Measuring normative endorsement of inclusive principles under ceiling effects: Evidence from Kazakhstan

The objective of the research is to examine the impact of brand name and pricing on Kazakhstan Gen Z consumers’ buying behavior, spending patterns, and other buying motives. Adopting a quantitative approach, the study administers a structured questionnaire following a comprehensive analysis of the relevant literature and gleaned responses from 300 Generation Z consumers after checking the reliability and validity of the instrument using Cronbach’s alpha. The selected hypotheses were tested using the Karl Pearson coefficient of correlation, which revealed that brand name significantly impacts Kazakh women, and price greatly influences both genders. The results observed that price has the most significant effect on the buying behavior of female and male Gen Z consumers in Kazakhstan, with 25 and 28 percent, respectively. Brand name and authenticity considerably influence female consumers, with 16 and 15 percent, whereas innovation, technology, and customization, with 20 and 14 percent, influence male consumers. The significant influence of branding and pricing on the buying behavior of Kazakhstan Gen Z customers is displayed, and the findings provide a focal context for developing marketing plans in this first-of-its-kind research article.

The transition to an innovative economy requires greater attention to creating favorable conditions for the commercialization of scientists’ developments and the possibility of realizing the accumulated scientific potential. This study aimed to examine the commercialization process in the Republic of Kazakhstan and identify factors influencing the development of academic entrepreneurship in universities. It examines a gradual change in legislation on technology transfer and the dynamics of implemented commercialization projects during 2016–2022. Structured interviews were conducted with academics of the biggest 14 universities in Kazakhstan with a sample of 209 respondents to identify factors influencing the desire of scientists to engage in academic entrepreneurship. The findings revealed that the most attractive factors for academics are flexible working hours (4.67 of 5), the opportunity to implement their own innovative ideas (4.12), and an increase in income (3.63). In turn, negative factors include the lack of qualified personnel (4.56), difficulties in legislation (4.27), and bureaucratic barriers (3.78). The study revealed that gender and age moderately affected scholars’ desire to engage in academic entrepreneurship (Cramer’s V = 0.3025). The greatest desire to start their own business was demonstrated by men aged 26-35 years and by women aged 36-45 years. The findings also show that the scientific fields positively affect the number of ready-made ideas, patents, and technologies that academics offer to businesses.

Acknowledgment
This study is funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13268750).

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).