The global COVID-19 pandemic has led to the self-isolation of people and the transformation of many economic and social processes into an electronic version thus contributing to the digitalization of all spheres. Being part of this environment, enterprises generate information resources to develop their desired image, which may vary according to the factors characterizing the information environment. Information capital is a comprehensive characteristic of an enterprise and determines its effectiveness and sustainability. The purpose of this study is to develop a toolkit that allows one to assess the information capital of an enterprise, reflecting its perception within the digital information environment. It is necessary to develop the methodology for the formation of such tools. As a result, a fuzzy-plural approach has been developed to evaluate the index of external information capital. This model allows us to assess the external information capital and to simulate its changes caused by various kinds of information events. The study of key elements, for example, the stability and tonality indices, index of target perception made it possible to systematize chaotic changes in the external environment and describe them using the Chen–Lee attractor model. The results of this study can be useful for researchers in the field of digital information analysis, in particular for the comparative analysis of enterprises and the assessment of their information capital.

The race of states for digital leadership faces the challenges of digital transformation foresight, providing combinatorial effects and acceleration of digitalization processes, contributing to a more efficient allocation of limited financial resources. This situation determines the relevance of the authors’ study, which aims to develop an appropriate model for the allocation of funds for digital technologies. The methodology of the study is based on the concepts of technological modes and cyclical development of the economy. The paper presents the authors’ definition of the concept “digital space,” which is new to Russian scientific literature. The key research method is mathematical modeling. The scientific novelty of the paper is chronological architectonics of digital transformations, built by the authors, and the model of distribution of financing under the investment constraints due to the trust limit of the society institutions, the time of fluctuations of the system of technological adaptation, and the response of the institutional environment to the digital limits of the transformations. The practical significance of the proposed models is the identified tandem interconnected digital technologies of the areas of combinatorial effects that accelerate the dynamics of Industry 4.0, as well as the resulting tools for the effective management of digital technology financing. The obtained theoretical and empirical results are desirable for the heads of government agencies, foundations, corporations, and development agencies, as well as enterprises interested in accelerating digital transformation and increasing strategic competitiveness in the digital space.

The purpose of this study is to develop a comprehensive algorithm for optimizing the interaction of economic entities with counterparties, taking into account the uncertainty of market conditions and the variety of behavioral strategies of participants. The developed algorithm aims to increase the stability and efficiency of the interactions between the economic entity under study and its counterparties, minimizing risks and optimizing cooperative and competitive strategies within the framework of existing market relations. The methodology uses game theory to devise interaction strategies using mutual influence indices, non-cooperative game principles, and payment matrices. The model analyzes various interaction scenarios with counterparties by using payment matrices and considering both competitive and cooperative conditions. The research methodology is supplemented by the calculation of integral estimates based on a set of financial and economic indicators, enabling the assessment of the impact of various interaction strategies on the overall efficiency of an economic entity. After testing the developed models, a set of data was obtained, which can be used to optimize strategic planning and manage the interaction of economic entities with counterparties. The developed algorithm is an effective tool for improving the operational analysis of enterprises, primarily in industrial sectors.