Will I or Will I Not? Explaining the Willingness to Disclose Personal Self-Tracking Data to a Health Insurance Company

Users of digital self-tracking devices increasingly benefit from multiple services related to their selftracking data. Vice versa, new digital as well as “offline” service providers, such as health insurance companies, depend on the users’ willingness to disclose personal data to be able to offer new services. Whereas previous research mostly investigated the willingness to disclose data in the context of social media, e-commerce and smartphone apps, the aim of our research is to analyze the influence of the privacy calculus of personal risks and benefits on the willingness to disclose highly personal and confidential self-tracking data to health insurance companies. To do so, we develop a conceptual model based on the privacy calculus concept and validate it with a sample of 103 respondents in a scenario-based experiment using structural equation modeling. Our results reveal that privacy risks always have a negative impact on the willingness to disclose personal data, while positive effects of privacy benefits are partly depending on the data sensitivity.

[1]  Tamara Dinev,et al.  An Extended Privacy Calculus Model for E-Commerce Transactions , 2006, Inf. Syst. Res..

[2]  Henner Gimpel,et al.  Quantifying the Quantified Self: A Study on the Motivations of Patients to Track Their Own Health , 2013, ICIS.

[3]  Stephen L. Vargo,et al.  Service Innovation in the Digital Age: Key Contributions and Future Directions , 2015, MIS Q..

[4]  Daniel R. Horne,et al.  The Privacy Paradox: Personal Information Disclosure Intentions versus Behaviors , 2007 .

[5]  Alain Yee-Loong Chong,et al.  An updated and expanded assessment of PLS-SEM in information systems research , 2017, Ind. Manag. Data Syst..

[6]  Ritu Agarwal,et al.  The Digitization of Healthcare: Boundary Risks, Emotion, and Consumer Willingness to Disclose Personal Health Information , 2011, Inf. Syst. Res..

[7]  R. Likert.  “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[8]  Amanda L. Forest,et al.  When Social Networking Is Not Working , 2012, Psychological science.

[9]  A. Parasuraman,et al.  SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. , 1988 .

[10]  Tom L. Roberts,et al.  Proposing the online community self-disclosure model: the case of working professionals in France and the U.K. who use online communities , 2010, Eur. J. Inf. Syst..

[11]  Hock-Hai Teo,et al.  The Role of Push-Pull Technology in Privacy Calculus: The Case of Location-Based Services , 2009, J. Manag. Inf. Syst..

[12]  Naresh K. Malhotra,et al.  Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model , 2004, Inf. Syst. Res..

[13]  Carol M. Mangione,et al.  Does the concordance between medical records and patient self-report vary with patient characteristics? , 2006, Health Services and Outcomes Research Methodology.

[14]  Hsin-Yi Huang,et al.  Examining the beneficial effects of individual's self-disclosure on the social network site , 2016, Comput. Hum. Behav..

[15]  S. Davies.  Re-engineering the right to privacy: how privacy has been transformed from a right to a commodity , 1997 .

[16]  Melanie Swan,et al.  Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 , 2012, J. Sens. Actuator Networks.

[17]  Steven A. Taylor,et al.  Measuring Service Quality: A Reexamination and Extension , 1992 .

[18]  Paul A. Pavlou,et al.  Building Effective Online Marketplaces with Institution-Based Trust , 2004, Inf. Syst. Res..

[19]  Deborah Lupton,et al.  Self-tracking cultures: towards a sociology of personal informatics , 2014, OZCHI.

[20]  David Gefen,et al.  The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online , 2010, Decis. Support Syst..

[21]  Selma C. Etter.  Database Nation the Death of Privacy in the 21st Century , 2000, Journal of Computing in Higher Education.

[22]  Kai Lung Hui,et al.  Overcoming Online Information Privacy Concerns: An Information-Processing Theory Approach , 2007, J. Manag. Inf. Syst..

[23]  Kai Lung Hui,et al.  Disclosure : Motivators and Measurements , 2006 .

[24]  T. White,et al.  Consumer Disclosure and Disclosure Avoidance: A Motivational Framework , 2004 .

[25]  Hyang-Sook Kim,et al.  What drives you to check in on Facebook? Motivations, privacy concerns, and mobile phone involvement for location-based information sharing , 2016, Comput. Hum. Behav..

[26]  Paul Benjamin Lowry,et al.  Information Disclosure on Mobile Devices: Re-Examining Privacy Calculus with Actual User Behavior , 2013, Int. J. Hum. Comput. Stud..

[27]  Straub,et al.  Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research , 2011 .

[28]  R. Laufer,et al.  Privacy as a Concept and a Social Issue: A Multidimensional Developmental Theory , 1977 .

[29]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[30]  D. Straub,et al.  Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly , 2012 .

[31]  Varun Grover,et al.  Investigating online information disclosure: Effects of information relevance, trust and risk , 2010, Inf. Manag..

[32]  M. Culnan,et al.  Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation , 1999 .

[33]  J. Phelps,et al.  Privacy Concerns and Consumer Willingness to Provide Personal Information , 2000 .

[34]  Deborah Lupton,et al.  Self-Tracking Modes: Reflexive Self-Monitoring and Data Practices , 2014 .

[35]  Kai Lung Hui,et al.  Inducing Customers to Disclose Personal Information to Internet Businesses with Social Adjustment Benefits , 2004, ICIS.

[36]  Cornelia Frings.  Das Messinstrument faktorieller Survey , 2010 .

[37]  Ramnath K. Chellappa,et al.  Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma , 2005, Inf. Technol. Manag..

[38]  Vincenzo Russo,et al.  Internet Users' Privacy Concerns and Beliefs About Government Surveillance: An Exploratory Study of Differences Between Italy and the United States , 2006, J. Glob. Inf. Manag..

[39]  J. P. Peter,et al.  A Comparative Analysis of Three Consumer Decision Strategies , 1975 .

[40]  M. Carlson,et al.  Panopticon.com: Online Surveillance and the Commodification of Privacy , 2002 .

[41]  Marko Sarstedt,et al.  Testing measurement invariance of composites using partial least squares , 2016 .

[42]  Rui Chen,et al.  Living a private life in public social networks: An exploration of member self-disclosure , 2013, Decis. Support Syst..

[43]  Heng Xu,et al.  Information Privacy Concerns: Linking Individual Perceptions with Institutional Privacy Assurances , 2011, J. Assoc. Inf. Syst..

[44]  Tom Green,et al.  Social anxiety, attributes of online communication and self-disclosure across private and public Facebook communication , 2016, Comput. Hum. Behav..

[45]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[46]  R. Bies,et al.  Consumer Privacy: Balancing Economic and Justice Considerations , 2003 .

[47]  Detmar W. Straub,et al.  Validating Instruments in MIS Research , 1989, MIS Q..

[48]  Alessandro Acquisti,et al.  Silent Listeners: The Evolution of Privacy and Disclosure on Facebook , 2013, J. Priv. Confidentiality.

[49]  S. Petronio.  Communication Boundary Management: A Theoretical Model of Managing Disclosure of Private Information Between Marital Couples , 1991 .

[50]  Heng Xu,et al.  Information Privacy Research: An Interdisciplinary Review , 2011, MIS Q..

[51]  Nils Urbach,et al.  Structural Equation Modeling in Information Systems Research Using Partial Least Squares , 2010 .

[52]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[53]  Torsten Eymann,et al.  The Unconscious App Consumer: Discovering and Comparing the Information-seeking Patterns among Mobile Application Consumers , 2014, ECIS.

[54]  Max E. Valentinuzzi,et al.  Understanding the human machine , 2004 .