Marat Urdabayev

This study aims to assess the relationship between R&D and economic growth in terms of their ability to understand R&D management. In the paper, the algorithm of actions was used, which allows ensuring interconnection, sequence of work, validity of the choice of the methods used, and defining key factors over a long period. The following methods of the empirical study were used: analysis of the provision of level development; regional analysis of the data; correlation analysis. Based on correlation analysis the impact of economic growth on R&D was investigated, which is expressed by such variables as the number of organizations engaged in R&D, internal expenditures in R&D, expenditures for technological innovations, number of employees in R&D. The data were obtained from the World Bank, the Eurasian Economic Union, and the statistical yearbook of Kazakhstan for 2009–2019. The results obtained show that all determinants correlate not only with the GDP but with each other as well. According to the findings, viewing the GDP level, there is a positive and negative correlation link between such two factors as ‘the number of research organizations’ and ‘R&D technological innovations’. These coefficients of correlation between GDP and independent factors selected for the analysis are significant, i.e. they can significantly affect the value of the GDP. The obtained results are useful in formulating the R&D development management strategy.

Acknowledgments
This study has been funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (grant IRN AP08052800 “Intellectual potential of the regions of the Republic of Kazakhstan: assessment and development prospects”).

This study aims to study Kazakhstan’s regions and identify places with the best potential for developing smart cities based on cluster analysis. To analyze the differentiation by the level of development, 17 regions of Kazakhstan are grouped according to 2020 data from the statistical bulletin of the National Bureau of Statistics of the Republic of Kazakhstan. The formation of groups of regions with different values of indicators was carried out based on agglomerative clustering using the single linkage, complete linkage, and Ward’s clustering methods. In agglomerative clustering, the algorithm groups regions based on observations into clusters, and indicators determine each area’s innovative development level. The instrument to build clustering is the “RStudio” software package. As a result, regions with their essential characteristics were identified, and an assessment of their prospects was obtained with the most significant potential for developing and managing “smart cities” – Atyrau region, Almaty city, and Astana city. The remaining clusters include regions where favorable conditions for the development of innovations have not yet been formed, which require more resources and efforts to build “smart cities.” Therefore, they should not be the first to implement this concept. They need a more balanced, integrated approach, ideally supported by experience in implementing the idea in more promising regions. In a sense, clustering also allowed for identifying potential (or even existing) innovation clusters in regions of Kazakhstan. The study results can be used in developing government programs to form smart cities and further study the potential of smart cities.

Type of the article: Research Article

Abstract
Export diversification and the growth of non-resource exports remain key strategic priorities for Kazakhstan, particularly for food manufacturing firms facing organizational and institutional obstacles to participation in international markets. We seek to assess managerial, institutional, organizational, and ownership-related drivers associated with direct and broader export participation, targeting food manufacturing enterprises in Kazakhstan. The empirical analysis uses the food sampling-sector domain of the 2024 World Bank Enterprise Survey for Kazakhstan. The complete national dataset contains 1,013 establishments, and the initial food-domain subsample comprised 128 establishments. After listwise deletion of observations with missing values, the final estimation sample consisted of n = 121 establishments in the direct export model and n = 121 establishments in the any-export model. The statistical analysis applies survey-weighted logistic regression and average marginal effects. The findings suggest that organizational capabilities are important for firms’ export operations. Digital presence (β = 2.030; p = 0.004), process innovation (β = 1.789; p = 0.010), and firm size (β = 1.491; p = 0.001) are positively associated with direct export participation. Broader export involvement is associated with foreign ownership and customs restrictions. Strengthening export competitiveness therefore requires improvements in organizational preparedness, innovation, digital communication, customs procedures, and logistics.

Acknowledgments
This study is funded by the Science Committee MSHE RK under grant IRN AP23489234 “Research on new instruments for the development of agrotechnological hubs in the regions of Kazakhstan to enhance the country’s competitiveness in the Eurasian region.”