Understanding the mechanism behind consumers’ purchase intentions toward AI recommendations: The trade-off between privacy and incentives

AI algorithms use personal data to deliver explicit and implicit incentive recommendations, widely employed by organizations to boost sales. However, it remains unclear which type of incentive consumers prefer and how the formation of purchase intentions is affected when they become aware that their personal data is used to generate these recommendations. Drawing on Perceived Value Theory and Social Exchange Theory, this study finds that consumers consistently prefer explicit incentives, which primarily influence purchase intentions through perceived functional value, whereas implicit incentives exert their effect through a balanced combination of perceived value mechanisms. Notably, under high privacy transparency, implicit incentives more strongly influence purchase intentions by boosting functional value and subsequently engaging a relatively balanced dual pathway, offering insights into the dynamic cost–benefit trade-offs in human-computer interactions.