Public safety is improved if individual users are able to make informed choices about what personal information they disclose online.
Privacy calculus is one way of describing the way in which individuals balance the perceived risks and benefits of online transactions (Krasnova et al. 2012; Dinev & Hart 2006). This depends on the way in which risks are described. However there is no consensus on a risk typology (Rosenblum 2007; Swedlow et al. 2009, p.237; Facebook Inc. 2017; Haynes & Robinson 2015). One of the objectives of this research will be to investigate the nature of risk associated with disclosure of personal data online based on monitoring actual user behaviour. The research sets out to address the following research questions:
- Is there a reliable typology for personal risk that can be used to analyse the privacy calculus that users engage in?
- What is the nature of the interactions and risks that users engage in when they use the Internet?
- Can the new risk typology be applied to existing empirical data?
- What effect will the new categorisation of risk have on the privacy calculus?
- Can these figures be used to improve the privacy calculus model in order to better predict online user behaviour?
Dinev, T. & Hart, P., 2006. An Extended Privacy Calculus Model for E-Commerce Transactions. Information Systems Research, 17(1), pp.61–80.
Facebook Inc., 2017. Facebook Privacy Basics. Available at: https://www.facebook.com/about/basics [Accessed April 7, 2017].
Haynes, D. & Robinson, L., 2015. Defining User Risk in Social Networking Services. Aslib Journal of Information Management, 67(1), pp.94–115.
Krasnova, H., Veltri, N.F. & Günther, O., 2012. Self-disclosure and Privacy Calculus on Social Networking Sites: The Role of Culture. Business & Information Systems Engineering, 4(3), pp.127–135.
Rosenblum, D., 2007. What Anyone Can Know: the privacy risks of social networking sites. IEEE Security & Privacy, 5(3), pp.40–49.
Swedlow, B. et al., 2009. Theorizing and Generalizing about Risk Assessment and Regulation through Comparative Nested Analysis of Representative Cases. Law & Policy, 31(2), pp.236–269.