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The present study investigates a nexus between digital public services (DPS) and international tourism empirically.

This article analyzes the nexus of DPS and international tourism by using the international sample of 23 European countries in the span of nearly 10 years from 2011 to 2019. Various econometric techniques, including the panel-corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model, are employed to confirm the author’s findings. Furthermore, the autoregressive distributed lag (ARDL) method is applied to measure the short- and long-run effects of DPS on international tourism developments.

Tourism is positively influenced by digitalization, implying that the enhancement of digital public service usage results in the development of the tourism industry. However, when looking at the effect of DPS in the short term, a negative impact can be found on tourism, as the density reported in the previous analysis stated a negative response to the tourism density. This effect spans the course of several facets, such as international tourism arrivals, international tourism receipt, international tourism, receipts (% of total exports) and global tourism expenditure (% of total imports). Although the result is unfavorable in the short term, digitalization promises great prospects for tourism in the long term. Notably, an improvement in economic growth, financial development as well a reduction in the pervasiveness of corruption and an improvement of environmental quality are transmission channels through which DPS have favorable influences on tourism activities.

The author’s findings are vital for managers and policymakers to establish a comprehensive grasp of digitalization's role in deciding tourist adoption. This is because digitalization has been proven to play a role in determining tourism adoption.

The present study is the first to examine the relationship between DPS and international tourism empirically. The author is also the first to distinguish the effects of digitalization in the short and long run.

This study aims to examine the perceived values of the metaverse when adopting it in the luxury hospitality business. Based on the cost–benefit perspective, this research provides solid theoretical contributions and actionable managerial recommendations.

An exploratory sequential mixed-method design was used. For the qualitative phase, 21 hotel managers and 24 hotel guests (who often stay in four-star and five-star hotels and resorts) were interviewed after showing them a series of videos about using the metaverse in the hotel business. Based on the results of the qualitative phase, the analytic hierarchy process method was used, and 476 valid questionnaires were analyzed.

The results highlight the perceived benefits (personalized services, immersive experience and positive brand image) and costs (lack of human touch, time and effort and security and privacy) of metaverse adoption for hotel managers and their guests. In addition, the study determines the weight of each value attribute of metaverse adoption for each travel stage (pre-travel, during travel and post-travel).

Regarding metaverse adoption, the research offers practical suggestions for luxury hotels. For instance, the cost of equipment and the time and effort required are perceived costs of metaverse adoption. To address these challenges, hotels may offer free equipment (e.g. VR headsets) and training to their guests to stimulate the use of the metaverse.

This study addresses a gap in the literature by presenting a conceptual framework for examining metaverse adoption in the luxury hotel scenario. Unlike using conventional models like the technology acceptance model or the unified theory of acceptance and use of technology to investigate a technology’s adoption, this study stands out by unraveling the topic through the lens of value proposition. The latter often comes from an efficient value co-creation process, which is indeed shaped by an adequate appreciation of the congruence of perceived values (i.e. perceived benefits and costs) of metaverse from hotel manager and guest perspectives.

This study aims to explore the diverse sources of electricity generation (coal, natural gas, oil and hydroelectricity) and their respective associations with economic growth and environmental quality.

This study uses static panel data analysis with a random effects model for six selected ASEAN countries (Indonesia, Malaysia, Filipina, Thailand, Vietnam and Myanmar) from 1994 to 2014.

This study reveals that economic growth in six selected ASEAN countries is enhanced by electricity generation from all sources, while the contribution of electricity production from hydroelectricity remains the largest and strongest. There is no environmental impact of electricity production from hydroelectric, whereas fossil fuel-based electricity production emits carbon dioxide, with coal sources being the largest contributor, followed by natural gas and oil.

Based on the results, these six ASEAN countries should invest more in hydropower projects, reduce the coal mix in power generation and promote clean coal technology to improve economic efficiency and environmental sustainability.

To the best of the authors’ knowledge, no research has examined the relationship between electricity production, environmental quality and economic growth in Southeast Asian nations. Therefore, the outcome of this study is expected to provide insightful results to supplement the framing and implementation of national and collective regional strategies for sustainable electricity generation in ASEAN countries.

This study aims to investigate the relationships between loan growth, loan losses and net income after the 2008 global financial crisis. This study further conducts a comparative analysis by considering the period of COVID-19.

This study uses panel data models such as one-step system GMM, random effects, fixed effects and OLS, with a data set of 131 Chinese commercial banks from 2009 to 2020.

The study finds no significant relationship between loan growth and future loan losses. However, after adjusting loan loss by net interest income (NII-adjusted loan loss), the study reveals that loan growth in the subsequent year decreases if NII-adjusted loan loss increases. The study also demonstrates the positive effect of loan growth on net income as newly expanded loans are funded at similar costs but offered at a lower rate compared with existing loans. During COVID-19, loan growth and net income were higher than in previous years.

The findings suggest that Chinese banks can increase lending to support the economy without sacrificing loan quality, emphasizing the importance of maintaining and enhancing credit policies and practices. Chinese banks should also continue to refine their pricing strategies for loans and deposits. The findings also imply that China's policy responses to the impact of COVID-19 could serve as lessons for future policy decisions.

This article aims to draw a conceptual model that integrates the view from the entrepreneurial event model with entrepreneurial education and prior self-employment experience. The model tests the role of entrepreneurial education on the formation of intentions to become an entrepreneur and examines whether prior self-employed experiences moderate the route from entrepreneurial education, entrepreneurial perceived feasibility (PF) and perceived desirability (PD) into the entrepreneurial intention (EI).

The authors operated on a sample of 389 master's students by applying Cronbach's alpha, exploratory factor analysis, confirmatory factor analysis and structural equation modelling to illustrate the links between constructs.

The study found that entrepreneurial education is positively correlated with PF, PD, and intention to enter entrepreneurial activities. PD is determined as a partial mediator in the entrepreneurial education–intention link and full mediator in PF and EI. Moreover, the study revealed that prior self-employed experiences serve as a positive moderator in the path from entrepreneurial education and PD to EI.

The study offers several recommendations based on research findings so as to nurture and promote entrepreneurial activities among master's students.

The current research provides novel insights about the relationship between entrepreneurial education and intentions to become an entrepreneur over and about the central antecedents in the entrepreneurial event model and moderation effects of prior self-employed experiences.

This study aims to investigate whether the extent of corporate disclosure, proxied by COVID-19-related disclosure, affects the dividend policy of listed firms.

The study uses a multinomial logistic regression model to examine the relation between corporate disclosure and the dividend policy of the 100 largest market-cap firms in Vietnam in 2021. The COVID-19 pandemic, with its unique impact on business operations, serves as the backdrop for this analysis.

The findings indicate that firms with more extensive COVID-19-related disclosure are more inclined to distribute dividends in the form of stocks or cash instead of omitting them.

This research contributes to the understanding of how corporate disclosure practices influence a firm’s financial decisions, particularly in the context of the COVID-19 pandemic. The findings hold implications for corporate financial decision-making during times of macroeconomic shock.

This study aims to investigate whether the participation in land rental markets helps to mitigate impacts by climate change on multidimensional poverty in Thailand and Vietnam.

The authors use precipitation data from the National Aeronautics and Space Administration (NASA) and self-reported shocks from the Thailand Vietnam Socio-Economic Panel (TVSEP) project to estimate climate change. Data from the TVSEP are also used to calculate a multidimensional poverty index (MPI). Fixed-effect logit panel regressions with interaction terms are implemented to analyze the above mentioned.

The results show that land rental markets are used as mitigation strategies to climate change in Thailand and Vietnam. The participation in land rental markets also reduces multidimensional poverty. However, as a mitigation strategy, land rental markets are only successful in certain circumstances.

The results show that there is potential in using land rental markets as mitigation strategies to climate change. Further research is needed to better understand which adaptation strategies, besides land rental market participation, and which combinations of different adaptation strategies are successful to mitigate negative effects induced by climate change.

The results show that there is potential in using land rental markets as mitigation strategies to climate change. Therefore, education in the participation in land rental markets and how to use them as a mitigation strategy can be a way to increase households' resilience to negative effects induced by climate change. Households make better decisions regarding their land when they are better informed on the functionality of land rental markets. Additionally, being better informed increases self-confidence to participate in land-rental markets.

Land rental markets as a mitigation strategy to climate change rarely have been studied, and if so, mainly the effect of leasing land has been studied. Additionally, the authors implement new measures of poverty – a multidimensional view on poverty which provides new insights into who are the poor and how they can be lift out of poverty.

This study addresses the empirical results of the spillover effect with export as the primary economic activity that enhances local businesses' total factor productivity (TFP). A learning mechanism is expected to be generated and used as the basis for the policy implication.

This study adopted the Cobb–Douglas function and multiple estimation approaches, including the generalized method of moments, the Olley–Pakes and the Levinsohn–Petrin estimation techniques. The findings were estimated based on the panel data of a Vietnamese local businesses survey conducted by the General Statistics Office of Vietnam (GSO) from 2010 to 2019.

The results showed that the highest TFP belongs to the businesses in the Southeast region, the Mekong Delta region, the mining industry and the foreign-invested enterprises. The lowest impacted TFP are businesses in the Northwest region and agricultural, forestry and fishery sectors. In addition, the estimated results also show that the positive spillover effect on TFP is shown through forward and backward linkage. The negative spillover effect is expressed through the backward and horizontal channels.

This study offers original empirical evidence on the learning mechanisms via which exports contribute to productivity improvement in a developing Asian economy, so making a valuable contribution to the existing academic literature in this domain. The findings of this research make a valuable contribution to the advancement of understanding on the many ways via which spillover effects manifest such as horizontal, forward, backward and supplied-backward linkage.

The study's findings indicate that it is advisable for governments to give priority to the development and improvement of forward and supply chain linkages between exporters and local suppliers. This approach is recommended in order to optimize the advantages derived from export spillovers. At the organizational level, it is imperative for enterprises to strengthen their technological and managerial skills in order to efficiently incorporate knowledge spillovers that originate from overseas partners and trade counterparts.

This study sheds new evidence on the export spillover effect on productivity in emerging economies, with Vietnam as the case study. The paper contributes to the research's originality by adopting novel methodological aspects to estimate local businesses' impact on total factor productivity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0373

Drought and salinity intrusion aggravated by climate change threaten agricultural livelihoods in Viet Nan's Mekong Delta. In response, authorities have built water management infrastructure for irrigation and salinity protection. This study assessed the impact of one such project, the Ba Lai dam in Ben Tre province, on the livelihoods of aquaculture farmers.

This study uses the Sustainable Livelihoods Framework to assess the impact of the Ba Lai dam on the livelihood capitals of 18 farming households in four communes, located both upstream and downstream of the dam.

The authors find that, apart from some positive effects, the dam has also brought negative environmental consequences, such as increased water pollution. The authors also find that farmers have responded to the changes by adapting their livelihood practices.

The samples were relatively small, encompassing four communes in Ben Tre province. On the other hand, this case study is instructive to the many ongoing infrastructure projects in the Vietnamese Mekong Delta.

The project have caused an increase in water-related social conflict.

The case of the Ba Lai dam provides a cautionary example for infrastructure-based water management plans, both in Viet Nam and more broadly. The study suggests the need to strengthen community participation and prioritize impacts of farmers' capital assets when constructing water management infrastructure for climate change adaptation.

The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed.

A Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree structure was generated by the Random Forest to retrieve a set of similar images for an input image. A KD-Tree structure is applied to determine a relationship word at leaves to extract the relationship between objects on an input image. An input image content is described based on class names and relationships between objects.

A model of image retrieval and image content extraction was proposed based on the proposed theoretical basis; simultaneously, the experiment was built on multi-object image datasets including Microsoft COCO and Flickr with an average image retrieval precision of 0.9028 and 0.9163, respectively. The experimental results were compared with those of other works on the same image dataset to demonstrate the effectiveness of the proposed method.

A balanced multibranch KD-Tree structure was built to apply to relationship classification on the basis of the original KD-Tree structure. Then, KD-Tree Random Forest was built to improve the classifier performance and retrieve a set of similar images for an input image. Concurrently, the image content was described in the process of combining class names and relationships between objects.