Enhancing trust in online grocery shopping through generative AI chatbots
研究通过访谈和372份用户问卷，整合精细加工可能性模型和现状偏差理论，分析生成式AI聊天机器人如何影响在线杂货购物中的信任，发现除感知遗憾规避外，其他因素均显著影响态度和信任。
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.