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Government service mini-programs have become an integral component of eGovernment in the Greater Bay Area, and successful eGovernment is necessary for building a smart city. Service quality and citizens' trust play a vital role in urban integration and in-depth cooperation in the Bay Area. The ubiquitous nature of mini-programs based on WeChat and Alipay provides excellent flexibility in accessing government services. Technology advantages, mutual recognition of cross-border data, and online transactions bring value and benefits to citizens. However, the mechanism of mini-program adoption has not been elaborated. Homogenization, conflict of regulations, and policy effectiveness are issues of great concern. This study employed Self-Determination Theory and Motivation Theory, proposed an empirical model based on the extended SOR paradigm, and aimed to identify the critical factors determining the intention of government service mini-program adoption from the user’s perspective. Six hundred and nine valid samples were collected from Macau, Hong Kong, Guangzhou, and Shenzhen through online survey platforms. The findings suggested that service quality, trust in eGovernment, ubiquity, and social influence constituted the determinants of intention to adopt. Service quality and ubiquity were salient determinants, and a great extent of service quality and ubiquity could promote perceived value and intention. Citizens' trust in government service mini-programs was reasonable, where benevolence, integrity, and competence were crucial indicators of trust. Social influence amplified and transmitted risk perception while perceived risk significantly reduced intention. Perceived value positively associated with the four determinants and enhanced user intention; it acted as a mediator with high explanatory power in the model. Government support received positive ratings from citizens; it negatively regulated the relationship between intention and the determinants respectively, implying that excessive intervention from the government could lead to inhibition. Finally, we proposed relevant implications and suggestions for the GBA government agents and policymakers

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.

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.

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.

Government service mini-programs have become an integral component of eGovernment in the Greater Bay Area, and successful eGovernment is necessary for building a smart city. Service quality and citizens' trust play a vital role in urban integration and in-depth cooperation in the Bay Area. The ubiquitous nature of mini-programs based on WeChat and Alipay provides excellent flexibility in accessing government services. Technology advantages, mutual recognition of cross-border data, and online transactions bring value and benefits to citizens. However, the mechanism of mini-program adoption has not been elaborated. Homogenization, conflict of regulations, and policy effectiveness are issues of great concern. This study employed Self-Determination Theory and Motivation Theory, proposed an empirical model based on the extended SOR paradigm, and aimed to identify the critical factors determining the intention of government service mini-program adoption from the user’s perspective. Six hundred and nine valid samples were collected from Macau, Hong Kong, Guangzhou, and Shenzhen through online survey platforms. The findings suggested that service quality, trust in eGovernment, ubiquity, and social influence constituted the determinants of intention to adopt. Service quality and ubiquity were salient determinants, and a great extent of service quality and ubiquity could promote perceived value and intention. Citizens' trust in government service mini-programs was reasonable, where benevolence, integrity, and competence were crucial indicators of trust. Social influence amplified and transmitted risk perception while perceived risk significantly reduced intention. Perceived value positively associated with the four determinants and enhanced user intention; it acted as a mediator with high explanatory power in the model. Government support received positive ratings from citizens; it negatively regulated the relationship between intention and the determinants respectively, implying that excessive intervention from the government could lead to inhibition. Finally, we proposed relevant implications and suggestions for the GBA government agents and policymakers

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