An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

Spatio-temporal modeling of lake's ecosystem and dynamism in response to changing environment: a case study of L. Ol Bolossat in Kenya

Lakes' ecosystems are vulnerable to environmental dynamisms prompted by natural processes and anthropogenic activities happening in catchment areas. The present study aimed at modeling the response of Lake Ol Bolossat ecosystem in Kenya to changing environment between 1992 to 2022 and its future scenario in 2030. The study used temperature, stream power index, rainfall, land use land cover, normalized difference vegetation index, slope, and topographic wetness index as datasets. A GIS-ensemble modeling approach coupling the analytical hierarchical process and principal component analysis was used to simulate the lake's extents between 1992 and 2022. Cellular Automata-Markov chain analysis was used to predict the lake extent in 2030. The results revealed that between 1992 and 2002, the lake extent shrunk by about 18%; between 2002 and 2012, the lake extent increased by about 13.58%; and between 2012 and 2022, the lake expanded by about 26%. The spatial-temporal changes exhibited that the lake has been changing haphazardly depending on prevailing climatic conditions and anthropogenic activities. The comparison between the simulated and predicted lake extents in 2022 produced Kno, Klocation, KlocationStrata, K standard, and average index values of 0.80, 0.81, 1.0, 0.74, and 0.84, respectively, which ascertained good performance of generated prediction probability matrices. The predicted results exhibited there would be an increase in lake extent by about 13% by the year 2030. The research findings provide baseline information which would assist in protecting and conserving the Lake Ol Bolossat ecosystem which is very crucial in promoting tourism activities and provision of water for domestic and commercial use in the region.

Spatio-temporal modeling of rangeland degradation in response to changing environment in the Upper Ewaso Ngiro River Basin, Kenya

Rangelands primarily provide forage for grazing and browsing animals, yet their ecosystems are degraded due to natural causes and anthropogenic activities such as pastoralism, tourism, and ranching. Increased rangeland detrimental effects led the present research to model the severity of rangeland degradation in the Upper Ewaso Ngiro River Basin (UENRB) in Kenya between 1986 and 2021 and predict the future scenario for 2031. The severity of rangeland degradation was analysed using the multi-criteria analytic hierarchical process and principal component analysis, while the cellular automata Markov chain-analysis model was used for prediction. The models utilized datasets including land-use land cover, surface albedo, bareness index, vegetation health index, soil moisture index, topographic wetness index, reconnaissance drought index, k-factor, slope, and population density. The findings indicated that rangeland degradation varied sporadically, with the reconnaissance drought index being the significant influencing parameter, contributing to about 19.2% of the total degradation. In average, between the years under study, non-rangeland zones covered 10.4%, while low, moderate, high, and very high degradability severity covered 15.3%, 49.1%, 25.2%, and 0%, respectively. Prediction results for the year 2031 revealed that non-rangeland zones will cover 5.3%, whereas low, moderate, high and very high will cover 18.1%, 39.2%, 37.4%, and 0%, respectively. The hybrid model proved to be effective in modeling rangeland degradation. The study recommends the county and national governments to propose and adopt by-laws on legislation to regulate the exploitation of natural resources in the study area in order to restore the rangelands.

Multi-criteria decision-making methods applied in health-insurance underwriting

This study attempts to structure methodologically the health insurance underwriting process by applying Multi-criteria Decision-making (MCDM) analysis in health insurance underwriting. This is done by assigning a score to each health insurance applicant which can be used to determine whether he or she is accepted, rejected or accepted with special terms and conditions (such as exclusions, additional waiting periods and/ or surcharge). The introduction of MCDM approaches in health insurance underwriting enables the quantification of the selection criteria, the increased standardization and automation of the process and its alignment through quantitative indicators with the risk tolerance/ risk appetite of the insurer, and there lie the novelties of this research. The proposed methodology can be readily implemented by insurers with added value in the underwriting, risk management and distribution (sales & marketing) functions, as well as in the profitability of the company or the level of premium paid by the insured.

Multiple Scenarios of Quality of Life Index Using Fuzzy Linguistic Quantifiers: The Case of 85 Countries in Numbeo

In economic development, in addition to comparing the gross domestic product (GDP) between nations, it is critical to assess the quality of life to gain a holistic perspective of their different aspects. However, the quality of life index (QOLI) is a subjective term that can be difficult to quantify. Although this composite index is typically calculated using universal weights proposed by experts to aggregate indicators, such as safety indexes, healthcare indexes, pollution indexes, and housing indicators, it is complicated to balance multiple dimensions whose weights are adjusted to account for different countries’ circumstances. Therefore, this paper aims to construct various scenarios of the QOLI, using linguistic quantifiers of the ordered weighted averaging (OWA) operator, and the 2-tuple linguistic model. Numbeo, one of the largest quality of life information databases, was used in this paper to estimate the QOLI in 85 countries. Uncertainty and sensitivity analyses were employed to assess the robustness of the QOLI. The results of the proposed model are compared with those obtained using the Numbeo formulation. The results show that the proposed model increases the linguistic interpretability of the QOLI, and obtains different QOLIs, based on diverse country contexts.

Environmental Fragility Zoning Using GIS and AHP Modeling: Perspectives for the Conservation of Natural Ecosystems in Brazil

The degradation of natural ecosystems triggers global environmental, economic, and social problems. To prevent this, it is necessary to identify the aptitude of priority areas for conservation or use by considering land fragility from multiple environmental and spatial perspectives. We applied the concept of environmental fragility to a hydrographic basin in southeastern Brazil that establishes (i) potential fragility levels according to slope, soil classes, geological domains, drainage hierarchy, and rainfall information using an algebraic map, and (ii) emerging fragility levels via the addition of the land-use parameters. The methodological approach involved the integration of the analytic hierarchy process (AHP) and weighted linear combination (WLC) into a geographic information system (GIS). The medium and slightly low fragility classes predominated in terms of potential (~60%), and emerging (~70%) environmental fragility models used to model the basin. The model indicated that high and extremely high potential fragilities were concentrated in the upper basin, a region that is considered a global biodiversity hotspot. The areas with high/extremely high classes of emerging fragility in the upper basin decreased, indicating that the natural cover classes and land-use types are not in danger. We also introduce acceptable conservation practices for land management and use according to the environmental fragility categories established in the present work. The methodology applied in this study can be replicated in other global ecoregions. It provides low-cost territorial and environmental zoning and flexible replication and can be adjusted by administrators who are interested in land-use planning.

Prioritization of Challenges for the Effectuation of Sustainable Additive Manufacturing: A Case Study Approach

Additive manufacturing (AM) is gaining significant importance, as demand for customized products is increasing nowadays. AM is one of the disruptive technologies of Industry 4.0, which can reduce waste generation, enabling sustainability. The adoption of sustainable practices in the manufacturing sector is due to the need of the current scenario to minimize harmful emissions and for human wellbeing. In this regard, AM technologies are integrated with sustainable manufacturing concepts to contribute toward sustainable AM (SAM), with various benefits from the design, manufacturing, use, and EoL perspectives. Still, many sustainability issues are associated with AM processes, namely limited speed and the uncertain performance of fabricated parts. From this viewpoint, it is essential to analyze the challenges associated with adopting SAM practices. This article presents identification and analysis of the potential challenges associated with adopting SAM practices. Fifteen SAM challenges have been identified from the literature survey and analyzed using the “Gray Technique for Order of Preference by Similarity to Ideal Solution” (G-TOPSIS) approach. The priority order of the challenges has been identified. The study identified that “training towards SAM benefits” and “limited materials recycling potential” were the significant challenges in adopting SAM practices in the manufacturing sector. The present study will help industry practitioners, decision makers, and researchers effectively analyze the challenges associated with SAM for its effective implementation. Researchers can utilize the findings of the study for establishing the guidelines for the adoption of SAM.

Habitat vulnerability in slum areas of India - What we learnt from COVID-19?

UN-Habitat identified the present COVID-19 pandemic as 'city-centric'. In India, more than 50% of the total cases were documented in megacities and million-plus cities. The slums of cities are the most vulnerable due to its unhygienic environment and high population density that requires an urgent implementation of public healthcare measures. This study aims to examine habitat vulnerability in slum areas to COVID-19 in India using principal component analysis and Fuzzy AHP based technique to develop slum vulnerability index to COVID-19 (SVI_covid-19). Four slum vulnerability groups (i.e. principal components) were retained with eigen-values greater than 1 based on Kaiser criterion - poor slum household status; lack of social distance maintenance; high concentrations of slum population and towns and mobility of the households. This study also mapped composite SVI_covid-19 on the basis of PCA and Fuzzy AHP method at the state level for a better understanding of spatial variations. The result shows that slums located in the eastern and central parts of India (particularly Uttar Pradesh, Bihar, Jharkhand, Odisha, West Bengal) were more vulnerable to COVID-19 transmission due to lack of availability as well as accessibility to the basic services and amenities to slum dwellers. Thus, the findings of the study may not only help to understand the habitat vulnerability in slum areas to COVID-19 but it will also teach a lesson to implement effective policies for enhancing the quality of slum households (HHs) and to reduce the health risk from any infectious disease in future.

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients' initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators.

MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services

Assessing and ranking private health insurance companies provides insurance agencies, insurance customers, and authorities with a reliable instrument for the insurance decision-making process. Moreover, because the world’s insurance sector suffers from a gap of evaluation of private health insurance companies during the COVID-19 outbreak, the need for a reliable, useful, and comprehensive decision tool is obvious. Accordingly, this article aims to identify insurance companies’ priority ranking in terms of healthcare services in Turkey during the COVID-19 outbreak through a multi-criteria performance evaluation methodology. Herein, alternatives are evaluated and then ranked as per 7 criteria and assessments of 5 experts. Experts’ judgments and assessments are full of uncertainties. We propose a Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) technique under an intuitionistic fuzzy environment to rank insurance companies. The outcomes yielded ten insurance companies ranking in terms of healthcare services in the era of COVID-19. The payback period, premium price, and network are determined as the most crucial factors. Finally, a comprehensive sensitivity analysis is performed to verify the proposed methodology’s stability and effectiveness. The introduced approach met the insurance assessment problem during the COVID-19 pandemic very satisfactory manner based on sensitivity analysis findings.

Decision Tree and AHP Methods Application for Projects Assessment: A Case Study

This research is dedicated to the modelling of decision process occurring during the implementation of construction projects. Recent studies generally do not assess the robustness of the decisions regarding the possible changes during the construction project implementation. However, such an assessment might increase the reliability of the decision-making process. We addressed this gap through a new model that combines the decision-making process modelling with the AHP method and includes the analysis of model stability concerning stakeholders’ behaviour. We used the Analytic Hierarchy Process (AHP) and Decision tree methods to model the decision-making process. The proposed model was validated on a case study of multiple construction projects. The assessment was performed from individual investor’s and independent expert’s perspectives. The criteria for the assessment were selected according to the principles of sustainability. We performed the sensitivity analysis, making it possible to assess the possible changes of the decisions depending on the potential patterns of the decision-makers’ behaviour. The results of the study show that, sometimes, small fluctuations in the project factors affect the project selection indicating the possible lack of the robustness of the project decisions.

Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

Establishing Merger Feasibility Simulation Model Based on Multiple-Criteria Decision-Making Method: Case Study of Taiwan’s Property Management Industry

The purpose of this study is to propose a feasible operational evaluation model for property mergers. It is expected that through the merger of enterprises, the comprehensive improvement of business management and the promotion of logistics supply resources will be effectively promoted, so that enterprises can effectively reduce operating costs and achieve maximum profits. This study uses the modified Delphi method and analytic hierarchy process method to find out the key factors of the common dilemmas in Taiwan’s property management companies, and the weight of their impact on the operation. Finally, we use the expected utility theory to develop a valuation model for whether the property is suitable for integration, and to evaluate this, the result is used as a reference indicator for merger operations. After 30 years of vigorous development in Taiwan’s property management companies, due to fierce market competition, most of the companies have reduced their profitability in the face of common dilemmas. The study found that the merger model should be accurately evaluated by the evaluation model. The sharing of logistics resources can indeed bring about the benefits of investment and marketing to the merger, and improve the profitability of the company. At the time of writing, there is no research on such a combined analysis of the property management industry in Taiwan. This research method uses multiple decision analysis theory and utility theory to develop a decision-making model that is suitable for consolidation. It can also be applied to the assessment of mergers in other fields, such as the clean service industry, real estate brokerage and other industry merger assessments. This is also the biggest contribution of this research paper.

Evaluation of Agricultural Extension Service for Sustainable Agricultural Development Using a Hybrid Entropy and TOPSIS Method

Agricultural extension service is the foundation of sustainable agricultural development. The evaluation and analysis of the agricultural extension service for sustainable agricultural development can provide an effective analytical tool for sustainable agriculture. This paper analyzes the influence of agricultural extension service on sustainable agricultural development, and constructs an evaluation system for sustainable agricultural development from the four dimensions of agricultural environment, society, economy, and agricultural extension service. This work proposes a framework based on the combination of technique for order performance by similarity to ideal solution (TOPSIS) and entropy method to evaluate the performance of the evaluation system. Taking three national modern agriculture demonstration zones in Suzhou in Jiangsu Province as a case study, the method was verified. Moreover, the main factors affecting sustainable agricultural development are discussed, and the improvement measures and management suggestions are also put forward to reduce the obstacles to sustainable agricultural development and improve sustainable agriculture practice.

A Statistical Framework for Assessing Environmental Performance of Quality Wine Production

The present work presents a statistical framework for analysing and evaluating the environmental performance of 21 wines (protected designation of origin or protected geographical indication) produced in Greece, through their complete lifecycle. For this purpose, the life cycle assessment methodology was used. It is well known that lifecycle thinking is a scientific approach that can support businesses in decision making towards sustainable consumption and production. However, such techniques provide a large amount of multi-dimensional data that are difficult to comprehend and interpret. Therefore, the application of an appropriate statistical framework to aid this assessment, which should be as unambiguous and reliable as possible, is needed. This statistical framework should be based on the lifecycle inventory results, on an appropriate multivariate technique such as principal component analysis, and on probability distributions, thereby providing an objective framework to assist the evaluation of the environmental performance of the products. Applying the proposed framework to 21 Greek wines, we found that the proposed framework could be used for categorizing the examined wines according to their environmental impact severity, as well as the impact types associated with them.

Analytic Hierarchy Process (AHP)-Based Aggregation Mechanism for Resilience Measurement: NATO Aggregated Resilience Decision Support Model

Resilience is a complex system that represents dynamic behaviours through its complicated structure with various nodes, interrelations, and information flows. Like other international organizations NATO has also been dealing with the measurement of this complex phenomenon in order to have a comprehensive understanding of the civil environment and its impact on military operations. With this ultimate purpose, NATO had developed and executed a prototype model with the system dynamics modelling and simulation paradigm. NATO has created an aggregated resilience model as an upgrade of the prototype one, as discussed within this study. The structure of the model, aggregation mechanism and shock parametrization methodologies used in the development of the model comprise the scope of this study. Analytic Hierarchy Process (AHP), which is a multi-criteria decision-making technique is the methodology that is used for the development of the aggregation mechanism. The main idea of selecting the AHP methodology is its power and usefulness in mitigating bias in the decision-making process, its capability to increase the number of what-if scenarios to be created, and its contribution to the quality of causal explanations with the granularity it provides. The parametrized strategic shock input page, AHP-based weighted resilience and risk parameters input pages, one more country insertion to the model, and the decision support system page enhance the capacity of the prototype model. As part of the model, the decision support system page stands out as the strategic level cockpit where the colour codes give a clear idea at first about the overall situational picture and country-wise resilience and risk status. At the validation workshop, users not only validated the model but also discussed further development opportunities, such as adding more strategic shocks into the model and introduction of new parameters that will be determined by a big data analysis on relevant open source databases. The developed model has the potential to inspire high-level decision-makers dealing with resilience management in other international organizations, such as the United Nations.

Multivariate Analysis for Assessing Irrigation Water Quality: A Case Study of the Bahr Mouise Canal, Eastern Nile Delta

Water scarcity and suitable irrigation water management in arid regions represent tangible challenges for sustainable agriculture. The current study aimed to apply multivariate analysis and to develop a simplified water quality assessment using principal component analysis (PCA) and the agglomerative hierarchical clustering (AHC) technique to assess the water quality of the Bahr Mouise canal in El-Sharkia Governorate, Egypt. The proposed methods depended on the monitored water chemical composition (e.g., pH, water electrical conductivity (ECiw), Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, and SO42−) during 2019. Based on the supervised classification of satellite images (Landsat 8 Operational Land Imager (OLI)), the distinguished land use/land cover types around the Bahr Mouise canal were agriculture, urban, and water bodies, while the dominating land use was agriculture. The water quality of the Bahr Mouise canal was classified into two classes based on the application of the irrigation water quality index (IWQI), while the water quality was classified into three classes using the PCA and AHC methods. Temporal variations in water quality were investigated, where the water qualities in winter, autumn, and spring (January, February, March, April, November, and December) were classified as class I (no restrictions) based on IWQI application, and the water salinity, sodicity, and/or alkalinity did not represent limiting factors for irrigation water quality. On the other hand, in the summer season (May, June, July, August, and October), the irrigation water was classified as class II (low restrictions); therefore, irrigation processes during summer may lead to an increase in the alkalinity hazard. The PCA classifications were compared with the IWQI results; the PCA classifications had similar assessment results during the year, except in September, while the water quality was assigned to class II using the PCA method and class I by applying the IWQI. Furthermore, the normalized difference vegetation index (NDVI) around the Bahr Mouise canal over eight months and climatic data assisted in explaining the fluctuations in water quality during 2019 as a result of changing the crop season and agriculture management. Assessments of water quality help to conserve soil, reduce degradation risk, and support decision makers in order to obtain sustainable agriculture, especially under water irrigation scarcity and the limited agricultural land in such an arid region.

COVID-19 and urban vulnerability in India

The global pandemic has an inherently urban character. The UN-Habitat's publication of a Response Plan for mollification of the SARS-CoV-2 based externalities in the cities of the world testifies to that. This article takes the UN-Habitat report as the premise to carry out an empirical investigation in the four major metro cities of India. The report's concern with the urban character of the pandemic has underlined the role of cities in disease transmission. In that wake, the study demarcates factors at the sub-city level that tend to jeopardize the two mandatory precautionary measures during COVID-19 - Social Distancing and Lockdown. It investigates those factors through a Covid Vulnerability Index. The Index devised with the help of Analytic Hierarchy Process demarcates the low, moderate, high, and very high vulnerable city sub-units. Secondly, UN-Habitat's one of the major action areas is evidence-based knowledge creation through mapping and its analysis. In our study, we do it at a granular scale for arriving at a more nuanced understanding. Thus, in harmony with the UN-habitat's we take the urban seriously and identify the gaps that need to be plugged for the pandemic cities of now and of the future.

Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality

Purpose

This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.

Design/methodology/approach

The target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.

Findings

Findings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.

Practical implications

This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.

Originality/value

The proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

A Multicriteria Decision Aid-Based Model for Measuring the Efficiency of Business-Friendly Cities

Local self-government has the task of enabling stable economic development, in addition to enabling a normal quality of life for citizens. This is why the state government should provide guidelines that will improve the local business climate, and by doing so enable local economic development. This can be done through the introduction of a business-friendly certification procedure, which is influenced by uncertain inputs and influences many output factors. Each local government has the important task of determining its rank of efficiency in this process. A number of methodologies developed to solve this problem are generally divided into two groups: Parametric and non-parametric. These two groups of methodologies could provide quite different results. Therefore, the purpose of this paper was to create a model using both approaches to achieve a balanced symmetrical approach that produces better results than each approach individually. For this purpose, the paper describes a multicriteria decision aid-based model of optimization to evaluate the effectiveness of this process, integrating classification, data envelopment analysis, and stochastic frontier analysis, as well as its application in a case study of business-friendly certification in the Republic of Serbia.

Environmental Determinants of a Country’s Food Security in Short-Term and Long-Term Perspectives

About 10% of the world population suffered from hunger in 2018. Thereby, the main objective of this research is the identification of environmental drivers and inhibitors of a country’s food security in the short and long run. The Food Security Index (FSI) was constructed from 19 indicators using Principal Component Analysis. Identification of the short- and long-run relationships between the FSI and environmental factors was realized with the pooled mean-group estimator for 28 post-socialistic countries for 2000–2016. Empirical research results showed that a country’s food security in the short run is affected by greenhouse gas emissions but boosted by the increase of renewable energy production. Reduction of carbon dioxide emissions, electrification of rural populations, access to clean fuels, renewable energy production, arable land, and forest area growth might be essential tasks in order to ensure countries’ food security in the long-run.

A Literature Review of the Concepts of Resilience and Sustainability in Group Decision-Making

The most critical decisions usually involve several decision makers with different roles and opportunities to commit key resources. Several group decision-making (GDM) approaches can support the identification of a joint or compromise decision in less conflicting settings, where there is a group of subjects (e.g’, partners) who pursue a common overall objective. However, considering the uncertainty in future events and complexity of modern-day systems, decision processes do not always produce beneficial results or give the participants a positive perception of their role in the process. Group decision-making should then take into consideration some aspects that might insure future resilience and sustainability, particularly the achievement of the objectives in view of future risks and the transparency and participation that are needed to limit problems in the implementation phase of the decision. The literature survey presented in this study identified a research gap regarding GDM. Differently from traditional GDM, which was first discussed in the early 1980s and whose body of knowledge is pretty defined, resilient and sustainable GDM (R&S GDM) is fairly new. The main objective of this study is then identifying the main attributes for supporting sustainable and resilient group decisions. To this aim, a preliminary focused systematic review was conducted to study the existing group decision-making methods in the literature and how the concepts of sustainability and resilience have been employed. After defining the search keywords and exclusion criteria for the individuation of the articles, the first screening process was carried out and the most relevant articles were selected. The last steps of the systematic review were the classification of the articles and the full paper examination to extract the main factors of R&S GDM. Seven attributes were listed as the key factors of R&S GDM. In light of those factors, a group decision process concerning an injection moulding line in Tajikistan was investigated. The case study highlighted that over self-confidence, information flow and transparency were the main reasons for faulty decisions, thus suggesting that information system and information fluidity play an important role in R&S GDM. Finally, the most important managerial implications of R&S GDM are reported.