Chatbot commerce - How contextual factors affect Chatbot effectiveness

The emergence of Chatbots has attracted many firms to sell their merchandise via chats and bots. Although Chatbots have received tremendous interest, little is understood about how different usage contexts affect Chatbots’ effectiveness in mobile commerce. Due to differences in their nature, not all shopping contexts are suitable for Chatbots. To address this research gap, this study examines how contextual factors (i.e., intrinsic task complexity that embraces shopping task attributes and group shopping environment, and extrinsic task complexity that entails information intensity) affect user perceptions and adoption intentions of Chatbots as recommendation agents in mobile commerce. Applying the lenses of Cognitive Load Theory (CLT) and Common Ground Theory (CGT), we perform an experiment and apply quantitative analytical approaches. The results show that Chatbots are more suitable in the context of one-attribute, information-light, and group-buying tasks, whereas traditional Apps are suitable for multi-attribute, information-intensive, and single-buying scenarios. These findings make important theoretical contributions to the IT adoption literature as well as to CLT and CGT theory by contextualizing the evolving state of Chatbot commerce and providing guidelines for designing better Chatbot user experiences, thereby enhancing user perceptions and adoption intentions.