Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies
January 13, 2020 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Weiyan Shi, Xuewei Wang, Yoo Jung Oh, Jingwen Zhang, Saurav Sahay, Zhou Yu
arXiv ID
2001.04564
Category
cs.HC: Human-Computer Interaction
Citations
99
Venue
International Conference on Human Factors in Computing Systems
Last Checked
3 months ago
Abstract
Intelligent conversational agents, or chatbots, can take on various identities and are increasingly engaging in more human-centered conversations with persuasive goals. However, little is known about how identities and inquiry strategies influence the conversation's effectiveness. We conducted an online study involving 790 participants to be persuaded by a chatbot for charity donation. We designed a two by four factorial experiment (two chatbot identities and four inquiry strategies) where participants were randomly assigned to different conditions. Findings showed that the perceived identity of the chatbot had significant effects on the persuasion outcome (i.e., donation) and interpersonal perceptions (i.e., competence, confidence, warmth, and sincerity). Further, we identified interaction effects among perceived identities and inquiry strategies. We discuss the findings for theoretical and practical implications for developing ethical and effective persuasive chatbots. Our published data, codes, and analyses serve as the first step towards building competent ethical persuasive chatbots.
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