Your search

The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.

The UTAUT-2 offers the most comprehensive assessment of individual acceptance and use of technology to date. In particular, the theoretical additions of “hedonic motivation”, “price value”, and “habit”, made the model suitable for studying technology in a consumer context. However, a review of the literature revealed that the construct of habit has been dropped from a large number of studies. There are several reasons for this, including that the technologies examined were relatively new for the respondents to form a routine behavior. Therefore, this study aims to explore whether the construct can be used as a key predictor of future intention to use an innovation rather than an acquired practice among technology users. For this purpose, a conceptual model based on the theoretical additions to the UTAUT-2 is proposed and analyzed with structural equation modeling (SmartPLS). Our results showed significant relationships between the predictors and the behavioral intention to use battery electric vehicles (BEV) technology, and, in particular, depicted the construct of habit as the strongest factor in the decision to adopt the technology. In light of our findings, the construct of habit (HT) should be used in research together with the other UTAUT-2 predictors to assess individuals’ perceptions of possible future habitual behaviors.

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.

Neuromarketing lies at the intersection of three main disciplines: psychology, neuroscience, and marketing, and it has been a successful neuroscientific approach for the study of real-life choices such as consumer behavior [1]. A current gap in the cosmetics field is the lack of published research studies, considering the marketing investment done yearly in this category. With the rapid economic expansion and the rise of social media in China, consumers' interest in beauty is growing. Even though the Chinese cosmetics sector is rapidly expanding, no studies have been done with Chinese consumers. This study aims to employ the same approach as previously done in consumer neuroscience studies to evaluate cosmetic brands' marketing strategy to understand better if immediate emotional responses can be measured using Electrodermal Activity (EDA). Here, we focus on cosmetics products advertisement as a model to understand consumer preference formation and choice. Eighteen Chinese female consumers were recruited between 19 and 37 years old. From the results obtained, it was understood that none of the participants have voted for the product advertisement for which they showed higher emotional arousal. However, it appears that the participants' preference is for the products for which the brand awareness is stronger since the product advertisements with more votes are the ones for the Korean brand used. The product advertisements with Asian faces were the ones with more votes, suggesting that Asian faces have engaged consumer preference. However, the product advertisements for the Brazilian brands, unknown to the Chinese public, were the ones with fewer votes, although, those product advertisements were the ones with more emotional arousal per minute. Those advertisements were also those with non-Asian faces, suggesting that this feature influenced voting decisions. From this study, it has been observed that Electrodermal Activity is a measure of emotional arousal that by itself cannot be translated into consumer engagement. Therefore, it is also proposed to evaluate brand awareness in future studies related to product advertisements. The physical features of the people included in the advertisements is also suggested to be further evaluated in future studies since a different cultural background seems to influence the consumers' engagement. Furthermore, using EDA to complement other neurophysiological tools like facial expression analysis is also suggested for future studies to have evidence about the nature of the emotions raised.

As the rate of change increases exponentially, organizations must adapt quickly to the business landscape's volatility, uncertainty, complexity, and ambiguity (VUCA). As a result, organizations must implement agile strategies and practices to ensure their responsiveness and readiness to any changes brought about by internal or external factors. With a greater number of changes, change agents are tasked with implementing various change management methodologies to ensure that change recipients accept change initiatives. This research will look at one of the methodologies used by change agents, the use of nudges from Thaler and Sunstein's Nudge Theory, which is a subtle intervention to influence an individual's decision-making with the goal of steering them towards a specific desired outcome; and analyze their effectiveness towards the change recipients when implemented. Change agents were interviewed on the application of Nudge Theory to change recipients when managing to change initiatives within their respective organizations. The results indicate that the use of nudges created by the change agents can significantly impact the level of resistance from the change recipients. If used correctly, the Nudge Theory can mitigate change resistance, and the success of a change initiative is higher. But, if change recipients are forced to comply, their resistance will be greater, affecting the organization overall.

In the last few years, the tourism industry has experienced rapid expansion and diversification, making it one of the fastest-growing financial industries in the world. Consequently, the hotel industry has significantly affected the environment's long-term viability. Many hotels have begun voluntarily implementing environmentally sustainable practices as they become more aware of their ecological footprint. There has been a great deal of discussion about the effects of hotel operations on the environment and tourism sustainability in Macau. It is because of these negative impacts that hoteliers have adopted green practices in an attempt to minimize them. By developing sustainability reports, hotels can set goals, measure performance, and manage change, resulting in better sustainability. It could also be viewed as a strategy to enhance the company’s sustainability reporting to ensure stakeholders know what the company does. The objective of this study is twofold based on the analysis of the official sustainability reports of four major hotel chains. Firstly, seven categories of sustainable practices effectively adopted by these chain hotels are identified and clusterized. Second, it is presented in which areas some hotels performed more efficiently than others, considering the UN Sustainable Development Goals (SDGs) as a reference. The results allow a comprehensive clusterized analysis of the industry in a highly developed gaming and entertainment area of South China and create a clear comparison between relevant players and their concerns about sustainability practices.

China growing awareness of sustainability has brought out relevant aspects to move towards a green environment. Since its subscription in 2016, China has been committed to implementing the Paris Agreement, and the Greater Bay Area (GBA) development plan prioritizes ecology and pursuing green development. The primary purpose of this research is to perceive the companies' insights concerning the implementation of sustainable buildings’ projects in Macau. For this multi-case study analysis, primary data was gathered from interviews with two groups involved in the construction projects’ lifecycle: Consultants and Contractors, to analyze different perceptions and concerns. The interviews considered two different themes about the main topic: (1) Perception on Companies’ Experience in Sustainable Projects; (2) Key Drivers towards Sustainable Buildings’ Projects’ Implementation. In conclusion, according to the analyzed data, it is essential to notice that companies’ background and the market particularities affect their corporate performance specially connected to the green construction frameworks. The data also indicate that it is necessary to move towards regulations and policies to change corporate and people's mindset.

This paper aimed to build up the theorical and conceptual understanding of future forecasting study of Macau’s GDP and Gross Gaming Revenue (GGR) by co-movement of economic indicators. Macau GDP and GGR showed co-movements with a number of time series economic indicators, including China’s exports and imports, China’s manufacturing PMI, non-manufacturing PMI, China's electricity production growth, share price of some Macau’s gaming operators, etc. These time series data can be found in statistics departments of China, Macau and Hong Kong, stock exchanges, and international organizations such as the International Monetary Fund (IMF), the World Bank, the World Trade Organization (WTO). Burns and Mitchell’s study in 1946 identified co-movements between economic indicators and being further carried out and developed leading, coincident and lagging indicators, which is essential for future econometric models and nowcasting techniques developments to study these co-movements. In particular, with the proper application of nowcasting techniques, future studies can exploit the data of leading and coincident economic indicators to forecast Macau’s GDP and GGR within an acceptable level of error. Since Macau is a “monotown,” where the gaming revenue makes a significant contribution to the economy. The forecasting of gaming revenue is crucial as it aids the gambling and tourism industries in preparing supply and provides information to policymakers to plan for the near future. This research also contributes to understand Macau’s economy by investigating its internal and external economic variables.

This research aims to evaluate a Macau tea brand's social media advertising effectiveness with neuromarketing tools, including physiological monitoring that can measure emotional arousal. This research bridges the gap of social media marketing on Instagram for brands through the neuromarketing method. Data from 40 respondents were collected with iMotions software using neuroscientific tools. This research uses the stimuli of Guanding Teahouse, a newly established Macau tea brand, to evaluate social media advertising effectiveness. The neuroscientific tools – Galvanic Skin Response (GSR) sensors, Eye-tracking, Facial Expression Analysis (FEA) and emotion analysis are used to do the experiment. The data analysis was drawn from one representative respondent to measure the emotions and attention on the Instagram advertisements. Video 1 recorded 9 GSR peaks and Video 2 recorded 12 GSR peaks, both videos attention is ranging between 96-98 indexes. Results show that advertising videos should focus more on the products than the model. Moreover, the participant is more interested in Video 2, but the effectiveness of advertising is showing a lower focus on the brand and the tea. Future studies should consider comparing the video advertising effectiveness of Instagram stories and Instagram reels to prevent disruption of video on the stories ad.

The Black-Scholes equation is famous for predicting values for the prices of Options inside the stock market scenario. However, it has the limitation of depending on the estimated value for the volatility. On the other hand, several Machine learning techniques have been employed for predicting the values of the same quantity. In this paper we analyze some fundamental properties of the Black-Scholes equation and we then propose a way to train its free-parameters, the volatility in particular. This with the purpose of using this parameter as the fundamental one to be learned by a Machine Learning system and then improve the predictions in the stock market.

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.

Citizens' trust in eGovernment is crucial for the successful implementation of new electronic services. This relationship in the Greater Bay Area (GBA) plays an essential role since the Government services rely on mobile mini-programs This study investigates the trust towards government service mini-programs in WeChat and Alipay. A user feedback questionnaire was designed, and a total of 609 valid samples were collected from Shenzhen, Guangzhou, Hong Kong, and Macau. The findings imply that competence, integrity, and benevolence are the key components of trust in e-government (TIEG). TIEG positively influences perceived value (PV), which positively affects citizens' Intention to adopt service mini-programs. PV significantly mediates the relationship between TIEG and Intention. Although TIEG does not effectively reduce perceived risk (PR), risk issues cannot be ignored in the adoption process. Finally, this article proposes relevant implications and suggestions for the GBA government agents and policy makers.

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.

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.

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.

Digital inclusion in Macao is in the very beginning stage, and disability inclusion practice on social media in producing and promoting accessible social media content and needs is hard to find. This study aims to analyse the factors that influence communication staff's practice of disability inclusion on social media by using the combined employee behaviour and communication process model to provide suggestions to management who wants to promote disability inclusion practice on social media.The Service Centre for the Deaf of the Macau Deaf Association (MDA) was selected as the object for this case study. The online social media used for MDA's communication was analysed, and semi-structured in-depth interviews with members of the Association were conducted. The research findings showed that, except for the reward structure, factors examined from the work environment in terms of organisation, supervision and co-workers; communication staff themselves; outcomes of accessible social media communication; audience and feedbacks show relations with disability inclusion practices on social media. The delivery of inclusive culture, the influential power of disability stakeholders and the positioning of social media platforms are the key influencing factors.The interesting part of this study is that people without disabilities seem to be excluded from the disability inclusion practice carried out on MDA's Facebook. Their social media content is highly accessible to deaf and hard-of-hearing audiences yet seems not to involve the general public. The study object is a good model for producing accessible content, yet the optimisation of promoting social media accessibility needs further exploration.

The classification of emotions based on facial expressions have been a new topic of research in recent years, especially in marketing and consumer behavior areas. However, there is lack of studies to understand how the research topic is developed in terms of bibliometric data. Therefore, the purpose of this work is to provide a bibliometric analysis of the research on the analysis of facial expressions for marketing and consumer behavior, identifying the state of the art, the latest research direction, and other indicators. We extracted data from Web of Science (WOS) platform, considering its core database, resulting in a total of 117 articles. The software Vosviewer was used to analyze the data and graphically visualize the results. This study indicates some of the most influential authors citations and coupling analysis in this specific field, identifies journals with the most published articles, and provide trends of the research area based on the analysis of keywords and corresponding number of articles per year. The results shows that 11 articles (9.4%) were cited more than 100 times, and the two most prolific authors published 5 articles, and the two most influential authors are Bouaziz Sofien and Pauly mark(270 citations) in this field. Of the 117 articles retrieved by WOS, more than 70% were published in high impact journals. The bibliometric analysis of the existing work in this study provides a valuable and reliable reference for researchers in this field and makes a reasonable prediction of the research direction trends.