Sima Sajjadiani
Sima Sajjadiani
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Who is leaving and why? the dynamics of high-quality human capital outflows.
Abstract This study proposes a unified, dynamic framework based on Turnover Event Theory (TET) to evaluate the effects of dismissals, layoff announcements, and voluntary turnover on subsequent work unit voluntary turnover.
Sima Sajjadiani
,
Alan M. Benson
,
John D. Kammeyer-Mueller
DOI
The social process of coping with work‐related stressors online: A machine learning and interpretive data science approach
Abstract People are increasingly turning to social media and online forums like Reddit to cope with work-related concerns. Previous research suggests that how others respond can be an important determinant of the sharer’s affective and well-being outcomes.
Sima Sajjadiani
,
Michael A. Daniels
,
Hsuan-Che (Brad) Huang
DOI
Using machine learning to translate applicant work history into predictors of performance and turnover
Abstract: Work history information reflected in resumes and job application forms is commonly used to screen job applicants; however, there is little consensus as to how to systematically translate information about one’s work-related past into predictors of future work outcomes.
Sima Sajjadiani
,
Aaron Sojourner
,
John D. Kammeyer-Mueller
,
Elton Mykerezi
DOI
Are bonus pools driven by their incentive effects? Evidence from fluctuations in gainsharing incentives
Abstract Bonus pools, in which a worker’s realized bonus depends both on a worker’s share of the pool (which serves as the incentive) and on the size of the pool (which is largely outside of the worker’s control), are a common method for distributing incentive pay.
Sima Sajjadiani
,
A.M. Benson
DOI
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