政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/134205
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109948/140897 (78%)
Visitors : 46095902      Online Users : 865
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大典藏 > College of Commerce > MBA Program > Theses >  Item 140.119/134205


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/134205


    Title: 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
    Authors: 劉育誠
    Liu, Yu-Cheng
    Contributors: 洪叔民
    Horng, Shwu-Min
    劉育誠
    Liu, Yu-Cheng
    Keywords: 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
    Date: 2020
    Issue Date: 2021-03-02 14:57:37 (UTC+8)
    Abstract: 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.
    Reference: 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.
    Description: 碩士
    國立政治大學
    企業管理研究所(MBA學位學程)
    105363007
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105363007
    Data Type: thesis
    DOI: 10.6814/NCCU202100249
    Appears in Collections:[MBA Program] Theses

    Files in This Item:

    File Description SizeFormat
    300701.pdf4081KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback