Mar 8, 1982 - I want to thank our Program Coordinator, Prof. ..... small businesses do engage in marketing research the benefits usually exceed the ... convenience of location, layout of store, credit and billing policy, retail store internal ...... Carrefour is a leading global retailer of food products (accounting for some 80% of.
THE IMPACT OF MARKETING MIX ON CUSTOMER SATISFACTION: A CASE STUDY DERIVING CONSENSUS RANKINGS FROM BENCHMARKING
AMY POH AI LING
DISSERTATION SUBMITTED IN PARTIAL FULFILMENT FOR THE DEGREE OF MASTER OF SCIENCE (QUALITY AND PRODUCTIVITY IMPROVEMENT)
FACULTY OF SCIENCE AND TECHNOLOGY NATIONAL UNIVERSITY OF MALAYSIA BANGI 2007
ii
DECLARATION
I hereby declare that the work in this dissertation is my own except for quotations and summaries which have been duly acknowledged.
16 April 2006
AMY POH AI LING P 37435
iii
ACKNOWLEDGEMENT I am deeply indebted to my supervisor Dr. Mohamad Nasir Saludin, for his constant support and assistance for the duration of my thesis. He has been a continual font of ideas, stimulating suggestions and encouragement helped me in all the time of research for and writing of this thesis. I have learnt a lot about all aspects of working both as part of a research team and as part of the wider research community. It is valuable to have someone close to the research activities as well as senior to the area. I want to thank our Program Coordinator, Prof. Madya Dr. Ahmad Mahir Razali for giving me permission to commence this thesis in the first instance and to do the necessary research work. I want to thank for his help, support, interest and valuable hints. Thanks to the lecturers in my courses that helped me in my studies and generously gave me idea to carry on in this project. To my research assistants, Chen Zhi Syin, Ivan Leong Jenn Jiang, Tan Ai Lee and Wong Xiao Wei, they have also made invaluable contributions to this thesis. I spent months working with them for my own good, and the result is that much of this work (Chapters 2, 3 and 4) was done in conjunction with them. Last but not least, I would like to express my gratitude to all those who gave me the possibility to complete this thesis.
AMY POH AI LING P 37435
iv THE IMPACT OF MARKETING MIX ON CUSTOMER SATISFACTION: A CASE STUDY DERIVING CONSENSUS RANKINGS FROM BENCHMARKING ABSTRACT This paper takes a cautionary stance to the impact of marketing mix on customer satisfaction, via a case study deriving consensus rankings from benchmarking on retail stores in Malaysia. Field research was conducted in Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International, and the homegrown retail store, Mydin Mohamed Holdings Berhad. With increasing globalization, local retailers find themselves having to compete with large foreign players by targeting niche markets. We build a model in deriving consensus rankings from benchmarking base on the marketing mix model, the traditional marketing paradigm, embodied in the well-known Marketing Mix frame work proposed by Borden and popularized as the 4Ps (Product, Price, Place, Promotion) by McCarthy. The marketing mix is the lens through which the contemporary customer perceives value in retail stores on 4Ps is examined. From the model, we analyze what is the best practice among the four elements derived from a consensus ranking, a ranking method to identify the best in class. The analysis will mainly depend on the outcome of what customer perceive towards the four marketing tactics. This paper discusses the introduction and use of a methodology for project ranking in Retail store and, in particular, illustrates the use of a particular solution method called ELECTRE. A goal of this research was to introduce a more objective methodology for the multicriteria outranking methodology as an alternative and more sustainable approach for benchmarking analysis in marketing sector.
Keywords: Marketing mix, Customer satisfaction, Retailing, Benchmarking, Multicriteria decision-making, ELECTRE methods
v CONTENT
Page DECLARATION
ii
ACKNOWLEDGEMENTS
iii
ABSTRACT
iv
CONTENTS
v
FIGURE LIST
x
ILLUSTRATION LIST
xi
TABLE LIST
xii
CHAPTER 1
INTRODUCTION
1
1.1
Research Description
1
1.2
Problem Statement
3
1.3
Background
5
1.3.1
5
Quantitative Marketing Research
1.4
Objectives of the Study
6
1.5
The Strength and Significance of the Study
7
1.6
Rationale of the Study
9
1.7
Specification of the Information Needed
10
1.8
Definition of Terms
11
1.8.1 1.8.2 1.8.3 1.8.4 1.8.5 1.8.6
11 11 12 12 12 13
1.9
Marketing Mix Customer Satisfaction Retailing Benchmarking Multi-criteria Decision Making ELETRE method
Conclusion
13
vi CHAPTER II
LITERATURE REVIEW
14
2.1
Introduction
14
2.2
Marketing mix
15
2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.2.6 2.2.7
16 16 16 17 17 17 19
2.3 2.4
Definition Product Decisions Price Decisions Place (Distribution) Decisions Promotion Decisions Criticism on Marketing Mix Model Limitations of the Marketing Mix Framework
Customer satisfaction
20
2.3.1
20
Measuring Customer Satisfaction
Benchmarking
22
2.4.1 Advantages of benchmarking 2.4.2 Competitive benchmarking 2.4.3 Advantage of the Benchmarking 2.4.4 Types of Benchmarking
22 22 23 24
2.5
Multi-Criteria Decision Models
28
2.6
Multi-Criteria Outranking Methodology - ELECTRE I
29
2.7
Retailing
31
2.7.1 Retail in Malaysia 2.7.2 Four types of retailers 2.7.3 Retail Activity in Malaysia: From Shop house to Hypermarket 2.7.4 Hypermarkets in Malaysia see strong growth
31 33 34
Retail Stores Profile
35
2.8.1 Tesco Stores (Malaysia) Sdn Bhd. 2.8.2 Carrefour - Magnificent Diagraph Sdn.Bhd. 2.8.3 Giant - Dairy Farm International (DFI) 2.8.4 Mydin Mohamed Holdings Berhad
35 37 38 39
Conclusion
39
2.8
2.9
34
vii CHAPTER III
RESEARCH METHODOLOGY
40
3.1
Introduction
40
3.2
Recognisance Survey
40
3.3
Questionnaire Construction
42
3.4
Test-Retest Reliability Checks
43
3.4.1 Cronbach's α (alpha) 3.4.2 Pre-Test 3.4.2.1 Reliability Statistics for Tesco 3.4.2.2 Reliability Statistics for Mydin 3.4.2.3 Reliability Statistics for Carrefour 3.4.2.4 Reliability Statistics for Giant 3.4.3 Overall Reliability Statistics
44 45 45 45 45 46 46
Sampling Methods and Sample Size
47
3.5.1 3.5.2
48 49
3.5
3.5.3
Simple Random Sampling Determine Sample Size – Statistical Sampling Concepts Assumptions for Simple Random Sampling
50
3.6
Data Collection
51
3.7
Illustration of Research Framework
52
3.8
Conclusion
55
viii CHAPTER IV
DATA ANALYSIS, INTERPRETATION
56
AND DISCUSSION 4.1
Introduction
57
4.2
Consensus Rankings from Benchmarking
59
4.3
Profile of Respondents
60
4.4
Descriptive Statistics
62
4.4.1
Marketing Mix Factor 4.4.1.1 Product Factor 4.4.1.2 Price Factor 4.4.1.3 Place/Distribution Factor 4.4.1.4 Promotion Factor 4.4.2 Marketing Mix Model, 4Ps 4.4.3 Motivating Factor 4.4.4 Cross tabulation Analysis 4.4.5 Descriptive Statistics Analysis of four retail stores 4.4.5.1 Tesco 4.4.5.2 Mydin 4.4.5.3 Carrefour 4.4.5.4 Giant
63 63 64 65 66 67 68 69 72 72 73 74 75
4.5
Benchmarking and Outranking-Satisfying Methodology
76
4.6
Benchmarking on Customer Satisfaction
83
4.6.1 Product Benchmarking 4.6.2 Price Benchmarking 4.6.3 Promotion Benchmarking 4.6.4 Place/Distribution Benchmarking
83 84 85 86
Conclusion
87
4.7
ix CHAPTER V
SUMMARY, CONCLUSION AND IMPLICATION
88
5.1
SWOT Analysis
88
5.1.1 Strength 5.1.2 Weakness 5.1.3 Opportunity 5.1.4 Threat
88 89 89 90
5.2
Conclusion
90
5.3
Directions for further research
91
5.4
Scope and Limitation of the Study
76
EXTENDED ABSTRACT - Technical Paper
108
REFERENCES
APPENDIX
92
A. Authorization Letter for the Research 1. 2. 3. 4.
Tesco Carrefour Giant Mydin
B. Letter Request of Contribution 1. 2. 3. 4.
Letter Request of Contribution - Tesco Letter Request of Contribution - Carrefour Letter Request of Contribution – Giant Letter Request of Contribution – Mydin
C. Questionnaires 1. 2. 3. 4.
Questionnaires - Tesco Questionnaires - Carrefour Questionnaires - Giant Questionnaires - Mydin
114 115 117 119 121 123 124 125 126 137 128 129 131 133 135
D. Major Retail Players in Malaysia
137
E. Classification of MCDM Method
138
x FIGURE LIST
Figure No.
Page
Figure 2.1
The Marketing Mix Model
15
Figure 4.1
Graph of S from Table 4.27 (C* ≥ 75 percent)
82
xi ILLUSTRATION LIST
Illustration No.
Page
Illustration 3.1
Selangor's Geographical Position
41
Illustration 3.2
Attribute – 4P’s – Retail Stores Mapping
52
Illustration 4.1
Graph of S from Table 4.27 (C* ≥ 75 percent)
82
xii TABLE LIST
Table No.
Page
Table 2.1
Types of Benchmarking
24
Table 2.2
Gross Domestic Product by Industry of Origin,
32
Malaysia 2000-2005 Table 3.1
Reliability Statistics – Tesco
45
Table 3.2
Reliability Statistics – Mydin
45
Table 3.3
Reliability Statistics – Carrefour
45
Table 3.4
Reliability Statistics – Giant
46
Table 3.5
Overall Reliability Statistics
46
Table 3.6
Level of Confidence
49
Table 4.1
Profile of Respondents – Gender
60
Table 4.2
Profile of Respondents – Ethnic
60
Table 4.3
Profile of Respondents – Marital Status
60
Table 4.4
Profile of Respondents – Age
61
Table 4.5
Profile of Respondents – Shopping Frequency
61
Table 4.6
Descriptive Statistics of Product Factor
63
Table 4.7
Descriptive Statistics of Price Factor
64
Table 4.8
Descriptive Statistics of Place/Distribution Factor
65
Table 4.9
Descriptive Statistics of Promotion Factor
66
Table 4.10
Descriptive Statistics of Marketing Mix Model, 4Ps
67
Table 4.11
Motivating Factor
68
Table 4.12
Motivating Factor * Gender Cross tabulation
69
Table 4.13
Motivating Factor * Ethnic Cross tabulation
69
Table 4.14
Motivating Factor * Marital Status Cross tabulation
70
Table 4.15
Motivating Factor * Age Cross tabulation
70
Table 4.16
Motivating Factor * Shopping Frequency Cross tabulation 71
Table 4.17
Descriptive Statistics for Tesco
72
Table 4.18
Descriptive Statistics for Mydin
73
Table 4.19
Descriptive Statistics for Carrefour
74
xiii Table 4.20
Descriptive Statistics for Giant
75
Table 4.21
Multicriteria matrix (Electre I)
76
Table 4.22
Retail stores Positioning Table
76
Table 4.23
Retail Stores’ Ranking Table
77
Table 4.24
Multicriteria Matrix
78
Table 4.25
Matrix of Concordance Ssubsystems (Jc)
78
Table 4.26
Concordance Matrix
79
Table 4.27
Outcomes of Concordance Test
80
Table 4.28
Product Benchmarking towards customer satisfaction
83
Table 4.29
Price Benchmarking towards customer satisfaction
84
Table 4.30
Promotion Benchmarking towards customer satisfaction
85
Table 4.31
Place/Distribution Benchmarking towards
86
customer satisfaction
1
CHAPTER 1
INTRODUCTION
1.1
RESEARCH DESCRIPTION
To excel and flaunt as a market leader in an ultramodern era and a globalize world where we barely can catch up with the changes, the organizations must strive not only to improve but also to commit into a continuous improvement climate, to harvest from its marketing strategies especially marketing mix model, benchmarking and company quality policy. Malaysia retail industry has been showing upward trends for quite some time. Growth in this sector is particularly spurring by the changing buying patterns of consumers and rising per capita income in the country. This paper takes a cautionary stance to the impact of marketing mix on customer satisfaction, via a case study deriving consensus rankings from benchmarking on multinational retail stores in Malaysia. Field research will be conduct in Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International, and the homegrown retail store, Mydin Mohamed Holdings Berhad. With increasing globalization, local retailers find themselves having to compete with large foreign players by targeting niche markets. This study continues to research the program component aspect by examining all four facets of the marketing mix, described here as product features, brand name, retail outlets, basic advertising message and retail pricing of a single consumer product.
2 Ranking and selecting projects is a relatively common, yet often difficult task. It is complicated because there is usually more than one dimension for measuring the impact of each project and more than one decision maker. This paper considers a real application of project selection for the marketing mix element, using an approach called ELECTRE. The ELECTRE method has several unique features not found in other solution methods; these are the concepts of outranking and indifference and preference thresholds. The ELECTRE method is explained and applied to the project selection problem using SPSS (Statistical Package for the Social Sciences) application. Results show that ELECTRE was well received by the decision makers and, importantly, provided sensible and straightforward rankings. Our contribution is to show the potential in Marketing mix model in deriving a consensus ranking in benchmarking. According to the feedback from the respondents, we dynamically rank out the best element to be benchmark.
3 1.2
PROBLEM STATEMENT
The decision problem faced by management has been translated into our market research problem in the form of questions that define the information that is required to make the decision and how this information obtained. Thus, in this paper, the decision problem regarding the marketing mix four Ps is translated into a research problem. The corresponding research problem is to assess whether the market would accept the consensus rankings derive from benchmarking result from the impact of marketing mix on customer satisfaction using a multi-criteria decision making outranking methodology. The project ranking problem is, like many decision problems, challenging for at least two reasons. First, there is no single criterion in marketing mix model which adequately captures the effect or impact of each element; in other words, it is a multiple criteria problem. Second, there is no single decision maker; instead the project ranking requires a consensus from a group of decision makers. (Henig and Buchanan and Buchanan et al.) Henig and Buchanan and Buchanan et al. have argued that good decisions come from good decision process and suggest that where possible the subjective and objective parts of the decision process should be separated. This separation enables the decision making process to move away from being unnecessarily subjective and toward a more objective orientation. A decision problem can be conceived as comprising two components; a set of objectively defined alternatives and a set of subjectively defined criteria. The relationship between the alternatives and the criteria is described using attributes, which are the objective and measurable features of alternatives, attributes form the bridge between the alternatives and the criteria. In Illustration 3.1 the alternative-attribute-criteria mappings are illustrated. Outranking relations, in most methods, are built using a concordance-discordance principle. More complexity and flexibility are required in the processing of efficient alternatives. And it is the solutions, not the criteria, which the marketing management is interested in.
4 Although it is not clearly stated in Simon (1977), we think that one of the main functions of review is learning and we believe that the best support that could be provided to organizations would be for learning. In many cases, we have observed that decision is treated as a one shot game whereas most decisions are more or less repetitive. Human memory has some known biases and, for that reason, cannot accurately analysis decisions ex post. However, very little seems to have been done in this domain up to now. There are many possibilities related to learning, review and ex post analysis. First, in some sense, a decision maker can learn the effect of the assignment he has given to the weights. Similarly, in outranking methods, the decision maker can learn to modify concordance and discordance factors (Roy and Skalka, 1985; Vetschera, 1986). Most of the failures arise because one does not take into account that a decision maker makes a decision according to a set of items (e.g., his preferences) that does not intervene explicitly in the decision making process itself but constrains it. This is what we call contextual knowledge. Let us also remind that, in the framework of decision making, due to the prominent lookahead component (Pomerol, 1995), the subjective and contextual data play an important role. Moreover, due to the incompleteness of the model, especially during the evaluation phases (Lévine and Pomerol, 1995), among the elements facilitating the cooperation are explanations and contextual knowledge, and the need to make them explicit and shared both by the system and the user (Brezillon and Abu-Hakima, 1995) and Brézillon (1996).
5 1.3
BACKGROUND
For the multinational corporation (MNC), the pursuit of a global marketing strategy encompassing a standardized marketing mix (M. Mix) strategy retains the promise of greater opportunities in the borderless marketplace (Dunning, 1993; Kustin, 1993; Roth, 1995). These strategies also offer the opportunity to develop higher quality products by obtaining greater efficiencies of production, through lower costs associated with economies of scale (Levitt, 1983), outsourcing (Kotabe, 1990; Keegan & Green, 2003), developing priority locations for manufacturing (Dunning, 1998), distribution (Rosenbloom, Larsen, & Metha, 1997) and economies of scope (Yip, 1989). Groonroos argues that the 4Ps framework has won an overwhelming acceptance among marketing practitioners, noticing that ‘‘. . . Marketing in practice has, to a large extent, been turned into managing this toolbox . . . ’’, a point shared by Goldsmith who argues that the ‘‘. . . time-honored concept of the 4Ps—the Marketing Mix . . . ’’ is the heart of the contemporary marketing management. 1.3.1
Quantitative Marketing Research
It is the application of quantitative research techniques to the field of marketing. It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four P's" of marketing: Product, Price, Place (location) and Promotion. As a social research method, it typically involves the construction of questionnaires and scales. People who respond (respondents) are asked to complete the survey. Marketers use the information so obtained to understand the needs of individuals in the marketplace, and to create strategies and marketing plans.
6 1.4
OBJECTIVES OF THE STUDY
The objectives of this research are defined clearly to ensure that the true decision problem is address. This research has two main objectives: 1.
To build an analytical connection between the customers satisfaction with the international marketing mix model, the four Ps and benchmarking. A. To determine products and services that meets the needs of customers. B. To observe value of price the intended customers willing to pay. C. To determine distribution channels the potential customer desire. D. To analyze impact of the business's promotion have on customers. E. To set a benchmark base on the marketing mix four Ps.
2.
To create perceive value and generate a positive response.
7 1.5
THE STRENGTH AND SIGNIFICANCE OF THE STUDY
Retailers need to generate a pool of information in order to introduce products and services that create value in the mind of customer. The value of what the customer perceived is a subjective one, the attributes that create value can not simply be deducted from common knowledge. Rather, data must be collected and analyzed. The purpose of this marketing research is to provide the facts and direction that managers need to make their more important marketing decision. The strength of this research lies on its specific focus on the connection between the customers satisfaction with the international marketing mix model, the four Ps and benchmarking. This research also underlines the impact of customer buying behavior base on the company quality policy. A survey of small business managers in Texas revealed that 84 percent of those who conducted formal marketing research projects in the past three years felt that the information obtained was worth the money spent. Overall, 58 percent said that they were able to incorporate the research findings into their decision-making process. Only six percent reported that they were not able to implement the results. Consequently, when small businesses do engage in marketing research the benefits usually exceed the costs. This research enable the retail stores to gain insight into future industry trends that will affect its business, get data and analysis in the most cost-effective and flexible way and draw on essential information without being overwhelmed by unnecessary detail.
8 It is anticipated that the findings of this research will harvest benefits as follow: 1. Elucidate a clear picture on the connection between the customers satisfaction with the international marketing mix model, the four Ps. 2. The four Ps are the parameters that the marketing manager can control, subject to the internal and external constraints of the marketing environment. 3.
Manifest a clear picture on the connection between the customers satisfaction with its company benchmarking strategy.
4. Develop the awareness on the impact of customer buying behavior base on the company quality policy. 5. Gain insight into future industry trends that will affect its business. 6. Get data and analysis in the most cost-effective and flexible way and draw on essential information without being overwhelmed by unnecessary detail. 7. Understand the customer. 8. Make value for customer. 9. Communicate the retail value to target market. 10. Help managers to look outside of themselves for solutions. 11. Contribute to the marketing theory (The marketing mix model, 4Ps). 12. Adding literature review to the marketing area. 13. Benefit to the retail stores participated (Tesco, Mydin, Carrefour and Giant).
9 1.6
RATIONALE OF THE STUDY
The Ministry of Finance expects the retail and wholesale sub-sector in Malaysia to growth by 8% from 6.3% recorded for the first half of the year 2006. In concord with this, the Malaysia’s GDP registered stronger-than-expected growth since 2003. A notable development has been the changing nature of FDI flows. Malaysia’s consumer lifestyle has been evolving and changing due in part to rising affluence and education levels. High profile international retailers and the global mass media have also played a hand in shaping consumer-buying behavior. Malaysians are becoming more westernized, sophisticated, and cosmopolitan. Since the emergence of the foreign-owned hypermarkets, Malaysians who live in urban areas have become accustomed to shopping for groceries at hypermarkets and supermarkets. The Malaysian retail scene is gearing up for intense competition with more new players and expansion plans undertaken by foreign players. As consumers become more cautious with their spending, retailers have had to become extremely price-competitive. The ongoing price war among major retailers continues to have an adverse effect on the small retailers, who may not be able to compete at lower prices. Company has become more aware of their marketing strategy and started benchmarking to measures and compares all its functions, systems and practices against strong competitors, identifying quality gaps in the organization, and striving to achieve competitive advantage locally and globally. However, it is note that the intense competition posed by foreign players will provide additional impetus for local retailers to leverage on retail technology to better understand consumer purchasing behavior, streamline operational procedures and to enhance efficiency.
10 1.7
SPECIFICATION OF THE INFORMATION NEEDED
The research identified the following factors as part of the choice criteria: Literature reviews from journal on the best practice for ranking in benchmarking were done. Further study has to be made on the ranking methodology to determine the best methodology to apply in this research project. Findings to gain a better understanding on the four selected retail stores: Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International, and the homegrown retail store, Mydin Mohamed Holdings Berhad. Determine the element of four Ps to be evaluate such as marketing mix criteria, quality of merchandise, variety and assortment of merchandise, service of store personnel, prices, convenience of location, layout of store, credit and billing policy, retail store internal benchmarking, customer satisfaction, company quality policy and customer buying behavior.
11 1.8
DEFINITION OF TERMS
1.8.1 Marketing Mix The marketing mix is a model of creating and implementing marketing strategies. It stresses the blending of various factors in such a way that both organizational and consumer objectives are attained. The elements are the marketing tactics, also known as the 'four Ps', the marketing mix elements are price, place, product, and promotion. When blending the mix elements, marketers must consider their target market. They must understand the wants and needs of the market customer then use these mix elements in constructing and formulating appropriate marketing strategies and plans that will satisfy these wants. These four P's are the parameters that the marketing manager can control, subject to the internal and external constraints of the marketing environment. The goal is to make decisions that center the four P's on the customers in the target market in order to create perceived value and generate a positive response. 1.8.2 Customer Satisfaction Customer satisfaction is a perception. It is also a question of degree. Providing quality products and services is all about meeting customer requirements. Customer satisfaction, a business term, is a measure of how products and services supplied by a company meet or surpass customer expectation. It is seen as a key performance indicator within business and is part of the four perspectives of a Balanced Scorecard. In a competitive marketplace where businesses compete for customers, customer satisfaction is seen as a key differentiator and increasingly has become a key element of business strategy. The four key steps for successful marketing are identified as understanding the customer, making value for customer, communicating the value to target market, and making it easy for the customer to buy.
12 1.8.3 Retailing Retailing refers to all activities directly related to the selling of small quantities of goods and services, at a profit, to the ultimate customers for personal consumption and nonbusiness use (Mohd-Said, 1990). Retail trading encompasses a wide variety of goods and services, ranging from household items to food and accessories. Guy (1980) for instance has categorized retail trade into three groups: (a) convenience goods which include groceries and daily provisions; (b) shopping or comparison goods which refer to relatively more expensive items bought at less regular intervals; and (c) specialty goods which are unique items that appeal to customers of the higher income level. 1.8.4 Benchmarking Benchmarking, also known as "best practice benchmarking" or "process benchmarking" is a process used in management and particularly strategic management, in which organizations evaluate various aspects of their processes in relation to best practice, usually within own sector. This then allows organizations to develop plans on how to adopt such best practice, usually with the aim of increasing some aspect of performance. Benchmarking may be a one-off event, but is often treated as a continuous process in which organizations continually seek to challenge their practices. 1.8.5 Multi-criteria Decision Making The choice of destination in relocation benchmark for marketing element for retailing management strategy, either price, product, place/distribution and promotion, can be performed using multiple criteria decision model (MCDM). Multiple Criteria Decision Model attempt to identify all alternatives and to quantify characteristics of these alternatives—attributes—in order to rank them in some consistent manner. MCDM can be divided into those that allow tradeoffs between attribute levels (“compensatory decision rules”) and those that do not, and those that explicitly incorporate risk, or uncertainty, and those that do not.
13 1.8.6 ELETRE method The simplest method of the ELECTRE family is ELECTRE I. The ELECTRE methodology is based on the concordance and discordance indices defined as follows. The ELECTRE I method is used to construct a partial ranking and choose a set of promising alternatives. ELECTRE II is used for ranking the alternatives. In ELECTRE III an outranking degree is established, representing an outranking creditability between two alternatives which makes this method more sophisticated and, of course, more complicated and difficult to interpret. In order to track the consensus ranking, the project itself has been broken into a number of four phases, the respondents collection was done in four different retail stores. 1.9
CONCLUSION
The information generated for this survey is use to adjust practices within the organization to continuously improve the retail stores’ products, pricing strategy, promotion strategy, place and distribution strategy, services, and processes base on the marketing mix model in order to more completely satisfy its customers. Literature reviews from journal on the best practice for ranking in benchmarking were done. Further study has to be made on the ranking methodology to determine the best methodology to apply in this research project.
14
CHAPTER II
LITERATURE REVIEW
2.1
INTRODUCTION
Knowledge is cumulative: every piece of research will contribute another piece to it. That is why it is important to commence all research with a review of the related literature or research, and to determine whether any data sources exist already that can be brought to bear on the problem at hand. This is also referred to as secondary research. Just as each study relies on earlier work; it will provide a basis for future work by other researchers. This stage involves a literature review on the status study of the international marketing mix model, customers satisfaction and the benchmarking methods. This stage also covers the background and recent reports of the selected retail stores to be survey such as Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International, and the homegrown retail store, Mydin Mohamed Holdings Berhad. According to the 2005 Global Retail Development Index TM, Malaysia’s GDP growth has recovered from the 2001 economic slowdown and stands at 6 percent. Its retail market remains fragmented, which helped boost it up one notch to the 18th position. Retail sales have grown up from 6 to 8 percent over the past two years and are expected to maintain the same rate. Although Malaysian consumers have embraced hypermarkets and department stores, discount retailers and convenience stores will likely become new vehicles for growth.
15 2.2
MARKETING MIX
The term "marketing mix" became popularized after Neil H. Borden published his 1964 article, The Concept of the Marketing Mix. Borden began using the term in his teaching in the late 1940's after James Culliton had described the marketing manager as a "mixer of ingredients". The ingredients in Borden's marketing mix included product planning, pricing, branding, distribution channels, personal selling, advertising, promotions, packaging, display, servicing, physical handling, and fact finding and analysis. E. Jerome McCarthy later grouped these ingredients into the four categories that today are known as the 4 P's of marketing. The marketing mix is a model of creating and implementing marketing strategies. It stresses the blending of various factors in such a way that both organizational and consumer objectives are attained. The elements are the marketing tactics, also known as the 'four Ps', the marketing mix elements are price, place, product, and promotion. The model was developed by Neil Borden (Borden, N. 1964) who first started using the phrase in 1949. When blending the mix elements, marketers must consider their target market. They must understand the wants and needs of the market customer then use these mix elements in constructing and formulating appropriate marketing strategies and plans that will satisfy these wants. Figure 2.1 The Marketing Mix Model
16 2.2.1 Definition These four P's are the parameters that the marketing manager can control, subject to the internal and external constraints of the marketing environment. The goal is to make decisions that center the four P's on the customers in the target market in order to create perceived value and generate a positive response. As Pedhazur and Schmelkin (1991, p. 164) have noted, “Even for people who speak the same language, words have different meanings, depending on, among other things, who speaks, to whom, in what context, at what time, and with what purpose . . . . The point is that the different terms reflect different outlooks, values, attitudes, and the like.” 2.2.2
Product Decisions
The term "product" refers to tangible, physical products as well as services. Although this typically refers to a physical product, it has been expanded to include services offered by a service organization. The specification of the product is one of the variables that a marketer has at his/her control. For example, the product can include certain colors, certain scents, and certain features. Lastly, in the broadest sense when a consumer purchases a product it also includes the post-sales relationship with the company. The post-sales relationship can include customer service and any warranty. 2.2.3
Price Decisions
The price is the amount paid for a product. In some cases, especially in business-tobusiness marketing this can also include the total cost of ownership (TCO). Total cost of ownership may include costs such as installation and other products required to deliver a complete functional solution.
17 2.2.4
Place (Distribution) Decisions
Place represents the location where a product can be purchased. It is often referred to as the distribution channel. It can include any physical store as well as virtual stores on the Internet. Distribution is about getting the products to the customer. 2.2.5
Promotion Decisions
In the context of the marketing mix, promotion represents the various aspects of marketing communication, that is, the communication of information about the product with the goal of generating a positive customer response. Promotion represents all of the communications that a marketer may insert into the marketplace. This can include TV, radio, and print advertising, as well as coupons, direct mail, billboards, and online advertising. One of the less well-defined areas in promotion is the role of a human sales force. On the other hand, consumers may rather purchase the product only when sold through the support of a known salesperson. In this case, the service, perceived or real can be defined as a feature of the product. 2.2.6
Criticism on Marketing Mix Model
Peter Doyle (Doyle, 2000) claims that the marketing mix approach leads to unprofitable decisions because it is not grounded in financial objectives such as increasing shareholder value. According to Doyle it has never been clear what criteria to use in determining an optimum marketing mix. Objectives such as providing solutions for customers at low cost have not generated adequate profit margins. Doyle claims that developing marketing based objectives while ignoring profitability has resulted in the dot-com crash and the Japanese economic collapse. He also claims that pursuing a ROI approach while ignoring marketing objectives is just as problematic. He argues that a net present value approach maximizing shareholder value provides a "rational framework" for managing the marketing mix.
18 Against Kotler's four P's, some claim that they are too strongly oriented towards consumer markets and do not offer an appropriate model for industrial product marketing. Others claim it has too strong of a product market perspective and is not appropriate for the marketing of services. Since 1960, the model has broadened beyond its origins in economic theory to encompass aspects of sociology and cognitive psychology (Hakansson and Waluszewski, 2005). Indeed, criticism of the 4Ps has centered on its inception in the production and supply context of the 1950s, and its appropriateness to later twentieth century marketing functions. Consequently, it has been extended with a further 3Ps of participants, process and physical evidence (Booms and Bitner, 1981), and an eighth P for personalisation, to reflect a services marketing orientation (Goldsmith, 1999). The growing importance of the political environment led Kotler (1984) to propose two additional Ps of political power and PR to the marketing mix. As marketing’s focus has moved to consumers and consumption, it has arguably broadened into an integrated and networked approach to organisational resources (Brownlie and Saren, 1992). This has accompanied the decline of mass markets and growth of specialisation, supported by database management and customer relationship marketing principles, which evolved into the one-to-one marketing opportunities developed on the internet. In spite of its deficiencies, the 4Ps remain a staple of the marketing mix. The subsequent Ps has yet to overcome a consensus about their eligibility and agreement over their practical application.
19 2.2.7
Limitations of the Marketing Mix Framework
The marketing mix framework was particularly useful in the early days of the marketing concept when physical products represented a larger portion of the economy. Today, with marketing more integrated into organizations and with a wider variety of products and markets, some authors have attempted to extend its usefulness by proposing a fifth P, such as packaging, people, process, etc. Today however, the marketing mix most commonly remains based on the 4 P's. Despite its limitations and perhaps because of its simplicity, the use of this framework remains strong and many marketing textbooks have been organized around it.
20 2.3 CUSTOMER SATISFACTION Customer satisfaction is a perception. It is also a question of degree. Providing quality products and services is all about meeting customer requirements. Customer satisfaction, a business term, is a measure of how products and services supplied by a company meet or surpass customer expectation. It is seen as a key performance indicator within business and is part of the four perspectives of a Balanced Scorecard. In a competitive marketplace where businesses compete for customers, customer satisfaction is seen as a key differentiator and increasingly has become a key element of business strategy. The four key steps for successful marketing are identified as understanding the customer, making value for customer, communicating the value to target market, and making it easy for the customer to buy. 2.3.2
Measuring Customer Satisfaction
Organizations are increasingly interested in retaining existing customers while targeting non-customers; measuring customer satisfaction provides an indication of how successful the organization is at providing products and/or services to the marketplace. Customer satisfaction is an ambiguous and abstract concept and the actual manifestation of the state of satisfaction will vary from person to person and product/service to product/service. The state of satisfaction depends on a number of both psychological and physical variables which correlate with satisfaction behaviors such as return and recommend rate. The level of satisfaction can also vary depending on other options the customer may have and other products against which the customer can compare the organization's products. Because satisfaction is basically a psychological state, care should be taken in the effort of quantitative measurement, although a large quantity of research in this area has recently been developed.
21 Work done by Berry, Brodeur between 1990 and 1998 defined ten 'Quality Values' which influence satisfaction behavior, further expanded by Berry in 2002 and known as the ten domains of satisfaction. These ten domains of satisfaction include: Quality, Value, Timeliness, Efficiency, Ease of Access, Environment, Inter-departmental Teamwork, Front line Service Behaviors, Commitment to the Customer and Innovation. These factors are emphasized for continuous improvement and organizational change measurement and are most often utilized to develop the architecture for satisfaction measurement as an integrated model. Work done by Parasuraman, Zeithaml and Berry between 1985 and 1988 provides the basis for the measurement of customer satisfaction with a service by using the gap between the customer's expectation of performance and their perceived experience of performance. This provides the measurer with a satisfaction "gap" which is objective and quantitative in nature. Work done by Cronin and Taylor propose the "confirmation/disconfirmation" theory of combining the "gap" described by Parasuraman, Zeithaml and Berry as two different measures (perception and expectation of performance) into a single measurement of performance according to expectation. According to Garbrand, customer satisfaction equals perception of performance divided by expectation of performance. The usual measures of customer satisfaction involve a survey with a set of statements using a Likert Technique or scale. In this paper, we use a 6 points Likert scale. The customer is asked to evaluate each statement and in term of their perception and expectation of the performance of the organization being measured.
22 2.4
BENCHMARKING
Benchmarking, also known as "best practice benchmarking" or "process benchmarking" is a process used in management and particularly strategic management, in which organizations evaluate various aspects of their processes in relation to best practice, usually within own sector. This then allows organizations to develop plans on how to adopt such best practice, usually with the aim of increasing some aspect of performance. Benchmarking may be a one-off event, but is often treated as a continuous process in which organizations continually seek to challenge their practices. 2.4.1
Advantages of benchmarking
Benchmarking is a powerful management tool because it overcomes "paradigm blindness." Paradigm Blindness can be summed up as the mode of thinking, "The way we do it is the best because this is the way we've always done it." Benchmarking opens organizations to new methods, ideas and tools to improve their effectiveness. It helps crack through resistance to change by demonstrating other methods of solving problems than the one currently employed, and demonstrating that they work, because they are being used by others. 2.4.2
Competitive benchmarking
Some authors call benchmarking "best practices benchmarking" or "process benchmarking". This is to distinguish it from what they call "competitive benchmarking". Competitive benchmarking is used in competitor analysis. When researching your direct competitors you also research the best company in the industry even if it serves a different location.
23 2.4.3
Advantage of the Benchmarking
1. A better understanding of the waits (expectations) of the customer because it is: based on the reality of the market estimated in an objectivist way. 2. A better economic planning of the purposes and the objectives to achieve in the company because they are: centered on what takes place outside controlled and mastered. 3. A better increase of the productivity: resolution of the real problems Understanding of the processes and what they produce. 4. Better current practices Search for the change many decisions practices of break. 5. A better competitiveness thanks to: a solid knowledge of the competition a strong implication of the staff new ideas on practices and tried techniques. Benchmarking has consequences which are beyond the process itself: it reforms all the levels of the company; modifies the process of manufacture of the product leads(drives); also reforms the hierarchical organization of the company, the product itself, and the state of mind of the employees.
24 2.4.4
Types of Benchmarking
There are a number of different types of benchmarking, as summarized below: Table 2.1 Types of Benchmarking Type
Description
Most Appropriate for the Following Purposes
Strategic
Where businesses need to improve overall
Re-aligning business
Benchmarking
performance by examining the long-term
strategies that have
strategies and general approaches that
become inappropriate.
have enabled high-performers to succeed. It involves considering high level aspects such as core competencies, developing new products and services and improving capabilities for dealing with changes in the external environment. Changes resulting from this type of benchmarking may be difficult to implement and take a long time to materialize. Performance or
Businesses consider their position in
Assessing relative level
Competitive
relation to performance characteristics of
of performance in key
Benchmarking
key products and services. Benchmarking
areas or activities in
partners are drawn from the same sector.
comparison with others
This type of analysis is often undertaken
in the same sector and
through trade associations or third parties
finding ways of closing
to protect confidentiality.
gaps in performance.
25 Continue… Process
Focuses on improving specific critical
Achieving
Benchmarking
processes and operations. Benchmarking
improvements in key
partners are sought from best practice
processes to obtain
organizations that perform similar work or quick benefits. deliver similar services. Process benchmarking invariably involves producing process maps to facilitate comparison and analysis. This type of benchmarking often results in short term benefits. Functional
Businesses look to benchmark with
Improving activities or
Benchmarking
partners drawn from different business
services for which
sectors or areas of activity to find ways of
counterparts do not
improving similar functions or work
exist.
processes. This sort of benchmarking can lead to innovation and dramatic improvements.
26 Continue… Internal
Involves benchmarking businesses or
Several business units
Benchmarking
operations from within the same
within the same
organization (e.g. business units in
organization exemplify
different countries). The main advantages
good practice and
of internal benchmarking are that access
management wants to
to sensitive data and information is easier;
spread this expertise
standardized data is often readily
quickly, throughout the
available; and, usually less time and
organization.
resources are needed. There may be fewer barriers to implementation as practices may be relatively easy to transfer across the same organization. However, real innovation may be lacking and best in class performance is more likely to be found through external benchmarking. External
Involves analyzing outside organizations
Where examples of good
Benchmarking
that are known to be best in class.
practices can be found in
External benchmarking provides
other organizations and
opportunities of learning from those who
there is a lack of good
are at the "leading edge". This type of
practices within internal
benchmarking can take up significant time business units. and resource to ensure the comparability of data and information, the credibility of the findings and the development of sound recommendations.
27 Continue… International
Best practitioners are identified and
Where the aim is to
Benchmarking
analyzed elsewhere in the world, perhaps
achieve world class
because there are too few benchmarking
status or simply because
partners within the same country to
there are insufficient”
produce valid results. Globalization and
national" businesses
advances in information technology are
against which to
increasing opportunities for international
benchmark.
projects. However, these can take more time and resources to set up and implement and the results may need careful analysis due to national differences.
Benchmarking is the concept of discovering what is the best performance being achieved, whether in your company, by a competitor, or by an entirely different industry. Benchmarking is a continuous process whereby an organization measures and compares all its functions, systems and practices against strong competitors, identifying quality gaps in the organization, and striving to achieve competitive advantage locally and globally. Another type of benchmarking is ranking method to identify the best in class that we practiced in this project. This method shall be discussed in chapter IV.
28 2.5
MULTI-CRITERIA DECISION MODELS
The choice of destination in relocation benchmark for marketing element for retailing management strategy, either price, product, place/distribution and promotion, can be performed using multiple criteria decision model (MCDM). Multiple Criteria Decision Model attempt to identify all alternatives and to quantify characteristics of these alternatives—attributes—in order to rank them in some consistent manner. MCDM can be divided into those that allow tradeoffs between attribute levels “compensatory decision rules” and those that do not, and those that explicitly incorporate risk, or uncertainty, and those that do not. For example, a simple ranking of alternatives in descending order by level of attributes (“elimination by aspects”, Holsapple and Whinston 1996) addresses neither tradeoffs nor risk. A standard method for addressing multi-criteria decision problems using compensatory decision rules is via value functions (Winston 1994). If it can be shown that the preferences of the decision maker satisfy a number of standard assumptions, including transitivity, preferential independence, difference independence and tradeoff independence, then we may define an additive value function to be applied to all alternatives i and thus generate the ranking we seek. Each single-attribute value function may be defined by discussions with the decision maker to translate attribute levels to a uniform scale; weights can be assessed using the swing weighting method or by direct tradeoffs. Gardener and Armstrong-Wright (2000) have applied this method to employee selection using a 0 to 3 scale value function and group attribute means for each weight. Multiattribute utility theory, a MCDM that explicitly models individual utility functions, a generalization of value functions, using principles developed by von Neumann and Morganstern over half a century ago (Winterfeldt and Edwards [1989]), addresses both tradeoffs and risk.
29 2.6
MULTICRITERIA OUTRANKING METHODOLOGY - ELECTRE I
The simplest method of the ELECTRE family is ELECTRE I. (Michael P. Johnson, 2002) The ELECTRE methodology is based on the concordance and discordance indices defined as follows. We start from the data of the decision matrix, and assume here that the sum of the weights of all criteria equals to 1. For an ordered pair of alternatives ( A j , Ak ), the concordance index where the performance score of
C
jk
A
C
is the sum of all the weights for those criteria
is least as high as that of Ak , i.e.
j
∑
=
jk
j, k = 1, …, n, j ≠ k
Wi ,
a j ≥a k
i:
Clearly, the concordance index lies between 0 and 1. The computation of the discordance index
d
jk
is a bit more complicated:
index is zero if
A
j
d
performs better than
d
jk
= max
i =1,..., m
I.e. for each criterion where
=0 if
A
A
k
ij
i =1,..., m
A
ij
ik
, i =1,...,m, i.e. the discordance
, j, k = 1, …, n, j ≠ k
ij
outperforms
difference in performance level between
a >a
on all criteria,. Otherwise,
k
a −a max a − min a ik
i =1,..., m
jk
k
ij
A
and
j
, the ratio is calculated between the
A
j
the maximum difference in score
on the criterion concerned between any pair of alternatives. The maximum of these ratios (which must lie between 0 and 1) is the discordance index. A concordance threshold c* and discordance threshold d* are then defined such that 0 ATT1 ( R2 ) = 3.91
ATT3 ( R3 ) = 3.71 > ATT3 ( R2 ) = 3.42 ATT4 ( R3 ) = 3.70 > ATT4 ( R2 ) = 2.95
Three criteria {1, 3, and 4} agree in considering R3 better than R2 . Only one criterion {2} considers R2 better than R3 . That is: ATT2 ( R2 ) = 3.73 > ATT2 ( R3 ) = 3.60
78 Interpreting the same procedure for all the other pairs of retail companies will obtain the Table 4.24. Table 4.24 Multi-criteria Matrix
R1 (Tesco) R2 (Mydin) R3 (Carrefour) R4 (Giant)
Weight
ATT1 (Product)
ATT2 (Price)
ATT3 (Promotion)
ATT4 (Place/Distribution)
4.42 3.91 4.10 3.90 1/4
3.94 3.73 3.60 4.02 1/4
3.97 3.42 3.71 3.76 1/4
3.90 2.95 3.70 3.92 1/4
Table 4.25 Matrix of Concordance Subsystems ( J c ) R1
R2
R3
R4
{1,2,3,4}
{1,2,3,4}
{1,3}
{2}
{1}
R1 R2 R3
Ø Ø
{1,3,4}
R4
{2,4}
{2,3,4}
{1}
{2,3,4}
The generic element J c ( Ri , R j ) of the matrix of Table 4.25 is given by:
J c ( Ri , R j ) = {j ∈ J = ATTi ( Ri ) ≥ ATT j ( R j )}; where: J = {1, 2, 3, 4}
Taking into account the weights assigned to the various criteria, a concordance index can be calculated for each pair of company ( Ri , R j ): C ( Ri ; R j ) = ∑ j∈J K j ; c
Where: K j is the weight assigned to the jth criterion.
79 For example, for the pair ( R3 , R2 ) we have:
C ( R3 , R2 ) = K1 + K 3 + K 4 = 1/4 + 1/4 + 1/4 = 0.75 (75 percent)
We therefore have a majority of criteria of 75 percent in favor of R3 with respect to R2 . Iterating the same procedure for other pairs or organizations, we obtain the concordance matrix of Table 4.26. Table 4.26 Concordance Matrix R1
R2
R3
R4
1
1
0.50
0.25
0.25
R1 R2 R3
0
0.75
R4
0.50
0.75
0
0.25
0.75
The concordance indicator in Table 4.26 varies between 0 and 1. It is equal to 1 only if there is unanimity or a majority of criteria that are 100 percent in favor of Ri with respect to R j . In order to decide on the superiority of one retail company with respect to another, the decision maker should set a concordance threshold C*. Generally, it is chosen to be a majority greater than or equal to 75 percent (simple majority tightened), that is: C* ≥ 0.75 (75 percent). Taking into account the database of Table 4.26 and the concordance threshold C* we have the following concordance test: 1 if C ( Ri ; R j ) ≥ C* Tc ( Ri , R j ) =
0 if otherwise The results of concordance test are shown in Table 4.27.
80 Table 4.27 Outcomes of Concordance Test R1
R2
R3
R4
1
1
0
0
0
R1 R2 R3
0
1
R4
0
1
0
0
1
The Electre I methodology considers another step: the construction of discordance test in order to take into account of an excessive “distance” (dissimilarity) between the scores ATT j ( R j ) and ATTi ( Ri ). The discordance test Td is fulfilled if the distance:
D ( R j , Ri ) = max [ ATT j ( R j ) - ATTi ( Ri )];
does not exceed discordance threshold D*. In order to simplify the analysis we suppose that the test of discordance is fulfilled by all pairs ( Ri , R j ).
The ideas behind the test of discordance may be summarized as follows. The outranking methods consists in examining the validity of the proposition “a outranks b”. The concordance test “measures” the arguments in favor of saying so, but there may be arguments strongly against that assertion (discordant criteria). The “discordant voices” can be viewed as vetoes. There is a veto against declaring that a outranks b if b is so much better than a on same criterion that it becomes disputable or even meaningless to pretend that a might be better overall than b. The logic of the test of discordance is quite similar to that on which statistical tests are based. Here as well, conventional levels of significance (like the famous 5 percent rejection intervals) are widely used. The decision maker decides the discordance threshold, that is he decides whether a hypothesis must be rejected or not.
81 If the discordance test is not passed alternatives a and b are said incomparable. They are too different to be compared. For instance, the comparison of a Rolls-Royce with a small cheap car is meaningless because the Rolls-Royce is incomparably better on many criteria but is also incomparably more expensive. Another example, concerns the comparison of projects that involve the risk of loss of human life. Should one prefer a more expensive project with a lower risk or a less expensive one with higher risk? One may advance that the projects are too different to be compared. Taking into account both the concordance and the discordance test we construct a binary outranking relation S. Given two generic retail companies ( Ri , R j ) we say that Ri outranks R j if and only if the concordance test and the discordance test are fulfilled, that is: Ri S R j if and only if Tc and Td fulfilled.
Because we suppose that the discordance test is passed by all pairs ( Ri , R j ) the outranking relation S coincides with the outcomes of concordance test of Table 4.27. That is: Ri S R j if and only if Tc fulfilled.
The relation S may be represented by the graph of Illustration and Figure 4.1.
82 Illustration 4.1 Graph of S from Table 4.27 (C* ≥ 75 percent)
Figure 4.1 Graph of S from Table 4.27 (C* ≥ 75 percent)
R1 Tesco
R3 R4 Giant
Giant
R2 Mydin
Now, R3 is the “ 2nd worst in class” and R2 is the “worse in class”. But R1 and R4 are not comparable structures: neither R3 outranks R4 nor the opposite. This is another important difference arising from the refusal of the ordering based on the average benchmarking.
83 4.6
BENCHMARKING ON CUSTOMER SATISFACTION
Benchmarking has consequences which are beyond the process itself: it reforms all the levels of the company; modifies the process of manufacture of the product leads(drives); also reforms the hierarchical organization of the company, the product itself, and the state of mind of the employees. Through benchmarking, we get better understanding of the customer because it is based on the reality of the market estimated in an objectivist way and a better economic planning of the purposes and the objectives to achieve in the company for they are centered on what takes place outside controlled and mastered. The management will get a better increase of the productivity, resolution of the real problems and understanding of the processes and what they produce. 4.6.1
Product Benchmarking
Table 4.28 Product Benchmarking towards customer satisfaction
Product Benchmarking 4.60
4.42
M ean
4.40 4.10
4.20 3.91
4.00
3.90
3.80 3.60 R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Tesco rank the highest on customer satisfaction towards product and it shall be the benchmark or guiding star for other retail stores. Mydin, Carrefour and Giant need to benchmark Tesco’s product strategy and improve to compete in the market.
84 4.6.2
Price Benchmarking
Table 4.29 Price Benchmarking towards customer satisfaction
Mean
Price Benchmarking 4.10 4.00 3.90 3.80 3.70 3.60 3.50 3.40 3.30
4.02 3.94 3.73 3.60
R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Giant rank the highest on customer satisfaction towards price and it shall be the benchmark or guiding star for other retail stores. It proofs that Giant’s “Everyday low price strategy” is a success. Tesco rank the second with mean value of 3.94, still in the competition mood with Giant. Mydin and Carrefour need to benchmark Giant’s pricing strategy and improve to compete in the market.
85 4.6.3
Promotion Benchmarking
Table 4.30 Promotion Benchmarking towards customer satisfaction
Mean
Promotion Benchmarking 4.10 4.00 3.90 3.80 3.70 3.60 3.50 3.40 3.30 3.20 3.10
3.97 3.71
3.76
3.42
R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Tesco rank the highest again on customer satisfaction towards promotion and it shall be the benchmark or guiding star for other retail stores. Tesco promotion strategy is well organized and effective; customers are aware of the latest promotion from the newspaper, flyers and promotion booklet. Giant and Carrefour are a little bit behind with the mean value of 3.76 and 3.71. Mydin rank the last, it need to benchmark Tesco’s promotion strategy, revise on its promotion strategy and improve to compete in the competitive market.
86 4.6.4 Place/Distribution Benchmarking
Table 4.31 Place/ Distribution Benchmarking towards customer satisfaction
Min
Place/Distribution Benchmarking 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00
3.90
3.70
3.92
2.95
R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Tesco rank the highest on customer satisfaction towards place and distribution and it shall be the benchmark or guiding star for other retail stores. The other three retail stores having very close mean value. Meaning the customer satisfaction towards place and distribution in four retail stores are well perceived. From 4.6.4, we conclude that Giant rank the second, followed by Carrefour and Mydin. They are to benchmark Mydin’s, and improve on its place and distribution strategy.
87 4.7
CONCLUSION
Outranking methods make it possible to deal with multicriteria benchmarking and avoid the shortcomings of the traditional methods based on the average aggregate monocriterion. If applied to the measurement of learning capability, they are a complete alternative to the traditional approach. They can support the behavioral theory of organizational analysis initiated by H. Simon (Biggiero and Laise, 2003a, b). In fact, even though H. Simon does not explicitly discuss the problem of criteria multiplicity nor does he apply outranking methods, the behavioral theory is nonetheless perfectly comparable with them. The “levels of aspirations” hypothesized by Simon can be associated with the threshold of concordance and discordance test. The lower the threshold assigned to the concordance test the lower the aspiration levels will be and hence the more the satisfying solutions will be. Outranking methods thus constitute a new and robust base on which to found the entire edifice of the behavioral theory of benchmarking applied to measurement of learning capability. They are a valid alternative to traditional methods, since they are equally rational and rigorous without suffering from its shortcomings and application limitations.
88
CHAPTER V
SUMMARY, CONCLUSION AND IMPLICATION
Global marketers ‘‘usually find that customer needs are much more in common than they might have seemed’’ (Yip, 2003, p. 214). There is an increasing emphasis on customer satisfaction as a means of affecting storechoice behavior (e.g., Weir, 2001) and although little research exists to substantiate it, it seems intuitive that satisfaction would also affect customer share. After taken a close look into the results, findings and discussions, the following SWOT analysis was born in the extended abstract technical paper. 5.1
SWOT Analysis
5.1.1
Strength
Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies in the sciences, business, government and engineering worlds. MCDM methods can help to improve the quality of decisions by making the decision-making process more explicit, rational, and efficient. It is not a coincidence that a simple search (for instance, by using google.com) on the web under the key words “multi criteria decision making” returns more than one million hits.
89 In a decisional process the making of choices derives from complex hierarchical comparisons among alternative options, which are often based on conflictual criteria, a large number of external variables plays a relevant role in orienting decision-making. The strength of multi-criteria decision-making methods (MDMM) are to aid decision-makers to be consistent with fixed ‘general’ objectives; to use representative data and transparent assessment procedures and to help the accomplishment of decisional processes, focusing on increasing its efficiency. The Electre I method, in which the criteria of the set of decisional alternatives are compared by means of a binary relationship, defined as ‘outranking relationship’, are more ‘flexible’ than the ones based on a multi-objective approach. 5.1.2
Weakness
An intriguing problem with decision-making methods which rank a set of alternatives in terms of a number of competing criteria is that oftentimes different methods may yield different answers (rankings) when they are fed with exactly the same numerical data. Thus, the issue of evaluating the relative performance of such methods is naturally raised. This, in turn, raises the question how can one evaluate the performance of such methods? Since it is practically impossible to know which one is the best alternative for a given decision problem, some kind of testing procedures need to be determined. 5.1.3
Opportunity
In this paper, a new approach has been carried out for the use of the ELECTRE: ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing the REality) model in marketing mix selection. This work shows that ELECTRE can be used successfully in deriving a consensus ranking in benchmarking to select the best in class.
90 5.1.4
Threat
In outranking approaches, the inaccuracy of the data can be modelled through the indifference and preference threshold (so-called pseudocriteria). Of course, threshold must be assessed for each criterion and for each problem separately. 5.2
CONCLUSION
It is not simply enough to identify the strengths, weaknesses, opportunities, and threats of the electre outranking method. In applying the SWOT analysis it is necessary to minimize or avoid both weaknesses and threats. Weaknesses should be looked at in order to convert them into strengths. Likewise, threats should be converted into opportunities. Lastly, strengths and opportunities should be matched to optimize the potential of a firm. Applying SWOT in this fashion can obtain leverage for a company (Marketing Strategy, 1998). Sensitivity analysis showed that, in general, the project rankings were considerably more sensitive to changes in the performances than they were to changes in the thresholds or weights. This is helpful and means that within a relatively wide band of preference, the same projects are considered important. Further, it requires the individual project sponsors to make the effort and ensure that the performance data is both accurate and defensible. As can be seen, the marketing manager should have rough outline of potential marketing activities that can be used to take advantage of capabilities and convert weaknesses and threats. However, at this stage, there will likely be many potential directions for the managers to pursue. Due to the limited resources that most firms have, it is difficult to accomplish everything at once. The manager must prioritize all marketing activities and develop specific goals and objectives for the marketing plan (Contemporary Marketing, 1992).
91 5.3
DIRECTIONS FOR FURTHER RESEARCH
The relationships between customer satisfaction and behavioral outcomes are probably much more complex than initially assumed. This study has looked only at a limited part of the puzzle of how customer satisfaction translates into behavioral outcomes. In what way consumer characteristics moderate the relationship between satisfactions and repurchase behavior is likely to be contingent on the product or service category and the buying and usage process for that category. Other consumer characteristics not included in this study, such as a propensity for variety seeking behavior or a recreational shopping orientation, could potentially be important in many retail industries. Further research on how the effects of satisfaction on behavior is moderated by different consumer characteristics would advance customer satisfaction research as well as be of great managerial significance. 5.4
SCOPE AND LIMITATION OF THE STUDY
The setting selected for conducting this marketing research was focus only on three multinational retail stores and a homegrown retail store due to time constrain. Field researches were conduct in Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International, and the homegrown retail store, Mydin Mohamed Holdings Berhad. The collection of primary data was based on a survey of 856 respondents who visit each respective retail outlets, the number in the sample limited due to the restrictions of time to complete the project and resources to support it.
92
EXTENDED ABSTRACT Technical Paper
93
THE IMPACT OF MARKETING MIX ON CUSTOMER SATISFACTION: A CASE STUDY DERIVING CONSENSUS RANKINGS FROM BENCHMARKING
DR. MOHAMAD NASIR SALUDIN, AMY POH AI LING, CHEN ZHI SYIN, IVAN LEONG JENN JIANG, TAN AI LEE, WONG XIAO WEI
ABSTRACT This paper takes a cautionary stance to the impact of marketing mix on customer satisfaction, via a case study deriving consensus rankings from benchmarking on retail stores in Malaysia. Field research was conducted in Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International, and the homegrown retail store, Mydin Mohamed Holdings Berhad. With increasing globalization, local retailers find themselves having to compete with large foreign players by targeting niche markets. We build a model in deriving consensus rankings from benchmarking base on the marketing mix model, the traditional marketing paradigm, embodied in the well-known Marketing Mix frame work proposed by Borden and popularized as the 4Ps (Product, Price, Place, Promotion) by McCarthy. The marketing mix is the lens through which the contemporary customer perceives value in retail stores on 4Ps is examined. From the model, we analyze what is the best practice among the four elements derived from a consensus ranking, a ranking method to identify the best in class. The analysis will mainly depend on the outcome of what customer perceive towards the four marketing tactics. This paper discusses the introduction and use of a methodology for project ranking in Retail store and, in particular, illustrates the use of a particular solution method called ELECTRE. A goal of this research was to introduce a more objective methodology for the multicriteria outranking methodology as an alternative and more sustainable approach for benchmarking analysis in marketing sector. Keywords: Marketing mix, Customer satisfaction, Retailing, Benchmarking, Multi-criteria decision-making, ELECTRE methods
ABSTRAK Kertas ini yang merupakan satu kajian kes pemerolehan darjat konsensus daripada penandarasan telah berjaya membukitkan bahawa terdapatnya kesan campuran pemasaran terhadap kepuasan pelanggan. Kajian penyelidikan telah dijalankan di Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International dan emporium tempatan, Mydin Mohamed Holdings Berhad. Dengan pembangunan globalisasi yang pesat, syarikat peruncitan tempatan terpaksa bersaing dengan pelabur asing. Sehubungan itu, satu model telah dibangunkan untuk memperoleh darjat konsensus daripada penandarasan berdasarkan model campuran pemasaran, yang merupakan paradigma pemasaran traditional dengan mempraktiskan 4P iaitu produk, harga, promosi dan tempat/pengedaran. Daripada model campuran pemasaran, kami telah membina hubungan analitik antara kepuasan pelanggan dengan model campuran pasaran iaitu 4Ps dan seterusnya penandarasan daripadanya serta menjana respon yang positif daripada penilaian yang diperoleh. Kertas ini memperbincangkan penggunaan kaedah pemerolehan darjat di syarikat peruncitan serta mempaparkan kebagusan kaedah penyelesaian Electre. Katakunci: Campuran pemasaran, kepuasan pelanggan, peruncitan, menandaras, pembuatan keputusan multi-criteria, kaedah ELECTRE
94 1. Introduction To excel and flaunt as a market leader in an ultramodern era and a globalize world where we barely can catch up with the changes, the organizations must strive not only to improve but also to commit into a continuous improvement climate, to harvest from its marketing strategies especially marketing mix model, benchmarking and company quality policy. Malaysia retail industry has been showing upward trends for quite some time. Growth in this sector is particularly spurring by the changing buying patterns of consumers and rising per capita income in the country. Ranking and selecting projects is a relatively common, yet often difficult task. It is complicated because there is usually more than one dimension for measuring the impact of each project and more than one decision maker. This paper considers a real application of project selection for the marketing mix element, using an approach called ELECTRE. The ELECTRE method has several unique features not found in other solution methods; these are the concepts of outranking and indifference and preference thresholds. The ELECTRE method is explained and applied to the project selection problem using SPSS (Statistical Package for the Social Sciences) application. Results show that ELECTRE was well received by the decision makers and, importantly, provided sensible and straightforward rankings. Our contribution is to show the potential in Marketing mix model in deriving a consensus ranking in benchmarking. According to the feedback from the respondents, we dynamically rank out the best element to be benchmark. The decision problem faced by management has been translated into our market research problem in the form of questions that define the information that is required to make the decision and how this information obtained. Thus, in this paper, the decision problem regarding the marketing mix four Ps is translated into a research problem. The corresponding research problem is to assess whether the market would accept the consensus rankings derive from benchmarking result from the impact of marketing mix on customer satisfaction using a multi-criteria decision making outranking methodology. 2. Literature Review The decision problem faced by management has been translated into our market research problem in the form of questions that define the information that is required to make the decision and how this information obtained. Thus, in this paper, the decision problem regarding the marketing mix four Ps is translated into a research problem. The corresponding research problem is to assess whether the market would accept the consensus rankings derive from benchmarking result from the impact of marketing mix on customer satisfaction using a multi-criteria decision making outranking methodology. The project ranking problem is, like many decision problems, challenging for at least two reasons. First, there is no single criterion in marketing mix model which adequately captures the effect or impact of each element; in other words, it is a multiple criteria problem. Second, there is no single decision maker; instead the project ranking requires a consensus from a group of decision makers. (Henig and Buchanan and Buchanan et al.) Henig and Buchanan and Buchanan et al. have argued that good decisions come from good decision process and suggest that where possible the subjective and objective parts of the decision process should be separated. This separation enables the decision making process to move away from being unnecessarily subjective and toward a more objective orientation. A decision problem can be conceived as comprising two components; a set of objectively defined alternatives and a set of subjectively defined criteria. The relationship between the alternatives and the criteria is described using attributes, which are the objective and measurable features of alternatives, attributes form the bridge between the alternatives and the criteria. In Illustration 3.1 the alternative-attribute-criteria mappings are illustrated.
95 Outranking relations, in most methods, are built using a concordance-discordance principle. More complexity and flexibility are required in the processing of efficient alternatives. And it is the solutions, not the criteria, which the marketing management is interested in. Although it is not clearly stated in Simon (1977), we think that one of the main functions of review is learning and we believe that the best support that could be provided to organizations would be for learning. In many cases, we have observed that decision is treated as a one shot game whereas most decisions are more or less repetitive. Human memory has some known biases and, for that reason, cannot accurately analysis decisions ex post. However, very little seems to have been done in this domain up to now. There are many possibilities related to learning, review and ex post analysis. First, in some sense, a decision maker can learn the effect of the assignment he has given to the weights. Similarly, in outranking methods, the decision maker can learn to modify concordance and discordance factors (Roy and Skalka, 1985; Vetschera, 1986). Most of the failures arise because one does not take into account that a decision maker makes a decision according to a set of items (e.g., his preferences) that does not intervene explicitly in the decision making process itself but constrains it. This is what we call contextual knowledge. Let us also remind that, in the framework of decision making, due to the prominent look-ahead component (Pomerol, 1995), the subjective and contextual data play an important role. Moreover, due to the incompleteness of the model, especially during the evaluation phases (Lévine and Pomerol, 1995), among the elements facilitating the cooperation are explanations and contextual knowledge, and the need to make them explicit and shared both by the system and the user (Brezillon and Abu-Hakima, 1995) and Brézillon (1996). 3. Research Methodology 3.1 Recognizance Survey A recognisance survey was carried out in order to locate the most suitable site for the research. The section take into consideration sites in Selangor area. Selangor is Malaysia's most populous state, with the nation's biggest conurbation, the Klang Valley. Selangor's geographical position in the center of Peninsular Malaysia contributed to the state's rapid development as Malaysia's transportation and industrial hub. Selangor has a population of 4,736,100 (2005 estimate); the state's ethnic composition consisted of Malays 41%, Chinese 37%, Indians 19% and other ethnic groups 3%. The selected data collection sites are Tesco Saujana Impian Kajang, Carrefour Alamanda Putrajaya, Giant Bukit Tinggi and Mydin Kajang. 3.2 Research Instrument The research objectives and frame of reference was defined beforehand, including the questionnaire's context of time, budget, manpower, intrusion and privacy. A non-comparative Likert scaling techniques was used. The level of measurement of a variable in mathematics and statistics is a classification that was proposed in order to describe the nature of information contained within numbers assigned to objects and, therefore, within the variable. The questionnaire is divided into 4 sections: customer information, marketing mix model, customer perception and motivating factor. Variables that are measured only nominally are also called categorical variables. The demography variables measured at a nominal level in Section 1 include gender, ethnic, marital status, age and how often do the respondents shop at the specific retail store. A typical test item in a Likert scale is a statement. The respondent is asked to indicate his or her degree of agreement with the statement or any kind of subjective or objective evaluation of the statement. In Section 2, a six-point scale is used in a forced choice method where the middle option of "Neither agree nor
96 disagree" is not available. The questions comprise four elements such as product, price, promotions, place/distribution; six questions are allocated for each of the 4Ps. Section 3 evaluates customer’s perception using the same scale as practice in Section 2 where Section 4, the last part of the questionnaire measure the factor that motivates respondents the most to patronize the specific retail store using the nominal measurement. We choose simple random sampling in the research for conceptually; simple random sampling is the simplest of the probability sampling techniques. It requires a complete sampling frame, which may not be available or feasible to construct for large populations. Even if a complete frame is available, more efficient approaches may be possible if other useful information is available about the units in the population. 3.3 Illustration of Research Framework Illustration 1 Attribute – 4P’s – Retail Stores Mapping
The illustration of Attribute - 4P’s - Retail Stores Mapping was built to sprout a better understanding on our study framework. It elucidates the main idea of how we determine the targeted attribute of the 4Ps and generate it in the questionnaire to meet out objectives. The relationship between the marketing mix, 4ps with the attributes lies in each P element were elucidate clearly linking to the four selected retail stores, namely Carrefour, Giant, Tesco and Mydin.
97 Once everybody agrees about the family of criteria, assuming that the alternatives are known, it remains to complete the decision matrix, i.e., to evaluate each alternative according to the criteria. This evaluation theoretically depends on the posterior aggregation procedure, but this fact is generally ignored by the designers so that the assessment is generally independent of the aggregation procedure. The system can support a direct assessment method, showing graphically to the decision making, the position of the various alternatives or transforming a pair wise comparison into a numerical (normalized) scale as, for example, in the so-called "Analytical Hierarchical Process"(AHP) (Saaty, 1980). In the framework of multi-attribute utility, the utilities of a given alternative, regarding each attribute, are jointly cardinal. They have consequently to be jointly evaluated (Pomerol & Barba-Romero, 1993). In this case, due to the difficulty either to verify the probabilistic independence or to help the decision maker to jointly evaluate the alternatives by solvability or by the mid-preference point method, the support of a Multicriteria Decision Making methodology should be very useful. 3.4 Data Collection The study was conducted in a Selangor area, the most populous state in Malaysia with approximately 4.19 million residents. At the time of the study, four retail stores were chosen as the research sites. The data were collected by means of questionnaire. Households were the target of the research during the surveyed period. First appointment was conducted with the personal in-charge in each retail store to request cooperation and approval for data collection and survey respond via formal letters from the Department of Mathematical Sciences, Faculty of Science and technology, National University of Malaysia. Field research was conducted in Tesco Saujana Impian Kajang, Carrefour Alamanda Putrajaya, Giant Bukit Tinggi and Mydin Mart Kajang. A simple random sample of 214 household’s respondents was obtained from each of the four retail stores; sum up a total of 856 respondents data. In our framework, we can think about objectives as aspiration levels defined for each criterion or alternately as very general goals. We manage to expose the relationship between the marketing mix, 4ps with the attributes lies in each P, it was elucidate clearly link to the four selected retail stores. The retail stores management uses the information so obtained to understand the needs of individuals in the marketplace, and to create strategies and marketing plans. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships. 3.5 Data Analysis and Interpretation The retail market place promotes continuous improvement to survive in a turbulent environment. It does so by creating, acquiring and transferring knowledge and modifying its behavior to reflect new knowledge. For that, benchmarking is the search for industry best practices that leads to superior performance (Camp, 1989). The benchmarking measurement of the retail stores considers a set of indicators and for this reason assumes the configuration of a multi-criteria analysis. The literature on retail stores and marketing mix model has identified four major underlying criteria essential to take place in the market place. They are as follows:
ATT1 : Product Attribute ATT2 : Price Attribute ATT3 : Promotions Attribute ATT4 : Place/Distribution Attribute
98 Multi-criteria benchmarking analysis of comparing the four retail stores (Tesco, Carrefour, Giant and Mydin) poses many problems. Since the “dominance” relation is usually not verified, there is not a “best in class organization”. Generally, an organization will show better performance on the basis of some indicators and worse performance on the basis of some others: “there is no single performance management enterprise system which is best in class across all areas” (Sharif, 2002, p. 76). However, in the absence of a superior “best in class” dominating organization, one cannot “search for industry best practice that leads to superior performance”, and thus cannot apply benchmarking analysis as advocated. The “best in class” is the organization with the maximum averaged value, computed by averaging the scores assigned to all the organizations on the basis of all the criteria. Moreover, this paper illustrates the advantages, in terms of flexibility and realism, connected to the application of the multi-criteria outranking methodology as an alternative and more suitable approach for benchmarking analysis of retail stores. That is, the aim of this paper is to show the contribution of the multi-criteria outranking methodology to the valuation of the retail stores in the market place in terms of benchmarking analysis. It enables the benchmarking of organizational learning capability without the necessity of an aggregate indicator obtained by averaging all scores assigned to the organizations on the basis of the different criteria. Consider four retail stores: R1 : Tesco
R2 : Mydin R3 : Carrefour R4 : Giant This averaging methodology is the peculiarity and the main disadvantage of the traditional approach, that is, the aim of this chapter is to show the contribution of the multi-criteria outranking methodology to the valuation of the impact of marketing mix on customer satisfaction of the four retail stores (Tesco, Carrefour, Giant, Mydin) in terms of benchmarking analysis. The application of outranking approach enables, unlike the traditional analysis, the benchmarking of the impact of marketing mix without the necessity of an aggregate indicator obtained by averaging all scores assigned to the organizations on the basis of the different criteria. Finally, the following section discusses the contribution of the outranking multi-criteria methodology to the benchmarking analysis of the impact of marketing mix on customer satisfaction. 3.6 Benchmarking and Outranking-Satisfying Methodology The outranking methodology is a family of algorithms developed by Operational Research (Roy, 1985; Vincke, 1992; Roy and Bouyssou, 1993; Pomerol and Barba-Romero, 2000). Of these, Electre I method will be introduced here. The input of the Electre I method is represented by a multi-criteria matrix as in Table 1, surrounded by a line containing the weights that the decision making assigns to each criterion. Table 1 Multicriteria matrix (Electre I)
ATT1 R1 (Tesco) R2 (Mydin)
R3 (Carrefour) R4 (Giant) Weight
ATT3
ATT4
(Promotion)
(Place/Distribution)
(Product)
ATT2 (Price)
4.42
3.94
3.97
3.90
3.91
3.73
3.42
2.95
4.10
3.60
3.71
3.70
3.90
4.02
3.76
3.92
1/4
1/4
1/4
1/4
99 From Table 1, the retail stores’ positioning is generated and shown in the table below: Table 2 Retail stores Positioning Table
ATT1
ATT2
ATT3
ATT4
(Product)
(Price)
(Promotion)
(Place/Distribution)
1st
Tesco
Giant
Tesco
Giant
2nd
Carrefour
Tesco
Giant
Tesco
3rd
Mydin
Mydin
Carrefour
Carrefour
4th
Giant
Carrefour
Mydin
Mydin
Table 3 Retail Stores’ Ranking Table Attributes
Retail Stores’ Ranking 1st
ATT1 (Product) ATT2 (Price)
Tesco
ATT3 (Promotion) ATT4 (Place/Distribute) Giant
Average (
2nd Carrefour
3rd Mydin
4th Giant
Giant
Tesco
Mydin
Carrefour
Tesco
Giant
Carrefour
Mydin
Tesco
Carrefour
Mydin
RN ) = [ ATT1 ( RN ) + ATT2 ( RN ) + ATT3 ( RN ) + ATT4 ( RN )]/4
R R Now, let us consider R2 and 3 . Taking into account the values in Table 1 it is evident that 3 is better than R2 for three criteria out of four (Marketing Model 4Ps). That is:
ATT1 ( R3 ) = 4.10 > ATT1 ( R2 ) = 3.91 ATT3 ( R3 ) = 3.71 > ATT3 ( R2 ) = 3.42 ATT4 ( R3 ) = 3.70 > ATT4 ( R2 ) = 2.95 Three criteria {1, 3, and 4} agree in considering better than
R3 better than R2 . Only one criterion {2} considers R2
R3 . That is: ATT2 ( R2 ) = 3.73 > ATT2 ( R3 ) = 3.60
100 Interpreting the same procedure for all the other pairs of retail companies will obtain the Table 4. Table 4 Multicriteria Matrix
ATT1
ATT2
ATT3
ATT4
(Product)
(Price)
(Promotion)
(Place/Distribution)
4.42
3.94
3.97
3.90
3.91
3.73
3.42
2.95
R3 (Carrefour)
4.10
3.60
3.71
3.70
R4 (Giant)
3.90
4.02
3.76
3.92
Weight
1/4
1/4
1/4
1/4
R1 (Tesco) R2 (Mydin)
c
Table 5 Matrix of Concordance Subsystems ( J )
R1
R2
R3
R4
{1,2,3,4}
{1,2,3,4}
{1,3}
{2}
{1}
R1 R2 R3
Ø Ø
{1,3,4}
R4
{2,4}
{2,3,4}
The generic element
{1} {2,3,4}
J c ( Ri , R j ) of the matrix of Table 5 is given by:
J c ( Ri , R j ) = {j ∈ J = ATTi ( Ri ) ≥ ATT j ( R j )}; where: J = {1, 2, 3, 4} Taking into account the weights assigned to the various criteria, a concordance index can be calculated for each pair of company (
Ri , R j ): C(
Where:
Kj
is the weight assigned to the
For example, for the pair ( C(
Ri ; R j ) = ∑ j∈Jc K j ;
jth criterion.
R3 , R2 ) we have:
R3 , R2 ) = K1 + K 3 + K 4 = 1/4 + 1/4 + 1/4 = 0.75 (75 percent) R
R
We therefore have a majority of criteria of 75 percent in favor of 3 with respect to 2 . Iterating the same procedure for other pairs or organizations, we obtain the concordance matrix of Table 6.
101 Table 6 Concordance Matrix
R1
R2
R3
R4
1
1
0.50
0.25
0.25
R1 R2 R3
0 0
0.75
R4
0.50
0.75
0.25 0.75
The concordance indicator in Table 6 varies between 0 and 1. It is equal to 1 only if there is unanimity or a
R
R
majority of criteria that are 100 percent in favor of i with respect to j . In order to decide on the superiority of one retail company with respect to another, the decision maker should set a concordance threshold C*. Generally, it is chosen to be a majority greater than or equal to 75 percent (simple majority tightened), that is: C* ≥ 0.75 (75 percent). Taking into account the database of Table 6 and the concordance threshold C* we have the following concordance test: 1 if C (
Tc ( Ri , R j ) =
Ri ; R j ) ≥ C*
0 if otherwise The results of concordance test are shown in Table 7. Table 7 Outcomes of Concordance Test
R1
R2
R3
R4
1
1
0
0
0
R1 R2 R3
0 0
1
R4
0
1
0 1
The Electre I methodology considers another step: the construction of discordance test in order to take into account of an excessive “distance” (dissimilarity) between the scores discordance test
ATT j R j (
) and
ATTi ( Ri ). The
Td is fulfilled if the distance: D(
R j Ri ATT j R j ATTi Ri , ) = max [ ( )( )];
does not exceed discordance threshold D*. In order to simplify the analysis we suppose that the test of discordance is fulfilled by all pairs (
Ri , R j ).
The ideas behind the test of discordance may be summarized as follows. The outranking methods consists in examining the validity of the proposition “a outranks b”. The concordance test “measures” the arguments in favor of saying so, but there may be arguments strongly against that assertion (discordant criteria). The “discordant voices” can be viewed as vetoes.
102 There is a veto against declaring that a outranks b if b is so much better than a on same criterion that it becomes disputable or even meaningless to pretend that a might be better overall than b. The logic of the test of discordance is quite similar to that on which statistical tests are based. Here as well, conventional levels of significance (like the famous 5 percent rejection intervals) are widely used. The decision maker decides the discordance threshold, that is he decides whether a hypothesis must be rejected or not. If the discordance test is not passed alternatives a and b are said incomparable. They are too different to be compared. For instance, the comparison of a Rolls-Royce with a small cheap car is meaningless because the Rolls-Royce is incomparably better on many criteria but is also incomparably more expensive. Another example, concerns the comparison of projects that involve the risk of loss of human life. Should one prefer a more expensive project with a lower risk or a less expensive one with higher risk? One may advance that the projects are too different to be compared. Taking into account both the concordance and the discordance test we construct a binary outranking
R R
relation S. Given two generic retail companies ( i , j ) we say that concordance test and the discordance test are fulfilled, that is:
Ri outranks R j if and only if the
Ri S R j if and only if Tc and Td fulfilled. Because we suppose that the discordance test is passed by all pairs ( coincides with the outcomes of concordance test of Table 7. That is:
Ri , R j ) the outranking relation S
Ri S R j if and only if Tc fulfilled. The relation S may be represented by the graph of Figure 1. Figure 1 Graph of S from Table 4.27 (C* ≥ 75 percent)
R1 Tesco
R3
R2
Giant
Mydin
R4 Giant
R3 is the “ 2nd worst in class” and R2 is the “worse in class”. But R1 and R4 are not comparable R structures: neither 3 outranks R4 nor the opposite. This is another important difference arising from the Now,
refusal of the ordering based on the average benchmarking.
103 3.7 Benchmarking On Customer Satisfaction Benchmarking has consequences which are beyond the process itself: it reforms all the levels of the company; modifies the process of manufacture of the product leads(drives); also reforms the hierarchical organization of the company, the product itself, and the state of mind of the employees. Through benchmarking, we get better understanding of the customer because it is based on the reality of the market estimated in an objectivist way and a better economic planning of the purposes and the objectives to achieve in the company for they are centered on what takes place outside controlled and mastered. The management will get a better increase of the productivity, resolution of the real problems and understanding of the processes and what they produce. Table 8 Product Benchmarking towards customer satisfaction
Product Benchmarking 4.60
4.42
M ean
4.40 4.10
4.20 3.91
4.00
3.90
3.80 3.60 R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Tesco rank the highest on customer satisfaction towards product and it shall be the benchmark or guiding star for other retail stores. Mydin, Carrefour and Giant need to benchmark Tesco’s product strategy and improve to compete in the market. Table 9 Price Benchmarking towards customer satisfaction
Mean
Price Benchmarking 4.10 4.00 3.90 3.80 3.70 3.60 3.50 3.40 3.30
4.02 3.94 3.73 3.60
R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Giant rank the highest on customer satisfaction towards price and it shall be the benchmark or guiding star for other retail stores. It proofs that Giant’s “Everyday low price strategy” is a success. Tesco rank the second with mean value of 3.94, still in the competition mood with Giant. Mydin and Carrefour need to benchmark Giant’s pricing strategy and improve to compete in the market.
104 Table 10 Promotion Benchmarking towards customer satisfaction
Mean
Promotion Benchmarking 4.10 4.00 3.90 3.80 3.70 3.60 3.50 3.40 3.30 3.20 3.10
3.97 3.71
3.76
3.42
R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Tesco rank the highest again on customer satisfaction towards promotion and it shall be the benchmark or guiding star for other retail stores. Tesco promotion strategy is well organized and effective; customers are aware of the latest promotion from the newspaper, flyers and promotion booklet. Giant and Carrefour are a little bit behind with the mean value of 3.76 and 3.71. Mydin rank the last, it need to benchmark Tesco’s promotion strategy, revise on its promotion strategy and improve to compete in the competitive market. Table 11 Place/ Distribution Benchmarking towards customer satisfaction
Min
Place/Distribution Benchmarking 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00
3.90
3.70
3.92
2.95
R1 (Tesco)
R2 (Mydin)
R3(Carrefour)
R4 (Giant)
We conclude that Tesco rank the highest on customer satisfaction towards place and distribution and it shall be the benchmark or guiding star for other retail stores. The other three retail stores having very close mean value. Meaning the customer satisfaction towards place and distribution in four retail stores are well perceived. From table 11, we conclude that Giant rank the second, followed by Carrefour and Mydin. They are to benchmark Mydin’s, and improve on its place and distribution strategy. 4. Discussion on SWOT analysis 4.1 Strength Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies in the sciences, business, government and engineering worlds. MCDM methods can help to improve the quality of decisions by making the decision-making process more explicit, rational, and efficient. It is not a coincidence that a simple search (for instance, by using google.com) on the web under the key words “multi criteria decision making” returns more than one million hits. In a decisional process the making of choices derives from complex hierarchical comparisons among alternative options, which are often based on conflictual criteria, a large number of external variables plays a relevant role in orienting decision-
105 making. The strength of multi-criteria decision-making methods (MDMM) are to aid decision-makers to be consistent with fixed ‘general’ objectives; to use representative data and transparent assessment procedures and to help the accomplishment of decisional processes, focusing on increasing its efficiency. The Electre I method, in which the criteria of the set of decisional alternatives are compared by means of a binary relationship, defined as ‘outranking relationship’, are more ‘flexible’ than the ones based on a multiobjective approach. 4.2 Weakness An intriguing problem with decision-making methods which rank a set of alternatives in terms of a number of competing criteria is that oftentimes different methods may yield different answers (rankings) when they are fed with exactly the same numerical data. Thus, the issue of evaluating the relative performance of such methods is naturally raised. This, in turn, raises the question how can one evaluate the performance of such methods? Since it is practically impossible to know which one is the best alternative for a given decision problem, some kind of testing procedures need to be determined. 4.3 Opportunity In this paper, a new approach has been carried out for the use of the ELECTRE: ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing the REality) model in marketing mix selection. This work shows that ELECTRE can be used successfully in deriving a consensus ranking in benchmarking to select the best in class. 4.4 Threat In outranking approaches, the inaccuracy of the data can be modeled through the indifference and preference threshold (so-called pseudocriteria). Of course, threshold must be assessed for each criterion and for each problem separately. 5. Conclusion It is not simply enough to identify the strengths, weaknesses, opportunities, and threats of the Electre outranking method. In applying the SWOT analysis it is necessary to minimize or avoid both weaknesses and threats. Weaknesses should be looked at in order to convert them into strengths. Likewise, threats should be converted into opportunities. Lastly, strengths and opportunities should be matched to optimize the potential of a firm. Applying SWOT in this fashion can obtain leverage for a company (Marketing Strategy, 1998). Sensitivity analysis showed that, in general, the project rankings were considerably more sensitive to changes in the performances than they were to changes in the thresholds or weights. This is helpful and means that within a relatively wide band of preference, the same projects are considered important. Further, it requires the individual project sponsors to make the effort and ensure that the performance data is both accurate and defensible. As can be seen, the marketing manager should have rough outline of potential marketing activities that can be used to take advantage of capabilities and convert weaknesses and threats. However, at this stage, there will likely be many potential directions for the managers to pursue. Due to the limited resources that most firms have, it is difficult to accomplish everything at once. The manager must prioritize all marketing activities and develop specific goals and objectives for the marketing plan (Contemporary Marketing, 1992). Outranking methods make it possible to deal with multicriteria benchmarking and avoid the shortcomings of the traditional methods based on the average aggregate monocriterion. If applied to the measurement of learning capability, they are a complete alternative to the traditional approach. They can support the behavioral theory of organizational analysis initiated by H. Simon (Biggiero and Laise, 2003a, b). In fact, even though H. Simon does not explicitly discuss the problem of criteria multiplicity nor does he apply outranking methods, the behavioral theory is nonetheless perfectly comparable with them. The “levels of
106 aspirations” hypothesized by Simon can be associated with the threshold of concordance and discordance test. The lower the threshold assigned to the concordance test the lower the aspiration levels will be and hence the more the satisfying solutions will be. Outranking methods thus constitute a new and robust base on which to found the entire edifice of the behavioral theory of benchmarking applied to measurement of learning capability. They are a valid alternative to traditional methods, since they are equally rational and rigorous without suffering from its shortcomings and application limitations. 6. Directions for Further Research The relationships between customer satisfaction and behavioral outcomes are probably much more complex than initially assumed. This study has looked only at a limited part of the puzzle of how customer satisfaction translates into behavioral outcomes. In what way consumer characteristics moderate the relationship between satisfactions and repurchase behavior is likely to be contingent on the product or service category and the buying and usage process for that category. Other consumer characteristics not included in this study, such as a propensity for variety seeking behavior or a recreational shopping orientation, could potentially be important in many retail industries. Further research on how the effects of satisfaction on behavior is moderated by different consumer characteristics would advance customer satisfaction research as well as be of great managerial significance. 7. Scope and Limitation of the Study The setting selected for conducting this marketing research was focus only on three multinational retail stores and a homegrown retail store due to time constrain. Field researches were conduct in Tesco Stores (Malaysia) Sdn Bhd, Carrefour of Magnificent Diagraph Sdn. Bhd., Giant of Dairy Farm International, and the homegrown retail store, Mydin Mohamed Holdings Berhad. The collection of primary data was based on a survey of 856 respondents who visit each respective retail outlets, the number in the sample limited due to the restrictions of time to complete the project and resources to support it. AUTHOR BIBLIOGRAPHY Amy Poh Ai Ling was born in Penang, also called Pearl of the Orient, an island in the Straits of Malacca, and also of one of the states of Malaysia, located on the north-west coast of peninsular Malaysia on 8th March 1982. She started her primary education in S.R.K.J. (C) Sin Ya and pursues her secondary education in S.M.J.K. Jit Sin in Bukit Mertajam, where she grew up. She then enrolled into the National University of Malaysia (UKM) and completed her first degree in BBA majoring Marketing. Her passion towards quality moved her one step further enrolling in Master of Science Majoring Quality and Productivity offered by the school of Mathematical Sciences in Faculty of Science and Technology in UKM. Currently she is a member of the National university of Malaysia (UKM) fellowship stationed in Kolej Ibrahim Yaakub, performing duties and serve throughout the year in students’ welfare. She later becomes a Quality Assurance Engineer in Sony where she applies and contributes her theory and knowledge of quality and productivity improvement towards the company. The author always has an enthusiasm for quality and productivity improvement studies. Annotated Bibliography: “Service Quality” Term paper: “Productivity Measurement”
107 REFERENCES Barlon, K. (2006) "The concept of the marketing mix" Presentation on marketing management, vol 1, September, 2006, pp 2-7-Oulu university –Finland. Belton, V., and Stewart, T. J., (2001), .Chapter 8: Outranking Methods,. Multiple Criteria Decision Analysis: An Integrated Approach, Kluwer Academic Publishers, Boston, MA, USA. Berry, Leonard L. and Ian H. Wilson (1977), Retailing: the next ten years. Journal of Retailing, 53 (Fall), 5–28. Borden, N. (1964) "The concept of the marketing mix" Journal of Advertising Research, vol 4, June 1964, pp 2-7. Bose, U., Davey, A.M. and Olson, D.L. (1997) .Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS., Omega, 25, 691-706. Brans, J.P. and Vincke, Ph. (1985) "A preference ranking organization method", Management Science, 31, 647-656. Brownlie, D. and Saren, M. (1992), “The four Ps of the marketing concept: prescriptive, polemical, permanent, and problematical”, European Journal of Marketing, Vol. 26 No. 4, pp. 34-47. Domenico Laise. Benchmarking and learning organizations: ranking methods to identify “best in class”. Benchmarking: An International Journal; Volume: 11 Issue: 6; 2004 Research Paper. Doole, I. and Lowe, R. (1999), International Marketing Strategy, International Thompson Business Press. London. Douglas, S. P., & Craig, S. C. (1983), International marketing research, Englewood Cliffs, NJ: PrenticeHall, Inc. Fernandez, I.P. McCarthy, T. Rakotobe-Joel. 2001. Benchmarking: An International Journal. Volume 8. Issue 4 Figueira, J., Greco, S. and Ehrgott, M. (Eds.) (2004) Multiple Criteria Decision Analysis: State of the Art Surveys, Springer, and New York. Frazier, Gary L. and Tasadduq A. Shervani (1992), Multiple channels of distribution and their impact on retailing. In R. A. Peterson (Ed.), The future of U.S. retailing: an agenda for the 21st century (pp. 217–238). New York: Quorum Books. Glynn, Carroll J., Susan Herbst, Garrett J. O'Keefe, and Robert Y. Shapiro, Public Opinion (1999) Government of Malaysia (2001), Eighth Malaysia Plan 2001-2005. Kuala Lumpur: Percetakan Nasional Malaysia Berhad Groonroos, C. (1997), “Keynote paper: from marketing mix to relationship marketing – towards a paradigm shift in marketing”, Management Decision, Vol. 35 No. 4, pp. 322-39. Hobbs, B.F., P. Meier, (2000), Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods, Kluwer Academic Publishers, Boston, MA, USA. Leyva-López, J-C. and Fernández-González, E. (2003) .A new method for group decision support based on ELECTRE III methodology., European Journal of Operational Research, 148, 14-27. Linkov, I., Varghese, A., Jamil, S., Seager, T.P., Kiker, G. and Bridges, T. (2004) .Multi-criteria decision analysis: A framework for structuring remedial decisions at the contaminated sites., In: Linkov, I. and Ramadan, A.B. (Eds.) Comparative Risk Assessment and Environmental Decision Making, Springer, New York, pp. 15-54. Louise Boulter. 2003. Benchmarking: An International Journal. Volume 10. Issue 6 Naresh K. Malhotra. Marketing Research: An Applied Orientation. International Edition. Fourth Edition. 2004 Rogers, M.G., M. Bruen and L.-Y. Maystre, (1999), .Chapter 3: The Electre Methodology,. Electre and Decision Support, Kluwer Academic Publishers, Boston, MA, USA. Triantaphyllou, E. (2000) Multi-Criteria Decision Making Methods: A Comparative Study, Kluwer Academic Publishers, Dordrecht. Triantaphyllou, E. and Sanchez, A. (1997) "A sensitivity analysis approach for some deterministic multicriteria decision making methods", Decision Sciences, 28, 151-194.
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APPENDIX A
AUTHORIZATION LETTER FOR THE RESEARCH
1. TESCO 2. CARREFOUR 3. GIANT 4. MYDIN
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APPENDIX B
LETTER REQUEST OF CONTRIBUTION
1. LETTER REQUEST OF CONTRIBUTION - TESCO 2. LETTER REQUEST OF CONTRIBUTION - CARREFOUR 3. LETTER REQUEST OF CONTRIBUTION - GIANT 4. LETTER REQUEST OF CONTRIBUTION - MYDIN
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APPENDIX C
QUESTIONNAIRE
1. QUESTIONNAIRE - TESCO 2. QUESTIONNAIRE - CARREFOUR 3. QUESTIONNAIRE - GIANT 4. QUESTIONNAIRE - MYDIN
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The Impact of Marketing Mix on Customer Satisfaction: A Case Study Deriving Consensus Rankings from Benchmarking on Tesco Stores (Malaysia) Sdn Bhd - Tesco Kajang Dear customer, Thank you for choosing our retail store and services. We would be glad if you could take a few minutes to complete this questionnaire. Thank you for your time assistance. CUSTOMER INFORMATION
I) Gender : II) Ethnic : III) Marital Status : IV) Age :
□ Male □ Malay □ Single □ 50 □ daily □ once a week or more □ 2 - 3 times a month □ once a month □ every 2- 3 months □ 2 - 3 times a year
MARKETING MIX MODEL Please rate with respect to the following Strongly Disagree 1
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Price 7) I can get a lower price if I buy additional similar items 8) This store offers the overall lowest price in the area 9) Maintains the best everyday price for most merchandise
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To be continued on the second page at the back
130 MARKETING MIX MODEL Please rate with respect to the following Strongly
10) The price of the product is reasonable 11) Consistently provides the best values for money 12) The price of the product is low throughout the year
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Place/ Distribution 13) Fast checkout 14) Convenient parking of vehicles 15) Close to where I live 16) Store atmosphere and decoration are appealing 17) This store layout makes it easy for me to find what I need 18) Convenient public transport to get to this store
Promotion 19) Advertised merchandise is always available 20) Offers coupons in newspaper advertisement 21) Seasonal promotions are available 22) I love shopping here because of the privilege card 23) I am well informed of the promotions held 24) The promotions are always attractive
CUSTOMER PERCEPTION Please rate with respect to the following Strongly
25) Overall, I am satisfied with this store
Disagree 1
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MOTIVATING FACTOR 26) What factor motivates you the most to patronize our store.
□ product
□ price
□ promotions
□ place/distribution
THANK YOU FOR YOUR COOPERATION ! PLEASE COME AGAIN.
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The Impact of Marketing Mix on Customer Satisfaction: A Case Study Deriving Consensus Rankings from Benchmarking on Magnificient Diagraph Sdn Bhd – Carrefour Putrajaya Dear customer, Thank you for choosing our retail store and services. We would be glad if you could take a few minutes to complete this questionnaire. Thank you for your time assistance. CUSTOMER INFORMATION
I) Gender : II) Ethnic : III) Marital Status : IV) Age :
□ Male □ Malay □ Single □ 50 □ daily □ once a week or more □ 2 - 3 times a month □ once a month □ every 2- 3 months □ 2 - 3 times a year
MARKETING MIX MODEL Please rate with respect to the following Strongly Disagree 1
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3
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5
Strongly Agree 6
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□ □ 2) Offers several brands to choose from in a category 3) Purchased products are usually found in good condition □ □ 4) Visual appearance of products in this store is pleasant 5) This store offers good maintenance and repair of good sold□ □ 6) Has the widest selection of national brand merchandise 1) This store offers high quality merchandise
Price 7) I can get a lower price if I buy additional similar items 8) This store offers the overall lowest price in the area 9) Maintains the best everyday price for most merchandise
□ □ □
To be continued on the second page at the back
132 MARKETING MIX MODEL Please rate with respect to the following Strongly
10) The price of the product is reasonable 11) Consistently provides the best values for money 12) The price of the product is low throughout the year
Disagree 1
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Strongly Agree 6
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Place/ Distribution 13) Fast checkout 14) Convenient parking of vehicles 15) Close to where I live 16) Store atmosphere and decoration are appealing 17) This store layout makes it easy for me to find what I need 18) Convenient public transport to get to this store
Promotion 19) Advertised merchandise is always available 20) Offers coupons in newspaper advertisement 21) Seasonal promotions are available 22) I love shopping here because of the privilege card 23) I am well informed of the promotions held 24) The promotions are always attractive
CUSTOMER PERCEPTION Please rate with respect to the following Strongly
25) Overall, I am satisfied with this store
Disagree 1
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Strongly Agree 6
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MOTIVATING FACTOR 26) What factor motivates you the most to patronize our store.
□ product
□ price
□ promotions
□ place/distribution
THANK YOU FOR YOUR COOPERATION ! PLEASE COME AGAIN.
Serial No
SURVEY FORM
133
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The Impact of Marketing Mix on Customer Satisfaction: A Case Study Deriving Consensus Rankings from Benchmarking on Giant of Dairy Farm International (DFI) Dear customer, Thank you for choosing our retail store and services. We would be glad if you could take a few minutes to complete this questionnaire. Thank you for your time assistance. CUSTOMER INFORMATION
I) Gender : II) Ethnic : III) Marital Status : IV) Age :
□ Male □ Malay □ Single □ 50 □ daily □ once a week or more □ 2 - 3 times a month □ once a month □ every 2- 3 months □ 2 - 3 times a year
MARKETING MIX MODEL Please rate with respect to the following Strongly Disagree 1
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Price 7) I can get a lower price if I buy additional similar items 8) This store offers the overall lowest price in the area 9) Maintains the best everyday price for most merchandise
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To be continued on the second page at the back
134 MARKETING MIX MODEL Please rate with respect to the following Strongly
10) The price of the product is reasonable 11) Consistently provides the best values for money 12) The price of the product is low throughout the year
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Place/ Distribution 13) Fast checkout 14) Convenient parking of vehicles 15) Close to where I live 16) Store atmosphere and decoration are appealing 17) This store layout makes it easy for me to find what I need 18) Convenient public transport to get to this store
Promotion 19) Advertised merchandise is always available 20) Offers coupons in newspaper advertisement 21) Seasonal promotions are available 22) I love shopping here because of the privilege card 23) I am well informed of the promotions held 24) The promotions are always attractive
CUSTOMER PERCEPTION Please rate with respect to the following Strongly
25) Overall, I am satisfied with this store
Disagree 1
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MOTIVATING FACTOR 26) What factor motivates you the most to patronize our store.
□ product
□ price
□ promotions
□ place/distribution
THANK YOU FOR YOUR COOPERATION ! PLEASE COME AGAIN.
Serial No
SURVEY FORM
135
:
The Impact of Marketing Mix on Customer Satisfaction: A Case Study Deriving Consensus Rankings from Benchmarking on Mydin Mohamed Holdings Berhad - Mydin Mart Kajang Dear customer, Thank you for choosing our retail store and services. We would be glad if you could take a few minutes to complete this questionnaire. Thank you for your time assistance. CUSTOMER INFORMATION
I) Gender : II) Ethnic : III) Marital Status : IV) Age :
□ Male □ Malay □ Single □ 50 □ daily □ once a week or more □ 2 - 3 times a month □ once a month □ every 2- 3 months □ 2 - 3 times a year
MARKETING MIX MODEL Please rate with respect to the following Strongly Disagree 1
2
3
4
5
Strongly Agree 6
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Product
□ □ 2) Offers several brands to choose from in a category 3) Purchased products are usually found in good condition □ □ 4) Visual appearance of products in this store is pleasant 5) This store offers good maintenance and repair of good sold□ □ 6) Has the widest selection of national brand merchandise 1) This store offers high quality merchandise
Price 7) I can get a lower price if I buy additional similar items 8) This store offers the overall lowest price in the area 9) Maintains the best everyday price for most merchandise
□ □ □
To be continued on the second page at the back
136 MARKETING MIX MODEL Please rate with respect to the following Strongly
10) The price of the product is reasonable 11) Consistently provides the best values for money 12) The price of the product is low throughout the year
Disagree 1
2
3
4
5
Strongly Agree 6
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Place/ Distribution 13) Fast checkout 14) Convenient parking of vehicles 15) Close to where I live 16) Store atmosphere and decoration are appealing 17) This store layout makes it easy for me to find what I need 18) Convenient public transport to get to this store
Promotion 19) Advertised merchandise is always available 20) Offers coupons in newspaper advertisement 21) Seasonal promotions are available 22) I love shopping here because of the privilege card 23) I am well informed of the promotions held 24) The promotions are always attractive
CUSTOMER PERCEPTION Please rate with respect to the following Strongly
25) Overall, I am satisfied with this store
Disagree 1
2
3
4
5
Strongly Agree 6
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MOTIVATING FACTOR 26) What factor motivates you the most to patronize our store.
□ product
□ price
□ promotions
□ place/distribution
THANK YOU FOR YOUR COOPERATION ! PLEASE COME AGAIN.
137 APPENDIX D
138
APPENDIX E
Classification of MCDM Method
Number of Decision Makers
Single
Operation Approaches
Group
Single Criterion Synthesis Approach base on MAUT
Type of Data
Deterministic
Stochastic
Fuzzy
Outranking Synthesis Approach based on Outranking Method
TOPSIS
MAVT
UTA
ELECTRE I
ELECTRE IS
ELECTRE II
SMART
MAUT
AHP
ELECTRE III
ELECTRE IV
ELECTRE TRI
Fuzzy Maximim
MELCHOIR
ORESTE
REGIME
PROMETHEE I
NAIADE
PROMETHEE II
Fuzzy Weight Sum
Rajah: Klasifikasi bagi kaedah MCDM ( Multicriteria Decision Making)
