Are AI tools better than traditional tools in literature searching? Evidence from E-commerce research

Abstract

This article examines the potential implications of Artificial Intelligence (AI) for literature search, comparing AI-based tools to conventional research methods. It also addresses the scarcity of academic literature on specific AI tools for research writing, posing four critical questions regarding accuracy, quality, uniqueness, and qualified uniqueness. Employing Algorithmic Theory and Data Dependency Theory, this project scrutinizes AI performance in algorithms, machine learning models, and data quality. Testing nine e-commerce topics using Scopus, Web of Science, Elicit, and SciSpace, the authors conclude that while conventional methods excel in accuracy and quality, AI tools show promise in uniqueness, complementing literature reviews. The findings also emphasize the judicious integration of AI tools and advocate for further research into new applications and diverse fields. Ultimately, this research offers highly relevant insights into leveraging AI tools to enhance conventional literature search practices in research and professional domains.