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研究生:黃馨瑩
研究生(外文):HUANG, HSIN-YING
論文名稱:利用利損交易雙因子模型,探討不持續使用Healthcare App的因素關係
論文名稱(外文):A Trade-off Dual-factor Model to Investigate Discontinuous Intention of Healthcare Apps: Perspective of Information Disclosure
指導教授:黃正魁黃正魁引用關係
指導教授(外文):HUANG,CHENG-KUEI
口試委員:陳彥良李永銘黃正魁
口試委員(外文):CHEN,YEN-LIANGLI,YUNG-MINGHUANG,CHENG-KUEI
口試日期:2018-06-28
學位類別:碩士
校院名稱:國立中正大學
系所名稱:企業管理系研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:42
中文關鍵詞:停止使用後期採用行為個人層次健康管理App實證研究
外文關鍵詞:Discontinuance usagePost-adoption behaviorIndividual levelHealthcare appsEmpirical study
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有越來越多的手機應用程式(Applications, App)被開發出來,使我們的生活更加便利並提高我們的生活品質,其中有一種稱為健康管理的App (Healthcare App),能夠透過蒐集使用者身體素質的數據資料,來了解使用者的健康行為,並藉著提供給使用者意見,以改善他們的身體健康。然而我們看見,人們在使用Healthcare App 一段時間之後,會產生一種現象,也就他們可能有停止去繼續使用這類 App 的意圖,這樣的情形是Healthcare App 提供者所不願看見的,因為這代表開發成本的增加以及日後利益的損失。為了探討停止使用的現象,本研究應用資訊系統生命週期理論,且將討論重點放在生命週期的最後一個階段,退出階段來做討論。接著,本研究提出了一個實證性研究,並發展一個利損交易雙因子(Trade-off Dual-factor)的研究模型,來瞭解造成使用者出現停止使用意圖的因素。這個雙因子模型的兩大理論基礎,分別為資訊揭露理論及期望確認理論。使用者可能擔心個人健康隱私被揭露,但又想要享受也滿意Healthcare App,其功能所提供的各樣健康幫助,因此,停止使用或不停止使用這類的App,成為一個利損交易的問題。為了深入探究相關決定因子,本研究透過線上問卷且蒐集了242份有效問卷,成為我們的研究樣本,隨後利用結構方程式(Structural Equation Modeling)於本研究中,進行樣本分析。結果顯示,我們所提出的研究模型,具有31%的解釋變異,同時根據研究發現與意涵,提供給研究者與實務者一些參考。
There are numerous mobile apps which have been developed for making our life more convenient and improving our quality of life. One type of apps is called healthcare apps. This type of apps is designed to help users for recording their health-related behaviors and to give advices about improving users’ physical condition. However, a phenomenon appears that users of healthcare apps may have an intention of quitting to continually use these apps. This indicates that the company of healthcare apps will increase costs and lose benefits for their developed apps. To find out the reason, we investigate the issue of information system adoption to discuss the last level, the retirement stage, in the field of management information systems. Hence, this study proposes an empirical study and develops a trade-off dual-factor model to dissect the reason for why users discontinue to adopt healthcare apps. The research model is based on the perspective of information disclosure and expectation-confirmation theory, respectively. Users may worry about the disclosure of individual healthcare privacy; however, on the other hand, they enjoy and satisfy the function of healthcare apps, proffering various healthcare-related assistances. To stop or not to stop this kind of apps turns into a trade-off issue. For delving into the determinants, we conduct an online survey and collect 242 qualified responses to be as our research samples. Structural equation modeling is employed to analyze these samples in this study. The result reveals that our research model explains 31% of the variance. The findings and implications provide the referral for researchers and practitioners.
致謝 i
Abstract ii
摘要 iii
Table of Contents iv
List of Tables vi
List of Figures vii
1. Introduction 1
2. Literature review 3
2.1. Perspective of information disclosure 3
2.1.1. Perceived privacy risk 4
2.1.2. Information sensitivity 5
2.1.3. Importance of information transparency 5
2.1.4. Regulatory protection 6
2.2. Perceived benefits of information disclosure 7
2.2.1. Expectation-confirmation theory 7
2.2.2. Perceived informativeness 8
2.2.3. Apps system reliability 9
2.3. IS and IT discontinuance 10
3. Research model and hypotheses 11
3.1. Control variable 12
3.2. Hypotheses from perspective of information disclosure (H1- H4) 12
3.3. Hypotheses from perceived benefits of information disclosure (H5- H10) 14
4. Research methodology 18
4.1. Questionnaire development 18
4.1.1. Item development 18
4.1.2. Basic information 19
4.2. Study design and procedure 19
5. Data analysis and results 23
5.1. Examined the measurement model 24
5.2. Examined the structural model 26
5.3. Result 28
6. Conclusions and implications 30
6.1. Theoretical implications 30
6.2. Practical implications 31
6.3. Limitations and future research 32
6.4. Conclusion 33
Appendix 34
Appendix A. Questionnaire items 34
Appendix B. 問卷問項 36
References 38

Aaker, D. A., & Norris, D. (1982). Characteristics of TV commercials perceived as informative. Journal of Advertising Research.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
Applause. (May 5, 2017). Smartphone Owners Use At Least Nine Different Apps Every Day. Retrieved from https://www.applause.com/blog/daily-app-usage-app-annie/
Apple.Com. A bold way to look at your health. Retrieved from https://www.apple.com/ios/health/
Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS quarterly, 13-28.
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS quarterly, 229-254.
Cao, X., & Sun, J. (2018). Exploring the effect of overload on the discontinuous intention of social media users: An SOR perspective. Computers in Human Behavior, 81, 10-18.
Center, P. R. (May 12, 2011). One in five adult internet users have gone online to find others with health concerns similar to their own. Retrieved from http://www.pewinternet.org/2011/05/12/peer-to-peer-healthcare-2/
Center, P. R. (May 26, 2010). Part 2: Concerns about the availability of personal information. Retrieved from www.pewinternet.org/2010/05/26/part-2-concerns-about-the-availability-of-personal-information/
Center, P. R. (September 5, 2013). Part 2: Concerns About Personal Information Online. Retrieved from http://www.pewinternet.org/2013/09/05/part-2-concerns-about-personal-information-online/
Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International journal of medical informatics, 87, 75-83.
Denscombe, M. (2006). Web-based questionnaires and the mode effect: An evaluation based on completion rates and data contents of near-identical questionnaires delivered in different modes. Social Science Computer Review, 24(2), 246-254.
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263-282.
Dinev, T., Bellotto, M., Hart, P., Russo, V., Serra, I., & Colautti, C. (2006). Privacy calculus model in e-commerce–a study of Italy and the United States. European Journal of Information Systems, 15(4), 389-402.
Dinev, T., Xu, H., Smith, J. H., & Hart, P. (2013). Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22(3), 295-316.
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of consumer research, 21(1), 119-134.
Ducoffe, R. H. (1995). How consumers assess the value of advertising. Journal of Current Issues & Research in Advertising, 17(1), 1-18.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
Furneaux, B., & Wade, M. (2010). The end of the information system life: a model of is discontinuance. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 41(2), 45-69.
Furneaux, B., & Wade, M. R. (2011). An exploration of organizational level information systems discontinuance intentions. MIS quarterly, 573-598.
Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.
Google.Com. Step up your fitness. Retrieved from https://www.google.com/fit/
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis . Uppersaddle River. Multivariate Data Analysis (5th ed) Upper Saddle River.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th ed. Uppersaddle River: Pearson Prentice Hall.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 62(1), 5-13.
iMedicalApps.com. (December 20, 2010). Google To Add Mobile Health Applications Category To Android Market. Retrieved from https://mobilemarketingwatch.com/google-to-add-mobile-health-applications-category-to-android-market-11998/
IQVIA. (November 7, 2017). Evidence and Impact on Human Health and the Healthcare System Retrieved from https://www.iqvia.com/institute/reports/the-growing-value-of-digital-health
Kang, Y.-S., & Kim, Y. J. (2006). Do visitors' interest level and perceived quantity of web page content matter in shaping the attitude toward a web site? Decision Support Systems, 42(2), 1187-1202.
Kang, Y. S., Min, J., Kim, J., & Lee, H. (2013). Roles of alternative and self-oriented perspectives in the context of the continued use of social network sites. International Journal of Information Management, 33(3), 496-511.
Kim, Y. J., & Han, J. (2014). Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization. Computers in Human Behavior, 33, 256-269.
Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
Li, H., Sarathy, R., & Xu, H. (2011). The role of affect and cognition on online consumers' decision to disclose personal information to unfamiliar online vendors. Decision Support Systems, 51(3), 434-445.
Li, H., Wu, J., Gao, Y., & Shi, Y. (2016). Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International journal of medical informatics, 88, 8-17.
Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & management, 42(5), 683-693.
Locke, E. (1976). The Nature and Cause of Job Satisfaction in handbook of industrial Organization. Chicago: Land McNally, 130.
Makovsky. (February 24, 2015). Fifth Annual “Pulse of Online Health” Survey Finds 66% of Americans Eager To Leverage Digital Tools To Manage Personal Health. Retrieved from http://www.makovsky.com/news/fifth-annual-pulse-of-online-health-survey-2/
Marcoulides, G. A. (1998). Modern methods for business research: Psychology Press.
Metzger, M. J. (2004). Privacy, trust, and disclosure: Exploring barriers to electronic commerce. Journal of Computer-Mediated Communication, 9(4), JCMC942.
Milne, G. R., & Gordon, M. E. (1993). Direct mail privacy-efficiency trade-offs within an implied social contract framework. Journal of Public Policy & Marketing, 206-215.
Nasco, S. A., & Bruner, G. C. (2008). Comparing consumer responses to advertising and non‐advertising mobile communications. Psychology & Marketing, 25(8), 821-837.
O'Neill, T. (1972). System reliability assessment from its components. Applied Statistics, 297-320.
Oh, L.-B., & Xu, H. (2003). Effects of multimedia on mobile consumer behavior: An empirical study of location-aware advertising. ICIS 2003 Proceedings, 56.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 460-469.
Pavlou, P. A. (2002). Institution-based trust in interorganizational exchange relationships: the role of online B2B marketplaces on trust formation. The Journal of Strategic Information Systems, 11(3-4), 215-243.
Perry, W. E. (1992). Quality concerns in software development: The challenge is consistency. Information Systems Management, 9(3), 48-50.
Phelps, J., Nowak, G., & Ferrell, E. (2000). Privacy concerns and consumer willingness to provide personal information. Journal of Public Policy & Marketing, 19(1), 27-41.
Pizzutti, C., & Fernandes, D. (2010). Effect of recovery efforts on consumer trust and loyalty in e-tail: a contingency model. International journal of electronic commerce, 14(4), 127-160.
Raab, C. D. (1998). The distribution of privacy risks: Who needs protection? The Information Society, 14(4), 263-274.
Recker, J. (2016). Reasoning about discontinuance of information system use. JITTA: Journal of Information Technology Theory and Application, 17(1), 41.
Recker, J. C. (2014). Towards a theory of individual-level discontinuance of information systems use. Paper presented at the Proceedings of the 35th International Conference on Information Systems.
Sharma, S., & Crossler, R. E. (2014). Disclosing too much? Situational factors affecting information disclosure in social commerce environment. Electronic Commerce Research and Applications, 13(5), 305-319.
Sheehan, K. B., & Hoy, M. G. (2000). Dimensions of privacy concern among online consumers. Journal of Public Policy & Marketing, 19(1), 62-73.
Siau, K., & Shen, Z. (2003). Mobile communications and mobile services. International Journal of Mobile Communications, 1(1-2), 3-14.
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: an interdisciplinary review. MIS quarterly, 35(4), 989-1016.
Solove, D. J. (2005). A taxonomy of privacy. U. Pa. L. Rev., 154, 477.
Taylor, G. (2011). The informativeness of on-line advertising. International Journal of Industrial Organization, 29(6), 668-677.
Techcrunch. (May 5, 2017). Report: Smartphone owners are using 9 apps per day, 30 per month. Retrieved from https://techcrunch.com/2017/05/04/report-smartphone-owners-are-using-9-apps-per-day-30-per-month/
Thong, J. Y., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International journal of human-computer studies, 64(9), 799-810.
Turel, O. (2015). Quitting the use of a habituated hedonic information system: a theoretical model and empirical examination of Facebook users. European Journal of Information Systems, 24(4), 431-446.
Vidmar, N., & Flaherty, D. H. (1985). Concern for personal privacy in an electronic age. Journal of Communication, 35(2), 91-103.
Weill, P., & Vitale, M. (1999). Assessing the health of an information systems applications portfolio: An example from process manufacturing. MIS quarterly, 601-624.
Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems, 12(12), 798.
Zahedi, F. (1987). Reliability of information systems based on the critical success factors-formulation. MIS quarterly, 187-203.


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