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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.

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.

Since early times, the effects of a booming sector in other sectors of a small economy have been of interest to scholars. There is a general perception that the booming Gaming sector has contributed to the overall growth in Macau through the trickle-down effect, passing on the benefits of growth to other sectors. After the liberalization of the gaming industry in 2002, this booming sector experienced several years of exponential growth, becoming the driving industry for Macao’s economy. Several scholars and researchers have dedicated their studies to the effects of the casino gaming industry as a booming sector in such a small economy. However, there is a gap in what concerns measuring the influence of the Gaming sector as a driving industry for several other sectors or following industries of Macau’s economy. The purpose of this research study is to investigate in what measure the Gaming sector in Macao leveraged the other economic sectors and how related or correlated are the different industries of Macao’s Economy. A protocol-driven understanding of the state of the art on the interrelations between economic sectors and different techniques used to study those inter-relations was conducted through a systematic literature review. Given the limited available data on the Gross Value Added (GVA), or Gross Domestic Product (GDP) on the supply side, as a central measure of economic activity in the different sectors, several possible interpolation models using auxiliary high-frequency data (indicators) were compared, to achieve the optimal model for interpolation of each variable. Several forecasts for the future performance of Macau's four major economic sectors were presented based on different regression techniques. Autoregressive Integrated Moving Average (ARIMA) models were developed to assess the dependence of the future performance of a sector’s GVA on its past performance. Optimal Vector Autoregressive (VAR) models were created to identify the explanatory power of some sectors of Macau’s economy in others. Based on available auxiliary data in high-frequency (quarterly) it was possible to interpolate the quarterly GVA per economic sector, available only in low-frequency (annually), for the major sectors of Macao’s economy. Some sectors have a considerable explanatory power on the performance of other sectors, however, the proposed regression models did not identify a clear relation between the performance of the Gaming sector and the performance of other major sectors from Macao’s economy

The stock market's inherent volatility and complexity pose significant challenges for investors seeking to optimize their strategies. This thesis addresses the critical need for improved forecasting methods in stock price prediction by proposing a hybrid approach that combines traditional machine learning (ML) techniques, specifically Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks, with sentiment analysis derived from financial news and social media platforms. The research establishes a theoretical framework integrating quantitative data, such as historical stock prices, with qualitative sentiment data to enhance prediction accuracy. The study involves the collection of a comprehensive dataset covering stock prices and sentiment scores from various sources, including news articles and social media posts, from January 2010 to December 2023. Rigorous data preprocessing techniques, including normalization and feature engineering, are employed to prepare the data for analysis. A comparative analysis of the SVM and LSTM models uses multiple performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and classification accuracy. The findings reveal that the LSTM model significantly outperforms the SVM model in predictive accuracy, demonstrating its capability to capture complex temporal dependencies inherent in financial time series data. Furthermore, integrating sentiment analysis significantly enhances the predictive performance of both models. Notably, transformer-based sentiment analysis techniques, such as BERT and DistilBERT, provide superior sentiment classifications compared to traditional methods like VADER and TextBlob. The empirical results indicate that incorporating sentiment data leads to an average accuracy improvement of 12.8% over models that rely solely on historical price data. This research contributes to the evolving field of financial forecasting by emphasizing the importance of a hybrid approach that amalgamates quantitative and qualitative data. The implications of these findings extend beyond academic research, offering valuable insights for investors and financial analysts seeking to leverage advanced predictive models to navigate market uncertainties. Ultimately, this dissertation advocates adopting sophisticated hybrid models that enhance stock investment strategies and decision-making processes in the finance sector.

This study sought to determine the strategy that allowed the Las Vegas Sands Corporation (LVS) to attain its leading status in the casino industry and to gain insight whether this status would continue given (i) the passing of the LVS founder, Sheldon Adelson, in January 2021, (ii)the sell-off of the company's Las Vegas properties early in 2022, and (iii) the firm's greater sensitivity to events in China caused by the company's increased reliance for most of its customers on the mainland China market. The study first identified the nature of the LVS competitive advantages when Adelson was directing the firm and then assessed whether these had been adversely impacted due to changes in the firm's markets, management or strategy. The study relied initially on the work of David Baron, Professor of Political Economy and Strategy at Stanford University who as early as 1981 advanced the view that corporate strategy needed to be divided in a Marketing Strategy (MS) and a Non-Market Strategy (NMS). The NMS component for LVS was critically important since government determined who could acquire a Macau casino concession and what level of visas would be provided to Mainland China gamblers to fill the Macau casinos. The key question became the nature of Adelson's Political Effectiveness as determined by the NMS he directed towards the China market. To resolve this issue, we adopted the Wellner & Lakotta proposal to extend Porter's Five Forces analytical framework by two additional dimensions, Government Interventors and Complementor Organizations. We concluded that it was highly likely that Goldman Sachs, the long-term financial backer of Sheldon Adelson, played a significant if not the major role in the success Adelson was able to achieve in the Greater China market.

Vehicles solely powered by electricity are a major technological innovation that combines individual transportation needs and environmental sustainability, yet their market penetration is low. Research has traditionally indicated factors such as the vehicle’s purchasing price, driving range, and charging time as the main barriers to adoption. However, the decision to adopt a technology also depends on what the technology represents to the user; therefore, other factors may be important to explain individuals’ behavior. This study is a quantitative and cross-sectional look at the behavioral intention to adopt battery electric vehicles (BEVs) technology in the context of Macau. The research builds on the unified theory of acceptance and use of technology 2 (UTAUT 2) (Venkatesh et. al., 2012) to explain the characteristics of the local consumers. Besides the addition of image and environmental concern to the theoretical model, the study also put forward and evaluate the construct of technology show-off, an original measure of the visible and experiential characteristics of a technology. A sample of 236 Macau residents was analyzed by structural equation modeling (SEM). The analysis of the data supported the explanatory and predictive power of our model and helped to describe the idiosyncrasies of local residents. The results provide insights related to individual technology acceptance that could be useful in designing more accurate strategies and fostering the uptake of BEVs in Macau or markets that share similarities

This thesis aims to explore how learning accounting for non-accounting major students could be enhanced through a comprehensive framework connecting individual attributes and learning approaches. While accounting is considered an important and mandatory discipline for business students, it is also considered a difficult subject which deters students’ engagement in the subject. Although the issue has been widely recognized, there are relatively less discussions on possible remedies. Learning accounting is a dynamic process that go beyond the traditional one-to-many learning and teaching settings, all factors revolving the students’ learning environment and process, altogether determine and affect the learning efficiencies. A meta-analysis was first performed to understand the standings of accounting education and to identify gaps within existing literatures in the field of management, accounting, tourism and general education researches. A documentary review on the curricula of all Macau HEI programs involving compulsory accounting courses was also conducted to understand the role and significance of accounting courses for non-accounting college students in Macau. Based on the literature review, this study borrows constructs from theory of planned behavior (TPB) models in its research framework. It attempts to provide a comprehensive framework to understand how students learn accounting efficiently by delving into individual learning styles of the students and their perceived helpfulness and self-efficacy, in respective to different learning techniques. Due to its exploratory nature, a mixed method of qualitative and quantitative methodology is adopted, where both focus group discussions and in-depth interviews are performed with non-accounting students to elicit understandings on student’s difficulties in learning accounting and their preferences for different learning styles. Then, a full-scale quantitative survey is conducted in a Macau higher education institute to assess the learning efficiencies of accounting in different situations. A triangulation with feedback from faculties and administrators are also elicited to provide alternate dimensions on the issues. With no surprise, there is no one best 3 teaching style or method for all students, this thesis contributes to accounting education research by provide a comprehensive diagnosis on the all-round learning stimulus and hindrance in learning accounting. At the end, recommendations are provided for accounting educators to emphasize on the uniqueness of students and to encourage a more dynamic and open environment for learning. Limitations of the studies are also discussed