Author
Listed:
- 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)
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.
Suggested Citation
Barna Laura-Eugenia-Lavinia & Hurducaci Gorea Corina-Cătălina & Ionescu Bogdan-Ștefan, 2025.
"From WOS to ChatGPT: Exploring Digital Transformation in Accounting through Keyword Analysis for ”Artificial Intelligence” and ”ERP Systems”,"
Proceedings of the International Conference on Business Excellence, Sciendo, vol. 19(1), pages 319-330.
Handle:
RePEc:vrs:poicbe:v:19:y:2025:i:1:p:319-330:n:1003
DOI: 10.2478/picbe-2025-0027
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:poicbe:v:19:y:2025:i:1:p:319-330:n:1003. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.