The aim of this study is to demonstrate the capabilities of the Jamovi software in analyzing complex economic models, using the case of examining the impact of various factors on enterprise innovation activity. Structural Equation Modeling (SEM) was employed to identify both direct and mediated relationships among economic and digital infrastructure, factors related to research and development (R&D), and innovation activity. The analysis results confirmed the positive impact of economic infrastructure on the level of R&D and the innovation activity of enterprises, as well as the mediating role of R&D factors in transmitting the effects of infrastructure. Meanwhile, the influence of digital infrastructure was found to be weak, highlighting the need for further development of digital technologies and their integration into economic activities. The findings and methodology of the study can be utilized to enhance the competitiveness of enterprises and to develop effective measures for state support of innovation activities in various regions.

Purpose: This study aims to investigate how green intellectual capital (GIC) impacts green competitive advantage (GCA) in Spanish wineries, further exploring the mediating roles of green technological innovation (GTI) and environmental management accounting (EMA) in the primary GIC-GCA linkage. Moreover, the wineries’ age, size and membership in a protected designation of origin (PDO) are introduced as control variables to enhance the accuracy of the examined cause-effect associations. Design/methodology/approach: This study, based on a theoretical framework developed by previous academic studies, uses partial least squares structural equation modeling (PLS-SEM) to analyze data collected from 207 Spanish wineries between September 2023 and February 2024. Findings: The findings indicate that GIC positively impacts the GCA of Spanish wineries, with GTI and EMA partially mediating this relationship. Originality/value: This study stands out for its incursion into the joint analysis of GTI and EMA as mediating factors in the GIC-GCA relationship, a perspective not previously examined in the academic literature, as well as for its contextualization in the wine business setting, thus tracing a novel path in the understanding of environmental management and wineries’ intellectual capital.

The essence of economic complexity is that some of the biggest social problems of our time begin to make sense only if we look at the systemic interactions that give rise to them. As an example of a systematic interaction, it is suggested that we look at the unemployment rate. Analyzing economic complexity offers a broad framework that can be applied to many social indicators. On issues related to social employment, the hypothesis of a relationship between a country's economic complexity and the unemployment rate is born. The discussion begins by examining how humans have been able to compartmentalize the process of knowledge production and organize complex interdependencies that in turn create extraordinary technologies. The purpose of this study is to prove or disprove the hypothesized relationship between the economic complexity index and the unemployment rate. Using the correlation and regression model, it was possible to identify an indirect relationship between the two indicators under study, thereby confirming the above hypothesis. Economic complexity offers a potentially powerful paradigm for understanding the key social issues and challenges of our time. Growth, development, technological change, income inequality and even unemployment are the visible results of hidden systemic interactions. Understanding the structure of these interactions and how they shape different socio-economic processes is therefore important in the study of economic complexity. The findings shed new light on the potential of economic complexity to track and predict the innovation potential of countries and interpret temporal dynamics. Economic growth could possibly pave the way for better and more unemployed populations.

The study analysed the key resources and conditions affecting the development of innovation activities in regional agroclusters. The study conducted a comprehensive analysis of the innovation potential of agroclusters, including the assessment of key factors such as human resources, research base, financial support, and technological infrastructure, using SWOT-analysis, index method and experts. The analysis revealed that the level of innovation potential of regional agroclusters was determined by the degree of interaction between enterprises, scientific institutions, and government structures. Funding of agro-industrial cluster in Kazakhstan was USD 2.5 bn, of which USD 0.8 bn was allocated for innovation, while in the Netherlands, USA (California and Iowa) these indicators were much greater, reaching USD 10-15 bn with innovation costs within USD 2.8-6.5 bn. For example, in Kazakhstan, a stable growth of agro-industrial complex by 3.6% per year has been observed since 2010, which contributed to an increase in labour productivity to USD 6.6 thsd per person – 30% greater than the indicators of Ukrainian farmers. The study found that the availability of specialised infrastructure (agrotechnoparks, technology transfer centres) contributed to accelerated innovation. The study also confirmed that the key barriers to innovative development of agroclusters included insufficient research and development funding, lack of qualified personnel, and weak cooperation between participants. For instance, the use of precision farming and artificial intelligence technologies can increase crop yields by 20% and reduce water use by 30%. In regions with active state support and access to investment, such as Kazakhstan, an increase in the number of start-ups and venture capital investments was observed. Based on the analysis, recommendations for developing the innovation potential of agroclusters were proposed, including the creation of incentive support measures, the development of educational programmes, and the introduction of digital platforms for technology exchange. The findings obtained can be used to develop strategies to support and stimulate innovation activity in regional agroclusters, which contributes to improving their competitiveness and efficiency

Amid rapid digitalization and the ongoing transformation of the economy, the need to revise approaches to the assessment and development of human capital is growing. This study aims to develop an empirically grounded competency map to support strategic human capital management in Kazakhstan’s entrepreneurial sector. The research employed a cross-sectional survey of 368 enterprises, selected through proportional stratified sampling to reflect the official structure of small, medium, and large businesses. The data was collected using an online questionnaire that included 12 key competencies: cognitive, soft, digital, environmental, and technological skills. Primary data processing employed indexing and tabular aggregation methods, while the analysis involved descriptive statistics, analysis of variance (ANOVA), and clustering techniques, implemented using Jamovi software. The results show that basic cognitive and soft competencies such as adaptability (59.9%) and environmental thinking (65.9%) are most common in all types of enterprises, while advanced digital and technological skills, including working with robotic systems (33.4%) and artificial intelligence (38.0%), remain underdeveloped., especially in the SME sector. ANOVA revealed statistically significant differences (p < 0.05) for 11 of the 12 competencies in terms of enterprise size, with large companies demonstrating a higher level of digital and technological skills. The findings formed the basis for the development of a visualized radar map reflecting the integrated competency profile by enterprise size. The results presented contribute to the formation of evidence-based strategies for the development of human capital under conditions of industrial and digital transformation.

The aim of this study is to identify the relationship between the level of digital literacy among the population and the innovation activity of enterprises across different regions of Kazakhstan. The research is motivated by the growing significance of digital competencies in enhancing innovation capacity, particularly in the context of regional disparities in technological readiness. A mixed-method approach was applied, including correlation analysis and structural equation modeling (SEM), using regional statistical data for the period 2021-2023. Key variables included R&D expenditures, digital infrastructure accessibility, and indicators of enterprise innovation activity. Data normalization (Z-score) and region grouping were used to ensure the comparability of results across newly established administrative boundaries. The results reveal a nuanced relationship that differs markedly across regions. In major urban centers such as Almaty and Astana, a clear and strong link is observed between digital literacy and innovation activity. In contrast, in many other regions, this relationship appears weak or even negative. Among the most impactful variables are household Internet access, external R&D funding, and the presence of innovation-oriented enterprises. Structural equation modeling highlights the indirect influence of digital infrastructure and innovation density on this relationship. Model fit statistics (CFI = 0.921; p = 0.207) support the adequacy of the analytical framework. These findings point to the importance of developing tailored strategies at the regional level – ones that address specific digital skill gaps and innovation barriers. The study offers practical implications for policymakers and enterprise leaders seeking to strengthen regional innovation ecosystems through targeted digital development.