Employee Satisfaction and Corporate Performance: Mining Employee Reviews on Glassdoor.com

Abstract

In recent years, Big Data has created significant opportunities for academic research in a wide range of topics within the social sciences. We contribute to this growing field by exploiting the unique social media data from Glassdoor.com. We extract anonymous employee reviews for textual analysis to reveal the relation between employee satisfaction and company performance. Using categories from corporate value studies, our analysis not only provide a “bird’s eye view,” but also provide specific aspects of employee satisfaction are responsible for driving these correlations. We found that while Innovation is the most important category for technology industry, Quality category drives retailing and financial industry. We confirmed the significant correlation between overall employee satisfaction and corporate performance and discovered categories that are negatively correlated with performance: Safety, Communication and Integrity. We hope that this research encourages other researchers to consider the rich environ that a text analytics methodology makes possible

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