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The 8th International Conference on Applied Energy – ICAE2016 ... Consumers prefer hybrid electric vehicles (HEVs) followed by plug-in electric vehicles ...
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ScienceDirect Energy Procedia 105 (2017) 2187 – 2193

The 8th International Conference on Applied Energy – ICAE2016

Customers' attitude on new energy vehicles’ policies and policy impact on customers' purchase intention Yiping Loua, Wenhuan Wanga, Xiaoguang Yanga,b * b

a China University of Petroleum (Beijing), No.18 Fu Xue Road, Changping District, Beijing, 102200, China Academy of Mathematics and Systems Science, CAS, No.55 East Road, Haidian District, Beijing, 100190, China

Abstract Under the background of worse air pollution and sluggish economic growth, green transportation is regarded as a powerful means to address these challenges and the governments in China's Capital Economic Circle (i.e. Beijing, Tianjin and Hebei) are making a great effort to promote new energy vehicles (NEVs). A series of new policies are issued to encourage the use of NEVs. A nature question is to know what the customers' attitude on the NEVs and NEVs' policies is, and what the effects of policies on customers' purchase intention will be. Based on a consumer survey, this paper explores the consumers’ attitudes on NEVs by statistical analysis, and investigates the effects of NEVs’ policies on consumers’ behavioral intention with a structural equation model (SEM ). The results indicate that: Consumers prefer hybrid electric vehicles (HEVs) followed by plug-in electric vehicles (PEVs), but consumers’ cognition of NEVs and related policies is insufficient. Policy preferences are different among consumers’ in Beijing, Tianjin and Hebei. Policies promote behavioral intention by easing perceived risks of consumers and the positive effects are transmitted through purchase willingness. There are correlations among perceived risks and differences among policies, so the effects of policies acting on each perceived risk vary widely.

© Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2017 2016The The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy.

Keywords: NEVs ; attitude; perceived risk; purchase willingness; behavioral intention; policy

1. Introduction The rapid develop ment of China’s economy imp roves the living standard of Ch inese people, but brings unpleasant pollution problems; in particu lar a large area of haze in recent years presents a serious threat to Chinese public health. The Capital Economic Circle has met the heaviest haze problem in China.

* Corresponding author. Tel.: +86-10-89733124; fax: +86-10-89733742. E-mail address: [email protected].

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.617

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Heightened concern on air pollution and economic develop ment has increased necessity of green transportation. In order to promote economic development wh ile improve the environment, the governments in the Capital Economic Circle are endeavoring to promote the use of NEVs. In fact, the latest data from China Association of Automobile Manufacturers shows that haze part icles in Beijing consist of vehicle exhaust 22.2%, coal 16.7%, dust16.3%, industry 15.7%. Higher energy efficiency in the transport sector is seen as a cornerstone of energy conservation [1]. Therefore the governments have issued a series of policies to encourage NEVs’ adoption [2]. It is important to know what the customers’ attitude is and how the policies affect customers. Emp irical research on the purchase intention of NEVs has revealed some factors affecting consumers’ behavior [3-6]. However, how these factors can affect consumers’ purchase wasn’t discussed clearly. NEVs in China have been developed to the stage of large scale demonstration and extension. Consumers’ concern and the effects of NEVs’ policies are t wo hot issues. This paper conducts a consumer survey which the hundreds of respondents are randomly selected in the area of Beijing, Tianjin and Hebei. The main research objective is to explore how policies exert influence on consumers’ behavioral intention through perceived risk. 2. Customers' attitude on NEVs' policies 2.1. Consumers’ cognition and preferences of NEVs A data analysis on the survey shows that: the vast majorit ies of consumers have heard of NEVs but don’t have a deeper understanding. Consumers prefer HEVs followed by PEVs. Descending order of the degree of concern about performance indicators are safety, power, reliab ility, convenience, cost and environmental p rotection. Consumers’ cognition and preferences of NEVs are shown in Table 1 and Fig.1. T able 1. Public cognition degree of NEVs Percent Unknown

6.3

Heard of it but don’t know

67.2

A better understanding of it

26.5

Fig. 1. (a) T ype preferences of NEVs; (b) Views about factors related to NEVs

2.2. Policy preferences Consumers’ awareness of related policy is generally inadequate, and the public gets policy information mainly through the med ia. Po licies on "subsidies" and "tax cuts" are the most popular but other policy

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preferences are different among respondents. Consumers in Hebei pay more attention to the traffic restrictions of non-Beijing license plate. Consumers in Tianjin show great concern for facilit ies and services. “Relax restrictions on traffic and purchase” is more popular in Beijing and Tianjin. T able 2. Customers' cognition of policy Policy

Know it or not (Yes)

Information Channels News Media Manufacturers Propaganda Friends Recommend

Others

Financial (subsidies, tax breaks) 25% Facilities and Services 25% T echnology , Quality 23.4%

80.88% 78.00% 86.38%

6.37% 15.60% 3.40%

6.37% 3.20% 3.40%

6.37% 3.20% 6.81%

Promotion Operations Security

87.60% 78.02%

0.00% 4.65%

12.40% 8.02%

0.00% 9.30%

25% 17.2%

T able 3. Policy preferences of respondents in Beijing, Tianjin, Hebei Beijing Personal car allowance Facilities and Services

Score 3.7 3.3

T ianjin Facilities and Services Personal car allowance

Score 4.0 3.4

T ax cuts

3.0

T ax cuts

2.7

Cancel traffic restrictions of non-Beijing license plate Relax restrictions on traffic and purchase

2.5 2.4

Cancel traffic restrictions of non-Beijing license plate Relax restrictions on traffic and purchase

Hebei Personal car allowance T ax cuts Cancel traffic restrictions of non-Beijing license plate

Score 3.6 3.3

2.6

Facilities and Services

3.0

2.4

Relax restrictions on traffic and purchase

1.9

3.2

3. Effects of NEVs' policies 3.1. Model and hypothesis Bauer [7] believes that any consumer is likely unsure that his expected results of the buying behavior is correct. The uncertainty of results of consumers’ purchase decision is the so-called perceived risk. Expectations of the possibility of dissatisfaction will h inder consumers ’ making decisions. Existing studies put the perceived risk into different d imensions [8-10]. This paper raised five dimensions of perceived risk according to the relevant features of NEVs. They are price risk, functional quality risk, time risk, physical security risk and social risk. Put forward the hypothesis H1 on this basis. H1: Consumers’ perceived risk about NEVs has negative effects on their willingness to buy. Take the market characteristics of Ch ina's NEVs and instinct features of humanity (Maslow's hierarchy of needs) into consideration, we suppose that H1’: the order of perceived risks about NEVs is d (security risk) > a (price risk) > b (time risk) > c (functional quality risk) > e (social risk). Internal factors and external information will affect the perceived risk of consumers [11-12]. Encouraging policy will provide consumers with a behavioral support and spiritual encouragement to some extent. Therefore we put forward the hypothesis H2. H2: Encouraging policy related to NEVs can ease consumers' perceived risk. According to the policy relevance, intensity and effectiv eness in China, we propose that H2’: the potency of policies acting on each perceived risk is sorted from strong to weak: a (effects on price risk) > b (effects on time risk) > c (effects on functional quality risk) > d (effects on security risk) &

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e (effects on social risk). Stone and Gronhaug [13] think that the dimensions of perceived risk are not necessarily independent of each other. We suggest that H3: Five dimensions of perceived risk of consumers to buy NEVs are associated with each other. Attitude contains cognition, emotion and action. There is a significant correlat ion between important attitudes and behavior, and they two are highly consistent. H4: Attitude towards the purchase of NEVs has a positive effect on the behavioral intention . 3.2. Results and discussion Five d imensions of perceived risk all have negative and significant correlat ions with purchase willingness and behavioral intention. Thus hypothesis H1 is proved. T able 4. Correlations without policy S1i

Willingness

Behavioral Intention

Perceived price risk

Pearson Correlation

1

-.278**

-.209**

Perceived time risk

Pearson Correlation

1

-.295**

-.220*

Perceived functional quality risk

Pearson Correlation

1

-.315**

-.247**

Perceived social risk

Pearson Correlation

1

-.485**

-.376**

Perceived security risks

Pearson Correlation

1

-.499**

-.390**

**. Correlation is significant at the 0.01 level (2-tailed).

S1i˖Perceived risk ˄i =1,2....5˅

T able .5. Variable label Label S1 S1a S1b S1c S1d

Variable Perceived risk protection without policy Perceived price Perceived time Perceived functional qualit y Perceived security

S1e

Perceived social

N1

Purchase willingness

Label S2 S2a S2b S2c S2d

Variable Perceived risk protection under policy Perceived price Perceived time Perceived functional quality Perceived security

S2e1

Perceived social: Saving and environmental

S2e2 N2

Perceived social: Status Behavioral intention

The path diagram of SEM was constructed based on a factor analysis. Variable labels are shown in Table5: Adjust the perceived risk score in reverse order, and then the meaning turns into perceived risk protection. The greater the perceived risks are, the smaller the perceived risk protections will be. Results of the model are shown in Fig 2 and the regressions for consumers’ purchase willingness and behavioral intention could be respectively written in the following way:

1

 u 6    u 6   X

1

 u 1   X 

(1) (2)

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ª6 D º «6 E » « » «6 F » «6 G » «6 H » ¬ ¼

ª6 D º « » «6 E » «6 F » « » «6 G » «6 H » « » «¬6 H »¼

ªº « » «» «» u 6   G  « » «» «» ¬ ¼

(3)

ªº « » «» «» « » u 6  G «» «» « » «¬»¼

(4)

Fig. 2. Path diagram of SEM

In Equation (1), perceived risk protection without policy (S1) has a significant influence on purchase willingness (N1) with a path coefficient of 0.67, and perceived risk protection under policy (S2) has a lower influence with a coefficient of 0.39, indicating that issues about perceived risk under the policy has diminished. Hypothesis H2 is proved to be right. Factor loadings reflect the relative importance of each variable on the common factors. In Equation (3) , factor loadings of S1a, b, c, d, e on S1 show that the order of perceived risks about NEVs is d > e > a, b > c. Hypothesis H1’ is partially correct. In Equation (4), factor loadings of variables on S2 are all increased and the increments show that the potency of policies acting on each perceived risk is sorted fro m strong to weak: c > b > a > d > e. Hypothesis H2’ is also partially correct. In Equation (2), purchase willingness (N1) has a positive effect on behavioral intention (N2) with a significant coefficient of 0.80. Hypothesis H4 is verified. Correlations among residuals proved the correlations among the corresponding variables. The level of perceived risk will change in different contexts [14]. So even though some relationships among perceived

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risks are significant but some not, correlations among perceived risk dimensions do exist. Hypothesis H3 is verified. 3.3. Model Fitting Model fitting parameters including CMIN, RMR, GFI, Baseline Co mparisons and RMSEA all show that the model has a good fitting. Thus hypotheses are all acceptable. 4. Conclusions This study provides some emp irical ev idence about customers’ attitude on NEVs ' policies and the impact of policies on customers. Po licies pro mote behavioral intention by easing perceived risks of consumers, and the positive effects are transmitted through purchase willingness. Policies have different effects on different dimensions of perceived risk. Specifically, policy on “functional quality” is the most successful in easing related perceived risk. Consumers put security at the first and it’s difficu lt to be ameliorated. Mainstream po licies on "subsidies" and "tax cuts" for NEVs are d isposable means in NEVs’ adoption, so the general views that financial policies are the most effect ive are refuted. Consumers pay more attention to the value of long-term use including functional quality, safety and infrastructure. Policies on in frastructure and use have greater incentive effects. Subsidies can't play a decisive role in the private purchase at the present stage, so the intensity of back slope needs to be increased by government. Getting rid of dependence on huge financial subsidies, the technological innovation, quality, infrastructure and new business models can help to develop China's NEVs market and imp lement green transportation. 5. Copyright Authors keep full copyright over papers published in Energy Procedia Acknowledgements The research is supported by the NSFC project (No. 71431008, 71532013) and the open fund of Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology, Nanning 530004, China.

Yiping Lou et al. / Energy Procedia 105 (2017) 2187 – 2193

References [1] Kihm A, Trommer S. The New Car Market For Electric Vehicles And The Potential For Fuel Substitution. Energy Policy; 73(2014),p.147-157 [2] Zheng SM, Yi HT, Li H. The impacts of provincial energy and environmental policies on air pollution control in China. Renewable and Sustainable Energy Reviews; 49(2015),p.386-394 [3] Ji PH. Empirical Research on the Factors that Affect Consumers’ Attitude Toward New Energy Vehicles,Northeast Normal University(China); 2014 [4] Wang YH., Wang Q. Factors Affecting Beijing Residents’ Buying Behavior of New Energy Vehicle:an Integration of T echnology Acceptance Model and Theory of Planned Behavior. Chinese Journal of Management Science; 21(special)(2013),p.691 [5] Diamond D. The impact of government incentives for hybrid-electric vehicles: Evidence from US states. Energy Policy; 37(3)(2009),p.972-983 [6] Ozaki R, Sevastyanova K .Going hybrid: An analysis of consumer purchase motivations. Energy Policy; 39(5)(2011),p.2217 [7] Bauer RA. Consumer behavior as risk raking.Dynamic Marketing for a Changing World,Proceedings of the 43rd Conference of the American Marketing Association; 1960,p.389-398 [8] Jacoby J, Kaplan L. The components of perceived risk.Proceedings of the 3rd Annual Conference ,Association for Consumer Research; 1972,p.382-393. [9] Bettman JR. Perceived risk and its components : a model and empirical test. Journal of Mark eting Research; 10(1973) , p.184-190. [10] [13] Stone RN, Gronhaug K. Perceived risk : further considerations for the marketing discipline. European Journal of Marketing; 27(3)(1993) , p.39-50 [11] Finucane ML, Alhakami A, Slovic P , Johnson SM. The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making; 13(1)(2000), p.1-17 [12] Weinstein ND. Unrealistic optimism about susceptibility to health problems:Conclusions from a community-wide sample. Journal of Behavior Medicine; 10(5)(1987), p. 481-500 [14] Mitchell VW, Boustani P. A preliminary investigation into post -purchase risk perception and reduction. European Journal of Marketing; 28(1994), p.56-71

Biography Y.P. Lou and W.H. Wang are master students and Ph.D. students respectively. X.G. Yang is a professor in Academy of Mathemat ics and Systems Science, CAS and China Un iversity of Petroleum (Beijing).

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