Marat Urdabayev

Type of the article: Research ArticleAbstractExport 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.AcknowledgmentsThis 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.”

In the context of the accelerating digital transformation of the economy, the importance of quantifying the impact of digital factors on the effectiveness of the higher education system is increasing. The purpose of the study is to analyze the impact of digitalization on the effectiveness of the educational process in higher education institutions in Kazakhstan. The methodological basis of the work consists of quantitative analysis methods, including the calculation of relative indicators of digitalization and educational effectiveness, as well as econometric modeling using the least squares method (OLS). The empirical base of the study was made up of statistical data from the Bureau of National Statistics of the Republic of Kazakhstan, including indicators of the number of students, graduates, teachers, as well as the provision of universities with computer technology and Internet access. The results of the study show that during the analyzed period, the availability of Internet computers increased from 11.64 to 13.28 units per 100 students (+14.1%), while the graduation rate increased from 23.57% to 26.51% (+2.94 p.p.). The econometric analysis revealed a positive relationship between the level of digitalization and the effectiveness of the educational process: an increase in the availability of Internet computers by 1 unit per 100 students is accompanied by an increase in the graduation rate by an average of 2.09 percentage points (R2 = 0.874). The results show that digitalization is a significant factor in improving the effectiveness of higher education, but its impact is primarily quantitative and does not fully reflect qualitative changes in graduate training.

Although smart-city benchmarking has produced many indices and rankings, cities still lack a practical way to assess whether successful initiatives can be transferred across institutional contexts and converted into implementable urban roadmaps. In this study, we aimed to develop and empirically test a paired donor–recipient “problem–solution” methodology that bridges comparative city analysis with implementation readiness gap assessment, addressing the persistent disconnect between smart-city benchmarking and actionable transfer guidance. The smart-city ecosystem was decomposed into eight functional dimensions covering digital foundations, service platforms, finance and procurement, innovation capacity, governance, legal adaptability, and citizen participation. The method was applied to the Ottawa-Astana pair using a systematic desk-based analysis of publicly available strategic documents, legislation and policy frameworks, and implementation materials (e.g., roadmaps, program guidelines, departmental plans, and monitoring outputs). Data were analyzed using a structured gap analysis algorithm employing a three-level qualitative compliance scale (Full Compliance, Partial Compliance, and Non-compliance) to assess recipient city status against donor benchmarks across all eight functional dimensions. The results reveal Astana’s partial compliance with the Ottawa benchmark, with moderate readiness and pronounced “hard-soft” asymmetry; that is, greater progress in regard to infrastructure and platforms, but persistent gaps in adaptive regulation, experimentation-friendly legal instruments, and participatory governance. These findings suggest that progressing toward a Smart City 2.0 model requires prioritizing regulatory sandboxes, adaptive procurement pathways for pilots, and scalable civic-tech mechanisms alongside continued investment in talent and innovation ecosystems—understood here as interconnected networks of universities, technology parks, civic-tech communities, and incubation infrastructure that collectively sustain capacity for technology absorption and local adaptation. The proposed paired framework is replicable and supports phased, actionable transfer roadmaps for policymakers.

Rapid urban growth and the spread of the concept of smart cities force an increasing need to understand how cities become “smart” and apply their experience where it will best take root. Understanding which experience will be most suitable is not a trivial task and requires labor-intensive analysis. This study aims to identify smart cities that are most similar to Almaty and Astana in terms of key indicators by applying quantitative methods. Using a sample of smart cities, this paper successively employs three methods—principal component analysis, hierarchical cluster analysis, and t-distributed stochastic neighbor embedding. The results showed that Denver and Ottawa are the closest to Almaty and Astana, followed by Ankara and Phoenix. The proposed methodology allowed us to assess the similarity of urban development conditions, with an assumption that similar development conditions determine approaches to the development of smart cities, and thus the relevance of experiences from other smart cities worldwide could be applied to Almaty and Astana. This approach is intended to contribute to the effectiveness of transferring advanced solutions of smart city development to the context of Kazakhstan. The obtained conclusions can be used to form recommendations for the development strategy of Almaty and Astana, as well as other cities facing similar challenges.

The motivation of academic staff in higher education institutions has become a critical issue, particularly in the context of rapid digital transformation. Faculty motivation is a crucial factor influencing the quality of education, research productivity, and the performance of universities. This study aimed to identify the main factors influencing motivation and to develop recommendations for educational institutions to enhance staff motivation. The research utilized bibliometric analysis through the VOSviewer program to explore relationships between core motivational factors. Initial data were collected via the ePORTAL system, which aggregates comprehensive information about faculty activities, including publications, participation in scientific projects, and teaching performance. The findings suggest that the current motivational systems in Kazakhstan's higher education institutions require significant modernization. Key challenges include insufficient financial support, excessive bureaucratic procedures, and limited career development opportunities. Moreover, adopting digital tools like ePORTAL can enhance the transparency and objectivity of assessing teaching staff performance, providing more substantial incentives for professional growth. The study offers recommendations for university management of educational institutions to increase academic staff motivation that can be adapted to each educational institution's unique profile and priorities. Future research should investigate the long-term effects of digitalization on teaching quality and explore how motivation strategies can be fine-tuned to address regional and institutional disparities. Such efforts will ensure faculty development aligns with national and international educational standards, ultimately enhancing the competitiveness and effectiveness of universities in a global context.

The potential for developing smart cities in Kazakhstan is evaluated using cluster analysis. Built on previous research focused on clustering the regions of Kazakhstan, this study applies the same method to the cities of the country. The analysis uses indicators related to human capital, infrastructure, education, information technology, production, and other factors to assess the potential of each city. The clustering is performed using Single Linkage, Complete Linkage, and Ward’s methods. The results show that Almaty and Astana are the cities with the highest potential for becoming smart cities. Aktobe is identified as a city with distinctive features that may help or hinder its development as a smart city. The remaining cities are clustered into two groups, with one group having the potential to catch up and maintain the trend of developing smart cities, while the other group is less suitable for starting smart city projects and may require more investment per capita. The study highlights the deep regional inequality affecting the potential to successfully develop and manage smart cities in Kazakhstan. The analysis also reveals some limitations and challenges in the data and variables used, including the lack of data for some variables and the difficulties in "translating" some factors and indicators into quantitative variables for clustering. The study concludes that future research should address these challenges and consider clustering inside certain regions to focus on their unique features. The study recommends launching pilot projects in small cities, with the most successful practices then scaled and implemented in the core smart cities and possibly Aktobe, if it manages to use its advantages to compensate for risks. Overall, this study provides insights into the potential of smart city development in Kazakhstan and can inform policymakers in their efforts to support smart city projects in the country.

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.

The level of digital readiness and the application of information and communication technologies (ICT) are key factors of any innovation policy. This research has highlighted the development of analysis of the degree of digital readiness and assessment methods of digital transformations, which can be used at various levels of business management to formulate digital transformation strategies. The present study investigates the theoretical framework in the field of innovation and spatial development considering the impact of the level of ICT. The research was conducted using index and economic-statistical methods based on a systematic approach. We developed a methodological tool adapted to the regional management level. The ICT development index, Krugman localisation index and Herfindahl-Hirschman index were modified to analyse digital readiness and ICT development at the regional level. The algorithm includes the following steps: assessment of the internet usage level; analysis of the degree of costs for the production of ICT; evaluation of the digital literacy rate of the population; evaluation of the degree of regional industry specialisation in the field of ICT. It was revealed that Kazakhstan’s regions have varying levels of ICT development, which is why they have different prerequisites and prospects for digitalising their economy. The agglomerations that could become “growth poles” of Kazakhstan’s knowledge-based economy were identified, such as Almaty city, Nur-Sultan city, Karaganda, and Aktobe regions. Government bodies can use the research findings for Kazakhstani territories’ digital modernisation.

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. AcknowledgmentsThis 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”).

The purpose of this research work is to analyze the concept of the “smart city” and its characteristics in literature, to assess the application of the concept in Kazakhstan using the example of the “Smart Aqkol” project in order to identify a number of advantages and disadvantages, problems and trends of innovative technologies in the cities of Kazakhstan. In the course of the research, the author carried out a literary review of the concept of “smart city” by domestic and foreign authors, and also considered the experience of implementing “Smart Aqkol” in Akkol. The methodology of the work is to systematize indicators and approaches to the analysis of smart cities projects using the example of “Smart Aqkol”. The author carried out a SWOT analysis to evaluate this project and form recommendations for future projects of smart cities in Kazakhstan. In conclusion of the study, conclusions were formulated about the feasibility of using the experience of “Smart Aqkol”, which can be used as an example for other cities of Kazakhstan in the implementation of similar projects. The study identified the advantages of the implemented experience based on “Smart Aqkol” and provided statistical evidence of the success of this project, which showed a clear example of the fact that in Kazakhstan there are prerequisites for the effective development of smart cities, similar to “Smart Aqkol”.