Multi-criteria Decision-making Techniques Research Articles

Successful Implementation of Disruptive Innovation by New Entrants: Modeling the Challenges Using Interpretive Structural Modelling

Successful implementation of disruptive innovation involves numerous challenges, especially for new entrants. This study tries to identify and analyze a set of critical challenges that might plague a new entrant to successfully implement disruptive innovation. Multicriteria decision-making (MCDM) technique of Interpretive Structural Modeling (ISM) was used to analyze the identified challenges, as well as to derive their interrelationship. The study was conducted in the context of the Indian manufacturing sector. The findings revealed that the lack of financial resources, lack of non-financial resources, and lack of top management support emerged as the three key challenges impeding disruptive innovation for manufacturing organizations in India. The motivation–opportunity–ability (MOA) model served as the basic theoretical underpinning for the study. The findings of the study are expected to help managers and policymakers to understand possible challenges associated with the implementation of disruptive innovation, and to develop counter strategies accordingly.

Mapping of groundwater potential zones of Khordha District using GIS and AHP approaches

The most dependable source of fresh water is groundwater. Groundwater supplies are severely threatened by a number of factors, including urbanization, industrialization, and population growth. The amount, quality and variables affecting groundwater supplies are significantly impacted by climate variability. The fall in groundwater levels is often exacerbated by poor quality surface water resources and unreliable monsoons. Therefore, in order to supplement the groundwater supply, it is important to locate and define the groundwater potential zone (GPZ). The analysis is conducted for the Khordha district, where groundwater rather is a primary source for agricultural uses. In order to determine the possible groundwater zones, many factors, including geomorphology, geology, elevation, slope, precipitation, soil type, soil texture, drainage density (DD), lineament density (LD), Land use/Land cover (LULC), and lineament density (LD), are constructed as separate layers in the geographical information system (GIS) backdrop. The multi-criteria decision-making technique and the Analytic Hierarchy Process (AHP), which enable pairwise evaluation of criteria impacting the potential zone, were utilized to establish the weights for the different layers and after that, the weighted overlay analysis (WOA) tool in ArcGIS10.8 was used to produce the final groundwater potential map. The output map of specified region was delineated into five new classes-very good, good, moderate, poor, and very poor of which 12% (325.1745 km2) falls under ‘very low’, 22% (603.9765 km2) under ‘low’, 26% (700.7715 km2) under ‘moderate’, 26% (694.2591 km2) under ‘high’, 14% (376.7553 km2) under ‘very high’. Approximately 1395 km2 area concerning 52% of study region, falls under ‘high’ and ‘very high’ categories of GPZ. Validation of the generated GPWZ map was done with data acquired from Central groundwater board. The accuracy assessment was done by kappa coefficient error matrix, and based on overall accuracy, the obtained map was 81.538% accurate to field value. As dependable results were produced with the proposed methodology, future management plans incorporating natural and artificial recharge practices can be created in these locations with effectiveness.

Development of RAMDOE: a new method for rapidly ranking alternatives with supplementary options and considering changes in criteria values

This paper delves into the development and validation of the RAMDOE method, a pioneering approach in multi-criteria decision making (MCDM) that seamlessly integrates the root assessment method (RAM) and design of experiments (DOE) techniques, addressing the inflexibility of traditional MCDM methods in accommodating adjustments in criteria ranges and the addition of new alternatives without necessitating a complete overhaul of the decision framework. Through empirical analysis, the study demonstrates the RAMDOE method's remarkable efficacy in precisely ranking alternatives, as illustrated through a practical case study focused on the selection of a supplier from a pool of seven candidates. One of the most notable aspects of the RAMDOE method lies in its capacity to formulate a regression equation that accurately captures the intricate relationship between alternative scores and criteria values, enabling decision-makers to seamlessly integrate new alternatives into the decision-making process without the cumbersome task of recalibration, thereby distinguishing it from conventional MCDM techniques such as TOPSIS (technique for order of preference by similarity to ideal solution), COPRAS (complex proportional assessment), MOORA (multiobjective optimization on the basis of ratio analysis), EDAS (evaluation based on distance from average solution) and CODAS (combinative distance-based assessment). The practical implications of these findings are profound, offering decision-makers across various domains a more efficient and adaptable framework to navigate complex decision scenarios. Particularly in contexts like supplier selection, where criteria ranges may vary significantly, the RAMDOE method provides decision-makers with a robust toolset to make informed decisions, presenting a promising avenue for addressing the dynamic nature of decision-making environments and enhancing the overall robustness and flexibility of MCDM processes in real-world applications

Advancements and innovations in requirements elicitation: Developing a comprehensive conceptual model

Requirements elicitation is a crucial phase in the software development lifecycle, ensuring that stakeholders' needs are understood and translated into system specifications. Traditional methods often face challenges such as ambiguity, inconsistency, and evolving requirements, leading to project delays and cost overruns. This review proposes a conceptual model that integrates advancements and innovations in requirements elicitation to address these challenges comprehensively. The proposed model leverages various techniques, including but not limited to, natural language processing (NLP), machine learning (ML), and human-computer interaction (HCI), to enhance the accuracy and efficiency of requirements elicitation. NLP techniques enable automated analysis of textual requirements documents, extracting key information and identifying implicit requirements. ML algorithms facilitate the prediction of potential changes in requirements based on historical data and project context, enabling proactive management of evolving requirements. Moreover, the integration of HCI principles in the requirements elicitation process enhances stakeholder engagement and collaboration. Interactive interfaces and visualization tools enable stakeholders to provide feedback in real-time, fostering a more iterative and participatory approach to requirement gathering. Additionally, techniques such as prototyping and simulation facilitate early validation of requirements, reducing the risk of misinterpretation and ensuring alignment with stakeholders' expectations. Furthermore, the proposed model emphasizes the importance of context-awareness in requirements elicitation. By considering the organizational, cultural, and environmental context of a project, the model adapts its elicitation strategies and techniques to suit specific needs and constraints. Context-awareness also enables the identification of implicit requirements that may not be explicitly stated but are crucial for the success of the system. The model also addresses the challenge of managing conflicting requirements by introducing a systematic approach to requirements prioritization and negotiation. By employing multi-criteria decision-making techniques, stakeholders can collaboratively prioritize requirements based on their importance, feasibility, and impact on project objectives. Conflict resolution mechanisms facilitate consensus-building and trade-off analysis, ensuring that the final set of requirements reflects the collective interests of all stakeholders. Additionally, the proposed model emphasizes the iterative nature of requirements elicitation, advocating for continuous refinement and validation of requirements throughout the development lifecycle. Feedback loops enable stakeholders to review and revise requirements based on evolving needs and changing circumstances, thereby enhancing the adaptability and resilience of the system. The conceptual model presented in this review represents a holistic approach to requirements elicitation, leveraging advancements and innovations in technology, human interaction, and context-awareness. By integrating these elements, the model aims to enhance the accuracy, efficiency, and stakeholder satisfaction in the requirements elicitation process, ultimately contributing to the successful delivery of high-quality software systems.

An Integrated Entropy-Weighted Sum Model Technique for Evaluating the Metrics of Cybersecurity under Uncertainty

Businesses and organizations handle large amounts of data that may be accessed unauthorized by hackers, so we need to protect this data and other resources from malicious users. Here comes the role of cybersecurity. Cybersecurity is a multidisciplinary field touching on the technical sciences as well as the social and behavioral sciences. Cybersecurity is the result of intelligent cyberattacks. Cybersecurity is crucial to protecting sensitive data stored on any device due to the increasing amount of information. So, this study evaluates cybersecurity using neutrosophic multi-criteria decision-making (MCDM) techniques such as entropy to obtain weight and the weighted sum model (WSM), for ranking and obtaining the best digital footprint of modern organization's cybersecurity. The cybersecurity estimation issue illustrates the validity and great performance of the presented method as (1) its capability to deal with uncertainty phenomena (2) its straightforwardness; and (3) its heightened capacity to discern alternatives. The presented case study shows that the best alternative of this study is A3 which refers to (Digital supply chain risk) as the best one of all alternatives.

Flood hazard analysis in Mumbai using geospatial and multi-criteria decision-making techniques

ABSTRACT Flood risk assessment remains a crucial element, particularly within locations highly susceptible to repeated flood occurrences. This study seeks to conduct an elaborate flood risk analysis for Mumbai, India based on an integrated method of geographic information systems and analytic hierarchy process (AHP). In this study, land use/land cover, average annual rainfall, elevation, drainage density, normalized difference vegetation index, distance from rivers as well as distance from roads are identified and considered. For this reason, the expert survey utilizes the AHP weights so as to find out the significance of these factors towards flooding danger. Investigations show a flood risk index and a corresponding map for Mumbai, where all areas are divided into risk zones very low to very high. Very high risks are particularly situated along rivers. Such details offer critical knowledge to policymakers who will undertake informed emergency preparedness measures designed to shield Mumbai's citizens and assets. Therefore, this research is considered as one of the modern techniques for flood risk assessment, which can be applied in other flood-affected areas worldwide.

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective.

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.

Synergistic Strategy of Sustainable Hospital Site Selection in Saudi Arabia Using Spherical Fuzzy MCDM Methodology

Site selection for a hospital is demanding and challenging work. It’s become more complicated when considering various infectious diseases. Multiple criteria and sub-criteria are considered for selecting the most suitable and efficient hospital location. In this paper, we find the most suitable location for multiple disease-related hospitals using the multi-criteria decision-making (MCDM) methodologies. The proposed study is done by the MCDM techniques, namely spherical Entropy and spherical VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. The decision-making for site selection is based on multiple experts’ opinions. Also, we use real data sources in spherical fuzzy numbers (SFN) to capture all its uncertainty. We proposed a new score function and an accuracy function of the SFN to evaluate crisp value from fuzzy numbers. The most prioritized criteria and sub-criteria are determined with their weights. We optimized the proposed site based on demand and criterion weight. Finally, sensitivity analysis and comparative analysis are conducted to check the stability and robustness of the result.

Critical Watershed Prioritization through Multi-Criteria Decision-Making Techniques and Geographical Information System Integration for Watershed Management

The Precambrian hard rock topography of the Manbhum-Singhbhum plateau, which is well known for its semi-arid climates prone to drought, is often seen in Purulia district in West Bengal, India. Despite the district’s middling groundwater capacity, 17 out of 20 blocks have exorbitant fluoride pollution in the groundwater that negatively impacts the health of local residents. Approximately 13% of the whole area suffers from severe erosion. It is evident that the river Kangsabati and its tributaries are not well fed by rainwater and thereby there is always a dearth of ground water. The aim of this study was to identify and prioritize integral watersheds in the Purulia area using Multi-Criteria Decision Making (MCDM) and Geographic Information Systems (GIS). The evaluation was carried out in the Bandu sub-watershed, which contains five micro watersheds: 2A2B5m, 2A2B5k, 2A2B5h, 2A2B5b, and 2A2B5j. The analysis considered five major factors: lithological properties, land use and land cover, soil erosion, groundwater recharge, and hydrogeomorphology. The weights of these criteria were determined by the Analytical Hierarchy Process (AHP) model, which was then prioritized using the Techniques for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. This study emphasized an integrated approach to assess watershed hazards and to establish rational conservation goals. The Central Ground Water Board (CGWB) of India report was referred during data analysis. As a result of this study, the 2A2B5k watershed emerged as the most critical due to its susceptibility across the analyzed parameters. This thorough plan demonstrated the usefulness of identifying watershed threads and prioritizing conservation efforts.

Identifying the Contributing Sources of Uncertainties in Urban Flood Vulnerability in South Korea Considering Multiple GCMs, SSPs, Weight Determination Methods, and MCDM Techniques

This study quantified uncertainties involved in assessing the future flood vulnerability in 33 urban areas with population exceeding designated thresholds in South Korea. The driver-pressure-state-impact-response (DPSIR) framework was utilized as the study procedure, integrating social, economic, and environmental factors. In addition, a total of 220 cases of combinations were examined, encompassing twenty general circulation models combined with shared socioeconomic pathway scenarios, five weight determination methods, and three multi-criteria decision-making (MCDM) techniques, as sources of inherent uncertainties in the process. The rankings of urban flood vulnerability (UFV) for the selected cities were comprehensively assessed considering all combinations, followed by an analysis of variance test to investigate contributing sources of uncertainties. As a result, Incheon and Busan were found to be vulnerable to flooding, while Yeongcheon and Andong appeared to be safe cities. Some cities exhibited wide ranges in their rankings, such as Daegu, Yangpyeon, and Jeongeup. The identified contributing sources were weighting (58%), MCDM (27%), and the combination of weighting and MCDM methods together (15%). This study revealed that weight determination methods and MCDM techniques are the primary sources of uncertainties in the assessment of future UFV instead of multiple GCMs and SSPs. This finding underscores the importance for decision-makers and stakeholders to carefully consider these uncertainties for sustainable flood risk management and prevention.

Using SMART Method for Multi-criteria Decision Making: Applications, Advantages, and Limitations

The applications of multi-criteria decision-making (MCDM) techniques are numerous. Simple Multi-Attribute Rating Technique (SMART) is a popular method for addressing MCDM problems with several criteria. The research investigates the SMART approach discussing how it is used, and its benefits and drawbacks, in decision-making situations. It looks at how it can be applied in choosing technology, improving healthcare systems, and managing the environment. SMART simplifies decision-making by comparing options based on factors. Yet it also has drawbacks such as biases in assigning weights and may not fully address the intricacies of certain decisions. The goal of the study is to enhance comprehension of SMART advocate for its use and propose combining it with intricate decision frameworks. Even though the SMART method is now widely used there is a lack of a thorough understanding of the method to identify its various applications. This paper aims to provide a comprehensive guide and a thorough overview of the SMART method to aid in decision-making and ranking in multi-attribute scenarios.

Hybrid multicriteria decision‐making model for process selection for drinking water treatment

AbstractThe overuse of pesticides in agriculture poses significant threats to human health and the environment. This study addresses the issue by proposing a hybrid multi‐criteria decision‐making (MCDM) method, utilizing the analytic hierarchy process (AHP), to select the most suitable pesticide removal method for drinking water treatment. Focusing on the priority and selection of treatment options for specific pesticide groups, such as diuron and cypermethrin, based on toxicity and physicochemical properties, the study concludes that nanofiltration is best option for diuron removal, while granular activated carbon adsorption is effective for cypermethrin. The innovative hybrid MCDM (AHP + TOPSIS and AHP + VIKOR) approach, unprecedented in assessing pesticide removal methods for drinking water treatment, proves promising in ranking treatment processes for pesticides. The study emphasizes the serious consequences of excessive pesticide use, highlights treatment solutions like NF and GAC adsorption, and introduces cutting‐edge hybrid MCDM techniques for selection treatment process that meet technical, economical, and environmental requirements. All of these factors combine to provide a novel approach not only used to select the best option from alternatives, but also to prioritize alternative processes in drinking water treatment at pesticide removal practices.

Modeling Livestock Procedures Toward Precision and Sustainable Livestock Farm in Era of Virtual Technologies: Lessons, Opportunities, Avenues of Digitization

Presently, contemporary and virtual technologies that are being embraced by many businesses have a favorable influence. For instance, biometric sensors of the Internet of Things (IoT), artificial intelligence (AI), big data (BD), blockchain technology (BCT), and so on in livestock permit the real-time observation of the health and behavior of animals. Also, monitoring helps prevent deterioration, diagnose injuries, and stress, and sustain productivity. The collected data from biometric sensors is analyzed by using BD securing the processed information and making it transparent for partners using BCT. Achieving safety and security through collaborating human robots (cobots) Accordingly, these technologies turn traditional livestock into precision livestock farms (PrLFs). Currently, the concept of precision serves as the cornerstone for the advancement of sustainable and user-friendly livestock farm management in many nations as well as globally. Hence, the objective of this study is to embrace the concept of precision and illustrate its influence on livestock farms toward sustainability of livestock farm. These objectives consider catalysts for analyzing and evaluating the sustainability of livestock farms that embrace contemporary technologies in their operations and practices. Moreover, we are constructing an evaluation model for obtaining the most sustainable livestock farm through harassing Multi-Criteria Decision Making (MCDM) techniques where each technique is responsible for a certain function. For instance, CRiteria Importance Through Inter-criteria Correlation (CRITIC) is utilized for obtaining the criteria’s weights and the combined compromise solution (CoCoSo) leverages the obtained weights from CRITIC for ranking the alternatives of livestock farms and recommends the most sustainable and worst alternative. The evaluation for alternatives is performed based on rating four main criteria related to sustainability’s pillars and thirteen sub-criteria. Finally, the utilized MCDM techniques are working under the authority of Triangular Neutrosophic Sets (TrNSs) to bolster these techniques when handling incomplete information and perplexed situations.

GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria

GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria

Performance measurement of factors affecting consumer experience in healthcare services using fuzzy and grey techniques

PurposeThe importance of consumer experience in service industries, particularly healthcare, is widely acknowledged as it captures the intricacies of quality management. In tandem with the emerging research trends that evaluate service excellence through user experience, this study renders a performance analysis of the dimensions of consumer experience that individually or collectively shape healthcare consumers’ perceptions of service quality.Design/methodology/approachA cross-sectional study was conducted across 13 mid-tier corporate hospitals to collect data from 438 patients. The data was processed through factor analysis in SPSS to confirm sample adequacy and factor extractability. Further, two independent multi-criteria decision-making (MCDM) tools, Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) and Grey Relational Analysis (GRA), were executed to render performance analysis of identified factors.FindingsUsing F-TOPSIS, factors such as “information” and “hospital environment” received higher performance ratings, while items related to “communication with doctors” and “humanistic care” received lower rankings. Minor yet anticipated deviations were observed while verifying performance scores using GRA. Nonetheless, both outcomes exhibited a strong correlation coefficient of 97.14%, confirming analytical consistency.Originality/valueHitherto, such usages of hybrid MCDM techniques have rarely been executed to convey a clear understanding of consumers’ experiences in healthcare services. Moreover, the findings provide a clear insight into consumers’ key response areas, which can further be translated to maximize consumer gratification, thus assisting healthcare managers in improving service performance and clinical decision-making.

Exploring machine vision measurements through innovative cyclic-lead-follower statistical technique: an experimental study

In metrology systems, machine vision systems are often utilized for non-contact inspection. The most important phase in ensuring measurement accuracy is camera calibration and estimation of pixel measurement errors, which establish the correspondence between image coordinates and object coordinates. Multiple calibration techniques improve the effectiveness of machine vision systems. However, a number of factors lead to variations in the camera calibration procedure, which must be optimized. This study explains a novel ‘Cyclic-Lead-Follower’ statistical methodology proposed for camera calibration and measurement to estimate the errors in pixel measurement, employing four slip gauges for measurement. Several multi-criteria decision-making techniques, including WSM, WPM, WASPAS, and TOPSIS, were used to optimize the results of the proposed Cyclic-Lead-Follower methods. The proposed Cyclic-Lead-Follower method improves the accuracy of the camera calibration and measurement system, which employs the exponential moving average statistical method when compared to the traditional calibration method. The proposed calibration method produces lower exponential moving average values than the traditional calibration method, with an average percentage error of approximately 46% in the exponential moving average. The use of an exponential moving average in a validation experiment of the Cyclic-Lead-Follower method decreased the measurement percentage errors, which were estimated to be 0.57%. The proposed method can be used in machine vision systems due to its robustness, accuracy, and cost-effectiveness.

Factoring Sector in Turkey: General Overview and Evaluation of Factoring Companies' Financial Performance by Multi-Criteria Decision-Making Techniques

Factoring Sector in Turkey: General Overview and Evaluation of Factoring Companies' Financial Performance by Multi-Criteria Decision-Making Techniques

Analysis of the quality of sustainability reports published by Brazilian companies: An analytic hierarchy process‐grey clustering approach

AbstractThis paper aims to assess the extent to which Brazilian companies adhere to the accuracy, balance, clarity, comparability, reliability, and timeliness criteria recommended by the Global Reporting Initiative (GRI) standards. A survey was conducted with experts in sustainability reporting with extensive experience in the Brazilian corporate context. The analytic hierarchy process was employed to assign weights to the criteria, and a grey fixed weight clustering model was proposed to analyze the collected data. Clarity and accuracy received the highest weights, which is concerning given the quality observed in sustainability reports. A lower emphasis on timeliness in sustainability reporting reflects many companies' publishing practices, prompting more efficient reporting. The majority of respondents perceive the quality of information in sustainability reports from Brazilian companies as falling within the medium maturity range, suggesting significant room for enhancement. The originality of this study lies both in the methodological approach, which combines multicriteria decision techniques capable of incorporating uncertainty related to data on sustainability reports, as well as in the results that provide information for improving sustainability reporting practices. The findings provide a foundation for companies and policymakers to enhance sustainability reporting, particularly in developing nations, promoting transparency, accountability, and international standard compliance.

Assessing tropical cyclone risk for improving mitigation strategies in Coastal Odisha, India.

Tropical cyclone causes large-scale devastation and destruction in the coastal plains of India, particularly in Odisha, which is the most cyclone-affected state in the country. Tropical cyclones are projected to be more powerful and widespread due to changing climate. Hence, the risk assessment of tropical cyclone is necessary to identify cyclone-risk areas in coastal Odisha which may help in the mitigation of the damages caused by cyclones. Therefore, this study utilizes geospatial techniques to produce a comprehensive risk map posed by tropical cyclones and to estimate the degree of risk for coastal districts of Odisha. For this, we evaluated the district-level cyclone risk for coastal Odisha using multi-criteria decision-making (MCDM) technique by considering 21 parameters for each of the four components of risk, i.e., exposure, hazard, vulnerability, as well as mitigation capacity. For each criterion, thematic raster map layers were created and weighted using a fuzzy analytical hierarchy process (FAHP). We prepared individual risk component maps using weighted overlay techniques and finally integrated all indices to create the risk map. The study shows that 13% area of the study area comes under a very high-risk zone whereas, 25% area comes under a high-risk zone. The central (Cuttack, northern parts of Khordha, and south-western parts of Jajpur district) and the eastern part (most of the parts of Jagatsinghpur, Kendrapara, and northern parts of Puri district) of the study area come under high to very high tropical cyclone impact zone. Almost 67% of the total area is highly vulnerable to tropical cyclones and mainly concentrated near the shoreline. The applied approach and results can assist the local authorities in identifying vulnerable and hazardous locations and developing workable solutions for the mitigation of revised cyclone threats in the coastal districts of Odisha.

Analyzing the Performances and Efficiencies of European Electric Vehicles in the European Union Market Using Decision Analysis Methods

Concerns about the environment have grown worldwide as carbon emissions, one of the primary drivers of global warming, continue to rise. As the transportation industry is one of the most greenhouse-gas-emitting sectors in the European Union (EU), the need for environmentally friendly solutions has been increasing progressively. In this regard, electric vehicles (EVs) play an important part in mitigating some of the adverse effects of road transportation on human health, the environment, and climate. Therefore, the popularity of electric vehicles in the EU has recently increased significantly because of climate change mitigation efforts. In this paper, we aim to analyze and compare the performance of European EVs with multi-criteria decision-making (MCDM) methods, namely preference selection index (PSI) and axial-distance-based aggregated measurement (ADAM) techniques, which do not need the criteria weights for measurement and a novel three-dimensional evaluation technique. The results of both MCDM methods reveal that the EVs of the Tesla Inc. and Geely Group models are found to be the best-performing vehicles. We also investigate the efficiency of the European EV market by the super-efficiency data envelopment analysis (DEA) technique, which enables us to evaluate among efficient units. The efficiency scores indicate that five EVs from three different manufacturer groups obtain efficiency levels. To the best of our knowledge, European EV market evaluation by integrated and comparative analysis has not been encountered for the EU on a group basis. The suggested MCDM and DEA techniques can benefit stakeholders while assessing the European EV market.