Building a Mathematical Model of Information and Analytical Support for Management Decision-Making in Territorial Community Development Based on Smart Specialization

This study develops a mathematical model of information and analytical support for decision-making in territorial community development based on smart specialization.  The model focuses on transforming heterogeneous data and expert assessments into a transparent system for prioritizing managerial actions, enabling the alignment of strategic development directions with the actual capabilities of communities and their institutional capacity to support initiative portfolios.  As a methodological framework, the DEMATEL approach is employed to formalize cause–effect interactions among key factors of information and analytical support, including data governance and quality, digital infrastructure, analytical capacity, stakeholder co-creation, innovation ecosystem alignment, and monitoring and project portfolio control.  Based on the matrix of direct influences, the total relation matrix is derived, and indicators of factor prominence and causality are calculated, allowing the determination of factor weights for subsequent integral assessment.  To demonstrate practical application, the model is tested on selected territorial communities in Ukraine.  Factor score profiles are constructed on a 1–5 scale, and an integral index of readiness of information and analytical support for smart specialization implementation is calculated.  The results show that the greatest contribution to readiness is provided by analytical capacity and portfolio control, highlighting the importance of management practices related to data quality, analytical procedures, scenario planning, and systematic monitoring of initiatives.  The practical value of the model is in its applicability as a decision-support tool for strategic units of territorial communities, as it provides a formalized procedure for data collection, assessment of factor interactions, prioritization, and performance monitoring.  This reduces the risk of fragmented decision-making and enhances the effectiveness of smart specialization management.