Anthropomorphic generative AI chatbots for enhancing customer engagement, experience and recommendation
Abstract
Purpose This research focuses on developing and testing a conceptual model that explores customer behavioural responses (engagement, experience and recommendation) towards generative artificial intelligence (AI)-enabled chatbots. It highlights the significant influence of anthropomorphic characteristics in enhancing perceptions of competence and warmth, further enhancing perceived authenticity. In addition, this study aims to investigate how the need for social interactions moderates these relationships. Design/methodology/approach This study used a self-administered questionnaire distributed on Prolific Academic to gather data from 282 eligible participants worldwide. This study uses a structural equation modelling approach to answer the research questions. Findings The findings reveal that anthropomorphic characteristics of generative AI-enabled chatbots are positively associated with perceived competence. Moreover, the findings show that the perceived competence and warmth of generative AI-enabled chatbots are significantly associated with perceived authenticity. Furthermore, the results highlight that the perceived authenticity of generative AI-enabled chatbots is positively associated with customer engagement, experience and recommendation. Finally, the results illustrate that the impact of anthropomorphic characteristics on perceived warmth is significantly moderated by the need for social interaction. Originality/value This study enriches the generative AI literature a…
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This capacity may strengthen user engagement, therapeutic alliance, and the sense of being understood.
“This capacity may strengthen user engagement [20], therapeutic alliance [21], and the sense of being understood [22].”
From Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues (2025) · cites this paper as [20]
Cites
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Celik, I. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107468 · Cited by 1,012 · see citing works
Intimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers
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Zhang, Q., Zhang, R., Xiong, Y., Sui, Y., Tong, C., & Lin, F.-H. (2025) · Journal of Medical Internet Research · doi:10.2196/78238 · Cited by 56 · see citing works
“This capacity may strengthen user engagement [20], therapeutic alliance [21], and the sense of being understood [22].”
- Authors
- Kumar, A., Shankar, A., Behl, A., Chakraborty, D., & Gundala, R. R.
- Affiliations
- Indian Institute of Management Ranchi (IN)
- Indian Institute of Management Visakhapatnam (IN)
- Keele University (GB)
- University of Wisconsin–Parkside (US)
- Corresponding author
- Behl, Abhishek
- Venue
- Journal of Consumer Marketing
- Publisher
- Emerald
- Published
- 2025-02-03
- Volume
- 42
- Issue
- 4
- Pages
- 472-483
- ISSN
- 0736-3761 (ISSN-L)
- 2052-1200
- OpenAlex
- W4407043540
- Cited by
- 66
- References
- 4
- Citations by year
- 2026: 44 · 2025: 22
- FWCI
- 26.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Consumer Marketing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- generative AI chatbots, customer engagement, customer experience, customer recommendation, perceived competence, perceived warmth, perceived authenticity, social response theory
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