@inproceedings{4faf9fb5e58b452cb69346caa7d2360c,
title = "⇜she is not just a computer⇝: Gender role of AI chatbots in debt collection",
abstract = "Chatbots have been empowered by Artificial Intelligence (AI) and rapidly applied to many industries. There is a call for more understanding of the effect of chatbots' social cues on business outcomes. This paper investigates how does the choice of chatbots' voice gender impacts customers' intention to repay overdue debt. Prior studies on gender differences have conflicting implications. Employing unique real business dataset, we find that for male customers, they are more willing to repay when served by female chatbots. However, female customers have no preference for the gender of chatbots. We finally explain the effect of chatbot gender in ten gender-stereotypical attributes (e.g., forceful and assertive of masculinity, gentle and warm of femininity). The results demonstrate that masculine attributes have significant negative effects on both male and female customers while feminine attributes only have significant (positive) effects on male customers. Based on the results, we further discuss the theoretical contributions and managerial implications.",
keywords = "Artificial Intelligence, Chatbot, Debt collection, Gender",
author = "Yiting Guo and De Liu and Ximing Yin and Xu, {Sean Xin}",
note = "Publisher Copyright: {\textcopyright} ICIS 2020. All rights reserved.; 2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020 ; Conference date: 13-12-2020 Through 16-12-2020",
year = "2021",
language = "English",
series = "International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global",
publisher = "Association for Information Systems",
booktitle = "International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive",
address = "United States",
}