Identifying and Prioritizing the Dimensions of Artificial Intelligence-Based Marketing Management in Iran’s Steel Industry: A Mixed-Methods Study

Abstract

This study aimed to identify and prioritize the dimensions of artificial intelligence-based marketing management in Iran’s steel industry. This applied/developmental study was conducted using a mixed qualitative–quantitative design. In the qualitative phase, the systematic grounded theory approach of Strauss and Corbin was employed. Participants included 16 experts, senior managers, commercial managers, sales managers, information technology managers, and university faculty members familiar with the steel industry, who were selected through purposive and snowball sampling until theoretical saturation was achieved. Data were collected through semi-structured interviews and analyzed using MAXQDA through open, axial, and selective coding. In the quantitative phase, the statistical population consisted of 400 employees of the commercial department of Isfahan Steel Company, from whom 196 participants were selected based on Morgan’s table through convenience sampling. The quantitative data collection instrument was a researcher-made questionnaire developed from the qualitative findings. Content validity was confirmed using CVR and CVI, and reliability was assessed using Cronbach’s alpha. Data were analyzed using SPSS and SmartPLS through structural equation modeling. The results of structural equation modeling indicated that all model paths were statistically significant, with t-values exceeding 1.96; therefore, all research hypotheses were supported. The overall goodness-of-fit index was 0.784, indicating a strong model fit. Based on the impact coefficients, artificial intelligence analytical capabilities ranked first with a coefficient of 0.342, followed by data-driven infrastructure with 0.298, organizational and managerial factors with 0.245, intelligent human resources with 0.215, implementation strategies with 0.196, and environmental and legal requirements with 0.187. These results indicate that analytical capabilities and data-driven infrastructure exert the strongest effects on artificial intelligence-based marketing management in the steel industry. The findings suggest that the successful implementation of artificial intelligence-based marketing management in Iran’s steel industry primarily depends on strengthening AI analytical capabilities and developing a robust data-driven infrastructure. Accordingly, steel companies should prioritize data quality, data integration, and analytical readiness before investing extensively in advanced AI-based marketing systems. They should also focus on human resource development, organizational support, and phased implementation strategies to ensure sustainable transition toward intelligent marketing.