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The purpose of this study is to create a methodological approach for identifying priority areas for science and technology (S&T) development and its empirical application within the city of Moscow. This research uncovers a wide range of multicultural and multidisciplinary global trends that will affect the development of major cities in an era of complexity and uncertainty, including the inherent complexity of urban contexts, demographic and socioeconomic trends, as well as scientific and ecological factors.

The methodological approach is based on classic foresight instruments. Its novelty lays in the blending of qualitative and quantitative methods specially selected as the most appropriate for the identification of S&T areas in an era of complexity and uncertainty, including horizon scanning, bibliometric analysis, expert surveys and the construction of composite indexes with respect to the scope and resources of the research and the selected object for empirical application – Moscow, which is one of the world’s largest megacities. The analysis was performed for the period of 2009–2018 and expert procedures took place in 2019.

As a result, 25 global trends were identified, evaluated and discussed over the course of an expert survey and subsequent expert events. Ten priority areas of S&T development were determined, including 62 technological sub-areas within them and the most important market niches for all identified technological sub-areas, which could be useful for the world’s megacities. The results of this study are illustrated using the construction sector. Based on the conducted research and results, a list of recommendations on S&T policy measures and instruments were suggested, including the creation of the Moscow Innovation Cluster, which by the end of 2023 contained more than 6,000 projects and initiatives, selected using the findings of this investigation.

This research contributes to the existing literature and research agenda of setting priorities for S&T development and shows how it can be done for a megacity. The blended foresight methodology that was created within the study satisfies the criteria of scientific originality, is repeatable for any interested researcher, is applicable to any other city in the world and demonstrates its high efficiency in empirical application. It could be used for creating new agenda items in S&T policy, setting S&T priorities for a megacity and integrating the results into decision-making processes. This study provides recommendations on the further implementation of the designed methodology and results into a policymaking system. Moreover, the example of the Moscow Innovation Cluster, which was created based on the results of our research, demonstrates these recommendations’ practical significance in real life, which is quite valuable. The limitation of this study is that it is not devoted to urban planning issues directly or the promotion of R&D areas; it is about setting promising S&T priorities in an era of complexity and uncertainty for megacities.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

This study aims to investigate and test the impact of competitive priorities, in terms of quality, speed, dependability, flexibility, cost and patient engagement, on patient satisfaction with healthcare services. It considers patients’ rather than managers’ points of view to collect responses about competitive priorities.

This research employed a cross-sectional survey design to analyze a sample of customers through an empirical study of 488 patients in Qatar’s healthcare service context.

The confirmatory factor analysis results show that competitive priorities and engagement positively and significantly impact patient satisfaction.

Researchers can use this methodology to explore the role of competitive priorities in different service contexts and sectors. The researchers conducted the study in Qatar; therefore, the results are not generalizable to all healthcare sectors. However, regardless of geographic location, the research approach can be used in healthcare.

Managers can employ the developed scales to diagnose competitive priorities and improve customer service experiences.

The paper is original as it suggests using competitive priorities as a measurement tool for predicting patient satisfaction compared to prior research that mostly measured competitive priorities based on internal perspectives (managers’ perspectives). Further, this paper is original because it depends on the external perspective (customers’ perspective) for the competitive priorities for measuring patient satisfaction.

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.

The tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about it is limited in Indonesia, especially at the provincial level and after the COVID-19 crisis. This study aims to estimate the TME in all provinces of Indonesia, test its differences in priority and non-priority areas before and after the COVID-19 crisis, analyze its spatial distribution and examine the determinant factor of TME

This study applies an input-output model to measure the TME of all provinces in Indonesia, an independent sample t-test to examine the similarity of TME in priority and nonpriority areas, a paired sample t-test to examine the similarity of it before and after the COVID-19 crisis, and spatial analysis to check its spatial relationship.

The result shows that regional TME ranges from 1.25 to 2.05 in 2019, which changed slightly over time. The empirical result shows the TME difference before and after the COVID-19 crisis, and there is a spatial correlation in terms of TME with the hot spots are clustered in the eastern region of Indonesia, However, there was a slight change in the position of hot spots during the COVID-19 crisis. Moreover, the spatial model shows that value-added and employment in agriculture, manufacturing, trade and transportation affect the size of TME.

This study contributes to the academic literature by providing the first estimate of the TME at the provincial level in Indonesia, comparing the it in priority and non-priority areas before and after the COVID-19 crisis, and mapping its spatial distribution.

This study aims to investigate the strategic priorities of green building projects and analyze energy consumption alternatives in green residence projects using two innovative methods.

This study uses two methods, decision-making trial and evaluation laboratory (DEMATEL) to measure strategic priorities and golden-cut quantum spherical fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) to analyze energy consumption alternatives.

The study reveals that sustainability and atmosphere are the most significant factors in determining the priorities of green residence projects, whereas innovation has a limited impact on addressing environmental challenges in the building sector. The ranking of energy use alternatives shows that sustainability issues and atmosphere quality of space heating and cooking are the top priorities, whereas other factors like white goods, water heating, lighting and space cooling are ranked lower.

This paper offers a significant contribution to the understanding of green buildings by introducing innovative methodological approaches. Theoretically, it uses the DEMATEL to enhance traditional analytical frameworks, marking a novel effort in understanding green residence projects. In addition, the golden-cut quantum spherical fuzzy TOPSIS method is introduced, offering a comprehensive decision-making framework for green projects, considering factors like energy consumption and economic feasibility. This combination of methodologies provides a holistic evaluation, emphasizing sustainability in green building construction. This study reveals untapped potential for environmental sustainability and energy efficiency, enriching the existing knowledge base.

The potential for massive economic growth exists in Samarinda City due to the intensification of activities in built-up areas. This suggests the potential for increased urban disease in the relocation of Indonesia’s new capital city to a location adjacent to Samarinda. One of the most striking impacts is the urban heat island (UHI). The increase in this phenomenon can be addressed effectively and efficiently through the provision and arrangement of appropriate vegetation-based actions. Therefore, this study aims to identify priority areas of green open space (GOS) based on UHI levels. In addition, this study also aims to present alternative mitigation measures to reduce the risk of disasters due to UHI.

A mixed-method approach was used in this research, involving an initial land surface temperature analysis to identify the UHI class. This analysis was complemented by quantitative spatial analyses, such as scoring, overlay and intersect methods, to determine the priority level class and the typology of GOS priority. A qualitative analysis was also conducted through data triangulation or comparison methods, such as examining existing land use, GOS priority maps and spatial plan policies.

The findings show that the total UHI area in Samarinda City was 6,936.4 ha in 2019 and is divided into three classifications. In Class 1, the UHI area is very dominant, reaching 87% of the total area. Meanwhile, the main results identified two priority classes of GOS in Samarinda, namely, the medium and high categories with an area of 960.43 ha and 113.57 ha, respectively. The results also showed that there were 17 typologies associated with five alternative mitigation measures: green industry, greening parking lots, improving urban green infrastructure and buildings, urban greening and mining restoration.

Specific to assessing UHI, image data were available only in medium spatial resolution, leading to a consequence of detailed accuracy. In addition, since the determination of mitigation considered local policies, the method should be used in other locations requiring adjustments to existing regulations, specifically those related to spatial planning.

This study makes a significant contribution to the understanding of the UHI phenomenon in Indonesia, especially in the urban areas of Kalimantan Island. In addition, the study presents new insights into alternative mitigation actions to reduce the risk of UHI. Innovatively, this study introduces a typology of regions associated with appropriate alternative mitigation actions, making it an important achievement for the first time in the context of this study.

Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study focuses on analyzing both demand and supply side response strategies to meet customer demand and reduce the impact of the shortage during supply disruptions.

According to the quantity of products that the external market can provide, the external supply scenarios were divided into sufficient-type external supply and learning-type external supply. A two-echelon perishable goods supply chain was analyzed, and three kinds of contingency strategy models for downstream retailers were investigated. First, in the sufficient external supply scenario, the optimal price and transshipment quantity to maximize retailer's profits is discussed. Second, in the scenario of learning-type external supply, this study analyzes the optimal decision in three mechanisms of the hybrid strategy and their application: price priority mechanism, quantity priority mechanism and price–quantity balance mechanism. Furthermore, the influence of penalty cost and supply on the priority orders of different mechanisms was studied.

Results show that comparing the two pure strategies (pricing strategy and transshipment strategy)it was noted that the hybrid strategy produces the best results in sufficient-type external supply scenario. In the learning-type external supply scenario, a numerical study has shown the existence of three areas in case of penalty cost and supplier's capacity, and each areas has different priority orders of the three mechanisms. Under the situation of learning external supply, the retailer's optimal strategy is affected by parameters such as penalty cost and supply volume.

The main innovation of the work lies in the following: First; the external supply situation was divided into sufficiency type and learning type, which improves the external situation faced by retailers after the outbreak of emergencies, helps retailers understand the external situation, conforms to the actual situation and has certain practical application value. Second; in the context of learning external supply, there are three coping strategies for retailers, including: Price priority mechanism, Quantity priority mechanism and Pricing and transshipment balance mechanism. This will help retailers make strategic choices, make more scientific management decisions and improve the supply chain emergency management theory. Third; the demand side response was managed through the change of external supply during supply side recovery period and supply disruption. The proposed model enables managing and analyzing supply disruption efficiently and effectively via handling uncertainty by considering all aspects of decision-making process. The proposed model can be applied in various fields such as vegetable and fruit, fresh food, etc.

This study aimed to compare mortality rate, length of stay (LOS) and hospitalization costs at different priority levels for a patient admitted to an intensive care unit (ICU) at a public tertiary hospital in Qazvin, Iran. This study also aimed to predict influencing factors on patients’ mortality, ICU LOS and hospitalization costs in different admission groups.

The authors conducted a retrospective cohort study among patients who mainly suffered from internal diseases admitted to an ICU of a public hospital. This study was conducted among 127 patients admitted to ICU from July to September 2019. The authors categorized patients into four groups based on two crucial hemodynamic and respiratory status criteria. The authors used a logistic regression model to predict the likelihood of mortality in ICU admitted patients during hospitalizations for the four prioritization groups. Furthermore, the authors conducted a multivariate analysis using the “enter” method to identify risk factors for LOS.

Results showed a statistically significant relationship between the priority of being admitted to ICU and hospitalization costs. The authors’ findings revealed that age, LOS and levels of consciousness had a predictability role in determining in-hospital mortality. Besides, age, gender, consciousness level of patients and type of the disease were mentioned as affecting factors of LOS.

This study’s findings emphasize the necessity of categorizing patients according to specific criteria to efficiently use available resources to help health-care authorities reduce the costs and allocate the budget to different health sectors.

The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.

This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.

The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.

This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.