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The Wisdom of Electronic Employee Crowds—Employee Reviews as a Data Source in Finance, Accounting, Economics, and Management Research: A Systematic Literature Review

Nils Gimpl

Intelligent Systems in Accounting, Finance and Management2025https://doi.org/10.1002/isaf.70007article
AJG 1
Weight
0.37

What the paper says

This study explores the wealth of information inherent in online employee reviews as an emerging resource in academic research. The focus is on the fields of finance, accounting, economics, and management, with an emphasis on how employee reviews contribute to our understanding of these areas. A systematic literature review (SLR) of 70 high‐quality articles highlights the insights gleaned from employee reviews. Their data points, such as employee satisfaction, employee outlook, evaluation of culture, management, and colleagues, and text comments are mainly used in (1) explaining and predicting firm performance, (2) predicting and understanding performance and satisfaction of specific job groups, and (3) CSR‐ and ESG‐related research. This SLR is important because the three main topics mentioned in which employee reviews are mainly used are spread across the fields of finance, accounting, economics, and management. This SLR therefore provides researchers with an important and necessary overview of the research already addressed across these fields. Furthermore, the SLR provides an overview of employer rating platforms utilized for academic research and methods used to harness employee reviews for research purposes. Here, a significant finding of this SLR is the predominant use of Glassdoor as a data source and the focus on US markets. The SLR concludes by proposing five potential avenues for future research, paving the way for a deeper understanding of the interplay between employee reviews (information) and organizational dynamics.

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https://doi.org/https://doi.org/10.1002/isaf.70007

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@article{nils2025,
  title        = {{The Wisdom of Electronic Employee Crowds—Employee Reviews as a Data Source in Finance, Accounting, Economics, and Management Research: A Systematic Literature Review}},
  author       = {Nils Gimpl},
  journal      = {Intelligent Systems in Accounting, Finance and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/isaf.70007},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.