1. NCCU Academic Hub
  2. 學術產出
  3. College of Commerce
  4. Theses
  5. Item

學術產出-Theses

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

題名 以TPE結合UTAUT2及HBM評估使用健康智慧手環之關鍵因素
Critical Factors of Adopting Smart Health Bracelet: The Perspectives of TPE, UTAUT2 and HBM
作者 劉育誠
Liu, Yu-Cheng
貢獻者 洪叔民
Horng, Shwu-Min
劉育誠
Liu, Yu-Cheng
關鍵詞 智慧手環
健康
科技-個人-環境
延伸整合科技接受模型
健康信念模型
Smart Bracelet
Health
TPE
UTAUT2
HBM
日期 2020
上傳時間 2-Mar-2021 14:57:37 (UTC+8)
摘要 本研究主要欲探討現代人在使用健康智慧手環的關鍵使用因素，因此使用了延伸整合型科技接受模型(UTAUT2)、科技-個人-環境模型(TPE)以及健康信念模型(HBM)這三個模型組合成本研究的研究模型。本人之所以要用此種模型是因感到近來使用者在購買產品時其實還會受到許多的個人背景、科技背景、環境背景的影響，故將TPE結合入本研究模型中；使用UTAUT2則是因為UTAUT2相較於UTAUT以及TAM的解釋力更高；而HBM則是因本次希望進行健康智慧手環相關的研究，且該模型在醫學方面已有多年被使用的經驗，諸多研究皆以此模型為基礎進行研究，且同樣有考慮使用者在科技背景、個人背景、環境背景以及同樣也有考慮干擾變數的影響，故與TPE及UTAUT2結合，最終本研究則以TPE結合UTAUT2及HBM作為研究模型。

本研究在設計問卷之前事先訪談了親友四位、一位資管學界專家、一位業界專家，將六位的意見統合起來，並透過TPE模型，結合UTAUT2以及HBM的各項構面後而生成問卷。透過線上問卷的方式共蒐集了505份問卷，最終替除掉無效問卷後以此為統計數字的基底，接著利用偏最小平方結構方程式模型(PLS-SEM)進行統計分析。最終在本研究的結尾則是依據本研究顯著之數據來建議業主應如何使的健康智慧手環的銷量提升。
This study explores the critical factors while adopting Smart Bracelet. Therefore, this study integral Unified Theory of Acceptance and Use of Technology (UTAUT2), Technological-Personal-Environmental (TPE) Framework and Health Belief Model (HBM) to build the research model. The reason of using this model is that when purchasing products, the users are affected by technological framework, personal framework, and environmental framework. Therefore, this study combines TPE to the research model, using UTAUT2 to obtain a higher explanatory power. Further, as many studies used HBM in medicine for many years, this study combines technological framework, personal framework, environmental framework, and the influence of interference variables. Therefore, HBM can be combined with TPE and UTAUT2. Overall, this research uses TPE combined with UTAUT2 and HBM as the research model.

This study interviewed four relatives, an information academic expert, and an industry expert. The questionnaire is adopted from the interviews, TPE model and various aspects of UTAUT2 and HBM. A total of 505 questionnaires were collected through online questionnaires; the invalid questionnaires were removed and used as the basis of statistics. Afterwards, statistical analysis was carried out using Partial Least Squares Structural Equation Model (PLS-SEM). Finally, this research suggests the way to increase the sales of the smart bracelet based on the significant data.
參考文獻 1. Abrahamson, E., & Rosenkopf, L. (1993). Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. Academy of management review, 18(3), 487-517.
2. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
3. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prince Hall, 249-259.
4. Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44.
5. Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & management, 44(8), 681-691.
6. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
7. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
8. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
9. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986, 23-28.
10. Becker, M. H. (1974). The health belief model and personal health behavior. Health education monographs, 2, 324-473.
11. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426.
12. Chau, P. Y., & Hui, K. L. (1998). Identifying early adopters of new IT products: A case of Windows 95. Information & management, 33(5), 225-230.
13. Chen, D., Lin, Z., & Lai, F. (2010). Crossing the chasm-understanding china`s rural digital divide. Journal of Global Information Technology Management, 13(2), 4-36.
14. Colquitt, J. A., Hollenbeck, J. R., Ilgen, D. R., LePine, J. A., & Sheppard, L. (2002). Computer-assisted communication and team decision-making performance: The moderating effect of openness to experience. Journal of Applied Psychology, 87(2), 402.
15. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
16. Darley, W. K., & Smith, R. E. (1995). Gender differences in information processing strategies: An empirical test of the selectivity model in advertising response. Journal of advertising, 24(1), 41-56.
17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
18. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132.
19. Deaux, K. and Kite, M. E. (1987) “Thinking About Gender.” in Analyzing Gender: A Handbook of Social Science Research. B. B. Hess and M. M. Ferree, (eds.) Newbury Park, CA: Sage Publications, 92–117.
20. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American sociological review, 147-160.
21. Emdad, A. F., Badamas, M., & Mouakket, S. (2009). Factors and impacts of low utilization of Internet: The case of Arab countries. Journal of International Technology and Information Management, 18(3), 2.
22. Fadil, P., Smatt, C., Segrest, S. L., & Owen, C. (2009). The moderating effects of technology on career success: can social networks shatter the glass ceiling?. Journal of International Technology and Information Management, 18(3), 8.
23. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research, 5, 177-189.
24. Glanz, K., Rimer, B. K., & Viswanath, K. (Eds.). (2008). Health behavior and health education: theory, research, and practice. John Wiley & Sons.
25. Gorsuch, R. L. (1983). Factor analysis Lawrence Erlbaum Associates. Hillsdale, NJ.
26. Grein, J., Ohmagari, N., Shin, D., Diaz, G., Asperges, E., Castagna, A., ... & Flanigan, T. (2020). Compassionate use of remdesivir for patients with severe Covid-19. New England Journal of Medicine, 382(24), 2327-2336.
27. Harcourt, M., Lam, H., & Harcourt, S. (2005). Discriminatory practices in hiring: Institutional and rational economic perspectives. The International Journal of Human Resource Management, 16(11), 2113-2132.
28. Harrison, J. A., Mullen, P. D., & Green, L. W. (1992). A meta-analysis of studies of the health belief model with adults. Health education research, 7(1), 107-116.
29. Haunschild, P. R., & Miner, A. S. (1997). Modes of interorganizational imitation: The effects of outcome salience and uncertainty. Administrative science quarterly, 472-500.
30. IDC. (2015). Fueled by Growing Demand for Smart Wearables, IDC Forecasts Worldwide Wearable Shipments to Reach 173.4 Million, Retrieved from https://www.businesswire.com/news/home/20150914005126/en/Fueled-by-Growing-Demand-for-Smart-Wearables-IDC-Forecasts-Worldwide-Wearable-Shipments-to-Reach-173.4-Million-by-2019
31. IDC. (2019)Worldwide Wearables Shipments Surge 94.6% in 3Q 2019 Led by Expanding Hearables Market, Says IDC. https://www.idc.com/getdoc.jsp?containerId=prUS45712619
32. IDC. (2020). Shipments of Wearable Devices Reach 118.9 Million Units in the Fourth Quarter and 336.5 Million for 2019, According to IDC. https://www.idc.com/getdoc.jsp?containerId=prUS46122120
33. IDC. (2020). Worldwide Quarterly Wearables Tracker.
34. IDC. (2020). Worldwide Wearables Market to Top 300 Million Units in 2019 and Nearly 500 Million Units in 2023, Says IDC. https://www.idc.com/getdoc.jsp?containerId=prUS45737919
35. Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health education quarterly, 11(1), 1-47.
36. Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & management, 35(4), 237-250.
37. Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—two competing perspectives on automatic use: A theoretical and empirical comparison. Information systems research, 16(4), 418-432.
38. Lee, H. J., Cho, H. J., Xu, W., & Fairhurst, A. (2010). The influence of consumer traits and demographics on intention to use retail self‐service checkouts. Marketing Intelligence & Planning, 28(1), 46-58.
39. Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: extending UTAUT2 with information quality. Sustainability, 11(11), 3141.
40. Lee, Y., & Kozar, K. A. (2008). An empirical investigation of anti-spyware software adoption: A multitheoretical perspective. Information & Management, 45(2), 109-119.
41. Levitt, B., & March, J. G. (1988). Organizational learning. Annual review of sociology, 14(1), 319-338.
42. Lewin, K. (1939). Field theory and experiment in social psychology: Concepts and methods. American journal of sociology, 44(6), 868-896.
43. Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS quarterly, 59-87.
44. Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS quarterly, 59-87.
45. Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology, 140, 44–53.
46. Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
47. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130.
48. Martinez‐Lopez, L., Martinez‐Lopez, F. J., Murray, K. B., Liang, J., & Häubl, G. (2010). ACT 2.0: the next generation of assistive consumer technology research. Internet Research, 20(3), 232-254.
49. Miller, N. E., & Dollard, J. (1941). Social learning and imitation, Retrive from https://psycnet.apa.org/record/1942-00109-000
50. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
51. Owusu Kwateng, K., Osei Atiemo, K., & Appiah, C. (2019). Acceptance and use of mobile banking: an application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118-151.
52. Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), 1210.
53. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134.
54. Pender, N. J. (1975). A conceptual model for preventive health behavior. Nursing outlook, 23(6), 385.
55. Ralston, A. (1976). Encyclopedia of computer science. Van Nostrand Reinhold Co..
56. Raman, A., & Don, Y. (2013). Preservice teachers` acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164.
57. Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85-93.
58. Rogers, E. (1983). M.; Diffusion of Innovations(3rd ed.), Free Press, New York.
59. Roscoe, J. T. (1975). Fundamental research statistics for the behavioral sciences / John T. Roscoe(2nd ed.), Rinehart and Winston, New York : Holt.
60. Rosenstock, I. M. (1974). Historical origins of the health belief model. Health education monographs, 2(4), 328-335.
61. Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health education quarterly, 15(2), 175-183.
62. Rosenwald, M. (2020). History’s Deadliest Pandemics, from Ancient Rome to Modern America. Washington Post–April 7, 2020,.
63. Saunders, G. H., Frederick, M. T., Silverman, S., & Papesh, M. (2013). Application of the health belief model: Development of the hearing beliefs questionnaire (HBQ) and its associations with hearing health behaviors. International Journal of Audiology, 52(8), 558-567.
64. Schumacker, R. E., & Lomax, R. G. (2004). A beginner`s guide to structural equation modeling(2nd ed.), Psychology Press, New York.
65. Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management, 45, 44-55.
66. Sheeran, P., & Abraham, C. (1996). The health belief model. Predicting health behaviour, 2, 29-80.
67. Shi, W., Shambare, N., & Wang, J. (2008). The adoption of internet banking: An institutional theory perspective. Journal of Financial Services Marketing, 12(4), 272-286.
68. Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management science, 42(1), 85-92.
69. Tauber, E. M. (1972). Marketing notes and communications: why do people shop?. Journal of marketing, 36(4), 46-49.
70. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
71. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS quarterly, 125-143.
72. Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on engineering management, (1), 28-45.
73. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation, Lexington, Mass. : Lexington Books.
74. Toth, N., & Schick, K. (2007). Overview of paleolithic archaeology. Handbook of paleoanthropology, 3, 1943-1964.
75. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
76. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
77. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
78. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
79. W. Richard SCOTT (1995), Institutions and Organizations. Ideas, Interests and Identities. M@ n@ gement, 17(2), 136-140.
80. Yang, S. (2013). Understanding undergraduate students` adoption of mobile learning model: A perspective of the extended UTAUT2. Journal of convergence information technology, 8(10), 969.
81. 工研院產業經濟與趨勢究中心. (2015). 穿戴式裝置的發展趨勢與機會. https://ieknet.iek.org.tw/iekppt/ppt_more.aspx?sld_preid=4384
82. 呂珮如. (2019).全球智慧穿戴裝置市場分析. 台灣雲端物聯網產業協會. http://www.twcloud.org.tw/files/file_pool/1/0j211517917521474259/2019.07-%E9%9B%B2%E5%8D%94%E6%A1%88%E4%BE%8B%E5%88%86%E6%9E%90-%E5%85%A8%E7%90%83%E6%99%BA%E6%85%A7%E7%A9%BF%E6%88%B4%E8%A3%9D%E7%BD%AE%E5%B8%82%E5%A0%B4%E5%88%86%E6%9E%90.pdf
83. 拓譜研究. (2019). 穿戴裝置產業2019年回顧與2020年展望. https://www.topology.com.tw/DataContent/report/%E7%A9%BF%E6%88%B4%E8%A3%9D%E7%BD%AE%E7%94%A2%E6%A5%AD2019%E5%B9%B4%E5%9B%9E%E9%A1%A7%E8%88%872020%E5%B9%B4%E5%B1%95%E6%9C%9B/14982
84. 紀佳佑. (2013). 運用整合性科技接受模式探討教師使用互動式電子白板的影響因素-以桃園縣國小為例.（未出版之碩士論文）.萬能科技大學.桃園。
85. 陳弘仁. (2014). 迎向智慧穿戴科技的大未來. 微系統暨奈米科技協會會刊, (31), 16-24.
86. 黃美玲. (2015). 穿戴裝置技術與市場趨勢. 證券服務, (634), 100-102.
87. 熊彼得. (1942). 資本主義，社會主義與民主.左岸文化 。
88. 鄭怡秀. (2017). 利用健康信念模式探討民眾使用智慧型手機健康管理應用程式行為之研究.
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
105363007
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105363007
資料類型 thesis
dc.contributor.advisor 洪叔民zh_TW
dc.contributor.advisor Horng, Shwu-Minen_US
dc.contributor.author (Authors) 劉育誠zh_TW
dc.contributor.author (Authors) Liu, Yu-Chengen_US
dc.creator (作者) 劉育誠zh_TW
dc.creator (作者) Liu, Yu-Chengen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Mar-2021 14:57:37 (UTC+8)-
dc.date.available 2-Mar-2021 14:57:37 (UTC+8)-
dc.date.issued (上傳時間) 2-Mar-2021 14:57:37 (UTC+8)-
dc.identifier (Other Identifiers) G0105363007en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/134205-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 105363007zh_TW
dc.description.abstract (摘要) 本研究主要欲探討現代人在使用健康智慧手環的關鍵使用因素，因此使用了延伸整合型科技接受模型(UTAUT2)、科技-個人-環境模型(TPE)以及健康信念模型(HBM)這三個模型組合成本研究的研究模型。本人之所以要用此種模型是因感到近來使用者在購買產品時其實還會受到許多的個人背景、科技背景、環境背景的影響，故將TPE結合入本研究模型中；使用UTAUT2則是因為UTAUT2相較於UTAUT以及TAM的解釋力更高；而HBM則是因本次希望進行健康智慧手環相關的研究，且該模型在醫學方面已有多年被使用的經驗，諸多研究皆以此模型為基礎進行研究，且同樣有考慮使用者在科技背景、個人背景、環境背景以及同樣也有考慮干擾變數的影響，故與TPE及UTAUT2結合，最終本研究則以TPE結合UTAUT2及HBM作為研究模型。

本研究在設計問卷之前事先訪談了親友四位、一位資管學界專家、一位業界專家，將六位的意見統合起來，並透過TPE模型，結合UTAUT2以及HBM的各項構面後而生成問卷。透過線上問卷的方式共蒐集了505份問卷，最終替除掉無效問卷後以此為統計數字的基底，接著利用偏最小平方結構方程式模型(PLS-SEM)進行統計分析。最終在本研究的結尾則是依據本研究顯著之數據來建議業主應如何使的健康智慧手環的銷量提升。		zh_TW
dc.description.abstract (摘要) This study explores the critical factors while adopting Smart Bracelet. Therefore, this study integral Unified Theory of Acceptance and Use of Technology (UTAUT2), Technological-Personal-Environmental (TPE) Framework and Health Belief Model (HBM) to build the research model. The reason of using this model is that when purchasing products, the users are affected by technological framework, personal framework, and environmental framework. Therefore, this study combines TPE to the research model, using UTAUT2 to obtain a higher explanatory power. Further, as many studies used HBM in medicine for many years, this study combines technological framework, personal framework, environmental framework, and the influence of interference variables. Therefore, HBM can be combined with TPE and UTAUT2. Overall, this research uses TPE combined with UTAUT2 and HBM as the research model.

This study interviewed four relatives, an information academic expert, and an industry expert. The questionnaire is adopted from the interviews, TPE model and various aspects of UTAUT2 and HBM. A total of 505 questionnaires were collected through online questionnaires; the invalid questionnaires were removed and used as the basis of statistics. Afterwards, statistical analysis was carried out using Partial Least Squares Structural Equation Model (PLS-SEM). Finally, this research suggests the way to increase the sales of the smart bracelet based on the significant data.		en_US
dc.description.tableofcontents 目錄
第壹章、 緒論 6
第一節、 研究背景與動機 6
第二節、 研究目的與問題 6
第三節、 研究流程 7
第貳章、 文獻探討 8
第一節、 穿戴裝置 8
一、 穿戴裝置之概述 8
二、 智慧手環與其功能之分類 10
三、 穿戴裝置之相關趨勢 10
第二節、 整合型科技接受模型結合之模型 14
一、 創新擴散理論 15
二、 理性行為理論 16
三、 計畫行為理論 18
四、 社會認知理論 19
五、 個人電腦使用模型 20
六、 科技接受模型及修正科技接受模型 22
七、 動機模型 25
八、 結合科技接受模型及計畫行為理論 26
第三節、 整合型科技接受模型之探討 27
一、 整合型科技接受模型之概念 27
二、 整合型科技接受模型之理論與架構 28
第四節、 延伸整合型科技接受模型之探討 30
一、 延伸整合型科技接受模型之概念 30
二、 延伸整合型科技接受模型的理論與架構 31
三、 延伸整合型科技接受模型(UTAUT2)之相關研究成果 33
第五節、 健康信念模型之探討 36
一、 健康信念模型的起源與發展 36
二、 健康信念模型理論與架構 36
三、 健康信念模型之實證研究 38
第六節、 科技–個人–環境模型之文獻探討 39
一、 科技背景： 39
二、 個人背景： 40
三、 環境背景： 40
第七節、 健康管理 43
一、 健康管理 43
第參章、 研究模型 44
第一節、 研究架構 44
第二節、 研究假設與推論 46
一、 科技背景： 46
二、 個人背景： 48
三、 環境背景： 50
第三節、 研究變數之定義 54
第四節、 問卷設計 55
一、 第一部分：基本資料 56
二、 第二部分：科技背景 57
三、 第三部分：個人背景 58
四、 第四部份：環境背景 59
第五節、 研究對象及資料收集 60
第六節、 研究分析方式 61
一、 敘述統計分析 61
二、 驗證性因素分析( Confirmatory Factor Analysis, CFA ) 61
三、 結構方程模型( Structural Equation Modeling, SEM ) 61
四、 信度與效度分析 62
第肆章、 研究分析 64
第一節、 正式問卷之樣本結構分布情況 64
第二節、 信度、收斂信度與區別效度分析 66
一、 信度： 66
二、 區別效度 67
第三節、 整體結構模式分析與研究假設驗證 68
一、 因素負荷量表 68
二、 構面顯著性 69
三、 干擾變數顯著性 71
第伍章、 結論與建議 73
第一節、 符合假說之相關建議 73
第二節、 研究限制與未來研究 74
一、 擴大樣本數 74
二、 問卷前測 75
第三節、 研究貢獻 75
一、 對學界貢獻 75
二、 對企業界貢獻 75
參考文獻 76

 
圖目錄
圖2.2.2理性行為理論(TRA)(Fishbein & Ajzen, 1975) 17
圖2.2.3計畫行為理論(TPB)(Ajzen, 1991) 19
圖2.2.4.1社會認知理論(Bandura, 1977) 20
圖2.2.4.2社會認知理論架構(Compeau & Higgins, 1995) 20
圖2.2.5個人電腦使用模型(Thompson et al.,1991) 21
圖2.2.6.1科技接受模型(Davis, 1989) 24
圖2.2.6.2 修正科技接受模型(Venkatesh & Davis, 2000) 25
圖2.2.7 動機模型(Davis et al., 1992) 26
圖2.3.1整合型科技接受模型UTAUT 28
圖2.3.2整合型科技接受模型(UTAUT)之架構圖(Venkatesh et al., 2003) 29
圖2.4.2 UTAUT2之架構圖(Venkatesh et al., 2012) 33
圖2.5.2 健康信念模型(Rosenstock, 1974a) 38
圖2.6.3 科技-個人-環境(Jiang et al., 2010) 42
圖3.1.1 以TPE結合UTAUT2以及HBM之模型 45
圖4.3.2.1 模型路徑係數 70

 
表目錄
表2.1.1 過去文獻認為穿戴裝置應有之特色整理 9
表2.1.3.1 全球五大穿戴廠商之出貨量(million)/市占率統計表(IDC, 2020) 12
表2.1.3.2 三大穿戴裝置類別之統計(million) (IDC, 2020) 13
表2.1.3.3 全球三大穿戴產品類型之出貨量(million)/市占及預估(IDC, 2020) 13
表2.2.1 UTAUT所整合的八個模型 14
表2.2.1.1創新擴散理論之核心構面與定義 15
表2.2.2理性行為理論之核心構面與定義 17
表2.2.3計畫行為理論核心構面與定義 18
表2.3.4.1社會認知理論之核心構面與定義(Venkatesh, 2003) 20
表2.2.5個人電腦使用模型之核心構面與定義 21
表2.2.6.1 修正科技接受模型之核心構面與定義(Venkatesh & Davis, 2000) 25
表2.2.7動機模型之核心構面與定義 26
表2.2.8 C-TAM-TPB之核心構面與定義 27
表2.3.2.1整合型科技接受模型之核心構面與定義(Venkatesh et al., 2003) 29
表2.3.2.2 UTAUT構面、理論基礎及干擾變數(Venkatesh et al. 2003) 30
表2.4.2.1 UTAUT2干擾變數整合說明(Venkatesh et al. 2012) 32
表2.4.2.2 UTAUT2之核心構面與定義(Venkatesh et al. 2012) 32
表2.4.3.1 延伸整合型科技接受模型相關之研究整理 34
表2.4.3.2 延伸整合型科技接受模型相關之研究整理(續) 35
表3.2 本研究之變數假說彙總 53
表3.3 本研究之變數定義彙總 54
表3.4.1 基本資料衡量問項表 56
表3.4.2科技背景衡量問項表 57
表3.4.3 個人背景衡量問項表 58
表3.4.4環境背景衡量問項表 59
表4.1.1 Age敘述統計分析表 64
表4.1.2 Experiment敘述統計分析表 65
表4.2.2 區別效度表 67
表4.3.1 因素負荷表 68
表4.3.2 構面顯著性 69
表4.3.3 干擾變數顯著性 72
表5.1.1 構面符合假說表 73		zh_TW
dc.format.extent 4179239 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105363007en_US
dc.subject (關鍵詞) 智慧手環zh_TW
dc.subject (關鍵詞) 健康zh_TW
dc.subject (關鍵詞) 科技-個人-環境zh_TW
dc.subject (關鍵詞) 延伸整合科技接受模型zh_TW
dc.subject (關鍵詞) 健康信念模型zh_TW
dc.subject (關鍵詞) Smart Braceleten_US
dc.subject (關鍵詞) Healthen_US
dc.subject (關鍵詞) TPEen_US
dc.subject (關鍵詞) UTAUT2en_US
dc.subject (關鍵詞) HBMen_US
dc.title (題名) 以TPE結合UTAUT2及HBM評估使用健康智慧手環之關鍵因素zh_TW
dc.title (題名) Critical Factors of Adopting Smart Health Bracelet: The Perspectives of TPE, UTAUT2 and HBMen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Abrahamson, E., & Rosenkopf, L. (1993). Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. Academy of management review, 18(3), 487-517.
2. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
3. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prince Hall, 249-259.
4. Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44.
5. Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & management, 44(8), 681-691.
6. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
7. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
8. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
9. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986, 23-28.
10. Becker, M. H. (1974). The health belief model and personal health behavior. Health education monographs, 2, 324-473.
11. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426.
12. Chau, P. Y., & Hui, K. L. (1998). Identifying early adopters of new IT products: A case of Windows 95. Information & management, 33(5), 225-230.
13. Chen, D., Lin, Z., & Lai, F. (2010). Crossing the chasm-understanding china`s rural digital divide. Journal of Global Information Technology Management, 13(2), 4-36.
14. Colquitt, J. A., Hollenbeck, J. R., Ilgen, D. R., LePine, J. A., & Sheppard, L. (2002). Computer-assisted communication and team decision-making performance: The moderating effect of openness to experience. Journal of Applied Psychology, 87(2), 402.
15. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
16. Darley, W. K., & Smith, R. E. (1995). Gender differences in information processing strategies: An empirical test of the selectivity model in advertising response. Journal of advertising, 24(1), 41-56.
17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
18. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132.
19. Deaux, K. and Kite, M. E. (1987) “Thinking About Gender.” in Analyzing Gender: A Handbook of Social Science Research. B. B. Hess and M. M. Ferree, (eds.) Newbury Park, CA: Sage Publications, 92–117.
20. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American sociological review, 147-160.
21. Emdad, A. F., Badamas, M., & Mouakket, S. (2009). Factors and impacts of low utilization of Internet: The case of Arab countries. Journal of International Technology and Information Management, 18(3), 2.
22. Fadil, P., Smatt, C., Segrest, S. L., & Owen, C. (2009). The moderating effects of technology on career success: can social networks shatter the glass ceiling?. Journal of International Technology and Information Management, 18(3), 8.
23. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research, 5, 177-189.
24. Glanz, K., Rimer, B. K., & Viswanath, K. (Eds.). (2008). Health behavior and health education: theory, research, and practice. John Wiley & Sons.
25. Gorsuch, R. L. (1983). Factor analysis Lawrence Erlbaum Associates. Hillsdale, NJ.
26. Grein, J., Ohmagari, N., Shin, D., Diaz, G., Asperges, E., Castagna, A., ... & Flanigan, T. (2020). Compassionate use of remdesivir for patients with severe Covid-19. New England Journal of Medicine, 382(24), 2327-2336.
27. Harcourt, M., Lam, H., & Harcourt, S. (2005). Discriminatory practices in hiring: Institutional and rational economic perspectives. The International Journal of Human Resource Management, 16(11), 2113-2132.
28. Harrison, J. A., Mullen, P. D., & Green, L. W. (1992). A meta-analysis of studies of the health belief model with adults. Health education research, 7(1), 107-116.
29. Haunschild, P. R., & Miner, A. S. (1997). Modes of interorganizational imitation: The effects of outcome salience and uncertainty. Administrative science quarterly, 472-500.
30. IDC. (2015). Fueled by Growing Demand for Smart Wearables, IDC Forecasts Worldwide Wearable Shipments to Reach 173.4 Million, Retrieved from https://www.businesswire.com/news/home/20150914005126/en/Fueled-by-Growing-Demand-for-Smart-Wearables-IDC-Forecasts-Worldwide-Wearable-Shipments-to-Reach-173.4-Million-by-2019
31. IDC. (2019)Worldwide Wearables Shipments Surge 94.6% in 3Q 2019 Led by Expanding Hearables Market, Says IDC. https://www.idc.com/getdoc.jsp?containerId=prUS45712619
32. IDC. (2020). Shipments of Wearable Devices Reach 118.9 Million Units in the Fourth Quarter and 336.5 Million for 2019, According to IDC. https://www.idc.com/getdoc.jsp?containerId=prUS46122120
33. IDC. (2020). Worldwide Quarterly Wearables Tracker.
34. IDC. (2020). Worldwide Wearables Market to Top 300 Million Units in 2019 and Nearly 500 Million Units in 2023, Says IDC. https://www.idc.com/getdoc.jsp?containerId=prUS45737919
35. Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health education quarterly, 11(1), 1-47.
36. Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & management, 35(4), 237-250.
37. Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—two competing perspectives on automatic use: A theoretical and empirical comparison. Information systems research, 16(4), 418-432.
38. Lee, H. J., Cho, H. J., Xu, W., & Fairhurst, A. (2010). The influence of consumer traits and demographics on intention to use retail self‐service checkouts. Marketing Intelligence & Planning, 28(1), 46-58.
39. Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: extending UTAUT2 with information quality. Sustainability, 11(11), 3141.
40. Lee, Y., & Kozar, K. A. (2008). An empirical investigation of anti-spyware software adoption: A multitheoretical perspective. Information & Management, 45(2), 109-119.
41. Levitt, B., & March, J. G. (1988). Organizational learning. Annual review of sociology, 14(1), 319-338.
42. Lewin, K. (1939). Field theory and experiment in social psychology: Concepts and methods. American journal of sociology, 44(6), 868-896.
43. Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS quarterly, 59-87.
44. Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS quarterly, 59-87.
45. Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology, 140, 44–53.
46. Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
47. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130.
48. Martinez‐Lopez, L., Martinez‐Lopez, F. J., Murray, K. B., Liang, J., & Häubl, G. (2010). ACT 2.0: the next generation of assistive consumer technology research. Internet Research, 20(3), 232-254.
49. Miller, N. E., & Dollard, J. (1941). Social learning and imitation, Retrive from https://psycnet.apa.org/record/1942-00109-000
50. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
51. Owusu Kwateng, K., Osei Atiemo, K., & Appiah, C. (2019). Acceptance and use of mobile banking: an application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118-151.
52. Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), 1210.
53. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134.
54. Pender, N. J. (1975). A conceptual model for preventive health behavior. Nursing outlook, 23(6), 385.
55. Ralston, A. (1976). Encyclopedia of computer science. Van Nostrand Reinhold Co..
56. Raman, A., & Don, Y. (2013). Preservice teachers` acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164.
57. Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85-93.
58. Rogers, E. (1983). M.; Diffusion of Innovations(3rd ed.), Free Press, New York.
59. Roscoe, J. T. (1975). Fundamental research statistics for the behavioral sciences / John T. Roscoe(2nd ed.), Rinehart and Winston, New York : Holt.
60. Rosenstock, I. M. (1974). Historical origins of the health belief model. Health education monographs, 2(4), 328-335.
61. Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health education quarterly, 15(2), 175-183.
62. Rosenwald, M. (2020). History’s Deadliest Pandemics, from Ancient Rome to Modern America. Washington Post–April 7, 2020,.
63. Saunders, G. H., Frederick, M. T., Silverman, S., & Papesh, M. (2013). Application of the health belief model: Development of the hearing beliefs questionnaire (HBQ) and its associations with hearing health behaviors. International Journal of Audiology, 52(8), 558-567.
64. Schumacker, R. E., & Lomax, R. G. (2004). A beginner`s guide to structural equation modeling(2nd ed.), Psychology Press, New York.
65. Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management, 45, 44-55.
66. Sheeran, P., & Abraham, C. (1996). The health belief model. Predicting health behaviour, 2, 29-80.
67. Shi, W., Shambare, N., & Wang, J. (2008). The adoption of internet banking: An institutional theory perspective. Journal of Financial Services Marketing, 12(4), 272-286.
68. Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management science, 42(1), 85-92.
69. Tauber, E. M. (1972). Marketing notes and communications: why do people shop?. Journal of marketing, 36(4), 46-49.
70. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
71. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS quarterly, 125-143.
72. Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on engineering management, (1), 28-45.
73. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation, Lexington, Mass. : Lexington Books.
74. Toth, N., & Schick, K. (2007). Overview of paleolithic archaeology. Handbook of paleoanthropology, 3, 1943-1964.
75. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
76. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
77. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
78. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
79. W. Richard SCOTT (1995), Institutions and Organizations. Ideas, Interests and Identities. M@ n@ gement, 17(2), 136-140.
80. Yang, S. (2013). Understanding undergraduate students` adoption of mobile learning model: A perspective of the extended UTAUT2. Journal of convergence information technology, 8(10), 969.
81. 工研院產業經濟與趨勢究中心. (2015). 穿戴式裝置的發展趨勢與機會. https://ieknet.iek.org.tw/iekppt/ppt_more.aspx?sld_preid=4384
82. 呂珮如. (2019).全球智慧穿戴裝置市場分析. 台灣雲端物聯網產業協會. http://www.twcloud.org.tw/files/file_pool/1/0j211517917521474259/2019.07-%E9%9B%B2%E5%8D%94%E6%A1%88%E4%BE%8B%E5%88%86%E6%9E%90-%E5%85%A8%E7%90%83%E6%99%BA%E6%85%A7%E7%A9%BF%E6%88%B4%E8%A3%9D%E7%BD%AE%E5%B8%82%E5%A0%B4%E5%88%86%E6%9E%90.pdf
83. 拓譜研究. (2019). 穿戴裝置產業2019年回顧與2020年展望. https://www.topology.com.tw/DataContent/report/%E7%A9%BF%E6%88%B4%E8%A3%9D%E7%BD%AE%E7%94%A2%E6%A5%AD2019%E5%B9%B4%E5%9B%9E%E9%A1%A7%E8%88%872020%E5%B9%B4%E5%B1%95%E6%9C%9B/14982
84. 紀佳佑. (2013). 運用整合性科技接受模式探討教師使用互動式電子白板的影響因素-以桃園縣國小為例.（未出版之碩士論文）.萬能科技大學.桃園。
85. 陳弘仁. (2014). 迎向智慧穿戴科技的大未來. 微系統暨奈米科技協會會刊, (31), 16-24.
86. 黃美玲. (2015). 穿戴裝置技術與市場趨勢. 證券服務, (634), 100-102.
87. 熊彼得. (1942). 資本主義，社會主義與民主.左岸文化 。
88. 鄭怡秀. (2017). 利用健康信念模式探討民眾使用智慧型手機健康管理應用程式行為之研究.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202100249en_US