Multi-criteria decision-making (MCDM) methods have been increasingly adopted in administrative research because of their ability to handle both ordinal and cardinal data, thus accommodating a wide range of decision contexts. These methods enhance the analytical robustness of case studies by systematically structuring criteria, weighting preferences and ranking alternatives, often incorporating both qualitative and quantitative judgments. In practical applications, MCDM techniques assist decision-makers such as executives, managers, supervisors and entrepreneurs in making choices that are more transparent, consistent and justifiable in complex situations, such as strategic planning, technology selection, supplier evaluation and resource allocation.
To examine the evolution of publications related to MCDM methods within the business, management and accounting fields, a systematic search was conducted using the Scopus database. Figure 1 illustrates this upward trend, reinforcing the importance of multi-criteria methods as an established approach in the academic literature.
The search strategy was designed to capture common orthographic and syntactic variations related to the topic, employing the following command applied to the title, abstract and keyword fields: TITLE-ABS-KEY (“multi-criteria decision” OR “multi criter decision” OR “multicriter* decision”). Specific filters were applied to ensure consistency and relevance of the results. First, the search was limited to the subject areas of business, management and accounting, focusing on the analysis of studies pertaining to administrative sciences. Next, the document type was restricted to scientific journal articles (journal articles), thereby excluding book chapters, conference papers and other publication types with less standardized indexing coverage in Scopus. The analyzed period spans 1974 to 2024. Although the year 2025 is still ongoing, Scopus provides early records of publications scheduled for this year, allowing a prospective view of scientific production. Based on the established criteria, 5,993 documents were identified that addressed the topic of MCDM in the context of business, management and accounting. Temporal analysis of the data revealed a steady increase in the use of MCDM methods over the past five decades. This significant growth in scientific output reflects not only the consolidation of these methods as relevant tools for organizational decision-making but also their expanding application in management-related research.
Until the mid-2000s, MCDM methods were predominantly applied in fields such as engineering and operational research. Beginning around 2007, a significant expansion of these methods into the field of management was observed, driven by the growing need to support complex decision-making based on multiple criteria, often qualitative or subjective. This trend was reflected in the substantial increase in the number of publications in the areas of business, management and accounting indexed in the Scopus database. The post-2007 period also marked a significant advancement in data digitalization and the growing popularity of business analytics in corporate environments, further reinforcing the recognition of MCDM methods as essential tools in descriptive, predictive and prescriptive analyses. These methods began to support strategic decisions in a structured and transparent manner in areas such as organizational planning, performance evaluation, technology and supplier selection and project management. Furthermore, the contemporary context of digital transformation, fueled by artificial intelligence as well as increasing demands for sustainable solutions aligned with the sustainable development goals (SDGs), the circular economy and innovation-driven entrepreneurship (such as data-oriented startups), has further expanded the application of MCDM methods. These methods are now used in multi-criteria evaluations of environmental impact, risk analysis, prioritization of sustainable projects and in defining strategies for more resilient, collaborative and responsible business models.
The application of MCDM methods is deeply rooted in the broader discipline of operations research (OR), which offers a systematic and quantitative foundation for analyzing complex decision problems. OR provides a suite of mathematical techniques designed to model, structure and optimize decision processes under multiple and often conflicting criteria. Within this context, MCDM stands out as a powerful subset, enabling decision makers to evaluate alternatives in scenarios that involve trade-offs, uncertainty and subjective preferences. In the field of business and management, the integration of MCDM into decision-making has become increasingly important, particularly in areas such as strategic planning, project selection, supply chain design, portfolio optimization and environmental sustainability (Yalcin, Kilic, & Delen, 2022; Kozłowska, 2022; Jain, Sahay, & Nupur, 2025). Therefore, the synergy between MCDM and OR exemplifies the role of mathematical methods as enablers of rationality and rigor in contemporary administrative practices. Recent methodological advances, such as the DARIA-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach (Wątróbski, Ziemba, Jankowski, & Karczmarczyk, 2022), have further strengthened this synergy by incorporating temporal dynamics into classical MCDM models, enhancing their applicability in sustainability assessment and longitudinal policy evaluation.
MCDM refers to a set of methods that enables the evaluation of alternatives based on multiple and often conflicting criteria, offering decision-makers a more robust analytical framework than unidimensional approaches. In the field of management, MCDM methods have been widely adopted to support decision-making processes in areas such as strategic planning, technology selection, project prioritization, environmental management and business analytics (BA) (Kozłowska, 2022; Yalcin et al., 2022; Keenan, 2024).
These methods are particularly valuable in contexts where trade-offs between economic, environmental, technical and social factors must be considered simultaneously (Kozłowska, 2022). As Kozłowska noted, MCDM techniques enable not only the evaluation of diverse and complex criteria, both qualitative and quantitative, but also support the ranking or selection of alternatives in a structured and transparent manner. Commonly used methods include the analytic hierarchy process (AHP), TOPSIS, VIKOR, ELECTRE, Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) and newer hybrid models incorporating fuzzy logic, temporal aggregation and machine learning (Saaty, 2005; Tzeng & Huang, 2011; Brans & Vincke, 1985; Opricovic & Tzeng, 2004; Zavadskas, Turskis, & Kildienė, 2014; Wątróbski et al., 2022). For instance, the DARIA–TOPSIS method introduces the dimension of performance variability over time, broadening the analytical power of classical MCDM frameworks. In this context, methodological advances, such as the Gaussian Analytic Hierarchy Process (AHP-Gaussian), have emerged to enhance the discriminative power of traditional AHP through statistical sensitivity adjustments (Santos, Costa, & Gomes, 2021).
According to a study by Yalcin et al. (2022), MCDM is closely tied to the evolution of BA, as it supports decision-making across descriptive, predictive and prescriptive analytics. The authors distinguish two broad families of MCDM: multi-attribute decision-making (MADM), which deals with a finite set of discrete alternatives and multi-objective decision-making (MODM), which handles problems with infinite alternatives and emphasizes Pareto-optimal solutions (Miettinen, 1999). MADM approaches include techniques based on pairwise comparisons (e.g. AHP and ANP), distance from ideal solutions (e.g., TOPSIS and VIKOR), outranking (e.g. PROMETHEE and ELECTRE) and utility theory (e.g. WASPAS and COPRAS) (Pinochet et al., 2024). On the other hand, MODM approaches include methods such as goal programming, epsilon constraint and interactive procedures such as NIMBUS and STEM.
The predominance of MADM methods over MODM methods in the field of management can be attributed to several contextual and methodological factors. These include the nature of managerial problems, the complexity of mathematical modeling, the familiarity of practitioners with certain tools and empirical evidence from recent literature reviews.
First, the nature of decision problems in management tends to favor MADM. Managerial decision-making frequently involves the selection, ranking or evaluation of a finite and discrete set of alternatives, such as suppliers, projects, technologies, strategies or policies. These alternatives are typically evaluated using multiple, often conflicting criteria that may be qualitative, subjective or ordinal in nature. This structure aligns directly with the scope of MADM methods, which are designed to support decision-making under such circumstances (Tzeng & Huang, 2011; Yalcin et al., 2022). For instance, choosing among four suppliers based on cost, delivery time, product quality and brand reputation represents a typical scenario in which methods such as AHP, TOPSIS or PROMETHEE are well-suited.
Second, MODM methods require continuous mathematical modeling, which is more common in engineering and operations research than in general management applications. These methods aim to find the optimal trade-offs in problems with infinite solution spaces, often involving objective functions and constraints that must be defined algebraically. Examples include optimizing the cost and delivery time simultaneously in a supply chain model using approaches such as goal programming, the epsilon-constraint method, or NSGA-II (Miettinen, 1999). While powerful, such techniques require a higher level of mathematical formulation and computational effort, which may not be practical or necessary for most management-oriented decisions.
Third, the practical adoption of MADM methods among practitioners and managers was significantly higher. Many managers and analysts prefer structured but intuitive approaches that can be implemented using familiar tools such as spreadsheets or decision-support software. As such, methods such as AHP, SAW, TOPSIS and PROMETHEE have become popular because of their ease of application, transparency and compatibility with real-world managerial decision processes (Kozłowska, 2022; Pant et al., 2024).
Finally, the empirical evidence from systematic reviews reinforces this pattern of use. According to Yalcin et al. (2022), over 80% of the reviewed studies applied MADM methods in administrative contexts. Similarly, Kozłowska (2022) highlighted that MADM methods dominate in technology selection, sustainability assessments and strategic decision-making in management. In contrast, MODM approaches are typically found in specific domains, such as logistics optimization, energy systems and industrial engineering, and are less common in organizational, behavioral or strategic management research.
Taken together, these findings suggest that MADM methods not only fit the typical structure of managerial problems but also offer practical advantages in terms of usability and implementation. Although MODM remains a critical area in operational research and quantitative optimization, MADM continues to be the preferred choice for most managerial decision scenarios because of its adaptability, interpretability and methodological accessibility.
Keenan (2024), through a scientometric analysis, confirms the growing importance of MCDM methods in management research, especially in disciplines that demand complex decision support systems (DSS). He highlights that MCDM has evolved from its early use in engineering and operational research to becoming an essential tool in managerial decision-making across sectors such as energy, healthcare, sustainability and supply chains. Altogether, the literature suggests that MCDM is not only a methodological domain but also a critical enabler of evidence-based management in a data-driven era.
An important conceptual distinction in the field of MCDM methods lies in the classification of compensatory and non-compensatory approaches. This distinction directly affects how criteria are treated in decision-making processes, especially in managerial contexts in which trade-offs among objectives are common.
Compensatory methods allow for high performance in some criteria to offset poor performance in others. These methods are generally based on aggregated utility functions such as weighted sums or weighted products, offering a holistic evaluation of alternatives (Tzeng & Huang, 2011). Typical examples include the AHP, the TOPSIS, Simple Additive Weighting (SAW), the Weighted Sum Model (WSM), the Weighted Aggregated Sum Product Assessment (WASPAS) and the Simple Weighted Sum Product Method (WISP), a more recent hybrid approach that combines additive and multiplicative models to simplify and improve decision analysis (Stanujkic, Popovic, Karabasevic, Meidute-Kavaliauskiene, & Ulutaş, 2023). As noted by Yalcin et al. (2022), such methods are well suited for environments where data are well structured, criteria are measurable and quantitative and trade-offs are acceptable, such as in strategic planning, performance assessments and technology selection processes.
In contrast, non-compensatory methods do not permit trade-offs between the criteria. A poor score in a single criterion may disqualify it as an alternative, regardless of its performance in other dimensions. These methods are especially useful when dealing with critical decisions in which certain requirements (e.g. safety, sustainability and compliance) must be met. Non-compensatory approaches are typically based on outranking relations or dominance rules. Notable examples include Elimination and Choice Expressing Reality (ELECTRE), PROMETHEE and ORESTE. According to Kozłowska (2022), such methods are effective in contexts involving the evaluation of high-risk technologies, environmental assessments and policy decisions, domains in which compromises may obscure unacceptable outcomes.
Fuzzy and uncertainty-based methods enhance decision-making by handling imprecision and modeling subjective preferences. Hybrid methods combine the strengths of different approaches to improve the robustness in complex scenarios (Almeida, Gomes, Pinochet, & dos Santos, 2025; Pinochet, Moreira, Fávero, dos Santos, & Pardim, 2023). Optimization-oriented MODM methods support decisions involving multiple conflicting objectives, guiding the search for optimal solutions aligned with specific goals.
As such, the selection of a compensatory or non-compensatory approach should be guided by the nature of the decision problem, the structure and reliability of the available data and decision-makers’ tolerance for trade-offs. Understanding these distinctions is essential for selecting the appropriate method and aligning the analytical approach with the organization’s strategic priorities and constraints. A comprehensive overview of the most commonly used and applied MCDM/MODM methods in the field of management is presented in Figure 2, titled “Taxonomy of Multi-Criteria Decision Making (MCDM) Methods: MADM and MODM Approaches.”
Selecting an appropriate MCDM method is a crucial step in addressing complex decision problems in the field of management. These problems often involve conflicting criteria and require tools capable of handling both quantitative and qualitative data while incorporating decision-makers’ preferences. One of the first aspects to consider is the type of decision problem. If the alternatives are discrete, the MADM methods are more suitable. MODM methods are preferred for continuous optimization problems with conflicting objectives.
The nature of the data also plays a key role. Fuzzy or probabilistic methods are more appropriate when dealing with subjective, imprecise, or uncertain information. By contrast, traditional methods are suitable when reliable quantitative data are available. Another important factor is the expression of preferences. Some methods require explicit weighting or pairwise comparisons, whereas others accept qualitative ranking. Compensation among criteria must also be assessed: compensatory methods allow trade-offs, whereas non-compensatory methods are better when poor performance in one criterion cannot be offset by another.
Additionally, ease of use, clarity of results and stakeholder understanding are vital, especially in organizational settings. Robustness, sensitivity analysis, computational support and time constraints should also guide method selection. Finally, the decision context and goals – whether strategic planning, investment prioritization or project evaluation – must align with the method’s structure and assumptions. Hybrid approaches have become increasingly popular because of their ability to provide both analytical precision and practical adaptability.
