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This research examines the relationship between market power and liquidity creation in the specific context of bank profitability in the Vietnamese banking sector.

The study applies the methodology proposed by Berger and Bouwman (2009) to demonstrate the creation of bank liquidity through a three-step procedure for investigating the relationship between market power and liquidity creation. The three steps include non-fat liquidity (NFLC), fat liquidity (FLC) and system generalized method of moments estimation for panel data.

This study finds that liquidity creation increases when a bank has high market power. Further, highly profitable banks positively impact the market power of banks with regard to liquidity creation, relative to less profitable banks. Moreover, bank size, capital, economic growth and interest rate negatively influence bank liquidity creation, while credit risk positively relates to bank liquidity creation.

Measurements used in this study are based on the works of Berger and Bouwman (2009). There are specific variations, relative to Basel III. In addition, other variables significantly impact bank liquidity creation that have not been considered in the models, and a quadratic model should have been considered to measure market power and bank liquidity creation.

This study suggests that managers should control the liquidity of their banks by supervising vulnerable characteristics that have been mentioned herein and emphasizing improvements in profitability. Further, the government may consider encouraging banks to generate more liquidity by modifying regulations concerned with market power or reinforcing policies about improving the transparent business environment.

This study characterizes an attempt to examine the influence of market power on the liquidity creation of banks in Vietnam, which represents one of the most dynamic systems in Asia, with several varied participating banks. The current study also examines the same within the specific context of the modifying impact of the profitability of banks.

Organizational pursuit of sustainability in multi-tier supply chain systems operating in unpredictable environments is often associated with the emergence of paradoxical tensions. This study aims to summarize and synthesize existing literature on managing various paradoxical tensions in supply chains (i.e. sourcing, making, delivering and reverse logistics) as organizations pursue sustainability transformation. It also strives to motivate new academic research inquiry into developing responses to sustainability paradoxes.

The study draws on a systematic literature review of 73 papers from the Web of Science database selected at the intersection of paradox, sustainability and logistics/ supply chain management (SCM). Applying paradox theory as a guiding lens, we investigate organizational strategies, practices and capabilities described in the literature to navigate sustainability paradoxes in supply chains.

The results assert that the success of sustainability transformation will depend on an organizational ability to recognize, accept and navigate paradoxical tensions in one's supply chain. This requires developing the dynamic capabilities of paradoxical leadership, strategic agility, innovativeness, collaboration with contextualization and governance. Successful sustainability transformation is not reliant on finding an optimal, final design but rather the continuous balancing of tensions inherent within or across the organizations that make up one's supply chain.

The research offers an integrative conceptual framework to guide organizations in navigating sustainability paradoxes in supply chains, embracing strategic, practice and capability levels. It also outlines opportunities for future research inquiries connected to this framework that are needed to build additional insight for addressing paradoxical tensions related to the pursuit of sustainable supply chain management.

This study takes a dynamic capabilities approach to navigating paradoxical tensions in pursuit of sustainable supply chain management.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

This paper aims to explore and analyse stakeholders’ perceptions of the development priorities and suggests more effective strategies to assist sustainable economic growth in the United Arab Emirates (UAE).

The authors use the World Bank data set, which collects various stakeholders’ opinions on the UAE development. First, the exploratory factor analysis has been applied to detect the main groups of development priorities. Second, the fuzzy cluster analysis has been conducted to detect the groups of stakeholders with different attitudes towards the importance of extracted groups of priorities. Third, clusters have been compared according to demographics, media usage and shared prosperity goals.

The two main groups of development priorities have been extracted by the exploratory factor analysis: economic priorities and sustainability priorities. Four clusters have been detected according to the level of motivation when it comes to the economic and sustainability priorities: Cluster 1 (High economic – High sustainability), Cluster 2 (High economic – Medium sustainability), Cluster 3 (High economic – Low sustainability) and Cluster 4 (Low economic – Low sustainability). Members of the cluster that prefer a high level of economic and sustainability priorities (Cluster 1) also prefer more diversified economic growth providing better employment opportunities and better education and training for young people in the UAE.

Limitations stem from the survey being conducted on a relatively small sample using the data collected by the World Bank; however, this data set allowed a comparison of various stakeholders. Future research should consider a broader sample approach, e.g. exploring and comparing all of the Gulf Cooperation Council (GCC) countries; investigating the opinions of the expatriate managers living in the UAE that are not from GCC countries; and/or including other various groups that are lagging, such as female entrepreneurs.

Several practical implications were identified regarding education and media coverage. Since respondents prioritize the economic development factors over sustainability factors, a media campaign could be developed and executed to increase sustainability awareness. A campaign could target especially male citizens since the analysis indicates that males are more likely to affirm high economic and low sustainability priorities than females. There is no need for further diversification of media campaigns according to age since the analysis did not reveal relevant differences in age groups, implying there is no inter-generational gap between respondents.

This paper contributes to the literature by comparing the perceived importance of various development goals in the UAE, such as development priorities and shared prosperity indicators. The fuzzy cluster analysis has been used as a novel approach to detect the relevant groups of stakeholders in the UAE and their developmental priorities. The issue of media usage and demographic characteristics in this context has also been discussed.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

This paper is a corpus-driven study of written electronic texts, particularly the “About Us” sections in the university websites of 41 members of the Asian Association of Open Universities (AAOU). This inquiry is important because it will provide an insight as to how AAOU members describe and portray themselves in the World Wide Web, a platform which is highly utilized in the field of distance education. This will also lead to the understanding of conventional knowledge among AAOU members during the period of study. The paper aims to discuss these issues.

Guided by Swales’ (1990) seminal work on genre analysis, the researchers conducted a three-part genre analysis which involved identification and analysis of moves, keywords, and concordance lines.

Results of the keyword analysis were conducted using the AntConc application. Anthony (2017) found that the top 100 keywords with positive keyness exemplified inherent characteristics of open and distance learning institutions as well as characteristics common to higher education institutions and their thrusts. The analysis also found that the use of adjectives and verbs with positive denotations is common in the “About Us” sections. Concordance for several keywords related to the overarching theme of the AAOU 2017 Conference then revealed that the AAOU members are active in the discourse about accessibility, assessment, and quality, while there is not much discourse on openness, inclusivity, and justice. Meanwhile, subscribing to equality and equity could still be a point of discussion among the AAOU members as the concordance analysis revealed more discourse on equality than equity as a goal and principle. Overall, results of the genre analysis resonated with previous studies of the academic genre as the “About Us” sections are promotional in both authoritarian and inclusive ways.

The inquiry will provide the members of the AAOU with an overview of their common communicative purposes, overused or underused keywords, and their usage of these words which they may opt to work on in the future.

Following the standard practice of using nutrition claims to denote food functionality, this study empirically explores Chinese consumers’ willingness-to-pay for functional processed meat products by using three nutrition claims (namely “increased calcium,” “containing omega-3”, and “reduced salt”) made on pork sausages. It also aims to outline the typical characteristics of Chinese consumer segments based on preferences.

A choice-based choice experiment is utilized to investigate Chinese consumers’ valuation on attributes of interest regarding functional sausage products. First-hand data was collected in the two cities of Xi'an and Beijing.

There are market potentials for domestic and/or imported functional processed meat products among Chinese consumers. Nutrition claims made on pork sausages are appealing to Chinese consumers, and therefore, monetarily rewarded by them. Being imported from a more developed country of origin could both positively and negatively impact consumers’ WTP for nutrition claims made on pork sausages. Furthermore, specific functional modification strategies should be taken into account when addressing different segments of the Chinese market. In addition, regional impacts between Xi'an and Beijing are implied in terms of consumers’ valuation for functional pork sausages.

Limitations in the current study are mainly two folds. First, the WTP estimation magnitudes are subject to a hypothetical bias by using a stated preference approach. Second, this study only focuses on pork sausages to explore consumers’ perceptions and selects three nutrition claims among many other relevant options.

Implications are provided for meat marketers and for Chinese official food policymakers, such that promoting meat products with a nutrition claim is an attractive marketing strategy for foreign food manufacturers in China, and more reformulated meat products with better nutritional compositions should be allowed in the Chinese market.

To the best of the author’s knowledge, this research is the first to fill in the literature blank on investigating the consumers’ valuation for functional meat in the emerging market of China. Because when taking Chinese consumers as a target market and evaluating their perceptions of food quality-related labeling and certifications, the existing literature is mainly limited to topics of product safety, organic/green products, and geographical origins. However, nutrition claims, as marketable credence attributes that associate closely to the main characteristics of the functional food products, have been explored to a much lesser extent among Chinese consumers.

The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations..

The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time.

The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates.

The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock.

To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

The purpose of this paper is to examine the determinants of users’ simulated experience in a virtual store and to show the subsequent impact of that experience on engagement. The outcome of that engagement is examined in relation to enjoyment, satisfaction and purchase intentions.

The method comprised an experiment comparing users’ perceptions of a standard 2D online clothing store with an enhanced, immersive one that aimed to provide shopping value approaching that of a traditional store by using a 3D experience where participants wore special glasses and a data glove.

Results demonstrate the major role of telepresence components in simulated experience and the critical role of that experience, along with hedonic and utilitarian values, in engagement. Purchase intention is influenced by satisfaction, which is in turn influenced by enjoyment and engagement. Engagement in turn is influenced by utilitarian and hedonic value and the experience of product simulation or telepresence, which is composed of control, colour and graphics vividness, and 3D authenticity. In the immersive, 3D environment, experience is more associated with engagement and enjoyment, leading to greater purchase intention. The immersive, 3D environment, thus, has the potential to rival traditional shopping in terms of experience, resulting in higher sales for retailers and satisfaction for consumers.

This work has evaluated a robust model of purchase intention and demonstrated it to hold not only in a 3D environment on a conventional computer platform, but also in an immersive one, where participants wear special glasses and a data glove.

The increasing energy consumption in residential buildings poses significant challenges to sustainability, economic efficiency, and environmental health. Despite the availability of numerous energy-saving strategies, their adoption remains inconsistent due to various barriers such as cost, awareness and technical limitations. This paper aims to assess energy-saving strategies mitigating high energy consumption in residential buildings.

The study adopted a quantitative research methodology. A carefully developed study questionnaire was distributed to a group of 20 Delphi experts to collect quantitative data. These experts possess extensive experience in the areas of energy use, energy management and energy savings. The questionnaire was also issued to the end users to gather quantitative data. The collected data were analyzed using descriptive analysis.

The study results indicate a strong preference for certain energy-saving measures, with energy-efficient appliances, occupant behavior adjustments and heating, ventilation and air conditioning (HVAC) system upgrades emerging as the most frequently adopted strategies. Measures such as installing smart thermostats and energy-efficient windows also ranked highly, reflecting their perceived effectiveness in reducing energy consumption. However, strategies like weatherizing buildings and using smart power strips were less frequently implemented, suggesting lower prioritization or barriers to adoption. The findings further emphasize the importance of HVAC-related maintenance and upgrades, highlighted by their top rankings and frequent use, while simpler actions, such as insulation and turning off lights, were ranked lower despite their potential for energy savings.

The study’s limitations include reliance on self-reported data, potential bias in participant responses and a lack of consideration for contextual factors such as regional climate, socioeconomic conditions and technological access that may influence the adoption of energy-saving measures.

This research is distinctive in its integration of the Delphi method with questionnaires to holistically evaluate and assess energy-saving measures for reducing excessive energy use in residential structures. By integrating expert consensus with empirical data from diverse residential contexts, it provides a comprehensive and nuanced understanding of effective energy management practices.