# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 1 Introduction  
With the increasing shift to online shopping, understanding customer trust has become a critical focus for e-commerce businesses aiming to retain customers and enhance their experiences. Trust is a key factor in shaping customer decisions, and it is influenced by various stages of the customer journey. Customer feedback plays a critical role in the e-commerce industry, offering a great source of information about customer experiences, preferences, and pain points. By systematically analyzing this feedback with various machine learning models [1], businesses can identify trends, improve customer satisfaction, and make data-driven decisions to foster growth and innovation. However, analyzing the vast amount of customer feedback efficiently is a significant challenge, necessitating advanced computational methods. Neural computation, through the use of large language models (LLMs), offers a sophisticated approach to this challenge, enabling the automation of feedback analysis and providing predictive insights into trust levels [2]. These models, rooted in deep learning architectures [3], are capable of understanding and predicting sentiments within customer reviews, unlocking new possibilities for analyzing trust at scale. This study investigates the use of LLMs to predict customer trust and understands its relationship with different stages of the customer journey in e-commerce by analyzing user feedback. While neural computation has demonstrated substantial power in predictive performance, capturing trust's nuanced dynamics across the e-commerce customer journey requires other approaches. Existing predictive models, though effective, do not always provide actionable insights regarding the interactions between various stages of the journey and their cumulative impact on customer trust. To bridge this gap, we incorporate Qualitative Comparative Analysis (QCA) [4], which allows us to explore the configurations ofcustomer experience elements that lead to positive or negative trust outcomes. By making use of the predictions of LLM-based sentiment analysis as the input for a QCA analysis, we are able to shift from purely predictive performance to a deeper understanding of how different conditions--such as app experience, delivery quality, and post-purchase support--interact to shape trust outcomes [5]. Therefore, to build upon the limited research on the relationship between the online customer journey and customer trust, we aim to address the following research question (RQ): _\"How can we utilize advanced ML models to analyze online customer reviews and understand the impact of different aspects on customer trust?\"_ We answer this question by fine-tuning LLMs on a dataset consisting of more than 900 online reviews from e-commerce platforms, and we conduct configurational modeling with QCA. By fine-tuning advanced LLMs, we aim to accurately identify key aspects of the shopping experience and their corresponding sentiments. For this purpose, we collected and manually annotated customer reviews from Trustpilot ([[https://www.trustpilot.com/](https://www.trustpilot.com/)]([https://www.trustpilot.com/](https://www.trustpilot.com/)), accessed on 8 July 2021), focusing on five leading e-commerce platforms. This dataset was used to train and evaluate two LLMs: TinyPixel/Llama-2-7B-bf16-sharded and Gemma. By offering a qualitative analysis of the outcome of these models in combination with QCA analysis, we aim to determine the impact of different aspects on customer trust and to identify potential improvements in e-commerce services based on these insights. The results highlight the effectiveness of LLMs in understanding and predicting customer trust, providing actionable business insights from raw textual data. This paper is structured as follows. Section 2 provides a literature review, covering customer trust in e-commerce and the application of LLMs in the business domain. Section 3 details the research methodology, including data acquisition, preparation, and annotation, language models, and QCA.