Akan Nurbatsin

The study aims to analyze the innovation potential of enterprises, explain the use of the binary regression methodology, and explore different indicators of the enterprises in the regions of Kazakhstan for building an effective management strategy. Methodological substantiation is based on the complex survey data analysis, provided by the World Bank Enterprises Survey (WBES) for Kazakhstan. WBES database has covered a sociological survey of enterprises, which was conducted using a random survey and representativeness among 1,296 enterprises, mainly in the production sector. Besides, the data were collected among companies regarding their experience in environmental perception (including innovation activity), in which they worked. The results have demonstrated that the age of the company, exporter status, type, sector, or activity – all these have a positive influence on the company’s tendency to innovations. However, as part of the study, it has found out that competitors in the marketplace and regions of activity of enterprises predominantly negatively affect the prospects of introducing innovation. It was also in evidence that the same factors (foreign ownership, advanced training, type, size, and sector of an enterprise) are essential determinants of product, technology, R&D, ICT, and innovation. Obtained results can be used by enterprises for building an effective management strategy of own business innovative development, as well as by local authorities to upgrade the competitive performance.

Acknowledgments
This study has been funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant “Priorities and mechanisms of inclusive regional development of Kazakhstan in the context of overcoming the economic recession” No. AP09259004).

This study aims to analyze the level of differences based on effectiveness, statistical dependence, and mutual influence of economic indicators for ranking the regions of Kazakhstan. In the paper, a systematic algorithm of actions is used, ensuring the interconnectedness, the sequence of work, and the validity of the methods used. Several model specifications were formulated to identify interregional differences in economic development indicators: the operating data environment analysis model (DEA) and the random effects regression model (RE). The information database of the Bureau of National Statistics of the Republic of Kazakhstan for 2010–2020 was used. The construction of the RE model was carried out in the SPSS program. A regression model with fixed and random effects in panel data was employed to determine the impact of the selected indicators on gross regional product produced (GRP). Based on the results, the influence of the physical volume of industrial products on GRP per capita in the regions of Kazakhstan for 11 years was revealed in both models with a high significance of coefficients. The study results can be used by public administration bodies that carry out effective strategic management by smoothing interregional differences. Moreover, they can determine the prospects for studying other parameters based on efficiency, statistical dependence, and mutual influence of economic indicators for ranking regions.

Acknowledgment
This study has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant “Priorities and mechanisms of inclusive regional development of Kazakhstan in the context of overcoming the economic recession” AP09259004).

Despite ongoing efforts to modernize the higher education sector in Kazakhstan, the integration of strategic management and the effective allocation of public investment continue to pose significant challenges. This study aims to assess the relationship between public investment in higher education, enrollment dynamics, and innovation activity measured through the share of innovative organizations. Official statistical data for 2004–2024 were obtained from the Bureau of National Statistics of Kazakhstan and the World Bank. The study employs econometric analysis methods, including the evaluation of multiple linear regression models, vector autoregressive models, and Granger causality tests. According to the ordinary least squares (OLS) regression results, the impact of public investment in higher education on the number of students was not statistically significant (β = –0.00035; p = 0.507), suggesting insufficient targeting of funding and inefficient channels of distribution of funds. However, the number of higher education institutions shows a statistically significant positive effect on enrollment (β = 8048.5; p = 0.002), indicating that institutional infrastructure and territorial accessibility play a more significant role in expanding access to higher education than the overall level of funding. In addition, the constructed regression model is characterized by a high explanatory power (R2 = 0.8665), which indicates the estimates’ reliability and the correctness of the choice of explanatory factors. The findings highlight the importance of coordinating investment policy in higher education with the goals of innovative development.

Acknowledgments
This paper has been funded by the Science Committee MSHE RK (Grant “Modernization of the quality assurance system of higher education in Kazakhstan based on digitalization: development of approaches, mechanisms and information base” BR24992974).