(How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis
ISSN: 1746-5265
Article publication date: 5 April 2022
Issue publication date: 12 May 2022
Abstract
Purpose
Enabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.
Design/methodology/approach
Theoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.
Findings
The authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.
Research limitations/implications
Major limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.
Originality/value
The authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success.
Keywords
Acknowledgements
The authors would like to thank the editor and two anonymous reviewers for the helpful comments on earlier versions of this paper.
Declarations of interest: none
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
Citation
Strohmeier, S., Collet, J. and Kabst, R. (2022), "(How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis", Baltic Journal of Management, Vol. 17 No. 3, pp. 285-303. https://doi.org/10.1108/BJM-05-2021-0188
Publisher
:Emerald Publishing Limited
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