Exploring the fit between mobile application service and application privacy

Abstract

Purpose With the popularity of mobile applications and increasing consumer awareness of application privacy, this paper aims to introduce a new construct of service-privacy fit (i.e. the perceived degree of match between the service of a mobile application and a privacy permission request) to predict consumers’ mobile application adoption. Design/methodology/approach Four experiments were carried out to test the hypothesized relationships. The first study investigated the direct impact of service-privacy fit on application adoption and the mediators of benefit expectancy and privacy concerns. The second, third and fourth studies further applied the moderated mediation model to analyze the moderating roles of benefit message type, regulatory focus type and privacy assurance. Findings The results show that service-privacy fit influences application adoption not only directly but also indirectly via the mediators of benefit expectancy and privacy concerns. Furthermore, the findings confirm the moderators of benefit message type, regulatory focus type and privacy assurance. Originality/value Drawn from the perspectives of task-technology fit and information boundary theory, this paper introduces a new construct of service-privacy fit as a determinant of application adoption. Grounded in privacy calculus theory, it further explains this relationship through mediating effects of benefit expectancy and privacy concerns. Furthermore, this paper proposes that benefit messages and privacy assurance are effective coping strategies to increase the benefit expectancy and reduce the privacy concerns of applications. Based on the perspective of regulatory fit theory, this study further shows that the effects of coping strategies rely on personal traits. The findings enrich the existing knowledge of mobile application adoption and application privacy, suggesting that practitioners should consider mobile consumers’ perception of service-privacy fit when developing applications.