The effects of designing conversational commerce chatbots with expertise cues

Drawing upon the computers-are-social-actors (CASA) paradigm, this study examined the effects of designing conversational commerce chatbots with expertise cues. Accordingly, these cues were operationalized through designation of chatbots as product-specific advisers, dialogues containing expertise-cued labels and social descriptors (e.g., "I am your personal expert adviser for sportswear!"), gender of chatbots, and appearance styles of chatbots. A within-subject laboratory experiment was conducted in which university undergraduates (n=71) viewed two videos displaying mock user-chatbot interactions — one featured the experimental condition (chatbots with expertise cues) while the other featured the control condition (chatbot without expertise cues). Compared against the control, the experimental condition elevated perceived source expertise of chatbots, platform trust ability and trust integrity, and purchase intention through the conversational commerce platform. The expertise cues effects on purchase intention was mediated by platform trust ability. Theoretical and practical implications are discussed through the lens of CASA and source credibility model in this paper.