Identifying the Core Factors of Intelligent Human Capital Management Using an Artificial Intelligence Approach in Government Organizations in Tehran Province

Abstract

This study aimed to identify and explain the factors constituting the core phenomenon of intelligent human capital management using an artificial intelligence approach in government organizations in Tehran Province. This applied study employed a sequential exploratory mixed-methods design. In the qualitative phase, grounded theory based on the Strauss and Corbin approach was used, and semi-structured interviews were conducted with experts in intelligent human capital management and professors of public administration. Participants were recruited through snowball sampling until theoretical saturation was achieved. In the quantitative phase, the statistical population comprised 9,117 employees of government organizations in Tehran Province, of whom 369 were selected using Cochran’s formula and stratified random sampling. Quantitative data were collected using a 15-item researcher-developed questionnaire encompassing two components: “key characteristics of intelligent management” and “differences from traditional management.” Face, content, and convergent validity were established, while reliability was confirmed using Cronbach’s alpha and composite reliability. Data were analyzed using structural equation modeling with SPSS, SmartPLS, and MAXQDA. Structural equation modeling indicated that the “key characteristics of intelligent management” component had a path coefficient of 0.842, whereas “differences from traditional management” had a path coefficient of 0.877, demonstrating their strong contributions to the core phenomenon. The corresponding coefficients of determination were 0.763 and 0.787, respectively. Furthermore, the path coefficient between AI-driven intelligent human capital management and the core phenomenon was 0.667, with an R² of 0.444, indicating that approximately 44% of the variance in the core phenomenon was explained by the central construct. The generally acceptable factor loadings further supported the explanatory adequacy of the proposed measurement structure and model. The findings demonstrate that intelligent human capital management in government organizations is primarily shaped by the distinctive characteristics of intelligent management and a substantive departure from traditional management practices. Data-driven decision-making, personalized career development, talent identification, enhanced transparency, and the redefinition of managerial and employee roles appear central to developing AI-enabled human capital management.