Scenario Planning for Digital Governance in Iranian Public Administration: Key Drivers, Critical Uncertainties, and Alternative Futures

Background and Purpose: The accelerating integration of artificial intelligence and digital technologies into public sector institutions has generated an urgent demand for anticipatory governance frameworks capable of navigating the structural uncertainties of digital transformation. Iran's public administration confronts this imperative under conditions of compounded institutional constraint- bureaucratic rigidity, fragmented data ecosystems, and underdeveloped algorithmic accountability mechanisms- that substantially complicate digital governance trajectories. This study constructs plausible alternative scenarios for digital governance in Iranian public administration through the horizon of ۲۰۳۵, foregrounding the institutional rather than technological determinants of governance futures. Methods: A mixed-methods design integrating Delphi expert elicitation, MICMAC structural cross-impact analysis, and Scenario Wizard computational scenario generation was employed. A purposive expert panel of ۳۲ specialists across public administration, digital policy, and AI governance domains participated in two Delphi rounds, validating ۳۸ indicators across seven digital governance dimensions: administrative efficiency, digital accountability, participatory capacity, algorithmic transparency, institutional coordination, regulatory coherence, and public trust. Cross-impact analysis of ۱,۴۴۴ variable relationships - yielding a matrix filling rate of ۵۶.۷%, indicative of high systemic interdependence- identified eight key drivers through structural influence-dependence mapping. The full combinatorial scenario space of ۳۶,۸۶۴ configurations (mathematically determined as ۴×۳×۴×۴×۴×۴×۳×۴) was evaluated for internal consistency, from which three strongly compatible scenarios emerged. Results: All eight key drivers cluster in the high-influence, high-dependence quadrant of the MICMAC map, confirming their bifaceted systemic leverage as both drivers of digital governance change and recipients of systemic influence. Assumption consistency analysis identifies algorithmic transparency (score: ۲۹) and public digital accountability (score: ۲۷) as the most adaptation-critical conditions- substantially outweighing AI process automation and technological capability in their systemic importance. Three scenarios are constructed: (۱) Integrated Digital Governance- an optimal configuration in which algorithmic transparency, comprehensive data protection, institutional coordination, and universal digital equity operate synergistically within a rights-protective regulatory architecture; (۲) Transitional Equilibrium- a partial modernization pathway characterized by selective AI deployment and incremental regulatory development without the inter-institutional coordination or accountability infrastructure required for transformative governance; and (۳) Institutional Paralysis- a critical trajectory in which governance fragmentation, regulatory failure, and algorithmic opacity converge to erode democratic legitimacy and deepen digital exclusion. Conclusion: The findings demonstrate that the realization of a preferred digital governance future in Iran is determined less by technological capability than by institutional capacity- the ability to embed transparent, participatory, and algorithmically accountable governance within durable normative and organizational architectures. The Integrated Digital Governance scenario is technically achievable but contingent on coordinated political commitment, sustained institutional investment, and regulatory coherence sustained across governance tiers and electoral cycles.

کلیدواژه ها:

Digital Governance ، Scenario Planning ، Public Administration ، Iran ، MICMAC ، Delphi Method ، AI Governance ، Algorithmic Transparency ، Futures Studies