The Effect of Perceived Warmth, Competence, and Social Presence of AI-Driven Chabots on Consumers’ Engagement and Satisfaction
Open accessAbstract
Recent research on chatbots has revealed consumers’ skeptical attitudes toward them in the form of less interaction and inconsistent use intentions. This study investigates how three social attributes of chatbots (perceived warmth, perceived ability, and social presence) affect consumers’ perceived trust in chatbots and their intention to use them. Furthermore, the moderating effect of Implicit theory on perceived trust, willingness to use, and interaction satisfaction was tested. An empirical analysis of a questionnaire survey of 303 consumers indicated that the perceived warmth, competence, and social presence of chatbots positively influence consumers’ trust in chatbots. Moreover, the perceived trust toward chatbots mediates the relationship between the perceived warmth, competence, and social presence of chatbots (IV), consumers’ continuous usage intention, and interaction (DV) satisfaction. Moreover, Implicit theory, as beliefs about the variability of human characteristics (such as intelligence, personality) gradually formed by individuals in interpersonal interactions, can affect individuals’ cognitive processing and behavioral decision-making. Implicit self-theory can be divided into Entity theory and gradient theory based on different beliefs. This article examines the applicability of Implicit self-theory in the field of trust and explores the moderating role of Implicit self-theory. Whether it is the entity theorist or gradient theorist, once they trust a chatbot, they will increase their willingness to use it and improve their interaction satisfaction. It also discusses ways to improve the design of chatbots and maximize their utility, providing theoretical and managerial implications for scholars and practitioners.
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Complacency creates a feedback loop in which users that perceive chatbots as more friendly, helpful and trustworthy are more likely to return, which in turn incentivizes providers to continue optimizing for this behavior.
“Third, it creates a feedback loop in which users that perceive return to them, which in turn incentivizes providers to continue optimizing for this behavior.”
From Complacent, Not Sycophantic: Reframing Large Language Models and Designing AI Literacy for Complacent Machines (2026) · cites this paper as [14]
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Germani, F., & Spitale, G. (2026) · arXiv · doi:10.48550/arxiv.2605.14544 · Cited by 1 · see citing works
- Authors
- Deng, Z., & Yan, J.
- Affiliations
- Changchun University of Finance and Economics (CN)
- Gachon University (KR)
- Corresponding author
- Yan, Jinzhe
- ORCID
- Yan, Jinzhe
- Venue
- SAGE Open
- Publisher
- SAGE Publications
- Published
- 2025-01
- Volume
- 15
- Issue
- 3
- ISSN
- 2158-2440 (ISSN-L)
- OpenAlex
- W4414164148
- Cited by
- 29
- References
- 20
- Citations by year
- 2026: 29
- FWCI
- 11.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- SAGE Open (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- chatbots, perceived warmth, perceived competence, social presence, trust in chatbots, intention to use, interaction satisfaction, implicit self-theories, entity theory, gradient theory, consumer attitudes toward AI, chatbot design
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