*Article* **Research Agenda on Multiple-Criteria Decision-Making: New Academic Debates in Business and Management**

**Fernando Castelló-Sirvent 1,2,\* and Carlos Meneses-Eraso <sup>3</sup>**


**\*** Correspondence: fernando.castello@esic.edu

**Abstract:** Systemic disruptions are becoming more continuous, intense, and persistent. Their effects have a severe impact on the economy in volatile, uncertain, complex, and ambiguous (VUCA) environments that are increasingly transversal to productive sectors and activities. Researchers have intensified their academic production of multiple-criteria decision-making (MCDM) in recent years. This article analyzes the research agenda through a systematic review of scientific articles in the Web of Science Core Collection according to the Journal Citation Report (JCR), both in the Social Sciences Citation Index (SSCI) and in the Science Citation Index Expanded (SCIE). According to the selected search criteria, 909 articles on MCDM published between 1979 and 2022 in Web of Science journals in the business and management categories were located. A bibliometric analysis of the main thematic clusters, the international collaboration networks, and the bibliographic coupling of articles was carried out. In addition, the analysis period is divided into two subperiods (1979–2008 and 2009–2022), establishing 2008 as the threshold, the year of the Global Financial Crisis (GFC), to assess the evolution of the research agenda at the beginning of systemic disruptions. The bibliometric analysis allows the identification of the motor, basic, specialized, and emerging themes of each subperiod. The results show the similarities and differences between the academic debate before and after the GFC. The evidence found allows academics to be guided in their high-impact research in business and management using MCDM methodologies to address contemporary challenges. An important contribution of this study is to detect gaps in the literature, highlighting unclosed gaps and emerging trends in the field of study for journal editors.

**Keywords:** bibliometric analysis; research agenda; multiple-criteria decision-making (MCDM); complexity; VUCA; VOSviewer; SciMAT

**MSC:** 90B50; 91B06; 60A86; 94D05; 68V30

### **1. Introduction**

In recent decades, systemic events that have accelerated the speed of economic, social, and political shocks have become widespread and frequent. Increasingly radical and intense disruptions impact society and companies by generating global challenges and affecting the stability of the economic system. Some examples of these shocks in recent years, such as the Global Financial Crisis, (GFC), sustainability challenges of the 2030 Agenda, COVID-19, war in Ukraine, sanctions against Russia, European energy crisis, risks of reversal of globalization, and political and military tensions with China, should be noted. This dynamic economic environment forces companies, governments, and investors to make increasingly agile decisions to try to eliminate bias in decision-making and minimize risks in professional endeavors. Advanced decision-making methods have been gaining popularity among managers and analysts in an increasingly common VUCA environment. The context of implicit uncertainty has changed the analysis paradigm,

**Citation:** Castelló-Sirvent, F.; Meneses-Eraso, C. Research Agenda on Multiple-Criteria Decision-Making: New Academic Debates in Business and Management. *Axioms* **2022**, *11*, 515. https://doi.org/ 10.3390/axioms11100515

Academic Editors: Amit K. Shukla and Darjan Karabaševi´c

Received: 19 August 2022 Accepted: 24 September 2022 Published: 29 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

affecting management sciences [1–7] and creating a need to articulate efficient mechanisms for decision-making in multiple fields [8–15].

The first radical shock of the contemporary era was the GFC that began after the bankruptcy of Lehman Brothers [16–18], a situation that created a need to develop better and more sophisticated methods for decision-making in such fields as finance, investment, and policymaking, among others. Society was transformed at great speed and academics progressively altered its predominant interests, as the generalization of VUCA environments transformed the archetypes of researchers and their priorities. Some emerging issues in the academic debate after 2008 are specified in the green strategies of the Sustainable Development Goals (SDG) [19–28], evaluation of decision-making systems based on the urgency and effectiveness of the applied models [23,29–37], and the impact disruptions in VUCA environments on global value chains [3,4,19,26,38–42].

The motivation for this research article is a drive to describe the structure of the academic agenda in detail and to identify the opportunities for high-impact publications related to MCDC. This study analyzes the academic discourse on multiple-criteria decisionmaking (MCDM) within the business and management areas before and after 2008. In addition, the change in the research agenda produced after the intensification of disruptions in VUCA environments is evaluated, and context is offered to understand the driving and emerging issues that articulate the new debate after 2008. The goal of this article is to present an overview and evolution of the research agenda, allowing academics to learn about new emerging trends, gaps not closed in the literature, and high-impact publication opportunities in the MCDM field of research. In addition, the findings of this study should provide a guide and a roadmap for journal editors to the evolution of the research agenda, facilitating the design of editorial lines and the preparation of special issues that may be thematically oriented to a more mainstream scope among scholars. The article is structured as follows. First, the materials and methods section is presented. Next, the results and discussion with a special focus on the opportunities detected for high-impact publications are reported. Finally, the conclusions of the study are formulated.

### **2. Materials and Methods**

The objective of this research article was to develop a comprehensive bibliometric analysis on the methodologies of multiple-criteria decision-making (MCDM). As per Zupic and Cater [ ˇ 43], the study followed this sequence: (1) design of the research; (2) collection of bibliometric data; (3) analysis and reporting of results; and (4) discussion of the findings. The search strategy for the literature review was an analysis of articles published in the Web of Science Core Collection (WoS-CC).

Based on the quality standards established by this study and the purpose of intertemporal comparison between periods, the WoS-CC was chosen, focusing the analysis on journals in the areas of business and management indexed in the Social Sciences Citation Index (SSCI) and the Scientific Citation Index Expanded (SCIE). Journals indexed in the Emerging Sources Citation Index (ESCI) were discarded. The use of Scopus was also ruled out, since the object of analysis began before the creation of Scopus and the inclusion of articles from this database would cause sample biases that would invalidate the analysis and conclusions of this research [44–47]. Thus, the WoS-CC was chosen as a source of robust bibliometric information [48,49], for its coverage [50] and its homogeneous availability throughout the period analyzed. The search discarded chapters, books and proceedings, and focused on articles. Based on the high heterogeneity of terms available to analyze the focus determined by the study, the first step taken was to identify the search terms and operators that would allow the identification of the relevant published studies. The PRISMA statement [51,52], widely used in systematic reviews in business and management research, was used [42,53–57]. In addition, the methodology proposed by Tavares Thomé et al. [58], which suggests that researchers should perform searches both backwards and forwards in order to reinforce the robustness of their studies and through this same practice eliminate the risk of search and/or approach biases, was followed. In this way, the main research

topics published on MCDM or MCDA in business and management journals—according to Web of Science categories—indexed in Journal Citation Reports® (JCR) were identified. Figure 1 shows the selection criteria and the Boolean search string applied to topic (TS), according to the information provided by title (TI), abstract (AB), author keywords (AK) or Keyword Plus® (KP). Figure 2 presents the flow diagram for new systematic reviews and reports the details of the search strategy according to each stage defined by the PRISMA statement [51]. way, the main research topics published on MCDM or MCDA in business and management journals—according to Web of Science categories—indexed in Journal Citation Reports® (JCR) were identified. Figure 1 shows the selection criteria and the Boolean search string applied to topic (TS), according to the information provided by title (TI), abstract (AB), author keywords (AK) or Keyword Plus® (KP). Figure 2 presents the flow diagram for new systematic reviews and reports the details of the search strategy according to each stage defined by the PRISMA statement [51].

**Figure 1. Figure 1.** Systematic literature review strategy [58]. Systematic literature review strategy [58].

journals have been created [59].

**Figure 2.** PRISMA 2020 flow diagram. Own elaboration according to Page et al. (2021) [51], Tavares Thomé et al. (2016) [58]. **Figure 2.** PRISMA 2020 flow diagram. Own elaboration according to Page et al. (2021) [51], Tavares Thomé et al. (2016) [58].

The resulting database was analyzed from a static approach for the total period available according to the criteria reported from the first record located (1Q 1979) and until the last available according to the date of the investigation (2Q 2022). In addition, two subperiods (1979–2008 and 2009–2022) were constructed for the comparative analysis of the academic discourse and the evolution of the scientific debate in the field. The year 2008 was chosen as it established important conjunctural and systemic changes, which were exemplified by the occurrence of the GFC. In addition, since 2008, academic production has increased based on a strong evolution of academic interest, and new fuzzy-related

The complexity of the world has been increasing progressively from 2008. In this year, there was an important financial event that began in September 2008 with the bankruptcy of Lehman Brothers [17] and the resulting consequences for the world's main economies (e.g., deep financial crisis in the United States and debt crisis in the Eurozone, quantitative easing of the Federal Reserve and the European Central Bank) [16,18,60–63], a subsequent period of high interdependence of economic agents and global value chains began, the continuing effects of which are further evidence of the fact that the process of generalization of VUCA environments is accelerating [1–3,6,64,65]. Bibliometric studies have analyzed academic discourse in comparative terms for two periods [66]. Based on its The resulting database was analyzed from a static approach for the total period available according to the criteria reported from the first record located (1Q 1979) and until the last available according to the date of the investigation (2Q 2022). In addition, two subperiods (1979–2008 and 2009–2022) were constructed for the comparative analysis of the academic discourse and the evolution of the scientific debate in the field. The year 2008 was chosen as it established important conjunctural and systemic changes, which were exemplified by the occurrence of the GFC. In addition, since 2008, academic production has increased based on a strong evolution of academic interest, and new fuzzy-related journals have been created [59].

relevance, the 2008 cutoff threshold has been used to analyze changes in trends in bibliometric terms [67–69]. According to the specific criteria selected, the applied method reports 909 articles published between Q1 1979 and Q2 2022, corresponding to journals indexed in JCR. Based on the size asymmetry of the two constructed subperiods (30 years vs. 13 years) and the The complexity of the world has been increasing progressively from 2008. In this year, there was an important financial event that began in September 2008 with the bankruptcy of Lehman Brothers [17] and the resulting consequences for the world's main economies (e.g., deep financial crisis in the United States and debt crisis in the Eurozone, quantitative easing of the Federal Reserve and the European Central Bank) [16,18,60–63], a subsequent period of high interdependence of economic agents and global value chains began, the continuing effects of which are further evidence of the fact that the process of generalization of VUCA environments is accelerating [1–3,6,64,65]. Bibliometric studies have analyzed academic discourse in comparative terms for two periods [66]. Based on its relevance, the 2008 cutoff threshold has been used to analyze changes in trends in bibliometric terms [67–69].

According to the specific criteria selected, the applied method reports 909 articles published between Q1 1979 and Q2 2022, corresponding to journals indexed in JCR. Based on the size asymmetry of the two constructed subperiods (30 years vs. 13 years) and the limitations that this asymmetry represents for a measurement of the academic impact of scientific production, the normalized impact per year (NIY) method was used, in accordance with Castelló-Sirvent [70]. This method calls for the weighing of total academic impact as measured by a count of citations and dividing this number by the number of years elapsed from the publication of an article to the date of completion of this study. Thus, the NIY highlights the most relevant information since it brings to the surface papers that mark an accelerated academic interest of a topic at a certain moment. This facilitates the process of comparing articles published in different subperiods. In this study, the NIY is used for the analysis of articles, and the NIY average for the analysis of the academic production of journals or subperiods.

A citations per document (CpD) ratio was also calculated [70] as an indicator of academic efficiency that divides the total citation count by the number of articles published in a period, or by an author, university, country or journal. In addition, the dynamic comparison of the top 25 articles by total citations and the top 25 articles by NIY for the same subperiod offers information on the articles that quickly become more interesting for researchers, particularly in shorter and more recent subperiods [70]. These types of articles were indicated in this research under the term "strong trend" (ST) and were shown in the results corresponding to the top 25 articles with the greatest impact of citations per year (NIY). This allowed this article to highlight research that did not manage to be placed in the top 25 articles with the greatest impact by total citations. In other words, the articles marked as ST are mainstream articles that arouse growing academic interest based on the trend accelerator that the NIY represents. [71]. The detailed analysis and evaluation of ST articles is likely to serve as an indicator of "hot topics" in the research agenda of a field of study [70,71]. Similarly, this research also applied the ST analysis to the main journals of both subperiods to identify "hot journals" as defined by having the greatest impact in comparative terms. ST analysis is a tool that enables academics to more easily select mainstream topics and journals, so that their research will be oriented to the emerging and most active topics in the area of study. It also helps researchers by offering an in-depth discussion on high-impact publication opportunities. [70,71].

The bibliometric software used was VOSviewer 1.6.17 [72,73] and SciMAT 1.1.04. [74]. VOSviewer enabled the analysis of international collaboration networks in MCDM research and the bibliographic coupling of articles. SciMAT showed the strategic diagrams of the two subperiods analyzed, identifying driving, basic, specialized and emerging themes. According to Cobo et al. [74–77], following Callon's preliminary approaches [78], the bibliometric analysis of topics with SciMAT identifies the thematic clusters in a two-dimensional plane that represents the centrality and density of the academic discourse, in order to know the academic field architecture [77] and visualize and evaluate its space–time path [75]. In this way, it is possible to identify the groups (clusters) of analyzed elements and their links with other related elements [73], according to the relative importance of the elements reviewed with respect to the academic corpus under study [79].

In this way, strategic diagrams built with SciMAT show four quadrants that are configured at the intersection of the X and Y axes, with the topics included in each quadrant being characterized in a specific way. The driving themes show high density and high centrality and, consequently, contribute in a relevant way to research and maintain links with their thematic networks, helping to articulate the research agenda. On the other hand, the emerging themes register low density and low centrality, since they are poorly developed, both in the area and in other areas. In the intermediate positions, the basic topics are shown, characterized by their low density and high centrality, reporting a limited development, although they suppose important contributions in other areas of knowledge. Specialized topics have a high density and low centrality, as they are well developed in the area, but in isolation from other areas.

SciMAT was used to know the typologies of themes for both subperiods and to understand the dynamic evolution of the discourse of academia on MCDM. For the analysis, the equivalence index [78] was used without network reduction, with association strength [80], based on the simple centers algorithm (min. 5; max. 12). In addition, core mapper, inclusion index and Jaccard's index [81] were reported.

### **3. Results and Discussion** This section is structured as follows. First, an overview of the academic production

**3. Results and Discussion** 

This section is structured as follows. First, an overview of the academic production linked to the study area is reported, both for the total period analyzed and for the two proposed subperiods. Second, the main authors, universities, and international collaboration networks identified are named. Third, the coupling clusters of articles generated in order to understand the construction of the academic debate are shown. Fourth, a general analysis of the dynamic evolution of the research agenda is carried out through the use of overlapping and evolution maps of the transformation process, and then a detailed analysis of this process is presented through strategic diagrams of each subperiod. Fifth, the publication opportunities detected are discussed. linked to the study area is reported, both for the total period analyzed and for the two proposed subperiods. Second, the main authors, universities, and international collaboration networks identified are named. Third, the coupling clusters of articles generated in order to understand the construction of the academic debate are shown. Fourth, a general analysis of the dynamic evolution of the research agenda is carried out through the use of overlapping and evolution maps of the transformation process, and then a detailed analysis of this process is presented through strategic diagrams of each subperiod. Fifth, the publication opportunities detected are discussed.

[80], based on the simple centers algorithm (min. 5; max. 12). In addition, core mapper,

*Axioms* **2022**, *11*, x FOR PEER REVIEW 6 of 38

inclusion index and Jaccard's index [81] were reported.

### *3.1. Academic Production 3.1. Academic Production*

The results of the longitudinal analysis show an incremental evolution (R<sup>2</sup> = 0.9293) of the scientific production on MCDM (Figure 3). As of 2009, an acceleration of the trend, coinciding with the international extension of the GFC is observed. Economic agents, companies and public administrations being forced to make quick and agile decisions in an increasingly interdependent VUCA environment is also evidenced. The results of the longitudinal analysis show an incremental evolution (R2 = 0.9293) of the scientific production on MCDM (Figure 3). As of 2009, an acceleration of the trend, coinciding with the international extension of the GFC is observed. Economic agents, companies and public administrations being forced to make quick and agile decisions in an increasingly interdependent VUCA environment is also evidenced.

**Figure 3.** Intertemporal evolution of academic production on MCDM. **Figure 3.** Intertemporal evolution of academic production on MCDM.

In sum, 909 articles published over 43 years (1979–2022) in journals from the business and management areas indexed in JCR were identified and retrieved. The articles generated an impact level of 37,904 citations. The annual averages of the NIY method previously discussed (Figure 4) show that the articles published during the years prior to the GFC (2002 and 2004) achieved greater academic impact per year, and a trend analysis corroborates a change in trend as of 2011. Academic interest in MCDM increased in terms of comparative annual impact. In sum, 909 articles published over 43 years (1979–2022) in journals from the business and management areas indexed in JCR were identified and retrieved. The articles generated an impact level of 37,904 citations. The annual averages of the NIY method previously discussed (Figure 4) show that the articles published during the years prior to the GFC (2002 and 2004) achieved greater academic impact per year, and a trend analysis corroborates a change in trend as of 2011. Academic interest in MCDM increased in terms of comparative annual impact.

**Figure 4.** NIY detail analysis. Annual average. **Figure 4.** NIY detail analysis. Annual average.

Table 1 shows a comparative analysis for both subperiods. NIY average clearly increases after the GFC. Given the asymmetry of years of the analyzed subperiods, CpD after the GFC is lower than the CpD before the GFC. Table 1 shows a comparative analysis for both subperiods. NIY average clearly increases after the GFC. Given the asymmetry of years of the analyzed subperiods, CpD after the GFC is lower than the CpD before the GFC.

**Table 1.** Published articles, citations and NIY. Detail by subperiods. **Table 1.** Published articles, citations and NIY. Detail by subperiods.


Source: Own elaboration.

However, the evidence found corroborates a higher production of articles per year after the GFC, as shown in the bibliometric analysis carried out by Liu [59]. The total number of articles published on MCDM is 2.6 times higher after 2008 and the academic impact of the articles published after 2008 (13 years) is 89% of the academic impact of the articles published before 2008 (30 years). However, the evidence found corroborates a higher production of articles per year after the GFC, as shown in the bibliometric analysis carried out by Liu [59]. The total number of articles published on MCDM is 2.6 times higher after 2008 and the academic impact of the articles published after 2008 (13 years) is 89% of the academic impact of the articles published before 2008 (30 years).

### *3.2. Main Authors, Universities and International Collaboration Networks 3.2. Main Authors, Universities and International Collaboration Networks*

The main authors with scientific production equal to or greater than 10 articles in MCDM fields are professors Zavdskas, Stewart, Ferreira, Greco, Turskis, Liao, Hashemkhani Zolfani and Ferreira (Table 2). The detailed analysis of academic efficiency reports an excellent performance in the number of citations per document of professors Greco (CpD = 69.2) and Stewart (CpD = 65.82). The main authors with scientific production equal to or greater than 10 articles in MCDM fields are professors Zavdskas, Stewart, Ferreira, Greco, Turskis, Liao, Hashemkhani Zolfani and Ferreira (Table 2). The detailed analysis of academic efficiency reports an excellent performance in the number of citations per document of professors Greco (CpD = 69.2) and Stewart (CpD = 65.82).

**Table 2.** Academic production of authors with more than 10 articles. **Rank Author Citations Articles CpD**  1 Zavadskas, Edmundas Kazimieras 1016 26 39.08 On the other hand, Table 3 shows the results of the analysis of universities of those researchers who achieved greater academic efficiency in the publication of MCDM articles from Taiwan (National Chiao Tung University; CpD = 224), Italy (University of Catania; CpD = 76.07) and Portugal (University of Coimbra; CpD = 75.10).

2 Stewart, Tj 724 11 65.82 3 Ferreira, Fernando A. F. 717 36 19.92 4 Greco, Salvatore 692 10 69.20


**Table 2.** Academic production of authors with more than 10 articles.

Source: Own elaboration.


**Table 3.** Academic production of universities with more than 10 articles.

Source: Own elaboration.

Figure 5 reports the connections between countries in international collaboration networks. According to the fractional counting method, the results show five co-authorship clusters between countries for the MCDM area. Cluster 1 (green) includes European countries (Portugal, France, Belgium and Greece) and the American continent (USA, Canada and Brazil). Cluster 2 (blue) includes England and European countries (Italy, Poland, Denmark, Germany). Cluster 3 (pink) links the academic production of countries such as Iran, Lithuania or Malaysia. Cluster 4 (red) shows the way in which European countries (Spain and Netherlands) and the Pacific area (India, Taiwan and Australia) connect their academic production through co-authorships. Cluster 5 (yellow) reports the connection of publication co-authors in MCDM from Finland, Switzerland, Scotland and South Korea.

The evaluation of academic production reports 27 countries with at least 10 articles for the period analyzed (Table 4). The detailed analysis of academic efficiency places Serbia, Netherlands and Denmark above 100 citations per article.

**Figure 5.** International collaboration networks. **Figure 5.** International collaboration networks.

The evaluation of academic production reports 27 countries with at least 10 articles **Table 4.** Collaboration networks in co-authorship. Countries with more than 10 articles.


Source: Own elaboration.

Among the countries with the greatest impact, located in positions 1 to 13 in the ranking, the USA, China, Spain, Portugal, England, Finland, France and Lithuania stand out for their low academic efficiency (CpD below average). These are countries that, in order to achieve academic impact, must increase their production of articles, but the average impact per article is low in comparative terms. In contrast, Australia, Scotland, India, Turkey, Brazil, Iran or Switzerland recorded little impact in comparative terms, given their high level of production, with CpD levels of fewer than 20 citations per article. Among the countries with the greatest impact, located in positions 1 to 13 in the ranking, the USA, China, Spain, Portugal, England, Finland, France and Lithuania stand out for their low academic efficiency (CpD below average). These are countries that, in order to achieve academic impact, must increase their production of articles, but the average impact per article is low in comparative terms. In contrast, Australia, Scotland, India, Turkey, Brazil, Iran or Switzerland recorded little impact in comparative terms, given their high level of production, with CpD levels of fewer than 20 citations per article.

22 Brazil 512 27 18.96 23 South Korea 368 13 28.31 24 Australia 308 22 14.00 25 Switzerland 287 15 19.13 26 Malaysia 263 12 21.92 27 Scotland 157 10 15.70

### *3.3. Bibliographic Coupling per Documents 3.3. Bibliographic Coupling per Documents*

Source: Own elaboration.

The analysis of bibliographic coupling per documents (Figure 6) set a minimum cutoff threshold of 150 citations of a document, establishing four clusters that include 36 central articles. The analysis of bibliographic coupling per documents (Figure 6) set a minimum cutoff threshold of 150 citations of a document, establishing four clusters that include 36 central articles.

*Axioms* **2022**, *11*, x FOR PEER REVIEW 10 of 38

**Figure 6.** Coupling of articles with at least 150 citations. **Figure 6.** Coupling of articles with at least 150 citations.

Cluster 1 (red) includes articles on the comparison of methods (e.g., VIKOR and TOP-SIS) [82,83], Qualiflex [84], Electre I [85], TOPSIS [86], fuzzy AHP [87], fuzzy MCDM [8,88– 90], and practical applications based on firm technological innovation capabilities under uncertainty [91] or tourism destination competitiveness [92]. Cluster 2 (green) focuses on supply chain management [19], portfolio optimization [93], social multicriteria evaluations [94] and methodological advances [10–12,20,95–97]. Cluster 3 (blue) focuses on advanced methodological aspects such as geometrical representations [98], combined Cluster 1 (red) includes articles on the comparison of methods (e.g., VIKOR and TOP-SIS) [82,83], Qualiflex [84], Electre I [85], TOPSIS [86], fuzzy AHP [87], fuzzy MCDM [8,88–90], and practical applications based on firm technological innovation capabilities under uncertainty [91] or tourism destination competitiveness [92]. Cluster 2 (green) focuses on supply chain management [19], portfolio optimization [93], social multicriteria evaluations [94] and methodological advances [10–12,20,95–97]. Cluster 3 (blue) focuses on advanced methodological aspects such as geometrical representations [98], combined compromise solutions [99], procedure under incomplete information [100], linear models [29] and worst methods in decision-making [101]. This cluster also includes seminal articles on practical applications of the MCDM methodology to virtual enterprises [102], robust portfolios [103] or port performance [104]. Cluster 4 (yellow) is located at a great distance from the three previous clusters and is made up of Romero's articles on goal programming [105,106], and peripheral articles on ranking methods [9], superefficiency [107], value efficiency [13] and data envelopment analysis [15].

### *3.4. Dynamic Evolution of the Research Agenda* The analysis of the overlapping map (Figure 7) reports the comparison of the domi-

*3.4. Dynamic Evolution of the Research Agenda* 

The analysis of the overlapping map (Figure 7) reports the comparison of the dominant academic production between subperiods. A total of 87 seminal articles articulated the debate before the GFC and 158 after the GFC. After 2008, the new research agenda incorporated 75 new articles that became central to shaping the scientific debate. In fact, in the VUCA era, the 83 articles from the previous period accounted for 51% of the 158 core articles that researchers used to build their references and advance science in fields such as MCDM. nant academic production between subperiods. A total of 87 seminal articles articulated the debate before the GFC and 158 after the GFC. After 2008, the new research agenda incorporated 75 new articles that became central to shaping the scientific debate. In fact, in the VUCA era, the 83 articles from the previous period accounted for 51% of the 158 core articles that researchers used to build their references and advance science in fields such as MCDM.

compromise solutions [99], procedure under incomplete information [100], linear models [29] and worst methods in decision-making [101]. This cluster also includes seminal articles on practical applications of the MCDM methodology to virtual enterprises [102], robust portfolios [103] or port performance [104]. Cluster 4 (yellow) is located at a great distance from the three previous clusters and is made up of Romero's articles on goal programming [105,106], and peripheral articles on ranking methods [9], superefficiency

*Axioms* **2022**, *11*, x FOR PEER REVIEW 11 of 38

[107], value efficiency [13] and data envelopment analysis [15].

**Figure 7.** Overlapping map. **Figure 7.** Overlapping map.

Important research that dominated the debate addressed the study of strategic performance [37], sustainable housing affordability [20], tourism policy implementation [108] or urban sewage sludge and transitions for eco-cities [22]. In this sense, the VUCA environments promoted research on blockchain [53,109], green supply chain management [42], new business models based in sharing economy [110], development of smart cities [111–114], mobility systems [115,116], fuzzy and MCDM methodologies to prioritize the best alternatives [90,117,118], wastewater treatment [119] and solution of challenges of Important research that dominated the debate addressed the study of strategic performance [37], sustainable housing affordability [20], tourism policy implementation [108] or urban sewage sludge and transitions for eco-cities [22]. In this sense, the VUCA environments promoted research on blockchain [53,109], green supply chain management [42], new business models based in sharing economy [110], development of smart cities [111–114], mobility systems [115,116], fuzzy and MCDM methodologies to prioritize the best alternatives [90,117,118], wastewater treatment [119] and solution of challenges of water management of developing countries [120,121].

water management of developing countries [120,121]. Figures 8 and 9 and Tables 5 and 6 report the results of the analysis carried out with SciMAT for both subperiods. The detailed evaluation based on the compared thematic clusters offers relevant information to guide future research according to the evolution of Figures 8 and 9 and Tables 5 and 6 report the results of the analysis carried out with SciMAT for both subperiods. The detailed evaluation based on the compared thematic clusters offers relevant information to guide future research according to the evolution of the research agenda.


the research agenda. **Table 5.** Cluster information. Subperiod 1 (1979–2008).

\* See Acronyms for details of acronyms. Source: Own elaboration.

Multi-criteria-decision-making 4 4 44.5 178 11.27 0.33 33.77 1 Preference-modelling 2 2 112 224 3.41 0.17 32.5 0.83 \* See Acronyms for details of acronyms. Source: Own elaboration. Emerging themes before 2008, year in which one of the biggest global economic crises broke out (GFC) as seen in Figure 8 and Table 5 were tangentially related to decision support systems [14], and after 2008, Figure 9 and Table 6 new academic debates arise about multi-objective optimization [30,93] and additive value functions [32]. Basic themes also evolved between both subperiods. Before 2008 (Figure 8; Table 5), multiple-criteria decisionmaking [82,88,89] was the most important basic theme, while after 2008 (Figure 9; Table 6) the theory of the firm [122,123] and the analysis of decision weights [97,124,125] became basic themes. Specialized topics were also transformed after 2008. Before 2008 (Figure 8; Table 5) the preference modeling theme stood out [103] and after 2008 (Figure 9; Table 6) the

Decision-support-systems 8 7 153.5 1228 21.55 0.5 9.19 0.33

niche themes that were identified where the preferences [126], distance measures [37,104] and impacts [36,124] themes. *Axioms* **2022**, *11*, x FOR PEER REVIEW 12 of 38

**Figure 8.** Strategic diagram of subperiod 1 (1979–2008). **Figure 8.** Strategic diagram of subperiod 1 (1979–2008).

**Table 6.** Clusters information. Subperiod 2 (2009–2022). **Cluster Name CDC \* CDHI DAC CDSC Centrality CR Density DR**  Environment 9 7 25.22 227 20.92 0.6 11.93 1 Models 141 34 35.99 5074 33.08 1 5.98 0.6 Preferences 14 9 21.79 305 19.04 0.4 5.39 0.6 Impacts 11 9 24.18 266 20.79 0.5 8.74 0.8 Multi-criteria 9 6 17 153 29.32 0.9 10.24 0.9 Weights 9 8 167.7 1509 28.11 0.8 3.06 0.2 Decision 12 8 33.83 406 23.58 0.7 1.77 0.1 Firms 7 4 9.29 65 20.95 0.6 4.56 0.5 Multiobjective-optimization 6 5 16.67 100 15.26 0.3 3.79 0.3 Distance-measure 4 3 27.25 109 10.53 0.1 6.67 0.7 Additive-value-functions 5 5 19.6 98 12.26 0.2 3.97 0.4 \* See Acronyms for details of acronyms. Source: Own elaboration. Before 2008 (Figure 8; Table 5) were the multiple-criteria decision-making [82,88,89], while after 2008 (Figure 9; Table 6) the theory of the firm [122,123] and the analysis of decision weights [97,124,125] became basic themes (Figure 8; Table 5), the motor themes were decisions and rankings, while the motor themes after 2008 (Figure 9; Table 6) were multicriteria [22,29,34,38,94,99,119,124,125,127,128], environment [41,120,124,129,130] and models [5,19,38,107,108,116]. The strong irruption of the 2030 Agenda in the academic debate is verified. The influence of the SDGs was articulated in a transversal way around the main research topics of the subperiod analyzed after 2008. The understanding of the models and methodologies for multicriteria decision-making in contexts of environmental and social sustainability helped to improve adherence and the impact of their application to reality. Emerging themes before 2008 (Figure 8; Table 5) were tangentially related to decision support systems [14], and after 2008 (Figure 9; Table 6) new academic debates arise about multiobjective optimization [30,93] and additive value functions [32]. The basic themes also evolved between both subperiods. Before 2008 (Figure 8; Table 5) were the multiple-criteria decision-making [82,88,89], while after the GFC (Figure 9; Table 6) the theory of the firm [122,123] and the analysis of weights in decision analysis [97,124,125] became basic topics. Specialized topics were transformed after 2008. Before 2008 (Figure 8; Table 5), preference modeling stood out [103], and after 2008 (Figure 9; Table 6), niche themes were preferences [126], distance measures [37,104], and impacts [36,124].

**Figure 9.** Strategic diagram of subperiod 2 (2009–2022). **Figure 9.** Strategic diagram of subperiod 2 (2009–2022).

*Axioms* **2022**, *11*, x FOR PEER REVIEW 13 of 38



were decisions and rankings, while the motor themes after 2008 (Figure 9; Table 6) were multicriteria [22,29,34,38,94,99,119,124,125,127,128], environment [41,120,124,129,130] and \* See Acronyms for details of acronyms. Source: Own elaboration.

models [5,19,38,107,108,116]. The strong irruption of the 2030 Agenda in the academic debate is verified. The influence of the SDGs was articulated in a transversal way around the main research topics of the subperiod analyzed after 2008. The understanding of the models and methodologies for multicriteria decision-making in contexts of environmental and social sustainability helped to improve adherence and the impact of their application to Figure 10 reports the details of the transformation of the academic debate on MCDM after 2008. The evolution map for both subperiods shows the connection of preceding and subsequent themes. Among others, four major conversion nuclei of the academic debate stand out: (a) rankings [9,11] towards models [5,19,38,108,116]; (b) MCDM [8,82,88,89,93] towards models, preferences, weights, decision, firms and distance measure [5,85,104,124–126,131]; (c) decision support systems [14] towards weights analysis [124,125]; (d) preference modeling [13,103] towards multiple criteria [38,95,124–128,132] and additive value functions [32].

and additive value functions [32].

reality. Emerging themes before 2008 (Figure 8; Table 5) were tangentially related to decision support systems [14], and after 2008 (Figure 9; Table 6) new academic debates arise about multiobjective optimization [30,93] and additive value functions [32]. The basic themes also evolved between both subperiods. Before 2008 (Figure 8; Table 5) were the multiple-criteria decision-making [82,88,89], while after the GFC (Figure 9; Table 6) the theory of the firm [122,123] and the analysis of weights in decision analysis [97,124,125] became basic topics. Specialized topics were transformed after 2008. Before 2008 (Figure 8; Table 5), preference modeling stood out [103], and after 2008 (Figure 9; Table 6), niche

themes were preferences [126], distance measures [37,104], and impacts [36,124].

Figure 10 reports the details of the transformation of the academic debate on MCDM after 2008. The evolution map for both subperiods shows the connection of preceding and subsequent themes. Among others, four major conversion nuclei of the academic debate stand out: (a) rankings [9,11] towards models [5,19,38,108,116]; (b) MCDM [8,82,88,89,93] towards models, preferences, weights, decision, firms and distance measure [5,85,104,124–126,131]; (c) decision support systems [14] towards weights analysis [124,125]; (d) preference modeling [13,103] towards multiple criteria [38,95,124–128,132]

**Figure 10. Figure 10.** Evolution map. Subperiod 1 (1979–2008) vs. subperiod 2 (2009–2022). Evolution map. Subperiod 1 (1979–2008) vs. subperiod 2 (2009–2022).

### *3.5. High-Impact Publishing Opportunities*

Proper understanding of the evolving research agenda offers important opportunities for high-impact publishing. There are multiple gaps not yet closed by the literature and in recent years issues have emerged that are of growing interest to scholars. Appendix A reports the top 25 articles and journals. From the perspective of the total impact expressed as the total count of citations for the subperiod prior to 2008 (Appendix A; Table A1), seminal articles on MCDM are located in the index with the highest impact [8–15,82,83,86–89,91,93,94,96–98,100,102,106,107,133], and those that stand out for their high performance expressed in academic efficiency through the normalized impact per year (NIY) method the seminal works of Opriocovic and Tzeng [82] on comparative analysis of VIKOR and TOPSIS (NIY = 132,11) and extended VIKOR method in comparison with outranking methods (NIY = 60,27), and Tone [107] on measure of superefficiency (NIY = 46,50). The academic performance of these three articles is markedly superior to the rest of the research indexed in the top 25 articles by citations.

Based on the asymmetry of years included in both subperiods, the NIY analysis for the top 25 articles by citations (Appendix A; Table A2) [19–23,29–37,84,85,92,95,99,101,108,134–137] offers relevant information on "hot topics" for scholars. This evaluation of WHAT research is likely to have a greater impact in the contemporary academic context. Thus, WHAT is determined from the NIY analysis, in order to identify mainstream themes for each of the two subperiods. In particular, the evaluation of the articles included in the top 25 per NIY that were not included in the top 25 articles by citations (Appendix A; Tables A3 and A4; second column: strong trend (ST), shows that before 2008, four very important articles

published in the years immediately prior to the 2008 stood out for NIY). These articles addressed topics of academic interest such as robust portfolio modeling [103,138], goal programming models [105] and multiobjective optimization [139]. The articles that are part of the top 25 per NIY after 2008 and that did not achieve enough total impact to be included in the top 25 per citations are articles with high academic efficiency, since they generate many citations in the trade-off per year since posting. Thus, given that subperiod 2 includes only 13 years, a detailed analysis of the "hot topics" allows us to identify mainstream topics in recent years. Given the high level of impact achieved by these articles in just a few years, issues such as linguistic analysis for smart health care emerge [140] and MCDM under uncertainty [141], decision-makers' psychological preferences [142], cognitive mapping [113,143–145], stochastic MCDM [146], big data analytics capabilities and firm performance [147], open innovation in SMEs [145], smart cities [113] and sustainable supplier selection for megaprojects [25,130,148].

The evidence found suggests that the reported topics are presented in the first line of advancement of scientific knowledge. These themes and their semantic connecting detail represent important high-impact publishing opportunities. In this sense, Appendix B (Figures A1–A11) offers detailed information on how to connect the topics based on the clusters of greatest interest in academia, so the "how" shows the connection between the subtopics through the cluster's networks of subperiod 2. These are thematic nuclei required with interest by the journals, and that make up the academic vanguard. Researchers can identify the way in which contemporary discourse is articulated and build their research on the environment (Figure A1), models (Figure A2), preferences (Figure A3), impacts (Figure A4), multiple criteria (Figure A5), weights (Figure A6), decisions (Figure A7), firms (Figure A8), multiobjective optimization (Figure A9), distance measures (Figure A10) and additive value functions (Figure A11).

On the other hand, after analyzing what to research and how to connect the topics to achieve maximum impact, it is necessary to look at where it is most effective to publish high-impact research. Analogously to the previous analysis, evidence is presented for both subperiods. Table A5 (Appendix B) shows the main journals of subperiod 1, ordered by total citations. Before 2008, the 10 journals with the greatest impact on MCDM were the *European Journal of Operational Research*, *Omega—International Journal of Management Science*, *Journal of the Operational Research Society*, *Decision Sciences*, *Tourism Management*, *Management Science*, *Technovation*, *IEEE Transactions on Engineering Management*, *Systems Research and Behavioral Science*, and *Group Decision and Negotiation*. The ComD evaluation offers indicators of high academic efficiency for three journals that manage to register at least 100 citations per article: *Tourism Management* (CpD = 544), *Systems Research and Behavioral Science* (CpD = 146), and the *European Journal of Operational Research* (CpD = 103.78).

Table A6 (Appendix A) shows the 25 journals with the greatest academic impact in the second subperiod analyzed, offering a useful guide to determine where to publish research on MCDM. According to the second column of Table A6, according to the strong trend (ST) indication, several journals are evidenced, characterized by their emerging impact within the field in comparative intertemporal terms. After 2008, 16 journals appear in the ranking that were not included in the same top 25 ranking of the previous subperiod. The finding of these journals is a very useful recommendation for scholars on where to direct their research on MCDM within the area of business and management once the systemic change in the research agenda after the 2008 took place. The strong trend (ST) evidence found shows 16 "hot journals" in the field: *Journal of Business Economics and Management*, *Technological Forecasting and Social Change*, *International Journal of Strategic Property Management*, *International Transactions in Operational Research*, *Journal of Enterprise Information Management*, *Transformations in Business and Economics*, *Journal of Business Research*, *Total Quality Management and Business Excellence*, *Socio-Economic Planning Sciences*, *International Journal Of Logistics Management*, *E a M: Ekonomie a Management*, *Business Strategy and the Environment*, *Engineering Construction and Architectural Management*, *Journal of Purchasing and Supply Management*, *and Tourism Management Perspectives*.

The results of this article should give researchers a better understanding of the structure of research done on MCDM and MCDA. Among the new contributions to the literature that this article represents is the evaluation of new motor topics though the SciMAT software, which permits the visualization of the most current mainstream thematic nuclei of the academic discourse on MCDM and MCDA.

Although the research was not designed or conducted as a meta-analysis, it can be deemed valuable due to the clarity of the information provided. As such, the findings of this study are relevant to the academic community and advance the scientific debate on MCDM. When information is focalized, it constitutes a resource that makes academic interaction more accessible for researchers, allowing for scholarly discussions based on decision-making traditions.

### **4. Conclusions**

The recent succession of economic, social, health, and war crises has generalized the irruption of a continuum of VUCA environments (volatile, uncertain, complex, and ambiguous) that have become global and hegemonic. The existing interdependence among the different systems has a substantial effect on companies, entrepreneurs, and investors, as well as governments and civil society, and forces accurate and rapid decisions to be made. On the other hand, shocks are becoming more frequent, deep, and persistent. In the contemporary era, the first recent systemic crisis that caused major economic and social disruption in most developed countries was the GFC. Since 2008, all kinds of disruptive events have taken place that force managers and governors to make quick and concrete decisions. Following 2008, academic interest in multiple-criteria decision-making (MCDM) or multiple-criteria decision-analysis (MCDA) has clearly increased. Articles published on these topics in scientific journals in the business and management area in recent years have achieved a much higher normalized impact per year (NIY). In addition to the increase in interest from academia, there has been an important paradigm shift causing the transformation of the research agenda. International collaboration networks have intensified, and Taiwan, USA, Serbia, China, and Spain are leaders in total citations. However, academic efficiency measured as citations per document (CpD) is very strong in Serbia, Netherlands, and Denmark and very weak in Australia, Scotland, India, Turkey, Brazil, Iran, and Switzerland. The theme construction of the scientific vanguard and bibliographic coupling per article analysis show four large clusters that contribute to a better understanding of the structure of the academic debate on MCDM.

The dynamic evolution of the research agenda shows a profound transformation of the driving, basic, and emerging issues, offering important recommendations on the construction of the academic debate. The evolution of specialized topics advises scholars on major emerging trends that allow them to increase the impact of their future research. From the perspective of the practical application of the methodologies analyzed, a finding of this study guides future academic production around "hot topics", such as mobility, management of scarce resources, such as water or energy, electrification, and electric vehicles, and new models of business based on blockchain or the sharing economy. In addition, from a particular approach, the evidence found suggests that future research on portfolio modeling, multiobjective optimization, or goal-programming models were emerging topics before 2008, but high-impact publication opportunities are focused on the study of the construction of cognitive maps and a better understanding of the psychological preferences of decision-makers, as well as the application of MCDM methodologies to specific problems, such as smart cities, green supply chain alternatives, open innovation in SMEs, and the impact of big data capabilities on firm performance.

The axes of systematic review and analysis carried out by this research offer new vectors to be explored and developed by scholars. In addition, researchers and journal editors can find in this article a roadmap to be used in the planning, design, and publishing of research in the field. Further value can be found in the identification of "hot topics" and changing trends defined on the basis of the methodologies used. The main contribution of this study is to offer a panoramic view of the field of research published in business and management areas on MCDM, and to help academics in understanding how the research agenda has been transformed. In addition, the identification of the change vectors of the internal structure of the area guides and advises researchers in their future academic production, informing about gaps not closed by the scientific literature. This research provides journal editors with specific guidance on new emerging topics and the forefront of the scientific debate on MCDM, as well as the uses and applications with the greatest academic impact.

A limitation of this study is in the bibliographic methodology used. Future research should complement the scientometric knowledge generated by this research with a qualitative approach that is provided by researchers who are building the cutting edge of science on MCDM. In addition, this research could include a classification of the MCDM methodology that identifies its evolution and its fields of application. Among others, future lines of research should hybridize bibliographic methodologies with others of a qualitative type, such as design and system thinking or focus group, to improve the understanding of the underlying motivations of the researchers, as well as sources of funding, that explain the transformation of the research agenda. Other research should delve into the detailed analysis of specific methodologies used for decision-making, such as fuzzy analytic hierarchy process (F-AHP) or fuzzy-set qualitative comparative analysis (fsQCA), extending the analysis to the construction and selection of sociotechnical transitions designed to achieve the SDGs.

**Author Contributions:** Conceptualization, F.C.-S.; methodology, F.C.-S.; software, F.C.-S.; validation, F.C.-S. and C.M.-E.; formal analysis, F.C.-S.; investigation, F.C.-S.; resources, F.C.-S.; data curation, F.C.-S.; writing—original draft preparation, F.C.-S.; writing—review and editing, F.C.-S. and C.M.-E.; visualization, F.C.-S. and C.M.-E.; supervision, F.C.-S.; project administration, F.C.-S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Data sharing not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Acronyms**


*Axioms* **2022**, *11*, 515
