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<p>Integrating data-driven decision-making into the Human Resources Management (HRM) function via Human Resources Analytics (HRA) applications is expected to make HR more rational in decision processes. This transformation will integrate HRM more into strategic decision-making processes. Therefore, it is crucial to assess the impact of HRA on strengthening HRM’s strategic role in organizations. For this purpose, an HRA capability measurement model is developed in this study to facilitate this assessment. In this context, an operational definition of the HRA capability construct was developed, and five subdimensions were determined. To evaluate the developed measurement model, which is designed as a Hierarchical Component Model (HCM), data were collected from HR professionals through a questionnaire. The collected data were analyzed using the nonparametric Partial Least Squares Structural Equation Modeling (PLS-SEM) method, which is particularly suitable for assessing HCMs. After excluding two items based on the analysis results, the revised measurement model demonstrated satisfactory validity and reliability, confirming its suitability for use in future studies.</p>

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Developing an organizational measurement instrument for HR analytics capability

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Muhammed Cagri Budak,
  • Ayberk Soyer

摘要

Integrating data-driven decision-making into the Human Resources Management (HRM) function via Human Resources Analytics (HRA) applications is expected to make HR more rational in decision processes. This transformation will integrate HRM more into strategic decision-making processes. Therefore, it is crucial to assess the impact of HRA on strengthening HRM’s strategic role in organizations. For this purpose, an HRA capability measurement model is developed in this study to facilitate this assessment. In this context, an operational definition of the HRA capability construct was developed, and five subdimensions were determined. To evaluate the developed measurement model, which is designed as a Hierarchical Component Model (HCM), data were collected from HR professionals through a questionnaire. The collected data were analyzed using the nonparametric Partial Least Squares Structural Equation Modeling (PLS-SEM) method, which is particularly suitable for assessing HCMs. After excluding two items based on the analysis results, the revised measurement model demonstrated satisfactory validity and reliability, confirming its suitability for use in future studies.