Author(s):

  • Ersin Dincelli
  • Xin Zhou

Abstract:

Wearable devices that allow users to track and monitor physical activities offer various benefits, such as improving self-awareness and promoting positive health-related behaviors. However, wearable devices also pose privacy risks associated with self-disclosure of sensitive personal data. Few empirical studies focus on users’ decision-making process regarding self-disclosure using wearable devices. Additionally, previous research tends to study current users while ignoring prospective and former users. This study proposes a research model to examine the role of privacy calculus and benefit structure on self-disclosure among prospective, current, and former users of wearable devices. In particular, we examine how perceived hedonic and utilitarian benefitsas well as users’ privacy calculus motivate users to disclose, and cease continued use. By examining the interplay between benefit structure and privacy calculus, this study aims to advance our knowledge on the crucial antecedents of self-disclosure on wearable devices.

Document:

https://aisel.aisnet.org/amcis2017/InformationSystems/Presentations/35/

References:
  1. Baumeister, R.F. 1982. “A Self-Presentational View of Social Phenomena,” Psychological Bulletin(91:1).
  2. Chen, R. 2013. “Member Use of Social Networking Sites—an Empirical Examination,” Decision Support Systems(54:3), pp. 1219-1227.
  3. Dhar, R., and Wertenbroch, K. 2000. “Consumer Choice between Hedonic and Utilitarian Goods,” Journal of Marketing Research(37:1), pp. 60-71.
  4. Dinev, T., and Hart, P. 2006. “An Extended Privacy Calculus Model for E-Commerce Transactions,” Information Systems Research(17:1), pp. 61-80.
  5. Gao, Y., Li, H., and Luo, Y. 2015. “An Empirical Study of Wearable Technology Acceptance in Healthcare,” Industrial Management & Data Systems(115:9), pp. 1704-1723.
  6. Gorm, N., and Shklovski, I. 2016. “Sharing Steps in the Workplace: Changing Privacy Concerns over Time,” SIGCHI Conference on Human Factors in Computing Systems (CHI). Santa Clara, CA.
  7. Gu, Z., Wei, J., and Xu, F. 2016. “An Empirical Study on Factors Influencing Consumers’ Initial Trust in Wearable Commerce,” Journal of Computer Information Systems(56:1), pp. 79-85.
  8. Klasnja, P., Consolvo, S., Choudhury, T., Beckwith, R., and Hightower, J. 2009. “Exploring Privacy Concerns About Personal Sensing,” 7th International Conference on Pervasive Computing. Nara, Japan, pp. 176-183.
  9. Krasnova, H., Spiekermann, S., Koroleva, K., and Hildebrand, T. 2010. “Online Social Networks: Why We Disclose,” Journal of Information Technology(25:2), pp. 109-125.
  10. Ledger, D., and McCaffrey, D. 2014. “Inside Wearables: How the Science of Human Behavior Change Offers the Secret to Long-Term Engagement,” Endeavour Partners(200:93), pp. 1-17.
  11. Lipman, V. 2014. “71% of 16-to-24-Year-Olds Want ‘Wearable Tech.’ Why Don’t I Even Wantto Wear a Watch?” Retrieved February 25, 2017, from https://goo.gl/L042TCLowry, P.B., Cao, J., and Everard, A. 2011. “Privacy Concerns Versus Desire for Interpersonal Awareness in Driving the Use of Self-Disclosure Technologies: The Case of Instant Messaging in Two Cultures,” Journal of Management Information Systems(27:4), pp. 163-200.
  12. Omarzu, J. 2000. “A Disclosure Decision Model: Determining How and When Individuals Will Self Disclose,” Personality and Social PsychologyReview(4:2), pp. 174-185.
  13. Patel, M.S., Asch, D.A., and Volpp, K.G. 2015. “Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change,” Journal of the American Medical Association(313:5), pp. 459-460.
  14. Petronio, S. 2002. Boundaries of Privacy. Albany, NY: State University of New York Press.
  15. Raij, A., Ghosh, A., Kumar, S., and Srivastava, M. 2011. “Privacy Risks Emerging from the Adoption of Innocuous Wearable Sensors in the Mobile Environment,” in: SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, pp. 11-20.
  16. Sun, Y., Wang, N., Shen, X.-L., and Zhang, J.X. 2015. “Location Information Disclosure in Location-Based Social Network Services: Privacy Calculus, Benefit Structure, and Gender Differences,” Computers in Human Behavior(52), pp. 278-292.
  17. Tang, P.C., Ash, J.S., Bates, D.W., Overhage, J.M., and Sands, D.Z. 2006. “Personal Health Records: Definitions, Benefits, and Strategies for Overcoming Barriers to Adoption,” Journal of the American Medical Informatics Association(13:2), pp. 121-126.
  18. Venkatesh, V., Thong, J.Y., and Xu, X. 2012. “Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology,” MIS Quarterly(36:1).
  19. Wieneke, A., Lehrer, C., Zeder, R., and Jung, R. 2016. “Privacy-Related Decision-Making in the Context of Wearable Use,” 20th Pacific Asia Conference on Information Systems (PACIS).Chiayi, Taiwan.
  20. Zhang, J., Dibia, V., Sodnomov, A., and Lowry, P.B. 2015. “Understanding the Disclosure of Private HealthcareInformation within Online Quantified Self 2.0 Platforms,” 19th Pacific Asia Conference on Information Systems (PACIS). Singapore.
  21. Zhao, L., Lu, Y., and Gupta, S. 2012. “Disclosure Intention of Location-Related Information in Location-Based Social Network Services,” International Journal of Electronic Commerce(16:4), pp. 53-90