Effects of cognitive absorption on continuous use intention of AI-driven recommender systems in e-commerce

Purpose: The applications of artificial intelligence (AI), natural language processing and machine learning in e-commerce are growing. Recommender systems (RSs) are interaction-based technologies based on AI that can offer recommendations for products for use or of interest to a potential consumer. Curiosity, focused immersion and temporal dissociation are often treated as the dimensions of cognitive absorption, so exploring them separately can provide valuable insights into their dynamics. The paper aims to determine the effect of the cognitive absorption dimensions namely focused immersion, temporal dissociation and curiosity independently on RSs continuous use intention. Design/methodology/approach: A quantitative research design was used to explore the effect of dimensions of cognitive absorption on AI-driven RSs continuous use intention in e-commerce. Data were gathered from 452 active users of Amazon through an online cross-sectional survey and were analysed using partial least squares structural equation modelling. Findings: The findings indicated that curiosity and focused immersion directly affect RSs continuous use intention, but temporal dissociation does not affect RSs continuous use intention. Originality/value: The current research focused on Amazon’s RSs that use AI and machine learning techniques. The research aimed to empirically explore the effects of the dimensions of cognitive absorption separately on AI-driven RSs continuous use intention in e-commerce. This research may be of interest to executives working in both public and private industries to better harness the potential of recommendations driven by AI to maximize RSs’ reuse and to enhance customer loyalty