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.
|Original language||English (US)|
|Title of host publication||International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive|
|Subtitle of host publication||Blending the Local and the Global|
|Publisher||Association for Information Systems|
|State||Published - 2021|
|Event||2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020 - Virtual, Online, India|
Duration: Dec 13 2020 → Dec 16 2020
|Name||International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global|
|Conference||2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020|
|Period||12/13/20 → 12/16/20|
Bibliographical notePublisher Copyright:
© ICIS 2020. All rights reserved.
- Artificial Intelligence
- Debt collection