Priority Weights Research Articles

An Integrated Fermatean Fuzzy Multi-attribute Evaluation of Digital Technologies for Circular Public Sector Supply Chains

The barriers to implementing circular supply chains are well explored, but very little is provided to understand how these barriers play in public sector supply chains. Consequently, the role of digital technologies in addressing these barriers in the circularity of supply chains in the public sector remains a gap. Thus, this study bridges these gaps by evaluating digital technologies according to their relevance in addressing the identified barriers. In particular, eight domain experts who have sufficient knowledge and expertise in the domains of the public sector and circular economy were asked to elicit judgments in order to (1) set a threshold that defines the list of barriers that are significant to supply chains in the public sector, (2) obtain the priority weights of these barriers through the criteria importance through intercriteria correlation (CRITIC), and (3) rank the identified digital technologies based on their relevance in addressing the identified barriers in public sector supply chains using combinative distance-based assessment (CODAS) method, all under a Fermatean fuzzy set environment to account for epistemic uncertainties in judgment elicitation processes. This novel integration of the CRITIC and CODAS methods augmented by Fermatean fuzzy sets forms the methodological contribution of this work. Findings show that barriers associated with regulations restricting the collection of wastes, poor demand or acceptance for environmentally superior technologies, lack of expertise, technology, and information, operational risk, immature recycling technologies, and information sharing and communication were considered critical in managing circular public sector supply chains. The analysis also revealed that ripple effect modeling, simulation, and artificial intelligence are the priority digital technologies. These digital technologies offer efficiency and flexibility to decision-makers in analyzing complex and dynamic scenarios before the deployment of any circularity initiative, providing crucial information in its design and implementation. This paper outlines several managerial insights and offers possible agenda for future research.

An Integrated Multicriteria Sorting Methodology with q-Rung Orthopair Fuzzy Sets for Evaluating the Impacts of Delays on Residential Construction Projects

This study offers an integrated evaluation methodology for construction project delay causes viewed as a multicriteria sorting (MCS) problem. Time, cost, and quality were the three project management factors considered as criteria to evaluate 38 identified delay causes. The priority weights were extracted using the integration of Weighted Influence Non-linear Gauge Systems (WINGS) and Level-Based Weight Assessment (LBWA) to capture the inherent interdependencies of the criteria. The sorting of 38 delay causes was performed using FlowSort. To handle the uncertainty and vagueness of the judgments of the decision makers in the evaluation process, q-rung fuzzy orthopair fuzzy sets (q-ROFS) were integrated within the proposed computational framework. The proposed novel q-ROF–WINGS–LBWA–FlowSort method was applied in an actual case study in residential construction projects. The delay causes were categorized under three categories of construction firm vulnerability into four levels of impact. In highly vulnerable construction firms, thirty-five delay causes have a high impact, two have a moderate impact, and one has the least impact. In moderately vulnerable and least-vulnerable construction firms, 32 and 28 delay causes have a medium impact, respectively. The results may provide insights for decision makers in highly vulnerable construction firms, i.e., small companies with limited resources and networks. Layers of sensitivity and comparative analyses were put forward to test the robustness of the approach.

Finite Control Set Model Predictive Torque Control Considering Dynamic and Steady‐State Control Performance for SMPMSM Drives

In order to improve the dynamic and steady‐state performance of the finite control set model predictive torque controlled surface‐mounted permanent magnet synchronous motor drive system, a novel finite control set model predictive torque control (FCS‐MPTC) with different dynamic and steady‐state control schemes is proposed in this paper. When the SMPMSM drive system operates at transient state, the optimal basic voltage vector of inverter is the vector that minimizes torque tracking error, the proposed FCS‐MPTC achieves fast dynamic response based on the optimal torque control. When the system operates at steady state, the relative importance of torque to the stator flux amplitude is calculated based on the quantitative analysis of minimum staggered errors between motor torque and stator flux amplitude control objectives, then the priority weighting factors based on the relative importance are determined online adaptively, the optimal control of motor torque and stator flux amplitude is achieved simultaneously. Finally, the comparison of the experimental results of proposed FCS‐MPTC with different control methods is implemented and research conclusion is shown. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

The information dissemination path of the sense of community of contextual beauty universe based on the hierarchical analysis method - the science fiction film “Dune” as an example

Abstract Since its introduction into China, the film Dune has sparked heated discussions among Chinese moviegoers. This paper explores the sense of cosmic community embedded in Dune, and its cinematic aesthetics conveyed to audiences through the information dissemination path. This paper identifies the hierarchical analysis method as the main research method, first identifies the definition of cosmic community in terms of content, and then conducts an appreciation of the sense of cosmic community in the contextual beauty of “Dune”. The algorithmic framework is established by building a model of the hierarchical analysis method, while the priority weight allocation coefficient is calculated afterward to determine the priority weight of the program, and the results are evaluated using the fuzzy preference evaluation method. The data showed that the highest ratio of movie viewers who liked science and technology very much or more was 36.09% and 35.49%, respectively, while the ratio of movie viewers who were interested in the topics of universe and space exploration was 48.8%. The rating screen effects and movie music scored out of 10, and the average audience rating was above 8.2. And Dune belongs to the topic of cosmic exploration, indicating that at least in terms of the movie audience, Dune already has a large audience base in the early stage, which is one of the sources of the movie’s enthusiasm. This paper is feasible and meaningful to explore the research direction of Dune’s cosmic community of contextual beauty from the contextual level.

Impact of Authentic Leadership traits in Engineering Sector using MCDM

This study aims to develop a conceptual model that establishes a connection between Authentic Leadership (AL), employee engagement, and organizational innovation within Pakistan's engineering sector. Employing a Multi-Criteria Decision-Making (MCDM) technique, the study investigates the interaction and impact of workers on organizational innovation, generating benefits for both industry practitioners and the academic community. The findings highlight the substantial influence of four key AL traits, namely balanced processing, internalized moral perspective, relational transparency, and self-awareness, on work engagement[8]. By applying the Analytic Hierarchy Process (AHP) and utilizing decision making software, the study identifies the internalized moral perspective as the most crucial criterion for AL outcomes, with a weight of 0.0483. Balanced processing (weight: 0.1220), relational transparency (weight: 0.20894), and self-awareness (weight: 0.25891) follow as subsequent priorities. These outcomes contribute to the development of theoretical understanding in the field. Furthermore, they provide valuable insights to the engineering sector, enabling the cultivation of employee engagement and the enhancement of organizational innovation.The research focuses on elucidating the relationships between AL, employee engagement, and organizational innovation in the context of Pakistan's engineering sector. By adopting a comprehensive MCDM approach, the study offers a thorough analysis of the factors influencing employee engagement, with particular emphasis on the identified AL traits. The findings underscore the significance of balanced processing, internalized moral perspective, relational transparency, and self-awareness as drivers of work engagement[9]. Moreover, the application of the AHP technique reveals the priority weights assigned to these traits, highlighting the critical role of the internalized moral perspective criterion in achieving AL outcomes.The results of this research carry implications for both practitioners and scholars. For the engineering sector in Pakistan, embracing and implementing AL practices can yield substantial benefits, fostering a work environment that stimulates employee engagement and drives organizational innovation. By prioritizing the identified AL traits, organizations can effectively channel their resources and efforts towards enhancing employee engagement and promoting innovation. From an academic standpoint, this study contributes to the existing literature by providing empirical evidence and a conceptual model that sheds light on the interplay between AL, employee engagement, and organizational innovation. The findings serve as a foundation for further theoretical development and future research endeavors in this domain.

Impact of Authentic Leadership traits in Engineering Sector using MCDM

This study aims to develop a conceptual model that establishes a connection between Authentic Leadership (AL), employee engagement, and organizational innovation within Pakistan's engineering sector. Employing a Multi-Criteria Decision-Making (MCDM) technique, the study investigates the interaction and impact of workers on organizational innovation, generating benefits for both industry practitioners and the academic community. The findings highlight the substantial influence of four key AL traits, namely balanced processing, internalized moral perspective, relational transparency, and self-awareness, on work engagement[8]. By applying the Analytic Hierarchy Process (AHP) and utilizing decision-making software, the study identifies the self-awareness as the most crucial criterion for AL outcomes, with a weight of 0.25891.Relational transparency Balanced processing (weight: 0.20894), Balanced processing (weight: 0.1220), and internalized moral perspective (weight: 0.0483) follow as subsequent priorities. These outcomes contribute to the development of theoretical understanding in the field. Furthermore, they provide valuable insights to the engineering sector, enabling the cultivation of employee engagement and the enhancement of organizational innovation.The research focuses on elucidating the relationships between AL, employee engagement, and organizational innovation in the context of Pakistan's engineering sector. By adopting a comprehensive MCDM approach, the study offers a thorough analysis of the factors influencing employee engagement, with particular emphasis on the identified AL traits. The findings underscore the significance of balanced processing, internalized moral perspective, relational transparency, and self-awareness as drivers of work engagement[9]. Moreover, the application of the AHP technique reveals the priority weights assigned to these traits, highlighting the critical role of the internalized moral perspective criterion in achieving AL outcomes.The results of this research carry implications for both practitioners and scholars. For the engineering sector in Pakistan, embracing and implementing AL practices can yield substantial benefits, fostering a work environment that stimulates employee engagement and drives organizational innovation. By prioritizing the identified AL traits, organizations can effectively channel their resources and efforts towards enhancing employee engagement and promoting innovation. From an academic standpoint, this study contributes to the existing literature by providing empirical evidence and a conceptual model that sheds light on the interplay between AL, employee engagement, and organizational innovation. The findings serve as a foundation for further theoretical development and future research endeavors in this domain.

Analyzing Interrelationships and Prioritizing Performance Indicators in Global Product Development: Application in the Chinese Renewable Energy Sector

Today’s global business landscape and intense market competition have heightened the significance of global product development (GPD) practices, making it necessary for organizations to evaluate GPD projects for sustained success. Existing research has primarily focused on independent and tangible performance metrics, neglecting their interdependencies and intangible nature in real-world scenarios. To address this gap, this study seeks to enhance the understanding of GPD performance by establishing a more sophisticated assessment approach. A hybrid decision-making trial and evaluation laboratory (DEMATEL) and analytical network process (ANP) method, known as DANP, was employed to determine the importance of evaluation metrics and their relationships. This research stands out by integrating financial, quality, time, environmental, and capability dimensions, along with their respective indicators, and presenting their interrelationships and priority weights for evaluating GPD projects. The proposed framework was validated through an in-depth case investigation conducted with a Chinese sustainable energy solutions company, involving extensive discussions with decision-makers. The results indicate that technological, innovation, and environmental indicators are the most critical metrics. The influential network relation map (INRM) derived from these findings offers practical recommendations to enhance GPD project performance, catering to policymakers and researchers in both managerial and theoretical contexts.

A Novel Framework of Public Risk Assessment Using an Integrated Approach Based on AHP and Psychometric Paradigm

Understanding how the public perceives various risks and hazards associated with our well-being and health is crucial for governments and policymakers. The present research aimed to assess the public perception of various risks and hazards associated with well-being and health. The study combined two well-known risk assessment approaches: the analytical hierarchical process (AHP) and the psychometric paradigm. Seven risk attributes were chosen from the risk perception literature to evaluate 27 risks and hazard activities using a survey questionnaire developed based on the psychometric paradigm literature. The collected data were then analyzed using the AHP to determine the priority weight for each risk attribute. The results showed that the most crucial risk attribute was voluntariness of risk, followed by chronic catastrophic and newness of risk. Furthermore, the study found that natural hazards were ranked the highest, followed by refugee influx and fire hazards. In contrast, the mobile phone was perceived as posing the lowest type of risk. Policymakers can use these findings to develop effective and sustainable risk communication strategies to help the governments to inform and educate the public about potential risks, improve coordination among agencies and stakeholders, and enhance public trust in government decision making.

Adoption of hydrogen‐fueled freight transportation: A strategy toward sustainability

AbstractFreight transport is critical to the development of the global economy, but it also increases greenhouse gas (GHG) emissions. As a result, global challenges, such as climate change, must be addressed. Hydrogen‐fueled transportation could be a viable, sustainable alternative by providing a clean, efficient, and long‐term alternative to fossil fuels, resulting in significantly lower GHG emissions. However, the usage of hydrogen as a fuel is still in its nascent stage, and different nations have initiated strategizing its production and consumption. Understanding the factors that may affect hydrogen's adoption as a fuel in the transportation and logistics sector becomes crucial. The present study attempts to fill this gap by identifying, categorizing, and prioritizing those factors. Initially, the factors have been identified from the literature and further finalized with the consultation with domain experts using the critical success factor theory. Further, relevant factors have been finalized and categorized using exploratory factor analysis. Afterward, the grey–ordinal priority approach is applied to determine the priority weights of the categories and their factors. The findings indicated that operational and environmental are the two most prioritized categories. Specifically, “Substituting Energy Dependency,” “Storage and Infrastructure Costs,” and “Ability to Mitigate Harmful Gases” are the top three prioritized factors for the adoption of hydrogen‐fueled transportation among stakeholders. According to the findings, the management of related businesses can collaborate as a business strategy to develop a robust hydrogen supply chain and secure the market stability of hydrogen fuel to ensure sustainable freight transportation.

Priority Based Transmission Rate Control with Neural Network Controller in WMSNs

Wireless Multimedia Sensor Networks (WMSNs) are networks of wirelessly interconnected sensor nodes equipped with multimedia devices, such as cameras and microphones. Thus a WMSN will have the capability to transmit multimedia data, such as video and audio streams, still images, and scalar data from the environment. Most applications of WMSNs require the delivery of multimedia information with a certain level of Quality of Service (QoS). This is a challenging task because multimedia applications typically produce huge volumes of data requiring high transmission rates and extensive processing; the high data transmission rate of WMSNs usually leads to congestion, which in turn reduces the Quality of Service (QoS) of multimedia applications. To address this challenge, This paper proposes the Neural Control Exponential Weight of Priority Based Rate Control (NEWPBRC) algorithm for adjusting the node transmission rate and facilitate the problem of congestion occur in WMSNs. The proposed algorithm combines Neural Network Controller (NC) with the Exponential Weight of Priority Based Rate Control (EWPBRC) algorithms. The NC controller can calculate the appropriate weight parameter λ in the Exponential Weight (EW) algorithm for estimating the output transmission rate of the sink node, and then ,on the basis of the priority of each child node , an appropriate transmission rate is assigned . The proposed algorithm can support four different traffic classes namely, Real Time traffic class (RT class); High priority, Non Real-Time traffic class (NRT1 class); Medium priority, Non Real-Time traffic class (NRT2 class); and Low priority, Non Real-Time traffic class (NRT3 class). Simulation result shows that the proposed algorithm can effectively reduce congestion and enhance the transmission rate. Furthermore, the proposed algorithm can enhance Quality of Service (QoS) by achieve better throughput, and reduced the transmission delay and loss probability.

The global value chain: Challenges faced by ASEAN least developed countries

We identify seven global value chain (GVC)-related challenges faced by the least developed countries (LDCs) of the Association of Southeast Asian Nations (ASEAN) region. The LDCs face some of the most rudimentary challenges related to trade participation and competitiveness. The challenges identified in the paper have been empirically validated using Fuzzy AHP modelling to decipher the priority weights. The findings reveal that ‘infrastructural and logistical constraints’ is the most imperative challenge followed by ‘limited FDI inflows’ and ‘inadequate development of SME clusters. The remaining challenges include: inadequate aid-for-trade, dismal LDC participation through the RTA/FTA route, lack of robust industrial policy, constraints in value chain upgradation, limited FDI. The study is beneficial for industry and provides useful inputs for policy-making. It helps in ascertaining optimal resource allocation for the purpose of enhancing trade competitiveness and economic development.

IDENTIFICATION OF GROUNDWATER POTENTIAL USING GEOGRAPHIC INFORMATION SYSTEMS AND REMOTE SENSING (CASE STUDY: MOJOKERTO REGENCY)

The impact of the drought problem in Mojokerto Regency resulted in thousands of people having access to clean water. So, it is necessary to plan the utilization of groundwater resources well. Therefore, more serious handling is needed in an effort to overcome the problem of groundwater supply. One of them is the availability of groundwater potential maps. This study aims to determine the identification of aquifer potential and groundwater potential in Mojokerto Regen cy in 2020. This research uses SPO T - 7 satellite imagery to see vegetation density using the NDVI method and uses 5 parameters, namely rainfall, soil texture, rock type/geology, slope, and land use. This study used the AHP method in determining the priority weight value of each parameter. Based on the results of the study, the results were obtained with 3 classifications with the largest area being an area with mode rate groundwater potential of 52,621.285 Ha and the smallest area being an area with low groundwater potential of 2,426.327 Ha. Previous research identified groundwater potential using the NDVI method with Landsat 8 imagery which has a resolution of 30 meters. Therefore, this research is expected to produce better vegetation density by using SPOT-7 imagery to obtain more detailed groundwater potential, because the resolution of the image SPO T -7 is 6 meters. So that this research can be considered in efforts to process the potential of groundwater resources in Mojokerto Regency

Transport Demand Management Strategy Priority Assessment Based on Expert Judgment

The main problem of transportation is the very high growth of vehicles causing congestion, resulting in various derivative impacts such as pollution, fuel waste, time value, and other environmental problems. This problem can be solved by Transportation Demand Management (TDM). TDM is a combination of various strategies, which strategy should be chosen whose priority depends on the conditions of each region. This research was conducted in a medium-scale city by determining the priority of TDM using the Analytical Hierarchy Process (AHP) tool. The final result of the judgment is the priority weight of the TDM strategy that will be applied with a CR value of < 10%, namely the pull strategy. This strategy is represented by improving public transport services and infrastructure (especially the integration of public transport services). This study shows that the strategy group with a high AHP Consensus Index (ACI) score means a high consensus among experts.

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

In view of the recent education sectoral transition to Education 4.0 (EDUC4), evaluating the preparedness of higher education institutions (HEIs) for EDUC4 implementation remains a gap in the current literature. Through a comprehensive review, seven criteria were evaluated, namely, human resources, infrastructure, financial, linkages, educational management, learners, and health and environment. This work offers two crucial contributions: (1) the development of an EDUC4 preparedness indicator system and (2) the design of a computational structure that evaluates each indicator and computes an aggregate preparedness level for an HEI. Using the full consistency method (FUCOM) to assign the priority weights of EDUC4 criteria and the rough set theory to capture the ambiguity and imprecision inherent in the measurement, this study offers an aggregate EDUC4 preparedness index to holistically capture the overall preparedness index of an HEI towards EDUC4. An actual case study is presented to demonstrate the applicability of the proposed indicator system. After a thorough evaluation, the results indicate that human resources were the most critical criterion, while health and environment ranked last. Insights obtained from the study provide HEIs with salient information necessary for decision making in various aspects, including the design of targeted policies and the allocation of resources conducive to implementing EDUC4 initiatives. The proposed indicator system can be a valuable tool to guide HEIs in pursuing EDUC4, resulting in a more effective and efficient implementation of this educational paradigm.

Some methods to derive the priority weights from the best–worst method matrix and weight efficiency test in view of incomplete pairwise comparison matrix

Some methods to derive the priority weights from the best–worst method matrix and weight efficiency test in view of incomplete pairwise comparison matrix

Self-Paced Broad Learning System.

Broad learning system (BLS), an efficient neural network with a flat structure, has received a lot of attention due to its advantages in training speed and network extensibility. However, the conventional BLS adopts the least square loss, which treats each sample equally and thus is sensitivity to noise and outliers. To address this concern, in this article we propose a self-paced BLS (SPBLS) model by incorporating the novel self-paced learning (SPL) strategy into the network for noisy data regression. With the assistance of the SPL criterion, the model output is used as feedback to learn appropriate priority weight to readjust the importance of each sample. Such a reweighting strategy can help SPBLS to distinguish samples from "easy" to "difficult" in model training, equipping the model robust to noise and outliers while maintaining the characteristics of the original system. Moreover, two incremental learning algorithms associated to SPBLS have also been developed, with which the system can be updated quickly and flexibly without retraining the entire model when new training samples are added or the network needs to be expanded. Experiments conducted on various datasets demonstrate that the proposed SPBLS can achieve satisfying performance for noisy data regression.

Environmental Supply Chain Performance Mapping of an Enterprise by Exploring a Novel Vague-Intellectual Approach

The authors notified the two momentous Research Gaps (RGs) via conducting the relevant literature survey. The authors found as first RG that there are still no mathematical models that could address the generalized trapezoidal fuzzy set (GTFs) based green supply chain performance measurement (GSCPM) multi-level hierarchical index for computing the performance of a production enterprise in % except in the forms of GTF set/scale/crisp value. Next, as the second research gap, the authors identified that a few research articles are published in the extent of degree of similarity approaches. Entire approaches are limited to recognize the weak metrics under assessment of two GTFN sets from experts and also not competent to measure the performance gap of metrics from its ideal value. The objective of research work is turned to overcome the identified two RGs. To fulfill the first RG, the authors first of all proposed the two GTFN set-based mathematical models, which are executed to compute the priority weights and appropriateness ratings (PWsaARs) for 1st level measures from 2nd level PWsaARs of metrics (discarded the requirement of PWsaARs data for 1st level measures from experts). Furthermore, the authors developed GTFN set-based novel fuzzy performance index (NFPI) approach (by combining the crisp as well as fuzzy percentage rule over FPI) to compute the performance in %. To address the second RG, the degree of similarity (DoS) approach is modified by introducing idea of negative and positive ideal solution into DoS (eliminate the need for assessment of two GTFN sets from experts). Next, modified DoS is applied over evaluated FPII (fuzzy performance importance index) to identify the weak and strong metrics and also quantify the GSCP gap of metrics from its ideal value. Eventually, the research work is demonstrated with empirical case research of an automobile parts manufacturing industry.

Determining the Main Resilience Competencies by Applying Fuzzy Logic in Military Organization

Military training programs have been developed to enhance soldier resilience competencies, which are necessary for soldiers to perform their duties effectively under stress. The ongoing military conflict in Ukraine and the experience of previous military missions abroad emphasize the need for effective training that helps soldiers recover quickly and continue their missions. However, selecting the most suitable resilience training program is challenging and the selection criteria need to be optimized to ensure the most needed competencies are considered. This study aimed to utilize a fuzzy MCDM method to establish the priority weight of decision-making criteria, identifying the core competencies necessary for soldier resilience training, and utilizing the fuzzy TOPSIS method to rank and select the most appropriate training program. The evaluation results were calculated using the MATLAB (R2020b) mathematical package developed by MathWorks. The application of the hierarchical MCDA model based on fuzzy sets theory indicated that mental agility is the most important competence in high-stress environments. The study found that the Mindfulness-Based Mind Fitness Training (MMFT) program, which is intended to regulate soldiers’ emotions, had the highest rank among evaluated options according to the combined FAHP sub-factor fuzzy weights and alternatives evaluation conducted using FTOPSIS. The study provides valuable information on the selection of military resilience training programs.

Supply chain mapping for improving “visilience”: A hybrid multi‐criteria decision making based methodology

AbstractSupply chain mapping is gaining heightened attention due to its vital role in improving supply chain visibility and resilience. Despite its crucial role in uplifting supply chain resilience, the critical elements of supply chain mapping are yet to be determined. The study adopts a twofold approach to identify and prioritize the dimensions and sub‐dimensions of supply chain (SC) mapping. At the first stage, through an extensive review of literature, 43 sub‐dimensions of SC mapping were identified. In the second stage, Gray ‐ DEMATEL‐based Analytic Network Process (GDANP) was employed by taking the input from 25 experts selected from Oil and Gas industry of an emerging market. The findings reveal three major dimensions of SC mapping followed by 15 sub‐dimensions. Among the dimensions, upstream mapping contains the highest priority weights, followed by midstream and downstream mapping. The findings suggest a step‐wise strategy to adopt SC mapping where upstream mapping should be given the first priority. The major contribution of this study is to develop a framework for measuring the extent of SC mapping of a firm using GDANP.

Pre-design CSFs (critical success factors) of building projects for Dubai's development boom

PurposeThis paper aims to identify the pre-design critical success factors CSFs pertaining to different types of construction projects in the rapidly growing city of Dubai, by adopting one of the multi-criteria decision-making (MCDM) tools, analytic hierarchy process (AHP).Design/methodology/approachA mapping process was utilized to filter and categorize CSFs gathered from a literature review. A total of 5 main and 27 sub-main criteria were derived following RIBA's Plan of Work 2020 (Royal Institute of British Architects). A questionnaire was distributed among experts in the construction industry to assess the development in vicinity of the Sheikh Zayed Road (SZR). An analysis was conducted to prioritize the CSFs as per experts' judgment.FindingsAccording to the experts' combined judgment, CSFs linked to client requirements (CR) are the most significant at 22.9% priority weight. Overall project review (OPR), project team (PT), project preparation (PP), previous projects feedback and experience (PPF) success factors achieved 21.4, 19.5, 18.9 and 17.2%, respectively. Within CR, sub-CSFs of client consultation and acceptance (CCA), responsibly matrix for stakeholders (RM), client track record (CTR) and client level of service (CLS) were prioritized by the experts.Research limitations/implicationsThis work was carried out by respondents based in UAE only, and focused on one urban stretch of Dubai, this could be replicated by employing experts from other countries and other case study areas within Dubai.Practical implicationsThis study would significantly have an impact on the education of current and future project managers, planners, engineers, architects and, most importantly, decision-makers on helpful pre-design strategies for enhancing their project success. This research could assist, direct and educate management, as the management provides/manages the necessary resources for projects. Furthermore, management also has the authority to choose project leaders/consultants that will make the project successful.Social implicationsConsolidating Dubai's phenomenal growth requires prioritizing socio-economic strategies in future development projects at the pre-design and pre-construction stage, which will improve the city's business environment and the quality of life for Dubai's residents. This opens up fresh possibilities over the next few years for integrating robust procedures for successful execution, monitoring and evaluation of future construction developments from the outset.Originality/valueAlthough many studies have identified various factors as important for successful building projects, few appear to have done a complete analysis using pre-design criteria to systematically categorize professional views on the relative relevance of CSFs. There is a lack in research examining the factors that led to Dubai's successful construction projects. This study attempts to bridge this gap and provide a prioritized framework reference model to assist project/asset managers and decision-makers.