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

Marc Roedenbeck and Petra Poljsak-Rosinski

Evidence-based HRM, 2022, vol. 11, issue 3, 519-537

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

Keywords: Soft HRM; Performance; Drivers; Artificial neural network; Non-linearity; Prediction (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ebhrmp:ebhrm-07-2022-0171

DOI: 10.1108/EBHRM-07-2022-0171

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