Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies
Open accessAbstract
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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Our recent work suggests that the perceived identity of a chatbot has significant effects on the persuasion outcome and interpersonal perceptions.
“Our recent work [52] suggests that as AI chatbots are quickly adopting human conversational capacities, the perceived identity of a chatbot has significant effects on the persuasion outcome and interpersonal perceptions.”
If a chatbot is presented with a human identity and tries to imitate human inquiries by asking personal questions, the uncanny valley effect can be elicited and make people feel uncomfortable.
“For instance, if a chatbot is presented with a human identity and tries to imitate human inquiries by asking personal questions, the UVE can be elicited and make people feel uncomfortable [52].”
Our study findings suggest that users respond better if the chatbot's identity is clearly presented.
“Furthermore, our study findings suggest that users respond better if the chatbot’s identity is clearly presented.”
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Understanding User Resistance Strategies in Persuasive Conversations
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- Authors
- Shi, W., Wang, X., Oh, Y. J., Zhang, J., Sahay, S., & Yu, Z.
- Affiliations
- University of California, Davis (US)
- Carnegie Mellon University (US)
- Intel (United States) (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-13
- OpenAlex
- W3000144571
- Cited by
- 93
- References
- 50
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 17 · 2023: 17 · 2022: 19 · 2021: 10 · 2020: 4
- FWCI
- 4.77
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- inquiry strategies, persuasion, charitable giving, interpersonal perception, perceived competence, perceived sincerity, human-chatbot interaction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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