Multi-criteria Decision Analysis Techniques Research Articles

A novel approach employing hesitant intuitionistic fuzzy linguistic Einstein aggregation operators within the EDAS approach for multicriteria group decision making

The Evaluation based on Distance from Average Solution (EDAS) is a multi-criteria decision analysis (MCDA) technique that uses various distances from average values to make decisions. It bears resemblance to other distance-based approaches like SPOTIS, VIKOR or TOPSIS, except that instead of positive and negative ideal solutions, it uses an average solution. For hesitant intuitionistic fuzzy linguistic term sets (HIFLTSs), we first define several operational laws and aggregation operators. As a follow-up, the hesitating intuitionistic fuzzy linguistic Einstein weighted averaging (HIFLEWA) operator is introduced. In this study, the EDAS technique is used to address a multi-criteria group decision making (MCGDM) issue by utilizing the suggested operational laws and aggregation operators for HIFLTSs, aiming to mitigate the uncertainties of decision makers (DMs). A representative numerical example is employed to illustrate the strength and logical soundness of our proposed method in identifying the optimal choice under the given limitations. A comparison examination with the existing TOPSIS approach is also performed to ensure that the results generated with EDAS are accurate.

Assessment of the geothermal potential zone of India utilizing GIS-based multi-criteria decision analysis technique

India, with 80 % energy demand reliant on fossil fuels, is the world's third-largest greenhouse gas emitter, necessitating a shift from fossil fuels to renewable energy. While solar and wind production has grown, geothermal energy remains untapped despite enormous prospects in the country. In this regard, India's first regional-scale geothermal potential (GP) map has been prepared using a GIS based on a multi-criteria decision analysis technique. Five thematic layers (geology, fault, heat flow, thermal spring, and earthquake epicenter) have been used to calculate GP index, which is divided into high, moderate, and low categories. The result shows that most geothermal drilling sites are distributed in the high GP category zone, showing high efficacy. Further, a joint study of the geothermal surface and reservoir temperature with the present study shows that the northern and western parts of India have significant potential for geothermal exploration. Our result also reveals that the projected CO2 emissions are approximately 2.6 gigatons (which is lower than other energy sources) for an output of 10 GW using geothermal energy in the next 75 years. This study can enhance geothermal prospect identification, cutting exploration costs, and site selection, and contributing to multiple UN Sustainable Development Goals.

Delineation of Groundwater Potential Zones for Sustainable Development and Planning using AHP and GIS Techniques in the Coal Mining Province of Mahan River Catchment Area

The present research aims to address the drinking water crisis in the Mahan River catchment area resulting from the disruption of groundwater availability due to extensive coal mining. The study uses GIS-based Multi-Criteria Decision Analysis (MCDA) to map the groundwater potential of the area by analysing several factors that affect groundwater availability, including rainfall, water depth, geomorphology, geology, soil, land-cover/land-use, and topographic characteristics derived from DEM. The groundwater potential map created using the MCDA technique classified the area into low, moderate, and high groundwater potential zones. The map was validated and verified using water table depth and electrical conductivity values available in the region, indicating that it can be used to identify groundwater recharging sites. The study’s results show that about 30% of the area has high groundwater potential, and more than 45% of the area has moderate groundwater potential. The information derived from the study can be used for sustainable management and proper planning of groundwater resources in the Mahan River catchment area. Overall, the study presents a useful approach to address the groundwater depletion problem resulting from coal mining activities in the Mahan River catchment area.

A framework for evaluation of construction and demolition waste management alternatives using MCDA techniques in the context of Dhaka City

Landfilling and dumping are the common strategies to manage generated construction and demolition waste (C&DW) in Dhaka City. These strategies of C&DW management (C&DWM) affect the environment adversely and fill the old landfill sites quickly. Besides, there isn’t any framework to manage C&DWs effectively. This study introduces a Multi-criteria Decision Analysis (MCDA)-based C&DWM framework for Dhaka City by incorporating the expanded Waste Management Hierarchy (E-WMH). The E-WMH principles are considered as the C&DWM alternatives. Through a comprehensive literature review, the criteria are determined and grouped into social, economic, environmental, and technical criteria to achieve sustainability and effectiveness. To construct the initial decision matrix, the ordinal data (i.e. rating) is collected through interviews with the subject-matter experts of Dhaka City. Also, the ranking system method is used to determine the weight of the criteria. Finally, the ELECTRE I and ELECTRE II MCDA methods are employed to rank the alternatives. Results show that reuse and recovery are suitable strategies for managing C&DWs in Dhaka city. Future studies may consider the hybridization of MCDA tools, quantitative data to form the initial decision matrix, and other mathematical models to weigh the criteria so that the performance of the framework can be enhanced.

An Integrated Multi-Criteria Decision Analysis and Structural Equation Modeling Application for the Attributes Influencing the Customer’s Satisfaction and Trust in E-Commerce Applications

Since the COVID-19 pandemic has forced most industries to embrace an online platform utilizing technological breakthroughs, it has significantly impacted our daily lives. Businesses that use marketplaces to sell and trade products to customers while increasing their participation through online shopping or e-commerce are among the sectors that take advantage of these situations. The current study set out to evaluate the level of customer satisfaction, trust, and service quality of the e-commerce application to enhance the system and provide a better shopping experience. Facebook, Shopee, Lazada, Shein, and TikTok were the five e-commerce platforms evaluated. The Philippines was the location of this study, and at least 200 people answered the survey, which was conducted in-person and online and consisted of 72 questionnaires. The researchers assessed twelve latent variables: perceived security, customer satisfaction, application interface, brand equity, tangibility, reliability, responsiveness, assurance, empathy, and information credibility. Structural equation modeling and multi-criteria decision analysis were used to analyze the data. The findings demonstrated that assurance, application interface, information credibility, and brand equity directly impacted service quality. Moreover, a direct and significant correlation exists between customer satisfaction and service quality. Customers’ trust is significantly impacted by their level of satisfaction and perception of security. The e-commerce apps were ranked using a multi-criteria decision analysis technique, which is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on tangibility, responsiveness, assurance, reliability, and empathy. Facebook received a low rating, whereas Shopee was ranked highest. To further enhance the services offered, the lowest rated application may use the results from the combination of the TOPSIS and SEM results. Additionally, application providers, managers, and researchers examining the user–software interaction of relevant e-commerce applications might utilize the study’s results to enhance their services regarding the purchasing experience and provide a sustainable service. Finally, this study is among the first to use the structural modeling approach to evaluate customer trust and satisfaction while integrating service quality and TOPSIS.

Agricultural land suitability analysis of Southern Punjab, Pakistan using analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) techniques

Agricultural land suitability analysis of Southern Punjab, Pakistan using analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) techniques

Assessment of the effects of different variable weights on wildfire susceptibility

In this study, wildfire susceptibility is mapped using various multi-criteria decision analysis techniques (AHP, SAW and VIKOR) and machine learning algorithms (MaxEnt and logistic regression) to reveal the response of models for wildfires. In this study, it is suggested that using natural weights generated by machine learning algorithms instead of artificial weights in MCDA methods can increase the reliability of susceptibility maps because wildfires have very close relationship with climatic, topographic and environmental variables. The contribution rates (natural weights) were obtained using machine learning algorithms and incorporated into MCDA methods to make the spatial relationships between variables more obvious. As a result, eight susceptibility maps were generated using MCDA methods, MaxEnt and logistic regression algorithms. Correlation analysis showed that using natural weights instead of artificial weights increased the correlation between MCDA methods and machine learning algorithms. Each correlation value increased by 10% on average and the highest increase was determined between VIKOR and logistic regression from 0.6286 to 0.7580 when natural weights were used. In addition, 1035 existing wildfire locations were used to evaluate the reliability of generated maps. The results showed that the average risk values of 1035 wildfire locations increased from 6.04 to 7.23 using AHP, from 0.66 to 0.79 using SAW and from 0.35 to 0.25 using the VIKOR method. This indicates a significant increase in the accuracy and reliability of susceptibility maps produced when natural weights determined by machine learning algorithms are used in MCDA methods.

Towards Efficient Energy Solutions: MCDA-Driven Selection of Hybrid Renewable Energy Systems

In these days of growing awareness on Renewable Energy Sources and their benefits, there is a major issue that needs to be considered, which is selection of suitable Hybrid Renewable Energy System (HRES) among the vast multitude of choices. In this paper, multiple Multi Criteria Decision Analysis (MCDA) techniques are proposed to select the best among a choice of six HRES Systems using different criteria including but not limited to net present cost (NPC), operating cost, renewability fraction, CO2 & NOx emissions etc. The criteria weights are obtained using Analytical Hierarchy Process (AHP) based on the preference order of the criteria. The decision matrix is obtained from the statistical data of the different HRES and using different MCDA techniques such as Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), rankings are given to the HRES systems based on their closeness to the ideal solution. These rankings are helpful in the selection of suitable HRES configuration for normal household purposes. Since cost aspects are given greater preference, the obtained rankings lean more towards economically affordable solutions without compromising on the number of units generated.

GIS-Based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) Techniques to Derive Flood Risks Management on Rice Productivity in Gishari Marshland

GIS-Based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) Techniques to Derive Flood Risks Management on Rice Productivity in Gishari Marshland

Synthesis of relevant information around non-core domains to support Multi-Criteria Decision Analysis (MCDA) for decision making.

Countries fundamentally base macro and micro decision making in the field of health on economic considerations, the budgetary impact of technologies being a major criterion. Nevertheless, the value of the technology of interest and its dimensions are more complex if we seek to take decisions based on the value itself. The use of structured and explicit approaches that require the assessment of multiple criteria that reflect the dimensions of this value may significantly improve the quality of the decision making. Multi-criteria decision analysis (MCDA) is a complementary decision-making tool that is able to systematically incorporate dimensions or domains such as ethical, organisational, legal, environmental and social considerations, as well as costs and benefits of medical interventions, together with the distinct perspectives of the interested parties. The objective of this article is to propose the implementation of analysis of non-core domains, in reports of Health Technology Assessment (HTA) agencies/units. To assess the scientific evidence on MCDA techniques a systematic review was conducted using structured searches in biomedical databases and websites of various HTA organisations. A consensus group was held using the nominal group technique and involving users of healthcare services, providers, managers and academics. Complementary, a survey was sent to HTA agencies to ascertain the degree of implementation of MCDA in their methods. 42 articles reporting the use of non-core criteria for the assessment of health technologies were included in the analysis. From these articles, a total of 216 non-core criteria were retrieved and categorised into domains by the researchers, and of these, 56 were classified as socioeconomic, 59 as organisational, 10 as legal, 8 as environmental and 47 as ethical, while 36 were considered to relate to other domains. The consensus group, based on the 216 non-core criteria obtained from the systematic review, proposed, and defined 26 criteria that participants considered necessary for decision making in healthcare. The consensus group did not consider that any of the domains should be given more weight than others or that any individual criteria should dominate. These approaches can serve as a framework of reference for a well-structured systematic discussion concerning the basis of individual criteria and the evidence supporting them.

Evaluating country performance in preventing industrial accidents: A multi-criteria decision analysis approach

This study presents a methodology that adopts a multi-criteria decision analysis (MCDA) approach to evaluate countries' performance in preventing industrial accidents, using accident records and combining three MCDA techniques (TOPSIS, Electre II, and CoCoSo) to create a robust ranking. Applied to OECD countries, the methodology provides valuable insights into countries' commitment to industrial accident prevention. It highlights the impact of European Union legislation, variations in the ranks of G7 economies, and identifies role models like Spain and Italy while shedding light on the neglect of regulations in developing countries like Hungary and Turkey. In a world where industrial accidents have far-reaching consequences, this study emphasizes the importance of sincerity and dedication in accident prevention efforts.

Climate change-derived environmental and physical factors influencing the socioeconomic development in the Himalayan region

Spatio-temporal fluctuation of climatic variables with the terrain characteristics and their inter-relationship is a priority for predicting flash-flood-induced landslide hazards over the fragile Himalayas. The present study addressed this anxiety by assimilating satellite data products and auxiliary datasets in the Bhagirathi River basin of the Indian Himalayas. Snow Covered Area (SCA) is a critical indicator of the ecosystem that influenced the flash flood along different terrain features such as Altitude, Hill-Gradient, and Aspect. GIS-based multi-criteria decision analysis (GIS-MCDA) technique is used to analyze the possible landslide zones and flood extent along the river basin, and MODIS Terra (MOD10A2) data products derived annual SCA is 4278 km2 for the year 2021, the analysis of geospatial maps at 25° intervals of Altitude, hill-gradient, and Aspect. The SCA distribution reveals that apart from the Altitude, the Aspect of the hill gradient significantly impacts snow accumulation. Hill-Gradient, ranging from 13.06 to 19.52, occupies 24.7% of the total area, and 45.3 to 51.83 are found without snow. The highest variation of SCA is along the Western direction (9.19%), followed by North-East (8.79%), while the least (3.78%) variance is in the Southwest direction. Additionally, it was found that many bridges, roads, and other properties are under threat in this study area, even with a moderate flash flood. Findings from this study provide the spatiotemporal status of SCA in various geological stress conditions during the last decades and probable landslide zones. This will be a preliminary pathway to policymakers in rehabilitation and early evacuation of human lives due to flash flood occurrence.

Comparative Analysis of MCDA Techniques for Identifying Erosion-Prone Areas in the Burhanpur Watershed in Central India for the Purposes of Sustainable Watershed Management

The degradation of land and increasing water scarcity are existing challenges for agricultural sustainability, necessitating the implementation of improved soil-conservation practices at the watershed scale. The identification and selection of critical/prone areas based on erosion-governing criteria is essential and helps in the execution of the management process for determining priority. This study prioritizes erosion-prone sub-watersheds (alternatives) based on morphometric parameters (multiple criteria) via five Multi-Criteria Decision Analysis (MCDA) approaches, i.e., AHP: Analytical Hierarchy Process; TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; VIKOR: VIseKriterijumska Optimizacija I Kompromisno Resenje; SAW: Simple Additive Weighting; and CF: Compound Factor. Based on their priority score, 19 sub-watersheds were classified into four priority classes: low priority (0–0.25), moderate priority (0.25–0.50), high priority (0.50–0.75), and very high priority (0.75–1). The results revealed that about 8.34–30.15% area of the Burhanpur watershed is critically prone to erosion, followed by 23.38–52.05% area classed as high priority, 7.47–49.99% area classed as moderate priority, and 10.33–18.28% area classed as low priority. Additionally, four indices—percentage of changes (∆P), intensity of changes (∆I), the Spearman rank correlation coefficient test (SCCT), and the Kendall tau correlation coefficient test (KTCCT)—were employed to compare the models. This study confirms the efficacy of morphometric parameters for prioritizing sub-watersheds to preserve soil and the environment, particularly in areas for which limited information is available.

Optimized landfill site selection for municipal solid waste by integrating GIS and multicriteria decision analysis (MCDA) technique, Hossana town, southern Ethiopia

Improper solid waste disposal has remained the major bottleneck for the socio-economic development particularly in urban and peri-urban areas of Ethiopia. This study was conducted with the prime aim of identifying an optimum municipal solid waste (MSW) management option subsequently selecting the best landfill site in Hossana town using integrated geographical information systems (GIS) and multicriteria decision analysis (MCDA) techniques. Accordingly, GIS integrated with MCDA was used to analyze the relative weight of each criterion and the overall suitability map where ten criteria were considered within their respective constraints. To create a landfill suitability index, environmental and socioeconomic factors such as distance from settlement, land use and land cover, lineament, slope, road, and wind direction/aspect were weighted. Population growth, waste generation rate, and waste volume/year were used to calculate the required landfill size. The study finding revealed that the town's MSW generation rate was 0.45 kg/cap/day, which was within the range of most of Ethiopian urban areas. On the other hand, the total daily and annual MSW generation was found to be 79.58 and 29,047 tonnes, respectively. Moreover, the study's findings revealed that 20.8 % of the solid waste was found was recyclable and the remaining 79.20 % was non-recyclable. Considering the future trend of waste generation, 19 ha of land in the city's Northern and Southern outskirts was chosen as a candidate landfill site with all the necessary suitability analysis. Therefore, optimized landfill site selection and better management system of MSW in urban and peri-urban areas could be achieved by the conjunctive application of MCDA and GIS.

Evaluation of forest fire risk based on multicriteria decision analysis techniques for Changzhou, China

With the rapid development of China's economy, an increasing number of cities are building forest parks or landscape forests to improve the quality of people's lives and protect the environment. However, forest fires are notoriously difficult to extinguish and have caused tragic losses to cities. Therefore, it is particularly important to conduct a comprehensive assessment of forest fire risk. This study focuses on urban forest fire risk to develop an integrated fire risk model to prevent forest fires. A risk assessment model covering different multicriteria decision analysis (MCDA) techniques was developed to analyze safety vulnerabilities and determine fire risk levels with the spatial analysis functions of geographical information system (GIS) software. Moreover, the accuracy of the model was verified using fire data downloaded from the NASA-FIRMS website. The area under the ROC curve (AUC) was used to evaluate the accuracy of each model. The results show that the AUC value of the combination weighting of game theory (CWGT) method is 0.700. The method has good accuracy and can be used to predict the forest fire risk in Changzhou City. The towns with the highest forest fire risk in Changzhou City were Tianmuhu, Daibu, and Xuebu, with average areas of 8354.77 ha, 5125.92 ha, and 5256.67 ha, respectively, and proportions of 36.20 %, 37.15 %, and 23.43 %, respectively. The conclusion of this study provides a reference and guide for local government management. At the same time, this study considers the emergency rescue capability index and comprehensively evaluates the risk over a time series of pre- and postfire. This is a new attempt and has made new contributions to this field.

Strategies for decommissioning small nuclear reactors in Brazil

The process of decommissioning nuclear reactors is a complex activity that involves various technical and administrative stages. Its main objective is to ensure the safety of the site, workers, the general public, and the environment during the execution of decommissioning activities, aiming for the release of the site from regulatory control. In the Brazilian context, it is essential to develop decommissioning strategies, taking into consideration the established technical and regulatory requirements, as well as following the guidelines of the Brazilian Nuclear Policy (BNP). Eight decommissioning strategies were proposed for small reactors, with different objectives and in different scenarios, encompassing 23 decommissioning approaches, divided into 6 groups: 1) decontamination and dismantling (DD); 2) radioactive waste (RW) management; 3) RW storage management; 4) human resources (HR) and knowledge management; 5) cost estimation; and 6) financial fund management. Additionally, 18 factors affecting the selection of these approaches were considered, taking into account particularities of the Brazilian context. A qualitative risk analysis was conducted using risk assessment techniques from the ABNT NBR ISO/IEC 31,010 standard, with a focus on the Multicriteria Decision Analysis (MCDA) technique. This qualitative analysis allowed for the evaluation of the approaches considering the current scenario and the future scenario, which includes possible changes in the BNP currently under discussion in the National Congress. The observations and results obtained in this study will be useful in guiding future efforts related to nuclear reactor decommissioning projects in Brazil. Based on the proposed strategies and considerations of safety, regulation, and governmental policies, it will be possible to plan and execute decommissioning activities more efficiently and safely.

Fundamentals of Construction Materials and Types of Building Using WPM Method

Building materials are construction materials. Natural resources such as branches, wood, clay, pebbles, and sand can all be used to build. Many naturally occurring compounds and some more and some less artificial man-made items are in use. Building materials are called from a range of sites and quarried and mined as jio goods. Must find new resources because the demand to cling to what is now available is expanding along with the world's population. Concrete has a long and illustrious history, but adobe bricks, stone, and clay were the primary building materials used throughout history. These products are still in demand, and any more quarry for their specific extractive industries in the same area causes issues. this is a list of construction supplies. Buildings and structures produce several types of building materials that are employed in the construction sector. Construction is used for projects to specify materials and processes, and project managers' materials and goods are utilized in this category by architects and construction. Blockwork, typically wood building materials, and cold, rolled steel frame are all considered modern construction techniques as opposed to slow ones. There are several applications for various building materials; as a result, you should always check with the manufacturer to be sure a product is good for your needs. Around the world, almost 11,000 new structures are constructed each day. Buildings, sidewalks, and skyscrapers the first being undersea constructions, mines, and bridges for building products, numerous structures up to various construction are used. This page discusses the various types of construction materials used in construction. A hotel and residential complex in midrice the trust is now in motion. Supported by types and earth steel wallers around the excavation the pile also holds the steel sheet. The equipment enters, and the image travels down the earth's curve before leaving the spot. On the right, is a large backhoe from the previous structure there following the ancient mounds around digging continues but pressure-painted setting up the concrete pile legs what is happening, in the movie, better-completed piles are visible in the nearby center and the far corners. Piledrivers in large groups work. Strengthening of columns and concrete pile caps construction is underway on an excavation in the middle of the center. A well-liked technique for multi-criteria decision analysis (MCDA) and multi-criteria decision making (MCDM) is the weighted product model (WPM). The weighted sum model is comparable to this (WSM). The primary distinction is that multiplication is used as the primary mathematical operation rather than addition. The decision-making model by multiplication in linking an attribute rating includes the Weighted Product (WP) technique. When attributes are multiplied, weight for attributes acts as a positive rank, whereas attribute rating acts as a negative rank for the cost attribute. Materials, Construction equipment, Labor, Finance, Enabling Expenses, Admin Expenses, Surplus Building, Roads, Bridges, Dams, Power, Railway, Mineral Plant, Transmission the result it is seen that Dams is got the first rank where as is the building is having the lowest rank. Dams is ranked first and industrial Building is ranked lowest.

Optimal Urban Waste Dumping Site Selection in Kalutara DS Division of Sri Lanka using GIS-based Multi-Criteria Decision Analysis

Improper waste dumping is an emerging issue in Sri Lanka because of the unsystematic urbanisation process. The main problem of the study is determining sites that are ideal for waste dumping. The use of improper waste disposal methods adversely affects the environment and human lifestyle. This study focuses on integrating a structured approach under multi-criteria decision analysis for waste dumping. The present study has been carried out in Kalutara DS Division, integrating Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Analysis (MCDA) and geospatial techniques to find suitable waste dumping sites. Ten different types of criteria were selected for the study in vector and raster format, namely, land use, population density, schools, religious places, government offices, coastal areas, hospitals, roads, railways, and environmentally sensitive areas. After finalising the criteria, the GIS-based AHP-MCDA methodology was used for weighting, standardisation, and normalisation by obtaining the opinions of experts and conducting literature surveys. The results of the study are sorted into not-suitable, less-suitable, and suitable dumping sites. Classified suitability maps revealed that 36.94% are not suitable, 53.06% are less suitable, and completely 9.9% are suitable for urban waste dumping sites. The spatial variance of suitable and not suitable waste disposal sites was found in the north, north-eastern, and western and central parts of the study area. Hence, based on the results indicated using such methods, it will be an effective manual for engineers, regional planners, and decision-makers to implement sustainable waste management in the study area in the future.

Application of multi-criteria decision analysis techniques and decision support framework for informing plant select agent designation and decision making.

The United States Department of Agriculture (USDA) Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents (Select Agents List) that threaten crops of economic importance to the United States and regulates the procedures governing containment, incident response, and the security of entities working with them. Every 2years the USDA DASAT reviews their select agent list, utilizing assessments by subject matter experts (SMEs) to rank the agents. We explored the applicability of multi-criteria decision analysis (MCDA) techniques and a decision support framework (DSF) to support the USDA DASAT biennial review process. The evaluation includes both current and non-select agents to provide a robust assessment. We initially conducted a literature review of 16 pathogens against 9 criteria for assessing plant health and bioterrorism risk and documented the findings to support this analysis. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for ensuring accuracy. Scoring criteria were adopted to ensure consistency. The MCDA supported the expectation that select agents would rank high on the relative risk scale when considering the agricultural consequences of a bioterrorism attack; however, application of analytical thresholds as a basis for designating select agents led to some exceptions to current designations. A second analytical approach used agent-specific data to designate key criteria in a DSF logic tree format to identify pathogens of low concern that can be ruled out for further consideration as select agents. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

Identifying the stressors hindering performance in the Indian construction industry: an empirical investigation

PurposeThis study attempts to analyse and rank the impact of work-related issues arising within the Indian construction industry in the context of the existing pandemic. Furthermore, this is the first attempt to provide strategies to overcome issues among a workforce that is highly contractual in nature and is currently witnessing the uncertainties of the pandemic's aftermath. To the best of the author's knowledge, few studies have highlighted the combined analysis of job insecurity, psychological stress and emotional exhaustion in the Indian construction industry.Design/methodology/approachThe “Best Worst Methodology” (BWM) has been used in this study to analyse and rank the key factors that eliminate negative characteristics among contractual construction employees. The BWM, outlined by Rezaei (2015, 2016), is a popular “multi-criteria decision analysis technique” due to its advantage of consistent results and lesser pairwise data requirements.FindingsThe study identified and ranked the socioeconomic impact of the three waves of the COVID-19 pandemic on construction sector employees in the Indian subcontinent. The results indicate that job insecurity has the most prominent impact, which ultimately produces psychological stress and emotional exhaustion among employees.Originality/valueTo achieve the objective of identifying and prioritising the criteria of adverse socioeconomic impacts during the pandemic and outlining plans of action for the construction industry, ten experts (civil engineers/managers) from ten different construction projects were involved in a mixed-method case study, which has never been explored in the Indian construction sector.