طراحی مدل حکمرانی داده در گمرک ایران با رویکرد کیفی اکتشافی با تمرکز بر سازوکارهای اجرایی و سیاستی

علی  سعدالدین; محمد علی عنایتی شیراز; سید محمد  زرگر

Authors

Ali Sadoddin Department of Industrial Management, ST.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0003-3666-6058
Mohammad Ali Enayati Shiraz Department of Business Management, And.C., Islamic Azad University, Andimeshk, Iran Author https://orcid.org/0000-0002-9970-5742
Seyyed Mohammad Zargar Department of Management , Se.C., Islamic Azad University, Semnan, Iran Author https://orcid.org/0000-0003-3708-0318

Keywords:

Data governance, Customs Administration of the Islamic Republic of Iran, thematic analysis, technical, organizational and strategic dimensions, data, driven model

Abstract

Objective: The present study aimed to design and explain a comprehensive conceptual model of data governance in the Customs Administration of the Islamic Republic of Iran with emphasis on executive, managerial, technological, and policy dimensions.

Methodology: This study was conducted using an exploratory qualitative approach based on grounded theory and thematic analysis. Research data were collected through 11 semi-structured interviews with experts in customs administration, data management, information technology, and policymaking. In addition, official documents including customs laws, the digital government transformation document, and the World Customs Organization Data Model were reviewed. During data analysis, 508 initial codes were extracted and subsequently organized through open, axial, and selective coding into 30 components, 12 categories, and four major dimensions. To ensure the credibility of findings, participant validation, peer review, and theoretical saturation techniques were applied.

Findings: The findings revealed that data governance in Iran’s customs administration is a multidimensional and interdisciplinary phenomenon requiring synergy among technological infrastructure, organizational structures, data-driven culture, and macro-level policymaking. Four principal dimensions were identified, including technical-technological, organizational-managerial, content-application, and strategic-international dimensions. The results further indicated that the major challenges involve weak data integration, lack of coherent data governance architecture, shortage of specialized human resources, insufficient data-driven organizational culture, sanctions-related technological limitations, and regulatory inefficiencies. The study also demonstrated that implementing data governance could improve managerial decision-making, enhance transparency, facilitate international trade, strengthen data security, and increase stakeholder trust.

Conclusion: The findings suggest that the transition toward data governance in Iran’s customs administration is not merely a technological transformation but also requires restructuring managerial systems, revising legal frameworks, developing digital infrastructures, strengthening organizational culture, and aligning with international standards. The proposed model can serve as a comprehensive roadmap for policymakers and customs managers to achieve digital transformation, intelligent governance, and data-driven decision-making.

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