1. Introduction
Within the Iranian urban context, "urban management fragmentation," arising from a multiplicity of stakeholders, serves as the primary structural barrier to integrating good governance with smart city development. This incoherence impedes the realization of accountability, civic participation, and the cohesive implementation of smart city programs. As a medium-sized city, Shahrekord similarly confronts various challenges, including inappropriate physical-spatial development patterns, deficient foundational and technological infrastructures, the absence of transparent public participation mechanisms, and inadequacies in organizational integration. Consequently, these issues have decelerated the progression of smart governance. The significance of the current study lies in proposing a scientific and comprehensive model of good urban governance components within the context of smartization. In doing so, it guides administrators beyond an exclusive focus on technological elements, steering them toward the enhancement of institutional capacities and human resources. Furthermore, this model assists policymakers in ensuring integration between the managerial structure (governance) and technical infrastructure (smart city features) during the formulation of high-level strategic documents and resource allocation.
Research Questions
What are the components of good urban governance based on a smart city approach in Shahrekord, and how can they be elucidated within an integrated, context-specific conceptual model?

2. Literature Review
Good governance and smart city development are inextricably linked; the realization of various smart city dimensions necessitates the establishment of an efficient governance framework. Rather than relying solely on emerging technologies, the success of such initiatives fundamentally depends on the quality of urban management and governance (underpinned by transparency, public participation, accountability, and efficiency) as the primary bedrock for development. However, a review of the literature revealed a significant theoretical and operational gap at the intersection of these two domains concerning mid-sized cities such as Shahrekord. Previous studies have predominantly focused on metropolises or strictly technological dimensions, thereby neglecting the institutional, participatory, and normative components essential for mid-sized urban centers. Consequently, the absence of a localized framework capable of prioritizing key components in alignment with local capacities was distinctly evident. The innovation of this study was twofold. First, it introduced an integrated model utilizing the Interpretive Structural Modeling (ISM) approach, which synthesized and stratified seven fundamental dimensions (e-participation, data-driven transparency, digital accountability, the rule of law in cyberspace, technological efficiency, equitable access to services, and data-driven governance) tailored to the specific context of Shahrekord. Second, it developed an actionable roadmap for local policymakers. Ultimately, by employing the ISM methodology, this research aimed to bridge the existing gap by offering a localized framework congruent with Shahrekord's managerial and technological landscape.

3. Methodology
The present study was applied and developmental in purpose, and descriptive and analytical in methodology. It employed Interpretive Structural Modeling (ISM) to elucidate the complex, hierarchical relationships among the variables. The research was conducted in two primary phases. In the first phase, a systematic literature review and expert panels (comprising specialists in urban management, information technology, geography, urban planning, and urban sociology) were utilized to comprehensively identify and extract 22 key components related to "good urban governance" with an emphasis on the smart city approach. In the second phase, structural matrices were formulated using the ISM method based on the experts' assessments of the pairwise relationships between the indicators. The output of this analysis was a hierarchical model that categorized the components into foundational (base), linkage, and dependent (outcome) levels, thereby delineating their causal network and interdependencies. The statistical population of the study consisted of academic experts and experienced managers in this field. Ultimately, a final sample of 26 individuals was selected employing purposive and snowball sampling techniques.

4. Discussion
The components of smartization have redefined the traditional principles of good governance into three distinct categories: governance-oriented, technology-enabling, and outcome-driven. Based on Interpretive Structural Modeling (ISM), these components form an 11-level hierarchical structure. In this framework, "smart and data-driven governance" (C4) serves as the most fundamental driver at the base, while elements such as "electronic participation" are positioned at the apex of the pyramid. Compared to previous studies, this research placed a particular emphasis on data-centricity and cybersecurity. In the context of Shahrekord, two independent and key components, namely "data-driven governance" (C4) and "vision and leadership" (C8), exhibited the highest driving power. A strategic focus on these drivers constituted the most effective pathway for transitioning toward smart urban governance.

5. Conclusion
Utilizing the Interpretive Structural Modeling (ISM) approach, the findings of this study explicitly demonstrated that smart, data-driven governance (C4) alongside vision and leadership (C8) serves as the most fundamental driving component (independent factor). Positioned at the base of the system, these elements exert the greatest influence over all other variables. Furthermore, the smart governance structure in Shahrekord comprises 11 hierarchical levels, spanning from data governance at the foundational tier to ultimate outcomes, such as e-participation and evidence-based policymaking, at the apex. Within this framework, domain-specific elements, including smart transportation, smart healthcare, and a smart economy, occupy the intermediate levels. Their successful realization is heavily dependent upon the reinforcement of both driving and linkage components. Moreover, achieving good governance within the smart city paradigm necessitates a transition from a purely techno-centric perspective toward a systematic, governance-oriented approach. Ultimately, the trajectory of governance transformation in Shahrekord needed to be strategic and phased. This process needed to commence with the fortification of governance and data infrastructures (the foundational layer), advance toward consolidating the linkage factors (the intermediate layer), and culminate in the execution of domain-specific smart projects (the outcome layer). It was solely through this sequential progression that a successful transition toward a smart, equitable, participatory, and sustainable city could be guaranteed.