Designing a Technology Transfer Model Based on the Implementation of the National Artificial Intelligence Development Program in the Petrochemical Industry

Hamidreza  Noori; Ahmadreza Kasraee; Seyyed Zabihollah  Hashemi

Authors

Hamidreza Noori Department of Technology Management, CT.C. , Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0005-3467-1667
Ahmadreza Kasraee Department of Industrial Management, CT.C. , Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0001-8606-1039
Seyyed Zabihollah Hashemi Department of Industrial Management, CT.C. , Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0003-0533-8898

Keywords:

Artificial Intelligence, Technology Transfer, Petrochemical Industry, National AI Development Program, Digital Transformation, PLS, SEM, Technology Strategy

Abstract

This study aimed to design and quantitatively validate a technology transfer model based on the implementation of the National Artificial Intelligence Development Program in the petrochemical industry. This applied, quantitative, cross-sectional study used a descriptive-correlational design. The statistical population consisted of managers, senior experts, technology specialists, digital transformation professionals, and other relevant personnel in the petrochemical industry in Tehran. Approximately 150 participants were selected through simple random sampling. Data were collected using a researcher-developed 26-item questionnaire covering six constructs: causal conditions, core phenomenon, strategies, contextual conditions, facilitating/intervening conditions, and consequences. Items were scored on a five-point Likert scale. Construct validity, convergent validity, discriminant validity, and composite reliability were examined. Because the Kolmogorov–Smirnov test indicated non-normal distributions for all variables, Partial Least Squares Structural Equation Modeling was performed using SmartPLS. All measurement indicators had significant loadings above the acceptable threshold, and composite reliability and average variance extracted values confirmed satisfactory reliability and convergent validity. All hypothesized structural relationships were statistically significant. Causal conditions had a strong positive effect on the core phenomenon (β = 0.904, t = 14.185), while the core phenomenon significantly affected strategies (β = 0.579, t = 7.313). Contextual conditions (β = 0.756, t = 3.091) and facilitating/intervening conditions (β = 0.628, t = 4.157) significantly influenced strategies. The strongest relationship was observed between strategies and consequences (β = 0.913, t = 17.262). Positive cross-validated communality values and Q² coefficients above 0.35 demonstrated satisfactory measurement quality and strong predictive relevance. The findings confirmed that AI technology transfer in the petrochemical industry is a multidimensional process in which operational and competitive pressures stimulate AI integration, contextual and intervening conditions shape strategic implementation, and appropriate strategies strongly determine technological, operational, environmental, and national outcomes.

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Designing a Technology Transfer Model Based on the Implementation of the National Artificial Intelligence Development Program in the Petrochemical Industry

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