Abstract
Purpose
This study aims to comprehensively identify supply chain risks and their causes, the factors influencing supply chain management and techniques to successfully mitigate and control supply chain risks in construction projects. This study developed a comprehensive framework showing various supply chain risks and how these risks that influence project execution are systematically identified and managed for the overall construction project success.
Design/methodology/approach
The research conducted was characterised by its descriptive, exploratory and quantitative nature. The collection of quantitative data was conducted by means of structured online questionnaires. The sample consisted of 205 construction project professionals who were selected randomly. This group included individuals with various roles in the construction industry, such as project managers, civil/structural engineers mechanical engineers, risk managers, architects, quantity surveyors, electrical engineers, construction managers, health, safety and environment managers, estate managers and other professionals. All participants were actively involved in construction projects located in the Gauteng province of South Africa. The data was analysed, using descriptive statistical methods, including factor analysis, reliability assessment and calculations of frequencies and percentages.
Findings
The result showed that predictable delivery, funding schedule, inventories, balanced demands, production capabilities, timely procurement, construction supply chain management coordination, delivery reliability, the proximity of suppliers, identification of supply chain risks in the conceptualisation stage of a project, identification of supply chain risks in the planning stage of a project, identification of supply chain risks in the execution stage and the reconciliation of material flows of the subcontractors with the contractors were identified as the key factors that influenced the construction supply chain management the most. The result also showed that subcontractor’s negative attitudes towards supply chain management, procurement delays, imbalanced demands, clients’ negative attitudes towards other project stakeholders, unpredictable delivery reliability, disorganised construction supply chain management approach, delayed funding, low delivery reliability, poor inventories, poor construction supply chain co-ordination, suppliers’ negative attitudes towards supply chain management and when the material flows of the subcontractors with the contractors are not reconciled were identified as the factors that have the greatest impacts on construction supply chain risks management.
Research limitations/implications
For future research, it is recommended to incorporate fourth industrial revolution) such as machine learning prediction models and algorithms, Artificial intelligence and blockchain to identify and manage supply chain, supply chain risks and project stakeholders involved in supply chain in construction projects. Green construction or sustainable construction was not fully covered in this study. The findings will be beneficial for sustainable construction projects in developing countries for sustainability, although it did not extensively cover green buildings and related risks.
Practical implications
Supply chain risk is one of the major challenges facing the construction industry because construction projects are complex by nature involving a lot of activities and participants with different responsibilities and tasks therefore it is highly recommended to implement the proposed frameworks in this paper from the conceptualisation stage to the execution stage, carefully identifying parties involved in supply chain, supply chain management, stakeholders, tasks, activities, responsibilities and supply chain risks generated as a result of the interactions between stakeholders involved in supply chain management and coordination to realise project objectives. The findings will be a foundation for identifying and managing supply risks in sustainable buildings in developing countries.
Social implications
Supply chain management is crucial in every enterprise. Managing supply chain risks is a major aspect of risk and disaster management and this implies that supply chain excellence is achievable by building communication, trust and mutual objectives, no blame culture, performance measurement, constant improvement and partnering.
Originality/value
The implementation of construction supply chain risk management framework involves assessing the impacts of these supply chain risks on the objectives of construction projects with respect to time, cost, safety, health, environment, stakeholders, financial performance, client satisfaction and quality.
Keywords
Citation
Okika, M.C., Vermeulen, A. and Pretorius, J.H.C. (2024), "A systematic approach to identify and manage supply chain risks in construction projects", Journal of Financial Management of Property and Construction, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JFMPC-09-2023-0057
Publisher
:Emerald Publishing Limited
Copyright © 2024, Michael Chuba Okika, Andre Vermeulen and Jan Harm Christiaan Pretorius.
License
Licensed re-use rights only
1. Introduction
Construction projects are characterised through short-term relationships, intrinsic complexities, several interruptions and interactions due to external and internal unexpected situations. The participation of numerous concentric stakeholders, rapidly varying environments, time and cost overruns and client dissatisfaction lead to uncertainties and risks in the project. Because of these different particularities and in terms of productivity, efficiency and performance, construction industry is behind other industries (Costa et al., 2019). These attributes contribute extensively to the presence of high risks in construction projects (Dikmen et al., 2018; El-Sayegh and Mansour, 2015; Siraj and Fayek, 2019). On this regard, managing supply chain risks (SCRs) ineffectively is a fundamental cause of poor reputation, delay, project failure and budget overrun (Carvalho and Rabechini Junior, 2015; Habibi and Kermanshachi, 2018). To identify the sources of SCRs is the important step to effectively manage SCRs because it could offer a base for the successive tiers (Siraj and Fayek, 2019). The contingency plan development for SCRs management happens to be more significant for construction industries in these days’ complex and large projects (Gunduz and Elsherbeny, 2020). Information, service, material and financial flows between involved stakeholders have a vital influence on the supply network and some risks are handled by a specific stakeholder (Luo et al., 2019). SCRs are directly connected to the associated project stakeholders, wherein interdependency among stakeholders is suggestively high, so, SCRs of construction tasks are managed safely through the consideration of stakeholders’ perspectives since every stakeholder from requirement to maintenance stage are exposed to numerous risks (Luo et al., 2019). In construction projects, ineffective inventory management leads to a bottleneck because of a great degree of interdependent activities and variability (Costa et al., 2019).
A usual issue that usually occurs in a construction supply chain (CSC) is complexity, project communication, material flow and the internal company’s communication (Thunberg and Fredriksson, 2018).
The paper’s objective is to carefully identify SCRs through a structured questionnaire focusing on construction industry professional, identify and analyse factors that affect supply chain management and the causes of SCRs and determine the ways to effectively mitigate and minimise these risks in construction projects. The study objective was to carry out a literature review on supply chain in construction, as well as SCRs and SCR management. The study’s two main objectives were:
to identify the factors that influence construction supply chain management (CSCM) and their extent levels; and
to identify the causes of construction supply chain risks (CSCRs) and their extent levels.
To support the objectives of the study, these two research questions were asked. Respondents were asked to identify:
the causes of SCRs; and
factors that influenced CSCM
The study focused on a systematic approach of identifying and managing risks associated with supply chain in construction projects in every phase.
The literature review aimed to identify essential areas of knowledge in the field of research and provide a summary of recent literature on the topic. The study aimed to create a framework for identifying and managing SCRs in construction, ensuring project success.
This work was divided into sections: Section 1 – introduction, Section 2 – literature review, Section 3 – data and methodology, Section 4 – reliability statistics of data collected, Section 5 – results and discussion and Section 6 – conclusion.
2. Literature review
2.1 Construction supply chain management
Construction projects go through multiple phases, each with its own set of participants. Research indicates a correlation between construction project risks and participant interactions (Koc and Gurgun, 2021; Yuan et al., 2021). CSC is a very complex system because many stakeholders and decision-makers are involved. Within the chains, there is an increase in uncertainty within the networks and more complex the networks are, the more uncertainty and risk will be.
CSCM involves the use of organisational, managerial and technological means to configure resources for the overall project success (Queiroz et al., 2022). According to Hussein et al. (2021), successful completion of a construction project requires typical CSC operations across its various phases. Costa et al. (2019) examined customer–supplier relationship in CSC and carried out the decision-making trial and evaluation laboratory (DEMATEL) analysis to expose the source of the obstacles to enhance the relationships. Luo et al. (2019) recognised 30 stakeholder-related SCRs and associated those risks with one or greater supply chain stakeholders: government, transporter, manufacturer, primary contractor, designer, client and subcontractor. Construction operations have a significant negative influence on the environment Chuai et al. (2021). There are increases in the need for coordination between precast factories and construction sites, as well as collaboration among all key stakeholders participating in construction activities.
These diverse construction project participants will generate diverse information sources at each stage of the project, in addition to their own information requirements. These problems can be related to a disorganised management style. Specifically, firms involved in the construction project tend to focus on their own manufacturing operations, efficiency and performance, resulting in errors throughout project execution. As a result, issues such as overcapacity, late purchase and delivery of materials and equipment and mistimed transportation occur regularly. Furthermore, throughout project execution, information need is gradually conveyed among the major project participants, and information deviation can increase. It is easy to produce information distortion and uncertainty (Liu et al., 2022). This results in unsatisfactory performance for the construction project and its collaborating firms (Golpîra, 2020; RezaHoseini et al., 2021). CSCM strives to deliver projects on time and at the lowest possible cost (economic and environmental) at the best time (RezaHoseini et al., 2021). By balancing diverse construction resources, communicating with all key participating firms and integrating construction information, CSCM assists leadership teams in optimising project and enterprise performance through the project-centred management paradigm.
Numerous risks are rooted across CSCs that are related to various stakeholders (Luo et al., 2019). Wuni et al. (2019) did a synthesis of 39 empirical papers on risks in sectional integrated construction and identified 30 serious risks. This sort of construction needs unique stakeholder composition, engineering, construction, design and supply chain management. These requirements create uncertainties and risks that undesirably influence modular incorporated projects success. Moreover, Derakhshanfar et al. (2019) systematically developed the taxonomy and terminology of delay risks. Some of the broadly adopted main risks classifications in the literature could be summarised as contractual, financial, managerial, social, economic, environmental and technical with respect to project risks (Kuo and Lu, 2013; Siraj and Fayek, 2019; Thunberg et al., 2017), whereas they are supply, external, control, demand and coordination with respect to SCRs in construction (Luo et al., 2019; Rudolf and Spinler, 2018). However, in spite of widespread studies in the CSCM research area, researchers did not study every supply chain life cycle phase discretely while linking methodically reviewed risks to the related stakeholders. Siraj and Fayek (2019) achieved a content evaluation based on the literature on identifying risks within the construction industry and observed that most researchers used expert interviews, a questionnaire survey and literature evaluation to identify risks.
2.2 Construction supply chain risks
CSCM provides evident benefits to the construction sector during the course of construction industrialisation. Nonetheless, unknown elements in the natural and social environment, such as broken capital chain and logistics interruption, frequently result in volatility and even disruption of CSC Ekanayake et al. (2022a, 2022b). Material management is a critical issue in construction activities, influencing CSC resilience through demand, procurement, transportation, delivery, inventory and other relationships. The emergence of modular buildings has presented CSC with new issues (Zhang and Yu, 2021). However, in recent years, researchers have discovered that CSC is influenced by a variety of internal and external factors, including the natural and social environment, as well as the construction project environment, resulting in CSC fluctuations and even disruptions (Ekanayake et al., 2022a, 2022b). As a result, CSC resilience promotes CSC stability in stormy scenarios by addressing elements such as responsiveness, flexibility and robustness (Kazancoglu et al., 2022), which is invaluable in ensuring construction project performance.
Furthermore, several researchers have investigated construction enterprise and CSC performance in terms of CSC disruption and reverse logistics (Kissi et al., 2020; Pushpamali et al., 2021). The existence of uncertain risks these days rises within the supply chain because of its globalisation, saturation and companies to use new risk evaluation concepts (Liker, 2019). Providers selection, resource allocation and variety of delivery determination are crucial parts of supply chain success (Deng et al., 2019) Shahbaz et al. (2019) studied the effect of supply chain capacities on logistic effectiveness for the construction projects and they concluded that alliance with stakeholders and flexibility with respect to labour, time, cost and other essential decisions is effective in logistic supply chain. The adaptability and convertibility of the supply chain to new conditions is referred to as resilience (Wieland and Durach, 2021), and thus CSC resilience can be defined as the ability of CSC to withstand fluctuations or disruptions by dynamically adjusting and optimising the structure of resources related to construction projects to cope with the impact of uncertain factors on CSC. Previous studies (Zainal Abidin and Ingirige, 2018; Abas et al., 2022; Ekanayake et al., 2022a, 2022b) examined the dynamics and impact of interlinked risks among construction organisations to improve resilience in dealing with disruptive events in construction projects. According to the CSC risk of large-scale engineering construction projects, systematic and structured situational risk management is performed to assure the project's successful delivery (Rudolf and Spinler, 2018).
Ahmed et al. (2019) recognised the critical limitations hindering the implementation of effective supply chain management in construction industries of developing nations such as UAE, Somalia, Ghana, Qatar, Kuwait, Egypt, India, Pakistan, Bangladesh, Iran, Saudi Arabia, Yemen and Argentina. The crucial elements recognised were regulations, incentives, government support, inadequate polices, top management or leadership, knowledge and lack of public awareness. Luo et al. (2018) concluded that supply chain excellence is achievable by building communication, trust and mutual objectives, no blame culture, performance measurement, constant improvement and partnering.
Panova and Hilletofth (2018) established that delays in the material supply and other resources, deficiency warehouse or overcapacity, shipment of goods and on-time loading are the potential risks within the supply chain. Reducing needless waste and tightening control are excellent ways to save money and enhance efficiency (Li et al., 2020). Paying attention to customer requests also helps to ensure the quality of construction operations and finished products (Le and Nguyen, 2022).
Luo et al. (2018) acknowledged that without effective communication, achieving success in supply chain in construction projects is not feasible. Communication is a crucial part of a CSC. The managers and suppliers fail to communicate relevant information and other changes on time. For a successful supply chain, communication must be efficient and effective between every stakeholder. Technology integration can essentially minimise the communication gap. Other studies recognised expertise, good weather, teamwork, skills, rewards, timely funding, regulations, laws and availability of technical staff as success factors (Gunduz and Yahya, 2015; Mashwama et al., 2017; Zakaria et al., 2017; Afolabi et al., 2019; Ahmadabadi and Heravi, 2019; Sfakianaki, 2019).
CSCs differ from industrial supply chains because they involve several stakeholders and complicated interactions throughout a project's lifecycle (Le et al., 2021). The CSC's transient nature and varied offerings make management more challenging since CSCs face daily operating risks, such as design modifications and material shortages, in addition to disasters like COVID-19 (Ndukwe et al., 2021; Raoufi and Fayek, 2021). Supply and demand risks are significant sources of operational uncertainty in CSCs (Pham et al., 2023).
Supplier capacity restrictions might cause supply interruptions, impacting material availability and project resource management. Uncertainty in material demand in construction projects can stem from budget constraints, schedule changes, late deliveries, equipment malfunctions and low productivity. Improving construction supply chain resilience (CSCR) is crucial for mitigating supply and demand risks in the construction industry (Shishodia et al., 2022; Siraj and Fayek, 2019; Chan, 2021; Raoufi and Fayek, 2021).
3. Methodology
The research conducted was characterised by its descriptive, exploratory and quantitative nature. The collection of quantitative data was conducted by means of structured online questionnaires. The sample consisted of 205 construction project professionals who were selected randomly in construction industry in Gauteng province, South Africa. This group included individuals with various roles in the construction industry, like project managers, civil/structural engineers, mechanical engineers, risk managers, architects, quantity surveyors, electrical engineers, construction managers, health, safety and environment (HSE) managers, estate managers and other professionals.
The selection criteria for participants required the following characteristics as shown in a selection criteria for participants.
active participants in the construction conceptualisation stages;
active participants in the planning stages of construction;
active participants in the design stages of construction;
active participants in the procurement stages of construction;
active participants in the contracting stages of construction;
active participants in the sub-contracting stages of construction;
active participants in production stages of construction;
active participants in execution stages of construction;
active participants in the CSC;
active participants in commissioning stages of construction;
active participants in the construction health, safety and environment management; and
active participants in the pre-contracting stages of construction.
During phase A of the dissemination of survey questionnaires to industry specialists within the construction industry, the sampling strategy was based on a random probability sample. Construction experts, specialists, leadership, construction and project managers who were actively involved in the construction business and executed construction projects to ensure that selected participants possessed the necessary skillset and knowledge on CSCM and CSCR management and relevant construction activities to successfully answer the survey questions.
3.1 Data collection
The primary data were collected from active project stakeholders as shown in a selection criteria for participants above. Secondary data were collected through a review of the relevant literature, articles and journals related to supply chain management in the construction industry. A total of 205 research questionnaires were distributed to participants active in the construction industry.
The data collection process commenced by administering a biographical questionnaire to ascertain the appropriate research participants in the first section. Section C, (specifically C5 and C6) encompassed the questions related to SCRs in construction projects. The data obtained from the questionnaire was coded, recorded and analysed using the Statistical Package for the Social Sciences (SPSS, V25). The items or questions in each section were coded for ease of analysis. For the five-point Linkert scale chosen, the extent scale was used to find out the extent in which each statement or item in the questionnaire influenced CSCM. In addition, “to no extent” was coded as a 1, “to a small extent” was coded as a 2, “to a moderate extent” was coded as a 3, “to a large extent” was coded as a 4 and “to a very large extent” was coded as a 5.
Factor analysis was conducted to identify the latent dimensions underlying the measured variables, as these variables are expected to exhibit correlations or anticipated correlations. The paper aims to investigate the impact of measured variables and examine the interrelationships among a predetermined set of defined, observed and quantifiable constructs. According to the guidelines provided in the SPSS manual, the Kaiser–Meyer–Olkin (KMO) measure and the Bartlett's Test of Sphericity are used to assess the suitability of the correlation matrix as an identity matrix, thereby determining the appropriateness of the factor model.
The questionnaire was structured in a way to identify SCRs, the factors that influence supply chain management and their levels of impacts and ways to effectively mitigate and manage SCRs in construction projects. All participants were actively involved in construction projects located in the Gauteng province of South Africa. Finally, the data underwent analysis using descriptive statistical methods, including factor analysis, reliability assessment and calculations of frequencies and percentages.
4. Data analysis
Exploratory factor analysis, empirical and theoretical reliabilities and frequencies and descriptives were the statistical approaches used to analyse the data collected. The study also used the KMO measure and Bartlett's test to measure the interrelationships among variables, thereby informed the decision to advance with the factor analysis of the collected data. A comprehensive set of 205 responses was obtained from the designated target population, which primarily comprised of individuals within the construction industry as described in the context of questionnaire design and target group identification. Table 1 shows the summary of the biographical data of the respondents who participated in the online survey.
From Table 1, out of the 205 responses from the online questionnaire, 16 respondents were quantity surveyors, 9 were architects, 7 were builders, 8 were project engineers, 9 were project administrators, 10 were safety officers/engineers/managers, 10 were risk managers, 20 were mechanical engineers, 13 were construction engineers, 18 were project managers, 8 were estate managers, 22 were electrical engineers, 25 were construction managers, 27 were civil/structural engineers and 3 respondents were other construction professionals. Table 2 shows age distribution of participants.
From the Table 2, out of the 205 respondents, 4 respondents were in the age group of 21–25 years, 16 were 26–30 years, 32 were 31–35 years, 42 were 36–40 years, 53 were 41–45 years, 46 respondents were 46 years and above. Table 3 shows the academic qualifications of the respondents.
From Table 3, 11 respondents out of the 205 respondents, which represented 5.4% of the total respondents, had post matric or diplomas as their highest academic qualifications; 55 (26.8%) had bachelor's degrees; 28 (13.7%) had honours degrees; 70 (34.1%) had master’s degrees; and 41 respondents, which represented 20.0% of the total respondents, had doctoral degrees. Table 4 shows the organisational size of the respondents.
From the Table 4, 72 respondents, which represent 35.1% of the total respondents, work in the small-sized industries and 74, which represent 36.1%, work at medium-sized industries and 59 of the respondents, which represent 28.8%, work in the large-scale construction industries. Table 5 represents the frequency distribution for question 1 (factors that influence CSCM).
Table 5 shows that predictable delivery, funding schedule, inventories, balanced demands, production capabilities, timely procurement, CSCM coordination, delivery reliability, the proximity of suppliers, identification of SCRs in the conceptualisation stage of a project, identification of SCRs in the planning stage of a project, identifying SCRs in the execution stage and the reconciliation of material flows of the subcontractors with the contractors were identified as the key factors that influenced the CSCM the most. Table 6 shows the extent in which the following factors contributed to the causes of CSCRs.
From Table 6, subcontractors’ negative attitudes towards supply chain management, procurement delays, imbalanced demands, clients’ negative attitudes towards other project stakeholders, unpredictable delivery, disorganised CSCM approach, delayed funding, low delivery reliability, poor inventories, poor CSC coordination, suppliers’ negative attitudes towards supply chain management and when the material flows of the subcontractors with the contractors are not reconciled were identified as the major causes of CSCRs. Table 7 shows the KMO and Bartlett’s test for research objective 1 (factors that influence CSCM and their extent levels).
From Table 7, the KMO measure of sampling adequacy is 0.902 which is greater than 0.6 and for Bartlett’s test of sphericity, the significance (sig) was zero (0.000) which is the p-value and it was less than 0.05 and this supports the factorability of the correlation matrix. Table 8 shows the KMO and Bartlett’s test for research objective 2 (to identify the causes of CSCRs and their extent levels.
From Table 8, the KMO measure of sampling adequacy is 0.895 which was greater than 0.6 For Bartlett’s test of sphericity, the significance (sig) is zero (0.000) which is the p-value and it was less than 0.05 and this supports the factorability of the correlation matrix.
4.1 Exploratory factor analysis for sections C5 and C6
With a sample size of 205, factor analysis was done to reduce the data or summarise using a lesser set of factors. This was achieved by intercorrelation identification of variables. To carry out factor analysis, the correlation matrix should show at least have some correlations of r = 0.3 or greater. Barlett’s test of sphericity should be p < 0.05, KMO values must be 0.6 or above. These values are shown as parts of the factor analysis result. Table 9 depicts the exploratory factor analysis for research objective 1.
From Table 9, the factors that influence CSCM were loaded on three factors with eigenvalues of 7.237, 1.589 and 1.105. These three factors explained 66.207% of the variance before rotation and 58.690% of the variance after rotation. Table 10 shows the correlation matrix for research objective 1 for section C5.
From Table 10, many of the correlations were bigger than 0.3. C5.14 and C5.1 had the correlation of 0.255, C5.12 and C5.1 had the correlation of 0.292, C5.8 and C5.1 had the correlation of 0.255, C5.2 and C5.3 had the correlation of 0.273, C5.13 and C5.2 had the correlation of 0.260, C5.14 and C5.13 had the correlation of 0.245, C4.6 and C4.14 had the correlation of 0.269, C5.14 and C5.7 had the correlation of 0.263, C5.14 and C5.5 had the correlation of 0.183, C5.14 and C5.11 had the correlation of 0.210, C5.10 and C5.1 had the correlation of 0.183 and C5.11 and C5.5 had the correlation of 0.278. Table 11 shows the communalities of research objective 1 – C5.
From Table 11, the extractions are all above 0.3 which means there was at least 30% common variance shared among them. Figure 1 shows the scree plot for section C5.
In Figure 1, variables 4 to 15 were not significant so they were not added in the rotation. For the first factor, the eigenvalue was around 7.2, for the second factor, the eigenvalue was around 1.5 and the third factor has the eigenvalue of 1.1. Table 12 depicts the rotated factor matrix for research objective 1 – C5.
From Table 12, C5.7, C5.11, C5.13, C5.9, C5.1, C5.15, C5.3 were grouped into factor 1 while C5.10, C5.12, C5.8, C5.14 and C5.2 were grouped into factor 2 while C5.6, C5.4 and C5.5 were grouped into factor 3.
Each item was grouped into factors based on the KPIs (time, cost, quality, safety and health, internal and external stakeholder, client satisfaction, financial performance, environment and information, technology and innovation) they are associated with.
Factor 1 contains items related to quality, people, IT and innovations, time, cost, safety and health, environment, internal and external stakeholders and client satisfaction (P, C, Q, IT&I, T, SH, I&ES, E and CS) and factor 2 includes items related to quality, people, IT and innovations, time, cost, internal and external stakeholders (P, C, Q, IT&I, T and I&ES), whereas factor 3 includes items related to internal and external stakeholders, cost, time, people (I&ES, P, C and T).
Table 13 represents the exploratory factor analysis for research objective 2 (causes of CSCRs) for section C6.
From Table 13, the causes of CSCRs were loaded on three factors with eigenvalues of 7.402, 1.484 and 1.181, respectively. These three factors explained 67.111% of the variance before rotation and 59.199% of the variance after rotation. Table 14 shows the correlation matrix for research objective 2.
From Table 14, many of the correlations were bigger than 0.3. C6.12 and C6.1 had the correlation of 0.244, C6.1 and C6.8 had the correlation of 0.264, C6.5 and C6.6 had the correlation of 0.295, C6.6 and C6.15 had the correlation of 0.224, C6.7 and C6.12 had the correlation of 0.241, C6.14 and C6.7 had the correlation of 0.287, C6.8 and C6.1 had the correlation of 0.264, C6.12 and C6.3 had the correlation of 0.294 and C6.15 and C6.6 had a correlation of 0.224.
Table 15 depicts the communalities for section C6.
From Table 15, the extractions are all above 0.3 which means there was at least 30% common variance shared among them. Figure 2 shows the scree plot for section C6.
In Figure 2, variables 4 to 15 were not significant so they were not added in the rotation. For the first factor, the eigenvalue was around 7,4, for the second factor, the eigenvalue was around 1.48 and the third factor has the eigenvalue of 1.18. Table 16 depicts the rotated factor matrix for research objective 2 – C6.
From Table 16, C6.11, C6.9, C6.1, C6.15, C6.13 and C6.5 were grouped into factor 1 while C6.4, C6.3, C6.2 and C6.7 were grouped into factor 2 and C6.12, C6.10, C6.8, C6.6 and C6.14 were grouped into factor 3.
Each item was grouped into factors based on the KPIs (time, cost, quality, safety and health, internal and external stakeholder, client satisfaction, financial performance, environment and information, technology and innovation) they are associated with.
Factor 1 contains items related to quality, people, IT and innovations, time, cost, financial performance, safety and health, environment, internal and external stakeholders and client satisfaction (P, C, Q, IT&I, T, SH, I&ES, E, FP and CS), factor 2 includes items related to quality, people, time, safety and health and internal and external stakeholders (P, Q, SH, T and I&ES) and factor 3 includes items related to internal and external stakeholders, cost, time, people, quality, clients satisfaction and financial performance (I&ES, P, C, Q, CS, FP and T).
5. Reliability statistics of data collected
5.1 Reliability of theoretical factors
To establish data consistency of the theoretical factors, the value of the Cronbach’s alpha (coefficient alpha was determined). A Cronbach alpha value of 0.7 and above signifies an acceptable internal consistency. Tables 17–24 showed Cronbach’s alpha values above 0.7. Table 17 shows the reliability statistics for research objective 1 – C5.
Table 17 depicts the reliability statistics for research object 1, which measures the internal consistency of the measured variables and the Cronbach’s alpha was 0.921 which confirms its reliability. Table 18 depicts reliability statistics for research objective 2 – C6.
Table 18 depicts the reliability statistics for research object 2, which measures the internal consistency of the measured variables. From Table 18, the Cronbach’s alpha was 0.926 which confirmed its reliability.
5.2 Reliability of empirical factors
To establish data consistency of the empirical factors, the value of the Cronbach’s alpha (coefficient values) was determined. Table 19 shows the reliability statistics for research objective 1 – C5.
The empirical reliability was carried out for all the three factors in section C5 to confirm their reliability. Table 19 shows the reliability statistics for section C5 – factor 1.
Table 19 shows the reliability statistics for C5 – factor 1, which measures the internal consistency of the measured variables. From Table 19, the Cronbach’s alpha was 0.883 which shows it was reliable. Table 20 depicts the reliability statistics for section C5 – factor 2.
Table 20 represents the reliability statistics for C5 – factor 2, which measures the internal consistency of the measured variables. The Cronbach’s alpha was 0.845 as shown in Table 20, therefore, it was reliable. Table 21 depicts the reliability statistics for section C5 – factor 3.
Table 21 depicts the reliability statistics for C5 – factor 2, which measures the internal consistency of the measured variables. From Table 21, the Cronbach’s alpha was 0.872, so it was reliable. Table 22 shows the reliability statistics for the empirical factor 1 for research objective 2 – C6.
The reliability of the empirical factors was carried out to determine their reliability. Table 22 shows the reliability statistics for research objective 2 – section C6 – factor 1.
Table 22 depicts the reliability statistics for C6 – factor 1, which measures the internal consistency of the measured variables. Table 22 shows that the Cronbach’s alpha was 0.882 and this confirms its reliability. Table 23 represents the reliability statistics for section C6 – factor 2.
Table 23 shows the reliability statistics for C6 – factor 2, which measures the internal consistency of the measured variables. From Table 23, the Cronbach’s alpha was 0.862 so it was reliable. Table 24 shows the reliability statistics for section C6 – factor 3.
Table 24 illustrates the reliability statistics for C6 – factor 3, which measures the internal consistency of the measured variables. From Table 24, the Cronbach’s alpha was 0.844, which shows it was reliable.
The reliability analysis shows that both the empirical and theoretical factors have Cronbach’s alpha values greater than 0.7, which confirms the internal consistency of the measured variables in each section.
6. Result and discussions
The respondents were asked to identify the factors that influence CSCM and their extent levels as depicted by Table 5. The result shows that predictable delivery, funding schedule, inventories, balanced demands, production capabilities, timely procurement, CSCM coordination, delivery reliability, the proximity of suppliers, identification of SCRs in the conceptualisation stage of a project, identification of SCRs in the planning stage of a project, identification of SCRs in the execution stage and the reconciliation of material flows of the subcontractors with the contractors were identified as the key factors that influenced the CSCM the most. The values of the Spearman’s coefficient were bigger than 0.3 which shows the items correlate and from Table 7, for the Bartlett's Test of Sphericity, the significance, p-value was 0.000 which was less than 0.05, which means the higher the probability of the factors that influence CSCM, the more they influence the project. The KMO Measure of Sampling Adequacy was 0.902, which was bigger than 0.6 which shows that factor analysis can be done. From Table 17, the Cronbach’s alpha for the theoretical factors was 0.921, which confirms its reliability. This shows that the theoretical factors were reliable because their values were greater than 0.7.
For the second research objective, the respondents were asked to identify the extent in which the following causes of SCRs influence construction projects as shown in Table 6, the result shows that subcontractor’s negative attitudes towards supply chain management, procurement delays, imbalanced demands, clients’ negative attitudes towards other project stakeholders, unpredictable delivery reliability, disorganised CSCM approach, delayed funding, low delivery reliability, poor inventories, poor CSC coordination, suppliers’ negative attitudes towards supply chain management and when the material flows of the subcontractors with the contractors are not reconciled were identified as the factors that have the greatest impacts on CSCRs management. The values of the Spearman’s coefficient were bigger than 0.3 which shows the items correlate and from Table 8, for the Bartlett's Test of Sphericity, the significance, p-value was 0.000 which was less than 0.05, which means the higher the probability of the factors that cause SCRs, the higher their influences on CSCM. The KMO Measure of Sampling Adequacy was 0.895 which was bigger than 0.6, which shows that factor analysis is feasible. From Table 18, the Cronbach’s alpha for the theoretical factors was 0.926, which confirms its reliability. The Cronbach’s alpha for the empirical factors (1, 2 and 3) for research objective 1 – C5 were 0.883, 0.845 and 0.872, respectively, and this shows that the factors were reliable. Also, the Cronbach’s alpha for the empirical factors (1, 2 and 3) for research objective 2 – C6 were 0.882, 0.862 and 0.844, respectively, and this shows that the empirical factors were reliable because the values were all above 0.7.
The Cronbach’s alpha values were all above 0.7 for all the measured variables, which indicated internal consistency for the measuring instrument.
6.1 Proposed new framework
6.1.1 Discussions.
Figure 3 depicts the CSCM approach fully incorporating factors that influenced supply chain management and methods to mitigate the impacts of these risks and the causes of SCRs on construction project success.
The framework shown in Figure 3 was designed by carefully analysing the major contributing factors to CSCRs and how these factors influenced CSCM and the success of the project and by paying attention to these areas and factors, supply chain resilience will be improved in construction projects. This new framework will help organisations to carefully and effectively assess and manage these contributing factors to CSCRs so that suppliers, vendors, distributors, wholesalers and retail partners’ relationships will be established and managed successfully. Organisations will effectively collaborate with internal and external stakeholders to establish contingency plans for critical supplies and services, effectively implement supply chain strategies that ensure efficient production, distribution and delivery of materials, labour and other resources, develop a global supply chain strategy that optimises logistics and reduce transportation costs and timely, continually and effectively Identify and assesses potential risks in the supply chain, including geopolitical, economic and operational risks for the overall construction project success.
The proposed framework depicted in Figure 3 will ensure that CSCR management and coordination are effective and every risk involved is carefully identified during the conceptualisation, planning and execution stages of the project and risk mitigation steps put in place to manage these risks. It is recommended to engage with suppliers, vendors and customers close to the project sites to guarantee timely, predictable and reliable deliveries and procurements. Balanced demands and updated inventories ensure the possibility of shortages and surplus of labour and materials are mitigated and eliminated. Sufficient production capabilities to meet production schedules and deadlines, timely and adequate project funding to ensure there are no delays in procurements and availability of labour and stakeholders’ positive attitudes towards supply chain management are crucial for the effective identification and management of CSCRs for a successful project completion. Research objective three will be discussed in Section 7.
7. Conclusion
Construction is a key element of sustainable development and poor supply chain management is one of the major challenges faced in construction due to the presence of many stakeholders, phases, activities, tasks and risks involved in executing construction projects. These risks if not carefully identified, assessed, analysed, monitored, controlled and mitigated throughout the project can lead to project delays, abandonment, failures, compromised safety and quality, reputational damage, claims, additional costs and extended project completion time and can negatively influence the overall key performance. Moreover, effective SCR management is a key element in achieving green buildings in developing countries.
SCRs are inevitable in every construction project, so this paper investigated the factors that influence CSCM and the causes of SCRs in construction projects and their level of impacts and proposed ways to improve construction supply chain resilience by identifying these factors that influenced CSCM. The methods used explored the relationships between project stakeholders with respect to CSCM and how these relationships affect supply chain management and associated risks generated in the project life cycle. To effectively mitigate SCRs and enhance supply chain management in construction projects, it is paramount that stakeholders fully understand how their attitudes, experiences, behaviours and competency directly and indirectly influence the outcome of a project and must encourage effective collaboration among project participants. To minimise costs, save time, avoid delivery delays and failures and avoid other uncertainties and risks associated with logistics, procurements and supply chain management, it is recommended to engage with suppliers, customers and vendors that are within favourable proximity without compromising the standard, safety, quality and environment.
From the results obtained, predictable delivery, funding schedule, inventories, production capabilities, timely procurement, CSCM coordination, delivery reliability, the proximity of suppliers, identification of SCRs in the conceptualisation stage of a project, identification of SCRs in the planning stage of a project, identification of SCRs in the execution stage and the reconciliation of material flows of the subcontractors with the contractors were identified as the key factors that influenced the CSCM the most because they showed highest extent levels. The findings will help project managers, project planners, project engineers and contractors to effectively plan project schedules and supply chain coordination from conceptualisation stage to finish. For a successful CSCRs management, it is crucial to have a reliable, timely and predictable delivery of materials, labour and resources as any delay will negatively influence the progress of any or other construction activities and incur additional costs. Identifying supply chains risks during the conceptualisation and planning stages is vital in putting risk mitigation strategies in place early enough to mitigate, minimise or eliminate these risks if they eventually occur during project contracting, procurement and execution phases. Funding schedule and updated inventories facilitate successful project delivery because any delay in funding, halts or delays project progress and this leads to extended project time and inventories facilitate effective supply chain coordination by ensuring delivery records are up to date and material flows of the subcontractors with the contractors are effectively reconciled to avoid shortages, discrepancies or excess supply of materials on site and for the overall successful CSCM coordination. The result also showed that to effectively manage CSCRs, subcontractors, clients and suppliers must display positive attitudes towards supply chain management and other project stakeholders because by not doing so, it will lead to communication breach and eventually result in a disorganised construction supply chain coordination which will either delay the project, cause the project to be abandoned or result in project failure. The proximity of suppliers is also important as to reduce cost and ensure timely and predictable delivery. So, to facilitate CSCM, every stakeholder must play an active role to ensure the flow of materials, resources and labour are coordinated efficiently and they are used effectively and ensure that demands are reconciled with supplies to realise project’s objectives with respect to time, safety, quality, cost and financial performance.
In summary, as developing countries embark on sustainable building adoption for sustainable development, construction industry professionals must ensure that CSCR management and coordination are effective and every risk involved is carefully identified during the conceptualisation, planning and execution stages of the project and risk mitigation steps put in place to manage these risks. Every aspect of construction KPIs such as time, safety, environment, cost, quality, financial performance, labour, materials, stakeholders etc. are carefully assessed, analysed, monitored and controlled to ensure that risks associated with supply chain will not adversely influence project delivery. It is recommended to engage with suppliers, vendors and customers close to the project sites to guarantee timely, predictable and reliable deliveries and procurements. Balanced demands and updated inventories ensure the possibility of shortages and surplus of labour and materials are mitigated and eliminated. Sufficient production capabilities to meet production schedules and deadlines, timely and adequate project funding to ensure there are no delays in procurements and availability of labour and stakeholders’ positive attitudes towards supply chain management are crucial for the effective identification and management of CSCRs for a successful project completion. The findings will facilitate successful project delivery when adopted by investors, governmental bodies, project managers, funders, host communities, private sectors, contractors, sub-contractors and other stakeholders involved in contracting and executing construction projects in South Africa and other developing countries for sustainability and decarbonisation in the construction industry locally and globally.
Figures
Figure 1.
Scree plot for research objective 1 – C5
Figure 2.
Scree plot for research objective 2 – C6
Figure 3.
Proposed new framework for managing supply chain risks in construction projects
Survey participants professions in the South African construction industry
| Profession | Frequency | % | Valid (%) | Cumulative (%) |
|---|---|---|---|---|
| Quantity surveyor | 16 | 7.8 | 7.8 | 7.8 |
| Architect | 9 | 4.4 | 4.4 | 12.2 |
| Civil engineer/structural engineer | 27 | 13.2 | 13.2 | 25.4 |
| Builder | 7 | 3.4 | 3.4 | 28.8 |
| Construction manager | 25 | 12.2 | 12.2 | 41.0 |
| Electrical engineer | 22 | 10.7 | 10.7 | 51.7 |
| Mechanical engineer | 20 | 9.8 | 9.8 | 61.5 |
| Estate manager | 8 | 3.9 | 3.9 | 65.4 |
| Project manager | 18 | 8.8 | 8.8 | 74.1 |
| Construction engineer | 13 | 6.3 | 6.3 | 80.5 |
| Project engineer | 8 | 3.9 | 3.9 | 84.4 |
| Project administrator | 9 | 4.4 | 4.4 | 88.8 |
| Safety officer/engineer/manager | 10 | 4.9 | 4.9 | 93.7 |
| Risk manger | 10 | 4.9 | 4.9 | 98.5 |
| Other construction professionals | 3 | 1.5 | 1.5 | 100.0 |
| Total | 205 | 100.0 | 100.0 |
Source: Authors’ own work
Age distribution of respondents
| Age group | Frequency | % | Valid (%) | Cumulative (%) |
|---|---|---|---|---|
| 21–25 years | 4 | 2.0 | 2.0 | 2.0 |
| 26–30 years | 16 | 7.8 | 7.8 | 9.8 |
| 31–35 years | 32 | 15.6 | 15.6 | 25.4 |
| 36 –40 years | 42 | 20.5 | 20.5 | 45.9 |
| 41–45 years | 53 | 25.9 | 25.9 | 71.7 |
| 46 years and above | 58 | 28.3 | 28.3 | 100.0 |
| Total | 205 | 100.0 | 100.0 |
Source: Author’s own work
Academic qualifications of the respondents
| Highest academic qualification | Frequency | % | Valid (%) | Cumulative (%) |
|---|---|---|---|---|
| Post matric certificate or diploma | 11 | 5.4 | 5.4 | 5.4 |
| Bachelor’s degree | 55 | 26.8 | 26.8 | 32.2 |
| Honours degree | 28 | 13.7 | 13.7 | 45.9 |
| Master’s degree | 70 | 34.1 | 34.1 | 80.0 |
| Doctorate degree | 41 | 20.0 | 20.0 | 100.0 |
| Total | 205 | 100.0 | 100.0 |
Source: Authors’ own work
Size of organisations of respondents
| Organisational size | Frequency | % | Valid (%) | Cumulative (%) |
|---|---|---|---|---|
| Small (1–100 staff) | 72 | 35.1 | 35.1 | 35.1 |
| Medium (101–500) | 74 | 36.1 | 36.1 | 71.2 |
| Large (501–5,000+) | 59 | 28.8 | 28.8 | 100.,0 |
| Total | 205 | 100.0 | 100.0 |
Source: Authors’ own work
Responses to the extent in which each of the following factors influence the construction supply chain management
| Survey items – C5 | To no extent | Small extent | Moderate extent | Large extent | Very large extent | Total | |
|---|---|---|---|---|---|---|---|
| C5.1 Identification of supply chain risks in the conceptualisation stage of a project | Count | 0 | 2 | 20 | 101 | 82 | 205 |
| Row N % | 0.0% | 1.0% | 9.8% | 49.3% | 40.0% | 100.0% | |
| C5.2 Identification of supply chain risks in the planning stage of a project | Count | 0 | 3 | 19 | 103 | 80 | 205 |
| Row N % | 0.0% | 1.5% | 9.3% | 50.2% | 39.0% | 100.0% | |
| C5.3 Identification of supply chain risks in the execution stage | Count | 0 | 5 | 37 | 96 | 67 | 205 |
| Row N % | 0.0% | 2.4% | 18.0% | 46.8% | 32.7% | 100.0% | |
| C5.4 The proximity of suppliers | Count | 3 | 7 | 46 | 121 | 28 | 205 |
| Row N % | 1.5% | 3.4% | 22.4% | 59.0% | 13.7% | 100.0% | |
| C5.5 The proximity of vendors | Count | 2 | 12 | 65 | 97 | 29 | 205 |
| Row N % | 1.0% | 5.9% | 31.7% | 47.3% | 14.1% | 100.0% | |
| C5.6 The proximity of customers | Count | 3 | 21 | 68 | 98 | 15 | 205 |
| Row N % | 1.5% | 10.2% | 33.2% | 47.8% | 7.3% | 100.0% | |
| C5.7 The reconciliation of material flows of the sub-contractors with the contractors | Count | 0 | 9 | 51 | 112 | 33 | 205 |
| Row N % | 0.0% | 4.4% | 24.9% | 54.6% | 16.1% | 100.0% | |
| C5.8 Delivery reliability | Count | 0 | 3 | 29 | 132 | 41 | 205 |
| Row N % | 0.0% | 1.5% | 14.1% | 64.4% | 20.0% | 100.0% | |
| C5.9 Construction supply chain management coordination | Count | 1 | 5 | 17 | 106 | 76 | 205 |
| Row N % | 0.5% | 2.4% | 8.3% | 51.7% | 37.1% | 100.0% | |
| C5.10 Timely procurement | Count | 1 | 2 | 27 | 121 | 54 | 205 |
| Row N % | 0.5% | 1.0% | 13.2% | 59.0% | 26.3% | 100.0% | |
| C5.11 Balanced demands | Count | 0 | 2 | 39 | 122 | 42 | 205 |
| Row N % | 0.0% | 1.0% | 19.0% | 59.5% | 20.5% | 100.0% | |
| C5.12 Inventories | Count | 0 | 7 | 47 | 109 | 42 | 205 |
| Row N % | 0.0% | 3.4% | 22.9% | 53.2% | 20.5% | 100.0% | |
| C5.13 Production capabilities | Count | 1 | 3 | 29 | 121 | 51 | 205 |
| Row N % | 0.5% | 1.5% | 14.1% | 59.0% | 24.9% | 100.0% | |
| C5.14 Funding schedule | Count | 0 | 2 | 14 | 103 | 86 | 205 |
| Row N % | 0.0% | 1.0% | 6.8% | 50.2% | 42.0% | 100.0% | |
| C5.15 Predictable delivery | Count | 0 | 4 | 22 | 130 | 49 | 205 |
| Row N % | 0.0% | 2.0% | 10.7% | 63.4% | 23.9% | 100.0% | |
Source: Authors’ own work
Responses for the extent in which the following factors contributed to the causes of construction supply chain risks – research objective 2 – C6
| Survey items – C6 | To no extent | Small extent | Moderate extent | Large extent | Very large extent | Total | |
|---|---|---|---|---|---|---|---|
| C6.1 When the material flows of the subcontractors with the contractors are not reconciled | Count | 1 | 2 | 34 | 132 | 36 | 205 |
| Row N % | 0.5% | 1.0% | 16.6% | 64.4% | 17.6% | 100.0% | |
| C6.2 Suppliers’ negative attitudes towards supply chain management | Count | 0 | 6 | 63 | 107 | 29 | 205 |
| Row N % | 0.0% | 2.9% | 30.7% | 52.2% | 14.1% | 100.0% | |
| C6.3 Vendors negative attitudes towards supply chain management | Count | 1 | 14 | 73 | 86 | 31 | 205 |
| Row N % | 0.5% | 6.8% | 35.6% | 42.0% | 15.1% | 100.0% | |
| C6.4 Third parties’ negative attitudes towards supply chain management | Count | 1 | 18 | 83 | 85 | 18 | 205 |
| Row N % | 0.5% | 8.8% | 40.5% | 41.5% | 8.8% | 100.0% | |
| C6.5 Procurement delays | Count | 0 | 5 | 29 | 125 | 46 | 205 |
| Row N % | 0.0% | 2.4% | 14.1% | 61.0% | 22.4% | 100.0% | |
| C6.6 Clients’ negative attitudes towards supply chain management | Count | 1 | 4 | 35 | 122 | 43 | 205 |
| Row N % | 0.5% | 2.0% | 17.1% | 59.5% | 21.0% | 100.0% | |
| C6.7 Subcontractor’s negative attitudes towards supply chain management | Count | 0 | 6 | 26 | 129 | 44 | 205 |
| Row N % | 0.0% | 2.9% | 12.7% | 62.9% | 21.5% | 100.0% | |
| C6.8 Imbalanced demands | Count | 0 | 6 | 44 | 122 | 33 | 205 |
| Row N % | 0.0% | 2.9% | 21.5% | 59.5% | 16.1% | 100.0% | |
| C6.9 Poor construction supply chain coordination | Count | 0 | 3 | 13 | 109 | 80 | 205 |
| Row N % | 0.0% | 1.5% | 6.3% | 53.2% | 39.0% | 100.0% | |
| C6.10 Clients’ negative attitudes towards other project stakeholders | Count | 2 | 3 | 36 | 122 | 42 | 205 |
| Row N % | 1.0% | 1.5% | 17.6% | 59.5% | 20.5% | 100.0% | |
| C6.11 Poor inventories | Count | 0 | 5 | 31 | 134 | 35 | 205 |
| Row N % | 0.0% | 2.4% | 15.1% | 65.4% | 17.1% | 100.0% | |
| C6.12 Delayed funding | Count | 0 | 2 | 13 | 109 | 81 | 205 |
| Row N % | 0.0% | 1.0% | 6.3% | 53.2% | 39.5% | 100.0% | |
| C6.13 Disorganised construction supply chain management approach | Count | 0 | 1 | 9 | 105 | 90 | 205 |
| Row N % | 0.0% | 0.5% | 4.4% | 51.2% | 43.9% | 100.0% | |
| C6.14 Low delivery reliability | Count | 0 | 2 | 29 | 137 | 37 | 205 |
| Row N % | 0.0% | 1.0% | 14.1% | 66.8% | 18.0% | 100.0% | |
| C6.15 Unpredictabledelivery | Count | 0 | 2 | 23 | 128 | 52 | 205 |
| Row N % | 0.0% | 1.0% | 11.2% | 62.4% | 25.4% | 100.0% | |
Source: Authors’ own work
KMO and bartlett’s test for research objective 1 (factors that influence construction supply chain management and their extent levels)
| KMO and Bartlett's test | ||
|---|---|---|
| Kaiser–Meyer–Olkin measure of sampling adequacy | 0.902 | |
| Bartlett's test of sphericity | Approx. chi-square | 1,845.508 |
| df | 105 | |
| Sig. | 0.000 | |
Source: Authors’ own work
KMO and bartlett’s test for research objective 2 (causes of construction supply chain risks and their extent levels)
| KMO and Bartlett's test | ||
|---|---|---|
| Kaiser–Meyer–Olkin measure of sampling adequacy | 0.895 | |
| Bartlett's test of sphericity | Approx. chi-square | 1,916.682 |
| df | 105 | |
| Sig. | 0.000 | |
Source: Authors’ own work
Exploratory factor analysis for research objective 1 (factors that influence construction supply chain management and their extent levels) – C5
| Total variance explained | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Factor | Initial eigenvalues |
Extraction sums of squared loadings |
Rotation sums of squared loadings |
||||||
| Total | % of variance |
Cumulative (%) |
Total | % of variance |
Cumulative (%) |
Total | % of variance |
Cumulative (%) |
|
| 1 | 7.237 | 48.246 | 48.246 | 6.854 | 45.693 | 45.693 | 3.770 | 25.133 | 25.133 |
| 2 | 1.589 | 10.591 | 58.837 | 1.175 | 7.831 | 53.524 | 2.908 | 19.388 | 44.520 |
| 3 | 1.105 | 7.369 | 66.207 | 0.775 | 5.167 | 58.690 | 2.126 | 14.170 | 58.690 |
| 4 | 0.995 | 6.633 | 72.840 | ||||||
| 5 | 0.640 | 4.268 | 77.108 | ||||||
| 6 | 0.602 | 4.016 | 81.124 | ||||||
| 7 | 0.521 | 3.473 | 84.596 | ||||||
| 8 | 0.413 | 2.752 | 87.348 | ||||||
| 9 | 0.377 | 2.511 | 89.859 | ||||||
| 10 | 0.331 | 2.205 | 92.064 | ||||||
| 11 | 0.299 | 1.994 | 94.058 | ||||||
| 12 | 0.269 | 1.796 | 95.854 | ||||||
| 13 | 0.239 | 1.593 | 97.447 | ||||||
| 14 | 0.219 | 1.463 | 98.910 | ||||||
| 15 | 0.164 | 1.090 | 100.000 | ||||||
Extraction method: Principal axis factoring
Source: Authors’ own work
Correlation matrix for research objective 1 – C5
| C5.1 | C5.2 | C5.3 | C5.4 | C5.5 | C5.6 | C5.7 | C5.8 | C5.9 | C5.10 | C5.11 | C5.12 | C5.13 | C5.14 | C5.15 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Correlation | C5.1 | 1.000 | 0.405 | 0.499 | 0.307 | 0.497 | 0.352 | 0.477 | 0.255 | 0.577 | 0.183 | 0.398 | 0.292 | 0.375 | 0.255 | 0.354 |
| C5.2 | 0.405 | 1.000 | 0.273 | 0.454 | 0.248 | 0.402 | 0.350 | 0.568 | 0.469 | 0.485 | 0.284 | 0.438 | 0.260 | 0.372 | 0.352 | |
| C5.3 | 0.499 | 0.273 | 1.000 | 0.313 | 0.441 | 0.336 | 0.505 | 0.344 | 0.501 | 0.305 | 0.500 | 0.336 | 0.405 | 0.160 | 0.423 | |
| C5.4 | 0.307 | 0.454 | 0.313 | 1.000 | 0.663 | 0.706 | 0.459 | 0.585 | 0.372 | 0.501 | 0.278 | 0.510 | 0.360 | 0.291 | 0.392 | |
| C5.5 | 0.497 | 0.248 | 0.441 | 0.663 | 1.000 | 0.718 | 0.648 | 0.450 | 0.489 | 0.402 | 0.501 | 0.434 | 0.561 | 0.183 | 0.525 | |
| C5.6 | 0.352 | 0.402 | 0.336 | 0.706 | 0.718 | 1.000 | 0.574 | 0.577 | 0.409 | 0.465 | 0.397 | 0.552 | 0.417 | 0.269 | 0.398 | |
| C5.7 | 0.477 | 0.350 | 0.505 | 0.459 | 0.648 | 0.574 | 1.000 | 0.467 | 0.622 | 0.426 | 0.625 | 0.530 | 0.621 | 0.263 | 0.643 | |
| C5.8 | 0.255 | 0.568 | 0.344 | 0.585 | 0.450 | 0.577 | 0.467 | 1.000 | 0.436 | 0.727 | 0.352 | 0.624 | 0.427 | 0.431 | 0.460 | |
| C5.9 | 0.577 | 0.469 | 0.501 | 0.372 | 0.489 | 0.409 | 0.622 | 0.436 | 1.000 | 0.382 | 0.534 | 0.390 | 0.530 | 0.325 | 0.530 | |
| C5.10 | 0.183 | 0.485 | 0.305 | 0.501 | 0.402 | 0.465 | 0.426 | 0.727 | 0.382 | 1.000 | 0.369 | 0.654 | 0.497 | 0.534 | 0.488 | |
| C5.11 | 0.398 | 0.284 | 0.500 | 0.278 | 0.501 | 0.397 | 0.625 | 0.352 | 0.534 | 0.369 | 1.000 | 0.493 | 0.573 | 0.210 | 0.564 | |
| C5.12 | 0.292 | 0.438 | 0.336 | 0.510 | 0.434 | 0.552 | 0.530 | 0.624 | 0.390 | 0.654 | 0.493 | 1.000 | 0.477 | 0.406 | 0.431 | |
| C5.13 | 0.375 | 0.260 | 0.405 | 0.360 | 0.561 | 0.417 | 0.621 | 0.427 | 0.530 | 0.497 | 0.573 | 0.477 | 1.000 | 0.245 | 0.690 | |
| C5.14 | 0.255 | 0.372 | 0.160 | 0.291 | 0.183 | 0.269 | 0.263 | 0.431 | 0.325 | 0.534 | 0.210 | 0.406 | 0.245 | 1.000 | 0.301 | |
| C5.15 | 0.354 | 0.352 | 0.423 | 0.392 | 0.525 | 0.398 | 0.643 | 0.460 | 0.530 | 0.488 | 0.564 | 0.431 | 0.690 | 0.301 | 1.000 | |
Source: Authors’ own work
Communalities for research objective 1 – C5
| Communalities | ||
|---|---|---|
| Initial | Extraction | |
| C5.1 | 0.520 | 0.382 |
| C5.2 | 0.506 | 0.389 |
| C5.3 | 0.409 | 0.405 |
| C5.4 | 0.638 | 0.706 |
| C5.5 | 0.735 | 0.815 |
| C5.6 | 0.671 | 0.745 |
| C5.7 | 0.661 | 0.702 |
| C5.8 | 0.658 | 0.722 |
| C5.9 | 0.571 | 0.579 |
| C5.10 | 0.679 | 0.761 |
| C5.11 | 0.539 | 0.561 |
| C5.12 | 0.588 | 0.567 |
| C5.13 | 0.603 | 0.559 |
| C5.14 | 0.353 | 0.343 |
| C5.15 | 0.597 | 0.568 |
Extraction method: Principal axis factoring
Source: Authors’ own work
Rotated factor matrix for research objective 1 – C5
| Rotated factor matrixa | |||
|---|---|---|---|
| Factor | |||
| 1 | 2 | 3 | |
| C5.7 | 0.727 | 0.249 | 0.334 |
| C5.11 | 0.706 | 0.216 | 0.130 |
| C5.9 | 0.685 | 0.294 | 0.152 |
| C5.13 | 0.662 | 0.298 | 0.179 |
| C5.15 | 0.653 | 0.344 | 0.156 |
| C5.3 | 0.593 | 0.154 | 0.171 |
| C5.1 | 0.571 | 0.116 | 0.208 |
| C5.10 | 0.231 | 0.818 | 0.197 |
| C5.8 | 0.227 | 0.739 | 0.353 |
| C5.12 | 0.336 | 0.605 | 0.298 |
| C5.14 | 0.165 | 0.560 | 0.046 |
| C5.2 | 0.252 | 0.538 | 0.190 |
| C5.6 | 0.296 | 0.332 | 0.740 |
| C5.4 | 0.188 | 0.407 | 0.711 |
| C5.5 | 0.550 | 0.102 | 0.708 |
Extraction method: principal axis factoring. Rotation method: Varimax with Kaiser normalisation
aRotation converged in six iterations
Source: Authors’ own work
Exploratory factor analysis for research objective 2 (causes of construction supply chain risks) – C6
| Total variance explained | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Factor | Initial eigenvalues | Extraction sums of squared loadings |
Rotation sums of squared loadings |
||||||
| Total | % of variance |
Cumulative (%) |
Total | % of variance |
Cumulative (%) |
Total | % of variance |
Cumulative (%) |
|
| 1 | 7.402 | 49.344 | 49.344 | 7.004 | 46.691 | 46.691 | 3.656 | 24.373 | 24.373 |
| 2 | 1.484 | 9.896 | 59.240 | 1.080 | 7.199 | 53.890 | 2.832 | 18.878 | 43.250 |
| 3 | 1.181 | 7.871 | 67.111 | 0.796 | 5.309 | 59.199 | 2.392 | 15.948 | 59.199 |
| 4 | 0.926 | 6.172 | 73.283 | ||||||
| 5 | 0.676 | 4.509 | 77.792 | ||||||
| 6 | 0.597 | 3.983 | 81.775 | ||||||
| 7 | 0.479 | 3.192 | 84.966 | ||||||
| 8 | 0.443 | 2.953 | 87.919 | ||||||
| 9 | 0.378 | 2.523 | 90.442 | ||||||
| 10 | 0.322 | 2.148 | 92.590 | ||||||
| 11 | 0.282 | 1.878 | 94.469 | ||||||
| 12 | 0.240 | 1.601 | 96.070 | ||||||
| 13 | 0.231 | 1.538 | 97.608 | ||||||
| 14 | 0.183 | 1.223 | 98.831 | ||||||
| 15 | 0.175 | 1.169 | 100.000 | ||||||
Extraction method: Principal axis factoring
Source: Authors’ own work
Correlation matrix for research objective 2 – C6
| C6.1 | C6.2 | C6.3 | C6.4 | C6.5 | C6.6 | C6.7 | C6.8 | C6.9 | C6.10 | C6.11 | C6.12 | C6.13 | C6.14 | C6.15 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Correlation | C6.1 | 1.000 | 0.343 | 0.531 | 0.448 | 0.564 | 0.306 | 0.445 | 0.264 | 0.512 | 0.443 | 0.450 | 0.244 | 0.427 | 0.413 | 0.535 |
| C6.2 | 0.343 | 1.000 | 0.656 | 0.699 | 0.375 | 0.530 | 0.415 | 0.498 | 0.383 | 0.475 | 0.458 | 0.326 | 0.397 | 0.417 | 0.322 | |
| C6.3 | 0.531 | 0.656 | 1.000 | 0.721 | 0.520 | 0.436 | 0.631 | 0.462 | 0.536 | 0.418 | 0.558 | 0.294 | 0.433 | 0.374 | 0.485 | |
| C6.4 | 0.448 | 0.699 | 0.721 | 1.000 | 0.439 | 0.524 | 0.518 | 0.615 | 0.386 | 0.457 | 0.436 | 0.344 | 0.331 | 0.430 | 0.390 | |
| C6.5 | 0.564 | 0.375 | 0.520 | 0.439 | 1.000 | 0.295 | 0.475 | 0.360 | 0.638 | 0.398 | 0.568 | 0.350 | 0.510 | 0.560 | 0.634 | |
| C6.6 | 0.306 | 0.530 | 0.436 | 0.524 | 0.295 | 1.000 | 0.571 | 0.503 | 0.422 | 0.711 | 0.361 | 0.467 | 0.360 | 0.379 | 0.224 | |
| C6.7 | 0.445 | 0.415 | 0.631 | 0.518 | 0.475 | 0.571 | 1.000 | 0.392 | 0.534 | 0.549 | 0.570 | 0.241 | 0.457 | 0.287 | 0.465 | |
| C6.8 | 0.264 | 0.498 | 0.462 | 0.615 | 0.360 | 0.503 | 0.392 | 1.000 | 0.420 | 0.566 | 0.482 | 0.458 | 0.306 | 0.486 | 0.347 | |
| C6.9 | 0.512 | 0.383 | 0.536 | 0.386 | 0.638 | 0.422 | 0.534 | 0.420 | 1.000 | 0.514 | 0.623 | 0.402 | 0.712 | 0.385 | 0.595 | |
| C6.10 | 0.443 | 0.475 | 0.418 | 0.457 | 0.398 | 0.711 | 0.549 | 0.566 | 0.514 | 1.000 | 0.435 | 0.541 | 0.456 | 0.528 | 0.396 | |
| C6.11 | 0.450 | 0.458 | 0.558 | 0.436 | 0.568 | 0.361 | 0.570 | 0.482 | 0.623 | 0.435 | 1.000 | 0.177 | 0.536 | 0.378 | 0.516 | |
| C6.12 | 0.244 | 0.326 | 0.294 | 0.344 | 0.350 | 0.467 | 0.241 | 0.458 | 0.402 | 0.541 | 0.177 | 1.000 | 0.328 | 0.549 | 0.348 | |
| C6.13 | 0.427 | 0.397 | 0.433 | 0.331 | 0.510 | 0.360 | 0.457 | 0.306 | 0.712 | 0.456 | 0.536 | 0.328 | 1.000 | 0.320 | 0.504 | |
| C6.14 | 0.413 | 0.417 | 0.374 | 0.430 | 0.560 | 0.379 | 0.287 | 0.486 | 0.385 | 0.528 | 0.378 | 0.549 | 0.320 | 1.000 | 0.527 | |
| C6.15 | 0.535 | 0.322 | 0.485 | 0.390 | 0.634 | 0.224 | 0.465 | 0.347 | 0.595 | 0.396 | 0.516 | 0.348 | 0.504 | 0.527 | 1.000 | |
Source: Authors’ own work
Communalities for section C6
| Communalities | ||
|---|---|---|
| Initial | Extraction | |
| C6.1 | 0.487 | 0.450 |
| C6.2 | 0.616 | 0.598 |
| C6.3 | 0.702 | 0.762 |
| C6.4 | 0.701 | 0.727 |
| C6.5 | 0.613 | 0.644 |
| C6.6 | 0.640 | 0.595 |
| C6.7 | 0.622 | 0.517 |
| C6.8 | 0.572 | 0.517 |
| C6.9 | 0.693 | 0.688 |
| C6.10 | 0.677 | 0.681 |
| C6.11 | 0.590 | 0.564 |
| C6.12 | 0.486 | 0.558 |
| C6.13 | 0.554 | 0.490 |
| C6.14 | 0.569 | 0.492 |
| C6.15 | 0.564 | 0.599 |
Extraction method: Principal axis factoring
Source: Author’s own work
Rotated factor matrix for research objective 2 – C6
| Rotated factor matrixa | |||
|---|---|---|---|
| Factor | |||
| 1 | 2 | 3 | |
| C6.9 | 0.746 | 0.218 | 0.289 |
| C6.5 | 0.741 | 0.200 | 0.232 |
| C6.15 | 0.725 | 0.146 | 0.228 |
| C6.13 | 0.627 | 0.202 | 0.238 |
| C6.11 | 0.620 | 0.403 | 0.131 |
| C6.1 | 0.586 | 0.283 | 0.165 |
| C6.4 | 0.240 | 0.768 | 0.283 |
| C6.3 | 0.472 | 0.728 | 0.097 |
| C6.2 | 0.220 | 0.678 | 0.300 |
| C6.7 | 0.468 | 0.504 | 0.209 |
| C6.12 | 0.209 | 0.092 | 0.711 |
| C6.10 | 0.306 | 0.338 | 0.687 |
| C6.6 | 0.133 | 0.491 | 0.580 |
| C6.14 | 0.397 | 0.171 | 0.552 |
| C6.8 | 0.210 | 0.462 | 0.509 |
Extraction method: principal axis factoring. Rotation method: Varimax with Kaiser normalisationa
aRotation converged in five iterations
Source: Authors’ own work
Reliability statistics for research objective 1 – C5 – factors that influence construction supply chain management and their extent levels
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.921 | 15 |
Source: Author’s own work
Reliability statistics for research objective 2 – the causes of construction supply chain risks and their extent levels
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.926 | 15 |
Source: Authors’ own work
Reliability statistics for section C5 – factor 1
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.883 | 7 |
Source: Authors’ own work
Reliability statistics for section C5 – factor 2
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.845 | 5 |
Source: Authors’ own work
Reliability statistics for section C5 – factor 3
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.872 | 3 |
Source: Authors’ own work
Reliability statistics for section C6 – factor 1
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.882 | 6 |
Source: Authors’ own work
Reliability statistics for section C6 – factor 2
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.862 | 4 |
Source: Authors’ own work
Reliability statistics for section C6 – factor 3
| Reliability statistics | |
|---|---|
| Cronbach's alpha | No. of items |
| 0.844 | 5 |
Source: Authors’ own work
References
Abas, M., Khattak, S.B., Habib, T. and Nadir, U. (2022), “Assessment of critical risk and success factors in construction supply chain: a case of Pakistan”, International Journal of Construction Management, Vol. 22 No. 12, pp. 2258-2266.
Afolabi, A., Ibem, E., Aduwo, E., Tunji-Olayeni, P. and Oluwunmi, O. (2019), “Critical success factors (CSFs) for e-procurement adoption in the Nigerian construction industry”, Buildings, Vol. 9 No. 2, p. 47.
Ahmadabadi, A.A. and Heravi, G. (2019), “The effect of critical success factors on project success in public–private partnership projects: a case study of highway projects in Iran”, Transport Policy, Vol. 73, pp. 152-161.
Ahmed, M., Thaheem, M.J. and Maqsoom, A. (2019), “Barriers and opportunities to greening the construction supply chain management: cause-driven implementation strategies for developing countries”, Benchmarking: An International Journal, Vol. 27 No. 3, pp. 1211-1237.
Carvalho, M.M.D. and Rabechini Junior, R. (2015), “Impact of risk management on project performance: the importance of soft skills”, International Journal of Production Research, Vol. 53 No. 2, pp. 321-340.
Chuai, X., Lu, Q., Huang, X., Gao, R. and Zhao, R. (2021), “China’s construction industry-linked economy-resources-environment flow in international trade”, Journal of Cleaner Production, Vol. 278, p. 123990.
Costa, F., Denis Granja, A., Fregola, A., Picchi, F. and Portioli Staudacher, A. (2019), “Understanding relative importance of barriers to improving the customer-supplier relationship within construction supply chains using DEMATEL technique”, Journal of Management in Engineering, Vol. 35 No. 3, p. 4019002, doi: 10.1061/(ASCE)ME.1943-5479.0000680.
Deng, Y., Gan, V.J., Das, M., Cheng, J.C. and Anumba, C. (2019), “Integrating 4D BIM and GIS for construction supply chain management”, Journal of Construction Engineering and Management, Vol. 145 No. 4, p. 4019016.
Derakhshanfar, H., Ochoa, J.J., Kirytopoulos, K., Mayer, W. and Tam, V.W. (2019), “Construction delay risk taxonomy, associations and regional contexts: A systematic review and meta-analysis”, Engineering, Construction and Architectural Management, Vol. 26 No. 10, pp. 2364-2388.
Dikmen, I., Budayan, C., Birgonul, M.T. and Hayat, E. (2018), “Effects of risk attitude and controllability assumption on risk ratings: observational study on international construction project risk assessment”, Journal of Management in Engineering, Vol. 34 No. 6, p. 4018037, doi: 10.1061/ASCE)ME.1943–5479.0000643.
Ekanayake, E.M.A.C., Shen, G.Q., Kumaraswamy, M.M. and Owusu, E.K. (2022a), “Identifying supply chain vulnerabilities in industrialized construction: an overview”, International Journal of Construction Management, Vol. 22 No. 8, pp. 1464-1477.
Ekanayake, E.M.A.C., Shen, G.Q., Kumaraswamy, M. and Owusu, E.K. (2022b), “A fuzzy synthetic evaluation of vulnerabilities affecting supply chain resilience of industrialized construction in Hong Kong”, Engineering, Construction and Architectural Management, Vol. 29 No. 6, pp. 2358-2381.
El-Sayegh, S.M. and Mansour, M.H. (2015), “Risk assessment and allocation in highway construction projects in the UAE”, Journal of Management in Engineering, Vol. 31 No. 6, p. 4015004, doi: 10.1061/(ASCE)ME.1943-5479.0000365.
Golpîra, H. (2020), “Optimal integration of the facility location problem into the multi-project multi-supplier multi-resource construction supply chain network design under the vendor managed inventory strategy”, Expert Systems with Applications, Vol. 139, p. 112841.
Gunduz, M. and Yahya, A.M.A. (2015), “Analysis of project success factors in construction industry”, Technological and Economic Development of Economy, Vol. 24 No. 1, pp. 67-80.
Gunduz, M. and Elsherbeny, H.A. (2020), “Operational framework for managing construction-contract administration practitioners’ perspective through modified Delphi method”, Journal of Construction Engineering and Management, Vol. 146 No. 3, p. 4019110, doi: 10.1061/(ASCE)CO.1943-7862.0001768.
Habibi, M. and Kermanshachi, S. (2018), “Phase-based analysis of key cost and schedule performance causes and preventive strategies: research trends and implications”, Engineering, Construction and Architectural Management, Vol. 25 No. 8, pp. 1009-1033.
Hussein, M., Eltoukhy, A.E., Karam, A., Shaban, I.A. and Zayed, T. (2021), “Modelling in offsite construction supply chain management: a review and future directions for sustainable modular integrated construction”, Journal of Cleaner Production, Vol. 310, p. 127503.
Kazancoglu, I., Ozbiltekin-Pala, M., Mangla, S.K., Kazancoglu, Y. and Jabeen, F. (2022), “Role of flexibility, agility and responsiveness for sustainable supply chain resilience during covid-19”, Journal of Cleaner Production, Vol. 362, p. 132431.
Kissi, E., Agyekum, K., Musah, L., Owusu-Manu, D.G. and Debrah, C. (2020), “Linking supply chain disruptions with organisational performance of construction firms: the moderating role of innovation”, Journal of Financial Management of Property and Construction, Vol. 26 No. 1, pp. 158-180.
Koc, K. and Gurgun, A.P. (2021), “Stakeholder-associated life cycle risks in construction supply chain”, Journal of Management in Engineering, Vol. 37 No. 1, p. 4020107.
Kuo, Y.C. and Lu, S.T. (2013), “Using fuzzy multiple criteria decision-making approach to enhance risk assessment for metropolitan construction projects”, International Journal of Project Management, Vol. 31 No. 4, pp. 602-614, doi: 10.1016/j.ijproman.2012.10.003.
Le, P.L., Jarroudi, I., Dao, T.-M. and Chaabane, A. (2021), “Integrated construction supply chain: an optimal decision-making model with third-party logistics partnership”, Construction Management and Economics, Vol. 39 No. 2, pp. 133-155, doi: 10.1080/01446193.2020.1831037.
Le, P.L. and Nguyen, N.T.D. (2022), “Prospect of lean practices towards construction supply chain management trends”, International Journal of Lean Six Sigma, Vol. 13 No. 3, pp. 557-593.
Li, S., Fang, Y. and Wu, X. (2020), “A systematic review of lean construction in mainland China”, Journal of Cleaner Production, Vol. 257, p. 120581.
Liker, J.K. (2019), Toyota Talent, McGraw-Hill.
Liu, Y., Chang, R.D., Zuo, J., Xiong, F. and Dong, N. (2022), “What leads to the high capital cost of prefabricated construction in China: perspectives of stakeholders”, Eng. Construct. Architect. Manag, doi: 10.1108/ECAM-06-2021-0538.
Luo, W., Shi, Y. and Venkatesh, V.G. (2018), “Exploring the factors of achieving supply chain excellence: a New Zealand perspective”, Production Planning and Control, Vol. 29 No. 8, pp. 655-667.
Luo, L., Qiping Shen, G., Xu, G., Liu, Y. and Wang, Y. (2019), “Stakeholder associated supply chain risks and their interactions in a prefabricated building project in Hong Kong”, Journal of Management in Engineering, Vol. 35 No. 2, p. 5018015, doi: 10.1061/(ASCE)ME.1943-5479.0000675.
Mashwama, N., Aigbavboa, C. and Thwala, D. (2017), “An assessment of the critical success factor for the reduction of cost of poor quality in construction projects in Swaziland”, Procedia Engineering, Vol. 196, pp. 447-453.
Ndukwe, C.V., Liu, J. and Chan, T.K. (2021), “Impact of covid-19 on the China-Australia construction supply chain”, in Lu, X., Zhang, Z., Lu, W. and Peng, Y. (Eds), Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate, Springer, pp. 1275-1291, doi: 10.1007/978-981-16-3587-8_87.
Panova, Y. and Hilletofth, P. (2018), “Managing supply chain risks and delays in construction project”, Industrial Management and Data Systems, Vol. 118 No. 7, pp. 1413-1431.
Pham, H.T., Pham, T., Truong Quang, H. and Dang, C.N. (2023), “Supply chain risk management research in construction: a systematic review”, International Journal of Construction Management, Vol. 23 No. 11, pp. 1945-1955.
Pushpamali, N.N.C., Agdas, D., Rose, T.M. and Yigitcanlar, T. (2021), “Stakeholder perception of reverse logistics practices on supply chain performance”, Business Strategy and the Environment, Vol. 30 No. 1, pp. 60-70.
Queiroz, M.M., Wamba, S.F., Jabbour, C.J.C. and Machado, M.C. (2022), “Supply chain resilience in the UK during the coronavirus pandemic: a resource orchestration perspective”, International Journal of Production Economics, Vol. 245, p. 108405.
Raoufi, M. and Fayek, A.R. (2021), “Identifying actions to control and mitigate the effects of the COVID-19 pandemic on construction organizations: preliminary findings, public works”, Public Works Management and Policy, Vol. 26 No. 1, pp. 47-55, doi: 10.1177/1087724X20969164.
RezaHoseini, A., Noori, S. and Ghannadpour, S.F. (2021), “Integrated scheduling of suppliers and multi-project activities for green construction supply chains under uncertainty”, Automation in Construction, Vol. 122, p. 103485.
Rudolf, C.A. and Spinler, S. (2018), “Key risks in the supply chain of large-scale engineering and construction projects”, Supply Chain Management: An International Journal, Vol. 23 No. 4, pp. 336-350, doi: 10.1108/SCM-09-2017-0292.
Sfakianaki, E. (2019), “Critical success factors for sustainable construction: a literature review”, Management of Environmental Quality: An International Journal, Vol. 30 No. 1, pp. 176-196.
Shahbaz, M.S., Soomro, M.A., Bhatti, N.U.K., Soomro, Z. and Jamali, M.Z. (2019), “The impact of supply chain capabilities on logistic efficiency for the construction projects”, Civil Engineering Journal, Vol. 5 No. 6, pp. 1249-1256.
Shishodia, A., Verma, P. and Jain, K. (2022), “Supplier resilience assessment in project-driven supply chains”, Production Planning and Control, Vol. 33 Nos 9/10, pp. 875-893, doi: 10.1080/09537287.2020.1837935.
Siraj, N.B. and Fayek, A.R. (2019), “Risk identification and common risks in construction: literature review and content analysis”, Journal of Construction Engineering and Management, Vol. 145 No. 9, p. 3119004, doi: 10.1061/(ASCE)CO.1943-7862.0001685.
Thunberg, M. and Fredriksson, A. (2018), “Bringing planning back into the picture–how can supply chain planning aid in dealing with supply chain-related problems in construction?”, Construction Management and Economics, Vol. 36 No. 8, pp. 425-442.
Thunberg, M., Rudberg, M. and Gustavsson, T.K. (2017), “Categorizing onsite problems: a supply chain management perspective on construction projects”, Construction Innovation, Vol. 17 No. 1, pp. 90-111, doi: 10.1108/CI-10-2015-0059.
Wieland, A. and Durach, C.F. (2021), “Two perspectives on supply chain resilience”, Journal of Business Logistics, Vol. 42 No. 3, pp. 315-322.
Wuni, I.Y. and Shen, G.Q. (2019), “Holistic review and conceptual framework for the drivers of offsite construction: a total interpretive structural modelling approach”, Buildings, Vol. 9 No. 5, p. 117.
Yuan, M., Li, Z., Li, X. and Luo, X. (2021), “Managing stakeholder-associated risks and their interactions in the life cycle of prefabricated building projects: a social network analysis approach”, Journal of Cleaner Production, Vol. 323, p. 129102.
Zainal Abidin, N.A. and Ingirige, B. (2018), “The dynamics of vulnerabilities and capabilities in improving resilience within Malaysian construction supply chain”, Construction Innovation, Vol. 18 No. 4, pp. 412-432.
Zakaria, S.F., Zin, R.M., Mohamad, I., Balubaid, S., Mydin, S.H. and Mohd Rahim, E.R. (2017), “Critical success factors in infrastructure projects”, AIP Conference Proceedings, Palembang, Indonesia: AIP Publishing LLC, Vol. 1903, No. 1, p. 70017.
Zhang, H. and Yu, L. (2021), “Resilience-cost tradeoff supply chain planning for the prefabricated construction project”, Journal of Civil Engineering and Management, Vol. 27 No. 1, pp. 45-59.
Further reading
Hernadewita, H. and Saleh, B.I. (2020), “Identifying tools and methods for risk identification and assessment in construction supply chain”, International Journal of Engineering, Vol. 33 No. 7, pp. 1311-1320.
Hill, R. (1998), “What sample size is ‘enough’ in internet survey research”, Interpers Comput Technol, Vol. 6 Nos 3/4, pp. 1-12.
Liao, L., Yang, C. and Quan, L. (2023), “Construction supply chain management: a systematic literature review and future development”, Journal of Cleaner Production, Vol. 382, p. 135230.
Obayi, R. and Ebrahimi, S.N. (2021), “A neo-institutional view of the transaction cost drivers of construction supply chain risk management”, Supply Chain Management: An International Journal, Vol. 26 No. 5, pp. 592-609.
Senthil, J. and Muthukannan, M. (2022), “Development of lean construction supply chain risk management based on enhanced neural network”, Materials Today: Proceedings, Vol. 56, pp. 1752-1757.
Acknowledgements
Funding: This research is funded by the university of Johannesburg, South Africa.