Continuance Intention in Traffic-Related Social Media: A Privacy Calculus Perspective

Abstract

Abstract Thanks to technological breakthroughs, drivers are enjoying traffic-associated social media to share and update information in place of the traditional ways using TV or radio. In this research, we integrate the IS success model and the privacy calculus model to elaborate on the factors of sustaining user’s continuance intention in information-sharing platforms. We collected data via online surveys from a community-based application that supports real-time traffic information sharing. The findings shed light on how perceived value and privacy concerns impact users’ continuance intention. While a higher level of perceived value enhances users’ continuance intention, privacy concerns do not influence users’ decision in continuing using the traffic app. This research provides initial efforts to investigate the effect of privacy paradox on users' continuance intention in the new context of a specific traffic-related social media platform.