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The advent of artificial intelligence technologies, exemplified by platforms such as ChatGPT, has created a profound global impact, with a particular resonance in education. This research aims to integrate the information systems success (ISS) model with the stimulus-organism-response (SOR) paradigm to explore how information quality and service quality individually, jointly, congruently, and incongruently affect higher education students’ trust in ChatGPT, satisfaction, and continuance usage intention.

We employed a sophisticated methodology - polynomial regression with response surface analysis - and conducted our study with a sample of 468 higher education students selected in Vietnam using a three-phase stratified random sampling approach to evaluate the hypotheses developed.

The findings indicate that both information and service quality influence higher education students’ trust directly and positively in ChatGPT, satisfaction, and continuance usage intention. Moreover, a balance between information quality and service quality can increase students’ level of trust in ChatGPT, satisfaction, and intention to continue using it. Nevertheless, a significant incongruity between information quality and service quality will likely reduce trust in ChatGPT, satisfaction, and the intention to continue using the service.

This research is one of the first to offer a shred of empirical evidence about how information quality and service quality interact congruently and incongruently with each other to trigger higher education students’ trust in ChatGPT, satisfaction, and continuance usage intention of ChatGPT. It thus offers valuable insights for leveraging the benefits and mitigating potential challenges associated with adopting this innovative technology in educational settings.

Little is known about how religious-related beliefs (i.e. karmic beliefs and beliefs in a just world) might affect consumers to engage in more environmentally friendly behavior. Drawing on the justice motive theory, this study aims to explore the individual, complementary, congruent and incongruent impacts of karmic beliefs and belief in a just world on consumers’ proenvironmental consumption.

A sample of 736 consumers was recruited by means of a mall-intercept survey in the most populus cities in Vietnam. An advanced technique – polynomial regression with response surface analysis – was used to test the hypothesized model, whereas the PROCESS macro approach was used to estimate indirect effects.

The findings indicate that while karmic beliefs do not directly affect proenvironmental consumption, they can inspire environmentally friendly behaviors through belief in a just world. High levels of both beliefs enhance proenvironmental consumption, but imbalanced levels erode such behaviors. Importantly, the findings revealed gender differences concerning the role of religious beliefs in influencing proenvironmental consumption among male and female consumers.

The findings of this study offered several theoretical and managerial implications for proenvironmental consumption.

The research provides new insights into how religious beliefs (karmic beliefs and belief in a just world) affect, interact, balance and imbalance with each other to trigger proenvironmental consumption. The research also contributes to the sustainable consumption literature by indicating the mediation roles of belief in a just world and proenvironmental consumption intention in the relationship between beliefs in karma and actual proenvironmental consumption behavior, as well as offering empirical evidence about the gender differences regarding these relationships.

This study explores the effect of local budget retention rate changes (RER) on total factor productivity (TFP) and its components in Vietnam.

The study employs a two-system generalized method of moments (GMM) estimator and data from 2012 to 2019 across all 63 provinces/cities of Vietnam.

The study finds that local budget retention rates significantly influence public investment, affecting scale and allocation efficiency. The reallocation of budgets between regions and from the central government to local levels incurs certain costs, often resulting in economically robust provinces experiencing reductions in their retention rates.

Recognizing the challenges of immediate structural budget changes due to cultural and historical factors, the study suggests a more gradual policy approach. It emphasizes the importance of policy predictability, as abrupt reductions in the retention rate lead to higher costs than gradual reductions, thus implementing budget policies with a clearer timeline. This study provides insight into local budget allocation regimes and their impact on productivity in transitioning countries.

First, the study provides fresh evidence of the impact of retention rate changes on TFP and its components in Vietnam. Second, the study provides insights into the mechanisms of the nexus of increased budget spending, capital efficiency and, most importantly, attaining improvement in education. We also offer further insights into inefficient budget allocation agents in Vietnam, especially in large cities, which should alert scholars to explore this topic further in the future.

This study aims to examine the relationships among fear of COVID-19, job stress, job commitment and intention to quit. It also investigates the boundary conditions of the impact of fear of COVID-19 on job stress and intention to quit, a research gap that has not been addressed yet.

This research focused on full-time frontline hotel employees who have been working in four- and five-star hotels in Ho Chi Minh City, Vietnam. A pilot test was performed before collecting formal data. The survey was conducted face-to-face on site so that any potential confusion could be clarified right away. 487 valid responses were analyzed using SPSS 28 and SmartPLS 4.

The majority of hypotheses were supported, with the results suggested that supervisor support contributes to reducing the tendency of hotel employees to quit their job and their job stress. Besides, government support is necessary to make staff feel secure during the pandemic.

This study contributes to pointing out central priorities in making decisions by hotel managers when facing crises. Managers need to focus more on measures to help their employees feel secure and should be available for guidance and feedback when nedeed. Additionally, supportive policies from the government, especially financial support, can provide hotel employees with resources they need to maintain a basic level of living in the face of a severe drop in their income. The study provides the hotel industry not only in Vietnam but also in countries with comparable settings with strategies to cope with unexpected events.

Research on intention to quit a job has mainly focused on a complex interplay of internal factors. However, the influence of fear of COVID-19 on intention to quit a job has not been studied in the context of Vietnamese hotel industry yet. During the COVID-19 pandemic, a number of hotels in Vietnam had to close due to a lack of visitors, which had a negative impact on human resources. Accordingly, fear, stress, commitment and intention to quit a job are the issues faced by staff.

This study aims to examine the relationship between blockchain technology (BCT) adoption and firm performance (FIP) mediated by cyber-security risk management (CSRM) in the context of Vietnam, a developing country. Besides, the mediating effect of risk-taking tendency (RTT) has been considered in the BCT–CSRM nexus.

Data is collected using a survey questionnaire of Vietnamese financial firms through strict screening steps to ensure the representativeness of the population. The ending pattern of 449 responses has been used for analysis.

The findings of partial least squares structural equation modeling demonstrated that CSRM has a positive effect on FIP and acts as a mediator in the BCT–FIP nexus. Furthermore, RTT moderates the relationship between BCT and CSRM significantly.

This study introduces the attractive attributes of applying BCT to CSRM. Accordingly, managers should rely on BCT and take advantage of it to improve investment resources, business activities and functional areas to enhance their firm's CSRM. Especially, managers should pay attention to enhancing their RTT, which improves FIP.

This study supplements the previous literature in the context of CSRM by indicating favorable effects of BCT and RTT. Additionally, this study identifies the effectiveness of RTT as well as its moderating role. Ultimately, this paper has been managed as a pioneering empirical study that integrates BCT, RTT and CSRM in the same model in a developing country, specifically Vietnam.

This study aims to identify the enabling factors for Building Information Modeling (BIM) adoption in Vietnamese construction enterprises and uncover their interrelationships. This will help stakeholders focus on controlling and allocating resources (time, personnel, and costs) appropriately to adopt BIM and differentiate themselves from fierce competition in the architectural, engineering, construction and operations (AECO) industry.

This study first identifies and evaluates 32 enabling factors for applying BIM in the Vietnamese construction industry according to the TOE extended framework. Afterwards, a hybrid questionnaire survey using a convenient sampling method is conducted to capture stakeholders' views. The exploratory factor analysis (EFA) and the partial least squares structural equation modelling (PLS-SEM) technique are then applied to identify the constructs of the enabling factors and their interrelationships.

The study extracts six constructs that could have a significant impact on the adoption of BIM in construction enterprises, namely: technical feasibility (TF), human resources and management (HRM), company business vision (CBV), political environment (PE), economic viability (EV), and legal aspects (LA). Based on eleven proposed hypotheses, the analysis results confirm nine hypotheses and show that the HRM, TF, and CBV have the strongest effects on managers in evaluating the factors for BIM.

The results of the study fill the gap in knowledge by discovering the interrelationships among the enabling factors for BIM adoption in construction enterprises. The results might support the construction enterprises and their stakeholders in increasing the application of BIM, and digital transformation in construction industry.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

Reconstruction of old buildings is often necessary after prolonged usage, but these projects encounter various complexities, leading to their failure. However, there is a lack of studies that focus on investigating the complexity of old building reconstruction initiatives. Thus, this study aims to examine the complexity of reconstruction projects for old buildings in Vietnamese urban areas.

Through a literature review and discussions with experienced practitioners, 12 significant complexity factors were identified. The study then utilized the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to analyze the interrelationships among these complexity factors.

The findings revealed intricate interdependencies among the complexity factors, visualized in a complexity map. The map highlighted the critical complexities of site compensation, clearance and the long project duration. Furthermore, a causal diagram categorized the complexity factors into net-effect and net-cause groups.

By examining the relationships and interactions among these factors, the study emphasizes the interconnected nature of complex systems and the influence of one factor on others. Therefore, decision-makers and stakeholders involved in reconstructing old buildings are suggested to possess a comprehensive understanding of these interdependencies to ensure effective decision-making.

This study explores the economic impact of the COVID-19 crisis on herding behaviour in the Australian equity market by considering liquidity, government interventions and sentiment contagion.

This study utilizes a daily dataset of the top 500 stocks in the Australian market from January 2009 to December 2021. Both predictive regression and portfolio approaches are employed to consider the impact of COVID-19 on herding intention.

This study confirms that herding propensity is more pronounced at the beginning of the crisis and becomes less significant towards later phases when reverse herding is more visible. Investors herd more toward sectors with less available information on financial support from the government during the financial meltdown. Conditioning the stock liquidity, herding is only detectable during highly liquid periods and high-liquid stocks, which is more observable during the initial phases of the crisis. Further, the mood contagion from the United States (US) market to Australian market and asymmetric herding intention are evident during the pandemic.

This is the first study to shed further light on the impact of a health crisis on the trading behaviour of Australian investors, which is driven by liquidity, public information and sentiment. Notwithstanding the theoretical contributions to the prior literature, several practical implications are proposed for businesses, policymakers and investors during uncertainty periods.

Of critical concern to the world is the need to reduce consumption and waste of natural resources. This study provides a multi-level exploration of the ways situational and transformational links between levels and challenges are related to the adoption and utilization of material flow cost accounting in Vietnam, to encourage green productivity.

Based on triangulation of public documents at different institutional levels and a set of semi-structured interviews, situational and transformational links and challenges for material flow cost accounting in Vietnam are examined using purposive and snowball sampling of key actors.

Using a multi-level framework the research identifies six situational and transformational barriers to implementation of material flow cost accounting and suggests opportunities to overcome these. The weakest links identified involve macro-to meso-situational and micro-to macro-transformational links. The paper highlights the dominance of meso-level institutions and lack of focus on micro transformation to cut waste and enable improvements in green productivity.

The paper identifies ways for companies in Vietnam to reduce unsustainability and enable transformation towards sustainable management and waste reduction.

The paper is the first to develop and use a multi-level/multi-time period framework to examine the take-up of material flow cost accounting to encourage transformation towards green productivity. Consideration of the Vietnamese case builds understanding of the challenges for achieving United Nations Sustainable Development Goal number 12, to help enable sustainable production and consumption patterns.