Enhancing Trust in Online Grocery Shopping through Generative AI Chatbots
Open accessAbstract
Generative Artificial Intelligence (GAI) is witnessing a lot of adoption across industries, but literature is yet to fully document the nuances of these applications. We develop a comprehensive framework for understanding the factors that affect trust in online grocery shopping (OGS) using GAI chatbots. Our exploratory study was conducted via interviews, which helped to build our model. We integrate the Elaboration Likelihood Model (ELM) and Status Quo Bias (SQB) theory to develop the Unified Framework for Trust on Technology Platforms. In our confirmatory study, by analyzing 372 responses from users, using structural equation modelling (SEM), we initially validate our path model. Subsequently, we used fuzzy set qualitative comparative analysis (fsQCA) to check the causal combinations to explain different trust levels. Apart from perceived regret avoidance, all of the other factors had a significant effect on attitude and trust. Perceived anthropomorphism moderated the associations between interaction quality, credibility, threat, and attitude.
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Interaction quality, credibility, and threat perceptions shape trust in generative AI chatbots.
“Chakraborty et al. [33] combine the Elaboration Likelihood Model and Status Quo Bias Theory to demonstrate how interaction quality, credibility, and threat perceptions shape trust in generative AI chatbots”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as Chakraborty et al. [33]
Perceived threat and regret avoidance shape trust formation.
“Chakraborty et al. [33] emphasize how perceived threat and regret avoidance shape trust formation”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as Chakraborty et al. [33]
Mixed-method contributions enhance contextual richness but generally rely on self-reporting and fail to address gaps in behavioral verification.
“Mixedmethod contributions (e.g., interviews plus surveys) enhance contextual richness but generally rely on self-reporting and fail to address gaps in behavioral verification [33].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [33]
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- Authors
- Chakraborty, D., Kumar Kar, A., Patre, S., & Gupta, S.
- Affiliations
- Symbiosis International University (IN)
- NEOMA Business School (FR)
- Corresponding author
- Gupta, Shivam
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 180
- Pages
- 114737
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W4398768358
- Cited by
- 162
- References
- 9
- Citations by year
- 2026: 76 · 2025: 73 · 2024: 13
- FWCI
- 23.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Business Research (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- 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 › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Keywords
- trust in technology, online grocery shopping, generative AI chatbots, anthropomorphism, perceived credibility, threat perception, interaction quality, attitudes toward technology, elaboration likelihood model, status quo bias
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