AI-Driven customer engagement in Tokopedia: Satisfaction mediation on chatbot and recommendation system based on UTAUT

Abstract

Artificial intelligence (AI) technologies, including chatbots and recommendation engines, are reshaping customer interaction in digital marketplaces, yet their ability to directly enhance engagement remains uncertain. This research examines the impact of Tokopedia’s AI-driven features, including the TANYA chatbot and automated product recommendations, on customer engagement, with satisfaction serving as an intermediary construct. Employing a quantitative explanatory design, data from 204 Tokopedia users in Jabodetabek were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results reveal that chatbot interaction and recommendation systems do not directly strengthen engagement, but both significantly improve satisfaction, which in turn enhances engagement. Satisfaction also mediates the effects of the chatbot and recommendation system on engagement. The findings confirm that AI influences engagement indirectly through user satisfaction, extending the UTAUT model by emphasizing satisfaction as a crucial psychological link in AI-driven e-commerce behavior