Impact of Recommendation Systems on AI-enabled Customer Experience Mediator Role of Perceived Usefulness and Perceived Trust

Abstract

Artificial intelligence (AI) is revolutionising the way customers interact with brands. AI-based customer experiences lack empirical research. This study aims to analyse how and to what extent integrating AI through recommendation systems in online purchases can lead to better AI-based customer experiences. We propose a theoretical model based on the theory of trust and commitment and the technology acceptance model. We conducted an online survey with customers who have experience with AI-powered online recommendation ads. We analysed 220 responses using PLS-SEM. The results of this research show that perceived trust in recommendation systems and customisation of recommendation systems have a positive impact on AI-based customer experience. Additionally, this study indicates that customisation of recommendation systems and controllability of recommendation systems have a positive impact on perceived usefulness and also have a positive impact on perceived trust. By examining the concept of controllability in AI-driven recommendation systems, an underexplored factor in the literature, this research offers a new perspective on AI-based customer experience. Finally, this research highlights the mediating role of perceived trust in the relationship between controllability and AI-based customer experience, and in the relationship between customisation and AI-based customer experience.