Developing an AI-Based Employer Branding Model in Iran's Electricity Industry

Document Type : Research Paper

Authors
1 Department of Management, Faculty of Management, CT.C,Islamic Azad University, Tehran, Iran
2 Department of Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 Department of Governmental Management, CT.C, Islamic Azad University, Tehran, Iran
10.30497/smt.2026.247410.3622
Abstract
The electricity industry, as a strategic sector in Iran, faces pressing workforce challenges such as talent shortages, brain drain, and reduced job attractiveness. Addressing these issues requires innovative approaches to human resource management, particularly employer branding. This study develops and validates an artificial intelligence–driven employer branding model designed for the unique context of Iran’s power industry. A three-phase mixed-method design was employed: grounded theory to identify conceptual dimensions, fuzzy analytic hierarchy process (FAHP) to prioritize components, and partial least squares structural equation modeling (PLS-SEM) to validate the model. The findings reveal three overarching dimensions—antecedents, capabilities, and outcomes—comprising fifteen validated components. Comparative analysis shows that academics prioritize sustainability and innovation, whereas practitioners emphasize efficiency and productivity. Results highlight the transformative role of AI-enabled tools such as predictive analytics, automated recruitment, and digital avatars in strengthening employee retention, job satisfaction, and organizational attractiveness. By integrating technological capabilities with strategic HR practices, this study advances the literature on employer branding and provides actionable insights for policymakers and managers in infrastructure industries undergoing digital transformation.
Keywords
Subjects


Articles in Press, Accepted Manuscript
Available Online from 23 May 2026

  • Receive Date 26 August 2025
  • Revise Date 15 February 2026
  • Accept Date 23 February 2026