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Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies

Published: 23 April 2020 Publication History

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.

Supplementary Material

ZIP File (paper714aux.zip)
post-survey.docx: the post-survey material in the study: supplementary.pdf: the supplementary material containing the experimental conditions, the chat interface, examples sentences for the persuasive appeals and the interaction effect illustrations
MP4 File (a714-shi-presentation.mp4)

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cover image ACM Conferences
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
April 2020
10688 pages
ISBN:9781450367080
DOI:10.1145/3313831
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 April 2020

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  1. behavior change
  2. crowdsourced
  3. empirical study that tells us about people
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  • (2024)Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported DataProceedings of the ACM on Human-Computer Interaction10.1145/36373648:CSCW1(1-35)Online publication date: 26-Apr-2024
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