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The business performances of firms are measured on a set of indicators (Financial Ratio Analysis Indicators or Balanced Scorecard Key Performance Indicators). Traditional benchmark analysis considers a set of criteria, though it generally synthesizes all the results, taking into consideration only an aggregate performance criterion (reductio ad unum approach). This methodology has many disadvantages, both theoretical and empirical. The purpose of this paper is to illustrate the advantages, in terms of greater flexibility and realism, related to the application of a multicriteria methodology.

The paper uses a tutorial approach. An exemplification of an outranking multicriteria methodology (ELECTRE type) is described.

The main findings of the paper can be summarized as: first, the evaluation of a business performance cannot generally be conducted by means of a unique criterion as in the traditional monocriterion benchmark analysis; second, when the evaluation of a firm is based on different genuine criteria, the performance has to be “satisfacing” and not maximizing; and third, the outranking methods are able to provide logically rigorous solutions to the genuine multicriteria benchmarking evaluation problems.

The paper provides practical implications useful for evaluating firm performances in many cases, also when each stakeholder (managers, shareholders, banks, etc.) assigns different “weights” to the decision criteria.

As a multicriteria evaluation is generally incompatible with a profit maximizing approach, the paper proposes a multicriteria performance measure approach that offers Simon's satisfacing solutions. The paper shows that satisfacing solutions to a multicriteria evaluation problem may be rigorously obtained through an outranking methodology (already introduced by other scholars).

In a previous paper the authors emphasized the advantages of multicriteria methodologies to evaluate business performance. The purpose of this paper is to highlight the metachoice problem that always arises in a benchmark multicriteria analysis that can be synthesized as follows: “how to choose an algorithm to choose?”

In order to perform a benchmark analysis, a set of criteria must be chosen. In the Balanced Scorecard approach, for example, key performance indicators (KPIs) are grouped in four different perspectives: financial, customer, internal processes and learning and growth. In this paper, the authors focus on multicriteria benchmark analysis applied to KPIs of the financial perspective. The paper considers a set of criteria used in financial statement analysis based on balance sheet, income statement and cash flow statement. A case study is described.

The main findings of the paper are when the evaluation of a firm is based on different genuine criteria, a metachoice problem arises: multicriteria ranking algorithms cannot be selected using a multicriteria algorithm; the choice of an algorithm ultimately depends on the subjective preference of the policy maker; and the authors metachoice solution to the benchmarking problem is in accordance with Simon’s satisfacing solution, describing a non-maximizing performance measurement methodology.

The paper provides several practical implications in all cases in which a ranking has to be assigned to a group of firms based on financial performances. More in general the problem is very relevant when a ranking has to be carried out with respect to a set of projects, a set of strategies, a set of organizational units, etc.

The adoption of a set of criteria is certainly an advantage to avoid uni-criterial myopic evaluation. However, this also creates some methodological problems. The paper demonstrates the “relativity” (subjectivity) of results of the evaluation process when there are many evaluation criteria, as in a benchmark context. This is a metachoice problem that cannot be solved by using another multicriteria algorithm.

This article presents a methodological application of decision support with the purpose of identifying and better aligning sustainable banking strategies. Those strategies are based on best practices declared by employees and conducted during efficient periods affecting sustainable production, the health quality of clients, the organization’s profitability and social impact on the local community across different sectors.

The approach involves a two-phase process: first, it employs directional data envelopment analysis (DEA) to benchmark knowledge based on employee opinions gathered through interviews to evaluate strategies related to banking services; then, using the best-worst method and ELECTRE outranking incorporating elements of fuzzy set theory based on an experienced decision-maker’s input, sustainable banking strategies are ranked according the different perspectives for leveraging outputs from the first step.

The outcomes yield a ranking of strategies, emphasizing the crucial role of technology in banking services while highlighting the need for more agile services to ensure customer satisfaction. This underscores the necessity of aligning with the market perspective, as fintech companies are reshaping the socio-technological-environmental landscape of financial services.

The research combined DEA and multicriteria analysis in the context of the banking sector, providing a comprehensive and analytically robust approach translated as a decision-making framework for promoting sustainability by aligning operational efficiency and social responsibility. These tools can guide banks in adopting more sustainable practices that benefit the institution, society and the environment.

Decisions in the banking sector encompass a wide array of concepts, from internal technical factors to customer feedback on service processes and offerings. The proposed approach considers decision analysis in complex environments, and the application developed in this study considered not only internal banking activity-oriented concepts but also the preferences of human agents developing them and the managerial perspective focused on issues involving components associated with sustainability.

By integrating DEA with multicriteria analysis, this study paves the way for a more efficient, environmentally conscious and socially responsible management scenario in the Brazilian banking sector. This research assesses operational efficiency and offers a comprehensive framework for selecting and implementing sustainable practices in the banking sector.

Policy makers are frequently challenged by the need to achieve sustainable development in cities and regions. Current decision-making processes are based on evaluation support systems which are unable to tackle the problem as they cannot take a holistic approach or a full account of actors. The purpose of this paper is to present a new generation of evaluation systems to support decision making in planning and regeneration processes which involve expert participation. These systems ensure network representation of the issues involved and visualization of multiple scenarios.

A literature review is used for both revising existing evaluation tools in urban planning and the built environment and highlighting the need to give stakeholders (industry, cities, operators, etc.) new tools for collaborative or individual decisions and to facilitate scaling up solutions. An overview of the new generation of decision support systems, named Multicriteria Spatial Decision Support Systems (MC-SDSS) is provided and real case studies are analyzed to show their ability to tackle the problem.

Recent research findings highlight that decisions in urban planning should be supported by collaborative and inclusive processes. Otherwise, they will fail. The case studies illustrated in this study highlight the usefulness of MC-SDSS for the successful resolution of complex problems, thanks to the visualization facilities and a network representation of the scenarios.

The case studies are limited to the Italian context.

These SDSS are able to empower planners and decision makers to better understand the interaction between city design, social preferences, economic issues and policy incentives. Therefore, they have been employed in several case studies related to territorial planning and regeneration processes.

This study provides three case studies and a review of the new MC-SDSS methodology which involve the Analytic Network Process technique to support decision-making in urban and regional planning.

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.

The purpose of this paper is to propose a multi-methodological approach for assessing the sustainability of urban projects. The proposed framework has the objective of addressing the different dimensions involved in the analysis (economic, environmental, social, technological, cultural, etc.), following the subsequent phases for the definition of the projects (from a very general masterplan to more detailed preliminary projects).

The multi-methodological approach is organized according to subsequent steps, involving the application of different evaluation methods, namely SWOT analysis, multicriteria analysis, discounted cash flow analysis and sensitivity analysis. With the aim of better illustrating the proposed approach, the method is applied on a real case concerning the project of a new boulevard in the city of Skopje.

The proposed approach resulted to be successful in structuring the complex planning context and in increasing and improving the information for the project, thus acting as a learning process.

The paper is based on the experimentation of an hybrid approach for addressing planning process. The multi-methodological framework can be useful in supporting decision making process in the domain of urban projects. In this sense, the proposed framework is able to communicate the planners’, designers’ and decision maker’s perspectives, to enhance transparency in the decision process and thus to increase acceptance.

The purpose of this paper is to offer a contribution to the study of integrated assessment procedures for evaluating the effectiveness of agri‐environmental policy strategies. While in the past the studies in this context have typically concentrated on the contents of methods in isolation, there is a growing trend towards methodological perspectives that support the linking of such methods. The focus here is on the combination of discrete multicriteria approaches used for handling qualitative information in a sequence of steps: the regime method, the evamix method and rough‐set analysis. The first two methods will be used to obtain a ranking of four alternative scenarios of agri‐environmental policies in a selected area of study, in this case, Sicily. The results obtained are discussed and re‐analysed by using the rough‐set approach as a recent meta‐analytical tool. Finally, the analysis findings are applied to an investigation into the potential effectiveness of agricultural policies in promoting sustainable rural development in Sicily.

This study aims to focus on the application of a multiple criteria decision analysis (MCDA) method to compare results presented by the Integrated Goals System based on the 12th edition of the Integrated Public Safety Areas (IPSAs) Award, which achieved goals established for Strategic Crime Indicators for the state of Rio de Janeiro, Brazil. The main objective of this research was to limit the compensatory effects of classification criteria on IPSAs that have achieved goals established for crime indicators by applying the MCDA method.

The methodology was based on the application of MCDA. The MCDA method selected and used was ELECTRE III, being implemented by J-Electre software.

Compared to results of the current method, the results of the ELECTRE III method showed a 94.87 per cent change in ranking positions revealed via the SIM method. This finding denotes the elimination of compensatory effects of the criteria. As a consequence, it can be affirmed that the application of resources by IPSA managers to reduce the prevalence of a single strategic crime indicator is no longer a success factor for awarding in the established goal system.

As limitations, it is possible to indicate the time cut used to carry out the research. The research may be extended to other issues of the productivity award.

The methodology applied with the use of ELECTRE III revealed that the government could reduce the cost with the incentive program to reduce criminal indices.

As the MCDA method is based on a binary pairwise comparison system, the methodology imposes a change of attitude on local managers fighting crime to reduce crime indicators and to consequently build a local sense of safety in IPSAs.

This research fills a gap in the literature because there are few studies using the MCDA method in the field of public security. The value of the work lies in the creation of a method that gives the decision-maker, of the law enforcement agency, an alternative to improve the process of rewarding by productividad of the integrated areas of public security.

Research on green certificate rankings in the MENA region primarily focuses on building scale, relying on the certified project count. This assessment approach overlooks the spatial factor, failing to capture their influence on the urban built environment, thus potentially undermining other efforts not reflected by the project count. This research aims to rank countries in the Middle East and Northern Africa (MENA) region based on their ongoing efforts regarding green neighbourhood certification.

This study employs a three-phase methodology to rank MENA countries' adoption of green neighbourhood certification systems: content analysis, multicriteria analysis (MCA) using the analytical hierarchy process (AHP) and spatial analysis.

Based on the content analysis, four major performance indicators were identified and the conventional ranking using projects count was presented. Using AHP, the MCA could rank the countries in the region according to their unique performance indicators score, clarifying the differences between conventional and AHP-based rankings. Finally, the spatial analysis phase uncovers shortcomings in the traditional ranking method, revealing inaccuracies and misrepresentations for several countries.

The study presents an innovative ranking methodology to monitor the green neighbourhood actions of countries in future development and establish a pioneering framework to evaluate the impact of green certifications within the region.

At the end of a building’s lifecycle, there are several limitations to the decision-making process (DMP). There is a lack of data available from the building’s history, the difficulty in assessing the condition of a building and the variety of stakeholders’ needs that have to be satisfied. The purpose of this paper is to answer the question: how would end-of-life (EOL) DMP change when buildings will have been digitally built? The answer will be illustrated through a conceptual framework.

A qualitative analysis of the existing literature has been performed to identify the elements within building information modelling (BIM) and advanced digital technologies that could be of support to the DMP. The findings have been collected and summarised in a conceptual framework that has been validated and enhanced through online interviews with industry experts.

The enhanced framework has identified that BIM technology would bring the benefit of providing the initial digital data source, from which machine learning and data analytics would then extract the relevant data needed to measure accurately the criteria during the analysis of the EOL options put on the table.

The findings of this research could contribute to developing the software modules making the bridge between BIM and machine learning technologies, to implement them in the EOL DMP.