Managing the Selection of Alternative Models of Industrial Clusters Taking into Account the Density of the Institutional Environment

Against the backdrop of the development of theoretical and practical interest in network forms of implementing the country’s innovation policy, the problem of improving the methodological basis for assessing the predictive performance of clusters remains poorly developed. Since clusters act as independent mesoeconomic systems, the assessment of the specifics of each model of such a system should take into account a whole range of socio-economic, geopolitical, and sectoral parameters. The purpose of the article is to present the results of a comprehensive situational modeling of three organizational network structures: autonomous production in the Perm region (model 1), production within a technology park (model 2), and production within an industrial cluster (model 3). The selection of models was carried out by analogy with the already implemented model of the Photonics cluster. The research methodology was the use of the author’s methodology for assessing institutional network potential. The research method was an assessment of the characteristics of the analyzed models with the help of independent experts from the EFQM group in Russia. Because of the simulation, it was found that for model 3 (production within an industrial cluster), the expected synergistic effect is underestimated by 21.17% on average; that is, quantitative indicators have a growth potential of 20–22%, respectively. This indicates an underestimation of the influence of the institutional environment or a higher likelihood of synergistic effects and the stability of the interaction system. The results of the study allowed us to formulate the conclusion that the completeness of the realizability of the interests and goals of the participants in each cluster model depends on the density of its institutional environment. The density of the institutional environment is a basic component for realizing network potential and determining institutional effectiveness.