Altynay Tyulkubayeva

The purpose of this study was to summarize the motivational factors in a project team, focusing on three key roles: team members, project managers, and project leaders. A systematic review was conducted using the PRISMA approach to identify relevant studies published between 2004 and 2024. 127 peer-reviewed articles were selected from the Web of Science database. The bibliometric analysis was performed using VOSviewer software to visualize trends and clusters. The bibliometric analysis revealed a surge in interest in research after 2021, highlighting the motivation’s special role in project management. Transformational and shared leadership are identified as the most motivating in projects. The study identified seven main categories of motivational factors in the project team: psychological factors, goal orientation, social factors, job design, training and development, financial incentives, and organizational support. However, these categories have their characteristics, emphasizing that motivation is not a universal concept but a dynamic and role-dependent construct. While team members are motivated by emotional support, clear goals, and rewards, project managers value strategic influence and professional development, and project leaders are driven by task complexity and intrinsic rewards. The noticeable gap in the research was the lack of attention to the motivation of the project leader. These findings offer a structured framework for improving team motivation and project performance across various organizational contexts.

Acknowledgment
Gratitude is extended to Professor Darren Dalcher for his valuable advice and insightful suggestions to start this article.

Type of the article: Research Article

Abstract
The application of artificial intelligence (AI) in enterprises presents new opportunities for growth in their market value. This study aims to evaluate the impact of AI applications in enterprises on the growth of enterprise market value and the transmission mechanism of these impacts. Using an enterprise AI application level as the independent variable, a regression model is constructed to analyze the long-term and short-term market value of the enterprise. This study uses relevant data from Chinese listed companies from 2014 to 2023 for analysis. Findings show that for every 1% increase in AI application level, the enterprise market value increases by 0.03% and the enterprise value multiple increases by 0.44%. Increasing the level of AI application in enterprises will enhance their ability to implement low-carbon measures and investors’ expectations of corporate profits, thereby increasing the market value of enterprises. High-quality talent within the enterprise and market share can enhance the impact of these two mechanisms. The application of AI in enterprises has different impacts on different industries and companies of different sizes. This study provides new empirical evidence for enterprise market valuation.

Acknowledgments
This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (IRN BR28713593 “Sustainable development of Kazakhstan’s economy in the context of new сhallenges: foresight, strategies and scenarios of modernization, institutions”).