Indigenous Indicators for Smart City Assessment in Iranian Metropolises: A Systematic Review and Qualitative Content Analysis

Abstract

The objective of this study was to identify and extract indigenous, context-sensitive indicators suitable for assessing smart city development in Iranian metropolitan areas. This study employed a systematic review with a qualitative content analysis approach. Thirteen peer-reviewed articles were selected based on relevance, methodological rigor, and conceptual contribution. Data were analyzed using NVivo 14 through open, axial, and selective coding to identify main themes and subthemes. The analytical process continued until theoretical saturation was achieved. Research rigor and trustworthiness were ensured through constant comparison, iterative review, and verification of coding consistency. Analysis resulted in four major themes: “Smart Governance and Management,” “Smart Infrastructure and Technology,” “Smart Environment and Sustainability,” and “Smart Social Capital and Quality of Life.” These themes encompassed 25 subthemes and more than 100 initial open codes. The findings revealed that smart city indicators in Iran are shaped primarily by governance limitations, environmental challenges, infrastructural constraints, and socio-cultural characteristics rather than purely technological drivers seen in global models. Key dimensions such as data transparency, digital participation, environmental monitoring, urban resilience, equitable access, and digital literacy emerged as essential components of an indigenous assessment framework. The study concludes that smart city assessment in Iran requires a hybrid model that integrates global standards with local contextual realities. The proposed framework highlights the need for data-driven governance, robust infrastructure, environmental sustainability, and human-centered development. These indicators provide a foundation for future policies, planning, and evaluation of smart city initiatives across Iranian metropolises.