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The Renewable Energy Directive 2009/28/EC of the European Union provides another element to cross-border cooperation by allowing Member States to fulfill their 2020 renewable energy sources (RES) targets by implementing joint projects in third countries through the cooperation mechanisms. The purpose of this paper is to assess the country risk, to support bilateral cooperation for RES electricity generation projects.

A multicriteria decision support methodology has been developed taking into account three evaluation parameters, namely, the investment framework, the social conditions and the energy and technological status. An additive value model has been constructed, and the UTilitès Additives (UTA) – UTA* (UTASTAR) disaggregation method has been implemented to infer the criteria weights. The obtained ranking of alternatives has been subjected to robustness analysis, and finally the proposed methodology has been applied to five North Africa countries, so as to draw key results.

The pilot application of the methodological approach proposed and the model developed was fully compatible with the decision maker’s ranking on a set of fictitious countries and facilitated the assessment of a country’s current situation with regards to its investment, social conditions and energy and technological status. The results regarding the five North African countries examined, indicated the country’s investment framework as the most important factor, from foreign investors’ perspective, affecting a country’s suitability for the implementation of RES projects through a cooperation mechanism and Morocco, as well as Tunisia as the countries with the most suitable conditions for a successful implementation of such projects.

To the best of authors’ knowledge, there are only very few studies trying to assess opportunities and risks emerging from the implementation of joint projects between European and third countries in the field of electricity generation from RES. There are even less studies using (UTASTAR) method on real-world decision-making problems, and almost none are dedicated to energy sector-related problems.

The purpose of this paper is to present a model to support governmental local managers in public budget optimization, based on an integration of methods. It was constructed to fill the gap related to weights definition in problematic, commonly performed subjective assessments. This model supports the decision making in budget distribution identifying the importance of sectors in local governments, captured by historical data.

The model was developed following three steps: the first step included the exploitation of the characteristics of local sectors represented by city departments and the data collection procedure using time series (TS). In the second one, the weights regarding the importance of each city department were calculated by the UTASTAR method and based on historical data from the first step. Finally, an objective function was formulated using linear programming and constraints based on law specifications, and as a result, an optimized projection for public budget distribution was performed.

The results demonstrated that the model can be more efficient to weights definition, considering the behavior of preferences by historical data and supporting local public resources optimization, also to comply with the legislation, being able to predict or project future values available on the budget.

The theoretical and practical implications are related with a novelty in recognizing the weights for criteria by a historical behavior of preferences. It can be bringing important directions for budget distribution. The main limitation detected in this study was the difficulty to formulate an assessment involving an integrated opinion from local managers and the population.

First of all, with the correct allocation of resources, the government has a greater advantage to capture investments from the negotiation with development entities and banks. Second, an efficient local government management can promote compliance with legislation and more transparent public policies.

The correct distribution of resources affects the life quality for citizens, since the government acts as a provider of essential services for the population like education, safety, health, particularly for citizens who depend exclusively on the services offered by the local government. Moreover, it can also affect the environment as resources for garbage collection, disposal services and sanitation and, finally, affect the city development such as infrastructure, taxes, etc.

It might be considered an original contribution mainly by the development of a procedure to capture values for weights by TS and meeting the manager’s requirements, based on analytical, statistical and mathematical tools integrated.

This paper attempts to determine effective push‐pull marketing strategies concerning olive oil in Greece, based on the analysis of consumers' and distributors' values and the comparison of importance that each group gives to different product characteristics.

In this paper, multicriteria analysis is used in order to identify olive oil market segments and the factors that affect the purchase behaviour of olive oil consumers. Consumers' preferences, attitudes and perceptions with regard to special characteristics of olive oil such as quality, packaging, image, odour, colour, etc. are explored. In addition, description and analysis of the marketing channels of olive oil in Greece is presented. Finally, consumers' preferences are compared to the judgments of distributors in order to identify useful similarities‐dissimilarities in their perceptions and attitudes, concerning the attributes of the product.

The study of the olive oil market in Greece shows the importance of the product for the Greek market. Findings also suggest that the olive oil market in Greece is very complex. The qualitative analysis shows that perceived quality is the only attribute of the product that is considered very important for both consumers and distributors. In addition, perceptual maps can be a useful tool for the comparative analysis of preferences between consumers and distributors.

The paper identifies key factors that influence the behaviour of Greek consumers and distributors regarding olive oil purchases. These factors and the comparison between the two groups have a great influence on the marketing decisions of agricultural products and food industry in general.

Consumers’ increasing need for safe and quality food has motivated this market research study. A customer satisfaction approach is followed to examine whether the quality of organic olive oil, the most important biological product that has recently entered the Greek food market, could affect consumers’ purchasing behavior. For this purpose a survey was conducted in the region of Thessaloniki, Greece. The implemented methodology is called MUlticriteria Satisfaction Analysis (MUSA) and takes into account that customers’ global satisfaction for organic olive oil depends on five criteria. The analysis showed that the average global satisfaction index was almost 78 percent. Health was considered to be the competitive advantage of organic olive oil. On the contrary, promotion and disposition seemed to be its greatest weaknesses. The price, along with the package, was also found to be significant. Finally, gives some recommendations for the continuing improvement of information and advertising, label information and image of the package, which are the weak characteristics of organic olive oil.

This paper aims to propose a hybrid multiple criteria decision-making (MCDM) technique for performance evaluation of banks in which the banks are assessed and ranked according to the criteria of the balanced scorecard (BSC) methodology and corporate social responsibility (CSR) views.

To clarify the performance of the proposed model, the MCDM technique was implemented in four banks in Iran as a pilot. First, proper criteria for banking industry are identified considering BSC and CSR. Consequently, analytic hierarchy process (AHP) and modified digital logic (MDL) techniques are used to determine the weights of criteria. The banks are ranked by fuzzy TOPSIS (FTOPSIS) and fuzzy VIKOR (FVIKOR). Using a combination of these techniques, four methods, namely, AHP-FTOPSIS, AHP-FVIKOR, MDL-FTOPSIS and MDL-FVIKOR, are obtained, each of which provides a different set of rankings for banks. Eventually, the obtained ranks are integrated using the Copeland method.

The results showed that the return on investment, debt ratio and lower energy consumption criteria are the most important, and enhancement of brand value, increasing customer loyalty and environmental care criteria have the lowest percentage of importance. Also, the final bank ranking is determined by the proposed method.

This paper identifies 6 criteria and 25 sub-criteria for evaluating the banks considering BSC and CSR viewpoints including some new sub-criteria that has not been considered before. Moreover, these hybrid approaches and especially MDL techniques have not been used by previous researchers.

In recent years, a number of researchers have attempted to make an integration of sustainability with supply chain risk management. These studies have led to valued insights into this issue, though there is still a lack of knowledge about the mechanisms by which sustainability-related issues are materialized as risks in the supply chain management.

The paper aims to provide a comprehensive framework to evaluate the sustainability risk in the supply chain management mechanism. To do so, a novel approach using the double normalization-based multiple aggregation (DNMA) approach under the intuitionistic fuzzy (IF) environment is extended to identify, rank and evaluate the sustainability risk factors in supply chain management.

To provide comprehensive sustainability risk factors, this study has conducted a survey using interview and literature review. In this regard, this study identified 36 sustainability risk factors in supply chain management of the manufacturing firms in five different groups of risk, including sustainable operational risk factors, economic risk factors, environmental risk factors, social risk factors, and sustainable distribution and recycling risk factors. The results of this paper found that the poor planning and scheduling was the important sustainability risk in supply chain management of the manufacturing firms, followed by the environmental accidents, production capacity risk, product design risk and exploitative hiring policies. In addition, the results of the study found that the extended approach was effective and efficient in evaluating the sustainability risk factors in supply chain management of the manufacturing firms.

Three aggregation methods based on the normalization techniques are discussed. A DNMA method is proposed under intuitionistic fuzzy sets (IFSs). To propose a broad procedure for identifying and classifying sustainability risk factors (ESFs) in supply chain management. To rank the sustainability risk factor, the authors utilize a procedure for evaluating the significance degree of the sustainability risk factor in supply chain management.

Achieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.

An integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.

The results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.

A new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

Agricultural sector not only meets the nutritional requirements of all living creatures but also generates the primary source of the raw material provided by various branches of industry to fulfill their functions. It is of great importance to increase studies on oilseeds which have an important role in Turkey's agricultural products. They are grown in almost all of the country, which are vital for the nutrition and many sectors. The main purpose of the study is to offer an integrated approach to determine potential warehouse locations for oilseeds.

This is the first study that integrates Delphi, analytical hierarchical process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), P-Median and Panel data analysis in a real case. This integrated approach consists of the following steps, respectively: (1) The criteria were determined by the Delphi method. (2) The weights of the criteria were calculated by AHP and the provinces with the highest oilseed warehouse potential in seven regions of Turkey were specified by TOPSIS. (3) Oilseed warehouse numbers and locations were obtained by P-Median. (4) In order to answer whether the distribution network is profitable in the future with the determined center locations, a forecast model based on panel data analysis was created. (5) Regional representatives were determined for 2030, and the distribution network was analyzed again. (6) The costs that arose in 2018 and 2030 were computed and compared by cost analysis. (7) The effect of the change in criteria weights on the alternative results was tested by scenario analysis.

The findings indicated that oilseed crop production potential and oilseed crop production area turned out to be the most important criteria. Furthermore, the results showed that this model is robust and suitable for warehouse location selection studies.

The study can serve as a guide for local and central policy makers with both the criteria it uses and the model it develops.

The main contribution of this study is that the integrated approach has been used for the first time in location selection in a real case.

Data envelopment analysis (DEA) is a mathematical method for the evaluation of the relative efficiency of a set of alternatives, which produces multiple outputs by consuming multiple inputs. Each unit is evaluated on the basis of the weighted output over the weighted input ratio with a free selection of weights and is allowed to select its own weighting scheme for both inputs and outputs so that the individual evaluation is optimized. However, several situations can be found in which the variability between weighting profiles is unsuitable. In those cases, it seems more appropriate to consider a common vector of weights. The purpose of this paper is to include a systematic revision of the existing literature regarding the procedures to determine a common set of weights (CSW) in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure. The discussion and findings of this paper provide insights into future research on the topic.

This paper includes a systematic revision of the existing literature about the procedures to determine a CSW in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure.

The discussion and findings of the literature review might insights into future research on the topic.

This papers revise the state of the art on the topic of models with CSW in DEA methodology and propose a systematic classification of the contributions with respect to several criteria. The paper would be useful for both theoretical and practical future research on the topic.

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.