Happiness, an inside job? Turnover prediction using employee likeability, engagement and relative happiness

Jose Berengueres, Guillem Duran, Dani Castro

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

2 Citations (Scopus)

Abstract

In this paper, we describe how to rank employees for risk of turnover by using data obtained from a happiness self-reporting app. Two data sources are used: daily happiness and social interactions. The data spans 2.5 years and 4,356 employees of 34 companies based in Barcelona. For each employee, we build features at three levels: individual, company level and social interaction graph level. We develop various turnover risk models and we compare how different features affect performance prediction. The results show that the top three features that explain turnover risk are: ratio of likes received (likeability), posting frequency (engagement), and relative happiness (employee happiness normalized by company mean). Surprisingly, a priori expected explanatory features such as mean happiness level and the ratio of likes (positivity), were not significant. Precision@50 = 80% out of a test set with 116 churns, sample size N=2k.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
EditorsJana Diesner, Elena Ferrari, Guandong Xu
PublisherAssociation for Computing Machinery, Inc
Pages509-516
Number of pages8
ISBN (Electronic)9781450349932
DOIs
Publication statusPublished - Jul 31 2017
Event9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
Duration: Jul 31 2017Aug 3 2017

Publication series

NameProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Other

Other9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Country/TerritoryAustralia
CitySydney
Period7/31/178/3/17

Keywords

  • Happiness
  • Information retrieval
  • Learning-to-Ranking
  • Privacy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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