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This study investigates the regulatory-operational dynamics of the premium market in Macau casinos using a constructivist grounded theory approach. Qualitative in-depth interviews were conducted with 25 participants from three stakeholder groups. The grounded theory analysis identifies seven core categories illustrating the impact of the new regulatory system on the premium direct and premium mass segments in casinos. These categories include regulatory changes and operational challenges, decline of gaming promoters, premiumization of the gaming market, new player retention strategies, cross-border player acquisition risks, presence of unauthorized agents in casinos, and non-gaming development amid international competition. These insights highlight four major regulatory impacts on the industry themes and operational trends, i.e., industry regularization, market premiumization, product diversification, and criminal fragmentation. This study also identifies specific regulatory mechanisms and operational management in premium gaming, such as premium player identification, enhanced operational procedures, multi-tiered market segmentation, and the provision of personalized services. Additionally, stakeholder perceptions of the new gaming regulatory system are explored, with casino and expert groups considering it necessary and junket participants finding it restrictive. From these findings, a conceptual model structured around regulatory functions, compliance, and improvement is developed to provide a theoretical regulatory framework that aligns with the characteristics of the gaming industry in Macau and possibly other gaming jurisdictions.
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As an important part of the global economy, family business have made a lot of contributions to the world economy. With the deepening of China's reform and opening up, the scale and volume of family business have increased dramatically in the national economy. In recent years, more and more family-owned enterprises have entered the list of “Top 500 Chinese Enterprises” and become an important part of the national economy. As a special enterprise organization, family business has certain characteristics. Most of the Chinese family business were founded in the early stage of China's reform and opening up, and now more and more family business are facing the problem that the founders need to leave the family business due to their advanced age. How to ensure the smooth succession of family business and avoid conflicts and contradictions in the process of succession is an important challenge faced by many family business. How to ensure that family business can continue to develop and maintain a high performance status is of great significance to the founders and their successors, as well as to the Chinese economy. Therefore, this research examines how the characteristics of successors affect enterprise performance based on the context of intergenerational succession in family business. This research is based on a multi-case study in which in-depth interviews were conducted with the founders and successors of five cases, which led to the conclusion that five successor characteristics have an impact on enterprise performance. They are inclusiveness, forward thinking, sociable and good communication, sense of social responsibility and sense of family mission. This finding has significant implications for the training of successors and the management of the succession process in Chinese family business. This study is conducive to the discovery of the mechanism of successor characteristics on enterprise performance as well as to make a realistic contribution to the research on the outcomes of family business in China.
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Artificial Intelligence (AI) is being applied in different areas of Administration and Management including finance, e-commerce, etc. Project Management (PM) is one area that may benefit from the use of AI to support project managers in making more accurate predictions, more quickly, such as deadline adjustments and cost updates, while at the same time helping with some of repetitive tasks of PM by relieving managers from these processes. Nevertheless, multiple aspects are still in consideration to allow AI to be widely adopted in PM, including lack of validated systems, including aspects of quality and prevalence, trust from users, market and specialists, and how the government will play a role to support the wider adoption of AI tools. This research explores the integration of Artificial Intelligence (AI) in Project Management and its potential to enhance four aspects: service quality, trust, prevalence, and government support. The proposed methodology employs a systematic literature review (SLR) combining with a quantitative survey to assess the current state of AI in project management. The SLR covers scholarly articles from 2016 to 2021, focusing on AI's impact on project management across various industries. The survey, conducted among 200 professionals, gathers insights into AI's perceived benefits and challenges in project management. The research findings indicate a positive inclination towards AI in project management, with respondents recognizing its potential to improve efficiency, support data-driven decisions, and enhance risk management. However, the study also reveals concerns regarding data quality, privacy, and the need for ethical considerations in AI applications. Most respondents agree on the necessity of government support to foster AI adoption and the importance of establishing trust in AI systems through transparency and security measures. The thesis concludes with recommendations for practitioners and policymakers to effectively leverage AI in project management. It proposes a framework including the development of training programs, the establishment of quality standards for AI services, and the promotion of public-private partnerships to drive innovation. The study emphasizes the importance of a multi-faceted approach to AI integration, considering technological, organizational, and ethical dimensions.
 
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                    Between 2000 and 2025
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                    Between 2020 and 2025
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- 2025 (3)
 
 
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                    Between 2020 and 2025
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