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題名:消費者對網路購物行銷策略與網路購物消費行為研究-以UTAUT、D&M ISS及TTF理論基礎探討兩岸行動支付
作者:陳東俊
作者(外文):Chen,Tung-Chun
校院名稱:國立高雄科技大學
系所名稱:國際企業系
指導教授:柯伯昇
學位類別:博士
出版日期:2019
主題關鍵詞:整合科技接受模型任務科技配適模型D&M資訊系統成功模型行動支付UTNUTD&M ISSTTFmobile payment
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2019年台灣電子商務營業額首次「行動支付」付款方式以49.6%的市佔率,變成台灣民眾選擇付款的首選付款方式,行動支付款機制提供了安全的收付款交易服務,為電子商務提供更優質的交易環境,現在行動支付儼然己成為進步便利生活不可或缺的重要一環。
本研究採用三種理論模型結合任務科技配適模式(TTF)、DeLone and McLean 資訊系統成功模型(D&M ISS)與整合科技接受模型(UTAUT)理論模型交叉比對驗證,探討行動支付款機制消費者的任務特徵、科技特徵、任務科技配適度、系統品質、資訊品質、服務品質、使用者滿意度、績效期望、努力期望、社會影響、便利條件及使用意圖等12個構面構成10大假說以解釋對行為意圖的影響。本研究採便利抽樣,以中國與台灣的網路購物消費者為研究對象,採問卷調查,有效問卷台灣地區消費者發放問卷1000份,回收614份;中國地區消費者發放問卷3000份,回收1387份。
本研究提出了一個結合三種各具不同優勢模型的整合模型。研究發現,對台灣以及中國的使用者而言,績效期望、社會影響、便利條件顯著影響使用意圖,二地的使用者都認為行動支付的使用者界面仍有進步空間,使用者期待行動支付帶來效率以滿足使用者預期。因行動支付系統容易受到資訊攔截、資安外洩等,以致使用者擔心安全並相當重視績效期望。使用者亦希望行動支付商擁有更好的程式編碼技術,以提供更安全可靠的行動支付工具。台灣、中國的使用者選擇使用行動支付會受到社會群體影響,受此新興風潮帶動而使用行動支付進行付款。但與中國使用者不同的是台灣使用者對於便利條件的反應,對台灣來說現行的支付方式如信用卡、貨到收款、超商取貨已慣行使用相當方便,以方便性來說並不是台灣使用者選擇行動支付的主要原因。
The "mobile payment" with the Taiwan’s e-commerce market share of 49.6% became the preferred payment method in 2019. The "mobile payment" mechanism not only provides more secure services for payment/collection transactions but also offers a much better e-commerce environment. This payment method has become an indispensable part in facilitating and improving daily life. It also replaces money and creates huge business opportunities.
This study uses three theoretical models combining Task-technology Fit model, Delone and McLean IS success model and Unified Theory of Acceptance and Use of Technology model theoretical model for exploring mobile payment mechanism and the interaction effect among various twelve variables (consumers’ task and technological characteristic; Fitness of task technology; system, information, and service quality; users’ satisfaction; performance and effort expectation; social influence; convenience condition and usage intention) comprising ten hypotheses and behavioral intentions. This research adopts the convenience sampling method using online survey to China and Taiwan online consumers. The researcher sends a total of 1000 survey collecting 614 valid replies in Taiwan and 3000 survey resulting in 1387 valid responses in China.
This research proposes an integration model combining three different advantages of various models. The research results show that performance expectation, social impact, and convenience conditions significantly affect usage intentions based on Taiwan and China survey results. It implies a couple of points. First, users in both countries believe that the user interface of mobile payment still has room for improvement. Second, users expect mobile payment to bring higher efficiency for meeting consumers’ expectations. Third, mobile payment is more susceptible to information interception for preventing leakage. Therefore, users are more concerned about security, care about performance expectations, and hope that mobile payment providers have better computer coding technology in order to provide better safe and reliable mobile payment tool. Regarding the significant effect on social impact, users’ choice is affected by others in social groups. This kind of phenomenon might become a trend and cause consumers to use mobile payment. Taiwanese users have shown an insignificant response to the convenience condition dimension, which is different from China users’ results. It is very common and convenient to use credit card, cash-on-delivery, and supermarket pick-up and so on as payment methods in Taiwan. So, convenience is not the main reason for Taiwanese users to choose mobile payment.
學術期刊
1.Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes 50, 179-211.
2.Ajzen, I., and Madden, T.J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology 22, 453-474.
3.Alalwan, A.A., Dwivedi, Y.K., and Rana, N.P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management 37, 99-110.
4.Atkinson, M., and Kydd, C. (1997). Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation. SIGMIS Database 28, 53–62.
5.Au, N., Ngai, E.W.T., and Cheng, T.C.E. (2008). Extending the Understanding of End User Information Systems Satisfaction Formation: An Equitable Needs Fulfillment Model Approach. MIS Quarterly 32, 43-66.
6.Bagozzi, R.P., and Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16, 74-94.
7.Bandura, A., and Cervone, D. (1986). Differential engagement of self-reactive influences in cognitive motivation. Organizational Behavior and Human Decision Processes 38, 92-113.
8.Beauvisage, T., and Mellet, K. (2020). Mobile consumers and the retail industry: the resistible advent of a new marketing scene. Journal of Cultural Economy 13, 25-41.
9.Beierle, F., Tran, V.T., Allemand, M., Neff, P., Schlee, W., Probst, T., Zimmermann, J., and Pryss, R. (2020). What data are smartphone users willing to share with researchers? Designing and evaluating a privacy model for mobile data collection apps. Journal of Ambient Intelligence and Humanized Computing 11, 2277-2289.
10.Bolen, M.C., and Ozen, U. (2020). Understanding the factors affecting consumers' continuance intention in mobile shopping: the case of private shopping clubs. International Journal of Mobile Communications 18, 101-129.
11.Cao, C.L., and Zhu, X.L. (2019). Strong anonymous mobile payment against curious third-party provider. Electronic Commerce Research 19, 501-520.
12.Chatterjee, S., Kar, A.K., and Gupta, M.P. (2018). Success of IoT in Smart Cities of India: An empirical analysis. Government Information Quarterly 35, 349-361.
13.Chau, N.T., Deng, H.P., and Tay, R. (2020). Critical determinants for mobile commerce adoption in Vietnamese small and medium-sized enterprises. Journal of Marketing Management 36, 456-487.
14.Chuang, L.-M., Chen, P.-C., and Chen, Y.-Y. (2018). The Determinant Factors of Travelers’ Choices for Pro-Environment Behavioral Intention-Integration Theory of Planned Behavior, Unified Theory of Acceptance, and Use of Technology 2 and Sustainability Values. Sustainability 10.
15.Compeau, D., Higgins, C., and Huff, S. (1999). Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study. MIS Quarterly 23, 145-158.
16.Compeau, D.R., and Higgins, C.A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly 19, 189-211.
17.Cui, Y., Mou, J., Cohen, J., Liu, Y.P., and Kurcz, K. (2020). Understanding consumer intentions toward cross-border m-commerce usage: A psychological distance and commitment-trust perspective. Electronic Commerce Research and Applications 39, 10.
18.Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Mis Quarterly 13, 319-340.
19.Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1989). User Acceptance of Computer-Technology - a Comparison of 2 Theoretical-Models. Management Science 35, 982-1003.
20.Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1. Journal of Applied Social Psychology 22, 1111-1132.
21.Deci, E.L., and Ryan, R.M. (1985). Intrinsic motivation and self-determination in human behavior. . New York: Plenum.
22.DeLone, W.H., and McLean, E.R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research 3, 60-95.
23.DeLone, W.H., and McLean, E.R. (2003). The DeLone and McLean model of information systems success: a ten-year update. J Manage Inform Syst 19, 9-30.
24.Dishaw, M.T., and Strong, D.M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management 36, 9-21.
25.Dwivedi, Y.K., Kapoor, K.K., Williams, M.D., and Williams, J. (2013). RFID systems in libraries: An empirical examination of factors affecting system use and user satisfaction. International Journal of Information Management 33, 367-377.
26.Fianu, E., Blewett, C., Ampong, G., and Ofori, K. (2018). Factors Affecting MOOC Usage by Students in Selected Ghanaian Universities. Education Sciences 8.
27.Fishbein, M., and Ajzen., I. (1975). Belief, Attitude, Intention and Behavior: an Introduction to Theory and Research. Addison-Wesley Boston, MA.
28.Fornell, C., and Larcker, D.F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research 18, 382-388.
29.Goodhue, D.L., and Thompson, R.L. (1995). Task-Technology Fit and Individual-Performance. Mis Quarterly 19, 213-236.
30.GSMA. (2020). 2020 The Mobile Economy(2020/06/22).
31.Igbaria, M., and Toraskar, K. (1992). Impact of End User Computing on the Individual: An Integrated Model. Information Technology & People 6, 271-292.
32.Im, I., Hong, S., and Kang, M.S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management 48, 1-8.
33.Kalinic, Z., Liebana-Cabanillas, F.J., Munoz-Leiva, F., and Marinkovic, V. (2020). The moderating impact of gender on the acceptance of peer-to-peer mobile payment systems. International Journal of Bank Marketing 38, 138-158.
34.Kim, C., Galliers, R.D., Shin, N., Ryoo, J.-H., and Kim, J. (2012). Factors influencing Internet shopping value and customer repurchase intention. Electronic Commerce Research and Applications 11, 374-387.
35.Kline, R.B. (2015). Principles and Practice of Structural Equation Modeling. Guilford Publications.
36.Ko, P.-S., and Chen, T.-C. (2020). Impact of Internet Finance on Bank Financial Risk Management. Basic & Clinical Pharmacology & Toxicology 126, 327-328.
37.Lee, J., Kim, K., Shin, H., and Hwang, J. (2018). Acceptance Factors of Appropriate Technology: Case of Water Purification Systems in Binh Dinh, Vietnam. Sustainability 10.
38.Li, X., Zhao, X.D., Xu, W.T., and Pu, W. (2020). Measuring ease of use of mobile applications in e -commerce retailing from the perspective of consumer online shopping behaviour patterns. Journal of Retailing and Consumer Services 55, 12.
39.Lin, T.-C., and Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management 45, 410-417.
40.Lin, X., Wu, R.Z., Lim, Y.T., Han, J.P., and Chen, S.C. (2019). Understanding the Sustainable Usage Intention of Mobile Payment Technology in Korea: Cross-Countries Comparison of Chinese and Korean Users. Sustainability 11, 23.
41.Lu, H.-P., and Yang, Y.-W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior 34, 323-332.
42.Mason, R.O. (1978). Measuring information output: A communication systems approach. Information & Management 1, 219-234.
43.Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research 2, 173-191.
44.Matthew, P., Ortiz, I., Cohen, A., Konkle, B.A., Streiff, M., Lee, M., Wages, D., Corash, L., de Alarcon, P., Benjamin, R., et al. (2005). Fresh frozen plasma prepared with amotosalen HCl (S-59) photochemical pathogen inactivation: transfusion of patients with congenital coagulation factor deficiencies. Transfusion 45, 1362-1372.
45.Molina-Castillo, F.J., Lopez-Nicolas, C., and de Reuver, M. (2020). Mobile Payment: The Hiding Impact of Learning Costs on User Intentions. Journal of Theoretical and Applied Electronic Commerce Research 15, 1-12.
46.Moon, J.-W., and Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Inf Manage 38, 217-230.
47.Moore, G.C., and Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research 2, 192-222.
48.Morosan, C., and DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management 53, 17-29.
49.Mousa Jaradat, M.-I.R., and Al Rababaa, M.S. (2013). Assessing Key Factor that Influence on the Acceptance of Mobile Commerce Based on Modified UTAUT. International Journal of Business and Management 8.
50.Nunnally, J.C., and Bernstein, I.H. (1994). The Assessment of Reliability. . Psychometric Theory, 3.
51.Nysveen, H., Pedersen, P.E., and Thorbjornsen, H. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. J Acad Mark Sci 33, 330-346.
52.Oliveira, T., Thomas, M., Baptista, G., and Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior 61, 404-414.
53.Oliver, R.L. (1997). Satisfaction: A Behavioral Perspective on the Consumer,. McGraw-Hill,.
54.Petter, S., DeLone, W., and McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems 17, 236-263.
55.Rogers, E.M., and Marcus, J.E. (1983). Advances in diffusion theory. In W. J. Paisley & M.
56.Butler (Eds.),. Knowledge utilization systems in education pp. 251-257.
57.Sarkar, S., Chauhan, S., and Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management 50, 286-301.
58.Seddon, P.B. (1997). A Respecification and Extension of the DeLone and McLean Model of IS Success. Information Systems Research 8, 240-253.
59.Shannon, C.E., and Weaver, W. (1949). The mathematical theory of communication.
60.Sharma, S.K., Gaur, A., Saddikuti, V., and Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology 36, 1053-1066.
61.Sharma, S.K., and Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management 44, 65-75.
62.Shaw, N., and Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management 45, 44-55.
63.Tam, C., and Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior 61, 233-244.
64.Tang, A.K.Y. (2019). A systematic literature review and analysis on mobile apps in m-commerce: Implications for future research. Electronic Commerce Research and Applications 37, 14.
65.Taylor, S., and Todd, P. (1995a). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing 12, 137-155.
66.Taylor, S., and Todd, P.A. (1995b). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research 6, 144-176.
67.Thompson, R.L., Higgins, C.A., and Howell, J.M. (1991). Personal Computing - toward a Conceptual-Model of Utilization. Mis Quarterly 15, 125-143.
68.Triandis, H.C. (1980). Values, attitudes, and interpersonal behavior. Nebr Symp Motiv 27, 195-259.
69.Vallerand, R.J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advances in Experimental Social Psychology, Vol 29 29, 271-360.
70.Veeramootoo, N., Nunkoo, R., and Dwivedi, Y.K. (2018). What determines success of an e-government service? Validation of an integrative model of e-filing continuance usage. Government Information Quarterly 35, 161-174.
71.Venkatesh, V., and Davis, F.D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Manage Sci 46, 186-204.
72.Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. Mis Quarterly 27, 425-478.
73.Venkatesh, V., Thong, J.Y.L., and Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly 36, 157-178.
74.Venkatesh, V.V., Morris, M.G., and Ackerman, P.L. (2000). A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. Organ Behav Hum Decis Process 83, 33-60.
75.Verkijika, S.F. (2018). Factors influencing the adoption of mobile commerce applications in Cameroon. Telematics and Informatics 35, 1665-1674.
76.Vongjaturapat, S., Chaveesuk, S., Chotikakamthorn, N., and Tongkhambanchong, S. (2015). Analysis of Factor Influencing the Tablet Acceptance for Library Information Services: A Combination of UTAUT and TTF Model. Journal of Information & Knowledge Management 14, 1550023.
77.Wang, Y., Wang, Y., and Lee, S.H. (2017). The Effect of Cross-Border E-Commerce on China's International Trade: An Empirical Study Based on Transaction Cost Analysis. Sustainability 9.
78.Warshaw, P.R. (1980). A New Model for Predicting Behavioral Intentions: An Alternative to Fishbein. Journal of Marketing Research 17, 153-172.
79.Wu, J., and Liu, D. (2007). The effects of trust and enjoyment on intention to play online games. Journal of Electronic Commerce Research - JECR 8.
80.Xu, C.Y., Mongo, P.A., and Ganiyu, S.A. (2020). Model construction and empirical study on mobile commerce user satisfaction. Current Psychology, 9.
81.Yi, M.Y., and Davis, F.D. (2003). Developing and Validating an Observational Learning Model of Computer Software Training and Skill Acquisition. Inform Syst Res 14, 146-169.
82.Zeithaml, V.A., Berry, L.L., and Parasuraman, A. (1996). The behavioral consequences of service quality. J Marketing 60, 31-46.
83.Zhang, L., Zhu, J., and Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior 28, 1902-1911.
84.Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems 54, 1085-1091.
85.Zhou, T., Lu, Y., and Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior 26, 760-767.
86.餘泰魁, and 楊淑斐 (2005). 線上學習系統使用意向之模式建構與比較分析研究. 台灣管理學刊 5, 311-337.

學術論文
1.吳宛欣 (2014). 運用UTAUT及TTF探討台灣店家對Beacon技術之廣告推播服務的接受度. In 工業工程與管理系碩士班 (臺北市: 國立臺北科技大學).
2.周立軒 (2004). 網誌的使用者與使用行為之研究. In 資訊傳播學系 (元智大學), pp. 1-103.
3.張宗榮 (2012). 以整合性科技接受模式及沉浸理論探討App之使用行為模式 - 以行動社群App為例. In 數位內容科技學系碩士班 (台中市: 國立臺中教育大學), pp. 99.
4.陳東俊 (2013). 台灣與中國電子商務問題之研究-以網路銷售交易模式探討消費者購物行為. In 財富與稅務管理研究所碩士在職專班 (高雄市: 國立高雄應用科技大學), pp. 105.
5.陳致良 (2011). 結合科技接受模式、計畫行為理論與準社會臨場感概念探討台灣地區微網誌行為意圖之研究. In 資訊管理學系碩士班 (新竹市: 中華大學), pp. 104.
6.楊惠合 (2004). 以科技接受模型探討數位元學習滿意度之研究. In 資訊管理學系碩士班 (彰化縣: 大葉大學), pp. 118.
7.蘇伯方 (2004). 即時傳訊軟體採用模式之研究. In 傳播管理研究所 (高雄市: 國立中山大學), pp. 134.

參考書藉
1.Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. In Action Control, pp. 11-39.
2.Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. (1992). Multivariate data analysis with readings / Joseph F. Hair, Jr. ... [et al.], 3rd ed. edn (New York: Macmillan).
3.Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. (1995). Multivariate data analysis with readings / Joseph F. Hair, Jr. ... [et al.], 4th ed. edn (Englewood Cliffs, N.J: Prentice Hall).
4.Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. (2010). Multivariate data analysis / Joseph F. Hair, Jr. ... [et al.], 7th ed. edn (Upper Saddle River, NJ: Prentice Hall).
5.Lew Nat (2017). 行動支付大解構:掌握新消費習慣 (四塊玉文創有限公司).
6.Thompson, J.K., Heinberg, L.J., Altabe, M., and Tantleff-Dunn, S. (1999). Exacting beauty: Theory, assessment, and treatment of body image disturbance (Washington, DC, US: American Psychological Association).
7.吳萬益、林清河 (2002). 行銷研究 = Marketing research / 吳萬益,林清河著, 初版 edn (台南市: 吳萬益發行).
8.張偉豪 (2011). SEM論文寫作不求人 = Structural equation modeling / 張偉豪編著, 一版 edn (高雄市: 三星統計發行).

網站資料
1.互聯網 (2017). 凱捷發佈2017全球支付報告 新支付生態面臨的挑戰.
2.吳睿睿 (2020). 最前線|《2020年全球支付報告》:下一個電商掘金地在東南亞.In36氪.
3.杭州微盤雲支付 (2017). 移動支付的優缺點是什麼?移動支付代理商須知.
4.雨果網 (2019). 2019年全球移動支付市場交易將達到1.08萬億美元,超60%電商流量來自移動端.
5.翁書婷 (2015). 圖解行動支付兩大模式,你的錢未來這樣用!.
6.財團法人台灣網路資訊中心 (2020). http://www.twnic.net.tw/ (2020/6/22).
7.財團法人資訊工業策進會 (2020). http://www.iii.org.tw/ (2020/6/22).
8.晨晰統計部落格(2011). https://dasanlin888.pixnet.net/ (2011/9/27). 晨晰統計顧問有限公司
9.陳冠榮 (2019). 網購市場成長,2019 年電子購物營收估破 2,000 億元大關.
10.經濟部統計處 (2018). 主要國家零售業電子商務發展概況.
11.資策會產業情報研究所 (2020). https://mic.iii.org.tw/news.aspx (2020/06/22).
12.劉士成 (2019). 行動支付優缺大剖析 網友們最在意的問題是什麼?. In 今日傳媒(股)公司.

 
 
 
 
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