Enhancing trust in online grocery shopping through generative AI chatbots

Debarun Chakraborty, Arpan Kumar Kar, Smruti Patre and Shivam Gupta

Journal of Business Research, 2024, vol. 180, issue C

Abstract: 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.

Keywords: Generative Artificial Intelligence (AI); Online Shopping; Elaboration Likelihood Model; Trust; Status Quo Bias Theory; Chatbots (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:180:y:2024:i:c:s0148296324002418

DOI: 10.1016/j.jbusres.2024.114737

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