The informational value of employee online reviews

Efthymia Symitsi, Panagiotis Stamolampros, George Daskalakis and Nikolaos Korfiatis ()

European Journal of Operational Research, 2021, vol. 288, issue 2, 605-619

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 modeling 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.

Keywords: Analytics; Employee online reviews; Topic modeling; Big data; Decision processes (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221720305269
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:288:y:2021:i:2:p:605-619

DOI: 10.1016/j.ejor.2020.06.001

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().