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This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

This paper uses the complex proportionality assessment (COPRAS) method to examine the driving factors of Industry 4.0 (I4) technologies for lean implementation in small and medium-sized enterprises (SMEs).

Adopting I4 technology is imperative for SMEs seeking to maintain competitiveness within the manufacturing sector. A thorough understanding of the driving factors involved is required to support the implementation of I4. For this objective, the multi-criteria decision-making (MCDM) tool COPRAS was used to efficiently analyze and rank these driving elements based on their importance. These factors can help small and medium-sized firms (SMEs) prioritize their efforts and investments in I4 technologies for lean implementation.

This study evaluates and prioritizes the nine I4 factors according to the perceptions of SMEs. The ranking offers significant insights into the factors SMEs consider more accessible and effective when adopting I4 technologies.

The author's original contribution is to examine I4 driving factors for lean implementation in SMEs using COPRAS.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

The purpose of the article is to identify relevant criteria for decision support in the implementation of waste-to-energy (WtE)-based systems.

The methodology is a simple case study with a qualitative approach. Five experts involved in the project of a thermoelectric power plant qualitatively evaluated, on a Likert scale, a decision model with 15 indicators derived from recent studies. The research object was the first stage of a project to implement a thermoelectric plant employing municipal solid waste (MSW) in southern Brazil.

The study identified 15 criteria supporting the decision-making process regarding WtE implementation for MSW in a mid-sized city in southern Brazil. The study identified that compliance with MSW legislation, compliance with energy legislation, initial investment and public health impact are the most influential criteria. The study offered two models for decision processes: a simplified one and a complete one, with ten and fifteen indicators, respectively.

The study concerns mid-sized municipalities in southern Brazil.

Municipal public managers have now a methodology based on qualitative evaluation that admits multiple perspectives, such as technical, economic, environmental and social, to support decision-making processes on WtE technologies for MSW.

MSW management initiatives can yield jobs and revenues for vulnerable populations and provide a correct destination for MSW, mainly in developing countries.

The main originality is that now municipal public decision-makers have a structured model based on four constructs (technical, economic, environmental and social) deployed in 15 indicators to support decision-making processes involving WtE and MSW management.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

This study aims to integrate the resource-based view (RBV) with other theories that consider external factors necessary to respond successfully to dynamic and uncertain entrepreneurial business conditions.

The paper introduces an multi-criteria decision-making (MCDM) approach, utilizing the axial-distance-based aggregated measurement (ADAM) method with weights determined by the preference selection index (PSI) method, to rank eight European countries based on the Global Entrepreneurship Monitor (GEM) data. Additionally, the paper extends the existing entrepreneurial ecosystem taxonomy (EET), offering an additional classification.

The performed analysis emphasizes the importance and necessity of involving different dimensions of EE in assessing the countries' entrepreneurship performance, which facilitates creating adequate policy measures.

The crucial limitations are assessments based only on the GEM data from a particular period, possibly leading to a certain bias. Future research should involve data from various resources to increase the results' reliability.

The ranking results and country classification obtained using the ADAM-based approach and two distinct taxonomies served as the basis for formulating tailored policy recommendations, aiming to formulate tailored policy implications for increasing the number of new entrepreneurs and improving innovativeness, sustainability and internationalization of existing entrepreneurs for each group of countries.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

Education 4.0 (E 4.0) represents a new paradigm in the field of education, which emphasizes a student-centric approach that allows learners to access education anytime, anywhere, tailored to their individual needs through modern-day technologies. The purpose of the study was to unearth the critical success factors (CSFs) essential for the successful implementation of E 4.0.

The CSFs were unearthed using a literature review and further the interrelationships were analysed using multi-criteria decision making (MCDM) approach.

The study unearthed 15 CSFs for the successful implementation of E 4.0. The most important factor for the successful implementation of E 4.0 was personalized learning which was found to be the casual factor. The other causal CSFs were clear vision and leadership for E 4.0, stakeholder involvement, data analytics in teaching and learning, inter-disciplinary learning and blended learning environments. The effect factors were digital citizenship-based education, teacher training and development for E 4.0, supportive environment, curriculum redesign for E 4.0, open educational resources, digital technologies, formative assessments, infrastructure for E 4.0 and sustainability in education.

This is the first study which unearthed the CSFs and found the interrelationships among them, thus contributing to the theory of technology organization environment.

This study represented a pioneering effort in understanding the CSFs underpinning the successful adoption of E 4.0, paving the way for a more personalized, tech-savvy and effective education system.