JOURNAL ARTICLE
Exploring Journal of Emerging Technologies in Accounting: A Content and Citation Analysis of JETA.
Published In: Journal of Emerging Technologies in Accounting, 2024, v. 21, n. 1. P. 29 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Göktürk, Ibrahim Emre; Güvemli, Batuhan; Sarısoy, Özkan 3 of 3
Abstract
This study presents a comprehensive bibliometric analysis of the Journal of Emerging Technologies in Accounting (JETA) from 2008 to 2022, the period since its indexing in Web of Science, focusing on authorship patterns, dominant topics, and citation trends. The findings highlight a significant concentration of authorship within JETA, suggesting an opportunity for enhancing diversity and introducing fresh perspectives through expanded authorial engagement. Acknowledging JETA's significant contributions to blockchain, text analysis/NLP, and AI, this study proposes an exploration into broader, interdisciplinary domains to further enrich the journal's thematic diversity, in alignment with global academic trends. The study further recognizes an opportunity for JETA to bolster its global impact by inviting contributions from underrepresented regions, such as South America, Australia, and Africa. Additionally, the analysis reveals a commendable trend in JETA toward collaborative and potentially interdisciplinary research, implying that fostering such collaborations could yield innovative research methodologies in the field. Data Availability: The data supporting the findings of this study can be provided upon request by contacting Batuhan Güvemli. JEL Classifications: M40; M41; M42; M49. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Emerging Technologies in Accounting. 2024/03, Vol. 21, Issue 1, p29
- Document Type:Article
- Subject Area:Education
- Publication Date:2024
- ISSN:1554-1908
- DOI:10.2308/JETA-2023-015
- Accession Number:176394423
- Copyright Statement:Copyright of Journal of Emerging Technologies in Accounting is the property of American Accounting Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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