Factors driving intention to use deep-learning chatbots for online shopping in Vietnam: the mediation role of attitude and trust

Abstract

This research offers a fresh perspective on deep-learning chatbot adoption, highlighting the crucial roles of trust and user perception in shaping attitudes and decisions. To explore deeper into this issue, the SOR model and an expanded TAM model, incorporating elements such as perceived enjoyment, perceived social presence and trust, has been proposed to explore the adoption of deep-learning chatbots for online shopping in an emerging market like Vietnam. This study follows a quantitative approach through Google Forms with 300 online retail customers in Vietnam. Data were analyzed through structural equation modeling. The results suggest that perceived social enjoyment and perceived social presence positively impact customers’ intention to use deep-learning chatbots both directly and indirectly through trust and attitude. However, attitude has no mediating effect on perceived social presence - intention to use relationship. This research highlights the importance of trust and enjoying the experience in influencing how people feel about and decide to use deep-learning chatbots. Managers are advised to prioritize strategies fostering transparency, reliability, and enjoyable interactions, recognizing the potential of these chatbots in enhancing user experiences and driving successful purchasing, especially in an emerging market like Vietnam