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系統識別號 U0026-1502201105270100
論文名稱(中文) 探討消費者拒絕使用行動加值服務之研究:結合「任務科技配適模式」與「社會認知理論」
論文名稱(英文) The Determinants of Consumer Resistance toward Mobile Value-Added Service: A Combination of Task-Technology Fit Model and Social Cognitive Theory.
校院名稱 成功大學
系所名稱(中) 企業管理學系碩博士班
系所名稱(英) Department of Business Administration
學年度 99
學期 1
出版年 100
研究生(中文) 王馨葦
研究生(英文) Hsin-Wei Wang
學號 r4893107
學位類別 博士
語文別 英文
論文頁數 151頁
口試委員 口試委員-陳鴻基
口試委員-鄭至甫
口試委員-吳仁和
口試委員-賴孟寬
口試委員-蔡東峻
指導教授-張心馨
中文關鍵字 知覺成本  知覺價值  任務科技配適模式  社會認知理論  拒絕使用意圖  行動加值服務 
英文關鍵字 Perceived Costs  Perceived Value  Task-Technology Fit Model  Social Cognitive Theory  Resistance to Use  Mobile Value-added Service 
學科別分類
中文摘要 近年來由於行動加值服務的市場成長快速,促成行動加值服務為通訊業者創造利潤。然如何改變消費者過去既有的行為模式,而願意嘗試使用新科技是值得探討的議題。因此,本研究以「任務科技配適模式」與「社會認知理論」為理論基礎發展研究架構,探討外部因素(即任務科技配適程度和使用者自我效能)、消費者內在評估機制(即轉換利益、知覺成本和知覺價值)和消費者行為意向(即拒絕使用行動加值服務的意願)彼此之間的關係。此外,消費者在使用不同型態的行動加值服務時,會因服務特徵的差異而產生不同的使用動機及認知,本研究藉由訊息傳遞、接觸服務、付款服務及遊戲服務四項來比較不同類型的行動加值服務,以分析行動加值服務的類型是否會干擾外部因素對消費者內在評估機制與行為意向。
本文的研究對象為未曾使用加值服務者,以便利抽樣法進行網路問卷調查,使用Structural Equation Model (SEM) 檢定研究變數之因果關係,資料分析結果顯示消費者對行動任務與科技配適的程度及自我使用能力的評估會同時影響消費者價值判斷的過程,最終會影響消費者拒絕使用行動加值服務之意願。在影響拒絕意圖的因素中,發現兩項有趣的結果:(1)知覺成本不但可以直接影響拒絕使用意圖,也可透過對知覺價值間接地影響拒絕使用意圖,由此可知知覺成本會在影響拒絕使用意圖中扮演很重要的因素;(2)自我效能與結果預期會受到任務科技配適的程度高低所干擾。為比較在不同使用經驗與不同類型的行動加值服務對整體研究架構之差異,由Multiple Group Analysis的結果顯示: (1)使用者過去所累積的經驗越多,則對行動加值服務的態度越正面;(2)消費者面對不同類型之行動加值服務時,所考量的因素也不相同,例如:消費者在使用任務導向型的服務時,較重視任務的績效;而在使用娛樂導向型的服務時,較重視個人的績效。此外,當消費者在使用任務導向與人員互動型服務時,較重視價值評估的過程;而在使用娛樂導向與人機互動型服務時,只注重娛樂的利益及試用過程是否繁複。
本研究有兩項主要的研究貢獻:(1)「任務科技配適模式」與「社會認知理論」在過去的研究領域主要用於解釋組織成員之工作績效,而本研究首次結合兩個理論應用於解釋消費者在轉換新服務過程,如何進行內在價值評估及形成拒絕使用的意圖;(2)對於行動加值服務的使用意願,在過去研究主要探討整體服務卻忽略不同類型服務間之特徵差異,而本研究以不同行動加值服務類型做影響消費者拒絕使用意圖因素的分析與比較,並提出不同行銷策略之建議。其主要目的是希望藉由此實證分析,可提供行動業者對於未來發展服務介面設計、服務內容、刺激消費動機,以作為提升消費者消費能力和認知價值等方案的重要參考依據。
英文摘要 The mobile value-added service market has seen significant growth in the past few years. The invention of a mobile value-added service has become a new opportunity for mobile service providers to create revenue. The most prominent obstacle for a new technology is getting customers to try it for the first time, because it requires a significant behavioral change. Therefore, this study extended Task-Technology Fit (TTF) with social cognitive theory (SCT) to investigate the relationships among those extrinsic factors (task-technology fit and self-efficacy), the intrinsic value of the assessment mechanism (perceived benefits, perceived costs and perceived value) and consumer behavioral intention (resistance to use). In addition, various mobile value-added services have several different characteristics, which lead to different consumers’ motives and perceptions. Thus, this study conducted four individual surveys including text messaging, contact service, payment service and gaming service to demonstrate the cross-service differences in the proposed model.
The research model was validated through an online survey of 839 respondents by a convenient sampling method. In the empirical results, this study adopted a Structural Equation Model (SEM) to test the interrelationships among all the research constructs and found that self-efficacy and task-technology fit have a simultaneous impact on perceived benefits and perceived costs, ultimately influencing perceived value and resistance to use. Among the predictors of resistance, this study also found two interesting consequences as follows: First, the perceived costs influenced user resistance both directly and indirectly through their effect on perceived value, which implied the important role of perceived costs in determining user resistance. Second, the mediating effect of task-technology fit on the relationship between self-efficacy and outcome expectancies was demonstrated. Furthermore, this study conducted Multiple Group Analysis to investigate the moderating effect of users’ prior experiences and mobile value-added service types on the research model and found two consequences. First, people with higher usage experience presented a positive attitude toward mobile value-added services than those with a lower one. Second, we found that there were notable differences among the determinants of resistance in diverse mobile value-added services. For example, people who conduct a goal-oriented task emphasized the performance-related outcome expectancies. But those people who conducted an experiential-oriented task paid more attention to personal outcome expectancies. People who completed a person-interactive service to complete their goal-oriented task paid attention to the value of the evaluation process. However, people who performed a machine-interactive service to finish their experiential-oriented task focused on the benefit of entertainment and the simple use process.
This study offered two important contributions to research and management. First, prior research applied TTF and SCT to explain the tasks’ performance of organization members. However, this study also provided the first empirical examination of both theories to understand when consumers consider about switching to the new services, how to evaluate the value of services and how they form their resistance intention. Moreover, previous research about mobile value-added service adoption has always investigated a single service or a whole service, but neglected the difference characteristics between services. Therefore, this study compared the customers’ behavior in different kinds of mobile services to provide insight into the issue, including the development of service context, the design of service interface, the improvement of consumer ability and perceived value about the service.
論文目次 TABLE OF CONTENTS
CHAPTER ONE INTRODUCTION 1
1.1 Research Background and Motivations 1
1.2 Research Objectives 4
1.3 Research Procedure 5
1.4 Research Structure 6
CHAPTER TWO LITERATURE REVIEW 8
2.1 Definition of Theory and Research Constructs 8
2.1.1 Task-Technology Fit Model 8
2.1.2 Social Cognitive Theory 13
2.1.3 Perceived Costs 17
2.1.4 Perceived Value 19
2.1.5 User Resistance 20
2.1.6 The Classification of Mobile Service 22
2.2 Research Hypotheses Development 23
2.2.1 Task-Technology Fit and Outcome Expectations 23
2.2.2 SCT: Self-efficacy and Perceived Benefits 25
2.2.3 Self-efficacy and Perceived Costs 27
2.2.4 Perceived Benefits, Costs, Value and User Resistance 28
2.2.5 Perceived Value and User Resistance 30
2.2.6 The Moderating Effect of Mobile Service Type 31
CHAPTER THREE RESEARCH DESIGN AND METHODOLOGY 32
3.1 The Conceptual Model 32
3.2 Construct Measurement 34
3.2.1 Task-Technology Fit 34
3.2.2 Self-efficacy 35
3.2.3 Perceived Benefits (Performance-Related Outcome Expectancies and Personal Outcome Expectancies) 37
3.2.4 Perceived Costs (Procedural Costs and Financial Costs) 38
3.2.5 Perceived Value 39
3.2.6 Resistance to Use 39
3.2.7 Measurement of Type of Mobile Service 40
3.2.8 Prior Experience of Mobile and Internet Services 40
3.2.9 Personal Demographic Information 41
3.3 Questionnaire Design and Pilot Test Procedures 42
3.4 Manipulation Check 43
3.4.1 Manipulation Check of Mobile Value-added Service Types 43
3.4.2 Manipulation Check of Simulations 48
3.5 Sample Plan 50
3.6 Data Analysis Procedure 51
3.6.1 Descriptive Statistic Analysis 51
3.6.2 Reliability and Validity of Measurement Constructs 51
3.6.3. Interrelationships among Research Variables 53
CHAPTER FOUR DATA ANALYSIS AND RESULTS 55
4.1 Descriptive Analysis 55
4.1.1 Data Collection 55
4.1.2 Characteristics of Respondents 58
4.1.3 Measurement Results for Research Variables 59
4.2 Reliability and Validity Tests 62
4.3 Common Method Bias 65
4.4 Structural Equation Model (SEM) 66
4.4.1 The Full Model 66
4.4.2 The Direct, Indirect and Total Effects 73
4.4.3 The Mediating Effect 75
4.5 Multiple Group Analysis 77
4.5.1 Testing of the Respondents’ Characteristics 77
4.5.2 Competing Models for Respondents with Different Experiences of Mobile Value-added Services 82
4.5.3 Testing of the moderating effect of mobile value-added service’s type 88
CHAPTER FIVE CONCLUSIONS AND RESEARCH CONTRIBUTIONS 97
5.1 Discussions and Conclusions 97
5.1.1 The Results of the Structural Equation Model 97
5.1.2 The Results of Multiple Group Analysis 102
5.2 Contribution and Implications 107
5.2.1 Research Implications and Contributions 107
5.2.2 Managerial Implications and Contributions 110
5.3 Limitations and Future Research 114
References 117

LIST OF TABLES
Table 3-1 Operational definition of constructs in this study 41
Table 3-2 Manipulations of mobile value-added service types 45
Table 3-3 Manipulation check of mobile value-added service type 47
Table 4-1 Characteristics of respondents 59
Table 4-2 Descriptive analysis for questionnaire items 61
Table 4-3 Reliability and validity test 63
Table 4-4 Discriminant validity 65
Table 4-5 The Fit Indices for the Structural Model 67
Table 4-6 The results of the measurment model 67
Table 4-7 The results of the structural model 72
Table 4-8 Direct, indirect and total effects 74
Table 4-9 Testing for mediation effects on the relationship between SE and PROE 76
Table 4-10 Testing for mediation effects on the relationship between SE and POE 77
Table 4-11 Sample demographics among types of mobile value-added service 78
Table 4-12 Research constructs among time of Internet Use 79
Table 4-13 Research constructs among frequency of using Internet services 80
Table 4-14 Research constructs among time of using Internet services 80
Table 4-15 Differences among type of mobile value-added service on research constructs 82
Table 4-16 Clusters of respondents with different Internet service experiences 83
Table 4-17 Differences among different Internet service experiences on research constructs 83
Table 4-18 The competing model for respondents with different Internet service experiences 86
Table 4-19 Testing for types of mobile value-added services as a moderator in the research model 94
Table 4-20 Comparison of different types of mobile value-added services 95
Table 5-1 A summary of the result of the structural model 99
Table 5-2 A summary of the path estimates among four types of mobile value-added services 105

LIST OF FIGURES
Figure 1-1 Flow Chart for this Research 6
Figure 3-1 The Conceptual Framework 33
Figure 3-2 The Scenario-Based Simulation for a Mobile Service Experience 49
Figure 3-3 The Interaction-Based Simulation for a Mobile Service Experience 49
Figure 4-1 The Process of Data Collection used in this study 57
Figure 4-2 The Structural Equation Model of the Full Model 71
Figure 4-3 The structural equation model for groups with different prior experience 87
Figure 4-4 The structural equation model for groups with different types of mobile value-added service 96
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