Employee Satisfaction in Online Reviews

Philipp Koncar*, Denis Helic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Employee satisfaction impacts the efficiency of businesses as well as the lives of employees spending substantial amounts of their time at work. As such, employee satisfaction attracts a lot of attention from researchers. In particular, a lot of effort has been previously devoted to the question of how to positively influence employee satisfaction, for example, through granting benefits. In this paper, we start by empirically exploring a novel dataset comprising two million online employer reviews. Notably, we focus on the analysis of the influencing factors for employee satisfaction. In addition, we leverage our empirical insights to predict employee satisfaction and to assess the predictive strengths of individual factors. We train multiple prediction models and achieve accurate prediction performance (ROC AUC of best model = 0.89 ). We find that the number of benefits received and employment status of reviewers are most predictive, while employee position has less predictive strengths for employee satisfaction. Our work complements existing studies and sheds light on the influencing factors for employee satisfaction expressed in online employer reviews. Employers may use these insights, for example, to correct for biases when assessing their reviews.
Original languageEnglish
Title of host publicationSocial Informatics - 12th International Conference, SocInfo 2020, Proceedings
EditorsSamin Aref, Kalina Bontcheva, Marco Braghieri, Frank Dignum, Fosca Giannotti, Francesco Grisolia, Dino Pedreschi
Pages152-167
Number of pages16
DOIs
Publication statusPublished - 1 Jan 2020
Event12th International Conference on Social Informatics: SocInfo 2020 - Virtual, Pisa, Italy
Duration: 6 Oct 20209 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12467 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Social Informatics
CountryItaly
CityVirtual, Pisa
Period6/10/209/10/20

Keywords

  • Employee satisfaction
  • Employer reviews
  • Kununu

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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