Open Journal of Social Sciences
Vol.03 No.03(2015), Article ID:54765,4 pages

A Comparative Study of Customer Value Drivers between Traditional Channels and Network Channel

Sili Wang1, Dahai Dong1,2

1Faculty of Management and Economics, Dalian University of Technology, Dalian, China

2China Business Executives Academy, Dalian, China


Received March 2015


With the rapid development of information technology, more and more customers start shopping online, this phenomenon has caused the performance of physical stores reducing. This article uses Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach to structure and evaluate the customer value drivers between traditional channels and network channels, finds out the different effect of the drivers in the two channels, then to guide practice.


Customer Value Drivers, Clothing Enterprises, Physical Stores, Online Stores, DEMATEL Method

1. Introduction

With the rapid development of information technology, more and more customers start to shopping online. Some companies that are successful in traditional market channels also begun to open up a new battlefield-the network channels to meet customers’ needs and enhance their competitiveness. In some developing countries, such as China, although online shopping has developed rapidly, it is still a new shopping way, thus the original ways of increasing customer values may not work in the network channels. For example, GOME is NO.2 in Appliance sales, while it is not prominent in online sales. When shopping online, the customer is not only consumers but also users of new technologies. The traditional channels and network channels have different shopping experiences. Customer focus and the driving force of the same elements to customer values are different either.

This paper uses the DEMATEL method to classify the driving factors of the traditional channel and online channel in the apparel industry. Through the analysis, this paper identifies the most fundamental driving factor (long drivers), the most direct driving factor (direct drivers) and most strongly influence driving factors of customer value in the two channels and compares the differences. We hope the research can provide reference to business practice.

2. Definition of Customer Value Driving Factors and Factor Analysis

A lot of literature point out that customer value driving factors can be divided into two categories: perceived gains and loss of profits. The driving factors mainly include product quality, service quality and price factors [1]-[7]. Based on this, this article uses perceive gain and loss of profits as the framework, Wolfgang (2001) [8], Dias and David (2002) [9] study, and explores the customer driving factors suitable for online and offline stores in apparel industry. Firstly, through the open customer interview, we get customer value driving factors in the shopping process of online and offline stores in apparel industry; then we invite three PHD in marketing major to analysis the interview result, and finally concludes 11 factors of gains and loss (product-related, service-related, brand-related). The result did not include the relationship related factors due to the research background is the online and offline stores of apparel industry in which service staffs has big mobility and little personal relationship with customers; on the other hand, customers are not sensitive to public relations and their upstream and downstream integration [10]-[12].

3. Questionnaire Development and Data Collection

3.1. Questionnaire Development

Questionnaire is divided into five parts. The first part introduces the academic rigor of the study to dispel the concerns of those who fill out the questionnaire; second part introduces the research background to define thinking background of by investigators; third part is the definition of all drivers; fourth part is the core data collection; fifth part is the demographic statistical information.

3.2. Data Collection

262 copies of surveys were distributed, excluding 55 vacancies item and invalid questionnaires, 217 valid questionnaires were collected, the effective rate is 72%. There are 113(42 male samples, 71 female samples) questionnaires in offline background and 134 (40 male samples, 64 female samples) questionnaires in online background. The sample groups include students, teachers, government workers and company staffs.

3.3. DEMATEL Method

DEMATEL (Decision-making Trial and Evaluation Laboratory) method was brought by the Bastille National Laboratory of United States (Gabus and Fontela, 1973) in the mid-1970s [12]. This method uses graph theory and matrix theory principle to analyze system factors.



The row values Ri are the overall direct and indirect effect of drivers on other barriers for EFCP. Similarly, the column values Dj represent the overall direct and indirect effects of all barriers on barrier j. We separately determined these results for each of the four companies.

Determine the overall importance or prominence (Pi) of barrier I and net effect (Ei) of barrier i using expressions (3) and (4).



3.4. Some Common Mistakes

Through twice repetitive calculation, we can draw the reason-result figure of the two additional customer value drivers and compared with the current reason-result figure. See Figure 1 and Figure 2.

In the two channels, the differences of customer value most direct drivers lie in the technical quality of product directly drive customer value in offline stores, while brand image directly drive customer value in online stores. Same points are service-related factors and product choice scope can most directly drive customer value

Figure 1. Customer value drivers of reasons―results in offline store.

Figure 2. Customer value drivers of reasons―results in online store.

in both two channels. Therefore, in order to increase customer value rapidly, companies should provide customers with fast, useful, comprehensive services, and give the customer a reasonable product choice scope.

In terms of the strongest drivers of customer value, drivers of the two channels are almost the same; the only difference is the impact strength. For offline stores, the impact of brand-related factors is stronger, while in online stores, two service-related factors (information usefulness and comprehensiveness) have stronger impact than brand self-concept consistency factor. So, clothing retail business need to follow their own channel characteristics, and improve the strongest factors of the channel in order to be targeted more efficiently.

4. Conclusions

The results showed that loss of profits factors and brand-related factors are the most fundamental drivers of customer value; product factors and service-related factors have the most direct impact on customer value; service-related factors and brand-related factors have the strongest impact on customer value. Meanwhile, the brand image is the most fundamental drivers of customer value in offline stores, but it is the most direct drivers of online stores [13] [14]. Technical quality is the most direct drivers of customer value in offline stores, but it is the most fundamental drivers for online stores. In offline stores, brand-related factors have stronger driving effect, while in online stores, two service-related factors (information usefulness and comprehensiveness) have stronger effect on customer value [15].

For closing companies that have both of the two channels, they need to figure out the common and differences of the drivers in those two channels, in accordance with their own resources and situation and choose the targeted factors to improve in order to help companies enhance customer value.


Thanks for the support by a grant from the National Natural Science Foundation of China Project (71272094) (71402014); the general project of Liaoning Province Department of Education Science Research (W2014008).

Cite this paper

Sili Wang,Dahai Dong, (2015) A Comparative Study of Customer Value Drivers between Traditional Channels and Network Channel. Open Journal of Social Sciences,03,53-56. doi: 10.4236/jss.2015.33011


  1. 1. Chen, Z. and Dubinsky, A.J. (2003) A Conceptual Model of Perceived Customer Value in e-Commerce: A Preliminary Investigation. Psychology and Marketing, 20, 323-347.

  2. 2. Myers, J.H. and Alpert, M.I. (1997) Determinant Buying Attitudes: Meaning and Measurement. Marketing Management, 6, 50-56.

  3. 3. Woodruff, R.B. and Gardial, S.F. (1996) Know Your Customer: New Approaches to Customer Value and Satisfaction. Blackwell, Cambridge, 321-323.

  4. 4. Ravald, A. and Grönroos, C. (1996) The Value Concept and Relationship Marketing. European Journal of Marketing, 30, 19-30.

  5. 5. Parasuraman, A. (1997) Reflections on Gaining Competitive Advantage through Customer Value. Journal of the Academy of Marketing Science, 25, 154-161.

  6. 6. Flint, D.J., Woodruff, R.B. and Gardial, S.F. (1997) Customer Value Change in Industrial Marketing Relationships: A Call for New Strategies and Research. Journal of Industrial Marketing Management, 26, 163-175.

  7. 7. Lapierre, J. (2000) Customer-Perceived Value in Industrial Contexts. Journal of Business & Industrial Marketing, 15, 122-140.

  8. 8. Ulaga, W. and Chacour, S. (2001) Measuring Customer-Perceived Value in Business Markets. Journal of Industrial Marketing Management, 30, 525-540.

  9. 9. Pihlens, D.S. and Lorena, D.R. (2002) Understanding the Drivers of Customer Value: The Fusion of Macro and Micro Modeling. Journal of Targeting, Measurement & Analysis for Marketing, 10, 269-281.

  10. 10. Zeithaml, V.A. and Berry, L.L. (1988) SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, 64, 12-40.

  11. 11. Johar, J.S. and Sirgy, M.J. (1991) Value-Expressive versus Utilitarian Advertising Appeals: When and Why to Use Which Appeal. Journal of Advertising, 20, 23-33.

  12. 12. Gabus, A. and Fontela, E. (1973) Perceptions of the World Problem Atique: Communication Procedure, Communicating with Those Bearing Collective Responsibility. DEMATEL Report No.1, Battelle Geneva Research Centre, Geneva.

  13. 13. Wu, C.W. and Chen, C.L. (2006) An Integrated Structural Model toward Successful Continuous Improvement Activity. Technovation, 26, 697-707.

  14. 14. Tzeng, G.-H., Chiang, C.-H. and Li, C.-W. (2007) Evaluating Intertwined Effects in e-Learning Programs: A Novel Hybrid MCDM Model Based on Factor Analysis and DEMATEL. Expert Systems with Applications, 32, 1028-1044.

  15. 15. Huberman, B.A. and Wu, F. (2006) Bootstrapping the Long Tail in Peer to Peer Systems. 10, 2-3.