(3.238.235.155) 您好!臺灣時間:2021/05/11 03:03
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:王淑燕
研究生(外文):WANG SHU-YAN
論文名稱:共享經濟影響因素之複雜模型研究
論文名稱(外文):Complex Model In Sharing Economy
指導教授:謝邦昌謝邦昌引用關係陳銘芷陳銘芷引用關係
指導教授(外文):Shia Ben-ChangChen Ming-Chi
口試委員:陳銘芷謝邦昌劉湘川李建裕何昶鴛鄭宇庭
口試委員(外文):Chen Ming-ChiShia Ben-ChangLiu Hsiang-ChuanLee Chien-YuhHo Chang-IuanCheng Yu-Ting
口試日期:2017-06-09
學位類別:博士
校院名稱:輔仁大學
系所名稱:商學研究所博士班
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:157
中文關鍵詞:共享經濟影響因素複雜模型結構方程模型DEMATEL方法IPA方法
外文關鍵詞:sharing economyinfluencing factorscomplex modelstructural equation modelDEMATEL methodIPA method
相關次數:
  • 被引用被引用:1
  • 點閱點閱:443
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
本文主要站在消費者的角度,從科技接受模型出發,結合共享經濟的O2O商業模式特徵,構建了影響中國共享經濟發展的九個因素的初始複雜模型,採用問卷調查的形式,獲取了實驗資料,使用改進的DEMATEL方法量化了共享經濟九個複雜因素之間的影響和被影響的關係,同時結合SEM方法對初始的複雜模型進行了驗證,提出了對複雜模型的優化建議,並建立了優化後的複雜模型及相關假設。基於此優化後的複雜模型,使用IPA方法對共享經濟發展的主要因素進行了分析,同時使用SEM方法,量化分析了該優化後的模型中各因素對共享经济的影響程度,分析结果及建议如下:
1.共享經濟的發展受感知有用性、感知易用性、主觀規範、經驗、信任、風險、價值、態度和行為意願等多個因素的影響
2.共享經濟模型中各因素之間存在相互影響和被影響的關係,其中信任對其他的因素影響最大,有用性受其他因素影響最大,易操作性、信任和風險是原因因素,而有用性和行為意願是結果因素。
3.消費者對共享經濟的感知有用性比較滿意,但對風險因素提出了較高的改進期望。
4.行為意願是共享經濟的最終表現,也是促進整個行業發展重要的落腳點。
5.在行為意願提升的關鍵因素中,信任是首要改進因素,感知有用性是其次改進因素,感知價值是優勢因素。

Sharing economy, also has been called collaborative consumption and collaborative sharing. It is a new type of economic model, a product of O2O business model, and a new trend of the market economy development. The development of China's sharing economy is concerned by both theoretical and commercial industry, and it is a frontier of emerging business.
This dissertation is conducted mainly from the perspective of consumers.
A complex model is developed with nine initial factors by combining both technology acceptance model and the features of the sharing economy of O2O business model. Questionnaire is adopted in order to obtain the experimental data. The modified DEMATEL method to quantify the influence relationship between the nine factors and SEM method are used to optimize the sharing economy model. Then, IPA method also is utilized for analyzing the main factors of shared economic complex model, at the same time, using the SEM method to find the quantitative impact degree on behavioral intention. Analysis results and suggestions for the sharing economic complex model are as following:
1. The factors include perceived usefulness, perceived ease of use, subjective norms, experience, trust, risk, value, attitude and behavior intention are the influence factors that affected the complex model of sharing economy.
2. The influence relationship between the nine factors of the sharing economy model is investigated. Trust has the greatest impact on the other factors. Perceived of usefulness receives the biggest impact from the other factors.Perceived easy of use, trust and risk factors are the cause factors. Perceived usefulness and behavior intention are the result factors.
3. Consumers are more satisfied with the perceived usefulness of the sharing economy, but have higher expectations to improve the Risk factors.
4. Behavior intention is the ultimate result of the sharing economy complex model, and is also the most important factor to promote the development of the whole share ecomic.
5. Among the key factors for the promotion of behavior intention, trust is the primary factor to be improved, and perceived usefulness is the second factor to be improved, and perceived value is a dominant factor.

目錄
提要 I
abstract III
謝 辭 V
目錄 VII
表目錄 X
圖目錄 XII
第壹章 緒論 1
第一節 研究背景與意義 1
壹、研究的背景 1
貳、問題的提出 6
參、研究的目的與意義 9
第二節 研究框架與主要內容 11
壹、研究的思路和框架 11
貳、主要研究的內容 13
第三節 本文特色、創新與不足 14
壹、本文特色與創新 14
貳、本文的不足 15
第貳章 共享經濟理論及綜述 17
第一節 共享經濟概述 17
壹、共享經濟的概念 17
貳、共享經濟的主要類型 19
參、共享經濟的本質及特點 20
肆、共享經濟的邊界和趨勢 22
第二節 相關理論綜述 23
壹、產權理論 23
貳、零邊際成本理論 25
參、感知價值理論 26
肆、消費者行為相關理論 27
第三節 相關研究綜述 31
壹、共享經濟影響因素的研究 31
貳、共享經濟的社會效益研究 32
第四節 相關方法綜述 34
壹、傳統DEMATEL方法介紹 34
貳、重要性--績效分析(IPA方法)介紹 38
參、結構方程模型介紹 40
第參章 共享經濟影響因素複雜模型構建 47
第一節 共享經濟的商業模式探討 47
壹、商業模式的概念 47
貳、共享經濟的商業模式 50
第二節 影響因素構建的理論基礎—科技接受模型簡介 51
壹、科技接受模型(TAM) 51
貳、第二代科技接受模型(TAM2) 53
參、整合型科技接受模型和使用模型 55
肆、科技接受模型3(TAM3) 57
伍、擴展之科技接受模型 60
第三節 共享經濟影響因素初始複雜模型建立及假設 63
壹、共享經濟影響因素初始複雜模型的建立 63
貳、初始的研究假設 63
第四節 共享經濟影響因素複雜模型定義 66
壹、感知有用性(PU) 66
貳、感知易用性(PEU) 67
參、主觀規範(SN) 67
肆、經驗(E) 68
伍、信任(Trust) 69
陸、感知風險(PR) 70
柒、感知價值(PV) 72
捌、態度(A) 74
镹、行為意願(BI) 74
第肆章 資料收集及描述性分析 75
第一節 問卷設計與資料獲取 75
壹、問卷設計 75
貳、問卷前測 76
參、問卷的發放和回收 76
第二節 調查對象基本資訊統計 77
壹、調查對象性別分佈 77
貳、調查對象年齡分佈 77
參、調查對象教育程度分析 78
肆、調查對象月收入狀況分析 79
伍、調查對象網購經驗分佈 80
第三節 共享經濟模式評價統計 81
壹、共享經濟模式總體評價 81
貳、共享經濟模式有用性評價與統計分析 82
參、共享經濟模式易用性評價與統計分析 83
肆、共享經濟模式主觀規範評價與統計分析 84
伍、共享經濟模式與移動支付相關性評價與統計分析 84
陸、共享經濟模式信任程度評價與統計分析 85
柒、共享經濟模式風險評價與統計分析 86
捌、共享經濟模式價值評價與統計分析 87
镹、共享經濟模式態度評價與統計分析 87
拾、共享經濟模式使用意願評價與統計分析 88
第四節 問卷信度及效度分析 89
壹、信度分析 89
貳、效度分析 91
第伍章 共享經濟影響因素初始複雜模型優化 95
第一節 使用改進DEMATEL方法對初始複雜模型的分析 95
壹、改進的DEMATE方法介紹 95
貳、使用改進的DEMATEL方法的計算過程 97
參、共享經濟的影響因素分析 99
第二節 使用結構方程模型對初始複雜模型的分析 100
壹、共享經濟結構方程模型設定 101
貳、使用結構方程模型對初始複雜模型的識別 102
參、初始複雜模型的測評模型的參數估計及檢驗 106
肆、初始複雜模型的測評模型評價 109
伍、各變數對行為意願的影響的量化分析 111
第三節 共享經濟影響因素初始複雜模型的優化 114
壹、對初始複雜模型優化的措施 115
貳、基於TAM3和DEMATEL的優化後的複雜模型及假設 116
第陸章 基於IPA方法的中國共享經濟發展主要 119
因素分析 119
第一節 中國共享經濟影響因素總體分析 119
壹、計算過程及結果展示 119
貳、主要結論 120
第二節 共享租車與租房兩種模式之間的影響因素比較分析 121
壹、計算過程及結果展示 121
貳、主要結論 124
第柒章 基於優化後複雜模型的共享經濟結果因 127
素—行為意願分析 127
第一節 模型設定與識別 127
第二節 基於通過檢驗的測評模型的參數估計與評價 131
壹、基於通過檢驗的測評模型的參數估計 131
貳、 基於通過檢驗的測評模型評價 134
第三節 優化後各變數對行為意願影響的量化分析 135
壹、各影響變數對行為意願的影響計算過程 135
貳、結果分析 138
第四節 使用IPA方法對共享經濟行為意願的改進分析 139
壹、使用外部模型計算各隱變數相對於顯變數的得分 139
貳、 基於IPA方法的共享經濟行為意願改進 141
第捌章 總結與展望 143
第一節 本文的主要結論 143
第二節 本文的研究展望 145
英文文獻 148


中文文獻
1.蕾切爾.博茨曼路.羅傑斯(2015),共享經濟時代,上海交通大學出版社。
2. 傑瑞米.裡夫金(2014),零邊際成本社會,中信出版社。
3.方針(2005),使用者資訊技術接受的影響因素模型與實證研究[博士論文]復旦大學資訊管理與資訊系統。
4.蔣曉敏(2014),基於O2O視角銀泰百貨連鎖經營商業模式的研究[碩士學位論文].浙江理工大學。
5.黎沖森(2014),O2O四大模式.經理人, 239,28-30。
6.楊永清,張金隆,李楠,楊光(2012)近距離移動支付用戶接受行為研究:基於消費者視角,圖書情報工作, 2,142-148。
7.尤丹蓉,陳毅文,王二平(2004).消費者認知風險概念模型的研究綜述.人類工學, 6(2),154-157。

英文文獻
1. Alain Yee-loong Chong, Felix T.S. Chan, Keng-Boon Ooi, (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia, Decision Support Systems 53(1):34-43.
2. Bauer, R.A., (1960). Consumer Behaviors Risk Taking. Dynamic Marketing for a Changing World. American Marketing Association,389-39
3. Botsman, R., & Rogers, R., (2011). What’s mine is yours: How Collaborative Consumption is changing the way we live: Collins London.
4. Broydrick, S. C., (1998). Seven laws of customer value. Executive Excellence, 15 (4):15.
5. Chitungo, S. K., Munongo, S., (2013). Extending the technology acceptance model to mobile banking adoption in rural Zimbabwe. Journal of Business Administration and Education,3(1):51-79.
6. CY Tang, CC Lai, (2014). Examining key determinants of mobile wallet adoption intention in Malaysia: an empirical study using the unified theory of acceptance and use of technology 2 model. International Journal of Modelling in Operations Management,4:3-4
7. D. Harrison McKnight, Vivek Choudhury, Charles Kacmar, (2002). Developing and Validating Trust Measures for e-Commerce: An Integrative Typology, Information Systems Research, 13(3):334-359.
8. David K, Gary Knight, (2005). Antecedents to internet-based purchasing: a multinational study, International Marketing Review, 22(4):460-473.
9. Davis F D., (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13:319-340.
10. Davis F D. (1986). Technology acceptance model for empirical testing new end-user information systems: Theory and results [Ph.D. Dissertation]. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, USA.
11. Featherman, Pavlou P.A., (2003). Predicting e-services adoption: A Perceived Risk Factors Perspective, Computer Studies, (59):451-474
12. Gao, T., Deng, Y., (2012). A study on users’ acceptance behavior to mobile e-books application based on UTAUT model. Paper presented at the Software Engineering and Service Science (ICSESS), IEEE 3rd International Conference.
13. Gilok Choi, HW Chung, (2013). Applying the Technology Acceptance Model to Social Networking Sites(SNS): Impact of Subjective Norm and Social Capital on the Acceptance of SNS, Interational Journal of Human-Computer Interaction, 29(10):619-628.
14. Huili, Y., Shanzhi, L., Yinghui, Y., (2013). A study of user adoption factors of mobile banking services based on the trust and distrust perspective. International Business and Management 6(2):9-14.
15. J Schepers, M Wetzels, (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects, Information & Management,44(1):90-103.
16. K Stokes, E Clarence, L Anderson, A Rinne, (2014). collaboriamo.org
17. Khaled M.S. Faqiha, Mohammed-Issa Riad Mousa Jaradatb, (2015). Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology: TAM3 perspective, Journal of Retailing and Consumer Services, 22:37-52.
18. KK Kim, B Prabhakar, (2004). Initial trust and the adoption of B2C e-commerce: The case of internet banking. ACM sigmis database, 35(2):50-64.
19. Lee M K O, Turban E, (2001). A trust model for consumer Internet shopping. International Journal of Electronic Commerce, 6(1): 75-91.

20. L. YiZi, T. Dingna, (2015). Effects of Consumer's Perceived Value on Mobile Shopping Willingness-Based on TAM and VAM models, Journal of Lanzhou Academic, 04.
21. OECD, (2015). Taxi services: Competition and regulation, OECD Competition Policy Round-tables , 7.
22. Paul A P, (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3): 69-103.
23. R Yadav, SK Sharma, A Tarhini, (2016). A multi-analytical approach to understand and predict the mobile commerce adoption, Journal of Enterprise Information Managenment, 29(2).
24. Roselius E., (1971). Consumer rankings of risk reduction methods. Journal of Marketing, 35(1): 56-61.
25. Salam A F, Lyer L, Palvia P, Singh R, (2005). Trust in e-commerce. Communications of the ACM, 48(2): 73-77.
26. Samuel, C.B., (2014). Collaborative consumption: sharing our way towards sustainability, Open Collections
27. Schulist, K. (2012). Collaborative consumption: A new form of consumption in a changing economy. University of North Carolina Wilmington.
28.V. Venkatesh and F. D. Davis, (1994). Modeling the determinants of perceived ease of use, in Proc. Int. Conf. Information Systems, Vancouver, BC, Canada, 213-227.
29. Vijayasarathy L R, (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41: 747-762.
30. Warshaw, P. R., (1980). A new model for predicting behavioral intentions: Analternative to Fishbein [J]. Journal of Marketing Research, 17:153-172.
31. Wood, C.M. and L.K. Scheer, (1996). Incorporating Perceived Risk into Model of Consumer Deal Assessment and Purchase Intent, Advances in Consumer Research, 23(1):399-404.
32. Koufaris M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2): 205-223.
33. Akturan U., Tezcan, N., (2012). Mobile banking adoption of the youth market: Perceptions and Intentions. Marketing Intelligence & Planning ,30(4): 444-459.
34. Alafeef, M., Singh, D., Ahmad, K., (2012). The influence of demographic factors and user interface on mobile banking adoption: A review. Journal of Applied Sciences 12(20):2082-2095.
35. AYL Chong., (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption,Expert Systems with Applications,40(4):1240-1247
36. AYL Chong, (2013). Predicting m-commerce adoption determinants: A neural network approach, Expert Systems with Applications, 40(2):523-530.
37. AYL Chong, (2013). Mobile commerce usage activities: The roles of demographic and motivation variables, Technological Forecasting and Social Change,80(7):1350-1359.
38. A Vellido, PJG Lisboa, K Meehan, (2000). Quantitative Characterization and Prediction of On-Line Purchasing Behavior: A Latent Variable Approach. International Journal of Electronic Commerce, 4(4):83-104.
39. Asif, M., Krogstie, J., (2012). Research issues in personalization of mobile services. International Journal of Information Engineering and Electronic Business, 4(4):1-8.
40. B Shao, X Meng, (2005). An empirical study on factors influencing consumer trust in China's B2C e-commerce, Science & Technology Progress and Policy, 7: 166-169.
41. B. Szajna, (1996). Empirical evaluation of the revised technology acceptance model, Manage. Sci, 42(1): 85-92.
42. C Baden-Fuller, MS Morgan, (2010). Business Models as Models. Long Range planning, 43(2-3):156-171.
43. Bentler, P. M, Chou, C. P, (1987). Practical issues in structural modeling[M]. Sociological methods & research, 16:78-117.
44. BG Edelman, (2017). Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment, American Economic Journal Applied Economics, 9(2):1-22
45. Botsman, R., & Rogers, R., (2010). What’s mine is yours. The Rise os Collaborative Consumption, Collins.
46. Brian J. Corbitt, Theerasak Thanasankit, Han Yi., (2003). Trust and e-commerce: a study of consumer perceptions. Electronic Commerce Research and Applications,2:203-215.
47. CF Chen, (2008). Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: Evidence from Taiwan, Transportation Research Part A: Policy and Practice, 42(4):709-717.
48. D. A. Adams, R. R. Nelson, P. A. Todd, (1992). Perceived usefulness, ease of use, and usage of information technology: A replication, MIS Quart, 16(2):227-248.
49. D. Gefen and D. W. Straub, (2000). The relative importance of perceived ease-of-use in IS adoption: A study of e-Commerce adoption, JAIS, 1(8):1-30.
50. DH McKnight, V Choudhury, C Kacmar, (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model, Journal of Strategic Information Systems ,11:297-323.
51. Davis F D, Bagozzi R P, Warshaw P R, (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35: 982-1003.
52. Davis, F.D., (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology, MIS Quarterly,3 (13):319-340.
53. DeLone, W. H., McLean, E., (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4):9-30.
54. DW Bedford, (2005). Empirical Investigation of the Acceptance and Intended Use of Mobile Commerce: Location, Personal Privacy and Trust. Dissertation, Mississippi State University
55. Dodds W B, Monroe K B., (1985). The effect of brand and price information on subjective product evaluations. Advances in Consumer Research, 12(1):85-90.
56. P Drucker, (1994). The Theory of the Business. Harvard Business Review, 72, (5) :95-104
57. E. Karahanna, DW. Straub, NL. Chervany, (1999). Information tech- nology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs, MIS Quart., 23(2):183-213.
58. Edelman, BM. Luca, D. Svirsky (2016), Racial discrimination in the sharing economy: Evidence from a field experiment ,working Paper 16-069,Harvard Business School.
59. E Chambers , M Patrocanio, (2012). Business Models and Value Creation. Umea School of Business, 124.
60. E Cody-Allen, R Kishore, (2006). An extension of the UTAUT Model with E-Quality, Trust, and Satisfaction Constructs. Proceedings of the 2006 SIGMIS Computer Personnel Research Conference, Claremont, CA.
61. F. D. Davis, (1989). Perceived usefulness, perceived ease of use and user ac- ceptance of information technology, MIS Quart., 13(3):319-340.
62. Featherman M S, Pavlou P A., (2003). Predicting e-services adoption: a perceived risk facets perspective international. Journal of Human-Computer Studies, 59(4): 451-474.

63. Felson, Marcus; Spaeth, Joe L., (1978). Community Structure and Collaborative Consumption: A Routine Activity Approach, The American Behavioral Scientist ,21(4): 614.
64. Fraiberge, S., A. Sundararajan (2015), Peer-to-peer rental markets in the sharing economy, NYU Stern School of Business Working Paper,10: 6.
65. Fred D. Davis, Richard P. Bagozziand Paul R. (1992). Warshaw Extrinsic and Intrinsic Motivation to Use Computers in the Workplace, Journal of Applied Social Psychology, 22(14):1111-1132
66. Gansky, L., (2010). The mesh: Why the future of business is sharing: Penguin.
67. Gefen D, Karahanna E, Straub D W., (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1): 51-90.

68. Golovin, S. (2014). The economics of Uber, http://bruegel.org.
69. Grabner-Krautera S, Ewald A K., (2003). Empirical research in on- line trust: A review and critical assessment. Int. J. Human- Computer Studies, 58: 783-812.
70. Greatorex M, Mitchell VW., (1993). Developing the perceived risk concept, Davies M et al, Emerging issues in marketing. Proceedings of Marketing Education Group Conference, Cardiff University, 405-415.
71. B Greenwood, S Wattal, (2015). Show me the way to go home: An empirical investigation of ride sharing and alcohol related motor vehicle homicide, Fox School of Business Research Paper, 15:54.
72. HKYau, TC Ho, (2015). The Influence of Subjective Norm on Behavioral Intention in Using E-Learning: An Empirical Study in Hong Kong Higher Education, Naternational Multi Conference of Engineers and Computer Scientists, 18-20, Hong Kong.
73. Hsu, C., Wang, C., Lin, J.C., (2011). Investigating customer adoption behaviors in mobile financial services. International Journal of Mobile Communications,9(5), 477-494.
74. Jacoby J, Kaplan L., (1972). The components of perceived risk, Venkatesan M, Proceedings of 3rd Annual
Conference Chicago: Association for Consumer Research. University of Chicago, 382-393.

75. Jarvenpaa, S.L., Staplesb, D.S., (2000). The use of collaborative electronic media for information sharing: an exploratory study of determinants, Journal of Strategic Information Systems ,9:129-154
76. JC Anderson, JA Narus, (1990). A model of distributor firm and manufacturer firm working partnerships, Journal of Marketing, 54 (1):42-58.
77. Jing Sun, Ting Chi, (2015). Key Factors Influencing the Adoption of Apparel Mobile Commerce: An Empirical Study of Chinese Consumers, Ama/acra Trinnial Conference.
78.K. Fren, J. Schor, (2017). Putting the sharing economy into perspective. Journal of Environmental Innovation & Societal Transitions,23:3-10
79. K. Blomqvist, (2008). The role of trust and contracts in the internationalization of technology-intensive Born Globals. J. Eng. Technol. Manage,25:123-135
80. K Stokes, E Clarence, L Anderson, A Rinne, (2014). collaboriamo.org. Books
81. Kim J K., (2005). Factors influencing consumers’ apparel purchasing intention in the C2C e-commerce market [Ph. D. Dissertation]. University of Nebraska, Lincoln, USA.
82. N Koenig-Lewis, A Palmer, (2010). Predicting young consumers' take up of mobile banking services. The International Journal of Bank Marketing, 28(5):410-432.
83.Lim Nena, (2003). Consumers’ perceived risk: sources versus consequences, Electronic Commerce Research and Application, 2(3):216-228.
84.Lim, Nena, (2003). The effects of experience and perceptions on consumers’ acceptance of on-line shopping. [PhD thesis]. School of Business, The University of Queensland.
85.Liu D R, Lin Y J, Chen C M, et al, (2001). Deployment of personalized e-catalogues: an agent-based framework integrated with XML metadata and user models. Journal of Network and Computer Application, 24(3):201-228
86.L YiZhi, T DingNa, (2015). Effects of Consumer's Perceived Value on Mobile Shopping Willingness-Based on TAM and VAM models, Journal of Lanzhou Academic, 4.
87.L YaoBin, Z Tao, (2005). An empirical analysis of factors influencing consumers’ initial trust under B2C environment. NanKai Business Review, 8(6): 96-101.
88.Lui H K, Jamieson R, (2003). Tri TAM: A model for integrating
trust and risk perceptions in business-to-consumer electronic commerce. In The 16th Bled e-Commerce Conference on e-Transformation. Bled, Slovenia,19:9-11.
89.M. Keil, P. M. Beranek, and B. R. Konsynsk, (1995). Usefulness and ease of use: Field study evidence regarding task considerations, Decision Supp. Syst., 13(1):75-91.
90. L Ming-Tsang, T Gwo-Hshiung, C Hilary , H Chih-Cheng, (2015). Exploring mobile banking services for user behavior in intention adoption: using new hybrid MADM model, Service Business, 9 (3):541-565
91. Miniard, P. W., Cohen, J. B., (1979). Isolating attitudinal and normative influences in behavioral intention models. Journal of Marketing Research, 6:102-110.
92. Nysveen, H., Pedersen, P. E., Thorbjørnsen, H., (2005b) Intentions to use mobile services: Antecedents and cross- service comparisons. Journal of the Academy of Marketing Science, 33:330-346.
93. Osterwalder, A., Pigneur, Y. ,Tucci, C., (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16(1):1-25.
94. Park, J., Yang, S.,, Lehto, X., (2007). Adoption of mobile technologies for Chinese consumers. Journal of Electronic Commerce Research, 8:356-367.
95. Pavlou P A, Gefen D., (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1):37-59.
96. Pavlou P, (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce Research and Applications, (3):101-134.

97. Porter C E, Donthu N, (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: the role of perceived access barriers and demographics. Journal of Business Research, 59(9): 999-1007.
98. Rajan Yadav, Sujeet Kumar Sharma, Ali Tarhini, (2016). A multi-analytical approach to understand and predict the mobile commerce adoption, Journal of Enterprise Information Management, 29(2).
99. Robert N. Stone, Kjell Grønhaug, (1993). Perceived Risk: Further Considerations for the Marketing Discipline, European Journal of Marketing, 27 (3):39-50.
100. Rogers, B., (2015). The social costs of Uber,Temple University Legal Studies Research Paper ,28.
101. Rosenblat, A. , L. Stark, (2015). Uber's drivers: Information asymmetries and control in dynamic work, Workshop Paper Prepard for the Winter School Labor in the On-demand Economy at the Centre for European Policy Studies in Brussels Belgium ,23-25
102. H Dehua, L Yaobin, Z Deyi, (2008). Empirical Study of Consumers’ Purchase Intentions in C2C Electronic Commerce, An empirical study of Tsinghua Science and Technology, June, 13(3):287-292
103. Stone RN, Gronhaug K., (1993). Perceived risk: further considerations for the marketing discipline. European Journal of Marketing, 27(3):372-394.
104. Tan, G.W.H., Ooi, K.B., Chong, S.C., Hew, TS., (2014). NFC Mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31(2):292-307.
105. Tsang, M. M., Ho, S. C., Liang, T. P., (2004). Consumer attitudes toward mobile advertising: An empirical study. International Journal of Electronic Commerce, 8(3):65-78.
106. Uber, (2014). DUI rates decline in Uber cities, http://blog.uber.com
107. Venkatesh V., Davis. F.D., (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science,46 (2):186–204
108. Venkatesh V, Davis F D., (1996). A model of antecedents of perceived ease of use: Development and test. Decision Science, 27:451-481.
109. Venkatesh V., (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation and emotion into the technology acceptance model. Information Systems Research, 11 (4):115-139
110. Venkatesh, V, (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2):273-315.
111. Venkatesh, V., (2000). Determinants of perceived ease of use: Integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model[J]. Information Systems Research, 11:342-365.
112. Venkatesh, V., Morris, M.G., G.B. Davis, F.D. Davis., (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3):425-478.
113. Walczuch R, Lundgren H., (2004). Psychological antecedents of institution-based consumer trust in e-retailing. Information & Management, 42(1):159-177.
114. Wood, C. M. and L. K. Scheer, (1996). Incorporating Perceived Risk into Model of Consumer Deal Assessment and Purchase Intent, Advance in Consumer Research, 23(1):399-404.
115. Woodruff, R.B., (1997). Customer Value: The Next Source for Competitive Advantage, Journal of the Academy of Marketing Science, 25(2):139-153.
116. Wu W.W, Lee, Y.T. (2007). Developing global managers’ competencies using fuzzy DEMATEL method. Expert Systems with Applications, 32 (2):499-507.
117. Y Chian-Son., (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research,13(2):104-121.
118. Zeithaml, Valarie A, (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence, Journal of marketing, 52(3):2-22.

電子全文 電子全文(網際網路公開日期:20220616)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
無相關點閱論文
 
系統版面圖檔 系統版面圖檔