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Measure what matters: descriptive and predictive metrics of HRM-pathway toward organizational performance

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  • Rajasshrie Pillai
  • Brijesh Sivathanu

Abstract

Purpose - To understand human resource (HR) practices outcomes on HR decision making, strategic human resource management (HRM) and organizational performance by exploring the HR data quality along with descriptive and predictive financial and non-financial metrics. Design/methodology/approach - This work utilizes the grounded theory method. After the literature was reviewed, 113 HR managers of multinational and national companies in India were interviewed with a semi-structured questionnaire. The collected interview data was analyzed with NVivo 8.0 software. Findings - It is interesting to uncover the descriptive and predictive non-financial and financial metrics of HR practices and their influence on organizational performance. It was found that HR data quality moderates the relationship between the HR practices outcome and HR metrics. This study found that HR metrics help in HR decision-making for strategic HRM and subsequently affect organizational performance. Originality/value - This study has uniquely provided the descriptive and predictive non-financial and financial metrics of HR practices and their impact on HR decision making, strategic HRM and organizational performance. This study highlights the importance of data quality. This research offers insights to the HR managers, HR analysts, chief HR officers and HR practitioners to achieve organizational performance considering the various metrics of HRM. It provides key insights to the top management to understand the HR metrics' effect on strategic HRM and organizational performance.

Suggested Citation

  • Rajasshrie Pillai & Brijesh Sivathanu, 2021. "Measure what matters: descriptive and predictive metrics of HRM-pathway toward organizational performance," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 71(7), pages 3009-3029, April.
  • Handle: RePEc:eme:ijppmp:ijppm-10-2020-0509
    DOI: 10.1108/IJPPM-10-2020-0509
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    Cited by:

    1. Neelam Kaushal & Rahul Pratap Singh Kaurav & Brijesh Sivathanu & Neeraj Kaushik, 2023. "Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis," Management Review Quarterly, Springer, vol. 73(2), pages 455-493, June.

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