Lazat Spankulova

This study aims to analyze the impact of logistics and information and communication technologies (ICT) on Kazakhstan’s economic growth, which requires rethinking search and management tools. The study used the methods of descriptive data statistics, checking the data for the normality of the distribution, and Spearman’s correlation analysis. The information database comprised the National Bank, the World Development Bank, and the national statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. The paper determined the dynamics of GDP per capita and logistics, including export and import, for 2005–2020. The construction of the correlation model was carried out in SPSS. Interestingly, the most significant negative relationship was revealed between database-related services and the population and the volume of communication services (.761*).
On the other hand, the results show a positive impact of ICT’s strong relationship with Kazakhstan’s logistics system. A close relationship was revealed between the volume of postal and courier activities and GDP per capita (.946*), and foreign trade turnover and exports and imports (.897*). Furthermore, the correlation analysis showed that the cost of investments in developing data processing services decreases with an increase in GDP and the volume of communication services. The findings of this study are relevant for governmental bodies operating in the field of logistics and transportation. Moreover, they are valuable for the digitalization of existing and designing new logistics systems as a factor in the development of the economy.

Acknowledgment
The study was carried out within the framework of grant funding IRN AP14869297 by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan “Priorities and mechanisms against rural women of Kazakhstan unequal access to the resources”.

Type of the article: Research Article

Abstract
This study investigates the relationship between tourism development and income inequality across regions of the Republic of Kazakhstan, using regional panel data for 2003–2024. The empirical analysis is based on an unbalanced panel of 16 regions, comprising 367 region–year observations. Fixed-effects regression models are employed to examine how two distinct dimensions of tourism development (tourism services per capita and tourist accommodation places per capita) affect income inequality measured by the regional Gini coefficient.
The results indicate that the intensity of tourism service provision does not have a statistically significant effect on income inequality, even after controlling for cross-sectional dependence. In contrast, tourism accommodation infrastructure capacity is positively and statistically significantly associated with regional income inequality across all model specifications. Additional results show that income inequality is significantly influenced by poverty incidence, income polarization, healthcare expenditures, and the share of the rural population.
The coefficients on the per-capita tourist accommodation variable are positive across all specifications. This indicates that the growth of this indicator contributes to increased income inequality. Moreover, the coefficients for the indicators ShServPop and ShServPop(–1) are significant. However, they cannot be relied upon, as Pesaran’s test rejects the hypothesis of cross-sectional independence for these specifications. This suggests that the growth of tourism infrastructure may exacerbate, rather than reduce, regional income differences due to capital concentration, skill-labor-oriented employment, and price effects.
The results highlight the need for complementary policies that promote inclusive tourism development and mitigate inequality-enhancing effects of tourism-related infrastructure investment.

Acknowledgment
This study was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan within the framework of the IRN grant project AP26198345 “Reducing socio-economic inequality in the regions of Kazakhstan through investments in health and improving the organization of the healthcare system.”

Type of the article: Research Article

Abstract
The purpose of this study is to determine the socio-economic and demographic factors of intra-regional inequality in Kazakhstan and their implications for cluster-based innovation in healthcare. A hybrid approach has been used, consisting of a systematic review of 181 publications using the PRISMA 2020 protocol and econometric analysis of the 2001–2024 panel data for the districts of Kazakhstan, consisting of 3,842 observations. Fixed-effects, cluster-robust, and hierarchical mixed-effects models were employed using standardized variables of population size, fertility, mortality, migration, criminality, and investments. The results reveal that the strongest and most robust predictor of intra-regional inequality in Kazakhstan is investment in fixed capital (β = 0.466, p < 0.01; β = 0.399, p < 0.01). Population size has consistently negative effects on intra-regional inequality in Kazakhstan (β = –0.240 to –0.256, p < 0.05 and p < 0.01). In the multilevel model, fertility increases intra-regional inequality in Kazakhstan (β = 0.114, p < 0.01), whereas mortality and net migration decrease it (β = –0.150 and β = –0.037, p < 0.01). The model explained 37.1% of the variance in intra-regional inequality in Kazakhstan (R2 = 0.371). The results suggest that without balanced investment and territorially differentiated policies, cluster-based innovation in healthcare can even reinforce rather than alleviate regional disparities.

Acknowledgment
This paper was carried out within the framework of the following grant projects funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan: IRN AP26198345 “Reducing socio-economic inequality in the regions of Kazakhstan through investment in and improvement of the organization ofthe healthcare system”.

Type of the article: Research Article

Abstract
This study systematically reviews methodological approaches in tourism crisis management research, examining 374 peer-reviewed articles indexed in Scopus and published between 2007 and 2025. Following PRISMA guidelines, 411 records were identified through a structured Scopus search, and 37 were excluded during screening (15 not substantively addressing crisis management in tourism and 22 falling outside the 2007–2025 window), yielding a final sample of 374 articles. Each article was classified along two dimensions: crisis type (COVID-19/health, war/political/terrorism, natural disaster, financial/economic, or general) and research methodology (paradigm, specific methods, analytical techniques). Classification drew on titles, abstracts, and author keywords rather than full texts; findings should be interpreted accordingly. Because the search string required at least one methodology-related term, the corpus represents the methodology-signaling segment of the literature, and reported shares should be read within that scope. The findings reveal a corpus dominated by qualitative research (56.4% of articles), led by interviews (34.2%) and surveys (21.7%). COVID-19 and health-related crises account for 57.2% of the corpus, confirming a strong post-2020 concentration. Experimental designs appear in 0.8% of articles and longitudinal designs in 2.7%. In comparison, mixed methods research remains at 5.9%, indicating persistent gaps in designs best suited to causal inference and to tracking crises over time. The study contributes a five-category crisis typology, an evidence-based diagnostic of methodological strengths and gaps, and a research agenda for methodological diversification. Practically, stronger and more diverse methodologies improve the evidence base for tourism organizations, destination authorities, and public administrators making decisions under uncertainty.