Anthropomorphic generative AI chatbots for enhancing customer engagement, experience and recommendation

Kumar, Aman; Shankar, Amit; Behl, Abhishek; Chakraborty, Debarun; Gundala, Raghava R.

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 and guides organizations in understanding consumer interactions for leveraging generative AI-enabled chatbots. Furthermore, this study contributes to the social response theory literature as this study investigates how user behavioural intentions towards generative AI-enabled chatbots are influenced by their perceived level of anthropomorphic characteristics.

Citation

Kumar, A., Shankar, A., Behl, A., Chakraborty, D., & Gundala, R. R. (in press). Anthropomorphic generative AI chatbots for enhancing customer engagement, experience and recommendation. Journal of Consumer Marketing, https://doi.org/10.1108/jcm-06-2024-6922

• Journal Article Type: Article
• Acceptance Date: Jan 20, 2025
• Online Publication Date: Feb 3, 2025
• Deposit Date: Apr 10, 2025
• Journal: Journal of Consumer Marketing
• Print ISSN: 0736-3761
• Publisher: Emerald
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1108/jcm-06-2024-6922
• Keywords: Artificial Intelligence, Generative AI, Anthropomorphism, Social Response Theory, Chatbots, Consumers
• Public URL: https://keele-repository.worktribe.com/output/1194483
• Publisher URL: https://www.emerald.com/insight/content/doi/10.1108/jcm-06-2024-6922/full/html