Designing an Indigenous AI-Based Smart Human Resource Management Model in the Petrochemical Industry Using a Grounded Theory Approach

Abstract

The present study aimed to design an indigenous model of artificial intelligence (AI)-based smart human resource management (SHRM) in the petrochemical industry. In terms of purpose, the study was applied, and in terms of nature, it was qualitative and based on the grounded theory approach. The research participants consisted of managers, specialists, and experts with direct experience or relevant expertise in the petrochemical industry in the fields of human resource management, information technology, digital transformation, strategic planning, organizational excellence, health, safety, and environment (HSE), and process management. Data were collected through 12 semi-structured interviews, which continued until theoretical saturation was achieved. Data analysis was conducted using the constant comparative method through the stages of open, axial, and selective coding. The findings resulted in the development of a paradigmatic model comprising six components: causal conditions, the central phenomenon, contextual conditions, intervening conditions, action–interaction strategies, and consequences. During axial coding, the central phenomenon was conceptualized as the “transition toward an indigenous, smart, data-driven, predictive, and human-centered human resource management system in the petrochemical industry.” The results indicate that smart human resource management in the petrochemical industry is not merely a technological project; rather, it constitutes a multidimensional organizational transformation whose success requires alignment among data, technology, people, culture, governance, safety, and indigenous contextual requirements.