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This study aims to understand New Zealand sheep farmers’ readiness toward sustainability transition by assessing their intentions of transition and adoption of sustainability tools, with information collection considered to mediate the intention–adoption relationship.

Based on the data collected from a survey of New Zealand sheep farmers in 2021, the empirical analysis was developed to investigate farmers’ perceptions of and attitudes toward readiness to move toward a sustainability transition. Structural equation modeling associated with principal component analysis was used to empirically test the theory of planned behavior constructs.

The results show that pressure from the public and the sheep industry, and the perceived controls of transition drive the intention of sustainability transition; farmers with higher intention of sustainability transition are found to be more likely to adopt sustainability tools. However, there is an attitude–behavior gap, wherein positive attitudes toward sustainability transition may not lead to a higher likelihood of adopting sustainability tools. There is no evidence of the mediating role of information collection on the intention–adoption relationship, while a positive effect was found in information collection on the adoption of sustainability tools.

The empirical evidence indicates that policymakers need to help increase the awareness of sustainable production and help farmers overcome barriers to achieving sustainable production by finding ways to turn intentions into adoption.

Being the first attempt to empirically assess farmers’ readiness toward sustainability transition, the study fills the gap of limited understanding of the link between sustainability transition intention and sustainable tools adoption in sustainability transition.

This paper aims to facilitate the joint assessment of issues related to the agricultural systems, i.e. agriculture, aquaculture and climate change (CC) response strategies, in the coastal districts with a panel of stakeholders; and to evaluate the level of agreement on the topics under consideration and potentially identify the most promising approaches to assessing CC affecting agricultural systems in the area and identify relevant and adequate response strategies.

Stakeholder Delphi technique – its assessment is an interactive method of qualitative research used to facilitate the interactive participation of varied and conceivably hierarchical and antagonistic stakeholder groups.

The most important result for the province is that the impact of climate change on agro-ecological landscapes (poverty and near river areas) is the duration of cold weather, more frequent and extreme. Moreover, Kendall’s W test gave a score of 0.547, which indicates a “strong” stakeholder agreement and the “confidence in ranks” being “high” in Thai Binh (Vietnam).

The scientific results in this study are intended to serve as relevant knowledge-inputs and direct contributions to capacity-building for the local partners (stakeholders and local authorities). These local partners are engaged in policy-making and effective planning and implementation of climate response measures in the districts in the province of Thai Binh.

This paper aims to investigate the decisions of listing for Vietnamese banks and the impact of listing on bank performance.

A longitudinal data set of 30 commercial banks in the period of 2006–2018 with various univariate and multivariate tests is used.

This study found that listing is positively associated with bank profitability. The results are consistent even after the control for potential endogeneity problems by propensity score matching methodology and Heckman selection bias models. Further analysis suggests some new alternative channels for the positive impact, namely, the increased quality of information disclosure, technological development and income diversification of commercial banks after listing.

Hence, this paper provides recommendations and policy implications for regulatory bodies regarding the listing of commercial banks in Vietnam.

The contributions to the literature are three-folds. First, this study contributes to a strand of literature on the impact of going public [initial public offering (IPO)/listing] of financial institutions on their performance. While the literature on non-financial firm performance post-going public is ample, few have directly considered the IPO/listing of banks and other financial institutions. Second, in further looking at the impact of listing on bank performance, this study also sheds some light on the new possible channels of the effect and provides evidence of new channels. Then, last but not least, the case of Vietnam could possibly yield interesting results for a transitory stock market. From the evidence, the recommendations and policy implications for a listing of Vietnamese banks are provided.

In Vietnam, there is a phenomenon that domestic tourists return to some tourist destinations despite their reputation of tourists being scammed. This study aims to focusses on Vung Tau City in Vietnam as a popular seaside destination to explain this interesting phenomenon.

Thereby, 22 domestic tourists who are repeated visitors and had experienced scams directly and/or indirectly in Vung Tau were interviewed and later took part in a self-evaluation.

The data shows that unreasonable charges, switching products, false reporting and dishonest middle-persons are the major dominant scams in the city. Furthermore, all visitors could be scammed regardless of gender, educational background or age. Even though scams may occur, the city remains to be a popular getaway destination for the Southern part of Vietnam due to its irreplaceable natural resources and visit intention of domestic tourists. The local authority has tried different ways to suppress issues related to tourist scams, however, due to their sociocultural and economic conditions, scams find ways to exist. Under this circumstance, domestic tourists tend to figure out how to handle scams by themselves whilst very few of them rely on the public sector to deal with them.

Finally, some practical strategies and recommendations are given as a reference for the local authority and businesses for long-term tourist business development.

This paper, respectively, investigates the common scams against domestic tourists in Vung Tau, reasons they still return to this destination, and finally, how these visitors attempt to avoid scams.

This paper aims to explore factors influencing apartment price volatility in the two biggest cities in Vietnam, Hanoi and Ho Chi Minh City.

The study uses the supply and demand approach and provides a literature review of previous studies to develop four main hypotheses using four determinants of apartment price volatility in Vietnam: gross domestic product (GDP), inflation rate, lending interest rate and construction cost. Subsequently, the Vector Error Correction Model (VECM) is used to analyze a monthly data sample of 117.

The research highlights the important role of construction costs in apartment price volatility in the two largest cities. Moreover, there are significant differences in how all four determinants affect apartment price volatility in the two cities. In addition, there is a long-run relationship between the determinants and apartment price volatility in both Hanoi and Ho Chi Minh City.

Limitations related to data transparency of the real estate industry in Vietnam lead to three main limitations of this paper, including: this paper only collects a sample of 117 valid monthly observations; apartment price volatility is calculated by changes in the apartment price index instead of apartment price standard deviation; and this paper is limited by only four determinants, those being GDP, inflation rate, lending interest rate and construction cost.

The study provides evidence of differences in how the above determinants affect apartment price volatility in Hanoi and Ho Chi Minh City, which helps investors and policymakers to make informed decisions relating to the real estate market in the two biggest cities in Vietnam.

This paper makes several recommendations to policymakers and investors in Vietnam to ensure a stable real estate market, contributing to the stability of the national economy.

This paper provides a new approach using VECM to analyze both long-run and short-run relationships between macroeconomic and sectoral independent variables and apartment price volatility in the two biggest cities in Vietnam.

This study aims to investigate the impact of AI chatbots on customer trust in AI chatbots for hotel services.

The probability sampling method was employed to develop a research sample. The research uses correlation analysis and structural equation modeling to analyze the data of 413 valid observations collected in the structured questionnaire survey in Vietnam.

The paper reports that empathy response, anonymity and customization significantly impact interaction. Empathy response is found to be the strongest influence on interaction. Meanwhile, empathy response and anonymity were revealed to indirectly affect customer trust. This paper also contributes several implications for hotel providers in emerging economies.

To the best of the authors’ knowledge, this is the first study to shed light on the role of AI chatbots in explaining customers’ behavior. The results provide an enhanced understanding of how the AI chatbot system influences customers’ decision-making. It has been used to plan the chatbot application and highlight which implementation issues need the most attention in the hospitality 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.

This study aimed to utilize Epistemic Network Analysis (ENA) for a thorough evaluation of policy documents concerning the digital transformation in Vietnam's higher education sector.

Adopting a quantitative ethnography approach, this research employed ENA to analyse a curated collection of 21 documents that specifically addressed higher education (HE) and digital transformation within Vietnam. The study also incorporated qualitative content analysis, utilizing the constant comparison method as outlined by Onwuegbuzie et al. (2009), for data coding. ENA facilitated the examination of connections among various policy aspects.

The study revealed a consistent overarching theme in Vietnam's digital transformation policies during and post-pandemic, focusing on key areas such as ADMINISTRATION, VISION, QUALITY, and INFRASTRUCTURE. However, a temporal shift in emphasis was observed: during the pandemic, policies were more focused on ADMINISTRATION and INFRASTRUCTURE, while post-pandemic, there was an increased emphasis on COLLAB, VISION, and TEACH_LEARN.

This research represents one of the initial efforts to showcase the utility and significance of ENA in analysing policy documents. It underscores ENA's potential in elucidating the complex interplay of policy elements in the context of digital transformation in higher education, particularly within a developing country setting.