Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach

This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research.

01. October 2023

Authors June Cao Zhanzhong Gu Iftekhar Hasan

Whom to contact

For Researchers

Professor Iftekhar Hasan, PhD
Professor Iftekhar Hasan, PhD

If you have any further questions please contact me.

Request per E-Mail

For Journalists

Mitglied der Leibniz-Gemeinschaft LogoTotal-Equality-LogoSupported by the BMWK