Abstract
South Korea endured early outbreaks and flattened the coronavirus curve without paralyzing economic systems. The critical factor that leads to the policy’s success is contact tracing using personal information. However, at the same time, the extensive use of personal information has raised social problems related to privacy loss. Even in devastating pandemics, balancing personal privacy and public safety remains a crucial issue. Thus, this study attempted to gain a deeper understanding of privacy disclosure for restaurant customers. We applied privacy calculus theory and risk-risk trade-off concepts to explain the relationship between two conflicting risks. i.e., privacy risk and health risk. We found that “risk substitutions” provide implications for how customers’ privacy perceptions change with the level of health risk and the importance of perceived benefit. Finally, we verified that institutional privacy protection directly influences disclosure intention. This study has implications for theory and practice.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are analyzed from the authors’ survey, and the data for all analyses is available upon request.
Notes
Since the late 1990s, concerns have been raised regarding the “dossier effect” that collecting a large number of innocuous data points could easily be de-anonymized and create a combined dataset with a startling amount of personal.
References
Aarts, H., Paulussen, T., & Schaalma, H. (1997). Physical exercise habit: On the conceptualization and formation of habitual health behaviours. Health Education Research, 12(3), 363–374.
Abramova, O., Wagner, A., Olt, C. M., & Buxmann, P. (2022). One for all, all for one: Social considerations in user acceptance of contact tracing apps using longitudinal evidence from Germany and Switzerland. International Journal of Information Management, 64, 102473.
Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514.
Ahn, N. Y., Park, J. E., Lee, D. H., & Hong, P. C. (2020). Balancing personal privacy and public safety during COVID-19: The case of South Korea. Ieee Access, 8, 171325–171333.
Aikin, K. J., Betts, K. R., Ziemer, K. S., & Keisler, A. (2019). Consumer tradeoff of advertising claim versus efficacy information in direct-to-consumer prescription drug ads. Research in Social & Administrative Pharmacy, 15(12), 1484–1488.
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, 30(1), 13–28.
Bansal, G., Zahedi, F. M., & Gefen, D. (2016). Do context and personality matter? Trust and privacy concerns in disclosing private information online. Information & Management, 53(1), 1–21.
Bauer, R. A. (1960). Consumer behavior as risk taking. In R. L. Hancock (Ed.), Dynamic Marketing for a Changing World. American Marketing Association.
Bhatt, P., Vemprala, N., Valecha, R., Hariharan, G., & Rao, H. R. (2022). User privacy, surveillance and public health during COVID-19–an examination of Twitterverse. Information Systems Frontiers, 1–16.
Brizek, M. G., Frash, R. E., McLeod, B. M., & Patience, M. O. (2021). Independent restaurant operator perspectives in the wake of the COVID-19 pandemic. International Journal of Hospitality Management, 93, 102766.
Brough, A. R., & Martin, K. D. (2021). Consumer privacy during (and after) the COVID-19 pandemic. Journal of Public Policy & Marketing, 40(1), 108–110.
Bulgurcu, B., Cavusoglu, H., & Benbasat, I. (2010). Information security policy compliance: An empirical study of rationality-based beliefs and information security awareness. MIS Quarterly, 34(3), 523–548.
Carlsson Hauff, J., & Nilsson, J. (2021). Individual costs and societal benefits: The privacy calculus of contact-tracing apps. Journal of Consumer Marketing.
Chan, E. Y., & Saqib, N. U. (2021). Privacy concerns can explain unwillingness to download and use contact tracing apps when COVID-19 concerns are high. Comput Human Behav, 119, 106718.
Chen, L., Zarifis, A., & Kroenung, J. (2017). The role of trust in personal information disclosure on health-related websites. Proceedings of the European Conference on Information Systems (ECIS), 1, 777–786.
Chin, W. W. (2010). Bootstrap cross-validation indices for PLS path model assessment. Handbook of partial least squares. Springer Handbooks of Computational Statistics.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.
Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104–115.
Culnan, M. J., & Bies, R. J. (2003). Consumer privacy: Balancing economic and justice considerations. Journal of Social Issues, 59(2), 323–342.
de Groot, J. I. M., Schweiger, E., & Schubert, I. (2020). Social influence, risk and benefit perceptions, and the acceptability of risky energy technologies: An explanatory model of nuclear power versus shale gas. Risk Analysis, 40(6), 1226–1243.
De Zwart, O., Veldhuijzen, I. K., Elam, G., Aro, A. R., Abraham, T., Bishop, G. D., Voeten, H. A., Richardus, J. H., & Brug, J. (2009). Perceived threat, risk perception, and efficacy beliefs related to SARS and other (emerging) infectious diseases: Results of an international survey. International Journal of Behavioral Medicine, 16(1), 30–40.
Dinev, T., & Hart, P. (2006). An extended privacy Calculus model for E-commerce transactions. Information Systems Research, 17(1), 61–80.
Dinev, T., Albano, V., Xu, H., D’Atri, A., & Hart, P. (2016). Individuals’ attitudes towards electronic health records: A privacy Calculus perspective. In A. Gupta, V. Patel, & R. Greenes (Eds.), Advances in healthcare informatics and analytics. Annals of information systems, 19. Springer.
Dzandu, M. D. (2023). Antecedent, behaviour, and consequence (abc) of deploying the contact tracing app in response to COVID-19: Evidence from Europe. Technological Forecasting and Social Change, 187, 122217.
Evermann, J., & Tate, M. (2012). Comparing the predictive ability of PLS and covariance analysis. Proceedings of the 33rd international conference on information systems (Orlando, FL).
Fahey, R. A., & Hino, A. (2020). COVID-19, digital privacy, and the social limits on data-focused public health responses. International Journal of Information Management, 55, 102181.
Fernandes, T., & Pereira, N. (2021). Revisiting the privacy calculus: Why are consumers (really) willing to disclose personal data online? Telematics and Informatics, 65, 101717.
Fox, G., van der Werff, L., Rosati, P., Takako Endo, P., & Lynn, T. (2022). Examining the determinants of acceptance and use of mobile contact tracing applications in Brazil: An extended privacy calculus perspective. Journal of the Association for Information Science and Technology, 73(7), 944–967.
Freeston, M. H., Rhéaume, J., Letarte, H., Dugas, M. J., & Ladouceur, R. (1994). Why do people worry? Personality and Individual Differences, 17(6), 791–802.
Gasser, U., Ienca, M., Scheibner, J., Sleigh, J., & Vayena, E. (2020). Digital tools against COVID-19: Taxonomy, ethical challenges, and navigation aid. The Lancet Digital Health, 2(8), e425–e434.
Goldberg, I., Wagner, D., & Brewer, E. (1997). Privacy-enhancing technologies for the internet. Proceedings IEEE COMPCON 97. Digest of Papers, 103–109.
Graham, J. D., Wiener, J. B., & Sunstein, C. R. (1995). Risk vs. risk: Tradeoffs in protecting health and the environment. Harvard university press.
Hair, J. F., Jr., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.
Han, Q., Lin, Q., Jin, S., & You, L. (2020). Coronavirus 2019-nCoV: A brief perspective from the front line. Journal of Infection, 80(4), 373–377.
Hansen, S. F., & Tickner, J. A. (2008). Putting risk-risk tradeoffs in perspective: A response to Graham and Wiener. Journal of Risk Research, 11(4), 475–483.
Hartley, K., & Jarvis, D. S. (2020). Policymaking in a low-trust state: Legitimacy, state capacity, and responses to COVID-19 in Hong Kong. Policy and Society, 39(3), 403–423.
Hassandoust, F., Akhlaghpour, S., & Johnston, A. C. (2021). Individuals’ privacy concerns and adoption of contact tracing mobile applications in a pandemic: A situational privacy calculus perspective. Journal of the American Medical Informatics Association, 28(3), 463–471.
Homans, G. C. (1961). The humanities and the social sciences. American Behavioral Scientist, 4(8), 3–6.
Hong, W., Chan, F. K. Y., & Thong, J. Y. L. (2019). Drivers and inhibitors of internet privacy concern: A multidimensional development theory perspective. Journal of Business Ethics, 168(3), 539–564.
Horberg, E. J., Oveis, C., Keltner, D., & Cohen, A. B. (2009). Disgust and the moralization of purity. Journal of Personality and Social Psychology, 97(6), 963–976.
Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. In M. Venkatesan (Ed.), Proceedings of the third annual conference of the Association for Consumer Research (pp. 382–393). Association for Consumer Research.
Jung, G., Lee, H., Kim, A., & Lee, U. (2020). Too much information: Assessing privacy risks of contact trace data disclosure on people with COVID-19 in South Korea. Frontiers in Public Health, 8, 305.
Kang, J.-W., & Namkung, Y. (2019). The role of personalization on continuance intention in food service mobile apps: A privacy calculus perspective. International Journal of Contemporary Hospitality Management., 31(2), 734–752.
Kim, M. J., & Denyer, S., 2020. A ‘travel log’of the times in South Korea: Mapping the movements of coronavirus carriers. The Washington Post. March 13 Retrieved from: https://www.washingtonpost.com/world/asia_pacific/coronavirus-south-korea-tracking-apps/2020/03/13/2bed568e-5fac-11ea-ac50-18701e14e06d_story.html
Kim, D., Park, K., Park, Y., & Ahn, J.-H. (2019). Willingness to provide personal information: Perspective of privacy calculus in IoT services. Computers in Human Behavior, 92, 273–281.
Kim, J., Kim, J., & Wang, Y. (2021). Uncertainty risks and strategic reaction of restaurant firms amid COVID-19: Evidence from China. International Journal of Hospitality Management, 92, 102752.
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.
Leppin, A., & Aro, A. R. (2009). Risk perceptions related to SARS and avian influenza: Theoretical foundations of current empirical research. International Journal of Behavioral Medicine, 16(1), 7–29.
Li, K., Cheng, L., & Teng, C. I. (2020). Voluntary sharing and mandatory provision: Private information disclosure on social networking sites. Information Processing & Management, 57(1), 102128.
Li, V. Q., Ma, L., & Wu, X. (2022). COVID-19, policy change, and post-pandemic data governance: A case analysis of contact tracing applications in East Asia. Policy and Society, 41(1), 01–14.
Luce, M. F., Bettman, J. R., & Payne, J. W. (2001). Emotional decisions: Tradeoff difficulty and coping in consumer choice. Monographs of the Journal of Consumer Research, 1, 1–209.
Ma, X., Qin, Y., Chen, Z., & Cho, H. (2021). Perceived ephemerality, privacy calculus, and the privacy settings of an ephemeral social media site. Computers in Human Behavior, 124, 106928.
Magnan, R. E., Gibson, L. P., & Bryan, A. D. (2021). Cognitive and affective risk beliefs and their association with protective health behavior in response to the novel health threat of COVID-19. Journal of Behavioral Medicine, 44(3), 285–295.
Maiman, L. A., & Becker, M. H. (1974). The health belief model: Origins and correlates in psychological theory. Health Education Monographs, 2(4), 336–353.
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet Users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355.
Margherita, A., Elia, G., & Klein, M. (2021). Managing the COVID-19 emergency: A coordination framework to enhance response practices and actions. Technological Forecasting and Social Change, 166, 120656.
Meinert, D. B., Peterson, D. K., Criswell, J. R., & Crossland, M. D. (2006). Privacy policy statements and consumer willingness to provide personal information. Journal of Electronic Commerce in Organizations (JECO), 4(1), 1–17.
Metzger, M. J. (2006). Effects of site, vendor, and consumer characteristics on web site trust and disclosure. Communication Research, 33(3), 155–179.
Morosan, C., & DeFranco, A. (2015). Disclosing personal information via hotel apps: A privacy calculus perspective. International Journal of Hospitality Management, 47, 120–130.
Nasser, A. A. N., & Nasser, A. A. N. (2020). Impacts of Trust in Government and Privacy Risk Concern on willingness to provide personal information in Saudi Arabia. International Journal of Management Science and Business Administration, 6(2), 7–18.
Park, S., & Tussyadiah, I. P. (2017). Multidimensional facets of perceived risk in mobile travel booking. Journal of Travel Research, 56(7), 854–867.
Parker, M. J., Fraser, C., Abeler-Dörner, L., & Bonsall, D. (2020). Ethics of instantaneous contact tracing using mobile phone apps in the control of the COVID-19 pandemic. Journal of Medical Ethics, 46(7), 427–431.
Prasetyo, Y. T., Castillo, A. M., Salonga, L. J., Sia, J. A., & Seneta, J. A. (2020). Factors affecting perceived effectiveness of COVID-19 prevention measures among Filipinos during enhanced community quarantine in Luzon, Philippines: Integrating protection motivation theory and extended theory of planned behavior. International Journal of Infectious Diseases, 99, 312–323.
Ribeiro-Navarrete, S., Saura, J. R., & Palacios-Marqués, D. (2021). Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy. Technological Forecasting and Social Change, 167, 120681.
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change1. The Journal of Psychology, 91(1), 93–114.
Roselius, T. (1971). Consumer rankings of risk reduction methods. Journal of Marketing, 35(1), 56–61.
Rust, R. T., Kannan, P., & Peng, N. (2002). The customer economics of internet privacy. Journal of the Academy of Marketing Science, 30(4), 455–464.
Shimshack, J. P., & Ward, M. B. (2010). Mercury advisories and household health trade-offs. Journal of Health Economics, 29(5), 674–685.
Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of business Research, 69(10), 4552–4564.
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989–1015.
Solove, D. J. (2007). I've got nothing to hide and other misunderstandings of privacy. San Diego Law Review, 44, 745.
Song, H. J., Yeon, J., & Lee, S. (2021). Impact of the COVID-19 pandemic: Evidence from the US restaurant industry. International Journal of Hospitality Management, 92, 102702.
Sreelakshmi, C. C., & Prathap, S. K. (2020). Continuance adoption of mobile-based payments in Covid-19 context: An integrated framework of health belief model and expectation confirmation model. International Journal of Pervasive Computing and Communications., 16, 351–369.
Tran, C. D., & Nguyen, T. T. (2021). Health vs. privacy? The risk-risk tradeoff in using COVID-19 contact-tracing apps. Technology in Society, 67, 101755.
Trepte, S., Scharkow, M., & Dienlin, T. (2020). The privacy calculus contextualized: The influence of affordances. Computers in Human Behavior, 104, 106115.
Viscusi, W. K., Magat, W. A., & Huber, J. (1991). Pricing environmental health risks: Survey assessments of risk-risk and risk-dollar trade-offs for chronic bronchitis. Journal of Environmental Economics and Management, 21(1), 32–51.
Weible, C. M., Nohrstedt, D., Cairney, P., Carter, D. P., Crow, D. A., Durnová, A. P., Heikkila, T., Ingold, K., McConnell, A., & Stone, D. (2020). COVID-19 and the policy sciences: Initial reactions and perspectives. Policy Sciences, 53(2), 225–241.
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), 1.
Yeong-Tsyr Wang, K., Wen-Hui, T., Chuang, T.-Y., & Lee, H.-J. (2021). Rethinking four social issues of the COVID-19 pandemic from social work perspectives. Asia Pacific Journal of Social Work and Development, 31(1–2), 45–51.
You, J. (2020). Lessons from South Korea’s Covid-19 policy response. The American Review of Public Administration, 50(6–7), 801–808.
Zhu, M., Wu, C., Huang, S., Zheng, K., Young, S. D., Yan, X., & Yuan, Q. (2021). Privacy paradox in mHealth applications: An integrated elaboration likelihood model incorporating privacy calculus and privacy fatigue. Telematics and Informatics, 61, 101601.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicting Interests
The Author(s) declare(s) that there is no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Table 5.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lee, E., Yoo, C.W., Goo, J. et al. Is Contact Tracing for Pandemic Relief or Privacy Menace?: a Lens of Dual-Calculus Decision. Inf Syst Front (2023). https://doi.org/10.1007/s10796-023-10420-7
Accepted:
Published:
DOI: https://doi.org/10.1007/s10796-023-10420-7