This article summarizes the arguments and counter-arguments in the scientific discussion on identifying the essential characteristics of human capital and key quantitative indicators of its evaluation. The article determined the evolutionary patterns of changing approaches to interpreting the essence of human capital. The study's main purpose is to form an integrated indicator of human capital assessment and identify the most relevant innovative drivers and inhibitors of its development. Systematization of literature sources and approaches to solving the human capital evaluation problem has shown a significant variation in both national approaches to solving the problem and their supranational counterparts. Given the lack of a unified approach to evaluating human capital, the article proposes an author's approach to solving the problem using the Fishburne formula and additive convolution. The relevance of the selection of normalized partial indicators to the integrated indicator is confirmed based on the Cronbach's alpha test. The composite human capital evaluation indicator includes several social, economic, and institutional indicators. Given the transformation of all components of the business environment and the national economy due to the formation of Industry 4.0, it is necessary to determine the most relevant innovative factors of human capital development. A sample of potential drivers and inhibitors of impact on the composite indicator of human capital evaluation, which have an innovative nature, is formed to achieve this goal. The panel data regression model was built. All calculations were performed using Stata 12/SE software product. Modeling results showed that most determinants of innovation development do not have a statistically significant impact on Human Capital Index and vice versa. Human Capital Index is positively influenced by information and communication technology exports but negatively influenced by the imports of computers, communications and services, and high-tech exports. At the same time, the growth of the Human Capital Index has a negative impact on the growth of the share of exports of computers, communications, and services in the structure of commercial imports and high-tech exports. The study results could be useful to scientists, public authorities, local governments, businesses, and entrepreneurs.

This article summarizes the arguments and counterarguments within the scientific debate on the identification of the main theoretical and practical principles of the functioning of innovative-industrial clusters in different countries, as well as the formalization of the impact of digitalization on their activities. The article summarizes scientific approaches to determining the main characteristics and features of the functioning of innovation-industrial clusters. In order to substantiate the theoretical background of the relationship between innovation-industrial clusters’ performance and digitalization processes, a bibliometric analysis of the main Scopus publications in this direction is carried out using the VOSviewer toolkit. That made it possible to identify the main essential and contextual clusters of scientific research on relevant topics to characterize the evolutionary patterns of their changes during the analysis period. In order to determine the empirical causality of the impact of digitalization on innovative and industrial development, an integral indicator of innovative and industrial development is developed. The Index considers the measurement parameters and regional features of industrial, entrepreneurial, and innovative development. Indicators were integrated using the principal components analysis and additive convolution. The study modelled the influence proxies of the digital economy on the integrated indicator of innovative and industrial development using panel data regression modelling in the Stata 14.2/SE software. In the paper, it is also identified those determinants of the digital development of the state that depends to the greatest extent on the volatility of the innovative and industrial development of the country using one-factor regression models. The study is conducted for the country sample with 10 countries, including Azerbaijan, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Poland, Romania, and Ukraine. The time horizon of the study covers the period 2009-2021 (or the latest available period). The research results can be useful to scientists, state authorities, and local governments.