Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323808 
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Year of Publication: 
2025
Citation: 
[Journal:] Intelligent Systems in Accounting, Finance and Management [ISSN:] 2160-0074 [Volume:] 32 [Issue:] 2 [Article No.:] e70007 [Publisher:] Wiley [Year:] 2025
Abstract: 
ABSTRACT 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.
Subjects: 
alternative data
employee reviews
employee satisfaction
employer rating platform
job satisfaction
wisdom of crowds
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Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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