Exploring Critical Factors Influencing the Resilience of the Prefabricated Construction Supply Chain

Exploring Critical Factors Influencing the Resilience of the Prefabricated Construction Supply Chain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Critical Factors Influencing the Resilience of the Prefabricated Construction Supply Chain Li Ma, Tianyang Liu, Hongwei Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3752539/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jan, 2025 Read the published version in Buildings → Version 1 posted You are reading this latest preprint version Abstract Prefabricated construction has less environmental pollution, less resource consumption, and high productivity. This new construction model is an important tool for the construction industry to achieve sustainable development. However, disruptions in the prefabricated construction supply chain (PCSC) frequently occur in practice, which seriously reduces the performance of prefabricated building projects. Improving the resilience of the prefabricated construction supply chain (RPCSC) is an urgent problem to be solved. This study first identified the factors influencing the RPCSC through a comprehensive literature review. Next, 13 experts were invited to summarize and integrate these factors, and 11 concepts were obtained. Finally, the fuzzy cognitive maps method is applied to evaluate the impact of these concepts on the RPCSC and the interaction between them. The results show that the main factors are relationship quality of members, laws and regulations, information exchange/analysis, Professional management personnel/Labor quality, and R&D. Moreover, this study reveals the effect of these factors individually or synergistically influencing the RPCSC. This study provides valuable insights for governments and members of the prefabricated construction supply chain. The related findings can help reduce the risk of disruption in the prefabricated supply chain, improving the productivity and low-carbon performance of prefabricated construction. Environmental pollution Prefabricated Construction Supply chain Resilience Fuzzy cognitive maps (FCMs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The construction industry improves the quality of human life and meets the needs of economic and social development through the construction of various types of buildings and infrastructure (Lee et al. 2017 ). However, the industry is typically characterized by substantial energy consumption and pollution, imposing a significant burden on both the environment and society (Ortiz et al. 2009 ; Li et al. 2014 ). Researches indicate that the construction industry accounts for 60% of global raw materials consumption, 40% of energy consumption, and 12% of water resource consumption (Bribián et al. 2011 ; Chen et al. 2016 ). Moreover, the construction industry experiences a high incidence of accidents, with construction workers facing a 50% higher risk of occupational injury or death compared to workers in other industries (Pinto et al. 2011 ; Cheng et al. 2012 ). These challenges increasingly impede the sustainable development of society. To address these unsustainable issues, many countries and regions have embraced a new construction approach, known as prefabricated construction, which has gained considerable favorability in the industry. Prefabricated construction includes three main stages, namely factory prefabrication, logistics transportation, and on-site assembly. In more detail, components are produced in off-site factories with automated production lines and then transported to the construction site to be assembled into buildings, which forms a complete supply chain (Mostafa et al. 2014 ). Compared to the traditional construction model, the prefabricated construction reduces environmental pollution and improves the safety and production efficiency. However, in practice, various uncertain events often occur in the prefabricated construction supply chain (PCSC), such as irregular interface design, machine failure, missing materials, traffic jams, and inconsistent information exchange (Hofman et al. 2009 ). These uncertain events often interrupt the regular operation of PCSC, leading to project schedule delays and increased costs (Li et al. 2014 ). Therefore, it is imperative to address these challenges to enhance the productivity and robustness of prefabricated construction. In the field of industrial supply chains, the concept of “supply chain resilience” is used to measure the ability of the supply chain to resist interference. Specifically, it refers to the capacity of the supply chain to continue operating during emergencies without compromising its performance or to reappear in a better state (Ponomarov and Holcomb 2009 ). Like other manufacturing supply chains, the PCSC must possess sufficient resilience to withstand risks and swiftly return to normal or ideal conditions. Existing studies have primarily focused on assessing the resilience of the prefabricated construction supply chain (RPCSC) and examining the impact of specific factors on the RPCSC, such as component production and transportation, skilled labor (Ekanayake et al. 2021 ; He et al. 2022). However, compared to other industrial supply chains, PCSC are affected by more factors in component production, transportation, and assembly. Additionally, there may exist intricate causal or synergistic relationships among these factors. Researchers are still unclear about what are the main factors affecting the RPCSC and how these factors function individually or interact with each other. These research gaps leave supply chain members with no idea of how to develop an effective strategy to improve the RPCSC. To address the issues mentioned above, this study attempts to identify the key factors influencing RPCSC and discern the interconnections between them. The related findings yielded possess the potential to furnish substantial enlightenment for participants within the prefabricated construction supply chain, enabling them to enhance the resilience of their supply chains. The research methods, results, and conclusions of this study are described in detail in the following sections. 2. Literature review 2.1. Prefabricated construction supply chain The prefabricated construction supply chain is the flow of funds, information, materials, and knowledge among general contractors, subcontractors, suppliers, and developers during the design, construction, transportation, assembly, and delivery of prefabricated buildings (Naim and Barlow 2003 ). Compared to traditional construction projects, the prefabricated construction supply chain (RPCSC) is characterized by its complexity due to several factors. Firstly, there is a longer chain caused by the involvement of two or more production environments, namely, the factory and the site (Koskela 2003 ). Secondly, there is a greater amount of design work and earlier design required for cast-in-situ construction due to the lead time associated with prefabrication (Han et al. 2022 ). Thirdly, there is a longer period required for error correction (Hussein et al. 2021 ). Lastly, there are higher requirements for dimensional accuracy (Luo et al. 2019 ). Consequently, while PCSC offers high production efficiency, it entails a range of uncertainties, such as machine failures, lack of production materials, traffic jams, and assembly component damage (Jiang et al. 2018 ). Practical experience has shown that these uncertainties and risk events often interrupt the PCSC, causing severe project schedule delays and cost overruns (Masood et al. 2021 ). Scholars and practitioners have made significant efforts to enhance the performance status of PCSC. Existing studies on the PCSC management primarily focus on three themes. First, recognizing the obstacles and driving factors for the development of the PCSC is crucial, given the long path towards large-scale applications (Chang et al. 2018 ; Hong et al. 2018 ; Arashpour et al. 2015 ). Second, the operation of the PCSC is highly complex and lacks standardization, resulting in diverse and uncertain supply chain risks (Polat 2008 ). Accurate identification and evaluation of these risks assist stakeholders to prevent and manage these risks more effectively (Wuni et al. 2019 ; Hsu et al. 2019 ). Additionally, the integration of PCSC has received significant attention. While the PCSC members strive to improve their individual interests through internal integration, the overall interests of PCSC are often overlooked (Zhong et al. 2017). Therefore, it is necessary to build an effective cross-organization cooperation mechanisms to coordinate strategies and activities among PCSC members. Technological advancements also play a vital role in enhancing the operational efficiency of PCSC. Several studies have explored the implementation of advanced technologies in the production, transportation, assembly, and information interaction of PCSC (Wong et al. 2010 ; Demiralp et al. 2012 ). 2.2. Resilience of the prefabricated construction supply chain The concept of “resilience” originated in materials science, ecology, and psychology (Ponomarov and Holcomb 2009 ; Adobor 2019 ). In materials science, resilience refers to the ability of a material to return to its original shape after deformation. In the context of ecology, resilience pertains to the degree, manner, and speed at which an ecosystem returns to its original structure and function after being disturbed (Doorn et al. 2019 ; Taşan-Kok et al. 2013 ). Since this concept is very suitable for describing the supply chain’s state when it is disturbed, resilience is introduced into the domain of supply chain management. The fundamental assumption underlying supply chain resilience is that not all risk events within the supply chain can be prevented. Rather, the focus lies in the ability of the supply chain network system to recover to its initial or ideal state following the occurrence of risks (Tukamuhabwa et al. 2015 ). The speed at which the supply chain returns to a normal state, encompassing aspects such as production, service, and supply ratios, serves as a reflection of the level of supply chain resilience (Wieland and Durach 2021 ). In relation to the dimensions of supply chain resilience, the initial perspective posits that supply chain resilience encompasses two dimensions: resistance and recovery. Conz and Magnani ( 2020 ) have highlighted that the resistance of a system to interference is composed of two pathways, namely, absorptive and adaptive. Consequently, supply chain resilience can be categorized into absorptive capacity, adaptive capacity, and restorative capacity, and this a viewpoint has gained consensus among the majority of scholars (Liu et al. 2017 ; Zhang et al. 2021). The evaluation of supply chain resilience represents the second primary concern. Diverse qualitative and quantitative approaches have been utilized to assess the extent of supply chain resilience and its impact on overall supply chain performance (Spiegler et al. 2012 ; Cai et al. 2018 ; Abimbola and Khan 2019 ). These studies serve as the starting point for the third research theme, which centers on enhancing supply chain resilience performance. Various resilience evaluation indicators provide guidance for bolstering the resilience of the supply chain. For example, pre-embedding and redundancy can fortify the absorptive capacity of the supply chain, dynamic logistics and information sharing of the supply chain can strengthen the restorative capacity of the supply chain, and procurement flexibility, delivery flexibility, and efficiency can enhance the adaptive capacity of supply chain (Pettit et al. 2019 ). Compared with other manufacturing supply chains, the PCSC exhibits low product standardization and faces challenges in transportation (Ekanayake et al. 2023 ). These factors increase the vulnerability of prefabricated construction, resulting in significant schedule delays and cost overruns. In the operation of the PCSC, disruptions such as machine failures, traffic congestion, and component damage are commonly encountered (Zhai et al. 2017 ). To effectively address the risks associated with the PCSC, several risk management models have been developed (Zhu and Liu 2022 ; Hsu et al. 2019 ;Wang et al. 2023 ). However, the practical utility of these models is limited due to the cumbersome nature of their application to potential risk event (Yingchao 2019 ). Furthermore, studies have conducted significant investigations into specific deficiencies of the PCSC. For example, RFID technology, block-chain technology, and cloud computing have been used to solve the poor information exchange (Demiralp et al. 2012 ; Wang et al. 2020 ; Du et al. 2017 ). Multi-objective optimization models are constructed to improve the resilience of transportation planning (Zhang and Yu 2021 ). Nevertheless, while these studies have offered some remedies for improving the RPCSC, they are still characterized by fragmentation. The existing body of research lacks a systematic identification and evaluation to the factors affecting RPCSC and their mechanisms of action, which prevents academics and practitioners from formulating a comprehensive RPCSC improvement plan. 3. Methodology The structured method adopted in this study is divided into two phases, and the specific research steps are shown in Fig. 1 . 3.1. Concepts identification 3.1.1. Factors screening To find the factors influencing RPCSC, this study first conducted a comprehensive literature review. The research team chooses Web of Science, Scopus, EBSCO, Spring, Taylor & Francis, and Emerald databases for literature search without limiting the publication time and type of literature. The keyword of prefabricated construction includes ‘“prefabricated building”, “prefabricated construction”, “industrialized building”, “industrialized construction”, “modular building”, “modular construction”; the keyword of the supply chain is “supply chain”. In this study, the meaning of “factor” is generalized. Our purpose is to search for and analyze any potential risks (such as political risks), practices (such as innovation), and other aspects (such as partnerships) that affect RPCSC. Therefore, keywords related to resilience include “resilience”, “risk”, “disruption”, “sustainability”, “uncertainty”, and “vulnerabilities”. Due to the overlapping coverage of several databases, duplication of literature is inevitable. After removing duplicates, a total of 47 related papers were obtained. Subsequently, these literatures were evaluated one by one, and finally identified 33 literatures that met the objectives of this study. All the factors influencing the RPCSC mentioned in these literatures are listed in Appendix A. 3.1.2. Taxonomy As can be seen from Appendix A, the literature involves more than 200 factors, which partially or entirely overlap. Therefore, it is necessary to summarize these factors into independent concepts. Delphi technology was adopted for this work, which is a method used for structuring a group communication process. In the case of conflicting and insufficient information, the Delphi technique is an excellent method for accurately finding consensus and making effective decisions (Linstone and Turoff, 1975). The research team first selected ten authors who published two or more papers on PCSC in peer-reviewed journals and contacted them through the email address in the paper. In the email, the information about the purpose and process of this study was added. Meanwhile, the research team invited these authors to participate in the study. Finally, nine experts responded, and 6 of them expressed their willingness to participate. Furthermore, to balance possible differences between theoretical research and practice, the research team contacted ten managers from prefabricated component manufacturers and prefabricated construction contractors. Seven managers agreed to participate in the study. Regarding the number of expert groups, there is no unanimous recommendation in the literature. Yong et al. (1989) point out that 7–15 is suitable for an expert panel. According to this criterion, the number of experts in the study is considered appropriate. This study was conducted from March to May 2023. First, the team eliminated duplicating factors in the literature and formed an initial list influencing RPCSC. Then, this list was sent to 13 experts via email, inviting them to summarize and merge the factors in the list and return the adjusted list. After the first round, the research team counted the opinions of all experts and sent the results to the experts again, asking them if they need to make changes to their previous opinions and sending back the second adjusted list. The second round adopted the same operation process as the first round. At the end of the third round, all the experts reached a consensus on integrating the factors influencing RPCSC, and 11 concepts were determined, as shown in Table 1 . Table 1 Research approach Code Concept Description Main factors involved C1 Performance of prefabricated components All issues related to components design, production. Performance of prefabricated components, Durability of prefabricated unproven, Geometric and dimensional intolerances C2 Construction of prefabricated building Factors related to the construction of prefabricated buildings construction technology of prefabricated components, machines breakdown, safety issues, Installation error of precast elements, C3 Policies and regulations The completeness and changes of laws and regulations related to the prefabricated building supply chain local government policy preferences, Implication of new laws/regulation, Political economy changes, Unreasonable site layout of prefabricated components, C4 Information exchange/sharing The type, quantity, form and medium of information exchange between supply chain members Communication breakdown/issues, Information loss, Inadequate IT systems, Information misuse C5 Transport risk All risks that may occur during prefabricated components and raw material transportation Transport disruptions including port stoppages, site logistics, Damage of prefabricated elements during transportation, C6 Research and development (R&D) The process and elements of supply chain members developing new technologies and new products Technology failure, cost of technology investment share, Cooperative Innovation, Absence of standard modular components, Monopoly of techniques by a few firms, Lack of R&D input C7 Decision alignment The degree of consistency of management decisions of supply chain members Conflict resolution, buffer space hedging, strategy alignment, Solution consistency, Inappropriate business strategies C8 Professional management personnel/ Labor quality The quantity and quality of managerial personnel, labor of the members of the prefabricated construction supply chain. Lack of highly skilled workers, Insufficient construction capacity, Lack of best management practices, Inaccurate cost estimation, operation efficiency C9 Relationship quality of members relationship The level of friendship and trust among members of the prefabricated building supply chain relationship coordination, Poor cooperation between multi-interface, trust between members, Stakeholders’ lack of awareness, C10 Supply-demand consistency The degree of matching between products and demand in the prefabricated building supply chain Variations and/or rework, Quality loss Supply-demand mismatch/shortages, Supply–demand mismatches or shortages, C11 Cost/profit sharing Reasonable and fair degree of cost and benefit distribution among members of assembly building supply chain. cost of technology investment share, transaction costs, 3.2. Fuzzy cognitive maps (FCMs) 3.2.1. Mathematical representation of fuzzy cognitive maps Fuzzy cognitive maps (FCMs) are a qualitative reasoning technique used to analyze the relationship between many interacting things. Its distinguishing feature is that it can use prior knowledge to calculate the state of complex systems, thereby expressing dynamic causal systems with feedback that are difficult to be represented by Tree structure, Bayes network, and Markov model. Cognitive maps are originated from graph theory. In 1986, Kosko introduced fuzzy relations into cognitive maps and proposed the concept of fuzzy cognitive maps, which can simulate the ambiguity of the real world better than cognitive maps. Because of its concise reasoning mode and easy operation, FCMs have been widely used in many aspects such as social economy, management, military, and other aspects, such as management problem diagnosis (Carriço and Guimarães 1997 ), urban design (Xirogiannis et al. 2004 ), relationship management in aviation services (Kang et al. 2004 ). The topological structure of FCMs modeling is a triple pattern G =( C , E , W ), Where C= { C 1 , C 2 ..., C n } represents the set of n concept nodes in FCMs; E={| C i , C j ∈ C } is the causal association directed arc between all nodes in FCMs (Directed arc means that node C i has a causal relationship or influence on C j ); W ={ w ij } is the weight of the directed arc . w ij represents the degree of influence of node C i on C j , and the value range is [-1,1], where: If w ij > 0, it means that w i has a positive effect on w j ; If w ij <0, it means that w i has a negative influence on w j ; If w ij =0, it means that w i has no effect on w j , and there is no arc connection between w i and w j . An FCMs with n concept nodes can be uniquely determined by an interaction matrix W=( w ij ) n×n . For example, Fig. 2 is a fuzzy cognitive map, and its corresponding interaction matrix W can be expressed as Eq. ( 1 ): $$W=\left[ {\begin{array}{*{20}{c}} 0&{{w_{12}}}&0&0&0&{{w_{16}}} \\ {{w_{21}}}&0&0&0&0&0 \\ 0&{{w_{32}}}&0&{{w_{34}}}&{{w_{35}}}&0 \\ 0&0&0&0&0&{{w_{46}}} \\ 0&0&0&{{w_{54}}}&0&0 \\ 0&0&{{w_{63}}}&0&{{w_{65}}}&0 \end{array}} \right]$$ 1 The reasoning mechanism of FCMs is the evolution process of an event based on its topological structure, in which each concept node C i represents a certain sub-event in the event, often driven by other sub-events, such as C j . The degree of the drive is determined by the causal (correlation) strength between C i and C j . This strength is the weight w ij of the directed arc in FCMs. The reasoning process of FCMs is realized by the recursive effect of the forward node on the backward node state, and the specific steps are as follows: a) Determine an initialized state vector A n (0) , b) Obtain the interaction matrix, with the help of expert knowledge and experience, c) Multiple iterative calculations of the initial state vector are carried out through Equations ( 2 ) and ( 3 ). When the final result satisfies A n (t) = A n (t + 1) , the iteration is stopped. At this time, FCM reaches A stable state, and the whole iteration process ends. $$A_{i}^{{(t+1)}}=f(A_{i}^{{(t)}}+\sum\limits_{{j=1,j \ne i}}^{n} {{w_{ji}}A_{j}^{{(t)}}} )$$ 2 Where \(A_{i}^{{(t+1)}}\) the value of concept C i at the step t + 1 , \(A_{i}^{{(t)}}\) is the value of the each interaction of the interconnected concept C j at step t , w ji is the weighted arc from C j to C i , and f is a threshold function to make sure the node concept value remains in the interval [0,1] and could be the Sigmoid threshold function: $$f=\frac{1}{{1+{e^{ - \lambda x}}}}$$ 3 Where λ > 0 determines the steepness of the continuous function f . The Sigmoid function is usually used when the concept interval is [0, 1]. 3.2.2. Modular construction After determining the factors influencing RPCSC, the steps of constructing FCM were executed. After completing the factors integration, we invited these experts to evaluate the impact of the factors on each other and the extent of these factors affect RPCSC. Experts were required to use “No”, “Very low”, “Low”, “Medium”, “High”, and “Very high” to express the ambiguous intensity of causality between concepts. The direction of influence between concepts was expressed by positive (+) or negative (-). Because the language of experts is vague, and the opinions of multiple experts are different, the linguistic values need to be converted into a digital weight through the triangular fuzzy number method to facilitate data processing. The conversion format of each linguistic value is shown in Fig. 3 . After the linguistic value conversion is completed, the fuzzification step was executed to convert the triangular fuzzy number into an accurate value. There are many defuzzification methods, and the area center of gravity method is the most common one. The specific calculation rules are shown in Eq. 4 . $${U^*}=\frac{{\sum\nolimits_{{i=1}}^{n} {({A_i} \times {L_i})} }}{{\sum\nolimits_{{i=1}}^{n} {{A_i}} }}$$ 4 Where n is the number of experts, A i represents area covered under each fuzzy set and L i represents midpoint of the fuzzy set on X-axis. 4. Results Equation ( 4 ) was applied to aggregate experts’ opinions to obtain the quantitative value W ij of the relationship between all conceptual nodes and the impact on RPCSC. On account of the simplicity of the FCMs calculations, the w ij values in the interval [-0.1, 0.1] are set to zero. The final interaction matrix W 1 is shown in Table 2 . According to the final interaction matrix, the cognitive map has been sketched (Fig. 4 ). Table 2 The final interaction matrix Code C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 RPCSC C1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.492 0.000 0.400 C2 0.431 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.292 C3 0.369 0.308 0.000 -0.231 0.277 0.477 0.000 0.415 0.000 0.000 0.877 C4 0.231 0.262 0.000 -0.569 0.954 0.923 0.000 1.000 0.892 0.554 0.754 C5 -0.369 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.277 0.000 -0.585 C6 0.985 0.754 0.000 0.000 -0.308 0.000 0.000 0.000 0.631 0.000 0.462 C7 0.400 0.000 0.000 0.000 -0.477 0.000 0.000 0.723 0.985 0.615 0.815 C8 1.000 0.985 0.000 0.600 -0.431 0.954 0.308 0.462 0.354 0.446 0.600 C9 0.000 0.000 0.000 0.985 -0.615 0.538 0.938 0.000 0.769 0.754 0.908 C10 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.800 C11 0.000 0.000 0.000 0.000 0.000 0.446 0.000 0.000 0.585 0.000 0.585 The iterative calculation is conducted according to Eqs. ( 1 ) and ( 2 ). In consideration of the simplicity of calculation and the speed of convergence of each concept, let λ = 1 (Baker et al. 2018). The iterative calculation results of each step are shown in Table 3 . Table 3 shows that after five iterations of calculation, the system reaches a steady-state. According to Table 3 , the changing trend of the state value of each concept is drawn (Fig. 5 ). It can be seen from Fig. 5 that the convergence speed of C9, C3, C4, C8, and C6 is fast. Their state values are higher than other factors when they fully converge, which shows that the Relationship quality of members, Policies and regulations, Information exchange/sharing, Professional technicians and management personnel/ Labor quality, and Research and development (R&D) are the most important factors influencing RPCSC. Other concepts, including Decision alignment (C7), Transport risk (C5), Cost/profit sharing (C11), Supply-demand consistency (C10), Performance of prefabricated components (C1), and Construction of prefabricated buildings (C2) are secondary factors influencing RPCSC. Table 3 The state value of concepts Iterative rounds C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0 1 1 1 1 1 1 1 1 1 1 1 1 0.4892 0.4664 0.8774 0.8157 0.5867 0.7549 0.6000 0.8022 0.9083 0.5025 0.5852 2 0.4654 0.4542 0.7227 0.6863 0.5012 0.6202 0.5065 0.6816 0.8162 0.4806 0.5005 3 0.4625 0.4533 0.7059 0.6567 0.4815 0.6071 0.4962 0.6485 0.7746 0.4782 0.4807 4 0.4625 0.4533 0.7024 0.6509 0.4815 0.6064 0.4930 0.6448 0.7268 0.4782 0.4807 5 0.4625 0.4533 0.7024 0.6509 0.4815 0.6064 0.4930 0.6448 0.7253 0.4782 0.4807 6 0.4625 0.4533 0.7024 0.6509 0.4815 0.6064 0.4930 0.6448 0.7253 0.4782 0.4807 5. Discussion Figure 5 reveals that the most crucial factor impacting the RPCSC is the relationship quality of members, which aligns with previous research findings that emphasize the significance of relationship quality as a key determinant of supply chain performance (Fynes et al. 2005 ; Shin et al. 2018 ). Effective communication and coordination are essential in PCSC for various stakeholders such as component manufacturers, designers, transporters, prime contractors, subcontractors, and owners. A prime example is the need for seamless communication among designers, component manufacturers, and construction contractors to ensure that design changes are aligned with component production capacity and assembly technology (Zhao et al. 2022 ). This process heavily relies on fostering a strong relationship among these members of the supply chain. However, Hofman et al. ( 2009 ) found that in practice, the relationship between the members of the PCSC is disconnected and distrusted, leading to frequent disruptions in PCSC. Therefore, the establishment of a solid, long-term relationship is of paramount importance for PCSC members aiming to enhance the RPCSC. The impact of policies and regulations on RPCSC is second only to the relationship quality. Wuni et al.(2019) pointed out that successfully implement prefabricated construction, it is crucial to have a comprehensive legal framework that encompasses the necessary qualifications, codes, standards, regulations, and policies. However, the current laws, regulations, and policies predominantly focus on the construction processes, structural durability, and material quality of traditional projects, neglecting the involvement of prefabricated construction and its associated supply chain activities (Ekanayake et al. 2021 ). Many behaviors exhibited by PCSC member companies are influenced by corporate culture and ethics, which often leads to opportunistic and noncompliant actions that significantly jeopardize the smooth operation of the supply chain. Moreover, Fig. 4 illustrates that in the system of factors influencing the RPCSC, the policies and regulations point to many other factors, but no one points to it, which indicates that this one is an independent and fundamental factor. Therefore, as the regulator and promoter of prefabricated construction, it is imperative for the government to promptly enhance relevant policies and regulations to guide and constrain the behavior of PCSC members, thereby reducing the risk of disruptions caused by participant misconduct. The exchange and sharing of information emerges as the third most crucial factor influencing the RPCSC. Previous research has extensively highlighted the challenges associated with information exchange in PCSC. Ekanayake et al. ( 2020 ) discovered that while prefabricated construction can significantly enhance construction efficiency, the absence of real-time information sharing often leads to fragmentation and disruptions among supply chain participants. For example, due to the lack of real-time exchange and sharing of assembly planning at the construction site, production planning at the component manufacturer, and transportation planning at the transportation company, project schedules are often delayed, and supply chain costs escalate (Luo et al. 2019 ). Consequently, scholars have emphasized the potential of information interaction platforms based on TI technology or block chain technology to enhance the efficiency of information exchange among members and bolster the RPCSC (Demiralp et al. 2012 ; Wang et al. 2020 ). Additionally, Fig. 4 indicates that the relationship quality influences the information exchange among supply chain participants, which indicates that a good relationship is a basis for their willingness to share and exchange information. This finding enlightens that in order to improve the operational efficiency and resilience of the entire PCSC, supply chain members must seek to establish good formal and informal relationships with upstream and downstream enterprises and even all other supply chain members. Wuni and Shen ( 2020 ) assert that effective stakeholder management strategy and scientific project planning and scheduling are important preconditions for the orderly operation of the PCSC. The formulation of strategy and plan depends on professional management personnel. However, as the prefabricated construction is still in the initial phase of promotion, professional management personnel and their experience are generally lacking, which has become one of the major obstacles that limit the RPCSC (Wen 2021 ). Additionally, practitioners have noted that quality defects in prefabricated construction are primarily caused by assembly errors and these errors are predominantly attributed to the low skill level of assembly workers (Ekanayake et al. 2021 ). Nevertheless, due to significant differences from traditional construction procedures, the current prefabricated construction sector suffers from a shortage of skilled labor. This shortage ultimately results in inadequate quality, cost overruns, schedule delays, and even disruptions to the entire supply chain. The research and development (R&D) is the last major factors influencing the RPCSC. While prefabricated construction plays a crucial role in promoting sustainable development in the construction industry, it currently suffers from a number of glaring deficiencies in its production, transportation, and assembly, leading to frequent interruptions of the PCSC. One limitation is the restricted range of component products available in prefabricated construction, which fails to fully satisfy the diverse and personalized demands of customers (Jiang et al. 2018 ). Additionally, the safety and durability of prefabricated construction are not entirely trusted due to incomplete technological development, particularly in relation to splice nodes (Fard et al. 2017 ). Moreover, the large size and weight of fabricated components impose higher demands on transportation and lifting equipment. In practice, failures in transportation and lifting equipment constitute a significant proportion of the causes behind supply chain disruptions. A prominent contributing factor to these challenges is the insufficient level of R&D of PCSC member enterprises. According to Wu et al.(Wu et al. 2019 ), prefabricated construction holds a relatively small market share compared to traditional cast-in-place construction methods, thus leading to potential diseconomies of scale resulting from substantial R&D investments. However, the prefabricated construction market shows promising growth potential, and substantial R&D investments can assist PCSC member enterprises in establishing early competitive advantages (Sabahi and Parast 2020 ). Therefore, it is imperative for these enterprises to appropriately increase their R&D investments. To further incentivize active R&D efforts of enterprises, the government can consider providing R&D cost subsidies to prefabricated construction member enterprises. 6. Conclusions and future research directions Compared to traditional construction forms, prefabricated construction offers several advantages including reduced environmental pollution, lower resource consumption, and increased productivity. However, in practice, the PCSC is frequently interrupted by a number of factors. In this context, how to improve the efficiency of the PPCSC and keep it running continuously and efficiently is a challenging issue. This study aims to identify the factors that impact the RPCSC by conducting a comprehensive literature review. Subsequently, these factors are integrated and summarized into 11 key concepts using the Delphi method. Finally, the fuzzy cognitive map method is employed to evaluate the relationship and degree of impact between these concepts on the RPCSC. The findings indicate that the relationship quality of members, laws and regulations, information exchange/analysis, professional management personnel/labor quality, and research and development (R&D) are the primary factors influencing the RPCSC. Figure 4 illustrates the impact between all 11 concepts. The findings of this study have substantial theoretical and practical implications. Currently, the theoretical research on RPCSC is limited. It is not clear what factors affect the RPCSC. Using literature review and Delphi method, this study identifies the factors affecting RPCSC, on the basis of which, this study explains the linkages between these factors using fuzzy cognitive map method. The relevant findings provide a direction for future research on the RPCS improvement mechanisms. Moreover, in practice, the vulnerability of the PCSC severely delays the construction schedule and leads to increased costs. Practitioners in the PCSC need to examine and evaluate the deficiencies in PCSC to enhance the PCSC through more effective measures. This study offers a pre-list that can assist them in effectively identifying the factors that influence the RPCSC and relationships among these factors. For example, this study finds that the relationship quality of members is the most crucial factor influencing RPCSC. Consequently, contractors, suppliers, and designers of prefabricated construction must carefully evaluate and improve their relationships with upstream and downstream supply chain members, emphasizing communication and interaction. Overall, the findings presented in this paper are helpful for industry professionals seeking to enhance the PPCSC and improve the prefabricated construction performance. Although this study has made significant theoretical and empirical advancements, it is crucial to acknowledge certain limitations. Initially, this study identified the factors that impact the RPCSC through an extensive examination of the existing literature. However, it is important to recognize that despite covering various aspects of the PCSC, there may still be limitations in terms of completeness of factor identification. In order to address this issue, future research can further expand the factor database by employing alternative methods such as large-scale interviews and multiple case studies. Another limitation of this study may lie in the sample size. While the number of experts involved in this research is adequate and represents the high level of knowledge in prefabricated construction theory and practice, a larger sample size would likely produce more precise findings. Thus, to obtain more reliable and trustworthy results, it is recommended to expand the pool of experts through various avenues in future studies. Finally, generalization of the results may be another limitation of this study. As the factors identified are based on existing literature, the applicability of the study’s conclusions to a specific country or region may be limited. To gain further valuable insights, future research could explore the factors influencing the RPCSC in various countries or industry backgrounds. Declarations Author contribution XXX, XXX, and XXX contributed to data collection and analysis; XXX designed the research and provided guidance on manuscript writing, and XXX wrote the manuscript. All authors have read and approved the final manuscript. Data availability The datasets used and/or analyzed in this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Acknowledgments This study was supported by Liaoning Province Social Science Planning Research key Project (NO.L23AGL010). 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J Constr Eng Manage 136(10):1116–1128 Wu G, Yang R, Li L, Bi X, Liu B, Li S, Zhou S (2019) Factors influencing the application of prefabricated construction in China: From perspectives of technology promotion and cleaner production. J Clean Prod 219:753–762 Wuni IY, Shen GQ (2020) Stakeholder management in prefabricated prefinished volumetric construction projects: benchmarking the key result areas. Built Environ Proj Asset Manag 10(3):407–421 Wuni IY, Shen GQ, Mahmud AT (2019) Critical risk factors in the application of modular integrated construction: a systematic review. Int J Constr Manag 22(2):133–147 Xirogiannis G, Stefanou J, Glykas M (2004) A fuzzy cognitive map approach to support urban design. Expert Syst Appl 26(2):257–268 Yingchao W (2019) Research on Risk Management of Prefabricated Construction Supply Chain Based on Immune Principle, IOP Conference Series: Earth and Environmental Science. IOP Publishing 052058 Zhai Y, Zhong RY, Li Z, Huang G (2017) Production lead-time hedging and coordination in prefabricated construction supply chain management. Int J Prod Res 55(14):3984–4002 Zhang H, Yu L (2021) Resilience-cost tradeoff supply chain planning for the prefabricated construction project. J Civ Eng Manag 27(1):45–59 Zhao S, Wang J, Ye M, Huang Q, Si X (2022) An evaluation of supply chain performance of China’s prefabricated building from the perspective of sustainability. Sustainability 14(3):1299 Zhu T, Liu G (2022) A Novel Hybrid Methodology to Study the Risk Management of Prefabricated Building Supply Chains: An Outlook for Sustainability. Sustainability 15(1):361 Supplementary Files AppendixA.docx Cite Share Download PDF Status: Published Journal Publication published 18 Jan, 2025 Read the published version in Buildings → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3752539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272161371,"identity":"c16a745b-cd01-4f39-9f24-505fb351e416","order_by":0,"name":"Li Ma","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Ma","suffix":""},{"id":272161372,"identity":"5f8237e6-4f10-4bd8-84a4-da838b85bf1a","order_by":1,"name":"Tianyang Liu","email":"data:image/png;base64,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","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Tianyang","middleName":"","lastName":"Liu","suffix":""},{"id":272161373,"identity":"8c4a8ea6-d5f9-4a05-beb5-dc4191df7a7c","order_by":2,"name":"Hongwei Fu","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2023-12-14 09:13:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3752539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3752539/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/buildings15020289","type":"published","date":"2025-01-19T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51046698,"identity":"b3592f64-93e6-4bcc-863b-6ae8d2778ba3","added_by":"auto","created_at":"2024-02-13 09:02:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87263,"visible":true,"origin":"","legend":"\u003cp\u003eResearch approach\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3752539/v1/1bb82a32b7b97f971af51aa5.png"},{"id":51045612,"identity":"1e305a48-4896-49eb-9a7c-3a8560b591b7","added_by":"auto","created_at":"2024-02-13 08:54:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34078,"visible":true,"origin":"","legend":"\u003cp\u003eA simple FCM\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3752539/v1/08f55aa044eeae36a5b89c4f.png"},{"id":51045613,"identity":"e2f26170-9a5d-44bc-b767-d1ef8710b66b","added_by":"auto","created_at":"2024-02-13 08:54:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65063,"visible":true,"origin":"","legend":"\u003cp\u003eThe membership function used to deffuzify linguistic values.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3752539/v1/475d3d0bdc25e701752af019.png"},{"id":51045616,"identity":"fbcb81c5-beeb-48ca-9156-c09c5a56dded","added_by":"auto","created_at":"2024-02-13 08:54:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154622,"visible":true,"origin":"","legend":"\u003cp\u003eCognitive map of factors influencing RPCSC\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3752539/v1/7628e11c86253a2e73d8a075.png"},{"id":51045614,"identity":"90df9e2f-9661-40d8-9203-469e50077675","added_by":"auto","created_at":"2024-02-13 08:54:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86279,"visible":true,"origin":"","legend":"\u003cp\u003eChange trend of concepts\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3752539/v1/a3cbc17b57829ed2150a00a0.png"},{"id":74371937,"identity":"cb80709f-5efa-4351-a3cb-16c29efa6670","added_by":"auto","created_at":"2025-01-21 15:46:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1368171,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3752539/v1/bc155431-364e-477c-9b5c-13cb80beb810.pdf"},{"id":51045617,"identity":"427c23ea-ee6b-4099-b577-eca07755de1b","added_by":"auto","created_at":"2024-02-13 08:54:19","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":26692,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-3752539/v1/e856d02c8f5e7acf989e3eed.docx"}],"financialInterests":"","formattedTitle":"Exploring Critical Factors Influencing the Resilience of the Prefabricated Construction Supply Chain","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe construction industry improves the quality of human life and meets the needs of economic and social development through the construction of various types of buildings and infrastructure (Lee et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the industry is typically characterized by substantial energy consumption and pollution, imposing a significant burden on both the environment and society (Ortiz et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Researches indicate that the construction industry accounts for 60% of global raw materials consumption, 40% of energy consumption, and 12% of water resource consumption (Bribi\u0026aacute;n et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, the construction industry experiences a high incidence of accidents, with construction workers facing a 50% higher risk of occupational injury or death compared to workers in other industries (Pinto et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These challenges increasingly impede the sustainable development of society. To address these unsustainable issues, many countries and regions have embraced a new construction approach, known as prefabricated construction, which has gained considerable favorability in the industry.\u003c/p\u003e \u003cp\u003ePrefabricated construction includes three main stages, namely factory prefabrication, logistics transportation, and on-site assembly. In more detail, components are produced in off-site factories with automated production lines and then transported to the construction site to be assembled into buildings, which forms a complete supply chain (Mostafa et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Compared to the traditional construction model, the prefabricated construction reduces environmental pollution and improves the safety and production efficiency. However, in practice, various uncertain events often occur in the prefabricated construction supply chain (PCSC), such as irregular interface design, machine failure, missing materials, traffic jams, and inconsistent information exchange (Hofman et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These uncertain events often interrupt the regular operation of PCSC, leading to project schedule delays and increased costs (Li et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, it is imperative to address these challenges to enhance the productivity and robustness of prefabricated construction. In the field of industrial supply chains, the concept of \u0026ldquo;supply chain resilience\u0026rdquo; is used to measure the ability of the supply chain to resist interference. Specifically, it refers to the capacity of the supply chain to continue operating during emergencies without compromising its performance or to reappear in a better state (Ponomarov and Holcomb \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Like other manufacturing supply chains, the PCSC must possess sufficient resilience to withstand risks and swiftly return to normal or ideal conditions.\u003c/p\u003e \u003cp\u003eExisting studies have primarily focused on assessing the resilience of the prefabricated construction supply chain (RPCSC) and examining the impact of specific factors on the RPCSC, such as component production and transportation, skilled labor (Ekanayake et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; He et al. 2022). However, compared to other industrial supply chains, PCSC are affected by more factors in component production, transportation, and assembly. Additionally, there may exist intricate causal or synergistic relationships among these factors. Researchers are still unclear about what are the main factors affecting the RPCSC and how these factors function individually or interact with each other. These research gaps leave supply chain members with no idea of how to develop an effective strategy to improve the RPCSC.\u003c/p\u003e \u003cp\u003eTo address the issues mentioned above, this study attempts to identify the key factors influencing RPCSC and discern the interconnections between them. The related findings yielded possess the potential to furnish substantial enlightenment for participants within the prefabricated construction supply chain, enabling them to enhance the resilience of their supply chains. The research methods, results, and conclusions of this study are described in detail in the following sections.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Prefabricated construction supply chain\u003c/h2\u003e \u003cp\u003eThe prefabricated construction supply chain is the flow of funds, information, materials, and knowledge among general contractors, subcontractors, suppliers, and developers during the design, construction, transportation, assembly, and delivery of prefabricated buildings (Naim and Barlow \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Compared to traditional construction projects, the prefabricated construction supply chain (RPCSC) is characterized by its complexity due to several factors. Firstly, there is a longer chain caused by the involvement of two or more production environments, namely, the factory and the site (Koskela \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Secondly, there is a greater amount of design work and earlier design required for cast-in-situ construction due to the lead time associated with prefabrication (Han et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thirdly, there is a longer period required for error correction (Hussein et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Lastly, there are higher requirements for dimensional accuracy (Luo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, while PCSC offers high production efficiency, it entails a range of uncertainties, such as machine failures, lack of production materials, traffic jams, and assembly component damage (Jiang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Practical experience has shown that these uncertainties and risk events often interrupt the PCSC, causing severe project schedule delays and cost overruns (Masood et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eScholars and practitioners have made significant efforts to enhance the performance status of PCSC. Existing studies on the PCSC management primarily focus on three themes. First, recognizing the obstacles and driving factors for the development of the PCSC is crucial, given the long path towards large-scale applications (Chang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hong et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Arashpour et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Second, the operation of the PCSC is highly complex and lacks standardization, resulting in diverse and uncertain supply chain risks (Polat \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Accurate identification and evaluation of these risks assist stakeholders to prevent and manage these risks more effectively (Wuni et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hsu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the integration of PCSC has received significant attention. While the PCSC members strive to improve their individual interests through internal integration, the overall interests of PCSC are often overlooked (Zhong et al. 2017). Therefore, it is necessary to build an effective cross-organization cooperation mechanisms to coordinate strategies and activities among PCSC members. Technological advancements also play a vital role in enhancing the operational efficiency of PCSC. Several studies have explored the implementation of advanced technologies in the production, transportation, assembly, and information interaction of PCSC (Wong et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Demiralp et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Resilience of the prefabricated construction supply chain\u003c/h2\u003e \u003cp\u003eThe concept of \u0026ldquo;resilience\u0026rdquo; originated in materials science, ecology, and psychology (Ponomarov and Holcomb \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Adobor \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In materials science, resilience refers to the ability of a material to return to its original shape after deformation. In the context of ecology, resilience pertains to the degree, manner, and speed at which an ecosystem returns to its original structure and function after being disturbed (Doorn et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Taşan-Kok et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Since this concept is very suitable for describing the supply chain\u0026rsquo;s state when it is disturbed, resilience is introduced into the domain of supply chain management. The fundamental assumption underlying supply chain resilience is that not all risk events within the supply chain can be prevented. Rather, the focus lies in the ability of the supply chain network system to recover to its initial or ideal state following the occurrence of risks (Tukamuhabwa et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The speed at which the supply chain returns to a normal state, encompassing aspects such as production, service, and supply ratios, serves as a reflection of the level of supply chain resilience (Wieland and Durach \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn relation to the dimensions of supply chain resilience, the initial perspective posits that supply chain resilience encompasses two dimensions: resistance and recovery. Conz and Magnani (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have highlighted that the resistance of a system to interference is composed of two pathways, namely, absorptive and adaptive. Consequently, supply chain resilience can be categorized into absorptive capacity, adaptive capacity, and restorative capacity, and this a viewpoint has gained consensus among the majority of scholars (Liu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al. 2021). The evaluation of supply chain resilience represents the second primary concern. Diverse qualitative and quantitative approaches have been utilized to assess the extent of supply chain resilience and its impact on overall supply chain performance (Spiegler et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Abimbola and Khan \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These studies serve as the starting point for the third research theme, which centers on enhancing supply chain resilience performance. Various resilience evaluation indicators provide guidance for bolstering the resilience of the supply chain. For example, pre-embedding and redundancy can fortify the absorptive capacity of the supply chain, dynamic logistics and information sharing of the supply chain can strengthen the restorative capacity of the supply chain, and procurement flexibility, delivery flexibility, and efficiency can enhance the adaptive capacity of supply chain (Pettit et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared with other manufacturing supply chains, the PCSC exhibits low product standardization and faces challenges in transportation (Ekanayake et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These factors increase the vulnerability of prefabricated construction, resulting in significant schedule delays and cost overruns. In the operation of the PCSC, disruptions such as machine failures, traffic congestion, and component damage are commonly encountered (Zhai et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To effectively address the risks associated with the PCSC, several risk management models have been developed (Zhu and Liu \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hsu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e;Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the practical utility of these models is limited due to the cumbersome nature of their application to potential risk event (Yingchao \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, studies have conducted significant investigations into specific deficiencies of the PCSC. For example, RFID technology, block-chain technology, and cloud computing have been used to solve the poor information exchange (Demiralp et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Du et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Multi-objective optimization models are constructed to improve the resilience of transportation planning (Zhang and Yu \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, while these studies have offered some remedies for improving the RPCSC, they are still characterized by fragmentation. The existing body of research lacks a systematic identification and evaluation to the factors affecting RPCSC and their mechanisms of action, which prevents academics and practitioners from formulating a comprehensive RPCSC improvement plan.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe structured method adopted in this study is divided into two phases, and the specific research steps are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Concepts identification\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Factors screening\u003c/h2\u003e \u003cp\u003eTo find the factors influencing RPCSC, this study first conducted a comprehensive literature review. The research team chooses Web of Science, Scopus, EBSCO, Spring, Taylor \u0026amp; Francis, and Emerald databases for literature search without limiting the publication time and type of literature. The keyword of prefabricated construction includes \u0026lsquo;\u0026ldquo;prefabricated building\u0026rdquo;, \u0026ldquo;prefabricated construction\u0026rdquo;, \u0026ldquo;industrialized building\u0026rdquo;, \u0026ldquo;industrialized construction\u0026rdquo;, \u0026ldquo;modular building\u0026rdquo;, \u0026ldquo;modular construction\u0026rdquo;; the keyword of the supply chain is \u0026ldquo;supply chain\u0026rdquo;. In this study, the meaning of \u0026ldquo;factor\u0026rdquo; is generalized. Our purpose is to search for and analyze any potential risks (such as political risks), practices (such as innovation), and other aspects (such as partnerships) that affect RPCSC. Therefore, keywords related to resilience include \u0026ldquo;resilience\u0026rdquo;, \u0026ldquo;risk\u0026rdquo;, \u0026ldquo;disruption\u0026rdquo;, \u0026ldquo;sustainability\u0026rdquo;, \u0026ldquo;uncertainty\u0026rdquo;, and \u0026ldquo;vulnerabilities\u0026rdquo;. Due to the overlapping coverage of several databases, duplication of literature is inevitable. After removing duplicates, a total of 47 related papers were obtained. Subsequently, these literatures were evaluated one by one, and finally identified 33 literatures that met the objectives of this study. All the factors influencing the RPCSC mentioned in these literatures are listed in Appendix A.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Taxonomy\u003c/h2\u003e \u003cp\u003eAs can be seen from Appendix A, the literature involves more than 200 factors, which partially or entirely overlap. Therefore, it is necessary to summarize these factors into independent concepts. Delphi technology was adopted for this work, which is a method used for structuring a group communication process. In the case of conflicting and insufficient information, the Delphi technique is an excellent method for accurately finding consensus and making effective decisions (Linstone and Turoff, 1975). The research team first selected ten authors who published two or more papers on PCSC in peer-reviewed journals and contacted them through the email address in the paper. In the email, the information about the purpose and process of this study was added. Meanwhile, the research team invited these authors to participate in the study. Finally, nine experts responded, and 6 of them expressed their willingness to participate. Furthermore, to balance possible differences between theoretical research and practice, the research team contacted ten managers from prefabricated component manufacturers and prefabricated construction contractors. Seven managers agreed to participate in the study. Regarding the number of expert groups, there is no unanimous recommendation in the literature. Yong et al. (1989) point out that 7\u0026ndash;15 is suitable for an expert panel. According to this criterion, the number of experts in the study is considered appropriate.\u003c/p\u003e \u003cp\u003eThis study was conducted from March to May 2023. First, the team eliminated duplicating factors in the literature and formed an initial list influencing RPCSC. Then, this list was sent to 13 experts via email, inviting them to summarize and merge the factors in the list and return the adjusted list. After the first round, the research team counted the opinions of all experts and sent the results to the experts again, asking them if they need to make changes to their previous opinions and sending back the second adjusted list. The second round adopted the same operation process as the first round. At the end of the third round, all the experts reached a consensus on integrating the factors influencing RPCSC, and 11 concepts were determined, as shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResearch approach\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain factors involved\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerformance of prefabricated components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll issues related to components design, production.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerformance of prefabricated components, Durability of prefabricated unproven, Geometric and dimensional intolerances\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruction of prefabricated building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactors related to the construction of prefabricated buildings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econstruction technology of prefabricated components, machines breakdown, safety\u003c/p\u003e \u003cp\u003eissues, Installation error of precast elements,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicies and regulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe completeness and changes of laws and regulations related to the prefabricated building supply chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elocal government policy preferences, Implication of new laws/regulation, Political economy changes, Unreasonable site layout of prefabricated components,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInformation exchange/sharing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe type, quantity, form and medium of information exchange between supply chain members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommunication breakdown/issues, Information loss, Inadequate IT systems, Information misuse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransport risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll risks that may occur during prefabricated components and raw material transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransport disruptions including port stoppages, site logistics, Damage of prefabricated elements during transportation,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch and development (R\u0026amp;D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe process and elements of supply chain members developing new technologies and new products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTechnology failure, cost of technology investment share, Cooperative Innovation, Absence of standard modular components, Monopoly of techniques by a few firms, Lack of R\u0026amp;D input\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecision alignment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe degree of consistency of management decisions of supply chain members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConflict resolution, buffer space hedging, strategy alignment, Solution consistency, Inappropriate business strategies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional management personnel/ Labor quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe quantity and quality of managerial personnel, labor of the members of the prefabricated construction supply chain.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLack of highly skilled workers, Insufficient construction capacity, Lack of best management practices, Inaccurate cost estimation, operation efficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelationship quality of members relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe level of friendship and trust among members of the prefabricated building supply chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erelationship coordination, Poor cooperation between multi-interface, trust between members, Stakeholders\u0026rsquo; lack of awareness,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupply-demand consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe degree of matching between products and demand in the prefabricated building supply chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariations and/or rework, Quality loss Supply-demand mismatch/shortages, Supply\u0026ndash;demand mismatches or shortages,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCost/profit sharing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReasonable and fair degree of cost and benefit distribution among members of assembly building supply chain.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecost of technology investment share, transaction costs,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Fuzzy cognitive maps (FCMs)\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Mathematical representation of fuzzy cognitive maps\u003c/h2\u003e \u003cp\u003eFuzzy cognitive maps (FCMs) are a qualitative reasoning technique used to analyze the relationship between many interacting things. Its distinguishing feature is that it can use prior knowledge to calculate the state of complex systems, thereby expressing dynamic causal systems with feedback that are difficult to be represented by Tree structure, Bayes network, and Markov model. Cognitive maps are originated from graph theory. In 1986, Kosko introduced fuzzy relations into cognitive maps and proposed the concept of fuzzy cognitive maps, which can simulate the ambiguity of the real world better than cognitive maps. Because of its concise reasoning mode and easy operation, FCMs have been widely used in many aspects such as social economy, management, military, and other aspects, such as management problem diagnosis (Carri\u0026ccedil;o and Guimar\u0026atilde;es \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), urban design (Xirogiannis et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), relationship management in aviation services (Kang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe topological structure of FCMs modeling is a triple pattern \u003cem\u003eG\u003c/em\u003e=(\u003cem\u003eC\u003c/em\u003e, \u003cem\u003eE\u003c/em\u003e, \u003cem\u003eW\u003c/em\u003e), Where C= {\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e..., \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e} represents the set of n concept nodes in FCMs; E={\u0026lt;\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e\u0026gt;|\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u0026isin;\u003cem\u003eC\u003c/em\u003e} is the causal association directed arc between all nodes in FCMs (Directed arc\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u0026gt; means that node \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e has a causal relationship or influence on \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e); \u003cem\u003eW\u003c/em\u003e={\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e} is the weight of the directed arc\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u0026gt;. \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the degree of influence of node \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e on \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, and the value range is [-1,1], where:\u003c/p\u003e \u003cp\u003eIf \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0, it means that \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e has a positive effect on \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e;\u003c/p\u003e \u003cp\u003eIf \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u0026lt;0, it means that \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e has a negative influence on \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e;\u003c/p\u003e \u003cp\u003eIf \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e =0, it means that \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e has no effect on \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, and there is no arc connection between \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eAn FCMs with n concept nodes can be uniquely determined by an interaction matrix W=( \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e)\u003csub\u003en\u0026times;n\u003c/sub\u003e. For example, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is a fuzzy cognitive map, and its corresponding interaction matrix W can be expressed as Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$W=\\left[ {\\begin{array}{*{20}{c}} 0\u0026amp;{{w_{12}}}\u0026amp;0\u0026amp;0\u0026amp;0\u0026amp;{{w_{16}}} \\\\ {{w_{21}}}\u0026amp;0\u0026amp;0\u0026amp;0\u0026amp;0\u0026amp;0 \\\\ 0\u0026amp;{{w_{32}}}\u0026amp;0\u0026amp;{{w_{34}}}\u0026amp;{{w_{35}}}\u0026amp;0 \\\\ 0\u0026amp;0\u0026amp;0\u0026amp;0\u0026amp;0\u0026amp;{{w_{46}}} \\\\ 0\u0026amp;0\u0026amp;0\u0026amp;{{w_{54}}}\u0026amp;0\u0026amp;0 \\\\ 0\u0026amp;0\u0026amp;{{w_{63}}}\u0026amp;0\u0026amp;{{w_{65}}}\u0026amp;0 \\end{array}} \\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reasoning mechanism of FCMs is the evolution process of an event based on its topological structure, in which each concept node \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents a certain sub-event in the event, often driven by other sub-events, such as \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e. The degree of the drive is determined by the causal (correlation) strength between \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e. This strength is the weight \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e of the directed arc\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u0026gt; in FCMs. The reasoning process of FCMs is realized by the recursive effect of the forward node on the backward node state, and the specific steps are as follows:\u003c/p\u003e\u003cp\u003ea) Determine an initialized state vector \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(0)\u003c/em\u003e,\u003c/p\u003e \u003cp\u003eb) Obtain the interaction matrix, with the help of expert knowledge and experience,\u003c/p\u003e \u003cp\u003ec) Multiple iterative calculations of the initial state vector are carried out through Equations (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and (\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When the final result satisfies \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(t)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(t\u0026thinsp;+\u0026thinsp;1)\u003c/em\u003e, the iteration is stopped. At this time, FCM reaches A stable state, and the whole iteration process ends.\u003c/p\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$A_{i}^{{(t+1)}}=f(A_{i}^{{(t)}}+\\sum\\limits_{{j=1,j \\ne i}}^{n} {{w_{ji}}A_{j}^{{(t)}}} )$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A_{i}^{{(t+1)}}\\)\u003c/span\u003e\u003c/span\u003ethe value of concept \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e at the step \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A_{i}^{{(t)}}\\)\u003c/span\u003e\u003c/span\u003e is the value of the each interaction of the interconnected concept \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e at step \u003cem\u003et\u003c/em\u003e, \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eji\u003c/em\u003e\u003c/sub\u003e is the weighted arc from \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e to \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003ef\u003c/em\u003e is a threshold function to make sure the node concept value remains in the interval [0,1] and could be the Sigmoid threshold\u003c/p\u003e \u003cp\u003efunction:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$f=\\frac{1}{{1+{e^{ - \\lambda x}}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere λ\u0026thinsp;\u0026gt;\u0026thinsp;0 determines the steepness of the continuous function \u003cem\u003ef\u003c/em\u003e. The Sigmoid function is usually used when the concept interval is [0, 1].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Modular construction\u003c/h2\u003e \u003cp\u003eAfter determining the factors influencing RPCSC, the steps of constructing FCM were executed. After completing the factors integration, we invited these experts to evaluate the impact of the factors on each other and the extent of these factors affect RPCSC. Experts were required to use \u0026ldquo;No\u0026rdquo;, \u0026ldquo;Very low\u0026rdquo;, \u0026ldquo;Low\u0026rdquo;, \u0026ldquo;Medium\u0026rdquo;, \u0026ldquo;High\u0026rdquo;, and \u0026ldquo;Very high\u0026rdquo; to express the ambiguous intensity of causality between concepts. The direction of influence between concepts was expressed by positive (+) or negative (-).\u003c/p\u003e \u003cp\u003eBecause the language of experts is vague, and the opinions of multiple experts are different, the linguistic values need to be converted into a digital weight through the triangular fuzzy number method to facilitate data processing. The conversion format of each linguistic value is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After the linguistic value conversion is completed, the fuzzification step was executed to convert the triangular fuzzy number into an accurate value. There are many defuzzification methods, and the area center of gravity method is the most common one. The specific calculation rules are shown in Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${U^*}=\\frac{{\\sum\\nolimits_{{i=1}}^{n} {({A_i} \\times {L_i})} }}{{\\sum\\nolimits_{{i=1}}^{n} {{A_i}} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere n is the number of experts, \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents area covered under each fuzzy set and \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents midpoint of the fuzzy set on X-axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eEquation (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was applied to aggregate experts\u0026rsquo; opinions to obtain the quantitative value \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e of the relationship between all conceptual nodes and the impact on RPCSC. On account of the simplicity of the FCMs calculations, the \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e values in the interval [-0.1, 0.1] are set to zero. The final interaction matrix \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. According to the final interaction matrix, the cognitive map has been sketched (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe final interaction matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eRPCSC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe iterative calculation is conducted according to Eqs.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In consideration of the simplicity of calculation and the speed of convergence of each concept, let λ\u0026thinsp;=\u0026thinsp;1 (Baker et al. 2018). The iterative calculation results of each step are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that after five iterations of calculation, the system reaches a steady-state. According to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the changing trend of the state value of each concept is drawn (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that the convergence speed of C9, C3, C4, C8, and C6 is fast. Their state values are higher than other factors when they fully converge, which shows that the Relationship quality of members, Policies and regulations, Information exchange/sharing, Professional technicians and management personnel/ Labor quality, and Research and development (R\u0026amp;D) are the most important factors influencing RPCSC. Other concepts, including Decision alignment (C7), Transport risk (C5), Cost/profit sharing (C11), Supply-demand consistency (C10), Performance of prefabricated components (C1), and Construction of prefabricated buildings (C2) are secondary factors influencing RPCSC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe state value of concepts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIterative rounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.5025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.4807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.4807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.4807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.4807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reveals that the most crucial factor impacting the RPCSC is the relationship quality of members, which aligns with previous research findings that emphasize the significance of relationship quality as a key determinant of supply chain performance (Fynes et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Shin et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Effective communication and coordination are essential in PCSC for various stakeholders such as component manufacturers, designers, transporters, prime contractors, subcontractors, and owners. A prime example is the need for seamless communication among designers, component manufacturers, and construction contractors to ensure that design changes are aligned with component production capacity and assembly technology (Zhao et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This process heavily relies on fostering a strong relationship among these members of the supply chain. However, Hofman et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that in practice, the relationship between the members of the PCSC is disconnected and distrusted, leading to frequent disruptions in PCSC. Therefore, the establishment of a solid, long-term relationship is of paramount importance for PCSC members aiming to enhance the RPCSC.\u003c/p\u003e \u003cp\u003eThe impact of policies and regulations on RPCSC is second only to the relationship quality. Wuni et al.(2019) pointed out that successfully implement prefabricated construction, it is crucial to have a comprehensive legal framework that encompasses the necessary qualifications, codes, standards, regulations, and policies. However, the current laws, regulations, and policies predominantly focus on the construction processes, structural durability, and material quality of traditional projects, neglecting the involvement of prefabricated construction and its associated supply chain activities (Ekanayake et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many behaviors exhibited by PCSC member companies are influenced by corporate culture and ethics, which often leads to opportunistic and noncompliant actions that significantly jeopardize the smooth operation of the supply chain. Moreover, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates that in the system of factors influencing the RPCSC, the policies and regulations point to many other factors, but no one points to it, which indicates that this one is an independent and fundamental factor. Therefore, as the regulator and promoter of prefabricated construction, it is imperative for the government to promptly enhance relevant policies and regulations to guide and constrain the behavior of PCSC members, thereby reducing the risk of disruptions caused by participant misconduct.\u003c/p\u003e \u003cp\u003eThe exchange and sharing of information emerges as the third most crucial factor influencing the RPCSC. Previous research has extensively highlighted the challenges associated with information exchange in PCSC. Ekanayake et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) discovered that while prefabricated construction can significantly enhance construction efficiency, the absence of real-time information sharing often leads to fragmentation and disruptions among supply chain participants. For example, due to the lack of real-time exchange and sharing of assembly planning at the construction site, production planning at the component manufacturer, and transportation planning at the transportation company, project schedules are often delayed, and supply chain costs escalate (Luo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, scholars have emphasized the potential of information interaction platforms based on TI technology or block chain technology to enhance the efficiency of information exchange among members and bolster the RPCSC (Demiralp et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicates that the relationship quality influences the information exchange among supply chain participants, which indicates that a good relationship is a basis for their willingness to share and exchange information. This finding enlightens that in order to improve the operational efficiency and resilience of the entire PCSC, supply chain members must seek to establish good formal and informal relationships with upstream and downstream enterprises and even all other supply chain members. Wuni and Shen (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) assert that effective stakeholder management strategy and scientific project planning and scheduling are important preconditions for the orderly operation of the PCSC. The formulation of strategy and plan depends on professional management personnel. However, as the prefabricated construction is still in the initial phase of promotion, professional management personnel and their experience are generally lacking, which has become one of the major obstacles that limit the RPCSC (Wen \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, practitioners have noted that quality defects in prefabricated construction are primarily caused by assembly errors and these errors are predominantly attributed to the low skill level of assembly workers (Ekanayake et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, due to significant differences from traditional construction procedures, the current prefabricated construction sector suffers from a shortage of skilled labor. This shortage ultimately results in inadequate quality, cost overruns, schedule delays, and even disruptions to the entire supply chain.\u003c/p\u003e \u003cp\u003eThe research and development (R\u0026amp;D) is the last major factors influencing the RPCSC. While prefabricated construction plays a crucial role in promoting sustainable development in the construction industry, it currently suffers from a number of glaring deficiencies in its production, transportation, and assembly, leading to frequent interruptions of the PCSC. One limitation is the restricted range of component products available in prefabricated construction, which fails to fully satisfy the diverse and personalized demands of customers (Jiang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, the safety and durability of prefabricated construction are not entirely trusted due to incomplete technological development, particularly in relation to splice nodes (Fard et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, the large size and weight of fabricated components impose higher demands on transportation and lifting equipment. In practice, failures in transportation and lifting equipment constitute a significant proportion of the causes behind supply chain disruptions. A prominent contributing factor to these challenges is the insufficient level of R\u0026amp;D of PCSC member enterprises. According to Wu et al.(Wu et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), prefabricated construction holds a relatively small market share compared to traditional cast-in-place construction methods, thus leading to potential diseconomies of scale resulting from substantial R\u0026amp;D investments. However, the prefabricated construction market shows promising growth potential, and substantial R\u0026amp;D investments can assist PCSC member enterprises in establishing early competitive advantages (Sabahi and Parast \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is imperative for these enterprises to appropriately increase their R\u0026amp;D investments. To further incentivize active R\u0026amp;D efforts of enterprises, the government can consider providing R\u0026amp;D cost subsidies to prefabricated construction member enterprises.\u003c/p\u003e"},{"header":"6. Conclusions and future research directions","content":"\u003cp\u003eCompared to traditional construction forms, prefabricated construction offers several advantages including reduced environmental pollution, lower resource consumption, and increased productivity. However, in practice, the PCSC is frequently interrupted by a number of factors. In this context, how to improve the efficiency of the PPCSC and keep it running continuously and efficiently is a challenging issue. This study aims to identify the factors that impact the RPCSC by conducting a comprehensive literature review. Subsequently, these factors are integrated and summarized into 11 key concepts using the Delphi method. Finally, the fuzzy cognitive map method is employed to evaluate the relationship and degree of impact between these concepts on the RPCSC. The findings indicate that the relationship quality of members, laws and regulations, information exchange/analysis, professional management personnel/labor quality, and research and development (R\u0026amp;D) are the primary factors influencing the RPCSC. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the impact between all 11 concepts.\u003c/p\u003e \u003cp\u003eThe findings of this study have substantial theoretical and practical implications. Currently, the theoretical research on RPCSC is limited. It is not clear what factors affect the RPCSC. Using literature review and Delphi method, this study identifies the factors affecting RPCSC, on the basis of which, this study explains the linkages between these factors using fuzzy cognitive map method. The relevant findings provide a direction for future research on the RPCS improvement mechanisms. Moreover, in practice, the vulnerability of the PCSC severely delays the construction schedule and leads to increased costs. Practitioners in the PCSC need to examine and evaluate the deficiencies in PCSC to enhance the PCSC through more effective measures. This study offers a pre-list that can assist them in effectively identifying the factors that influence the RPCSC and relationships among these factors. For example, this study finds that the relationship quality of members is the most crucial factor influencing RPCSC. Consequently, contractors, suppliers, and designers of prefabricated construction must carefully evaluate and improve their relationships with upstream and downstream supply chain members, emphasizing communication and interaction. Overall, the findings presented in this paper are helpful for industry professionals seeking to enhance the PPCSC and improve the prefabricated construction performance.\u003c/p\u003e \u003cp\u003eAlthough this study has made significant theoretical and empirical advancements, it is crucial to acknowledge certain limitations. Initially, this study identified the factors that impact the RPCSC through an extensive examination of the existing literature. However, it is important to recognize that despite covering various aspects of the PCSC, there may still be limitations in terms of completeness of factor identification. In order to address this issue, future research can further expand the factor database by employing alternative methods such as large-scale interviews and multiple case studies. Another limitation of this study may lie in the sample size. While the number of experts involved in this research is adequate and represents the high level of knowledge in prefabricated construction theory and practice, a larger sample size would likely produce more precise findings. Thus, to obtain more reliable and trustworthy results, it is recommended to expand the pool of experts through various avenues in future studies. Finally, generalization of the results may be another limitation of this study. As the factors identified are based on existing literature, the applicability of the study\u0026rsquo;s conclusions to a specific country or region may be limited. To gain further valuable insights, future research could explore the factors influencing the RPCSC in various countries or industry backgrounds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXXX, XXX, and XXX contributed to data collection and analysis; XXX designed the research and provided guidance on manuscript writing, and XXX wrote the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Liaoning Province Social Science Planning Research key Project (NO.L23AGL010).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbimbola M, Khan F (2019) Resilience modeling of engineering systems using dynamic object-oriented Bayesian network approach. 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Int J Prod Res 55(14):3984\u0026ndash;4002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Yu L (2021) Resilience-cost tradeoff supply chain planning for the prefabricated construction project. J Civ Eng Manag 27(1):45\u0026ndash;59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao S, Wang J, Ye M, Huang Q, Si X (2022) An evaluation of supply chain performance of China\u0026rsquo;s prefabricated building from the perspective of sustainability. Sustainability 14(3):1299\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu T, Liu G (2022) A Novel Hybrid Methodology to Study the Risk Management of Prefabricated Building Supply Chains: An Outlook for Sustainability. Sustainability 15(1):361\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental pollution Prefabricated Construction, Supply chain, Resilience, Fuzzy cognitive maps (FCMs)","lastPublishedDoi":"10.21203/rs.3.rs-3752539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3752539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrefabricated construction has less environmental pollution, less resource consumption, and high productivity. This new construction model is an important tool for the construction industry to achieve sustainable development. However, disruptions in the prefabricated construction supply chain (PCSC) frequently occur in practice, which seriously reduces the performance of prefabricated building projects. Improving the resilience of the prefabricated construction supply chain (RPCSC) is an urgent problem to be solved. This study first identified the factors influencing the RPCSC through a comprehensive literature review. Next, 13 experts were invited to summarize and integrate these factors, and 11 concepts were obtained. Finally, the fuzzy cognitive maps method is applied to evaluate the impact of these concepts on the RPCSC and the interaction between them. The results show that the main factors are relationship quality of members, laws and regulations, information exchange/analysis, Professional management personnel/Labor quality, and R\u0026amp;D. Moreover, this study reveals the effect of these factors individually or synergistically influencing the RPCSC. This study provides valuable insights for governments and members of the prefabricated construction supply chain. The related findings can help reduce the risk of disruption in the prefabricated supply chain, improving the productivity and low-carbon performance of prefabricated construction.\u003c/p\u003e","manuscriptTitle":"Exploring Critical Factors Influencing the Resilience of the Prefabricated Construction Supply Chain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 08:54:14","doi":"10.21203/rs.3.rs-3752539/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf6a5627-4f62-4df4-91d4-fd1aa1077c61","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-21T15:39:39+00:00","versionOfRecord":{"articleIdentity":"rs-3752539","link":"https://doi.org/10.3390/buildings15020289","journal":{"identity":"buildings","isVorOnly":true,"title":"Buildings"},"publishedOn":"2025-01-19 00:00:00","publishedOnDateReadable":"January 19th, 2025"},"versionCreatedAt":"2024-02-13 08:54:14","video":"","vorDoi":"10.3390/buildings15020289","vorDoiUrl":"https://doi.org/10.3390/buildings15020289","workflowStages":[]},"version":"v1","identity":"rs-3752539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3752539","identity":"rs-3752539","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}