A Conjoint Analysis of Chinese Mobile Payment: Heavy and Light Users

다국어 초록 (Multilingual Abstract)

Along with the mobile commerce market’s rise and an increasing number of smart phone users, the mobile payment industry grows rapidly. In the past few years, Chinese mobile payment industry has achieved rapid development and has become the biggest market worldwide. This research aims to assess the relative importance that Chinese consumers attach to different attributes of mobile payment and further compare the differences between heavy and light users.
By conducting a conjoint analysis using 206 valid questionnaires, we found that location based service is considered as the most significant attributes and the most attractive mobile wallet is one available in both department and convenience stores with financial transaction function and provide local life O2O services and customized electronic bills analysis functions. The results also showed significant differences between heavy and light users. While heavy users prefer hedonic attributes, light users consider utilitarian attributes more attractive. Additionally, from the results of discriminant analysis, we derived discriminant equations for heavy and light shoppers.

목차 (Table of Contents)

• 1. Introduction 1
• 1.1 Research Background 1
• 1.2 The definition of M-payment and M-wallet 1
• 1.3 The market performance of mobile payment 2
• 1.4 The mobile payment in China 3
• 2. Theoretical Background and Literature review 5
• 2.1 Review of related overseas research 5
• 2.2 Review of related domestic research 7
• 3. Research purpose 7
• 4. Theoretical background and hypotheses 8
• 4.1 MEC Theory 8
• 4.2 Utilitarian and hedonic attributes 8
• 4.3 Perceived risk 9
• 4.4 Heavy and light users segmentation 10
• 4.5 Hypotheses 10
• 5. Research method 11
• 5.1 Study1: The utility of mobile wallet attributes 11
• 5.1.1 Attributes and profiling 11
• 5.1.2 Measurement and result 15
• 5.1.3 Differences between heavy and light users 18
• 5.2 Study2: Consumer classification by discriminant analysis 21
• 6. Conclusion and Limitation 24
• Reference 26
• Appendix 32