From WOS to ChatGPT: Exploring Digital Transformation in Accounting through Keyword Analysis for ”Artificial Intelligence” and ”ERP Systems”

Barna Laura-Eugenia-Lavinia (), Hurducaci Gorea Corina-Cătălina () and Ionescu Bogdan-Ștefan ()
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Barna Laura-Eugenia-Lavinia: Bucharest University of Economic Studies, Bucharest, Romania
Hurducaci Gorea Corina-Cătălina: Bucharest University of Economic Studies, Bucharest, Romania
Ionescu Bogdan-Ștefan: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2025, vol. 19, issue 1, 319-330

Abstract: This study investigates how the concepts of “Artificial Intelligence” and “ERP Systems” are represented in the specialized literature indexed in the WOS database and compares them with the suggestions generated by ChatGPT. The paper explores the digitalization in the field of accounting through a detailed analysis of keywords extracted from scientific articles and from queries to an advanced AI model. The methodology used includes bibliometric analysis performed with the help of VOSviewer and Bibliometrix software, which allow the identification of term co-occurrence networks, the evolution of research topics and semantic clusters. In addition, set operations are applied to calculate the overlap between the two sources, using metrics such as the Jaccard index and cosine similarity, to measure the degree of similarity between the identified concepts. The obtained results provide an insight into how the topics related to technological innovations are addressed in academic research compared to AI suggestions, highlighting significant convergences and discrepancies. The study contributes to understanding the digital transformation process in accounting, proposing future directions for optimizing indexing and information management in the field.

Keywords: Artificial Intelligence; ERP systems; accounting; ChatGPT; digital; concepts (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:19:y:2025:i:1:p:319-330:n:1003

DOI: 10.2478/picbe-2025-0027

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