1. Introduction
The smart village paradigm provides a holistic framework for rural settlement planning, aiming to enhance living standards while fostering socio-economic and environmental sustainability. Rooted in digital connectivity and technological integration, a smart village aligns local infrastructure with modern ICT solutions to optimize operational efficiency and cost-effectiveness, all while preserving context-appropriate traditional construction and design practices (Dolvi et al., 2019). Bridging the digital divide between urban and rural networks is vital for sustained national progress, making the integration of smart technologies essential for rural communities such as those in Meshginshahr County. These communities face severe structural challenges, including high unemployment, over-reliance on mono-crop agriculture, limited job diversification, suppressed income levels, inadequate public services, and escalating environmental and energy pressures. Addressing these multi-faceted issues demands a cost-effective, low-impact strategy that accelerates rural development, underscoring the urgency of implementing smart village initiatives in the region. Consequently, this study empirical investigates the critical driving and inhibiting factors governing smart village transformation in the central district of Meshginshahr County to inform targeted planning and policy intervention.

2. Methodology
Conducted as an applied, descriptive-analytical investigation, this study utilized a dual-track data collection strategy incorporating both documentary/library research and field surveys. Theoretical foundations and regional baseline data were established through documentary analysis, while empirical field data were gathered via structured questionnaires administered to both local residents and rural development experts across the central district of Meshginshahr County. Statistical processing encompassed descriptive metrics (frequencies, percentages, central tendencies, and dispersion) alongside inferential analyses. The Kolmogorov-Smirnov test was initially applied to verify data normality. Structural Equation Modeling (SEM) via LISREL software was then employed on the expert dataset to determine factor loadings for drivers and barriers, while the non-parametric binomial test evaluated resident perceptions. Finally, the Kruskal-Wallis test (executed in SPSS) was utilized to rank the surveyed villages based on their relative driver and inhibitor profiles.

3. Findings
The structural equation modeling revealed that physical-infrastructural barriers ( ), economic constraints ( ), managerial bottlenecks ( ), environmental challenges ( ), and socio-cultural limitations ( ) represent the most significant inhibitors to smart rural development, respectively. Conversely, human capital ( ), physical infrastructure ( ), environmental assets ( ), socio-cultural readiness ( ), and economic incentives ( ) emerged as the primary driving factors. Spatial ranking via the Kruskal-Wallis test indicated that Vali Abad (score: 182.65), Baris (score: 169.17), and Naser Abad (score: 163.53) face the highest structural inhibition, whereas Kojang (score: 51.05), Alni (score: 48.53), and Barzil (score: 17.56) exhibit the lowest barriers. In terms of promoting factors, Alni (score: 231.59), Kojang (score: 147.05), and Parikhan (score: 93.13) ranked highest in smart development readiness, while Naser Abad (score: 21.57), Baris (score: 52.60), and Vali Abad (score: 9.65) demonstrated the lowest driving momentum.

4. Discussion and Conclusion
The smart village concept offers an effective path toward sustainable rural development by synthesizing endogenous, bottom-up community assets with exogenous technological innovations. By cultivating human capital, expanding cooperative networks, and driving localized economic innovation, this approach empowers rural areas—particularly those within urban spheres of influence that possess greater proximity to digital infrastructure, expert management, and technical institutions. However, because smart village implementation is highly place-specific, success depends on a region's social-ecological characteristics, community absorptive capacity, and tailored socio-economic innovations. Given that physical infrastructure represents the primary barrier identified in this study, immediate priority must be given to establishing robust digital and physical foundations. Overcoming these infrastructural constraints—paired with targeted capacity building, economic diversification, adaptive governance, and a shift in policy perception treating rural areas as high-value investments rather than cost centers—will provide a scalable model for smart rural transformation across Meshginshahr County and similar regional contexts nationwide.