# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 2 Literature Review
### LLMs in Business Domain  
Neural computations and machine learning techniques have revolutionized the way businesses analyze and understand customer behavior in e-commerce [28]. These advanced methods, powered by deep learning and neural networks, enable the processing of vast amounts of customer data, such as reviews, transaction histories, and clickstream data, to uncover patterns that were previously difficult to detect [29]. One of the most significant developments in this area is the use of Large Language Models (LLMs), which leverage neural computation to interpret and predict customer sentiments, preferences, and trust levels or predict the relevance of information based on textual data [30]. These models, trained on massive datasets, can comprehend the subtleties of human language, allowing businesses to gain nuanced insights into customer satisfaction, decision-making, and overall experience. As e-commerce continues to grow, the ability to analyze customer feedback at scale is critical for creating personalized recommendations, improving customer service, and driving loyalty. LLMs such as GPT and BERT offer a unique advantage by not only identifying customer needs but also predicting future behavior based on past interactions. Given their capacity to handle complex language patterns and adapt to diverse contexts, LLMs have immense potential to transform customer behavior analysis in e-commerce, providing businesses with powerful tools to enhance engagement and trust.  
LLMs are increasingly being used in the business and e-commerce domains for various applications. In the literature, LLMs are leveraged for their advanced Natural Language Processing (NLP) capabilities, which allow for sophisticated analysis and automation of text-related tasks. LLM can be used to automate the customer service process. For example, LLMs are employed to create chatbots and virtual assistants that can understand and respond to customer inquiries with high accuracy, providing 24/7 support [31]. Additionally, they are used to automate responses to frequently asked questions, reducing the workload on human agents [32; 33].  
Another application of LLMs in business is the analysis of customer feedback, reviews, and social media posts to gauge public sentiment toward products and brands. By leveraging these advanced models, businesses can gain insights into customer opinions, detect emerging trends, and make well-informed decisions based on the data collected [29; 34].  
Moreover, LLMs enhance recommendation systems by analyzing user behavior, preferences, and feedback to suggest relevant products [35; 36]. They provide more accurate and personalized recommendations compared to traditional methods.  
Additionally, LLMs can improve the accuracy and relevance of search results on e-commerce platforms by understanding the context and intent behind user queries [37]. They enhance the overall search experience for users. To the best of our knowledge, this study is the first to explore the capability of LLMs in predicting trust sentiment and its associated aspects within the e-commerce domain.