Aknur Zhidebekkyzy
57192831004
Publications - 6
EXAMINING THE CRITICAL SUCCESS FACTORS FOR THE EFFICIENCY OF GREEN ENERGY PROJECTS
Publication Name: Polish Journal of Management Studies
Publication Date: 2024-06-24
Volume: 29
Issue: 2
Page Range: 328-345
Description:
In recent times, there has been a remarkable exponential interest in green energy projects, driven by their potential to significantly decrease greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. However, such high-tech projects' complexity, expensiveness, and high uncertainty level necessitate new ways of increasing efficiency. Therefore, this study, with its clear aim to evaluate the efficiency of green energy projects and identify the critical success factors that can enhance efficiency, remains a compelling and relevant research endeavour. The study employed a rigorous methodology, using multilinear regression analysis to survey 123 project managers from Kazakhstan. This allowed for a comprehensive comparison of the efficiency level of green energy projects and low-tech projects. Research results show that green energy projects exhibit the highest percentage of schedule overrun at 23.9% and cost overrun at 32.7%, indicating substantial delays in project completion and extra expenses in budget compared with low-tech projects. Moreover, green energy projects show the least favourable results (7.3) regarding technological performance. The study reveals the following critical success processes enhancing project efficiency: project planning, scope and cost management, communication, and team management. Based on the processes of the Project Management Body of Knowledge (PMBoK) Guide, an algorithm for managing green energy projects was developed. This tool equips project managers with a process-based map, enabling them to run their projects effectively and enhance their efficiency.
Open Access: Yes
Catalysing responsible production: Evaluating the impact of EPR system on manufacturing enterprises
Publication Name: Journal of International Studies
Publication Date: 2024-01-01
Volume: 17
Issue: 2
Page Range: 178-190
Description:
Responsible production has become increasingly vital in the global sustainability discourse, particularly in manufacturing. The extended producer responsibility (EPR) system is a critical policy mechanism that encourages manufacturers to reduce their environmental impact. Despite its growing significance, comprehensive studies assessing its effectiveness are sparse. Our research aims to address this gap by evaluating the influence of the EPR system on responsible production practices in European manufacturing enterprises. We employed the difference-in-differences (DiD) method to assess the impact, analysing panel data from 27 manufacturing enterprises across the Czech Republic, Poland, Slovakia, Romania, Estonia, Hungary, and Bulgaria, from 2010 to 2022. This method was chosen to mitigate endogeneity concerns. The results from the DiD analysis reveal a statistically significant positive impact of the EPR system on the circular material use rate, with an average increase of 10,5%. These findings indicate that the EPR system effectively enhances circular material use within the electronics manufacturing industry, a critical sector for advancing environmental sustainability.
Open Access: Yes
REGIONAL DISPARITIES AND DUAL DYNAMICS: ECONOMIC GROWTH AND INCOME INEQUALITY IN KAZAKHSTAN
Publication Name: Economics and Sociology
Publication Date: 2024-01-01
Volume: 17
Issue: 2
Page Range: 241-255
Description:
This study examines the complex relationships between economic growth and income inequality in different regions of Kazakhstan, revealing the nuances of their interaction. The article aims to assess the long-term and short-term effects of economic growth on income inequality in both forward and reverse directions across the regions of Kazakhstan. Employing region-specific time series data allowed us to examine the bidirectional impact of economic growth on inequality, using an error correction model (ECM) to describe short-run and long-run relationships. The results highlight that the relationship between economic growth and income inequality is heterogeneous across regions, reflecting each area's unique economic and social landscapes. The estimation results support the hypothesis of an inverted U-shaped Kuznets curve linking GRP per capita to inequality with varying starting points for different regions. Regarding the inverse relationship, we identified a positive causal relationship for the West Kazakhstan, Zhambyl and Pavlodar regions, indicating that increased income inequality stimulated economic growth. The study also highlights the significant role of trade, labour force, investment and government consumption in shaping these relationships.
Open Access: Yes
Assessing the impact of artificial intelligence on project efficiency enhancement
Publication Name: Knowledge and Performance Management
Publication Date: 2024-01-01
Volume: 8
Issue: 2
Page Range: 109-126
Description:
The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency.
Open Access: Yes
Perceptions and practices of academic excellence: Insights from university stakeholders
Publication Name: Knowledge and Performance Management
Publication Date: 2025-01-01
Volume: 9
Issue: 2
Page Range: 246-261
Description:
The study analyzes how academic excellence is conceptualized within Kazakhstani universities, focusing on two key internal stakeholder groups: faculty members and administrative staff. While academic excellence has become a global priority, little empirical evidence exists on how it is interpreted in emerging higher education systems. The paper addresses this gap by examining the Kazakhstani case, where government-led excellence initiatives are still in their early stages. A quota-based survey was conducted across 42 universities, producing weighted responses from 832 faculty and 155 administrators. Quantitative data were processed with IBM SPSS Statistics 25, employing descriptive statistics, Welch’s t-test, and two-way ANOVA to compare perceptions between the groups. Despite a broad consensus on the multidimensional nature of academic excellence (positive agreement averaged > 94%), the results reveal consistent differences in their interpretation of core parameters. Of the 32 indicators tested, only four showed no statistically significant difference between faculty and administrators: faculty numbers (p = 0.246), academic reputation and stakeholder recognition (p = 0.701), graduate employability and employer satisfaction (p = 0.106), and student enrollment (p = 0.588). Overall, administrators assigned systematically higher importance to institutional characteristics, enabling components, and barriers across all thematic blocks. Consistent with the conceptual framework integrating institutional and stakeholder perspectives, these patterns indicate that external policy pressures and role-specific responsibilities shape interpretations of excellence. These findings provide a data-driven basis for designing initiatives that couple system-level reforms with participatory governance and co-created metrics, thereby improving the translation of policy into practice.
Open Access: Yes
AI-DRIVEN PUBLIC ADMINISTRATION: EXPERT INSIGHTS ON ADOPTION AND IMPLEMENTATION
Publication Name: Economics and Sociology
Publication Date: 2026-01-01
Volume: 19
Issue: 1
Page Range: 172-194
Description:
Artificial intelligence (AI) is increasingly transforming public administration, yet empirical evidence from developing countries remains limited. This study explores the current use, key challenges, and enabling conditions of AI adoption in Kazakhstan’s public administration system. The study employs an exploratory qualitative design based on semi-structured interviews with 20 experts from government, academia, and related professional domains. The data were analyzed using thematic analysis in ATLAS.ti to identify key themes. The findings show that AI adoption is in a transitional stage, supported by strong government initiatives and shifting from digitalization to its use in decision support and predictive analytics for more proactive public services. While a number of pilot projects and practical applications are already in place, broader adoption remains constrained by interrelated barriers, including data limitations, skills gaps, infrastructural constraints, and regulatory uncertainty. The results also identify a corresponding set of enabling conditions, such as institutional support, human capital development, data governance improvements, and cross-sector collaboration, which can facilitate further progress. By linking systemic barriers with corresponding enabling conditions, the study clarifies how AI adoption unfolds in practice and identifies actionable directions for policy and implementation.
Open Access: Yes
