Inducing consumers' self-disclosure through the fit between Chatbot's interaction styles and regulatory focus

With rising concerns among consumers over the sharing of personal information in the digital era, it is increasingly difficult to collect abundant and timely consumer data for modern online marketing practices. This paper suggests that well-designed AI agents (e.g. chatbots) can play a vital role in online marketing strategy by enhancing consumers' willingness to self-disclosure personal information. More specifically, based on the stereotype content model (SCM) and regulatory focus theory, we examined how the interaction effect between the chatbot interaction style and individual regulatory focus influences consumers' willingness to self-disclosure. The results from four studies (N = 1075) indicate that chatbots with warm interaction styles lead to a stronger willingness to self-disclosure for promotion-focused consumers than those who are prevention-focused, while chatbots with a competent interaction style lead to a stronger willingness to self-disclosure for prevention focused consumers than those who are promotion-focused. Further, consumers' trust in chatbots acts as a psychological mechanism in the disclosure induction process. This paper contributes to AI agent-related research by providing insights into how to enhance the positive effect of consumer-chatbot interaction and by offering suggestions for future research.