# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 4 Results
### Configurations from QCA Analysis  
In the QCA analysis of understanding the antecedents of positive trust, three distinct configurations were identified (Table 8). In the table, \(\bullet\) and \(\circ\) indicate the presence and absence of the condition, respectively.  
Solution 1 highlights the importance of positive perception of delivery as a sufficient condition to generate trust. Similarly, according to solution 2, positive evaluation of the selection stage can play the same role. Both of these solutions exhibit a high consistency of over 0.95, with coverage values of 0.73 and 0.28, respectively. Solution 1 is the most dominant configuration, and the unique coverage of 0.22 suggests that this solution alone explains 22% of the cases where positive trust is observed, indicating that a favorable delivery process is a critical driver of trust in e-commerce. In contrast, solution 3 shows that recovery alone is not a sufficient condition to achieve trust unless it is accompanied by a positive evaluation of the delivery stage. Overall, these findings reveal multiple pathways to achieving positive trust in e-commerce underscoring the complexity of the phenomenon. Furthermore, delivery plays a prominent role in two of the solutions, highlighting the role of this stage of the customer journey in contributing to trust formation.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Review Number** & **Customer Feedback** \\ \hline
1 & I did not love the dress, but great customer service, I will recommend them. \\ \hline
2 & I hate them, where is my order \\ \hline
3 & very hard to navigate. I will delete the app \\ \hline
4 & good packaging but fake products. Stay away \\ \hline
5 & free and fast delivery. I like these guys \\ \hline
6 & I love the dress, great customer service, I will recommend them. \\ \hline
**Review Number** & **Model’s Prediction** \\ \hline
1 & Trust: Positive, Customer\_service: Positive, Item\_quality: Negative \\ \hline
2 & Trust: Negative, Shipping: Negative \\ \hline
3 & Trust: Negative, App\_experience: Negative \\ \hline
4 & Trust: Negative, Packaging: Positive, Item\_quality: Negative \\ \hline
5 & Trust: Positive, Shipping: Positive \\ \hline
6 & Trust: Positive, Customer\_service: Positive, Item\_quality: Positive \\ \hline \hline \end{tabular}
\end{table}
Table 7: Predictions generated by the Llama model.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Conditions** & **Solution 1** & **Solution 2** & **Solution 3** \\ \hline Selection: Positive & & \(\bullet\) & \\ Selection: Negative & & & \\ Delivery: Positive & \(\bullet\) & & \\ Delivery: Negative & & & \(\circ\) \\ Recovery: Positive & & & \(\bullet\) \\ Recovery: Negative & & & \\ \hline Consistency & 0.995 & 0.952 & 0.988 \\ Coverage & 0.732 & 0.279 & 0.570 \\ Unique coverage & 0.223 & 0.067 & 0.112 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Configurations for positive trust.  
The QCA analysis for conditions leading to negative trust identified two configurations that are sufficient to explain the outcome (Table 9).  
Solution 1 highlights the importance of the recovery stage, as negative experiences with any post-purchase processes such as returns, refunds, and customer service are sufficient to foster negative trust. The configuration shows a very high consistency of 0.95, and a high coverage of 0.65. Solution 2 demonstrates that the combination of the absence of positive perception of selection and recovery, combined with negative evaluation of delivery, is another pathway leading to negative trust. In addition to high consistency, the unique coverage of 0.27 shows the high number of cases that are explained exclusively by this configuration. This shows that the combination of a poor delivery experience without a positive compensation in other stages of the customer journey process can play a significant role in damaging trust.