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Article

Building Information Modeling Execution Drivers for Sustainable Building Developments

by
Ibukun O. Famakin
1,
Idris Othman
2,
Ahmed Farouk Kineber
3,4,*,
Ayodeji Emmanuel Oke
1,5,6,
Oludolapo Ibrahim Olanrewaju
7,
Mohammed Magdy Hamed
8 and
Taiwo Matthew Olayemi
1
1
Department of Quantity Surveying, Federal University of Technology Akure, 340110 Akure, Nigeria
2
Department of Civil & Environmental Engineering, University Technology PETRONAS, Seri Iskandar 32610, Malaysia
3
Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
4
Department of Civil Engineering, Canadian International College (CIC), Zayed Campus, 6th October City, Giza 12577, Egypt
5
CIDB Centre of Excellence, Faculty of Engineering and Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
6
School of Social Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
7
Wellington School of Architecture, Victoria University of Wellington, Wellington 6140, New Zealand
8
Construction and Building Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT), B 2401 Smart Village, Giza 12577, Egypt
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 3445; https://doi.org/10.3390/su15043445
Submission received: 21 November 2022 / Revised: 28 December 2022 / Accepted: 30 December 2022 / Published: 13 February 2023
(This article belongs to the Special Issue Sustainable Management of Construction Projects)
Browse Figures
Review Reports Versions Notes

Abstract

:
The need for continuous global improvement in the construction industry’s current state is inevitable. This pursuit for advancement is to benefit all concerned stakeholders in the construction industry, and innovation has been acknowledged as this improvement measure. Interestingly, Building Information Model (BIM) is a typical example of such innovation in the construction industry. It circumvents human errors, lessening project costs, strengthening productivity and quality, and reducing the project delivery time. This analysis investigates the factors influencing BIM implementation in construction in developing nations. A comprehensive literature review was performed to determine what factors contribute to BIM adoption. These drivers were categorized using exploratory factor analysis (EFA). Partial Least Square Structural Equation Modeling (PLS-SEM) was also used with a questionnaire survey of 100 Nigerian building engineering professionals. Findings from the model highlight the most critical drivers of sustainable BIM deployment. The study’s conclusion will serve as a guideline for policymakers in developing nations that want to finish successful projects by avoiding BIM implementation drivers and improving the accomplishment of building projects via the usage of BIM.

1. Introduction

By 2030, the architectural, engineering, construction, and operations (AECO) business will have contributed almost 15% of the global Gross Domestic Product (GDP) [1,2]. The products of the construction industry provide the backbone of society and other sectors of the economy via the creation of vital infrastructure and the built environment [3,4,5]. As a result, the impact on the economy is likely more significant than the direct GDP contribution. Nearly 40% of annual energy usage, 32% of CO2 emissions, and 25% of created garbage in Europe may be attributed to the AECO business [6,7]. Additionally, the building sector in many emerging nations has experienced significant shifts to meet local economic goals [8]. As a result, several low-income nations have strengthened their financial systems [9,10]. Multiple timetable delays are expected for building projects in these nations [11,12,13]. Lack of acceptance of new technologies and ideas, such as Building Information Modeling (BIM) [14], Blockchain [15], the Internet of Things (IoT) [16,17], and Industry 4.0 [18], has also contributed to widespread productivity problems in the sector. Thus, the construction sector in developing nations fails to achieve government goals for the community and clients, and there is a pressing need to create resource-efficient “total success building projects” [19].
Because BIM is at the forefront of this field’s literature, it is increasingly used in various contexts and coupled with other elements for a successful approach throughout a project’s planning and execution [20]. BIM is an intelligent 3D model-based system that provides engineers, architects and construction managers with data and tools for improved building and infrastructure planning, design, construction, and management, as defined by Autodesk [21]. BIM’s latent potential to increase productivity across the entire life cycle (from design to construction to maintenance) is well established [22,23]. The requests for technology to address systematic and recurrent challenges that stubbornly persist have led to drastic changes in BIM [24]. These issues can be categorized as having a negative impact on productivity, cost, and time management. As a result, BIM has been recognized as a vital tool for lifecycle management that may considerably improve the quality of a building’s lifespan [25,26].
Although there are numerous obvious benefits to using BIM, its full potential has not been realized. The current state of adoption [27], the definition and delineation of the barriers [28,29], and the identification of the drivers [2,30,31] in both developed and developing countries have all been the focus of numerous related studies that have sought to untangle a Gordian knot of barriers to BIM adoption. As a result, there has not been a concerted effort to study what motivates builders to adopt BIM [32]. While many studies have investigated BIM practices and the efficacy of these methods in industrialized nations, only a tiny amount of work has been done to investigate the drivers of BIM elaborately. The word “drivers” describes a management procedure essential to guaranteeing success and displays characteristics that, if correctly done, would secure a firm’s competitive performance [33,34]. The term “drivers” also refers to a feature necessary to achieve success. For instance, Olanrewaju, et al. [2] discovered that the critical determinants for adopting BIM in Nigeria’s AECO business include building lifecycle visualization, improved business performance, regulated whole-life expenditures and environmental parameters, improved quality and higher sustainability, as well as enhanced performance and cooperation. Eadie, et al. [30] noted several essential factors, including but not limited to clash detection, pressure from the government, pressure from competitors, correct construction sequencing, and cost savings through less rework. Clash detection, improved cooperation, and reduced costs were also highlighted as primary motivators for the widespread use of BIM in Australia by Rodgers, et al. [35]. Combining anecdotal evidence with the body of information shows that there are typical motivations for BIM implementation.
This study seeks to contribute to the body of knowledge further by introducing a comprehensive set of drivers that can enhance BIM adoption and determine the significant drivers stakeholders of the construction industry need to concentrate on to encourage the use of BIM for sustainable use building development. As a result, the following research question guided this empirical study: What are the significant drivers of BIM adoption for sustainable building development? Therefore, the current job is the first to fill this void by quantitatively emphasizing these drivers and the influence of BIM via establishing the linkages between input variables using structural equation modeling (SEM). Additional goals include fostering extensive critical discussion and debate among industry stakeholders and strategy consultants and stimulating additional investigation and research within the academic community to accelerate the widespread adoption of BIM in modern practice.

2. State of BIM Adoption in Nigeria for Sustainable Building Development

BIM adoption in Nigeria has continued to receive traction among built environment researchers [22,23,25,26,27]. However, it has been reported that the adoption level for BIM in the Nigerian construction industry is still shallow in the building lifecycle stages [27,36]. The design phase has more use cases compared to other lifecycle phases. BIM is mainly used to design buildings to attract the client’s attention. Continuous integration of BIM for all the building lifecycle stages is almost nonexistent. Only one project (Eko Atlantic) has fully integrated BIM in Nigeria from the design to use phase [37,38]. Although, many upcoming projects are beginning to maximize the value of BIM because BIM is underutilized in the Nigerian construction industry.
BIM is a powerful tool that can enhance construction projects throughout their life cycle [39,40]. BIM can enhance sustainable building development by providing many benefits, including sustainability assessment, clash detection, quantity estimation, and valuable information for the maintenance phase of buildings [41,42,43]. Given these benefits, there is a need for the Nigerian construction industry to increase BIM uptake for sustainable building development.

3. Drivers of BIM in the Building Industry

The lack of proper records and data management is a common problem in the construction sector, which has a detrimental effect on the whole project lifecycle [2]. According to Saka and Chan [44], the construction sector is famously sluggish in adopting new digital technologies, such as BIM, which has stifled the sector’s development and prevented it from keeping up with the times. Adopting BIM provides several advantages to the construction industry, such as simplified project administration and faster turnaround times Olanrewaju, et al. [2]. Furthermore, the study found that process digitalization and economics, construction, visualization and productivity, sustainability and efficiency were the four major drivers of Implementing BIM in Nigeria. Decision-making is facilitated, and construction productivity is increased by using BIM, as indicated by Stransky and Dlask [45]. Similar to what was said by Eastman, et al. [46], BIM enhances communication and coordination within a project’s team. Cost estimate and management are two other areas where BIM has been shown to shine in research [2,47]. The early discovery of design clashes before project execution is one way BIM may save money, as stated by Chahrour, et al. [48]. In addition, it has been recognized as a resource that helps teams work together efficiently and creatively [49,50,51].
Green-BIM, which stands for BIM to Lessen the Environmental Impact of Construction, has also been recognized as an essential instrument in promoting sustainable construction and structures [52,53]. With examples and suggestions for enhancement, Amarasinghe and Soorige [54] showed how BIM might be used in building lifecycle assessment (LCA). Moreover, BIM’s inherent visualization capabilities are a strong incentive for its adoption since it allows the customer to preview their planned building before construction begins realistically. Because of this, the design team has more leeway to incorporate the client’s feedback into the final product [2,46]. To facilitate the visualization and management of issues, Lin and Hsu [55] implemented BIM by way of a web-based application programming interface (API). This exemplifies BIM’s potential for providing early visibility into issues and work progress. Olanrewaju, et al. [2] identified many categories of BIM drivers, and Table 1 provides a review of these drivers as retrieved from the examined literature

4. Research Methods

The literature evaluation served as the basis for developing a testable conceptual model of the research strategy [104]. There are three steps involved in conceptual modeling: (1) identifying the model’s constructs, (2) classifying those constructs, and (3) establishing their connections [105]. The methodology used to explain the model’s outcomes is depicted in Figure 1. In addition, as shown in Figure 1, the research strategy was borrowed from Kineber, et al. [106].

4.1. Construct Validity Analysis

Exploratory Factor Analysis (EFA) was used to categorize the BIM Drivers-related components (Table 2) by reviewing the appropriate literature to determine the crucial BIM Drivers. Validity was assessed using EFA by evaluating the non-dimensionality, reliability, and validity of measurement components for each concept (i.e., the measurement models). Principal Component Analysis (PCA) was favored over competing methods because it is both dependable and conceptually easy to understand [107]. Furthermore, Varimax rotation was employed instead of straight oblimin or Promax because it better distributes the workload among the available variables [108]. In light of this, factor analysis was performed on the 100 completed questionnaires collected from the current study’s 100 participants, using the 35 previously described factors [109].

4.2. Analytical Technique

The SEM method was used to analyze the BIM drivers. Multiple visible and unobservable factors are brought to light by the SEM technique [110,111]. SEM is a powerful method for addressing mistakes caused by variables [112]. In this work, we used the SEM technique to identify the connections between the relevant BIM drivers by looking at how the predefined indicators relate to each build [113]. Byrne [114] said that, in cases where concept analysis methodologies were not strictly adhered to, SEM has lately become a recognized non-experimental study methodology.
Similarly, research published in the MIS Quarterly and cited by Ringle, et al. [115] corroborated the growing popularity of this method over time, and it is also a widely used instrument in the social sciences [116]. A Partial Least Square (PLS) model, encompassing both reflective and formative aspects, has been undertaken to develop the link among BIM drivers based on the purpose of this study. However, the PLS-SEM analysis in this research is broken down into three distinct but interrelated evaluations (measurement model, structural model, and standard method variance) [117].
The Common Methods Variance (CMV) was used to calculate the Common Methods Bias (CMB) [117]. Since data collecting has the potential to bring up trigger issues [118,119,120], CMB seeks to explain the mistake examination outcomes. As a result, it is crucial to recognize these difficulties and issues to recognize any CMV. In light of this, a formal and systematic one-factor assessment [121,122] was used, as proposed by Harman’s analysis (1976). The measurement model provides an improved understanding of the relationship between observations and their interpretation [123]. Convergent validity (which looks at how well different measures agree with one another) and discriminant validity (which investigates how well different concepts are measured against one another) can be used to decide whether or not to use the measure [124,125].

4.3. Questionnaire Design and Data Collection

The questionnaire survey was designed on a five-point Likert scale to obtain data from professionals knowledgeable about BIM in the Nigerian construction industry.

4.3.1. Target Population

Due to the limited schedule, financial constraints, familiarity with the area and ease of data collection, this research focused on the Ondo State construction industry. The target population will comprise professionals in the construction industry, viz, Quantity Surveyor, Architect, Engineers, and Project Manager. For the uniqueness of this study in terms of numbers, the census method was adopted. The census method was appropriate for this study because the total number of all proposed respondents is manageable.

4.3.2. Sample Frame

This is the primary material or device from which a sample is drawn. It lists all population members who can be sampled and may include individuals, householders or institutions (Wikipedia). The sample frame in this research is the professionals in the construction industry working within Ondo state.

4.3.3. Sampling Techniques

Sampling techniques involve the selection of a section of a population in order to define the characteristics of the entire population. There are two sampling techniques: non-random sampling (also known as non-probability sampling) and random sampling (also known as probability sampling). In the non-random sampling method, the samples are gathered in a manner that does not afford all the members of the population equal opportunity of being included in the sample, while in the random sampling method, all the members of the population have an equal opportunity of being included in the sample. This study adopted non-random sampling techniques. The reason for choosing this method is that respondents are picked based on convenience concerning their availability, accessibility, proximity and by other means decided by the researcher.

5. Results

5.1. EFA Analysis

Factor analysis was also used to analyze the significant drivers of the acceptance of BIM knowledge in Nigeria’s building industry. This analysis explored and detected the relationship among variables and categorized the factors concisely and comprehensively. The data obtained passed Bartlett’s Test of Sphericity for enough correlation between the variables, and the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy indicated that the data might be used for factor analysis. Bartlett’s Test of Sphericity will provide a positive result if the data or the sampling is suitable for factor analysis. KMO was used to conduct the sampling adequacy test, and the findings suggested that 81.6% of the data gathered met the criteria for factor analysis.
Bartlett’s test is highly significant (p-value = 0.000), suggesting that the correlation is an identity matrix. This means that the correlation matrix of all the items listed has a significant correlation at the 5% level and thus, exploratory factor analysis is suitable for the data (degree of freedom = 69, approximate chi-square = 1276.7).
The rotatable component matrix shows the 32 factors impacting the widespread use of BIM in Nigeria’s building sector. The elements mentioned above have a significant association of 9 levels in the correlation matrix, indicating the viability of employing EFA. In the Nigerian construction business, BIM is driven by a model of nine different applications. In Table 2, we can see that the factors have been sorted using the varimax rotation and that each variable significantly impacts every application. After omitting uncorrelated drivers (D2, D16, D21 and D31) due to low loading, the six extracted components were named as follows: Standards, Knowledge, Software, Legalization, Management, and Training.

5.2. Common Method Bias

When attempting to depict the error variance connected to the measured variables, common technique bias is a variance calculation that can affect the reliability of the study [117,126]. Single-factor analysis [127] was used to determine the traditional technique variance in the suggested model. Common technique bias is shown to have no effect on results when the overall variation of variables is less than 50% [121]. According to the results, the first group of variables accounts for 22.53% of the total variation; as a result, the standard deviation of the results is too little to have any effect [121].

5.3. Measurement Model

The measurement model defines the link between the elements and their latent construct as of this measurement point [123]. The PLS-SEM method requires analyzing discriminant and convergent validity [128] for the reflected measurement items (BIM drivers).
Two or more measurements (BIM drivers) of the same construct are discussed regarding their degree of coherence and organization [124,129]. A subset of construct and convergent validity may be evaluated using the reliability index. There are a few different tests that may be used in PLS-SEM to get a rough idea of the convergent authenticity of the suggested constructs [130]: Average Variance Extracted (AVE), Cronbach’s alpha ( α ) and composite reliability scores ( ρ c ).
Every one of the (BIM drivers) in Table 3 has a composite dependability of 0.60 or above and is therefore accepted [37,38]. On the other hand, Table 3 demonstrates that the Cronbach alpha was 0.60, indicating moderate to high dependability in line with the recommendations of Perry, et al. [131]. In addition, the AVE was used to examine the convergence of the construct variables, and its calculation is as follows [130,132]:
A V E = ∑ λ i 2 ∑ λ i 2   + ∑ var ( ε i )
where A V E is the average variance extracted; λ i is the component loading of each item to a latent variable, and var ε i = 1 − λ i 2 . AVE values above 0.5 are considered acceptable [130]. As a result, the measurement variables explain at least 50% of the measurement variation [133]. All the estimated AVE values (Table 3) for the various constructs in this investigation are greater than 50%, as calculated using the PLS 3.0 program. Results such as this prove that the measurement model is internally consistent and convergent. This means that no additional constructs in the study model are quantified by the measurements used to evaluate each construct. Hulland [124] argues that an external load value of 0.70 is optimal but that a value of 0.40 or higher is acceptable, provided the analysis performed is explanatory. Figure 2 shows the initial PLS model with no loading less than 0.500. The loading is significant because it affects the reliability of the final model.
Discriminatory validity testing is becoming increasingly important in the SEM research process [134,135]. It is designed to verify that the examined notion is empirically different or unique [125]. Throughout this research, discriminant validity is examined in terms of a set of methods:
(a)
Fornell–Larcker criteria;
(b)
Hetrotrait–Monotrait Criterion Ratio (HTMT).
Table 4 shows that the BIM benefits constructs are accepted and authorized based on the Fornell and Larcker criterion, where the square root of the AVE needs to be greater than the correlation between the build indications and variables [130,136].
The second technique is Hetrotrait–Monotrait Criterion Ratio (HTMT). To evaluate the discriminative validity of variance-based SEMs, HTMT estimates the precise correlation between the two constructs, assuming they were measured correctly. According to Hair, et al. [125], if the HTMT score is below 0.85 or above 0.90, the two buildings are not interchangeable. If the model’s constructors are highly conceptually similar, then the HTMT value should be less than 0.90, and if they are highly conceptually distinct, it should be less than 0.85. The HTMT values for all investigated factors are shown in Table 5. Therefore, the results have shown sufficient discriminating validity.

5.4. Structural Model Analysis

The analysis’s primary purpose was to ensure the viability of the suggested curriculum. Using the p-value and outer weight (β) at the 95% confidence interval (CI0.95) [137,138], this method examines the robustness and statistical significance of the original dataset selection and, by extension, the observed path coefficient. To ensure the accuracy of the calculated path coefficients [139], a bootstrapping process is used to randomly resample the original data set, creating fresh samples of the exact size as the initial data set [19]. An indicator of the significance of one construct’s influence on another, the route coefficient is represented by the value shared by each path [140]. We examined the endogenous construct’s route importance by calculating the standardized path coefficients (β) and p-values (Figure 3). Table 6 and Figure 3 both displayed bootstrapping findings. Figure 3 shows the significance of each of the drivers group on BIM adoption for sustainable building development graphically.

6. Discussion

The research explored 32 underlying strategies for overcoming obstacles to implementing BIM in Nigeria’s construction industry; these were further categorized into 23 by ranking. From this study, providing continuous employee training about BIM, more technical professionals should be encouraged and established, and a strategic initiative to drive transformation in the construction industry through the use of information modeling were the three highest rank strategies to aid the adoption of BIM technology in the construction industry. By providing consistent training on BIM technology’s nitty-gritty, construction professionals will find it easy to use BIM over traditional methods. Consistent encouragement of technical professionals will have a significant impact on influencing construction professionals. However, Aka, et al. [141] established three techniques, which are to form a BIM institute for the development of young graduates, deter customers from the old ways of construction via cost, and develop BIM execution enforcement bodies, which he later simplified into one entity for affordability reasons. The three were consolidated into one: regulatory organizations charged with enforcing the use of BIM. In addition, he concluded that the Nigerian government should establish a BIM institute to train recent graduates in the field for at least six months, ensure that the endorsement process for BIM projects is more cost-effective and time-efficient than the conventional approach, and mandate the use of BIM for large-scale construction projects. It is essential to urge customers and other parties involved in construction to make BIM adoption a standard requirement for all projects. The adoption and implementation of BIM will be enhanced by the support of the clients, contractors, and government [142,143]. This claim is valid in several international settings, including Singapore, Norway, Denmark, and Finland. For instance, several sizeable Norwegian construction clients insist on using open-format BIM for most of their projects [43]. There is a need to provide information about BIM to construction businesses working in developing nations such as Nigeria to increase its acceptance and implementation there. This will increase awareness of BIM’s benefits and the technical expertise required for its adoption [141]. Spreading this knowledge would require concentrated hard work and incorporating professional bodies, industry, and academia [43].
BIM awareness may be increased by fostering a vibrant research community and federal building authorities focusing on BIM expansion nationally [144]. Further, Ahmed and Kassem [41] provided a catalogue and a collection of drivers/determinants that BIM experts may use to conduct multiple studies of the BIM implementation process, providing evidence and insights to decision-makers across various industries. Conversely, working together on a project may boost innovation and make it easier to make choices. If you want to choose how to incorporate and manage technology in your field, you need to have a clear vision for the future. To improve students’ access to and equity in IT, institutions can benefit from acquiring BIM knowledge and generating funds by fostering excellent relationships with the community, public, business sector, and alumni [42]. There is a need to provide young software developers with the proper training and resources to create BIM-related software that can be used nationally, establishing BIM standards to checkmate the construction industry. In Singapore, industry foundation classes (IFC) were adopted as the standard for BIM implementation. This body is responsible for transforming the industry in the country by establishing a standard to checkmate the industry.

7. Conclusions

BIM is critical to efficiently delivering sustainable buildings, and developing countries, such as Nigeria need to increase BIM uptake. This study explored the drivers of BIM adoption for sustainable building development using EFA and PLS-SEM. The EFA revealed six unique BIM drivers for sustainable building developments: Standards, Knowledge, Software, Legalization, Management, and Training. The PLS-SEM also showed that the standard-related driver category strongly impacts BIM adoption. Currently, many developing countries such as Nigeria lack critical BIM standards that govern the use of BIM. This is due to the lack of government support for BIM implementation in many developing countries.

7.1. Conceptual and Empirical Contributions

Particularly in emerging nations with many unknowns, the created model highlights the necessity of BIM implementation drivers. The model emphasizes the essential factors influencing BIM adoption. By capitalizing on these factors, policymakers and other government institutions may develop a strategy to increase BIM usage in the AECO sector. Firstly, the study analyzed all the primary factors that encourage BIM implementation in the AECO industry. This lays the groundwork for further research into the factors that encourage BIM adoption in the AECO sector, particularly for sustainable building development. As a result, the theoretical framework developed in this study may be used to improve BIM acceptance in Nigeria and other developing countries by pinpointing the specific drivers of BIM implementation that are most crucial to these countries. The research also makes several significant theoretical and practical advances, including:
  • While there are several pieces of research on BIM deployment in industrialized nations, there is little on the topic in Nigeria [32]. The present research fills this void by analyzing the key factors influencing the widespread adoption of BIM;
  • The study’s model represents the first predictive model to be built in the construction industry to quantify the impact of drivers for BIM use for sustainable building development in the AECO sector. Hopefully, this resource will accelerate the spread of BIM in underdeveloped nations. This approach is empirical since it seeks to achieve what no other researchers have done: investigate the theoretical links between the various components that make up the “BIM implementation drivers”.

7.2. Managerial Implications

Building industry experts may maximize their effect by gaining insight into the below managerial implications as they get an appreciation for the factors that drive BIM implementation:
  • It offers AECO company’s critical drivers that can be implemented to deal with the problems and obstacles connected with BIM adoption, leading to greater client satisfaction due to higher-quality visualization;
  • It facilitates choice-making by analyzing the effects of BIM drivers throughout the project’s lifecycle.

7.3. Limitations and Areas for Future Studies

The following are the limitations of this study and areas for future research:
  • The sample size used for the study is small. Future studies should include more respondents to the survey in order to improve the generalization of the research;
  • In terms of geographical scope, the work is limited to Nigeria only. Future research can target other developing countries in Africa to examine the drivers for BIM adoption because different countries may have different motivators;
  • This study also showed that standards-related drivers significantly affect BIM adoption for sustainable developments compared to other categories of drivers. Future studies could focus on addressing the BIM standards gaps in developing countries.

Author Contributions

Research Idea: A.F.K. Conceptualization, A.F.K., A.E.O. and I.O.F.; Methodology, A.F.K.; Software, A.F.K.; Validation, I.O.F., I.O., A.F.K., A.E.O., O.I.O., M.M.H. and T.M.O.; Formal Analysis, A.F.K.; Investigation, T.M.O.; Resources, A.E.O. and I.O.F.; Data Curation, A.F.K.; Writing—Original Draft Preparation, A.F.K., O.I.O. and M.M.H.; Writing—Review and Editing, I.O.F., I.O., A.F.K., A.E.O., O.I.O., M.M.H. and T.M.O.; Visualization, A.F.K. and M.M.H.; Supervision, A.F.K., A.E.O. and I.O.F.; Project Administration, A.E.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received funding from the YUTP, 1/2021 (015LC0-367).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to express their utmost gratitude to the YUTP, 1/2021 (015LC0-367) for funding this research, and to the University Tecknologi PETRONAS.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Research design.
Figure 1. Research design.
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Figure 2. The PLS initial model.
Figure 2. The PLS initial model.
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Figure 3. Path analysis.
Figure 3. Path analysis.
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Table
Table 1. Major Drivers of BIM Technology Adoption.
Table 1. Major Drivers of BIM Technology Adoption.
S/NDrivers for BIMReferences
D1A strong comprehension of the BIM procurement process[56,57,58]
D2Create a BIM Technology for construction policy[26,59]
D3Encourage stakeholder cooperation in the building sector in Nigeria[2,60]
D4Encouragement should be given to aspiring software engineers[61,62]
D5Accept the BIM specifications for the supply chain for construction[58,63]
D6The software packages chosen should work together[20,64]
D7Provide continuous employee training about BIM[65,66]
D8More technical professionals should be encouraged[67,68]
D9Adequate propagation of BIM knowledge to the construction firms in Nigeria[26,27]
D10Developing the construction industry’s BIM perspective[69,70]
D11Projects are carried out in an integrated manner[71,72]
D12Strengthening the legal environment for BIM adoption in the construction industry[73,74]
D13Using cloud computing to develop locally optimized software and standards[75,76]
D14Use of supply chain process and advanced procurement for designs[77,78]
D15Use of a scientific approach[79,80]
D16Regulation of BIM usage by the government[81,82]
D17Increase cooperation between the public and commercial sectors in implementing BIM[83,84]
D18Promote stakeholder cooperation[39,40]
D19Supply chain and BIM may work together if the BIM elements are correctly integrated.[85,86]
D20People management is key to implementing BIM[87,88]
D21Standardize the BIM process and define the procedure for its utilization[89,90]
D22Organize adequate seminars for proper understanding and interpretation of BIM[26,91]
D23Certain training to implement the latest BIM equipment[92,93]
D24Commitment through the investment of BIM[94,95]
D25Identification of the type of group and the software to use[96,97]
D26Given Proper training for BIM users[92,93]
D27Align manufacturers of BIM applications to simplify their concept[94,98]
D28Consistent publication of practices and skills necessary for BIM adoption strategy adoption[26,27]
D29The evolution of BIM standards on a national and international scale[98,99]
D30BIM accreditation[99,100]
D31Instruction and the presentation of a rationale for implementing BIM[26,101]
D32Established a strategic initiative to drive transformation in the construction industry by the use of information modeling[102,103]
Table
Table 2. Related components of the construction activities.
Table 2. Related components of the construction activities.
Drivers12345678
D10.762
D2 * 0.379
D3
D4 0.539
D5 0.753
D6 0.594
D7 0.545
D8 0.834
D9 0.644
D10 0.602
D11 0.626
D12 0.590
D13
D14
D15 0.667
D16 * 0.351
D170.581
D180.565
D190.597
D20 0.673
D21 * 0.353
D22
D23 0.614
D24
D25 0.633
D26
D27 0.757
D280.575
D29 0.532
D30 0.639
D31 * 0.394
D320.500
* Deleted drivers due to low loading.
Table
Table 3. Constructs reliability and validity analyses.
Table 3. Constructs reliability and validity analyses.
ConstructsCronbach’s AlphaComposite ReliabilityAverage Variance Extracted (AVE)
Training0.7700.7370.584
Knowledge0.7950.8280.617
Legalization0.7570.7630.526
Management0.7550.8120.591
Software0.7430.8060.581
Standards0.7960.8600.551
Table
Table 4. Discriminant validity analysis (Fornell–Larcker).
Table 4. Discriminant validity analysis (Fornell–Larcker).
ConstructsTrainingKnowledgeLegalizationManagementSoftwareStandards
Training0.696
Knowledge0.5860.785
Legalization0.4620.3010.725
Management0.5210.3440.5010.769
Software0.5110.3510.3180.4000.762
Standards0.6100.4430.4480.5720.5370.742
The roots of AVE are shown in bold.
Table
Table 5. Discriminant validity (HTMT).
Table 5. Discriminant validity (HTMT).
ConstructsTrainingKnowledgeLegalizationManagementSoftwareStandards
Training
Knowledge0.800
Legalization0.7540.470
Management0.7290.4960.806
Software0.7100.4990.4880.586
Standards0.7840.5800.6220.7830.739
Table
Table 6. Relative path for the model.
Table 6. Relative path for the model.
PathsBSDT Statistics (|O/STDEV|)p Values
Training -> BIM Implementation Drivers0.1870.0277.0100
Knowledge -> BIM Implementation Drivers0.1900.0424.5650
Legalization -> BIM Implementation Drivers0.1580.0384.1860
Management -> BIM Implementation Drivers0.2100.0464.5870
Software -> BIM Implementation Drivers0.1900.0335.7430
Standards -> BIM Implementation Drivers0.3830.0448.6740
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MDPI and ACS Style

Famakin, I.O.; Othman, I.; Kineber, A.F.; Oke, A.E.; Olanrewaju, O.I.; Hamed, M.M.; Olayemi, T.M. Building Information Modeling Execution Drivers for Sustainable Building Developments. Sustainability 2023, 15, 3445. https://doi.org/10.3390/su15043445

AMA Style

Famakin IO, Othman I, Kineber AF, Oke AE, Olanrewaju OI, Hamed MM, Olayemi TM. Building Information Modeling Execution Drivers for Sustainable Building Developments. Sustainability. 2023; 15(4):3445. https://doi.org/10.3390/su15043445

Chicago/Turabian Style

Famakin, Ibukun O., Idris Othman, Ahmed Farouk Kineber, Ayodeji Emmanuel Oke, Oludolapo Ibrahim Olanrewaju, Mohammed Magdy Hamed, and Taiwo Matthew Olayemi. 2023. "Building Information Modeling Execution Drivers for Sustainable Building Developments" Sustainability 15, no. 4: 3445. https://doi.org/10.3390/su15043445

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