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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

COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.

This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.

This dissertation identifies factors driving consumer shopping behavior within the realm of live-streaming commerce, an area fast emerging in the e-commerce domain. Live-streaming shopping or commerce involves real-time interaction and entertainment with traditional online shopping, forming a unique endogenous environment where consumers can contact sellers or influencers directly. The study employed quantitative surveys that identified some of the main determining factors of consumer behavior within this context. The findings show that the significant factors in determining consumer behavior are trust and engagement, which are strongly influenced by the credibility and authenticity of the live streamer. Another significant finding is the role of social interaction and community building in providing consumers with a sense of belonging and validation, enhancing their confidence and purchase intention. Moreover, it highlights how marketing strategies of flash sales, limited-time offers, and partnerships with influencers make their way into the system to help invoke engagement and impulsive buying behavior among consumers. The implications of these findings extend to e-commerce platforms and marketers. Any improvements in features leading to trust, engagement, and interactivity within the community would drive higher customer satisfaction and sales. According to researchers, working partnerships with believable influencers and more extensive integrations of real-time marketing might further activate live-streaming commerce. This study thus fills a gap in the existing body of literature by detailing the drivers of consumer behavior toward live-stream commerce. It also identifies areas of future research on the current studies, including developing technologies and the cultural variances in the impact of live-stream commerce, including ethical considerations. These results are principle for guiding work on potential live-stream commerce in the digital age for anybody from workers to academicians

In the wave of digital transformation, Chinese banks have taken digital and scenario-based finance as primary strategic goals. The goal is to revolutionize the mobile banking experience and encourage frequent use of mobile banking services. However, assessing customer satisfaction with the various financial and contextual services mobile banking provides is crucial. The main objective of this study is to propose a model based on users' perception of financial usage in mobile banking scenarios and how the development of mobile banking finance and scenarios affects users' choice motivations. The study examined the interview records of 12 mobile banking users through qualitative in-depth interviews and utilized Nvivo qualitative analysis software to analyze the interview content. Through repeated thinking, sorting, and differentiating the data, nine core coding categories were formed. The coding was further refined and deepened to include Financial professionalism, Security, Marketing Stimulation, Innovative Products, Use Experience, Strong Relationship, Trust, Perceived usefulness, and Willingness to use. Based on these categories, a theoretical model of user willingness in the financial scenario of mobile banking has been proposed by referring to the optimized TAM model. The results may provide support to the banking industry in Macau in understanding customers' needs and fostering the positive development of mobile finance and the scene field in Macau

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.

The spontaneous symmetry breaking phenomena applied to Quantum Finance considers that the martingale state in the stock market corresponds to a ground (vacuum) state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena completely ignores the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (martingale). In this paper, we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock markets.

The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...

The potential of blockchain technology extends beyond cryptocurrencies and has the power to transform various sectors, including accounting and auditing. Its integration into auditing practices presents opportunities and challenges, and auditors must navigate new standards and engage with clients effectively. Blockchain technology provides tamper-proof record-keeping and fraud prevention, enhancing efficiency, transparency, and security in domains such as finance, insurance, healthcare, education, e-voting, and supply chain management. This paper conducts a bibliometric analysis of blockchain technology literature to gain insights into the current state and future directions of blockchain technology in auditing. The study identifies significant research themes and trends using keyword and citation analysis. The Vosviewer software was used to analyze the data and visualize the results. Findings reveal significant growth in blockchain research, particularly from 2021 onwards, with China emerging as a leading contributor, followed by the USA, India, and the UK. This study provides valuable insights into current trends, key contributors, and global patterns in blockchain technology research within auditing practices, and future research may explore thematic areas in greater depth.

In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...]

With the rapid development of digital media, internet celebrity live streaming has become a key factor in influencing the consumer decision-making of adolescents, presenting unique regional characteristics in different socio-cultural and economic contexts. This study investigates the differences in consumption habits among adolescents in Macau and Mainland China and their impact on the innovation and reform of the commercial model of internet celebrity live streaming. The methodology employs a questionnaire survey and data analysis to systematically compare the consumption behavior of adolescents in Macao and mainland China, collecting live streaming consumption habits of adolescents in both regions. Statistical methods are used to compare and analyze the consumption patterns within the regions. The analysis indicates that influencers, as internet celebrities with a large number of fans on social media, have a significant impact on adolescents' consumption decisions through their recommendations and evaluations. Firstly, the convenience and diversity of e-commerce platforms provide adolescents with a wealth of consumption choices, such as characteristics and usage effects of products. Secondly, the recommendations and evaluations of influencers have become an important reference for adolescents' consumption. Results show that adolescents in Macau tend to seek entertainment and interaction in their consumption of internet celebrity live streaming, whereas those in Mainland China place greater emphasis on the practicality of the live streaming content and the cost-effectiveness of the products. Moreover, the study reveals the roles of socio-cultural and economic levels in the differences in consumption between the two regions. Based on these insights, it is recommended that live streaming platforms should advance the innovation and reform of their business models to cater to different market characteristics—such as optimizing content recommendation algorithms, enhancing interactive elements, and improving the integration of e-commerce features, thereby promoting business sustainability and economic benefits

Projects are tactical and operational initiatives, and achieving specific outcomes through projects can help organizations achieve strategic goals. The effective use of project management tools and techniques is essential to achieve successful results, since the goal is to maximize the realization of the project's plan by effectively using the budget, time, and resources provided by the project owner to achieve the project's original purpose. The Project Management Maturity Model (PMMM) is a tool for measuring project management capabilities and is essential to improve project and portfolio performance in different industries. The main objective of this research is to analyze and characterize the maturity level and capacity of the IT industry in Macau and HengQin based on the assessment of the PMMM. The research also aims to assess and compare the maturity level in the IT industry in Macau and HengQin. An online survey was conducted and sent to IT project managers from Macau and HenqQin. A total of 34 responses were collected, divided into 3 different parts: Part I - General Information, Part II - Project Management Areas, and Part III - Perception. The results indicate that, in general, Project Managers state that their companies do not follow Project Management standards and best practices, classifying as Low and Very Low essential PM areas such as Planning and Scheduling (68%), Scope (61%) and Communications (64%). From a comparison perspective, project managers in Macau follow less formal frameworks than Hengqin in managing the triple constraints of the project. The collected data also indicate that Macau's communication management and stakeholder engagement are less mature than Hengqin's. Furthermore, the data indicate that maturity level is not necessarily related to education level, which means not higher education has a higher maturity level. Recommendations are provided for the IT industry in both areas, and specific comments are provided for each group or professionals. In conclusion, this work allows a novel characterization and a better understanding of the Project Management adoption and maturity level of the IT Industry in Macau and Hengqin

Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.