Key Drivers for BIM-Enabled Materials Management: Insights for a Sustainable Environment

Abstract

The value of Building Information Modelling (BIM) is widely discussed within all construction stages including the data-driven culture across building processes and sustainable impact in the long term. Yet, there is a need to explore the opportunities of BIM in improving construction materials management (CMM) as a core function of supply chain management. Due to the dearth of studies on BIM potential in improving CMM within the sustainability context, the authors examine the effectiveness and efficiency of BIM-enabled materials management, via three data streams: a literature review, an online survey, and interviews with subject matter experts in the field. This study aims to explore the drivers of BIM-enabled sustainable construction materials management. This is the preliminary study designed to test the initial hypotheses based on an online questionnaire application to derive tacit knowledge from industry and academic experts, followed by short interviews with respondents. Grounded in the comprehensive literature review, 24 indicators were defined for survey purposes. Preliminarily, 206 experts from 10 countries responded to the survey. The results show significant differences in the ranking of the indicators among the five factors. In developing countries, among two groups, industry sample awareness scales demonstrated lower understanding than among academic experts. Another finding relates to the significant agreement in scaling the importance of opportunities among industrial and academic sector experts. The research adds knowledge to deepen the understanding of opportunities of BIM-enabled materials management as a part of building project sustainable performance for industry policy and decision-makers. It also brings attention to the lack of sustainability awareness amongst industry experts in developing countries. Although materials constitute a solid part of any construction project cost, there is still a collaboration gap among designers, materials management, and, more broadly, supply chain management experts.

1. Introduction

The construction industry in Kazakhstan accounts for up to 6% of the GDP according to the Government Report on Development 2021 [1] and the market was valued at USD 20.8 billion in 2022 [2]. Despite this high GDP percentage, it comes with concerns about the environmental and societal impact of construction activities. As a result of the inescapable construction, renovation, and demolition activities, huge amounts of waste are generated annually, causing severe environmental problems, affecting human health, and damaging the landscape. The massive quantities of construction and demolition waste are a big problem in many parts of the world [3].

One of the major pitfalls of building projects is poor materials management since any process gap has an inevitable impact on the cost and schedule of deliverables, excessive waste, and low productivity [4]. A range of studies focused on deficiencies in construction materials management (CMM), highlighting issues related to the following: (i) Poor communication between the process actors [5]; (ii) Quality and availability of materials [5]; (iii) Process gaps related to materials delivery, storage, inventory, and disposal [6]. Materials management issues are also often discussed within the supply chain context in the literature, where procurement and logistics are included in material management or supply chain interchangeably.

Building Information Modelling (BIM) is the fundamental for digital transformation of the orthodox Architecture Engineering and Construction (AEC) industry traditionally resistant to changes. According to Rathnasinghe et al. [7], the potential of BIM was underestimated to the level of just another “software”, whereas it is a one-of-a-kind platform for stakeholders’ collaboration throughout a project’s lifecycle. However, as proven over time, digital technologies are constantly evolving to capture additional needs and functionalities for multidisciplinary construction and AEC contributors [8]; today, BIM capabilities and features can be fully used to simplify the complex and fractured industry in one environment.

It is also reported that information technologies play a crucial role in sustainable supply chain development [9]. Hence, industry sustainable goals can be achieved effectively via the BIM enabling flexible and transparent interactions among supply chain network participants. Despite the myriad of benefits and opportunities of BIM discussed in the scientific literature, no comprehensive analysis has been conducted on BIM opportunities for the core function of the supply chain, i.e., materials management in the context of sustainability.

Motivated by the research gap at the intersection of BIM and CMM and need to deliver an updated perspective on BIM’s potential in improving sustainability, this study aims to explore and analyse drivers for BIM-enabled materials management within the sustainability context. The study sought to delineate the drivers of BIM-enabled materials management derived from the tacit knowledge of practitioners in comparison with academics.

Hence, this paper aims to explore the drivers of BIM-enabled sustainable construction materials management. This paper has the following objectives:

(1) To investigate the effect of BIM-enabled materials management on sustainability.

(2) To explore and analyse the mediating/moderating factors between BIM-enabled materials management and sustainability.

(3) To determine the qualitative strength of identified factors and their interrelationships based on industry stakeholders’ responses and the multivariate statistical analysis technique called structural equation modelling.

There are two main knowledge-increasing contributions of this study to the research field. The first contribution is to delineate the opportunities within BIM-enabled materials management. The second contribution is the provision of specific management knowledge for the CMM, especially the level of awareness of practitioners of BIM potential in improving the sustainability of building projects through materials management. The study also provides valuable insights into technology diffusion research and highlights drivers that require practitioners’ and industry leaders’ attention to promote BIM-enabled materials management.

The remainder of this study is structured as follows: Section 2 refers to the literature review and hypotheses development. Section 3 describes the research design. The data analysis is detailed in Section 4. The research discussion and implications, alongside findings, are presented in Section 5. Finally, the conclusion and future research directions are provided in Section 6. The list of abbreviations used in this study is presented in

Table 1. List of Abbreviations.

Abbreviation	Definition
AEC	Architecture Engineering and Construction
AVE	Average Variance Extracted
BIM	Building Information Modelling
CMM	Construction Material Management
GDP	Gross domestic product
GIS	Geographic information system
HTMT	Heterotrait–Monotrait Ratio
ISO	International Organization for Standardization
NFI	Normed Fit Index
PLS	Partial Least Squares
SEM	Structural Equation Modelling
SRMR	Standardized root mean square residual
VIF	Variance Inflation Factor

.

2. Drivers to BIM-Enabled Sustainable CMM: Literature Review

The construction industry faces several structural problems rooted in its fragmented nature that hinder its efficiency and productivity compared to other industries [10,11]. Thus, project-based activities typically involve multiple stakeholders, that temporarily collaborate to deliver one-off results [11]. This structural feature led to the prevalence of the subcontracting approach in the construction field, including material supply obligations. Being a network of companies and individuals, material supply further compounds the complexity of the temporary project organization. The risk allocation practices in such complex constructs are often skewed toward subcontractors, causing an imbalance in risk and reward. Therefore, network-based industries have traditionally been resistant to change and slow in adopting new technologies and practices. However, BIM which was conceptualized in the 60s construction industry has transformed the construction industry. Being the main industry advancement, BIM enables continual information exchange across the discipline within the project needs [12]. It is a shared knowledge resource that has already brought a range of quantifiable and non-quantifiable advantages to industry players.

Contemporary materials management in the construction industry is discussed within BIM-enabled supply chain management. By leveraging the BIM across the supply chain network stakeholders can enhance collaboration, improve communication, increase data integration, and streamline the flow of information, resulting in more efficient and effective project delivery [12,13]. Yet, the lack of collaboration and transparency, adversarial relationships, legacy systems, and fragmented information hinder BIM adoption in construction supply chain operations [10,14,15,16].

A range of studies discuss the opportunities of BIM extension to supply chain components in the context of sustainability. One direction encompasses studies that explore the opportunities of integrating certain supply chain components into BIM. In a recent study, Honic et al. [17] and Atta et al. [3] propose material passport digitalization using BIM to improve building materials handling throughout the lifecycle stages and assess project sustainability. According to Atta et al., 2021, several studies emphasized that the lack of evidence-based technical data is the major hindrance to developing sustainable construction. It is also reported that having extensive information on material properties at the design stage can significantly improve decision making [17]. This data may include guidance on safety, circularity, and dismantling. BIM application is suggested for site operation monitoring systems to improve communication with the supply chain via visualization models managed in the Cloud [18] and integration with geographic information systems (GISs) [19]. The wider vision is to use Enterprise Resource Management as the platform to incorporate BIM processes into functional divisions across the supply chain network that work in heterogeneous environments of a range of software [20,21].

Another direction of studies envisions a circular supply chain, therefore, proposing blockchain technology to integrate the whole constituents of the supply chain network [22]. It is reported that, within construction, blockchain technology is in its infancy [23]. Nonetheless, blockchain technology has many potential applications in building environments, such as a sharing bank of BIM families for reusable items developed by designers [24], smart contracts [25], etc. The underlying reason for all efforts is to gain transparency, i.e., real-time tracking of the actual status of materials through the supply chain.

2.1. Knowledge Gaps

The existing knowledge gaps relate to the commonly used fragmentary research approach and monodirectional studies. Thus, many studies have been conducted in materials management and green materials management, BIM-enabled supply chain management and sustainability through BIM. The literature review revealed that there is a dearth of studies on materials management in construction and no in-depth connection between BIM-enabled materials management to sustainability issues. Materials might comprise up to 60% of the building project cost [26]. The project schedule is extremely dependent on lead times and different aspects of materials procurement and delivery. Yet, on close investigation, a massive amount of waste generated by the building industry resulted from poor materials handling [24,27]. In this regard, it is important to delineate the potential opportunities of BIM application to CMM in the context of sustainability. Therefore, a mixed methods approach was applied to collect professionals’ and practitioners’ opinions and secondary sources of evidence as presented in research studies. Since the “BIM and materials management” keywords combination produced no results, the literature review is conducted using the keywords “BIM”, “Construction supply chain”, and “Construction materials management”. The research was performed primarily on articles related to BIM-enabled supply chains. The opportunities of BIM expansion to materials management in the context of sustainable growth are hardly discussed in the literature. Therefore, opportunities were extracted from the articles related to the construction supply chain through an extended analysis of existing studies, reports, and case studies.

2.2. BIM-Enabled Materials Management towards Sustainable Decision Making

In a broader sense, BIM is the process of creating and managing digital information in construction projects that involves a range of stakeholders. This is further magnified by the need to align with a dispersed network of supply chain participants [8]. Besides bringing a platform for collaboration at both intra- and inter-organizational levels, a BIM-based supply chain generates a wealth of data and analytics enabling data-driven decision making. Tracking supply chain performance metrics provides a way for businesses to identify areas for improvement, optimize processes, and make more accurate decisions on materials procurement, logistics, and resource allocation [8]. Disruptions associated with workforce, materials, and equipment clashes in the construction process account for a productivity loss of up to 25 per cent [28]. BIM facilitates the visualization of project schedules and sequencing via real-time tracking of materials availability and delivery. Indeed, this allows for revealing potential bottlenecks, dependencies, and discrepancies in the construction process, thereby improving scheduling and sequencing decisions.

Hypothesis 1 (H1). 

BIM-enabled materials management has a significant impact on sustainable decision making.

2.3. BIM-Enabled Materials Management’s Link to Procurement, Logistics, Safety, and Cost

Being popularized at the design and construction stages, BIM gradually moves towards materials management [29], bringing a higher level of visibility into the supply chain by integrating data on materials into the digital model. It enables network participants to track the availability, procurement, and delivery of materials more effectively, ensuring timely and accurate supply chain operations. Among other benefits, the BIM built-in clash detection feature performed at the early phase of design contributes to the speed and safety of building operations. It enables the detection of clashes between building systems, components, or materials and their resolution in the virtual model before the construction phase. BIM allows further leverage of the simulation of different scenarios to reduce on-site rework and disruptions in the supply chain that occur from conflicts between design and construction requirements [30]. BIM could produce quantity take-off and estimation of materials derived from digital models. Thus, the model stores the data on the material’s properties, such as volume, weight, etc., but it does not include information about the material’s location, supplier, and manufacturer. To calculate lead time, transportation cost, customs payments, and material delivery information, users need to switch between different software products. This consumes additional time and cost. Therefore, material-related data extracted from the digital model can be linked to the supply chain system to maintain accurate procurement, inventory records, and tracking throughout the project lifecycle [31].

Hypothesis 2 (H2). 

BIM-enabled materials management has a significant impact on procurement.

Hypothesis 3 (H3). 

BIM-enabled materials management has a significant impact on logistics.

Hypothesis 4 (H4). 

BIM-enabled materials management has a significant impact on safety.

Hypothesis 5 (H5). 

BIM-enabled materials management has a significant impact on cost.

2.4. Procurement, Logistics, Safety, and Cost towards Sustainable Decision-Making

Within supply chain boundaries, sustainability could be identified as making decisions that imply not only profit for the company but also benefits for society while minimizing the ecological footprints of those decisions [32]. Each component contributes to long-term value. Thus, daily operations include sourcing materials from suppliers who adhere to ethical and sustainable practices, while, at the same time, prioritizing local content to reduce embodied carbon emissions, support local communities, and apply product lifecycle assessment for conscious decision making. Logistics efficiency refers to transportation route optimization to reduce fuel consumption via order consolidation, eco-friendly packaging practices, or using fuel-efficient vehicles. Safety is another important aspect of sustainable supply chain management. This includes taking all mandatory and necessary preventive measures to avoid costly accidents and legal liabilities, thereby ensuring employees’ well-being and environmental protection. Every decision comes with a cost. In practice, cost-effectiveness is a value judgement that relies on the following: (i) Total cost ownership; (ii) Cost-benefit analysis; (iii) Return on investment. Yet, there will always be trade-offs between short-term costs and long-term sustainable benefits.

Hypothesis 6 (H6). 

Procurement positively affects sustainable decision making.

Hypothesis 7 (H7). 

Logistics positively affects sustainable decision making.

Hypothesis 8 (H8). 

Safety positively affects sustainable decision making.

Hypothesis 9 (H9). 

Cost positively affects sustainable decision making.

2.5. Mediating Effects in the BIM-Enabled Materials Management and Sustainable Decision Making Linkage

The mediating effects of the construct refer to the intermediate processes that come into operation with BIM-enabled materials management and the impact of these intermediaries on sustainability-conscious decision making. BIM enables real-time data sharing among stakeholders, including architects, engineers, contractors, suppliers, and subcontractors. It has the potential to improve coordination by establishing an integrated platform for network participants to access and exchange information. Information collaborative management facilitates the supply chain by reducing errors, and decreasing data transmission delays or information distortion in engineering [33]. Hence, BIM can be leveraged to improve materials management [34]. For example, with BIM, many problems of prefabricated buildings and the off-site construction methods adopted can also be resolved by using a detailed digital model. BIM enables better planning and coordination of prefabricated components throughout the whole prefabricated building lifecycle management [35]. During the construction phase, the BIM integrated management process contributes to supply chain efficiency, on-site labour, and waste reduction [36]. Since procurement, logistics, safety, and cost contribute to sustainable decision making, they can be assessed as mediators in the proposed construct.

Hypothesis 10 (H10). 

The link between BIM-enabled materials management and sustainable decision making is mediated by procurement.

Hypothesis 11 (H11). 

The link between BIM-enabled materials management and sustainable decision making is mediated by logistics.

Hypothesis 12 (H12). 

The link between BIM-enabled materials management and sustainable decision making is mediated by safety.

Hypothesis 13 (H13). 

The link between BIM-enabled materials management and sustainable decision making is mediated by cost.

2.6. Interrelationship between Procurement, Logistics, Safety, and Cost

Because procurement, logistics, safety, and cost components are interconnected, they have a significant impact on building project delivery and the success of operation. The interrelationship is complex and dynamic where effective coordination and alignment between components contribute to supply chain risk minimization. While procurement influences logistics by shaping supplier relationships [37], well-established logistics adhere to the culture of safety. The safety cost link is known within the “safety pays” concept. Safety is associated with funds allocated for the safety budget, cost of accidents, and return on investment in safety [38]. In the long run investing in safety measures leads to cost saving.

Hypothesis 14 (H14). 

Procurement positively affects logistics.

Hypothesis 15 (H15). 

Logistics positively affects safety.

Hypothesis 16 (H16). 

Safety positively affects cost.

Implementing BIM-enabled supply chain management largely depends on trust and transparency throughout each stage of stakeholder engagement. It implies a collaborative and integrated approach to BIM adoption, setting data exchange standards, and enabling interoperability across software modules used by supply network participants. BIM-enabled supply chain management brings a range of opportunities in the context of sustainability. This study provides a non-exhaustive list of benefits mostly discussed in the literature. To limit the scope of the study to what extent BIM-enabled materials management has the potential to improve the sustainability performance of construction projects, the list of drivers derived from the literature review is shown in

Table 2. Sustainable construction drivers due to BIM-enabled materials management.

Scales Items		How Much Do You Agree That the Following Drivers Improve Sustainability in Construction Due to BIM-Enabled Materials Management? Drivers: Positive Results (Output) of the Process (BIM-Enabled Materials Management)
		Statement	Explanation	Derived Based on Studies
I Materials Management	DMM1	Material traceability and transparency	To mitigate the environmental impact of the construction materials’ lifecycle cost-effectively. To optimize materials consumption: governance through materials lot and serial traceability and transparency	Elghaish et al. [24], Zhineng [42], and Ruparathna and Hewage [43]
	DMM2	Inventory management visibility	To promote usage of project leftovers and surplus materials (use/reuse/maintenance)	Zhu Na et al. [29] and Islam et al. [44]
	DMM3	Fewer, less toxic, and more durable materials usage	To improve materials planning and enable commodity re-design so they are manufactured using different, fewer, less toxic, and more durable materials (waste management)	Elghaish et al. [24]
	DMM4	Materials specifications for facility management	To facilitate transferring construction to facility management: greater control over replacements, refurbishments, and renewals (cost reduction)	Machete et al. [45] and Zhineng [42]
II Procurement	DP5	Material planning and ordering	To enhance data-sharing with manufacturers/suppliers, to improve procurement reliability via consistent use of data for better material planning and ordering	Zhu Na et al. [29] and Le et al. [30]
	DP6	Market differentiation for construction companies that adopt sustainable practices	To increase the inventory management system’s visibility for proper tracking of project resources and project leftovers to reduce over-ordering	Walker et al. [44], Jorma Kinnunen et al. [46], and Pero et al. [47]
	DP7	Material and corporate sustainability assessment	To cooperate with local manufacturers/suppliers as alternative sources and, therefore, minimize embodied carbon material purchasing	Elghaish et al. [24]
	DP8	Investment in sustainable construction technologies and materials	To create shared value approaches with manufacturers/suppliers and facilitate the assessment of product and corporate sustainability in general	David Worford [48], Beorkrem [49,50], and Kar and Jha [51]
III Logistics	DL9	Delivery Scheduling	To improve material delivery scheduling by having real-time data on material characteristics and properties	Xiaoqiu Ma [33] and Bajomo et al. [52]
	DL10	Shipping routes and mode selection optimization	To optimize the shipping routes and mode selection by having real-time data on material characteristics and properties	Getuli et al. [18] and Zhineng [42]
	DL11	Emissions reduction	To reduce emissions by distributing the load in each vehicle by having real-time data on material characteristics, properties, and locations	Thöni et al. [9] and Walker et al. [44]
	DL12	Delivery cost decrease	To reduce the cost of delivery by enhancing shipment track and transporting goods from one place to another	Getuli et al. [18] and Xiaoqiu Ma [33]
IV Safety	DS13	Hazard-based design choices	To evaluate the likelihood of hazard-based design choices for making safety-conscious decisions (e.g., material selection and clash detection)	Deng et al. [27]
	DS14	Workplace, operation, and maintenance safety	To reduce the production of waste on construction sites (e.g., material cut-offs) to improve workplace safety performance	Getuli et al. [18], Ruparathna and Hewage [43], and Islam et al. [53]
	DS15	Building evacuation performance	To improve evacuation performance of building layout design under various fire scenarios	Tang et al. [54] and Chatzimichailidou and MA [55]
	DS16	Innovation and competition in sustainable construction practices	To facilitate transferring construction projects to facility management: greater control over replacements, refurbishments, and renewals	Ying Xie et al. [56]
V Cost	DC17	Cost savings through streamlined processes	To cut operational costs through time and resource optimization and increase net profit from the construction materials supply chain by minimizing risks and errors	Xiaoqiu Ma [33]
	DC18	Supply-demand planning, work order scheduling	To reduce cost by improving supply–demand planning, work order scheduling, and forecasting	Nadeem et al. [57], Magill et al. [28], and Le et al. [30]
	DC19	Resource management cost reduction	To reduce the additional cost of purchasing, inventory, and disposal associated with over-purchasing materials	Zhu Na et al. [29] and Porwal and Hewage [35]
	DC20	Process waste cost decrease	To decrease process waste by letting resource planning systems sustainably process operational activities, while BIM efficiently improves sustainability at the design stage	Xiaoqiu Ma [33] and Zhineng [42]
VI Decision Making	DDM21	Streamlining data workflow for decision-making	To optimize, streamline, and automate data workflow and improve decision-making by providing real-time data	Getuli et al. [18], Papadonikolaki [8], and Bajomo et al. [52]
	DDM22	Stakeholders’ collaboration and data sharing	To enhance dialogue and data sharing among stakeholders	Getuli et al. [18], Papadonikolaki [8], and Atta et al. [3]
	DDM23	Sustainability awareness and education	To improve data workflow efficiency by implementing integrated solutions rather than adopting a fragmented solution	Atta et al. [3], Magill et al. [28], and Ruparathna and Hewage [43]
	DDM24	Building’s performance and life-long environmental impact tracking	To analyse a building’s performance and quantify a building’s life-long environmental impact before construction	Dickson and Pavía [58]

. These drivers echo the ISO 19650 Standard [39] for Industrial Asset, ISO 10845 Construction Procurement [40], and ISO 20400 Sustainable Procurement [41]. The standard principles on asset physical and functional characteristics provision for decision making throughout the building lifecycle, collaborative approach for stakeholders with improved data sharing practices and asset management during the operational phase are presented in the materials management, cost, safety, and decision-making scale items. The standard principles on developing collaborative commitment for sustainable purchasing are presented in the procurement, logistics, cost, and safety scales items.

3. Research Design

3.1. Study Measures

Within the scope of this study, the block with drivers has been analysed as part of a preliminary study. A detailed literature review was conducted to extract the most common and significant drivers discussed by researchers. Thus, twenty-four (24) potential drivers of materials management were integrated into the BIM derived from the comprehensive literature review, followed by the solicitation of researchers and practitioners in the field to validate the drivers. Figure 1 represents the methodology flowchart that describes the methods applied in detail.

According to the research methodology a conceptual model was developed in such a way as to provide an easily understood, hypnotized linkage and to test the assumed relationships, as shown in Figure 2. The intention is to evaluate the importance of each driver within the industry and country context. There are no other fundamentally different types of models. The most comprehensive analysis of sustainability practices in materials management is presented by Kar and Jha [51]. However, the authors did not consider BIM-enabled materials management as proposed in our study. It was administered to construction practitioners with experience in material supply and management, as well as academics and researchers in the AEC industry. The questionnaire survey was chosen as the primary data collection method. Its benefits are acknowledged in the scientific literature since it is easy to administer, anonymous, flexible, cost-effective [59], and can capture the tacit knowledge of respondents [60].

The data was collected online through the survey with approximately 10–15 min to complete. The questionnaire consisted of 4 (four) sections; the first section introduces the main objective, indicates the targeted audience, and clarifies privacy issues. The second section is dedicated to the respondent’s details, i.e., demographics. The third section comprises three blocks of closed-ended statements on drivers, barriers, and improvement strategies for sustainable construction facilitated by BIM-enabled materials management. Each block is closed by an open-ended question to allow respondents to answer based on their complete knowledge, feeling, and understanding; the fourth section that concludes the survey also utilizes an open-ended question to encourage participants to share an overall opinion on the study objective. Typically, the same question presented in these formats produces different results. Therefore, the intention of combining the questions’ format is to attain both measuring the importance of drivers derived from the literature review and to gain valuable information on a research subject. Unlike the twenty-four (24) closed-ended questions in each block, all four (4) open-ended questions were designed as non-compulsory for the respondents’ convenience. Thus, to avoid the issue of unanswered questions the survey is configured to not allow respondents to proceed without selecting the response option, except for open-ended questions. Although respondents were allowed to return to previous pages if required, the multiple-response option was disabled.

The 5-point Likert scale of measurement was applied to rate the level of agreement on the drivers, known as 1 (strongly disagree), 2 (somewhat disagree), 3 (neither agree nor disagree), 4 (somewhat agree), and 5 (strongly agree). It is the most universal method where the main advantage is the possibility of respondents to self-report the scale of their agreement or disagreement. Another convenience is that it contains a midpoint, which reduces the extreme residuals, thereby providing respondents with the option of expressing a neutral opinion. It is believed that this advantage could minimize response bias. The scale is common in construction and engineering research to quantitatively evaluate opinions, attitudes, or behaviour by applying a range of techniques for data analysis.

To not limit the dataset for any statistical analysis, the statements were not grouped. Although factors were seemingly related to a certain stage of materials management, they were not grouped to ensure unbiased perception. To test the validity of the survey the pilot study was carried out before launching the main survey. In pilot testing the survey was distributed to a small sample, focusing on industry professionals and academics to evaluate the content quality in pursuing reliability to a greater extent. The feedback was received from three professors with a speciality in civil engineering (USA, Turkey, and Hong Kong), two postgraduate research fellows (Kazakhstan and Iran), and two project managers (Nigeria and UK). The feedback included comments on the survey design, targeted audience, structure, content, and clarity of wording. According to the recommendations provided, the questions were simplified, open-ended questions were added, and the targeted audience was specified in the introduction part.

3.2. Sample Justification

The questionnaire was developed in three languages (Kazakh, Russian, and English) using the Qualtrics platform and distributed via personalized emails with the invitation and a web link generated by Qualtrics. Unlike the number of respondents, the number of distributions cannot be identified, since the experts were requested to forward the survey to professionals they deemed appropriate. As the topic is cross-disciplinary, the participants were targeted and purposely approached. For this study, the primary targeted audience was the construction industry practitioners, and the secondary targeted audience was the academics/researchers. Originally, the intention was to collect first-hand information from experienced practitioners. In this regard, the researcher’s competence in state and industrial supply chain management activities facilitated the participants’ selection. Thus, experts from 14 construction companies and 5 universities specialized in the AEC field participated in the survey. The data collection followed a robust ethical protocol that guaranteed respondents confidentiality of their data which would be used only for research purposes and not be disseminated without prior written consent. Considerately, to improve the response rate, the participants were informed that the research outcomes would be freely available post-publication.

In August 2023, the survey was distributed to the targeted sample. Out of all questionnaires sent, 206 were retrieved and only 118 were recognized as valid for further analysis; only 100% completed responses were considered. A total of 88 responses were excluded due to incompleteness or misplaced information, resulting in a response rate of 57% with 118 valid samples. Randomly chosen respondents were interviewed afterwards based on their responses to the survey to identify the clarity of the statements and overall awareness of sustainability issues in CMM. During post-survey interviews, it is revealed that often respondents have expertise in a certain area and are not necessarily familiar with the whole construction project lifecycle. The study is cross-functional at the intersection of construction and the supply chain. Thus, the respondents who work in material management sometimes were not familiar with BIM-technologies opportunities.

To estimate the cause–effect relationships of sustainability drivers in construction in path models that consider latent variables to be partial least squares, a structural equation modelling technique was applied using Smart PLS software version 4. The study adopted an exploratory research design (quantitative and qualitative methods).

4. Data Analysis

4.1. Respondent Profile

According to the respondents’ profile, the construction sector is male-dominated. The most common degree is BSc for practitioners, while MSc and PhD are more common for academics. The experts have specialities in architecture, civil engineering, quantity surveying, safety, project management, and construction supply chain management. Most participants are from developing countries and have 0–10 (69), 10–20 (39), or more than 20 (10) years of experience associated with the AEC industry (

Table 3. Respondent profile.

Experience/Education	BSc	MSc	PhD	Other
0–10 years	36	22	6	5
11–20 years	21	14	3	1
Above 20 years	3	4	3

). The focus group is industry practitioners (82), as presented in Figure 3, where 36 were academics, 4 respondents were from developed countries, and 114 were from developing countries, respectively. While Iraq and Pakistan are represented only by scholars, respondents from Iran and Kazakhstan are represented by both academics and industry experts. Respondents from other countries are fully represented by industry professionals.

4.2. Descriptive Statistics

Mean values of responses to the opportunities-related questions ranged from 4.02 to 4.58. Standard deviation values were between 0.75 to 1. Responses are more dispersed and less condensed around the mean value. Data is not evenly distributed with the negative skewness (−0.55 to −2.55). Excess kurtosis values ranged from −0.92 to 7.64 indicating a deviation from normality. The results are presented in

Table 4. Descriptive statistics.

Indicator	Mean	SD	Excess Kurtosis	Skewness	Indicator	Mean	SD	Excess Kurtosis	Skewness
DMM1	4.472	0.942	4.744	−2.206	DS13	4.361	0.822	0.928	−1.229
DMM2	4.528	0.849	4.524	−2.103	DS14	4.333	0.913	0.274	−1.169
DMM3	4.306	0.876	−0.924	−0.774	DS15	4.264	0.986	0.616	−1.18
DMM4	4.542	0.763	3.294	−1.871	DS16	4.444	0.815	3.892	−1.785
DP5	4.583	0.777	7.641	−2.55	DC17	4.403	0.892	1.386	−1.497
DP6	4.125	0.815	−0.467	−0.552	DC18	4.472	0.816	1.731	−1.553
DP7	4.389	0.792	0.703	−1.169	DC19	4.361	0.855	2.934	−1.599
DP8	4.472	0.745	0.709	−1.242	DC20	4.222	0.931	1.152	−1.202
DL9	4.333	0.913	0.703	−1.281	DDM21	4.403	0.877	3.319	−1.781
DL10	4.333	0.882	0.121	−1.089	DDM22	4.528	0.745	2.323	−1.642
DL11	4.208	0.942	−0.811	−0.74	DDM23	4.472	0.763	0.557	−1.244
DL12	4.028	1	−0.373	−0.653	DDM24	4.417	0.795	0	−1.075

.

4.3. Reliability and Validity

The response method to estimate both indicators and response variables is subject to common method bias. It is assessed using Harman’s single-factor test, using SPSS software, version 27. According to the results, a single-factor solution explained 47.037% of the total variance (

Table 5. Total Variance Explained.

Extraction Sums of Squared Loadings
Total	% of Variance	Cumulative %
28.221	47.037	47.034
Extraction Method: Principal Axis Factoring. a. 1 factor extracted. 3 iterations required.

). Since the result does not exceed the threshold value of 50% [61], the common bias is at an acceptable level.

Items with factor loading less than the recommended threshold of 0.600 were not revealed. The reliability of the responses is tested by Cronbach’s Alpha and Composite Reliability, showing values higher than the recommended the minimum of 0.700 [61]. The validity of the construct is tested using the Average Variance Extracted (AVE) method. All values are higher than 0.500, thus representing adequate convergence [61]. Multicollinearity is measured by employing the Variance Inflation Factor (VIF). VIF values less than 5 show a moderate correlation in the dataset. The results are presented in

Table 6. Item loading, reliability, and validity.

	Drivers
	Outer Loading	Cronbach’s Alpha	CR (rho_c)	AVE	VIF
Materials management		0.784	0.862	0.610
DMM1	0.746				1.543
DMM2	0.803				1.707
DMM3	0.690				1.348
DMM4	0.874				2.065
Procurement		0.851	0.901	0.696
DP5	0.718				1.395
DP6	0.859				2.483
DP7	0.888				2.632
DP8	0.860				2.386
Logistics		0.812	0.878	0.645
DL9	0.857				2.537
DL10	0.887				2.531
DL11	0.677				1.413
DL12	0.776				1.830
Safety		0.876	0.915	0.729
DS13	0.839				2.086
DS14	0.886				2.913
DS15	0.871				2.691
DS16	0.819				1.801
Cost		0.898	0.929	0.766
DC17	0.895				3.406
DC18	0.865				2.447
DC19	0.915				3.901
DC20	0.823				2.208
Decision Making		0.828	0.886	0.661
DDM21	0.840				2.355
DDM22	0.865				2.443
DDM23	0.726				1.667
DDM24	0.814				1.936

.

It is observed that most factor loadings are greater than 0.7, while they are less than 0.7 in two cases (0.69 and 0.68). As a rule of thumb, the threshold for the factor loading is at least |0.4| for the factor to be considered important. Therefore, variables are not excluded from the analysis. Another observation presented in

Table 7. Discriminant validity—cross-loading.

	Drivers
	DMM	DP	DL	DS	DC	DDM
DMM1	0.746	0.564	0.457	0.446	0.539	0.507
DMM2	0.803	0.522	0.535	0.634	0.643	0.657
DMM3	0.690	0.630	0.591	0.565	0.368	0.488
DMM4	0.874	0.661	0.724	0.662	0.695	0.660
DP5	0.703	0.718	0.553	0.538	0.647	0.640
DP6	0.604	0.859	0.664	0.593	0.500	0.663
DP7	0.623	0.888	0.698	0.643	0.677	0.755
DP8	0.603	0.860	0.610	0.526	0.559	0.704
DL9	0.624	0.667	0.857	0.671	0.686	0.647
DL10	0.685	0.612	0.887	0.688	0.644	0.602
DL11	0.607	0.560	0.677	0.589	0.477	0.496
DL12	0.459	0.597	0.776	0.485	0.631	0.522
DS13	0.608	0.547	0.597	0.839	0.546	0.702
DS14	0.643	0.599	0.671	0.886	0.680	0.680
DS15	0.549	0.514	0.692	0.871	0.601	0.661
DS16	0.735	0.691	0.650	0.819	0.657	0.698
DC17	0.612	0.638	0.710	0.609	0.895	0.726
DC18	0.666	0.632	0.732	0.741	0.865	0.753
DC19	0.566	0.602	0.616	0.584	0.915	0.676
DC20	0.694	0.631	0.596	0.604	0.823	0.703
DDM21	0.639	0.642	0.735	0.802	0.796	0.840
DDM22	0.653	0.656	0.535	0.648	0.787	0.865
DDM23	0.632	0.711	0.469	0.517	0.512	0.726
DDM24	0.496	0.712	0.542	0.614	0.528	0.814

refers to the factor loadings that are greater than the variables’ cross-loadings. To identify whether the measurements are related, discriminant validity is tested using the Fornell–Lacker criterion and Heterotrait-Monotrait ratio. The results are presented in

Table 8. Discriminant Validity. Fornell–Lacker criterion. Heterotrait–Monotrait ratio.

	DC	DDM	DL	DMM	DP	DS
DC	0.875	0.933	0.889	0.854	0.818	0.815
DDM	0.809	0.813	0.854	0.919	0.997	0.932
DL	0.762	0.710	0.803	0.923	0.915	0.900
DMM	0.729	0.746	0.747	0.781	0.936	0.889
DP	0.717	0.831	0.760	0.760	0.834	0.797
DS	0.730	0.803	0.765	0.745	0.692	0.854

Note: The elements below the diagonal and higlighted are Fornell-Lacker criterion values. The elements above the diagonal are the Heterotrait-Monotrait ratio values.

close to 1 indicating a lack of discriminant validity.

4.4. Structural Model

The structural model presented in Figure 4 is developed according to the conceptual model. To measure the model fit, the standardized root mean square residual (SRMR) is assessed as suggested by Hair et al. [62]. Additionally, the Normed Fit Index (NFI) is employed as an incremental fit metric. The SRMR value of 0.097 is less than the 0.10 threshold. The NFI is 0.621. The closer the value is to 1 the better the model fit, while a value higher than 0.9 is considered as a good fit. The NFI is known to be sensitive to the number of parameters influencing the NFI results. In this case, the model is considered satisfactory but can be improved.

In the next step, the bootstrapping method is applied to assess path coefficients, t-values, and p-values for both direct and indirect relationships. Firstly, the hypotheses associated with direct relationships are analysed. As presented in

Table 9. Tested hypotheses (direct relationship).

Hypotheses		Beta (B)	T-Value	p Value	Results
H1	Materials Management -> Sustainable Decision Making (DMM -> DDM)	0.004	0.035	0.972	Not Accepted
H2	Materials Management -> Procurement (DMM -> DP)	0.760	9.254	0.000	Accepted
H3	Materials Management -> Logistics (DMM -> DL)	0.401	2.814	0.005	Accepted
H4	Materials Management -> Safety (DMM -> DS)	0.394	2.534	0.011	Accepted
H5	Materials Management -> Cost (DMM -> DC)	0.415	2.837	0.005	Accepted
H6	Procurement -> Sustainable Decision Making (DP -> DDM)	0.467	4.281	0.000	Accepted
H7	Logistics -> Sustainable Decision Making (DL -> DDM)	−0.217	2.378	0.017	Accepted
H8	Safety -> Sustainable Decision Making (DS -> DDM)	0.365	3.370	0.001	Accepted
H9	Cost -> Sustainable Decision Making (DC -> DDM)	0.382	2.915	0.004	Accepted
H14	Procurement -> Logistics (DP -> DL)	0.456	3.242	0.001	Accepted
H15	Logistics -> Safety (DL -> DS)	0.471	3.200	0.001	Accepted
H16	Safety -> Cost (DS -> DC)	0.421	3.082	0.002	Accepted

, the direct effect of materials management on sustainable decision making (β = 0.004, t = 0.035, p = 0.972) is positive but not significant. Therefore, Hypothesis 1 is rejected. However, in the other 11 cases, the impact is positive and statistically significant. Hence H2, H3, H4, H5, H6, H7, H8, H9, H14, H15, and H16 are supported.

Although the relationship between materials management and sustainable decision-making is found to be insignificant, mediation analysis (

Table 10. Tested hypotheses (mediation analysis).

H10	Materials Management -> Procurement -> Sustainable Decision Making (DMM -> DP -> DDM)					Accepted
H11	Materials Management -> Logistics -> Sustainable Decision Making (DMM -> DL -> DDM)					Not Accepted
H12	Materials Management -> Safety -> Sustainable Decision Making (DMM -> DS -> DDM)					Accepted
H13	Materials Management -> Cost -> Sustainable Decision Making (DMM -> DC -> ODM)					Accepted
Hypothesis		Indirect Effect			BI 2.5–97.5%
		Beta (B)	T-Value	p-Value
H10 DMM -> DP -> DDM		0.355	3.817	0.000	0.182	0.545
H11 DMM -> DL -> DDM		−0.087	1.608	0.108	−0.225	−0.013
H12 DMM -> DS -> DDM		0.144	2.049	0.041	0.031	0.311
H13 DMM -> DC -> DDM		0.159	1.983	0.047	0.039	0.381

) revealed that materials management impacts on sustainable decision making are mediated by procurement (β = 0.355, t = 3.817, and p = 0.000); safety (β = 0.144, t = 2.049, and p = 0.041); and cost (β = 0.159, t = 1.983, and p = 0.047). The impact is positive and statistically significant; therefore, H10, H12, and H13 are supported. However, logistics (β = −0.087, t = 1.608, and p = 0.108) were not found to mediate the relationship between materials management and sustainable decision making. Hence, the H11 is rejected.

Although the logistics were not determined as the mediator between materials management and sustainable decision making, within the specific indirect effect analysis several observation findings were reported (

Table 11. Specific indirect effect.

	Beta (B)	Mean	SD	T-Value	p Value
DMM -> DP -> DDM	0.355	0.350	0.093	3.817	0.000
DMM -> DP -> DL -> DDM	−0.075	−0.079	0.042	1.793	0.073
DMM -> DP -> DL -> DS -> DDM	0.059	0.065	0.043	1.383	0.167
DMM -> DP -> DL -> DS -> DC -> DDM	0.026	0.024	0.015	1.744	0.081
DMM -> DP -> DL	0.346	0.346	0.117	2.951	0.003
DMM -> DP -> DL -> DS	0.163	0.162	0.079	2.077	0.038
DMM -> DP -> DL -> DS -> DC	0.069	0.072	0.045	1.539	0.124
DMM -> DL -> DDM	−0.087	−0.093	0.054	1.608	0.108
DMM -> DL -> DS -> DDM	0.069	0.068	0.036	1.908	0.056
DMM -> DL -> DS -> DC -> DDM	0.030	0.028	0.018	1.642	0.101
DMM -> DL -> DS	0.189	0.186	0.089	2.116	0.034
DMM -> DL -> DS -> DC	0.079	0.076	0.038	2.068	0.039
DMM -> DS -> DDM	0.144	0.147	0.070	2.049	0.041
DMM -> DS -> DC -> DDM	0.063	0.062	0.042	1.495	0.135
DMM -> DS -> DC	0.166	0.172	0.094	1.760	0.078
DMM -> DC -> DDM	0.159	0.148	0.080	1.983	0.047
DP -> DL -> DS	0.215	0.217	0.103	2.093	0.036
DP -> DL -> DS -> DC	0.090	0.096	0.058	1.561	0.119
DP -> DL -> DS -> DDM	0.078	0.087	0.057	1.379	0.168
DP -> DL -> DS -> DC -> DDM	0.034	0.032	0.020	1.729	0.084
DP -> DL -> DDM	−0.099	−0.104	0.053	1.867	0.062
DL -> DS -> DDM	0.172	0.179	0.084	2.051	0.040
DL -> DS -> DC -> DDM	0.076	0.070	0.036	2.078	0.038
DL -> DS -> DC	0.198	0.199	0.086	2.314	0.021
DS -> DC -> DDM	0.161	0.153	0.072	2.228	0.026

). Materials management has a positive and statistically significant impact on logistics through procurement (β = 0.346, t = 2.951, and p = 0.003) and on safety through logistics (β = 0.189, t = 2.116, and p = 0.034). Procurement’s impact on safety is mediated by logistics (β = 0.215, t = 2.093, and p = 0.036). And, finally, logistics’ relationships with cost and sustainable decision making are mediated by safety (β = 0.198, t = 2.314, and p = 0.021 and β = 0.172, t = 2.051, and p = 0.040, respectively).

5. Discussion

This study explores the role of industry professionals’ and academics’ perceptions of BIM-enabled materials management in improving sustainable decision making in construction through its direct impact and mediated by identified factors, namely procurement, logistics, safety, and cost in cross-country environment. As hypothesized, the outcomes of analysis with the application of structural equation modelling and Smart PLS (version 4) confirmed eleven direct paths out of twelve suggested and three mediated impacts out of four suggested.

5.1. Materials Management

Overall findings reveal a positive but insignificant direct impact of materials management on sustainable decision making, thereby rejecting H1. Silva et al. [63] reported on new waste governance policies emerging globally following the shifting waste paradigm from “prevention of waste” to sustainable materials management. Researchers conclude that society is in a transitional state towards holistic circular systems. Furthermore, with rapidly increasing materials consumption, failure to find more sustainable ways of materials management has detrimental implications for the environment, economy, and society globally [64]. And yet, from the results of this study, it is clear there is still a lack of awareness of materials management’s impact on sustainable decision making within the construction industry. This corresponds to the existing studies emphasizing the low interest in BIM adoption for materials management compared to other AEC fields [34]. Another explanation is the current low implementation status of BIM in developing countries, which is reported by several studies [65,66].

Poor materials management is one of the critical challenges of building projects [29,34,36]. From the survey results it is observed that, while working in isolation, design engineers tend to underestimate the importance of the supply chain in a range of aspects such as supplier selection, lead times, regulatory requirements, etc. And, materials specialists who are familiar with both design and supply chain fields have no vision of how to integrate design, construction, and materials supply processes. Therefore, sustainability opportunities are unclear and hidden. This observation relates to the complexity of construction derived from industry characteristics, such as high fragmentation and temporary nature, etc., making it more prone to the decentralization and isolation of its activities. Traditionally, once the design is completed and approved, the material bills are sent to the supply chain management department “as is” to arrange material procurement and timely delivery. Hence, wrong material delivery, materials shortage, or excess, reworks, and other risks are associated with poor material management due to insufficient details for proactive decision making [67]. Researchers emphasize the necessity of a collaborative and integrated approach to make the smooth information flow real for the scattered supply chain participants [13]. It implies adopting BIM technology, developing unified data exchange formats, and securing interoperability within the range of unintegrated software systems operated by supply chain players [68]. Another trend is to apply blockchain and smart contracts technologies to BIM-enabled supply chains to address still unsolved issues with data incompatibility and interoperability that prevent the traceability of information [69].

5.2. Procurement

All the direct paths of procurement from materials management (H2) to sustainable decision making (H6) and logistics (H14), including materials management’s impact on sustainable decision making mediated by procurement (H11), are positive and significant. This finding aligns with green construction procurement initiatives taken under the United Nations Sustainable Development Goals. Many studies delved into research on the inclusion of sustainable criteria in procurement processes. The promotional strategies are based on electronic procurement and sustainable procurement paradigms. The first one is expanded due to internet proliferation, thereby changing the modes of data management. Utilizing online platforms for purchasing purposes became a feasible and secure option that completely changed traditional paper-based procurement practices. The latter means purchasing materials with cost-effective lifecycles that cause minimal adverse environmental and societal effects [70].

5.3. Logistics

Both materials management (H3) and procurement (H14) have a direct impact on logistics, while logistics influence safety (H15) and sustainable decision making (H7). Logically these paths have the same degree of impact inversely and on the cost. Implications of green logistics both onsite and offsite improve the overall construction project performance, providing operational efficiency and less hazardous environmental emissions [37]. On the other hand, according to the mediation analysis, logistics do not mediate the relationship between materials management and sustainable decision making (H11). Hence, according to the respondents’ perceptions, material delivery nuances could not be influenced by materials management data. In reality, material planning as a part of materials management involves managing logistics to ensure materials’ timely delivery. Therefore, to check this finding sample size, an increase will be considered; otherwise, it contradicts existing knowledge in the field.

5.4. Safety

Materials management (H4) has a direct positive impact on safety, while safety impacts sustainable decision making (H8) and cost (H16). Due to the latter, safety (H12) acts as a mediator between materials management and sustainable decision making. There is no doubt that moving, handling, and storing activities, especially with hazardous materials, are the most universal challenges safety personnel face every day. It is crucial to integrate risk management into purchasing practices, considering the highest fatal injury rate of construction among other industries. Followed by safety, there is a range of preventive measures for logistics, known as driver-assistance systems, fatigue programs, ergonomic seats, etc. In contrast, having detailed specifications of obtainable materials for safety technologies will increase the implementation of safety innovations during the whole project lifecycle [71]. Ignoring any safety aspect leads not only to environmental or societal damage but can be extremely costly due to direct and indirect costs associated with an accident. The latter may include legal fees, expenses related to medical treatment reputation loss, etc. Thus, safety influences all three pillars of sustainability. Therefore, it has the potential to facilitate the operationalization of sustainable development [72].

5.5. Cost

Finally, materials management influences the cost (H5), while cost contributes to sustainable decision making (H9) and, consequently, cost mediates the relationship between materials management and sustainable decision making (H13). Other indicators are mostly considered within environmental and safety dimensions, cost is fully associated with the economic aspect of sustainability. Whereas each item might potentially increase the cost of the project, due to procurement policies, delivery issues, safety precautions, etc. These findings support previous lack of knowledge on materials specifications accompanied by communication issues and poor material planning leading to cost over-run [73].

5.6. Implications

The theoretical contribution of this study is that it provides an in-depth analysis of opportunities in the perception of materials management’s role in improving construction project performance. Despite the rising sustainability concerns regarding material consumption globally, research is scarce in the field [34]. Therefore, providing a conceptual model for understanding the causal relationships between materials management and sustainable decision making in construction is the key theoretical contribution of this study. The findings of this study establish that materials management cannot directly promote sustainability in decision making. The relationship between the two constructs is not immediate and straightforward but mediated by other indicators, defined as procurement, safety, and cost, while the logistics effect is questionable.

Additionally, the proposed approach is novel in several ways. Firstly, it is the first attempt to address project sustainable performance from another angle, whereas most contemporary studies focus on BIM potential in safety improvement, building lifecycle approach, etc. Secondly, it is the first attempt to assess and compare the awareness of industry professionals and academics in construction materials management issues. Finally, it is the first attempt to collaborate with cross-functional experts in developing integrated solutions. Thus, materials management is not an isolated non-core part of a building project, as often perceived. It operates at the junction of procurement, logistics, and inventory, considerably influencing project delivery in terms of time, cost, quality, productivity, and waste [74]. Therefore, its processes significantly contribute to all three dimensions of sustainability.

The developed framework would also provide clarity to the managers when assessing the initial project. Long-term cost-effectiveness refers to a well-informed decision made by the project customer, which leads to savings throughout the whole asset’s lifecycle. The customer’s willingness to finance sustainable design is contingent upon their understanding of not just the environmental and societal consequences but also the potential economic hazards resulting from any mishap. It is imperative to assess and address risks from a holistic viewpoint where project delivery and operation will be executed in a well-balanced and rational manner, considering all three elements of sustainability [69]. Sustainability considerations are no longer optional but are becoming a mandatory part of global policy. Another knowledge-increasing contribution of this study is that it derives the data from developing countries, including Kazakhstan, which is known to be very challenging due to low visibility and transparency.

5.7. Research Limitations

The limitations of the study firstly are derived from the methodology applied for the data collection and analysis. The most common survey research limitations are low validity of closed-ended questions, response bias if the respondent does not fully understand the question, and missing data. To address these issues open-ended questions were added allowing respondents to elaborate on their points, non-random sampling was applied to deliberately approach the targeted population, and incomplete responses with missing data were excluded from the analysis. Another limitation is related to the knowledge of the topic of research. Thus, the study is cross-functional; to better understand the process flow, respondents should be familiar with the whole construction project lifecycle. Authors associate the number of incomplete surveys with the lack of questions clarity to the particular subject matter experts. Major experts have work experience either in design and construction or in supply chain, with limited knowledge of sustainability practices. Although the researchers arranged pre-sessions to broaden audience knowledge on the CMM processes, the study evaluates the respondents’ attitude towards sustainability issues as a major construct in project delivery.

Therefore, further research opportunities shall be concentrated on other qualitative analysis methods via interviews and observations to attain more accurate data.

6. Conclusions

The sustainable development concept was introduced more than thirty years ago. It is popularized by world organizations and has, thus, been brought into the mainstream of policy discussion. Reportedly, it has already penetrated all industries. And yet, its expansion in construction is disproportional, where the importance scales toward the environmental dimension rather than the economic and social. So, compared to other dimensions, sustainability practices have higher industry-wide standards that target environmental performance improvement. In contrast, the focus on short-term project profit makes investment in long-term, sustainable initiatives less favourable. Being a highly fragmented industry, construction is more susceptible to this issue. Long-term cost-effectiveness is an informed decision of the project customer that results in savings over the building’s lifespan. Whether he would be willing to pay for sustainable design depends on his knowledge not only of environmental and societal impacts but also the economic risks from any accident. It is necessary to capture risks from a project lifecycle perspective. Sustainability is not just an environmental issue. All construction-related decisions should be made with sustainability in mind to ensure long-term growth.

In general, the concept is still not adequately perceived industry-wide in developing countries. Other environmental benefits should be explored more. In particular, economic benefits have the potential to induce industry decision makers to consider sustainability initiatives more seriously.

Within the scope of this study, Building Information Modelling (BIM) potential is examined in addressing materials management issues. The BIM has evolved as an effective instrument of sustainable construction, given a range of social, economic, and environmental benefits. Many studies have proved that BIM has had a significant impact on building project sustainable performance. This includes the following: (i) Enhanced design, record accuracy, saving time and contingencies, and cost decreases at the planning stage; (ii) Improved work schedule and safety; (iii) Errors and rework reductions at the operation stage. When it comes to the post-construction stage, BIM provides an opportunity to produce high-quality deliverables for end-users. Therefore, it is the right time to expand its potential in materials management in terms of improving supply chain sustainability. More specifically, the potential is explored through the prism of the economic dimension, which can be more interesting for industry players.

Overall findings of this study establish that most industry-related respondents are unaware of BIM-enabled materials management or BIM-enabled supply chain management opportunities. The results confirmed the importance of four out of five groups of the sixteen opportunities’ indicators extracted from the literature in the field. Comparative analysis shows that, in developed countries, sustainability awareness is higher for both representatives (industry and academics); the reverse is rather true in developing countries. BIM-enabled supply chain management is currently gaining momentum within the scientific community. In contrast, despite sustainability practice implementation and BIM adoption being widely discussed in scientific circles, both concepts are still nascent in developing countries.

Although it is revealed that materials management’s impact on sustainable decision-making is not immediate and straightforward, the contribution of procurement, logistics, safety, and cost to sustainable decision making is expectedly confirmed by the research outcomes. Furthermore, the relationship between materials management and sustainable decision-making is mediated by procurement, safety, and cost. In contrast, logistics do not act as a mediator in the link between materials management and sustainable decision making. Regarding improving sustainability through BIM-facilitated materials management, respondents conclude that any concept adoption or system implementation depends heavily on organizational goals. Yet, if the company would work on process optimization to reduce cost and losses by applying BIM technologies, it still indirectly contributes to industry sustainable development. Thus, it is believed that, ultimately, any improvements in the economic dimension would have a positive impact on environmental and social aspects.

Firstly, there is a high need for building project stakeholders to collaborate with materials management professionals at the early design stage. The importance of subject matter experts’ collaboration is highlighted in all studies examined within the literature review exercise. This research recommendation is to facilitate teams’ work in a BIM environment. In a broader vision, the integration scope should cover all activities related to building resilient supply chain networks. This includes the following: (i) Building trust among stakeholders by forming alliances with the construction supply chain actors and improving sustainable productivity; (ii) Creating an environment for both the public and private sectors where the individual actors can enter, align their processes, and add their value to the overall industry’s sustainable development. Secondly, a more detailed analysis could be conducted to focus on the economic benefits of system integration. Thus, working separately, individual stakeholders rely on different software solutions, which can be several even within one company. Functionalities’ decentralization causes unwholesome situations with process and data conflicts amongst supply chain participants. These findings could direct scholars toward exploring the ways of integrating construction supply chain processes.