Designing a Smart Model for Implementing Industrial Policies Based on Article 4 of the Seventh Development Plan: A Case Study of Megamotor Saipa Company

Document Type : Original Article

Authors

1 PhD Candidate, Department of Management, ST.C., Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Management, ST.C., Islamic Azad University, Tehran, Iran.

Abstract
This research was conducted with the objective of designing an intelligent implementation model for industrial policies based on Article 4 of the 7th Development Plan, specifically focusing on Megamotor Saipa. The primary problem involves transforming macro-level industrial policies into operational smart mechanisms at the organizational level by leveraging modern data-driven governance approaches and digital technologies, such as the industrial metaverse and digital twins. The methodology follows a mixed-methods (qualitative-quantitative) design, utilizing Grounded Theory in the qualitative phase and Structural Equation Modeling (SEM) in the quantitative phase. During the qualitative stage, data were collected through semi-structured interviews with automotive experts, senior managers, and technology specialists, which were then analyzed through rigorous open, axial, and selective coding. In the quantitative stage, the relationships between the model’s dimensions were tested using SEM and statistical analysis software, confirming the overall model fit and validity. The findings reveal that successful implementation is driven by causal factors including strategic leadership, sustainable financing, and governance mandates. Furthermore, contextual conditions such as digital infrastructure, policy stability, and organizational culture act as facilitators, while intervening conditions like international sanctions, employee resistance, and financial limitations function as deterrents. Executive strategies include digital twin development, metaverse-based training, reverse engineering, and industrial consortiums, leading to increased productivity, innovation, and enhanced organizational sustainability. Ultimately, the proposed model suggests that transitioning toward intelligent implementation requires a shift from traditional management to data-driven digital governance.

Keywords

Subjects

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  • Receive Date 30 September 2025
  • Revise Date 15 October 2025
  • Accept Date 26 November 2025