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Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment.

This paper introduces a concept proposal for accessing driving behavior in public transportation through Mobile Crowd Sensing (MCS), as part of a long-term research project on Advanced Public Transportation System (APTS). The proposed concept makes use of mobile device's accelerometer and passengers' qualitative evaluation to identify aggressive driving behavior, which is believed to be a major factor for unnecessary accidents and fuel consumption. A survey and comparison of IT services (mobile applications and websites) provided by Macau Government and private bus companies in Macau, regarding bus-related information, such as fares, routes and route diversions is also provided.

Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.

Association Rule Mining by Aprior method has been one of the popular data mining techniques for decades, where knowledge in the form of item-association rules is harvested from a dataset. The quality of item-association rules nevertheless depends on the concentration of frequent items from the input dataset. When the dataset becomes large, the items are scattered far apart. It is known from previous literature that clustering helps produce some data groups which are concentrated with frequent items. Among all the data clusters generated by a clustering algorithm, there must be one or more clusters which contain suitable and frequent items. In turn, the association rules that are mined from such clusters would be assured of better qualities in terms of high confidence than those mined from the whole dataset. However, it is not known in advance which cluster is the suitable one until all the clusters are tried by association rule mining. It is time consuming if they were to be tested by brute-force. In this paper, a statistical property called prior probability is investigated with respect to selecting the best out of many clusters by a clustering algorithm as a pre-processing step before association rule mining. Experiment results indicate that there is correlation between prior probability of the best cluster and the relatively high quality of association rules generated from that cluster. The results are significant as it is possible to know which cluster should be best used for association rule mining instead of testing them all out exhaustively.

The degree of economic integration in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), as reflected in the mobility of trade and capital flows, has been strengthened by free trade agreements, but obstacles including border effects, capital controls, differences of exchange rate systems and inadequate cross-regional coordination remain. Digital renminbi (e-CNY) has been tested in Shenzhen, a core GBA city since April 2020. If e-CNY is adopted in the GBA, the area will effectively become a single currency zone. Whether the GBA constitutes an “optimum currency area” (OCA) depends on its degree of economic integration. This paper computes real interest rate differential (RID), uncovered interest rate differential (UID) and deviation from purchasing power parity (PPD) of each regional pair based on data of interest rates, exchange rates and price indexes from 2016M2 to 2022M7. All UID, PPD and RID series have means within about 1 percent point from 0, indicating high degrees of financial integration, real integration and economic integration. With the exception of Guangdong-Macau RID, all series are stationary, implying mean-reverting behavior. Hence, the parities are expected to hold both in the short run and in the long run, which is a condition for an OCA in the GBA. Furthermore, the regression analysis finds that the test launch of e-CNY in Shenzhen (adjusted for the COVID-19 outbreak) has significant impacts on all RIDs, Guangdong-Macau PPD and Hong Kong-Macau PPD. With merely two and a half years of test launch, the introduction of e-CNY already had impacts on overall economic integration in the GBA.

The Guangdong-Hong Kong-Macau Greater Bay Area (GBA) was first conceptualized in 2016, which aimed to facilitate trade and finance liberalization among the three regions. The trade and financial environment of the GBA is unique. Due to the “one country, two systems” principle, Mainland China, Hong Kong and Macau are considered to be trading partners bounded by WTO rule, but bilateral free trade agreements have been signed between Mainland China and Hong Kong, and between Mainland China and Macau, but not between Hong Kong and Macau. Furthermore, each of the three regions circulates a local currency subject to its own exchange rate policy, with Hong Kong Dollar and Macau Pataca currently pegged to the US Dollar. These affect the mobility of trade and capital flows in the area. Hence, this paper applies the widely-used price-based approach due to Cheung et al. [5] to analyze the degrees of real and financial integration in the GBA based on interest rates, exchange rates, and price indexes data from January, 2016 to November, 2021. The real interest differential (RID), uncovered interest differential (UID) and the deviation from purchasing power parity (PPD) between each regional pair have means that are statistically and economically close to zero, implying high real and financial integration in the GBA. The unit root tests for stationarity also confirm that the time series are mean-reverting, so the economic integration in the GBA in the long run is foreseeable.

Objective: As a world tourist destination, Macao is inevitably under the impact of the COVID-19 pandemic. However, the market of integrated resorts in Macao are shared by only a few casino concessionaries, together forming an oligopoly. While the firms attempted to adjust price, quantity and quality of their hotel services in response to the pandemic, they could not overlook the strategic interactions with other players in the market. Hence, this paper aims to investigate the possible impact of the pandemic on the oligopolistic strategies in the integrated resort market in Macao. Methodology: Application of a theoretical model of differentiated oligopoly to this six-firm case shows that price differences across firms depend on their quality differentiation. In order to analyze these price differences empirically, this paper collects data of hotel room rates of the integrated resorts from November, 2019 to mid-August, 2020, covering the periods before and after the outbreak of COVID-19. Originality: In the existing literature, there is a lack of studies of the oligopoly in the hospitality industry of Macao. Furthermore, the effect of COVID-19 is still ongoing, so this present paper is one of the first to perform such analysis. Results: The regression of each of the hotel price differentials on the COVID-19 dummy variable shows that COVID-19 has statistically significant impacts on almost all the price differentials. Intuitively, MGM and Wynn were in the high-price segment before and after the outbreak, while other firms switched positions in the low-price segment during the pandemic. One obvious downstream movement was by Conrad. According to the proposition derived from the theory, these imply that COVID-19 should have significant impact on the quality differentiation of the firms. Practical implications: The results are in line with the observations that the integrated resorts have rolled out staycation packages according to preferences of local residents. These quality adjustments observed in Macao’s hospitality industry currently only involved variable inputs rather than fixed inputs of production; therefore, the impact of COVID-19 should be seen as short-term effects. Keywords: Covid-19; Differentiated oligopoly; Hospitality industry; Hotel room rate; Oligopolistic market structure; Pricing strategy.

This paper is motivated by two observations in the large civil aircraft (LCA) industry. (1) Boeing and Airbus are significantly different in the degree of offshoring. (2) The degree of offshoring also changes among different aircraft models. To offer an explanation, this paper focuses on issues related to fragmentation. Existing literature has established the tie between fragmented technology and offshoring. However, it is assumed that production can be fragmented readily and at no cost; and only exogenous global economic factors have impact on the degree of fragmentation. This model distinguishes itself from others by incorporating endogeneity in fragmentation. A final-good firm can spend on R&D specifically for its own fragmented technology. As a result, the final-good firm can optimally choose the portion of components to be offshored. A strategic trade policy model is used to show that the degree of offshoring depends on the firm's own cost of production, the host country's cost of production, the global state of technology as well as the government trade policies. In particular, export subsidy and subsidy on R&D of fragmented technology are shown to be policy substitutes. Keywords: Fragmentation; Offshoring; Outsourcing; Aircraft; Export subsidy; R&D subsidy; Boeing; Airbus JEL classification: F12; F13; F23; L13

Air pollution is a major concern issue on Macao since the concentration levels of several of the most common pollutants are frequently above the internationally recommended values. The low air quality episodes impacts on human health paired with highly populated urban areas are important motivations to develop forecast methodologies in order to anticipate pollution episodes, allowing establishing warnings to the local community to take precautionary measures and avoid outdoor activities during this period. Using statistical methods (multiple linear regression (MLR) and classification and regression tree (CART) analysis) we were able to develop forecasting models for the main pollutants (NO2, PM2.5, and O3) enabling us to know the next day concentrations with a good skill, translated by high coefficients of determination (0.82–0.90) on a 95% confidence level. The model development was based on six years of historical data, 2013 to 2018, consisting of surface and upper-air meteorological observations and surface air quality observations. The year of 2019 was used for model validation. From an initially large group of meteorological and air quality variables only a few were identified as significant dependent variables in the model. The selected meteorological variables included geopotential height, relative humidity and air temperature at different altitude levels and atmospheric stability characterization parameters. The air quality predictors used included recent past hourly levels of mean concentrations for NO2 and PM2.5 and maximum concentrations for O3. The application of the obtained models provides the expected daily mean concentrations for NO2 and PM2.5 and maximum hourly concentrations O3 for the next day in Taipa Ambient air quality monitoring stations. The described methodology is now operational, in Macao, since 2020.

People with intellectual disabilities need vocational training and support in order to be able to get into the work market and maintain their workplace. In Macau SAR, China, the vocational training ecosystem still operates in fully classic, in-person, fashion, which means it is susceptible to pandemic situations such as COVID-19. This causes a big disruption to the training when isolation measures are in place. Our goal is to study the introduction of serious games for vocational training of people with disabilities in Macau. This work presents a study to assess the training benefits of serious games and usability factors, understand the acceptability and adoption factors/benefits of serious games for vocational training for people with intellectual disabilities and associated stakeholders in Macau.

A growing number of people are becoming aware of the environmental footprint that our actions have on the environment. Research indicates that a key factor leading to the adoption of an electric vehicle is consumers’ high environmental concern. Indeed, the environmental concern (EC) construct is commonly associated with the purchase of sustainable and eco-friendly products in literature. Our study challenges this assumption. We examined how the environmental factor influenced the behavioral intention of Macau residents to adopt battery-electric vehicle (BEV) technology. For this purpose, we conducted a study based on the UTAUT-2 framework and used structural equation modeling (SmartPLS) to analyze the data. As a result, the choice of vehicles did not depend on the consumers’ level of concern. It appeared that consumers strongly perceived the benefits of a cleaner environment, however, when it comes to technology, environmental benefits are nice to have, rather than the primary incentive to purchase BEVs. Researchers should consider the role of environmental concern as a background factor in technology acceptance models, rather than a direct predictor of behavior. It is also recommended that marketers correctly consider this element when developing their product communications strategies, to appeal to the desired segment of customers.

Technology research offers several theories and models to explain how individuals accept and use technology innovations. While these often focus on the technical aspects of the innovation, they tend to downplay the affective component of technology. Recognizing that the adoption of technology is also determined by what it means and represents to the users, this paper aims to fill the gap in the literature by studying the effects of social influence and image on the behavioral intention to adopt a technology. We used structural equation modeling (SmartPLS) to analyze data collected from 238 self-administrated surveys regarding the behavioral intention of Macau residents to use battery electric vehicles. The result showed significant relationships among the variables in the model and depicted the construct of image as a strong factor in the adoption decision. Our findings suggest that social influence may not exhibit substantial impact in the case of innovations in their initial phase and, more importantly, the construct of image could be included as a key predictor of behavioral intention in technology acceptance models, particularly in contexts where the choices that consumers make are public, and therefore subject to judgments from the members of the community.

Despite the general good intentions towards the environment, individuals tend to adopt traditional internal combustion vehicles. Drawing from technology research, this study focuses on the impact of society - in the form of subjective norm and image – on the behavioral intention to adopt a technology. More precisely, this study seeks to explore to which extent societal influences drive the behavioral intention to adopt battery electric vehicles (BEV) technology. A self-administered survey was used for this purpose. The analysis of the data from a sample of 111 respondents showed significant relationships between the predictors and the target behavioral outcome. The study also revealed that subjective norm and image are particularly significant factors for the segment of BEV owners. The findings suggest that marketers and practitioners incorporate social elements into their product communication strategies in order to encourage the uptake of environmentally-sound technologies.

This study analyzes the green marketing strategies with specific reference to the hotel industry. The concept of green marketing in this sector is crucial due to the growing expected importance of tourism in the future of global economy and its potential impact on social and economic development; this is true particularly in areas with relevant volumes of tourist arrivals. In this sense, we carried out an exploratory research in the hotel industry of the Special Administrative Region (SAR) of Macao in order to: highlight the primary motivations that underlie interventions geared towards the eco-sustainability of hotels, the services they offer and point out the problems, issues, and future prospects in the development of green marketing, as well as explore the role of eco-sustainable values in hotels’ online communication policies. In order to reach these aims a qualitative research was carried out with a semi-structured questionnaire (face-to-face interviews) to a group of hotels. The research was finished by an analysis of their websites, in order to verify possible references to the steps taken to protect the environment.

Over the years Macao has been exposed to different cultures and has been influenced by various political and economic interests. The booming casino economy has ultimately transformed the city into the largest gambling hub in the world. In spite of the consensus about Macao's shiny future, there are factors (such as the large reliance on a single industry) and socio-economic problems (such as labor shortage, unequal income distribution, and inflationary pressure) that moderate the optimism. By making use of the Chatterjee and Nankervis' convergent and divergent process model for management, this paper examines how global, regional, and local forces have impacted the economic development process, form and type of organizations in Macao. The paper also suggests that the government implement a framework that develops and diversifies the economy but also takes into consideration the social needs of the community.

The decision to accept and use technology innovations has long been a source of debate across disciplines due to the complexity involved in predicting behavior. Recognizing that the subject is vast and fragmented, this paper examines the mainstream technology works to assist researchers to understand, conceptualize and select the most appropriate theoretical framework for their study. Starting with the pioneering effort on Diffusion of Innovations (DOI/IDT), the analysis considers the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM/TAM-2/TAM-3), the Value-based Acceptance Model (VAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT-2) among the most important. A review of the key literature is vital to assessing and identifying research trends, as well as contributing to the discussion of emerging technologies such as Artificial Intelligence (AI), Augmented Reality (AR), Blockchain, Cloud Computing, Internet of Things (IoT), Mobile Apps, etc. Suggestions for future research paths are also provided.