Digital leadership and AI performance assessment impact on organisational performance: Role of empowerment and engagement

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Type of the article: Research Article

In the era of digital transformation, organizations are increasingly integrating digitalization and artificial intelligence to enhance employee behavior and organizational outcomes. This study examines the associations among digital leadership, AI-based performance assessment, employee empowerment, work engagement, and organizational performance in a Chennai-based IT company in India. Specifically, the study investigates the direct and indirect effects of digital leadership and AI performance assessment on organizational performance through employee empowerment and work engagement. Data were collected from 373 IT employees using an online survey conducted between June and August 2025 and analyzed using partial least squares structural equation modelling (PLS-SEM). A stratified random sampling technique based on organizational job levels (entry, mid, and senior) was adopted to ensure adequate representation of hierarchical positions within the organization. Hypothesis testing revealed that digital leadership and AI performance assessment significantly enhance employee empowerment and work engagement (β = 0.490, 0.415, 0.527; p < 0.001), which in turn positively influence organizational performance (β = 0.383, 0.477, 0.195, 0.287; p < 0.001, 0.033). Furthermore, employee empowerment and work engagement significantly mediate the relationships between digital leadership, AI performance assessment, and organizational performance (β = 0.135, 0.199, 0.265, 0.244; p < 0.001). In addition, a multi-group analysis was conducted to examine differences across employee hierarchical levels. The findings highlight that transformations in modern workplaces and digitalized HR practices contribute to organizational performance across the workforce, while ensuring that employees from different hierarchical levels are adequately represented in the sample.

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    • Figure 1. Conceptual framework
    • Table 1. Demographic profile
    • Table 2. Measurement scales
    • Table 3. Measurement items analysis
    • Table 4. Reliability and average
    • Table 5. Discriminant validity
    • Table 6. Model fit assessment
    • Table 7. Effect size (f²)
    • Table 8. Coefficient of determination (R²) and predictive relevance (Q²)
    • Table 9. Direct path results (hypothesis testing)
    • Table 10. Mediation analysis
    • Table 11. Multi-group analysis (MGA) by employee level
    • Conceptualization
      Gayathiri G., Prabu G.
    • Formal Analysis
      Gayathiri G., Sindu Bharathi S. K., Prabu A.
    • Investigation
      Gayathiri G., Prabu G., Prabhavathy R
    • Methodology
      Gayathiri G., Prabu G.
    • Project administration
      Gayathiri G., Sindu Bharathi S. K., Prabhavathy R, Prabu A.
    • Software
      Gayathiri G., Prabu G., Prabhavathy R
    • Supervision
      Gayathiri G., Prabu G., Prabu A.
    • Writing – original draft
      Gayathiri G.
    • Writing – review & editing
      Gayathiri G., Prabu G., Prabu A.
    • Data curation
      Prabu G., Sindu Bharathi S. K., Prabhavathy R
    • Validation
      Prabu G., Sindu Bharathi S. K., Prabu A.
    • Visualization
      Prabu G., Sindu Bharathi S. K., Prabhavathy R
    • Resources
      Sindu Bharathi S. K., Prabhavathy R, Prabu A.