Aknur Zhidebekkyzy

This paper aims to quantify the impact of selected demographic, financial, and economic factors on the propensity to do business in the taxi sector of the sharing economy. The sample comprised 375 taxi drivers from the Czech Republic and Slovak Republic. Data were collected using the query method via a questionnaire in April 2022. The structure of the respondents is divided into shared taxi service providers (N = 294) and traditional taxi service providers (N = 69). The study selected 14 factors: demographic (4), financial (7), and economic (3). The SEM approach was applied to evaluate the hypotheses. Shared taxi providers have a stronger propensity to do business than traditional taxi drivers. Demographic characteristics of a traditional taxi driver are the most significant factors with a strong influence on the propensity to do business (βS = 0.525 > βT = 0.425). On the other hand, the financial and economic characteristics of shared taxi drivers strongly influence the propensity to do business (βT = 0.565 > βS = 0.212). The characteristics of the enterprise are on the verge of significance in relation to the tendency to do business with shared taxi drivers, as opposed to traditional taxi drivers. For traditional taxi drivers, there is a strong influence of the characteristics of the enterprise on the propensity to do business (βT = 0.476 > βS = 0.026). This study contributes to understanding how participating in sharing economy may stimulate the propensity to do business.

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.

Acknowledgments
This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).

Project managers from different areas struggle to find universal set of factors to achieve high project efficiency. However, the project efficiency assessment varies across industries, making it difficult to apply general concepts and guidelines in the specific project environment. The study’s main purpose is to assess the impact of organizational support on project efficiency in different areas and identify critical processes that increase it. The paper presents quantitative research results by using data from 261 project managers in the Republic of Kazakhstan during 2018–2019. The research sample consists of commercialization projects, scientific projects, social projects, and projects from the service industry. The research findings show the different quality levels of top management support and its high correlation with project efficiency. Furthermore, the study reveals the specific processes for each investigated type of project that highly correlate with project efficiency dimensions. Commercialization projects that intensively use the organizational support processes were the most efficient projects by showing the best result in cost overrun (15%) and the second resulted in schedule overrun (17%). These findings have theoretical and practical implications, supporting project managers in increasing their project efficiency by choosing the critical, proper project management tools and techniques.

Innovations play an important role in achieving competitiveness and long-term economic growth at all levels of the economic hierarchy. More recently, the role of sustainability-oriented innovation in achieving economic growth has become equally important. However, there is a scientific debate about the possibility of effective commercialization of sustainability-oriented innovations. The existence of such scientific debate, as well as the absence of citation-based systematic literature reviews, became a prerequisite for conducting a comparative literature review of research on the effective commercialization of conventional and sustainability-oriented innovations. The purpose of the study is to conduct a comparative review and analysis of research on the commercialization efficiency of conventional and sustainability-oriented innovations. The analysis was conducted on the basis of a citation-based systematic literature review method. The results show that sustainable innovation research is more focused on the relationship between commercialization and firm performance. In the field of conventional innovations, research trends have shifted from studying the impact of technology transfer office (TTO) size, staffing, compensation practices to how strategic factors affect the efficiency of commercialization. In the area of sustainability-oriented innovation, the issues of the strategic orientation impact cause the most sustained interest, while managerial concerns and the stringency of environmental regulations have been actively explored in the past few years. It was revealed that the commercialization efficiency of sustainability-oriented innovations is characterized by so called sustainability-oriented criteria. A distinctive output criterion for commercialization efficiency of sustainability-oriented innovation is the improvement of the firm’s image. In addition, content analysis identified possible research directions to be investigated.

The study analyzes the implementation of responsible production practices in Kazakhstan’s manufacturing sector, an area of growing importance for sustainable development in emerging economies. The aim is to examine how environmental, operational, and social factors influence production processes and community contributions. Data were collected through an online survey conducted between May and August 2024, using a voluntary response sampling method. To enhance the representativeness of the findings, the sectoral distribution of the responding enterprises was analyzed post-collection. The survey captured top, middle, and line management responses to comprehensively assess sustainability practices. Structural equation modeling (PLS-SEM) was employed to evaluate the relationships between ecological operational efficiency, environmental protection, resource efficiency, waste management, social factors, product manufacturing, and community contribution. The findings reveal that environmental protection, efficient resource use, and effective waste management significantly strengthen manufacturing performance and enhance companies’ contributions to community development. In contrast, improvements in ecological operational efficiency and workplace social initiatives do not directly impact production outcomes. The study concludes that integrating sustainable environmental and resource management practices into production processes is crucial for enhancing both manufacturing performance and broader societal outcomes. The findings offer practical implications for industrial managers, policymakers, and stakeholders aiming to promote sustainability transitions in manufacturing sectors.

Acknowledgment
This study was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19576593).