Unpacking Passive Innovation Resistance and Openness to Data Sharing in Pay-as-You-Drive Tariff Acceptance

Drawing on innovation resistance theory, the present study investigates the influence of cognitive and situational passive innovation resistance on the acceptance of data-driven tariff models in the car rental sector (i.e. pay-as-you-drive). A structural equation modeling (SEM) approach was used to analyze survey data from Germany (N = 599) and the United States (N = 585). The findings indicate that passive innovation resistance has a detrimental effect on openness to data sharing and increases risk perceptions, which in turn influence attitudes towards tariffs and behavioral intention. The findings of this study offer valuable implications for practitioners seeking to increase acceptance of personalized and behavior-based tariff offerings. The implications of this study relate to the importance of tackling psychological barriers and improving data transparency in order to enhance user acceptance. The present study makes a valuable contribution to extant literature by combining innovation resistance theory with privacy and acceptance research in a novel empirical context.