Abstract

Currently, the value of the information and communication technology development index in Russia is increasing every year, but it still lags far behind the values of world leaders. The specifics of the domestic experience of digital transformation of all spheres of the economy indicates that the main changes are more often carried out in separate vectors, rather than in a complex. This is a definite problem for the development of the digital ecosystem of the industry – a complex system of interconnected objects competing on a single digital platform and designed to solve specific tasks set for industry needs. And it should be considered not only at the level of state support, but also at the level of combining the forces of the state with science and private business. To do this, it is necessary to create stimulating factors in order to attract investment, which is especially important in the housing and communal services sector. The purpose of this study is to analyze the factors that make up the architecture of the digital ecosystem of housing and communal services, as well as to assess their mutual influence on each other. Methods of analysis and synthesis, economic-statistical and comparative methods, as well as mathematical modeling were used in the work. The authors analyzed current foreign and domestic scientific publications on the research topic, electronic collections of analytical research centers, and official statistical reports of state bodies. The results are a description of a three-factor model of the digital ecosystem of housing and communal services, consisting of performance indicators of digital services, competence characteristics of human resources and a set of functional digital services for housing and communal services. Conclusion. Considering the digital ecosystem of housing and communal services in a complex of three interrelated factors, it is possible to improve the quality of managerial decision-making in matters of modernization of digital management of housing and communal services. Moreover, mathematically describing the dependencies of the factors of the digital ecosystem, it is possible to move to the level of determining specific thresholds, after reaching which, the housing and communal services ecosystem will acquire an optimal configuration of elements and a higher digital potential.

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