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Artificial neural network in soft HR performance management: new insights from a large organizational dataset

Author

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  • Marc Roedenbeck
  • Petra Poljsak-Rosinski

Abstract

Purpose - This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R2/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered. Design/methodology/approach - The study builds on a dataset (n > 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006). Findings - The analysis reveals a substantial artificial neural network model performance (R2 > 0.75) with an excellent fit statistic (nRMSE

Suggested Citation

  • Marc Roedenbeck & Petra Poljsak-Rosinski, 2022. "Artificial neural network in soft HR performance management: new insights from a large organizational dataset," Evidence-based HRM, Emerald Group Publishing Limited, vol. 11(3), pages 519-537, December.
    • Handle: RePEc:eme:ebhrmp:ebhrm-07-2022-0171
    DOI: 10.1108/EBHRM-07-2022-0171
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