Discovering Chatbot's Self-Disclosure's Impact on User Trust, Affinity, and Recommendation Effectiveness
Year: 2021
Type: preprint
Study design: Clinical Trial
Abstract: In recent years, chatbots have been empowered to engage in social conversations with humans and have the potential to elicit people to disclose their personal experiences, opinions, and emotions. However, how and to what extent people respond to chabots' self-disclosure remain less known. In this work, we designed a social chatbot with three self-disclosure levels that conducted small talks and provided relevant recommendations to people. 372 MTurk participants were randomized to one of the four groups with different self-disclosure levels to converse with the chatbot on two topics, movies, and COVID-19. We found that people's self-disclosure level was strongly reciprocal to a chatbot's self-disclosure level. Chatbots' self-disclosure also positively impacted engagement and users' perception of the bot and led to a more effective recommendation such that participants enjoyed and agreed more with the recommendations.
Source: arXiv (Cornell University)
Authors: Kai-Hui Liang, Weiyan Shi, Yoojung Oh, Jingwen Zhang, Zhou Yu
Institutions: Columbia University, University of California, Davis
Language: English
Topic: AI in Service Interactions
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3171135336