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

Przemysław Tomczyk (), Philipp Brüggemann (), Niklas Mergner () and Maria Petrescu ()
Additional contact information
Przemysław Tomczyk: Kozminski University
Philipp Brüggemann: FernUniversität in Hagen
Niklas Mergner: FernUniversität in Hagen
Maria Petrescu: Embry-Riddle Aeronautical University

A chapter in Advances in Digital Marketing and eCommerce, 2024, pp 141-148 from Springer

Abstract: Abstract 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 specific AI-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.

Keywords: Artificial intelligence; AI; AI-based tools; literature review; SLR; systematic literature review (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-62135-2_15

Ordering information: This item can be ordered from
http://www.springer.com/9783031621352

DOI: 10.1007/978-3-031-62135-2_15

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().