Optimizing AI Social Chatbots for Relational Outcomes: The Effects of Profile Design, Communication Strategies, and Message Framing
Alvin Zhou, Wan‐Hsiu Sunny Tsai, Linjuan Rita Men
Abstract
With more corporations incorporating artificial intelligence (AI) tools like social chatbots into their public communication practices, we explore optimal chatbot designs for organization-public relational outcomes. Our 2 × 2 × 2 factorial web experiment with eight custom-designed chatbots showed that communication strategies (verbal and non-verbal social cues such as emoji, memes, filler words, and response delay) had a significant impact (Cohen’s d = 0.536, standardized coefficient β = 0.438) on chatbot social conversation, the central antecedent to the investigated relational outcomes such as trust in business. Furthermore, the effect of chatbot social conversation is partially mediated by perceived organizational listening, highlighting the importance of listening and its related theories and practices in automated business communication.