Factors Affecting the Use of ChatGPT for Obtaining Shopping Information

Abstract

ChatGPT transforms the shopping experience by providing responses in human‐like language about products, services, and brands to customers. This study investigated the influential drivers of intention to use ChatGPT to obtain shopping information. We extended the “extended unified theory of acceptance and use of technology” UTAUT2 by incorporating the direct and moderating effects of trust and technology anxiety. To test the model on data from 412 respondents, a hybrid Partial Least Squares—Artificial Neural Network (PLS‐ANN) approach was employed. This approach combines the strengths of PLS for modeling complex variable relationships and ANN for capturing nonlinear dependencies and interactions. PLS analysis identified performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, and trust as significant drivers of ChatGPT usage. The associations between the intention to use ChatGPT and its predictors are negatively moderated by trust and technology anxiety. ANN analysis revealed that trust has the highest effect on the choice to use ChatGPT, followed by facilitating conditions, performance expectancy, hedonic motivation, and effort expectancy. By extending the UTAUT2 framework and applying the PLS‐ANN method, this study advances the theoretical understanding of technology adoption and provides practical insights for marketers and developers of AI‐driven text generators. It emphasizes the importance of building trust and alleviating technology anxiety to promote wider adoption of ChatGPT. The broader significance of this research lies in its contribution to shaping the future of retail and e‐commerce strategies by encouraging a more informed and user‐centric development of AI technologies in the shopping domain.