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Revealing Research Themes and Trends in the Thirty Top Ranked Accounting Journals through Natural Language Processing (NLP): 1997-2017

Fri, October 19, 1:45 to 3:25pm, Hyatt Regency Greenwich, TBA

Abstract

Objective: This study aims to reveal themes and trends for accounting research for last twenty years by utilizing natural language processing (NLP) and text mining techniques.
Method: To address our aim, we generated a corpus consisting of over forty thousand references through multiple searches in EBSCOhost-Business Source Premier, Scopus and ScienceDirect to gather data for thirty- top ranked A* and A accounting journals. These journals were listed and categorized by Australian Business Deans Council(ABDC). Based upon predetermined inclusion and exclusion criteria, elimination of 24,474 non-empirical references and references with no abstracts resulted in 16,449 abstracts. Analyses and data visualization were performed using JMP13 Pro Text Explorer, R studio, R and Tableau.
Findings: The text mining analyses revealed fifteen distinct clusters(C) including (C-1) General accounting, (C-2) Audit, (C-3) Organizational, (C-4) Market/trade, (C-5) Accounting standards, (C-6) Accounting education, (C-7) International, (C-8) Corporate governance, (C-9) Tax, (C-10) Earnings, (C-11) Corporate social responsibility, (C-12) Banking, (C-13) Government and public sector, (C-14) Accounting history and country comparison, and (C-15) Financial forecasting.
Conclusions: The interpretation of the fifteen clusters and their trends across the years revealed that there has been some shrinkage in certain clusters over time (e.g., C-5, C-6, C-10, C-14) while some others are growing (e.g. C-8, C-11, C-12).
Implications for Policy or Practice: Overall, the findings of the study are expected to help educators, researchers, practitioners, and policy-makers to develop a future research agenda. For example, the decreasing trend in certain clusters such as accounting education (C-6) calls the attention of educators and practitioners.

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