在這次 COVID-19 大流行中，世界各地的許多政府都推出了手機聯繫追踪應用程序，以預防、減少和控制 COVID-19 的傳播。泰國政府推出「Mor-chana」應用程序，該應用程序旨在對新冠患者的密切接觸者進行跟踪。然而，如果公眾不願意使用這些技術，它們就毫無用處。在現有使用接觸者追踪應用意圖的研究中，雖然有少數研究創建了初步的理論框架，大多數先前的研究都是以總結敘述性統計分析為主，缺乏一個全面及完整的理論架構。因此，本研究希望通過理論觀點來檢驗是什麼讓人們願意手機聯繫追踪應用程序的因子。本研究通過借鑒先前文獻和先前確定的前因中的理論，開發了一個情境化的用戶接受模型。本研究通過對泰國用戶的調查收集資料來測試此模型。最後，使用偏最小二乘結構方程建模方法 (PLS-SEM) 對從泰國的 353 名泰國公民獲得的數據進行實證測試。研究結果支持了大部分了假設，並凸顯了以感知風險和感知收益相關的以人為本、以技術為中心和社會背景因素的重要性。此外，感知風險和收益會影響使用意向。然而，人口統計變數和感知的績效風險並未影響作為先前研究目的的使用意圖。本研究提供的見解可以幫助政府、政策制定者和應用程序開發人員減輕現有的影響並製定更好的策略來提高采用率，從而幫助我們的社會阻止下一種傳染病的傳播。

In this COVID-19 pandemic, many governments worldwide are implementing mobile phone contact tracing apps to prevent, reduce, and control the spread of COVID. Aiming to track and minimize the spread of COVID-19 in their country, The Thai government launched ‘Mor-chana’ An application designed to anonymously detect if users had contact with COVID-19 positive individuals. However, these contact tracing technologies are ineffective if the public is not willing to use them. There has been research conducted that has examined the intentions to use contact tracing applications. Researchers have created a theoretical framework and most of the prior research has utilized quick surveys. What separates this research paper from previous work is that this study’s objective is to examine what factors influence people’s willingness to use contact tracing applications through theoretical frameworks. Moreover, we collected results from prior literature and added a new factor. This was achieved by developing a contextualized acceptability model by drawing on theories from prior literature and antecedents identified earlier. This model was then tested on Thai users in Thailand. Finally, a partial least square structural equation modeling approach (PLS-SEM) was used on data obtained from 353 Thai citizens in Thailand for empirically testing. The result supports many hypotheses and highlights the importance of human-centered, technology-centered, implementation context, and social-context factors associated with privacy calculus, which contain perceived risk and perceived benefit. Moreover, it was found that perceived risk and benefit lead to intention to use the covid-19 tracking application. However, the demographic and perceived performance risks have been found to not affect the intention of use, as prior research has suggested. The insights contributed by this study can help government, policymakers, and application developers to implement better strategies to increase the adoption rate to ultimately, help society stop the spread of the next contagious disease.

Altmann, S., Milsom, L., Zillessen, H., Blasone, R., Gerdon, F., Bach, R., . . . Abeler, J. (2020, May 4). Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study. JMIR mHealth and uHealth, 8(8), e19857.
Anderson, J., & Gerbing, D. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
Awad, N., & Krishnan, M. (2006). The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Q.,, 30(1), 13-28.
Bansal, G., F.M. Zahedi, & D. Gefen. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decis. Support. Syst., 49(2), 138-150.
Chan, S. (2020). COVID-19 contact tracing apps reach 9% adoption in most populous countries. Retrieved from https://web.archive.org/web/20201126160643/: https://sensortower.com/blog/contact-tracing-app-adoption
Chowdhury, M., Ferdous, M., Biswas, K., Chowdhury, N., & Muthukkumarasamy, V. (2020, June 19). COVID-19 Contact Tracing: Challenges and Future Directions. Preprints Preprint posted online.
Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6).
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
Culnan, J., M., & Bies., R. J. (2003). Consumer privacy: Balancing economic and justice considerations. Journal of social, 59(2), 323-342.
Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-Commerce transactions. Information Systems Research, 17(1), 61-80.
Dinev, T., Xu, H., Smith, H., & Hart, P. (2013). Information privacy and correlates : An empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22, pp. 295-316.
E. Karahanna, D.W. Straub, & N.L. Chervany. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2).
Eastlick, M. A., Lotz, S. L., Warrington, & P. (2006). Understanding online B-to-C relationships: An integrated model of privacy concerns, trust, and commitment. Journal of business research, 59(8), 877-886.
Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., . . . Fraser, C. (2020). Quantifying SARS–CoV-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491).
Garrett , P. M., White, J. P., Lewandowsky, S., Kashima, Y., Perfors, A., Little, D. R., . . . Dennis , S. (2021). The acceptability and uptake of smartphone tracking for COVID-19 in Australia. Plos one, 16(1), e0244827.
Garrett PM, Wang Y, White JP, Hsieh S, Strong C, Lee Y-C, . . . Yang C-T. (2021). Young Adults View Smartphone Tracking Technologies for COVID-19 as Acceptable: The Case of Taiwan. International Journal of Environmental Research and Public Health, 18(3), 1332.
Georgieva, I., Beaunoyer, E., & Guitton, M. J. (2021). Ensuring social acceptability of technological tracking in the COVID-19 context. Computers in Human Behavior, 116, 106639.
Gostin, L., JD, & LLD. (2006). Public health strategies for pandemic influenza: ethics and the law. JAMA, 295(14), 1700-1704.
Hair, J. F., J., F., Risher, J. J., Sarstedt, Ringle, & C.M. (2019). When to use and how to report the results of PLS-SEM. . European business review.
Henseler, J., Dijkstra, T., Sarstedt, M., Ringl, C., Diamantopoulos, A., Straub, D., & Calantone, R. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods, 17(2), 182-209.
Huh, Y.U., Keller, F.R., Redman, T., & Watkins. (1990). A.R. Data quality. Information and Software Technology, 32(8), 559-565.
Idrees, M., S., Nowostawski, M., Jameel, & R. (2021). Blockchain-based digital contact tracing apps for COVID-19 pandemic management: Issues, challenges, solutions, and future directions. JMIR medical informatics, 9(2), e25245.
Jamieson, Daniel, R., & Kathleen, H. (2020). Conspiracy theories as barriers to controlling the spread of COVID-19 in the US. Social Science & Medicine, 263, 113356.
Jason, B., Joel, K., Alvin, T., Chai, S. H., Lai, Y., Janice, T., & Tang, A. Q. (2020). BlueTrace: A privacy-preserving protocol for community-driven contact tracing across borders. Government Technology Agency-Singapore.
Joel Hellewell. (2020, April). Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Glob Health, 8(4), 488-496.
Jonker, M., Bekker-Grob, E. d., Veldwijk, J., Goossens, L., Bour, S., & Rutten-Van Mölken, M. (2020). COVID-19 Contact Tracing Apps: Predicted Uptake in the Netherlands Based on a Discrete Choice Experiment. JMIR mHealth and uHealth,, 8(10), e20741.
Joshua, M. E., Jon, P., Derek, C., & Ross, A. H. (2008). Coupled contagion dynamics of fear and disease: mathematical and computational explorations. PLoS One, 3(12), e3955.
Kahnbach, Leonie, Dirk Lehr, Brandenburger, J., Mallwitz, T., Jent, S., . . . Janneck., M. (2021). Quality and Adoption of COVID-19 Tracing Apps and Recommendations for Development: Systematic Interdisciplinary Review of European Apps. Journal of Medical Internet Research, 23(6), e27989.
Kaplan, B., & Litewka, S. (2008). Ethical challenges of telemedicine and telehealth. Cambridge Quarterly of Healthcare Ethics, 17(4), 401-416.
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
King, W.R., & Epstein. (1983). B.J. Assessing information system value: An experimental sludy. Decision Sciences, 14(1), 34-45.
Koohikamali, M., Peak, D. A., & Prybutok, V. R. (2017). Beyond self-disclosure: Disclosure of information about others in social network sites. Computers in Human Behavior, 69, 29-42.
Krasnova, H., Spiekermann, S., Koroleva, K., & Hildebrand, T. (2010). Online Social Networks: Why We Disclose. Journal of information technology, 25(2), 109-125.
Kwon, Bum, C., Courtland, V., Stephanie, E. ,., Hyung, W. ,., Paul, E., . . . Jina, H.-Y. (2020). Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App. JMIR mHealth and uHealth, 8(12), e21733.
Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020, October ). Applications of machine learning and artificial intelligence for COVID-19(SARS-CoV-2) pandemic. a review, 139(20), 110059.
Laufer, R. S., & Wolfe, M. (1977). Privacy as a Concept and a Social Issue: A Multidimensional Developmental Theory. Journal of social Issues, 33(3), 22-42.
Lazarus, H., & T. McManus. (2006). Transparency Guru: an interview with Tom McManus. J. Manage. Dev., 25(10), 923-936.
Leins, K., Culnane, C., & Rubinstein, B. I. (2020). Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 with technological interventions. Medical Journal of Australia, 213(1), 6-8.
Lewandowsky , S., Dennis, S., Perfors, A., Kashima, Y., White, J. P., Garrett, P., . . . Yesilada, M. (2021). Public acceptance of privacy-encroaching policies to address the COVID-19 pandemic in the United Kingdom. PLoS ONE, 16, e0245740.
Li, Y. (2011). Empirical studies on online information privacy concerns: literature review and an integrative framework. Commun. Assoc. Inf. Syst., 28, 453-496.
Lockey, S., Edwards, M. R., Hornsey, M. J., Gillespie, N., Akhlaghpour, S., & Colville, S. (2021). Profiling adopters (and non-adopters) of a contact tracing mobile application: Insights from Australia. International Journal of Medical Informatics, 149, 104414.
Machida, M., Nakamura, I., Saito, R., Nakaya, T., Hanibuchi, T., Takamiya, T., . . . Inoue, S. (2021). Survey on usage and concerns of a COVID-19 contact tracing application in Japan. Public Health in Practice, 2, 100125.
Meier, Y., Meinert, J., Krämer, & N. (2021). Investigating factors that affect the adoption of Covid-19 contact-tracing apps. A privacy calculus perspective.
Miller, J., & Doyle. (1987). B.A. Measuring the effectiveness of computer-based information systems in the financial services sector. MIS Quarterly, 11(1), 107-124.
Mohsen Jozani, Emmanuel Ayaburi, Myung Ko, & Kim-Kwang Raymond Choo. (2020). Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective. Computers in Human Behavior.
Montjoye, Y.-A. d., Ramadorai, T., Valletti, T., & Walther, A. (2021). Privacy, adoption, and truthful reporting: a simple theory of contact tracing applications. Economics Letters, 198, 109676.
N.K. Malhotra, S.S. Kim, & J. Agarwal. (2004). Internet users' information privacy concerns (IUIPC): the construct, the scale, and a causal model. Inf. Syst. Res., 15(4), 336-355.
Nuzzo, A., T., O., C., Raskar, R., DeSimone, . . . R. (2020, September). Universal Shelter-in-Place Versus Advanced Automated Contact Tracing and Targeted Isolation: A Case for 21st-Century Technologies for SARS-CoV-2 and Future Pandemics. In Mayo Clinic Proceedings, 95(9), 1898-1905.
Peng, C., & Kim, Y. (2014). Application of the stimuli-organism-response (SOR) framework to online shopping behavior. Journal of Internet Commerce, 13(3-4), 159-176.
Pichayada, P. (2021, Feb 8). Data privacy concerns over Thailand's COVID-19 contact tracing app amid new wave of cases. Retrieved from channelnewsasia: https://www.channelnewsasia.com/news/asia/transparency-thailand-covid19-contact-tracing-app-mor-chana-14096014
Salathé, M., Althaus , C. L., & Neher, R. (2020, March). COVID-19 epidemic in Switzerland: on the importance of testing, contact tracing and isolation. Swiss Med Wkly, 150, 1-3.
Sarah, D., Claudia, R. S., John, K., Alexandra, L. ,., Gabriel, R., Anne, M. V., . . . Sander, v. d. (2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 1-30.
Saw, Y. E., Tan, E.-Q., Liu , J. S., & Liu, J. C. (2021). Predicting public uptake of digital contact tracing during the covid-19 pandemic: results from a nationwide survey in Singapore. Journal of medical Internet research, 23(2), e24730.
Sharma, N., Basu, S., & Sharma, P. (2021). Sociodemographic determinants of the adoption of a contact tracing application during the COVID-19 epidemic in Delhi, India. Health Policy and Technology, 100496.
Sharma, S., Singh, G., Sharma, R., Jones, P., Kraus, S., & Dwivedi, Y. K. (2020). Digital Health Innovation: Exploring Adoption of COVID-19 Digital Contact Tracing Apps. IEEE Transactions on Engineering Management.
Shaw, N. (2002). Capturing the technological dimensions of IT infrastructure change: A model and empirical evidence. ournal oftheAIS,, 2(8), 1-33.
Sheeran, P., & Sheina, O. (1999). Augmenting the theory of planned behavior: roles for anticipated regret and descriptive norms. Journal of Applied Social Psychology, 29(10).
Slyke, C. V., Shim , J., Johnson, R. D., & Jiang , J. J. (2006). Concern for Information Privacy and Online Consumer Purchasing. Journal of the Association for Information Systems, 7(6), 16.
Steve, J., & Lawarée, J. (2021). The adoption of contact tracing applications of COVID-19 by European governments. Policy response to COVID-19, 4(1), 44-58.
Stiglbauer, B., Weber, S., & Batinic, B. (2019). Does your health really benefit from using a self-tracking device? Evidence from a longitudinal randomized control trial. Computers in Human Behavior, 94, 131-139.
Stone, E., Gueutal, H., Gardner, D., & McClure, S. (1983). A field experiment comparing information-privacy values, beliefs, and attitudes across several types of organizations. J. Appl. Psychol, 68(3), 459-468.
Tassanai, K. (2020, May 11). A new world for data privacy . Retrieved from Norton Rose Fulbright: https://www.nortonrosefulbright.com/-/media/files/nrf/nrfweb/contact-tracing/thailand-contact-tracing.pdf?revision=8e2d0a90-8ff1-4e18-9fc5-ab8a3840f145&la=en-th
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization. Journal of Management Information Systems, 11(1).
Tianshi, L., Cobb, C., Baviskar, S., Agarwal, Y., Li, B., Bauer, L., & Hong, J. I. (2021). What Makes People Install a COVID-19 Contact-Tracing App? Understanding the Influence of App Design and Individual Difference on Contact-Tracing App Adoption Intention. Pervasive and Mobile Computing, 101439.
Urs Gasser, Marcello Ienca, James Scheibner, Joanna Sleigh, & Effy Vayena. (2020). Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid. The Lancet Digital Health, 2(8), e425-e434.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3).
Waldo, J., Lin, H., & Millett, L. (2007). Engaging Privacy and Information Technology in a Digital Age. National Academies Press, Washington,.
Wasko, M. M., & Faraj, S. (2000). It is what one does: why people participate and help others in electronic communities of practice. The journal of strategic information systems, 9(2-3), 155-173.
White, J. P., Lewandowsky, S., Kashima, Y., Perfors, A., Little, D. R., Geard, N., . . . Dennis, S. (2021). The acceptability and uptake of smartphone tracking for COVID-19 in Australia. PLOS ONE, 16(1).
Xia, Y., & Lee, G. (2020). How to return to normalcy: fast and comprehensive contact tracing of COVID-19 through proximity sensing using mobile devices. arXiv preprint , arXiv:2004.12576.
Xu, H., Teo, H.-H., Tan, B. C., & Agarwal, R. (2009). The Role of Push-Pull Technology in Privacy Calculus: The Case of Location-Based Services. Journal of Management Information Systems, 26(3), 135-174.
Xu, H., X. Luo, J.M. Carroll, & M.B. Rosson. (2011). The personalization privacy paradox: an exploratory study of decision making process for location-aware marketing. Decis. Support. Syst., 51(1), 42-52.
Xu, Heng, Hock-Hai, T., Bernard, C. T., & Ritu, A. (2009). The role of push-pull technology in privacy calculus: the case of location-based services. Journal of management information systems , 26(3), 135-174.
Yasaka, T. M., Lehrich, B. M., & Sahyouni, R. (2020, April). Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App. JMIR mHealth and uHealth, 8(4), e18936.
Yuen-C, T. (2021, January 5). Parliament: Covid-19 Over 4.2 million, or 78% of residents, using TraceTogether. Retrieved from The straitstimes: https://www.straitstimes.com/singapore/politics/over-42-million-or-78-of-residents-using-tracetogether
Zhao, L., Lu, Y., & Gupta, S. (2012). Disclosure intention of location-related information in location-based social network services. . International Journal of Electronic Commerce, 16(4), 53-90.
Zhou, T. (2011). Examining the Critical Success Factors of Mobile Website Adoption. Online Information Review, 35(4), 636–652.