The effect of privacy concerns, risk, control, and trust on individuals’ decisions to share personal information: A game theory-based approach

Abstract

The rapid developments and innovations in technology have created unlimited opportunities for private and public organizations to collect, store and analyze the large and complex information about users and their online activities. Data mining, data publishing, and sharing sensitive data with third parties help organizations improve the quality of their products and services and raise significant individuals’ privacy concerns. Privacy of personal information remains subject to considerable controversy. The problem is that big data analytics methods allow user’s data to be unlawfully generated, stored, and processed by leaving users with little to no control over their personal information. This quantitative correlational study measures the effect of privacy concerns, risk, control, and trust on individuals’ decisions to share personal information in the context of big data analysis. The key research question aimed to examine the relationship among the variables of perceived privacy concerns, perceived privacy risk, perceived privacy control, and trust. Drawing on Game Theory, the study explores all the game players’ actions, strategies, and payoffs. Correlation analysis was used to test these variables based on the research model with 418 internet users of e-services in the United States. The overall correlation analysis showed that the variables were significantly related. Recommendations for future studies are to explore e-commerce, e-government, and social networking separately, and data should be collected in different regions where many factors can affect the privacy concerns of the individuals.