# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 3 Materials and Methods
### Configurational Analysis with Qualitative Comparative Analysis  
Traditional studies examining the relationship between customer trust and its antecedents in e-commerce often rely heavily on various forms of regression analysis. However, as highlighted by [45], these techniques face three significant limitations by relying on the assumptions of symmetric and linear interactions among variables. To overcome these limitations, researchers have increasingly turned to methodologies that allow for more nuanced configurations of variables. One such approach that has gained prominence is Qualitative Comparative Analysis (QCA) and its various extensions, such as Fuzzy-set QCA (FsQCA) [4]. QCA is widely used for identifying causal patterns and relationships between an outcome of interest, such as customer trust, and its potential antecedents. This method excels in situations where the relationships between variables are complex, and it enables the exploration of multiple causal paths to the same outcome. In this study, the outcome of interest is trust towards the e-commerce platform, and to complement the described LLMs, we aim to understand what combination of aspects and corresponding sentiments results in positive or negative trust. Similarly to [39], instead of focusing on individual aspects in this part of the analysis, the goal is to understand the impact of the stages of the customer journey experienced with the e-commerce platform. One can specify three distinct stages in this process: (i) selection, (ii) delivery, and (iii) recovery. The first stage, selection, includes information related to the customers' interaction with the platform until making a purchase decision: app experience, information, product availability, product features, pricing, and payment. The second stage, delivery, focuses on the shipping process and the experience with the product, as expressed by the following attributes: shipping, delivered product status, packaging, and item quality. The third stage, recovery, captures any possible interactions with the company after the product is received, as measured by the aspects of refund process, return process and customer service. For each stage and review pair, two variables can be defined [39]: (i) one capturing whether there is any positive sentiment mentioned towards the stage, and (ii) another capturing whether there is any negative sentiment mentioned towards the stage. For example, 'Selection Positive' will take on the value 1 if at least one of the six related aspects is mentioned positively in the review, and 0 otherwise. In this study, according to this variable specifications, we utilize the basic, crisp version of QCA, as all the values in the data are either 0 or 1. As the initial step of the analysis, in the necessity analysis, we assessed whether any single condition was required for the outcome to occur. Following that, we identified combinations of the three stages and their negations that were sufficient to achieve either positive or negative trust. The modeling was conducted using various R packages, and we followed the recommended steps and procedures [45]. The results of the necessity analysis, presented in Tables A1 and A2, reveal several important insights regarding the conditions influencing trust in e-commerce platforms. First, positive evaluation regarding the second stage, delivery, emerges as the most consistent and relevant factor for fostering positive trust. The high consistency and relevance indicate that it is a key driver of customer confidence. Second, positive perception in the first stage, selection, while less consistent than delivery, is still highly relevant for building positive trust, suggesting that factors like app experience and product availability play an important role, though not as universally. Third, positive evaluation of the third stage, recovery, encompassing refund and customer service processes, shows lower consistency, indicating that positive trust can still be achieved, even when these post-purchase processes are not consistently strong.