an engine fan belt and brake pads). Car Knowledge ..... speed of return, the total response rate and the choices made (Dunn et al., 2003). ...... Taylor & Francis.
Front Matter
Accelerating the Demand for Low Emission Vehicles: A Consumer Led Perspective
A thesis presented for the degree of Doctor of Philosophy at the University of Aberdeen
Craig Lee Morton
B.A. (Hons.) Economics (University of Stirling) M.Sc. Ecological Economics (University of Edinburgh)
2013
I
Front Matter
I declare that this thesis has been composed and completed by myself and it has not been accepted in any previous application for a degree. All quotations have been distinguished by quotation marks and the sources of information specifically acknowledged.
Craig Lee Morton University of Aberdeen
II
Front Matter THESIS ABSTRACT Low Emission Vehicles (LEVs) represent a car classification which utilizes advancements in automotive technology to address policy objectives associated with energy security and greenhouse gas emissions. The effectiveness of LEVs at addressing these objectives will not only depend on their technical performance but also on their levels of adoption by consumers. With LEVs encompassing cars which are significantly different compared to conventional market options, the understanding of consumer response to these vehicles remains limited. This thesis addresses this limitation in existing knowledge by providing a detailed examination of consumer demand for LEVs in the UK. Through the application of psychometric methods, this thesis assesses the influence of socio-psychological constructs over LEV preference. A bespoke conceptual framework has been developed to provide insights regarding the influence of attitudes, emotions and values. This framework was applied to the design of a self completion household questionnaire distributed over the cities of Newcastle upon Tyne and Dundee. The meanings placed on car ownership are measured alongside the concept of innovativeness to determine if these traits are likely to hinder or advance LEV adoption. Additionally, past research has tended to consider consumer demand for LEVs at a market level perspective with little attention given to social stratification. This thesis advances knowledge in this area by producing a structural analysis of the emerging market for LEVs. Noteworthy consumer segments are identified, described and
compared
according
to
their
socio-psychological
profiles,
socio-economic
characteristics and LEV preferences. Principle results of the thesis are that socio-psychological constructs account for a greater degree of variance compared to socio-economic characteristics when explaining evaluations of LEVs. The propensity to consider cars through symbolic, emotive and functional meanings tends to decrease assessments made concerning the functional capabilities of LEVs. The concept of innovativeness displays a positive influence over preferences towards LEVs indicating that these vehicles are being considered as innovations. Moreover, heterogeneous consumer segments with unique socio-psychological and demographic III
Front Matter profiles are emerging in the LEV market. These segments display a range of LEV preference structures from those which hold high preferences and are likely to characterize early adopters to those which exhibit low preferences and are likely to represent non-adopters.
IV
Front Matter ACKNOWLEDGEMENTS Throughout the development of this thesis, I have been indebted to a number of different individuals and organisations for their support. The supervision team of Professor Jillian Anable, Professor John Nelson and Dr Christian Brand have provided valuable guidance on the scientific approach best suited to the project whilst offering keen insights relating to the output generated. At a wider level, individuals associated with the Centre of Transport Research at the University of Aberdeen have continually encouraged me to demonstrate my potential by the production of an original contribution to academic knowledge. The research environment provided by the department for Geography and Environment has assisted in cultivating the ideas connected to this thesis. Specifically, I am grateful for the encouragement offered by Thomas Birch, Michael Bumsted, Paul Ledger and Christina Noble who shared an office with me throughout my time in Aberdeen. Annual assessments and progress reviews have been a reoccurring feature in the landscape of this thesis. The efforts of Dr Stuart Archer, Dr Clare Bond and Dr Lorna Philip as panel members have been exceptionally valuable in identifying problems with the approach and suggesting areas for improvement. The continuation of original scientific research in the UK is dependent on the funding provided by Research Councils and other institutions who can appreciate the value of acquiring new insights. This research project has been made possible through the provision of a grant by the UK Energy Research Centre which I am most grateful for. I have been fortunate enough to present the research included in this thesis to a number of different audiences during the course of the project (detailed in section 10.6 if the Appendix). Conference organisers at the Universities’ Transport Study Group, Royal Geography Society, Scottish Transport Applications and Research, Transport Research Arena and Transportation Research Board have all been kind enough to offer me the opportunity
V
Front Matter to discuss my study. I would like to extend my gratitude to these conferences and I look forward to offering further contributions in the future. The progress of academic understanding in the social sciences often relies on the efforts of volunteers that participate in empirical research. This study would not have been possible without the respondents that freely gave up their time to provide their opinions on the topic of inquiry. I would like to express my appreciation to all of these individuals, who make advancements in knowledge of social phenomenon possible.
VI
Front Matter TABLE OF CONTENTS TITLE PAGE..............................................................................................................
I
THESIS ABSTRACT...................................................................................................
III
ACKNOWLEDGMENTS.............................................................................................
V
TABLE OF CONTENTS...............................................................................................
VII
DEFINITIONS OF ACRONYMS...................................................................................
XIII
LIST OF FIGURES......................................................................................................
XIV
LIST OF TABLES........................................................................................................
XVI
CHAPTER 1: INTRODUCTION…………………………………………………………………………............
1
1.1 Overview of Thesis..................................................................................................
1
1.2 Policy Environment……………………………………………………………………………………………… 7 1.2 Thesis Objectives……………………………………………………………………………….…...............
9
1.3 Contribution to Academic Knowledge………………………………………………….................
11
1.4 Wider Research Impact………………………………………………….......................................
12
1.5 Thesis Structure…………………………………………………...................................................
13
CHAPTER 2: LITERATURE REVIEW…………………………………………………............................
16
2.1 Introduction………………………………………………………………………………………………..........
16
2.2 Econometric Demand Analysis ……………………………………………………………….............
17
2.2.1 Theoretical Basis…………………………………………………..........................................
17
2.2.2 Applied Research………………………………………………….........................................
20
2.2.2.1 General Vehicle Market………………………………………………….......................
20
2.2.2.2 Low Emission Vehicle Market…………………………………………………..............
23
2.2.3 Overview of Econometric Demand Analysis…………………………………………........
28
2.3 Socio-Psychological Examination…………………………………………………........................
30
2.3.1 Theoretical Basis…………………………………………………..........................................
31
2.3.2 Applied Research………………………………………………….........................................
37
2.3.2.1 Psychometric Modelling………………………………………………….......................
37
2.3.2.2 Qualitative Assessment…………………………………………………........................
39
VII
Front Matter 2.3.3 Overview of Socio-Psychological Examination……………………………………………..
43
2.4 Market Structure and Consumer Segmentation………………………………………………….
45
2.4.1 Theoretical Basis…………………………………………………..........................................
46
2.4.2 Applied Research………………………………………………….........................................
50
2.4.3 Overview of Market Structure and Consumer Segmentation……………………….
54
2.5 Chapter Summary…………………………………………………................................................
56
CHAPTER 3: METHODOLOGY - CONCEPTUAL FRAMEWORK…………………………………...
58
3.1 Introduction………………………………………………….........................................................
58
3.2 Conceptual Frameworks: Past Developments and Applications…………………………..
59
3.3 Conceptual Framework Developed in This Thesis………………………………………….......
64
3.3.1 Innovativeness………………………………………………….............................................
66
3.3.2 EV Attitudes………………………………………………….................................................
70
3.3.2.1 Car Meanings: Symbolic, Emotive and Functional………………………………..
71
3.3.2.2 Car Attitudes…………………………………………………........................................
75
3.3.4 Value Orientation…………………………………………………........................................
76
3.4 Instrument Development…………………………………………………....................................
78
3.4.1 Psychometric Measurement…………………………………………………........................
79
3.4.2 Innovativeness: Measurement Instruments…………………………………….............
84
3.4.3 EV Attitudes: Measurement Instrument………………………………………………….....
87
3.4.4 Car Meanings: Measurement Instrument…………………………………………………...
88
3.4.5 Car and EV Emotions: Measurement Instruments……………………………………....
89
3.4.6 Car Attitudes: Measurement Instruments…………………………………………………...
90
3.4.7 Value Orientation: Measurement Instrument………………………………………........
92
3.4.8 LEV Preferences: Measurement Instrument…………………………………………........
94
3.5 Chapter Summary…………………………………………………................................................
96
CHAPTER 4: METHODOLOGY - SURVEY DEVELOPMENT……………………………………….....
99
4.1 Introduction………………………………………………….........................................................
99
4.2 Survey Development…………………………………………………...........................................
100
4.2.1 Design…………………………………………………..........................................................
100
VIII
Front Matter 4.2.2 Structure…………………………………………………......................................................
102
4.2.3 Scale Format…………………………………………………................................................
105
4.2.4 Online Survey…………………………………………………...............................................
107
4.2.5 Pilot…………………………………………………..............................................................
108
4.3 Sampling Strategy and Survey Administration…………………………………………………....
110
4.3.1 Site Selection…………………………………………………...............................................
110
4.3.2 Policy Site………………………………………………….....................................................
111
4.3.3 Comparison Site…………………………………………………...........................................
114
4.3.4 Sample Size Requirement…………………………………………………............................
117
4.3.5 Survey Distribution Requirement…………………………………………………................
122
4.3.6 Sampling Strategy…………………………………………………........................................
123
4.4 Ethical Considerations in Applied Human Research……………………………………….......
125
4.5 Chapter Summary…………………………………………………................................................
128
CHAPTER 5: RESULTS - VARIABLE DEVELOPMENT…………………………………………………...
130
5.1 Introduction………………………………………………….........................................................
130
5.2 Description of Sample………………………………………………….........................................
131
5.2.1 Car Details and Usage…………………………………………………..................................
131
5.2.2 Socio-Economic Characteristics and Household Profiles……………………………...
133
5.2.3 Overview of Sample and Population Comparison…………………………………….....
135
5.2.4 Comparison of Study Sites…………………………………………………...........................
135
5.2.5 Subsamples: Car Details and Usage………………………………………………….............
135
5.2.6 Subsamples: Socio-Economic Characteristics and Household Profiles………....
137
5.2.7 Overview of Subsample Comparison…………………………………………………...........
138
5.3 Measurement of Powertrain Preferences…………………………………………………...........
138
5.3.1 Primary Car Preferences…………………………………………………..............................
139
5.3.2 Secondary Car Preferences…………………………………………………..........................
140
5.3.3 Primary and Secondary Car Preference Comparison…………………………………...
140
5.3.4 Study Site Preference Comparison…………………………………………………..............
142
5.3.5 Additional Dichotomous Variable Preference Comparisons………………………...
143
5.3.6 Respondent Confidence in Powertrain Evaluation Exercise………………………….
146
IX
Front Matter 5.3.7 LEV Option as Highest Preference…………………………………………………...............
148
5.3.8 Preference Aggregation…………………………………………………...............................
150
5.3.9 Overview of Powertrain Preferences…………………………………………………...........
150
5.4 Measurement of Adoptive Innovativeness………………………………………………….........
151
5.5 Measurement of Socio-Psychological Constructs………………………………………..........
154
5.5.1 Introduction to Method…………………………………………………...............................
154
5.5.2 Principal Components Analysis: Survey Scales……………………………………….......
157
5.5.3 Car Meanings Scale…………………………………………………......................................
158
5.5.4 Car Emotions Scale…………………………………………………......................................
160
5.5.5 Car Knowledge and Importance Scale………………………………………………….........
161
5.5.6 Car Attitudes Scale………………………………………………….......................................
163
5.5.7 EV Emotions Scale…………………………………………………........................................
165
5.5.8 EV Attitudes Scale…………………………………………………........................................
166
5.5.9 Communication Determinants of Innate Innovativeness Scale…………………....
167
5.5.10 Psychological Determinants of Innate Innovativeness Scale……………………...
169
5.5.11 Life Principles Scale…………………………………………………....................................
173
5.5.12 Overview of Socio-Psychological Measurements……………………………………....
174
5.6 Chapter Summary…………………………………………………................................................
177
CHAPTER 6: RESULTS - SOCIO-PSYCHOLOGICAL MODELLING…………………………………..
179
6.1 Introduction………………………………………………….........................................................
179
6.2 Correlation Analysis………………………………………………….............................................
179
6.2.1 Introduction to Method…………………………………………………...............................
180
6.2.2 Powertrain Preferences…………………………………………………...............................
182
6.2.3 Value Orientation…………………………………………………........................................
183
6.2.4 Innovativeness………………………………………………….............................................
189
6.2.5 EV Attitudes………………………………………………….................................................
191
6.2.6 LEV Preferences…………………………………………………...........................................
196
6.2.7 Overview of Correlation Analysis………………………………………………….................
198
6.3 Regression Analysis………………………………………………….............................................
202
6.3.1 Introduction to Method…………………………………………………...............................
203
X
Front Matter 6.3.2 Innovativeness………………………………………………….............................................
205
6.3.3 EV Attitudes………………………………………………….................................................
209
6.3.4 LEV Preferences…………………………………………………...........................................
213
6.3.5 Overview of Regression Analysis………………………………………………….................
216
6.4 Chapter Summary…………………………………………………................................................
218
CHAPTER 7: RESULTS - MARKET STRUCTURE ANALYSIS……………………………………….....
219
7.1 Introduction………………………………………………….........................................................
219
7.2 Introduction to Method…………………………………………………......................................
224
7.3 Structure of the LEV Market…………………………………………………................................
224
7.3.1 Segmentation Variables…………………………………………………...............................
224
7.3.2 Hierarchical Clustering………………………………………………….................................
226
7.3.3 K-Means Clustering…………………………………………………......................................
229
7.3.4 Analysis of Variance………………………………………………….....................................
230
7.4 Cluster Descriptions…………………………………………………............................................
232
7.4.1 Cluster Label and Size…………………………………………………..................................
232
7.4.2 Powertrain Preferences…………………………………………………...............................
233
7.4.3 Socio-Economic Characteristics…………………………………………………..................
234
7.4.4 Current Car Details…………………………………………………......................................
235
7.4.5 Socio-Psychological Profiles………………………………………………….........................
237
7.5 Chapter Summary…………………………………………………................................................
245
CHAPTER 8 – DISCUSSION AND CONCLUSIONS………………………………………………….......
250
8.1 Introduction………………………………………………….........................................................
250
8.2 Research Objectives Revisited………………………………………………….............................
250
8.2.1 Develop and apply a conceptual framework of LEV preference………………....
251
8.2.2 Conduct a segmentation analysis of the emerging market for LEVs…………....
259
8.2.3 Transform research output into recommendations for decision makers……..
263
8.3 Policy Recommendations…………………………………………………………………………………….
270
8.4 Limitations…………………………………………………...........................................................
272
8.4.1 Sample Size…………………………….…………………………………………………...................
272
XI
Front Matter 8.4.2 Sample Representativeness………………………………………………….........................
273
8.4.3 Subsample Comparisons…………………………………………………..............................
274
8.4.4 Scale Development…………………………………………………......................................
275
8.4.5 Adoptive Innovativeness………………………………………………….............................
275
8.4.6 Socio-Psychological Stratification…………………………………………………................
276
8.4.7 Household Demand………………………………………………….....................................
277
8.4.8 Fleet Demand…………………………………………………..............................................
278
8.5 Future Research…………………………………………………..................................................
279
8.6 Concluding Remarks…………………………………………………............................................
283
9.0 REFERENCES…………………………………………………........................................................
285
10.0 APPENDIX…………………………………………………..........................................................
312
XII
Front Matter DEFINITIONS OF ACRONYMS
Acronym
Term
AFV
Alternatively Fuelled Vehicle
CNG CO2 CVI
Compressed Natural Gas Carbon Dioxide Cluster Validity Indices
EC EV DCM DfT DOI GHG GT HEV
European Commission Electric Vehicle Discrete Choice Models Department for Transport Diffusion of Innovations Theory Greenhouse Gas Grounded Theory Hybrid Electric Vehicle
HFCV ICE LEV
Hydrogen Fuel Cell Vehicle Internal Combustion Engine Low Emission Vehicle
LPG MNL MPG NAM NEP OLS PCA PHEV
Liquefied Petroleum Gas Multinomial Logit Miles per Gallon Norm Activation Model New Ecological Paradigm Ordinary Least Squares Principal Components Analysis Plug-in Hybrid Electric Vehicle
PiP RUT TAM TPB TRA UK US ULCVD VRC
Plugged in Places Random Utility Theory Technology Acceptance Model Theory of Planned Behaviour Theory of Reasoned Action United Kingdom United States of America Ultra Low Carbon Vehicle Demonstrations Variance Ratio Criterion
VBN
Values Beliefs Norms Theory
Definition Type of vehicle not fuelled by Petrol or Diesel Type of alternative fuel Chemical compound Classification of statistical procedure used to determine optimum cluster quantities Government body Type of vehicle fuelled by electricity Preference model Government department Marketing theory Category of chemical compound Qualitative theory Type of vehicle fuelled by a combination of conventional fuel and electricity Type of vehicle fuelled by hydrogen Type of conventional vehicle Type of vehicle technology utilising advancements in automotive to reduce GHG emissions Type of alternative fuel Statistical procedure Evaluation metric for vehicle fuel efficiency Behavioural theory Behavioural theory Statistical procedure Statistical procedure Type of vehicle similar to a HEV but contains a battery pack which can be recharged from an external source UK Government LEV policy Behavioural theory Behavioural theory Behavioural theory Behavioural theory Country Country UK Government LEV policy Statistical procedure used to determine optimum cluster quantities Behavioural theory XIII
Front Matter LIST OF FIGURES Figure 2.1: Illustration of a DCM for LEVs Figure 2.2: Illustration of the Theory of Planned Behaviour Figure 2.3: Illustration of the Technology Acceptance Model Figure 2.4: Illustration of the Value Beliefs Norms Model Figure 2.5: Illustration of the Diffusion of Innovation Theory Figure 3.1: Extended Decision Processes for the LEV Market Figure 3.2: The Influence of Psychological and Situational Factors over Car-Buyer Behaviour Figure 3.3: Conceptual Framework of EV Purchase Intentions Figure 3.4: Conceptual Framework of Fuel Efficient Car Purchase Behaviour Figure 3.5: Semiotic Map of HEV Adoption Figure 3.6: The Conceptual Framework Developed for This Thesis Figure 3.7: Decision Process of Innovation Adoption Figure 3.9: Illustration of the symbolic, emotive and functional car meanings Figure 3.10: Cognitive hierarchical model of human behaviour Figure 3.9: Illustration of the information pack offered to survey respondents including verbal description, graphical illustration and attribute matrix Figure 4.1: Ultra Low Carbon Vehicle Demonstration and Plugged-in Places Sites Figure 4.2: Installed Charge Point Infrastructure in (a) UK and (b) Tyne and Wear Metropolitan Area Figure 4.3: Charge Point Infrastructure of Potential Comparison Sites (a) Dundee (b) Cardiff (c) Plymouth and (d) Sheffield Figure 4.4: Zones selected in (a) Newcastle upon Tyne (d) Dundee Figure 5.1: Respondent Stated Confidence in the Powertrain Evaluation Exercise Figure 5.2: Frequency Distribution of Technology Ownership Figure 6.1: Value Orientation in the Conceptual Framework Figure 6.2: Innovativeness in the Conceptual Framework Figure 6.3: EV Attitudes in the Conceptual Framework Figure 6.4: LEV Preferences in the Conceptual Framework Figure 6.5: Illustration of a Two Variable OLS Regression Model Figure 7.1: Illustration of a Dendrogram XIV
Front Matter Figure 7.2: Distance Matrix for a Hierarchical Cluster Analysis Figure 7.3: Illustration of a K-Means Cluster Solution Figure 7.4: Dendrogram of Hierarchical Clustering Figure 7.5: Cluster Powertrain Preferences Figure 7.6: Cluster Loadings for Innate Innovativeness Constructs Figure 7.7: Quantity of Household Technology Owned Figure 7.8: Cluster Loadings for Car Meanings and Emotions Constructs Figure 7.9: Cluster Loadings for Car Attitudes Constructs Figure 7.10: Cluster Loadings for EV Attitudes and Emotions Constructs Figure 7.11: Cluster Loadings for Value Orientation Constructs Figure 8.1: Conceptual Framework Developed for this Thesis Figure 10.1: Introduction Page to the Pre-Testing Procedure Figure 10.2: Example of Construct Evaluation Figure 10.3: Illustration of Online Version of Household Survey
XV
Front Matter LIST OF TABLES
Table 1.1: Acronyms and Definitions Associated with Vehicle Powertrains Table 3.1: Characteristics of Innovators Table 3.2: Item Pool for Ambition Table 3.3: Rotated Factor Output for the Ambition Item Pool Table 3.4: Response Frequencies of Selected Statements from the Ambition Item Pool Table 3.5: Judge’s Appraisal of Selected Statements from the Ambition Item Pool Table 3.6: Innate Innovativeness Measurement Scale - Psychological Determinants Table 3.7: Innate Innovativeness Measurement Scale - Communication Determinants Table 3.8: EV Attitudes Scale Table 3.9: Car Meanings Measurement Scale Table 3.10: Car/EV Emotions Scale Table 3.11: Car Attitudes Scale Table 3.12: Car Knowledge and Importance Scale Table 3.13: Life Principles Scale Table 4.1: Survey Section Order Table 4.2: Alteration to Questions Table 4.3: Main Features of the Ultra Low Carbon Vehicle Demonstrator and Plugged In Places Government Policies Table 4.4: Study Site Comparison Table 4.5: Sample Size Requirements for Population Accuracy Table 4.6: Minimum Survey Distribution Table 5.1: Comparison of sample and population car details and usage Table 5.2: Comparison of sample and population socio-economics Table 5.3: Comparison of subsample car details and usage Table 5.4: Comparison of subsample socio-economic characteristics Table 5.5: Percentage Response Frequencies from Powertrain Evaluation Exercise – Next Primary Car Purchase Intentions Table 5.6: Percentage Response Frequencies from Powertrain Evaluation Exercise – Next Secondary Car Purchase Intentions XVI
Front Matter Table 5.7: Comparison between Primary and Secondary Powertrain Preferences Table 5.8: Comparison between Newcastle and Dundee Powertrain Preferences Table 5.9: Comparison between Additional Dichotomous Variable Powertrain Preferences Table 5.10: Correlation Analysis between Confidence Level and Powertrain Preference Table 5.11: LEV Powertrain as Highest Preference Table 5.12: Unique LEV Powetrain Highest Preferences Table 5.13: Aggregate Variables of Powertrain Preference Table 5.14: Percentage of Technology Ownership Stated by Respondents Table 5.15: Aggregate Variables of Adoptive Innovativeness Table 5.16: Levels of Acceptability in Cronbach’s Alpha Table 5.17: Levels of Acceptability in the KMO Test of Sampling Adequacy Table 5.18: Rotated Factor Matrix - Car Meanings Table 5.19: Rotated Factor Matrix - Car Emotions Table 5.20: Rotated Factor Matrix - Car Knowledge and Importance Table 5.21: Rotated Factor Matrix - Car Attitudes Table 5.22: Rotated Factor Matrix - EV Emotions Table 5.23: Rotated Factor Matrix - EV Attitudes Table 5.24: Rotated Factor Matrix – Communication Determinants of Innate Innovativeness Table 5.25: Rotated Factor Matrix – Psychological Determinants of Innate Innovativeness Table 5.26: Rotated Factor Matrix - Life Principles Table 5.27: Overview of Socio-Psychological Factors Table 6.1: Correlation Analysis between Powertrain Preferences for Next Main Car Purchase Table 6.2: Correlations Analysis between Life Principles and Communication Determinants Table 6.3: Correlation Analysis between Life Principles and Psychological Determinants Table 6.4: Correlation Analysis between Life Principles and Car Meanings Table 6.5: Correlation Analysis between Life Principles and Car Attitudes Table 6.6: Correlation Analysis between Adoptive Innovativeness and Psychological Determinants Table 6.7: Correlation Analysis between Adoptive Innovativeness and Communication Determinants Table 6.8: Correlations Analysis between EV Attitudes and Car Meanings Table 6.9: Correlation Analysis between Car/EV Emotions and EV Attitudes XVII
Front Matter Table 6.10: Correlation Analysis between Car Attitudes and EV Attitudes Table 6.11: Correlation Analysis between Adoptive Innovativeness and LEV Preferences Table 6.12: Correlation Analysis between LEV Preferences and EV Attitudes Table 6.13: Summary of Value Orientation Significant Correlations Table 6.14: Summary of Innovativeness Significant Correlations Table 6.15: Summary of EV Attitudes Significant Correlations Table 6.16: Summary of LEV Preferences Significant Correlations Table 6.17: Summary of Dependent Variables Included in the Innovativeness Regression Models Table 6.18: Summary of Independent Variables included in the Innovativeness Regression Models Table 6.19: Regression Analysis Explaining Adoptive Innovativeness through the Constructs Extracted from the Innate Innovativeness Scales Table 6.20: Summary of Dependent Variables Included in the EV Attitudes Regression Models Table 6.21: Summary of Independent Variables Included in the EV Attitudes Regression Models Table 6.22: Regression Analysis Explaining EV Attitudes using Constructs Extracted from the Car Meanings and Car Attitudes Scales Table 6.23: Summary of Dependent Variables Included in the LEV Preference Regression Models Table 6.24: Summary of Independent Variables included in the LEV Preferences Regression Models Table 6.25: Regression Analysis Explaining LEV Preferences using Constructs Extracted from the LEV Attitudes Scale and Adoptive Innovativeness Table 7.1: Overview of Segmentation Variables used in the Cluster Solution Table 7.2: Variance Ratio Criterion for Cluster Solutions Table 7.3: Initial Cluster Centres from the Hierarchical Analysis Table 7.4: Final Cluster Centres from the K-means Analysis Table 7.5: ANOVA of Segmentation Variables Table 7.6: Cluster Labels and Sizes Table 7.7: Socio-Economic and Household Characteristics of the Clusters XVIII
Front Matter Table 7.8: Current Car Details and Usage Patterns of the Clusters Table 7.9: Key Features of the Clusters Table 7.10: Summary of Cluster Loadings – Ranked from Lowest (light) to Highest (dark) Table 10.1: Item Pools for Car Specific Constructs Table 10.2: Item Pool for Innovativeness Specific Constructs Table 10.3: Distribution Schedule for Long Benton Area of Newcastle upon Tyne Study Site Table 10.4: ANOVA of Segment Description Variables
XIX
Chapter One: Introduction
CHAPTER
1
INTRODUCTION “If I’d asked my customers what they wanted, they would have said ‘a faster horse’.” Henry Ford 1.1 OVERVIEW OF THESIS
Since its invention over a century ago, the passenger vehicle 1 has diffused rapidly across the 0F
world, displaying universal appeal in different societies and economies. In the United Kingdom, car ownership has increased from under 2 million registered in 1950 to over 27 million in 2011 (DfT, 2012a). As disposable incomes have increased (ONS, 2012a), the cost of car purchasing has decreased in real terms (ONS, 2012b), so that a growing proportion of households now own more than one passenger vehicle (DfT, 2011a). The underlining factors influencing the widespread adoption of passenger vehicles are related to the benefits attributed to ownership. A car grants the owner (and their household) a high degree of mobility, allowing for quick point to point transport with the ability to carry passengers and luggage in a protected environment. These characteristics mean that passenger vehicles are widely used for personal, social and economic activities. Indeed, passenger vehicle use is the dominant transport mode for all common activities with over 69% of commuter trips in the UK conducted by car or van and 70% of leisure activities (DfT, 2011b). Not only is passenger vehicle use an important aspect of daily life in the UK, vehicle manufacturing contributes significantly to UK economic output. UK car production has a notable history and, whilst a number of the major manufacturers have now been incorporated into multinational conglomerates, the industry is still considered a world leader in innovation and style. According to the Society of Motor Manufacturers and 1 The term passenger vehicle is used in this context to refer to a light duty vehicle primary used by private individuals and households to transport passengers, more generally referred to as a car.
1
Chapter One: Introduction Traders (SMMT, 2013), UK automotive manufacturing accounted for 10.9% of UK exports and directly employed 720, 000 workers. Forecasting future trends, vehicle ownership and use is expected to continue to increase in the UK (Whelan, 2007). Whilst vehicle ownership brings a large number of benefits to individuals and society, it is not without its associated costs. The purchase of a passenger vehicle often represents the highest single item of expenditure for a household’s budget in any one year. Moreover, operating costs 2 associated with cars have the potential to absorb significant proportions of 1F
household incomes (ONS, 2012b). In addition to these private expenses, which are internalised to household incomes, a number of external costs are imposed on society. The oil based fuels that conventional passenger vehicles operate on emit gaseous compounds which have negative implications. Particulate matter, which is an emission more common in older diesel fuelled vehicles, has been shown to negatively affect public health by increasing respiratory disease (Yim and Barrett, 2012; Valavanidis et al., 2008). Emissions of sulphur dioxide and volatile organic compounds reduce the resilience of the built environment to weathering, notably stone based buildings which are common in historic sites and have heritage importance (Brimblecombe and Grossi, 2007; Cowell and Apsimon, 1996). Greenhouse gases, such as carbon dioxide, are known to influence radiative forcing, altering the energy balance of the atmosphere, which has implications for the climate and other related systems (IPCC, 2007). Oil itself has concentrated spatial distributions with a high proportion of global reserves (UKERC, 2009) controlled by an organisation of countries leading to monopolistic market conditions (OPEC, 2008) which raises concerns over security of supply and stability of price levels (ITPOES, 2008). These costs 3, often referred to as 2F
externalities (Buchanan and Stubblebine, 1962), are a type of market failure whereby a cost is imposed onto a third party without any form of compensatory payment to account for the disutility experienced. One of the greatest challenges for the next half century relates to the question of how to satisfy personal mobility whilst reducing the effects of the externalities so far detailed.
2
fuel, maintenance, insurance, parking and annual registration taxes It should be noted that there are additional externalities associated with passenger vehicle use and ownership such as car crime, mobility implications, safety concerns and congestion which are themselves important though not covered in this thesis
3
2
Chapter One: Introduction The UK Government has passed legally binding legislation to reduce greenhouse gas emissions by 80% in 2050 based on emissions levels in 1990 (Great Britain, Climate Change Act, 2008). To achieve this goal, a series of 5 year carbon budgets have been specified to ensure adequate steps are taken to attain the stated objective. Additionally, this act established a Committee on Climate Change which has been tasked with advising government in relation to emissions levels and budgets whilst impartially reporting on progress made. In the Committee’s latest report (CCC, 2012), the reduction in carbon intensity of new passenger vehicle sales in 2011 has outperformed estimated trajectories. It is important to determine what is influencing this promising trend in market activity to ensure that conditions are correct to sustain it. The quantity of greenhouse gases emitted from passenger vehicles is influenced by a number of factors such as driving behaviour, vehicle use patterns and road conditions. Perhaps most importantly, the car individuals choose to buy and use can have substantial effects on the carbon intensity of the owner’s mobility. A car purchased today can be expected to have an operational lifespan in excess of ten years (Lemp and Kockleman, 2008) leading to technological lock-in effects. For transport to contribute towards the carbon budget requirements, the carbon intensity of vehicles sold is required to continually decrease 4. New technologies are being developed and integrated into vehicles to assist in 3F
this transition to a low carbon vehicle fleet. The term selected to refer to such vehicles in this thesis is Low Emission Vehicle (LEV). LEVs represent a car classification which utilizes advancements in automotive technology to address policy objectives associated with greenhouse gas emissions and energy security. A number of alternative systems have been proposed though, as it currently stands, the only system developed to a standard required for mainstream market deployment is the pathway employing electrification of the powertrain 5 (Hoyer, 2008; Romm, 2006). This 4F
technological pathway incorporates vehicles which are similar in function to conventional internal combustion engine (ICE) vehicles to those that are significantly different. Specifically, some LEVs being designed have unique attributes such as limited range, 4
5
Holding all other factors, such as vehicle miles travelled, constant. Refers to the primary system used to provide power to the vehicle, for instance, an internal combustion engine or battery.
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Chapter One: Introduction reduced operating costs and reduced emissions levels which have yet to be observed in the mainstream market. A summary of a number of powertrain classifications used throughout the literature is offered in Table 1.1. Table 1.1: Acronyms and Definitions Associated with Vehicle Powertrains Acronym
Term
Definition
AFV
Alternative Fuelled Vehicle
Defines a class of vehicle which operate from a non petrol or diesel fuel source. Tends to be associated with compressed natural gas and liquefied petroleum gas vehicles
BEV
Battery Electric Vehicle
A vehicle category which utilises an electricity storage battery to operate an electric motor which propels the vehicle
EV
Electric Vehicle
Similar to a BEV though the energy can also be sourced from a hydrogen fuel cell
HEV
Hybrid Electric Vehicle
Defines a class of vehicle which is fuelled by an internal combustion engine but has the ability to capture the energy usually lost under braking to charge a small battery. The energy stored in this battery is then used to assist in vehicle propulsion
ICE
Internal Combustion Engine
A conventional vehicle which combustible fuel to propel the vehicle
LEV
Low Emission Vehicle
A vehicle category which utilises advancements in automotive technology to reduce the emissions of a vehicle
PHEV
Plug-in Hybrid Electric Vehicle
Similar to a Hybrid Electric Vehicle but has an enlarged battery pack which can be recharged from an external power source. This energy can then be used to assist or to entirely propel the vehicle
utilises
These novel vehicle attributes make it challenging to predict likely consumer response to LEVs based on previously observed market data. Yet, with LEVs having the potential to significantly contribute to prominent societal objectives, their successful adoption is viewed as an important aspect of government policy (DfT, 2009a). Previous attempts to transition to alternative vehicle powertrains have been largely unsuccessful. For example, the UK 4
Chapter One: Introduction Government has provided a number of financial incentives in the past to enhance the adoption of LEVs (Brevitt, 2002; Ward-Jones, 2007) though registrations still account for less than 1% of the national fleet (DfT, 2012b). New policies have recently been positioned in the UK market to assist the uptake of Electric Vehicles (EVs) including a Plug-in Vehicle Grant and an exemption from annual registration taxes. Moreover, local policy initiatives have been deployed with the deployment of EV public trials and the installation of EV infrastructure. Whilst no specific LEV sales target has been adopted in the UK (Transport Committee, 2012), the Committee on Climate Change has recommended that 1.7 million electric vehicles be incorporated into the national fleet by 2020 increasing to 11 million in 2030 (CCC, 2012). In addition, the European Commission has expressed an aspiration to halve the use of conventionally fuelled vehicles in urban areas by 2030 increasing to an absolute reduction by 2050 (EC, 2011). Uptake of the recently released generation of EVs has been slower than originally anticipated, with total licensed EVs well under 3,000 in 2011, compared to expectations of 13,000 by the end of 2011 (CCC, 2012), and with the total registrations of vehicles eligible for the plug-in grant reported as reaching only 3,293 at the end of December 2012. This coupled with the recent spate of cancellations of planned EV models from major manufacturers and the on-going difficult economic conditions in the UK and Europe is of significant concern given the ambitious targets for EV deployment for later on in the decade, which therefore threatens to also endanger long-term objectives. The limited success of previous transitions to LEV powertrains is likely to be explained by a combination of different factors such as immature technical development, changing market conditions, lack of policy ambition and insufficient infrastructure. However, it is argued in this thesis that a lack of attention paid by decision makers to demand side, as opposed to supply side, factors led to an incomplete understanding of consumer response to LEVs. This can be clearly observed in the turbulent history of California’s zero emission vehicle policy (Collantes and Sperling, 2008) and in the lack of commercial success displayed by General Motor’s first EV to be positioned in the mainstream market, the EV1 (Westbrook, 2001). Initial research into consumer preferences was largely focused on calculating the value consumers placed on the novel functional characteristics of LEVs such as limited range, home refuelling and reduced operating costs (Mannering and Train, 1985). This research was often conducted using econometric models which assumed that consumers were well 5
Chapter One: Introduction informed rational agents that structure their behaviour in a utility maximising manner. Whilst this claim is partially true, it does not adequately account for the influence of attitudes values and emotions, which have been shown to have significant influence over behaviour (Fleming et al. 2008; Gatersleben et al. 2002). Recently, research has progressed by examining the influence of attitudes over vehicle purchasing behaviour (Choo and Mokhatarian, 2004; Peters et al. 2011a; Ozaki and Sevatsyanova, 2011) through the application of behavioural theories, such as Ajzen’s (2005) Theory of Planned Behaviour. Nevertheless, this is a relatively new area of research that is rapidly changing the market and policy debate. Additionally, with LEVs becoming available on the mainstream vehicle market, determining the likely adopters of these vehicles and what areas of the market will be receptive to them is an important issue. This knowledge will assist policy makers to better assess the potential for carbon savings and to target market interventions to help achieve this. Whilst research has investigated consumer response to different policy measures (Ewing and Sarigollu, 1998; de Haan et al. 2007; Diamond, 2009; Ryan et al. 2009), comparatively little work has examined how to target these measures at the correct consumer groups. Furthermore, the research has often taken a holistic approach overlooking the importance of sub-markets and unique consumer segments which can behave in an idiosyncratic manner. This thesis aims to provide insight relating to the socio-psychological determinants that have the potential to influence consumer preference for LEVs. Furthermore, this thesis aims to assess the consumer structure of the LEV market in order to identify and profile the emerging consumer groups. To achieve these aims, a quantitative cross-sectional approach has been employed based on data generated from a self completion household survey.This survey was distributed over the study sites of Newcastle upon Tyne and Dundee and extensively measures social and psychological characteristics to act as components in an explanatory model of LEV preference. The results from this stage have then been taken forward to form the basis of a segmentation analysis of the emerging LEV market. A bespoke conceptual framework has been developed which integrates components sourced from different theories of behaviour. Firstly, drawing on work conducted by Steg (2005) and Dittmar (1992), measurements of symbolic and emotive car connection have been taken to 6
Chapter One: Introduction complement functional considerations. Secondly, the concept of innovativeness, as it is defined in Rogers (1995) Diffusion of Innovation theory, has been measured to investigate what influence this has over LEV preference. Thirdly, value structures (Stern, 2000) have been measured to examine the importance of biospheric, altruistic and egoistic constructs. To complement these components, socio-economic characteristics alongside current car details assist in providing additional richness to the analysis. 1.2 POLICY ENVIRONMENT With the passenger vehicle market being of significant importance to society, it is unsurprising that it is also an active area of government policy. A complex taxation system exists within this sector covering registration, use and circulation which generates a substantial proportion of government revenue (£33.1 billion in 2011 (HMTreasury, 2012)). With the UK Government expressing a continued desire to accelerate the uptake of LEVs in the UK (DfT, 2013a), adapting the current policy environment to favour the diffusion of LEVs has been a popular strategy for government intervention. This section provides a brief overview of the policy agenda surrounding LEVs in the UK since the start of the millennium. The types of policy so far enacted can be partitioned into those which are based in the supply or demand side of the market. As this thesis is specifically focused on consumer demand for LEVs, only the demand side policies are discussed. UK Government policy was partially motivated by the Kyoto Protocol (UN, 1998), enacted by the United Nations Framework Convention on Climate Change, which set mandatory emission reduction targets coming into effect in 2005. The UK was included as an Annex 1 county and required to achieve a reduction in carbon dioxide emissions of 12.5% on 1990 emissions levels between the enforcement period 2008-2012. To achieve this, the UK Government set out its vision for a transition to a low carbon energy system (DTI, 2003). Going beyond the commitments of the Kyoto Protocol, the UK Government expressed an aspiration to achieve a carbon dioxide emission reduction of 60% by 2050. Emission reductions in the transportation sector were viewed as an important aspect of achieving this ambition, with the government stating a desire to encourage the adoption of new low carbon vehicle technologies. Concentrating specifically on vehicle use, the government established the Powering Future Vehicles 7
Chapter One: Introduction initiative (DfT, 2002; DfT, 2003: DftT, 2004) which developed a detailed roadmap including 10 primary areas of action. One of these areas of action specifically focused on encouraging consumer adoption of LEVs through the application of fiscal policy and consumer engagement to disseminate knowledge and information. An official target was set by the Powering Future Vehicles initiative that, by 2012, 10% of new car sales in the UK would emit less than 100 grams of CO2 per kilometre. Reviewing this target against what has actually been achieved, recent vehicle licensing statistics show that 8.6% of cars registered in 2012 emitted less than 100 grams of CO2 per kilometre (DfT, 2013b). The Transport Committee of the House of Commons (HoC, 2004; HoC, 2006) provided an early critical analysis of the progress made in achieving the Government’s ambition of a low carbon passenger vehicle system and shed light on why this target was missed. Evidence provided to the committee demonstrated a lack of advancement with the sales target for LEVs. The Department for Transport’s white paper (DfT, 2003) was singled out as offering no significant contributions in the area of passenger cars whilst the Department for Trade and Industry were criticised for a lack of ambition. The reform of Vehicle Exercise Duty in 2001 to reflect CO2 emissions as opposed to engine size was lauded for being a world-leader in combining vehicle taxation with emission levels but was viewed as being ineffective in offering an incentive to purchase a less polluting vehicle. Similarly, the PowerShift grant provided by the Energy Savings Trust to encourage the adoption of LEVs was criticized for not providing adequate funding to meet the demands of the market. The publication of Lord Stern’s review on the economics of climate change (Stern, 2006) proved to be a significant motivator in fields relating to sustainability and energy. The consequences of unmitigated climate change to the worldwide economic system were estimated to cost between 5 to 20% of global GDP per annum. Conversely, the cost of action to stabilise CO2 atmospheric concentrations at 550 parts per million (and thus limit global temperature increase to 2 degrees Celsius) was assessed to be equivalent to 1% of global GDP per annum. The review states that an 80% reduction in worldwide CO2 emissions will be required by 2050 to stabilise CO2 concentrations. Responding to this, the UK Government enacted legally binding targets to reduce domestic CO2 emissions by 26% by 2020 and 80% by 2050 based on emission levels in 1990 (Great Britain, Climate Change 8
Chapter One: Introduction Act, 2008). A unified strategy was developed by the government which accounted for all sectors of the economy to put the UK on a trajectory to meet these targets (HMGovernment, 2009). Specifically relating to transport, the UK Government set a target to reduce CO2 emissions from the non-traded transport sector by 16% by 2020 based on 2005 emissions levels (DfT, 2009). Furthermore, a reduction of 40% in new car carbon emissions by 2022 (based on 2007 emissions levels) was also targeted. The King Review of low carbon cars (King, 2007; King, 2008) demonstrated that significant cuts in CO2 emissions from passenger vehicles were technically feasible, with a 90% reduction possible by 2050. Moreover, a short-term decrease of 30% was attainable simply through widespread adoption of currently available technology. To realise this potential, long term and committed government policy is called for with significant consumer engagement. Information campaigns were viewed as important to spur smarter consumer choices facilitated through the adoption of eco-labels in sales environments. Taking account of these recommendations, the UK Government made £400 million available to LEV projects with £250 million of this fund earmarked to incentivize the adoption of LEVs through fiscal policies (DfT, 2009). 1.3 THESIS OBJECTIVES To achieve the aforementioned aims, this thesis addresses three primary research objectives which represent different stages of analysis. Each research objective is associated with a number of research questions which are addressed through statistical analysis and interpretation of results. Objective 1: Develop and apply a conceptual framework of LEV preference In this stage of the thesis, a framework is developed incorporating components from different theories of behaviour. In essence, the framework is tailored to achieve a number of goals. To begin, the framework progresses academic knowledge by bringing together a number of concepts which have received relatively little attention in the area of car choice. Additionally, the research output identifies socio-psychological constructs which have 9
Chapter One: Introduction influence over LEV preferences and evaluations, providing policy makers with a detailed understanding of dynamics in this market. The research questions associated with this stage are: 1.1 Does an individual’s innovativeness explain variance in the LEV market? 1.2 What is the relationship between general car meanings and car attitudes with specific attitudes towards EVs? 1.3 Do values significantly relate to other socio-psychological constructs?
Objective 2: Conduct a segmentation analysis of the emerging market for LEVs Stage two utilises the findings observed through the application of the conceptual framework as the basis for a market segmentation analysis. The topic of market structure has recently gained traction as LEVs have begun to enter the mainstream vehicle market. The principal aim of this stage is to provide insight relating to the emerging structure of the LEV market with emphasis placed on the identification of unique consumer groups. The research questions associated with this stage are: 2.1 Are heterogeneous consumer segments being formed in the emerging market for LEVs? 2.2 What are the defining features of each segment? 2.3 Do these segments conform to theoretical expectations? Objective 3: Transform research output into recommendations for decision makers The final research objective positions this thesis in a policy relevant manner by examining how the results generated and the insights attained can assist decision makers to develop and target interventions to accelerate LEV adoption. The research questions associated with this stage are: 3.1 Are preferences for LEVs influenced by local policy?
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Chapter One: Introduction 3.2 Are any barriers to adoption identified through the analysis which can be directly addressed by policy? 3.3 Can the market segments identified inform the design and targeting of policy intended to influence the uptake of LEVs? 1.4 CONTRIBUTION TO ACADEMIC KNOWLEDGE With the majority of the previous research in this field taking a distinctly functional approach, this thesis contributes to academic understanding by measuring sociopsychological constructs and through examining how these constructs can be used to explain LEV preference. The constructs of symbolism and emotion have been shown to have significant influence over car usage (Peters et al., 2011a; Lois and Lopez-Saez, 2009). This thesis measures these two constructs with reference to the meanings individuals place on car ownership and then examines how these constructs relate to LEV preferences. In addition, an individual’s receptivity to new products and technologies has been shown to be influenced by the concept of innovativeness (Roehrich, 2004). This thesis determines the extent to which innovativeness is influencing preferences in the emerging market for LEVs. To achieve these broad objectives, a bespoke conceptual framework has been developed which incorporates components from a number of different theories. This type of approach has recently become popular among researchers (Lane and Potter, 2007; Peters et al. 2011a) and offers some significant benefits. It provides the researcher with a high degree of flexibility allowing for frameworks to be tailored to the task at hand. However, a distinct limitation of this approach is the inability to directly compare results to those attained in other applied studies. With this in mind, this thesis provides insights relating to integrating aspects from different theories by a detailed discussion relating to the procedure followed in the development of the framework. In a similar fashion, the segmentation analysis conducted in this thesis takes an original approach by examining the market structure during the development phase of the market, as opposed to when the market is already mature. Often, analysis examining the diffusion of innovations is undertaken after the innovation in question has been fully diffused and is, in 11
Chapter One: Introduction essence, a historical inspection (Mahajan et al., 1990). By examining the market during its development phase, this thesis provides a new insight relating to the early stages of adoption which can be critical to a successful diffusion. In addition, this thesis provides knowledge relating to the socio-psychological profiles of the market segments, further illustrating how the components contained in the conceptual framework interact and perhaps indicating that groups of consumers may hold similar preferences towards LEVs, though these preferences may indeed be influenced by different factors. 1.5 WIDER RESEARCH IMPACT This thesis is taking place in an area of dynamic public debate and political activity. LEVs are an important innovation and have the potential to address a number of prominent societal objectives. Their successful adoption is viewed as an important requirement to a transition to a low carbon society in the UK. In addition, the UK currently contains a significant vehicle manufacturing sector which has the potential to gain a competitive advantage in LEV production. This thesis contributes to the discussion in this area in two principal ways. Firstly, through the application of the conceptual framework, a more inclusive understanding relating to what socio-psychological constructs are influencing LEV preferences has been attained. This information is of relevance to policy makers as it will provide an enhanced comprehension of consumer dynamics in this market. This knowledge can act as a starting point when considering how policy may interact with attitudes and behaviour. In addition, the segmentation analysis provides a valuable insight relating to the structure of the emerging market. This will be useful to policy makers when considering how to effectively target policy to optimise associated impact. More broadly, with the UK Government expressing a desire to transition the UK to a low carbon trajectory (CCC, 2008; HMGovernment, 2011), it will be important to match low carbon consumption with low carbon production. The segmentation analysis conducted provides a detailed illustration of the emerging market through an extensive profiling of consumer groups which incorporates constructs that may not be considered in conventional
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Chapter One: Introduction market research. With this in mind, the results of this thesis will be of use to private companies operating in the UK that are acting as producers and retailers for LEVs. 1.6 THESIS STRUCTURE This section briefly describes the chapter structure by discussing the contents of each chapter. Chapter 1: Introduction Contained in this chapter is a general introduction to the thesis, stating the background of the field and the relevance of the research. Discussion points include the current market and policy environment alongside how this thesis contributes to academic knowledge whilst providing guidance to political and market strategy. Chapter 2: Literature Review To effectively ground this thesis in the context of previous inquiry, an extensive review of the past study in this field is offered. Research studies are introduced, appraised and compared to provide insight relating to what activity has already taken place and assist in identifying areas that have received relatively little attention. Additionally, a brief introduction to the relevant theories utilised by researchers is offered. Chapter 3: Methodology - Conceptual Framework This chapter provides an overview to the conceptual framework that has been developed to address the research objectives and questions outlined in the introduction. Initially, an appraisal of previously applied frameworks in this field is offered before the framework designed for this thesis is detailed. Components incorporated in the framework are described followed by an overview of the instruments designed and included in the household survey. Chapter 4: Methodology - Survey Development
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Chapter One: Introduction The conceptual framework described in the proceeding chapter has been applied through a self completion household survey distributed over two sites in the UK. This chapter describes the manner in which the survey was developed and covers topics including structure, design, piloting, site selection, sampling and distribution. Chapter 5: Results – Variable Development The first of the results chapters begins by examining the basic structure of the sample attained, notably the socio-economic and current car details of respondents. However, the main objective of this chapter is to calculate the variables that are utilised in the socio-psychological models and market structure analysis. This is done in three stages by examining powertrain preferences, technology ownership and socio-psychological constructs. Chapter 6: Results – Socio-Psychological Modelling The second of the results chapters focuses on the first research objective by assessing the validity of the conceptual framework. Through the application of correlation and regression analysis, the structure of the framework is evaluated to determine if expected conceptual links between the framework components are present. Chapter 7: Results – Market Structure Analysis The final results chapter approaches the second research objective and applies a market segmentation analysis to examine the structure of the emerging market for LEVs. Respondents are grouped based on their shared characteristics into heterogonous market segments. These segments are profiled based on their principal features. Chapter 8: Discussion and Conclusions Having presented the main findings of the conceptual framework in the three results chapters, the discussion chapter critically appraises how well the results are able to answer the research objectives and questions. Moreover, emphasis is given to the third research objective through a detailed discussion regarding to how the 14
Chapter One: Introduction knowledge gained from this thesis can be used to inform government policy. Following this, the limitations experienced are highlighted and the potential for future research is outlined.
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Chapter Two: Literature Review
CHAPTER
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LITERATURE REIVEW 2.1 INTRODUCTION Interest in the market demand for passenger vehicles has spawned a rich field of academic literature which spans multiple subjects and draws on theories from psychology, sociology, anthropology, economics and marketing. The application of different theories has been driven by the inherent challenges of examining a practice that interacts with a broad range of disparate yet interconnected dimensions of social science. Perhaps the genesis of academic enquiry relates to the importance of this business sector to a number of large economies. Vehicle manufacturing in the United States, United Kingdom, European Union and Japan (Rubenstein, 2008; Heneric et al., 2005; Shimokawa, 1994) has a long history and still contributes significantly to the performance of national economies. With this in mind, it is unsurprising to observe that understanding the competitive market dynamics of the industry is of importance to society in general and governments in particular (NAIGT, 2009; EC, 2012). This chapter reviews the previous research which has been conducted in the field of LEV demand to frame this thesis. The questions posed by previous research are examined to determine what areas have received academic attention. Following this, the methodologies utilised to answer these research questions and the results generated are critically assessed to identify areas of success and gaps in the current knowledge base. This review has been partitioned into a number of sections to provide a structured critique of the existing literature. To begin, econometric modelling, which represents the dominant paradigm in the field of LEV demand, is discussed to demonstrate the contributions it has offered whilst also highlighting the aspects which this paradigm overlooks. The remaining parts of this review concentrate on these overlooked areas and examine the work that has so far taken place and to outline the areas of focus and intended contribution to knowledge 16
Chapter Two: Literature Review of this thesis. In each instance, the theory which underpins the literature is presented to underline the embedded assumptions and limitations. 2.2 ECONOMETRIC DEMAND ANALYSIS Market modelling and demand analysis includes aspects such as explaining consumer preferences towards certain vehicle characteristics, predicting likely consumer response to vehicles with certain attribute configurations and forecasting markets. Research that falls into this category tends to rely on econometric methods to produce structured statistical models that generally concentrate on objective factors such as the instrumental performance levels of the vehicles or the basic socio-economic characteristics of the consumers. Until recently, this approach has tended to dominate the applied research examining demand for LEVs. This section of the literature review discusses the contribution that research in this area has made whilst emphasising the limitations by discussing the importance of aspects which are not addressed by the approach. 2.2.1 Theoretical Basis The market for passenger vehicles has a number of characteristics which makes it well suited to econometric modelling. It is extensive in nature displaying a high degree of market activity with the vehicles being complex and highly diversified. The market is often regulated and monitored by governments leading to data being publicly available. Additionally, this data is often collected at a disaggregate level allowing for analysis of individual consumer preferences. These factors have combined to make the passenger vehicle market ideal for the application of econometric modelling. The principal framework used by researchers in conducting studies using this approach is derived from the field of econometrics and the theory of Random Utility. At the centre of this theory is the assumption that consumers base their purchasing decision primarily on their assessment of the utility they could derive from the attributes which define a product. Radom Utility Theory (RUT) (Manski, 1977) contains two primary components; firstly the systematic component which represents the attributes of any choice, such as the characteristics of a vehicle, that are evaluated by consumers. The second is a random 17
Chapter Two: Literature Review component containing variables that prevent choice from being a completely rational action such as exogenous situational aspects which are difficult to measure and therefore cannot be directly accounted for by a model. Consumers are conceived as rational agents conducting choice decisions with the purpose of maximising their utility. When presented with a choice of mutually exclusive alternatives, consumers will appraise the alternatives based on the quality of their attributes and make a choice that attains the highest utility. Data on choices can be attained from two primary sources. When the alternatives under investigation are actively traded in a market, the market data concerning what alternatives are chosen and the related attribute specification of these alternatives can be observed. This is often termed Revealed Preference data as it concerns market transactions which have actually occurred. In certain circumstances, Revealed Preference data may be unavailable, such as in the case of non-market goods or when a good in question is not actively traded. In these circumstances, researchers model preferences towards goods using hypothetical situations, often referred to as choice experiments, whereby individuals are provided with information relating to the product specification, such as attribute and price levels, and are asked to state their preference for these goods. An example of a choice experiment related to LEVs is presented in Figure 2.1 (Ewing and Sarigollu, 1998). These Stated Preference techniques are primarily used to model consumer preferences towards LEVs due to the limited size of the current market. This approach is generally referred to as Discrete Choice Modelling (DCMs) and often takes the form of logistic regression analysis based on the work done by McFadden (1973). Ewing and Sarigollu (1998) provide an effective and concise discussion relating to RUT, DCM and how they have been applied to investigate preferences towards LEVs whilst a more detailed presentation of the theory is offered by Hensher et al. (2005) and Train (2009).
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Chapter Two: Literature Review
Figure 2.1: Illustration of a DCM for LEVs (Ewing and Sarigollu, 1998) Logistic regression analysis has a number of different variants which have been utilised to model the automotive market. Binomial Logit Models are employed when the choices of the experiment are one of two discrete outcomes, such as in the case of the market choice of whether to buy or not to buy a product. When the outcome variable can take on more than two values, Multinomial Logit Models (MNL) are often used to predict choices. In circumstances where the outcome variable is in a ranked format, such as when alternatives are placed in order of preference, Ordinal Logit Models can be employed. More recently, Mixed Logit Models have been developed (McFadden and Train, 2000) that aim to address a number of the limitations of conventional logistic modelling such as the problems associated with the independence of irrelevant alternatives 1 (Arrows, 1963). Logistic modelling is 0F
suitable for the analysis of both stated and revealed preference data and, recently, modelling techniques have been progressed through the integration of revealed and stated preference data into joint mixed logit models which achieve superior model fits and are less susceptible to some of the statistical biases, such as multicollinearity, which have been beset other logistic models (Brownstone et al. 2000).
1
The independent of irrelevant alternatives is an axiom of decision theory and states that the choices made in an experiment should not be influenced by alternatives which were not included.
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Chapter Two: Literature Review 2.2.2 Applied Research Whilst the automotive industry has been an area of research focus since its early conception (Edison, 1902; Edmonds, 1923; Hayford, 1934; Brems, 1956), it was not until the 1970s and the development of DCMs and logistic regression analysis that academics were able to produce complex models in an attempt to explain market dynamics and forecast market developments. This section of the review presents these complex models and discusses how they have been iteratively improved. Following this, literature related specifically to econometric analysis of the LEV market is appraised to demonstrate the insights which have been provided and the aspects which have been overlooked. 2.2.2.1 General Vehicle Market One of the earliest pieces of work to make use of these econometric methods was conducted by Lave and Train (1979) who developed a disaggregate model of the passenger vehicle market to provide insight relating to how consumers structure their auto-choice decisions. Breaking from past research, which only included a number of vehicle characteristics and often made use of aggregate market data, this study incorporates a mixture of socio-economic characteristics of the car buyers alongside an extensive set of car attribute variables leading to a complex model. The key findings are that increases in fuel price affect the probability of choosing a fuel efficient car, individuals with lower levels of formal education are more likely to buy large cars, young individuals have a higher preference for car performance and high income individuals are more likely to buy expensive cars, a tendency the authors label the prestige effect. Lave and Train’s model is pioneering in its approach to the automotive model through its focus on auto-type choice at the individual consumer level and has set the tone for a significant proportion of the research to follow. Taking a more inclusive perspective of the automotive market, Agarwal and Ratchford (1980) develop an econometric model which includes demand and supply components allowing for market equilibrium estimations to be made. Focusing specifically on the demand estimation component, a number of models are specified to explain consumer preference for a range of different vehicle attributes. The primary conclusions are that high incomes are associated with a greater willingness to pay for ride smoothness and internal car space whilst the intention of a consumer to use their cars for long distance journeys is associated with a 20
Chapter Two: Literature Review general willingness to pay for enhanced car attributes. More generally, the results of the model imply that consumer specific variables such as socio-economic characteristics have a relatively small influence over consumer preference for vehicle attributes. These results perhaps indicate that profiling consumers likely to purchase a certain type of vehicle based only on their socio-economic characteristics may be an ineffective approach. Both Lave and Train (1979) and Agarwal and Ratchford (1980) have focused on explaining consumer preferences for passenger vehicles based on the specification of the cars, the intended car use or the characteristics of the individuals. This form of analysis provides insight relating to what car specifications prove desirable in different circumstances. Berkovec (1985) is less interested in explaining current market conditions but instead attempts to predict future market trends. The simulation model developed to forecast the market contains three primary components relating to vehicle production, vehicle scrappage and consumer demand derived from a DCM. Two scenarios are developed from the model which describes market conditions between the periods 1978-1984 and 1978-1990. Appraising how the model predictions compare to observed market data, the forecasts made for stock growth rates and scrappage levels are a good fit whilst light truck and new vehicle sales are somewhat divergent. Large divisions between forecasts and observations are seen for imported car sales and light truck sales. The recession in the US economy during the early 1980s is stated to be the underlining condition that generated the division between forecasts and observed market data. From this, it can be proposed that previous trends and current market conditions can, in certain circumstances, provide an ineffective basis for predicting future market trajectories due to the inability to incorporate exogenous and unpredictable factors. Early research in demand modelling of the car market tended to consider the purchase of a single car. With the incidence of households owning more than one car having increased throughout much of the industrialised world (Dft, 2011a), this single car modelling approach is significantly limited by not accounting for the dynamic nature of household fleet management. Manski and Sherman (1980) conducted one of the first investigations into household fleet composition. Through the specification of a MNL model which used observed market data, the authors examine what combination of cars households tend to 21
Chapter Two: Literature Review hold in their fleets. Key findings are that multicar households tend to own cars of dissimilar size and function supporting the hypothesis for functional specialisation. Mannering (1983) conducts a similar study through analysis of survey data using simultaneous equation models. Whereas Manski and Sherman’s study examined vehicle purchasing decisions, Mannering inspected how a household shares its fleet among family members under different market conditions. Vehicle allocation decisions are assumed to be dependent on two primary determinants. The first determinant relates to household activity choices which specifies characteristics connected to trip purpose. The second determinant relates to the characteristics of the household fleet and the relative bargaining power of household members. Key findings of the model are that the impact of fuel prices on vehicle use are significantly affected by household income with more affluent households being affected much less than those on lower incomes. Additionally, households tend to substitute use away from their least fuel efficient vehicle during times of fuel price increases. The literature so far described has taken a distinctly objective approach to the explaining consumer preferences for passenger vehicles and in the prediction of future market trends. Observable variables relating to the socio-economic characteristics of the individuals purchasing the vehicle or the functional attributes of the vehicle itself have formed the explanatory variables of the models. However, these approaches do not account for the influence that unobservable or subjective factors have over car use and ownership. Factors such as personal motivations (Steg et al., 2001a) and bonds with possessions (Chandler and Schwarz, 2010) have been shown to influence car use and ownership. In an attempt integrate these factors with passenger vehicle demand modelling, Choo and Mokhtarian (2004) developed a DCM based on a MNL specification which incorporated measurements of travel attitudes, personality, lifestyle and mobility factors to help explain vehicle type choice. Principal components analysis was used to estimate a number of the unobservable variables such as if an individual tends to associate car use with stress, the self image of their personality and what factors inform their lifestyles. The dependent variable used in estimating the model was specified by the type of vehicle a respondent most often drives split into eight categories ranging from small car to sports utility vehicle.
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Chapter Two: Literature Review Some of the key findings of the model are that individuals who have a dislike for car travel tend to prefer luxury cars, those classified as organisers are more likely to drive moderate cars whilst calmer people are more often minivan drivers. Respondents who are frustrated with their lifestyle are less likely to drive luxury cars and SUVs, whilst status seekers tend to have higher preferences for luxury and sports cars. This research is successful in demonstrating that aspects of an individual’s character, such as their personality or lifestyle, have an influence over vehicle preferences. However, the explanatory power of the model is modest with 17.7% of the variance in vehicle type explained by the independent variables. Moreover, the manner in which the socio-psychological factors are included in the model is unstructured leading to a lack of clarity regarding construct hierarchy. This section has provided an overview to some of the most influential papers in the field of market modelling and demand analysis for passenger vehicles that have taken an econometric approach. This review was not intended to be exhaustive but rather provide insight relating to how researchers have approached the subject, what methodologies have been used, how these methodologies have been developed and how effective they have been. For a more inclusive review of the early literature in this field, Mannering and Train (1985) provide a succinct analysis. 2.2.2.2 Low Emission Vehicle Market Whilst the initial research in modelling the passenger vehicle market took a holistic perspective, researchers soon began to focus on specific dimensions and niche sectors. Of interest to this thesis is the analysis of the market for LEVs which began almost in parallel with the general market modelling. Interest in the market demand for LEVs was primarily initiated by the two oil shocks experienced in the US during the 1970s, leading to concerns over the volatility of future oil prices and availability of supply (Harding, 1999; Raskin and Shah, 2006). With US oil production at the time declining and following the trajectory estimated by Hubbert (1956), interest in alternative fuels was sparked. One of the earliest studies examining the market for LEVs was conducted by Train (1980) who utilised the disaggregate market model developed in Lave and Train (1979) as a basis. However, instead of being used to explain revealed consumer preferences, the model was 23
Chapter Two: Literature Review adapted to be used in a predictive capacity. Six different alternative powertrain technologies were identified as being most feasible to reach market deployment including four varieties of EVs, one HEV and a hydrogen powered vehicle. Two scenarios were formulated to estimate market composition in the year 2000 and 2025. The first scenario reflects the most likely case and includes three EV powertrains whilst the second is more optimistic and contains all six of the LEV powertrains. In the most likely scenario, EVs are forecast to remain a niche market, only representing 2.2% of the market in 2000 and 2.5% of the market in 2025. For the more optimistic scenario, EV market penetration is estimated to be 9% in 2000 and 9.6% in 2025 whilst hydrogen vehicles only attain 0.59% and 0.62% market share. HEVs attain a higher share of the market with 6.3% penetration in 2000 increasing to 6.6% in 2025. The market forecasts by Train (1980) predict a modest degree of LEV market penetration even under optimistic conditions. Beggs et al. (1981) developed a DCM to assess what might be motivating this low level of demand for EVs in particular. Respondents were asked to rank order 16 different vehicles specified by 9 vehicle attributes. One of the principal findings of this study is that consumers place a high value on vehicle range which has a negative impact on preferences for EVs. Vehicle operating costs are a clear advantage of EVs over conventional vehicles though consumers tend to discount these savings. The model estimates discount rates ranging from 9% per year for individuals with high annual incomes increasing to 22% per year for individuals with low incomes. Thus, the operating costs savings of EVs do not adequately counterbalance the range limitation leading the authors to conclude that the market application of EVs is likely to be limited. Drawing from the work conducted by Train (1980) and Beggs et al. (1981), Calfee (1985) applied a DCM to estimate consumer demand for EVs through a niche market analysis. Respondents were asked to make a selection from 3 vehicles in 30 different experiments. The experiments were designed so that a number of different scenarios could be developed which include EVs with different performance levels. As was expected, predicted market shares for EVs was highly dependent on the levels of vehicle attributes such as price, operating cost, range and top speed. Market forecasts for EVs with modest ranges and significant price premiums, those that were most likely to be technically achievable at the 24
Chapter Two: Literature Review time, had virtually no market. Only in the scenarios where the EV had vehicle attributes on par with or superior to conventional vehicle options did the predicted EV market share increase to a significant level. A research team based at the University of California conducted a project (Bunch et al., 1993; Golob et al., 1993; Bunch et al., 1995) which examined preferences towards alternative liquefied and gaseous fuelled vehicles alongside conventional ICE vehicles and EVs. A unique feature of some of the alternative powertrains being developed in this period was their ability to operate using different fuel types, such as a vehicle that has the capacity to run on liquefied petroleum gas alongside conventional petrol. Another unique aspect of this project was that the choice experiment provided to each respondent was customized according to their specific socio-economic characteristics and current car details. This survey tailoring was employed to make the choice experiments appear more realistic. Key findings of this project were vehicle range displayed diminishing marginal utility, indicating that consumers value range improvements at lower levels more highly than improvements when vehicle range is already high. Reductions to vehicle emission levels displayed increasing marginal utility, signifying that consumers have relatively higher preferences for vehicles with significant, as opposed to incremental, improvements to vehicle emissions. Respondents hold strong positive preferences for clean-fuelled vehicles which have functional performance levels similar to conventional vehicles. Moreover, vehicles with a duel fuel capability are preferred to those that can only run from one fuel source. Additionally, vehicle use levels are simulated to estimate levels of fuel consumption and to predict the impact of EV charging. By the year 2005, clean fuelled vehicles were predicted to have attained a high degree of market penetration with over 40% of new car scales being fuelled by natural gas, whilst EVs, methanol fuelled cars and conventional cars each attain around 20% of the market. These market predictions are far removed from those estimated by Train (1980) and from those observed in the market (DoT, 2012) which raises doubts regarding the validity of the methodological approach. Taking a different approach, Greene (1985) acknowledges the previous findings which have indicated limited range is a significant barrier to the uptake of EVs and constructed a 25
Chapter Two: Literature Review longitudinal model to estimate daily vehicle use. Based on a large data set of driving activity, containing data for 2290 households recorded over 30 consecutive refuels, the model is extensive in its scope. The key finding of the model is that 95% of daily travel recorded in the US is less than 100 miles, identifying a large potential market for limited range vehicles. A clear limitation of this study is its complete focus on driving range. The model specified does not account for considerations such as other attributes unique to EVs or consumer preferences in general. Where this study excels however is that it indicates the aversion shown to EVs is perhaps not based on a rational appraisal of how a limited range is likely to affect travel behaviour and mobility. This finding suggests that other less tangible aspects are potentially influencing preferences towards LEVs making their appraisal transcend a purely economic evaluation of the associated costs and benefits. Up to this point, the research reviewed in relation to modelling preferences for LEVs has approached the subject from a distinctly functional perspective and has concentrated on objective measures relating to vehicle attributes and socio-economic considerations. Mau et al. (2008) use a different approach and examine if the degree of market penetration of HEVs and Hydrogen Fuel Cell Vehicles (HFCVs) affects preference levels, a factor they label the neighbour effect. To achieve this, a DCM following a MNL procedure is employed to appraise if preferences are higher in scenarios with higher degrees of LEV uptake. Results support the neighbour effect hypothesis for HEVs, with stated preferences significantly higher in the scenarios with higher levels of HEV market uptake, however not in the case of HFCVs. These findings suggest that sociological factors such as social norms are holding an influence over LEV preference. Taking a similar approach, and drawing on the work conducted by Choo and Mokhtarian (2004), Sangkapichai and Saphores (2009) consider the motivations behind the relatively high level of HEV uptake in California. Attitudinal data relating to beliefs about energy and the environment was analyzed using principal components analysis whilst preferences for HEVs were measured using a choice experiment. Principal research findings are that interest in HEVs is motivated by attitudes towards global warming, petrol prices and access to high occupancy vehicle lanes. Additionally, considerations for air quality and health only marginally influenced preference towards HEVs, indicating that individuals may not consider 26
Chapter Two: Literature Review their transport choices to significantly affect these issues. Whilst this study has demonstrated that socio-psychological constructs are indeed influencing LEV preferences, the structure of the constructs included is un-specified with no clarification of construct hierarchy which limits the structural validity of the model. More recently, attention has returned to forecasting market trends for LEVs to provide insight to policy makers relating to the timing and scope of any market transition. Eggers and Eggers (2011) examine potential adoption rates of HEVs, EVs and PHEVs in the German market through the application of a choice-based conjoint adoption model. Key findings of this model are that LEVs could account for 75% of new car sales in ten years time but demand for LEVs does not pick up until five years into the future. Of this LEV demand, over 50% is made up of demand for HEVs with PHEVs and EVs attaining lower market shares. However, the model makes some optimistic assumptions, such as EVs in ten years time will only attract a 20% price premium compared to ICE vehicles and have ranges of 155 miles, PHEVs are assumed to have a price premium of 10% with an all electric range of 60 miles whilst HEVs are predicted to have comparable prices to conventional ICE vehicles. Results indicate that consumers are still highly price sensitive and that a 20% increase in LEV prices over those assumed would reduce predicted market penetration in ten years to 32.9%. With this in mind, it can be proposed that the predictions of this study should only be considered as a best case scenario due to their dependence on significant technological advancement. Also appraising the German vehicle market, Lieven et al. (2011) examine what types of vehicles are most suited to the inclusion of an EV powertrain based on driver preferences. Specifically, vehicle types were examined based on their susceptibility to vehicle price and range limitation barriers. The evidence suggests that the vehicle types most suited to EV powertrains are city or micro cars, sports utility vehicles or offroaders and sports or leisure cars. In essence, individuals that tend to drive these types of vehicle tend also to display less aversion to vehicle price premiums and range limitations. From these findings, market share of EVs is set at a potential of 4.2% for primary cars and 6% for secondary cars. Whilst the argument for evaluating market potential based solely on aversion to price premiums and range limitations appears logical, it significantly reduces what is generally considered to be a
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Chapter Two: Literature Review complex purchasing decision (Heffner et al., 2007). This limitation raises concerns regarding the robustness of the approach and the validity of the predictions made. Taking a slightly different approach to forecasting LEV market adoption, Musti and Kockelman (2011) estimated the fleet composition in Austin, Texas, in the year 2034. To achieve this, a microsimulation model was developed that included components related to vehicle ownership based on a DCM and vehicle use patterns based on a forecasts of vehicle miles travelled. Concentrating on the DCM, respondents were asked to select from a list of twelve car types (such as compact, pickup truck etc.) which included a HEV and PHEV option, across four different predefined scenarios that varied on such aspects as expectations of fuel price, environmental concerns and LEV tax reductions. Key findings are that expectations for increased petrol prices are associated with higher preferences for HEVs and PHEVs. Interestingly, providing information concerning the environmental implications of vehicles at the decision stages positively influences preferences for PHEVs but not for HEVs. Relating to market forecasting, under trend market conditions, PHEVs are predicted to account for 6.1% of the local vehicle fleet and 6.4% for HEVs by 2034. The scenario which generates the highest degree of LEV market penetration includes a feebate taxation system where the market shares are 4.7% and 14.4% respectively. These forecasts are distinctly low and are far removed from those predicted by Eggers and Eggers (2011) leading to questions over which represents a valid assessment of market potential. Indeed, the forecasts estimated by the research team at the University of California (Bunch et al., 1993; Golob et al., 1993; Bunch et al., 1995) have already demonstrated that predictions of future activity in this market can be widely inaccurate. This is perhaps due to the inherent instability present in this emerging market leading to exogenous factors (Berkovec, 1985) which are difficult to account for. Moreover, the complexity of the purchase decision (Heffner et al., 2007) and the wide variation in use behaviours (Miller, 2001) suggest that any such market predictions, which are based on highly reduced models, should be considered with caution. 2.2.3 Overview of Econometric Demand Analysis The preceding section provided a detailed examination of the literature which utilises econometric models to explain consumer preferences and forecast trends in the passenger 28
Chapter Two: Literature Review vehicle market. Partitioned into two different sub-sections, studies assessing the general vehicle market and LEV preferences have been presented and discussed. This research theme has, until recently, been the dominant paradigm applied in the field of LEV demand and has provided a number of keen insights. The quantitative nature of the car market has allowed for the development of DCMs which explain consumer choices in different situations (Lave and Train, 1979). These models have been refined to enhance their validity and expanded to include a more complete representation of the market (Mannering and Train, 1985). The output from these disaggregate models is useful to policy makers by offering insights relating to market response to different stimuli. Whilst modelling market conditions at the sector level is valuable in its own right, DCM can be scaled down to examine niche markets and subsectors. Of specific interest to this thesis are consumer preferences for LEVs which have benefited from detailed attention (Ewing and Sarigollu, 1998). Researchers have examined aspects including sensitivity to vehicle range limitations, price premiums and the discount rates applied to future operating cost savings (Beggs et al., 1981; Calfee, 1985). Furthermore, the market potential for LEVs has been extensively examined, with early market predictions ranging from distinctly muted (Train, 1980; Musti and Kockleman, 2011) to exceptionally optimistic (Bunch et al., 1995; Eggers and Eggers, 2011). More recently, researchers have integrated attitudinal measurements into DCMs to expand from the hitherto functional approach favoured. Results indicate that the addition of psychological aspects related to the individual purchasing the car and the sociological environment present in the market significantly affect preferences. Dimensions connected with lifestyles, personality and transportation attitudes significantly influence the type of car an individual drives (Choo and Mokhtarian, 2004). The degree of market uptake of LEVs in particular influences the level of preference expressed by an individual (Mau et al., 2008) whilst environmental concerns, the prestige of HOV lane access and attitudes towards fuel prices have all been identified as motivations for LEV adoption (Sangkapichai and Saphores, 2009).
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Chapter Two: Literature Review Whilst these recent pieces of research have demonstrated that socio-psychological aspects are exerting a significant influence in the emerging market for LEVs, the models tend not to be based on a structured design. Specifically, socio-psychological constructs have tended to be introduced into models in a single batch alongside socio-economic characteristics (Sangkapichai and Saphores, 2009) with little attention paid to how these constructs are related to one another. This significantly limits the validity of the models, as conceptual distance and level of abstraction have been shown to appreciably influence human behaviour (Trope and Liberman, 2010). Moreover, the models developed consider the consumer market as a single entity, disregarding the importance of market segments which often have idiosyncratic features and profiles which are distinctly different from other consumer groups. Responding to these highlighted limitations, new research themes in LEV demand are emerging. Firstly, the importance of socio-psychological constructs have been strengthened by the development and application of conceptual frameworks which demonstrate that LEV preferences are influenced by a complex assortment of different constructs positioned at different levels of abstraction. This has been complemented by the application of qualitative methods which have identified novel aspects which hold influence over consumer reaction to LEVs. Secondly, new research has revealed that a significant degree of stratification exists in the market for LEVs with the structure suggesting unique segments are beginning to emerge. The remainder of this chapter discusses the research so far conducted in these two areas to illustrate what has so far been learned and to highlight areas which have yet to be examined. 2.3 SOCIO-PSYCHOLOGICAL EXAMINATION In the previous section, this review examined consumer demand for vehicles, often through the application of DCMs or other econometric based approaches. For the purposes of LEV demand, this has been applied by utilising hypothetical choice experiments to elicit preferences for vehicles where no revealed market data exists. Turrentine and Sperling (1992) provide a strong critique of these modelling approaches stating that they require respondents to be well informed about the alternatives presented in the choice experiments 30
Chapter Two: Literature Review in order to make effective comparisons. With LEVs embodying novel attributes which consumers have no experience with, Turrentine and Sperling question whether respondents can make informed decisions in such circumstances. Following this critique, researchers in this field have drawn on theories and methods from subjects such as psychology, sociology, and anthropology to address these highlighted limitations. This research approach has been less concerned with predicting and forecasting consumer preferences and more focused on explaining preferences through an examination of motivations, attitudes and behaviours which are often less substantively rational (Simon, 1973) and therefore less suited to economic analysis. This section first offers an overview of the theories which have been applied in this area before examining the empirical research which has taken place. 2.3.1 Theoretical Basis Applied research has drawn from two distinct theoretical approaches to assessing the importance of socio-psychological constructs over consumer response to LEVs. The first of these approaches, generally referred to as psychometric modelling, uses quantitative techniques to measure unobservable characteristics such as attitudes, values and emotions to assess their importance over LEV preferences. These psychometric models are often employed using regression analysis or structural equation modelling to assess their validity. Alternatively, researchers have used qualitative assessment techniques to evaluate the importance of socio-psychological constructs. Employing interviews or focus groups, research participants are generally asked to reflect on and express their opinions relating to LEVs with trends in responses evaluated by the researcher. This later approach can often inform the development of quantitative instruments in the former. These two theoretical approaches are introduced and described in this section commencing with psychometric modelling and concluding with qualitative assessment. The Theory or Planned Behaviour (TPB) as defined by Ajzen (1991), which itself is an extension of the previously formatted Theory of Reasoned Action ( Fishbein and Ajzen, 1975; Ajzen and Fishbein, 1980), attempts to explain why individuals behave in certain ways or make certain decisions. Applications of the theory are widespread in the social sciences such as health studies (Godin and Kok, 1996), transportation behaviour (Evans and Norman, 1998) and sports psychology (Bozionelos and Bennett, 1999). An illustration of the theory is 31
Chapter Two: Literature Review presented in Figure 2.2 which states the main conceptual components and how they are related. The theory requires the assumption that intentions act as a good predictor of behaviour. Intentions themselves are influenced by three primary determinants. Firstly, individuals form attitudes about behaviours based on the likely consequences which relate to the expected outcome. Secondly, behaviours are associated with social norms based on how the individual perceives the responses to a behaviour by an external audience. Thirdly, the theory accounts for the fact that not all behaviours are directly controllable and often there are different degrees of perceived behavioural control. In sum the theory states that intentions and, ultimately, behaviours are determined by beliefs relating to the consequences attached to a behaviour, how this behaviour will be perceived by society and the volitional control an individual has over the situation. Normative Beliefs
Attitude towards Behaviour
Control Beliefs
Social Norms
Behavioural Beliefs
Perceived Behavioural Control
Intention
Behaviour
Figure 2.2: Illustration of the Theory of Planned Behaviour (Ajzen, 1991) Armitage and Conner (2001) provide a critical appraisal of the research which has applied the TPB and highlight a number of areas of contention. Firstly, the TPB is often applied through the subjective measure of intention to perform a behaviour. This subjective measure, whether it be through intention, desire or self prediction, often provides better model fits compared to more objective measures or observations of revealed behaviour. Secondly, perceived behavioural control often proves to be the primary determinant in the model yet debate still exists relating to the specific strategy used to measure it. Thirdly, social norms are often found to be the weakest determinant though this may be a result of 32
Chapter Two: Literature Review ineffective measurement practices. Though the TPB does contain a number of important limitations, a primary advantage is related to its versatility. This versatility has led to it being widely applied allowing for straightforward comparisons between different research projects Viewed as a specialised extension of the TPB, the Technology Acceptance Model (TAM) (Davis, 1989; Bagozzi et al. 1992) and its subsequent revisions (Venkatesh and Davis, 2000; Venkatesh and Bala, 2009) present a deterministic model aimed at explaining user response to technology. Initially designed to examine acceptance of information technology in workplaces, the TAM has subsequently been applied in other related fields such as ecommerce (Gefen et al., 2003) and on-line gaming (Hsu and Lu, 2004). According to the TAM, the adoption or use of a technology is influenced by two primary determinants. Firstly, the perceived usefulness of the technology is appraised by an individual based on considerations such as the technology’s output quality and relevance to the activity or job. Secondly, individuals evaluate the perceived ease of use of the technology based on such aspects as enjoyment and complexity. An illustration of these conceptual constructs and how they are linked together is provided in Figure 2.3. Perceived Usefulness
Behavioural Intention
Use Behaviour
Perceived Ease of Use
Figure 2.3: Illustration of the Technology Acceptance Model (Davis, 1989) Whilst the TAM has generally been well received by the academic community, its application has somewhat focused in the field of information technology. Turner et al. (2010) provide a systematic literature review of empirical TAM studies and determine that previous 33
Chapter Two: Literature Review applications have tended to rely on measurements of behavioural intention as opposed to observed adoption. This is important because perceived ease of use and perceived usefulness are more likely to be related to behavioural intention and not use behaviour, leading to concerns over the validity of the primary determinants. Bagozzi (2007) discusses the legacy of TAM and offers a proposal for a paradigm shift to propel the theory to more widespread acceptance and application. Specifically, Bagozzi describes the weakness between the intention and behaviour linkage and views it as an area which could benefit from additional study. In addition, whilst there are occasions when the adoption and use of technology is purely an individual decision, it is often the case, especially in the workplace, that the decision is taken at a group level. Further improvements to the structure of the TAM may consider addressing this importance of group action to broaden the scope of the model. With environmental issues such as climate change and damage to local environments gaining more traction in society over the last 25 years, creating a framework to explain environmental behaviour became an important issue. The Value-Belief-Norm Theory (VBN) (Stern et al. 1999) is an integrated theoretical framework containing components drawn from different models with the purpose of explaining pro-environmental behaviour. Included in the VBN theory are components taken from the Norm Activation Model (NAM) (Schwartz, 1977) and the New Ecological Paradigm (NEP) (Dunlap et al. 2000) which are integrated through the inclusion of components related to awareness of consequences and ascription of responsibility to certain situations or actions. An illustration of the components included in this psychometric model is provided in Figure 2.4. Developing theoretical approaches by combining elements of different theories together is becoming more common in applied research. It can provide an interdisciplinary perspective on an issue, potentially uncovering new features which would have remained hidden whilst using standard models. However, there are a number of limitations such as the inability to directly compare research findings with other projects and how to initially specify which elements to take from which theories.
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Chapter Two: Literature Review
Figure 2.4: Illustration of the Value Beliefs Norms Model (Stern et al., 1999) The TPB, TAM and VBN theory, though not economic in their approach, share the similarity that they all explain individual behaviour through a structured framework suited to statistical analysis. In essence, they are reductionist in nature and attempt to confine human action to a limited and predefined list of determinants. Moreover, these psychometric models often concentrate on individualistic behaviour and do not adequately account for social and interpersonal behaviour. To counter this reductionist approach, researchers have developed qualitative techniques which are an umbrella category for methods that often employ small samples of participants and are aimed at attaining a more in-depth perspective on a topic of interest. Qualitative research can be employed in a number of different fashions such as interviews, focus groups and ethnographies. In the LEV field, semi-structured interviews with individuals or households have become more popular with researchers. In a semi-structured interview, respondents are asked a number of preformatted questions with the dialogue being allowed to progress in a natural fashion (Longhurst, 2003). Participants are encouraged to answer questions in an open format which best reflects their views, attitudes and opinions. The nature of semi structured interviews makes the data collected applicable to multiple forms of analysis and examination. One such method is Grounded Theory (GT) (Glaser and Strauss, 1967) which is a bottom up approach where theory and understanding is discovered through 35
Chapter Two: Literature Review the examination of data. No hypothesis is formulated in advance with researchers encouraged to examine the data from multiple viewpoints. GT has primarily been applied in the social sciences though has found traction in transportation research. Gardner and Abraham (2007) examine motivations for driving to work whilst Pappu and Mundy (2002) investigate strategic buyer-seller relationships in transportation based transactions. However, the approach is not without its limitations with concerns being voiced relating to how GT approaches the concepts of theory, ground and discovery (Thomson and James, 2006). In relation to car use and ownership, it is apparent that symbols and their related meanings are often attached to specific types of car (Dittmar, 1992) and can frequently assist in representing personal identity (Belk, 1988). The broad method of Semiotics (Eco, 1979; Clarke, 1987) allows researchers to examine the effect of signs, symbols and their associated meanings on human behaviour. In a similar fashion to GT, Semiotics was initially employed in the social sciences though has recently become more popular in transport related research. Divall and Revill (2005) use the approach to conceptualise the innovative and controversial aspects of transport history whilst Wagner (2006) examines the visual semiotics of road signs. To summarise this section, researchers examining the importance of socio-psychological constructs over individual behaviour have approached the topic from two distinct angles. Whilst attitudes, opinions and emotions cannot be directly observed, they can be indirectly measured through the application of psychometric analysis. This allows for the development of quantitative models which explain certain behaviours through a defined list of determinants that hold influence over the intention to act in a specific manner. Conversely, researchers following a constructivist ontology reject the application of statistical methods in social science and instead qualitatively examine and interpret narratives to identify shared themes. Qualitative approaches are often open in nature, granting a degree of flexibility by allowing research participants to express their specific points of view on a topic which are not constrained by the expectations or hypotheses of the researcher. The next section in this chapter discusses how these forms of approach have been applied in the analysis of demand for LEVs by presenting and critically assessing the relevant literature. 36
Chapter Two: Literature Review 2.3.2 Applied Research In a similar fashion to the econometric demand analysis section, the applied research which examines the importance of socio-psychological constructs has been partitioned into two categories. These categories reflect the theoretical approaches utilised by the researchers with the first concerned with psychometric modelling whilst the second employs qualitative analysis. This section of the chapter presents and discusses the key findings so far made in this area whilst highlighting the topics which remain unexplored. 2.3.2.1 Psychometric Modelling Approaching the issue of vehicle fuel efficiency from a psychological perspective, Peters et al. (2011a) develop a model which integrates components sourced from the TPB and NAM alongside a measurement of symbolic motives to identify the primary psychological determinants influencing how individuals conceive fuel efficiency when purchasing a new vehicle. The model components include attitude, personal norm and perceived behavioural control which themselves are influenced by social norms, response efficacy, symbolic motives and problem awareness. Independent variables related to these determinants are calculated using principal components analysis whilst the dependent variable in the model is the carbon dioxide emissions of the current car owned. Interpreting the results of the model, it appears as though individuals tend to associate new technology, new fuels and environmental soundness with fuel efficient vehicles. Conversely, fuel efficient vehicles are also associated with less power, slower acceleration and small size. In addition, safety concerns, decreased comfort and a boring image are not associated with fuel efficient vehicles. However, perhaps the most important finding of this project is that symbolic motives related to vehicle ownership are negatively influencing personal norms connected to fuel efficiency, indicating that this may be restricting preferences for fuel efficient vehicles. Whether or not this is a result of fuel efficient vehicles not being associated with desirable symbols or being connected with the undesirable symbols remains to be determined. Examining the attitudinal statements that have gone into the formation of the factor associated with symbolic motives, elements of car appeal, instrumental function, car importance and car uniqueness are included. However, there is no mention of
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Chapter Two: Literature Review attachment, identity or personal representation leading to only a partial measurement of symbolic motive. A significant limitation of the TPB relates to the tendency of applied research to rely upon a measurement of behavioural intention as opposed to observed behaviour. In reference to the adoption of green products and behaviours, this is generally referred to as the attitudeaction gap whereby individuals tend to state high preferences for the environment but do not translate this attitude into the purchase of environmentally friendly products (Kollmuss and Agyeman, 2002). Lane and Potter (2007) explore this phenomenon and the possible implications it may have for the adoption of LEVs. In their study, the authors employ the TPB to define the concept and note that, with attitudes being more closely related to intentions as opposed to actual behaviour, it is possible that exogenous factors are influencing the link between intention and behaviour. In a similar piece of research, Peters et al. (2011b) further examine the importance of socio-psychological constructs over behavioural intentions by developing an integrated theoretical framework, which incorporates components sourced from the TPB and TAM, it in an attempt to build an explanatory model of preferences for EVs. Specifically, the components perceived ease of use from the TAM and social norms from the TPB are measured. Social norms appear to be influencing EV preferences whilst perceived ease of use is only significant in one of the five models calculated. Jansson et al. (2011) overcome this limitation by sourcing a sample that includes both adopters and non-adopters of Alternatively Fuelled Vehicles (AFVs) in Sweden, allowing for the model to reflect the influence of explanatory components on observed behaviour. The theoretical framework utilised in this study is the VBN theory applied through a binary logistic regression analysis with the dependent variable being if a respondent had adopted a AFV or not. A number of interesting results are seen in the model, notably that socioeconomic characteristics alone are a poor indicator of AFV adoption. Conversely, the attitudinal constructs incorporated into the model explain a significantly greater percentage of the variance. Specifically, egoistic values, ascription of personal responsibility for environmental issues and personal norms all hold statistically significant explanatory power. The findings of this project, along with others which include attitudinal components, indicate that socio-psychological constructs are an important consideration in the adoption of LEVs. 38
Chapter Two: Literature Review 2.3.2.2 Qualitative Assessment One of the earliest projects to investigate the market for LEVs without relying on econometric based modelling was conducted by a research team based at the Institute of Transportation Studies within the University of California at Davis (Kurani et al. 1994; Kurani et al. 1996; Turrentine and Kurani, 1998). This project offers a detailed examination of household demand for EVs. The nature of the project is multifaceted and structured to overcome the limitations of previous research. To begin, the researchers developed and employed an Interactive Stated Lifestyle-Preference Interview. This interview used data collected though a week-long travel diary to structure a Purchase Intention and Range Estimation Game aimed at deriving a detailed understanding of how households consider their vehicle requirements. The main findings of this project are that previous studies have improperly framed EVs as a replacement to a current vehicle in household fleets as opposed to a new component. In the interactive interviews, households often explored how to allocate an EV among their activities and tended to conclude that a 100 mile vehicle range is enough to meet their transport needs. This finding indicates that households, when given the opportunity and the right information, actively participate in evaluating adaptive behaviours linked with fleet management. Households were likely to consider range limitations in their second vehicles to be of reduced importance in comparison to their primary vehicle. Home recharging was seen as the most important EV asset whilst those respondents more concerned about the environment tended to have slightly higher likelihoods to select an EV. This project has had a profound impact on the field of LEV demand which was, at the time, reliant on econometric modelling techniques. This qualitative and in-depth approach has provided significant insight into how households evaluate their fleets and respond to LEV technology. At a more general level, Turrentine and Kurani (2007) make use of semi structured interviews to investigate how households consider the fuel efficiency of their vehicles. No household from the 57 interviewed made firm commitments to attaining higher fuel economy in their next vehicle purchase and often lacked the basic financial skills required to evaluate the benefits and costs associated with improved efficiency. This finding shares parallels with research which has shown that drivers tend not to rationally appraise fuel 39
Chapter Two: Literature Review efficiency expressed in miles per gallon (Anable et al., 2008; Larrick and Soll, 2008). Additionally, few households accurately tracked fuel prices and often based their responses to fuel economy questions on the cost of their most recent refuel. Surprisingly, current HEV owners did not display superior knowledge or capability with fuel efficiency, though households which used a separate credit card for fuel payments had a better grasp of how fuel price and fuel efficiency translate into financial implications. These results indicate that households generally do not consider fuel efficiency in a systematic or economically rational manner and so cannot be assumed to make informed financial decisions regarding potential LEV purchases which often require cash flow extrapolations over 5 years into the future. Focusing on the symbolic meanings being developed and communicated by HEV owners in California, Heffner et al. (2007) conducted semi-structured interviews with 25 HEV households to examine their thoughts on the social and personal meanings attached to HEV ownership. With HEVs being available in California for over 10 years and in excess of 250, 000 being sold in the US in 2006, expectations were that enough time had passed for a symbolic framework to be established around HEV ownership. Data were analysed using semiotics to develop illustrative maps relating to the symbolic structures households connect to their HEVs. Throughout the exercise, emphasis was placed on keeping the complexity and richness of the narrative, in what the authors refer to as thick description, which rejects simplification or reductionism. Having thoroughly reviewed the household narratives, the authors identified a number of common themes, referred to as denotations, which tend to be shared among participants. These denotations are orientated around issues regarding environmental preservation, opposition of foreign wars related to oil, management of personal and household finances, reducing the support to oil producing nations and multinational oil companies and embracing new technologies. The authors note that previous references to HEV owners as environmentalists or technology enthusiasts are too simplistic. Findings suggest that HEV owners actively manage their identities through their car and use it as part of their self narratives, providing support to the view that car ownership and use transcends functional considerations.
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Chapter Two: Literature Review Whereas Heffner et al. examine the possible motivations of HEV adoption in California, Dutscheke et al. (2011) examine acceptance of LPG and CNG vehicles from a German perspective to determine what lessons can be learned from consumer reaction to these vehicles. Through the application of an extensive literature review and 12 in-depth interviews with adopters, the authors research what the primary motivations were for adopting a CNG or LPG powered car. From the literature, a number of mixed results were found with some studies indicating that 75% of German drivers are aware of CNG and LPG vehicles when directly asked, decreasing to only 20-28% of drivers when not prompted with the name of the technology. The main concerns regarding adoption relate to the limited infrastructure, restricted range, initial investment, low reliability and safety of the vehicles. Primary motivations for adoption were financial in nature with environmental concerns often ranked as secondary. Most often, adopters became aware of CNG and LPG vehicles though communicated experiences from friends, family and work colleagues. The in-depth interviews proved useful in expanding on a number of issues identified in the literature review. Firstly, economic advantage is supported as a primary motivation but a general enthusiasm for technology is also regularly mentioned. Prior concerns relating to the vehicles are often mitigated by experience and planning. Information from actual users of the vehicle was held in high regard whilst official sources were thought to be affected by vested interests. Social response to adoption tended to be varied with some adopters praised whilst others were subjected to derision. As the diffusion of the current generation of LEVs is sparse, it often proves challenging to identify current adopters that are willing to participate in research. To overcome this obstacle, the prevalence of LEV trials has increased, due in no small part to a more active role being played by governments and manufacturers. Caperello and Kurani (2012) investigate response to PHEVs in a six week trial in California primarily through a series of interviews supplemented by pre and post trial surveys, in car data recorders and research field notes. The authors subject the data to thematic analysis to identify common issues. Through this analysis, the authors observe that participants were often confused about PHEV operation even after an extended period of time with the vehicle, supporting Axen and Kurani’s (2008) finding of low public awareness of PHEV technology, though they stated how 41
Chapter Two: Literature Review recharging quickly became part of the household routine. Concerns were often raised regarding recharge etiquette such as when at a friend’s house or restaurant. Additionally, using a PHEV often changed participant’s driving behaviour with a number referring to attempts to maximise the in car miles per gallon (MPG) display through optimising gear shifting and acceleration. The last primary theme relates to the concept of financial payback with participants keen to examine the financial implications of owning a PHEV though the calculations they put forward are often fragmented. Graham-Rowe et al. (2012) conduct a similar study in the UK with EV and PHEV trials where participants were provided access to the vehicles for a seven day period. The sample is split with 20 participants each being assigned to an EV and PHEV. Semi-structured interview were undertaken directly after a participant had completed the trial period and then analyzed using GT to identify core categories of response and potential barriers to adoption. Through this procedure, six different response categories have been identified. Firstly, participants raised concerns relating to the up-front cost of the vehicles being significantly higher than conventional cars. In a similar fashion to Caprello and Kurani (2012), when participants attempted to conduct a financial analysis of the benefits and costs, they often confused terms and used imprecise procedures. Concerns were also raised about the lifespan of the battery packs having the potential to significantly affect operating cost projections. The second theme relates to vehicle confidence with participants tending to raise concerns regarding the capability of EVs in certain circumstances. This translated into reduced levels of pleasure participants received from driving EVs, indicating the importance of emotive response. The use of EVs and PHEVs by participants often required adaptations to their behaviour which a number viewed as a hassle. The in-car display received mixed reviews by participants with some considering it to be distracting whilst others benefited from the feedback. Additionally, participants tended to consider recharging the battery of the EV or PHEV as dead time. Moreover, participants also stated mixed responses to the supposed environmental benefits of driving an EV or PHEV. A number stated that driving one of these vehicles bestowed a feel-good factor whilst others expressed the view that it reduced their perceptions or guilt. However, many participants were more sceptical about the environmental implication and 42
Chapter Two: Literature Review questioned the reduction in carbon emissions attributed to these vehicles, perhaps indicating the impact of a number of critical articles that have appeared in the mass media (BBC, 2012). Comparable to the findings of Dutschke et al. (2011), social response to the vehicles was often mixed, with a number of participants expressing felt embarrassment when driving linked with the feeling of being on display and standing out, whilst others conveyed feelings of inferiority when their vehicle was compared to a more glamorous car. Conversely, a cohort of participants did experience positive social response, finding that they received esteem from their social networks. Lastly, participants tended to believe that EVs and PHEVs were still a work in progress, which was seen as a significant barrier to adoption with concerns relating to rapid depreciation of value and the technology becoming quickly dated which shares parallels with a secondary finding from Caprello and Kurani (2012) who found respondents still considered PHEVs to be a future technology. 2.3.3 Overview of Socio-Psychological Examination Early research examining consumer demand for LEVs focused almost entirely on objectively measureable variables, such as the functional attributes of the vehicles and the socioeconomic characteristics of the consumer, to construct statistical models aimed at explaining preferences. These models came under criticism in regards to the manner in which the preference data is sourced and in the reductionist approach (Turrentine and Sperling, 1992). Research which has examined cars as a social artefact has shown that cars are a possession embedded with personal meanings, social identity and shared experience (Miller, 2001; Dittmar, 2001). To account for these subjective aspects, researchers have drawn from theories and approaches present in the fields of psychology, sociology and anthropology. Through the application of theoretical frameworks, researchers have evaluated the influence of socio-psychological constructs in the emerging LEV market. A distinction can be made between the situational factors associated with instrumental features of the vehicle, government policy and infrastructure provision and the psychological factors linked to attitudes, beliefs and social norms (Lane and Potter, 2007). Concentrating on the psychological factors, the concept of fuel efficiency tends to be associated with environmental soundness, new technology and new fuels but also to negative aspects such as less power and smaller sized vehicles (Peters et al., 2011a). Concerning intended and 43
Chapter Two: Literature Review actual adoption of LEVs, this process appears to be motivated by a variety of distinct constructs including social norms (Peters et al., 20011b) and value structures (Jansson et al., 2012). Utilising qualitative methods, researchers have examined how individuals consider LEVs through the application of household interviews. Narratives have been evaluated to identify shared themes which appear to be influencing vehicle evaluations. Research of this nature has found that LEVs were perhaps improperly framed in previous econometric analysis due to the reliance on quantifiable information (Kurani et al., 1994). The use of reflexive techniques, which allow respondents to consider at length the implications of LEV adoption, have demonstrated that LEV aversion appears to diminish after prolonged deliberation (Kurani et al., 1996). Additionally, the value of LEVs is not entirely constrained by their associated purchase and operating costs, but can also be defined as a symbolic expression of an owner’s identity, which is often complex in nature (Heffner et al., 2007). Previous studies which have generally referred to LEV adopters as environmentalist or technology enthusiasts may well be overly simplistic. Examining the subjective experiences of LEV trial participants, themes associated with social response, fuel efficiency optimisation and technology development were found to be salient (Caperello and Kurani, 2012; Graham-Rowe et al., 2012). However, research examining the importance of socio-psychological constructs has tended to overlook the importance of dynamic aspects. The market tends to be represented in black and white terms, with individuals partitioned into those that state they intended to adopt a LEV and those that do not. Individuals are generally neither considered based on their likelihood of LEV adoption nor how their adoption rates vary over time. Moreover, the emerging LEV market tends to be regarded as distinctly homogenous with individuals only varying on a small number of characteristics. Little attention is given to how individuals can be grouped together based on their shared similarities and contrasted against other heterogeneous groups. Indeed, the profiles of these groups are often highly distinctive, and suggest that certain groups may respond idiosyncratically in a given situation. The next section of this review presents and discusses the limited literature which has examined the
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Chapter Two: Literature Review issue of market structure to demonstrate what areas have so far been examined and identify aspects which remain unexplored. 2.4 MARKET STRUCTURE AND CONSUMER SEGMENTATION In the early years of vehicle manufacturing, the choices available to consumers were limited with only a few companies producing cars which were highly generic in nature. As the market developed, the variety of vehicles available for purchase become more diversified with niche sub markets emerging and vehicles being designed for specific purposes. Manski and Sherman (1980) refer to the modern automotive market as offering a vehicle universe to consumers. This market trend is set to continue through the addition of more personalised features aimed at connecting an owner more intimately with their car. It is unsurprising that this car universe is matched by a rich and varied consumer base. Whilst each driver has a unique character, certain drivers are likely to share similarities with others. Sub cultures of drivers have emerged, often focused on a market niche (Miller, 2001). Moreover, certain driver types are often associated with different car types and vice versa (Westfall, 1962). Market environments that include heterogeneous groups of consumers are often suitable to segmentation analysis and the automotive market clearly has a rich environment for segments to form and develop. From a transportation perspective, market segments are important as they often have unique travel behaviours and idiosyncratic responses to stimuli (Anable, 2005). In relation to LEVs, those segments which are more receptive to LEVs are important as they may act as gatekeepers to the mainstream market. In essence, some market segments can be of critical importance to the future adoption of a new product or innovation and can have profound influence over the diffusion process, or lack thereof. This section firstly describes the theoretical foundations of market segmentation before presenting the research in the LEV field which has specifically examined the market from a segmentation perspective.
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Chapter Two: Literature Review 2.4.1 Theoretical Basis Market segmentation itself is a somewhat broad term which can be equally applicable when describing the sub culture of joyriding as to the purchase of a specific vehicle type. To keep this section focused, the discussion put forward here limits its scope to market segmentation as it has been conceptualised when examining the adoption of new innovations. Indeed, market segmentation and the adoption of innovations are often related concepts with companies that are considering releasing a new product or innovation into the market keen to determine what target market segment is likely to be most receptive to it. Within this field, the approaches that have gained most traction can be partitioned into two main categories. The first of these categories is concerned with the mathematical modelling of market diffusion of innovations whilst the second focuses more on the segments themselves and details their main features. The work conducted by Bass (1969) has been significant in directing the research in the first category though the formulation of a growth model for consumer durables. In this model, consumers are partitioned into those that are innovators and are among the first to adopt an innovation, and those that are imitators. Innovators are conceived as being individuals that are not influenced in their adoption decision by the actions of other members of a social system whilst imitators, conversely, are influenced. The critical assumption made in this theory is that the probability of the timing of an initial purchase by an imitator is based on a function of the number of previous buyers. Thus, the number of innovators that initially adopted the product will have significant implications for the trajectory of the market adoption curve. The mathematic specification of the model is formulated so that the diffusion will initially go through an exponential growth phase, followed by a peak and then an exponential decline in adoption rates. The timing of the peak, alongside the rates of adoption increase and decrease either side of this peak, will be dependent on the initial market conditions. The initial specification of the Bass Diffusion Model was widely accepted by the academic community, most notably in the marketing and economic fields, and initiated a significant degree of further academic research. Mahajan et al. (1990) present an overview of the developments which have taken place with the model improving to account for multiple 46
Chapter Two: Literature Review stages of adoption, diffusion through space as well as time and the effects of competing brands. In essence however, the Bass Diffusion Model remains a tool useful in forecasting and predicting adoption levels. It operates at an aggregate market level and provides little insight relating to why certain consumers in a market adopt faster than others. In sum, it is helpful at answering the questions relating to when and where an innovation will be adopted but sheds little light on the questions relating to why an individual decides to adopt an innovation. Rogers (1995) proposes the Diffusion of Innovations Theory (DOI) to address the question of why certain individuals have a greater propensity to behave in an innovative manner. This theory puts the individual at the centre of analysis, being less predictive in nature and more explanatory. Multifaceted in its approach, the DOI theory incorporates elements which define the decision making process of whether to adopt an innovation, the influence of social networks and their structure along with how the innovation is critically appraised by individuals. Of particular interest to market segmentation, the DOI theory partitions consumers, based on the time it takes them to adopt an innovation, into five main categories. Those individuals that are among the first to adopt a new product are referred to as innovators whilst those entering the market directly after are labelled early adopters. Following these two segments is the bulk of the market, partitioned into the early majority and the late majority. The segment which takes the longest time to adopt an innovation is referred to as laggards, who tend to have the most prolonged innovation adoption decision process. Within the DOI theory, levels of adoption tend to follow a normal distribution with an Scurve cumulative distribution, as displayed in Figure 2.5. This representation can make it seem as if there is a smooth transition between the different market segments. However, Moore (1999) argues that a gap often exists between the early adopters and the early majority, who are viewed as being more pragmatic. This gap reflects why the large majority of innovations are not widely adopted by society as the needs and requirements of the mainstream market are often much different compared to the preferences of innovators and early adopters.
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Chapter Two: Literature Review
Market Penetration S-Curve: Cumulative Distribution Bell Curve: Temporal Distribution
Innovators
Early Adopters
Early Majority
Late Majority
Laggards
Time
Figure 2.5: Illustration of the Diffusion of Innovation Theory (Rogers, 1995) A common criticism of the DOI theory reflects its positive stance on innovation, considering individual innovativeness to be a desirable trait and often overlooking a more critical appraisal of the associated social and personal implications of adoption. Moreover, the theory tends to neglect issues surrounding non-adoption of an innovation. Approaching this later issue, Bagozzi and Lee (1999) develop a framework which accounts for both acceptance and resistance to innovations. This framework structures the adoption decision as a combination between a goal setting and goal striving procedure. Moreover, Kleijnen et al. (2009) define a conceptual framework to explore consumer resistance to innovation and propose the major components to be postponement, rejection and opposition. These components share similarities with the reoccurring theme in the LEV field which attempts to identify barriers to adoption though often approaches investigations from a deductive stance.
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Chapter Two: Literature Review Closely related to the concept of innovation diffusion is the approach generally referred to as Strategic Niche Management which represents a comprehensive management and analysis tool designed to assist in understanding how a technological innovation progresses from an idea to the successful introduction in the mainstream market. Initially created as a strategy to assist in shifting technological systems to a more environmentally sustainable trajectory (Schot et al., 1994; Hoogma et al., 2005, Caniels et al., 2008; Schot and Geels, 2008), Strategic Niche Management defines how a technological innovation can surmount a dominant technological regime. The establishment of technological niches where an innovation can evolve is viewed as a critical stage. These technological niches provide a protected space whereby artificial support mechanisms can be enacted to allow the innovation to be insulated from the competition of the mainstream market. Experiments and demonstration projects can take place within the technological niche to further refine the innovation and generate information and knowledge regarding consumer response. Once the innovation has progressed to a level whereby it can survive without the support system of the technological niche, the protected space is removed and the innovation transfers into a market niche. If the innovation is successful in the market niche, it may diffuse and shift the current technological regime onto a different pathway. Applications of Strategic Niche Management in the field of transportation have principally focused on the transition to environmentally sustainable pathways. van der Laak et al., (2007) used Strategic Niche Management to evaluate the successes and failures of three biofuel experiments in the Netherlands. In a similar piece of research, van Eijck and Romijn (2008) examined the prospects for a specific biofuel feedstock through an analysis of the technological landscape, regime and niche. Specifically related to the nature of this thesis, Lane (2007) applied Strategic Niche Management to assess a project trailing EVs in the UK. This project was based in Coventry and utilized fleet operators as participants to examine the application of EVs in a commercial role. The presence of Strategic Niche Management illustrates how the diffusion of an innovation should never be considered as a standalone market phenomenon and that only through the application of a systems level perspective will a researcher grasp the complexities involved with taking an innovation from a niche market into the mainstream. Whilst this thesis concentrates on the consumer adoption of
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Chapter Two: Literature Review LEVs, it is important to note that this relates to only one aspect of a broader approach geared to transitioning technological regimes to a sustainable path. 2.4.2 Applied Research Throughout the literature that has so far been examined, the question of what type of individual would be more receptive to a certain vehicle, whether it be a LEV or otherwise, has often been framed as being reliant on a small number of variables. Lave and Train (1979) included socio-economic and demographic characteristics in their disaggregate model of auto-type choice and found that older people are less influenced by vehicle performance whilst people with lower levels of formal education tend to place higher values on large cars. From the demand analysis conducted by Ararwal and Ratchford (1980), the authors propose that large comfortable cars should be targeted at suburban homeowners that have moderate degrees of education for use in family vacations. Specifically focusing on LEVs, Potoglou and Kanaroglou (2007) found that demand is lower for individuals aged over 45 and higher if an individual holds a university degree. Socio-economic and demographic variables are often easily observed and quantified leading to straightforward incorporation into econometric models and application in sales environments. Recently however, more attention has been given to the influence sociopsychological constructs hold over the adoption of LEVs. Jansson et al. (2011) found that attitudinal constructs related to the VBN theory held significantly more explanatory power over the adoption of AFVs compared to socio-economic variables. Peters et al. (2011) apriori segment their sample based on the level of stated interested in EVs and find that the different segments are influenced by different attitudinal constructs. However, these studies have tended to overlook the structure of the LEV market and instead have used the variables included in their explanatory models to make inferences concerning what types of consumer are likely to have higher preferences for LEVs. The profiling of different individuals based on their shared characteristics has only received limited attention. One of the earliest studies to focus specifically on market segmentation was conducted by Feldman and Armstrong (1975) who examined the introduction of the rotary engine car produced by Mazda, which was viewed as being innovative and substantially different from 50
Chapter Two: Literature Review current ICE vehicles of the time. In this study, three hypotheses are proposed, that Mazda innovators in two geographically separated markets are similar, that innovators have significantly different characteristics from later adopters and that innovators have significantly different characteristics compared to buyers of conventional ICE vehicles. Individuals included in the analysis are defined by three categories of variable, firstly their social, attitudinal and personality variables such as personal competence and venturesomeness. Secondly, perceptions of product characteristics such as complexity and compatibility are measured whilst the last variable category relates to socio-economic characteristics. To a degree, Feldman and Armstrong were somewhat ahead of the research being conducted in the LEV field, which at the time was focused primarily on vehicle characteristics, by including measurements of personality and attitude. From an analysis of the data, the authors state that the first and third hypotheses are well supported though less support is found for the second hypothesis. However, four out of the five personality and attitudinal characteristics do show statistically significant differences between innovators and later adopters. With this in mind, these results support the findings of Jansson et al. (2011) that the attitudes of innovators in the automotive market are significantly different compared to non-adopters or those that adopt later in the product diffusion. Focusing on segmentation applied in the LEV market, Skippon and Garwood (2011) examine consumer response to EVs in the UK though the use of a vehicle trial to reduce the psychological distance by providing participants with experience of the vehicles. A sample of 58 participants is provided access to an EV and then asked to complete a post trial survey, which included personality trait measurements, participant evaluations of the EV and a vignette exercise (Rossi and Nock, 1985) to observe the attribution of symbolic meaning to these vehicles. This data was then analyzed using hierarchical clustering which employed the within group linkages method with distances measured by a Euclidean procedure. From this analysis, four market segments have been identified and were initially defined by their involvement with cars and their concern for the environment. Discussing the structure of the four segments, the segment which included participants who exhibit both high involvement with cars and high concern for the environment tend to hold the most sceptical attitudes 51
Chapter Two: Literature Review towards EVs. Conversely, participants with low car involvement and low environmental concerns were among the most favourable towards EVs. These results are distinctly surprising, as environmental concern has been identified in other studies to be a significant factor in LEV adoption (Sangkapichai and Saphores, 2009; Kurani et al. 1994). Conducting a segmentation analysis with individuals that have participated in an EV trail is a significant strength of this project, however, most of the participants experience with the EV was limited to a 10 mile journey with others allowed access to the EV for one evening. With such a short level of exposure, it is questionable whether psychological distance has been reduced by any significant amount. Furthermore, the project only included 58 participants, restricting the inferences that can be drawn from the results. In addition, participants were sourced from employees of a national energy firm and are not representative of the UK populace. Lastly, with the segments identified only described by two characteristics, the richness of the discussion is limited. These limitations, linked with the surprising results attained, suggest this study should be interpreted with caution. Using a much larger sample size and a more extensive measurement of psychological constructs, Borthwick and Carreno (2012) conduct an analysis of Scottish drivers in an attempt to identify segments which are receptive to LEVs. A large sample size has allowed for a much richer cluster solution containing three primary categories of variables. Firstly, psychological factors are measured related to the components included in the TPB and VBN theory. In addition, situational factors are also included which reflect social conditions and physical structures such as government policy and vehicle infrastructure based on the framework proposed by Lane and Potter (2007). Lastly, response to potential government policies, such as rebates and alterations to parking fees based on carbon dioxide emissions, are examined. Through the application of a cluster analysis using a K-means method, three market segments were identified each displaying significantly different structures for the three primary variable categories. For the market segment with the most positive preferences for LEVs, psychological factors appear to have primary influence whilst in the remaining two segments it is situational factors. Notably, the segment with the most positive preferences for LEVs tended to 52
Chapter Two: Literature Review consider environmental degradation their responsibility and to reinforce this with strong personal norms. In addition, the three segments tended to rank potential government policies in the same order of preference though statistically significant differences between the clusters were observed for how receptive each cluster would be to proposed policies. In sum, this project appears to have successfully integrated psychological and situational factors into a unified framework followed by the identification of unique market segments with structures which prove highly informative. Whilst market segments are often categorized by the personal characteristics of the consumer, analysis can also be conducted at a more aggregate level. Approaching the diffusion of LEVs from this direction, Pridmore and Anable (2012) examine the spatial distribution of HEVs and ethanol fuel ICE vehicles in Sweden. A primary objective of this project is to test the neighbour effect, initially proposed by Mau et al. (2008), and determine if interpersonal influences are affecting the spatial diffusion of these vehicles. To achieve this, the authors utilise exploratory spatial data analysis to classify areas which exhibit spatial autocorrelation of these vehicles. From the data analysis, two areas of relatively high market uptake of these vehicles, around the cities of Stockholm and Gothenburg, have been detected. Identification of these two hotspots provides support to the neighbour effect though does not provide insight relating to consumer motivations for adoptions. Additionally, with Stockholm operating a congestion charging zone which these vehicles are exempt from (those registered before 2009), it is likely this policy will have significant influence over adoption rates. A similar analysis has been conducted by Campbell et al. (2012) who investigate the spatial diffusion of potential LEV innovators in Birmingham, UK. Through the application of a literature review, a socio-economic profile of potential LEV adopters has been developed. This profile contains the characteristics age, home ownership, household type, car ownership, income levels and education. Data was sourced from the UK census and then analyzed using a hierarchical cluster procedure with Ward’s method. From this analysis, clusters have been identified with the DOI theory utilised to assign where each cluster is likely to be placed in the diffusion timeframe. Results indicate that 9% of Birmingham’s population share the characteristic profile of early adopters which is in keeping with 53
Chapter Two: Literature Review theoretical expectations of this segment’s market size. This early adopter cluster tends to be located north of Birmingham city, are middle aged with a distinctly high percentage of home ownership living in detached or semi-detached properties with a higher tendency to be employed in professional or managerial roles.
Additionally, this cluster tends to be
populated by individuals that rely on cars to travel to work and are members of multicar households. Based on the research findings, the authors make a number of policy recommendations aimed at enhancing adoption rates. Firstly, that information campaigns be targeted at the identified early adopter market segment and the geographical locations this segment is predominantly resident in. This is sensible, as awareness is still viewed as an apparent barrier to consumer adoption of LEVs (Axsen and Kurani, 2008). Secondly, that LEV infrastructure such as refuelling points be located in these geographical areas to assist households in transitioning to a LEV. Understanding is less clear on this point with research examining recharge habits having tended to find that individuals are more likely to recharge their EVs from home or workplaces (Garling, 2001; Turrentine et al. 2011; Everett et al., 2011) so it is questionable how much public recharge points will be utilised. However, an argument can be put forward that recharge points can act as a means of generating interest in EVs and to demonstrate the support these vehicles have from the government. Additionally, with this research project utilising a data source which, at the time of publication, was over ten years out of date, the results are dependent on the socioeconomic and demographic structure of Birmingham remaining stable. Furthermore, the adopter profiles have been largely based on work conducted in the US with the implicit assumption that these characteristics are directly transferable to UK adopters. Having said this, there is still clear merit in the approach of this project and the authors do state that forthcoming research using primary data will help to establish firm research findings. 2.4.3 Overview of Market Structure and Segmentation With LEVs beginning to enter the mainstream automotive market, academic and industry attention is shifting towards identifying consumer groups which are likely to be more receptive to these vehicles. The current car market is highly diversified both in terms of the vehicles available for purchase and the individuals purchasing them. The large degree of 54
Chapter Two: Literature Review social stratification present in the car market allows for the application of segmentation analysis which categorises groups of consumers based on their shared characteristics. Understanding the profiles of these segments and how they respond to different stimuli is viewed as an important aspect of market and government policy (DfT, 2009a; CCC, 2012). Providing optimum market conditions to early adopters can prove to be essential to a successful diffusion process (Bass, 1969). The research that has so far been conducted in this area can be partitioned into two categories. The first category examines the formation of consumer segments by examining their profiles to identify key features and characteristics. Through the establishment of innovator profiles defined by their socio-economic and demographic variables, census data has been examined to determine locations where early adopters are likely to cluster so that market interventions can be spatially targeted (Campbell et al., 2012). Moreover, spatial analysis can be employed to observe the formation of adoption hotspots at a regional and urban level (Pridmore and Anable, 2012). Through the addition of psychometric assessment, segment profiles can be augmented through the inclusion of psychological characteristics to provide a more complete description of their features (Skippon and Garwood, 2011; Borthwick and Carreno, 2012). Structure analysis of the emerging market for LEVs remains a developing topic, with few empirical studies having so far been conducted. A number of prominent areas remain unexplored and would benefit from specific attention. Notably, segment profiling has so far only considered a limited number of aspects which are often related to car specific issues. A more detailed examination of the psychological and sociological characteristics of the segments emerging in this market is required, with consideration given to traits not specifically connected to cars. The importance of values and individual innovativeness has so far received partial examination yet may well hold a significant role in the adoption of LEVs. Moreover, segments have tended to be profiled according to their preference or attitude towards a single LEV powertrain. With the future automotive market likely to incorporate LEVs of different specifications, it is necessary to develop an appreciation of how consumers are considering different powertrain options.
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Chapter Two: Literature Review 2.5 CHAPTER SUMMARY This chapter presented and discussed the literature which has so far examined demand in the emerging market for LEVs. Split over three sections, past research has been grouped dependent on theoretical approach with key findings highlighted and limitations discussed. Early research in this field utilised statistical models based on econometric theories to predict demand for LEVs (Train, 1980; Calfee, 1985). Initially, these models only considered functional attributes of the vehicles themselves or easily observed characteristics of the consumers. The work conducted by the research team at the University of California at Davis (Turrentine and Sperling, 1992; Turrentine and Kurani, 1998) was fundamental in demonstrating that reliance on econometric modelling was subject to a number of distinct biases and was portraying a partial understanding of consumer reaction to LEVs. Preferences for LEVs appear to be unstable, significantly influenced by the information presented and the time granted for reflection. Moreover, the attitudes held by an individual appear to hold an influence over LEV preferences and thus need to be accounted for in research. Responding to this critique, researchers followed a number of different strategies to account for the importance of socio-psychological factors. Firstly, attitudinal measurements were incorporated in the econometric models and demonstrated that aspects such as lifestyle, personality and environmental concern could be successfully integrated with socio-economic characteristics to predict car type choice (Choo and Mokhtarian, 2004) and HEV adoption (Sangkapichai and Saphores, 2009). Other researchers based their models on psychometric theories and determined that constructs such as social norms (Peters et al., 2011b) and value orientations (Jansson et al., 2011) hold influence over LEV preferences. Embracing qualitative methods, researchers have expanded understanding of how individuals consider LEVs with issues related to status and symbolism (Heffner et al., 2007), emotive response (Graham-Rowe et al., 2012) and vehicle etiquette (Caprello and Kurani, 2012) all being salient. However, research examining the influence of socio-psychological constructs has tended to consider the problem from a market level perspective, and has inadequately examined the importance of different consumer groups.
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Chapter Two: Literature Review Most recently, research has turned to understanding the social stratification of the market by grouping individuals based on shared similarities. Providing a proof of concept, Feldman and Armstrong (1985) demonstrate that significant differences in socio-economic and psychometric profiles exist between consumer groups in reference to an automotive innovation. Specifically examining the emerging market for LEVs, research has so far found that segments can be profiled in accordance with their relative levels of car importance and environmental concern (Skippon and Garwood, 2011) and also the significance of psychological and situational factors (Borthwick and Carreno, 2012). Moreover, the spatial positioned of LEV early adopters in urban areas has been examined to determine optimum location for charging infrastructure (Campbell et al., 2012) and to determine if diffusion hotspots are present (Pridmore and Anable, 2012). However, issues in this area remain unexplored with an incomplete understanding of how consumers respond to different LEV variants and a lack of attention paid to examining more general socio-psychological traits. The next chapter presents the conceptual framework which has been developed for this thesis by detailing the constructs it includes and how these constructs are hypothesised to be related to one another. This framework has been developed with the existing literature in mind, addressing the gaps in current knowledge which have been identified in this review. Specifically, socio-psychological constructs which have been found in related fields to hold influence over the adoption of innovations have been incorporated to determine their influence over LEV preferences. Moreover, the results attained from the conceptual framework have been used as a basis for a market structure analysis aimed at identifying notable segments and profiling them in accordance with their prominent features. With the market for new passenger vehicles in the UK being evenly split between private households and fleets, it is challenging to produce an effective assessment of both aspects of the market in a single project. With this in mind, the decision was made to focus this thesis solely on private household passenger vehicle demand.
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Chapter Three: Conceptual Framework
CHAPTER
3
METHODOLOGY - CONCEPTUAL FRAMEWORK
3.1 INTRODUCTION Research examining consumer demand for LEVs has been an active area of academic inquiry for the past thirty years. The preceding Literature Review chapter has assessed the key studies so far conducted and discussed how they relate to one another. From this appraisal, it is apparent that areas still remain unexplored. Econometric analysis utilising discrete choice modelling has been widely applied (Train, 1980, Beggs et al., 1981; Calfee, 1985; Bunch et al., 1995; Mau et al., 2008). More recently, researchers in this field have begun to consider the usefulness of psychometric models (Lane and Potter, 2007; Jansson et al., 2011; Peters et al., 2011a) which examine the influence of socio-psychological constructs over LEV preferences. Moreover, with LEVs beginning to enter the mainstream market, attention has turned to evaluating the structure of the emerging market by assessing consumer stratification (Skippon and Garwood, 2011; Borthwick and Carreno, 2012). With research in these two specific areas only recently becoming active, gaps in the knowledge base remain present. To achieve an original contribution, this thesis has been developed to address these unexplored areas to allow academic understanding in this field to advance. The research objectives and questions attached to this thesis, which are provided in summary format in Table 3.1, have been devised to allow for a number of these gaps to be addressed. Firstly, a conceptual framework has been developed which determines if LEV preferences are being influenced by socio-psychological concepts. Incorporated in this framework are constructs which have been shown to affect related forms of behaviour but, as yet, have not been sufficiently examined in reference to preferences for LEVs. The results attained from this stage of the thesis assist in forming the basis for a segmentation analysis 58
Chapter Three: Conceptual Framework which examines the structure of the emerging market for LEVs. This chapter firstly describes conceptual frameworks that have previously examined the adoption of LEVs before detailing the framework developed for this thesis, defining which constructs have been included in it and how these constructs are conceptually linked. To conclude, the development of the measurement instruments connected to each of the framework constructs is described. 3.2 CONCEPTUAL FRAMEWORKS: PAST DEVELOPMENTS AND APPLICATIONS To place the conceptual framework developed for this thesis in context, a brief review of previously designed frameworks developed to examine LEV adoption is offered in this section. For a field which has seen an extended period of active research, it is somewhat surprising to discover a lack of illustrated frameworks. This deficiency perhaps originates from the ascendancy of econometric analysis when examining demand for LEVs which tends not to make use of illustrated frameworks. In total, five frameworks have been identified from the literature review (which are initially discussed in Section 2.3.1 of the Literature Review chapter) and cover a number of different approaches to illustrating which constructs are influencing LEV demand. In their critique of econometric modelling applied to quantify demand for LEVs, Turrentine and Sperling (1992) develop an extended decision process model which defines the stages leading to LEV adoption. Illustrated in Figure 3.1, the framework is split over two primary stages with the first stage displaying how the choice set is initially developed whilst the second stage demonstrates the process involved in progressing from choice set to purchase decision. This framework exhibits the hypothesized steps required to progress from the recognition of purchase intention to the selection of a specific vehicle. Socio-psychological constructs are incorporated with attitudes hypothesized to hold influence over brand and fuel while tastes are conceptually liked to information searching. Moreover, the influence of social stimuli is defined with the presence of innovators likely to affect the formation of the choice set.
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Chapter Three: Conceptual Framework
Figure 3.1: Extended Decision Processes for the LEV Market (Turrentine and Sperling, 1992) More recently, conceptual frameworks have been proposed which place more emphasis on the importance of socio-psychological constructs. Lane and Potter (2007) present a framework (displayed in Figure 3.2) which illustrates the factors influencing car-buyer behaviour based on a review of the literature. In this framework, the authors make the distinction between the influence of psychological and situational factors. The psychological factors are drawn from two behavioural theories, the Theory of Planned Behaviour (TPB) and the Values Beliefs Norms (VBN) theory, which emphasise the importance of values, beliefs and attitudes over behaviour. Whilst this conceptual framework is useful in its separation of situational from psychological factors, it lacks conceptual clarity with the potential for overlap on both sides of the framework which perhaps limits its suitability to applied research. For instance, the TPB and VBN theory are often viewed as mutually exclusive in the explanation of behaviour (Kaiser et al., 2005) though recent research has examined if value orientation, a construct from the VBN model, can be integrated into the TPB (de Groot and Steg, 2007).
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Chapter Three: Conceptual Framework
Figure 3.2: The Influence of Psychological and Situational Factors over Car-Buyer Behaviour (Lane and Potter, 2007)
The two frameworks so far discussed have been purely conceptual, illustrating how different constructs are potentially associated with LEV adoption and car buying behaviour. Peters et al. (2011a) develop a model (illustrated in Figure 3.3) which integrates constructs sourced from three different theories and proceeds to empirically apply this framework to determine its effectiveness. The constructs referred to as relative advantage, compatibility, trialability and observability have been sourced from the Diffusion of Innovation Theory (Rogers, 1995) and reflect opinions regarding the features of the innovation. The construct ease of use has been taken from the Technology Acceptance Model (Davis, 1989) and measures perceptions relating to simplicity of application whilst the construct social norms is sourced from the TPB (Azjen, 1991) to account for social expectations.
Figure 3.3: Conceptual Framework of EV Purchase Intentions (Peters et al., 2011a)
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Chapter Three: Conceptual Framework This framework was applied using a household survey with results proving to be encouraging, with relative advantage, social norm, compatibility and ease of use all holding significant explanatory power over the intention to purchase a LEV. However, the layout of the framework is perhaps overly simplistic, with the potential for overlap and multicollinearity in the structure. For example, it can be proposed that the compatibility of an LEV is likely to be influenced by the social norms attached to it whilst a LEV’s relative advantage may also be linked to its ease of use. Providing a more structured framework, Peters et al. (2011b) integrate constructs from the TPB and the VBN theory to explain the purchase of a fuel efficient car. In this framework (displayed in Figure 3.4), the principal constructs of attitude, personal norm and perceived behavioural control are themselves explained by sub-constructs. This framework presents a detailed overview of the sociopsychological factors that have the potential to influence the purchase of fuel efficient vehicles. The empirical application of this framework broadly supports the predefined structure, with all but four of the hypothesized relationships being found to be statistically significant.
Figure 3.4: Conceptual Framework of Fuel Efficient Car Purchase Behaviour (Peters et al., 2011b) So far, the conceptual frameworks discussed have been developed from established theories and then tested for their applicability in relation to LEV preferences and purchase intentions. Heffner et al. (2007) use a different approach to examining which concepts hold influence over LEV demand. Through a qualitative assessment of factors motivating the adoption of Hybrid Electric Vehicles (HEVs), a number of semiotic maps (an example is 62
Chapter Three: Conceptual Framework offered in Figure 3.5) have been developed which provided a structural illustration of how different factors are linked in respondent dialogue. Incorporated in these semiotic maps are constructs associated with attitudes towards technology, fuel efficiency and environmental concerns. This results driven approach allows the maps to be tailored to the individual respondent, providing a flexible solution, though it is not suited to test-retest validation.
Figure 3.5: Semiotic Map of HEV Adoption (Heffner et al., 2007)
This review of the conceptual frameworks developed by researchers to examine LEV purchasing behaviour has brought a number of important issues to light. Firstly, whilst this field has been an active area of research for the past 30 years, there is a lack of illustrated 63
Chapter Three: Conceptual Framework conceptual frameworks so far developed. This deficiency leads to a lack of conceptual clarity regarding how constructs are related to one another and the distance they are removed from the behaviour being explained. However, previous conceptual frameworks have demonstrated the importance of socio-psychological constructs by finding evidence of the influence of attitudes, values and emotions hold over LEV purchase intentions and adoption. In the next section of this chapter, the conceptual framework developed for this thesis is presented and discussed. Whilst this conceptual framework was being developed, emphasis was placed on addressing the limitations of previous frameworks by ensuring the structure of the constructs and how they are integrated is logical, conforms to theoretical expectations and can be tested using empirical analysis. 3.3 CONCEPTUAL FRAMEWORK DEVELOPED IN THIS THESIS Previous conceptual frameworks which have been developed and applied in this field have demonstrated the importance of socio-psychological concepts over preferences towards LEVs (Lane and Potter, 2007). Additionally, incorporating constructs sourced from different theories has proved rewarding by displaying theory compatibility (Peters et al., 2001b). The conceptual framework developed for this thesis adopts a similar approach by incorporating constructs sourced from related fields which have the potential to influence LEV preferences but have yet to be examined in relation to LEV demand. Displayed in Figure 3.6, this conceptual framework contains three overarching constructs. The first of these aspects concerns innovativeness (Roehrich, 2004) which has been utilised in other fields to explain the adoption of new technologies such as consumer durables (Blythe, 1999), internet shopping (Citrin et al., 2000) and information technology (Agarwal and Prasad, 1998; Schillewaert et al., 2005). This thesis examines if the concept of innovativeness holds influence over preferences towards LEVs. The second overarching construct examines EV attitudes by determining the influence of car meanings and general car attitudes over how individuals evaluate the functional capabilities of EVs. Previous academic inquiry has taken a distinctly functional approach to examining EV attitudes which provides only a partial understanding of how individuals consider their cars. Little attention has been given to symbolic and emotive car meanings 64
Chapter Three: Conceptual Framework which have been shown to influence car use behaviour (Steg, 2005). This thesis broadens the discussion by investigating the influence of symbolic, emotive and functional car meanings over EV attitudes. Additionally, attitudes linked to cars in general have been shown in past empirical research to influence perceptions of and attitudes towards LEVs (Sangkapichai and Saphores, 2009; Ozaki and Sevatsyanova, 2011). This thesis measures a number of car attitudes including concerns for the environment, perceived car importance and opinions regarding car technology to determine what affect these constructs hold over preferences for EVs in particular. The third overarching construct has been sourced from the VBN theory (Stern et al., 1999) and concerns value orientations. Previous research has found that value orientation has a significant influence over related behaviours such as acceptability of energy policies (Steg et al., 2005) and pro-environmental behaviour intention (Garling et al., 2003). This thesis investigates if value orientations hold an influence over innovativeness, EV attitudes and, ultimately, LEV preferences. The following sections offer a detailed discussion relating to these three aspects and their associated constructs followed by an overview of the instruments selected and developed to measure them. The conceptual framework has been developed so that it can be applied to a range of alternative powertrain technologies. Whilst, in this thesis, the framework is set-up to principally evaluate attitudes towards EVs, this aspect of the framework can be altered to measure attitudes towards other forms of low carbon technology such as hybrid or fuel cell vehicles. In this sense, the conceptual framework has a degree of flexibility built in allowing it to be validated against different forms of innovation in the automotive market.
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Chapter Three: Conceptual Framework
Figure 3.6: The Conceptual Framework Developed for This Thesis
3.3.1 Innovativeness In his integrated theory describing the nature of how innovations are diffused and adopted in a social system, Rogers (1995) provides insight related to a number of issues that are potentially important in understanding consumer response to LEVs. The characteristics of the innovations themselves are discussed as having direct influence over the successful diffusion, or lack thereof, of innovations. Those innovations that are relatively advantageous compared to current products, are easily compatible with current systems, are not complex or difficult to use and are straightforward to try and observe the associated results are likely to have high diffusion potential. Indeed, these considerations have already been examined in reference to LEVs (Peters et al., 2011a) and have been found to significantly influence LEV preferences. However, the diffusion of innovations theory is unique in its approach, placing equal importance on understanding the nature of the adopter as well as the innovation. The decision journey embedded within the theory is illustrated in Figure 3.7 and offers a 66
Chapter Three: Conceptual Framework detailed understanding of the likely steps individuals take when considering adopting an innovation.
Figure 3.7: Decision Process of Innovation Adoption (adapted from Rogers (1995)) Separated into distinct stages, individuals firstly become aware of an innovation in the knowledge stage through active and passive sources, such as exposure to adverts or discussions with friends. If the innovation is of interest to an individual, they enter the persuasion stage whereby further information is attained and judgements are made concerning potential suitability. Once the individual begins to form attitudes and preferences towards an innovation, they enter the decision stage where the evaluation transfers from being a mental exercise to a practical one. If a decision to adopt is taken, the innovation will be attained and then goes through a regularisation procedure to integrate it with the owner’s lifestyle. The final stage is the post-purchase assessment whereby, if an innovation is well reviewed, its ownership is confirmed whilst, if dissonance is experienced, its ownership is discontinued.
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Chapter Three: Conceptual Framework This adoptive decision process is unique to each individual in both content and the duration required to progress through each stage. The total length of time that an individual takes to complete the process, going from initial awareness to adoption, is associated with their level of innovativeness. Those individuals that have a relatively rapid process can be considered more innovative compared to their slower counterparts. It is this concept of innovativeness that can be highly distinctive in separating those individuals that are likely to be receptive to new products from the rest of the market. Those individuals that have relatively short adoptive decision processes, being more likely to acquire an innovation early in its diffusion, are referred to as Innovators in the Diffusion of Innovation theory. Conversely, individuals that tend not to keep updated related to innovations and have extended adoptive decision processes are labelled Laggards. Through empirical analysis of diffusions, Roger’s offers a profile of characteristics that tend to define Innovators. These characteristics are split into three categories referring to the socio-economic, psychological and communication characteristics and are presented in Table 3.2. Innovativeness is not a static concept but varies dependent on the situational context. An individual, placed into a certain environment, may behave in a highly innovative manner though, if conditions were different, their behaviour may be more reflective of a Laggard. Midgley and Dowling (1978) account for this by conceptualising innovativeness at different levels of abstraction. To begin, the concept of innate innovativeness is introduced at the highest level of abstraction and is influenced by psychological and sociological traits. Separating innate innovativeness from adoptive behaviour is a number of intervening variables such as interest in the product, communicated experiences and situational effects. In this sense, innate innovativeness is defined as a personality trait that is held by all individuals and, whilst not directly observable, can be indirectly measured which allows individuals to be classified by their relative degree of innate innovativeness.
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Chapter Three: Conceptual Framework Table 3.1: Characteristics of Innovators (Rogers, 1995) Socio-Economic
Psychological
Communication
Education [+] Literacy [+] Higher social status [+] Upward social mobility [+] Favourable attitude towards credit [+]
Empathy [+] Dogmatism [-] Ability to deal with abstractions [+] Rationality [+] Intelligence [+] Favourable attitude towards change [+] Ability to cope with uncertainty [+] Favourable attitude towards education [+] Fatalism [-] Achievement motivation [+] High aspirations [+] Favourable attitude towards science [+]
Social participation [+] Interconnectedness with social system [+] Cosmopolitanism [+] Change agent contact [+] Mass media exposure [+] Exposure to interpersonal networks [+] Active information seeking [+] Knowledge of innovations [+] Opinion leadership [+] Belonging to a highly connected system [+]
[+] positively affecting innovativeness [-] negatively affecting innovativeness
This conceptualisation of innovativeness, though set out with thought and deliberation, is not universally accepted in the academic community. Burns (2007) rejects the proposition that innate innovativeness is a separate personality construct due to its lack of predictive validity in empirical applications. Instead, the author positions innovativeness within a theoretical framework concerning variety seeking behaviour though does not test this through empirical application. In Burn’s framework, innovativeness is conceived as being only one manifestation of an individual’s desire to add variety to their existence. This lack of predictive validity is discussed by Roehrich (2004) in a review of the instruments that have been developed to measure innate innovativeness and their applications in empirical research aimed at examining adoptive behaviour. The results of this research suggest that innate innovativeness is only weakly related to adoption of innovations. From this lack of explanatory power, the authors call for continued research to further examine the relationship between the concept of innate innovativeness and the adoption of innovations. Specifically, an integrated framework is required that holds a strong theoretical foundation to allow for the improved development of measurement scales. Perhaps part of this lack of empirical clarity in the connection between innate and adoptive innovativeness originates from the intervening variables which separate these two 69
Chapter Three: Conceptual Framework concepts. Goldsmith et al. (1995) frame the issue at three different levels of abstraction encompassing innate innovativeness (referred to as Global Innovativeness in their model), domain specific innovativeness and adoptive innovativeness (adoptive behaviour). Building on previous work (Goldsmith and Hofacker, 1991), a measurement of these three separate concepts suggests that domain specific innovativeness can be considered as an intermediate step between innate and adoptive behaviour. From this examination of the literature, it is apparent that the concept of innovativeness has been defined and measured using a number of different approaches. Moreover, the findings of empirical research suggest that innovativeness is an important determinant of adoptive behaviour (Manning et al., 1995; Lu et al., 2005; van Rijnsoever and Donder, 2009). However, in Roehrich’s (2004) review of innovativeness measurements, the author notes that pre-existing instruments lack an effective theoretical basis which leads to a disconnect between empirical application and conceptual foundation. Responding to this, this thesis has developed a new set of measurement instruments which are linked to the generalisations of Innovators (detailed in Table 3.2) defined in Rogers (1995) Diffusion of Innovation Theory. Specifically, two innate innovativeness measurement scales have been developed
which focus on
psychological and
communication determinants of
innovativeness. Additionally, adoptive innovativeness has been measured by observing the quantity of household technology owned. This multi measurement approach allows the concept of innovativeness to be examined from different perspectives. With LEVs being considered to represent a type of disruptive innovation (Christensen, 1997), this thesis determines if an individual’s level of innate and adoptive innovativeness affects their preferences towards these vehicles. 3.3.2 EV Attitudes From the analysis of past research which has examined consumer demand for EVs, researchers have repeatedly found that evaluations of the functional performance of these vehicles significantly affect their likelihoods of adoption. Attributes such as range (Beggs et al., 1981), purchase price (Ewing and Sarigollu, 1998) and fuel cost (Potoglou and Kanaroglou, 2007) have been observed to influence preferences towards EVs. A relatively unexplored area relates to what are the motivating factors behind these evaluations. To 70
Chapter Three: Conceptual Framework provide insights related to this important aspect of LEV preference formation, this thesis examines the potential precursors of EV attitude. These potential motivators are separated into two distinct groups with the first examining the meanings attached to car ownership and use whilst the second measures attitudes related to cars in general. 3.3.2.1 Car Meanings: Symbolic, Emotive and Functional Previous applications of preference models in the LEV demand field have taken a distinctly functional approach by focusing on the importance of instrumental vehicle characteristics. To a degree, this approach has been insightful in revealing some of the more objective barriers to LEV adoption. Additionally, it has provided information relating to what LEV characteristics, if improved, would likely cause the biggest increase in demand. However, a purely functional approach does not provide adequate attention to the less observable influences over human behaviour. Research conducted by Steg (2005) has found that considerations relating to the symbolic meanings and emotive attachments placed on cars influence user behaviour. To determine if these constructs hold influence over EV attitudes and preferences in conjunction with functional considerations, they have been incorporated into the conceptual framework and are illustrated in Figure 3.7. Related to the emotions people attach to their possessions
Emotion Crossovers exist between these three
Symbolis
Function
Related to the quantifiable specification of possessions
Related to identity and the status people associated with possessions Figure 3.8: Illustration of the symbolic, emotive and functional car meanings Symbolic Car Meaning 71
Chapter Three: Conceptual Framework Possessions can act as social indicators to declare status, association with particular groups and issues related to the value structures of their owners. In essence, symbols are embedded with social meaning to convey characteristics about the owner that are not easily observed. McKracken (1986) discusses the relationship that exists between owner and object stating that the meanings attributed to possessions can reside in three primary locations. Firstly, the culturally conceived world acts as a store of meaning which is augmented by connotations held by the object itself and the individual consumer. However, these meanings do not remain static but are being continually altered and reinterpreted by advertisements, the fashion system and consumer rituals. Examining the owner-object relationship from a different perspective, Belk (1988) inspects how possessions contribute to a sense of self. Belk argues that possessions can be viewed as a means by which owners extended their self identities to a social audience. Similar to the meanings associated with possessions, an individual’s sense of self and identity is not a static concept but changes over an individual’s life cycle. For possessions to act as symbolic extensions of personal identity, allowing owners to associate themselves with certain social groups and values, an appropriate communication platform is required. Belk et al. (1982) examine how individuals interpret the symbols associated with consumer goods and find that the skills necessary to decipher symbolic meanings are not evenly spread through society. These skills begin to emerge during adolescence and are highly utilised during early adulthood. Additionally, these skills are influenced by social class, gender and the life stage of the individual. Holt (1995) discusses the differences in consumer behaviour as a typology of practices, with interpersonal considerations supported by institutional frameworks which provide a structure to socially portrayed consumption. A two way process exists in which individuals manipulate the social meanings associated with possessions to fit their identities whilst the possessions themselves influence the individual’s character. Richins (1994) argues that the meanings associated with possessions are not solely linked to instrumental characteristics but can also be observed by how an owner values a possession. Moreover, a distinction is made between the public and private meanings a possession can hold, demonstrating that the same object can hold different meanings to different audiences.
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Chapter Three: Conceptual Framework From this review, it is apparent that the symbolic meanings placed on possessions can have significant influences over consumption and use behaviour. Whilst Heffner et al. (2007) has taken a qualitative approach to examining the influence of symbolic considerations on attitudes towards and preferences for LEVs, this thesis employs quantitative techniques to measure symbolic attachment to cars and assess its influence over EV attitudes. This construct of the conceptual framework assists in determining if symbolism is a factor in the emerging LEV market and, if so, what role it is playing. Emotive Car Meaning Whilst connection to possessions is often associated with the symbolic meanings linked to them, Ball and Tasaki (1992) argue that attachment is also strongly related to emotional significance. This statement appears intuitive and it is difficult to perceive an individual that views a particular object as an expression of their identity and a conveyance of their values without the formation of a positive emotional connection. This emotive connection may not be apparent at the beginning of the owner-object relationship but instead be formed by repeated use and shared experiences. Thomson et al. (2005) examine the emotional ties individuals form to their possessions and find that they are constructed by considerations of affection, connection and passion. In a similar piece of research, Chandler and Schwartz (2010) inspect the influence of use and ownership patterns on the formation of ownerpossession bonds. Specifically, they find that if an owner forms a friendship with their cars and tends to consider them in an anthropomorphic context, this affects the owner’s willingness to replace their car. In an attempt to structure the research activity concerning emotions and their influence over individual behaviour, Hirschman and Stern (1999) developed an inclusive framework to illustrate how emotions influence human behaviour. Drawing on research conducted in the cognitive, hedonic and compulsive research fields, a model is specified where emotional response is defined as being a range between highly positive to highly negative. This constitutes the current emotional state of an individual which influences decision making ability and, as a result, behaviour. Current emotional state along with emotive connection to a possession has the potential to influence human behaviour. Elster (1998) discusses the limitations of economic theory in reference to emotional considerations. Current economic models are isolated from the 73
Chapter Three: Conceptual Framework concepts of emotive response leading to an incomplete representation of economic action. However, it is apparent that emotional state does not only shape economic action through its influence over the cost and benefits associated with any choice, but can also affect an individual’s ability to act in a rational manner. In a thought provoking critique of the academic and policy system currently dominant in transportation, Sheller (2004) argues that individualistic rational choice models distort the understanding of reality by not accounting for emotive considerations which can often be complex and unintuitive. This viewpoint is shared by Steg et al. (2001b) who state that, whilst rational models of human behaviour based on instrumental considerations of the options available are useful in policy assessment, they do not provide an inclusive picture relating to how individuals conceive transport choices. Through an examination of motives related to car use and its perceived attractiveness, symbolic-affective considerations were often viewed as being as important as reasoned-instrumental aspects in evaluations. Moreover, it is often the case that individuals are hesitant to state how important symbolic-affective motives are, instead being eager to present their actions in a rational manner, making it challenging to assess the importance of these factors. In a related piece of research, Steg (2005) further examines the influence of symbolic, emotive and functional vehicle considerations over behaviour and transport choice. Building on a structure initially proposed by Dittmar (1992), a theoretical framework is proposed and applied to measure these different constructs and observe their interaction with transport behaviour. Results of the analysis indicate that symbolic and affective motives significantly influence the utility derived from car use. Individuals place a high value on the positive emotions received and status attained from driving. Additionally, different groups of individuals place differing levels of symbolic and emotive importance on car use, with those individuals that have high car use and positive car attitudes holding stronger emotive and symbolic connections to their cars. This last point highlights the importance of considering the automotive market as a collection of unique market segments, which have their own individual characteristics, as opposed to a single homogenous entity. To summarise, emotive connection to possessions has been found in past research to influence ownership and use behaviour. Moreover, current models of behaviour do not 74
Chapter Three: Conceptual Framework include this influence leading to lack of completeness in model specification. Currently, the interaction between emotions and LEV adoption is an unexplored area of the field. In this thesis, emotive connection to cars has been measured to examine the influence this concept has over EV attitudes and, ultimately, LEV preferences. Functional Car Meaning Perhaps the most basic use value associated to cars concerns their functional capacity to enhance the mobility of their owner. Having the ability to serve most forms of transportation need, cars are widely used in the UK for trip based activities such as commuting to work, attending social events and shopping (DfT, 2011b).
Technical
specifications of modern cars run to tens of pages, detailing hundreds of different attributes. These are often grouped by system, such as braking or interior decor, to allow customers to review the headline performance features. With cars being positioned as a technical artefact, it is clear to understand the reasons behind why early research in LEV demand took a distinctly functional approach to assessing consumer preferences (Mannering and Train, 1985). The objective nature of the car lent itself to statistical analysis whereby the importance of different performance variables was quantified. Often, technology progresses in an incremental fashion with marginal improvements in performance levels such as the gradual increase in processing power observed in silicon chips (Moore, 1965). LEVs in general and EVs in particular follow a different trajectory and have been classified as disruptive innovations (Christensen, 1997) due to their unique configurations of performance attributes. Whilst some features in LEVs are improved compared to conventional vehicles, others are reduced. This makes it challenging to predict consumer response to these vehicles through a simple analysis of functional attributes. Nevertheless, instrumental performance is still viewed as an important aspect of vehicle purchasing decisions and so has been incorporated in the conceptual framework. 3.3.2.2 Car Attitudes In addition to the meanings individuals place on cars, attitudes held regarding cars in general are likely to influence attitudes towards EVs and, ultimately, preferences for LEVs. At a general level, Choo and Mokhtarian (2004) integrate measurements of transport 75
Chapter Three: Conceptual Framework attitudes into an econometric model of car type choice. The findings of this analysis suggest that small car drivers are motivated by concerns for the environment whilst owners of sports utility vehicles are influenced by attitudes associated with travel freedom. Specifically focusing on LEVs, Sangkapichai and Saphores (2009) examine demand for HEVs in California by inspecting the influence of attitudinal motivations. Factor analysis is utilised to measure a number of different attitudes linked to cars with the output being used as explanatory variables in a DCM. The results of this research support the view that attitudes connected to cars affect likelihood of LEV adoption, with concerns linked to global warming, energy efficiency, air quality and the health impacts of car use holding significant influences. In a similar piece of research, Ozaki and Sevastyanova (2011) investigate the motivations of HEV adoption in the UK through the application of factor analysis and find that environmental concerns, attitudes towards fuel independence and attitudes towards car technology (such as a desire to be a pioneer and opinion leader) are all motivating factors. This thesis has incorporated car attitudes into the conceptual framework in an effort to validate previous research findings whilst including a number of novel attitudes which have yet to receive attention in this field. Attitudes linked to environmental concerns of car use, car costs and car technology have been measured to determine if the results of past empirical research are reliable. Additionally, measurements regarding perceived car importance and car knowledge have been included to observe if these constructs hold influence over LEV preferences. Chandler and Schwartz (2010) found that perceived car importance holds a significant influence over vehicle replacement time. This thesis extends this principle by determining if those individuals that consider their car to be essential items, in terms of both function and attachment, are either more or less likely to adopt a LEV. Furthermore, Lai (1991) found that pre-existing knowledge regarding a product significantly affects the adoption of a new innovation. This thesis examines this issue by observing if knowledge concerning cars in general and LEVs in particular significantly influences preferences towards LEVs. 3.3.4 Value Orientation An individual’s value orientation can be considered to represent the underlining principles which motivate their behaviour. Values are often hypothesized to exist at a high level of 76
Chapter Three: Conceptual Framework abstraction and act as the foundation for psychological constructs such as attitudes, beliefs and opinions. This can be clearly seen in the conceptual framework developed by Vaske and Donnelly (1999) which is displayed in Figure 3.8. This framework positions values as the characteristic furthest removed from behaviour yet linked to it via their influence over attitudes, norms and intentions. Referred to as a cognitive hierarchical model of human behaviour, this framework shares significant similarities with the VBN theory (Stern et al., 1999) in its positioning of values in reference to other construct categories. The importance of value orientation over behaviour has been assessed in empirical research with findings tending to support the link between values, attitudes and behaviour. Collins and Chambers (2005) examined the influence of value orientations over commuter travel behaviour and preferences for public transport. Findings of Collins and Chambers’ research suggest that values tend to influence beliefs which in turn affect transport preferences and behaviours. Hansla et al. (2008) studied the relationship between values associated with environmental concerns, self enhancement and self transcendence to determine how these value structures influence the willingness to pay for green electricity. The results of Hansla et al.’s research indicate that self transcendence and environmental concerns positively influence attitudes towards green electricity and willingness to pay for it.
Behaviours Behavioural Intentions
Numerous Faster to change Peripheral Specific to situation
Attitudes and Norms Value Orientation
Values
Few in number Slow to change Central to beliefs Transcend situation
Figure 3.9: Cognitive hierarchical model of human behaviour (Vaske and Donnelly, 1999)
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Chapter Three: Conceptual Framework In a research project which integrates three separate studies, de Groot and Steg (2008) investigate the influence of value orientation over environmentally significant behaviour. A scale measuring the value structures associated with biospheric, egotistic and altruistic life principles is repeatedly tested with the factor output remaining consistent. The results of the analysis signify that attitudes towards recycling are significantly influenced by egoistic values whilst altruistic and biospheric values were found to significantly influence intention to donate to humanitarian and environmental charities. The results of this research demonstrate that value orientation can be reliably measured and can be used to explain attitudes towards and intentions related to environmental behaviours. A review of the empirical research examining the influence of value orientation over behaviour indicates that value structures are a significant determinant. So far, little research has been conducted which inspects the influence of value orientation over preferences towards LEVs. This study has incorporated value orientation into the conceptual framework to test if value structures are influencing attitudes towards innovation in general, car attitudes and car meanings. Recently, Jansson et al. (2011) have applied the VBN theory to determine its explanatory power over LEV adoption in Sweden. Findings of this study signify that egoistic values are positively influence adoption rates. Consequently, it proves interesting to compare these findings to the results of this thesis to determine if similar observations are made. 3.4 INSTRUMENT DEVELOPMENT The previous section has outlined the conceptual framework developed for this thesis, describing the constructs which it includes and how these constructs are linked together. To apply this framework, it is necessary to attach a measurement instrument to each construct to allow for the concepts to be calculated and statistically analyzed. This section of the chapter discusses the selection and construction of these measurement instruments to allow for the framework to be operationalised. To begin, a brief discussion relating to the practice of psychometric measurement is offered, reviewing the recommended practice and detailing how this has been applied in this thesis. Following this, the specific measurement instrument attached to each construct is described with their internal structures justified. 78
Chapter Three: Conceptual Framework 3.4.1 Psychometric Measurement Scrutinising the constructs included in the conceptual framework, it is apparent that the majority are associated with characteristics that cannot easily be observed. Sociopsychological constructs such as attitudes and values form the theoretical antecedents of behaviour but are challenging to bound and quantify. Thurstone (1928) provides initial guidance on the topic of attitude measurement, describing the nature of the task alongside the development of measurement instruments and application. Attitudes are defined as inclinations, ideas, notions and convictions related to a specific topic which an individual can express more or less affinity with. In essence, attitudes exist on a continuum, similar to objective phenomenon such as temperature and mass, and thus can be quantified. Opinions are defined as signifying the verbal expression of attitudes and can therefore be used as symbolic representations. Accordingly, attitude measurement scales can be constructed which include opinion statements related to an attitude which individuals can express endorsement or rejection of. From this early discussion, the measurement of attitude has become widespread in social research. By measuring attitude, a researcher has the potential to stratify a social system according to a topic of interest, to determine the proportion of individuals that hold different magnitudes of attitude on a subject whilst examining their more observable features such as socio-economic characteristics. Moreover, attitudes and related psychometric measurements have become widely utilised in the explanation of different forms of behaviour. Fishbein and Ajzen (1975) develop the Theory of Reasoned Action which used measurements of attitudes concerning behaviour and subjective norms to explain behavioural intentions. This theory has been subsequently revised into the Theory of Planned Behaviour (Ajzen, 1991) through the inclusion of perceptions relating to behavioural control. A similar theory has been developed by Triandis (1977) which describes interpersonal behaviour through the determinants of attitudes, social factors and affects which influence intentions whilst positioning habits as an intermediate determinant between intention and behaviour. Furthermore, Gibbons et al. (2009) incorporate attitudes about personal vulnerability into an explanatory model of risk behaviour.
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Chapter Three: Conceptual Framework From the literature review, it is evident that measurements of attitudes have been widely used in an explanatory capacity to describe human behaviour. Consequently, the constructs included in the conceptual framework applied in this thesis have been largely based on socio-psychological factors. In total, eight attitudinal scales have been applied with six of these scales being originally developed. Whenever a scale has been originally developed, the procedure outlined by Oppenheim (2000) has been followed. Specifically, an initial item pool, which includes a large quantity of attitude statements relating to a certain construct, has been generated. This is followed by pre-testing of the attitude statements to determine which best reflect the underlining construct. This pre-testing procedure involved using a panel of thirty judges sourced from an academic peer group to first state their particular stance on each statement included in the item pool before rating how well they consider it to reflect the underlining construct. The data attained in this phase were examined using descriptive statistics, which provided insights relating to the structure of response, and factor analysis, which demonstrated how statements were linked together. To provide an example of this pre-testing procedure, the psychological construct of ambition has been measured due to it being generalised as a determinant of innate innovativeness. The item pool produced for this construct is illustrated in Table 3.3 which contains fourteen unique statements. From this initial item pool, two statements were selected to be included in the final measurement scale. The selection process was based on the structure of the factor output, the dispersion of response and the appraisal of the judges. The specific selection criterion to evaluate the effectiveness of the statements was as follows: 1. Does the statement explain a significant degree of the variation in the factor analysis? 2. Does the statement have a dispersed response structure? 3. Is the statement positively appraised by the judges? Ideally, selected statements will account for a substantial amount of the variance in the factor analysis and have an evenly distributed response structure, signifying that the statement can successfully separate individuals in reference to the construct of interest. 80
Chapter Three: Conceptual Framework Moreover, selected statements should be well appraised by the judging panel who were provided with an overview of each of the constructs being evaluated. Table 3.2: Item Pool for Ambition 1. I’m rarely satisfied with where I am in life and always look to better myself in the future 2. I’m never satisfied with my current position in life and continually look to the future 3. I regularly think of ways I can improve my life and set out plans to turn these dreams into reality 4. I’m happy with my position in life and don’t think it needs improving 5. I am a very ambitious person setting high standards and expectations for myself 6. I always aspire to be something more than I am 7. I like to separate myself from the crowd by achieving exceptional standards of excellence 8. In life, if you’re not going forwards you’re going backwards 9. You can measure the success of my life by listing my accomplishments 10. I am capable of accepting a life that is not perfect but is satisfactory 11. Making advancements in my life is not worth the effort 12. I like to set goals for myself that I strive to achieve 13. I have a strong desire to achieve my goals in life 14. We are only here for a short period of time so it is important to make the most of it
Table 3.4 presents the rotated factor analysis from the item pool which identified five different statement groupings. The statements which have been highlighted in grey are distinctive, in so much that they only load on one factor, and are lead statements in their factor, explaining a high proportion of the variance in the data.
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Table 3.3: Rotated Factor Output for the Ambition Item Pool Statement
1
2
Factor 3
4
0.456
I’m rarely satisfied with where I am in life and always look to better myself in the future
0.95
I’m never satisfied with my current position in life and continually look to the future
0.815
I regularly think of ways I can improve my life and set out plans to turn these dreams into reality
0.687
0.303
I’m happy with my position in life and don’t think it needs improving
0.525
0.414
I am a very ambitious person setting high standards and expectations for myself 0.391
I like to separate myself from the crowd by achieving exceptional standards of excellence
0.353 0.667
0.81 -0.34
0.43 0.565 0.429
You can measure the success of my life by listing my accomplishments
0.777
I am capable of accepting a life that is not perfect but is satisfactory
0.723 0.813
Making advancements in my life is not worth the effort I like to set goals for myself that I strive to achieve
0.397
0.817
I always aspire to be something more than I am
In life, if you’re not going forwards you’re going backwards
5
0.47
0.6
I have a strong desire to achieve my goals in life
0.566 0.565
We are only here for a short period of time so it is important to make the most of it
0.921
The three statements highlighted in the factor analysis were further scrutinised for their suitability. To begin, their response structures were inspected to determine if the statement had been successful in attaining a range of different responses. The response distribution for these three statements can be viewed in Table 3.5. The results of this indicate that the second and third statements attain responses in all seven categories, though the distribution of the responses is somewhat positively skewed, whilst the first statement has a more even response distribution but has zero values on two of the response categories. 82
Chapter Three: Conceptual Framework
Table 3.4: Response Frequencies of Selected Statements from the Ambition Item Pool Strongly Disagree
Disagree
Slightly Disagree
1. I’m rarely satisfied with where I am in life and always look to better myself in the future
0.0%
17.2%
2. I am a very ambitious person setting high standards and expectations for myself
10.3%
3. I’m never satisfied with my current position in life and continually look to the future
6.9%
Statement
Slightly Agree
Agree
Strongly Agree
13.8%
24.1% 34.5% 0.0%
10.3%
31.0%
24.1%
17.2% 10.3% 3.4%
3.4%
17.2%
34.5%
17.2% 10.3% 6.9%
6.9%
Neutral
The assessments relating to how well each of the selected statements reflects the underlining psychological construct are displayed in Table 3.6. For this exercise, the judges were asked to evaluate each statement and declare if they considered it to be a poor, average or good representation of the construct. The results demonstrate that the second and third statements attain a high proportion of positive appraisals with 72.4% of the judges stating a good representation in both cases. Conversely, the first statement attains a lower appraisal with 31% of judges considering it to be a poor reflection of ambition. Table 3.5: Judge’s Appraisal of Selected Statements from the Ambition Item Pool Statement
Poor
Average
Good
1. I’m rarely satisfied with where I am in life and always look to better myself in the future
31.0%
6.9%
62.1%
2. I am a very ambitious person setting high standards and expectations for myself
24.1%
3.4%
72.4%
3. I’m never satisfied with my current position in life and continually look to the future
24.1%
3.4%
72.4%
An identical assessment procedure has been followed for all of the measurement scales originally developed for this thesis. The initial item scales can be viewed in their entirety in Section 10.1 of the Appendix. The remainder of this section presents the finalised measurement scales selected and developed to measure the constructs included in the conceptual framework. 83
Chapter Three: Conceptual Framework 3.4.2 Innovativeness: Measurement Instruments From examining how the concept of innovativeness has been defined in the academic literature alongside the instruments developed to measure it, it is apparent that a universally accepted approach is not currently present. The terminology utilised to describe innovativeness has often led to confusion regarding what is being discussed, with different layers of abstraction commonly being employed. The measurement techniques so far developed have often proved to have weak relationships with adoptive behaviour leading to questions over their predictive validity (Roehrich, 2004). With these criticisms in mind, this thesis has taken a multifaceted approach to the measurement of innovativeness in order to triangulate on its true nature. To begin, two socio-psychological scales have been developed which examine the concept of innate innovativeness. Midley and Dowling’s (1978) definition of innate innovativeness being constructed from psychological and sociological traits holds parallels with Roger’s (1995) separation of the generalised characteristics of Innovators found in empirical research by personality and communication behaviour. Taking guidance from these previous studies, the first scale has been developed to reflect the psychological determinants of innate innovativeness whilst the second scale measures the associated communication determinants. Taking Roger’s characteristics of Innovators (as defined in Table 3.2) as a starting point and selecting the determinant which have been proven in empirical research to repeatedly share relationships with the adoption of innovations, statements have been developed linked to the psychological and communication determinants of innovativeness and incorporated into scales.
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Table 3.6: Innate Innovativeness Measurement Scale - Psychological Determinants Statement
Determinant
A. Making sure I always make the correct decision is something that is important to me
Rationality
B. I prefer to let other people make decisions when I am not completely sure about the situation
Uncertainty
C. Science has no impact on how I live my life
Science
D. I’m always looking for ways to alter my life to make it better
Change
E. I have confidence in myself in making the right decision in complicated situations
Uncertainty
F. I rarely use the things I learned in formal education in my daily life
Education
G. I enjoy learning about new things
Education
H. I’m a very ambitious person setting high standards and expectations for myself
Ambition
I. I’m never satisfied with my current position in life
Ambition
J. Compulsive behaviour usually governs my purchasing decisions
Rationality
K. I quickly incorporate new ideas into how I live my life
Change
L. My friends and family would consider me to be a highly innovative
Self Report
M. I’m usually one of the first people to acquire the latest consumer technology
Self Report
N. I really enjoyed my science classes at school
Science
Focusing firstly on the psychological determinants scale, the characteristics measured and their associated statements are presented in Table 3.7. Two statements have been attached to each determinant alongside two self reports linked to innovativeness and early adoption of technology. Relating to the communication determinants scale, the characteristics measured and their associated statements are displayed in Table 3.8. In this instance, one statement has been associated with each individual determinant.
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Table 3.7: Innate Innovativeness Measurement Scale - Communication Determinants Statement A. I regularly participate in activities such as sports, clubs and/or associations that have a formal structure
Determinant Social participation
B. I have a small group of friends who all know each other well and share similar interests
Connected social system
C. My friends and family would say I was a cosmopolitan person
Cosmopolitanism
D. I have frequent contact with people working with new consumer technology E. I keep up-to-date with consumer technology by reading newspapers/magazines, websites or relevant TV shows F. Friends and colleagues regularly come to me about advice concerning new consumer technology G. I often know about the next ‘must have’ piece of consumer technology before it is released into the market H. I regularly seek information about the latest consumer technology I. I often socialise with people from a large variety of different backgrounds
Change agent contact Mass media exposure Opinion leadership Knowledge of innovations Active information seeking Highly connected system
Alongside these two scales developed to measure the abstract concept of innate innovativeness, the conceptual framework includes a measure of adoptive innovativeness. Whilst adoptive innovativeness is conceived as being related to the time taken for an individual to adopt a specific innovation, with those relatively early to adopt considered more innovative, this is not possible to directly observe in relation to LEVs. Moreover, adoptive innovativeness can be situation specific, perhaps only activating in certain markets or for certain innovations, and not necessarily universal across all contexts. To address these challenges, a broad measurement technique has been developed for this thesis to measure this concept. Asking individuals to state how long they consider it would take them to adopt an LEV (which is an instrument used by Eggers and Eggers (2011)) can appear to be an abstract exercise and may be interpreted in different ways by survey participants. Conversely, their current ownership of consumer technology is a much more tangible concept. With this in 86
Chapter Three: Conceptual Framework mind, a list of current consumer technologies has been created containing household and electronic innovations recently released in the mainstream market. Respondents have been asked to evaluate the list and state if they currently own the technology or not. In addition, respondents can state if they have an intention to own the technology in the near future allowing for a measurement of expected adoptive innovativeness. From these variables, it is possible to calculate a number of statistics such as cumulative adoption of household technology and also non-adoption levels. The list of technology specified, whilst being extensive, is not exhaustive and is restricted to household and electronic technologies that respondents are likely to have knowledge of. 3.4.3 EV Attitudes: Measurement Instrument The early research examining demand for LEVs determined that the functional characteristics of the vehicles are likely to significantly influence preferences towards them. The implication of limited range has been identified as a primary barrier to EV adoption alongside price premiums and lack of infrastructure. Conversely, a number of EV functional attributes may have a positive influence over individual preferences. EVs have considerably reduced operating costs compared to conventional vehicles and have the opportunity to be recharged from decentralised locations such as the home and workplace. To observe the influence of these negative and positive functional considerations over preferences, an attitudinal scale has been developed to measure a number of these important aspects.
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Chapter Three: Conceptual Framework
Table 3.8: EV Attitudes Scale Statement
Construct
A. Electric cars are relatively more expensive to purchase but can pay for themselves in lower fuel costs
Operating cost
B. I think I can fulfil all my transport needs with an electric car that has a range of 100 miles before recharging
Range anxiety
C. I would value the ability to refuel my car from home
Home charging
D. Electric cars don’t offer enough performance
Performance
E. I would feel relatively less safe in an electric car
Safety
F. I think it would be easy for me to find places to plug in an electric car
Decentralized fuelling
G. Electric cars are less reliable than conventional cars
Reliability
H. I think electric cars would be complicated to use
Simplicity
The internal structure of this attitudinal scale is displayed in Table 3.9 which contains eight different statements connected to eight unique constructs. Alongside aspects which have been proven in past empirical research to hold influence over preferences, this scale includes a number of novel constructs which have received relatively muted attention. Specifically, the statements associated with EV reliability, safety and complexity have been included to observe their affect on preferences as current empirical knowledge in these areas is lacking. 3.4.4 Car Meanings: Measurement Instrument To measure the constructs associated with symbolic, emotive and functional car meanings, an integrated scale was selected from the literature. Specifically, a scale proposed by Richins (1994), which is based on a statement set initially outlined by Dittmar (1992), was chosen due to its ability to be configured for use in different contexts. From the initial twenty-three items specified, a scale has been developed by selecting four suitable statements to be attached to each of the three constructs. These 12 statements alongside the construct they have been attached to are presented in Table 3.10.
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Chapter Three: Conceptual Framework
Table 3.9: Car Meanings Measurement Scale Statement*
Construct
A. Allow me to be efficient in my daily life and work
Function
B. Provide enjoyment
Emotive
C. Allow me to express myself
Symbolic
D. Be a sensible financial decision
Function
E. Be beautiful or attractive in appearance
Function
F. Provide me with social status
Symbolic
G. Have a lot of practical usefulness
Function
H. Make others think well of me
Symbolic
I. Provide emotional security
Emotive
J. Improve my appearance or the way I look
Symbolic
K. Improve my mood
Emotive
L. Be a hassle
Emotive
*I think a car most of the time can....
This scale made use of an anchor sentence to put each of the specific statements in context. Respondent were asked to think of cars in a general sense and consider how well each of the statements reflected their own attitude. Statements associated with the three different constructs have been randomly distributed through the scale to avoid grouping effects (such as universal response formats to all statements connected to a specific construct). 3.4.5 Car and EV Emotions: Measurement Instruments To attain an additional measurement relating to emotive car meaning, two attitudinal scales have been developed. Taking guidance from Hirchman and Stern’s (1999) inclusive framework previously discussed, the scales have been developed to evenly measure the assignment of positive and negative emotions. Respondents have been asked to consider how strongly they tend to associate cars with each of the stated emotions in a general manner. Following this structure, the scales provide a measurement of the general level of emotive connection to cars as opposed to situation specific emotive responses. Both scales follow the format displayed in Table 3.11 with the first scale measuring general level of
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Chapter Three: Conceptual Framework emotive connection to cars on the whole whilst the second scale asks respondents to consider their emotive response to EVs in particular. Table 3.10: Car/EV Emotions Scale Statement* A. Pride B. Stress C. Happiness D. Apprehension E. Excitement F. Boredom G. Pleasure H. Embarrassment I. Affection J. Irritation
Construct Positive Negative Positive Negative Positive Negative Positive Negative Positive Negative
*Most of the time I associate a car/EV with the following emotion…
3.4.6 Car Attitudes: Measurement Instruments Previous research has shown that attitudes related to transport can often affect how individuals structure their car purchase decisions. Choo and Mokhtarian (2004) found that travel attitudes hold influence over the type of vehicle an individual drives whilst Ozaki and Sevastyanova (2011) found that the purchase of a hybrid vehicle was motivated by sociopsychological constructs linked with environmental concerns, identity, fuel independence, technology interest and social norms. To observe the influence general car attitudes may have over LEV preferences, two scales has been developed to measure a number of potentially important socio-psychological constructs. The first scale is displayed in Table 3.12 and contains a measurement of environmental attitudes, which have been shown in previous research to influence LEV preferences. Specifically focused on car use and ownership, respondents are asked to consider their views relating to the environmental aspects of cars. These statements have been structured around two constructs included in the VBN theory (Stern et al., 1999). Firstly, respondents state their awareness of the environmental consequences of car use before detailing their ascription of responsibility. In addition, respondents declare their willingness to act by stating if they would be prepared to pay more for a car that has lower pollution levels.
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Chapter Three: Conceptual Framework
Table 3.11: Car Attitudes Scale Statement
Construct
A. I think it is my responsibility to reduce the environmental impact of driving my car
Environment
B. In my car I like to keep things as simple as possible C. Owning cutting edge car technology is something that appeals to me E. I have the ability to affect how exposed I am to increases in fuel price G. When buying a car the purchase cost is my number one concern H. I am willing to spend more on a car that has lower pollution levels I. I am concerned about the environmental impact of driving my car K. I worry about how much of my money I spend on filling up my car M. I am willing to spend more on a car that has better fuel economy
Technology Technology Cost Cost Environment Environment Cost Cost
To examine respondent sensitivity to vehicle expenditure, this scale includes statements to measure attitudes towards purchase and operating costs. Aspects related to fuel efficiency and independence alongside purchase costs and fuel expenses have been examined. With previous studies highlighting the importance of advanced technology embedded within LEVs and the potential attractiveness this feature holds for adopters, two statements have been included to measure respondent attitude towards car technology. The first statement positions the construct in a positive manner and asks respondents to state if they are attracted to cutting edge car technology whilst the second uses a negative approach and asks if the respondent prefers a simple car. The second scale related to Car Attitudes is presented in Table 3.13 and contains statements focused on two different constructs. The first of these constructs examines the degree of knowledge a respondent has concerning cars. Measured over three different statements, respondents are first asked about their knowledge of the new types of car powertrains becoming available on the market before being requested to state their acquaintance with the mechanics of cars and their practical ability to fix car malfunctions. The influence of car knowledge over LEV preferences has seen limited attention in empirical research. Innovation theory suggests that those individuals that are more knowledgeable about a product category are likely to need a relatively small degree of time to evaluate new innovations (Rogers, 1995). By including a measured of car knowledge in the conceptual framework, this thesis determines what affect car knowledge has over LEV preferences. 91
Chapter Three: Conceptual Framework Table 3.12: Car Knowledge and Importance Scale Statement
Construct
A. I know a lot about the new types of cars (such as hybrids or pure electric cars) being released into the car market
Car Knowledge
B. I know how my car engine works on a mechanical level C. I’m capable of conducting most forms of car repair (such as replacing an engine fan belt and brake pads) F. My car is the most important thing I own G. Without my car, my life would become very difficult H. If my car was stolen, I’d feel as if I had lost a part of myself I. The car I drive is irreplaceable J. I consider my car to be part of the family K. I often treat my car as if it were a person (talk to it, give it a name etc.)
Car Knowledge Car Knowledge Car Importance Car Importance Car Importance Car Importance Car Importance Car Importance
The second construct integrated in this scale measures the degree of importance placed on car ownership. This has been approached from two different angles, firstly by determining the degree to which a respondent considers their car to be essential due to the instrumental benefits it offers before progressing to more abstract concepts such as car personification and relationship formation. Anthropomorphic tendencies surrounding car use are well documented (Dant, 2004; Sheller, 2004) though their affect over LEV preferences remains unexplored. This thesis offers some insight into this area by determining if those individuals that place high degrees of instrumental and personal importance on car ownership are more or less likely to hold positive attitudes towards EVs.
3.4.7 Value Orientation: Measurement Instrument Research in LEV demand has tended to concentrate on the importance of environmental attitudes and their influence over preferences (Sangkapichai and Saphores, 2009). With LEVs embodying a number of important environmental aspects, such as reduced emissions levels and increased energy efficiencies, this is an obvious connection. In qualitative interviews with individuals concerning LEVs, the environmental aspects of these vehicles appear to be salient in perceptions and considerations (Graham-Rowe et al., 2012). However, an aspect that has received relatively little attention concerns the influence of other value orientations. Choo and Mokhatarian (2004) found that the lifestyle of an individual can significantly influence the type of car they drive. Specifically, respondents that 92
Chapter Three: Conceptual Framework classify themselves as workaholics are less likely to own a small car whilst those that self categorise as status seekers are more likely to drive sports cars.
Table 3.13: Life Principles Scale Statement
Construct
A. Equality (equal opportunity for all)
Altruistic
B. Respecting the Earth (harmony with other species)
Biospheric
C. Social Power (control over others, being dominant) D. Unity with Nature (fitting into nature)
Egotistic Biospheric
E. A World at Peace (free of war and conflict)
Altruistic
F. Wealth (acquiring material possessions and money) G. Authority (the right to lead and command)
Egotistic Egotistic
H. Social Justice (correcting injustice)
Altruistic
I. Protecting the Environment (preserving nature)
Biospheric
J. Influential (having an impact on people and events)
Egoistic
K. Helpful (working for the welfare of others)
Altruistic
L. Preventing Pollution (protecting natural resources)
Biospheric
M. Ambitious (hard working and aspiring)
Egoistic
The final scale including in the conceptual framework is displayed in Table 3.14 and approaches the topic of value orientation by measuring the life principles that tend to guide behaviour. Specifically, guidance is taken from the VBN theory (Stern et al., 1999) and the life principles associated with the altruistic, egotistic and traditional values have been selected. This scale has been applied using Steg et al.’s (2005) specific approach in which the traditional construct has been altered to reflect biospherical considerations. Respondents have been presented with 13 individual principles linked to the 3 different value orientations and are asked to state how well each principle reflects how they structure their behaviour. These measurements have been taken forward to determine the influence of value orientation over innovativeness, car attitudes and car meanings.
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Chapter Three: Conceptual Framework 3.4.8 LEV Preferences: Measurement Insutrument In order achieve an understanding of how consumers are appraising LEVs in the emerging market, an accurate measurement of preferences towards these vehicles must first be established. With LEVs only recently becoming available on the mainstream market and with adoption rates still relatively low, attaining revealed market data concerning actual vehicle purchases is difficult. To overcome this challenge, an evaluation exercise has been developed to measure respondent stated preference towards LEVs. This evaluation exercise measures respondent preferences across 4 different LEV powertrain options alongside 2 conventional ICE options. However, there are a number of concerns relating to the accuracy of preference measurement in hypothetical situations. Armitage and Conner (2001) discuss this point in their critique of the TPB, finding that the conceptual gap between intention and behaviour represents a limitation of the theory. This is referred to as the attitude-action gap by Lane and Potter (2007) in their examination of LEV adoption and is a prevalent issue in environmental behaviour research (Kollmus and Agyeman, 2002). In addition, Turrentine and Sperling (1992) question the appropriateness of traditional choice modelling exercises applied in the LEV market, arguing that, as respondents are likely to
have no prior
experience with and little knowledge relating to LEVs, their stated preferences are likely to be unstable and susceptible to framing bias. To address these stated challenges, the powertrain evaluation exercise developed for this thesis was designed to provide all respondents with the information necessary to make an informed decision. Specifically, an information pack was created which details the unique nature of the LEV powertrain options included in the exercise, stating how they differed from conventional powertrains and each other. Clearly, information must be provided in a concise and unbiased manner so that adequate knowledge is imparted without overburdening respondent cognitive capabilities. With this in mind, the information pack was designed to be accessible by avoiding any form of advanced terminology.
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Chapter Three: Conceptual Framework
Figure 3.10: Illustration of the information pack offered to survey respondents including verbal description, graphical illustration and attribute matrix An example of the information pack relating to the Plug-in Hybrid and Pure EV powertrain options is provided in Figure 3.9 with the full information pack being available in Section 10.3 of the Appendix. Specific attention was paid to ensuring that information concerning the different LEV powertrains is provided in a consistent fashion to facilitate direct comparison. Taking inspiration from the work conducted by Axsen and Kurani (2008), a graphical illustration of the vehicle is offered displaying the types of fuel source each 95
Chapter Three: Conceptual Framework powertrain can operate from. In addition, a more traditional attribute matrix was developed detailing the relative performance levels of the functional powertrain features which was based on the examples provided by Ewing and Sarigollu (1998). The four LEV powertrain options included in the evaluation exercise are Mild Hybrid, Full Hybrid, Plug-in Hybrid and Pure Electric Vehicles. As highlighted in the introductory chapter, the electric powertrain pathway has been selected as the basis from which to measure LEV preferences as it is currently the one most ready to be deployed in the mainstream market (Romm, 2006; Hoyer, 2008). Each respondent is provided with the exact same information pack, containing static Powertrain parameters, to ensure that the preferences stated are for the same powertrain formats in each instance. Once the information pack has been reviewed by a respondent, they progress to the evaluation exercise proper. Respondents are asked to state on a 7 point Likert scale how likely they would be to consider each option in their next vehicle purchase from highly unlikely to highly likely. The rationale for framing the exercise linked to next vehicle purchase is linked to the assumption that individuals hold more stable preferences for their next vehicle purchase compared to some pre-specified time in the future. Whilst it may prove interesting to measure how powertrain preferences are likely to be structured in 2025, it is unlikely that an accurate measure of these preferences can be taken. In a similar fashion, the specification of the powertrains in the evaluation exercise is drawn from current technology as opposed to expectations of future powertrain technology. Alongside the four LEV powertrain options, respondents are asked to express preferences for conventional petrol and diesel ICE powertrains to add an element of realism to the exercise and frame it within conditions that are present in the current market. 3.5 CHAPTER SUMMARY This chapter has discussed the development of a conceptual framework which addresses the specific research objectives and questions attached to this thesis. To begin, an overview of conceptual frameworks previously developed in demand for LEVs has been offered. This examination has allowed for insights regarding the structural layouts, integrated constructs and theoretical foundations of previous frameworks to be made. From this examination, it is 96
Chapter Three: Conceptual Framework apparent that few authors have formally illustrated their frameworks, instead tending to rely on verbal descriptions and results tables to display where relationships are present. Where formal illustrations are displayed, they have shown a tendency of authors to integrate constructs sourced from different theoretical frameworks when researching this field. Having made these observations, the conceptual framework developed for this thesis was presented with the integrated constructs described and justified. This framework uses a psychometric approach by measuring socio-psychological constructs to determine how they relate to one another and influence preferences for LEVs. Different levels of abstraction have been utilised to demonstrate hypothesized links between framework constructs. The personality trait of innovativeness has been measured from an innate and adoptive perspective to observe the role it is playing in this emerging market. In addition, attitudes towards the functional capabilities of LEVs have been taken which are themselves described by the meanings and attitudes individuals hold regarding cars in general. Moreover, measurements relating to value orientation have been made to determine their influence over other framework constructs. Following the description of the conceptual framework, the instruments developed and selected to evaluate each construct are presented. The socio-psychological constructs have been measured through the application of attitudinal scales. An overview of the procedure followed to create these scales is offered, discussing aspects such as item pool generation, the use of judging panels in pre-testing and the criteria for statement selection. LEV preferences have been assessed through the application of a powertrain evaluation exercise containing six different options. The structure of the exercise has been made with respondent unfamiliarity with LEV powertrains in mind, ensuring that the information provided is straight forward to interpret and that the different options can be easily compared. Having discussed the development of the conceptual framework associated with this thesis, the next chapter progresses to detailing the practicalities of empirical application. Topics
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Chapter Three: Conceptual Framework linked to survey development, sample size requirements and site selection are discussed alongside issues associated with ethical considerations.
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Chapter Four: Survey Development
CHAPTER
4
METHODOLOGY - SURVEY DEVELOPMENT
4.1 INTRODUCTION Traditionally, research examining the diffusion and adoption of innovations has taken a historical perspective, describing how a particular innovation spread through a social system. This approach holds a number of benefits, allowing for different innovation diffusions to be compared noting similarities and differences. Moreover, this method allows for key events which significantly influence the diffusion path of an innovation to be identified and discussed. Knowledge generated from this research activity has allowed for theories such as the Diffusion of Innovations (Rogers, 1995) to be developed which provides generalisations based on observations of common aspects of diffusion. However, this retrospective approach means that knowledge generated regarding a particular innovation cannot be used to enhance its rate of adoption. With LEVs being viewed as a strategically important innovation (DfT, 2009a; CCC, 2012), an understanding of their diffusion potential will make a useful addition to policy development. This thesis approaches the issue of innovation diffusion from a different direction, by investigating consumer response to LEVs at the initial introduction of the product. The previous chapter has discussed the development of a conceptual framework proposed to examine constructs which have the potential to influence preferences towards LEVs but have yet to be investigated in applied research. This chapter discusses the specific methodological approach utilised in this thesis to apply this conceptual framework to achieve an understanding of consumer response to LEVs from the initial introduction of these vehicles into the mainstream market. The focus primarily is on the development and
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Chapter Four: Survey Development administration of a household survey which incorporates the measurement instruments detailed in the previous chapter. 4.2 SURVEY DEVELOPMENT Car purchasing is one of the most common types of consumer behaviour, with a large proportion of adults having experience of this transaction. Often, a car purchase represents the largest single form of expenditure for a household in a given year (ONS, 2012b) with individuals ordinarily considering the purchase over a number of months before deciding which specific car to buy (Kiel and Layton, 1981). The widespread nature of this market means that the population of interest for this thesis is decentralised and accessible for research. To test the appropriateness of the conceptual framework developed, it is necessary to collect data for each of the constructs included in it. To achieve this, a self completion household survey has been produced which contains the measurement instruments (discussed in Chapter 3, Section 3.4) and can be viewed in Section 10.3 of the Appendix. Surveys represent the most common data collection tool used in the social sciences (Murray, 1999), allowing research participants to provide their responses autonomously. The production of an effective survey is a complex task, involving issues linked to design, structure and wording which is required to be suitable for a wide audience. This section discusses the primary aspects of the survey development stage of the thesis, detailing the production process from initial prototype to final version including aspects linked to format of scales, layout of sections and survey piloting. 4.2.1 Design The basic design of a survey is the first aspect a respondent notices and may significantly influence their first impressions. Alongside the covering letter, survey design is likely to affect how a respondent approaches the survey and, ultimately, if they choose to participate in the research. Aesthetic appearance of the survey can influence the level of response and the quality of data attained (Fink, 2008). The questions a survey includes need to be well constructed to ensure accessibility and clarity (Murray, 1999). In cases where a survey includes questions which are vague, this can lead to the data attained becoming subjected to bias (Preston, 2009). This section discusses the manner in which the survey was 100
Chapter Four: Survey Development designed for this thesis, beginning with the essential aspects of question structure before detailing considerations linked with visual format. A principal aim of this thesis is to attain insight regarding consumer dynamics in the mainstream car market. To achieve this, the survey design must be accessible to all consumers. To ensure this is the case, the style of wording utilised when developing the questions and other aspects of the text has been chosen with care. Simplicity and clarity were key issues when determining the structure of questions and related text to minimise the chance of ambiguity. The use of advanced terminology, industry or academic jargon and acronyms has been avoided to mitigate confusion. However, this approach can lead to a tendency to make the survey overly simplistic, perhaps leading to some respondents feeling patronized. With this in mind, it is more appropriate to frame the issue of wording as a balance between clarity and substance. Turning to the subject of question wording, a number of notable issues have been accounted for throughout question development. Questions with two meanings, commonly referred to as double-barrelled questions, have been avoided to ensure respondents are only appraising a single aspect at any one time. Similarly, questions that are termed as double negatives have been rephrased to improve comprehension. A more challenging point to address has been the issue of loaded questions, whereby a respondent is directed to respond in a desired manner. With LEVs embodying a number of traits that have the potential to benefit society, it is a temptation for respondents to select socially acceptable answers or attempt to identify what the correct response is. In incidences where this is apparent, it may lead to attitudes not being accurately measured. To account for this tendency, questions have been designed to allow respondents to answer in a natural manner by avoiding tones or structures that may indicate that one particular answer is preferable to another. During the development of the survey, twelve different versions were constructed, progressing from the initial prototype and then undergoing iterative improvements before a final version was selected. A template has been created which is universally applied throughout the survey. Each section is associated with a brief introduction detailing its 101
Chapter Four: Survey Development purpose and how it should be completed. Questions have been embedded within boxes so that they can be easily recognized. All sections have been labelled and numbered with greyscale being employed to help respondents distinguish questions and observe their locations. Whilst text has been kept to a minimum, bold type face has been used to make respondents aware of the most important points. In an attempt to differentiate this survey from marketing circulars, the University of Aberdeen’s seal has been prominently positioned on the front cover of the survey and on the delivery envelope. This design strategy has been adopted to ensure the survey is viewed as being professionally constructed and thus provides a sense of legitimacy to the exercise. Respondents should consider the survey to be attractive and a worthwhile endeavour knowing that their efforts are valued and that their response will assist not only academic knowledge but also address social objectives. 4.2.2 Structure The order in which the sections included in the survey are delivered may influence the speed of return, the total response rate and the choices made (Dunn et al., 2003). Information presented in early sections of the survey is likely to frame the exercise in a certain manner and influence later responses (Preston, 2009). Additionally, respondent concentration is likely to diminish over time (Boksem et al., 2005), leading to less effort being expended in later sections. With these reflections in mind, time has been taken to consider the likely implications of different section orders in the process of developing an appropriate structure for the survey. Examining the constructs included in the conceptual framework, it is apparent that a number, such as innovativeness and life principles, are somewhat abstract in nature and have no direct link to cars in general. Considered individually, this can lead to confusion among respondents that expected to complete a survey about cars and are instead being asked to state their level of rationality or cosmopolitanism. When determining the survey structure, a key issue was to develop a logical progression to avoid for this potential source of confusion. To achieve this aim, sections have been grouped by theme to make survey
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Chapter Four: Survey Development completion as straightforward as possible. The exact section order is outlined in Table 4.1 which states the section title and provides a brief summary of the purpose of each section. Sections relating to current car details and travel patterns have been placed at the beginning of the survey to ease respondents into the question and answer process. Directly following this, the survey advances by examining attitudes related in cars in general and car purchasing behaviour. The next stage introduces the concept of LEVs to the respondent and asks them to express their preferences towards a selection of powertrains. Having successfully measured LEV preference, sections that include items that may directly influence these preferences, such as environmental considerations and EV functional capabilities, are now presented. To conclude the survey, the sections progress to examining more general respondent characteristics such as innovativeness and value orientation before asking respondents to state their basic socio-economic characteristics. This last section can often prove to be a point of contention, with some respondents considering questions related to family structure and income an invasion of privacy. However, these variables are essential in determining the representativeness of the sample. By positioning this section at the end of the survey, respondents are likely consider the effort they have already expended and decide to finish the survey in its entirety
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Chapter Four: Survey Development Table 4.1: Survey Section Order No. 1 COVERING LETTER 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Section Title and Description
Introduces the recipient to the research topic, generating initial interest and covers a number of ethics requirements
INTRODUCTION QUESTIONS
Routing questions that, based on the answer, informs respondents of which sections of the survey should be completed
CAR OWNERSHIP AND USE
Attains data related to current car characteristics and use patterns
TRAVEL BEHAVIOUR
Measures the frequency in which a respondent uses different modes of travel
CAR MEANING SCALE
Scale assessing respondent assignment of functional, symbolic and emotive meaning to car use and ownership
CAR EMOTIONS SCALE
An additional measurement relating to emotive connection with cars
FUTURE AND PREVIOUS CAR CHOICE
Determines future car type expectations alongside car brand and type loyalty
IMPORTANT CAR CHARACTERISTICS
Top three vehicle characteristics which influence their car appraisal and purchasing decisions
CAR KNOWLEDGE AND IMPORTANCE SCALE
Inspects how important a car is, the knowledge and understanding concerning cars and experience with EVs in particular
INFORMATION PACK
Providing information relating to the practical nature of the vehicles
POWERTRAIN EVALUATION EXERCISE Measure preferences for LEVs
CAR ATTITUDES SCALE
Measures constructs which have been shown in previous research to influence attitudes and preferences towards LEVs
EV EMOTIONS SCALE
Identical structure to the Car Emotions Scale though focus is shifted to EVs in particular
EV ATTITUDES SCALE
Measures attitudes linked to EV functional performance and ability
COMMUNICATION DETERMINANTS OF INNOVATIVNESS SCALE
Innate innovativeness scale examining association with a number of social determinants PSYCHOLOGICAL DETERMINANTS OF INNVATIVENESS SCALE Innate innovativeness scale examines individual personality traits and attitudes linked to innovative behaviour
TECHNOLOGY OWNERSHIP
Measurement of adoptive innovativeness of household technology
LIFE PRINICIPLES SCALE
Measure value orientation of biospheric, altruistic and egotist value structures
SOCIO-ECONOMICS
Captures information regarding respondent economic and household characteristics
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Chapter Four: Survey Development 4.2.3 Scale Format A high proportion of the instruments developed in this thesis’s conceptual framework involve the measurement of socio-psychological constructs. Measurement scales are a commonly used mechanism to quantify these variables throughout academic research (Green and Rao, 1970). The manner in which a measurement scale is presented to a respondent is likely to influence their response to it. How the instrument is framed, the order in which they are delivered, the context in which they are set and how they are grouped together can affect respondent decisions (Heap et al, 1992). This section of the chapter discusses the manner in which measurement scales have been delivered by the survey based on recommendations made by the literature. Oppenheim (2000) discusses the development and application of measurements scales and details three primary scale formats. Firstly, Thurstone’s (1928) approach involves developing a list of opinion statements which have been assessed by a judging panel and assigned a value based on how positively inclined each statement is to the attitude. Respondents are asked to accept or reject each statement based on if it is an accurate representation of their attitude with a mean score then calculated. Guttman’s (1950) Scalogram analysis rank orders statements based on their degree of positive orientation to an attitude, so that the first statement presented to a respondent is completely negative whilst the last statement is completely positive. Respondents are asked to state whether or not they endorse each statement with an expectation that, if they endorse a particular statement, they will endorse all statements which follow it. Likert (1932) took a slightly different approach to attitude measurement by formatting a scale which allows respondents to state the degree to which each statement reflects their particular attitude. To determine the effectiveness of these three different scale formats, Tittle and Hill (1967) conducted an empirical evaluation and determined that the Likert format is superior in both reliability of measurement and as a predictor of behaviour. With these results in mind, the Likert scale format was selected to measure the socio-psychological constructs of this thesis. The specific format of a Likert scale can be considered as the product of two different aspects. The first aspect reflects the terms used to evaluate each statement and the second to the quantity of response categories provided. These aspects are detailed in the following 105
Chapter Four: Survey Development discussion with the findings from empirical research being used to select the appropriate format. Likert scales often incorporate anchor terms which define the polarity of the scale and assist respondents in structuring their choices. The range of terms utilised is varied and includes phrases such as (dis)agree, (un)likely and (un)important. The format of anchor terms, including the specific phrases used and if these phrases progress in a logical manner, can significantly affect the response distribution, mean and skewness of a scale (French-Lazovik and Gibson, 1984). Bendig (1953) offers initial guidance on the use of anchor terms in measurement scales by examining the change in scale reliability under different anchor formats. Specifically, three different anchor formats were applied where only the scale ends were labelled, only the middle was labelled and both the ends and the middle were labelled. Results of this study suggest that scale reliability increases when both the ends and the middle of the scale are labelled. More recently, Weng (2004) examined the influence of anchor terms on Cronbach’s alpha scores and test-retest reliability. In this study, two different anchor term formats were examined with the first having only the ends of the scale labelled whilst the second had all response categories labelled. The results of this analysis demonstrate that the alpha scores of the two different formats are comparable whilst the full label scale outperforms the end label scale in regards to stability under testretest evaluation. Taking into consideration these findings, the scales applied in this thesis have all response categories labelled. Using different quantities of response categories can lead to disparities in scale output. Inspecting the influence of response categories on the measurement of personality, Symonds’ (1924) early research examined the influence of scale coarseness over reduction in reliability by noting the difference between true and obtained values across eleven different interval quantities. The results of this initial investigation indicate that a scale with 7 intervals is optimum in generating reliable results. More recently, research has continued in this field with the assistance of advanced statistical techniques. Cicchetti et al. (1985) use a computer simulation model to explore the effect of the number of response categories over scale reliability with the results indicating that scale reliability increases steadily up to 7 intervals but does not significantly increase past this. In a similar study, Cook and Beckman 106
Chapter Four: Survey Development (2009) compare the effectiveness of a 5 and 9 interval scale by examining study validity in medical assessments. The results of this analysis demonstrate that a 5 and 9 interval scale are comparable when assessing scale reliability but that a 9 interval scale provides better levels of accuracy. In an attempt to identify optimum scale length, Preston and Colman (2000) conduct an extensive study examining response quantities ranging from 2 to 11 intervals. Evaluation indices comprised scale reliability, validity and discriminating power with the results indicating that a 2, 3 and 4 interval scale offer inferior performance compared to a 7 interval scale. Furthermore, scale reliability tends to decreases when the quantity of response categories exceeds 10. From this review, the empirical research seems to suggest that a 7 interval scale is optimum for producing reliable results and thus has been selected as the number of response categories for this thesis. 4.2.4 Online Survey Whilst a paper based survey was selected as the principal data collection method, an online version of the survey was developed to allow respondents to submit their scripts electronically. The use of internet based surveys has seen rapid growth over the past decade and is now a widely applied method of data collection in academic research (de Leeuw, 2012). The swift adoption of online surveys is attributed to their comparative advantage over the traditional methods of postal, telephone and in-person sampling. Sills and Song (2002) outline these advantages which include decreases in the time required to deliver, respond, enter and clean the data with transaction costs also being reduced. Additionally, online surveys provided enhanced flexibility through the use of routing questions whilst moderating the possibility of investigator or participant error by defining appropriate response formats and necessary answers. These strengths make online surveys an appealing option to researchers but a number of limitations should be considered before selection. Soloman (2001) discusses the methodological challenges of administrating an online survey stating that computer expertise and infrastructure coverage is not yet advanced to allow for a representative sample of the general population to be collected. Moreover, online surveys tend to suffer from lower response rates compared to traditional methods, perhaps linked to privacy and data protection concerns (Gunn, 2002).
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Chapter Four: Survey Development With participants having the option of responding either by post or electronically, the limitations discussed in the preceding paragraph are mitigated. The online version was constructed to directly replicate the structure and wording of the paper version to ensure that results attained across the two different mediums were compatible. The SNAP 1 0F
software package was used to construct the online version which was accessed through a University of Aberdeen hyperlink 2. A screenshot displaying the introduction page of the 1F
online survey can be viewed in Section 10.3 of the Appendix. Responses were automatically received by a POP3 email address, transferred into a spreadsheet package and then integrated with the postal responses. 4.2.5 Pilot With the conceptual framework developed, constructs described and measurement instruments designed, a pilot phase was undertaken to assess suitability of the survey and to identify any apparent design flaws. This pilot utilised a draft version of the survey and followed a similar administration process to that developed for the main survey (discussed in the forthcoming Section 4.3.6). The pilot phase was based in the city of Aberdeen with four areas selected using the Index of Multiple Deprivation (Scottish Government, 2009) to ensure a representative distribution. A drop and collect procedure was followed whereby households received a hand delivered survey and were made aware of a specific evening when completed surveys would be collected. If a respondent had not completed the survey by the collection date but desired to participate, a second collection date was set. In total, 200 surveys were delivered to households in Aberdeen city with 48 being completed representing a response rate of 24%. Of this response, only one was received using the online version of the survey. To better appraise the effectiveness of the online version, a further 100 one-page letters were distributed inviting households to participate using the online survey. Of this additional distribution, a further six electronic versions were completed. The data set attained from the pilot was subjected to a similar data analysis procedure as that developed for the main survey. Data was coded into SPSS and then cleaned to detect 1 2
Information on this software can be viewed at http://www.snapsurveys.com The exact URL was http://www.abdn.ac.uk/~r12cm9/carsurvey
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Chapter Four: Survey Development and correct for input errors. This led to refinements to the data entry template, allowing for streamlined input of the main survey. Descriptive and univariate statistical analysis were employed to examine the basic format of the data. Attitudinal measurement scales were subjected to Exploratory Factor Analysis to determine the internal data structure. In addition, during the collection procedure respondents were asked if they had any particular problems with aspects of the survey and were encouraged to provide their input. This appraisal of the pilot provided valuable insights related to areas which could be improved or altered to enhance survey effectiveness. The information attained in the pilot led to a number of changes being made to the final version of the survey. Table 4.2 lists alterations made to specific attitude statements, stating the original and updated version. The first statement in the table was initially phrased as a double negative and so has been changed to improve comprehension. The statement reflecting attitudes towards big oil companies was removed due to a high degree of neutral responses and replaced with a statement examining LEV complexity. The last statement in the table is a self report on innovativeness and the pilot data suggested that responses to this statement were negatively skewed. To achieve a more distributed response frequency in the final survey, the term “highly innovative” was used.
Table 4.2: Alteration to Questions Section
Original Statement
Updated Statement
LEV Attitudes
I do not think it would be difficult for me to find places to plug in an electric car.
I think it would be easy for me to find places to plug in an electric car.
LEV Attitudes
I like the idea of electric cars because they mean less money will go to the big oil companies.
I think electric cars would be complicated to use.
Psychological Determinants of Innovativeness
My friends and family would consider me to be an innovative person.
My friends and family would consider me to be a highly innovative person.
In addition to inspecting individual and groups of questions, the pilot data was also utilised to examine the appropriateness of the survey section structure. From this appraisal, a number of changes have been initiated to improve the clarity of the survey. During the data 109
Chapter Four: Survey Development entry procedure, it was apparent that respondents were repeatedly making input errors, such as omitted variables, on the car characteristics section. This section was initially sourced from Lane (2005) and was included to assess what car attributes are important to a respondent when making a car purchase. On further assessment, it was concluded that the section in its pilot format was cumbersome, including 24 different car features, and that this was likely the cause of the input errors. To correct this, the section was redesigned to make it more straightforward and has taken an open approach to response. In addition to this section alteration, changes have been made to the covering letter to simplify the introduction to the survey. 4.3 SAMPLING STRATEGY AND SURVEY ADMINISTRATION This section describes the relevant practicalities associated with the administration of the household survey. To begin, the process of site selection is outlined which states and then justifies the choice of locations. Following this, the requirements for sample size is discussed to allow for inferences and generalisations to be made about the relevant populations. To conclude the section, time is taken to explain how the survey was administered. The literature relating to best practice is presented and implications for this thesis discussed. 4.3.1 Site Selection The decision relating to where to source survey respondents from can have a significant impact on the results generated. Respondents in certain geographical locations may be subject to factors that are not present in the general population. Moreover, the decision of whether or not to select a single or multiple site approach will significantly influence the versatility of the study. A principal advantage of selecting multiple sites to conduct a research study is the ability to compare the aggregated results in order to identify any site specific variation. Result variation based on site location potentially indicates the existence of unique environment factors. One such environment factor relevant in the LEV market is the UK Government’s introduction of Ultra Low Carbon Vehicle Demonstrator (ULCVD) sites and Plugged-in Places (PIP) electric vehicle infrastructure initiatives in 2009 (DfT, 2009a; DfT, 2009b). These initiatives have introduced a number of plug-in electric vehicle trials and charge points in various locations throughout the UK with the objectives being to examine 110
Chapter Four: Survey Development vehicle and infrastructure effectiveness, collect usage data and highlight any unforeseen problems before mainstream market introduction. The main features of these two policy initiatives are detailed in Table 4.3. Table 4.3: Main Features of the Ultra Low Carbon Vehicle Demonstrator and Plugged In Places Government Policies Ultra Low Carbon Vehicle Demonstrator
Plugged In Places
£25 million in Government funding provided
£20 million of Government funding provided
8 demonstration sites selected
A further £10 million made available from the strategic investment fund
350 vehicles used Vehicles required to have emissions of less than 50 grams of carbon dioxide per kilometre Members of the public used in the trials Information gathered on usage patterns and user perceptions
3 initial sites selected with a further 5 sites coming online in phase two Project aims to establish front runners in the adoption of LEVs Facilitate an early market in the selected sites
These initiatives have exposed the population of these areas to LEVs, providing visual and practical experience of the vehicles and their associated infrastructure. It is proposed that this experience will not only work towards the specific goals previously outlined but also potentially influence the LEV preferences of the relevant population. This proposition has been tested by examining the aggregate preference data between a site that have been exposed to LEV initiatives and a site that has not to determine if any significant differences are present. The next two sections discuss the rationale for selecting a site which has been a recipient of these policy interventions and a comparison site of similar characteristics which has not been exposed to the ULCVD or PIP initiatives. 4.3.2 Policy Site The sites that have been selected as ULCVD and PIP are listed and illustrated in Figure 4.1. The PIP scheme initially allocated funding for 3 sites (London, Milton Keynes and North East England) with subsequent additional funds becoming available leading to a second wave of sites being selected (Northern Ireland, Scotland, Greater Manchester, East of England and Midlands). There were 8 winning bids for the ULCVD scheme which tended to be 111
Chapter Four: Survey Development consortiums formed between private automotive firms, regional development agencies, universities and private consultancies. A number of these bids are not directly related to conventional passenger vehicles for the mainstream car market, with one London scheme focusing on fleet users whilst the consortium based in Didcot is researching electric sports cars. With this thesis focusing on the general consumer market, these two sites are not suitable and thus have been removed from consideration. Of the remaining sites, three have been the recipients of both the ULCVD and PIP policy interventions. The remainder of this section discusses the suitability of each site to be utilised in this study.
Ultra Low Carbon Vehicle Demonstrations
First Wave (blue) London Milton Keynes North East England
Second Wave (green) Northern Ireland Scotland Greater Manchester East of England Midlands
Plugged-in Places
Glasgow North East England Coventry and Birmingham Oxford London Middlesex London (fleet) Didcot (sports)
Figure 4.1: Ultra Low Carbon Vehicle Demonstration and Plugged-in Places Sites Examining the geographical spread of the schemes, it is apparent that there is a high concentration, especially relating to the ULCVD scheme, around the Greater London area. 112
Chapter Four: Survey Development The transport system for London is governed by Transport for London (TfL) that was created in 2000 by the Greater London Authority Act of 1999 (HMGoverment, 1999). This governing system distinguishes London’s transport from the rest of the UK both in terms of bureaucratic structure and development vision. Within TfL, the London Electric Vehicle Partnership has been established as a separate division to oversee EV related issues (Mayor of London, 2009). Unique incentives related to plug-in vehicles are in operation within the Greater London area including exemption from the London Congestion Zone charges and free parking and recharging offered by some London Borough Councils. The concentration of recharge infrastructure located in the Greater London area is expected to be extensive and will be uniformly integrated by the Source London scheme 3. 2F
As the area in and around London possesses many unique characteristics that are not shared by other areas of the UK, it can be argued that London represents a unique case with a distinct market environment. With the high concentration of activity relating to the automotive market in general and LEVs in particular, it is likely that any research conducted in London would not be representative of the UK as a whole but rather be reflective of a separate market. This in itself is interesting and shares similarities with the spatial analysis of diffusion hotspots as discussed by Pridmore and Anable (2012) but is not well suited to the objectives of this thesis. With these points in mind, the decision was taken to exclude sites in the South East of England as candidates for the policy site. Outside of the South East of England, there is one site that has been selected to host both the PIP and ULCVD schemes. The North East of England, through the Regional Development Agency (One North East) in partnership with other consortium members, was successful in their bid to win funding for these two schemes. Charge Your Car 4 was established in 2010 as 3F
the PIP operator for the North East of England and has currently installed 275 5 charging 4F
points in the Tyne and Wear Metropolitan Area with 1300 charging points expected to be installed by the end of 2013. Switch EV 6 was established in September 2010 as the ULCVD 5F
3
Details available at https://www.sourcelondon.net/ Details available at: http:// http://chargeyourcar.org.uk/ 5 As of 6th January 2013 6 Details available at: http:// http://vehicletrial.switchev.co.uk/ 4
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Chapter Four: Survey Development operator for the North East of England and has been operating with 44 vehicles made available to the general public and private organisations. Four different EV models are operating in this trial including the Nissan Leaf, Peugeot iOn, Smith Edison Minibus and Land Rover Range Rover. Interested parties can apply to lease a LEV for six months with prices ranging from £200 per calendar month to £400. Consortium partners have been running a public engagement and awareness campaign with regular project updates published in local media (Wardle, 2013). Early market research indicates that 34% of the local populace have brand awareness of Charge Your Car whilst 60% have seen a charging post. From this, it is anticipated that public attitudes towards LEVs are better informed as a result of these policy interventions which has the potential to positively influence preferences. In order to determine if this assertion can be corroborated, the North East of England (specifically, Newcastle upon Tyne) has been selected as the policy site in which this thesis has been applied. 4.3.3 Comparison Site To determine if preferences in Newcastle upon Tyne have been influenced by specific contextual factors associated with the outlined policy interventions, it is necessary to compare this policy site to a similar area where these aspects are not present. This comparison site is required to be analogous to Newcastle though have yet to be exposed to the installation of charging infrastructure or LEV trials. To determine an appropriate comparison site, three distinct aspects have been examined comprising of charge point diffusion, socio-economic characteristics and transport behaviour profiles. Figure 4.2a displays the current diffusion of charge points around the UK 7, exhibiting a 6F
significant degree of installed infrastructure. The charge point infrastructure currently installed in the Tyne and Wear Metropolitan Area is presented in Figure 4.2b which shows a substantial spatial diffusion, particularly in Newcastle. The first step in identifying an appropriate comparison site was to select a number of metropolitan areas which have yet to be exposed to either LEV trials or LEV infrastructure. Four different sites were identified
7
Details available at: http://www.zap-map.com/
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Chapter Four: Survey Development comprising of Dundee, Cardiff, Plymouth and Sheffield. Figure 4.3 displays the charge point infrastructure installed in these areas, showing that all potential comparison sites have either zero or relatively low levels of charge point diffusion.
(a)
(b)
Figure 4.2: Installed Charge Point Infrastructure in (a) UK and (b) Tyne and Wear Metropolitan Area
Having identified four potential comparison sites based on their lack of exposure to LEV trials and infrastructure, the selection process progressed to evaluating the socio-economic 115
Chapter Four: Survey Development and travel behaviour profiles of these areas. The results of this analysis are presented in Table 4.4 which compares the four potential comparison sites to Newcastle across characteristics such as economic activity, population, car ownership and car use. In total, population statistics were selected covering nine distinct characteristics with each being assessed based on the similarity with Newcastle.
(a)
(b)
(c)
(d)
Figure 4.3: Charge Point Infrastructure of Potential Comparison Sites (a) Dundee (b) Cardiff (c) Plymouth and (d) Sheffield In regards to economic activity, Dundee and Sheffield appear to be well matched to the Gross Value Added per capita of Newcastle whilst Cardiff has a distinctly higher value and Plymouth a lower value. In terms of area population, Plymouth and Cardiff appear to be well matched to Newcastle whereas Dundee contains fewer residents and Sheffield markedly more. Dundee and Plymouth have the closest values to Newcastle in regards to total number of cars licensed whilst Dundee and Sheffield are the most comparable when examining cars per household. For the car vehicle miles travelled, none of the potential comparison sites offers a particularly good match, with Plymouth and Dundee being the 116
Chapter Four: Survey Development closest though they are still substantially different. The average daily commute is similar across all listed sites, with Dundee being the closest match to Newcastle whilst Cardiff is the most divergent. Driving license attainment for eligible residents is notably low in Newcastle, with Dundee attaining the closest comparable value whereas Plymouth is the most distant. The final two variables relate the car usage measured by total number of car trips and total driver mileage per annum. In this instance, Dundee and Sheffield are the most similar to Newcastle whilst Cardiff and Plymouth are the furthest removed. Table 4.4: Study Site Comparison Statistics
Newcastle
GVA per capita (GBP)F Population
G,H
Cars Licensed
I J
Dundee
Cardiff
17 909
22 234
16 479
17 904
279 100
145 570
345 400
256 600
551 800
84 818
50 644
133 189
102 563
200 979
A
B
1.29
1.12E
1 719C
871D
1 643E
26B
21C
23D
24E
66A
69B
75C
79D
70E
381A
399B
439C
461D
403E
3 287A
3 497B
3 898C
3 916D
3 269E
1.03
1.26
Car Traffic Volume (million miles) K
1 054A
532B
Average Daily Commute (minutes) L
27A
Driving License Attainment (%)M Car Trips Per Annum N Average Driver Car Mileage O A
B
C
D
Sheffield
18 919
1.03
Mean Cars per Household
Plymouth
C
D
E
– North-East – Scotland - Wales – South West – South Yorkshire Sources: F – ONS, 2011a G – ONS, 2012c H – SNS, 2011 I – DfT, 2012c J – DfT, 2010a K – DfT, 2010b L – DfT, 2010c DfT, 2010e O – DfT, 2010f.
M
– DfT, 2010d
N
–
From this analysis, it is apparent that no one potential comparison site is universally superior to the others in regards to its similarity to Newcastle. However, this assessment has been successful in illustrating relative equivalence, showing that Dundee proves to be similar to Newcastle across a number of different socio-economic and transport behaviour characteristics. Specifically, Dundee is the closest match to Newcastle with regards to gross value added per capita, cars per household, length of commute, driving license attainment and annual driver mileage. As a result of this assessment, Dundee has been selected as the comparison site for this thesis. 4.3.4 Sample Size Requirement Survey research in social science often aims to develop an understanding of a population’s attitudes, characteristics and preferences on a topic of interest. The population can be small 117
Chapter Four: Survey Development when the topic of interest is highly specific or the geographical area bounded. Conversely, the population can also be large, such as in instances where the topic of interest is universal. When the population is large, attempting to attain data for the entire population can prove expensive in time and financial resources. In order to overcome this, researchers often take samples of the population. A sample can be thought of as a subset of individuals used to represent a larger population for the purpose of statistical analysis. A key consideration which needs to be addressed relates to the size of the sample required so that the information extracted from it is valid and reliable. However, populations can vary in size, raising questions over what constitutes an appropriate sample size in different circumstances. Furthermore, the type of variables measured by a survey can be varied including those which are continuous, ordinal, categorical and dichotomous leading to different sample requirements. Additionally, the types of statistical analysis applied to data have associated requirements for sample size. This section discusses these issues by examining the recommendations provided in the literature. Firstly, the minimum sample size required to accurately reflect a population is determined before inspecting the minimum sample size necessary to conduct the statistical manipulations employed in this thesis. Statisticians have developed expressions in order to assist researchers in determining the required sample size necessary to accurately reflect a population under different data formats. Cochran (1977) has developed two formulas to calculate minimum sample size for continuous and categorical data which are detailed in Equations 4.1 and 4.2.
Equation 4.1
Continuous Data
𝑛=
(𝑡)2 ×(𝑠)2 (𝑑)2
where n = minimum sample size
t = alpha value in each tail s = standard deviation of the population d = acceptable margin of error for mean
Equation 4.2
Categorical Data
𝑛=
(𝑝)(𝑞)×(𝑡)2 (𝑑)2
where (p)(q) = estimate of variance
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Chapter Four: Survey Development Focusing on the expression associated with continuous data, this formula contains three principal components. Firstly, the margin of error denotes the acceptable random sampling error contained in a survey’s results. More generally, the larger the margin of error, the less confident a researcher can be that the results of the survey accurately reflect the true values present in the population. Secondly, the alpha value denotes the probability that differences determined by statistical analysis are in fact not present in the population. The third component is associated with the estimated standard deviation of the population which measures the degree of dispersion associated with a particular variable. For commonly examined phenomenon, such as age or height, this statistic is often available and can be easily incorporated. However, for novel research which includes instruments not previously applied, this is unknown and must be estimated. Bartlett et al. (2001) offer advice on this issue and states that, for seven point measurement scales, a value of 1.167 should be utilised for the standard deviation of the population as it is representative of 98% of all possible responses. Bartlett et al. provides an overview of Cochran’s formulas and details the minimum sample size requirements for continuous and categorical data under different parameter and population values. The sample size recommendations for different population quantities have being calculated using Equations 4.1 and 4.2 with the results being displayed in Table 4.5. Once population size exceeds 10, 000, the level of sample size required increases only slightly. Having examined the statistical theory underpinning sample size requirements, some recommendations for this thesis can be put forward. The choice of whether to calculate the minimum sample requirements is dependent on the variety of data collected. As this survey employs both continuous and categorical data, the minimum sample associated with both these data formats has been calculated. An alpha level of 0.05 was selected to reflect an acceptable level of risk that the true margin of error for the sample is greater than the acceptable marginal error of 5%. (this corresponds to a t value of 1.96). Consulting Table 4.5, it is apparent that for populations in excess of 10, 000 a sample size of 119 respondents is required for continuous data and 370 respondents for categorical data.
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Chapter Four: Survey Development Table 4.5: Sample Size Requirements for Population Accuracy Population Continuous Data (marginal error = .03) alpha = .10 alpha = .05 alpha = 0.01 Size t = 1.65 t = 1.96 t = 2.58 100 200 300 400 500 600 700 800 900 1000 1500 2000 4000 6000 8000 10000
46 59 65 69 72 73 75 76 76 77 79 83 83 83 83 83
55 75 85 92 96 100 102 104 105 106 110 112 119 119 119 119
68 102 123 137 147 155 161 166 170 173 183 189 198 209 209 209
Categorical Data (marginal error = .05) p = 0.50 t = 1.65
p = 0.50 t = 1.96
74 116 143 162 176 187 196 203 209 213 230 239 254 259 262 264
80 132 169 196 218 235 249 260 270 278 306 323 351 362 367 370
p = 0.50 t = 2.58
87 154 207 250 286 316 341 363 382 399 461 499 570 598 613 623
The use of two study sites creates an extra layer of complexity when deciding on minimum sample sizes. A question can be put forward relating to whether or not the populations of the study sites should be combined together or treated separately. The answer to this question is dependent on the level of comparability of the two study sites. In a certain respect, these two sites have been selected based on their similarities across socioeconomic and transport characteristics whilst being different relating to their level of exposure towards LEV technology. With this in mind, and to position this thesis on the side of caution, it was decided to treat these studies as separate populations. In addition to the type of data collected by a survey, different forms of statistical analysis have their own general rules related to specific sample size requirements. Green (1991) provides insights on this issue related to conducting regression analysis by comparing the results of different sample sizes based on power analysis. The results of this examination shed doubt on the use of constant quantities and instead find support for sample sizes determined by the quantity of independent variables used in the model. Specifically, a recommended minimum of 50 respondents plus 8 additional respondents for every independent variable is suggested for regression analysis. Bartlett et al. (ibid.) supports this 120
Chapter Four: Survey Development approach and states that a general rule is to have 5 respondents for each explanatory variable included in the model as a minimum with a recommended level of 10 respondents per variable. For the application of factor analysis, Bartlett et al. and Kline (1993) recommend a minimum sample of 100 respondents whilst Comrey and Lee (1992) offer a more precise graduated scale which classifies sample sizes of 100 as poor, 200 as fair, 300 as good, 500 as very good and 1, 000 as excellent. Furthermore, authors have suggested minimum respondent-tovariable ratios with Hair et al. (1998) recommending 20 respondents for every variable included in a measurement instrument whilst Everitt (1975) suggests 10 respondents for every variable. These general rules can appear attractive to applied researchers due to their ease of calculation, however they are unlikely to prove universally applicable in all situations. Mundfrom et al. (2005) examined the relationship between population and sample solutions in accordance with congruence coefficients. Results of this analysis showed that minimum sample requirements were smaller when the levels of variable communality were high and in instances when the ratio of factors to variables was high. These findings suggest that absolute recommendations, which do not account for the specific nuances of the research environment, should be used with caution and should not singularly determine the required sample size. With reference to the sample size requirements necessary so that the coefficients calculated in regression and factor analysis are accurate representations of the true values of the population, some recommendations can be put forward. The conceptual framework developed for this thesis includes 6 separate explanatory constructs leading to the inclusion of up to 25 independent variables in the regression analysis. Using Green’s (ibid.) recommendations of fifty initial respondents plus an extra 8 for every included independent variable, a minimum sample size of 250 respondents is required. In regards to factor analysis, the largest measurement scale developed in this thesis contains 14 statements. Based on Hair et al’s. (ibid.) recommendation of 20 respondents per variable included in the factor analysis, the minimum sample required to conduct factor analysis is 280 respondents.
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Chapter Four: Survey Development 4.3.5 Survey Distribution Requirement The minimum sample size required to accurately represent the population is 119 per site for continuous data and 370 per site for categorical data. In regards to the minimum sample required to conduct statistical analysis, a sample size of 250 respondents is recommended for regression analysis and 280 respondents for factor analysis. Thus, erring on the side of caution, a required sample size of 370 respondents per site has been selected as necessary for this thesis. The next question to approach concerns the quantity of surveys required to be distributed in order to achieve these requirements. Table 4.6 calculates the required number of surveys to be distributed in order to attain a 600, 800 or 1000 sample given an expected response rate varying from 100 to 10 percent. This example shows that the expected response rate can have a large influence on the quantity of surveys distributed. Research examining the factors affecting survey response rates has been a continuing feature of academic inquiry over the last half century (Cox et al. 1974; Kanuk and Berenson, 1975). Yu and Cooper (1983) examine different survey strategies and find that those which are based on personal and telephone interviews and include monetary incentive attain significantly better response rates. In a similar study, Edwards et al. (2002) concentrated on postal surveys with results suggesting that, among others, surveys which are personalised, use colour, originate from a University institution and include pre-paid return envelopes have superior response rates. Approaching the issue from a different direction, Goyder and Leiper (1985) investigate the general decline in survey response rates since the 1960s and state that part of this phenomenon can be explained by increasing levels of privacy related objections. Table 4.6: Minimum Survey Distribution Response 600 required 800 required 1000 required Rate 100% 600 800 1000 90% 660 880 1100 80% 750 1000 1250 70% 857 1143 1429 60% 1000 1333 1667 50% 1200 1600 2000 40% 1500 2000 2500 30% 2000 2667 3333 20% 3000 4000 5000 10% 6000 8000 10000 122
Chapter Four: Survey Development Clearly, survey response rates can be influenced by a wide range of different factors which need to be considered. During the survey development stage of this thesis, a 24% response rate was achieved during the pilot. However, this was attained under a drop and collect approach which is not replicated in the main survey. With this in mind, this research project has adopted a cautious approach and has based its distribution strategy on an expected response rate of 20%. To achieve 370 responses per study site given this expectation, a distribution of 1850 surveys is required for each study site. 4.3.6 Sampling Strategy The primary aim of this thesis is to develop a detailed understanding of how the mainstream automotive market is likely to respond to the introduction of LEVs. To achieve this, it is necessary to attain a sample which is representative of this defined population. To ensure that is the case, a stratified random sampling approach was employed over the two preselected study sites. Initially outlined by Nyeman (1934), stratified random sampling is a method which allows researchers to take account of subpopulations within a given populace and has become widely accepted in applied research (Cochran, 1977). Urban areas are often comprised of zones with unique characteristics which focus on specific activities (Harris and Ullman, 1945; Dear and Flusty, 1998). Even in zones which are primarily residential in nature, there are disparities in the housing format and resident composition. To account for this spatial variance in population distribution, stratified random sampling has been utilised in this thesis. Stratified random sampling is often employed to increase the probability of extracting a representative sample from a population. By specifying spatial zones which account for variations in population spatial distribution from which to sample from, a researcher can enhance the probability that the sample extracted is representative. However, before the stratification procedure can take place, a metric must be identified which provides information of the spatial distribution of the population to allow for zones to be identified. In this thesis, the Index of Multiple Deprivation (IMD) (Scottish Government, 2009; DCLG, 2010a) has been employed to highlight zones based on their relative levels of prosperity. The IMD was initially developed during the 1970s and provides information regarding human deprivation which is defined as unmet needs caused by a lack of resources. 123
Chapter Four: Survey Development Specifically, the IMD is a composite measurement including 6 components, termed domains, covering household income, employment levels, health, education, crime and the living environment. This information is provided at Lower Super Output Area (LSOA) resolution thus making it suitable to distinguish the relative levels of deprivation in different urban areas. Based on the IMD maps developed by Rae (2010), three areas in each study site have been identified to represent an area of high, medium and low deprivation. These areas are highlighted in Figure 4.4a and 4.4b and form the basis for the sampling strategy.
(a)
(b)
Figure 4.4: Zones selected in (a) Newcastle upon Tyne (d) Dundee
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Chapter Four: Survey Development Having specified the stratum for each study site, the decision process progressed to identifying distribution streets and candidate households. In this instance, a random sampling procedure was followed to ensure the selection of respondents within each zone is unbiased. Initially, the primary collector roads in each of the zones were identified. From these collector roads, every other feeder road branching off was selected for survey distribution. Within each of these feeder roads, every other household was selected to receive a survey. An example of one of the distribution schedules developed for this research project is presented in Section 10.4 of the Appendix. The survey was distributed by hand around the study sites based on the distribution schedules developed. Between 200 and 300 surveys were distributed in any given day dependent on the level of housing density. In total, two weeks were required for each study site to distribute the 2000 allocated surveys. In order to enhance survey response, a £100 monetary incentive was offered and pre-paid return envelopes were included though, due to financial constraints, a reminder was not feasible. 4.4 ETHICAL CONSIDERATIONS IN APPLIED HUMAN RESEARCH One of the principal challenges in conducting behavioural research is that individuals tend to operate differently when they know they are being observed or evaluated. To generate the most accurate understanding of behaviour, it is necessary to provide a natural environment. In essence, the influencing effect of the researcher’s presence, in both direct and indirect circumstances, is required to be mitigated. This has led researchers in the past to use questionable methods which have strayed across ethical bounds (Schuklenk, 2000). To ensure this thesis is conducted in a principled manner, ethical frameworks have been consulted to assist in determining what is deemed acceptable and unacceptable practice. The development of such a framework is inherently difficult as ethics, by their nature, are not a universal law meaning objective rules are, to all intensive purposes, impossible to completely agree on. In addition, what is deemed to be acceptable tends to differ between fields of inquiry and specific situational contexts. However, a number of guidance points have been accepted as good practice by the academic community (Strangor, 2010). Firstly, the research experimental design should be critically examined to determine if it is likely to 125
Chapter Four: Survey Development cause physical damage or psychological stress to a participant. The first point is likely to be straightforward to assess though the second can prove a significant challenge. It is often difficult to estimate the likely manner in which an experiment will be interpreted by a participant leading to uncertainties regarding potential outcomes. For instance, what may be taken by one participant as an innocuous question may be construed by another in a different, unplanned and, potentially, harmful manner. With this in mind, it is important for the researcher to acquire external views on the experimental design in an attempt to account for any unforeseen possibilities. For this thesis, three external judges examined the survey for ethical concerns and found no areas which may cause psychological stress. A topic which has specific relevance for research examining human behaviour is the issue of consent. Participants must have the freedom to join and, if desired, leave the research at any time without any undue hindrance. However, as previously noted, volunteers may behave differently compared to non-volunteers. Focusing specifically on surveys, those individuals that choose to take part often have different profiles compared to nonvolunteers (Reuss, 1943). This is indeed a challenging issue to approach, researchers are caught between a desire to make their work as valid as possible yet are aware of the ethical implications of non-consented observation. Related to this issue is the manner in which consent is granted. An adult that is in full control of their faculties has the capacity to make an informed decision on whether to participate or decline. Studies involving participants that do not have this capacity, such as children, are required to reach a stricter level of informed consent which often necessitates the approval of a guardian. With this point in mind, children have been removed from the sampling frame in this thesis. During participant recruitment and experiment application, it is necessary for a researcher to behave in an honest manner. Issues relating to the nature of the research, for whom it is being conducted and the desired purpose should be presented truthfully. However, there are situations where the provision of certain knowledge at the beginning of an experiment can have direct implications over participant conduct. Related to this thesis, explicitly informing participants that this study is interested in the demand for cars with reduced emissions levels may influence their responses. This situation is possible whenever a socially sensitive topic is being examined. To account for this, a researcher can employ deception 126
Chapter Four: Survey Development (Christensen, 1988; Kimmel, 2012) in order to misdirect participant attention away from any social triggers in order to attain valid observations. This can be conducted in two different ways [1] actively and [2] passively. Active deception involves manipulation of the truth and should be avoided. Conversely, passive deception involves not revealing certain aspects of the experiment until after they have been conducted. To a degree, the survey section order selected for this thesis (discussed in Section 4.1) can be considered a form of passive deception, with the order chosen based on a desire to ensure responses in the powertrain evaluation exercises are not overly influenced by the remaining contents of the survey. Conducting quantitative social research through the application of surveys often requires the information generated to be stored for future examination and analysis. Storing information which has been provided in confidence by research participants raises a number of ethical issues (Johnson, 2009). Firstly, privacy is a concern with the information being surrendered by participants, such as household income levels and disabilities, being regarded as sensitive. Secondly, the security of data and the manner in which it is stored is an important subject to be addressed to ensure only authorised individuals can gain access. In this thesis, these issues have been dealt with in two ways. To begin, respondent personal information, such as names and contact details, is kept in a separate file from their responses to survey questions to ensure the acquisition of one file on its own would not jeopardize information security. In addition, survey information is stored on password protected computers and has not been transmitted over unsecure networks. During the methodological development of this thesis, the ethical issues discussed in this section were considered and appropriate action taken to address any questionable aspects. In addition to this, the University of Aberdeen offers guidance on ethical issues and has produced a checklist which is required to be completed to grant ethical approval for applied research. This checklist has been completed for this research and can be inspected in Section 10.5 of the Appendix.
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Chapter Four: Survey Development 4.5 CHAPTER SUMMARY The application of empirical research in the social sciences requires detailed consideration to ensure that the results generated are reliable, valid and, ultimately, of value. This chapter has discussed issues related to the application of this thesis and has drawn on best practice from the academic literature to assist in structuring an effective empirical analysis. To begin, this chapter examines issues related to survey development. Aspects associated to survey format, structure and the use of measurement scales are initially discussed before more practical topics linked to the use of online versions and survey piloting are addressed. In total, the survey created contains 19 different sections over 12 pages and has been structured to allow for respondents to transition logically from one section into the next. Additionally, the order of survey sections has been selected to minimise the influence certain elements of one section may have on the stated responses in another. An online version of the survey has been developed to allow study participants to respond either via post or electronically. During the survey pilot, both the online and paper versions of the survey were assessed and changes made to improve survey effectiveness. Proceeding to more practical issues, the sections discussing sampling strategy and administration cover topics associated with population characteristics and site selection. A two study site approach was selected with surveys being evenly distributed across the cities of Newcastle upon Tyne and Dundee. These sites were chosen in order to examine the effect of recent policy initiatives over LEV preference. To ensure the survey attains a representative sample of the population and is capable of calculating valid and reliable results, a stratified sampling method was adopted so that the initial survey distribution evenly covered UK demographics. A sample size requirement of 370 respondents per site was selected so that the sample attained was an accurate representation of the general populace and capable of performing the statistical analysis techniques chosen for this thesis. Two study sites were selected to allow for differences between these sub-samples to be assessed in an effort to determine if LEV market interventions linked to UK Government policy had influenced preferences towards these vehicles.
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Chapter Four: Survey Development The final section of this chapter discusses the ethical implications of applied research in human behaviour including aspects connected to respondent consent, researcher honesty and data security. The principal points to note are that only adults have been included in the sample frame for this study, information is presented in an unbiased manner, the data generated from the survey application is securely stored and that research study has undergone appraisal using the University of Aberdeen’s ethical checklist. The next three chapters take the data set attained from the survey application and employ a variety of statistical techniques to attain results which can be used to answer the initial research objectives and questions posed. Firstly, the general structure of the data set is examined to determine if a representative sample has been attained before multivariate statistics are applied to assess the determinants of LEV preference and the structure of the emerging LEV market.
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Chapter Five: Results – Variable Development
CHAPTER
5
RESULTS - VARIABLE DEVELOPMENT 5.1 INTRODUCTION As outlined in Chapters 3 and 4, the conceptual framework developed for this thesis was used to design a self completion household survey administered in both Newcastle upon Tyne and Dundee. In total, 552 responses from an initial distribution of 4000 surveys were received, with 46 responses removed due to lack of completion leading to a net achieved sample size of 506 and a response rate of 12.7%. As discussed in detail in Chapter 4, Section 4.3.4, this sample size is sufficient to perform factor, regression and cluster analysis on continuous data with the appropriate level of confidence. However, the sample size is not sufficient enough to warrant making confident inferences in reference to categorical data and thus interpretations of this variety should be assessed with caution. This results chapter is comprised of three distinct sections. The first section examines the basic profile of the data set to determine if a representative sample of the population has been achieved. Specifically, current car details and socio-economic characteristics are compared to known population statistics. Additionally, the profiles of the two study sites are compared to assess their degree of similarity. In the second section, a number of variable sets are developed to be used in the forthcoming results chapters. Firstly, the variables linked to the powertrain evaluation exercise have been examined to inspect preference structures and determine how these structures differ between sample cohorts. In a similar fashion, the variables connected with technology ownership have been examined to calculate measurements of adoptive innovativeness. The last section of this chapter focuses on the attitudinal measurement scales which have been evaluated using
130
Chapter Five: Results – Variable Development factor analysis to identify latent variables linked to the socio-psychological constructs described in the conceptual framework. 5.2 DESCRIPTION OF SAMPLE A primary aim of this thesis is to provide insights relating to the nature and structure of the emerging market for LEVs. To achieve this, a self completion household survey was distributed over two study sites. A stratified random sampling strategy (described in Chapter 4, Section 4.3.6) was followed in an attempt to attain a representative sample of the populace. Additionally, the sampling strategy was applied identically in the two study sites in an effort to achieve comparable sub-samples. To determine if the survey achieved a representative sample of the populace, it is necessary to compare their basic structures. This section examines the dataset in a descriptive fashion to approach this requirement. To begin, an overview of the car details and usage characteristics of the sample is presented and compared to the known values for the population. Additionally, the socio-economic and household profiles are compared. 5.2.1 Car Details and Usage The general characteristics of the sample relating to current car details and usage patterns are presented in Table 5.1. These have been paired with known UK population statistics to determine how well the sample reflects the demographic composition of these areas. The sample attained in this thesis appears to over-represent drivers with 93.7% of the sample holding a driving license compared to 73% of the general population. Likewise, the sample under-represents non-car households whilst over-representing single car households. Whilst the survey was designed to be accessible to non-car drivers, initial expectations were that it would prove to be more popular with individuals that drive. In relation to whether the car was bought new or used, the sample substantially overrepresents new car buyers compared to the general car market. This divergence is a concern, as those individuals purchasing new cars are likely to have unique variables influencing their car purchasing decisions. However, as LEVs are presently being released into the mainstream market, new car buyers are perhaps the most likely to acquire one. 131
Chapter Five: Results – Variable Development Therefore, this overrepresentation of new car buyers may provide better insight relating to the determinants of LEV preference.
Table 5.1: Comparison of sample and population car details and usage Variable
Category
Driving License AttainmentA Cars in HouseholdA Purchase New or Used Fuel TypeD Parking ProvisionE
Engine Size
Annual Car MileageA Usual Car Purchase Price (GBP) A
B
C
Sample
Yes No 0 1 2 or more Used Brand New Petrol Diesel Other No Provision Dedicated on-street Dedicated off-street Garage 1 lt or less 1 – 1.5 lt 1.5 – 2 lt 2 – 2.5 lt 2.5 – 3 lt 3 lt or more Mean Standard deviation Mean Standard deviation D
E
Population
93.7% 6.3% 10.6% 53.7% 35.7% 57.9% 42.1% 61.6% 36.2% 2.2% 14.7 6.9% 33.6% 44.8% 2.9% 32.1% 54.4% 6.1% 2.7% 1.8% 8260 4995 10027 F
6105
73% 27% 25% 42% 33% 77.8%B 22.2%C 68.7% 30.8% 0.5% 11.5% 17.5% 27% 44% 4.5% 31.9% 49.9% 6.7% 4.4% 2.5% 8430 G 10140 G
-
Sources: – DfT, 2010d – SMMT, 2010 – SMMT, 2012 – DfT 2012d – DfT, 2012e – DCLG, 2010b – ONS, 2010.
Concerning car fuel type, the split between Petrol and Diesel cars in the sample appears to follow the general market, though Diesel is slightly overrepresented.
This similarity
between sample and population continues when considering household parking provision though there is a slight overrepresentation of dedicated on street with an underrepresentation of dedicated off-street. Engine size is well matched between sample and population with the same being true for annual car mileage. In terms of usual car purchase price, no direct value for the population could be sourced. Instead, a proxy is calculated using average household weekly expenditure on car purchases which is
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Chapter Five: Results – Variable Development multiplied to determine ten-year expenditure 1. Comparing this proxy statistic with that 0F
attained by the sample, the two values appear well matched. 5.2.2 Socio-Economic Characteristics and Household Profiles A comparison between the socio-economic and household characteristics of the sample with known population statistics is displayed in Table 5.2. Examining the age distribution, the sample over-represents older members of the population with the categories 51-65 years and 65 years and over being relatively large. Younger individuals are significantly underrepresented with the age group 18-30 comprising only 6% of the sample compared to 22% of the population. The gender split is somewhat skewed with males being overrepresented in the sample though the discrepancy is not large. Regarding employment status, a crossover with the age profile of the sample can be observed with a greater proportion of the sample retired compared to the population. This is of concern as the transport behaviour of retired individuals (Stead, 2001) is often different compared to those in employment meaning the factors influencing retired individual’s car preferences may be different. The sample over-represents individuals with a high degree of formal education, with individuals that hold a university level qualification having a significantly higher presence. This type of discrepancy is commonly observed in applied surveys of the general populace (Benson, 1946; Edgerton et al., 1947) with those individuals that hold relatively higher levels of formal education tending to be more willing to participate in survey research. In the survey, gross household income levels have been measured on an interval scale and can be directly compared to population statistics. In this instance, the sample appears to be well matched to the income distribution of the population. This is slightly surprising as gross household incomes tend to be related to levels of formal education, with those individuals that hold a university level education likely to have relatively high lifetime incomes, referred to as the sheepskin affect (Hungerford and Solon, 1987). Household composition is well matched though there is a slight overrepresentation of households containing two individuals. In reference to household tenure, the sample over-represents owner occupiers whilst under-representing privately rented properties. This observation may also be a result 1
Assumed to be the average car lifetime
133
Chapter Five: Results – Variable Development of the positively skewed age distribution of the sample, with older individuals more likely to be in a mature life-cycle stage and own their homes (Lydall, 1955; Wells and Gubar, 1966; Speare, 1970). Table 5.2: Comparison of sample and population socio-economics Variable
Category
Age (years) A
18-30 31-50 51-65 65 or older Male Female Full time employment Part time employment Unemployed Economically inactive Retired No formal education GCSE/Standard grades A-level/Highers Bachelors degree Postgraduate degree Professional qualification Less than 10, 000 10 - 30, 000 30 - 50,000 50 - 70.000 70 - 90,000 More than 90, 000 1 2 3 4 5 6 or more Owner occupier Privately rented
Gender A Employment Status B
Education LevelC
Gross Household Income (GBP)B
Individuals in HouseholdD
Housing StatusE A
B
C
Sample Population
D
E
6% 27% 37% 30% 59.1% 40.9% 46% 9% 1% 4% 40% 8.3% 16.5% 17.7% 25.4% 19.7% 12.3% 7% 40% 28% 14% 7% 6% 25.2% 47.2% 12% 12.6% 2.6% 0.4% 87.8% 10.4%
22% 35% 23% 20% 49.17% 50.83% 42% 16% 5% 18% 19% 11.5% 23.2% 31% 23.8% 2 1F
10.5% 9% 44% 24% 12% 5% 6% 29% 35% 16% 13% 4% 2% 66.4% 16.6%
Sources: – ONS, 2011b – ONS, 2012d – ONS, 2012e – ONS, 2012f – ONS, 2012g.
2
Degree level not separated into undergraduate and postgraduate in national statistics
134
Chapter Five: Results – Variable Development 5.2.3 Overview of Sample and Population Comparison Having compared the basic profile of the sample attained in this thesis, covering car ownership and use details alongside socio-economic and household characteristics, with those of the general populace, a mixed result is observed. Some of the comparison variables are highly similar, with engine size, annual car mileage and household incomes analogous between sample and populace. However, a number of prominent differences have also been identified with car ownership levels alongside respondent age and education levels being divergent. From this analysis, it can be concluded that the sample is representative of the general population in relation to some characteristics, but not others. To determine if this lack of representativeness is likely influence the validity of the results generated in the Socio-Psychological Modelling chapter, basic respondent characteristics are used as control variables in the regression analysis. 5.2.4 Comparison of Study Sites The principal rationale for employing a two site approach in applying the conceptual framework was to observe if levels of LEV preference in one site which has benefited from government led interventions in this market (Newcastle upon Tyne) are significantly different from a comparable site (Dundee) which has not received these interventions. In order for this to be a valid assessment, it requires the subsamples containing the Newcastle and Dundee respondents to be similar in their profiles. To determine if this similarity has been achieved, the profiles of the Newcastle and Dundee subsamples have been examined. The first aspect to consider is the size of the two subsamples. As a proportion of the total sample, the Newcastle subsample contains 52.9% of respondents whilst the Dundee subsample contains 47.1%. With both subsamples being of approximate size, this assists in the application of hypothesis testing (Yates, 1934). 5.2.5 Subsamples: Car Details and Usage Variables associated with current car ownership and use are displayed in Table 5.3. With all statistical parameters being available in this stage of the analysis, hypothesis testing was utilised to determine if observed differences between the study sites are statistically significant. Driving license attainment, the quantity of cars in the household fleet and engine fuel type are similar across both subsamples. This similarity extends to engine displacement 135
Chapter Five: Results – Variable Development and usual car purchase price. However, a number of discrepancies are also observed with the Newcastle subsample being more representative of individuals that purchase their cars used as opposed to new whilst the Dundee subsample contains respondents that tend to have higher annual car mileages. This difference in car mileage is likely linked to the different transportation infrastructure and service provision of the two sites, with Newcastle benefiting from the Metro light-rail system leading to a reduced requirement for car based mobility (ONS, 2013). Additionally, parking provision is unequally distributed across the subsamples with Dundee containing a higher prevalence of respondents with access to a garage whilst Newcastle respondents are more likely to have no provision. This divergence possibly indicate the limitations of being reliant on an single metric (in the case of this thesis, the Index of Multiple Deprivation) when developing a distribution schedule for a stratified random sampling frame as aspects such as neighbourhood design (Aditjandra et al., 2012) are not directly accounted for. Table 5.3: Comparison of subsample car details and usage Variable Category Newcastle Driving License Attainment Yes 94.4% No 5.6% Cars in Household 0 11.4% 1 54.9% 2 or more 33.8% Purchase New or Used* Used 63.4% Brand New 36.6% Fuel Type Petrol 63.2% Diesel 35.1% Other 1.7% Parking Provision** No Provision 17.7% Dedicated on street 8.6% Dedicated off street 37.1% Garage 36.6% Engine Size 1 lt or less 3.8% 1 – 1.5 lt 33.3% 1.5 – 2 lt 53.8% 2 – 2.5 lt 6.2% 2.5 – 3 lt 1.4% 3 lt or more 1.4% Annual Car Mileage** Mean 7584 Standard deviation 3978 Usual Car Purchase Price Mean 9940 (GBP) Standard deviation 6377 Significant at the *. 0.05 level **. 0.01 level
Dundee 92.9% 7.1% 9.7% 52.1% 38.2% 51.8% 48.2% 59.6% 37.6% 2.8% 11.1% 5.1% 30.1% 53.7% 2% 31% 55.8% 6.1% 4.1% 1.5% 8853 5830 10390 5922
136
Chapter Five: Results – Variable Development 5.2.6 Subsample: Socio-Economic Characteristics and Household Profiles A comparison of the socio-economic and household characteristics is presented in Table 5.4 and exhibit a number of similarities and clear disparities. Newcastle respondents tend to be younger, with a higher proportion of the subsample in the 31-51 age band whilst the Dundee subsample has a high proportion of its respondents over 65 years of age. In regards to the gender split, the Newcastle subsample is closer to parity whilst more males are contained in the Dundee subsample though this difference is not statistically significant (pvalue of .056). Table 5.4: Comparison of subsample socio-economic characteristics Variable
Category
Age (years)**
18-30 31-50 51-65 65 or older Male Female Full time employment Part time employment Unemployed Economically inactive Retired No formal education GCSE/Standard grades A-level/Highers Bachelors degree Postgraduate degree Professional qualification Less than 10, 000 10 - 30, 000 30 - 50,000 50 - 70.000 70 - 90,000 More than 90, 000 1 2 3 4 5 6 or more Owner occupier Privately rented
Gender Employment Status**
Education Level**
Gross Household Income (GBP)
Individuals in Household
Housing Status Significant at the *. 0.05 level **. 0.01 level
Newcastle Dundee 7% 32% 38% 23% 55.1% 44.9% 53.5% 11.6% 1.1% 2.9% 30.7% 4.9% 17.7% 14.3% 32.3% 21.1%
5% 21% 36% 38% 63.4% 36.6% 37.5% 6.4% 1.7% 4.7% 49.8% 12.3% 15.3% 21.6% 17.8% 17.8%
9.8% 5.5% 39.9% 25% 13.7% 8.1% 8.1% 23.3% 46.2% 11.5% 15.3% 3.4% 0.4% 87.5% 11.4%
15.3% 9.6% 35.8% 30.7% 13.8% 5.5% 4.6% 27.4% 48.1% 12.7% 9.7% 1.7% 0.4% 88.6% 8.9% 137
Chapter Five: Results – Variable Development
The difference in the age distribution transfers to employment status with Dundee respondents more likely to be retired whereas Newcastle contains relatively more individuals in fulltime employment. Levels of formal education also display some notable disparities with the Newcastle respondents more likely to hold a degree level qualification. Interestingly, the Dundee subsample’s education profile is well matched to the general population. The final three variables are comparable across both subsamples. Gross household income is well matched with similar proportions of respondents contained in each band. This similarity extends to household profile with a comparable distribution of household residents and an analogous split between home owners and renters. 5.2.7 Overview of Subsample Comparison Having compared the subsamples associated with the two study sites in relation to their basic profiles, it is apparent that a number of similarities and notable differences are present. In regards to current car details, engine size and usual car purchase price is comparable across the subsamples whilst parking provision and annual car mileage tends to differ. For socio-economic characteristics, the age and education distribution of the subsamples are divergent whereas the income and household profiles are well matched. This analysis demonstrated that the subsample profiles are not exactly compatible with some clear disparities observed. This has implications for later sections which compare the subsamples in relation to LEV preferences meaning caution is required when interpreting the results. 5.3 MEASUREMENT OF POWERTRAIN PREFERENCES The results attained from the powertrain evaluation exercise are of significant importance, providing a first impression relating to the magnitude of preference towards LEVs whilst also acting as the dependent variables in the forthcoming regression models. This section presents and discusses the results derived from the powertrain evaluation exercise with emphasis given to examining preference trends and highlighting related implications. Firstly, the results for household main car are examined before progressing to results provided from multi-car households which expressed second car powertrain preferences. To 138
Chapter Five: Results – Variable Development conclude this section, a number of comparisons between sample cohorts are conducted to identify any statistically significant differences in powertrain preferences. 5.3.1 Primary Car Preferences This section focuses on powertrain preferences for main household cars. Each respondent was asked to state how likely they would be to consider each powertrain in their next main car purchase. As with the other survey sections, responses were measured on a negative to positive 7 point Likert scale with the anchor points going from “Highly Unlikely” to “Highly Likely”. Table 5.5: Percentage Response Frequencies from Powertrain Evaluation Exercise – Next Primary Car Purchase Intentions Powertrain Petrol Diesel Mild Hybrid Full Hybrid Plug-in Hybrid Pure EV
Highly Unlikely
Unlikely
Slightly Unlikely
Neutral
Slightly Likely
Likely
Highly Likely
11.7 12.4 30.8 32.5
6.2 4.8 10.5 12.7
6.2 4.8 13.6 15.2
7.5 7.1 11.3 19.8
14.2 13.7 17.7 10.9
17 25.6 9.2 6.3
37.2 31.5 6.9 2.5
42.6
19.4
13.3
8.9
7.9
4.3
3.6
67.6
14.3
4.3
2.8
4.1
4.1
2.8
Table 5.5 displays the percentage response rate for each point on the Likert scale for all powertrain option considered. The results follow expectations with preferences tending to decrease as the proportion of electrification in the powertrain increases. Diesel receives the highest proportion of positive response (70.8% compared to 68.4% for Petrol) though Petrol does receive the highest proportion of “Highly Likely” response (37.2% compared to Diesel’s 31.5%). The proportion of positive response gradually decreases through the LEV options with 33.8% of respondents expressing some degree of positive response for a Mild Hybrid reducing to 11% for a Pure EV. The Mild Hybrid option appears to have attained a significantly higher proportion of positive response compared to the other LEV options. This result provides support to the view that Mild Hybrids are likely to act as a bridge between conventional ICE technologies and electric power delivery (Chan et al., 2009; Raskin and Shah, 2006; Turrentine et al., 2006). 139
Chapter Five: Results – Variable Development 5.3.2 Secondary Car Preferences In addition to preferences relating to household main car, the survey measures powertrain preferences for multi-car households in relation to household second car. The specification of the evaluation exercise remains constant with the only aspect changed being that respondents were asked to state their likelihood to consider each powertrain option in their second cars as opposed to their main cars. In this stage of the choice experiment, 105 out of the 506 survey responses were included in the analysis. Table 5.6: Percentage Response Frequencies from Powertrain Evaluation Exercise – Next Secondary Car Purchase Intentions Powertrain
Highly Unlikely
Slightly Unlikely
Unlikely
Neutral
Slightly Likely
Likely
Highly Likely
Petrol
9.1
5.2
5.8
9.7
16.9
23.4
29.9
Diesel
8.5
5.2
7.2
9.2
15
29.4
25.5
Mild Hybrid
30.3
9.2
12.5
11.2
19.7
8.6
8.6
Full Hybrid Plug-in Hybrid Pure EV
34.2
11.8
17.1
17.8
9.9
8.6
0.7
40.1
22.4
11.2
4.6
11.8
7.2
2.6
63.8
11.2
4.6
7.2
2
8.6
2.6
Table 5.6 displays the percentage response rate for each point on the Likert scale for all powertrain option considered. The aggregate preferences for each powertrain option follow the same trend as those observed in the main car stage, though subtle differences are present. Firstly, the proportion of positive response for the Plug-in Hybrid Powertrain option surpasses that of the Full Hybrid. Secondly, with the exception of Full Hybrids, LEV powertrain preferences attain an increase in positive responses for second cars compared to main cars. This can perhaps be accounted for by the tendency of multi-car households to have higher levels of household income 3. 2F
5.3.3 Primary and Secondary Car Preference Comparison Having asked respondents from multi-car households to complete the choice experiment first for the main household car and then for their second car, it proves interesting to
3
A Spearman’s correlation analysis between gross household income and quantity of cars in the household produces a significant positive coefficient of 0.376
140
Chapter Five: Results – Variable Development compare these preference structures together and examine if any statistically significant differences are present. Previous research has proposed that multi-car households may have a tendency to display higher preferences for LEVs in their secondary household car compared to their primary household car (Kurani et al., 1996). This powertrain diversification allows households to take advantage of the low running costs of LEVs whilst mitigating the perceived disadvantages such as range and size limitations. With the data originating from related samples, a Wilcoxon Signed Rank test (Wilcoxon, 1945) was conducted where the primary and secondary preferences for each powertrain options are compared.
Table 5.7: Comparison Between Primary and Secondary Powertrain Preferences Exact Sig. (2-tailed)
Point Probability
Preference Variable
Mean RanksA
Z
Petrol Preferences
[22.93][19.17]
-.380a
.713
.006
Diesel Preferences
[22.92][23.73]
b
.007**
.000
Mild Hybrid Preferences
[17.45][22.97]
-.335b
.744
.001
Full Hybrid Preferences
[20.28][20.87]
-1.344a
.183
.003
Plug-in Hybrid Preferences
[17.48][17.56]
-2.457a
.013*
.000
[14.79][16.00]
a
.002**
.000
Pure EV Preferences A
-2.681
-3.028
[Primary Car Preference] [Secondary Car Preference] *Significant at the 0.05 level ** Significant at the 0.01 level
The results of the comparisons between primary and secondary powertrain preferences are presented in Table 5.7 where a number of statistically significant differences are observed. Firstly, the null hypothesis for the diesel powertrain option is rejected with multi-car households tending to have lower preferences for a diesel engine in their secondary car compared to their primary car. This result is intuitive, with second cars commonly having lower annual mileages compared to primary cars (Greene, 1985), the benefits of a diesel engine are greater in a primary as opposed to secondary car. Furthermore, the null hypothesis is rejected for Plug-in Hybrid and Pure EV powetrain options with mult-icar households tending to have higher preferences for these powertrains in their secondary car compared to their primary car. This finding supports the proposition that LEVs currently hold greater market potential as secondary as opposed to primary vehicles. 141
Chapter Five: Results – Variable Development 5.3.4 Study Site Preference Comparison Having so far inspected the results of the powertrain evaluation exercise for the entire dataset, the analysis now progresses to comparing the results between the two study sites. One of the principal reasons for choosing a multi-site approach to the survey distribution was to observe if there were any significant differences in preferences between the two sites. Preferences for the conventional powertrain options of Petrol and Diesel are skewed to the positive side of the scale whilst those related to the LEV options are negatively skewed. To address this, non-parametric testing has been employed to conduct the hypothesis testing. Specifically, Mann-Whitney-U tests (Mann and Whitney, 1947) have been utilised to test the hypothesis that preference structures between the Newcastle and Dundee sub-samples are significantly different. Table 5.8: Comparison Between Newcastle and Dundee Powertrain Preferences Preference Variable Petrol Preferences Diesel Preferences Mild Hybrid Preferences Full Hybrid Preferences Plug-in Hybrid Preferences Pure EV Preferences A
Mean Rank
MannWhitney U
Wilcoxon W
[202][198] [197][196] [181][208] [180][212] [189][202] [192][198]
19521.5 19217 16319.5 16117 17774.5 18508.5
40842.5 39317 34655.5 34838 36302.5 37036.5
A
Z -.411 -.076 -2.385 -2.900 -1.247 -.644
Sig. (2tailed) .681 .940 .017* .004** .212 .520
[Dundee] [Newcastle] *Significant at the 0.05 level ** Significant at the 0.01 level
Examining the results from the analysis which are presented in Table 5.8, it is apparent that a number of statistically significant differences in preferences between the two sites have been identified. Preferences for the Petrol, Diesel, Plug-in Hybrid and Pure EV powertrain options display no significant difference across the two study sites. However, preferences for the Mild Hybrid and Full Hybrid powertrain options do display significant differences in their structures. There are a number of potential explanations for these observed differences in powertrain preference across the two study sites. Firstly, the independent subsamples of the Dundee and Newcastle populations are not exactly compatible in their basic profiles. Whilst care has been taken to make the survey distribution as similar as possible across the two study areas, 142
Chapter Five: Results – Variable Development following the same methodology and using the same selection criterion, a number of notable discrepancies are present (discussed in Section 5.2.5). Secondly, with Newcastle having been the recipient of two government backed market interventions in relation to LEVs (discussed in Chapter 4, Section 4.3.1), public awareness and understanding of LEVs may potentially be greater in Newcastle compared to Dundee. A-priori expectations were that, if any difference was to be observed between the preference levels of the two study sites, Newcastle would hold the more positive preference. In this sense, expectations have been supported in the case of Mild and Full Hybrids, were the Newcastle subsample holds significantly higher preferences for these powertrains compared to Dundee respondents. The disparities between the basic profiles of the two subsamples means this conclusion is only tentative, and that further research is required to justify this claim. 5.3.5 Additional Dichotomous Variable Preference Comparisons Having compared powertrain preferences dependent on whether or not the respondent was situated in the Dundee or Newcastle study site, it proves interesting to repeat this procedure for a number of additional dichotomous variables. To achieve this, a number of dummy variables have been calculated to identify respondents which met the conditions required to be placed in one group or another. The results of this exercise are displayed in Table 5.9. i.
Gender
Previous studies in this field have found differences in LEV preference dependent on gender (Mannering, 1983; Bunch et al. 1993; Ewing and Sarigollu, 1998) and its effect in wider marketing is well documented (Vigorito and Curry, 1998; Koc, 2002). To determine if these previous results can be corroborated in this thesis, a Mann-Whitney-U test has been conducted to examine if powertrain preferences differ dependent on gender. The results of this analysis demonstrate that a number of differences do indeed exist between males and females in relation to LEV preferences. Preferences for Petrol, Diesel, Full Hybrid and Pure EV powertrain options do not exhibit any statistically significant differences based on gender. However, there are significant differences for the powertrain options Mild Hybrid and Plug-in Hybrid. Females tend to 143
Chapter Five: Results – Variable Development state higher preferences for these powertrain options compared to male respondents. This finding is in keeping with previous research which generally found females to hold relatively higher preferences for LEVs ii.
Current Powertrain
The engine type which an individual currently operates may potentially affect their preferences towards the powertrain they plan to have in their next car. To test if this is the case, the sample was split into two cohorts dependent on the fuel type of the current vehicle. As expected, there was a statistically significant difference for petrol and diesel powertrain preferences. Those respondents which currently own a petrol car tend to have higher preferences for the petrol powertrain option compared to current diesel car owners whilst the opposite is true for the diesel powertrain preferences. It is unsurprising that those respondents the currently own petrol cars would tend to have higher preferences for the petrol powertrain option whilst current diesel owners tend to have higher preferences for the diesel powertrain option. Somewhat surprisingly, there was also a statistically significant difference for Pure EV powertrain preferences with current petrol owners tending to have higher preferences for this powertrain option. This result was unexpected though the difference is only just statistically significant at the 95% level. With current diesel owners already revealing a willingness to pay a price premium for lower operating costs, it was expected that they would tend to have higher preferences for LEV powertrain options, especially Mild Hybrid powertrains which share distinct similarities with Diesel. This result perhaps indicates that targeting current diesel owners with information concerning LEVs may not be an optimum strategy with a broader approach which includes current petrol drivers potentially being more rewarding.
144
Chapter Five: Results – Variable Development Table 5.9: Comparison Between Additional Dichotomous Variable Powertrain Preferences Mean MannWilcoxon Sig. (2Preference Variable Ranks Whitney Z W tailed) U I. Gender [male][female] [195][201] 48175 -1.346 .178 Petrol Preferences 17547 [202][189] 16955 27981 -1.172 .241 Diesel Preferences [183][214] 15056 44459 -2.696 .007** Mild Hybrid Preferences [192][206] 16925 47060 -1.242 .214 Full Hybrid Preferences [186][212] 15696 45586 -2.273 .023* Plug-in Hybrid Preferences [191][205] 16745 -1.453 .146 Pure EV Preferences 46635 II. Current Powertrain [Petrol][Diesel] [237][125] Petrol Preferences 7591 18176 -9.790 .000** [149][262] 35658 Diesel Preferences 7217 -9.890 .000** [191][188] .808 Mild Hybrid Preferences 16652 26805 -.242 [197][184] Full Hybrid Preferences 16234 26819 -1.063 .288 [196][183] Plug-in Hybrid Preferences 15979 26419 -1.157 .247 [198][179] 25853 .045* Pure EV Preferences 15413 -2.001 III. University/Professional Education [attained][not attained] [188][216] 44572 Petrol Preferences 16606 -2.397 .017* [188][207] 44410 Diesel Preferences 16680 -1.652 .099 [203][180] Mild Hybrid Preferences 15948 28038 -1.990 .047* [189][201] 29680 Full Hybrid Preferences 17277 -1.091 .275 [208][175] 27417 Plug-in Hybrid Preferences 15171 -2.958 .003** [207][177] 15423.5 27669.5 Pure EV Preferences -3.130 .002** IV. Garage [owned][not owned] [201][189] 17757.5 39493.5 -1.088 .277 Petrol Preferences [187][195] 17443.5 33374.5 -.762 .446 Diesel Preferences [178][200] 15754.5 31330.5 -2.025 .043* Mild Hybrid Preferences [177][204] 15600.5 31353.5 -2.495 .013* Full Hybrid Preferences [190][191] 17936 33689 -.115 .908 Plug-in Hybrid Preferences [185][195] 17050.5 32803.5 -1.136 .256 Pure EV Preferences *Significant at the 0.05 level ** Significant at the 0.01 level
iii.
Holders of University or Professional Qualifications
As with gender, the level of formal education attainment has been shown in previous studies to affect LEV preference (Potoglou and Kanaroglou, 2007) and is a common segmentation variable used in market research studies. In this instance, the sample is split based on the attainment (or lack thereof) of a university or professional level qualification. It is observed that there are four statistically significant differences identified by the
145
Chapter Five: Results – Variable Development analysis. Firstly, those respondents without a professional or university level qualification are more likely to select a Petrol powertrain compared to those with. Conversely, those respondents who hold a university or professional qualification are more likely to consider a Mild Hybrid, Plug-in Hybrid and Pure EV powertrain compared to those without. These results support the findings of previous research, demonstrating that level of formal education is a valid indicator of LEV preference. iv.
Garage
Currently, to externally recharge the battery pack of a Plug-in Hybrid or Pure EV a user is required to connect the vehicle to a wired power source. With this in mind, it is possible that those respondents who cannot easily achieve this may have relatively adverse preferences to plug-in vehicles (Axsen and Kurani, 2012). This may occur when respondents only have access to street parking or no formal parking provision. To test whether or not this is the case, the sample has been split based on garage access. Examining the powertrain preferences of these groups, two statistically significant differences can be observed which are somewhat surprising. It was expected that respondents with garage access would have higher preferences for Plug-in Hybrid and Pure EVs though what is observed is respondents without garage access have higher preferences for Mild and Full Hybrids. This finding is difficult to explain as it goes against expectations. Further study is required in this area to determine if this finding is valid or spurious and, if validated, to identify what is motivating this difference in preference. 5.3.6 Respondent Confidence in Powertrain Evaluation Exercise The powertrain evaluation exercise is likely to be a novel experience for most respondents that are not used to considering different powertrain options. Furthermore, the addition of new technologies may lead to confusion. Whilst care has been taken in the development of the exercise, it is still possible that respondents may remain uncertain in their stated choices. To examine the effectiveness of the information pack provided (discussed in Chapter 3, Section 3.4.8), which details the primary features of the powertrain options, respondents were asked to evaluate their decision process and state their confidence in
146
Chapter Five: Results – Variable Development being able to make correct decisions in the evaluation exercise. Results of this post exercise assessment are presented in Figure 5.1. The majority of respondents (69.7%) state some degree of confident response in their assessment of the choices made whilst 21.6% are unconfident in their decision making. From this basic examination, it can be tentatively concluded that the information pack provided has been successful in supplying enough information for respondents to be generally confident in their decisions. However, this assertion cannot be corroborated without the use of a control group which did not have access to the information.
Figure 5.1: Respondent Stated Confidence in the Powertrain Evaluation Exercise Having examined the aggregated results, it is important to determine if this confidence level interacts with powertrain preferences. To achieve this, a correlation analysis has been conducted between stated confidence level and preferences. Results of this analysis are displayed in Table 5.10 with no statistically significant correlation present. This finding indicates that powertrain preferences are independent of the level of confidence expressed 147
Chapter Five: Results – Variable Development by a respondent relating to the evaluation exercise. Specifically, a respondent stating either high or low levels of confidence was no more likely to select any specific powertrain option compared to other participants. Table 5.10: Correlation Analysis Between Confidence Level and Powertrain Preference* Statistic Pearson Correlation Sig. (1-tailed)
Petrol Diesel Mild Hybrid Full Hybrid Preferences Preferences Preferences Preferences
Plug-in Pure EV Hybrid Preferences Preferences
-.052
.037
-.079
-.016
-.041
-.039
.149
.232
.061
.376
.211
.223
* How confident are you that you made the right decision in the exercise above?
5.3.7 LEV Option as Highest Preference Due to the nature of the response format used in the evaluation exercise, respondents were allowed to express positive preferences for multiple powertrain options. For instance, whilst a respondent may state a likely response to considering a Full Hybrid powertrain in their next vehicle purchase, this preference may be surpassed by a highly likely response for one of the conventional powertrain options. This section examines the propensity of respondents to select one of the LEV powertrain options as their highest preference with the results of this investigation being presented in Table 5.11.
Table 5.11: LEV Powertrain as Highest Preference Number of Respondents
Percentage of Respondents
Overall LEV Highest Preference
97
24.2%
Unique LEV Highest Preference
57
14.2%
Statistic
Briefly discussing the method for addressing this issue, the first statistic, labelled Overall LEV Highest Preference, refers to the quantity of respondents that select an LEV option as their highest preference. However, with preferences not required to be mutually exclusive, it is possible for a respondent to state the same preference category for multiple powertrain options. In instances where a respondent has a conventional and LEV powertrain option as their highest preference (for instance, Mild Hybrid and Petrol both ranked as Likely), a conservative approach has been taken and a new statistic calculated. The statistic labelled 148
Chapter Five: Results – Variable Development Unique LEV Highest Preference refers to respondents that hold an LEV powertrain option as their uniquely highest preference. Furthermore, this variable has been separated based on which LEV powertrain option holds this unique highest preference with the results displayed in Table 5.12. In circumstances where two LEV powertrain options hold the highest preference (for instance, Plug-in Hybrid and Pure EV both ranked Highly Likely), a conservative approach has been taken and the powertrain with the lowest level of electrification has been assigned the preference.
Table 5.12: Unique LEV Powetrain Highest Preferences Number of Respondents
Percentage of Respondents
Mild Hybrid
20
5%
Full Hybrid Plug-in Hybrid Pure EV
7 16 14
1.8% 4% 3.5%
Powertrain
Inspecting the results of this analysis, almost a quarter of respondents that took part in the evaluation exercise hold a LEV as their highest preference. However, when removing the respondents that state joint highest preference for both an LEV and conventional powertrain option, the percentage of respondents stating uniquely high LEV preferences decreases to 14.2%. Appraising how this 14.2% is distributed between the different LEV options, the Mild Hybrid powertrain option attains the highest proportion of unique LEV highest preferences followed by Plug-in Hybrid and Pure EVs. The Full Hybrid powertrain option receives the lowest proportion of LEV highest preferences. The reason behind this is that a number of respondents stated equally highest preferences for Mild and Full Hybrid options. In these circumstances, the preference was assigned to the Mild Hybrid option. These results suggest that 14.2% of the sample appear to be considering the adoption of a LEV and that all four of the LEV options are receiving relatively equal proportions of this stated preference. With the UK household car market selling 929, 440 new cars in 2012 (SMMT, 2013) and assuming the sample attained in this thesis is representative of this market, this represents a diffusion potential of 131,980 LEVs. However, with only 27, 841 LEVs being sold in 2012, only a small degree of this diffusion potential is being realised (SMMT, 2013). 149
Chapter Five: Results – Variable Development 5.3.8 Preference Aggregation A concern of using a Likert response format in an evaluation exercise is that these measurement variables are not classified as being continuous in nature. This limits the suitability of the data to linear modelling and has the potential of introducing a degree of bias into analysis (Poole and O’Farrell, 1971). To overcome this limitation, the results from the evaluation exercise have been aggregated to convert the data into continuous variables. The first aggregate variable reflects average preferences to Mild and Full Hybrid powertrain options whilst the second corresponds to average preference to Plug-in Hybrid and Pure EVs. This split follows a technical aspect with the first aggregate variable representing LEV powertrain options that are fully reliant on conventional fuel whilst the second relates to vehicles with the ability to charge the battery pack externally. A summary of these two aggregate preference variables is provided in Table 5.13. These aggregate preference variables are taken forward in the next results chapter to test the conceptual framework. Table 5.13: Aggregate Variables of Powertrain Preference Aggregate Variable Label
Constituting Variables
Mean Hybrid Preferences
• •
Mild Hybrid preferences Full Hybrid preferences
Mean Plug-in Preferences
• •
Plug-in Hybrid preferences Pure EV preferences
5.3.9 Overview of Powertrain Preferences This section has presented the results of the powertrain evaluation exercise which measured preferences across six different powertain options. Firstly examining the basic structure of preferences, results tend to follow expectations with preferences decreasing as the proportion of powertrain electrification increases. The Mild Hybrid option attains the highest level of positive responses of the four LEV options included whilst the Pure EV option attains the lowest. Following this, hypothesis testing was employed to determine if any significant differences in powertrain preference existed between sample cohorts. To begin, preferences for primary and secondary household cars were compared with the results indicating that respondents have preferences for a Diesel as a primary as opposed to secondary car and for Plug-in Hybrid and Pure EVs as secondary as opposed to primary cars. 150
Chapter Five: Results – Variable Development
Following this, the preference structures of the Newcastle and Dundee subsamples were compared with the results signifying that Newcastle respondents have significantly higher preferences for Mild and Full Hybrid vehicles. In a similar fashion, dummy variables were used to identify sample cohorts to examine a number of expected differences in preference. Female respondents were found to have higher preferences for Mild and Plug-in Hybrid vehicles compared to males whilst owners of Petrol cars have higher preferences for Pure EVs compared to Diesel drivers. Moreover, respondents with a university or professional qualification had significantly lower preferences for the Petrol option and significantly higher preferences for Mild Hybrids, Plug-in Hybrids and Pure EVs. To determine if the evaluation exercise was capable of accurately measuring powertrain preferences, respondents were asked to state their perceived confidence in the choices they made. Results of this are encouraging, indicating that 69.7% are confident in their ability to make correct decisions. Taking the aggregated results of the evaluation exercise and determining the proportion of respondents that selected a LEV as their highest preference, the results indicate that 14.2% of drivers may potentially consider adopting some form of LEV in their next car purchase. 5.4 MEASUREMENT OF ADOPTIVE INNOVATIVENESS The second set of variables to be used in a dependent capacity during model construction relates to adoptive innovativeness. Usually, this concept is assessed for a single innovation with the measurement connected to the time interval between the innovation becoming available to the public and being adopted by an individual. This raises some clear challenges, firstly the research cannot commence until the diffusion process is complete and results are often dependent on respondent recall of purchasing behaviour. To overcome this, this thesis has taken a more holistic and less cognitively challenging approach whereby respondents were asked to state their ownership of a pre-specified list of consumer technology. Specifically, respondents were allowed to state if they currently own the technology, intend to own it in the near future or have no current intention to own it.
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Chapter Five: Results – Variable Development Table 5.14: Percentage of Technology Ownership Stated by Respondents Intended Do not Intend Technology Own to Own to Own Smart Phone 32.3% 14.2% 53.5% HDTV 72% 15.7% 12.3% HD Satellite 40.5% 10% 49.5% Netbook 18.4% 11% 70.6% PV Tiles 1.8% 18.4% 79.8% Heat Pump 0.2% 8.2% 91.6% 3D TV 5.6% 11.2% 83.2% Media Centre PC 13.5% 14.5% 71.9% Wireless Home Internet 65.3% 7.4% 27.3% Underfloor Heating 5.8% 7.4% 86.9% Tablet PC 10.3% 22.6% 67.1% Combination Boiler 66.9% 10.2% 22.8% GPS 35.6% 13.2% 51.2% Blueray Player 15.6% 10.4% 74% Touchscreen PC 4.8% 14.1% 81.1% eReader 14.6% 19% 66.3% Digital Camcorder 25.5% 9.4% 65.1%
Examining the results for the individual technologies presented in Table 5.14, it is apparent that HD televisions, combination boilers, wireless home internet, HD satellite, GPS and smart phones all display a market uptake of over 30%. Conversely, heat pumps, photovoltaic tiles, 3D televisions and underfloor heating all have adoption rates of less than 6%. The most highly desired items of technology are tablet PCs and photovoltaic tiles. The results of this exercise are presented as a frequency distribution in Figure 5.2. Looking firstly at the quantity owned, it is observed that the distribution is normal in nature though with a long right tail containing those respondents that have high levels of technology ownership. This lends support to the Diffusion of Innovation Theory (Rogers, 1995) which is based around a normal distribution. Relating to intention to own, it is apparent that the distribution is positively skewed with a large proportion of respondents stating no intention to purchase any of the listed technologies in the near future. Lastly, the variable relating to quantity of technology neither owned nor desired follows a normal distribution with a negative skewness. 152
Chapter Five: Results – Variable Development
Response Frequency (pcercentage)
30 25 20 15 10 5 0 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Quantity of Consumer Technology Own
Intend to Own
Don't Intend to Own
Figure 5.2: Frequency Distribution of Technology Ownership Having measured respondent ownership of each individual technology option included, the data has been used to calculate a number of aggregate variables which are displayed in Table 5.15. The first relates to the total quantity of consumer technology owned by a respondent, the second the total quantity desired and the third the total quantity neither owned nor desired. These variables are utilised in forthcoming data analysis as measurements of adoptive innovativeness.
Table 5.15: Aggregate Variables of Adoptive Innovativeness Aggregate Variable Label Total Owned Total Desired Total Not Owned
Variable Description The total quantity of household technology currently owned The total quantity of household technology desired to be owned in the near future The total quantity of household technology neither currently owned nor desired to be owned
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Chapter Five: Results – Variable Development 5.5 MEASUREMENT OF SOCIO-PSYCHOLOGICAL CONSTRUCTS In total, the survey developed for this thesis incorporates nine attitudinal measurement scales totalling one hundred separate statements (detailed in Chapter 3, Section 3.4). It is a necessary requirement to reduce this data to a more manageable quantity to make it of use in later analysis stages. Factor analysis has been used to achieve this goal by assisting with the identification of latent socio-psychological constructs. This section provides an introduction to the statistical method, justifies the specific procedure followed and then presents and describes the results attained from the analysis. 5.5.1 Introduction to Method The results so far have concentrated on the basic structure of the dataset, employing univariate and descriptive statistics to examine response frequencies alongside central tendencies and levels of data dispersion. This section progresses the analysis by examining how variables can be assessed concurrently as opposed to separately, often referred to as multivariate statistics. The first stage of this analysis is to subject parts of the data set to a technique called factor analysis. The main purpose of this form of analysis is to determine if a large group of variables can be adequately explained by a smaller number of unobserved constructs, referred to as factors. Factor analysis is a statistical method frequently applied in the social sciences (Bartholomew et al., 2008) to measure socio-psychological constructs which cannot be directly observed, and is often utilised to identify latent variables that are present within datasets. Furthermore, it is a method that has seen application in the LEV demand field (Peters et al., 2011b; Jansson et al., 2011; Ozaki and Sevatsyanova, 2011). However, Costello and Osbourne (2005) state that knowledge relating to the relative strengths and weaknesses of factor analysis is scarce and often inadequately referenced. Factor analysis can be conducted using a number of different procedures, each with its own particular nuances in the manner in which the factor output is determined (Field, 2009). Principal Component Analysis (PCA) is a commonly applied variant of factor analysis employed in the social sciences to identify latent variables and in the development of psychometric models. Primarily a data reduction technique, the appropriateness of PCA compared to conventional factor analysis remains an area of academic debate with Fabrigar 154
Chapter Five: Results – Variable Development et al. (1999) stating the results from the different procedures can diverge when communalities 4 are low. Conversely, Velicer and Jackson (1990) find no substantial 3F
empirical difference in results attained from PCA and other forms of factor analysis. Additionally, Joliffe and Morgan (1992) describe PCA’s applicability for further statistical analysis, such as the capability of components to act as independent variables in linear regression (Marx and Smith, 1990). With these considerations in mind, PCA has been selected as the factor analysis method for this thesis. It is general practice when conducting factor analysis to follow a two stage procedure. To begin, the specific factor analysis procedure (such as PCA) is applied to identify the unobserved variables that can represent the variances of multiple observed variables. Following this, the initial output is subjected to rotation which alters the axis position with the aim of making the output clearer, more pronounced and thus further revealing the embedded structure. Two main categories of rotation are possible [1] orthogonal rotations which assume that the factors within the data are uncorrelated and [2] oblique rotation which relaxes this assumption. Varimax rotation (Kaiser, 1958) exists within the first category and is the most commonly applied rotation technique (Abdi, 2003). In essence, Varimax rotation forces the output to become bipolar in nature, maximising larger loadings whilst minimising smaller ones. This provides assistance to the researcher when interpreting the output by making the factors more unique and visually apparent. With these considerations in mind, Varimax rotation has been utilised in this thesis. Once the factor output has been generated, it is important to assess its quality to determine its suitability for subsequent analysis. A popular method to achieve this is through the calculation of Cronbach’s alpha (Cronbach, 1951) which tests the reliability of a factor be assessing its internal consistency. Peterson (1994) conducted a meta-analysis of how alpha scores are rated in empirical research and found that acceptable scores range from 0.5 for preliminary analysis to 0.9 for applied research (presented in Table 5.16). In this thesis, Cronbach’s alpha has been calculated for each factor identified in the analysis for the purposes for quality appraisal.
4
Which measures the total variance of a single variable explained by all the factors extracted in the analysis
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Chapter Five: Results – Variable Development Table 5.16: Levels of Acceptability in Cronbach’s Alpha (Peterson, 1994*)
Davis (1964)
Recommended Level Prediction for individual Above .75 Prediction for group of 25-50 .5 Prediction for group over 50 Below .5
Kaplan and Saccuzzo (1982)
Basic research Applied research
Author
Situation
Murphy and Davidshofer Unacceptable level (1988) Low level Moderate to high level High level
.7-.8 .95 Below .6 .7 .8-.9 .9
Nunnally (1967)
Preliminary research Basic research Applied research
.5-.6 .8 .9-.95
Nunnally (1978)
Preliminary research Basic research Applied research
.7 .8 .9-.95
*All references embedded in this table can be viewed in Peterson (1994)
However, doubts have been raised concerning the suitability of Cronbach’s alpha with Sijtsma (2009) stating the method suffers from significant limitations in its ability to evaluate reliability and internal consistency whilst Bernardi (1994) found that low alpha scores do not necessarily suggest spurious factor output. To provide additional quality tests, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy has been calculated for each scale to determine the proportion of variance likely to be caused by latent variables whilst Bartlett’s test of sphericity has been calculated to determine the suitability of the scale to undergo structure detection (Dziuban and Shirkey, 1974). Mooi and Sarstedt (2011) offer guidance on how to interpret the KMO test result (presented in Table 5.17) whilst Bartlett’s test offers a significance result with values over 0.05 indicating sphericity to be present. Moreover, the variables included within each scale have been critically examined to determine if they are appropriate and make intuitive sense.
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Chapter Five: Results – Variable Development Table 5.17: Levels of Acceptability in the KMO Test of Sampling Adequacy (Mooi and Sarstedt, 2011) Adequacy of the KMO Value Correlations Below 0.50 Unacceptable 0.50–0.59 Miserable 0.60–0.69 Mediocre 0.70–0.79 Middling 0.80–0.89 Meritorious 0.90 and higher Marvellous Once a rotated factor analysis has been generated, the next step is to determine the degree to which each respondent can be represented by each factor. This is achieved by assigning each respondent a factor score calculated by examining how they respond to each of the items included in the factor. A significant advantage of using factor scores when developing statistical models is that it limits the possibility of multicollinearity as factors extracted from the same analysis, by their nature, should be independent of each other (where orthogonal rotation has been applied). However, Glass and Maguire (1966) discuss the main problems associated with calculating factor scores and note that, under certain conditions, the independence of factor output may not directly transfer when factor scores are determined. To account for this, the regression method (Harris, 1967) for calculating factor scores has been selected for this thesis as it maximises the degree to which a factor score correlates with the original factor thus increasing measure validity (DiStefano et al., 2009). 5.5.2 Principal Components Analysis: Survey Scales This section presents the results of the PCA conducted on each of the measurement scales. The scales have been analyzed in the order in which they appear in the survey (detailed in Chapter 4, Section 4.2.2) commencing with the Car Meanings scale and concluding with Life Principles. A brief introduction to each scale is offered before presenting a number of key statistics alongside the rotated factor output. To assist in interpretation, statement loadings have been arranged by order of magnitude with those that are less than 0.3 being hidden whilst those statements associated with a specific factor have been highlighted in grey. Following this, each identified factor is discussed and an appropriate label attached which is used to refer to the factor in later stages of analysis. Lastly, a summary of the main results is
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Chapter Five: Results – Variable Development offered which underlines the key findings of the analysis and discuss how the results will be used in later analysis. 5.5.3 Car Meanings Scale This scale includes 12 statements linked to the constructs of symbolic, emotive and functional car meaning discussed in Chapter 3, Section 3.3.2.1. Each construct has been assigned 4 attitudinal statements based on work conducted by Dittmar (1992). An anchor sentence has been used to position the scale being “I think a car most of the time can...” with respondents asked to state to what degree they agree with each individual statement. The results of the PCA are presented in Table 5.18 and show that 2 factors have been extracted from the data. This is, initially, somewhat surprising as it was expected 3 factors would be extracted orientated around the 3 constructs. Looking firstly at Factor 1, it is clear that statements reflecting symbolic and emotive car meaning have been combined together. Within this factor, the statements with the largest coefficients are centred on the symbolic dimension with the emotional statements being secondary. Elements of status, personal expression, image improvement and appearance are included in this factor. On further reflection, it appears these results may not prove to be as counterintuitive as initially considered. Whilst it can be proposed than symbolic and emotive car meanings are separate constructs at the conceptual level, it still remains within reason that these constructs can be related to one another. In essence, it is likely that any individual expressing a strong symbolic attachment to their vehicle is also likely to reinforce this with a strong emotional connection.
The second factor predominantly contains statements linked to functional vehicle characteristics such as usefulness and financial efficiency. A statement that appears to be out of place is associated with the provision of enjoyment which was intended to be linked with emotive vehicle connection and therefore should be better suited to Factor 1. A possible explanation for this is that the word provide can be conceived in a functional manner as the provision of a service. With this observation in mind, future applications of
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Chapter Five: Results – Variable Development this scale may consider re-wording this statement to ensure it connects with the intended construct.
Table 5.18: Rotated Factor Matrix - Car Meanings Statements* (KMO - .868 Bartlett’s sig .000) Improve my appearance or the way I look Make others think well of me Provide me with social status Improve my mood Provide emotional security Be beautiful or attractive in appearance Allow me to express myself Allow me to be efficient in my daily life and work Be a sensible financial decision Provide enjoyment Have a lot of practical usefulness Be a hassle Variance Explained Cronbach’s Alpha
Factor 1 .881 .877 .857 .764 .749 .710 .672
41.3% .907
2
.344 .703 .676 .631 .616 -.574 15.3% .659
*I think a car most of the time can...
Factor 1: Meaning: Symbolism and Emotion - The first factor contains a mix of symbolic and emotive car meaning statements with the symbolic statements tending to attain larger coefficients and thus being more dominant. Factor 2: Meaning: Function - The second factor predominantly contains functional car meaning statements with the exception of the statement “provide enjoyment” which was initially included as an emotion statement. Whilst results from this scale have not followed expectations, some interesting findings have been observed. Factor 1 clearly illustrates the intrinsic connection between the constructs of symbolic and emotive meaning which individuals place on their vehicles. Factor 2 shows that functional vehicle considerations are separable, perhaps viewed in a more objective light. A degree of overlap is likely to exist between all 3 constructs perhaps indicating that refinements to the scale could be made through a re-specification of the 159
Chapter Five: Results – Variable Development attitudinal statements with oblique factor rotation. This re-specification will allow for the factors extracted to be correlated with one another. 5.5.4 Car Emotions Scale In order to measure respondent emotive connection to cars in greater detail, the Car Emotions scale was developed which includes 10 different emotive states. Evenly split between positive and negative emotive associations, a sentence completion approach was used with the anchor sentence “Most of the time I associate a car with the following emotions…”. Table 5.19 displays the PCA for this scale showing 2 factors having been extracted each containing 5 statements. Looking firstly at Factor 1, it is apparent that all of the emotions with positive association have been grouped together. Turning the attention to Factor 2, it is similarly observed that all of the emotions with negative association have been assembled. Table 5.19: Rotated Factor Matrix - Car Emotions Factors Statements* (KMO - .828 Bartlett’s sig .000) 1 2 Happiness .841 Excitement .825 Pride .806 Pleasure .783 Affection .702 Irritation .831 Stress .806 Apprehension .791 Boredom .736 Embarrassment .731 Variance Explained 34.5% 28.5% Cronbach’s Alpha .852 .847 *Most of the time I associate a car with the following emotions…
Factor 1: Positive Car Emotions – comprising of statements linked with positive emotional connection to cars. Factor 2: Negative Car Emotions – containing all of the negative emotions comprised in the original scale.
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Chapter Five: Results – Variable Development This scale has followed expectations with positive and negative emotional associations separating to form two unique and clear factors. However, thinking in such polar terms can, in certain circumstances, be overly simplistic. It is entirely conceivable that car users will experience multiple emotive states, included in the different factors, during a single journey. To overcome this, the anchor sentence was constructed to connect with primary emotional association rather than, for instance, considering emotional reaction under specific conditions. However, there is still a degree of simplicity imposed by the scale structure which reduces the richness of measurement associated with emotive car connection. 5.5.5 Car Knowledge and Importance Scale The third survey scale is a mixture of two different themes. Firstly, statements were included that measure the perceived importance of car ownership. This has taken the form of statements linked to how integral car use is for an individual and how connected an individual feels to their car. Secondly, statements were included that measure car knowledge, including such aspects as mechanical competency and awareness of LEVs. The rotated PCA for this scale is presented in Table 5.20 showing that 2 factors have been identified. Factor 1 includes statements which were included to measure the degree to which a respondent considers their car to be an important possession. This factor incorporates issues related to cars being essential to the operation of everyday life alongside aspects linked to the anthropomorphic importance of a car. Factor 2 clearly relates to car knowledge including elements such as understanding of the mechanical operation of cars and the ability to fix malfunctions.
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Chapter Five: Results – Variable Development Table 5.20: Rotated Factor Matrix - Car Knowledge and Importance Factors
Statements (KMO - .732 Bartlett’s .000)
1 .816
I consider my car to be part of the family If my car was stolen, I'd feel as if I had lost a part of myself The car I drive is irreplaceable My car is the most important thing I own I often treat my car as if it were a person Without my car, my life would become very difficult I know how my car works on a mechanical level I’m capable of fixing any rudimentary problems with my car
2
.806 .783 .686 .645 .507 .878 .807
I know a lot about the new types of cars (such as hybrid and electric cars) being released into the car market
.714
Variance Explained
35.5%
22.7%
Cronbach’s Alpha
.805
.772
Factor 1: Car Importance – measures the degree to which an individual considers their car to be a vital possession required for the operation of everyday life. Additionally, this factor links with a more personal connection to cars, such as anthropomorphising and relationship formation. Factor 2: Car Knowledge – links with knowledge concerning and capability with the mechanical operation of vehicles such as being able to diagnose and fix malfunctions. Furthermore, this factor connects with knowledge of new powertrain technologies. With this scale including elements with little overlap, it was expected that clear factors would be extracted from the analysis. The factor scores generated from these components provide measures relating to how important a car is to a respondent and their level of knowledge associated with cars. On reflection, it may have proved beneficial to include in this section statements associated with driving skill. However, this may have proved challenging as being a skilful driver is generally considered a desirable trait and thus would be susceptible to positive response bias.
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Chapter Five: Results – Variable Development 5.5.6 Car Attitudes Scale Examining previous research conducted in the field of LEV demand, certain car attitudes have been identified which appear to influence LEV preferences (see Chapter 3, Section 3.3.2.2 for a discussion of this point). This scale has been developed to include a number of these attitudes and contains statements linked to environmental concerns of car use, perceptions of car cost and car technology. During the initial stages of analysis, the decision was taken to remove the statement associated with attitudes towards fuel independence due to its low coefficient loadings on multiple factors. In total, 8 statements were included in the analysis with 3 factors having been extracted which are displayed in Table 5.21. The first factor to be extracted is associated with concern for the environmental impact of car use. This is linked with aspects of responsibility and the willingness to pay a premium for a car that has lower pollution levels. What is of particular interest concerning this factor is the last highlighted statement which reflects willingness to pay more for a car which has higher fuel efficiency. This finding supports the view that individuals are beginning to comprehend the link between fuel efficiency and environmental impact. The second factor extracted includes two statements connected with car costs with the first statement reflecting operating costs whilst the second concerns initial purchase price. Both of these statements are orientated around a desire to reduce the costs associated with car ownership. The final factor includes two statements with the first expressing a desire for car simplicity which holds a positive coefficient whilst the second indicates a preference for advanced car technology though this holds a negative coefficient. This arrangement makes intuitive sense, with simplicity and advanced technology being arranged as opposite interests. Unfortunately, the alpha value associated with this factor is markedly low, indicating internal inconsistency. This perhaps indicates that attitudes linked to car simplicity and car technology are not as connected as initially expected. With these considerations in mind, the decision was taken to remove this factor from further analysis.
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Chapter Five: Results – Variable Development Table 5.21: Rotated Factor Matrix - Car Attitudes Statements (KMO - .716 Bartlett’s .000) I am concerned about the environmental impact of driving my car I am willing to spend more on a car that has lower pollution levels I think it is my responsibility to reduce the environmental impact of driving my car I am willing to spend more on a car that has better fuel economy I worry about how much of my money I spend on filling up my car When buying a car the purchase cost is my number one concern
1 .866
Factors 2
.826 .822 .521
.392 .788 .700 .788
In my car I like to keep things as simple as possible Owning cutting edge car technology is something that appeals to me Variance Explained Cronbach’s Alpha
3
.307 32.9% .785
-.702 16.0% .350
14.1% .238
Factor 1: Car Environment – this factor provides a measurement of concerns relating to the environmental consequences of car use and willingness to act on these concerns. Factor 2: Car Cost – measures the importance of car costs from both an operating and purchase perspective. Factor 3: Car Simplicity – this factor reflects desire to own a car which is simple to operate without the addition of advanced technology. However, a low Cronbach’s alpha means this factor is not utilised in later stages of analysis. In sum, this scale has been successful in identifying two distinct constructs related to car attitudes. Factor 1 includes aspects linked with environmental car considerations in its statement structure whilst Factor 2 provides a basic measurement of concern linked to the costs attributed to car ownership. Factor 3 appears to lack internal consistency and, whilst a measurement associated to a desire for car technology would prove useful, this factor is not employed in later analysis. 164
Chapter Five: Results – Variable Development 5.5.7 EV Emotions Scale Having applied a scale measuring emotive connection to cars in general, this scale follows a similar format but instead asks respondents to consider their emotive response to EVs in particular. The layout is identical to that of the Car Emotions scale though the anchor phrase has been changed to “most of the time I associate an electric car with the following emotions...” to reflect the different scale onus. The results of the PCA are displayed in Table 5.22. Table 5.22: Rotated Factor Matrix - EV Emotions Statements* (KMO .864 Bartlett’s .000) Happiness Pleasure Excitement Pride Affection Irritation Embarrassment Stress Boredom Apprehension Variance Explained Cronbach’s Alpha
Factors 1 .850 .841 .804 .803 .684
47.7% .880
2
.397 .848 .782 .769 .765 .744 19.1% .863
*most of the time I associate an electric car with the following emotions...
Factor 1: Positive EV Emotions – this factor provides a measurement of positive emotional connection to EVs. Factor 2: Negative EV Emotions – contains a number of negative emotions to assess the ascription of negative emotional connections to EVs. Whilst coding the data associated with this scale, it became apparent that a large number of respondents (n = 105) decided to express universally neutral scores across all EV emotions. This perhaps reflects a difficulty in respondent ability to connect emotive responses to objects they have yet to experience. Nevertheless, the results from the PCA are similar to those of the Car Emotions scale in that the first factor is associated with positive emotive connection whilst the second links with negative emotions. The most telling difference between the two scales is in the internal factor structure. It is apparent that, within Factor 165
Chapter Five: Results – Variable Development 2, the negative emotion of embarrassment increases from being the last loading statement in the Car Emotions scale to being the second highest in the EV Emotions scale. A possible reason for this change is that respondents may conceive EVs currently as an inferior car thus connecting it with feelings of inadequacy. Slightly surprisingly, apprehension is the least important emotion in the second factor indicating its relative lack of importance. It was expected that, with EVs symbolizing advanced technology, that apprehension would be a prominent emotive response. 5.5.8 EV Attitudes Scale A commonly accepted influence over preferences towards EVs is associated with their unique functional performance profiles which tend to be viewed as generally inferior to conventional vehicles (Calfee, 1985; Graham-Rowe et al., 2012). Individuals often use conventional vehicles as the benchmark for comparison of key characteristics such as range, price and performance (Anable et al., 2008). This scale incorporates a number of these potential concerns alongside aspects of functional enhancement over 8 statements which have been extracted into 2 factors. Examining the rotated factor output presented in Table 5.23, it is apparent that statements connected with negative EV attitudes have been grouped together in Factor 1. This factor includes aspects linked with concerns over safety, reliability and lack of simplicity. Conversely, the statements connected with positive EV attitudes have been grouped together in Factor 2. This factor is comprised of issues connected with price premium recovery resulting from lower operating costs, home refuelling and ability to cope with a 100 mile range. These two factors are likely to prove effective in separating individuals that hold either positive or negative attitudes towards EV functional capabilities in later stages of analysis.
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Chapter Five: Results – Variable Development Table 5.23: Rotated Factor Matrix - EV Attitudes Statements (KMO .705 Bartlett’s .000) Electric cars are less reliable than conventional cars I would feel relatively less safe in an electric car I think electric cars would be complicated to use Electric cars don’t offer enough performance
Factors 1 .794 .778 .768 .518
I think I can fulfil all my transport needs with an electric car that has a range of 100 miles before recharging Electric cars are relatively more expensive to purchase but can pay for themselves in lower fuel costs
Cronbach’s Alpha
-.334 .715 .657
I would value the ability to refuel my car from home I think it would be easy for me to find places to plug in an electric car Variance Explained
2
.588 .497 28.6%
19.5%
.701
.491
Factor 1: Negative EV Attitudes – this factor contains statements connected to negative attitudes of EV functional capability such as lack of reliability and complexity. Factor 2: Positive EV Attitudes – measures positive associations with the functional capacity of EVs such as valuing decentralised fuelling and reduced operating costs. The statement linked with EV performance loads on both factors and, surprisingly, holds the smallest coefficient in both cases. This statement was expected to clearly distinguish between positive and negative attitudes. The fact that this statement loads on both factors could indicate the ambiguity of the term performance whereas a more specific term such as acceleration or driveability may have elicited a clearer result. 5.5.9 Communication Determinants of Innate Innovativeness Scale This scale provides the first measurement of innate innovativeness as detailed in Chapter 3, Section 3.3.1. Focusing on social and communication behaviour, this scale was constructed by attaching a statement to each of the determinants proposed by the Diffusion of
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Chapter Five: Results – Variable Development Innovations theory. In total, 9 statements were included with 2 factors being extracted from the analysis displayed in Table 5.24. The first factor includes 5 statements connected with information seeking and provision behaviour. Specifically, aspects of opinion leadership, mass media exposure and knowledge of innovations are represented. The second factor includes 4 statements associated with social behaviour relating to whether an individual has a small or broad social network and if they participate in structured social groups such as clubs and societies. Of particular interest is the positive coefficient held by the last statement in this factor. Concerning participation in small and inwardly orientated social groups, it was expected this statement would achieve a negative coefficient given the nature of the other statements in this factor, which are focused on broad and integrated social activity. This finding may indicate that this statement has been unsuccessful in attaching to the assigned theoretical determinant and so should be re-specified in future research. Table 5.24: Rotated Factor Matrix – Communication Determinants of Innate Innovativeness Factors Statements (KMO .831 Bartlett’s .000) 1 2 Friends and colleagues regularly come to me about advice concerning .890 new consumer technology I often know about the next ‘must have’ piece of consumer .885 technology before it is released onto the market I regularly seek information about the latest consumer technology I keep up-to-date with consumer technology by reading newspapers/magazines, websites or relevant TV shows I have frequent contact with people working with new consumer technology My friends and family would say I was a cosmopolitan person I often socialise with people from a large variety of different backgrounds I regularly participate in activities such as sports, clubs and/or associations that have a formal structure I have a small group of friends who all know each other well and share similar interests
.881 .751 .526
.426 .738 .723 .674 .355
Variance Explained
40.2%
16.8%
Cronbach’s Alpha
.865
.548 168
Chapter Five: Results – Variable Development Factor 1: Comm: Information Seeking and Provision - contained within this factor are statements that generally connect with information concerning innovativeness. Specifically, aspects relating to desire to attain information about innovations, knowledge of innovations and acting as a source of information about innovations are represented. Factor 2: Comm: Social Activity - this factor is focused on social behaviour such as the degree to which a respondent has a wide and varied social network, participation in formal groups and if a respondent would be classified as cosmopolitan. The statement relating to contact with individuals promoting the adoption of innovations (referred to as Change Agents) has a significant loading on both factors. This could be explained by the fact that Change Agents often act as a source of information, which connects well with the information seeking and provision aspect of Factor 1. Additionally, Change Agents tend to be well integrated into social networks, which attaches to the social activity theme of Factor 2. Both the factors extracted from this scale are straightforward to interpret and their structures display a clear separation in the communication determinants of innate innovativeness. Innovativeness appears to follow two distinct themes concerning communication behaviour. Firstly, those individuals that proactively seek information whilst acting as an information source for their social networks concerning innovations may indeed be more receptive to innovations early in their diffusion. Additionally, those individuals that possess broad and diversified social networks increase their likelihood of being exposed to new innovations. 5.5.10 Psychological Determinants of Innate Innovativeness Scale The second innate innovativeness scale shifts the focus to the psychological determinants which have been found to be related to the adoption of innovations . Two statements have been attached to each of the determinant proposed in the Diffusion of Innovation theory whilst also including two additional self report statements relating to innovativeness and
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Chapter Five: Results – Variable Development adoption of consumer technology. In total, 14 statements are contained within this scale with 4 factors having been extracted with the analysis displayed in Table 5.25. Factor 1 contains 5 statements which are associated with a number of psychological determinants of innovativeness including ability to cope with uncertainty, decision making capability and ambition alongside the self report on innovativeness. Three out of the 5 statements that have been assigned to this factor have additional coefficient loadings in excess of 0.3 on other extracted factors. This finding is perhaps indicative of the significant degree of overlap between the psychological determinants. For instance, whilst it is expected that individual rationality and ability to cope with uncertainty would be related to innate innovativeness, these determinants are also likely to be related to each other. With this inference in mind, a suggestion for future research would be to re-specify the scale and use an oblique rotation. Factor 2 contains 4 statements associated with the determinants relating to attitudes towards science and education. The coefficients display a positive orientation to this factor which expresses openness to new ideas, enjoyment of science and the active use of knowledge attained in formal education in everyday life. Science, technology and education are fields where innovation is taking place with the Diffusion of Innovation theory suggesting that those individuals that hold positive attitudes towards these fields are likely to also express positive attitudes towards innovations. Therefore, it is expected this factor will share a positive relationship with adoptive innovativeness. Factor 3 contains three statements which are associated with distinct determinants. The first of these links to ambition through a desire for personal progress whilst the second is attached to a positive attitude towards change expressed by a propensity to behave in a proactive manner in enhancing quality of life. The final statement included in this factor concerns rationality, approached by measuring desire to make correct decisions, which connects with procedural and substantive rationality (Simon, 1973; Simon, 1986).
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Chapter Five: Results – Variable Development Table 5.25: Rotated Factor Matrix – Psychological Determinants of Innate Innovativeness Factors Statements (KMO .825 Bartlett’s .000) 1 2 3 I have confidence in myself in making the right decision in complicated situations I prefer to let other people make decisions when I am not completely sure about the situation I quickly incorporate new ideas into how I live my life I’m a very ambitious person setting high standards and expectations for myself My friends and family would consider me to be an innovative person Science has no impact on how I live my life I really enjoyed my science classes at school I rarely use the things I learned in formal education in my daily life I enjoy learning about new things
.808 -.734 .512 .497
.308 .307
.494
.478
.380 .317
.441
-.745 .687 -.618 .554 .701
I’m never satisfied with my current position in life I’m always looking for ways to alter my life to make it better Making sure I always make the correct decision is something that is important to me Compulsive behaviour usually governs my purchasing decisions I’m usually one of the first people to acquire the latest consumer technology
Variance Explained Cronbach’s Alpha
4
.670 .607
-.435 .756 .595
27.3% .737
11.4% .589
9.2% .450
8.1% .381
Factor 1: Psych: Decision Making and Ambition - This factor contains 5 statements drawn from a mix of different determinants including aspects of decision making ability and attitudes towards change whilst also incorporating a self report of innovativeness. Factor 2: Psych: Science and Education - Four statements have been included in this factor related to 2 determinants. The first 2 statements are associated with attitudes towards science whilst the remaining 2 statements connect with attitudes towards education. Factor 3: Psych: Aspiration - sharing similarities with Factor 1, this factor includes determinants linked to ambition, attitudes towards change and
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Chapter Five: Results – Variable Development rationality. Indeed, this factor also has coefficients greater than 0.3 on 3 of the statements included in Factor 1. Factor 4: Psych: Compulsive - Whilst Factor 3 loads positively on the statement linked with respondent rationality, Factor 4 loads negatively and also includes a statement associated with compulsive behaviour. In addition to these 2 statements, this factor contains the self report linked with early adoption of consumer technology. The final factor contains 2 statements with the first linked to the determinant of rationality whilst the second concerns a self report on early adoption of consumer technology. During the scale development, the first statement highlighted in this factor, associated with compulsive purchasing behaviour, was incorporated into the scale as a negative measurement of rationality. With previous empirical literature suggesting that compulsive purchasing behaviour is related to a loss of self control (Raab et al., 2011) and diminished decision making ability (Kellett and Bolton, 2009), this link seemed valid. However, the structure of this factor suggests that compulsive behaviour is associated with the early acquisition of consumer technology which can be considered a form of innovative behaviour. In essence, this finding does not support the link between rationality and innovativeness though this disparity may either be a result of the erroneous assumption of a connection between compulsive behaviour and rationality or the low internal consistency of this factor. The statement overlap between Factors 1, 3 and 4 illustrates the level of interconnectedness when considering innate innovativeness from a psychological perspective. This leads to the factor output appearing less distinct compared to the other scales so far discussed. Whilst there may well be issues related to clarity when considering this scale, the statements displaying a degree of overlap should not be rejected out of hand before considering if the overlap can be explained. In this instance, the statement overlap observed does appear to be logically distributed. However, statement overlapping can lead to difficulties in assigning statements to factors. Additionally, statement overlap makes the calculation of Cronbach alphas more challenging. For instance, Factor 4’s Cronbach alpha is
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Chapter Five: Results – Variable Development .381 when only including the highlighted statements but increases to .667 when all statements with a coefficient in excess of 0.3 are included. 5.5.11 Life Principles Scale The final scale included in the survey has been sourced directly from external literature and relates to individual value orientation (described in Chapter 3, Section 3.3.4). Respondents were presented with 13 life principles and asked to state how much a particular principle actively guides their lives. Previous applications of this scale have found that 3 factors are commonly extracted which is supported by the results of this analysis presented in Table 5.26. Table 5.26: Rotated Factor Matrix - Life Principles Statements (KMO .792 Bartlett’s .000) 1 Protecting the Environment .841 Unity with Nature .815 Respecting the Earth .813 Preventing Pollution .803 Authority Social Power Influential Wealth Ambitious Social Justice Helpful Equality A World at Peace .371 Variance Explained 28.5% Cronbach’s Alpha .858
Factors 2
.839 .743 .688 .586 .583
18.9% .734
3
.304
.752 .722 .659 .480 9.9% .668
Factor 1: Biospheric Principles – concerned with preserving and fitting into nature, protecting natural resources and harmony with other species. Factor 2: Egotistic Principles – connects to control over others, a desire to command, having high aspirations and the acquisition of material wealth. Factor 3: Altruistic Principles – measures a desire for equal opportunity, social freedom and justice and working for the welfare of others. 173
Chapter Five: Results – Variable Development Factor 1 contains the life principles orientated around biospheric concern including aspects of environmental protection and preventing pollution. The second factor is linked to egotistic principles and includes aspects relating to social dominance, aspiration and wealth accumulation. The final factor connects with social goals including the desire to live in a world at peace, being helpful and desiring social equality. These factors are extracted in a clear fashion and, whilst a small degree of statement overlap does exist, this does not appear to be substantial. Examining the coefficients within the factors, it is apparent that the environmental factor displays coefficients that are all close in value. The range of coefficient values is wider for the egotistic and altruistic factors indicating that they perhaps lack the same degree of internal consistency, supported by the decreasing values for Cronbach’s alpha. Whilst this scale is not directly related to cars, LEVs or innovativeness, it has been included for two principal reasons. Firstly, with values being positioned in psychological theory as the basis for attitude and behaviour (Vaske and Donnelly, 1999), this thesis examines how value orientations interact with other socio-psychological constructs included in the conceptual framework. Secondly, value orientations are used in the market structure analysis to provide additional insights relating to the internal structure of consumer segments. It is expected that individuals who display positive factor scores relating to biospheric values will be more inclined to express higher preferences towards LEVs. For the factors linked with egoistic and altruistic values, their effect on LEV preferences is more challenging to predict and it will be interesting to observe what influence, if any, these two factors have. 5.5.12 Overview of Socio-Psychological Measurements In total, 9 separate PCA have been conducted on the measurement scales included in the survey. From these analyses, 100 individual statements have been reduced to 22 factors. To provide an overview of the data reduction, Table 5.27 offers a summary of the scales which have been examined and the factors that have been extracted.
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Table 5.27: Overview of Socio-Psychological Factors Scale Factor Label Car Meanings Meaning: Symbolic and Emotion Meaning: Function
Factor Description Relates to the symbolic and emotive meanings placed on car ownership such as representation of status and viewing cars as sources of positive emotions Functional car meanings such as cars being an efficient tool and a good financial investment
Car Emotions
Positive Car Emotions Negative Car Emotions
Measures the positive emotions connected to cars such as happiness and excitement Measures the negative emotions connected to cars such as embarrassment and anxiety
Car Knowledge and Importance
Car Importance Car Knowledge
Measures knowledge relating to the mechanical operating of cars, ability to fix car malfunctions and knowledge of LEVs Focuses on the importance of car ownership from a life enabling and personal perspective
Car Attitudes
Car Environment Car Cost
Concerns relating to the environmental consequences of car use and felt responsibility Measures concerns related to the purchase and operating costs associated with cars
EV Emotions
Positive EV Emotions Negative EV Emotions
Measures the positive emotions connected to EVs such as happiness and excitement Measures the negative emotions connected to EVs such as embarrassment and anxiety
EV Attitudes
Negative EV Attitudes Positive EV Attitudes
Concerns related to EV functional capabilities such as safety, reliability and complexity Positive attitudes connect to EV performance including cost and decentralised fuelling
Communication Determinants
Comm: Information Seeking and Provision Comm: Social Activity
Actively seeking information concerning innovations and acting as a source of this information to social networks Social activity such as cosmopolitanism, a broad social networks and social engagement
Psychological Determinants
Psych: Decision Making and Ambition Psych: Science and Education Psych: Aspiration Psych: Compulsive
Positive views relating to decision making ability linked with an ambitious nature Positive views relating to the importance of science and education Degree of aspiration such as a desire to better oneself and social progression Tendency to act compulsively without the application of reasoned thought
Life Principles
Biospheric Principles Egotistic Principles Altruistic Principles
Including principles such as protecting the environment and preventing pollution Including principles such as social power and material wealth Including principles such as social justice and helpfulness 175
Chapter Five: Results – Variable Development Each of the factors identified by the analysis has been described in accordance to their statement structure followed by an assessment of their legitimacy and the degree to which they adhere to expectations. A number of scales have produced markedly clear factor output with structures that appear sensible and theoretically justifiable. Falling into this category are the factors extracted from the Car Knowledge and Importance, EV Attitudes and Communication Determinants of Innate Innovativeness scales. Conversely, some factors are more challenging to interpret, with structures that require additional assessment. The factors extracted from the Car Meanings scale provide an appropriate example, which was considered, a priori, to incorporate three distinct socio-psychological constructs whereas results demonstrated the existence of only two. Whilst, in this instance, the result did not follow expectations, it has still proved insightful by suggesting that individuals may consider the symbolic and emotive aspects of car use and ownership together as opposed to separately. Appraising the two scales linked with the measurement of innate innovativeness, there has been some notable differences in the structure of the factor outputs. Relating to the Communication Determinants scale, two clear factors have been extracted whilst for the Psychological Determinants scale, a more indistinct picture has emerged. This perhaps indicates that the communication determinants of innate innovativeness have a more defined structure whereas the psychological determinants are relatively overlapping. To conclude, this section’s primary purpose has been to measure the socio-psychological constructs that are included in the conceptual framework developed for this thesis (see Chapter 3, Section 3.3). Appraising the results generated by the PCA, the factors extracted from the measurement scale appear to provide an adequate representation of the constructs specified. Whilst the results of this section are interesting in their own right, the factors extracted are utilised in the forthcoming results chapter to assist in the specification of models intended to provide answers to the primary research objectives posed (see Chapter 1, Section 1.2). Specifically, the factors are included in a number of correlation analyses to determine their relationship to LEV preferences and each other. After this, the factors are examined according to their suitability in explaining LEV preferences through the application of regression analysis. 176
Chapter Five: Results – Variable Development 5.6 CHAPTER SUMMARY This chapter has presented the initial results from the household survey which applied the conceptual framework developed for this thesis. To begin, key statistics were calculated regarding the socio-economic and current car details of the sample and then compared to known population parameters. For certain characteristics, the sample appears to accurately represent the general UK populace whilst in other areas clear divergences have been identified. A similar situation is present when comparing the Newcastle and Dundee subsamples, which display certain similarities and differences. These results may affect the degree to which the results attained in this thesis and their related interpretations can be generalised to the wider population and the degree to which the Dundee and Newcastle subsamples can be compared on other characteristics. To determine what impact the disparity between the socio-economic profile of the sample and the general population may have, these variables are entered as control variables in the regression analysis presented in Chapter 6. Following this, the results of the powertrain evaluation exercise were presented and discussed. A number of statistically significant differences in preference structures were identified between sample cohorts. A primary objective of this thesis is to determine if government funded initiatives linked to LEV adoption (outlined in Chapter 4, Section 4.3.1) have been successful at influencing preferences towards these vehicles in the areas in which they have been enacted. The two study sites of Newcastle and Dundee were selected to test this assertion, with the results demonstrating that the Newcastle subsample holds comparatively higher preferences for Mild and Full Hybrids. These results suggest that the government funded initiatives may have influenced preferences towards LEVs in their application areas. This will be further examined in Chapters 6 and 7. The third section of this chapter presented results relating to the measurement of adoptive innovativeness, firstly describing the adoption rates of specific technologies before computing aggregate measurements of adoption. The results of this analysis demonstrate that the instrument developed has been successful at measuring adoption rates of technologies which are at different stages of the diffusion process. Taking this technology 177
Chapter Five: Results – Variable Development specific data, three aggregate variables have been calculated which display cumulative ownership of household technology, cumulative desire to own household technology in the near future and cumulative non-adoption levels. The final section relates to the measurement of the socio-psychological constructs included in the conceptual framework. Factor analysis has been utilised to reduce the scale statements to latent constructs. The results of the analysis of each of the measurement scales included in the survey are presented and discussed. In certain areas, the factors extracted have conformed to expectations though in other areas a number of unexpected results have been identified. The next chapter takes the results generated in these four sections and applies correlation and regression analysis to determine where relationships between the variables developed exist.
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CHAPTER
6
RESULTS - SOCIO-PSYCHOLOGICAL MODELLING 6.1 INTRODUCTION The previous results chapter used the primary data attained through the application of the household survey to develop a number of variables linked to the components included in the conceptual framework (Chapter 3, Section 3.3). These variables are now utilised to construct a series of statistical models with the output being utilised to answer the first set of research questions which assess the validity of the framework (Chapter 1, Section 1.2). To begin, correlation analysis is conducted to identify where relationships between variables exist in the data set. Following this, regression analysis is used to develop a number of explanatory models to determine which constructs described in the conceptual framework hold significant influence over each other. 6.2 CORRELATION ANALYSIS Through the application of data aggregation and factor analysis, a number of variables have been developed associated with the constructs included in the conceptual framework (see Table 5.13, 5.15 and 5.27 in Chapter 5). These variables have been measured with the intention to utilise the data in order to answer the research objectives associated with this thesis. Before this can be achieved, it is necessary to identify where statistically significant relationships exist between the variables. To accomplish this, correlation analysis is utilised. The results attained are checked against a priori expectations to establish construct validity. The structure of the conceptual framework is used as a starting point, with conceptual links between different constructs tested.
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Chapter Six: Results – Socio-Psychological Modelling Specifically, the correlation analysis is partitioned into five main stages which examine different aspects of the conceptual framework. To begin, the relationships between the measurements of powertrain preference are examined. This initial stage of the analysis assesses if the aggregation of LEV preferences is valid (described in Chapter 5, Section 5.3.8). Following this, the interaction between value orientation and the socio-psychological constructs of innate innovativeness, car meanings and car attitudes is scrutinised. This stage approaches research question 1.3 which asks if value orientations significantly interact with other socio-psychological constructs. The third stage of the analysis determines if innate innovativeness is related to adoptive innovativeness whilst the fourth stage inspects the relationship between car meanings, car attitudes and EV attitudes. To conclude, the relationships that LEV preferences hold with adoptive innovativeness and attitudes towards EVs is examined. However, before the results of these analysis stages are presented and discussed, a brief introduction to correlation analysis is offered which describes the main features of the procedure and how it is applied in this thesis. 6.2.1 Introduction to Method Correlation analysis is a form of statistical evaluation commonly utilised in the natural and physical sciences and is widely applied in psychological research (Field, 2009). Employed in the appraisal of bivariate association, correlation analysis identifies the existence (or lack thereof) of a statistical relationship between two variables. Through an examination of the values of two variables, correlation analysis determines if specific values of one variable tend to be associated to specific values of another. This relationship can take two primary formats. The first is referred to as positive correlation and is present when the values of two variables tend to move in the same direction, either increasing or decreasing together. The second is labelled negative correlation and exists when the values of two variables have a propensity to move in opposite directions, so as the value of one variable increases, the other decreases and vice versa. If the values of two variables are entirely unrelated to one another, this is commonly termed independence. To illustrate the magnitude of any identified relationship, the correlation coefficient (often denoted as r) is expressed which can take a value ranging from +1, which represents a perfect positively correlated relationship, to -1, which indicates a perfect negatively correlated relationship.
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Chapter Six: Results – Socio-Psychological Modelling Initially proposed in its full form by Pearson (1920), correlation analysis provides a useful method of statistical appraisal by allowing the identification of relationships between different phenomenon. From this initial inception, thirteen different procedures have been specified to calculate the correlation coefficient using algebraic, geometric and trigonometric approaches (Rodgers and Nicewonder, 1988). Pearson’s Product Moment Correlation is the most commonly applied variant and determines if a relationship exists between two variables by examining their covariance divided by the product of their standard deviations. A prominent shortcoming of Pearson’s procedure is the requirement for the variables utilised to follow the assumptions of parametric statistics (Siegel, 1957). In the social sciences, it is common to attain variables which are not normally distributed or are measured on ordinal scales. To account for these conditions, Spearman (1904) developed a non-parametric form of correlation analysis, commonly referred to as Spearman’s Rank Correlation. The formulaic structure of this correlation procedure is identical to that of Pearson’s Product Moment with the principal differences being the specific values of the variables are transformed into their associated ranks. The dataset associated with this thesis contains a mixture of parametric and non-parametric variables. Specifically, certain variables are not normally distributed and, in cases, are measured on an ordinal scale. With this in mind, correlation analysis is conducted using Spearman’s Rank procedure in instances where non-parametric variables are present. With this thesis being exploratory in nature, two tailed tests of significance are used with significant relationships highlighted. Interpreting the size of the correlation coefficient is often dependent on which field of inquiry the research has taken place. In social science, Cohen (1988) categorises coefficients of .1 as being low, .3 as being medium and .5 as being large. Hemphill (2003) empirically evaluates these classifications and finds that the upper boundary for large correlations would only represent the 89th and 97th percentile of results in two prominent meta-analyses in psychology (Meyer et al., 2001; Wilson and Lipsey, 1993). Accounting for this, Hemphill states that the empirical distribution of correlation results indicates that a coefficient less than .2 should be classified as small, between .2 and .3 as medium and those of above .3 as large.
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Chapter Six: Results – Socio-Psychological Modelling 6.2.2 Powertrain Preferences The correlation analysis initially focuses on the powertrain preference variables which were calculated in the evaluation exercise (discussed in Chapter 5, Section 5.3). A correlation matrix is calculated solely including powertrain preferences to observe how they relate to each other with the results displayed in Table 6.1. All four of the LEV powertrain options are positively correlated with each other, indicating that respondents tend to display similar preferences for LEVs. The correlation coefficient between the Plug-in Hybrid and Pure EV options is high demonstrating a strong relationship which is to be expected with these options sharing similar characteristics. The lowest correlation coefficient between LEV options is observed among Mild Hybrids and Pure EVs which can be explained by these two options being the furthest apart in vehicle attributes of the LEV options. Table 6.1: Correlation Analysis Between Powertrain Preferences for Next Main Car Purchase Variable
Petrol
Diesel
Mild Hybrid
Full Hybrid
Plug-in Hybrid
Petrol
1.000
Diesel
-.140**
1.000
Mild Hybrid
-.144**
.024
1.000
Full Hybrid
-.264**
-.043
.608**
1.000
Plug-in Hybrid
-.265**
-.204**
.427**
.663**
1.000
Pure EV
-.235**
-.261**
.243**
.437**
.651**
Pure EV
1.000
**. Significant at the .01 level
Perhaps the most interesting observation to emerge from this analysis is related to Diesel preferences. Whereas Petrol preferences are negatively correlated with all four LEV options, Diesel preferences are only negatively correlated with Plug-in Hybrid and Pure EV preferences. Specifically, there is no statistically significant relationship between Diesel preferences and Mild or Full Hybrid preferences. This finding contrasts with those identified in Chapter 5, Section 5.3.5 and potentially indicates that individuals that currently prefer Diesel powertrains have yet to form preferences for these two LEV options. This may represent an opportunity to positively position hybrid powertrain options with consumers who have high preferences for diesel. Moreover, the introduction of Diesel based Hybrids into the market in 2013 (SMMT, 2013) may further enhance this opportunity. 182
Chapter Six: Results – Socio-Psychological Modelling
More generally, these results provide support to the use of aggregate preference variables (outlined in Chapter 5, Section 5.3.6). With Mild Hybrid and Full Hybrid preferences alongside Plug-in Hybrid and Pure EV preferences displaying strong correlation coefficients, they can be combined together without any substantial reductions in data richness. The forthcoming correlation analysis utilises the aggregate variables of Mean Hybrid Preferences and Mean Plug-in Preferences in the place of the individual powertrain preferences. As this thesis is specifically interested in LEV preference formation, preferences towards Petrol and Diesel powertrain options will no longer feature. 6.2.3 Value Orientation Positioned on the far left of the conceptual framework and illustrated in Figure 6.1, value orientation acts as a basis on which attitudes and, ultimately, behaviours are constructed (Vaske and Donnelly, 1999; Stern et al., 1999). The principles an individual selects to guide their lives are likely to influence a significant proportion of their conduct. In this thesis, three different value structures have been measured based on a scale initially developed by Steg et al. (2005). Firstly, principles connected to biospheric issues such as preventing pollution and protecting the environment have been measured. Additionally, value structures linked to egoistic and altruistic life principles are also included.
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Chapter Six: Results – Socio-Psychological Modelling
Communication Determinants Psychological Determinants Biospheric Egoistic
Symbolic Value Orientation
Altruistic
Emotive Functional Car Importance Car Knowledge Car Environment Car Costs Car Technology
Figure 6.1: Value Orientation in the Conceptual Framework In this section, four separate correlation analyses are conducted based on the links proposed in the conceptual framework. To begin, the relationship between life principles and innate innovativeness is assessed by examining how value structures are linked to the factors extracted from the communication and psychological determinants of innovativeness scale. Following this, the interaction between life principles and the meanings individuals place on car ownership and use is examined. Lastly, the connection between general car attitudes and value orientations is scrutinized to determine if specific attitudes regarding cars are linked to the life principles an individual holds.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.2 Correlations Analysis between Life Principles and Communication Determinants Variable Biospheric Principles
Biospheric Principles
Egotistic Principles
Altruistic Principles
Comm: Info
Comm: Social
1
Egotistic Principles
.000
1
Altruistic Principles
.000
.000
1
-.003
.179**
.031
1
.079
.199**
.222**
.000
Comm: Info Comm: Social
1
**. Significant at the 0.01 level
The output from the correlation analysis between life principles and the communication determinants of innovativeness is presented in Table 6.2 which displays a number of significant relationships. Firstly, Egoistic Principles are positively related to both the factors associated with communication determinants. This result suggests that those individuals that are motivated by values such as ambition, social power and the acquisition of material wealth also tend to have knowledge of innovations, provide this knowledge to their social networks and exist in a highly connected social system. Additionally, Altruistic Principles displays a relatively strong relationship to the social communication determinants, indicating that those individuals that are motivated by values such as equality and social unity tend to be well integrated into a broad and diversified social network.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.3: Correlation Analysis between Life Principles and Psychological Determinants Variable
Bio Principles
Ego Principles
Alt Principles
Psych: Decision and Ambition
Psych: Science and Education
Psych: Aspiration
Bio Principles
1
Ego Principles
.000
1
Alt Principles
.000
.000
1
Psych: Decision and Ambition
.031
**
**
1
Psych: Science and Education
.108*
.009
.004
.000
1
Psych: Aspiration
.162**
.351**
.115**
.000
.000
1
Psych: Compulsive
-.001
.218**
.065
.000
.000
.000
.201
.140
Psych: Compulsive
1
*. Significant at the 0.05 level **. Significant at the 0.01 level
The correlation analysis between life principles and the psychological determinants of innate innovativeness is presented in Table 6.3. In this instance, all three of the value constructs share multiple relationships with psychological determinants. Biospheric Principles are positively related to the psychological determinants linked to attitudes towards science and education and also personal aspiration. This result suggests that individuals that are motivated by principles such as protecting the environment and unity with nature also tend to be aspiring and consider science and education to be important aspects of their lives. Egoistic Principles display the strongest degree of interaction, being positively connected with three out of the four psychological determinants. From this, it can be proposed that those individuals who are motivated by material wealth and social power are also likely to be personally ambitious, have a desire to make correct decisions, aspiring and tend to act compulsively. The final value orientation, connected with Altruistic Principles, shares a positive relationship with the psychological determinant associated with decision making and ambition and also personal aspiration. This finding implies that individuals that structure their lives in accordance with an altruistic values also tend to be ambitious, aspiring and confident decision makers.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.4: Correlation Analysis Between Life Principles and Car Meanings Variable Biospheric Principles
Biospheric Egotistic Altruistic Principles Principles Principles
Meanings: Meanings: Symbolism Function and Emotion
1
Egotistic Principles
.000
1
Altruistic Principles
.000
.000
1
Car Meanings: Symbolism and Emotion
.036
.292**
-.029
1
-.057
.046
.046
.000
Car Meanings: Function
1
**. Significant at the 0.01 level
Shifting the focus to the relationships shared between value orientation and car specific socio-psychological constructs, Table 6.4 presents the results of the correlation analysis between life principles and car meanings. The analysis identified one significant relationship to be present, with individuals that are motivated by Egoistic Principles such as personal ambition and authority tending to associate car ownership and use with symbolic and emotive meanings. This finding implies that individuals motivated by egoistic value structures are also likely to consider their cars as a status symbol, an expression of their identity and as a source of positive emotions. The final correlation analysis in this section examines the relationship between life principles and car attitudes. The results of this analysis are presented in Table 6.5 and, interestingly, all three value structures interact to some degree with the factors associated with car attitudes, suggesting that value orientation is significantly related to the attitudes individuals hold concerning cars. Biospheric Principles are, unsurprisingly, strongly correlated with concerns related to the environmental consequences of car use but also to perceived car importance. This result suggests that those individuals that are motivated by a biospheric value structure tend also to consider it their responsibility to reduce the environmental consequences of car use, are willing to pay more for a car with lower pollution levels and increased fuel efficiency whilst also considering their cars to be an important possession in both functional and personal terms.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.5: Correlation Analysis Between Life Principles and Car Attitudes Variable
Bio Principles
Ego Principles
Alt Principles
Car Knowledge
Bio Principles
1
Ego Principles
.000
1
Alt Principles
.000
.000
1
Car Knowledge
.065
**
-.008
1
Car Importance
.109*
Car Environment Car Cost
.167
Car Importance
Car Environment
.066
.003
.000
1
**
-.066
.106
*
-.002
.033
1
.007
**
.116
*
**
.029
.000
.488
.179
.181
Car Cost
1
*. Significant at the 0.05 level **. Significant at the 0.01 level
Egoistic Principles share relationships with knowledge the factors Car Knowledge and Car Cost, suggesting that those individuals who structure their lives according to egoistic values claim to know how a car operates at a mechanical level, are capable of repairing car malfunctions and have knowledge concerning LEV powertrains. Moreover, these individuals are also likely to be concerned about the financial costs associated with car ownership in terms of both purchase and operating costs. Lastly, Altruistic Principles are significantly linked to concerns regarding the environmental implications of car use and to car costs. From these results, it can be suggested that those individuals who are motivated by altruistic value structures are more likely to consider it their responsibility to reduce the environmental damage caused by car use yet are also concerned about the costs attributed to car ownership. These findings may indicate that, whilst altruistic individuals have a desire to adopt a LEV based on their environmental concerns, they may only do so if the decision was financially justified. However, the coefficients associated with these relationships are weak suggesting that, whilst still significant, concerns associated with the environmental implications of car use and car costs are only moderately important for those individuals which hold altruistic value structures.
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Chapter Six: Results – Socio-Psychological Modelling 6.2.4 Innovativeness The conceptual framework measures the construct of innovativeness from a number of directions. Specifically adoptive innovativeness is measured by noting the quantity of technology owned and desired whilst innate innovativeness is measured from both a psychological and communicative perspective (discussed in Chapter 3, Section 3.3.1). If this thesis has been successful at measuring the same concept (innovativeness) across these three different instruments, it is expected that significant relationships will be present between them. Figure 6.2 illustrates the conceptual links proposed in relation to innovativeness.
Communication Determinants Psychological Determinants
Innate Innovativeness
Adoptive Innovativeness
Figure 6.2: Innovativeness in the Conceptual Framework To begin the analysis, the relationships between adoptive innovativeness and the factors extracted from the communication determinants of innovativeness scale are examined. A number of significant relationships have been identified between these variables and are displayed in Table 6.6. It is observed that the factors connected to information seeking and provision behaviour exhibit a number of strong correlations, being positively related to the total quantity of technology owned and desired whilst being negatively related to the quantity not owned. The factor linked to social activity, including aspects such as if an individual has a wide social network, is positively correlated with the quantity of technology owned whilst being negatively correlated with the quantity not owned, though the coefficients are relatively smaller. From these findings, it can be proposed that individuals who actively seek information and act as a source of information to their social networks concerning innovations tend to have higher levels of adoptive innovativeness. General social activity may have a supporting role by facilitating this information seeking and provision behaviour.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.6: Correlation Analysis Between Adoptive Innovativeness and Communication Determinants Variable
Comm: Info
Comm: Social
Total Owned
Comm: Info
1.000
Comm: Social
.000
1.000
Total Owned
.366**
.139**
1.000
Total Desired
.254**
.085
.121**
Total Not Owned
-.427
**
-.144
**
-.789
**
Total Desired
Total Not Desired
1.000 -.645**
1.000
**. Significant at the 0.01 level
Next, the relationships between adoptive innovativeness and the factors associated with the psychological determinants of innovativeness are investigated. A number of significant relationships are observed in the analysis which is displayed in Table 6.7. The factors linked with attitudes towards science and education alongside compulsive behaviour are positively correlated with the quantity of technology owned, the quantity of technology desired and negatively correlated with the quantity of technology not owned. Specifically, Psych: Compulsive displays a number of strong correlations, suggesting it is particularly well associated with adoptive innovativeness. Furthermore, the factor associated with ambition and decision making is positively correlated with quantity of technology owned whilst negatively correlated with quantity of technology not owned. The only factor extracted from this scale not to display any form of significant correlation to adoptive behaviour relates to individual aspiration. This result suggests that individual aspiration may not be an effective indicator of adoptive innovativeness. In general, these findings provide support to the proposition that innate innovativeness from a psychological perspective can be measured and is linked with adoptive innovativeness.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.7: Correlation Analysis Between Adoptive Innovativeness and Psychological Determinants Psych: Decision Variable Making and Ambition Psych: Decision Making 1.000 and Ambition
Psych: Science and Education
Psych: Aspiration
Psych: Compulsive
Psych: Science and Education
.012
1.000
Psych: Aspiration
.011
.038
1.000
Psych: Compulsive
.009
-.006
.016
1.000
**
.073
.332
**
.033
.232
Total Owned
**
.171
.134
Total Desired
-.036
.201
Total Not Owned
*
-.102
**
-.216
-.064
Total Owne d
**
1.000
**
.121
**
-.389
** **
-.789
Total Desired
Total Not Owned
1.000 **
-.645
1.000
**. Significant at the 0.01 level *. Significant at the 0.05 level
6.2.5 EV Attitudes Considerations associated with the functional capabilities of EVs have been found to represent a significant barrier to EV uptake. Range anxieties (Bunch et al., 1993), aversion to price premiums (Beggs and Cardell, 1980) and high discount rates (Beggs et al., 1981) have been identified in past research to be issues limiting adoption. One aspect which has yet to be examined relates to the factors which are linked to appraisals of EV functional capability. This thesis provides insights on this aspect by examining if attitudes associated to the instrumental abilities of EVs are related to car meanings, emotive connection and general attitudes towards cars. The conceptual links proposed in regards in EV Attitudes are illustrated in Figure 6.3.
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Chapter Six: Results – Socio-Psychological Modelling
Symbolic Emotive
Car Meanings
Functional EV Attitudes
Car Importance Car Knowledge Car Environment
Car Attitudes
Car Costs Car Technology
Figure 6.3: EV Attitudes in the Conceptual Framework To begin, a correlation analysis is conducted which determines if attitudes regarding the instrumental characteristics of EVs are associated with the symbolic, emotive and functional meanings individuals place on car ownership and use. The results of this analysis are presented in Table 6.8 with two significant relationships having been identified. Specifically, negative attitudes towards the instrumental capabilities of EV are positively linked with symbolic, emotive and function car meanings. This finding suggests that those individuals who consider their cars as an expression of their identity, a provider of enjoyment and as a sensible financial decision tend also to hold negative attitudes towards the functional performance of EVs such as concerns related to safety, reliability and complexity. Examining the relative magnitude of these relationships, it appears that those individuals who consider their cars in symbolic and emotive terms display the greater degree of aversion to EVs.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.8: Correlations Analysis Between EV Attitudes and Car Meanings Variable Meaning: Symbolism and Emotion Meaning: Function Negative EV Attitudes Positive EV Attitudes
Meaning: Symbolism and Emotion
Meaning: Function
Negative EV Attitudes
Positive EV Attitudes
1 .000
1
.235**
.161**
1
.009
-.090
.000
1
**. Significant at the 0.01 level
Further inspecting the relationship between emotions and EV attitudes, Table 6.9 presents results of a correlation analysis which includes emotive connection to cars in general, EVs in particular and EV attitudes. Observing firstly how car and EV emotions interact with one another, a noteworthy counterintuitive relationship is identified. Positive Car Emotions shares significant positive relationships with both Positive EV Emotions and Negative EV Emotions. This finding indicates that individuals who have a positive emotional connection to cars in general also hold positive and negative emotional connection to EVs in particular. This somewhat mixed result potentially indicates that individuals may not necessarily transfer the positive emotive connection they hold to cars in general directly to EVs. The interaction between Negative Car Emotions and Negative EV Emotions is clearer with a positive relationship being present suggesting that individuals who tend to associate cars with negative emotions are likely to transfer these emotions over to EVs.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.9: Correlation Analysis Between Car/EV Emotions and EV Attitudes Variable Positive Car Emotions Negative Car Emotions
Positive Car Emotions
Negative Car Emotions
Positive EV Emotions
Negative EV Emotions
Negative EV Attitudes
Positive EV Attitudes
1 .000
1
Positive EV Emotions
.377
**
.101
1
Negative EV Emotions
.241**
.268**
.000
1
Negative EV Attitudes
.211**
.063
-.089
.359**
1
Positive EV Attitudes
-.017
.095
.386**
-.012
.000
1
**. Significant at the 0.01 level *. Significant at the 0.05 level
Examining how car and EV emotions interact with EV attitudes, three significant relationships are observed. Firstly, Positive Car Emotions is positively related to Negative EV Attitudes, indicating that individuals who associate cars in general with pride, happiness, excitement, pleasure and affection tend also to hold negative attitudes towards the functional capabilities of EVs. Moreover, the emotions individuals associate with EVs in particular appear to be strongly connected to their EV attitude counterparts, with Positive EV Emotions positively associated with Positive EV Attitudes and Negative EV Emotions positively related to Negative EV Attitudes. These findings indicate that emotive connection to EVs is related to how individuals are evaluating the functional characteristics of EVs, and that associating EVs with positive emotions may lead to improvements in attitudes associated with EV instrumental capability.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.10: Correlation Analysis Between Car Attitudes and EV Attitudes Car Knowledge
Car Importance
Car Environment
Car Cost
Negative Positive EV EV Attitudes Attitudes
Car Knowledge
1
Car Importance
.000
1
Car Environment
-.002
.033
1
.181**
.029
.000
1
**
**
**
**
1
.302** .207**
.000
Car Cost Negative EV Attitudes Positive EV Attitudes
.317
.087
-.201
-.122*
-.159
.137
1
**. Significant at the 0.01 level *. Significant at the 0.05 level
To conclude, a correlation analysis is conducted between the socio-psychological factors extracted from the EV Attitudes and the Car Attitudes scales. The results of this analysis are displayed in Table 6.10 with seven noteworthy relationships having been identified. Firstly, Car Knowledge displays a strong positive correlation with Negative EV Attitudes, suggesting that those individuals that have knowledge about the mechanical operation of cars in general and EV powertrains in particular tend to consider EVs to be functionally inferior. Moreover, Car Environment exhibits a strong positive relationship with Positive EV Attitudes whilst holding a negative correlation with Negative EV Attitudes. These findings indicate that individuals who are concerned about the environmental consequences of car use and are willing to act on these concerns are more likely to hold favourable attitudes relating to the functional capabilities of EVs. Somewhat surprisingly, Car Importance is negatively correlated with both Negative and Positive EV Attitudes. A similar result is observed for Car Cost which is positively related to both Negative and Positive EV Attitudes. These counterintuitive results may perhaps be explained by the internal composition of these factors. To illustrate this point, consider Car Cost, which contains statements associated with concerns for both the running and operating costs of cars. It is entirely feasible that an individual would hold concerns for both the upfront and lifetime costs of a car which may lead to them to hold favourable attitudes towards the reduced running costs associated with EVs yet have unfavourable attitudes linked to price premiums. 195
Chapter Six: Results – Socio-Psychological Modelling 6.2.6 LEV Preferences The final aspect of the conceptual framework examined with correlation analysis concerns preferences towards LEVs. The structure of the framework hypothesizes that LEV Preferences are directly linked to Adoptive Innovativeness and EV Attitudes. These proposed relationships are illustrated in Figure 6.4 and have been assessed with the results discussed in this section.
Adoptive Innovativeness
LEV Preferences
EV Attitudes
Figure 6.4: LEV Preferences in the Conceptual Framework The correlation analysis conducted between the measurements of adoptive innovativeness and preferences towards LEVs is presented in Table 6.11. Examining the results, four significant relationships are observed and are in keeping with expectations. Firstly, the variable Total Desired, which reflects the quantity of household technology desired to be owned in the near future, is positively related to Mean Hybrid and Plug-in Preferences. Moreover, the variable Total Not Owned, which measures the quantity of household technology neither currently owned nor desired, is negatively related to both LEV preference variables. Somewhat surprisingly, Total Owned, which measures the quantity of household technology currently owned, is not significantly related to either LEV preference variables. These results indicate that desire to own technology, which can be conceived as a measure of aspirational innovativeness, is a valid indicator of preferences towards LEVs. Additionally, those individuals that tend neither to own nor desire to own household technology are more likely to have lower preferences for LEVs.
196
Chapter Six: Results – Socio-Psychological Modelling Table 6.11: Correlation Analysis Between Adoptive Innovativeness and LEV Preferences Variable
Mean Plugin Preferences
Mean Hybrid Preferences
Total Total Owned Desired
Mean Hybrid Preferences
1
Mean Plug-in Preferences
.432**
1
Total Owned
.091
.056
1
Total Desired
.163**
.160**
.053
1
**
**
**
**
Total Not Owned
-.168
-.142
-.777
-.656
Total Not Owned
1
**. Significant at the 0.01 level
Table 6.12 presents the results of the correlation analysis examining the relationships which exist between LEV Preferences and EV Attitudes. Scrutinizing the output of the analysis, three significant relationships have been identified. Firstly, Mean Hybrid Preferences are negatively related to Negative EV Attitudes, suggesting that those individuals that are concerned about the reliability, safety and complexity of EVs are less likely to hold positive preferences towards Hybrid vehicles in particular. Additionally, Mean Plug-in Preferences are negatively related to Negative EV Attitudes whilst being positively related to Positive EV Attitudes. These results imply that those individuals that have positive attitudes towards the functional capabilities of EVs, such as decentralised fuelling and reduced operating costs, tend to hold positive preferences towards Plug-in LEVs. Table 6.12: Correlation Analysis Between LEV Preferences and EV Attitudes Variable
Mean Hybrid Preferences
Mean Plugin Preferences
Negative EV Attitudes
Mean Hybrid Preferences
1
Mean Plug-in Preferences
.432**
1
**
**
1
.224**
.000
Negative EV Attitudes Positive EV Attitudes
-.136
.080
-.177
Positive EV Attitudes
1
**. Significant at the 0.01 level
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Chapter Six: Results – Socio-Psychological Modelling 6.2.7 Overview of Correlation Analysis To summarize, this section utilised correlation analysis to examine how the constructs measured by the conceptual framework relate to one another. To begin, the measurements of powertrain preferences were compared to observe which specific options included in the evaluation exercise are linked. The results of this analysis indicate that preferences for the four LEV powertrains are positively correlated, suggesting those individuals that exhibit high preferences for one LEV option are likely to hold positive preferences for the remaining three. Moreover, the preferences between the plug-in and non plug-in LEV options are highly correlated, indicating that the aggregation of preferences based on these two categories is valid. Focusing on the interaction of Diesel preferences with the LEV options, it is observed that significant negative relationships exist with Plug-in Hybrids and Pure EVs but not Mild and Full Hybrids. These findings indicate that those individuals that display high preferences for Diesels may yet to form negative preferences for the non plug-in LEV options, and that individuals considering a Diesel car may still prove to be receptive to LEV technology. Following this, the analysis progressed by examining how the measurements of value orientation interact with the socio-psychological constructs of innate innovativeness, car meanings and car attitudes. The findings of this stage of the analysis are summarised in Table 6.13 and suggest that individuals who are motivated by egoistic value structures tend to display high levels of innate innovativeness, consider their cars to hold symbolic and emotive meanings whilst also being knowledgeable regarding car operation and concerned about car costs. Individuals that base their behaviour on altruistic principles are likely to be moderately innovative, though possess a well integrated social network, are somewhat concerned about the environmental consequences of car use and also the costs attributed to car ownership. Conversely, those individuals that are motivated by a biospheric value structure are the least likely to be innately innovative, though do display tendencies for personal aspiration and positive attitudes towards science and education, consider their cars an important possession whilst, unsurprisingly, display significant concerns related to the environmental implications associated with cars.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.13: Summary of Value Orientation Significant Correlations Construct of Interest
Statistically Significant Correlations
Biospheric Principles
Psych: Science and Education [+] Psych: Aspiration [+] Car Importance [+] Car Environment [+]
Egoistic Principles
Comm: Information Seeking and Provision [+] Comm: Social [+] Psych: Decision Making and Ambition [+] Psych: Aspiration [+] Psychl: Compulsive [+] Meaning: Symbolism and Emotion [+] Car Knowledge [+] Car Cost [+]
Altruistic Principles
Comm: Social [+] Psych: Decision Making and Ambition [+] Psych: Aspiration [+] Car Environment [+] Car Cost [+]
The next stage of the analysis examined the measurements of innate and adoptive innovativeness to determine how these constructs are connected. The main findings of this stage are summarised in Table 6.14 with the factors associated with decision making and ambition, attitudes towards science and education as well as compulsive purchase behaviour all sharing significant relationships with the measurements of adoptive innovativeness. Moreover, both factors extracted from the communication determinants scale also hold significant correlations with adoptive innovativeness. These findings assist in validating the measurements of innovativeness as it is applied in this thesis, with expected relationships being present between different measurements.
199
Chapter Six: Results – Socio-Psychological Modelling Table 6.14: Summary of Innovativeness Significant Correlations Construct of Interest
Statistically Significant Correlations
Total Owned
Psych: Decision Making and Ambition [+] Psych: Science and Education [+] Psych: Compulsive [+] Comm: Information Seeking and Provision [+] Comm: Social [+]
Total Desired
Psych: Science and Education [+] Psych: Compulsive [+] Comm: Information Seeking and Provision [-]
Total Not Owned
Psych: Decision Making and Ambition [-] Psych: Science and Education [-] Psych: Compulsive [-] Comm: Information Seeking and Provision [-] Comm: Social [-]
Previous research in this field has identified the importance of EV functional capabilities in influencing preferences towards these vehicles, yet relatively little work has looked into how the appraisals of the instrumental capabilities of EVs interact with other sociopsychological constructs. This thesis investigates this area by observing how attitudes towards EV functional performance interact with the meanings individuals place on car ownership, emotive connection to vehicles and more general attitudes towards cars. The results of this stage of the analysis are summarised in Table 6.15 and indicate that individuals that tend to hold negative EV attitudes are also likely to consider their cars to embody symbolic, emotive and functional meanings such as representing their personality, acting as a source of positive emotions and assisting them in the operation of their everyday lives. Relating to the factors associated with general car attitudes, knowledge concerning cars in general and LEVs in particular is positively correlated with Negative EV Attitudes, implying the more an individual know about cars, the more likely they are to hold negative attitudes about EV functional capability. Concerns relating to the environmental consequences of car use are significantly linked to both EV attitude factors, suggesting that those individuals that feel the environmental implications of car use are their responsibility and are willing to act
200
Chapter Six: Results – Socio-Psychological Modelling on these considerations are more likely to hold positive attitudes towards EVs and less likely to hold negative attitudes. Table 6.15: Summary of EV Attitudes Significant Correlations Construct of Interest
Statistically Significant Correlations
Negative EV Attitudes
Meaning: Symbolism and Emotion [+] Meaning: Function [+] Positive Car Emotions [+] Negative EV Emotions [+] Car Knowledge [+] Car Importance [-] Car Environment [-] Car Cost [+]
Positive EV Attitudes
Positive EV Emotions [+] Car Importance [-] Car Environment [+] Car Cost [+]
The final part of this section is related to the aggregated measurements of LEV preferences and determines if they are significantly related to the adoption of innovations and to EV attitudes with the results summarised in Table 6.16. In reference to adoptive innovativeness, the quantity of household technology desired as well as the quantity not owned share significant relationships with preferences for both Hybrid and Plug-in LEVs. Examining how attitudes towards the functional capabilities of EVs interact with preferences towards these vehicles, the results follow expectations with Positive EV Attitudes positively related to LEV preferences whilst negative attitudes are negatively related. More generally, these results indicate that the more adoptively innovative an individual is and the degree to which they hold positive opinions of the functional capabilities of EVs, the more likely they are to hold positive preferences towards LEVs.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.16: Summary of LEV Preferences Significant Correlations Construct of Interest
Statistically Significant Correlations
Mean Hybrid Preferences
Total Desired [+] Total Not Owned [-] Negative EV Attitudes [-]
Mean Plug-in Preferences
Total Desired [+] Total Not Owned [-] Negative EV Attitudes [-] Positive EV Attitudes [+]
To sum, the results of this section generally support the structure of the conceptual framework, with constructs linked to framework components tending to be related to one another in an expected fashion. However, a number of expected correlations between variables have not been observed and, in other areas, somewhat counterintuitive relationships have surfaced. The next section of this chapter progresses the statistical analysis through the application of multiple regression to determine if the relationships identified in this section can be used in an explanatory capacity. 6.3 REGRESSION ANALYSIS In the correlation analysis, the variables developed in Chapter 5 were examined to identify statistically significant relationships between constructs of the conceptual framework. The results attained have assisted in shaping the explanatory models detailed in this section by proving initial guidance on where significant relationships exist between framework constructs. This section of the chapter specifies three groups of explanatory models through the application of regression analysis to further examine the structural validity of the conceptual framework by producing evidence to answer research questions 1.1 and 1.2. To begin, a regression analysis is conducted to determine if the measurements of innate innovativeness hold significant influence over adoption of household technology. Following this, models are developed to explain attitudes towards the instrumental capabilities of EVs through the symbolic, emotive and functional meanings placed on car ownership and general car attitudes. To conclude, a model of LEV preferences is constructed which uses the measurements of adoptive innovativeness and EV attitudes as explanatory variables.
202
Chapter Six: Results – Socio-Psychological Modelling Before this analysis is conducted, an overview of multiple regression is offered which discusses the primary features of the technique and how it is applied in this thesis. 6.3.1 Introduction to Method Initially proposed by (Galton, 1885), regression analysis is a statistical procedure frequently employed in empirical research due to its ability to test theoretical proposals. Whereas correlation analysis provides information on the relationships that exist between two variables, regression analysis allows the researcher to develop models which can be used to determine how the values of a number of independent variables influence the value of a dependent variable. The nature of the analysis allows researchers to make inferences relating to causation (Freedman, 1997) though this must be supported through theoretical logic. Regression analysis can be employed in two distinct capacities, firstly in an explanatory manner whereby a group of independent variables is used to explain the variation of a single dependent variable or secondly in a predictive manner whereby the specific values of a group of independent variables assist in predicting the value of a single dependent variable. Often employed in psychometric research, regression analysis has been used to apply the Theory of Planned Behaviour (Hrubes et al., 2001), Value Belief Norm Theory (Steg et al., 2005) and the Technology Acceptance Model (Szajna, 1996). Regression analysis can be applied using a number of different procedures which are suited to different data structures. Ordinary least squared (OLS) is a type of regression which is best suited when the dependent variable is measured in a continuous manner and is normally distributed. If these assumptions are supported, then OLS predicts optimum estimators. In incidence where these assumptions are not supported, such as when the dependent variable is ordinal or categorical in nature, then generalised linear modelling or logistic regression can be used. In its simplest form, OLS can be illustrated graphically by a model where only two variables are present. One of these variables is referred to as the dependent variable and represents the outcome the research is interested in explaining or predicting. The other variable is referred to as the independent variable and the model examines if this variable can be used to predict specific values of the dependent variable. Figure 6.5 provides a graphical 203
Chapter Six: Results – Socio-Psychological Modelling illustration of an OLS model with only two variables. This hypothetical model examines how vehicle miles travelled is affected by the price of fuel with the data points observed depicted as grey circles. The OLS procedure fits a straight line through the data points (referred to as Model line in Figure 6.5) which minimises the difference between what the model would predict the number of vehicles miles travelled would be for a given price of fuel and that which is actually observed. The difference between predicted values and observed values is commonly referred to as the model residual. Model line Price of fuel
Mean line Mean residual
Model residual
Figure 6.5: Illustration of a Two Variable OLS Regression Model
Vehicle miles travelled
In correlation analysis, the correlation coefficient is calculated to provide information regarding the strength of the relationship between two variables. A similar concept exists in regression analysis whereby the coefficient of determination, commonly referred to as the rsquared (R2), is calculated to express the total variance explained by the specified model (Studenmund 2010). Returning once again to Figure 6.5, the broken horizontal line depicted represents the mean score of the observed values of the dependent variable. The R-squared statistic uses the residuals of the model and the residuals of this mean score to determine if the model fitted is superior in predicting values of the dependent variable compared to simply using its mean value.
204
Chapter Six: Results – Socio-Psychological Modelling Within the OLS regression method, a number of different procedures can be followed which determine how data is entered into the model (Field, 2009). Firstly, there is the forced entry method whereby all of the possible independent variables are entered into the model simultaneously. Secondly, a hierarchical entry procedure can be followed where blocks of variables are entered in stages to examine how the model changes when different blocks of independent variables are utilised. Finally, variables can be entered in a stepwise manner whereby a mathematical procedure determines which variables are entered in which order by examining each independent variable’s semi-partial correlation with the dependent variable. In this thesis, a hierarchical entry method has been employed whereby conceptual framework components are included in the first block followed by socio-economic characteristics. This method has been selected to firstly observe the relative power of these two variable sets and to determine if the moderate divergence between the sample and the population (discussed in Chapter 5, Section 5.2) is an area of concern. 6.3.2 Innovativeness The concept of innovativeness holds a primary position in the conceptual framework developed for this thesis, acting as a theoretical link to the Diffusion of Innovation theory (Rogers, 1995) and as a potential influencing factor over LEV preferences. To determine if innovativeness was successfully measured, two regression models are specified which utilise the six factors extracted from the two innate innovativeness scales in an explanatory capacity to observe their influence over household technology ownership which is assumed to represent adoptive innovativeness. This model specification conforms to the definition of innovativeness proposed by Midgley and Dowling (1978), which positions innate innovativeness at a higher level of abstraction and, therefore, as a deterministic feature of adoptive innovativeness.
205
Chapter Six: Results – Socio-Psychological Modelling Table 6.17: Summary of Dependent Variables Included in the Innovativeness Regression Models Variable Label
Variable Description
Total Owned
The total quantity of technology owned
Total Not Owned
The total quantity of technology neither owned nor intended to be owned in the near future
Table 6.17 provides an overview of the dependent variables utilised in this stage of the analysis. As initially described in Chapter 3, Section 3.3.1, this thesis measures adoptive innovativeness by examining the quantity of household technology currently owned, intended to be owned in the near future and neither owned nor intended to be owned. Two of these measurements of adoptive innovativeness are utilised as dependent variables to determine if the measurements of innate innovativeness can be used to explain the adoption of innovations. The variable linked with the quantity of household technology intended to be owned is omitted from the analysis as it is highly skewed and therefore does not meet the assumptions of linear regression analysis. The independent variables included in the regression models specified in this section are summarized in Table 6.18. The independent variables are entered in two different stages. To begin, the six constructs extracted from the two innate innovativeness scales associated with communication and psychological determinants are entered into the model. Following this, the variables of age, gender, level of education and household income are entered to observe the influence of socio-economic variables over adoptive innovativeness.
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Chapter Six: Results – Socio-Psychological Modelling Table 6.18: Summary of Independent Variables Included in the Innovativeness Regression Models Variable Label Stage One Comm: Information Seeking and Provision Comm: Social Activity Psych: Decision Making and Ambition Psych: Science and Education Psych: Aspiration Psych: Compulsive Stage Two
Variable Description Actively seeking information concerning innovations and acting as a source of this information to social networks Social activity such as cosmopolitanism, a broad social network and engagement in formal social activities Positive views relating to decision making ability linked with an ambitious nature Positive views relating to the importance of science and education Degree of aspiration such as a desire to better oneself and social progression Tendency to act compulsively without the application of reasoned thought
Age
Current age
Gender
Dummy variable to distinguish males and females (female = 1) Dummy variable to represent the attainment of a university or professional level qualification (attainment = 1) Gross household income
Education Income
The output from the regression models is displayed in Table 6.19. In both regression models,
four innate innovativeness factors hold significant explanatory power. The factors measuring knowledge and information relating to innovations, general social activity and compulsive behaviour positively influence the quantity of household technology owned whilst negatively influencing the quantity not owned. Of these variables, Comm: Information Seeking and Provision (β of .635 and -.935) and Psych: Compulsive (β of .674 and -.1.063) hold the strongest influence whereas Comm: Social Activity is of secondary importance. In the total quantity owned model, the factor associated with decision making and ambition holds a positive influence whilst in the total quantity not owned model the factor associated with attitudes towards science and education holds a negative influence. All of these results are in keeping with expectations, with the innate innovativeness constructs positively influencing the adoption of household technology. Additionally, these innate innovativeness measurements explain a significant proportion of the variance in
207
Chapter Six: Results – Socio-Psychological Modelling adoptive innovativeness, with 21.7% of the variance in technology owned and 28.5% of the variance in technology neither owned nor desired accounted for. Table 6.19: Regression Analysis Explaining Adoptive Innovativeness Through the Constructs Extracted from the Innate Innovativeness Scales Total Not Total Owned Owned Independent Variable β
Stage One
(Constant) Comm: Information Seeking and Provision Comm: Social Activity Psych: Decision Making and Ambition Psych: Science and Education Psych: Aspiration Psych: Compulsive
(Constant) Comm: Information Seeking and Provision Comm: Social Activity Psych: Decision Making and Ambition Psych: Science and Education Psych: Aspiration Psych: Compulsive Gender Age Education Income R2 **. Significant at the 0.01 level *. Significant at the 0.05 level
Sig.
β
Std. Error
Sig.
4.311 .635**
.110 .133
.000 .000
10.397 -.935**
.140 .170
.000 .000
.258* .271* .134 .058 .674**
.115 .114 .118 .116 .123
.025 .018 .256 .615 .000
-.409** -.133 -.473** -.026 1.063**
.146 .145 .151 .147 .157
.005 .359 .002 .860 .000
R2
Stage Two
Std. Error
.217 2.839 .710**
.731 .127
.000 .000
.237* .101 -.036 .013 .615** -.069 -.005 -.330 .695**
.108 .109 .119 .110 .123 .220 .008 .241 .093 .319
.028 .356 .762 .909 .000 .753 .527 .170 .000
10.344 1.017** -.402** .042 -.172 .089 -.856** .171 .032** -.009 -.664**
.285 .936 .163
.000 .000
.138 .140 .152 .141 .157 .281 .010 .308 .119 .374
.004 .762 .259 .526 .000 .544 .002 .977 .000
The inclusion of socio-economic variables in stage two offers some additional insights. Firstly, the R2 changes between the different stages of the models are significant, meaning that socio-economic variables have the ability to explain variance not accounted for by the innate innovativeness factors. Household income level holds the largest significant affect (β of .695 and -.664) over technology adoption, positively influencing ownership rates and negatively influencing non-adoption. Additionally, age positively influence the quantity of 208
Chapter Six: Results – Socio-Psychological Modelling household technology neither owned nor desired, indicating that older individuals are less likely to be adoptively innovative. 6.3.3 EV Attitudes Previous studies in this field have highlighted the importance of functional considerations which are significantly influencing EV preferences (Beggs et al., 1981; Calfee, 1985; Ewing and Sariggollu, 1996). This section of the regression analysis examines if attitudes towards cars in general, the meanings individuals place on car ownership and emotive connection to EVs can be used to explain attitudes connected to the instrumental capabilities of EVs. To achieve this objective, two regression analyses are conducted utilising the factors extracted from the EV Attitudes scale as dependent variables. These dependent variables are summarized in Table 6.20 with one being orientated around positive attitudes towards EV functional performance whilst the other is negatively positioned. Table 6.20: Summary of Dependent Variables Included in the EV Attitudes Regression Models Variable Label
Variable Description
Negative EV Attitudes
Concerns related to EV functional capabilities such as safety, reliability and complexity
Positive EV Attitudes
Positive attitudes connect to EV performance including aspects of decentralised fuelling, lower operating costs and home recharging
The independent variables used to explain the variance in attitudes connected to the instrumental performance of EVs are the factors extracted from the Car Meanings, Car Knowledge and Importance, Car Attitudes and EV Emotion scales. Eight independent variables are utilised in total and are concisely described in Table 6.21. A two stage entry procedure is followed with the socio-psychological constructs entered in stage one with the socio-economic variables entered in stage two.
209
Chapter Six: Results – Socio-Psychological Modelling Table 6.21: Summary of Independent Variables Included in the EV Attitudes Regression Models Variable Label Stage One Car Knowledge Car Importance Car Environment Car Cost Meaning: Symbolism and Emotion Meaning: Function Positive EV Emotions Negative EV Emotions Stage Two
Variable Description
Measures knowledge relating to the mechanical operation of cars, ability to fix car malfunctions and knowledge of LEVs Focuses on the importance placed on car ownership from both a life enabling and personal perspective Concerns relating to the environmental consequences of car use, felt responsibility and willingness to act Measures concerns related to the purchase and operating costs associated with car ownership Relates to the symbolic and emotive meanings placed on car ownership such as representation of status and viewing cars as sources of positive emotions Connects with functional car meanings for instance viewing cars as an efficient tool and a good financial investment Measures the positive emotions connected to EVs such as happiness and excitement Measures the negative emotions connected to EVs such as embarrassment and anxiety
Age
Current age
Gender
Dummy variable to distinguish males and females (female = 1)
Education
Dummy variable to represent the attainment of a university or professional level qualification (attainment = 1)
Income
Gross household income
The results from the regression analyses are presented in Table 6.22 with a number of variables displaying significant explanatory power over EV attitudes. Focusing first on the model where Negative EV Attitudes are employed as the dependent variable, the variable Car Knowledge, which measures aspects such as if an individual knows how cars work on a mechanical level, if they would feel confident conducting car repairs and if they know about LEV powertrains, holds a positive influence (β of .170). This finding implies that knowledge relating to cars in general and LEVs in particular is negatively influencing attitudes towards the functional capabilities of EVs. Additionally, the variables Meaning: Symbolism and Emotion (β of .128) and Meaning: Function (β of .118) both hold significant explanatory power and display positive beta coefficients. Thus, the degree to which cars are considered 210
Chapter Six: Results – Socio-Psychological Modelling in symbolic, emotive and functional terms is influencing the level of negative attitudes held regarding EVs. The importance of emotive considerations is further supported by the significant effects held by the variables Positive EV Emotions (β of -.115) and, to a stronger degree, Negative EV Emotions (β of .287) which suggest that emotive connection to EVs is likely to influence attitudes towards their instrumental performance. Moreover, the variable Car Importance (β of -.238) holds a strong negative influence over negative attitudes towards EVs, which suggests that the degree to which an individual perceives car ownership as an essential aspect of life and also in anthropomorphic terms is affecting evaluations relating to the functional performance of EVs. The results observed in the first regression model are mostly supported by affects observed in the second model which uses Positive EV Attitudes as the dependent variable. The variables measuring symbolic, emotive and functional car meanings as well as the assignment of positive emotions to EVs continue to hold significant explanatory power. Additionally, the variables Car Cost (β of .174) and Car Environment (β of .145) become significant with positive beta coefficients indicating that attitudes towards the environmental implications of car use as well as concerns for the financial costs attributed to car ownership positively influence attitudes towards the functional capabilities of EVs. Regarding the variable Car Importance, this holds significant negative explanatory power in both models, indicating that attitudes associated with the essential nature of car ownership and a tendency to personify cars negatively affects both positive and negative attitudes towards EVs. This counterintuitive result is perhaps linked to the internal structure of Car Importance which contains two different yet related aspects which may interact with EV attitudes in different ways. Specifically, it is possible that items measuring the essential nature of items contained within Car Importance may be motivating one of these findings whereas the items measuring personal connection to cars is motivating the other.
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Table 6.22: Regression Analysis Explaining EV Attitudes using Constructs Extracted from the Car Meanings and Car Attitudes Scales Negative EV Attitudes Positive EV Attitudes Independent Variable Std. Std. β Sig. β Sig. Error Error Stage One (Constant) Car Knowledge Car Importance Car Environment Car Cost Meaning: Symbolism and Emotion Meaning: Function Positive EV Emotions Negative EV Emotions R2
-.035 .170** -.238** -.049 .036 .128* .118* -.115* .287**
.045 .052 .046 .050 .046 .052 .055 .050 .049 .282
.445 .001 .000 .331 .440 .015 .031 .021 .000
.031 .080 -.137** .145** .174** -.134* -.122* .315** -.049
.045 .052 .046 .050 .046 .052 .055 .050 .049 .248
.489 .128 .003 .004 .000 .011 .026 .000 .323
(Constant) Car Knowledge Car Importance Car Environment Car Cost Meaning: Symbolism and Emotion Meaning: Function Positive EV Emotions Negative EV Emotions Gender Age Education Income R2
-.889** .155** -.201** -.060 .062 .143** .108 -.117* .274** .208 .007* -.124 .074
.314 .053 .053 .051 .048 .052 .055 .050 .050 .112 .003 .101 .040 .301
.005 .004 .000 .234 .192 .007 .053 .020 .000 .065 .027 .219 .064
-.387 .020 -.074 .155** .163** -.118* -.116* .286** -.044 .371** .005 -.045 -.104**
.308 .052 .052 .050 .047 .052 .054 .049 .049 .110 .003 .099 .039 .296
.210 .708 .158 .002 .001 .022 .034 .000 .374 .001 .169 .651 .008
Stage Two
**. Significant at the 0.01 level *. Significant at the 0.05 level
Variables measuring socio-economic characteristics are entered in stage two of the models to determine what effect they may have over attitudes towards EVs. A number of these variables hold significant explanatory power indicating that demographics are potential motivators in this area. Specifically, individual age holds a significant positive influence over Negative EV Attitudes implying that older individuals are more likely to consider EVs to be functionally inferior to conventional cars. Additionally, the dummy variable for gender holds a significant positive affect in the Positive EV Attitudes model indicating that females have a greater propensity to hold positive opinions of the instrumental capabilities of EVs. Interestingly, the variable measuring gross household income holds a negative influence 212
Chapter Six: Results – Socio-Psychological Modelling over Positive EV Attitudes, suggesting that higher incomes are motivating aversion to EVs, perhaps implying that EVs are not considered prestigious. 6.3.4 LEV Preferences The concluding set of regression models included in this section relate to the final aspect of conceptual framework which concerns preferences towards LEVs. The structure of the framework proposes that LEV preferences are influenced by levels of adoptive innovativeness and attitudes towards EV functional capabilities. To test the appropriateness of this structure, two regression models are constructed. The dependent variables utilised in this section are the aggregated preference measures described in Chapter 5, Section 5.3.8 which have been summarised here in Table 6.23. Table 6.23: Summary of Dependent Variables Included in the LEV Preference Regression Models Variable Label
Variable Description
Mean Hybrid Preferences
Aggregate variable reflecting the average preferences stated towards Mild and Full Hybrids in the powertrain evaluation exercise
Mean Plug-in Preferences
Aggregate variable reflecting the average preferences stated towards Plug-in Hybrid and Pure EVs in the powertrain evaluation exercise
The independent variables employed in this section are taken from three distinct sources. Firstly, the measurement of household technology not owned is taken as the appropriate measurement of adoptive innovativeness. This selection is based on the results observed in the correlation analysis of Section 6.2.6, which illustrated this variables’ association with LEV preferences, and the regression analysis of Section 6.3.2 which indicated this variable’s causal relationship with the measurements of innate innovativeness. Secondly, the constructs extracted from the EV Attitudes scale are selected as appropriate measurements of opinions related to the instrumental performance of EVs. Thirdly, socio-economic variables are entered in stage two of the models to observe their influence over LEV preferences. Included in this is a dummy variable to distinguish respondents from the Newcastle and Dundee subsamples to determine if study site location has an influence over 213
Chapter Six: Results – Socio-Psychological Modelling LEV preference. All of the independent variables employed in this section of the analysis are summarized in Table 6.24. Table 6.24: Summary of Independent Variables included in the LEV Preferences Regression Models Variable Label
Variable Description
Stage One Total Not Owned Positive EV Attitudes Negative EV Attitudes Stage Two
Measures the quantity of household technology neither owned nor desired Positive attitudes connect to EV performance including aspects of decentralised fuelling, lower operating costs and home recharging Concerns related to EV functional capabilities such as safety, reliability and complexity
Age
Current age
Gender
Income
Dummy variable to distinguish males and females (female = 1) Dummy variable to represent the attainment of a university or professional level qualification (attainment = 1) Gross household income
Study Site
Dummy variable to distinguish study site (Newcastle = 1)
Education
The results of the regression analysis are displayed in Table 6.25 and tend to follow a priori expectations. In stage one, five out of the six variables included in the two models hold significant explanatory power. The variable Total Not Owned, which measures the quantity of household technology neither owned nor desired, holds a significant negative influence (β of .077) over preferences towards Plug-in and Hybrid LEVs. This result suggests that an individual’s adoptive innovativeness can be used as an indicator of their preferences towards LEVs. Moreover, attitudes towards the functional capabilities of LEVs are found to influence preferences towards these vehicles. Negative EV Attitudes, which measures concerns about the safety, reliability and complexity of EVs, negatively influences preferences towards Hybrid (β of .201) and Plug-in (β of -.253) LEVs. Furthermore, Positive EV Attitudes, which includes aspects such as decentralised fuelling, home recharging and reduced operating costs, displays a large positive influence (β of.381) over preferences towards Plug-in LEVs in particular. 214
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Table 6.25: Regression Analysis Explaining LEV Preferences using Constructs Extracted from the LEV Attitudes Scale and Adoptive Innovativeness Mean Hybrid Preferences Mean Plug-in Preferences Variable Std. Std. β Sig. β Sig. Error Error Stage One (Constant) 3.935 .263 .000 2.870 .231 .000 Total Not Owned -.077** .025 .002 -.069** .022 .002 Negative EV Attitudes -.201* .087 .021 -.253** .076 .001 Positive EV Attitudes .164 .088 .063 .381** .077 .000 2 R .045 .102 Stage Two (Constant) 2.848 .695 .000 .963 .608 .115 Total Not Owned -.059* .027 .031 -.075** .024 .002 Negative EV Attitudes -.235** .090 .009 -.259** .079 .001 Positive EV Attitudes .100 .093 .284 .341** .082 .000 Gender .413* .199 .039 .362* .175 .040 Age -.007 .007 .310 .015* .006 .012 Education -.062 .193 .750 .320 .170 .061 Income .065* .078 .032 .099 .069 .149 Study Site .357* .177 0.44 .088 .155 .571 2 R .084 .143 **. Significant at the 0.01 level *. Significant at the 0.05 level
In stage two of the model, socio-economic variables are included to observe their influence over LEV preferences. In the Mean Hybrid Preference model, the dummy variables representing respondent gender, household income and study site location hold significant influence, indicating that if a respondent is female, has a relatively high income or is from the Newcastle subsample, then they are more likely to hold positive preferences for Hybrid vehicles. In the Mean Plug-in Preference model, the variables Age, which measures the age of an individual, positively influence preferences for Plug-in LEVs. This result somewhat contradicts what was observed in the EV Attitudes models, where age was found to positively influence negative attitudes towards EVs. This contradiction in results is puzzling and may indicate a spurious effect in the dataset. These results support the findings of previous research, demonstrating that attitudes towards the instrumental performance levels of LEVs significantly influence preferences towards these vehicles. Moreover, adoptive innovativeness displays a significant affect 215
Chapter Six: Results – Socio-Psychological Modelling indicating that LEVs are being considered as innovations with Laggards being less inclined to hold positive preferences towards them. However, whilst each model is significant in its ability to explain LEV preferences, neither offers a particularly good fit to the data. Both display low values for the R2 which suggests that, whilst adoptive innovativeness and attitudes towards the functional capabilities of EVs are significant in explaining preferences towards LEVs, they are not particularly powerful influencing factors. 6.3.5 Overview of Regression Analysis This section of the chapter utilised regression analysis to produce a number of explanatory models to determine the structural validity of the conceptual framework developed in this thesis. Split over three sub-sections, measurements of innate innovativeness were first employed to determine their explanatory power over the quantity of household technology owned which is employed as a proxy measurement of adoptive innovativeness. Results of this section indicate that constructs associated with psychological and communication determinants of innate innovativeness significantly affect the quantity of household technology owned and also the quantity not owned. Specifically, factors measuring knowledge relating to innovations, social activity, decision making and ambition, attitudes towards science and education as well as compulsive purchasing behaviour all significantly explained the quantity of household technology adopted. These findings suggest that innovativeness is successfully measured in this thesis and that measurements of innovativeness at different levels of abstraction can be integrated in a single model. Following this, the analysis progressed by developing models concerned with explaining attitudes towards the functional capabilities of EVs. In this instance, factors extracted from the Car Knowledge and Importance, Car Meanings, Car Attitudes and EV Emotions scale were employed in an explanatory capacity. Results from the models suggest that the ascriptions of symbolic, emotive and functional meanings to car ownership are negatively influencing attitudes towards the instrumental performance of EVs. Specifically, considering a car to be a status symbol, an improver of mood and as an important functional tool increases the likelihood of considering EVs to be instrumentally inferior to conventional cars. The importance of emotive considerations is further supported by the significance of factors associated with the ascription of emotions to EVs. The results indicate that 216
Chapter Six: Results – Socio-Psychological Modelling connecting EVs with positive emotions will improve evaluations of their functional performance. Moreover, factors associated with the concerns related to environmental implications of car use and the costs attributed to car ownership appear to positively influence EV attitudes. The results of these two regression models indicate that attitudes towards the functional performance of EVs may themselves be formed by general attitudes concerning cars, the meanings placed on car ownership and the emotive connection to EVs specifically. The final set of analyses conducted in this section examined the influence of adoptive innovativeness and attitudes towards the functional capabilities of EVs over preferences towards LEVs. Two regression models were constructed to firstly inspect preferences towards Hybrid vehicles before scrutinizing Plug-in vehicle preferences. Adoptive innovativeness, measured by the quantity of household technology not owned, held a significant influence in both models. Additionally, both positive and negative attitudes towards the instrumental performance of EVs significantly affected preferences towards these vehicles. These results suggest that the adoptive innovativeness and attitudes towards the functional capabilities of EVs can be used as valid indicators of LEV preference. In all of the regression models developed, a two stage entry procedure has been followed with conceptual framework constructs included in the first stage and socio-economic variables integrated in the second stage. The rationale for this approach was to test the sensitivity of the results to demographic changes. With the sample extracted for this thesis not exactly reflecting the characteristics of the general populace, this has the potential to limit the ability to generalize the findings. Results of this two stage entry procedure indicate that socio-economic variables hold significant influence in a number of the specified models. Specifically, gross household income and age have an influence in the models describing innovativeness and EV attitudes. Additionally, gender has a significant influence over EV attitudes whilst levels of education significantly affect LEV preferences. These findings indicate that generalizing the results of this thesis to the wider population should be done with caution. However, in all models specified, socio-psychological constructs explained a greater degree of the variance compared to socio-economic characteristics, implying that socio-psychological attributes prove to be superior indicators. 217
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6.4 CHAPTER SUMMARY The main purpose of this chapter has been to take the variables developed in the previous results chapter and test the validity of the conceptual framework. Partitioned into two main sections, the first utilised correlation analysis to identify relationships between framework components. The results are generally in keeping with expectations, with the majority of the proposed conceptual links being observed and following theoretical principles. The second section employed regression analysis to determine if certain framework components can be used in an explanatory capacity. Six different regression models were specified examining the concepts of innovativeness, EV attitudes and LEV preferences. The findings of the analysis tended to follow the expected conceptual links, with constructs which were proposed to influence certain concepts usually displaying significant explanatory power. However, a cause for concern relates to the final set of regression models constructed, which used the measurements of adoptive innovativeness and EV attitudes to explain preferences towards LEVs. Whilst the proposed relationships are found to be present and significant, the goodness of fit of the model is markedly low suggesting that adoptive innovativeness and EV attitudes are not particularly effective in explaining preferences towards LEVs. With the intention of the conceptual framework being to test new aspects which had received muted attention in the academic literature as opposed to being an inclusive model of behaviour, this lack of explanatory power should perhaps not prove to be overly concerning. The next chapter utilises the results attained in the correlation and regression analysis to develop a structural model of the emerging market for LEVs. Specifically, cluster analysis is employed to segment the market and identify groups of consumers that share similar characteristics. The results of this analysis assist in answering the second set of research questions attached to this thesis by firstly examining the key features of the segments identified before determining if these segments conform to theoretical expectations.
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Chapter Seven: Results – Market Structure Analysis
CHAPTER
7
RESULTS - MARKET STRUCTURE ANALYSIS 7.1 INTRODUCTION The mainstream automotive market offers a wide range of vehicle specifications alongside a diversity of vehicle architectures. It is unsurprising that this high degree of variation on the supply side is matched by a rich multiplicity of driver types. Whilst every individual driver has a unique set of characteristics and attitudes, they are likely to share similarities with one another. For instance, a sports car driver will generally find they have more in common with other sports car drivers compared to drivers of compact cars. These groups of drivers, who share similar characteristics and attitudes, are often referred to as market segments. Understanding the structure, dynamics and responses of market segments is seen as a critical aspect of market strategy (Myers and Tauber, 2011). Through improved understanding of these segments, firms and other organisations can better position and develop their products to fulfil the desires of consumers and grow their market share. This chapter applies a market research approach to the dataset in order to determine if segments are being formed in the emerging market for LEVs and, if this is the case, what structure they are taking. A critical assessment of the segments identified is offered to provide insights regarding their primary features. Additionally, the segments identified are compared to determine where similarities and differences are present. It is envisaged that through the successful identification of market segments which are likely to play important roles in the diffusion of LEVs, policy can be targeted effectively. To begin, an overview of the statistical method employed is provided before the results of the analysis are presented and discussed.
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Chapter Seven: Results – Market Structure Analysis 7.2 Introduction to Method Cluster analysis is an exploratory technique that analyzes a dataset of heterogeneous independent objects 1 , which can be multidimensional in nature, to determine if 0F
homogenous groups of objects can be formed. This form of analysis is commonly used in market research (Punj and Stewart, 1983) with the objective of partitioning a given market into unique segments. The knowledge generated from such an analysis can be utilised to better understand the dynamics of the market and to effectively target interventions at the desired group of consumers. Furthermore, cluster analysis is a popular method in the social sciences (Bartholomew et al., 2008) due to its ability to offer insights relating to stratification and classification within a social system. Cluster analysis can be conducted using two distinct methods which are discussed in this section. Hierarchical cluster analysis (Johnson, 1967; Murtagh, 1983) is a method which examines the similarities between objects included in a dataset and iteratively merges or separates objects based on an assessment of mutual difference. This form of cluster analysis can be visualised in a graphical format, referred to as a Dendrogram, which illustrates a nested set of data partitions. Figure 7.1 offers a simple example of the method containing six unique objects. The manner in which the clusters are formed can be conducted in either an agglomerative or divisive manner. Agglomerative integration involves commencing with individual objects and iteratively merging objects together based on similarity until all objects are combined. Conversely, divisive separation begins with all objects combined in one cluster and iteratively separates out sub-clusters of objects based on dissimilarity. Referring to Figure 7.1, these different approaches to cluster formation can be visualised by the direction in which the Dendogram is read, with left to right signifying agglomeration and right to left representing divisive separation.
1
For this thesis, each object represents the data profile of each survey respondent
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Chapter Seven: Results – Market Structure Analysis Objects 6 3
1
5
2
4
Distance
Figure 7.1: Illustration of a Dendrogram To provide an example of a cluster analysis conducted using an agglomeration method, the first necessary stage is to develop a distance matrix which denotes levels of similarity between the objects included in a dataset. An illustration of the structure of a distance matrix is provided in Figure 7.2 where the values for 𝑑 represent the distances between the relevant objects. This distance can be calculated in a number of different formats (Mooi and
Sarstedt, 2011) such as Euclidian distance, squared Euclidian distance, Chebychev distance and city-block distance. Once two objects are merged together, they represent a unique cluster and are provided with a space in the distance matrix with this process iteratively repeating. The technique utilised in this thesis to measure distance between clusters is Ward’s method (Ward, 1963) and was selected based on its ability minimise variance between objects within clusters thus optimising the within cluster homogeneity.
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Chapter Seven: Results – Market Structure Analysis
1 2 3 4 5 6
1
2
0 ⎡ 𝑑2,1 ⎢ ⎢𝑑3,1 ⎢𝑑4,1 ⎢ ⎢𝑑5,1 ⎣𝑑6,1
0 𝑑3,2 𝑑4,2 𝑑5,2 𝑑6,2
3
0 𝑑4,3 𝑑5,3 𝑑6,3
4
0 𝑑5,4 𝑑6,4
5
6
0 𝑑6,5
⎤ ⎥ ⎥ ⎥ ⎥ ⎥ 0⎦
Figure 7.2: Distance Matrix for a Hierarchical Cluster Analysis The second category of cluster analysis techniques is generally referred to as NonHierarchical clustering with the most frequently applied variant being K-means analysis (Hartigan, 1975; Hartigan and Wong, 1979). K-means analysis does not rely on the application of a distance matrix, nor does it iteratively merge (or divide) objects together based on similarity (or dissimilarity). Instead, K-means analysis starts with a pre-defined number of clusters and initial cluster centroid starting points and then analyzes each individual object to determine if the solution can be improved by the reassignment of an object to a new cluster. This procedure is repeated until no further object reassignments can be made to improve the structure of the solution. Figure 7.3 provides a basic illustration of the K-means procedure. In this hypothetical example, there are six unique objects (represented by black circles) included in the data set with the analysis conducted based on a two cluster solution. Two random cluster centroids are placed into the solution (represented by white circles) with the six unique objects being assigned to the centroid closest to them. After objects have been assigned to an initial centroid, the centroids are recalculated based on the values of their assigned objects. After this stage, the positions of the objects in relation to the centroids are re-evaluated and any object which is now closer to another centroid compared to the centroid to which it is currently attached is reassigned. This procedure repeats with centroids being recalculated and objects reassigned until a stable solution is determined, where no further object reassignments are required.
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Chapter Seven: Results – Market Structure Analysis
a.
c.
b.
d.
Figure 7.3: Illustration of a K-Means Cluster Solution The K-means technique can prove an effective form of cluster analysis in situations where the number of clusters embedded within a data set is already known and in circumstances when the size of the dataset is extensive. However, Hierarchical and Non-Hierarchical techniques should not be considered as mutually exclusive alternatives to one another. Mooi and Sarstedt, (2011) detail a two-stage analysis procedure whereby Hierarchical analysis is firstly conducted on a data set to provide an initial insight relating to the appropriate quantity of clusters to base a solution on. The Dendogram calculated provides a visual representation of cluster formation, allowing for an assessment of the suitability of different cluster quantities. Additionally, the variance ratio criterion (Calinski and Harabasz, 1974) can be calculated to appraise inter-cluster homogeneity and intra-cluster heterogeneity and is applicable to a large variety of different data formats (Milligan and Cooper, 1985). The cluster centroids calculated in this stage are then used as initial seed points for the K-means cluster analysis. In this thesis, a two stage cluster procedure was adopted based on Mooi and Sarstedt’s (2011) recommendations.
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Chapter Seven: Results – Market Structure Analysis 7.3 STRUCTURE OF THE LEV MARKET The previous section provided an introduction of the statistical theory which forms the basis of cluster analysis. Any specific cluster solution can use a large number of different combinations of statistical parameters which affect how the solution is calculated and the structure of the final result. In this thesis, a two stage approach is used to calculate the solution which firstly employs Hierarchical cluster analysis using Ward’s method to identify clusters and squared Euclidean distance to measure cluster separation. Following this, the initial cluster centroids calculated in the first stage are used as seed points for a K-means analysis which refines the solution by making iterative alterations to cluster memberships. The forthcoming discussion firstly offers an overview to the exact statistical procedure followed to calculate the final cluster solution. Aspects linked to the variables utilised to determine differences and similarities between car owning survey respondents, the quantity of clusters deemed appropriate to base the solution on and how the solution changed between the two different stages are detailed. Following this, the cluster solution is presented and discussed firstly covering powertrain preferences, socio-economic and current car details before examining the socio-psychological profiles of the identified segments. To conclude the chapter, the primary features of the cluster analysis are summarised with the main findings assessed. 7.3.1 Segmentation Variables The first stage in a market segmentation study is to determine how to distinguish groups of consumers from one another. To achieve this, a list of variables must be identified that are likely to differentiate these groups. These variables are based around the characteristics and attitudes of the consumers which have been found to be distinctive in a given market. For this thesis, variables have been selected based on the results of the correlation and regression analysis sections presented in Chapter 6. Specifically, variables which have been found to be significantly associated with and have explanatory power over LEV preferences and their antecedents in the conceptual framework have been selected as segmentation variables. An overview of these variables is presented in Table 7.1.
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Chapter Seven: Results – Market Structure Analysis Table 7.1: Overview of Segmentation Variables used in the Cluster Solution Variable Label
Variable Description
Mean Hybrid Preferences
Mean preferences towards Mild and Full Hybrid vehicles
Mean Plug-in Preferences Mean preferences towards Plug-in Hybrid and Pure EVs Meanings: Symbolism and Emotion Meanings: Function
Symbolic and emotive car meaning such as considering cars to be a status symbol and linking cars to mood improvement Functional car meaning such as considering cars to be a sensible financial decision and to have practical usefulness
Total not Owned
Total quantity of household technology neither owned nor desired
Positive EV Attitudes
Positive attitudes connect to EV performance including aspects of decentralised fuelling, lower operating costs and home recharging
Negative EV Attitudes
Concerns related to EV functional capabilities such as safety, reliability and complexity
Car Environment
Concerns relating to the environmental consequences of car use, felt responsibility and willingness to act
Car Cost
Measures concerns related to the purchase and operating costs associated with car ownership
A number of these segmentation variables may prove to be similar in nature, for instance, Mean Hybrid Preferences are likely to be associated with Mean Plug-in Preferences. Where this relationship is a consequence of the variables being different measurements of the same construct, this can introduce a bias into the analysis. This bias originates from double counting the importance of the construct by including multiple variables into the cluster analysis which measure it. To ensure this is not the case, a correlation matrix including all of the segmentation variables is specified and large correlations are identified. From this assessment, a number of statistically significant relationships between the segmentation variables were found. However, only one of the relationships 2 identified were particularly 1F
strong (above 0.3) meaning the opportunity for double counting in the segmentation variables is low. 2
r of .432 between Mean Hybrid Preferences and Mean Plug-in Preferences
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7.3.2 Hierarchical Clustering In the first stage of the analysis, the segmentation variables are used to group respondents using a Hierarchical cluster analysis employing Ward’s method with the interval measure being specified by squared Euclidean distance. As the segmentation variable list includes three different metrics (factor scores for the socio-psychological constructs, integers for technology ownership and real numbers for LEV preferences), the data went through an initial standardisation procedure. The Hierarchical cluster solution is displayed in Dendrogram format in Figure 7.4. The Dendogram offers initial insights regarding how the data set is being partitioned and what quantity of respondents are being assigned to different clusters. From an initial visual inspection, it is possible to consider what might be an appropriate quantity of clusters to base the solution on. From a general observation, by reading the Dendogram from right to left, it is evident that the separation points between a 4, 5 and 6 cluster solution are close. This suggests that a significant degree of respondent variation is occurring in and around this distance and thus is an appropriate place to consider cluster quantity. However, determining the optimum number of clusters to base the solution on from an assessment of the Dendogram is reliant on subjective interpretation.
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6 Cluster 5 Cluster 4 Cluster
Figure 7.4: Dendrogram of Hierarchical Clustering To overcome this subjective approach to determining the appropriate number of clusters to base a solution on, authors have proposed a number of techniques to assess the suitability of different cluster solutions. Kryszczuk and Hurley (2010) discuss the applicability of Cluster Validity Indices (CVI) which can be used to examine cluster solutions with different cluster quantities and determine which provides the most effective representation of the market. Mooi and Sarstedt (2011) recommend using the Variance Ratio Criterion (VRC), initially proposed by Calinski and Harabasz (1974), to determine the optimum number of clusters. This metric is defined in Equation 7.1.
227
Chapter Seven: Results – Market Structure Analysis Equation 7.1
VRCk = (SSB / (k-1)) / (SSW/(n-k))
In this equation, SSB represents the sum of squares between the clusters, SSW the sum of squares within the clusters and k is the number of clusters in the solution. In this sense, the VRC is equal to the F value calculated in a one way ANOVA test. To determine the optimum number of clusters the value for ωk needs to be calculated for each cluster solution defined in Equation 7.2. Equation 7.2
ωk = (VRCk+1 - VRCk) – (VRCk - VRCk-1)
The solution with the lowest ωk value will both minimise the variation within segments, thus maximising within cluster homogeneity, whilst at the same time maximising the variation between segments, thus optimising between cluster heterogeneity. The following analysis applies this assessment metric to the 4, 5 and 6 cluster solution with the results presented in Table 7.2. Firstly examining the F values for each of the cluster solutions, it is apparent that, with the exception of the 5 cluster solution, as the quantity of clusters increases, the F values tend to decrease. This is reflected in the Sum of F Values statistics being highest for a 3 cluster solution and lowest for a 7 cluster. However, when examining the values of ωk, it is clear that a 5 cluster solution achieves the lowest value for this assessment. As a result of this analysis, a 5 cluster solution was selected as an appropriate basis for this thesis with cluster centroids being calculated in accordance with this cluster quantity.
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Chapter Seven: Results – Market Structure Analysis Table 7.2: Variance Ratio Criterion for Cluster Solutions Variable Mean Hybrid preferences Mean Plug-in preferences Total not Owned Meaning: Symbolism and Emotion Meaning: Function Negative EV Attitudes Positive EV Attitudes Car Environment Car Cost Sum of F ωk
3 Cluster
4 Cluster
5 Cluster
6 Cluster
7 Cluster
28.64 16.20 840.21 1.63 0.33 2.46 1.69 1.94 1.09 894.20
140.23 83.13 63.96 51.23 326.38 588.71 5.45 2.46 4.32 2.65 11.93 5.17 8.29 3.83 12.68 7.79 1.50 2.68 574.73 747.66 492.40 -246.74
54.61 67.60 520.59 2.64 4.14 8.21 4.74 10.10 1.22 673.85 4.76
77.23 108.05 382.78 8.57 3.20 5.29 6.64 5.91 7.14 604.80
7.3.3 K-Means Clustering Having calculated an initial cluster solution using the Hierarchical method, the next stage in the analysis involves conducting a K-means cluster solution. To achieve this, the cluster centroids for the segmentation variables which were calculated during the Hierarchical cluster analysis are used as initial starting points for a K-means solution. A five cluster quantity is used in both stages of the analysis with eleven iterations required to alter cluster centroids. The initial cluster centres calculated in the Hierarchical analysis and the final cluster centres calculated in the K-means solution are presented in Table 7.3 and 7.4. Table 7.3: Initial Cluster Centres from the Hierarchical Analysis Variable Mean Hybrid preferences Mean Plug-in preferences Total Not Owned Meaning: Symbolism and Emotion Meaning: Function Negative EV Attitudes Positive EV Attitudes Car Environment Car Cost
1 1.56 1.11 11.26 -.56289 .49489 .03649 -.48744 -.44454 -.37556
2 1.22 1.14 11.59 1.37516 .45483 .69177 .19763 -.20793 .35839
Cluster 3 4 5 3.86 4.56 3.59 2.24 4.97 1.70 11.42 8.11 5.20 -.04654 -.45113 .62063 -.04277 -.11136 .02031 -.07387 -.42674 .00697 .06296 .67294 -.25101 .10409 .69119 -.02959 -.00096 .03664 .08724
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Chapter Seven: Results – Market Structure Analysis Reviewing how the cluster centroids have changed between the Hierarchical and K-means stages, it is apparent that a number of alterations have taken place. Clusters 1, 3 and 4 remain markedly stable with only slight changes in their centroid positions. Cluster 2 and 5 exhibit higher degrees of modification, with Cluster 2 becoming less adoptively innovative (variable Total not Owned increasing) whilst the factor measuring symbolic and emotive car meanings for Cluster 2 and 5 is appreciably different. Specifically, the factor measuring the symbolic and emotive meaning placed on car ownership decreased for Clusters 2 and 5. Table 7.4: Final Cluster Centres from the K-means Analysis Variable
Cluster
1
2
3
4
5
Mean Hybrid Preferences
2.01
1.87
4.47
4.51
3.04
Mean Plug-in Preferences
1.25
1.20
3.26
3.07
2.10
10.21
14.35
12.57
8.34
4.93
Meaning: Symbolism and Emotion
.04153
.03877
-.25889
.01465
.23937
Meaning: Function
.34431
.10829
.12351
-.09861
.12016
Negative EV Attitudes
.16634
.20453
.01906
-.44639
-.03895
Positive EV Attitudes
-.14874
-.10796
.24279
.19427
-.25522
Car Environment
-.34519
-.20481
.23986
.39348
.00839
Car Cost
.09533
-.31311
-.11262
.16127
-.02076
Total not Owned
According to Mooi and Sarstedt (2011), it is preferable to have complete stability in the cluster centroids across the two stages of the analysis. Complete stability can be considered to enhance the reliability of the solution, displaying a consistency between different analysis stages. Evaluating the stability of the cluster solution for this study, it is clear that the majority of the segmentation variables have remained constant across the two analysis stages, indicating that the solution itself is appreciably stable. 7.3.4 Analysis of Variance To determine if heterogeneous clusters have been calculated in the K-means analysis, an analysis of variance (ANOVA) is conducted which includes the segmentation variables used to calculate the solution. The results of this ANOVA are presented in Table 7.5. Inspecting these results, it is apparent that there are statistically significant differences between at least two clusters for all nine of the segmentation variables at the 95% confidence level. This 230
Chapter Seven: Results – Market Structure Analysis finding indicates that the solution is successful at identifying clusters which are significantly different in their profiles. In the forthcoming section, where the clusters identified in the analysis are presented and described, only those variables which displayed statistically significant differences between the cluster centroids are discussed.
Table 7.5: ANOVA of Segmentation Variables
Between Groups
Sum of Squares 494.002
Within Groups
552.680
372
1.486
1046.682 287.813
376 4
71.953
522.445
372
1.404
Variable Mean Hybrid Preferences Mean Plug-in Preferences Total not Owned
Total Between Groups Within Groups Total Between Groups Within Groups
Meaning: Symbolism and Emotion Meaning: Function
Negative EV Attitudes
Positive EV Attitudes
Car Environment
Car Cost
810.259 3911.843
df
Mean Square 4 123.501
F
Sig.
83.126
.000**
51.233
.000**
376 4 977.961 588.712
.000**
617.961
372
1.661
4529.804 9.230
376 4
2.308
Within Groups
349.588
372
.940
Total Between Groups
358.819 7.685
376 4
1.921
Within Groups
269.421
372
.724
Total Between Groups
277.107 19.278
376 4
4.819
Within Groups
346.484
372
.931
Total Between Groups
365.762 14.215
376 4
3.554
Within Groups
344.805
372
.927
Total Between Groups
359.019 28.539
376 4
7.135
Within Groups
340.719
372
.916
Total Between Groups
369.257 10.392
376 4
2.598
Within Groups
360.584
372
.969
Total
370.976
376
Total Between Groups
2.456
.045*
2.653
.033*
5.174
.000**
3.834
.005**
7.790
.000**
2.680
.031*
Difference significant at the * 0.05 level ** 0.01 level
231
Chapter Seven: Results – Market Structure Analysis 7.4 CLUSTER DESCRIPTIONS Having discussed the principal steps undertaken to achieve a cluster solution which provides an effective partition of the dataset, the next stage is to examine the characteristics of the clusters identified to determine their primary features. This section offers a discussion relating to the profiles of each of the five clusters identified in the analysis. Firstly, a label is assigned to each cluster to assist in the discussion of their primary attributes. Next, the powertrain preferences for each cluster are inspected before examining the socio-economic characteristics and current car details. Following this, the socio-psychological profile of each cluster is examined commencing with individual innovativeness before inspecting the constructs focused on car specific attitudes and life principles. 7.4.1 Cluster Label and Size The assignment of a label to each cluster at the start of the examination of their primary features allows for the results to be discussed in a clear fashion. Whilst it may not be entirely obvious why certain labels have been assigned from the outset, as the interpretation of the results progresses, the labels assigned to each cluster will begin to make sense. Table 7.6 provides an overview of the labels whilst also stating the percentage of respondents assigned to each cluster. Table 7.6: Cluster Labels and Sizes Cluster Number
Cluster Label
Percentage of Sample
Percentage of Newcastle
Percentage of Dundee
10.88
12.72
19.9
9.55
10.35
Cluster 2
Environmental Cynics (EC) Weekend Drivers (WD)
Cluster 3
Early Adopters (EA)
19.6
9.53
10.07
Cluster 4
Keen Greens (KG)
18
Cluster 5
Car Enthusiasts (CE)
18.8
11.57 9.53
6.44 9.26
Cluster 1
23.6
232
Chapter Seven: Results – Market Structure Analysis 7.4.2 Powertrain Preferences The powertrain preferences of each cluster, which include the four LEV options alongside conventional Petrol and Diesel, are displayed in Figure 7.5. Examining the results, there is a significant degree of separation in preferences between clusters with the Early Adopters and Keen Greens displaying relatively high preferences for the LEV options whilst the Weekend Drivers and Environmental Cynics have comparatively low preferences. Focusing on the Early Adopter’s and Keen Green’s preference structures, the powertrains Mild and Full Hybrids are comparable to the conventional options, which indicates these clusters represents consumers that are the most likely to adopt an LEV in their next vehicle purchase. Conversely, there are a number of clusters which display relatively low preferences for LEVs, with the Environmental Cynics and Weekend Drivers holding universally low preferences for all four LEV options. These results indicated that these clusters would best represent non-adopters in the near-term market based on an assessment of their preference structure.
6.000 5.000 4.000 3.000 2.000 1.000 0.000 Petrol Preferences**
Diesel Preferences
Environmental Cynics
Mild Hybrid Full Hybrid Plug-in Hybrid Pure EV Preferences** Preferences** Preferences** Preferences**
Weekend Drivers
Early Adopters
Keen Greens
Car Enthusiasts
Figure 7.5: Cluster Powertrain Preferences (difference significant at the ** 0.01 level)
233
Chapter Seven: Results – Market Structure Analysis 7.4.3 Socio-Economic Characteristics The socio-economic characteristics of each cluster, which are often used as a foundation for conventional market research, are presented in Table 7.7. Looking at the cluster profiles, it is apparent that Weekend Drivers and Early Adopters tend to be older respondents whilst the Car Enthusiast and Keen Greens are relatively younger. These observations transfer when considering employment status where Weekend Drivers are the most likely to be retired contrasting with Car Enthusiasts where over 70% are in employment. Examining household income and levels of education, these variables appear to be connected with the Car Enthusiasts and Keen Greens containing the highest proportion of respondents with a university level qualification and incomes in excess of £50, 000 per annum whereas the Weekend Drivers displays the lowest values for these variables. When examining cluster socio-economics with LEV preferences, the results suggest that approaching the explanation of LEV preference by solely examining the influence of demographic characteristics may only provide a partial understanding of the underlining dynamics. To illustrate this point, consider the characteristic of age, which has been found in previous research (Dagsvik et al., 2002) to be negatively associated with LEV preferences. The results of this solution suggest that, whilst the cluster representing the oldest respondents (Weekend Drivers) does indeed hold comparatively low LEV preferences, the second oldest cluster (Early Adopters) displays the highest preference level for LEVs. This finding supports the view that clusters formed on the basis of socio-economic characteristics alone are insufficient in grasping the complexities of this emerging market.
234
Chapter Seven: Results – Market Structure Analysis Table 7.7: Socio-Economic and Household Characteristics of the Clusters Cluster Variable Category EC WD EA Gender Male 67.4% 64% 55.4% Female 32.6% 36% 44.6% Highest level of No Formal Education 6.7% 13.5% 8.2% academic GCSE 20.2% 21.6% 9.6% achievement* A-level 19.1% 20.3% 17.8% BA/BSc 30.3% 13.5% 27.4% MA/MSc/PhD 14.6% 20.3% 19.2% Professional 9% 10.8% 17.6% Employment Employed part time 3.4% 8% 6.8% status* Employed full time 43.8% 33.3% 36.5% Self employed part time 1.1% 1.3% 4.1% Self employed full time 7.9% 4% 6.8% Unemployed 05 0% 0% Retired 39.3% 53.3% 43.2% Full time education 2.2% 0% 0% Disabled 1.1% 0% 2.7% Looking after 1.1% 0% 0% children/home/family Gross household Less than 10, 000 0% 9% 2.8% income (GBP)** 10-30, 000 40% 52.2% 39.4% 30-50, 000 30% 25.4% 28.2% 50-70, 000 18.8% 7.5% 11.3% 70-90, 000 7.5% 1.5% 9.9% More than 90, 000 3.8% 4.5% 8.5% Age (years)** Mean 57 63 59 Standard Deviation 16.7 12.7 12.1
KG 57.4% 42.6% 2.9% 5.9% 19.1% 26.5% 29.4% 16.2% 7.4% 51.5% 0% 5.9% 2.9% 29.4% 0% 1.5%
CE 67.6% 32.4% 4.2% 9.9% 19.7% 26.8% 29.6% 9.9% 11.4% 51.4% 0% 8.6% 0% 21.4% 4.3% 1.4%
1.5%
1.4%
1.5% 2.9% 28.8% 20% 39.4% 32.9% 18.2% 20% 4.5% 10% 7.6% 14.3% 53 49 13.7 15.6
Difference significant at the * 0.05 level ** 0.01 level EC – Environmental Cynics WD – Weekend Drivers EA – Early Adopters KG – Keen Greens CE – Car Enthusiasts
7.4.4 Current Car Details The type of car an individual currently drives is likely to influence their future vehicle preferences. Anable et al. (2008) examined car purchasing behaviour finding that individuals tend to use their current cars as a benchmark when comparing potential options. Table 7.8 characterises each cluster by their current car details and usage patterns with a number of noteworthy differences being apparent. Concerning the number of vehicles in the household fleet, Weekend Drivers are the most likely to be single car households, perhaps 235
Chapter Seven: Results – Market Structure Analysis resulting from the tendency of this cluster to be populated by retired individuals on reduced incomes, whilst the Environmental Cynics cluster contains the most multi-car households.
Table 7.8: Current Car Details and Usage Patterns of the Clusters Variable Cars in household**
Fuel type* Frequency of car wash*
Engine size**
Annual mileage (miles) * Usual car expenditure (GBP) *
Category 0 1 2 3 or more Petrol Diesel Never 3 – 4 times a year Once a month 2 – 3 times a month Weekly 1.0 – 1.5 1.6 – 2.0 2.1 – 2.5 2.6 – 3.0 3.0 or more
EC 3.4% 46.1% 37.1% 13.5% 60% 40% 4.8% 34.5% 45.2% 11.9% 3.6% 33.8% 56.3% 6.1% 2.4% 1.2%
Mean Standard Deviation Mean Standard Deviation
8811 4000 9441 5044
WD 1.4% 74.3% 18.9% 5.4% 67.6% 32.4% 10.7% 37.3% 34.7% 10.7% 6.7% 34.8% 54.5% 7.6% 1.5% 1.5%
Cluster EA 2.7% 63.5% 31.1% 2.7% 66.7% 33.3% 6.9% 54.2% 23.6% 11.1% 4.2% 29.2% 69.2% 1.5% 0% 0%
KG 1.5% 51.5% 39.7% 7.5% 70.2% 29.8% 9% 49.3% 32.8% 10.4% 1.5% 37.1% 58.1% 3.2% 0% 1.6%
CE 1.4% 50.7% 36.6% 11.3% 47.8% 52.2% 2.9% 31.9% 43.5% 10.1% 11.6% 18.8% 57.8% 12.5% 7.8% 3.1%
6698 8741 9233 8470 3317 5492 6122 4655 9074 10492 10008 12336 5619 4780 5703 7684
Difference significant at the * 0.05 level ** 0.01 level EC – Environmental Cynics WD – Weekend Drivers EA – Early Adopters KG – Keen Greens CE – Car Enthusiasts
Relating to the fuel type of their current cars, Car Enthusiasts are the most likely to own a Diesel car whilst the Keen Greens have the highest propensity to drive a Petrol car. This result is somewhat surprising, as with Keen Greens also representing drivers that tend to have high annual mileages, expectations were that this cluster would have a greater tendency to drive Diesel cars. This observation may perhaps be partially explained by the tendency of Keen Greens to own cars with small engine displacements which are less likely to be available in a Diesel option. In contrast, Car Enthusiasts have the highest likelihood of having a car with an engine size in excess of 2 litres. Moreover, Car Enthusiasts tend to wash 236
Chapter Seven: Results – Market Structure Analysis their cars more frequently than other clusters and spend considerably more when purchasing a car, indicating an enhanced time and financial commitment to car ownership. Linking these car details back to powertrain preferences, it is apparent that the clusters which tend to spend the least when purchasing a car (Environmental Cynics and Weekend Drivers) also have the lowest expressed preferences for LEVs. Moreover, Weekend Drivers tend to be from single car households that have relatively low annual mileages, indicating that they would receive a reduced benefit from the lower operating costs attributed to LEVs. Conversely, the Early Adopters and Keen Greens, which display the highest level of LEV preference, also tend to have the highest annual mileages, allowing them to receive significant benefit from the lower operating costs of LEVs, whilst also being the most likely to own a car with an engine size lower than 2 litres, perhaps signifying their lack of desire for car performance. 7.4.5 Socio-Psychological Profiles Having so far presented and discussed the cluster solution in reference to powertrain preferences, socio-economic and current car details, the discussion now progresses by examining the socio-psychological profiles of the clusters. The measurements of innovativeness have been presented in two different formats with Figure 7.6 relating to the innate innovativeness factors extracted from the psychological and communication determinants sales whilst Figure 7.7 represents the level of household technology owned by each cluster. Firstly, the factors extracted from the scales related to innate innovativeness are illustrated in a bar chart. Secondly, Figure 7.7 displays the quantity of household technology owned by each cluster which is used as a measure of adoptive innovativeness with the assumption being the more household technology owned by an individual, the greater their adoptive innovativeness. The internal structures of the innate innovativeness factors are presented and discussed in Chapter 5, Section 5.5.9 and 5.5.10. In this solution, clusters were found to hold significantly different loadings on four out of the six innate innovativeness factors. Specifically, the factors associated with information concerning innovations from the communication determinants scale and the factors connected to attitudes towards science and education, a 237
Chapter Seven: Results – Market Structure Analysis desire for personal aspiration and a tendency to behave compulsively from the psychological determinants scale are included in Figure 7.6.
0.800
Construct Loading
0.600 0.400 0.200 0.000 -0.200 -0.400 -0.600 -0.800
Comm: Information Psych: Science and Seeking and Education** Provision*
Psych: Aspiration* Psych: Compulsive**
Environmental Cynics
Weekend Drivers
Keen Greens
Car Enthusiasts
Early Adopters
Figure 7.6: Cluster Loadings for Innate Innovativeness Constructs (difference significant at the * 0.05 level ** 0.01 level) Inspecting the cluster loadings on these factors, it is apparent that Car Enthusiasts and Keen Greens display a relatively high degree of innate innovativeness. Scrutinizing the Car Enthusiasts innovativeness profile in greater detail, it appears that this cluster often knows about innovations early in their diffusion, holds positive attitudes towards science and education, is personally aspiring and can also behave compulsively. Conversely, the Weekend Drivers are comparatively less innately innovative, tending to have no knowledge relating to innovations, do not consider science and education to be particularly important whilst being unlikely to act in a compulsive manner. Contrasting these factor loadings with adoptive innovativeness, it is evident that those clusters which tend to display relatively high levels of innate innovativeness, such as Car Enthusiasts, also tend to own high levels of household technology whilst the opposite is true for clusters with relatively low levels of innate innovativeness.
238
Chapter Seven: Results – Market Structure Analysis
Quantity of Household Technology Owned
9.000 8.000 7.000 6.000 5.000 4.000 3.000 2.000 1.000 0.000
Total Owned** Environmental Cynics
Weekend Drivers
Early Adopters
Keen Greens
Car Enthusiasts
Figure 7.7: Quantity of Household Technology Owned (difference significant at the ** 0.01 level) Comparing innovativeness to LEV preferences, it appears as if there is a partial overlap. The Weekend Drivers, which hold the lowest levels of LEV preference, also display low levels of innate and adoptive innovativeness. Conversely, Keen Greens exhibit high levels of LEV preference and innovativeness. However, the cluster displaying the highest levels of adoptive and innate innovativeness (Car Enthusiast) holds distinctly muted LEV preference, whilst the cluster with the highest LEV preferences (Early Adopters) displays low levels of innovativeness. In sum, innovativeness and LEV preference appears to be connected when examining the profile of some clusters, but not in others, indicating that innovativeness is only an important characteristic of some segments in the LEV market.
239
Chapter Seven: Results – Market Structure Analysis 0.400
Construct Loading
0.300 0.200 0.100 0.000 -0.100 -0.200 -0.300 -0.400
Meaning: Symbolism and Emotion* Environmental Cynics
Meaning:Function*
Weekend Drivers
Early Adopters
Negative Car Emotions** Keen Greens
Car Enthusiasts
Figure 7.8: Cluster Loadings for Car Meanings and Emotions Constructs (difference significant at the * 0.05 level ** 0.01 level) Shifting the focus of the analysis to car specific constructs, Figure 7.8 presents the cluster loadings for the factors extracted from the Car Meanings (discussed in Chapter 5, Section 5.5.3) and Car Emotions (discussed in Chapter 5, Section 5.5.4) scales. Regarding the factor associated with the assignment of symbolic and emotive meanings to car ownership, Car Enthusiasts display a positive loading, indicating that they tend to consider their cars as an extension of their identity and a source of positive emotions. Conversely, Early Adopters load negatively on this factor, signifying a lack of emotive attachment to cars and a reluctance to consider their cars as status symbols. For the second factor extracted from this scale, linked to functional car meanings, most clusters load positively indicating that the majority of the market considers cars to have instrumental value. Environmental Cynics display the highest positive loading in this instance, demonstrating that this cluster tends to consider cars as a good financial investment and as an efficient possession to own. The last factor in Figure 7.8 measures the allocation of negative emotions when considering cars in general. The results reveal that Keen Greens tend to associate cars with stress, apprehension, boredom, embarrassment and irritation. This finding may indicate that Keen Greens are disenchanted with conventional cars, perhaps being a motivation for their relatively high preferences for LEVs. 240
Chapter Seven: Results – Market Structure Analysis
0.500
Construct Loading
0.400 0.300 0.200 0.100 0.000 -0.100 -0.200 -0.300 -0.400
Car Importance* Environmental Cynics
Car Knowledge**
Weekend Drivers
Car Environment**
Early Adopters
Keen Greens
Car Cost* Car Enthusiasts
Figure 7.9: Cluster Loadings for Car Attitudes Constructs (difference significant at the * 0.05 level ** 0.01 level) Keeping with the general car theme, Figure 7.9 displays the cluster loadings for the factors extracted from the Car Attitudes (discussed in Chapter 5, Section 5.5.6) and Car Knowledge and Importance (discussed in Chapter 5, Section 5.5.5) scales. Relating to the first factor in this figure, Environmental Cynics hold a distinctly positive loading, suggesting that this cluster tends to consider cars to be valuable in their ability to facilitate everyday life and also on a more personal level. This finding supports the observation made in the previous scale, where Environmental Cynics were found to attach a high degree of functional meanings to car ownership. These two observations, taken together, indicate that Environmental Cynics consider their cars to be an essential personal item embedded with a significant degree of instrumental value. Regarding the second factor presented in this figure, which measures the degree to which an individual has practical skills with a car and knowledge of LEV powertrains, it is apparent that Weekend Drivers display a negative loading on this factor whilst Car Enthusiasts hold a positive loading. These observations suggest that Car Enthusiasts are more engaged with cars, having knowledge of their mechanical operation, whilst Weekend Drivers may only hold knowledge of car operation. The last two factors included in Figure 7.9 have been extracted from the Car Attitudes scale and measure concerns related to the environmental consequences of car use and for the 241
Chapter Seven: Results – Market Structure Analysis costs attributed to car ownership. Examining the first of these factors to begin, it can be observed that Environmental Cynics and Weekend Drivers hold a negative loading whilst Early Adopters and Keen Greens display positive loadings. These observations have a direct overlap with LEV preferences, indicating that attitudes towards the environmental implications of cars remains an important feature of market segments and can be useful in distinguishing those individuals that may be interested in adopting an LEV from those who are not. For the last factor, measuring concern for both the upfront purchase cost and operating costs attributed to car ownership, it is apparent that Weekend Drivers tend to load negatively whilst Keen Greens are more likely to hold positive loadings. In the case of Weekend Drivers, this negative loading may be partly motivated by this cluster’s low annual mileage, with this relatively low car usage acting to reduce this cluster’s exposure to fluctuations in car operating costs. Conversely, with Keen Greens tending to have the highest annual mileages, this cluster will spend significantly more than other clusters on car fuel and would therefore have the most to gain by adopting a LEV. The last set of constructs associated with cars is displayed in Figure 7.10 and presents cluster loadings on the factors extracted from the EV Attitudes (discussed in Chapter 5, Section 5.5.8) and EV Emotions (discussed in Chapter 5, Section 5.5.7) scales. Focusing on EV attitudes to begin, there appears to be a mirroring effect, with those clusters which tend to load positively on one of the factors loading negatively on the other. Environmental Cynics and Weekend Drivers hold positive loadings on Negative EV Attitudes, suggesting that these clusters are concerned about the reliability, safety and complexity of EVs, whilst loading negatively on Positive EV Attitudes. Conversely, Early Adopters and Keen Greens tend to hold positive attitudes towards the functional capabilities of EVs, considering EVs to be a good financial investment and valuing decentralised fuelling, whilst considering the potential limitations of EV ownership to be unimportant. Moreover, Car Enthusiasts display a significant negative loading on Positive EV Attitudes, indicating that this cluster may consider EVs to be functionally inferior compared to conventional cars.
242
Chapter Seven: Results – Market Structure Analysis 0.400 0.300 0.200 0.100 0.000 -0.100 -0.200 -0.300 -0.400 -0.500 Positive EV Emotions* Environmental Cynics
Negative EV Emotions* Negative EV Attitudes** Positive EV Attitudes** Weekend Drivers
Early Adopters
Keen Greens
Car Enthusiasts
Figure 7.10: Cluster Loadings for EV Attitudes and Emotions Constructs (difference significant at the * 0.05 level ** 0.01 level) A similar loading structure is observed when examining the constructs which measure emotive attachment to EVs. In this instance, Environmental Cynics and Car Enthusiasts are more likely to associate EVs with negative emotions such as irritation, embarrassment and apprehension whilst Keen Greens have a tendency to associate EVs with positive emotions such as happiness, pleasure and affection. Overlaying cluster attitudes and emotions regarding EVs with their preferences towards these vehicles, a noticeable trend can be observed. Those clusters which hold negative attitudes towards the functional capabilities of EVs whilst being unlikely to associate EVs with positive emotions also hold the lowest preferences towards EVs. Conversely, clusters which tend to consider EVs to be functionally proficient are less likely to associate EVs with negative emotions whilst also tending to hold relatively high preferences for EVs. These observations indicate that there is an association between the emotions which clusters attach to EVs, the attitudes they hold concerning the instrumental performance of EVs and their preferences towards EVs.
243
Chapter Seven: Results – Market Structure Analysis 0.400
Construct Loadings
0.300 0.200 0.100 0.000 -0.100 -0.200 -0.300 -0.400
Biospheric Principles*
Environmental Cynics
Weekend Drivers
Keen Greens
Car Enthusiasts
Egoistic Principles**
Early Adopters
Figure 7.11: Cluster Loadings for Value Orientation Constructs (difference significant at the * 0.05 level ** 0.01 level) The final set of socio-psychological constructs examined in this section are those that were extracted from the Value Orientation scale (discussed in Chapter 5, Section 5.5.11). Specifically, the cluster loadings associated with biospheric and egoistic value structures are displayed in Figure 7.11. An examination of the results indicates that loadings on these factors tend to follow a mirrored structure, with a positive loading on one factor followed by a negative loading on another. To illustrate this point, consider Car Enthusiasts who are the most likely to be motivated by an egoistic value structure whilst being unlikely to be concerned by biospheric considerations. Opposed to this outlook are Early Adopters and Keen Greens which consider biopsheric motivations to be important whilst displaying a lack of interest in egoistic pursuits.
Comparing these value structure loadings with LEV
preferences, it is evident that clusters which display biospheric value structures (Early Adopters and Keen Greens), tend to hold relatively high preferences for LEVs whilst those clusters which do not consider biospheric motivations to be important (Environmental Cynics and Car Enthusiasts) display relatively low LEV preferences.
244
Chapter Seven: Results – Market Structure Analysis 7.5 CHAPTER SUMMARY With LEVs beginning to enter the mainstream market, academic and industry attention is shifting towards determining what types of consumer are most likely to consider them. This chapter presented results of a segmentation analysis which adds towards academic knowledge in this area through an examination of the structure of the emerging market for LEVs. To achieve this, a two stage cluster analysis was applied to the dataset attained from the household survey. To begin, a Hierarchical cluster analysis using Ward’s method was followed to attain an initial appreciation of the respondent partitions which exist within the dataset. A visual inspection of the Dendogram coupled with a calculation of the VRC suggested that 5 clusters offered an effective separation. Having determined the appropriate number of clusters to base the final solution on, the centroids calculated in the Hierarchical stage where utilized as seed points for a K-means analysis which formed the final cluster solution discussed in this chapter. Hypothesis testing was employed to ensure that the cluster solution was successful at identifying clusters which were significantly different in their profiles. The final cluster solution was found to contain clusters which were significantly different from one another in regards to their LEV preferences, socioeconomic characteristics, current car details and socio-psychological profiles. Following the statistical analysis, the profiles of the clusters were presented and discussed. This discussion commenced with an examination of the powertrain preference structures of the clusters before inspecting the socio-economic characteristics and current car details. The next stage of the discussion concerned an assessment of the socio-psychological composition of the clusters, involving such aspects as innovativeness, attitudes towards cars and value orientations. This examination of cluster profiles allowed for detailed descriptions of the clusters to be established, permitting a comprehensive evaluation of their primary features. A summary of the primary features of each cluster is provided in Table 7.9 whilst an overview of how the clusters are relatively ranked is offered in Table 7.10 with those clusters that have a low rank for a particular variable are lightly shaded whereas those clusters that attain the highest rank are darkly shaded.
245
Chapter Seven: Results – Market Structure Analysis Table 7.9: Key Features of the Clusters Cluster Label Environmental Cynics
Key Features Display low LEV preferences Are the most likely to be multi-car households Hold medium levels of innovativeness Are unconcerned about the environmental consequences of car use Consider their cars to hold a high degree of functional meaning Are likely to attach negative emotions to EVs
Weekend Drivers
Display low LEV preferences Tend to be older drivers with low levels of household income and formal education Have the lowest annual car mileages Hold low levels of innovativeness Do not consider their cars to be important nor have knowledge relating to cars Consider EVs to be functionally inferior
Early Adopters
Display high LEV preferences Hold low levels of innovativeness Are concerned about the environmental implications of cars Hold positive attitudes about the functional capabilities of EVs
Keen Greens
Display high LEV preferences Have the highest annual car mileages Hold medium levels of innovativeness Are likely to associate EVs with positive emotions Are motivated by biospheric life principles
Car Enthusiasts
Display medium LEV preferences Have high levels of household income and formal education Wash their cars the most frequently and are the most likely to own a diesel car Prefer cars with large engines and spend the most when buying cars Hold high levels of innovativeness Place a high degree of symbolic and emotive meaning on car ownership Have a high degree of knowledge relating to car mechanics and operation Are motivated by egoistic life principles
The analysis identified clusters which display LEV preferences that are significantly different from one another. Towards the lower end of the preference scale, the clusters labelled Environmental Cynics and Weekend Drivers hold the lowest preferences towards LEVs. These clusters share a number of addition similarities, with both having a lack of concern for the environmental consequences of car use whilst being unlikely to be motivated by biospheric life principles. Moreover, both these clusters tend to hold negative attitudes to the functional capabilities of EVs whilst being unlikely to associate EVs with positive 246
Chapter Seven: Results – Market Structure Analysis emotions. However, these clusters also display some significant differences with Environmental Cynics, who tend to consider their cars to be an important possession in both terms of its functional capabilities and personal significance, being the most likely to be multicar households whilst also being more adoptively innovative. Conversely, Weekend Drivers are the least innately and adoptively innovative cluster whilst holding the lowest annual car mileages and being the most likely to be single car households, potentially linked to their higher likelihood to be retired. On the other side of the preference scale, Early Adopters and Keen Greens exhibit distinctly high preferences for LEVs whilst holding the lowest preferences for the conventional powertrain options. Both these clusters display concerns for the environmental implications of car use and are motivated by biospheric life principles. Moreover, these clusters consider EVs to be functionally capable, valuing the reduced operating costs and the ability to recharge at home, whilst being less likely to associate EVs with negative emotions. Conversely, these clusters are also significantly different on a number of issues. Whilst Keen Greens tend to be younger and display high levels of innate and adoptive innovativeness, Early Adopters are more likely to be older and exhibit low levels of innate and adoptive innovativeness. Positioned in the middle of the LEV preference scale are Car Enthusiasts who display moderate preferences towards LEVs, though these are substantially lower than their preferences for conventional powertrains. On initial inspection, Car Enthusiasts display a number of features which would potentially indicate openness to LEVs. Firstly, Car Enthusiasts are the most innately and adoptively innovative cluster, displaying knowledge of innovations, positive attitudes towards science and education whilst also owning a large quantity of household technology. Moreover, Car Enthusiasts consider their cars to be symbolic representations of their identities and a source of positive emotions whilst also stating an awareness of LEVs and knowledge of the mechanical operation of vehicles. However, potentially limiting the attractiveness of LEVs to this cluster are their lack of concern for the environmental consequences of car use and general lack of interest in biospheric life principles. Additionally, Car Enthusiasts tend to hold negative attitudes of the functional capabilities of EVs and view them as being a source of negative emotions. 247
Chapter Seven: Results – Market Structure Analysis Table 7.10: Summary of Cluster Loadings – Ranked from Lowest (light) to Highest (dark) Variable
EC
WD
Clusters EA
KG
CE
Socio-Economics Education Income Age Current Car Details Cars in household Engine size Annual mileage Usual car expenditure Innovativeness Innate Innovativeness: Comm Innate Innovativeness: Psych Adoptive Innovativeness Car Meanings Meaning: Symbolism and Emotion Meaning: Function Car Attitudes Car Importance Car Knowledge Car Environment Car Cost EV Emotions and Attitudes Positive EV Emotions Negative EV Emotions Negative EV Attitudes Positive EV Attitudes Life Principles Biospheric Principles Egoistic Principles Having examined the profile of the clusters identified by the analysis, a number of general conclusions can be made. Firstly, clusters may hold similar preferences for LEVs, but these preferences may be motivated by different cluster characteristics. Whilst Early Adopters are potentially attracted to LEVs as a result of the environmental connotations that surround these vehicles and their dissatisfaction with conventional cars, Keen Greens are perhaps 248
Chapter Seven: Results – Market Structure Analysis more drawn to the innovativeness of LEVs as a result of their innate and adoptive innovativeness tendencies. Conversely, the Environmental Cynics may hold negative preferences towards LEVs due to their propensity to consider a car to be an important possession embedded with functional meaning whereas Weekend Drivers may hold low LEV preferences as a result of their relatively reduced car usage meaning the value they would receive from the lower operating costs of LEVs would be limited. To summarize, this chapter presented and discussed results from a structural analysis of the emerging market for LEVs. Five unique clusters have been identified based on their preferences towards LEVs, socio-economic characteristics, current car details and sociopsychological features. The results of this analysis show the value of including sociopsychological considerations in cluster descriptions. Clusters which initially appear similar when considering the objective aspects linked to socio-economic and current car details prove to be distinctly different when examining their socio-psychological features. This finding indicates that a focus only on objective aspects when conducting a market segmentation analysis may potentially lead to a misclassification of different consumers and that an appreciation of the attitudes, opinions and emotions of market segments is required to accurately partition the market.
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Chapter Eight: Discussion and Conclusions
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DISCUSSION AND CONCLUSIONS
8.1 INTRODUCTION The previous three chapters presented and discussed detailed statistical analysis on the dataset attained from the application of the household survey developed for this thesis. As a result of this extensive analysis, it may prove difficult to recall the initial purpose of the thesis, how it was designed and the method selected to apply it. This chapter provides an overview of the entire thesis from original conception to the final thoughts on results interpretation. The conceptual framework is critically assessed to determine its structural validity and what aspects could be improved. The methodology selected to apply the household survey is evaluated to consider its suitability and identify any significant limitations. The results of the statistical analysis are examined to discover the knowledge which is embedded within them. To provide a structure for this discussion, the research objectives and questions attached to this thesis are approached utilizing the evidence gathered to determine what insights have been gained. Emphasis is placed on the last research objective, which draws together the evidence gathered to provide guidance points for policy. To conclude this chapter, recommendations for future research in this field are suggested which would build on the results of this thesis and examine areas which remain unexplored. 8.2 RESEARCH OBJECTIVES REVISITED As initially outlined in Section 1.2 of the introductory chapter, this thesis addresses three overarching research objectives each with a number of research questions attached to them. This section of the chapter appraises how the application of the adopted 250
Chapter Eight: Discussion and Conclusions methodology provides evidence to address these objectives and answer the associated questions. Each research objective is taken in order with the findings of the results chapters being interpreted to propose answers to the specific questions. 8.2.1 Develop and apply a conceptual framework of LEV preference The first research objective explored in this thesis involved the development and application of a conceptual framework used to examine preferences towards LEVs. This framework, which is displayed in Figure 8.1, was designed following an extensive review of the previous literature which identified a number of gaps in existing knowledge. Specifically, three overarching constructs were incorporated in the framework that have been observed in related fields to be significant motivators of behaviour but have yet to be examined in the context of LEV preferences. The first of these overarching constructs reflects individual innovativeness which has been measured from an innate and adoptive perspective. The second overarching construct examines if the attitudes individuals form regarding the functional performance of EVs are being influence by the meanings they place on car ownership and use as well as their general attitudes towards cars. The third overarching construct positions value orientation as a basis from which all other forms of sociopsychological factors and, ultimately, behaviour originate. The framework’s structure is deterministic in nature with the most abstract constructs positioned to the left whilst the most tangible are positioned to the right. Value orientations are positioned as the foundation of all other socio-psychological constructs which in turn hold influence over preferences for LEVs. Each construct included in the framework has an instrument attached allowing for quantitative measurement. LEV preferences were measured through the application of a powertrain evaluation exercise which included four different options based on the electric powertrain pathway. All the socio-psychological constructs were measured through the application of attitudinal scales following a Likert format (Likert, 1932). For the scales measuring value orientation (Steg et al., 2005) and car meanings (Dittmar, 1992), the measurement instruments have been sourced from previously applied studies. For all other components measuring socio-psychological constructs, the measurement instruments were designed originally for this thesis based on a review of the literature and extensive piloting. 251
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Figure 8.1: Conceptual Framework Developed for this Thesis Associated with this research objective are three questions which focus on specific aspects of the conceptual framework’s structure. These questions are connected to the three overarching constructs which form the main components of the framework. The remainder of this section presents each of these research questions and discusses how they can be addressed from the evidence gathered. 1. Does an individual’s innovativeness explain variance in the LEV market? The first question associated with this research objective is connected to the first overarching construct incorporated in the conceptual framework which provides a measurement of individual innovativeness and determines if this trait holds an influence over LEV preferences. Innovativeness itself is separated into two sub components which exist at different levels of conceptual abstraction. The most abstract construct is that of 252
Chapter Eight: Discussion and Conclusions innate innovativeness (Midgley and Dowling, 1978) which measures an individual’s propensity to behave in an innovative manner by examining key psychological and sociological factors which have been shown in previous research to be associated with early adoption of innovations. The second and more tangible construct is that of adoptive innovativeness, which has been measured in this thesis by noting the quantity of household technology owned by an individual with the assumption being that the more household technology owned, the higher the degree of adoptive innovativeness. Innate innovativeness was measured through the application of two attitudinal scales with the first examining the psychological determinants of innovativeness whilst the second assesses the communication determinants. These two scales were originally developed for this thesis with the results of the principal components analysis presented in Chapter 5, Section 5.5.9 and 5.5.10. Appraising the quality of these results for both scales, the KMO test of sampling adequacy is rated as being meticulous whilst Bartlett’s test of sphericity is significant indicating that the scales are appropriate for structure detection. Relating to the factors extracted from the scales, the Cronbach’s alpha scores, which measure internal consistency, range from .865 to .361. Three different measurements of adoptive innovativeness were taken which measure the total quantity of household technology owned, the total quantity desired and the total quantity not owned. Results for these measurements are presented in Chapter 5, Section 5.4 and are in keeping with expectations with the variables measuring the quantity of household technology owned and the total quantity not owned tending to follow a normal distribution which is a principal aspect of the Diffusion of Innovations theory (Rogers, 1995). Having evaluated the measurements associated with the overarching construct of innovativeness, the analysis progressed to examining how these different measurements are related. As illustrated in the conceptual framework, innate innovativeness is positioned at a higher level of abstraction and is conceptually linked to adoptive innovativeness. Correlation analysis was utilized to determine where significant relationships exist between these two constructs with the results displayed in Chapter 6, Section 6.2.4. In regards to the four factors extracted from the psychological determinants scale, three hold significant correlations with measurements of adoptive innovativeness whilst the same is true for both 253
Chapter Eight: Discussion and Conclusions of the factors extracted from the communication determinant scale. Of these, the factors measuring the information regarding innovations and compulsive behaviour display particularly strong coefficients, indicating their relative superiority in indicating the adoption of innovations. Taking the analysis one step further by observing if the measurements of innate innovativeness can be used to explain levels of adoptive innovativeness, two regression models were specified with the results presented in Chapter 6, Section 6.3.2. Examining the explanatory power of the two models, it is apparent that both are effective at explaining a significant degree of the variation in the adoption of innovations. Moreover, the model which utilized the quantity of household technology not owned (a negative measurement of adoptive innovativeness) attains a higher value for the coefficient of determination (R2 of .374 compared to .319). The final stage of analysis necessary to answer this research question examines how adoptive innovativeness can be used to explain preferences towards LEVs. Correlation analysis was employed to examine the relationships which exist between these variables with the results presented in Chapter 6, Section 6.2.6. In this instance, significant results are observed between the variables measuring the quantity of household technology desired (r of .163 and .160) and the quantity not owned (r of -.168 and -.142) and the measurements of LEV preference but, surprisingly, not the quantity owned. Following this, regression analysis was utilized to determine if these observed relationships transfer into causal inferences with the results presented in Chapter 6, Section 6.3.4. The total quantity of household technology not owned was used as an explanatory variable to determine the influence of adoptive innovativeness over mean preferences towards Hybrid and Plug-in LEVs. In both models, this measurement of adoptive innovativeness holds a significant negative effect (β of -.077 and -.069). These findings indicate that the construct of innovativeness is a significant determinant of preferences towards LEVs. Specifically, individuals that display low levels of adoptive innovativeness have a higher propensity to display low preferences for LEVs and are therefore more likely to be categorized as Laggards in this emerging market. With adoptive innovativeness being a less abstract and more observable individual characteristic, these findings indicate that it can be used as a valid indicator or LEV preferences.
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Chapter Eight: Discussion and Conclusions 2. What is the relationship between general car meanings and car attitudes with specific attitudes towards EVs? The second question associated with this research objective is linked to the second overarching construct incorporated in the conceptual framework. In this instance, attitudes towards cars in general and the meanings placed on car ownership are positioned as influential factors over attitudes relating to the functional capabilities of EVs. The purpose of this structure is to begin to identify motivations associated with positive and negative opinions relating to the instrumental performance of EVs. To examine this aspect of the conceptual framework, it was first necessary to produce accurate measurements of these socio-psychological constructs. The results associated with this initial step can be viewed in Chapter 5, Section 5.5.3 to 5.5.8 where principal components analysis was employed to extract these unobserved variables from the attitudinal scales developed to measure them. For all of the scales associated with these constructs, evaluations of their suitability for structure detection (Bartlett’s test of sphericity) and the presence of latent variables in the data (KMO test) have all proven to be satisfactory. The Cronbach’s alphas calculated for each construct are generally acceptable though a number of low values were calculated. Specifically, the construct measuring concerns regarding the costs attributed to car ownership (Car Cost) and the construct assessing positive attitudes towards the functional capability of EVs (Positive EV Attitudes) both have Cronbach’s alpha scores of less than 0.5 which indicates internal inconsistency. These findings suggest that improvements could be made to the scales examining these constructs through a re-specification of the constituting statements attached to them so that future applications attain a higher quality measurement. To test the conceptual links defined by the framework, two different forms of statistical analysis were utilized. The first involved correlation analysis to determine if significant relationships exist between the constructs and what affect these relationships have. The results of this analysis are presented in Chapter 6, Section 6.2.5 and display a number of significant results. Firstly, the symbolic, emotive and functional meanings individuals place on car ownership and use are positively correlated (r of .211 and .107) with negative 255
Chapter Eight: Discussion and Conclusions attitudes relating to the functional performance of EVs. More generally, individuals that consider their vehicles as status symbols, sources of positive emotion and to embody functional value have a higher propensity to consider EVs to be unreliable and complicated to use. The importance of emotive constructs is further supported by the finding that the emotions individuals attach to EVs are related to their opinions of EV instrumental capacity. Thus, if an individual links EVs with irritation, embarrassment and boredom then they are more likely to consider EVs to be unsafe and lacking performance. Turning the attention to the relationship between general car attitudes and EV attitudes, as expected, the degree to which an individual holds concerns relating to the environmental consequences of car use and a willingness to act on these concerns is positively correlated (r of .302) with positive attitudes towards EVs whilst being negatively correlated (r of -.159) with Negative EV Attitudes. This result indicates that individuals who are concerned about the environment are likely to value EV decentralized charging and consider EVs to offer adequate performance levels. In addition, the level of knowledge and mechanical competency an individual states they have with cars is positively related (r of .317) to Negative EV Attitudes, indicating individuals who are generally interested in cars tend to hold negative opinions of the functional capabilities of EVs. Following this, regression analysis was used to determine the extent to which the constructs associated with car attitudes and car meanings in general can be used to explain evaluations of the functional capability of EVs. The results of this analysis are presented in Chapter 6, Section 6.3.3 with two models being specified to look at Negative EV Attitudes and Positive EV Attitudes. Both of the models explain a significant proportion of the variance in the dependent variables (R2 of .301 and .296) with the socio-psychological constructs proving to be better predictors compared to socio-economic characteristics. The findings of these models are generally in keeping with expectations and support the hypothesized structure of the conceptual framework. Key results indicate that the meanings individuals place on car ownership and use, the emotions associated with EVs and the attitudes held regarding cars in general hold an influence over appraisals of the instrumental capacities of EVs. More generally, evaluations of EVs are not only influenced by the attitudes and emotions held towards EVs, but also the attitudes and emotions held towards cars on the whole.
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Chapter Eight: Discussion and Conclusions 3. Do values significantly relate to other socio-psychological constructs? The final question attached to this research objective is associated with the third overarching construct embedded in the conceptual framework. Positioned at the far left of the model, and therefore at the highest level of abstraction, value orientations were hypothesized to act as a basis on which all other framework components originate. A scale was employed to provide a measurement of biospheric, egoistic and altruistic life principles based on Steg et al.’s (2005) specification. Comparing the output from the principal components analysis (detailed in Chapter 5, Section5.5.11) to previous applications of this scale (de Groot and Steg, 2007; de Groot and Steg, 2008), the factors extracted in this thesis are generally in keeping with past results with a clear separation between the three life principles. A number of small divergences can be found, mostly attributed to the internal structure of the factors with the constituting statements appearing in slightly different orders. From this first stage of the analysis, it can be proposed that valid measurements of the three life principles incorporated in the conceptual framework have been made, supported by strong Cronbach’s alpha scores, clear factor output and a high degree of compatibility with previous applications of this scale. The next stage necessary in addressing this research question was to determine how the three life principles interact with other socio-psychological constructs included in the conceptual framework. To achieve this, a number of correlation analyses were conducted to identify significant relationships. The results of this analysis are presented in Chapter 6, Section 6.2.3 with a number of significant correlations identified. Firstly examining how Biospheric Principles interact with other socio-psychological constructs, there is no interaction with the factors associated with communication determinants of innate innovativeness, however significant positive relationships are observed with the factors measuring personal aspiration (r of .162) and attitudes towards science and education (r of .108) of from the psychological determinants. From this, it can be proposed that individuals who are motivated by biospheric values are unlikely to be innately innovative. In regards to constructs measuring general car attitudes, it is unsurprising that Biospheric Principles shares a significant positive correlation (r of .488) with concerns relating to the
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Chapter Eight: Discussion and Conclusions environmental consequences of car use though there is also a significant interaction (r of .109) with the construct measuring perceived car importance. Shifting the attention to how Egoistic Principles interact with other socio-psychological constructs, significant positive correlations are observed between a substantial proportion of the factors associated with innate innovativeness. This finding indicates that individuals who have affinity with egoistic principles tend also to be innately innovative. Examining how this life principle relates to car specific constructs, a significant positive correlation (r of .292) is observed with the construct measuring symbolic and emotive car meaning, suggesting that individuals who are motivated by egoistic pursuits are also likely to consider their cars as status symbols and to be embedded with positive emotion. Moreover Egoistic Principles is also positively correlated with Car Knowledge (r of .167) and Car Cost (r of .179), indicating that egoistic individuals tend to have practical knowledge of car mechanics whilst holding concerns relating to the costs attributed to car ownership. The final value orientation measured by the conceptual framework, which focused on Altruistic Principles, shares a significant relationship (r of .204) with the factor measuring the social aspects of innate innovativeness and also the factor measuring personal aspiration (r of .115) and a decision making (r of .140). This finding suggests that individuals who are motivated by altruistic interests have a moderate propensity for innate innovativeness. Additionally, altruistic individuals are also more likely to hold concerns for the environmental consequences of car use (r of .106) and the costs attributed to car ownership (r of .116). In sum, the results observed during the application of correlation analysis between the measurements of life principles and other socio-psychological constructs provides support to the proposition that value orientations are significantly associated with innate innovativeness, car meanings and car attitudes.
Individuals that hold biospheric and
altruistic value structures tend to hold concerns for the environmental implications of cars and thus may well be attracted to the environmental aspects of LEVs. Conversely, individuals that have an egoistic value structure tend to hold higher levels of innate
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Chapter Eight: Discussion and Conclusions innovativeness, indicating that they are more likely to be drawn to the advanced technology embedded within LEVs. 8.2.2 Conduct a segmentation analysis of the emerging market for LEVs The second research objective linked to this thesis examines the structure of the emerging market for LEVs by assessing the social stratification of the consumer base. The automotive market in general contains a substantial level of variety both in terms of the cars available for purchase and the individuals purchasing the cars. Whilst every individual is unique, it is hypothesized that certain individuals share similarities with each other and can therefore be classified into groups based on their shared characteristics. As LEVs become widely available for purchase in the mainstream market, academic attention is beginning to shift to determining what types of individuals will be more receptive to these vehicles and how policy can be designed to target individual segments. Previous studies have examined the socio-psychological profiles of potential LEV adopters and non-adopters (Skippon and Garwood, 2011; Borthwick and Carreno, 2012) and also the spatial distribution of adopters (Campbell et al., 2012; Pridmore and Anable, 2012). This thesis segments the emerging market for LEVs based on the results of the conceptual framework. Three specific research questions are connected with this research objective and the remainder of this section is focused on using the evidence attained from the market structure analysis to answer these questions. 1. Are heterogeneous consumer segments being formed in the emerging market for LEVs? The first question attached to this research objective examines the hypothesis that segments, which have unique profiles, are forming in the emerging market for LEVs. In order to determine if this is the case, a cluster analysis was conducted on the dataset attained from the household survey which found a five cluster solution to provide an effective partition of the respondents. These five clusters were assessed using ANOVA to determine if they were statistically different from each other in their profiles. This testing was done over two different stages with the first stage determining the difference between clusters in 259
Chapter Eight: Discussion and Conclusions reference to the segmentation variables (discussed in Chapter 7, Section 7.3.1) used to separate them. The results of this first stage of testing can be viewed in Chapter 7, Section 7.3.4 which shows the five clusters identified are significantly different from one another on all nine of the segmentation variables used in the analysis. The second stage of testing involved an examination of the remaining variables included in the survey dataset inclusive of socio-economic characteristics, current car details, technology ownership and sociopsychological constructs. The results of this stage of testing can be viewed in Section 10.5 of the Appendix whereby 61 variables were examined with 51 proving to display significantly different values between the five clusters at the 95% confidence level. The results attained over the two stages of testing provide evidence to suggest that unique segments are forming in the emerging market for LEVs. These segments display unique profiles which can be described using a mixture of different characteristics. Firstly, objective characteristics, which refer to the more quantifiable and easily observed features of the segments such as gender and income, can form one basis of the profiling. Indeed, the use of easily observed variables often encompasses the entirety of the cluster profiling conducted in traditional market segmentation analysis. However, this thesis demonstrates that the inclusion of socio-psychological constructs provides additional richness to the segment profiles, further distinguishing them from one another. Moreover, the socio-psychological constructs prove just as effective at identifying significant differences between segments compared to the more traditional objective characteristics. 2. What are the defining features of each segment? The second question linked to this research objective is concerned with the principal features of the segments identified. With the household survey taking measurements on a large array of different respondent characteristics, this has allowed for a detailed profile of each segment to be formed. These profiles are discussed in detail in Chapter 7, Section 7.4 with each of the five market segments identified having a unique structure. Specifically focusing on their preferences for LEVs, a number of clusters may hold similar levels of preference, though form these preferences for different reasons. To illustrate this point, in
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Chapter Eight: Discussion and Conclusions this section clusters are compared to demonstrate the similarities in preference and differences in cluster profiles. The Environmental Cynics and Weekend Drivers hold the lowest levels of LEV preference, likely linked to their lack of concern for the environmental impacts attributed to car use and their wider disregard of biospheric life principles. Moreover, both of these clusters tend to consider EVs to be functionally inferior to conventional cars and to associate EVs with negative emotions. However, these two clusters differ on a number of important issues, indicating that their low LEV preference may be influenced by unique motivations. Weekend Drivers represent individuals with low car usage, meaning their ability to benefit from the reduced operating costs of LEVs is limited. Additionally, this cluster tends to spend the least when purchasing cars and have the lowest household incomes, meaning their ability to pay LEV price premiums is likely diminished. Moreover, Weekend Drivers tend to be single car households, suggesting that any car they purchase must be capable of meeting all their desired transportation needs, whilst Environmental Cynics are the most likely to be multicar households. Examining how these two segments are positioned in regards to the concept of innovativeness, it is apparent that Weekend Drivers exhibits low levels of both adoptive and innate innovativeness whereas Environmental Cynics rank moderately on these metrics. With reference to car specific socio-psychological constructs, Environmental Cynics are more likely to consider cars to have a high degree of functional value, be important possessions enabling life and to hold knowledge relating to car mechanics. Holding moderate LEV preferences are Car Enthusiasts, who display preferences for LEVs which are below those held for conventional powertrains. Car Enthusiasts are a highly distinctive cluster, spending considerably more than other clusters when purchasing cars, are the most likely to own a diesel car and cars with a large engine displacement, indicating a taste for car performance. This cluster scores highly on innate and adoptive innovativeness and places a high degree of symbolic and emotive attachment to cars. However, this innovativeness does not appear to be transferring to the LEV market, perhaps as a result of this cluster’s lack of concern for biospheric values and skepticism relating to the functional attributes of EVs.
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Chapter Eight: Discussion and Conclusions Expressing the highest preferences for LEVs are Early Adopters and Keen Greens. Both clusters holds similar levels of preference for LEVs compared to conventional powertrains which indicates these clusters may represent innovators in this market. These clusters are quite comparable in their socio-economic and current car characteristics, though Early Adopters represents somewhat older drivers whilst Keen Greens are more likely to hold a university level education. Moreover, both clusters are concerned about the environmental implications of car use, are motivated by biospheric life principles and hold positive opinions regarding the functional capabilities of EVs. However, these clusters are also different in a number of important aspects, with Keen Greens displaying relatively high levels of innate and adoptive innovativeness whereas Early Adopters exhibit distinctly low levels of innovativeness across both levels of abstraction. Thus, the motivation for Early Adopter’s to consider purchasing a LEV for the next car is unlikely to originate from their degree if innovativeness. Moreover, Early Adopters have an aversion to considering cars to hold symbolic or emotive meaning whilst Keen Greens tend to associate conventional cars with negative emotions and EVs with positive emotions. This last point indicates that Keen Greens may be dissatisfied with conventional cars whilst considering LEVs capable of addressing this discontentment. 3. Do these segments conform to theoretical expectations? The final question associated with this research objective is concerned with whether the output from the segmentation analysis is compatible with theoretical principles. When designing the conceptual framework linked to this thesis, the first overarching construct, which measures the concept of innovativeness, was sourced from the Diffusion of Innovation theory (Rogers, 1995). One of the primary features of the Diffusion of Innovation theory is the proposition that a market can be partitioned according to the length of time it takes an innovation to attain market saturation. Consumers are categorized in accordance with how long it takes them to move through the adoption decision process (discussed in Chapter 3, Section 3.3.1) which reflects their degree of innovativeness. Those market segments which tend to become aware of and interested in an innovation either before or close after its release and have a relatively short adoption process are referred to as
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Chapter Eight: Discussion and Conclusions Innovators whilst those individuals who are among the slowest to adopt are labelled Laggards. Inspecting the results attained in this thesis in reference to the expectations of the theory, a partial overlap can be observed. Within the cluster solution, Early Adopters and Keen Greens have relatively high preferences for all four LEV powertrain options. These findings indicate that these clusters may represent individuals who are more receptive to and therefore more likely to adopt a LEV. Conversely, Environmental Cynics and Weekend Drivers have distinctly low preferences for the LEV powertrain options, perhaps indicating these clusters represent Laggard market segments. Moreover, Keen Greens displays high preferences for LEVs alongside high levels of innate and adoptive innovativeness whilst Weekend Drivers rank lowest for all three of these metrics. However, there are two clear disparities in this trend with Car Enthusiast, who exhibit the highest degree of innate and adoptive innovativeness but medium to low LEV preferences, whilst Early Adopters, who display high levels of LEV preference but hold relatively low levels of innate and adoptive innovativeness. These finding indicate that individual innovativeness appears to be an important feature in some segments, but not others. Individuals that display distinctly high levels of innate and adoptive innovativeness may not consider LEVs to represent a desirable innovation due to their perceived lack of functional performance and incompatibility with emotive and symbolic car attachment. In sum, LEVs may not follow a traditional diffusion process as they may well prove to be more successful with segments who display low levels of innovativeness whilst being viewed as un-desirable by segments that exhibit high levels of innovativeness. 8.2.3 Transform research output into recommendations for decision makers With this thesis being conducted in a field of active government policy (DfT, 2009a; CCC, 2012; Transport Committee, 2012) and with academic research often assessed through its contribution to society (Bornmann, 2012), the final research objective attached to this thesis is associated with demonstrating how the findings of the research can be of benefit to 263
Chapter Eight: Discussion and Conclusions decision makers. LEVs have the potential to address a number of prominent societal objectives, such as reducing greenhouse gas emissions and local pollution levels alongside mitigating dependency on oil and thus increasing the level of energy security. However, the ability of LEVs to contribute towards these objectives will be directly connected to their levels of adoption by mainstream consumers. If LEVs undergo a rapid diffusion and are generally well accepted by consumers, then emissions levels associated with vehicle operation and the susceptibility of the UK to external energy shocks will be mitigated. Conversely, if adoption rates are relatively muted, LEVs will remain in a niche market meaning other mechanisms will be required to address the societal objectives outlined. At the commencement of this thesis, only three LEVs 1 were available for purchase in the 0F
mainstream UK automotive market. During the last three years, new manufacturers have entered the market whilst those manufacturers which were already present have diversified their LEV options. Between 2007 and 2012, new registrations of LEVs have increased by 67.3% with a 217.9% increased in pure EV registrations (SMMT, 2013). However, the absolute number of LEVs on UK roads remains markedly low and well below the targets set by the Committee on Climate Change (CCC, 2012). By producing a structural profile of the consumer segments forming in the LEV market, policy can be focused on likely adopters whilst barriers reducing the likelihood of some segments from adopting a LEV can be mitigated. This thesis initially associated three questions with this research objective which are approached in this section using the evidence presented in the results chapters. 1. Are preferences for LEVs influenced by local policy? The approach to study site selection and survey administration designed to apply the conceptual framework was significantly influenced by this research question which is associated with assessing the impact of current government LEV market interventions. As described in detail in Chapter 4, Section 4.3.1, the UK Government has initiated two policies in selected sites throughout the UK which are intended to enhance the adoption of LEVs. Specifically, a LEV demonstration project has allowed the general public to trial LEVs whilst charging infrastructure associated with the operation of plug-in LEVs has been installed in 1
Toyota Prius, Honda Civic Hybrid and Tesla Roadster
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Chapter Eight: Discussion and Conclusions public areas. To determine if theses policy initiatives have influenced preferences towards LEVs, a two site approach was used when administering the household survey. Newcastle upon Tyne was selected to represent the policy site due it its exposure to both market interventions whilst Dundee was chosen as a comparison site due it its similarity to Newcastle in regards to the socio-economic and travel behaviour characteristics of the population (discussed in Chapter 4, Section 4.3.3) yet having no exposure to the policy initiatives. The subsamples associated with these study sites were compared to determine if preferences for LEVs in Newcastle were significantly higher compared to those held in Dundee. The comparison of the two subsamples associated with the study sites was conducted in three principal ways. Firstly, the results of the powertrain evaluation exercise (detailed in Chapter 5, Section 5.3.4) were examined to determine if any significant differences in preferences exist between Newcastle and Dundee respondents. With the population of Newcastle having been exposed to the two policy initiatives previously outlined, expectations were that the Newcastle subsample would display higher preferences for LEVs compared to the Dundee subsample. From this analysis, the null hypothesis was accepted in regards to the preference structures for Petrol, Diesel, Plug-in Hybrid and Pure EV powertrains. However, significant differences in the preference structures were identified for Mild and Full Hybrid, with Newcastle respondents displaying significantly higher preferences for these two powertrains compared to the Dundee respondents. These results provide evidence to the proposition that the LEV market interventions introduced by the UK Government have had an effect on preferences towards these vehicles. However, it is surprising that differences in preferences are observed for the Hybrid LEV options, as the detailed government interventions were focused on Plug-in powertrains. To determine if the study site a respondent originates from significantly influences their preferences for LEVs, a dummy variable was included in the regression analysis. The results of this can be viewed in Chapter 6, Section 6.3.4. Specifically, this dummy variable was included in stage two of the entry procedure to determine if it could explain any variance not already accounted for by the socio-psychological constructs. The variable holds a positive significant influence in the Mean Hybrid Preference model, indicated study site is a 265
Chapter Eight: Discussion and Conclusions valid indicator of preferences towards Hybrid vehicles. This finding supports the results observed in the preference comparison, which suggested that Newcastle respondents hold significantly different preference structures for the Mild and Full Hybrid options included in the powertrain evaluation exercise. The market structure analysis provides further supports to this proposition, with the assignment of Newcastle and Dundee respondents to different segments being of particular interest (detailed in Chapter 7, Section 7.4.1). For the five segments identified by the analysis, the internal composition of four of these clusters is evenly split between respondents of the two study sites. However, for the Keen Green segment, there are a significantly higher proportion of Newcastle respondents contained within this cluster compared to Dundee respondents (64.3% to 35.8%). This result indicates that the Newcastle subsample contains a relatively higher proportion of respondents who are likely to be categorized as innovators and early adopters in the market for LEVs. Taking this interpretation one step further, it can therefore be proposed that the Newcastle car market is relatively more primed for the introduction of LEVs with a greater proportion of the market likely to be potential LEV adopters. In sum, the evidence found in reference to this research question generally supports the proposition that the aforementioned UK Government initiatives have significantly influenced the population of the policy sites. With the expressed purpose of these initiatives being to establish frontrunners and to facilitate an early market for LEVs, these results suggest that the policies have been affective in meeting their objectives. 2. Are any barriers to adoption identified through the analysis which can be directly addressed by policy? The second question attached to this research objective is broad in its approach and asks if the results of the analysis have found barriers to the adoption of LEVs. Previous research has already shed light on a number of adoption barriers, mostly associated with the functional attributes of LEVs (Beggs et al., 1981; Calfee, 1985; Potoglou and Kanaroglou, 2007). With this thesis approaching the topic of LEV demand by examining socio266
Chapter Eight: Discussion and Conclusions psychological constructs, there is a potential for this project to identify less tangible adoption barriers. To determine if this has been the case, two specific aspects of the results have been evaluated. The first of these aspects relates to the second overarching component of the conceptual framework which utilized general car meanings and car attitudes to explain how individuals are forming opinions relating to the specific functional capabilities of EVs. The findings of this analysis are presented in Chapter 6, Section 6.3.3 with a number of significant results having been found. Assessing which of the independent variables hold influence over Negative EV Attitudes, the first notable result is Car Knowledge holds a positive effect. This result suggests that the more an individual holds knowledge of cars in general and LEVs in particular, the more negative their opinions regarding the instrumental capabilities of EVs. This result is of importance as those individuals who are knowledgeable about certain products often act as opinion leaders, providing information to their social networks (Iyengar et al., 2011). Secondly, the variables Meaning: Symbolism and Emotion and Meaning: Function also hold a positive influence in this model. These two constructs measure the assignment of symbolic, emotive and functional meaning to car ownership and use. These findings indicate that the association of cars with personal expression, positive emotion and functional value negatively influences perceptions of EVs. Moreover, the constructs Positive and Negative EV Emotions, which measure the connection between EVs and emotive response, also hold influence in this model, further supporting the importance of emotions in how individuals evaluate the functional capabilities of EVs. Thus, decision makers may wish to consider strategies which attempt to connect LEVs with positive emotions, as this is likely to enhance how consumers evaluate the instrumental value of these vehicles. The second aspect which has the potential to highlight barriers to LEV adoption involves the segments identified in the market structure analysis discussed in Chapter 7. The principal features of a number of the segments provide insights regarding possible adoption barriers, and how these barriers are grouped in different segments. Firstly, the Environmental Cynics and Weekend Drivers tend to hold the least positive preferences towards LEVs whilst sharing a number of similarities and notable differences in their profiles. Both of these 267
Chapter Eight: Discussion and Conclusions segments consider EVs to be functionally inferior compared to conventional cars, indicating that relative performance levels are still an important issue Moreover, these segments are not concerned about the environmental consequences of car use or motivated by biospheric life principles, suggesting that they are unlikely to be attracted to the green image of LEVs. In addition, Environmental Cynics consider cars to have a high degree of functional value, to be an important possession which enables their lives and hold understanding of car operation. These results indicate that interest in cars in general appears to be associated with lower preferences for LEVs, though why this should be the case still remains unclear. Positioned in the middle of the preference scale is the Car Enthusiast market segment which contains individuals that tend to display high levels of innate and adoptive innovativeness yet do not transfer these innovative tendencies into the LEV market. Possible barriers restricting the LEV preferences of Car Enthusiasts include the fact that this cluster tends to consider their cars to represent symbolic and emotive car meanings whilst being generally motivated by egoistic pursuits. Moreover, this cluster is inclined to hold unfavourable views relating to the functional capabilities of EVs and to associate EVs with negative emotions. Interpreting these results, it can be proposed that Car Enthusiasts, whilst generally reflecting an innovative segment of the population, are unlikely to be among the early adopters of LEVs, potentially due to their consideration of EVs to be functionally inferior to conventional cars and the incompatibility of LEVs with their identities and values. These latter issues tend to be overlooked by Government policy as they are challenging to approach. However, without an appreciation of the importance of ensuring LEVs are compatible with a wide range of lifestyle and value orientations, their adoption by a diversity of consumers remains unlikely. 3. Can the market segments identified inform the design and targeting of policy intended to influence the uptake of LEVs? The final question attached to this research objective specifically focuses on the market structure analysis and asks if the segments identified can assist in targeting government and market policies to enhance the uptake of LEVs. To address this topic, it is useful to partition 268
Chapter Eight: Discussion and Conclusions the segments identified in the market structure analysis into three sections. The first section contains the segments Environmental Cynics and Weekend Drivers which both hold low preferences for LEVs and most likely reflect non-adopters or Laggards in the market. The profile of these two segments indicates a significant aversion to LEVs, which is unlikely to be mitigated by short or medium term market intervention. Indeed, the aversion appears to be significantly entrenched, with these segments neither considering the environmental consequences of car use to be their responsibility nor the functional capabilities of EVs to be particularly valuable. These findings suggest that the Environmental Cynics and Weekend Driver market segments would unlikely benefit from policies focused on enhancing LEV adoption. At the other end of the LEV preference scale, the second section contains the segments Early Adopters and Keen Greens who display markedly high preferences for LEVs. Indeed, these two clusters have LEV preferences comparable to those for conventional vehicles, indicating their distinct likelihood to consider an LEV in their next car purchase. Knowing the features of these segments allows decision makers to better target policy and market interventions to the individuals who will be most receptive to it, rather than use the broad measures currently favoured. Moreover, the principal features of these market segments provides insights regarding what type of interventions may be more effective, with Keen Greens likely to be receptive to policy which focuses on the environmental benefits of LEVs whilst Early Adopters are likely to be open to the position of LEVs as prominent innovations. Targeting these two segments with policy initiatives and market interventions during the short term has the potential to enhance the probability of LEV adoption. The final section includes the Car Enthusiast market segment who display a number of encouraging characteristics, such as high levels of innovativeness and an interest in and connection to cars, but hold relatively muted preferences for LEVs. This market segment could perhaps benefit from medium term policy initiatives intended to shift the symbolic meanings associated with LEVs away from environmental considerations and more towards their embodiment of advanced technologies. If LEVs continue to be regarded as functionally inferior to conventional cars on issues related to performance, it is unlikely that individuals who fit the Car Enthusiast profile will be attracted to them. 269
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More generally, current UK Government policy in the LEV market is focused on fiscal incentives and the installation of charging infrastructure. This thesis has demonstrated the important role socio-psychological constructs hold in the segments emerging in this market. As understanding regarding these socio-psychological aspects improves, government policy has the opportunity to adapt to take account of these less observable but still significant influences over behaviour. Traditional methods of behavioural modification, often following a market regulation design (Seddon, 2003), may need to be re-evaluated to determine if they are fit for purpose in a more socially diversified society. 8.3 POLICY RECOMENDATIONS The implications for policy which have emerged out of an interpretation of evidence gathered in this thesis have covered a number of interrelated issues. This section provides a synthesis of the most policy relevant outputs which many be of use to decision makers in this field. In a similar fashion to the King Report (King, 2007; King, 2008), a number of recommendations are made followed by a short description. More focus needs to be provided regarding the influence of EV trials and the installation of charging infrastructure over LEV preferences. Through an examination of the preference structure of respondents from Newcastle and Dundee, this thesis has found initial evidence that areas which have been the recipients of EV trials and infrastructure installation have had their preferences primed for LEVs. The UK Government should consider expanding these initiatives to new areas to further support the emerging market for LEVs. Consideration should be given to the importance of emotional attachment to cars in general, EVs in particular and the way these emotional attachments can affect attitudes about the functional performance of EVs.
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Chapter Eight: Discussion and Conclusions Emotions have been identified in this thesis to be a significant factor influencing evaluations of the instrumental capabilities of EVs. If individuals associate EVs with positive emotions such as happiness and enjoyment, they are unlikely to be concerned about the functional implications of EVs. These results suggest that the UK Government may want to consider actively attempting to associate EVs with positive emotions through information campaigns and marketing to influence how individuals perceive their instrumental characteristics. Individuals that hold knowledge concerning cars in general need to be convinced of the virtues of EVs. With the factor measuring the degree to which individuals hold mechanical competence with cars negatively influencing evaluations of the functional capabilities of EVs, it is possible individuals who consider themselves knowledgeable regarding cars may not regard EVs to be instrumentally capable. Individuals that have experience and knowledge relating to products can be a key source of information to their social networks. To ensure that positive opinions regarding LEVs are disseminated, the UK Government may want to consider targeting individuals that hold knowledge relating to cars to ensure the information they pass on regarding LEVs is fair and balanced. LEVs need to be connected with attractive symbolic, emotive and functional car meanings. The evidence from the application of the conceptual framework indicates that the assignment of symbolic, emotive and functional meanings to car ownership negatively influences the appraisal of EVs. To make LEVs more attractive to the general market, the UK Government may want to evaluate what specific meanings are being associated with LEVs followed by an analysis to determine if these meanings can be altered to improve LEV desirability. The innovativeness dimension of LEVs should be more notably featured to balance the prominent environmental symbolism associated with LEVs.
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Chapter Eight: Discussion and Conclusions Whilst environmental motives are still a significant indicator of interest in LEVs, their dominance in the emerging market might be hindering the potential of LEVs with the general market. The UK Government should consider a shift in focus and put more emphasis on LEVs embodying advanced technologies to attract more interest from individuals who value the innovativeness attributes of cars. 8.4 LIMITATIONS Whilst every effort has been made to ensure this thesis presents research which is as robust as possible, with the correct application of the scientific method in order to generate valid and reliable results, a number of limitations have been experienced. These limitations affect the quality of the results generated by the analysis which in turn influences the interpretations made and the generalisations proposed. This section of the chapter discusses these limitations and reflects on their implications. The first three limitations discussed involve concerns relating to the methodology whilst the last five limitations are more concerned with possible improvements to the conceptual approach. 8.4.1 Sample Size In order for this thesis to propose inferences regarding the nature of the general population, it is a requirement that a sufficient sample size be attained. To achieve this objective, statistical theory was firstly examined to determine the minimum sample required based on the size of the population of interest and the type of statistical analysis employed (discussed in Chapter 4, Section 4.3.4). The minimum sample required was dependent on the type of data collected and the variety of the analysis used, ranging from 119 for continuous data increasing to 370 for categorical data. A key question in reference to this aspect of the data collection regards whether or not to treat the two study sites as separate or as a combined population. The decision was made to treat the sites as separate populations, thus a minimum sample of 370 was required for each site when conducting analysis on categorical data. The actual sample attained was 267 respondents for the Newcastle subsample and 238 for the Dundee subsample which meets the minimum sample requirements for continuous 272
Chapter Eight: Discussion and Conclusions data and in order to conduct factor, cluster and regression analysis but not for an examination of categorical data. The expected response rate to the survey was 20%, based on experiences from similar research studies and what was observed in the survey pilot, leading to a distribution of 2000 surveys to each study site. The response rate actually attained in the main survey was 12.7%, indicating that initial expectations were overly optimistic. Potential implications due to the attained sample sizes are generalizations associated with categorical variables, such as females being more likely to hold higher preferences for LEVs, must be interpreted with caution and additional research is required to verify propositions of this nature. 8.4.2 Sample Representativeness A distribution schedule based on a stratified random sampling technique was developed in order to increase the probability of extracting a representative sample from the population. The Index of Multiple Deprivation was utilized as a partition metric to select areas which exhibit different levels of economic and social prosperity. Specifically, three areas were identified in each study site to represent an area of high, medium and low deprivation with the surveys being distributed over these areas. In order to determine if a representative sample has been collected, it is necessary to compare key features and characteristics of the sample to known values for the population. The results of this analysis are presented and discussed in Chapter 5, Section 5.2. With population statistics only being available for these features and characteristics at the national level, the decision was made to compare the sample as a whole, as opposed to the separate subsamples associated with the two study sites, to the population values. Interpreting this analysis, it is apparent that for some characteristics, the sample attained for this thesis provides a close match to the values present in the national population whilst, for other characteristics, there are some clear disparities. Implications of these disparities between the sample attained for this thesis and the general population are that the interpretations of the findings must be conducted with caution, as significant results identified in this thesis may not necessarily be transferable. In an attempt to determine the influence of socio-economic variables, a two stage entry was used in the regression analysis detailed in Chapter 6, Section 6.3.2 to 6.3.4. Generally, these variables explained less 273
Chapter Eight: Discussion and Conclusions variance in the respective dependent variable compared to the socio-psychological constructs, suggesting that any differences between the socio-economic profiles of the sample and the population are unlikely to have substantial implications. Moreover, it is important to consider what the general population is defined as in the context of this thesis. During the sample-population comparison, the assumption was made that the population of interest for this thesis was synonymous with the national population, as values for the national population in reference to socio-economic characteristics and transportation behaviour are available. However, it can be argued that the correct population of interest for this study is better defined as those individuals that are active consumer in the passenger vehicle market. It is possible that this population of car buyers does not share the same socio-economic profile as the national populace. This point, whilst valid, proves challenging to support, as the characteristics and features of car buyers as a population cohort are unknown and therefore cannot be used to determine the representativeness of the sample attained in this thesis. 8.4.3 Subsample Comparisons The decision to employ a two study site approach was motivated by a desire to compare and contrast these two sites together. Newcastle was selected as one of the study sites due to its exposure to two government backed market interventions which has seen the installation of EV charging infrastructure and public EV trails. Dundee was selected as a comparison sites due to its similarity with Newcastle in reference to the socio-economic characteristics and transport behaviour of the populace yet having not been a recipient of these aforementioned government policy initiatives. The results of this comparison between the basic profiles of the two subsamples associated with the study sites can be viewed in Chapter 5, Section 5.2.4. In this instance, hypothesis testing was used to determine where significant differences between the subsamples are present. Findings of the analysis suggest that the subsamples are comparable on some characteristics, but not others. Notably, the Dundee subsample contains a higher prevalence of older respondents which are more likely to be retired. Concerning transport behaviour, the Newcastle subsample contains respondents which are more likely to buy 274
Chapter Eight: Discussion and Conclusions their cars used and to have lower annual car mileages. Possible implications for these divergences are that the differences observed between these two subsamples in reference to LEV preferences are potentially motivated by differences in the basic profiles of the subsamples as opposed to the exposure of Newcastle to the government policy interventions. Moreover, whilst these divergences are unlikely to influence the profiles of the segments identified in the cluster analysis, but may alter the quantity of the population categorized by each segment. 8.4.4 Scale Development The household survey developed to apply the conceptual framework contains nine different scales designed to measure socio-psychological constructs. Of these nine scales, seven have been originally developed in this thesis. A limitation associated with the application of novel scales is that the structure of the output attained cannot be compared to previous empirical research. Moreover, the clarity and quality of the output is difficult to predict before the scale has been applied to a populace large enough to make the application of factor analysis robust. Critically assessing the structure of the factor output for the original scales developed (discussed in Chapter 5, Section 5.5.2), it is apparent that there has been some areas of success. Notably, the scales measuring emotive connection to cars in general and EVs in particular generate clear output, though considering emotive response in such polar terms may be regarded as overly simplistic. The first scale to measure car attitudes, associated with how important a car is and knowledge relating to cars, also generates and well structured output though the second scale requires improvements to enhance the quality of the factors measuring car cost and car simplicity. Similarly, the scale measuring the communication determinants of innate innovativeness produces clear factor output, though the scale which measures psychological determinants requires attention to improve the internal consistency of the last two factors extracted. 8.4.5 Adoptive Innovativeness In this thesis, adoptive innovativeness has been measured by observing the total quantity of household technology owned, total desired and total not owned. The primary advantage of 275
Chapter Eight: Discussion and Conclusions this measurement instrument relates to its simplicity of operation. Individuals tend to have a good recollection of the items which they own and those which they do not. Moreover, measuring the actual quantity of household technology owned provides a tangible metric, unlike asking an individual when they expect to adopt a certain innovation, which can depend on the individual’s ability to forecast future purchases and is susceptible to the intention-behaviour gap (Kollmus and Agyeman, 2002). However, this simplicity of operation also creates a number of prominent limitations which reduce the quality of the measurement. Firstly, the list of technology from which individuals state their ownership has been subjectively constructed by the author. What may be considered a form of innovative household technology by one individual may not be shared by others. Secondly, the measurement instrument integrates different varieties of household technology into a unified scale. Clearly, there are a number of sub divisions which exist within the theme of household technology, such as innovations which are associated with audio or visual technology, those which are connected with energy and those which are linked to household appliances. Thirdly, any scale developed in this fashion is susceptible to temporal drift, with different innovations being introduced to the market frequently and older innovations reaching market maturity. Scales measuring adoptive innovativeness in this manner will need to be re-evaluated regularly to ensure their effectiveness. Future research measuring adoptive innovativeness may want to consider these limitations and develop a more refined measurement scale to address them. 8.4.6 Socio-Psychological Stratification One of the significant findings of the regression section of the Socio-Psychological Modelling chapter was that the constructs included in the conceptual framework tended to explain a greater proportion of the variation in the designated dependent variables compared to more objective socio-economic characteristics. Moreover, the findings observed in the Market Structure Analysis chapter have shown that the segments forming in the market for LEVs have unique socio-psychological profiles. These socio-psychological profiles are distinctly different from each other and appear to be a significant feature of the segments.
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Chapter Eight: Discussion and Conclusions A common criticism of segmenting by socio-psychological construct is associated with the difficulty in measuring these unobservable characteristics. Whereas it is relatively straightforward to assess an individual’s socio-economic characteristics, such as their age, gender and level or education, determining their level of innate innovativeness or their value orientation is a much more involved task. This can limit the application of market segmentation which utilizes socio-psychological profiling in practical market situations. However, the diffusion of digital technology and the adoption of social networking may offer a possible solution to this. Society is becoming more comfortable with sharing personal information (Acquisti and Gross, 2006) through online services. This additional information, linked to more personal aspects of an individual’s character as well as documenting their online and real world behaviour, can be utilized to develop a more inclusive profile of consumers. This principle has been partly adopted by online retail and advertising companies, who now customize consumer marketing based on browsing histories and user profiles (Cho et al., 2002). Indeed the integration of information available online regarding an individual to produce targeted marketing has already been proposed, with Li et al. (2013) incorporating social relationships into an e-commerce recommender. Therefore, it is proposed that social stratification based on socio-psychological constructs is likely to become less complicated due to the advancement of digital technology leading to applicability in general market environments. 8.4.7 Household Demand Psychometric analysis, which investigates the influence of socio-psychological constructs over behaviour, is often limited to examining individual action which is conducted by a sole agent. For the majority of goods, such as the purchase of a pair of jeans or a cup of coffee, this approach is clearly valid. In regards to a new car purchase, this decision is often taken at the household level and tends to involve an interaction between family members. This situation introduces an additional layer of complexity to the examination of household demand for LEVs. Traditional econometric models are based on the principle of an individual desiring to maximize their utility which becomes more challenging when multiple family members have an influence over the decision made. Davis (1976) examined decision making in the household and found that family units engage in collective negotiation and bargaining in order to achieve a consensus on household purchases. Moreover, Manser and 277
Chapter Eight: Discussion and Conclusions Brown (1980) have demonstrated that heterogeneous utility functions can be simultaneously assessed to determine the allocation and distribution of shared goods within a household. With this thesis taking a distinctly individualist perspective to examining demand for LEVs, there has been little attention paid to these issues of household decision making. Individual utility is not synonymous with household utility, and thus the optimum car purchase decision that an individual makes may not necessarily be duplicated within the household. Issues concerning how household members negotiate to assess the suitability of different potential new cars are not captured in this study. However, potential exists to adapt the conceptual framework to include the influence of family members. Indeed, the conceptual framework itself could be integrated into a larger and more inclusive model of joint decision making which captures household and social influence. 8.3.8 Fleet Demand From the outset, this thesis focuses on the potential private individual demand for LEVs, examining the possible socio-psychological constructs which hold influence in this emerging market. During 2012, slightly over two million passenger vehicles were registered for the first time in the UK (SMMT, 2013). Of this, 45.5% were registered by households for private use with the remainder being registered by business fleet operators. With over half of new car sales being made by businesses, this sector of the market has the largest influence on the composition of the national fleet. Moreover, with cars registered to businesses tending to have annual mileages twice as large as household cars (DfT, 2011b), these cars contribute significantly more to the issues of carbon emissions and energy security. Business fleets have the potential to influence purchasing decision in the household market, with individuals capable of trialling certain vehicles at their places of work (Nesbitt and Sperling, 2001). The decision processes underpinning the acquisition of passenger vehicles by businesses remains an underexplored area, though the research which has been conducted has found that popular conceptions of fleet managers as highly rational individuals who undertake advanced cost benefit analyses are perhaps misplaced (Nesbitt and Sperling, 1998). 278
Chapter Eight: Discussion and Conclusions Recently, research has been conducted in the UK which has examined the influence of fiscal incentives over purchasing behaviour in the business fleet market finding that current tax structures may put pure EVs at a disadvantage compared to diesel fuelled vehicles (Potter and Atchulo, 2013). Whilst it might be tempting to consider the business and household passenger vehicle market as distinctly separate, it is possible that a number of constructs which are integrated in the conceptual framework for this thesis may hold influence in the business fleet market. To provide an example for this point, consider the construct of innovativeness, which in this research has been defined as the propensity of an individual to behave in an innovative manner through the early adoption of an innovation. This construct can be transferred into the corporate environment (Rutherford and Holt, 2007) to explain entrepreneurial behaviour. In sum, whilst this thesis has developed and applied a conceptual framework to explain household LEV preference, it is possible to re-design this framework and apply it in the business market to determine if similar or different findings arise. 8.5 FUTURE RESEARCH Whilst consumer demand for LEVs has been an active area of research for the past thirty years, there are still areas which remain unexplored. This thesis has examined the importance of socio-psychological constructs over preferences for LEVs and has demonstrated that these unobservable consumer characteristics can be measured and do indeed influence demand in this market. The previous section has discussed a number of significant issues which has limited the effectiveness of this study, some of which concern the application whilst others relate to refinements required in the conceptual framework and measurement instruments. A number of areas of potential future research can be sourced from these discussed limitations, such as categorizing the domains of adoptive innovativeness or measuring the socio-psychological profile of business fleet managers. Conversely, there are topics in this field, which are not directly related to this thesis, which the author considers would benefit from future academic attention. These areas are discussed in the remainder of this section, whereby the issues are phrased in terms of a research question with a justification of its importance.
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Chapter Eight: Discussion and Conclusions 1. What role will alternative car ownership models have in shaping demand for LEVs? In a thought provoking article which discusses the transition from traditional car ownership models towards a model of access-based consumption, Bardhi and Eckhardt (2012) highlight the growth of car sharing services in the US and the potential implications this has. These initiatives are also established in the European market, with ZipCar 2 having a significant 1F
presence in the UK whilst the Autolib 3 scheme in Paris allows for users to access EVs. 2F
Moreover, peer-to-peer car sharing schemes are also emerging, allowing individuals to provide access to their cars when not being used or to access a neighbour’s car. This topic also displays a synergy with the Peak Car phenomenon (Goodwin, 2013), with examines the issue of car ownership appearing to plateau in a number of industrialized countries. Determining what influence these new models of car ownership have for LEV adoption may provide insights on new forms of diffusion based on access. 2. Can parallels between previous energy or transport innovations be made to assist in predicting the diffusion trajectory of LEVs? Past innovations which share similarities with LEVs have already undergone widespread diffusion. A number of these innovations have been broadly adopted whilst others have been less successful. By examining the diffusion histories of similar technologies, this may provide insights related to the market potential of LEVs and how this potential can be enhanced. Innovations such as the microwave (Decareau, 1986; Oropesa, 1993), photovoltaic tiles (Arkesteijn and Oerlemans, 2005; Faiers et al., 2007) and combination boilers (Herring et al., 2007) may offer knowledge on possible barriers to adoption which can be transferred into an LEV context. 3. Does the consumer decision journey for LEVs follow the general pattern found in innovation theory?
2 3
http://www.zipcar.co.uk/ http://www.autolib.fr/autolib/
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Chapter Eight: Discussion and Conclusions In his descriptive theory of innovation diffusion, Rogers (1995) details a five stage procedure which takes an individual from the acquisition of knowledge regarding an innovation to the confirmation of its virtues after adoption. A number of similar approaches can be identified in the Marketing literature, such as the Purchase Funnel (Court et al., 2009) which describes how an initial consideration set of potential products undergoes evaluations, such as elimination by attribute, until one product remains. By investigating the decision journey related to the adoption of an LEV, new insights can be generated relating to if LEVs are likely to follow a traditional adoption process or if novel aspects are likely to hold influence. Additionally, the decision journeys of different market segments may not be identical and identifying any segment idiosyncrasies will provide a more detailed understanding of segment dynamics. 4. Are preferences for LEVs unstable and, if so, how can preferences be positively stabilized? Random Utility Theory (Manski, 1977), which serves as the foundation of traditional econometric modelling employing DCMs in this field, generally assumes that individuals preferences are complete, reflexive, transitive and continuous (Crouch, 1979). Implicit within this set of assumptions is the concept of stability, which relates to the volatility of an individual’s preferences. With knowledge relating to LEVs being sparsely distributed throughout the general populace, it can be proposed that LEV preferences are likely to be unstable and significantly influenced by information and experience. Prospect theory (Kahneman and Tversky, 1979) provides a framework for examining individual decisions under uncertainty, though has yet to be applied in reference to LEVs. Aspects which are present in this theory and also in the emerging field of behavioural economics, such as priming effects, the influences of biases, the presence of heuristics, the propensity of risk aversion, the importance of reference points and the effects of framing may all hold influence over an individual’s perceptions of LEVs. These aspects have received muted attention in this field and could benefit from focused examination. 5. What are the mechanisms which will assist in LEVs transitioning from niche market to mainstream? 281
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So far, research in the field of LEV demand has tended to examine the market either by assessing consumer reaction or by investigating the technical practicalities of the vehicles. More recently, infrastructure provision has been researched to determine the importance of charging points to consumer LEV preferences, optimal spatial distributions and the effects on the electricity grid (Axsen and Kurani, 2008; Wirges et al., 2012). So far, an integrated perspective has yet to be conducted which combines aspects of consumer demand, technical performance, institutional structure, policy mixture and infrastructure instalment. Transition theory (Geels, 2002; Geels and Schot, 2007) offers a framework on which these different aspects can be combined to examine the development of LEVs from niche market application to mainstream acceptance. Future research may wish to consider utilizing this framework to provide an encompassing perspective of this market and how it interacts and is affected by other social and physical structures. 6. What impact are LEVs likely to have over individual and household mobility? LEVs in general and EVs in particular offer a number of vehicle characteristics which are significantly different compared to conventional ICE vehicles. Concentrating on EVs, trials of these vehicles have already found that users tend to alter their refuel patterns away from third party operators and towards home and work based charging (Everett et al., 2011). Additionally, a significant majority of the participants in an EV trial expressed concerns over the suitability of the vehicle to meet all mobility requirements due to the reduced range (Turrentine et al., 2011). However, there are a number of additional potential implications that a household might encounter by incorporating an EV into their fleet. Firstly, cars may be reassigned away from a his-and-her classification towards an allocation based on trip purchase. Moreover, trips might be abandoned if the correct vehicle is unavailable. This can be considered a form of trip evaporation whereby certain mobility requirements are left unfulfilled. This potential aspect of EV adoption may have implications for the mobility of households.
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Chapter Eight: Discussion and Conclusions 8.6 CONCLUDING REMARKS This thesis has gone through a multistage process commencing with an overview of the topic of interest and concluding with a discussion concerning potential research directions. An extensive examination of the previous research conducted in this field assisted in identifying areas which have yet to be examined and would benefit from specific attention. The conceptual framework developed contains a number of interlinked components aimed at providing insights relating to how consumers will respond to the introduction of LEVs into the mainstream car market. The focus has been placed on examining the importance of socio-psychological constructs, which measure values, attitudes and opinions, to determine how these less tangible aspects are influencing preferences towards LEVs. Measurement instruments were integrated into a household survey and applied over two study sites to gather an appropriate sample. This sample has been examined using a variety of statistical analyses in an effort to turn the data into evidence necessary to answer the research objectives and questions initially posed. The results have been presented over three different chapters, commencing with an analysis of the basic structure of the dataset attained and concluding with a structural assessment of the emerging market for LEVs. Hypothesis testing allowed for comparisons to be made between different respondent cohorts and to identify where these cohorts differ in their preferences for LEVs. Principal components analysis was employed to extract the socio-psychological constructs contained in the attitudinal scales. Taking the variables developed in the first results chapter, correlation analysis was used to investigate where statistical relationships exist between these variables and to determine if the links proposed by the conceptual framework could be supported. This was followed by the application of regression analysis to develop a number of psychometric models to explain three principal sections of the conceptual framework. The final results chapter conducted a structural analysis of the dataset to determine if unique respondent clusters can be identified and to profiles these segments according to their primary features and characteristics. Using the knowledge generated in the results chapters to answer the research objectives and questions, it is apparent that a number of new insights have been achieved. Firstly, the 283
Chapter Eight: Discussion and Conclusions conceptual framework designed has been generally supported, with expected relationships identified and constructs successfully measured. However, a number of improvements could still be made specifically regarding the output attained from a number of the attitudinal scales. Secondly, the market structure analysis has demonstrated that unique market segments are forming with reference to LEVs and that a number of these segments appear to follow theoretical expectations. Thirdly, the evidence attained has been assessed to determine what insights can be offered to policy makers and other organizations operating in this market. Throughout the course of this thesis, new topics linked to LEV demand have been uncovered which would benefit from future research. With socio-psychological modelling still being a new area of this field, additional constructs could be examined for their influence over LEV preference.
Moreover, the fields of behavioural economics and
innovation theory still offer novel avenues for research with the potential to investigate consumer decision journeys, the significance of vehicle framing and the influence of priming. With knowledge relating to consumer reaction to LEVs growing rapidly, a significant future research objective may wish to consider how to effectively integrate this with technical aspects associated with vehicle usage and infrastructure deployment.
284
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Appendix 10.0 APPENDIX The Appendix contains a number of sections which underpin areas of this thesis but have not been included in the main body of text. 10.1 MEASUREMENT SCALES: ITEM POOLS Each of the socio-psychological measurement scales originally designed for this thesis was constructed from a larger pool of items. This section of the Appendix details these item pools grouped by those focuses on car specific constructs followed by items specifically examining the construct of innovativeness.
Table 10.1: Item Pools for Car Specific Constructs Car Cost 1. 2. 3. 4. 5.
I tend to go for the larger size of engine for any new car I buy I tend to go for the basic model whenever buying a new car My current car acts as my benchmark for comparing potential new car purchases to The purchase cost of a new car is my number one concern when considering a purchase When buying a car I will always pay a bit more and go for the option that gives me better performance 6. The costs of running a car are insignificant compared to the costs of purchasing a new car 7. The fuel economy of a vehicle plays a primary role in my purchasing decision 8. I think about the costs associated with my car as being unavoidable and so don’t consider them much 9. I calculate and compare the likely running costs of any new vehicle I am considering purchasing 10. I am willing to spend more for a car that has superior fuel economy (cheaper to run) 11. I am willing to spend more for a car that has lower pollution levels 12. Rising fuel prices are something I will be taking into account when I buy my next new car 13. Fuel efficient cars are relatively more expensive but can pay for themselves in lower fuel costs 14. Environmentally friendly cars are too expensive for me to consider 15. The Road Tax band my new car sits in is something I consider important when I am buying a new car 16. I keep a note of how much it costs me to fill up my tank and how long my tank will usually last me for 17. I don’t mind spending more for my new car to get added features 18. I have a good idea how fuel efficient my current car is compared to others on the road 312
Appendix 19. How quickly a car depreciates after I buy it is an important factor for me 20. The running costs of new vehicles are unlikely to increase in the near future 21. I don’t like to think about how much my car costs me Car Technology 1. When buying my next car I want to be able to customize and optimise as many settings as possible 2. I don’t like the thought of my car telling me the best way to drive it 3. I like to keep things as simple as possible in my car 4. I like having the latest technology in my car but don’t want to it to be complicated to use 5. I want my car to be able to connect to all my other consumer gadgets (like my phone, laptop, home internet etc.) 6. The invention of the car is one of Mankind’s greatest achievements 7. Making sure my car has cutting edge technology in it is an important factor to me 8. I would never consider buying a car that operated with very new technology 9. I don’t like reading long manuals when I buy a new car 10. The technology used by modern cars has progressed substantially over the last 20 11. years 12. I like my car to have only tried and tested technology in it 13. I don’t care how a car works; I just need it to get me where I want to go 14. Being at the forefront of new car technology is something that appeals to me 15. I have seen Hybrid cars (such as the Toyota Prius) on the roads and would like to know more about them Driving Behaviour 1. I’m very proud of the car I drive and how I drive it 2. The joy I get from driving my car is just as important as its transportation function 3. I like to impress my friends with my driving ability. 4. Driving a slow car would not fit in well with my personality 5. I enjoy the adrenaline rush of driving my car in an positive manner 6. I am often anxious when I need to drive and so limit my driving as much as possible 7. I often travel large distances so having a car that is not range limited is important 8. Driving a car is much more stressful compared to other modes of transport 9. I need a car that fits in well with my work. 10. The ability to refuel my car from home is a feature that would appeal to me. 11. When I am driving with other people in the car I will make sure I’m being as safe as possible 12. I try to drive in a manner that makes sure I get the most fuel efficiency out of my car (for example, steady acceleration and deceleration, sticking to 70mph on motorways) 13. I never consider any other ways of getting to where I want to go and automatically think my car will be the best way 14. The main purpose of a car is to get me when I want to go 15. I think service stations are unpleasant and do not like having to use the facilities. 16. I enjoy driving a vehicle that I can accelerate quickly 17. The speed limit on motorways should be increased 18. I often get annoyed with other drivers on the road 19. I always stick to speed limits when driving 313
Appendix 20. I think I can fulfil all my daily transport needs with a car that has a range of 100 miles 21. I usually only use my car for journeys I would have difficulty doing on foot or on bike 22. Sporty cars are much better to drive than normal cars 23. I find driving to be dull and boring 24. I am a very cautious driver never doing any form of risky manoeuvre Environment 1. 2. 3. 4.
I am concerned about the environmental impact driving my car causes. I’d like to buy a less polluting car but am concerned it will not meet my transport needs The pollution generated from my car use is unavoidable The amount of cars on the roads in the UK is having a severe impact onto the natural environment 5. I regularly take measures to reduce my environmental impact 6. There is no point in changing my behaviour to be greener if no one else is making an effort. 7. I don’t think my car use contributes to environmental problems 8. I think it is everyone’s responsibility to do something to reduce their car’s environmental impact 9. To reduce pollution people should not be allowed to use their cars for journeys they 10. could easily walk 11. I think cars should be more heavily regulated by the Government to reduce pollution. 12. Car technology will help solve the problems of climate change and oil running out 13. I try to reduce my car use whenever possible to be more environmentally friendly 14. In order to reduce pollution people should only use cars for essential journeys 15. It’s the job of the car makers to produce cars that are less polluting Independence and Fatalism 1. When driving past service stations I often worry about how expensive the cost of fuel will be 2. I worry about how much of my money goes on filling up my car 3. I often feel like a lack control over my own life 4. The issues surrounding energy security (such as the supply of oil) are completely outside my sphere of influence 5. My actions can change how exposed I am to changes in fuel price 6. Oil is going to run out no matter what I do so it doesn’t matter what car I buy 7. I have the ability to positively affect the natural environment around me 8. My life is determined by my actions 9. Oil is going to start running out soon so it is important to make what we have left go further 10. I have complete control over my life 11. I think the price of oil will increase dramatically over the coming years 12. I want to release myself from the constraints put on me by big oil companies 13. I don’t believe that people have predetermined destinies 14. Most of the important things in life often come down to a lucky break or a chance encounter 15. I don’t like to be at the mercy of the cost of car fuel 16. I can easily change the way I travel about if the price of fuel becomes too high 17. Big oil companies are so powerful that I just have the accept whatever they choose to 314
Appendix do 18. There is no point in me changing my behaviour to help World issues if no one else does the same 19. Our lives are more controlled by fate than by our own actions 20. I don’t have any choice but to pay the price of petrol, however high it goes 21. I run my car, my car doesn’t run me 22. I often feel helpless in the face of World issues Symbolism 1. I like to keep things fresh and regularly change and upgrade the things I own 2. Being the first person out of my friends and colleague to get the latest consumer technology is important to me 3. My car is the most important thing that I own 4. I like to distinguish myself from my friends and colleagues by the car I drive 5. I tend to judge people by the items they own 6. Having the right brands around me helps me to maintain my image 7. I don’t care what my car looks like so long as it does what I need it to do 8. The car I own tells people about the person I am 9. For me a car is simply a way of getting from A to B 10. My car is an expression of my personality 11. You can tell a lot about a product by what its brand is 12. I don’t put much faith in branding and do not let it affect my purchasing decision 13. Being seen owning the best brands is important to me 14. The car I drive lets people know about my values 15. My car is part of the family 16. I develop a relationship with my car 17. I like my car to fit in with my outlook on life 18. You can tell a lot about a person by the things they own 19. I express my personality through my possessions 20. I like to be seen using the latest technology 21. I like to customize my car to add my own unique character to it
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Appendix Table10.2: Item Pool for Innovativeness Specific Constructs Ambition 1. I’m rarely satisfied with where I am in life and always look to better myself in the future 2. I’m never satisfied with my current position in life and continually look to the future 3. I regularly think of ways I can improve my life and set out plans to turn these dreams into reality 4. I’m happy with my position in life and don’t think it needs improving 5. I am a very ambitious person setting high standards and expectations for myself 6. I always aspire to be something more than I am 7. I like to separate myself from the crowd by achieving exceptional standards of excellence 8. In life, if you’re not going forwards you’re going backwards 9. You can measure the success of my life by listing my accomplishments 10. I am capable of accepting a life that is not perfect but is satisfactory 11. Making advancements in my life is not worth the effort 12. I like to set goals for myself that I strive to achieve 13. I have a strong desire to achieve my goals in life 14. We are only here for a short period of time so it is important to make the most of it Change 1. I’m always looking for ways that I can alter my life to make it better 2. If everything stayed the same, my life would be a boring place 3. I regularly try new things to see if they fit in with my life 4. I can quickly incorporate new ideas into how I live my life 5. I like to keep things fresh by continually changing the things around me 6. My life is constantly in flux with new things coming in and old things going out 7. I usually associate a changing environment/situation with opportunity 8. I don’t like changing my behaviour just because everyone else is doing so 9. Social trends come and go but I stay the same 10. Whenever I am confronted with change I try to embrace it rather than maintain the status quo (way things are) 11. “If it isn’t broke, don’t fix it” is a rule that governs my behaviour 12. I’m quite happy keeping things the way they are 13. I’m happy with the way my life is and wouldn’t consider changing it Communication 1. I enjoy reading about the latest consumer technology releases 2. I regularly seek information about the latest consumer technology 3. More often than not I am the person who will introduce my friends and colleagues to a new form of consumer technology 4. Friends and colleagues regularly come to me about advice concerning new consumer technology 5. I often know about the next must have piece of consumer technology before it is release onto the market 6. My friends usually come to me when they need an opinion relating to technology 7. I have frequent contact with people working with new consumer technology 316
Appendix 8. I volunteer in the local community and/or give up my time unpaid for social/community reasons 9. My social network spans a wide variety of people 10. I regularly participate in activities such as sports, clubs and/or associations that have a formal structure 11. Most of the information about new consumer technology comes to me through friends or adverts 12. I enjoy experiencing other cultures and ways of life 13. My beliefs, morals and world view are not restricted to one perspective but instead draw from multiple cultures and societies 14. I regularly make professional and/or social trips to a variety of cities 15. I draw information about new consumer technology from multiple social networks 16. I move between groups of friends that do not regularly interact with each other 17. My social network is not restricted by social or economic boundaries (such as class level, education or religion) 18. I like to be placed on company call sheets to let them keep me updated regarding their latest product releases 19. I subscribe to/read magazines, industry journals or other forms of media that inform me about the latest consumer technologies 20. I would consider myself to be a cosmopolitan person 21. I have a small group of friends who all know each other well and share similar interests Education 1. 2. 3. 4. 5. 6.
My learning didn’t stop when I left school Education is a waste of time and hasn’t helped me in the slightest The problems of the world can often be attributed to a lack of understanding Your entire life is a learning process I enjoy learning about new things I like to keep myself up to date on the latest thinking in the fields I have a professional or private interest in 7. I prefer to take things for granted rather than develop a complete understanding about them 8. I regularly read books and information on topics I don’t know much about 9. I’m a very curious person always wanting to know why things are the way they are 10. More public funding should be allocated to the education system in this county as it does such fantastic work 11. Expanding my understanding of the world is one of my most important life goals 12. I rarely use the things I learned in formal education in my daily life 13. A strong education makes life a lot easier 14. The world would be a better place if people had a higher degree of education Rationality 1. I am a highly compulsive person letting my emotions and gut instincts dictate my behaviour 2. When making important decisions I always listen more to my heart than to my head 3. If I want to do something I’ll do it and worry about the consequences later 4. I usually prefer to go for the simple option when confronted with a decision regardless of the pros and cons 317
Appendix 5. Making sure I always make the correct decision is something that is important to me 6. I like to appraise every aspect of a potential purchase before deciding whether or not to buy it 7. I tend to spend a decent amount of time considering important decisions before I act 8. Whenever I make an expensive purchase I conduct a reasoned evaluation of all the costs and benefits 9. Once I have made up my mind to do something it doesn’t matter if any other information or evidence comes to light, I’ll still do it 10. Once I make a decision I usually stick to it regardless if it turns out to be the wrong thing to do 11. Habit and compulsive behaviour usually govern my purchasing decisions 12. I review most of the important decisions I make afterwards to see if they turned out good or bad 13. I like to keep track of the decisions I make to see if I can learn from any errors 14. Ensuring I make consistent decisions is important to me 15. When I make decisions I tend to use “rules of thumb” I’ve learned in the past Science and Technology 1. I have a personal or professional interest in the latest consumer technology 2. I am usually one of the first people to acquire the latest technology 3. I often modify new technology I purchase to make it work better to suit my specific needs 4. Technology advances so quickly now I don’t bother to keep up. 5. Technology should progress at a steady rate rather than suddenly shift 6. I have an intuitive grasp of how most technology functions 7. I can usually understand technology very quickly 8. There are some areas of our lives and beliefs that science should keep away from 9. Technology can protect us from any form of natural event (such as flooding and drought) 10. Advances in technology are detrimental to social cohesion and the sense of community 11. Scientific breakthroughs are fundamental to the progression of human society 12. New technology is causing the degradation of human society 13. I am usually cautious when it comes to new technology 14. I prefer to keep things as simple as possible and do not like technology I have to learn how to use. 15. Science has no impact onto how I live my life 16. I really enjoyed my science classes at school 17. I often need someone to show me how to use any new technology before I feel comfortable using it 18. Science is so abstract these days it doesn’t mean much in the “real world” 19. There is no more noble an endeavour than the scientific search for truth 20. I enjoy finding out how new technology works on a technical level 21. I don’t think advancements in science have made a big difference to my life Technical 1. If something I own breaks down I’ll always try to fix it myself first before I consider replacing it 2. I have a good grasp of how most things I own (such as your car, mobile phone, laptop 318
Appendix etc.) work 3. I have confidence in myself to resolve a problem or malfunction in most of the possessions I own 4. I wouldn’t know where to begin if one of my items of consumer technology (mobile phone, laptop, digital camera) malfunctioned 5. I’d rather learn how to maintain my own possessions that pay someone to do it for me 6. If I tried to fix something that broke down myself I’d more than likely make it worse 7. If my hot water boiler broke down I’d feel comfortable trying to fix it myself 8. I can make rudimentary repairs to my car only needing the help of a qualified mechanic in exceptional circumstances 9. I like to participate in Do It Yourself (DIY) activity around my house 10. I find following technical instructions, guidelines and schematics easy to follow (such as the building of flat packed furniture) 11. I like to modify the things I buy to optimise their performance 12. I don’t like the hassle of trying to fix things 13. Customizing my possessions allows me to put my own personal touch on the things I use Uncertainty 1. 2. 3. 4. 5. 6.
I don’t like uncertain situations where I don’t know exactly what the outcomes will be I find it easy to cope in situations where outcomes are highly uncertain I don’t like making decisions when I have doubts in my mind I enjoy situations with an element of uncertainty as it can add to the excitement I will never do something without knowing first what the consequences will be I prefer to let other people make decisions when I am not completely sure about the situation 7. The biggest rewards usually come from making decisions in uncertain situations 8. There are few certainties in life, only less risky alternatives 9. I don’t need to know all the ins and outs of a situation before I am ready to act 10. My life is a very stable place with everything moving at a steady rate 11. Life is full of uncertainty but that doesn’t stop me from doing the things I want to do 12. I have a lot of confidence in myself in making the right decision in complicated situations 13. I often find myself in situations where I am unsure of what to do 14. By seeking more information relating to a situation I can make it less uncertain for myself
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Appendix 10.2 MEASUREMENT SCALES: INITIAL TESTING In order to identify the best statements to measure the designated construct from the item pools originally specified, a pre-testing procedure was followed. Thirty judges sourced from an academic peer group firstly stated their particular score on a seven point Likert scale for each item and then rated each item as either a good, average or bad reflection of the outlined construct. This section of the Appendix illustrates the form which made this pretesting procedure possible.
Figure 10.1: Introduction Page to the Pre-Testing Procedure
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Figure 10.2: Example of Construct Evaluation
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Appendix 10.3 SURVEYS Throughout the course of this thesis, a number of different household surveys have been applied. This section of the Appendix presents three of these surveys. Firstly, an illustration of the online survey version is offered. Following this, the main survey which was distributed in Dundee and Newcastle is presented. To conclude, a follow up survey which was utilised examine the validity of the market structure analysis is presented.
Figure 10.3: Illustration of Online Version of Household Survey
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10.4 SURVEY DISTRUBTION During the survey development, distribution schedules were created for each study site. This section of the Appendix offers an example of a distribution schedule which was developed for the Long Benton area of Newcastle. Table 10.3: Distribution Schedule for Long Benton Area of Newcastle upon Tyne Study Site Thoroughfare
Road
Goathland Avenue (E) Skelder Avenue Eishort Way Glenfield Road Lambourne Aveue Hailsham Avenue Whitfield Drive Beech Grove Denbeigh Place Tenbury Crescent Edenbridge Crescent West Farm Avenue (W) Oxford Close Clipsham Close Charnwood Avenue Blackfriars Way Whitefriars Way Monks Park Way
Reference LB1 LB2 LB3 LB4 LB5 LB6 LB7 LB8 LB9 LB10 LB11 LB12 LB13 LB14 LB15 LB16 LB17 LB18
Notes Mature semi detached housing Mature semi detached housing Mature semi detached housing Semi detached housing, mix between well kept and not Semi detached housing Mature semi detached housing, some flats at end of street Mature semi detached housing Mature terraced housing, front gardens bay windows Semi detached housing Mature semi detached housing Semi detached housing Mixture of housing types, flats and modern developments Modern development, terraced town houses Semi detached housing, well kept Mostly flats from a mixture of developments Mostly flats from a mixture of developments Modern development, town houses with flats Semi detached houses, well kept
Estimated Households 140 20 4 60 15 40 35 55 15 60 20 300 10 4 20 20 10 10
Distribution Schedule 70 10 2 30 7 20 17 27 7 30 10 130 5 4 10 10 5 5
Distribution Risk High Low Low Medium Low Low Low Medium Low Medium Low High Low Low Low Low Low Low 338
Somervyl Avenue West Farm Avenue (road at end) Stoneleigh Avenue
LB19 LB20
Modern housing development, semi detached houses 1970s flat development
LB21
Byland Road Kingsdale Road Camsey Close Merlin Place Grassholm Place Shearwater Avenue Kestrel Place Ancaster Avenue Purbeck Road Chesters Avenue
LB22 LB23 LB24 LB25 LB26 LB27 LB28 LB29 LB30 LB31
Mixture of housing types, mostly semi detached housing with some modern developments 1970s flat development Mature semi detached housing Mixture of different flat developments Split between semi detached housing and flat development Semi detached housing Mature semi detached housing Mature semi detached housing Mature semi detached housing Mature semi detached housing Mostly semi detached housing from a number of different developments - modern and 1970s Flat complexes from what looks to be the same development Flat complexes from what looks to be the same development Mostly mature terraced housing with gardens from the same development, some retirement bungalows Flats surrounding road but no signs of access Mix of two developments, 1970s mature terraced housing with gardens and modern semi detached TOTAL Total for All Sites Total Distribution Schedule Total Number of Streets Refined Distribution
Rowanberry Road
LB32
Chester Crescent
LB33
Knowle Place
LB34
The Chesils West Farm Wynd
LB35 LB36
30 20
Appendix 15 Low 10 Low
400
180 High
20 20 20 10 10 30 25 30 30 300
10 10 10 5 5 15 22 15 15 130
Low Low Low Low Low Low Low Low Low High
100
50 High
20
10 Low
15
7 Low
0 150
0 Low 75 High
2068 8725 2115 71 1195
983
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Appendix 10.5 ETHICAL CHECKLIST As this thesis involved the participation of human subjects, the University of Aberdeen’s ethical checklist was followed to ensure these dimensions had been acknowledged. A copy of this checklist is provided in this section. UNIVERSITY OF ABERDEEN RESEARCH ETHICAL REVIEW CHECKLIST Research ethics refers to the moral principles underpinning research at all stages, from developing a project grant application, data collection, to writing up and dissemination of findings. The University is committed to promoting and facilitating the ethical conduct of research conducted by all its staff and postgraduate and undergraduate students. This checklist (or an equivalent college, school or discipline specific checklist) should be used for every research project that involves human participants.
This includes surveys or
interviews, focus groups or observation techniques. It must be completed before potential participants are approached to take part in any research. Where a college, school or discipline specific ethics approval process has already been undertaken, completion of this checklist should not be required. The checklist aims to identify whether or not a full application for ethics approval needs to be submitted, and should be used in conjunction with appropriate college, school or department ethical review guidelines. The Principal Investigator, or where the PI is a student, the supervisor, is responsible for ensuring that the checklist review is undertaken, and for exercising appropriate professional judgement. Where a research project is being undertaken outwith a College (e.g. by staff within the University Administration), the checklist should be completed and signed off by a relevant line manager.
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Appendix Name and status of applicant (e.g. staff/postgraduate or undergraduate student) and relevant School/Department Craig Morton Postgraduate student College of Physical Sciences, School of Geosciences, Department of Geography If student – name of supervisor Dr. Jillian Anable (primary) Professor John Nelson (secondary internal) Dr. Christian Brand (secondary external) Title and brief description of proposed research project and intended participant group. PhD Title: Accelerating the Demand for Low Emission Vehicles: A consumer led perspective Description of Research Application: The data collection and analysis associated with this research study will be conducted in 2 stages. Stage 1 will be a household questionnaire asking subjects to provide information relating to their current vehicle, their attitudes and preferences
towards
vehicles,
specified
personality
characteristics
and
general
socioeconomic characteristics. Stage 2 will be an in-depth 1-to-1 interview conducted with subjects sourced from Stage 1 aimed at increasing the richness of the data such as elements that would be more difficult to capture in a conventional survey It is not expected that either stage 1 or 2 of this research study will cause harm or discomfort to subjects. There is a potential for some subjects to prove reluctant to provide what they may deem as sensitive information (such as socioeconomic characteristics). To alleviate this concern subjects will be informed that the study will strictly follow Data Protection Act legislation and that their personal details will be kept secure and never released or referred to directly unless if exemptions conditions as detailed in the Data Protection Act are requested. 341
Appendix
Declaration:
I have read the relevant college/school/department and funding council
guidelines for conducting research with Human Participants.
YES
If the answer to any of the questions 1 – 13 below is YES, further information should be provided and guidance sought. In all cases involving research by students, whether or not any question is answered YES, the form should be submitted to your supervisor for signature. 1.
(i) Is the study externally funded? If Yes, (ii) please state
(i) Yes
which funding agency and; (iii) whether the funding (ii) UK Energy Research agency requires proof of Ethical approval
Centre (iii) Yes
2.
Does the study involve clinical populations (i.e. have
No
participants been identified as a result of their status as a patient)? 3.
Does the study involve children (under 18 years)?
4.
Does the project involve vulnerable adults such as individuals with mental health problems or learning
No No
disabilities, or prisoners or young offenders up to the age of 21? 5.
Does the study involve participants who are unable to
No
give informed consent? 6.
Does the study involve any clinical procedure?
No
7.
Are drugs, placebos or other substances to be
No
administered to participants, or will the study involve invasive or potentially harmful procedures of any kind? 8.
Could the study induce psychological stress or anxiety, or
No
cause harm or negative consequences beyond the risks encountered in normal life? 9.
Is pain or more than mild discomfort for subjects likely to
No 342
Appendix result from the study? 10. Does the project involve the collection of material that
Yes
could be considered of a sensitive personal, medical or psychological nature? 11. Does the project involve the use of animals and
No
procedures not covered by the Animal Scientific Procedures Act 1986? 12. Does the project use covert research techniques?
No
13. Will the subjects of the study include staff or students of
Yes
the University? Where you have answered “YES” to any question please provide further information in the box below. If you wish to make a fuller response please submit this on a separate sheet Further information:
(10)
- Some subjects may consider collection of socioeconomic characteristics to be
invasive. (13) – Staff of the University of Aberdeen will be used during the testing and pilot phase of this study with their information used improve study performance
343
Appendix If you are a member of staff and have answered “NO” to all of the questions, then no further action will required, and the completed checklist should be filed with your research records. In all cases involving research by students (i.e. whether or not any question is answered YES) the form should be submitted to your supervisor for signature. If you are an undergraduate student the form together with your project proposal should be submitted to your supervisor in the first instance. You should also retain a copy for your own reference. If you have answered “YES” to any of the questions, you may have to apply to a relevant Ethics Committee for approval and the form should be sent to the relevant School or College Research Ethics Committee and guidance sought. Principal Investigator
Supervisor (where appropriate)
Signed
Signed ________________________________
Date
13th June 2011
Date __________________________________
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Appendix 10.6 MARKET STRUCTURE: ANOVAS This section of the Appendix presents results from the ANOVA conducted on all the variables considered in the description of the segments identified in the Market Structure Analysis chapter. In total, 61 variables have been examined with 51 displaying significantly different values between the five clusters. Table 10.4: ANOVA of Segment Description Variables Sum of Squares 214.350 How many cars have Between Groups you owned in your Within Groups 19533.719 lifetime? Total 19748.069 Between Groups 7.797 How many cars are in Within Groups 204.192 your household? Total 211.989 Between Groups 127.946 How many years have Within Groups 2560.192 you had this car for? Total 2688.137 Was this car brand Between Groups .556 new of used when you Within Groups 89.314 got it? Total 89.870 Between Groups 3.133 What is the engine Within Groups 73.250 size? Total 76.383 If no, how many Between Groups 1.144 people do you share Within Groups 54.927 this car with? Total 56.072 What is the total Between Groups 255980508.258 annual mileage of this Within Groups 7812992216.671 car? Total 8068972724.929 Between Groups How much do you generally spend when Within Groups buying a car? Total How long do you tend Between Groups to keep your car Within Groups before replacing it? Total Between Groups When do you plan to Within Groups buy your next car? Total Between Groups Upgrade of cosmetic Within Groups aspect of car Total Upgrade of Between Groups performance aspect of Within Groups
df
Mean Square 4 53.588 370 52.794 374 4 1.949 371 .550 375 4 31.986 362 7.072 366 4 .139 363 .246 367 4 .783 332 .221 336 4 .286 190 .289 194 4 63995127.064 345 22646354.251 349 108345484.06 433381936.248 4 2 10957261949.29 327 33508446.328 4 11390643885.54 331 2 30.010 4 7.503 2096.810 334 6.278 2126.820 338 18.376 4 4.594 754.386 192 3.929 772.762 196 .155 4 .039 30.695 362 .085 30.850 366 .696 4 .174 23.447 359 .065
F Sig. 1.015 .399
3.542 .007 4.523 .001 .565 .688 3.550 .007 .990 .414 2.826 .025
3.233 .013
1.195 .313 1.169 .326 .458 .766 2.666 .032 345
Appendix car
Total Between Groups Upgrade of comfort Within Groups aspect of car Total Between Groups What type of fuel do Within Groups you tend to buy? Total Between Groups How regularly do you Within Groups wash your car? Total Do you have a Between Groups personalised number Within Groups plate? Total Do you usually buy the Between Groups same type of car Within Groups you’ve owned in the Total past? Do you usually buy the Between Groups same brand of car Within Groups you’ve owned in the Total past? Between Groups Frequency of car use Within Groups as driver Total Between Groups Frequency of car use Within Groups as passenger Total Between Groups Frequency of bus use Within Groups Total Between Groups Frequency of train use Within Groups Total Between Groups Frequency of bicycle Within Groups use Total Between Groups Frequency of walking Within Groups Total Between Groups Frequency of airplane Within Groups use Total Between Groups Likelihood of purchase Within Groups of Petrol for main car Total Between Groups Likelihood of purchase Within Groups of Diesel for main car Total Likelihood of purchase Between Groups of Mild Hybrid for Within Groups main car Total
24.143 1.716 33.889 35.604 9.599 330.080 339.678 9.392 316.309 325.700 .311 33.656 33.966 1.861 86.529
363 4 359 363 4 362 366 4 362 366 4 353 357 4 365
.429 .094
4.543 .001
2.400 .912
2.632 .034
2.348 .874
2.687 .031
.078 .095
.814 .517
.465 .237
1.962 .100
.332 .239
1.390 .237
.939 1.821
.516 .724
23.515 5.757
4.084 .003
1.123 6.551
.171 .953
20.147 5.427
3.712 .006
7.140 5.804
1.230 .298
10.315 5.475
1.884 .113
3.350 .705
4.750 .001
19.070 4.240
4.497 .001
6.312 4.224
1.494 .203
123.652 2.785
44.397 .000
88.389 369 1.326 4 87.063 365 88.389 369 3.755 657.373 661.128 94.059 1945.941 2040.000 4.490 2332.069 2336.560 80.590 1926.566 2007.156 28.559 2048.904 2077.464 41.262 1954.573 1995.834 13.402 251.825 265.227 76.280 1577.338 1653.618 25.247 1571.225 1596.472 494.609 1036.080 1530.690
4 361 365 4 338 342 4 356 360 4 355 359 4 353 357 4 357 361 4 357 361 4 372 376 4 372 376 4 372 376
346
Appendix Likelihood of purchase of Full Hybrid for main car Likelihood of purchase of Plug-in Hybrid for main car
Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Likelihood of purchase Within Groups of Pure EV for main Total car Total Between Groups Mean LEV preference Within Groups Total Between Groups Mean Hybrid Within Groups preferences Total Between Groups Mean Plug-in Within Groups preferences Total Between Groups Mean Conventional Within Groups preferences Total Between Groups Total Owned Within Groups Total Between Groups Total Desired Within Groups Total Between Groups Total Not Owned Within Groups Total Between Groups Gender Within Groups Total Between Groups Age of respondent Within Groups Total Highest level of Between Groups academic Within Groups achievement Total Between Groups Gross household Within Groups income Total Between Groups Number of children in Within Groups the household Total Between Groups Number of adults in Within Groups the household Total Meaning: Symbolism Between Groups and Emotion Within Groups
498.856 628.078 1126.934 423.941 660.584 1084.525 179.002 727.247 906.249 473.842 383.308 280.923 664.231 494.002 552.680 1046.682 287.813 522.445 810.259 33.185 713.955 747.141 1391.141 1252.551 2643.692 621.722 1044.113 1665.836 3911.843 617.961 4529.804 .969 87.296 88.265 8477.513 75933.194 84410.708 35.056 741.878 776.933 37.407 524.516 561.924 7.241 234.452 241.693 6.581 148.513 155.094 9.230 349.588
4 372 376 4 372 376 4 372 376 145 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 368 372 4 370 374 4 349 353 4 369 373 4 369 373 4 372
124.714 1.688
73.866 .000
105.985 1.776
59.684 .000
44.750 1.955
22.891 .000
95.827 126.895 .000 .755 123.501 1.486
83.126 .000
71.953 1.404
51.233 .000
8.296 1.919
4.323 .002
347.785 103.290 .000 3.367 155.431 2.807
55.377 .000
977.961 588.712 .000 1.661 .242 .235
1.033 .390
2119.378 206.340
10.271 .000
8.764 2.005
4.371 .002
9.352 1.503
6.222 .000
1.810 .635
2.849 .024
1.645 .402
4.088 .003
2.308 .940
2.456 .045
347
Appendix Total Between Groups Meaning: Function Within Groups Total Between Groups Positive Car Emotions Within Groups Total Between Groups Negative Car Emotions Within Groups Total Between Groups Positive EV Emotions Within Groups Total Between Groups Negative EV Emotions Within Groups Total Between Groups Negative EV Attitudes Within Groups Total Between Groups Positive EV Attitudes Within Groups Total Between Groups Comm: Info Within Groups Total Between Groups Comm: Social Within Groups Total Between Groups Psych: Decision Within Groups Total Between Groups Psych: Science and Within Groups Education Total Between Groups Psych: Aspiration Within Groups Total Between Groups Psych: Compulsive Within Groups Total Between Groups Biospheric Principles Within Groups Total Between Groups Egotistical Principles Within Groups Total Between Groups Altruistic Principles Within Groups Total Between Groups Car Knowledge Within Groups
358.819 7.685 269.421 277.107 4.970 329.427 334.396 17.352 317.271 334.624 11.045 351.477 362.522 12.850 360.019 372.869 19.278 346.484 365.762 14.215 344.805 359.019 64.770 294.005 358.775 8.070 352.884 360.954 7.091 354.026 361.116 22.510 305.876 328.385 10.587 329.681 340.268 56.725 289.640 346.365 12.605 370.815 383.420 15.724 338.181 353.905 2.915 363.072 365.987 9.501 348.801
376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 372 376 4 367
1.921 .724
2.653 .033
1.242 .886
1.403 .232
4.338 .853
5.086 .001
2.761 .945
2.922 .021
3.213 .968
3.319 .011
4.819 .931
5.174 .000
3.554 .927
3.834 .005
16.193 .790
20.488 .000
2.018 .949
2.127 .077
1.773 .952
1.863 .116
5.627 .822
6.844 .000
2.647 .886
2.986 .019
14.181 .779
18.214 .000
3.151 .997
3.161 .014
3.931 .909
4.324 .002
.729 .976
.747 .561
2.375 .950
2.499 .042 348
Appendix Total Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total
Car Importance Car Environment
Car Cost
358.302 16.673 352.677 369.349 28.539 340.719 369.257 10.392 360.584 370.976
371 4 367 371 4 372 376 4 372 376
4.168 .961
4.337 .002
7.135 .916
7.790 .000
2.598 .969
2.680 .031
10.6 RESEARCH OUTPUT Over the course of this thesis, a number of different papers have been produced and presented. This section of the Appendix presents the abstracts of these papers in chronological order. Electric Vehicles: Will Consumers Get Charged Up? Universities' Transport Study Group Annual Conference 2011 Climate change programmes around the globe are relying heavily on the electrification of private transport to achieve carbon reduction targets. Currently, the main focus is on electric vehicles (EVs) in particular, which are novel technologies, including fully electric, plug-in hybrid and range extended electric vehicles. In general, mainstream consumers have no experience with EVs. This presents a significant challenge to the investigation and prediction of the consumer response to such vehicles. In order to accelerate the market, more evidence is needed on the willingness of consumers to respond to this technology and under what combination of fuel prices, incentives, infrastructure provision, technical performance, individual and societal norms success is most likely to be achieved. This paper presents a systematic review of the international evidence to understand consumer behaviour relating to the uptake of cars in general and EVs in particular. The literature falls into two broad categories (i) theoretical texts relating to socio-technical transitions, instrumental, symbolic and affective motives and consumer segmentation; (ii) empirical evidence based on (a) qualitative and conventional questionnaire surveys eliciting consumer attitudes and perceptions of (alternatively fuelled) vehicle attributes; (b) revealed
349
Appendix and stated preference surveys of consumer behaviour regarding a variety of vehicle powertrains and (c) consumer responses to EVs before and after (small-scale) vehicle trials. In order to synthesise this evidence, this paper will present a conceptual framework of EV adoption to incorporate the socio-psychological, functional and symbolic motives present in the literature. Moreover, insights as to how behavioural antecedents are likely to prevail in different consumer segments will be included, which goes beyond the typical diffusion theory classification of early adopters and mainstream consumers. Suggestions as to novel research methodologies are also offered. The results presented will underpin future primary data collection being undertaken by the authors as part of the Consumers and Vehicles sub-project of the Energy Technologies Plugin Vehicle Economics and Infrastructure programme and as part of a PhD project funded by the UK Energy Research Centre. Consumer Response to Low Emission Vehicles: The potential of novel design methodologies RGS-IBG Postgraduate Mid-Term Conference 2011 The challenges of energy security and environmental impacts of personal vehicle transport are encouraging the UK Government to reconsider personal vehicle transport. A variety of technologies are being developed to address these challenges but a clear understanding of the likely consumer response to these potential technologies has yet to be developed. Previous studies have investigated the functional attributes of alternative vehicle powertrains by utilizing discrete choice modeling generating useful insights relating to consumer preferences. These studies have been limited by elements of framing bias, respondent asymmetrical information and unstable preferences. The application of in-depth product design methodologies can address these limitations of conventional choice modeling. These methodologies employ a more detailed context setting to inform the subject of the activity and provide a structured game environment for them to display their preferences. This study will incorporate aspects of stated preference, consumer 350
Appendix decision making, problem solving and budget allocation. The objective of this methodology is to produce a more realistic simulation of the purchasing environment and allow subjects to design the vehicle that best reflects their desires. The development of this method has been informed from the product design literature and a review of current industry practice. Consumer Reaction to and Acceptance of the Introduction of New Low Emission Technologies into the Passenger Vehicle Market UK Energy Research Centre Summer 2011 New technologies and innovations are being released into the consumer market at an ever increasing rate. Some of these technologies are advancements of a preexisting theme sustaining existing market conditions whilst others disrupt the status quo. Low Emission Vehicles are often categorized as a prime example of a disruptive technology as they break rather than alter the mould of what a car should be and how it should perform. Disruptive technologies tend to have high polarization in their success rates; they’re either a hit or a failure. Low Emission Vehicles not only provide the personal mobility service expected by consumers but also provide benefits to society by mitigating energy insecurity and environmental degradation. In this sense their success is linked to the accomplishment of key societal level objectives leading to the application of governmental level interventions in order to assist this technological transition. It would be ill advised to intervene in a situation where there is little preexisting understanding of the governing conditions. Uninformed intervention at best would be ineffective and at worst counterproductive. A detailed understanding of emerging consumer behavior in the market for Low Emission Vehicles is necessary to determine how best to proceed in enhancing the benefits of these cars. This research study aims to develop this understanding by conducting consumer research in this emerging market. Drawing on theories from social science, psychology, economics and technology studies this research study investigates the likely consumer responses to Low Emission Vehicles whilst attempting to develop a predictive model of consumer choice of next generation vehicles using mixed methodology. It is expected that on conclusion of this research study we will be able to
351
Appendix provide guidance on how best to direct consumer behavior in this market in order to accelerate the shift towards these vehicles. Market Segmentation Analysis of the Low Emission Vehicle Market RGS-IBG Conference 2011 Low emissions technologies in the personal vehicle market hold great potential for addressing some of the significant environmental and energy security challenges the UK may face over the coming decades. Many technologies are either near market deployment or are being introduced currently. These technologies range from alternative powertrains such as Battery Electric Vehicles to milder vehicle interventions such as low resistance tyres. The market from these new technologies is beginning to emerge. Understanding the possible market dynamics will assist policy makers and automotive manufacturers in developing their strategy. This research project aims to produce a detailed analysis of this new market. A baseline will be established outlining the current market environment. A market segmentation methodology will be employed in order to identify consumer groups in this new market. These consumer segments will be grouped according to their preferences for and attitudes towards vehicles and personal transport in general and low emission vehicle technologies in particular. Specific attention will be given to market and consumer dynamics detailing how the market is likely to develop in different scenarios and subjected to different stimuli. Initial results will be displayed and discussed with an outline provided from future research progression. Diffusion Analysis of the Emerging Market for Low Emission Vehicles Universities' Transport Study Group Annual Conference 2012 A large degree of public and private funding is being allocated to accelerating the introduction of Low Emission powertrains for passenger cars, especially plug-in Hybrid and Pure Battery Electric Vehicles (EVs). If these new vehicles are to make a significant contribution towards moving the UK to a more sustainable personal transportation system, a 352
Appendix detailed understanding of the likely consumer demand for them is a fundamental requirement. The success of these new vehicles will be as much dependent on their desirability to customers as to their technical ability. This paper draws upon Roger‟s Diffusion of Innovation Theory to understand the potential importance of consumer „innovativeness‟ as a pre-cursor to at least the early adoption of new vehicle technology. It presents preliminary results from an extended household pilot self completion survey conducted in Aberdeen City which respondents were asked questions relating to both conventional vehicles and Low Emission Vehicles (e.g. electric powertrains). These questions included aspects of Consumer Culture Theory in addition to an innovation scale that covers the three main variations of innovativeness that have been identified in the literature: (1) personality and communication traits (also referred to as innate innovativeness), (2) adoptive innovativeness that has further been segmented into (a) general adoption of consumer technology and (b) specific preferences towards Low Emission Vehicles. The results will be presented using Factor and Correlation analysis and will aim to understand the relative importance of the constructs with respect to consumer preference towards Low Emission Vehicles. Our findings suggest that innovativeness can be measured both through adoptive behaviour, psychological inclination and communication activity with these 3 constructs showing a degree of interaction. It proved more challenging to identify interactions between these 3 constructs and the local measure of innovativeness in the LEV market suggesting that innovative behaviour has yet to be “switched on” in this setting. Demand Drivers in the Emerging Market for Low Emission Vehicles in Scotland Scottish Transport Applications and Research Conference 2012 Moving towards a more sustainable transport system within Scotland has been a primary objective of the Scottish Government for a considerable length of time. Specifically looking at the high dependence on private vehicle use, the associated problems of road accidents, urban pollution, congestion and energy security are clearly evident. Whilst attempting to reduce this private vehicle dependency is a worthy endeavour, it is likely that the majority of passenger trips will be conducted in private vehicles for the foreseeable future. Rather than focusing on changing the quantity of transport demand satisfied by passenger vehicle use, it
353
Appendix may prove fruitful to consider changing the type of private vehicle consumers operate. Low Emission Vehicles (LEVs) have been developed to address some of these outlined problems and are ready to be introduced into the mainstream automotive market. How successful they are at reducing these problems will be dependent on consumer reaction to and adoption of these LEVs. Traditionally, demand for a vehicle has been estimated using formally rational decision making models where consumers are represented as self interested utility maximizers basing their decision primarily on the price and specification of the vehicles. Whilst this approach has considerable merit, it is clear that consumers take into consideration other factors when deciding what car to purchase. To account for this, we aim to augment the traditional perspective by employing a dual framework approach. Firstly, we apply a model developed on the principles put forward in the Diffusion of Innovation Theory to address the predictive nature of this research. Secondly, we have developed a 3 construct framework which includes functional, symbolic and emotive vehicle characteristics to observe what influence these have over LEV preference formation. Results will be presented at this conference from an initial distribution of 1996 household self completion questionnaires that were distributed in Dundee city. Diffusion Analysis of the Emerging Consumer Market for Low Emission Vehicles Transport Research Arena 2012 A large degree of public and private funding is being allocated to accelerating the introduction of Ultra Low Emission powertrains for passenger cars, especially Plug-in Hybrid and Pure Battery Electric Vehicles (EVs). If these new vehicles are to make a significant contribution towards increasing energy security whilst decreasing levels of air pollution and greenhouse gas emissions, a detailed understanding of the likely consumer demand for them is a fundamental requirement. The success of these new vehicles will be as much dependent on their desirability to customers as to their technical ability. This paper draws upon Roger’s (1995) Diffusion of Innovation Theory to understand the potential importance of consumer ‘innovativeness’ as a pre-cursor to at least the early adoption of new vehicle technology. It presents preliminary results from a household self 354
Appendix completion survey conducted over two case study sites (Newcastle-upon-Tyne and Dundee) in which respondents were asked questions relating to both conventional vehicles and Ultra Low Emission Vehicles (e.g. electric powertrains). These questions included aspects of Consumer Culture Theory (Arnould and Thompson, 2005) in addition to an innovation scale that covers the three main variations of innovativeness that have been identified in the literature: (1) personality traits (also referred to as innate innovativeness) and (2) adoptive innovativeness that has been further segmented into (a) general adoption of consumer technology and (b) specific preferences towards Low Emission Vehicles. Additionally, a conceptual framework including the constructs of Function, Emotion and Symbolism will be employed to investigate how respondents assign meaning to cars and what impact this has onto their preference for Low Emission Vehicles. This framework draws from work conducted by Dittmar (1992) and Steg (2005). An attitude scale has been developed that will measure these 3 latent variable constructs with the expectation that respondents who place more emphasis onto the symbolic aspects of cars will be more positively inclined to Ultra Low Emission Vehicles. Conversely, we expect respondents that pace more emphasis onto the functional aspects of cars to be less positively included to Ultra Low Emission Vehicles. The results will be presented using Factor and Regression Analysis and will aim to understand the relative importance of the constructs with respect to consumer preference towards Ultra Low Emission Vehicles. The results will contribute to understanding dynamic processes of consumer adoption of EVs and will inform policy relating to possible approaches to increase adoption rates. Whilst this research is focused on the UK market it is believed the results and understanding obtained will have translational benefits to the wider European context. Consumer Structure in the Emerging Market for LEVs: A market segmentation analysis Universities' Transport Study Group Annual Conference 2013 Low Emission Vehicles (LEVs) are scheduled for widespread release in the mainstream passenger vehicle market in the forthcoming vehicle cycle. Their successful adoption is 355
Appendix viewed as being of strategic importance in order to accomplish a number of prominent societal objectives. However, significant uncertainties remain regarding the nature of consumer reaction to these vehicles. This research aims to provide insight relating to consumer response to LEVs by examining the structure of the emerging market. This has been conducted using a cluster analysis, based on data attained from a household survey, which has partitioned the market into unique segments. A bespoke research framework incorporating components sourced from the Diffusion of Innovation Theory, Value Belief Norm Theory and transport psychology has been developed to form the basis of the segmentation procedure. Specifically, the concept of individual innovativeness has been measured at an innate and adoptive level alongside respondent ascription of symbolic, emotive and functional meanings to car ownership and use. Results suggest that unique market segments are forming in this emerging market, with a number showing distinctly high preferences for LEVs. Innovativeness appears to hold a mixed role, potentially enhancing likely adoption rates in some segments but not others. Those market segments that exhibit high levels of symbolic and emotive car connection may indeed be less likely to consider Factors Influencing Demand in the Emerging Market for Low Emission Vehicles Transportation Research Board Annual Meeting 2013 Low Emission Vehicles (LEVs) are being developed and brought to market in order to address the issues surrounding greenhouse gas emissions linked with climate change, high urban pollution levels and concerns linked to energy security. How successful these vehicles are at mitigating these issues depends on their desirability to consumers. This research project takes a new approach to the investigation of consumer response to the introduction of LEVs by specifying a Research Framework which includes components that have received relatively little attention in this field of enquiry. Specifically, measurements relating to symbolic and emotive car meanings have been taken alongside the more traditional functional considerations of LEVs. Additionally, the construct of innovativeness has been measured from an innate and adoptive perspective to examine its influence over LEV preference. Three preference models have been constructed using multiple regression analysis to examine interactions between the Research Framework components. The data 356
Appendix has been collected through the application of a self completion household questionnaire distributed over the cities of Dundee and Newcastle-upon-Tyne in the United Kingdom. Initial results indicate that symbolic and emotive meanings as well as individual innovativeness are having statistically significant influence over LEV preference.
357
