Harnessing the “Wisdom of Employees” from Online Reviews
(A2019-8186)

AUTHORS

Panagiotis Stamolampros, University of East Anglia; Nikolaos Korfiatis, Norwich Business School - University of East Anglia; Konstantinos Chalvatzis, University of East Anglia; Dimitrios Buhalis, Bournemouth University

KEYWORDS

Employee eWOM; Structural Topic Modeling; Machine Learning

ABSTRACT

Web platforms where people voice their opinions and experiences attract considerable interest from scholarly thought. The focus, however is on consumption-based experiences shared from customers in post transactional contexts whereas other, perhaps valuable, online information sources are neglected. An untapped source of online articulation that of employee online reviews offers new opportunities for the study of research phenomena of interest and managerial practices. At this article we study how the digital voice of employees and specifically the feedback to management could be of practical value for firms and we showcase the informational value of unstructured data (review text). A probabilistic topic model the structural topic model is used to analyze the review text. A Correspondence Analysis based on the topic loadings across the tourism and hospitality sectors identifies differences among them and provide further support against the “one size fits all” approach.

REFERENCES

Authors would like to thank Glassdoor Inc. for kindly providing the data used in this study. We are particularly thankful to Andrew Chamberlain (Glassdoor’s Chief Economist) and Glassdoor’s Data Science team for useful comments and suggestions.