16卷 第2期
HSE University, Saint Petersburg, Russia
纸质出版日期：2025-03-24
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(2025). Leveraging People Analytics for Employer Branding: A Text Mining Study of Employee Reviews in IT Industry. 华夏人力资源管理学报, 16(2), 97-120.
Mahar Daniyal Haider.Leveraging People Analytics for Employer Branding: A Text Mining Study of Employee Reviews in IT Industry[J].Journal of Chinese Human Resources Management,2025,16(02):97-120.
(2025). Leveraging People Analytics for Employer Branding: A Text Mining Study of Employee Reviews in IT Industry. 华夏人力资源管理学报, 16(2), 97-120. DOI： 10.47297/wspchrmWSP2040-800506.20251602.
Mahar Daniyal Haider.Leveraging People Analytics for Employer Branding: A Text Mining Study of Employee Reviews in IT Industry[J].Journal of Chinese Human Resources Management,2025,16(02):97-120. DOI： 10.47297/wspchrmWSP2040-800506.20251602.
The rise of crowdsourced job reviews has provided valuable insights into employer branding. This study applies text-mining techniques to analyse employee reviews and identify key employer value propositions (EVPs) in the U.S. IT industry. A dataset of 21
482 employee reviews from 11 Fortune 500 IT companies was collected from Indeed.com
followed by bigram analysis and Structural Topic Modeling (STM) for extracting recurring themes and sentiments. The bigram analysis identified work-life balance and competitive compensation as the most positively associated factors
whereas job security emerged as the most frequently criticized aspect. The STM analysis identifies five dominant EVPs that IT professionals prioritize. Further
findings indicate a growing emphasis on management quality and economic value over time
while employee joy at work has declined since 2012. These insights have practical implications for HR managers and policymakers aiming to enhance employer branding strategies. This study contributes to the field of people analytics by integrating computational text-mining methods to analyze employee feedback
offering a data-driven approach to understanding workforce sentiments. Limitations and future research directions are discussed in relation to expanding the study across different industries and geographical contexts.
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