This paper aims to explore the pathways through which artificial intelligence (hereinafter – AI) contributes to enhancing new quality productivity, based on empirical analysis of Chinese listed enterprises. The analysis covers panel data from 12,880 observations for 2013-2022, excluding companies from the financial and construction sectors. Based on the data of Chinese A-share listed enterprises, this study systematically explores the driving mechanism and practice path of AI technology on enterprises’ new quality productivity. By constructing AI technology application indicators through machine learning and text analysis methods, combined with the heterogeneity perspective (enterprise attributes, industry characteristics, and regional policies), the empirical test finds that AI technology significantly enhances enterprises’ new quality productivity. Its core paths include intelligent supply chain management, digital innovation efficacy enhancement, and information asymmetry alleviation. The results show that AI has a statistically significant positive effect on new quality productivity (coefficient = 1.18, p < 0.01). In addition, it was found that the key channels of impact are digital innovations (effect = 0.465, p < 0.01), supply chain efficiency (effect = 0.121, p < 0.01) and reduction of information asymmetry (effect = -0.053, p < 0.01). Heterogeneity analysis shows that the empowering effect of AI is particularly significant in SOEs, labor-intensive industries, hightech manufacturing industries and regions with high government financial support. This study provides theoretical and empirical evidence for differentiated policy-making and emphasizes that AI technology needs to be combined with organizational characteristics and the external environment to accelerate the sustainable development of new quality productivity.

Modern agricultural production needs to increase efficiency and sustainability against the background of digitalization and environmental challenges. The purpose of this study is to analyze the impact of precision marketing on the economic performance and sustainable development of agro-industrial enterprises using the example of China, as well as to assess the applicability of the Chinese experience to the agricultural sector in Kazakhstan. The methods used are econometric analysis of panel data and cross-country comparative research. Based on the panel data of ten representative listed agribusinesses in China from 2014 to 2023, the empirical study uses regression analysis and fixed effects model. The results show that the input of selling expenses is significantly positively correlated with the growth of operating revenues, with a marginal benefit as high as 1:17.04, highlighting the central role of precision marketing in resource allocation. At the same time, the strategy significantly impacts the economic efficiency and sustainable development of agribusinesses through reducing resource wastage. The coefficient of determination was R2 = 0.735, which indicates a high explanatory power of the model, and each increase in marketing costs of 100 million yuan was accompanied by an average increase in revenue of 5.165 billion yuan, which indicates a high marginal return (17 times higher than the underlying investment). Special emphasis is placed on the potential of Sino-Kazakh cooperation in the application of precision agriculture technologies such as smart irrigation and drone inspection.

With the accelerating ageing of the population, there is an increasing need for older citizens to adapt to using digital healthcare solutions, including Health Information Systems (hereinafter – HIS), as an important element of affordable medicine. The primary purpose of this study is to examine the use and acceptance of HIS among senior citizens in Turkey who are actively employed or capable of working, using the Technology Acceptance Model (hereinafter – TAM) as the theoretical framework. A quantitative research design was applied, including survey data from 221 elderly individuals and a comparative dataset from 50 middleaged and 56 elderly participants. The results showed that self-efficacy (β = 0.73, p < 0.001) and facilitating conditions (β = 0.77, p < 0.001) significantly predicted perceived ease of use, which in turn was significantly related to perceived usefulness (β = 0.73, p < 0.001). However, neither perceived usefulness nor perceived ease of use significantly affected attitude or behavioral intention among elderly participants. T-tests revealed no statistically significant differences in HIS acceptance between middle-aged (33–40) and elderly (65–76) groups across all factors (p > 0.05). The analysis results indicated that the physical, motor and cognitive skills of elderly individuals who are active in working life or able to work are in better condition than their peers. Accordingly, the usage and acceptance levels of HIS among middle-aged and elderly individuals are almost at the same level. However, it has been determined that some improvements will improve the usage level.

Today, digital transformation in the Kyrgyz Republic is becoming a key area of government policy, particularly in light of the rapid growth in demand for electronic services and inclusive technological solutions. The purpose of this study is to assess regional differences and socio-demographic features of digitalization in the Kyrgyz Republic, and to identify barriers and drivers of digital transformation. The research methodology is based on an interdisciplinary approach, incorporating desk analysis of regulatory and strategic documents, statistical data, and international ratings, as well as a mass survey (N = 1102 respondents) and 18 in-depth interviews with representatives of government agencies, businesses, and NGOs. Descriptive statistics methods were employed for quantitative analysis, and qualitative data were analysed using thematic coding. The initial data indicate a significant disparity between regions and age groups: in 2024, 65% of households had access to fixed Internet, but in Bishkek this figure reached 91%, while in Batken region it was only 47%. Digital literacy among young people (aged 18-29) is 81%, while for citizens aged 60 and above, it is only 44%. The results show that the key barriers to digitalization are lack of infrastructure (28% of respondents), high cost of services (23%) and problems with information security (18%). However, educational initiatives and the development of regional infrastructure are among the proposed solutions that are being considered. Future research should focus on developing a digital inclusion index, creating models for assessing cyber resilience, and examining mechanisms to enhance trust in electronic services among vulnerable populations.

In the context of digital transformation and media polarization, companies are increasingly resorting to integrated marketing communications (hereinafter – IMC) in order to ensure brand consistency and improve the effectiveness of communication strategies. This scientific study aims to conduct a bibliometric analysis of scientific literature on evaluating the effectiveness of IMC, in order to identify key theoretical and methodological approaches, dominant thematic areas, and the evolution of metrics from 1991 to 2021. The study utilizes bibliometric analysis with the Bibliometrix tool in the R environment and a sample of 320 publications from the Scopus database as its source base. The empirical basis for this research is an array of 30 peer-reviewed articles on BMI assessment and measurement selected from the same database, covering a period from 1. The research includes an analysis of the ratios of keywords, co-citation mapping and analysis of publication dynamics in order to identify thematic clusters, leading researchers and the intellectual structure of scientific fields. The results allowed us to identify five main research areas: (1) conceptual foundations of BMI; (2) brand capital and consumer behaviour; (3) valuation models and ROI indicators; (4) integration of digital media; and (5) BMI in the global and emerging market. It was found that over the last three decades there has been a shift from theoretical discussion to applied research, with particular focus on digital transformation. Future research should focus on developing an efficiency index, examining the long-term effects of integrated communication and its adaptation to digital and crosscultural environments.

Today, the active implementation of machine learning (hereinafter – ML) methods in public administration opens up new opportunities for forecasting, impact assessment and decision support, while simultaneously generating various challenges. The present study is aimed at a systematic review of scientific publications devoted to applying ML methods in the field of public administration, with an emphasis on identifying thematic areas, ethical and institutional challenges. The initial data set included 524 publications obtained using targeted search queries in the Scopus and Web of Science databases for the period 2014-2024. Data filtering was performed using SQLite, thematic mapping was performed in the VOSviewer environment, and metadata was structured using the Elicit tool and subsequent manual encoding. The analysis results allowed us to identify four functional areas of ML application in public administration: transparency and ethics, resource allocation and service provision, institutional design, and technical integration. Despite significant progress in the models’ technical implementation and predictive accuracy, in many cases, mechanisms for equity, transparency, and citizen participation have been poorly implemented. The scientific novelty of the work lies in the interdisciplinary synthesis and development of a typology of institutional challenges that arise when implementing ML systems in public administration. The prospects for further research are related to the empirical validation of decisions, the development of ethical audit methods, and institutional training for responsible, sustainable, and contextually adaptive use of algorithmic tools in the public administration system.

In conditions of gender imbalance and territorial asymmetry, women’s access to financial resources remains limited. The study aims to assess the level of financial inclusion of women in Kazakhstan based on an analysis of structural factors and their relationship with the transformation of the employment structure. The empirical base of the study is based on official statistics from the Bureau of National Statistics, World Bank reports, as well as industry reviews and specialized databases on the dynamics of small business lending. Structural modelling using the partial least squares method (PLS-SEM) and the Random Forest algorithm is used as a methodological basis. The results show that the GRP variables and the share of wages in GDP have the greatest impact on the parameters of women’s entrepreneurial lending in Kazakhstan. According to the partial least squares model, the coefficients of determination were R² = 0.935 for  volume of loans and R² = 0.822 for their number, which confirms the high explanatory power of the model. The regional analysis confirms the presence of spatial heterogeneity: the greatest synchronicity in lending dynamics is observed in Zhambyl, Turkestan, and East Kazakhstan regions; the lowest - in Mangystau and Atyrau. The results justify the need for financial support tools that consider regional specificities and are consistent with employment and professional adaptation programs to eliminate institutional barriers and improve financial inclusion effectiveness. Future research could focus on assessing financial support mechanisms and analyzing the interaction between employment and digitalization programs for women’s entrepreneurship.

In the context of the rapid digital transformation of Kazakhstan’s economy, financial technologies (FinTech) have become a key factor in modernizing the financial sector. The study aims to develop a comprehensive methodology for assessing and implementing FinTech in Kazakhstan’s financial sector, as well as analyzing their impact on economic performance and operational efficiency. The research methodology includes theoretical and methodological analysis, regression modeling, SWOT analysis, content analysis, as well as methods of quantitative and qualitative assessment: paired t-test and analysis of variance (ANOVA). The initial data is based on statistics from the Kazakh fintech industry, the results of a survey of 2000 respondents, and operational and financial indicators from Kaspi Bank. The results indicate a significant impact of FinTech, including a 30% reduction in transaction costs, an increase in non-cash payment share from 45% to 83%, an increase of more than 200% in the number of active mobile banking users, and an increase from 52 points to 78 in satisfaction levels. Regression analysis showed a significant impact of factors such as investments in fintech (β = 0.28, p < 0.01) and the level of transaction security (β = 0.31, p < 0.05) on operational efficiency. These results demonstrate that financial technologies lead to increased business efficiency, greater access to financial services, and faster digital transformation. Future research may be aimed at assessing the impact of new technologies, such as blockchain and artificial intelligence, on the financial sector’s sustainability, the development of regulatory mechanisms, and the increasing digital inclusion of various population groups.

In the context of digital transformation and rapid globalization, the analysis of the contribution of universities to the formation of intellectual capital as a source of inclusive regional growth becomes particularly relevant. This article aims to identify the relationship between the level of intellectual capital in universities and regional imbalances in Kazakhstan. To achieve this goal, we use a methodology that includes constructing an integral intellectual capital index based on three components - human, structural, and relational - using normalization and factor analysis as well as regression analysis. Official data from the Bureau of National Statistics for 20 Kazakhstani regions for the years 2015, 20, and 23 was used as empirical evidence. The results showed that the university’s intellectual capital index had a statistically significant effect on GRP per capita growth, confirming its role as an engine of economic development. Moreover, the strength and direction of the impact vary depending on the level of IC: regions with high IC tend to benefit from its growth, while in low-IC regions the effect may be neutral or even negative. In addition, the impact on nominal income turned out to be insignificant (p=0.857) and the Gini coefficient showed a significant increase with IQ increase (coefficients of 0.371 and 0. 370; p=0.008). This may indicate a concentration of benefits among individual social groups, emphasizing the need for regional policy adaptation in higher education and science. Future research should focus on in-depth analysis of individual universities’ innovation potential, knowledge transfer, and interactions with regional economies.

Despite the growing importance of interdisciplinary approaches in tourism studies, there remains a lack of structured analytical frameworks for clustering regional tourism products and aligning them with effective marketing tools. The present study aims to develop a theoretical and methodological model for clustering regional tourism products based on bibliometric analysis, followed by comparing clusters with effective marketing promotion tools. The method used is a systematic bibliometric analysis of 245 peer-reviewed publications from 2010 to 2023, selected from the Web of Science Core Collection database using the PRISMA protocol. The analysis was carried out using RStudio (Biblioshiny package) and VOSviewer, which allowed us to build maps of co-authorship, co-quoting and co-use of keywords. The results of the analysis revealed four thematic clusters: (1) sustainable development and innovation in tourism, (2) quality of service and tourist satisfaction, (3) cultural and event tourism, and (4) gastronomic tourism and territorial identity. Based on them, the Matrix Design model has been developed to ensure consistency between the types of travel products and specific promotion tools. As part of the empirical testing, a study of the Mangystau region of Kazakhstan was presented, confirming the model’s applicability: problems of fragmented positioning of the region were identified, as well as solutions for digital segmentation, cluster management and strategic branding were proposed. Future research should explore cross-regional applications of the model and examine the integration of digital technologies, such as AI and data analytics, to further optimize marketing strategies for regional tourism development.