TY - GEN
T1 - Happiness, an inside job? Turnover prediction using employee likeability, engagement and relative happiness
AU - Berengueres, Jose
AU - Duran, Guillem
AU - Castro, Dani
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/7/31
Y1 - 2017/7/31
N2 - 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.
AB - 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.
KW - Happiness
KW - Information retrieval
KW - Learning-to-Ranking
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=85040240805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040240805&partnerID=8YFLogxK
U2 - 10.1145/3110025.3110132
DO - 10.1145/3110025.3110132
M3 - Conference contribution
AN - SCOPUS:85040240805
T3 - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
SP - 509
EP - 516
BT - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
A2 - Diesner, Jana
A2 - Ferrari, Elena
A2 - Xu, Guandong
PB - Association for Computing Machinery, Inc
T2 - 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Y2 - 31 July 2017 through 3 August 2017
ER -