Bridging the chatbot connection: the role of AI-driven chatbot affordances in e-commerce purchase intentions
Abstract
Purpose: This paper explores how AI-driven chatbots, using social support and affordance theories, influence consumer behavior and purchase intentions (PIs) in e-commerce, addressing challenges like limited understanding and security concerns. Design/methodology/approach: A quantitative study was conducted to collect data from 385 customers who had interacted with AI-driven chatbots on e-commerce platforms. Purposive sampling was employed to ensure the relevance of the respondents. Partial least squares-structural equation modeling (PLS-SEM) analysis was performed using SmartPLS 4 to test the hypothesized relationships and analyze the data. Findings: The results show a small direct effect of chatbot affordances on PI, with stronger indirect effects via customer satisfaction (CS) and trust. Notably, trust does not directly influence PI, suggesting a reliance on word-of-mouth. Customer engagement (CE) plays a minor mediating role, highlighting the importance of emotional and experiential factors. Originality/value: This pioneering study within the Sri Lankan context addresses the underexplored area of AI-driven chatbots and their influence on PI. Additionally, the study provides nuanced insights into the mediating mechanisms of CS, trust and engagement in the relationship between chatbot affordances and PI.
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Marketing lift depends on maintaining the user's sense of control, not just on improving capability cues.
“This demonstrates that marketing lift depends on maintaining the user's sense of control, not just on improving capability cues (e.g., [22,23,40]).”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [22]
Satisfaction rises via problem-solving, confirmation, usefulness, and interaction/service quality.
“Satisfaction rises via problem-solving, confirmation, usefulness, and interaction/service quality.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [22]
The utility chain dominates: quality/usefulness leads to attitude/satisfaction, which leads to intention.
“Utility chain dominates: quality/usefulness → at-titude/satisfaction → intention.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [22]
Satisfaction declines when designs trigger reactance or cognitive/affective overload, which depress downstream evaluations.
“It declines when designs trigger reactance or cognitive/affective overload, which depress downstream evaluations.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [22]
Purchase intention/adoption intention is mostly positive via utility/trust, but can decrease under high autonomy.
“Mostly positive via utility/trust, but can decrease under high autonomy”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [22]
Satisfaction/CX/perceived service quality is mostly positive, but design can introduce negative affect (reactance, overload).
“Mostly positive, but design can introduce negative affect (reactance, overload)”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [22]
High-autonomy cues can reduce intention via powerlessness/threat-to-freedom, unless urgency/scarcity raises benefit salience and attenuates resistance.
“High-autonomy cues can reduce intention via powerless-raises benefit salience and attenuates resistance. ness/threat-to-freedom, unless urgency/scarcity”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [22]
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Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty
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The effect of social presence and chatbot errors on trust
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Balaskas, S. (2026) · Information · doi:10.3390/info17030222 · Cited by 8 · see citing works
- Authors
- Yatawara, K. M. B., Sampath, T., Kalupahana, P. L., Rathnayake, S. O., Jayasuriya, N., & Rathnayake, N.
- Affiliations
- Sri Lanka Institute of Information Technology (LK)
- University of Tasmania (AU)
- ORCID
- Venue
- International Journal of Sociology and Social Policy
- Publisher
- Emerald
- Published
- 2025-12-05
- Volume
- 46
- Issue
- 7-8
- Pages
- 908-928
- ISSN
- 0144-333X (ISSN-L)
- 1758-6720
- OpenAlex
- W4416939349
- Cited by
- 1
- References
- 10
- Citations by year
- 2026: 1
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Sociology and Social Policy (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- purchase intention, e-commerce, social support theory, affordance theory, customer satisfaction, consumer trust, customer engagement, word of mouth, Sri Lankan consumers
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