Designing a Data-Driven Digital Transformation Deployment Model for Iranian Public Organizations: An Empirical Investigation

Abstract

This study aimed to design and structurally explain a data-driven digital transformation deployment model for Iranian public organizations using expert judgment and interpretive structural modeling. This applied and exploratory study was conducted using a mixed-methods design with an interpretive structural modeling approach. The study population consisted of senior managers, digital transformation specialists, data governance experts, information technology managers, public administration scholars, and consultants familiar with digital transformation in Iranian public organizations. A purposive sample of 21 experts from Tehran participated in the study. Data were collected through literature review, semi-structured expert interviews, and an ISM questionnaire based on pairwise comparison of the finalized components. The extracted components were refined through expert review, and the contextual relationships among them were determined using the structural self-interaction matrix. The initial and final reachability matrices were then developed, transitivity was applied, hierarchical levels were identified, and MICMAC analysis was used to classify the components according to driving power and dependence power. The ISM results revealed a seven-level hierarchical model. Digital leadership and strategic commitment were positioned at the deepest level and had the highest driving power, indicating their foundational role in the deployment process. Data governance and regulatory alignment, together with inter-organizational coordination and ecosystem collaboration, formed the next driving layer. Integrated digital infrastructure and interoperability and human resource digital competence were identified as key enabling components. Data quality, security, and privacy management emerged as a central linkage variable. Process redesign and organizational agility and evidence-based decision-making culture were placed at the intermediate transformation level. Citizen-centric digital service design and performance monitoring and accountability were identified as highly dependent outcome components. The findings indicate that data-driven digital transformation in Iranian public organizations is a systemic, hierarchical, and interdependent process that should begin with leadership commitment, governance alignment, coordination, infrastructure development, and human capability building before progressing toward service redesign, evidence-based decision-making, and accountability.