# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 6 Conclusions  
In this study, we explored the application of two advanced LLMs for predicting trust in e-commerce. In the research, we employed and fine-tuned two LLMs, TinyPixel/Llama-2-7B-bf16-sharded and Gemma, to assist in processing online customer reviews. The fine-tuning of these LLMs enabled us to automate the aspect extraction and sentiment classification processes, which are integral parts of analysis. By leveraging LLMs, we were able to identify trust indicators in customer feedback, reducing the manual analysis. This process involved training the models on annotated data and using them to predict key aspects such as shipping, product quality, and customer service, along with their corresponding sentiments. For training both models, we used the Google Colab Pro environment, and training each model took approximately 2 h. Our analysis revealed that these models significantly enhanced the accuracy of trust prediction by effectively analyzing customer feedback and sentiments. To the best of our knowledge, no prior research has employed LLMs specifically for this purpose, making our approach and dataset unique. Furthermore, making use of the predictions produced by LLMs, we illustrated how to use a configurational approach to shed lights of the important stages of the customer journey process having a high impact on the formation of trust. The combination of LLMs and QCA provided a more comprehensive framework for understanding trust in e-commerce. LLMs excelled at accurately predicting trust levels and associated sentiments from customer reviews, automating a task that would otherwise be labor-intensive and error-prone. However, QCA added an explanatory layer by revealing how different customer journey stages interacted to shape overall trust. This synergy between predictive performance and explanatory power is one of the core contributions of this study. It demonstrates that machine learning models can provide accurate predictions, but configurational methods like fsQCA are needed to explain the interactions that lead to those predictions. For future research, we recommend training other machine learning models such as RoBERTa or Llama, or combining different models to augment training data. Performing similar experiments on larger datasets could confirm the scalability and effectiveness of the proposed approach. As manual data annotation is a very labor-intensive task, additional synthetic annotated data could be utilized to address the scarcity of manually annotated data and to improve the model's performance on infrequent aspects. By employing fine-tuned LLMs for generating high-quality synthetic data, we can efficiently expand a limited training dataset, focusing on underrepresented aspects such as payment, refund, and product availability. In a future paper, the different NLP analysis steps could be combined: a fine-tuned BERT model for aspect detection with a fine-tuned LLM-based ABSA model to handle cases where aspects are not explicitly mentioned as nouns, and Llama for sentiment analysis. Additionally, integrating real-time user feedback to continuously refine and adapt the models could ensure sustained accuracy and relevance in a dynamic market environment. Furthermore, in future research, various interpretability techniques can be incorporated in the model-building and analysis process. These could include simple approaches, such as feature importance and attribution methods, and also more advanced techniques, such as surrogate models (LIME and SHAP). This addition can help to explain the decision-making process of our large language models (LLMs). These techniques will allow decision makers to better understand the contribution of specific aspects--such as customer service, shipping, and product quality--toward predicting trust in a brand or product. By integrating both predictive performance and interpretability, one could provide a comprehensive analysis of trust, enhancing transparency at both the global and individual prediction levels.