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In this study, the deployment of an integrated Quality Function Deployment (QFD) decision framework is presented to help cities design targeted strategies to become “smart”. Arguably smart cities leverage advanced technologies to enhance their smartness to improve everyday urban life. To this end, a QFD – Analytic Hierarchy Process – Analytic Network Process (QFD-AHP-ANP) framework is proposed to deliver guidance for selecting the appropriate mix of smart technologies based on the specific smart needs of each city.

The AHP and ANP methods are incorporated into QFD to enhance its methodological robustness in formulating the decision problem. AHP accurately captures and translates the “Voice of the Experts” into prioritized “Smart City” dimensions, while establishing inter-relationships between these dimensions and “Smart City Technologies”. Meanwhile, ANP explores tradeoffs among the technologies, enabling well-informed decisions. The framework’s effectiveness is evaluated through an illustrative application in the city of Thessaloniki.

Applying the framework to this real-world context confirms its practicality and utility, demonstrating its ability to particularize local, social, political, environmental and economic trends through the resulting mix of technologies in smart urban development strategies.

The importance of this study lies in several aspects. Firstly, it introduces a novel QFD decision framework tailored for smart city strategic planning. Secondly, it contributes to the operationalization of the smart city concept by providing guidance for cities to effectively adopt smart technologies. Finally, this study represents a new field of application for QFD, expanding its scope beyond its traditional domains.

This paper aims to develop a crisis readiness framework for road traffic crisis response for law enforcement agencies in the United Arab Emirates (UAE).

A Delphi method was used that combined questionnaire-based survey and the analytical hierarchy process to collect quantitative and qualitative data from an expert panel of crisis readiness professionals on how they prioritise and weigh the different strategic criteria, sub-criteria and performance indicators in the context of law enforcement agencies’ traffic response.

The findings of this paper resulted in the identification, ranking and validation of ten key dimensions of crisis readiness clustered into three distinct sets of priority rankings: response planning, resources, training and coordination; information management and communication and risk and hazard assessment; and early warning, legal and institutional frameworks, recovery initiation and property protection. The results additionally established the relative priority of sub-criteria for each criterion and validated a broad set of key performance indicators (KPIs) for the top six ranked criteria.

The findings are based on a single case study focused on a specific area of operation within crisis response and one group of organisations of the UAE police sector. This potentially places a constraint on the wider generalisation of the findings to different operational areas and agencies, as they may have different priorities or organisational conditions that have implications for the framework application and the relative importance of certain criteria and sub-criteria.

This paper provides strategic guidance in the form of a prioritised list of criteria, sub-criteria and KPIs that can direct efforts to optimise different dimensions of crisis readiness at a strategic and operational level.

This paper makes an original contribution in identifying the key criteria and performance indicators of crisis readiness for road traffic situations. The findings contribute a comprehensive strategic readiness framework that supports planning and decision-making for the development of organisational capacities that can enhance response times of police to road traffic crises. This framework ranks dimensions of crisis readiness and key sub-criteria in order of priority and validates the key components of crisis readiness that can support practitioners to structure, standardise and benchmark key processes and elements of crisis response.

Refractory materials are now used in all major industries that demand high-temperature resistance, including petrochemicals, steel, cement and aviation. Businesses must decrease operating costs, enhance product technology, sell well and manage corporate risks in decision-making, notably supplier selection, to be more competitive. The study aims to determine the key criteria and factors of supplier selection and to evaluate the importance of the key factor of the supplier selection criteria for the refractory materials manufacturers in Taiwan.

Analytical hierarchy process (AHP) is used to rank these factors for the decision maker. The AHP method is suitable for verifying refractory supplier selection criteria and providing references. The weighted loss scores for each supplier are then determined using the relative importance as the weights. Supplier selection criteria are ranked using their aggregate weighted loss scores. The provider with the lowest loss score should be chosen.

Product quality is the most significant of the five criteria: product quality, production technology, logistics capacity, service capability and supplier background. Professionalism is the most significant aspect of product quality, whereas equipment and capacity are vital in manufacturing techniques. The studies also show that the delivery rate is essential for logistics and service capabilities.

This research has important implications for refractory suppliers in promptly fine-tuning the production and service to enhance customer satisfaction, which is key to business sustainability.

The application of an AHP technique to a real-world industrial issue is what makes this research unique. This research addressed one of the most critical topics in supply chain operations by offering better judgement for supplier selection via the use of suitable quantitative methodologies.

This study aims to adapt analytical hierarchy process (AHP) for choosing the optimal location for sport facilities. The location of a sports facility contributes significantly to its potential success or failure. Therefore, factors affecting such location-related decisions must be carefully studied and prioritized in a systematic fashion.

This study develops a seven-step framework which may be used to decide on a location from among several alternatives. Through an extensive literature review, this study first determines the factors affecting sports facility location selection and then applies AHP steps by asking several sports facility owners and managers to assess importance of the criteria.

This study determined the sport facility location selection factors as “ease of access,” “facility features,” “financial issues,” “neighborhood” and “market,” and further divided each factor into its subfactors. To illustrate the framework of using AHP as a tool to select the right location for sport facilities, we chose three candidate locations and scored them according to the calculated weight scores of the criteria, identifying the strengths and weaknesses of each location.

This study provides several managerial implications that may guide sport facility investors in choosing the right location.

This study presents a method to evaluate different factors for different actors of sport industry in a systematic way. Private investors can use the method for securing sufficient number of potential customers in a well-selected location. Government institutions and public policymakers can use the method, possibly with different sets of factors, to decide on the location of public sports facilities to maximize the number of visitors or to reach disadvantaged or underserved populations.

This framework of AHP method can help private and public investors and policymakers evaluate and make the optimal decision for choosing sports facility locations. This study contributes both to sport management theory and practice as well as to operation management literature. This study also refined the scattered factors in the literature of selecting a sport facility site in a more understandable and adaptable way.

Although smart city studies have increased recently, smart city discussions are made based on general concepts not specific to the region. The region-specific local smart city strategy in the built environment is key to climate resilience in the built environment in the face of natural disasters. The purpose of this paper is to focus on the smart environment, which expresses the spatial dimension of smart cities. This research defines a region-specific smart city model and revealing the role of this model in the resilience against disasters of the built environment.

The analytical hierarchy process (AHP) method was conducted in four steps. In Step 1, the authors suggested new smart environmental assessment criteria with climatic and geographical data within the scope of the collective mind of the region. In Step 2, they determined the expert group to evaluate within the scope of the AHP method and then compared the significance levels of the current and suggested smart environmental assessment criteria by the AHP method.

From the results, it turned out that smart urbanization processes, which are trying to relate to local characteristics, are of great importance in terms of ensuring urban resilience. The results also highlight that the existing smart environmental assessment criteria in the literature are insufficient to ensure the climatic resilience of the built environment in the face of natural disasters.

The study is in an intermediary section, which has a gap in the literature due to its subject. Although it has focused on an acute problem and a current research problem, the lack of literature on the field has been a limitation. Determining the cities where the field studies would be conducted has been a major limitation. For an objective hypothesis test within the scope of the AHP method, the sample group should consist of experts working in smart city projects in cities that are in the top 3 in five different smart city rankings, where field studies are conducted. Within this limited cluster, creating a large sample group was an important limitation.

This research looks into the existing gaps of the relation between climate resilience of the built environment and the local smart city approach. This examination will foster a holistic approach in the practice of sustainable smart city in the built environment, thus reinforcing urban resilience and climate studies in the context of smart cities.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

Megaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.

The study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.

It was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.

The contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.

Effective leaders have emerged as the cornerstone of project success. The major purpose of this paper is to propose a framework to categorize and prioritize leadership competencies for project managers in megaprojects.

In the first stage, this study utilizes PMBOK 6th Edition, IPMA ICB 4.0 frameworks to develop a hierarchy-based four clusters of leadership competencies. In the second stage, a Fuzzy-AHP (Analytic Hierarchy Process) approach was employed to prioritize the leadership competencies for an organization dealing in megaprojects. Finally, using ordinal priority approach (OPA), the results of Fuzzy-AHP method are validated.

Based on PMBOK, IPMA and literature, the proposed framework deduced twenty-four leadership competencies and grouped them in four clusters. The Fuzzy-AHP results indicate that among clusters, people competencies cluster is ranked most important, followed by perspective, practice, and innovation competencies. Considering the sub-categories and global weights, culture/values, governance, interpersonal skills, and development and growth emerged as the most important leadership competencies. The results from OPA corroborate the findings of Fuzzy-AHP method.

Megaprojects are characterized by massive investments and extensive economic and social impact. The proposed framework would be an important aid for policymakers to develop suitable strategies and programs to inculcate leadership competencies that would lead to successful project managers and improved project performance.

The need for this research stems from the need to integrate popular project management frameworks in leadership competencies development in project based organizations. The proposed integrated framework, based on PMBOK 6th Edition and IPMA ICB 4.0 competency frameworks, is an original contribution to understand and prioritize leadership competencies for megaproject success.

Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.

Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.

The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.

This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.