Examining the Influence of Sustainable Construction Supply Chain Drivers on Sustainable Building Projects Using Mathematical Structural Equation Modeling Approach

Abstract

This study focuses on the results of examining the impact of Sustainable Construction Supply Chains (SCSC) on sustainable success (OSS) throughout the life of a project in developing countries. While previous research has explored the challenges of implementing SCSC in these regions, limited attention has been given to the overall impact on sustainable success. To address this gap, a conceptual model was developed based on an extensive literature review, and data were collected through a survey involving 70 building professionals in Egypt. The findings indicate that the adoption of SCSC drivers has a significant influence on OSS during the construction phase, ranging from moderate to high. These results provide valuable insights for policymaking in developing nations, as they highlight the importance of overcoming barriers to SCSC adoption and promoting these drivers to ensure successful project completion. Ultimately, implementing SCSC approaches will contribute to improved project outcomes in the construction industry.

1. Introduction

In certain developing countries, the building industry has experienced substantial transformations to address general fiscal aims [1]. Despite these changes, the building industry in these countries remains fundamentally collegial due to the inability to sustain international standards for viable development. Building schemes often encounter various problems including non-completion, delays in project schedules, budgetary issues, high risks, low quality, and lack of achieving desired goals [2,3]. Due to the reduced sector investment, several schemes may be suspended or cancelled [4]. Overall, the construction industry in emerging economies fails to accomplish the tasks of authorities, consumers, and the public, trailing other industries and their counterparts [5]. Construction in Egypt consistently faces the same persistent issues due to disparities within social and economic factors. The low wages, high unemployment, and safety concerns present a vulnerable environment for the industry [6]. The high level of risk is caused by significant currency fluctuations, a scarcity of sound business decision-making, and restrictions on investment strategies [7]. The significant variables accountable for delaying projects in Egypt were established in a broader context [8]: The challenges faced in construction finance include inconsistencies from the client (owner) in ensuring timely payment, impromptu changes to the design, and inadequate building management.

The role of Sustainable Construction Supply Chains (SCSC) is crucial in light of the issues above. SCSC generates value through connections in the end-consumer’s downstream and upstream supply of services and products. As a result, an SCSC involves multiple entities including distribution, upstream supply, and the final user [9,10]. It can also be described as the ability to foresee disruptions, endure them, react to them, and bounce back effectively from them [11,12,13]. Based on the research available, it can be observed that the adoption of sustainable tradition might significantly mitigate or address socio-economic and ecological problems [14]. Alternatively, SCSC is defined as resource management to fulfil the stakeholders’ expectations to establish greater sustainability and resilience within a company’s supply chain [15]. The published research on SCSC highlights the lack of systematic analysis that incorporates sustainability, particularly in emerging nations [16]. It aligns due to the absence of studies, a common phenomenon in these countries.

Nonetheless, between the significant research conducted, Pettit et al. [17] contended that SCSC is essential for a viable SC and increases the system’s intricacy. Chowdhury et al. [18] emphasised the significance of applying the system thinking (ST) method to tackle the growing intricacy. ST entails viewing the environment as a strong structure where all components are interconnected, and a single task cannot be accomplished in isolation [19,20,21]. Therefore, the autonomous realisation of SCSC is impossible, and a comprehensive system assessment is necessary. It highlights a lack of information in the present research, which constituted the objective of this research. This study’s main aim is to assess and identify the impact of SCSC on OSS.

The current study investigates the lack of knowledge concerning the correlation between Supply Chain Sustainability (SCSC) and Sustainable Success (OSS) in the construction industry. The study takes a mathematical approach via the Partial Least Square (PLS) modelling procedure to uncover this relationship. The results of this study will provide a valuable basis for policy-making concerning the building industry, particularly in Egypt, where understanding of SCSC benefits is limited. By adopting SCSC, the study hopes to eliminate unwanted costs and improve quality, leading to an economic benefit in a rapidly transforming milieu. This study’s results would also offer a real-time perception of the operations within the SCSC linkages, reducing the risk for building companies. Results derived from this study are expected to encourage companies to regulate and improve their logistics and practices, ultimately leading to an OSS. It will increase sustainability, reduce cost reduction, improve efficiency, and provide more significant benefits for construction businesses. The methodology used for this study is detailed in the relevant literature review. Likewise, this study’s expected findings were deliberated within the background of the available texts. The primary research finding was highlighted in the final section of this paper. Commendations for future investigations followed it. This study is significant because it guides policy-makers in the building industry in making cognisant judgments concerning SCSC, leading to increased sustainability and success.

2. Literature Review

2.1. Sustainable Supply Chain Implementation

A supply chain is a network that connects individuals, resources, organisations, and processes involved in the manufacturing and distribution of the product(s) [22]. It encompasses everything from the origin of material supply (i.e., supplier) to the producer (i.e., manufacturer) and ultimately reaches the consumer through final distribution [23]. It involves overseeing the progression of services and goods from conception to the manufacturing of the final product [24]. Modern supply chains are intricate systems where multiple companies collaborate across various stages to deliver a diverse range of products to end-users [25]. In contemporary supply chains, numerous companies collaborate at different stages to collectively provide a wide array of products to end-users [26]. Supply chain management (SCM) integrates all aspects of a company’s operations into a unified system [27]. The Sustainable Supply Chain Management (SSCM) framework incorporates the three pillars of sustainability, namely financial, social, and environmental, throughout the entire manufacturing lifecycle. This lifecycle encompasses activities such as product design, manufacturing, raw material sourcing, processing, packaging, warehousing, shipping, distribution, utilisation, returns, and disposal [28,29]. The Sustainable Supply Chain Management (SSCM) system can adeptly and proficiently oversee the interconnected economic, social, and environmental factors within international supply chains [30]. In order to attain sustainable supply chains, stakeholders must fulfil financial, social, and environmental prerequisites [31]. The underlying concept is that by meeting consumer demands and the associated criteria, competition can be preserved. In recent years, Sustainable Supply Chain Management (SSCM) has gained significant recognition, leading to a surge in scholarly research output. Such sustainable supply chains contribute to value management (VM) enhancement [32]. SSCM is a systematic process aimed at enhancing the value of a product. It involves examining and optimising the performance of individual components and their associated costs to improve the overall value of the product [33]. In the context of construction projects, Sustainable Supply Chain Management (SSCM) can offer significant advantages. Implementing SSCM at an early stage of projects can result in long-term cost and time savings, ultimately leading to higher returns on investments and increased cost efficiencies [34]. SSCM enables the substitution of more cost-effective materials and technologies without compromising the functionality of the product [35]. SSCM plays a crucial role in enhancing the efficiency of construction supply chains by reducing costs through supply chain integration, while simultaneously maintaining a high-quality service delivery. This makes the supply chains more economically viable [36]. Within supply chain management (SCM), the focus has predominantly been on addressing the social aspect of sustainability, while comparatively giving less attention to the economic and environmental dimensions [37]. The goal of sustainable supply chains is to integrate social, economic, and environmental aspects into traditional, cost-effective supply chain management approaches. A sustainable supply chain can be defined as a collaborative interface between companies within the supply chain that delivers comprehensive social and environmental benefits to all involved stakeholders [38,39]. It encompasses the endeavours of companies to address the human and environmental impact of their product’s journey within the supply chains, starting from the sourcing of raw materials through production, storage, and distribution [37,40,41].

2.2. Sustainable Supply Chain Management (SSCM)

Mukherjee and Mandal [42] utilised the Interpretive Structural Modelling (ISM) technique to analyse significant considerations in managing the photocopier remanufacturing business within the realm of sustainable practices. The study revealed that the work environment, utilisation of returns, and marketing challenges related to remanufactured goods exerted the most significant influence. Factors such as product design, remanufacturing technology and equipment, proper planning of disassembly and reassembly, and the role of skilled and experienced workforce exhibited the highest degree of dependency. Through an examination of the interactions among key enablers, this research aimed to transform a supply chain into a truly sustainable entity, Faisal [43] proposed a methodology for effectively integrating sustainable practices within a supply chain, using a hierarchical model based on the ISM technique. The study identified key factors with significant driving and dependency power, including consumer concerns about sustainable practices, regulatory frameworks, knowledge of sustainable practices in the supply chain, and metrics for quantifying sustainability benefits. Grzybowska [44] examined the facilitators of sustainability in supply chains and their interrelationships. Sixteen enablers were identified, with top management commitment and the adoption of acceptable reverse logistic practices (Environmental performance) found to have the highest driving and reliance power. Hussain [44] introduced a modelling framework that examined multiple supply chain enablers, their interactions, and proposed options for establishing sustainable supply networks. The study explained the concept of triple bottom line sustainability encompassing environmental, social, and economic aspects, and identified relevant enablers. An ISM approach was utilised to establish relationships between different enablers for each sustainability dimension. The findings from ISM were then incorporated into an Analytical Network Process (ANP) using an ISM approach. This integration entailed analysing a variety of prospective alternatives in order to determine the best option(s) for developing long-term supply chains. The analysis revealed that the consumer voice, governmental constraints, and risk management had the most effect and dependency on the outcomes. Diabat et al. [45] developed a model to analyse the factors that influence the adoption of green supply chain management (GSCM) practices in organisations. The study highlighted three elements with the most driving and reliance power: government rules and legislation, reverse logistics and green design, and quality integration. Mathiyazhagan et al. examined the applicability of GSCM ideas [46] The research was divided into two phases: obstacle identification and qualitative analysis. To comprehend the interdependence of the 26 obstacles identified by a survey of the literature, feedback from industry professionals, and input from academia, an ISM analysis was conducted. As a result, a hierarchical sustainable framework was proposed to evaluate the obstacles to the adoption of green supply chain management (GSCM) in organisations by Dashore and Sohani [47,48]. After identifying a total of 14 barriers, a structural model was constructed using the ISM approach. The barriers that exhibited the highest driving and dependency power were the absence of a government initiative framework for GSCM practitioners and the suppliers’ adaptability in transitioning towards GSCM. This was conducted in response to the requirements of green-enabled practices in the Indian mining industries, Muduli et al. [48] explored different behavioural variables that impact GCSM practices and their inter-connectedness. The ISM approach was used to examine the interrelationships between the identified behavioural components. Top management support and green innovation were recognised as the components with the greatest influencing and reliant power. Several variables crucial for adopting GSCM relevant to the Indian manufacturing industry were highlighted by Luthra et al. [49]. By employing the ISM approach, a contextual connection between these factors was established. Among the ten evaluated criteria, international environmental accords and cutting-edge green practices demonstrated the highest driving and dependency power. This empirical study technique was employed as part of the research methodology, Kumar et al. [50] gathered primary data to collect and to rank several factors associated with successful consumer engagement in implementing green ideas within a supply chain. To establish contextual links among the variables, an ISM-based model was utilised. The study investigated ten factors, with the two factors exhibiting the highest driving and reliance power identified as the level of customer awareness and the presence of green labelling [51]. In order to understand the interplay between the obstacles, an ISM technique was employed. The obstacles with the most significant driving and dependency forces were attributed to poor knowledge and insufficient planning and forecasting. To address this, a multi-criteria group decision-making (MCGDM) model was developed within a fuzzy environment was created by Kannan et al. [52] To guide the selection of the optimal third-party reverse logistics provider (3PRLP), a combination of the ISM methodology and the fuzzy approach using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis was employed. The study revealed that reverse logistics cost exerted the most significant driving force, while the technical/engineering capability requirement exhibited the highest dependency. The aim was to uncover and consolidate the relationships between crucial variables when choosing the most suitable third-party reverse logistics provider, Kannan et al. [52] employed the ISM technique. The study revealed that reverse logistics functions and third-party logistics services possessed the highest driving and reliance power. This determination was made using an ISM approach, Sarkis et al. [53] examined 11 barriers to the deployment of environmentally friendly manufacturing (ECM) processes were investigated. Inadequate design-for-environment (DFE) interfaces and inappropriate evaluation and appraisal methodologies were identified as the issues having the most driving and dependence power. Raut et al. [14] utilised the ISM methodology to identify the factors that influence and hinder the implementation of green supply chain management (GSCM) practices in Nigerian construction enterprises. The study highlighted that the main obstacles to adopting GSCM practices included a lack of public awareness, limited understanding of environmental implications, insufficient commitment from senior management, and inadequate legal enforcement and government support. The ISM approach was employed to analyse these factors. Balasubramanian [39] introduced a hierarchical sustainability framework to evaluate the 12 obstacles to implementing green supply chain management (GSCM) in the construction industry of the United Arab Emirates (UAE). The study identified the two factors with the most significant impact on the situation as a lack of resources and a lack of understanding among stakeholders. In order to integrate GSCM methods in the Indian car sectors, Luthra et al. [40] identified a total of 11 barriers, and a contextual relationship among these barriers was established. The absence of government assistance programs, market competition, and uncertainty emerged as the barriers with the highest driving and reliance power, as determined by a hierarchical structural model developed using the ISM approach. Using ISM, Sandeep et al. [41] identified fifteen key enablers for the integration of green principles into the Indian automotive supply chain. The factors with the highest influence were government assistance, regulation, and relative advantage. Eswarlal et al. [42] employed the ISM technique to evaluate 14 critical aspects of sustainable development in India, specifically focusing on the implementation of renewable energy projects. Leadership, sustainable growth, and return on investment were identified as the most influential factors with significant dependencies. Eswarlal et al. [43] identified 14 key critical success factors (CSFs) for the implementation of renewable energy projects to promote sustainable development. It was found that sustainable growth, return on investment, and public awareness had the highest driving and dependency power. These factors were crucial in determining the suitable location for the establishment of a wind farm; Kang et al. [44] created a comprehensive evaluation model, considering various aspects that needed to be considered. The ISM approach was employed to establish the interconnections between the requirements for each criterion. The significance of the criteria was determined using a fuzzy analytic network technique, and the overall performance of the wind farm projects was predicted. The ISM approach was utilised in this study; Muduli and Barve [45] identified possible obstacles to greening efforts in the Indian mining industry. The operational waste management plan and a lack of senior management commitment emerged as the two factors with the highest driving and dependency power. To address the unique circumstances surrounding the coral reefs, turtles, and variety of pelagic fish in Bunaken Marine Park and manage it as a sustainable tourism destination, an institutional model was developed by Kholil and Tangian [46] who used the ISM technique. In the research project, a total of nine primary criteria and fifteen supporting criteria were defined. The aspects of control that exhibited the highest driving power and reliance power were identified as setting the number of visits and enhancing community engagement. Among the sub-criteria, the national parks board demonstrated the strongest motivating force, while public and environmental as well as marine non-governmental organisations (NGOs) played a significant role in enabling forces. According to Muduli et al. [49], GSCM performance in the mining sector has influenced human behaviours. Their study revealed and assessed these behavioural characteristics. Additionally, their study focused on identifying 10 obstacles to the adoption of GSCM techniques in the foundry industry. Manikanda Prasath [54] utilised an ISM methodology to explore the interconnections among the barriers. The lack of governmental oversight and legislation emerged as the most influential motivating factor, while the lack of adoption of new technologies exhibited the highest reliance. The study focused on examining the relationships between the 13 main obstacles that hinder the implementation of energy conservation in China. According to Wang et al. [55], the factors that had the greatest influence were a lack of technological experience and a lack of knowledge regarding energy conservation. These factors were particularly relevant to the Taipei Metropolitan Municipal Solid Waste Management (MSWM) operations aimed at reducing air pollution. Dos Muchangos et al. [56] established a total of 18 criteria with the highest driving and reliance power identified in relation to fuel or non-renewable energy usage, air pollutants, and waste generation. Additionally, nine key considerations were identified for selecting a supplier based on corporate social responsibility (CSR). The factors with the most influential driving and dependency forces were identified as underage labour and safety measures. Mangla et al. [57] established various performance-focused criteria for the implementation of green supply chain management (GSCM) in their company. During their empirical study on green supply chain management (GSCM) practices among Micro, Small, and Medium-sized Enterprises (MSMEs) in India. Hence, the identification of crucial components necessary for the implementation of sustainable supply chain management (SSCM) practices and their impacts becomes imperative [58]. Based on the analysis of existing literature, it can be inferred that numerous research studies have investigated the adoption of sustainable practices across various countries and industries. However, there is a scarcity of studies focusing on the significance of implementing sustainable supply chain management (SSCM) techniques and addressing sustainable practices specifically in the Egyptian sector. This highlights the necessity for further research on the application of sustainable practices in Egyptian businesses.

2.3. Overall Sustainable Success

The concept of sustainability has received extensive attention from academia [59,60]. However, updating the tactics and techniques for achieving sustainability goals for a project is a complicated process [61]. Creating an equilibrium between the environmental, economic, and social aspects [59,62]. Increased focus on the construction industry’s sustainability has led to exploring effective methods for incorporating this perspective into everyday business operations [63]. The crucial elements that can speed up the integration of SCSCs during the initially planned phases are the quest for a benchmark of imaginative corporate social responsibility to improve sustainability for corporations to adopt [64]. Three key sustainability measurements-environmental, economic, and social-can be linked to the impact of SCSCs on construction projects.

2.3.1. Economic

Economic paybacks are central to stimulating the SCSC implementation concerning sustainable construction. It is a widely recognised application due to its steady influence on the economic aspect through the calculation cost of managing risks [65]. The estimated total cost of the project and the funding requirement tend to be categorised into different stages to envision and determine the budget for each stage [66]. Likewise, the SCSC’s incorporation of additional project features could impact financial efficiency. For example, the ability to make future predictions by adopting the projects can lead to increased understanding and collaboration among involved parties. It can result in decreased waste, shorter project timelines, reduced project expenses, and improved management of construction projects [65,67].

2.3.2. Environmental

The data required for performance financing in SCSCs is considered as the project proposal process. Engineers could assess building performance via SCSCs at the project plan’s preliminary phases and establish it. Therefore, they can quickly assess proposal alternatives to make a faultless selection to reiterate an environmentally friendly plan [68]. Mainly, the SCSCs mechanisms have several procedures for energy and material consumption analysis and electrical and mechanical components of the building. Therefore, it can offer comprehensive information concerning energy reduction and resource intake [69].

2.3.3. Social

According to the Western Australian Council of Social Services, social sustainability refers to conditions that enable present and upcoming generations to establish a liveable and healthy society. These societies are characterised by fairness, diversity, interconnectedness, and democracy and offer a high standard of living. The concept comprises informal and formal processes, structures, relationships, and systems [70]. The benefits of the social component of sustainability are considered to expedite extra facets of sustainability that enhance the standard of living, healthy living, and comfortability [70,71]. Concerning sustainability, the social principle has concentrated on different descriptions and theories. Thus, independent and dependent elements can be classified into two sets concerning the relationship with SCSCs. The more qualitative the pendulous social sustainability elements which might be projected using other measurements, the more the SCSCs could provide different aspects of the environmental conditions, lightning, and energy performance.

2.4. Key Drivers of SCSC Implementation and OSS

The SCSC is a process that connects all individuals, activities, organisations, and resources needed for manufacturing and product distribution [22]. The SCSC includes entire supply chain phases, starting from the material supply source (i.e., the supplier) to the manufacturer or producer and concluding with the final distribution of the product to the end consumer [23]. The SCSC is a systematic approach that oversees the progression of goods and services from conception to production of a finished product [24]. Modern SCSCs are intricate systems in which multiple companies collaborate across different phases to provide a variety of goods to customers [25]. While minimising the likelihood of uncertainties and disturbances and enhancing the litheness of the SCSC, independent companies must collaborate [26]. The SCSC integrates all three pillars of sustainability, namely financial, social, and environmental aspects, throughout the product manufacturing lifecycle [27]. The product lifecycle spans design and manufacturing to sourcing raw materials, warehousing, packaging, distribution, shipping, return, utilisation, and disposal [28,29]. The SCSCs may effectively and proficiently handle interlinked social, environmental, and economic components within the global supply chain [30]. To attain sustainable SCs, participants must satisfy environmental, social, and economic demands [31]. The theory is that competition will be sustained to meet consumer demands and criteria. Sustainable SCs have gained significant recognition, leading to increased scholarly research outputs in recent years. VM is improved by such sustainable SCs [32]. The SCSC method is a systematic approach to enhancing products’ value. Thus, it involves evaluating and improving individual components’ performance and associated costs to elevate the product’s overall value [33]. Utilising SCSC in building projects can provide significant benefits. Implementing SCSC early in projects can result in long-term cost and time savings, leading to greater investment returns and increased cost efficiencies [34]. SCSC increases by replacing cheaper technologies and materials without compromising the product’s functionality [35]. SCSC aids in enhancing the functionality of construction SCs through cost reduction via the incorporation of a supply chain while maintaining a more outstanding quality delivery of service, thus making it more sustainable [36]. The social sustainability within SCSC was addressed compared to the environmental and economic components [37]. The objective of SCs is to incorporate environmental, economic and social components into traditional SCSCS cost-effective techniques. Viable SC is described as a crossing point between businesses in an SC that presents all-inclusive environmental and social benefits to the entire SC connections [38,39]. It encompassed corporations’ determinations to address the environmental and human effect of their products’ pathways within SCs. It starts from raw material sourcing to storing, production, and marketing [37,40,41]. An SCSC can withstand or avoid the impacts of interruptions and recuperate rapidly from interruptions. Thus, an SCSC can avert or endure the impacts of interruptions and recuperate in an economic and timely manner [40]. SCSC has continuously been an essential factor in assuring corporations’ achievement. SC pliability exclusively no longer means managing risk [72]. Thus, it is established that managing risk comprises being more accepted over competitors to tackle interruptions within SCs. Moreover, SCSC provides benefits to companies through completive advantage [39]. It is noteworthy that the dimension of SCSC is to create a resilient structure. The sustainability level needed by an organisation largely depends on the context [73]. The SCSC is normally affected by the antecedents of capability, vulnerability, orientation, and SCSC plan [74,75].

Based on the literature review, many drivers of SCSC adoption were identified. These are summarised in

Table 1. Key drivers to adopting SCSC.

Code	Enablers	Studies
En1	Highest Organizational Support	[76,77]
En2	Flexibility	[17,78]
En3	Discernibility	[17,76,78,79]
En4	Fitness	[77,80]
En5	Affinity	[76,77]
En6	Quality assurance	[81]
En7	Receptiveness	[81,82]
En8	High-tech Ability	[81,82,83]
En9	Swiftness	[77,80]
En10	Safe Keeping The Supply Chain	[80]
En11	Partnership	[17,79]
En1	Instant Faith	[77,78]
En13	Revenue and Risk Sharing	[84,85]
En14	Data Distribution	[86,87]
En15	Elastic System	[82,88]
En16	Philosophy of risk management	[81,82]
En17	Data Safety	[89]
En18	Tactical Risk Design	[75,90]
En19	Commercial Communal	[91]
En20	Accountability	[88,92]
En21	Eventuality Design	[81]
En22	Security Standard	[82]
En23	Elastic Shipping	[81]
En24	Supply Efficacy	[77,80]
En25	Clearness	[81,82,83]
En26	Self-Rule	[81]
En27	Market Understanding	[86,87]
En28	Firmness	[17,78]
En29	Control	[17,76,78,79]
En30	Reliability	[75,90]
En31	Suitable Arrangement	[17,79]
En32	Equanimity	[82]

. The literature showed that the adoption of SCSC is not thoroughly investigated. Further, the accessible research concerning the adoption of SCSC is scattered and comprises various autonomous papers and publications. Hence, additional research concerning this fertile area seems irrefutable and justifiable. These research works would lay the basis for improved SCSC adoption-based systems within the construction industry and could expand relationships among significant stakeholders and investors. Based on the literature review conducted, this study, as indicated in Figure 1, hypothesised that:

H₁: A significant relationship exists between OSS and SCSC.

3. The Study Design and Methods

A study approach is initiated by creating a conceptual model, which serves as a condensed version of the literature review on a particular subject. The purpose of the conceptual model is to establish transitional hypotheses or theories which can be tested using experiential proof [93]. The conceptual modelling process involves three stages: (1) identifying the model’s constructs that will be included, (2) organising the model’s construct into categories, and (3) defining the existing relationships among the model’s constructs [94]. This study’s research design was embraced by Kineber et al. [95]. It is further illustrated in Figure 2.

A questionnaire survey tool was used to collect data from participants involved in design supply including architects, designers, quantity surveyors, and constructors. Participants in the building industry include traditional sub-constructors and constructors, management experts, employees, construction directors, and operatives working on project sites.

3.1. Analysis Construct Validity: EFA Assessment

The two commonly used statistical techniques, i.e., Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA), were employed to analyse the data [96]. This study applied the CFA to assess the significant components of different variables within a specific hypothesis. On the other hand, exploratory factor analysis (EFA) was employed to gain insight into the correlations and components underlying a few essential assemblies [97]. When defining the initial solution, no prior hypothesis is needed in principal component analysis (PCA), whereas in EFA, it is required [98]. Thompson [99] indicated that Principal Component Analysis (PCA) is the automatic selection within different statistical software bundles. Thus, it is more widely used in exploratory factor analysis (EFA).

Additionally, the Varimax rotation technique (VRT) is commonly ideal over Promax or direct Oblimin as it reduces load distribution between variables [100]. The VRT is ideal for Factor Analysis (FA) since it establishes a technique that reduces the number of factors without changing their interpretability [101,102]. The variables are measured as a distinct model across relevant categories [103]. Therefore, 32 factors were identified for this study, and the questionnaires were distributed to 90 respondents, generating appropriate data for FA [96,104].

3.2. Methodical Approach (Structured Equation Modelling)

Four models from the literature were examined to explore the impact of Supply Chain Sustainability Certification (SCSC) concerning the overall sustainability presentation of construction schemes. The aim was to compare these models and determine the most effective approach for incorporating SCSC into the development of sustainable building projects. The models evaluated were Multiple Linear Regression (MLR), Structural Equation Modelling (SEM), System Dynamics (SD), and Artificial Neural Network (ANN). The regression equation was not utilised due to non-observed variables, a significant limitation when using regression analysis [105]. As a result of the type of data employed by this study, it was deemed unsuitable to use system dynamics, as the data were not time related.

Additionally, the artificial neural network is a prediction technique, although this research aimed to assess the impact of SCSC adoption on the general sustainability presentation of building projects. Therefore, the Structural Equation Modelling (SEM) approach was employed, as it allowed for describing the connection among various quantifiable and non-quantifiable variables relevant to the study requirements. Amaratunga et al. [106] reported that Structural Equation Modelling (SEM) is a valuable technique for addressing inaccuracies within variables. The SEM method was applied in this study to construct a model and establish the association between SCSC (drivers) and the overall sustainability performance of building projects. Byrne [107] noted that Structural Equation Modelling (SEM) has emerged as an established technique for non-experimental research, particularly in cases involving testing methods for research hypotheses that are yet to be fully established. Likewise, Ringle et al. [108] noted that the SEM approach had gained strength and popularity over the years. The publication of various studies in periodicals, including the MIS Quarterly, supports it. Moreover, Yuan et al. [109] can be concluded that SEM is a widely recognised and commonly applied methodology for data analyses in behavioural sciences.

The SEM approach was used in this research since it has been commonly applied in the building industry [60,102]. The study aimed to show the correlation between SCSC (drivers) and OSS by employing the Partial Least Squares (PLS) model. This model consisted of formative and reflective variables and was used to assess the performances within SCSC and their effects on OSS. The PLS model is a statistical method that can handle complex relationships and identify latent variables that are difficult to measure directly. In this approach, the model measurement was applied to explain the connections between the construct (SCSC) and the observed pointers (drivers).

Reflective variables are the traditional ones where those measured variables are an effect of the underlying constructs. In contrast, formative variables are constructs where the measured variables create the construct. By using reflective and formative variables in the PLS model, this research could understand the correlation between SCSC and OSS. Additionally, the PLS model is a suitable method when the sample size is relatively small, and the data may not meet the assumptions of other methods such as covariance-based structural equation modelling. Overall, the PLS model helps investigate complex relationships and determine the impact of various factors on a specific outcome. By employing this approach in the study, the scientists might study the effect of SCSC on OSS within the building industry [110].

3.2.1. Common Method Bias (CMB)

The CMB denotes the discrepancy that can be attributed to the shared technique of data collection instead of the constructs or instruments themselves [111]. Under certain conditions, the data collected in the field may be inflated or hinder the examination of the intended relationships, thereby leading to difficulties [112,113], given that all of the data analysed by this paper was derived from a solitary basis and is personal and subjective. Thus, it is crucial to resolve these concerns to ascertain potential data discrepancies. Harman and Harman [114] described and utilised a conventional one-factor test in a study to address this issue [115]. FA has shown a specific factor which explains a more significant proportion of the variance [113].

3.2.2. Convergent Validity (CV)

The CV refers to the similarity level or agreement among binary or more measures or tools to assess similar constructs [116]. It is considered a component of convergent validity. Concerning PLS, an estimated construct’s CV is assessed via three tests [117]. These tests comprised the Average Variance Extracted (AVE), Composite Reliability Score (Pc), and Cronbach’s Alpha (α). The recommended Pc value is 0.7, representing the upper limit of a modest CV [118]. In experimental analysis, values of 0.60 or higher, and in some cases 0.70 or higher, are generally considered acceptable for most studies [119]. The final test conducted to evaluate the constructs’ CV in a model is the AVE. An AVE score of 0.5 or above is considered good convergent validity [119].

3.2.3. Discriminant Validity (DV)

The DV asserts that the examined constructs are different, and dimensions do not measure identical characteristics in Structural Equation Modelling (SEM) [120]. It has been noted that demonstrating DV requires that the correlations among measures or tools must be both atypical and relatively low [121].

3.2.4. Path Model

This research aims to employ the SEM method and identify the factors that drive SCSC implementation. To achieve this, the study involves identifying and measuring path coefficients. Specifically, the underlying or path correlations concerning the SCSC adoption drivers (£) and µ (i.e., OSS) were postulated. As a result, the essential association among $\mathsf{\mu }, \pounds \mathrm{a}\mathrm{n}\mathrm{d} €1$ within the analytical model, considered as a core association, is expressed in an equation below [122]:

$$\mu =\beta \mathrm{\pounds }+\mathrm{€}1.$$

(1)

The coefficient path connecting the SCSC implementation drivers was represented by $\beta$, while (€1) indicates the variance residual, likely to be present in the operational strength. Comparable to $\beta$ within the regression model, the standardised regression weight is also β, and the signs must align with the estimations of the model and are significant empirically. One major challenge is to test the path coefficient’s significance represented by $\beta$. Thus, addressing these concerns requires using the bootstrapping technique within the SmartPLS3.2.7 statistical package. It was used to compute the path coefficient’s standard errors. The computation was conducted using 5000 sub-samples following the recommended standard in the literature [123].

Conversely, the theorised hypothesis was tested following Henseler et al. [123]. The structural equation proposed in this study for the SCSC adoption drivers’ constructs was calculated for the PLS model; it identified the concealed relationships between the model’s construct and equation. The proposed structural equations for the constructs of drivers of SCSC adoption were designed for the PLS model, which identifies the internal correlations of the constructs and equation.

4. Results

4.1. Common Method Bias (CMB)

The CMB refers to an inconsistency that can compromise the analysis’s validity and attempts to calculate the associated error with the analysed variables [60,124]. To address the common method bias in the model suggested, a one-factor analysis was performed to ascertain the variance related to conventional techniques [114]. Studies have shown that CMB is unlikely to significantly impact the data gathered when the overall variables’ variance is less than fifty percent [115]. This study’s findings revealed that the initial factors explained 41.3% of the variance. Therefore, CMB is unlikely to significantly impact the results since it is below the 50% threshold [115].

4.2. EFA for SCSC Implementation Drivers

This research focused on the adoption of SCSC and its impact on OSS in the construction sector of Egypt. The EFA analysis on the study sample comprised 70 cases and was determined using abstemiously short responses. Likewise, this study included all relevant tests [104]. The first section of the survey tool deals with the participant’s demographic features. The subsequent sections were related to the drivers of SCSC implementation and overall Sustainable Success (OSS). A five-point Likert Scale, containing 1 (very low) to 5 (very high), was used to categorise the answers. The use of this scale is extensively contained in the existing literature [102,125,126,127]. Different concepts (or constructs) were used to assess the correlation’s factorability. These comprised the Kaiser–Meyer–Olkin (KMO) technique for sampling adequacy.

The KMO measure is commonly utilised to define if correlations among variables are appropriate for FA by evaluating the partial correlations of the variables [128]. The collected data were suitable for factor analysis, as confirmed by Bartlett’s Sphericity Test, which assesses the relationship between constructs. This test determines if the sampling method or data set is acceptable for FA. KMO was applied to test for the correspondence of factors. The analysis revealed a KMO value of 0.82, which implies that 82% of the data collected was fit for FA [128]. The study found that the p-values for Bartlett’s test were less than 0.001, 146 as the degree of freedom (DF), and an estimated Chi² of 496. It confirmed the necessity of performing Bartlett’s test for this study and indicated a serious relationship in the data matrix. The results also showed that the correlation matrix for all the listed items was correlated at a 5% significant level. Thus, it confirmed the suitability of performing EFA, and the results were consistent with the previous studies [103,129]. To examine the explained variance by the SCSC domains, the principal component analysis (PCA) was performed to discover the SCSC drivers in the construction industry. The results showed the presence of five components or factors, each having eigenvalues more significant than one. These components explained 23.25%, 18.4%, 15.2%, 11.9%, and 8.6% of the variance.

The matrix’s anti-image transverse construct matrix is above 0.5, which supports incorporating individual variables within FA. Likewise, the preliminary commonalities were computed to evaluate the individual variables’ variances explained by the factors. Values less than 0.3 imply that the variables concerning the factor solution are unfit. The preliminary commonalities in this study surpass the threshold, and the entire factor loadings are above 0.5 (

Table 2. Communalities of 32 drivers (DR) related to SCSC adoption.

Drivers	Initial	Extraction
DR1	1.000	0.723
DR2	1.000	0.770
DR3	1.000	0.804
DR4	1.000	0.812
DR5	1.000	0.651
DR6	1.000	0.808
DR7	1.000	0.891
DR8	1.000	0.711
DR9	1.000	0.812
DR10	1.000	0.670
DR11	1.000	0.793
DR12	1.000	0.780
DR13	1.000	0.770
DR14	1.000	0.786
DR15	1.000	0.784
DR16	1.000	0.840
DR17	1.000	0.818
DR18	1.000	0.874
DR19	1.000	0.814
DR20	1.000	0.841
DR21	1.000	0.706
DR22	1.000	0.742
DR23	1.000	0.805
DR24	1.000	0.645
DR25	1.000	0.707
DR26	1.000	0.820
DR27	1.000	0.713
DR28	1.000	0.798
DR29	1.000	0.774
DR30	1.000	0.844
DR31	1.000	0.704
DR32	1.000	0.795

).

Evaluation of the scree test revealed a clear break after components 1 and 2 (Figure 3). The point at which the curve of the slop began to flatten off indicates the number of components that need to be generated by the study. The SCSC implementation drivers’ varimax factor (rotated) matrix of the Egyptian construction industry comprised five significant drivers that are appropriate for depicting the effects of SCSCs. The component clustering built on the rotated varimax is presented in

Table 3. Factor loadings of SCSC implementation drivers.

Drivers	Components
	1	2	3	4	5
D1			0.516
D2			0.743
D3					0.529
D4					0.788
D5					0.514
D6					0.549
D7	0.783
D8	0.507
D9	0.712
D10	0.694
D11	0.555
D12			0.653
D13	0.660
D14	0.666
D15	0.563
D16		0.765
D17		0.769
D18		0.783
D19				0.778
D20		0.706
D21	0.563
D22				0.614
D23	0.577
D24				0.540
D25				0.583
D26	0.685
D27	0.602
D28		0.576
D29			0.583
D30			0.514
D31		0.555
D32		0.612

Note: Method of Extraction: Principal Component Analysis; Method of Rotation: Varimax with Kaiser Normalisation.

. Each variable is weighted heavily in one group only. While there is no set method for factor identification, classifying these components was considered suitable for this study. The factors identified were Knowledge, Planning, Management, Collaboration, and Compatibility (Table 3). A preliminary explanation of these five main clusters highlights the significance of these constructs.

4.3. Common Method Bias (CMB)

Single-factor analysis was used in this study to assess the potential influence of common method variance on the results [114]. If the factors’ total variance is below 50%, it implies that CMB did not affect the data [115]. The study revealed that the primary components or factors explained 48.4% of the variance. Thus, the results showed that CMB did not significantly influence the results since it is below 50% [115].

4.4. Analytical Model

The analytical model describes the correlation among the analysed variables and their fundamental concealed constructs [130]. To assess the weighty dimension items OSS and SCSC Drivers) in SEM-PLS, it is essential to assess both the discriminant validity (DV) and convergence validity (CV) [131]. The CV studies the level of agreement and consistency among binary or more elements (or indicators) of the construct(s) [116]. The evaluation of convergent validity in SEM-PLS is a part of construct validity. It is assessed using different tests, including average variance extracted $\left(AVE\right)$, Cronbach’ alpha $\left(\alpha \right),$ and composite reliability $\left(Pc\right)$ [117].

Based on the statistical results summarised in Table 3, it can be inferred that the SCSCs and OSS’s $Pc$ was above 0.60, indicating that they met the necessary criteria [119,132]. Although the composite reliability for the SCSC drivers and OSS was more significant than 0.60, indicating their acceptability, the Cronbach Alpha values in Table 1 were also found to be 0.60, suggesting medium to high consistency [133]. The AVE was calculated to assess the construct variables’ CV using the equations below [117]:

$$AVE=\frac{\sum {\lambda }_i^2}{\sum {\lambda }_i^2+\sum var({\mathsf{\epsilon }}_i)},$$

(2)

where AVE is extracted from average variance, and ${\lambda }_i$ is the factor loading of each variable to an underlying variable and

$$var\left({\epsilon }_i\right)=1-{\lambda }_i^2.$$

(3)

The AVE should be at least 0.5 to be considered acceptable [117]; this indicates that the dimension factors account for ~50% of the total variance [134]. The results presented in Table 1 indicate that the assessed values of AVE constructs were above 50%, suggesting that the analytical model is internally consistent and convergent. It further suggests that the analytical variables for individual constructs are adequately defined and did not measure other models’ construct(s). On the other hand, Hulland [94] argued that an outer factor loading value of 0.70 is more suitable, though 4.0 or above is deemed acceptable in exploratory studies. The external loadings of the dimensions within the preliminary model comprising the external loadings are deemed acceptable [95] (

Table 4. The result of convergent validity.

Model Constructs	Cronbachs Alpha	Composite Reliability	Average Variance Extracted (AVE)
Compatibility	0.826	0.884	0.658
Knowledge	0.951	0.959	0.748
Management	0.916	0.937	0.749
OSS	0.824	0.894	0.738
Planning	0.95	0.959	0.772
collaboration	0.87	0.911	0.72

).

Evaluating discriminant validity (DV) is a crucial and increasingly common practice in structural equation modelling (SEM) studies [135,136]. Discriminant validity is commonly used to confirm that the measured constructs are empirically different or unique [120]. Thus, the methods below were used to evaluate the discriminant validity.

i.

Fornell–Larcker Criterion,

ii.

Cross Loading, and

iii.

Hetrotrait–Monotrait Criterion Ratio (HTMT)

Table 5. Discriminant validity analysis (Fornell–Larcker).

Constructs	Compatibility	Knowledge	Management	OSS	Planning	Collaboration
Compatibility	0.811
Knowledge	0.77	0.865
Management	0.722	0.856	0.865
OSS	0.556	0.664	0.626	0.859
Planning	0.741	0.809	0.845	0.624	0.878
collaboration	0.706	0.792	0.795	0.511	0.787	0.849

shows that the calculated discriminant validity of SCSC and OSS drivers’ implementation constructs, evaluated using Fornell and Larcker’s criterion, is within the acceptable range. It suggests that the constructs are empirically distinct and not overlapping. The AVE’s square root was more significant than the correlations among the indicators and constructs. Thus, it implied acceptable DV [117,137].

This study utilised the HTMT as another approach to evaluating discriminant validity. The HTMT is a newly developed technique to assess variance-based SEMs by calculating the precise relationship among the evaluated constructs. The acceptable HTMT values must be 0.85 and 0.90, showing that the model constructs are distinct [138,139]. Supposedly, the constructs are conceptually and theoretically comparable; the HTMT values must be less than 85 and 0.95. The HTMT results revealed that all studied constructs had acceptable DV, as summarised in

Table 6. HTMT Discriminant Validity.

Constructs	Compatibility	Knowledge	Management	OSS	Planning	Collaboration
Compatibility
Knowledge	0.858
Management	0.821	0.812
OSS	0.642	0.739	0.706
Planning	0.817	0.847	0.8	0.697
collaboration	0.832	0.808	0.803	0.598	0.862

.

Cross-loading criteria were analysed to determine SCSC adoption drivers and the OSS construct’s construct validity. This analysis aimed to confirm that the variable loading of the latent construct was above the cross-loadings from the constructs of other models [140]. The results indicate that the loadings indicators of constructs are higher than the other latent variables cross loading of similar raw as revealed by the analysis of cross loading (

Table 7. Cross loadings results.

Drivers	Compatibility	Knowledge	Management	Planning	Collaboration	OSS
D3	0.831	0.6	0.687	0.672	0.541	0.485
D4	0.714	0.472	0.454	0.388	0.547	0.299
D5	0.845	0.689	0.619	0.68	0.622	0.461
D6	0.846	0.708	0.56	0.617	0.585	0.527
D10	0.59	0.804	0.627	0.612	0.593	0.523
D11	0.779	0.887	0.797	0.76	0.686	0.683
D13	0.66	0.904	0.775	0.727	0.693	0.556
D14	0.652	0.891	0.82	0.747	0.699	0.558
D15	0.609	0.843	0.77	0.742	0.756	0.521
D7	0.733	0.928	0.765	0.719	0.702	0.599
D8	0.607	0.82	0.616	0.66	0.686	0.584
D9	0.685	0.834	0.729	0.614	0.657	0.566
D2	0.548	0.653	0.856	0.657	0.591	0.484
D29	0.629	0.733	0.882	0.78	0.736	0.422
D30	0.655	0.813	0.907	0.81	0.813	0.563
D12	0.593	0.744	0.849	0.702	0.687	0.566
D1	0.69	0.751	0.83	0.69	0.592	0.672
D16	0.691	0.693	0.706	0.897	0.648	0.585
D17	0.554	0.639	0.612	0.816	0.64	0.49
D18	0.632	0.687	0.749	0.942	0.69	0.59
D20	0.641	0.69	0.793	0.913	0.702	0.514
D28	0.789	0.829	0.804	0.855	0.742	0.575
D31	0.566	0.683	0.715	0.836	0.695	0.557
D32	0.657	0.736	0.793	0.882	0.71	0.524
D19	0.5	0.609	0.6	0.635	0.84	0.361
D22	0.613	0.684	0.645	0.666	0.832	0.428
D24	0.621	0.669	0.731	0.62	0.84	0.433
D25	0.652	0.719	0.716	0.744	0.881	0.502
Economics	0.642	0.659	0.639	0.597	0.473	0.886
Environmental	0.387	0.545	0.523	0.539	0.402	0.874
Social	0.357	0.482	0.42	0.457	0.437	0.815

). It suggests that the uni-dimensionality of each construct in the model has been supported.

4.5. Second-Order Analysis

In this study, the SCSC drivers’ adoption constructs were considered determinative (or formative), and it is usually challenging to predict more significant relationships between the evaluations of formative measurement models. Moreover, significant relationships between determinative variables are problematic, indicating collinearity [140]. The study utilised VIF values to detect collinearity among the determinative variables within model constructs for SCSC drivers’ adoption. Since the study considered the determinative-reflective type of the first-order model, it was necessary to evaluate the absolute values of VIF. The values of VIF are less than 3.5 (Table 6), indicating that the model’s constructs had independently underwritten the adoption of SCSC. Moreover, the four-first-order subscales for SCSC drivers had a significant path coefficient $\left(\beta \right)$, suggesting they contributed independently to the implementation of SCSC drivers (

Table 8. Formative constructs analysis. p-value <0.001.

Paths	B	Standard Deviation (STDEV)	T Statistics	p Values
Compatibility -> SCSC Implementation	0.105	0.016	6.723	0
Knowledge -> SCSC Implementation	0.353	0.025	13.893	0
Management -> SCSC Implementation	0.221	0.017	12.755	0
Planning -> SCSC Implementation	0.257	0.024	10.812	0
collaboration -> SCSC Implementation	0.15	0.018	8.214	0

). Similarly, the four-first-order subscales for SCSC implementation drivers had a robust external loading or path coefficient $\left(\beta \right)$.

4.6. Structural Model Analysis

A critical aspect concerning this study is testing the study’s propositioned hypotheses. The validity of the proposition of the model was examined within the context and based on the perspective of the bootstrapping process [141]. The bootstrap method is used to measure the numerical significance and consistency of the dataset. It assesses the path coefficient, including the p-value and external loading, at a confidence interval of 95% (C10.95). Therefore, verifying the significance of the proposed hypotheses in the model is essential [141,142]. The bootstrapping technique randomly resamples the original data set to produce multiple samples of similar dimensions as the initial set of data, enabling the testing of statistical significance and data reliability. The bootstrapping technique is helpful in testing the reliability of the data set since it enables resampling randomly of the initial set of data to produce distinct samples of similar dimensions, which can help measure the error of the path coefficient [142].

The path coefficient is a crucial component in structural equation modeling (SEM) and measures the degree of influence one variable has on another variable in a model. Thus, it estimates the direction and strength of the correlation among binary variables. A path coefficient can be positive, indicating a direct and positive relationship; negative, indicating a direct and adverse relationship; zero, indicating no direct relationship between the two variables. The path coefficient is explained as the variation on the conditional variable for unit variation within the independent variable, constantly retaining the remainder. The path coefficients are typically represented in the path diagram and can be used to test specific hypotheses concerning the connection among the model’s variables. The importance of the path coefficients is determined by the p-values, which indicate whether the correlation among the variables is statistically significant. The path coefficients can also calculate a particular variable’s total effect and indirect impact on a different variable within the model. Overall, the path coefficient is an essential statistical measure in SEM that helps to gain insights concerning the correlation among variables and their effect on the general model [143]. In Figure 4 a theoretical link was proposed between the OSS and £ constructs, and this link was further investigated in the operational model. The operational model contains several linear equations describing relationships among the concealed variables. The working relationship amongst the $\mu , \in 1, \mathrm{a}\mathrm{n}\mathrm{d} \pounds$ equations can be considered as the inner connection within the model [122].

The linear equation representing the structural model indicates that the path coefficient (β) linking the SCSC drivers’ adoption construct to OSS is a vital model component. Additionally, the expected accumulated residual variance is denoted as $\in 1$. The normalised weight of regression is comparable to the load within the multiple regression analysis and must support the prediction of the model while also being significant statistically. However, the challenge relates to the mode of measuring the impact of the coefficient path $\left(\beta \right)$. Addressing this concern required using a bootstrapping technique in the SmartPLS3.2.7 statistical software package to calculate the path coefficient ‘errors and ensure the model’s validity. Five hundred sub-samples were analysed following Henseler et al. [123]. Hence, the t-test statistics were established using bootstrapping to test the hypothesis.

In summary, an operational equation model was established using PLS to investigate the drivers of SCSC adoption. The model included a single equation that captured the internal connection between the constructs and the drivers. The normalised p-value $\left(\beta \right)$ and the pathway significance of the internal relationships were formed and portrayed in Figure 4. Likewise, testing the significance of the pathway requires a bootstrapping analysis which was performed, and the results showed that the effects of OSS and SCSC implementation were positive and significant $(p=0.0005, \beta =0.667)$. Hence, the two prominent faces in these two significant features in this study, namely SCSC implementation and OSS, are consistent.

4.7. Explanatory Operational Model’s Power

The results indicate that the analytical model has high reliability for each item and discriminant and convergent validity. Determining the explanatory power of the structural model requires the explanation of the independent variables through the evaluated model variance. PLS algorithm, which supports squared multiple correlations (R²), was used to determine the R² concerning the model’s dependent variable. R² determined through the PLS algorithm was comparable to R² in conventional regression analysis [144].

The analytical model showed strong consistency of individual elements, discriminant, and discriminant validity. Evaluating the structural model’s exploratory requires examining the variance within the dependent variable. The R² was determined using the PLS algorithm, and the value was comparable to traditional regression and signified the total conditional variables’ variance explained by the independent variables. The higher value of R² signifies a more substantial predictive capacity of the operational model. The R² values in this paper were computed through the Smart PLS algorithm, and the R² was adjusted for the project’s success; the primary conditional variable within the model was 0.445. It suggests that the exogenous latent variable, SCSC drivers, could explain 44.0% of sustainable success.

Table 9. The coefficient of determination (R²).

Exogenous Latent Variable	R2	Adj R2	Explained Size
OSS	0.445	0.445	Moderate-High

shows the R² values. Thus, the results implied that the dimension explained by SCSC drivers was moderate to high and is concurrent with the existing literature [142].

The effect size, also known as f², can be calculated by measuring the variation within R2 values after removing the autonomous construct from the model. It is employed to determine if the omitted construct significantly influences the conditional constructs. Computation of the dimension effect was performed using the equation below [144]:

f² = (R²_included − R²_excluded)/(1 − R²_excluded).

(4)

Proposed recommendations for evaluating the weight dimension include values of f² $\ge 0.02, \ge 0.15 \mathrm{a}\mathrm{n}\mathrm{d} \ge 0.35$. It indicated small, medium, and enormous effect dimensions of the exogenous construct(s) on the endogenous construct(s) [145]. The result of f² demonstrated that the exogenous construct’s dimension effect concerning the project’s success was significant, having a large effect size of SCSC drivers at f² = 1.234.

4.8. Analytical Significance of the Operational Model

The ability of an operational model to assess its analytical effectiveness is an essential factor. In order to determine the redundancy measures, the blindfolding protocol was used to cross-validate each conditional variable. It indicated that the autonomous construct has analytical significance for this study’s analysis of the conditional construct. It is further shown by OSS Q² values which are more significant than zero- 0.311 [146].

Table 10. Analytical Significance (Q²).

Endogenous Latent Variable	SSO	SSE	Q2 (=1 − SSE/SSO)
Indicators of Project Success	177.000	121.996	0.311

shows that the Q² value is above 0, proving the model is outstanding analytical significance.

4.9. The IPMA (Importance Performance Matrix Analysis)

The SEM-PLS approach indicates the comparative importance of an independent element in explaining the conditional variable within the model path [131,146]. By taking into account the performance of each variable, IPMA increases on findings of the PLS-SEM. The outcomes are assessed based on two perspectives, i.e., performance and importance. These are essential in identifying management actions that need to be taken. This dual assessment of importance and performance dimensions in IPMA is crucial for decision-making [131]. To identify key areas that require improvement in management actions (or a particular model area of focus), the operational model employs the total importance (or effects) and mean value of latent construct dimensions or performance. In this study, the dependent variable used was IPMA for SCSC drivers.

Table 11. Total Effects and Significance of the IPMA.

Predictor	Significance	Performance
SCSC drivers execution	1.2	67.4

presents the level of significance and performance of the exogenous variable (SCSC drivers).

5. Discussion

5.1. Impact of SCSC Implementation Drivers on OSS

Sustainable construction supply chains are crucial in successfully executing sustainable building projects. Such supply chains aim to minimise the negative environmental, social, and economic impacts associated with the production, transportation, and disposal of construction materials. By adopting sustainable supply chain practices, construction companies can reduce the carbon footprint of their operations, minimise waste, and promote the use of renewable resources. For instance, sourcing materials from local suppliers and using recycled or repurposed materials can significantly reduce the environmental impact of a construction project. Moreover, sustainable construction supply chains can ensure workers producing and transporting construction materials are treated fairly and provided with safe working conditions. It can help improve a project’s social sustainability, are crucial for building trust, and maintain good relationships with local communities.

Furthermore, by promoting sustainable practices across the supply chain, construction companies can reduce the risk of project delays, cost overruns, and legal issues. It is because sustainable supply chain practices often promote efficient and transparent procurement and delivery systems, which can improve project planning and management. Thus, sustainable construction supply chains are vital for ensuring that sustainable building projects are successful. By promoting sustainability across the supply chain, construction companies can lessen their management’s environmental, economic, and social effects while also improving project planning and management. The proposed model shows that SCSC implementation can increase the OSS by 45.5%. This finding is with Lam et al. [147]. Through implementing a strategic risk planning approach that strengthens corporate social responsibility, a flexible SCSC structure can be achieved, allowing for a just-in-time approach. Such a strategy would enhance the speed and visibility of the SCSC [76].

Additionally, strategic risk planning has a balancing effect on adaptability [76]. In general, a just-in-time approach and flexibility facilitate the implementation of SCSC since they promote minimal raw resources, ultimately leading to increased sustainability [148]. A just-in-time approach and flexibility are essential to facilitate SCSC by promoting the utilisation of the minimum amount of raw resources, enhancing sustainability [148]. The SCSC will develop due to the integration, exchange, and sharing of information through SC associates, as argued by Katsaliaki et al. [149]. However, the floods that occurred in Thailand in 2011 had a significant impact on the supply chains of automobile and computer manufacturers. These high-risk supply chains are prone to instability and are ineffective, resulting in substantial losses when interrupted. To ensure the enduring tactical paybacks of SCSC, mitigating the associated risks and threats has become crucial. Likewise, environmental concerns and social responsibility could pose risks to SCSC. Regarding environmental concerns, resource wastage and contamination can tarnish a company’s reputation and result in revenue loss and brand damage. Thus, low-carbon strategies are becoming increasingly vital. Although governments have tried to reduce greenhouse gas emissions since the Paris Agreement, it has also brought new threats to supply chains across various industries such as agriculture, transportation, and tourism, all of which are closely linked to CO₂. Achieving sustainability goals also creates new risks for conventional supply chains, making it imperative to study these risks. Several research studies have been conducted from an industrial perspective. For instance, Choirun et al. [150] studied risk management concerning the supply agricultural supply chain, which uncovered poor risk management to establish a sustainable supply chain system. Chowdhury and Quaddus [151] assessed SCSC threats concerning Garment Industry. The study identified many desired approaches for flexibility to lessen vulnerabilities. It included creating communication channels concerning buyers and suppliers, developing alternate capabilities, improving efficiency and skills, approving mechanisms for quality control, utilising information and communication technology (ICT), enhancing security systems, and projecting and responding to consumer demands. Diverse experiments had distinct characteristics such as the primary risks in cloth manufacturing related to labour utilisation and extended working hours.

5.2. Framework for Implementing Sustainable Construction Supply Chains

The recommended framework is presented in Figure 5. After verifying the SCSC drivers’ correlation via the recommended model, the framework was established to comprise the essential SCS drivers in the Egyptian construction sector. Thus, the SCSC drivers must be fulfilled before effectively introducing SCSC in the Egyptian building business, demanding more attention from officials. Since the implementation of SCSC requirements is a certified factor from the operational model concerning SCSC implementation drivers, the recommended framework explained and linked particular variables which could serve as the basis for the SCSC adoption [152]. More significantly, the SCSC application needs primarily encompass validated variables using operational and measurement models [153]. Therefore, a particular model construct’s analysed factors (or items) were founded, and all the study’s paths were supported and confirmed (Figure 5). The sub-sections below depict the framework’s items established from the recommended model.

5.2.1. Knowledge

The significance of knowledge in building schemes is irrefutable [154]. The SEM-PLS model recommends this factor as having the most significant impact on the SCSC implementation drivers. It had an external factor (knowledge) loading of 0.353. A strong relationship exists between sustainable construction supply chains and knowledge. Sustainable construction supply chains aim to lessen the construction industry’s deleterious social and environmental consequences while providing high-quality and cost-effective building materials and services. One key aspect of sustainable construction supply chains is using innovative and environmentally friendly materials and technologies [155]. It requires knowledge of the latest developments in sustainable construction and the ability to apply them effectively within the supply chain. Additionally, implementing sustainable practices requires the involvement of various stakeholders including suppliers, manufacturers, contractors, and designers [156]. Knowledge sharing and collaboration between these stakeholders are crucial to ensure that sustainable practices are implemented throughout the supply chain. Another critical factor in sustainable construction supply chains is the management of resources and waste [157]. It requires knowledge of the environmental impacts of construction activities and the ability to implement strategies to minimise waste and promote resource efficiency. Effective resource management requires collaboration and knowledge sharing between supply chain investors to identify waste reduction and resource optimiation opportunities. Overall, knowledge plays a central role in the development and implementation of sustainable building supply chains [158]. Effective knowledge management and sharing among stakeholders can promote innovation and best practices, leading to more sustainable construction practices and a reduced environmental impact.

5.2.2. Planning

A significant relationship exists between sustainable construction supply chains and planning. Sustainable construction supply chains aim to reduce the construction industry’s undesirable social and environmental consequences while providing high-quality and cost-effective building materials and services. Effective planning is essential to achieving these goals. The second factor was correlated to “planning”. The impact of the “Planning” on the drivers of SCSC. It had an outer factor loading of 0.257. It suggested that participants and professionals’ and professionals’ success concerning the SCSC application is greater than the medium level (Median range). One key aspect of planning for sustainable construction supply chains is identifying and evaluating environmental and social risks and opportunities [159]. It requires understanding the potential impacts of building activities on society and the environment and the ability to identify and evaluate alternative materials, processes, and technologies that can reduce these impacts [160]. Effective planning can help to identify opportunities to optimise resource use, reduce waste, and promote sustainability throughout the supply chain. Another critical factor in planning sustainable construction supply chains is aligning goals and objectives among stakeholders. It requires collaboration and communication among various stakeholders including suppliers, manufacturers, contractors, and designers [161]. Effective planning can help to identify common goals and objectives among stakeholders and develop strategies for achieving them. Overall, planning is essential in developing and implementing sustainable building supply chains. Effective planning can help to identify risks and opportunities, align goals and objectives, and develop strategies for promoting sustainability throughout the supply chain. It can result in more effective resource utilisation, reduced waste, and a lower environmental impact of construction activities [162].

5.2.3. Management

The principal component three was correlated to “Management”, with an external loading of 0.221. It ranked third on the scale of SCSC application driver’s factors. Driver factors for SCSC implementation. Supply chain management as a construct embodied three key factors, i.e., product, environment, and management. Project managers pursuing a successful supply chain management initiative must consider these three factors in their designs [163]. Studies suggested that managing human and environmental resources are the primary drivers of SCSC implementation based on a model for applying corporate social responsibility [164]. Applying the structural modelling equation by Paulraj and Chen [165] revealed that tactical supply management is determined by technology uncertainty and supply. However, the uncertainty of demand does not have a significant influence on supply management, though studies showed correlations between the performance and strategic supply management [165].

5.2.4. Collaboration

The fourth scale of SCSC application required factors that were correlated to “Collaboration”. It had an outer loading of 150. Collaborative activities implementation of sustainable, innovative supply chain revealed positive correlations between cooperative activities and green certification programs in medium and small businesses and large companies [166]. There is a general belief that a company’s size does not moderate the correlation between independent collaborative activities and sustainable green construction activities. Nevertheless, other links were supported by the analytical model. Hence, the company’s size partly moderated the correlations of collaborative activities with SCSC adoption activities and ecological performance [166]. Thus, implementation activities are essential in enhancing collaborative activities with vendors and suppliers for a sustainable and innovative supply chain. Moreover, it contributed to the innovative sustainable supply chain practice and sustainability via collaborative activities in the supply chain process [166].

5.2.5. Compatibility

Compatibility was the last scale factor driver component needed for the SCSC application. The complex index structure and unempirical weight index setting could not acclimate the objective of integrating the supply chain for choosing partners. Thus, a partner (or compatibility) assessment model revealed that PCA modelling is feasible and effective [167]. Application of diffusion of innovation theory concerning adopting a particular product’s supply chain revealed that the exporter’s adoption of a particular product(s) supply chain is governed by not only the apparent relative advantage and apparent compatibility but by apparent complexity and awareness [168]. Technological compatibility with significant suppliers and buyers correlated positively with collaboration and environmental monitoring. Concerning logistical integration, the relationship was found only with suppliers’ environmental compatibility with increased major suppliers [169].

5.3. Theoretical and Empirical Contributions

This study’s proposed systematic method highpoints the importance of implementing SCSC drivers, especially in developing countries. The model stresses the critical adoption of SCSC drivers’ Implementation. Policymakers and relevant authorities can use this approach to develop a framework that facilitates SCSC placement within the AECO area and overcomes acknowledged SCSC drivers’ Implementation. Furthermore, this study assessed SCS and OSS interaction drivers for adoption in Egypt’s construction sector by assessing significant drivers for SCSC implementation. This research provides a sound basis for advanced revisions on SCSC adoption drivers within the AECO area. Additionally, the statistical background developed in this paper can help identify the SCSC adoption drivers that must be implemented to enhance the viable applications in Egypt and other comparable emerging nations. This research has made various empirical and theoretical contributions to the field as follows:

• This research provides a theoretical role by identifying and conceptualising additional ideas that could be incorporated into the theoretical outline including the effect of SCSC driver adoption concerning OSS during project lifecycles.
• The lack of research on SCSC driver implementation in developing nations like Egypt is critical, as adopting sustainable practices is vital for economic development and environmental sustainability. Developing countries face unique challenges that require a different approach than advanced countries, and, therefore, it is essential to examine the elements that initiate the SCSC implementation in such nations. By thoroughly assessing the significant SCSC drivers for implementation with OSS, this research has delivered a valuable understanding of the elements critical for promoting sustainable practices in the AECO sector. Policymakers and relevant authorities can use these findings to develop effective action plans to overcome the acknowledged SCSC driver implementation barriers in developing countries. Furthermore, the study’s findings are significant because they demonstrate that the adoption of SCSC is not limited to advanced countries. Developing nations like Egypt can also take steps to implement sustainable practices by identifying and addressing the unique challenges they face. In this way, the research has been underwritten to develop a more wide-ranging understanding of SCSC driver implementation in the global context.
• The proposed model is anticipated to drive the implementation of SCSC drivers in developing countries. This practical contribution examines the theoretical connections between the binary concepts of SCSC adoption drivers and OSS throughout the building project lifecycle, which has not been fully explored in the current literature.

5.4. Managerial Implications

Experts in the construction industry can utilise the decision-making insights from this study to evaluate how SCSC driver implementation impacts OSS at different stages of the building project lifecycle. It can be achieved as follows:

• The study provides AECO companies with a list of significant drivers of SCSC that can be addressed to overcome the challenges and hurdles associated with implementing SCSCs, ultimately enhancing client satisfaction through improved quality assurance.
• The proposed model can potentially be a valuable tool for policymakers, construction professionals, and relevant authorities, striving to improve sustainable implementation practices in the AECO industry. By providing a predictive framework for understanding the relationship between SCSC driver implementation and OSS, this model can help identify key drivers that should be prioritised to promote the sustainable application in building schemes. Furthermore, this research can lay a foundation for further studies and analysis in the field of SCSC driver implementation and its impact on OSS, particularly in developing countries like Egypt. Overall, this study offers a priceless understanding of the constraints and opportunities related to implementing SCSC in the construction industry and offers a novel approach to understanding the complex relationship between SCSC driver implementation and OSS.
• The study’s contribution is particularly relevant for decision-makers in the AECO industry who seek to improve the adoption of SCSCs. By clearly understanding the significant SCSC drivers that need to be addressed, this study can aid decision-making in addressing the problems and hurdles of implementing sustainable constructions. It can lead to higher client satisfaction through improved quality visualisation.
• Moreover, the analytical approach proposed in this study offers a framework for decision-making concerning SCSC driver implementation on OSS throughout the building project lifecycle. This framework can support policymakers in recognising and prioritising the most critical drivers that need to be addressed, thereby enabling a more effective and efficient deployment of SCSCs in building projects.

5.5. Limitation of Study and Direction of Future Research

Although this study made some significant contributions, it is likewise limited in many ways and thus needs to be recognised to guide upcoming research as follows:

• This study has certain geographical limitations that need to be considered when interpreting the findings. The survey tool used in this research was administered solely to building experts located in southwestern Egypt, thus making it difficult to generalise the results to other regions. Therefore, future studies are recommended to extend the geographical scope beyond this study by including more regions in Egypt as well as similar developing nations to enhance the validity and generalisability of research findings.
• One limitation of this cross-sectional study is the lack of consideration for historical and organisational perspectives on SCSC adoption. To gain a more comprehensive understanding of the interface between SCSC adoption challenges and OSS in the building project lifecycle, future research should be longitudinal in nature. This will enable researchers to track changes over time and provide a deeper insight into the complexities associated with SCSC adoption.
• Third, this study focused on the SEM-PLS applications to evaluate the links between OSS and SCSC drivers in construction projects—lifecycle through theoretic conceptualisation. Hence, future studies might focus on the documentation of the level of viable adoption through the adoption of theory, comprising the Technology Acceptance Model (TAM), Innovative Diffusion Theory (IDT), and Technology organisation and environment model (TOEM).

6. Conclusions

This study has shown a good impact of SCSC driver’s Implementation on OSS. However, this technique is not used regularly by the construction sector in emerging nations. This study used a questionnaire survey tool to obtain quantitative data from construction experts in Egypt. Stakeholders from the Egyptian building industry take part in this research. Consequently, the PLS-SEM model was applied to establish an experientially authenticated model. The model results would aid the building industry participants in removing hurdles that avert the adoption of SCs. The study showed how they could lessen the cost of projects and increase construction success in Egypt and some emerging nations. Even though the analysis of SCSC implementation by the building sector in Egypt is the particular focus of this paper, the inferences made could be transferable to similar emerging nations with comparable economic conditions and where similar results are not accessible.