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DOES BOT GENDER MATTER? THEORY AND EVIDENCE FROM A HIGH-TENSION SERVICE CONTEXT

  • Yiting Guo
  • , De Liu
  • , Sean Xin Xu
  • , Ximing Yin

Research output: Contribution to journalArticlepeer-review

Abstract

Despite the increasing use of AI-powered voicebots, our understanding of how the choice of bot gender may impact service outcomes in high-tension service contexts, such as debt collection, remains limited. To address this gap, we drew on the tensions-based view of customer relationships and gender stereotype theory to hypothesize how and when voicebot gender matters in high-tension service contexts. We tested our hypotheses using a proprietary dataset of debt collection calls made by AI voicebots. We found that female voicebots increase the odds of a positive repayment intention by 28.3%. This gender effect is more pronounced when service encounters begin with higher tension, such as during weekdays or with initially uncooperative customers. We further show that the gender effect can be explained by the advantages of female voicebots in reducing behavioral and emotional tension during service interactions.

Original languageEnglish (US)
Pages (from-to)627-1641
Number of pages1015
JournalMIS Quarterly: Management Information Systems
Volume49
Issue number4
DOIs
StatePublished - Dec 1 2025

Bibliographical note

Publisher Copyright:
© 2025 University of Minnesota. All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 5 - Gender Equality
    SDG 5 Gender Equality

Keywords

  • Artificial intelligence
  • gender stereotype
  • high-tension service contexts
  • human-computer interaction (HCI)
  • tensions-based view
  • voicebot

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