RMIT Vietnam

Having geographical proximity and a high volume of trade with China, the first country to record an outbreak of the new Coronavirus disease (COVID-19), Vietnam was expected to have a high risk of transmission. However, as of 4 April 2020, in comparison to attempts to containing the disease around the world, responses from Vietnam are seen as prompt and effective in protecting the interests of its citizens, with 239 confirmed cases and no fatalities. This study analyzes the situation in terms of Vietnam’s policy response, social media and science journalism. A self-made web crawl engine was used to scan and collect official media news related to COVID-19 between the beginning of January and April 4, yielding a comprehensive dataset of 14,952 news items. The findings shed light on how Vietnam—despite being under-resourced—has demonstrated political readiness to combat the emerging pandemic since the earliest days. Timely communication on any developments of the outbreak from the government and the media, combined with up-to-date research on the new virus by the Vietnamese science community, have altogether provided reliable sources of information. By emphasizing the need for immediate and genuine cooperation between government, civil society and private individuals, the case study offers valuable lessons for other nations concerning not only the concurrent fight against the COVID-19 pandemic but also the overall responses to a public health crisis.

This study examines the trend of financial inclusion in Asia and its impact on financial efficiency and financial sustainability. For this purpose, the study employs a sample of 31 Asian countries during the period spanning from 2004 to 2016. Composite indicators for the three financial dimensions are constructed using principal component analysis (PCA) based on normalized variables. We find that the trends are fluctuating across countries and there is no clear pattern in several cases. The findings are robust to different normalization techniques. Furthermore, the impact of financial inclusion on financial efficiency and sustainability is analysed using Feasible Generalized Least Squares (FGLS). The estimation results indicate that growing financial inclusion negatively affects financial efficiency while favourably influences financial sustainability. The findings hold for the whole sample as well as across the two subsamples of countries with different income levels. This implies that while there are policy synergies between growing financial inclusion and maintaining financial sustainability, proper attention needs to be paid to the side effect of financial inefficiency associated with increasing financial inclusion.

BACKGROUND: There has been a growing interest in disability and poverty on the international research and policy stages. Poverty assessments for persons with disabilities may be affected by the experience of extra costs associated with a disability. OBJECTIVE: This article provides a systematized review of the global literature on the direct costs associated with living with a disability at the individual or household level. METHODS: We searched three databases for peer-reviewed journal articles that estimated extra costs associated with disability: Econlit, SocIndex and PubMed. RESULTS: We found 20 such studies conducted in 10 countries. These studies were predominantly from high-income countries. Although studies were heterogeneous (e.g., in terms of disability measures and cost methodologies), estimated costs were sizeable and some patterns were consistent across studies. Costs varied according to the severity of disability, life cycle and household composition. Highest costs were observed among persons with severe disabilities, and among persons with disabilities living alone or in small sized households. CONCLUSIONS: More quantitative evidence is needed using rigorous methods, for instance evidence based on longitudinal data and as part of policy evaluations. More internationally comparable data on disability is required for the quantitative evidence to develop, especially in low- and middle-income countries where studies are scarce. Qualitative and participatory research is also needed, especially to investigate unmet needs, and the consequences of extra costs.

Abstract Entrepreneurship has been viewed as a critical contributor and an economic engine in a country for creating new jobs and it is crucial for graduates to alter their mindset to become self-employed. Thus, it is necessary to synthesize the factors that impact the entrepreneurial intentions (EI) of students at tertiary level. The aim of this research is twofold; first to identify the factors which have been most studied in the literature and second, to determine which factors are less explored to measure the EI of students. This research adopts the systematic review approach to identify various studies conducted between 2005 to June 2022. The paper further adopted citation analysis and identified the 36 most impactful studies in this area of research. Next, the thematic analysis was conducted and seven main themes (factors) (cognitive, personality, environmental, social, educational, contextual and demographic) of EI determinants were identified. The analysis of the papers clearly demonstrated that the TPB model and cognitive factors dominate this area of research. Furthermore, over half of the studies are conducted in Asia, hence it is important to explore other regions such as Africa, America and Europe and other comparative studies between various regions. The study offers avenues for future research and practical implications of the study for the practitioners.

We examine the determinants of financial sector development in Asia and the Pacific from 1995 to 2011. In terms of economic growth, over the last twenty years the region has outperformed other parts of the world and has also experienced major developments in its traditionally bank-dominated financial system since the 1997 Asian financial crisis. We apply the dynamic generalized method of moments to a panel data set of twenty-six economies in the region. The estimations were done for the whole panel as well as for subpanels of developed and developing economies. We find that better governance and institutional quality foster financial sector development in developing economies while economic growth and trade openness are key determinants of financial depth in developed economies.

As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digital abilities. Results show that economic status and parents’ level of education are positively correlated with digital literacy. Students from urban schools have only a slightly higher level of digital literacy than their rural counterparts, suggesting that school location may not be a defining explanatory element in the variation of digital literacy and resilience among Vietnamese students. Students’ digital literacy and, especially resilience, also have associations with their gender. Moreover, as students are digitally literate, they are more likely to be digitally resilient. Following SDG4, i.e., Quality Education, it is advisable for schools, and especially parents, to seriously invest in creating a safe, educational environment to enhance digital literacy among students.

Abstract This paper aims to explore the benefits of Blended Learning towards students’ learning experiences at an offshore campus of an Australian university located in Ho Chi Minh City, Vietnam. At the university campus, the Blended Learning practice in use is the displacement of content. Content displacement refers to a portion of the learning content and activities for a course being delivered online via the Learning Management System. Learning not only happens in face-to-face sessions at a given time but is extended to online spaces as well, happening anywhere at any time at students’ preference. The focus of this research is its usefulness and effectiveness in promoting interactions between students and their peers, their teachers, and course materials. An online survey, which was designed based on a set of validated questions, was used to collect data from sixty-six students enrolled in eight Blended Learning courses. The analysis of the survey results provides empirical evidence to the claim that students’ perception of their learning experiences at the university was beneficially impacted as a result of the Blended Learning environment in each of their classes. Specifically, factor analysis using oblique rotation method identifies a clear factor structure across survey questions, representing four dimensions of benefits: Engagement, Flexibility of learning, Online learning experience, and Self-confidence. In addition, significant differences between the clusters on these factors indicate that students vary in their responses towards the benefits of Blended Learning and their experience with a Blended Learning approach.

This article discusses the challenges and implications of artificial intelligence powered chatbot (AI-Chatbots) in nursing education. Chat Generative Pre-trained Transformer (ChatGPT) is an AI-Chatbot that can engage in detailed dialog and pass qualification tests in various fields. It can be applied for drafting course materials and administrative paperwork. Students can use it for personalized self-paced learning. AI-Chatbot technology can be applied in problem-based learning for hands-on practice experiences. There are concerns about over-reliance on the technology, including issues with plagiarism and limiting critical thinking skills. Educators must provide clear guidelines on appropriate use and emphasize the importance of critical thinking and proper citation. Educators must proactively adjust their curricula and pedagogy. AI-Chatbot technology could transform the nursing profession by aiding and streamlining administrative tasks, allowing nurses to focus on patient care. The use of AI-Chatbots to socially assist patients and for therapeutic purposes in mental health shows promise in improving well-being of patients, and potentially easing shortage and burnout for healthcare workers. AI-Chatbots can help nursing students and researchers to overcome technical barriers in nursing informatics, increasing accessibility for individuals without technical background. AI-Chatbot technology has potential in easing tasks for nurses, improving patient care, and enhancing nursing education.

Purpose This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research. Design/methodology/approach A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered. Findings Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations . Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization , where detection is contributed by only seven articles. Research limitations/implications This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. Originality/value This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

The roles of institutional quality on economic growth are still heavily debated in the literature. This paper investigates the impacts of institutional quality on economic growth for 29 emerging economies over the 2002-2015 period by employing System Generalized Method of Moments (SGMM) estimators. We find the significant positive impacts of institutional quality on economic growth. The institutional quality impedes the positive effects of foreign direct investments (FDIs) and trade openness on economic growth. However, institutional quality improvement can mitigate the competition brought by trade openness in the areas FDIs operate to optimize their spill-over effect.

Abstract In response to growing environmental challenges, there is an urgent need to understand how corporations can leverage new technologies to boost sustainability and eco‐innovation. This study addresses this need by investigating Artificial Intelligence adoption (AIA) influence on green innovation (greenovation) performance among Chinese firms as China's expanding digital economy and severe ecological pressures make it unique study context. Specifically, panel data on 8722 firm‐year observations from Chinese listed firms from 2008 to 2017 is analyzed to test the relationship. The main findings show that higher AIA is associated with increased greenovation, measured through green patents. This positive effect is more pronounced among privately‐owned enterprises versus state‐owned enterprises. Additionally, financial analysts are found to strengthen the AI‐greennovation link through information dissemination and scrutiny. Importantly, the study findings are robust and validated through a battery of tests, including change regression, instrumental variable methods, propensity score match (PSM), and sysGMM. Overall, this study provides novel empirical evidence that AI holds promise as an enabler of corporate eco‐innovation. The findings have crucial implications for research and practice regarding leveraging digital technologies for sustainability, especially in emerging economies like China that is undergoing rapid technological change.

Identity sharing systems, regardless of their architectural models, share common vulnerabilities. These systems compel users to divulge personal information and furnish proof of identity for accessing services, leaving them susceptible to data breaches that can culminate in identity theft and jeopardize online data security. While blockchain technology offers a potential remedy, delivering enhanced security, immutability, and traceability, it simultaneously raises pertinent concerns surrounding privacy and transparency. The integration of zero-knowledge proof (ZKP) technology has emerged as a promising solution, particularly in enhancing privacy within the transparent blockchain ecosystem. Our paper conducts an exhaustive survey of the existing literature, with a particular focus on the assimilation of ZKP technology into blockchain for the secure sharing of user identities. We undertake a critical evaluation of the advancements achieved in this domain, pinpoint the formidable challenges that must be confronted, and uncover nascent opportunities for further exploration. Our contribution transcends the realms of mere summarization and analysis; we go a step further by offering recommendations drawn from real-world case studies and delineating future research directions.

The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing.

Cloud computing as an emerging high technology has been recognised by organisations and individuals for a wide range of potential applications. Since the concept's first appearance in 2007, the authors found a dominant amount of studies in the non-technological domains, including attempts to define and categorise cloud computing and the challenges and issues of the technology's adoption. Nonetheless, few researches are dedicated to determine the drivers of adopting cloud computing thus the literature is limited on this topic. As more adopters are becoming familiar with the technology and implementing cloud computing in their daily work, understanding what drives their adoption decision is essential to create opportunities for future cloud technologies to be tailored and aligned with the consumer's needs thus promotes exploitations of the technology's promising applications. This research takes a quantitative approach by developing and validating a theory-based conceptual model. Among the theories that are commonly applied in Information Systems research, the authors found Technology-Organisation-Environment framework can encapsulate the adoption's factors into one big picture. The authors conducted a secondary data analysis on the recent large-scale survey of IBM to investigate the drivers and barriers of cloud computing adoption. Structural Equation Modelling and Partial Least Square statistical methodologies provide rigid scientific procedures to validate the conceptual model. This study contributes a statistically validated conceptual model of the drivers and barriers of cloud computing adoption.

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

Artificial intelligence (AI) is revolutionizing consumer–provider interactions by changing the nature of online purchases. This study uses the social support theory to investigate consumer purchase intentions by combining AI technology, consumer social media engagement, and consumer experience. Online surveys are conducted with 467 Chinese social media users who had experience with online purchasing and AI technology. Partial Least Squares Structural Equation Modelling (PLS-SEM) is used to examine the data and proposed hypothesis. This study finds that AI positively affects consumer experience and consumer engagement on social media. Similarly, a positive relationship exists between social media engagement and consumer experience, leading to a more satisfied consumer and amplified purchase intentions. Additionally, affective attachment moderates the relationship between consumer satisfaction and purchase intention. The results reveal that AI can be used on social media to improve consumer experience and increase customer satisfaction levels and purchase intention. We also provide tips for developing flawless service business models. Marketers should explore making social media posts more engaging by using vibrant images and videos to attract customers and prompt them to create, circulate, and share said content on various social media networks.

The paper aims to investigate the impact of ESG practice on firms’ financial performance in the context of U.S. market from 2018 to 2020. The paper examines a sample of 57 U.S. non-financial firms belonging to the S&P 500. The Two-Stage Least Squares (2SLS) estimation is employed with an instrumental variable - the political views of the states where the studied firms are located. The paper shows that having a better practice of ESG could enhance firms’ financial performance measured by ROA, ROE, and TobinQ. These findings are consistent with the stakeholder-focused theory instead of shareholder-focus perspective. In addition, the magnitude of the influence of the ESG practice on TobinQ is significantly higher than that of the ESG-ROA and ESG-ROE relations. It reveals that the ESG benefits could make the firms appear more attractive to investors, creating higher market values of the firms’ assets and then higher TobinQ ratio. Not as the TobinQ enhancement, the significant improvement in ROA and ROE would be realized in the long run rather than short term. The low managerial ownership in the U.S. market may increase the chance of ESG overinvestment by the firms’ managers, hence reducing firm value. However, under the pressure of the investors’ strong demand for socially responsible investing, the U.S. firms tend to become involved in ESG activities, obtaining a strong stakeholder commitment and thus creating additional firm value in the long run.

Purpose The purpose of this paper is to provide a structured literature review (SLR) and systematic insight into logistics outsourcing research, in order to identify gaps in knowledge, and provide future research direction in the logistics outsourcing discipline. Design/methodology/approach This paper identifies and synthesises information from academic journals and examines research designs and methods, data analysis techniques, geographic locations, industry engagements, year of publication, publishers, university and author affiliations. A collection of online databases from 1991 to 2016 were explored, using the keywords “third-party”, “logistics” and “outsourcing” in their title and/or abstract, to deliver an inclusive listing of journal articles in this discipline area. Based on this approach, a total of 263 articles were found and data were derived from a succession of variables. Findings There has been a significant increase and concentration by researchers over the past 25 years focussing on logistics outsourcing. A need to implement best practice has been universally identified, owing to increasing global supply chain complexity. This analysis shows that only seven literature reviews were published in the logistics outsourcing discipline. Additionally, this study revealed that 42 per cent of the work in this field was found to be survey based and this trend in research indicates the development of the structural equation modelling and multi-criterion decision-making methods. Investigations between 2010–2012 and 2015–2016 accounted for 151 of the total 263 articles published. Finally, reverse logistics is an area that requires special attention. Research limitations/implications This paper is limited to a review of academic articles obtainable from online databases, containing the words “logistics” and “outsourcing” in the title and/or abstract. Additionally, only papers from high quality, peer-reviewed journals were evaluated. Other academic sources such as books and conference papers were not included in this study. Originality/value This review will provide an increased understanding of the existing state of current research, trends and future research directions in the logistics outsourcing discipline.

Ports play a critical role in the economies of many countries in the ASEAN region. Efficient ports facilitate a country's imports and exports, and the “relative efficiency” of a port is evaluated based on its efficiency compared to others in its group. This study analyzes the relative efficiencies of 50 ASEAN container ports and terminals. These ports are categorized according to their container handling system and location (those located in a riverbank connected to the sea are called “inland seaports” and those by the seaside are called “seaports”). The traditional output-oriented data envelopment analysis method is applied, and measures of super-efficiency constant returns to scale are estimated in order to compare the units situated on the efficiency frontier. The findings may support port managers in the ASEAN region to make decisions on whether to increase container traffic. In addition, policymakers may consider the evaluation results in deciding whether to improve the trans-ASEAN transport network and ASEAN trade competitiveness.

This study examines the relationship between employee voice arrangements and employees' trust in management using data from the 2007 A ustralian W orker R epresentation and P articipation S urvey of 1,022 employees. Drawing on social exchange theory and employee relations literature, we test hypotheses concerning the relationships between direct and union voice arrangements, perceived managerial opposition to unions and employees' trust in management. Consistent with our predictions, after controlling for a range of personal, job and workplace characteristics, regression analyses indicated that direct voice arrangements were positively related to employees' trust in management. Union voice arrangements and perceived managerial opposition to unions were negatively related to trust in management. The article concludes by highlighting the study's implications for management practice and avenues for further research.