Digitalization of talent planning in IT sector: Mediating role of HR policies and practices

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In the highly dynamic digital era of the information technology industry, agile talent planning and human resources (HR) management strategies are essential for maintaining a competitive edge. This study aims to determine the impact of HR policies and practices as a mediator between the effectiveness of talent planning and digitalization tools and techniques within the IT sector. The research population comprised IT companies in Delhi (and its national capital region), India. Using the convenience sampling technique, a sample of 106 respondents was selected out of 168 contacted, including human resource professionals and managers. The study employed regression analysis and structural equation modeling. The results reject that digitalization tools and techniques do not significantly increase the effectiveness of talent planning in IT organizations (beta coefficient = 0.455 at p-value < 0.001). Additionally, the paper shows that digitalization tools and technologies significantly affect HR policies and practices (beta coefficient = 0.826 at p-value < 0.001). The findings reject the suggestion that there is no significant positive relationship between HR practices and policies and the effectiveness of talent planning in the IT sector (beta coefficient = 0.425 at p-value < 0.001). Furthermore, the study rejects the perception that HR policies and practices do not mediate the relationship between digitalization tools and techniques and the effectiveness of talent planning (beta coefficient = 0.351 at p-value < 0.001). These insights will contribute to developing effective HR strategies that align with technological advancements and foster organizational growth.

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    • Figure 1. Conceptual model
    • Figure 2. Structural model using AMOS SEM
    • Table 1. Demographics of respondents
    • Table 2. Internal consistency, reliability, and convergent validity
    • Table 3. HTMT ratio – Discriminant validity
    • Table 4. Fornell-Larcker criterion – Discriminant validity
    • Table 5. R-square and R-square adjusted
    • Table 6. Path coefficients for the constructs
    • Conceptualization
      Swati Yadav, Shikha Kapoor, Sandeep Kumar Gupta
    • Data curation
      Swati Yadav
    • Formal Analysis
      Swati Yadav
    • Methodology
      Swati Yadav
    • Resources
      Swati Yadav, Shikha Kapoor, Sandeep Kumar Gupta
    • Software
      Swati Yadav
    • Validation
      Swati Yadav, Shikha Kapoor, Sandeep Kumar Gupta
    • Visualization
      Swati Yadav
    • Writing – original draft
      Swati Yadav
    • Investigation
      Shikha Kapoor, Sandeep Kumar Gupta
    • Supervision
      Shikha Kapoor, Sandeep Kumar Gupta
    • Writing – review & editing
      Shikha Kapoor, Sandeep Kumar Gupta