Examining Humanized Bot Interaction for the Retail Revenue Growth
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As agents move from recommending to executing and optimizing offers, fairness and price fairness risks become structurally more significant.
“However, as agents move from recommending to executing and optimizing offers, these risks become structurally more significant [65].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [65]
Fairness constructs primarily function as boundary-strengtheners; for example, price fairness enhances the engagement to continuous purchase intention relationship.
“Fairness constructs primarily function as boundary-strengtheners (e.g., price fairness enhancing the engagement → continuous purchase intention relationship [65])”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [65]
In many studies, conversion is proxied via self-reported ordering/behavioral intention rather than verified execution.
“In many studies,”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [65]
Behavioral lift appears when systems reduce friction (latency/steps) or improve option fit (recommendation relevance).
“Behavioral lift appears when systems reduce fric-havioral intention rather than verified execution. tion (latency/steps) or improve option fit (recom-mendation relevance).”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [65]
Observed conversion/purchase behavior is positive when friction drops or recommendations fit, but not universal.
“Positive when fric-tion drops or rec-ommendations fit, but not universal”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [65]
Cites
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Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 1× · see citing works
“GenAI can dynamically tailor pricing communication based on customer profiles, purchase history, and real-time market data (Dwivedi et al. 2021).”
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059 · cited 2× · see citing works
“…has emphasized the crucial role of anthropomorphism in chatbot conversations and its impact on consumer attitudes toward chatbots (Araujo 2018; Huang & Rust 2021; Sheehan et al. 2020) In line with this study, we propose that as the level of anthropomorphism in chatbot services increases, user…”
Gaver, W. W. (1991) · Proceedings of the SIGCHI conference on Human factors in computing systems Reaching through technology - CHI '91 · doi:10.1145/108844.108856 · Cited by 1,536 · cited 1× · see citing works
“This idea corresponds with Gaver (1991) who emphasized that technology’s affordances are linked to users’ interactions with technological entities.”
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Developing and validating a service robot integration willingness scale
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Understanding Web-based learning continuance intention: The role of subjective task value
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Customer service chatbots: Anthropomorphism and adoption
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The impact of chatbots on customer loyalty: a systematic literature review
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How does artificial intelligence create business agility? Evidence from chatbots
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Balaskas, S. (2026) · Information · doi:10.3390/info17030222 · Cited by 8 · see citing works
- Authors
- Kumar, N., Garg, P., Gupta, N., & Upreti, K.
- Affiliations
- Christ University (IN)
- Amity University (IN)
- CHRIST (Deemed to be) University, Delhi NCR (IN)
- Corresponding author
- Kumar, Nishant
- Venue
- Journal of Revenue and Pricing Management
- Published
- 2025-08-03
- Volume
- 25
- Issue
- 1
- Pages
- 25-40
- ISSN
- 1476-6930 (ISSN-L)
- 1477-657X
- OpenAlex
- W4412869987
- Cited by
- 3
- References
- 12
- Citations by year
- 2026: 3
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Revenue and Pricing Management (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- chatbots, human-bot interaction, anthropomorphism, social presence, personalization, media richness, customer engagement, purchase decision, trust, price fairness, e-commerce, customer loyalty
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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