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Artificial Intelligence Chatbots Versus Human Agents in Customer Satisfaction: The Role of Warmth and Competence

Ye Zhang et al.

Journal of Interactive Marketing2025https://doi.org/10.1177/10949968251366265article
AJG 3ABDC A
Weight
0.41

Abstract

This article investigates the impact of artificial intelligence (AI) chatbots and human agents on customer satisfaction by applying the Stereotype Content Model, focusing on warmth and competence as parallel mediators. It further examines how relational and transactional exchanges moderate these effects. In addition, the authors explore customer retention to understand how these factors influence long-term customer behavior. They used two datasets: 887 chat transcripts from a retail investment firm (Study 1) and 989 participants from a controlled Prolific experiment (Study 2). The authors used Linguistic Inquiry and Word Count for linguistic analysis and conducted content analysis to categorize chat types. Results indicate that AI chatbots outperform in competency-driven, transactional communications, whereas human agents excel at fostering trust through warmth in relational exchanges, enhancing satisfaction and retention. By integrating text-based process measures with self-reported satisfaction ratings and a behavioral retention measure, this study offers valuable insights into how different service interactions impact both customer satisfaction and long-term loyalty.

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https://doi.org/https://doi.org/10.1177/10949968251366265

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@article{ye2025,
  title        = {{Artificial Intelligence Chatbots Versus Human Agents in Customer Satisfaction: The Role of Warmth and Competence}},
  author       = {Ye Zhang et al.},
  journal      = {Journal of Interactive Marketing},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/10949968251366265},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.