The informational value of employee online reviews

Efthymia Symitsi, Panagiotis Stamolampros, George Daskalakis, Nikolaos Korfiatis

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper investigates the informational value of online reviews posted by employees for their employer, a rather untapped source of online information from employees, using a sample of 349,550 reviews from 40,915 UK firms. We explore this novel form of electronic Word-of-Mouth (e-WOM) from different perspectives, namely: (i) its information content as a tool to identify the drivers of job satisfaction/dissatisfaction, (ii) its predictive ability on firm financial performance and (iii) its operational and managerial value. Our approach considers both the rating score as well as the review text through a probabilistic topic modelling method, providing also a roadmap to quantify and exploit
employee big data analytics. The novelty of this study lies in the coupling of structured and unstructured data for deriving managerial insights through a battery of econometric, financial and operational research methodologies. Our empirical analyses reveal that employee online reviews have informational value and incremental predictability gains for a firm’s internal and external stakeholders. The results indicate that when models integrate structured and unstructured big data there are leveraged opportunities for firms and managers to enhance the informativeness of decision support systems and in turn, gain competitive advantage.
Original languageEnglish
Pages (from-to)605-619
Number of pages15
JournalEuropean Journal of Operational Research
Volume288
Issue number2
Early online date12 Jun 2020
DOIs
Publication statusPublished - 16 Jan 2021

Keywords

  • Analytics
  • Big data
  • Decision processes
  • Employee online reviews
  • Topic modeling

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