Human resources analytics (HRA) applications are of theoretical and practical interest to both researchers and practitioners. While organizations have begun to implement HRA applications, there is currently no established approach for measuring their performance. This study aims to address this gap in the literature by proposing a new approach for measuring the performance of HRA applications.
This study proposes a hybrid approach that combines the cumulative belief degree (CBD) and partial least squares structural equation modeling (PLS-SEM) to measure organizational HRA performance.
The performance measurement approach proposed in this study has the capacity to reveal the total HRA performance level of an organization, while also providing the opportunity to measure the performance of the subdimensions that make up HRA. These subdimensions include data sufficiency, technological capability, workforce capability, application level of HRA and organizational climate. This approach has the potential to assist organizations that do not currently utilize HRA in their operations to make an informed decision regarding the implementation of HRA and enables organizations to assess their potential performance if they were to implement HRA.
The proposed approach allows organizations to assess the performance of analytical applications in the human resources (HR) field. This assessment can be carried out at both the pre-implementation and post-implementation stages of HRA applications. Therefore, the approach provides a valuable contribution to organizations, enabling them to enhance their capabilities in this domain. Consequently, the study addresses a significant gap in practical research. Furthermore, in terms of the applicability of the developed HRA performance measurement model to diverse analytical domains, it paves the way for the advancement of other performance measurement studies.
The HRA performance assessment process encompasses multiple interrelated HRA subdimensions and performance indicators that can be measured using different scales. It is therefore essential to implement a flexible methodology that can convert diverse forms of evaluation into a unified scale and integrate them in order to effectively manage the inherent complexities and uncertainties associated with the assessment process. In this regard, the CBD approach proves particularly effective. In the CBD approach, a fuzzy set of linguistic terms is used to convert the performance indicator scores into a common scale and therefore takes into account the uncertainty inherent in the assessment process. In addition, it is also proposed to use the PLS-SEM method to combine CBDs.
