A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce

Abstract

The primary goal of this study is to predict and analyze customer trust in e-commerce by leveraging neural computation within large language models (LLMs) alongside configurational approaches. We employ LLMs to predict trust levels based on customer reviews, applying artificial intelligence to analyze key aspects of the e-commerce experience, such as customer service, refund processes, item quality, and shipping. To extend beyond predictive performance, we integrate Qualitative Comparative Analysis (QCA) to identify the causal relationships between trust and various stages of the customer journey, including selection, delivery, and post-purchase support (recovery). This dual approach not only showcases the power of neural computation in predicting trust outcomes but also provides a deeper understanding of how specific configurations of customer experience elements contribute to either positive or negative trust. By combining machine learning techniques and QCA, this study contributes to the application of LLMs and configurational approaches, offering novel insights into the drivers of trust in e-commerce.