Exploring AI's Role in Literature Searching: Traditional Methods Versus AI-Based Tools in Analyzing Topical E-Commerce Themes

This paper delves into the evolving landscape of Artificial Intelligence (AI) and its impact on literature reviews, exploring the potential of AI-based tools in comparison to traditional methods. Acknowledging AI's transformative influence on diverse sectors and its ethical implications, the study addresses the scarcity of academic literature on specificAI-based tools like ChatGPT. The research poses four critical questions comparing the accuracy, quality, uniqueness, and qualified uniqueness of AI-based tools against traditional methods. A theoretical framework based on Algorithmic Theory and Data Dependency Theory is employed to scrutinize the AI's performance in terms of algorithms, machine learning models, and data quality. The methodology involves testing nine topical e-commerce themes using Scopus, Web of Science, Elicit, and SciSpace, leading to nuanced conclusions. While traditional methods excel in accuracy and quality, AI-based tools demonstrate potential in uniqueness, emphasizing their complementary role in literature reviews. The study underscores the importance of judiciously integrating AI-based tools in literature reviews and advocates for continued research exploring new applications and diverse fields. Ultimately, the paper provides valuable insights into leveraging AI-based tools to enhance traditional literature review practices in research and professional domains.