Framework for adoption of generative AI for information search of retail products and services
International Journal of Retail & Distribution Management
ISSN: 0959-0552
Article publication date: 6 November 2024
Issue publication date: 21 January 2025
Abstract
Purpose
Generative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.
Design/methodology/approach
We conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.
Findings
Results show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.
Originality/value
Consumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.
Keywords
Citation
Gupta, A.S. and Mukherjee, J. (2025), "Framework for adoption of generative AI for information search of retail products and services", International Journal of Retail & Distribution Management, Vol. 53 No. 2, pp. 165-181. https://doi.org/10.1108/IJRDM-05-2024-0203
Publisher
:Emerald Publishing Limited
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