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.

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.

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”).

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.