Introduction:This study was conducted with the aim of designing an indigenous and integrated model of geospatial knowledge governance for the Islamic Republic of Iran in the non-military sector. The central research question was: “What is the pattern of geospatial knowledge governance in the Islamic Republic of Iran, and what are its dimensions and components?” The research did not begin with pre-determined hypotheses within a fixed theoretical framework. The term “governance” here encompasses all leadership, policy-making, facilitation, knowledge-sharing, administration, and regulatory processes in the field of cartography across a social system and geospatial knowledge governance framework, including the establishment and development of the National Spatial Data Infrastructure (NSDI) in the non-military domain for all terrestrial and marine areas of the Islamic Republic of Iran. The logic of geospatial knowledge governance is grounded in policy-making and knowledge-sharing for stakeholders in this industry.

Methodology: The research methodology employed a sequential exploratory mixed-methods design, beginning with qualitative methods followed by quantitative methods. The qualitative phase adopted an interpretive and subjective paradigm, grounded theory approach, inductive reasoning, and grounded theory strategy. The quantitative phase followed a positivist and objective paradigm, theory-driven approach, deductive reasoning, and survey/field strategy. The study involved two distinct populations: qualitative and quantitative. In the qualitative phase, grounded theory was utilized. Qualitative findings revealed that the expected functions of geospatial knowledge governance in the Islamic Republic of Iran form a dynamic network of policy, institutional, technological, and social actions—from causal conditions (necessities and problems) to consequential outcomes (macro-national achievements)—ultimately leading to the realization of a systematic and integrated geospatial data governance model. The resulting paradigmatic model was derived from 502 open codes, 219 axial codes, and 21 selective codes.

Results and Discussion:
Causal Conditions: These are the factors and drivers that shape the central phenomenon. They include: increasing pressure on natural resources and environmental crises such as land subsidence, water scarcity, and flooding; the need to create coherence in geospatial data to eliminate dispersion and duplication across organizations; responding to mega-crises such as water crisis, drought, land subsidence, intentional fires, environmental degradation, and crisis management through reliable geospatial data; dispersion of geospatial data and lack of metadata standardization; support for macro-level policy-making (sustainable development, national security, spatial justice); rapid growth of geospatial technologies and the need for their management; lack of coordination among governmental and private organizations; and absence of a standardized and comprehensive national spatial data infrastructure.

Contextual Conditions: These are the environmental or structural conditions in which the phenomenon occurs. They include: establishment of a national spatial data infrastructure as a sustainable foundation for geospatial knowledge governance; development of a data-centric culture in macro- and micro-level decision-making; complex and multi-layered organizational structure in the country; ecosystem and environmental conditions of the country; capacity-building of human resources in geospatial data and emerging technologies; limitations in specialized human resources and technical-engineering capacity; financial and budgetary constraints for infrastructure development; political and legal environment with restrictions and ambiguities; and institutional and organizational coordination between governmental entities, private sector, and academic community.

Intervening Conditions: These are factors that can positively or negatively influence the interaction between causal and contextual conditions. They include: adoption of emerging technologies such as big data, artificial intelligence, and block chain in geospatial data management; legal and policy support to ensure the enforceability of geospatial knowledge governance; development of data standards and protocols to ensure data compatibility; prevention of data monopolization and centralization through decentralization and broad stakeholder participation; support and guidance from senior managers and policymakers; access to new technologies and standards; level of inter-organizational participation and collaboration; organizational culture and acceptance of change; and assurance of geospatial data security, intellectual property, metadata, and licensing.

Strategies: These are actions taken to manage or respond effectively to the central phenomenon. They include: standardization of data and metadata; establishment and development of an integrated national spatial data infrastructure; creation of geospatial data-sharing platforms; establishment of inter-organizational and cross-sectoral networking; fostering collaboration among organizations, private sector, and non-governmental entities; leveraging emerging technologies and geospatial information networks compatible with digital transformation; strengthening entrepreneurship and commercialization of geospatial data; development of indigenous software and databases; crowdsourcing and public participation in data production; capacity-building through education, research, and international collaboration; human resource capacity-building and empowerment of specialists; and financial management, investment, and optimal resource allocation.

Consequences: The following eleven outcomes represent both direct and indirect results of the central phenomenon and implementation of strategies. These outcomes of designing the geospatial knowledge governance model and establishing the National Spatial Data Infrastructure (non-military) in the Islamic Republic of Iran, along with strategy implementation, include: enhancement of Iran's position in regional and global geospatial knowledge governance; increased productivity of financial, natural, and human resources in geospatial data management; improved transparency, accountability, public trust, and longevity and value of geospatial data; creation of data infrastructure for crisis and risk management; improved decision-making and governance over natural resources and the environment; enhanced quality of national and local policy-making and decision-making; realization of sustainable development and spatial justice; development of national and international participation and collaboration; growth of the digital economy through value creation from geospatial data; reduction of costs, duplication, and resource wastage; and reduction and control of damages from environmental and social crises resulting from poor data management.

Conclusion:
Quantitative findings demonstrated that the dimensions of governance, policy and institutions; stakeholder participation; capacity building and human resources; interactions and policy; governance, policy and institutions; infrastructure and technology; processes and data quality; strategies and innovation; security and data rights; and consequences and sustainable development significantly influence geospatial knowledge governance in the Islamic Republic of Iran.
Keywords: Geospatial Knowledge Governance; Spatial Data Infrastructure; Mixed-Methods; Grounded Theory; I.R.Iran.