On the formation of state innovation support programs: a taxonomy and synthesis of universal measures
Year: 2025
Type: article
Abstract: This article addresses the relevant scientific and practical task of enhancing the adaptability and substantiation of state innovation support programs in the context of high uncertainty and budgetary constraints. Based on a content analysis of 47 federal and regional state programs (2010-2025), which identified and systematized 1,843 measures, a stable trend towards the unification of program tools was revealed (increasing from 69.2% to 81.3% over 15 years). The theoretical and methodological framework of the study is the concept of a universal innovation system [1–4], within which policy measures are viewed as instruments for targeted impact on key system elements: human capital (maximizing actor engagement) and the institutional environment (minimizing transaction costs) [5, 6]. The main result of the work is the synthesis of seven universal models of measures (UMM), possessing the properties of core and activation premise invariance, modularity, adaptability, and measurability. For each UMM, the invariant activation premise, adaptation parameters, and a system of key performance indicators (KPIs) are defined. Based on the proposed taxonomy, an algorithm for forming an adaptive state program has been developed, representing a sequence of steps from diagnosing systemic dysfunctions to verified activation and parameterization of UMMs. The practical significance of the research lies in creating a toolkit that enables public authorities to reduce the time and transaction costs of developing program documents, enhance the substantiation of managerial decisions, and ensure the comparability of results. Future research directions include the development of software to automate the program formation process, the formalization of synergy effect assessments [9], and the adaptation of the methodology to other areas of public administration.
Language: English
Topic: Innovation Policy and R&D
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7204236382