Isaac Owusu Asante
Atlab Md Hossin
Artificial intelligence (AI) is reshaping the online shopping experience. However, there is limited information on consumers’ interaction with AI elements embedded in electronic commerce (e-commerce) platforms and the behavioral outcomes of such interactions. AI application studies have focused on consumers’ reluctance to use AI-powered services due to failed machine-human conversations. On the contrary, this study exploits the bright side of AI applications in e-commerce. It applies the stimuli-organism-response (S-O-R) paradigm to examine the effects of AI elements on consumer engagement attitudes, beyond purchase intentions, towards e-commerce platforms. Specifically, it examined the impact of chatbot efficiency, image search functionality, recommendation system efficiency, and automated after-sales service on consumer engagement. Furthermore, the study examined the moderating role of consumers’ attention to the social comparison of consumption choices on the relationships between the AI capability elements and consumer engagement. The partial least square-structural equation modeling (PLS-SEM) approach was employed in analyzing 464 responses collected via an online survey from consumers of different e-commerce platforms. The findings indicate that AI capability elements, directly and indirectly, attract consumers’ observable engagement behaviors. Also, attention to social comparison dampens the positive effects of chatbot efficiency and automated after-sales service on behavioral engagement. In contrast, it positively moderates the impact of recommendation system efficiency. The study contributes to academia by introducing consumers’ attention to social comparison to advance the understanding of consumer engagement with AI applications in e-commerce. Practitioners can gain insight into improving consumer experience on e-commerce platforms.