This study proposes a research framework that integrates personal innovativeness, perceived risk, cost and enjoyment with technology acceptance model (TAM). We used the proposed model to explore antecedents of consumers’ behavioral intention to adopt M-commerce (mobile commerce).
Excluding missing answers and invalid questionnaire, 477 valid responses were collected. In addition to confirmatory factor analysis (CFA), we used structural equation modeling (SEM) to examine relationships among constructs in the proposed model. Our findings indicated that the younger group (under 30) had lower stickiness on M-commerce. Among constructs, perceived enjoyment had the most significantly influence on behavioral intention, and followed by attitude, perceived ease of use, perceived usefulness and perceived risk. Our research results could be guidance and reference to M-commerce service providers to improve and operate their services.

This study proposes a research framework that integrates personal innovativeness, perceived risk, cost and enjoyment with technology acceptance model (TAM). We used the proposed model to explore antecedents of consumers’ behavioral intention to adopt M-commerce (mobile commerce).
Excluding missing answers and invalid questionnaire, 477 valid responses were collected. In addition to confirmatory factor analysis (CFA), we used structural equation modeling (SEM) to examine relationships among constructs in the proposed model. Our findings indicated that the younger group (under 30) had lower stickiness on M-commerce. Among constructs, perceived enjoyment had the most significantly influence on behavioral intention, and followed by attitude, perceived ease of use, perceived usefulness and perceived risk. Our research results could be guidance and reference to M-commerce service providers to improve and operate their services.

Chapter 1 Introduction 1
1.1 Background 1
1.2 Research motivation 3
1.3 Research purpose 4
1.4 Research Process 5
Chapter 2 Literature Review 7
2.1 Mobile Commerce (M-commerce) 7
2.2 Technology Acceptance Model (TAM) 20
2.3 Personal Innovativeness 25
2.4 Perceived Enjoyment 27
2.5 Perceived Cost 28
2.6 Perceived Risk 30
Chapter 3 Research Methodology 37
3.1 Research Framework 37
3.2 Hypotheses 37
3.3 Operational Definitions of Variables 42
3.4 Questionnaire Design and Pilot Test 43
3.5 Subject and Data Collection 46
3.6 Analysis Method 47
Chapter 4 Data Analysis and Result 52
4.1 Sample Description 52
4.2 Reliability Analysis 58
Chapter 5 Conclusion 66
5.1 Research Finding and Discussion 66
5.2 Academic and Managerial Implication 72
5.3 Research Limitation 75
5.4 Suggestions for Future Research 76
References 77
Appendix 85

List of Figures
Figure 1.1 Research process 6
Figure 2.1 Framework for M-commerce 9
Figure 2.2 Generation of wireless mobile communications technology 13
Figure 2.3 The different types of wireless network 14
Figure 2.4 The coverage of WiMAX & Wi-Fi 15
Figure 2.5 Global customers & trend on types of communications services16
Figure 2.6 Mobile users share in Taiwan 18
Figure2.7 4G card and home sharing device 18
Figure 2.8 Technology acceptance model (TAM) 20
Figure 3.1 Research framework 37
Figure 3.2 Analysis process of SEM 49
Figure 4.1 Empirical results of proposed model 64
Figure 5.1 Forecasting ASP (average sales price) of smartphone70

List of Tables
Table 2.1 Participants in the M-commerce market9
Table 2.2 M-commerce application types and items 10
Table 2.3 Categories and items of M-commerce application 11
Table 2.4 The 13 popular M-commerce items of application in Taiwan11
Table 2.5 Fee rates and provider of WiMAX 19
Table 2.6 3G card providers and 80% discount on fee rates 19
Table 2.7 Review of empirical studies regarding TAM 22
Table 2.8 Overview of diverse constructs of perceived risk 34
Table 3.1 Operational Definitions of Variables 42
Table 3.2 Constructs and Measurement Items 43
Table 3.3 Reliability test on pilot test 46
Table 3.4 Preliminary fit criteria 50
Table 3.5 Criteria of overall model fit 50
Table 3.6 Criteria on fit of internal structural model 50
Table 4.1 Data collection 52
Table 4.2 Demographic characteristics of the respondents (n=477) 53
Table 4.3 Experience in wireless and M-commerce (n=477) 54
Table 4.4 Purposes of using M-commerce (n=477) 55
Table 4.5 Purposes of using M-commerce for 2 groups 55
Table 4.6 Preference of service toward using M-commerce (n=477) 56
Table 4.7 Preference of service toward using M-commerce for two groups57
Table 4.8 Cronbach’s α coefficient 58
Table 4.9 Factor loading of items 59
Table 4.10 Convergent validity 61
Table 4.11 Discrimination validity 61
Table 4.12 Fit indices and analysis results of the measurement model 62
Table 4.13 Fit indices and analysis results of the structural model 63
Table 4.14 Path coefficients and hypotheses test 65
Table 5.2 Direct, indirect and total effect of each construct on behavioral intention72

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