Designing a Strategic Human Resource Management Model in the Era of Digital Transformation with Emphasis on Artificial Intelligence Applications: A Mixed-Methods Study

Abstract

Objective:  The present study aimed to design and validate a strategic human resource management model aligned with digital transformation requirements by examining the role of artificial intelligence applications in enhancing organizational human capital management.

Methods and Materials: This research adopted a sequential exploratory mixed-methods design conducted in two phases. In the qualitative phase, a meta-synthesis based on the seven-step Sandelowski and Barroso method was performed to analyze 95 scholarly articles published between 2000 and 2025, complemented by semi-structured interviews with 28 experts including senior HR managers, digital transformation consultants, and university scholars selected through purposive sampling until theoretical saturation was achieved. Thematic analysis was used to identify key dimensions and components of strategic HR transformation. In the quantitative phase, a researcher-developed questionnaire consisting of 42 items derived from qualitative findings was distributed among 384 human resource managers and specialists from organizations operating in information technology, financial, manufacturing, and service sectors in 2025 using stratified random sampling. Reliability and validity of the instrument were confirmed through expert review and internal consistency measures. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4, complemented by Interpretive Structural Modeling (ISM) and MICMAC analysis to determine hierarchical relationships among model components.

Findings: Structural model results demonstrated that transformation infrastructure significantly predicted strategic AI applications (β = .68, p < .001), while digital organizational culture exerted a strong positive effect (β = .54, p < .001). Ethical challenges showed a significant negative influence on AI implementation (β = −.35, p < .001). Artificial intelligence applications significantly enhanced talent acquisition (β = .71), talent development (β = .66), talent retention (β = .63), and HR utilization effectiveness (β = .60). The model explained 62% of the variance in AI applications and achieved strong overall goodness of fit (GOF = 0.624), indicating robust explanatory and predictive power.

Conclusion: The findings confirm that artificial intelligence serves as a strategic enabler of human resource transformation when supported by technological infrastructure, digital organizational culture, and ethical governance mechanisms. The proposed model provides an integrated framework that balances algorithmic efficiency with human-centered management and demonstrates that successful digital HR transformation requires coordinated investment in technology, organizational learning, and responsible AI practices to achieve sustainable competitive advantage.