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

Marc Roedenbeck (Faculty of Business, Computing and Law, Technical University of Applied Sciences Wildau, Wildau, Germany)
Petra Poljsak-Rosinski (Faculty of Business Administration, Berlin International University of Applied Sciences, Berlin, Germany)

Evidence-based HRM

ISSN: 2049-3983

Article publication date: 26 December 2022

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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 <0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one.

Originality/value

Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance.

Keywords

Citation

Roedenbeck, M. and Poljsak-Rosinski, P. (2022), "Artificial neural network in soft HR performance management: new insights from a large organizational dataset", Evidence-based HRM, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EBHRM-07-2022-0171

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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