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Optimal strategies for improving organizational BIM capabilities: PLS-SEM approach
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Praveena Munianday,
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Postgraduate Student, School of Housing, Building and Planning, Universiti Sains Malaysia, 11800
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Penang, Malaysia. Email address: praveenamuniandy20@gmail.com
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Afiqah R. Radzi,
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Postgraduate Student, Faculty of Built Environment, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
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Email address: nurafiqah279@gmail.com
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Muneera Esa, Ph.D.,
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Senior Lecturer, School of Housing, Building and Planning, Universiti Sains Malaysia, 11800 Penang,
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Malaysia. Email address: muneera_esa@usm.my
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Rahimi A. Rahman, Ph.D.,
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Senior Lecturer, Faculty of Civil Engineering Technology, Universiti Malaysia Pahang, 26300 Kuantan,
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Malaysia. Email address: arahimirahman@ump.edu.my (corresponding author)
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ABSTRACT
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Building information modeling (BIM) is an emerging approach to managing the design, construction,
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operation, and maintenance of projects. However, the lack of organizational BIM capabilities prevents BIM
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from being generally adopted across the architecture, engineering, and construction (AEC) industry.
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Therefore, AEC organizations must develop strategic plans to support BIM implementation and ensure that
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the anticipated benefits of BIM are realized. This study identifies the underlying factors and strategies
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related to organizational BIM capabilities and develops a structural equation model to establish their causal
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relationships. A systematic literature review of 26 articles and semi-structured interviews with BIM
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practitioners provided 19 factors and 14 strategies. A total of 121 BIM practitioners evaluated the criticality
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of the factors and strategies through a survey. The collected data were analyzed using the Kruskal–Wallis
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test, exploratory factor analysis (EFA), and partial least-squares structural equation modeling (PLS-SEM).
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The factor analysis classified the factors into two groups: organizational BIM capabilities and
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organizational capabilities; and strategies into three groups: capability requirement, organizational culture,
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and organizational competitiveness. The structural equation model revealed the organizational culture to
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positively affect both organizational and BIM capabilities. Moreover, organizational competitiveness is
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shown to positively influence organizational capabilities. These results provide evidence for the
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development of strategies for implementing BIM. Industry practitioners may use these strategies to develop
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strategic plans and prioritize efforts in a more effective manner. With the findings of this research, users
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will have a better understanding of the relationships between factors and strategies that are associated to
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organizational BIM capabilities.
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INTRODUCTION
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In recent years, architecture, engineering, and construction (AEC) organizations have increasingly focused
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on Building Information Modelling (BIM). According to the National Institute of Building Sciences (NIBS
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2021), BIM can be defined as “a digital representation of physical and functional features of a facility.” In
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the last decade, BIM has emerged as a primary tool for managing buildings throughout their lifecycles.
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From the conception to deconstruction of a facility, BIM is an information resource that can be shared and
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used for informed decision-making. It has addressed many previously unsolved issues, with computer-
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based solutions for building maintenance (Alvanchi et al. 2021). In the United Kingdom (UK), the industry-
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wide use of BIM increased from 13–74 % between 2011–2018 (National Building Specification 2018).
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Similar trends could be observed in developed countries such as the United States (US), Europe, and
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Australia (Charef et al. 2019). Despite the popularity of BIM, its implementation has been modest in other
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countries, because of diverse concerns (Ahuja et al. 2020).
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However, this surge is distributed over a wide range of usage levels (Sacks et al. 2018). A recent
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study reported that BIM adoption is still primarily limited to Levels 0–1, with only a limited number of
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practitioners utilizing fully integrated and interoperable BIM systems (Oraee et al. 2019). Moreover, BIM
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is not widely used in many developing countries, applied primarily for low maturity level tasks, such as
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visualization and clash detection (Chan et al. 2019). Efforts have been undertaken to understand the reason
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for the difficulty in the implementation of BIM. According to a previous study, the attitudes, technological
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and managerial obstacles, and environmental impediments, differ in different AEC contexts (Charef et al.
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2019). Therefore, there appears to be no consistent BIM adoption route. The integration of BIM with
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different environments is, thus, a continuous process (Wang and Lu 2021).
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This knowledge gap can be filled by identifying the underlying factors and strategies related to
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organizational BIM capabilities, and developing a structural equation model to establish their causal
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relationship. This study aims to identify: (1) the underlying factors impacting organizational BIM
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capabilities; (2) underlying strategies for improving organizational BIM capabilities; and (3) the
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relationships among these factors and strategies. A systematic review of 26 published studies and semi-
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structured interviews with 15 BIM practitioners, revealed 19 factors that influenced organizational BIM
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capabilities, and 14 strategies for improving organizational BIM capability. The BIM practitioners were
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provided the option of evaluating the criticality of the factors and strategies. The data acquired were
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analyzed using the Kruskal–Wallis test, exploratory factor analysis (EFA), and partial least squares
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structural equation modeling (PLS-SEM). This study contributes to an elaborate understanding of the
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elements that influence organizational BIM capabilities and strategies for improving them. The results of
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this study will be helpful to AEC organizations and industries (e.g., vertical and horizontal), and individual
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businesses. Additionally, the results can assist policymakers, industry stakeholders, and governments, in
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identifying the optimal methods for effective BIM implementation.
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BACKGROUND INFORMATION
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Factors Affecting Organizational BIM Capabilities
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Varying working conditions constitute a major obstacle for BIM adoption. Highly qualified or competent
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professionals adopted the new technology with ease and learned how to utilize it (Sargent et al. 2012).
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Succar et al. (2013) reported that traditional education with or without a degree has historically concentrated
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on acquiring theoretical knowledge. In the implementation of new technology, a person's attitude toward
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the advancement determines their risk acceptance level. As a result, many AEC professionals, especially in
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developing countries, express concerns about BIM use. Many perceive BIM as a "disruptive technology"
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that threatens conventional construction procedures. Meanwhile, a person's individual BIM competencies
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is dependent on their personal qualities, professional knowledge, and technical abilities necessary to
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integrate a BIM activity or produce a BIM-related output. These include the individual BIM competencies
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distinct to a person, irrespective of their job, and not the competencies of groups, organizations, or teams.
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They may be professionals, tradesmen, academics, or students from any specialty. Furthermore, the lack of
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collaboration among industry professionals and groups has led to a poor understanding of the BIM process
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and interoperability issues (Oraee et al. 2019). Most BIM adoption frameworks have not entirely examined
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the soft human behavioral or organizational variables that impact the BIM implementation competency,
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despite evidence that specify these aspects to be crucial in BIM delivery success (Haron et al. 2015).
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Moreover, a recent study showed BIM delivery success to be significantly influenced by the staff
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experience. In addition to qualification being an essential factor affecting BIM adoption, competence-
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related factors could be the greatest contributors to total delivery success (Mahamadu et al. 2019b). Using
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an organization's BIM experience as a major criterion, aligned with contractor and consultant selection
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theories, prior performance was emphasized as an essential factor (Mahamadu et al. 2019a). Existing
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frameworks for measuring competence, generally focus on process maturity or the availability of technical
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infrastructure, instead of historical indications (Chen et al. 2014). Prior expertise with BIM has emerged as
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the most important qualification required in the pre-qualification and selection environment.
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BIM implementation involves a sophisticated procedure that necessitates the use of expert technical
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capabilities. Information technology (IT) professionals are essential for the appropriate selection of
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hardware, installation of software, and for providing a continuous support toward BIM implementation. In
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the context of a project, the diversity of BIM software used raises the problem of data interoperability.
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Therefore, professional assistance in resolving important issues may ease BIM implementation. Based on
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this, the competence of a professional or an organization can be assessed by the amount of professional
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assistance obtained in hardware/software selection and BIM implementation. The inadequacy of technically
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trained workforce would impede BIM implementation (Ahuja et al. 2020; Chan et al. 2019). In contrast,
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Qin et al. (2020) reported that the number of BIM specialists and technical personnel exerted a minimal
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influence adoption of BIM. Instead, it had a significant impact on the workflow and pattern of the
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companies and the human aspects of the (such as perceived ease of use, perceived usefulness, and intention
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to use).
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Managers proficient with BIM ought to lead their teams in the analysis of unverified design data
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and the verification of the shared data within the project team. In the event of a modification, the leader
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must create an environment where its impact is minimal. Additionally, influential, and motivational skills
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may assist BIM leaders in fostering a collaborative work atmosphere and overcoming resistance to data
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sharing (as a BIM application obstacle). Instead, collaboration may assist overcome negativity towards BIM
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adoption. It is also possible to motivate subordinates to change by demonstrating the benefits of BIM
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(Mirhosseini et al. 2020). The organizational culture of a corporation in facilitating learning is also crucial
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for encouraging positive attitudes toward new technologies (Alsabbagh and Khalil, 2017). A positive
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attitude toward technology can improve learner acceptability levels, a critical aspect in the effective
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adoption of new technologies (Yoo and Han, 2013). Therefore, a positive corporate culture may help
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organizations gain a competitive advantage in adopting new and essential knowledge about the required
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competencies and values for BIM.
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Utility acceptance is an essential organizational intent for actual implementation. In a previous
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study, the level of organizational acceptability was determined by identifying the process through which an
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organization was willing to adopt, implement, or urge other organizations to use BIM (Lee et al. 2015). The
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first step toward learning BIM is a shift in attitudes and willingness to expend time and effort in learning.
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Even if all the organizational personnel fail to entirely understand BIM's technical design processes, they
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should be aware of specific BIM applications. It would allow them to create meaningful data that might
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help them in their everyday work. The AEC industry's organizational structure and processes are generally
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fixed, with each project stakeholder performing their respective tasks. To a certain extent, BIM
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implementation is impeded by this difficulty in changing the organizational models, processes, roles, and
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work content, inside the organization. For current organizational models and workflows, BIM offers limited
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advantages in deployment, which generally entails process-related and organizational task modifications
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during integration (Arayici 2011).
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The commitment of senior management is an essential factor for the successful adoption of BIM,
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as it is considerably influenced by corporate executives (Lee et al. 2015). High-ranking authorities play a
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vital role in addressing technology-related challenges, by introducing changes to job profiles, duties, and
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resolving conflicts of interest. Before deploying a new technology, such as BIM, senior executives require
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to be educated on its merits and hazards. If an organization's policy supports BIM, organizational support
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becomes natural. Succar et al. (2013) illustrated that the senior management support, such as training and
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encouraging employees to utilize BIM in their daily tasks, is important for BIM adoption.
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BIM standards are essential documentations for BIM implementation, which refer to processes that
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must be improved and enhanced over time. These documentations describe the requirements for a
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standardized procedure for creating, maintaining, and disseminating construction data, using BIM. To
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ensure the utility of information throughout the project life cycle, open systems and standardized data must
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be developed. Governments, including the Malaysian government, publish documents to ensure a consistent
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approach to BIM implementation in public projects. Whereas such standards or guidelines are typically
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attributed to public projects, additionally, organizations can develop BIM standards for private projects,
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which are compatible with most organizations within the industry. Such steps include organizing for BIM
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implementation; developing information exchange-capable systems; establishing modeling guidelines,
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standards, and best practices (possibly through successful pilot projects); and promoting, liaising, and
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presenting BIM initiatives with other stakeholders (Wong et al. 2010).
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In addition, financial support for early set-up costs is crucial for BIM adoption, especially for small
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and medium-sized businesses. Therefore, the senior management of any organization ought to be prepared
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to monetarily support the continued development of BIM in their organizational operations. Projects
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involving BIM application typically involve several offices and locations, with teams working in silos and
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pursuing their distinct interests (Oraee et al. 2019). Further, BIM-authoring software may present additional
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technological challenges. Therefore, BIM leaders must build and maintain strategic partnerships with their
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BIM-authoring software suppliers, consultants, contractors, and the external BIM community. A strategic
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viewpoint is required for smooth transitions, when changes occur during the BIM implementation. This
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may be performed by progressively expanding the engagement of its members in change activities, such as
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planning and decision-making, through time. Moreover, the stakeholders require a strategy for utilizing
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their acquired information and lessons. While approving, authorizing, and verifying BIM-based information,
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change management is necessary (Mirhosseini et al. 2020). An organization’s investment in BIM research
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and development is a positive indication of its potential for using the technology (Succar 2010).
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Software, hardware, and data/networks are the different elements of the technology-related
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equation. It is possible to migrate from drawing-based workflow to object-based workflow using a BIM
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tool (BIM Stage 1 requirement). It comprises resources, activities/workflows, products/services, leadership,
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and management. For example, a model-based collaboration requires cooperation methods and database
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sharing skills (BIM stage 2 requirement) (Succar et al. 2012). This may significantly influence the
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organizational BIM capabilities. Organizations can self-assess or rely on recommended criteria for internal
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benchmarking for performance management or assessment of their suitability to tender for projects, based
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on the weighted priority of qualifying criteria (Kam et al. 2014; Mahamadu et al. 2017; Succar 2009). This
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enables organizations to monitor their position with respect to their BIM capabilities and their areas of
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improvement. A major obstacle to BIM adoption is the cost of implementation. The knowledge of the area
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of focus for investments in BIM capacity building can assist in optimal adoption (Barlish and Sullivan
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2012). Organizations can improve their BIM capabilities by concentrating on specific BIM targets, derived
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from a previous benchmarking aspect.
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Lastly, the standard process of evaluating BIM capability, based on the evidence of contribution to
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successful project delivery in practice, is extremely important. The importance of BIM performance and
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BIM capability must be acknowledged to be an essential part of BIM execution plans (BEPs). Thus, the
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contributions of distinct BIM capacity elements inside an organization and their influence on the various
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aspects of BIM delivery success can be assessed appropriately in the future. As part of the pre-qualification
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and selection process, the importance of prioritizing the standard process of evaluating BIM capability,
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based on its contribution to project success must be acknowledged through standards, such as the UK
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Publicly Available Specifications (PAS) (Mahamadu et al. 2017).
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Strategies for Improving Organizational BIM Capabilities
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Standardization. The AEC industry implements a variety of standards and technological procedures, such
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as interoperable programs and methods of exchanging information, to encourage the creation of integrated
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teams. The coordination techniques across project teams, and the standardization of building components
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and associated characteristics, are important for improved outcomes during BIM implementation (Eastman
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et al. 2011). Therefore, the standardization of BIM rules and processes is required to ensure successful BIM
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implementation (Azhar and Olsen 2011). Additionally, the development of BIM-related technical processes
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and standards can ensure a highly smooth collaborative environment with fewer issues than before. Thus,
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the senior management of an organization ought to provide a clear organizational strategic plan to improve
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BIM capabilities.
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Policy. BIM policy is considered to be one of the most important influencing factors in BIM deployment.
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Existing practices and survey data show that the AEC industry is still dependent on conventional working
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procedures, with the absence of BIM in the contractual environment. To ensure complete BIM
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implementation, an effectively defined policy must be established at both macro and micro adoption levels.
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The introduction of BIM gradually in the contractual context is essential (Eastman et al. 2011). In addition,
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to apply BIM on construction projects, policies that offer a clear vision about the project delivery techniques,
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the quality of procedures, and the consistency of information throughout the AEC organizations, ought to
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be adopted in general (Kassem et al. 2014). The identification of policy as a crucial conceptual component
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of BIM operations is another perspective (Succar 2009). Therefore, organizations must additionally develop
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internal BIM policies to improve their BIM capabilities.
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Training and Education. AEC industry stakeholders come from a wide range of cultural and racial
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backgrounds, which greatly diversifies their BIM experiences. Therefore, AEC organizations should devise
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less steep learning curves for BIM practitioners (Azhar and Olsen 2011). Furthermore, effectively designed
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training and education programs assist in upskilling employees and expanding their knowledge of BIM
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ideas and technologies (Ahn et al. 2016). Individual characteristics, training intervention design and
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delivery, workplace contextual factors, and training performance evaluations, can be considered as the
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major categories of training and education (Gegenfurtner et al. 2009). In addition, evaluations should be
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based on the trainee learning outcomes, behavioral reactions and expectations on their experience of change
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from the training programs, and the extent of work performance improvement resulting from new
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information and skills. Comprehensive training and education are crucial for meeting and exceeding end-
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user demands and supporting a long-term emphasis on continuous development (Peansupap and Walker
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2006). Considering that BIM is a recent technology, there would be industry participants with varying levels
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of expertise, leading to results with varying quality. To maximize BIM performance, organizations and
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vendors should work together to develop methods to facilitate easier BIM training and learning for new
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hires (Azhar 2011). In addition, training programs could be designed to satisfy various requirements,
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ranging from global and standard, to specific and advanced (Singh et al. 2011).
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Motivation. Adriaanse et al. (2010) highlighted the importance of personal and external motivating
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elements to incorporate information and communications technology (ICT), such as BIM, in the AEC
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environment. Personal motivation may be defined as the degree of eagerness of people to advantageously
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adopt new technology. In construction, motivation is determined by the perceived advantages and
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disadvantages of a technological application, in addition to satisfying strict deadlines and maintaining a
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short-term working relationship (Green et al. 2005). The existence of contractual agreements for BIM
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adoption and the presence of a seeking stakeholder are examples of external motives (Adriaanse et al. 2010).
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This indicates the impact of AEC rivals, collaborators, and other stakeholders. Moreover, creating a
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learning-friendly atmosphere is essential for successful BIM deployment. To enunciate environments where
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workers feel free to experiment and take risks, learning-oriented organizations aim for the presence of
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connected practices and attitudes inside an organization that enable individuals to build their abilities and
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learning(Klein and Knight 2005). Reflective learning during BIM organizational transformation is a type
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of "deconstruction" (a different method of performing a task) and "reconstruction" (correcting something
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for the better) (Kokkonen and Alin 2016). Employees can easily find BIM implementation efficiencies
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through a learning-by-doing approach (Arayici 2011). This strategy is a unique approach to improving
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organizational BIM capabilities.
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Cultural readiness. There may be opposition to the introduction of BIM. Effective communication is
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crucial, allowing people to experience involvement in the implementation process, while simultaneously
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being educated about organizational practices, expectations, and goals (Ahn et al. 2016). Organizational
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cultures that embrace change and possess a unified set of values and goals are highly likely to implement
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new initiatives. A change management program is critical when implementing BIM to avoid resentment
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among employees (Arayici 2011). Prospective clients should develop and maintain a positive mindset
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before BIM adoption begins. This indicates that controlling the organization's preparedness for change is
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essential to deploy BIM successfully (Khosrowshahi and Arayici 2012). Managers must also include users
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as early as feasible. The input of BIM users should be handled to gather their requirements, remarks,
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responses, and approval (Arayici 2011). Implementation leaders must identify and analyze the causes of
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objections to BIM tools and systems and effectively drive consensus throughout the implementation process
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(Lee et al. 2015). Change agents also play essential roles in building skills and abilities on behavior
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modification, transform attitudes, and actions, toward BIM tools and ideas (Succar et al. 2013).
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Network relationships. Problems related to information exchange, integration, IT systems, and software,
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require the organizations driving the implementation process to work collaboratively with external vendors,
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consultants, supply chain partners, and internal divisions (Arayici 2011). Whereas most AEC organizations,
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especially small and medium-sized (SME), lack the in-house knowledge or resources to cope with BIM
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implementation, it is critical to access high-quality external consultants and software suppliers. Software
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providers may also serve as consultants on occasion. During BIM implementation, it is critical to establish
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long-term connections with external entities and supply chain partners. Maintaining tight and trustworthy
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contacts with other organizations with a wealth of BIM-related knowledge provides opportunities to gain
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expertise in BIM applications (Abbasnejad et al. 2020).
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Process and performance management. To help comprehend the BIM implementation processes,
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businesses may use a BIM maturity model that enables the determination of an organization’s maturity goal.
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These tools may be used for various purposes, from determining preparedness to gauging an organization's
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capabilities, and conducting internal benchmarking. While maturity models and tools vary in breadth and
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application, those with effectively defined phases can serve as a roadmap to help organizations progress
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toward higher maturity states. Models and methods for maturity evaluation may be divided into three main
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categories: project-oriented, similar to the virtual design and construction (VDC) Scorecard (Kam et al.
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2014), organization-oriented, similar to the BIM maturity measurement (MM) (Succar et al. 2012), and
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macro maturity models (Succar and Kassem 2015). Owing to this diversity, objectives must be specified
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before BIM tools are selected. In addition to a maturity model that can provide a reference framework for
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BIM implementation monitoring, data collection methods and tools (such as questionnaires and interviews)
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should be used to examine BIM-enabled processes and components. BIM leaders and managers may then
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use the information collected from performance evaluations to verify the adherence of the performance of
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BIM practices to the defined BIM plans and policies.
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The use of external benchmarking tools and data renders it possible to compare the BIM
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performances among organizations (Du et al. 2014). This is aimed at gathering the necessary information
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that organizations require for the identification and execution of long-term improvement plans. Successful
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BIM adoption is highly dependent on ingrained tacit knowledge, which increases the difficulty of
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duplication. According to Baden-Fuller and Winter (2005), effective cross-boundary knowledge transfer
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can be typically accomplished by moving knowledgeable individuals between organizations, creating
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industry networks among members of different organizations, or by replicating practices through regular
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and repeated observations. Each AEC organization should carefully analyze its circumstances to
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appropriately connect the best practices of BIM with its business operations. There is no one-size-fits-all
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approach to BIM implementation (Abbasnejad et al. 2020).
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Research Gap & Positioning this Study
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According to previous studies, discovering and comprehending organizational BIM capabilities is critical
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for boosting BIM adoption. Different organizations respond differently to BIM capabilities and strategies.
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Therefore, AEC organizations must navigate the influencing factors of organizational BIM capabilities.
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However, there is a dearth of evidence in the literature with respect to the influence of various BIM variables
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on the BIM capabilities of an organization (Tai et al., 2020). In a first attempt, this study bridges this
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knowledge gap by identifying the underlying factors and strategies affecting organizational BIM
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capabilities.
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METHODOLOGY
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Survey Development
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A questionnaire survey is a method of gathering random data systematically. It has been frequently utilized
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to acquire expert opinions in the field of construction management. Thus, a questionnaire survey was
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developed and used in this study to collect the data. To begin with, this study employs a systematic literature
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review (SLR) approach to perform a thorough review of the existing literature to generate a list of potential
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strategies and capability factors. The SLR is divided into two sections. The first step was to conduct a
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comprehensive search for existing construction management publications using the 'title/abstract/keyword'
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feature in the Scopus database. The terms ‘building information modeling’ OR ‘building information
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modeling’ AND ‘capability’ OR ‘capabilities’ were employed to accomplish this. The search was
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conducted on November 6, 2020. Based on the search code, 205 articles were retrieved. Then, unrelated
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articles were excluded after examining the title, abstract, and content. As a result, 26 articles were finally
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determined to be valid for further analysis.
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In addition to the SLR, the survey development process entails a two-step approach to ensure the
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survey's appropriateness and rationality. First, fifteen semi-structured interviews with BIM managers were
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conducted to find any additional variables missing from the existing body of knowledge. After each
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interview, a summary was made and sent to the respondents for validation purposes. Then, a survey was
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developed using data from the SLR and interviews. Variables with similar meanings were combined,
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resulting in fourteen strategies and nineteen capability factors. Tables 1 and 2 summarize the strategies and
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capability factors synthesized from the SLR and interviews. Second, the survey was reviewed by three
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professors in the construction management field to eliminate unclear statements and assure proper use of
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technical jargon.
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On the front page of the survey, the study objectives and contact details were displayed, followed
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by two parts. The first part includes questions about the respondents' backgrounds and organizations. This
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component is essential to assess the respondents' reliability. The second part consists of a list of the
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strategies for improving organizational BIM capabilities identified. Respondents were asked to rank the
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criticality of the strategies on a five-point Likert scale (1 = not critical, 2 = less critical, 3 = neutral, 4 =
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critical, and 5 = extremely crucial). The third part includes a list of the identified factors that are affecting
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the organizational BIM capabilities. Respondents were asked to score the criticality of the factors on a five-
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point Likert scale (1 = not critical, 2 = less critical, 3 = neutral, 4 = critical, and 5 = extremely crucial). The
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five-point Likert scale is popular because of its ability to give clear results (Zhang et al. 2011). Nonetheless,
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at the end of the survey, respondents are given space to describe and evaluate any additional strategies and
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capability factors.
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Data collection
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The target population includes all BIM practitioners. The sample was nonprobability in this investigation
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due to the lack of a sampling frame (Zhao et al. 2015). Nonprobability sampling can be used to create a
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representative sample when a truly random sampling approach cannot be utilized to select responders from
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the entire population (Patton 2001). Respondents might be chosen for the study based on their desire to
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participate (Wilkins 2011). As a result, a snowball sampling method was used to determine the overall
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sample size. It has also been employed in earlier construction management work since it enables data and
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response collection and sharing via referral or social networks (Mao et al. 2015).
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BIM practitioners who have been directly involved in the AEC industry were contacted to
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determine the initial respondents. After that, the responders who had been identified were requested to share
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information on others they deemed appropriate based on their industrial or academic experience. Two
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follow-ups were sent to the target populations two weeks following the first contact to boost the survey's
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success rate. As a result, a total of 121 valid responses were collected.
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The demographic background of the 121 BIM practitioners regarding their experience in the
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industry and BIM is illustrated in Table 3. 70% of the respondents have 2 or more years of experience in
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the AEC industry. Among all the respondents, 63 consisting of 52.1% have 2-5 years of experience, 13.2%
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have 6–9 years of experience, and 5.0% have more than 10 years of experience. These results reflect great
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experience in construction. Regarding BIM, 69.4% of the respondents have used BIM in 1 to 5 projects,
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19.0% of the respondents have used BIM technology in 6 to 10 projects, and 11.6 % have used BIM
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technology for more than 10 projects. Since BIM is a relatively recent technology in the industry, the
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respondent backgrounds satisfy this survey.
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ANALYSIS AND RESULTS
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Statistic Package for the Social Sciences (SPSS) version 23.0 was used to conduct exploratory factor
348
analysis. In contrast, SmartPLS 3 (Ringle et al. 2015) was employed to statistically test the hypotheses
349
based on structural equation modeling using the partial least squares (PLS) approach.
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Kruskal-Wallis Test
351
Nonparametric tests were used to analyze the data because collected data were not always normally
352
distributed. The Kruskal-Wallis test was conducted to verify if there were significant differences among
353
the respondents. According to Siegel and Castellan (1988), the significant difference is proved when the
354
asymptotic significance value is lower than 0.05. The Kruskal-Wallis test results show that all the
355
asymptotic significance values are greater than 0.05, indicating no significant differences among the
356
stakeholders.
357
358
Normalization method
359
The normalization method was used in this study because it provides a better interpretation of the data,
360
particularly for selecting significant capability factors. Capability factors data with normalized values of at
361
least 0.50 were identified as significant capability factors. The normalization method was adopted from the
362
work of Chan et al. (2015). In this study, the method was used to transform the minimum mean value to 0.
363
In contrast, the maximum mean value was transformed into a value of 1. Then, the other mean values were
364
transformed to decimal values between 0 and 1. Based on Table 4, there are 15 significant capability factors
365
with normalization values of greater than 0.50.
366
Exploratory Factor Analysis (EFA)
367
Then, exploratory factor analysis (EFA) was employed to determine the factors underlying the BIM
368
implementation strategies and capability factors. EFA helps to regroup and reduce many interrelated
369
variables into a smaller and more relevant set of constructs (Norusis 2008). There are primarily two types
370
of factor analysis: exploratory factor analysis and confirmatory factor analysis. Exploratory factor analysis
371
aims to uncover the constructs influencing a set of responses. In contrast, confirmatory factor analysis
372
determines if a certain set of constructs influences responses in a predicted way. In this study, exploratory
373
factor analysis was performed to uncover the multiple dimensions of BIM implementation strategies and
374
capability factors because the number of common factors and their associated components is determined
375
by this method. Otherwise, the variables should be grouped by the researchers that need to be validated by
376
confirmatory analysis.
377
The sample size ratio to the number of variables method was used to determine the sample size for
378
the EFA method. Gorsuch (1983) recommended that the minimum ratio value should be 5.00. Accordingly,
379
the sample size ratio to the number of variables is 8.64 for the strategies data and 6.36 for the capability
380
factors data. Therefore, using the aforementioned rules of thumb, the sample size for this study is adequate
381
The suitability of the strategies data and capability factors data for EFA was assessed using the
382
Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity. In this study, the data was deemed to be
383
appropriate for the analysis because the KMO value was 0.820 (strategies) and 0.882 (capability factors),
384
which was significantly higher than the minimum value of 0.80 (Pallant 2020). The result of Bartlett's test
385
of sphericity is 588.118 (strategies) and 772.056 (capability factors) with a significance value of 0.000,
386
indicating that the correlation matrix is significant at p < 0.05 and thus is not an identity matrix. Therefore,
387
the data are suitable for factor analysis.
388
Principal component analysis (PCA) was selected as an extraction method to identify underlying
389
grouped factors. PCA is a common technique used in construction management to group variables (Ma et
390
al. 2020; Le et al. 2014). Hair et al. (2008) illustrated that the significant factor loading for a sample size of
391
approximately 100 is 0.60. Thus, a cut-off factor loading of 0.60 was used to screen out weak indicators of
392
common factors. For strategies data, 11 strategies were finally considered in the factor analysis, from which
393
three components are extracted. The three components explain approximately 67.33% of the total variance,
394
which is more than the 60% needed for adequate construct validity (Ghosh and Jintanapakanont 2004). For
395
the capability factors data, 11 capability factors were finally considered in the factor analysis, from which
396
two components are extracted. The two components explain approximately 64.08% of the total variance.
397
Then, the Cronbach's alpha reliability test was run to ensure that the factors were appropriately
398
grouped. The Cronbach's alpha coefficients ranging from 0.716 to 0.877 were greater than the required
399
minimum of 0.60 (Nunnally 1994). Therefore, each construct possessed good internal consistency. Tables
400
5 and 6 summarize the final EFA results and Cronbach's alpha values.
401
Hypotheses for Structural models
402
Based on the EFA method in the last section, the following six hypotheses are developed to examine
403
relationships between the strategies and capability factors:
404
• Hypothesis H1: Capability requirements positively affect organizational BIM capabilities
405
• Hypothesis H2: Capability requirements positively affect organizational capabilities
406
• Hypothesis H3: Organization culture positively affects organizational BIM capabilities
407
• Hypothesis H4: Organization culture positively affects organizational capabilities
408
• Hypothesis H5: Organization competitiveness positively affects organizational BIM capabilities
409
• Hypothesis H6: Organization competitiveness positively affects organizational capabilities
410
Partial Least-Squares Structural Equation Modeling (PLS SEM)
411
Structural equation modeling (SEM) was used to test the hypotheses. Observed variables can be directly
412
measured using SEM; latent variables can be inferred from the observed variables. A structural equation
413
model consists of measurement models and structural models. A measurement model shows the relationship
414
between each observed variable and its latent variable. A structural model shows the relationships between
415
latent variables. There are two types of SEM: covariance-based SEM (CB–SEM) and partial least-squares
416
SEM (PLS-SEM). PLS-SEM addressed non-normal datasets and small sample sizes better than CB–SEM
417
(Hair et al. 2014). It is also best used for exploratory research with theoretical models that are not well-
418
formed (Jöreskog and Wold 1982).
419
PLS-SEM produces a set of measurement models and structural models. First, the validity of the
420
measurement model is assessed using composite reliability, loadings of variables on the corresponding
421
construct, and average variance extracted (AVE). Internal consistency reliability is measured using
422
composite reliability and Cronbach’s alpha, which should be more than 0.7. (Hair et al. 2011). Next, the
423
indicator reliability is assessed using loadings of variables on the corresponding construct, with a value of
424
at least 0.4. (Hair et al. 2011). Then, the convergent validity is assessed using the AVE, and it should have
425
a value greater than 0.5. (Hair et al. 2011). After that, discriminant validity is assessed. Discriminant validity
426
refers to the degree to which a given construct is different from other constructs (Hulland 1999). For
427
adequate discriminant validity, the square root of the AVE of each construct should be higher than the inter-
428
construct correlation, and a measurement item’s loading on its respective construct should exceed the cross-
429
loadings (Fornell and Larcker 1981). Finally, the structural model validity is assessed using the significance
430
and relevance of the structural model relationships.
431
Measurement Model Evaluation
432
Convergent validity. Establishing good measurement models is a prerequisite to testing the structural
433
model. Therefore, the reliability and validity of the measurement models should be assessed. Based on the
434
results presented in Table 7 and Figure 1, the loading of all variables and AVE values exceeded the
435
recommended value of 0.4 and 0.5, which indicate a satisfactory level of convergent validity of the
436
indicators and constructs, respectively (Hair et al. 2011). Furthermore, the estimated composite reliability
437
values and Cronbach’s alpha values of all constructs are above the required threshold of 0.7, which indicates
438
that internal consistency and reliability are acceptable (Hair et al. 2011).
439
Discriminant validity. The results (Table 8) show that the square-rooted AVEs (highlighted diagonal
440
values) for the constructs were greater than the correlation coefficients between any two latent constructs
441
as presented in the corresponding rows and columns (off-diagonal values), suggesting an adequate level of
442
discriminant validity of latent constructs. Furthermore, discriminant validity was evaluated using analysis
443
of cross-loadings (Chin 1998). The results (Table 9) show that all variables loaded higher on the construct
444
they were theoretically specified to measure compared to other constructs in the model, demonstrating
445
discriminant validity of the constructs.
446
Structural model evaluation. Finally, the bootstrapping technique was applied to estimate the significance
447
of path coefficients and test the hypotheses. In this study, the number of bootstrap samples was 5,000 (Hair
448
et al. 2011). The critical t-value for a two-tailed test was 2.58 (significance level = 0.01) (Henseler et al.
449
2009). The results showed that the path coefficients for Hypotheses 3, 4, and 6 were positive and significant
450
at the 0.01 level, implying that these three hypotheses were supported. However, Hypothesis 1, 2, and 5
451
received a low path coefficient with a t-value below 2.58, indicating that it was not supported.
452
453
DISCUSSION
454
Relationship between ‘Organization Culture’ and ‘Organizational BIM Capabilities’
455
The study outcomes demonstrate that organizational culture positively and substantially influences the BIM
456
capabilities of AEC organizations. Positive and significant path coefficients at the 0.01 and 0.1 levels attest
457
to this. Therefore, the Hypothesis H3 is supported. Schein (2004) defined three levels of organizational
458
culture: artifacts (including observable symbols, mission, and vision statements), espoused beliefs and
459
values, and basic underlying assumptions (Seidel-Sterzik et al. 2018). A great organizational culture
460
exhibits positive attributes that contribute to improved organizational BIM capabilities. In contrast, a
461
dysfunctional organizational culture elicits characteristics that may impede even the most successful
462
organizations. Employee creativity enhancement, altering staff attitudes toward technology, and employee
463
motivation are all discussed in this segment of the study. The results of this study agree with those of
464
previous studies, which showed that the BIM implementation team must explicitly create a change
465
management program and be aware of the necessity to evaluate the ramifications of a project
466
(Khosrowshahi and Arayici 2012). BIM adoption necessitates modifications, which relate to an increase of
467
positive attitude toward technology among workers and a user acceptance approach. The willingness of the
468
workers of an organization to use BIM may substantially aid the development of the organizational BIM
469
capability. However, AEC professionals may find it difficult to change their work culture (Saka and Chan
470
2020). BIM involves a learning curve that entails comprehending the technology as well as the people and
471
processes involved in its implementation (Rahman and Ayer 2019). Ultimately, the impediments to BIM
472
implementation are most likely to be addressed by an organizational culture that is open to change and
473
possesses shared common values and goals. Therefore, a key improvement strategy is to implement
474
organizational measures, such as promoting employee innovation and employee attitudes toward BIM
475
adoption, which will impact organizational BIM capability.
476
Relationship between ‘Organization Culture’ and ‘Organizational Capabilities’
477
According to the results, organizational culture exerts a favorable and substantial impact on organizational
478
competence. Table 10 demonstrates organizational culture to exhibit the strongest association with the other
479
hypotheses on the organizational capabilities construct. Furthermore, the route coefficient value of 0.288
480
supports this. Therefore, the Hypothesis H4 is supported. The importance of organizational culture in
481
strengthening organizational capabilities was discussed previously. The results of this study validate
482
previous studies, suggesting that organizational competencies, such as support from senior management are
483
critical for effective BIM adoption, especially in early stages (Arayici 2011). The extent to which senior
484
management recognizes the importance of and participates in the adoption of BIM and its implementation
485
is referred to as organizational support. Top managers may play enabling roles in this process, such as
486
encouraging, committing, supporting, and empowering employees. In addition, other organizational
487
qualities, such as providing adequate resources, the finest product, services, and a reasonable pricing
488
structure, are also essential. The results of this study agree with Patel (2021), who reported that every
489
organization should acquire the required BIM software, while considering the total cost. The cost of license
490
upgrades, hardware updates, an initial investment in the software purchase, installation, and training, are
491
all direct and indirect expenses connected with BIM software that play a major part in decision making.
492
This ought to be balanced by stakeholders who benefit financially from the use of BIM, in terms of
493
increased productivity (improved cost, quality, and time). Furthermore, with technological advancements,
494
BIM software must be updated with the required technological features to solve important challenges for
495
improved project performance. Software functionality, interoperability, user-friendliness, BIM standards
496
and regulations, data security and privacy protocols, potential capabilities of application
497
integration/extension, and accessibility of BIM software, are important technical aspects to be considered
498
as crucial organizational capabilities, which are strongly related to organizational culture.
499
Relationship between ‘Organizational Competitiveness’ and ‘Organizational Capabilities’
500
According to the results of the study, organizational competitiveness exerts a positive and substantial effect
501
on the organizational capabilities. Positive and significant path coefficients at the 0.01 levels attest to this.
502
Therefore, the Hypothesis H6 is supported. One of the most important elements of this approach is to form
503
partnerships with BIM experts, which will impact the organizational capabilities. This result supports the
504
study by Oraee et al. (2019), during BIM implementation. It is critical to develop strategic and long-term
505
collaborations with external entities and supply chain partners. Maintaining tight and trusting relationships
506
with other entities possessing a wealth of BIM knowledge increases the opportunity to gain expertise and
507
learn about BIM implementation. Further, this study agrees with Zhang et al. (2018), who believed that the
508
incentive and urgency to adopt BIM in organizations may be determined by its history and project portfolio
509
(Eadie et al. 2013). According to Arayici (2011), an organization's previous BIM expertise and forward-
510
thinking senior management, who are supportive of the process, are critical elements for successful BIM
511
acceptance and implementation. Thus, organizational capabilities are strongly influenced by organizational
512
competitiveness.
513
514
Theoretical Implications and Contribution
515
This study bridges a gap in the existing body of knowledge by focusing on organizational BIM capabilities.
516
It highlights the important variables influencing organizational BIM capabilities and strategies for
517
enhancing them. This study provides a thorough knowledge of organizational factors contributing to BIM
518
capabilities. Therefore, researchers and academics can use this construct to propose frameworks for
519
improving organizational BIM capabilities. In addition to previously known constructs, the distinct latent
520
construct proposed in this study may pique the attention of researchers, prompting future work into the
521
participation of AEC organizations with respect to BIM implementation and capacity building. This study
522
shows that while some organizational BIM capabilities appear important in existing literature, they are less
523
frequent in practice. Therefore, to improve organizational BIM capabilities, culture, competencies, and
524
capabilities, should be prioritized for attaining effective organizational BIM capabilities. This is a crucial
525
result, because it identifies specific areas for development that should be implemented to help organizations
526
improve their BIM capabilities and prevent unnecessary organizational changes.
527
Managerial Implication
528
This study offers a comprehensive overview of the essential organizational BIM capability criteria for the
529
effective implementation of BIM at the organizational level. Owing to the limited resources available to
530
adopt BIM, organizations should prioritize resources and focus on the critical factors, rather than all
531
elements, to improve their BIM capabilities. Furthermore, by classifying these variables and providing a
532
thorough knowledge of the latent structure of the factors affecting BIM capabilities, a CEO or Managing
533
Director of an organization can gain awareness about the underlying components of such factors.
534
Organizations may also use the important drivers and strategies identified in this study to improve
535
organizational BIM capacity.
536
Limitations and Future Work
537
Despite the relevance of these results, there are limitations to the study, which should be addressed in future
538
endeavors. First, the sample size was small (N=121). However, the use of the PLS-SEM approach and
539
bootstrapping technique reduced the potential problem caused by small sample sizes. Future studies may
540
consider a large sample size to test the model. Second, the nonprobability sampling approach was used,
541
owing to the lack of a sampling frame for this study, because it was difficult to construct a sampling frame.
542
Notwithstanding the inherent limitation, this sampling approach can be used to obtain a representative
543
sample (Patton, 2001), and it has been recognized to be appropriate when respondents are not randomly
544
selected from the entire population but selected based on their willingness to participate in the study
545
(Wilkins, 2011). Third, the data were primarily interpreted within the context of Malaysia. Therefore, the
546
results should be applied to other countries with caution and appropriate adjustments. Thus, a wider scope
547
of data collection across different countries and regions can enhance the optimal strategies for improving
548
organizational BIM capabilities. However, the results of this study still provide valuable insights into
549
organizational BIM capabilities. Future studies can aim to build roadmaps based on the study's results while
550
tailoring to local demands.
551
CONCLUSION
552
The improvement of the BIM capacity of an organization requires a combination of organizational
553
capabilities, capability requirements, organizational culture, and competitiveness. Therefore, organizations
554
must possess the resources to adapt and provide the finest products and services for their workforce to
555
improve their BIM capabilities. In addition, the pertinent senior personnel in an AEC organization play a
556
critical role in providing BIM direction to their colleagues. The organization must first identify its expertise
557
and develop a consistent procedure for evaluating its BIM capability to retain its competent workers.
558
Organizational competitiveness, culture, and competencies, contribute to an effective transition that will
559
result in improved BIM capabilities. When the eleven key factors and strategies are compared, it can be
560
observed that the adequacy of resources to implement BIM and the establishment of strategies to satisfy
561
customers requirements are the most important factors and strategies for improving organizational BIM
562
capability.
563
Overall, the results show that organizational culture positively and substantially impacts BIM
564
capabilities in the AEC industry. This indicates that the healthier the organizational culture, the better the
565
BIM capabilities of the organization. Among AEC organizations, organizational culture exerts the greatest
566
positive effect on the overall organizational capabilities. This indicates that the more planned and
567
responsive the organizational culture is, the greater the organizational capabilities. Finally, the study asserts
568
that organizational competitiveness positively and substantially impacts the organizational capabilities.
569
This indicates that the stronger the level of organizational competitiveness, the greater the level of
570
organizational capabilities. This study improves the understanding of the important drivers and methods in
571
supporting organizational BIM capabilities.
572
ACKNOWLEDGEMENT
573
We are grateful to the industry practitioners that agreed to participate, whose kind cooperation, time, and
574
effort made this work possible.
575
DATA AVAILABILITY STATEMENT
576
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature
577
and may only be provided with restrictions (e.g., anonymized data).
578
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Table 1. List of strategies for improving organizational BIM capabilities
Code
Strategies for improving organizational BIM capabilities
References
SBIM1
Change staff attitude towards new technology
Succar et al. (2013); Lattuch and Hickey (2019);
SBIM2
Encourage creativity among staffs
Olatunji (2019)
SBIM3
Motivate staffs in helping each other
Olatunji (2019)
SBIM4
Provide the necessary BIM training
Jones (2020); Harris et al. (2015); Olatunji (2019);
Lattuch and Hickey (2019); Rice and Martin (2020)
SBIM5
Have internal BIM policies
Wong et al. (2010); Wang et al. (2019)
SBIM6
Hire competent supervisors to provide guidance
Abbasnejad et al. (2020); Mirhosseini et al. (2020)
SBIM7
Ensure database is sufficient for BIM-based projects
Succar et al. (2012); Succar and Kassem (2015);
Abbasnejad et al. (2020); Gouda et al. (2020)
SBIM8
Create a partnership with BIM expert companies
Wong et al. (2010)
SBIM9
Establish strategies to cater to client’s demand on BIM
Jones (2020); Wong et al. (2010)
SBIM10
Hire BIM experts into the company
Abbasnejad et al. 2020; Mirhosseini et al. (2020)
SBIM11
Ensure good company history
Lattuch and Hickey (2019)
SBIM12
Provide rewards and recognitions to staffs
Olatunji (2019)
SBIM13
Have top management providing clear company direction
Wong et al. (2010); Subki and Mahadzir (2019);
Lattuch and Hickey (2019); Olatunji (2019)
SBIM14
Prepare staffs for the demanding BIM-based construction projects
Succar et al. (2013); Harris et al. (2015)
Table 2. List of factors affecting organizational BIM capabilities
Code
Factors affecting organizational BIM capabilities
References
FBIM1
Staffs have enough BIM experience
Mahamadu et al. (2019a); Mahamadu et al. (2019b);
Mahamadu et al. (2017); Succar et al. (2013); Interview
FBIM2
Staffs have adequate academic qualifications
Mahamadu et al. (2019a); Mahamadu et al. (2019b); Succar
et al. (2013)
FBIM3
Company has sufficient BIM experience
Mahamadu et al. (2019a); Mahamadu et al. (2019b); Ahuja
et al. (2018); Mahamadu et al. (2017); Succar et al. (2013)
FBIM4
Company has a standard process of evaluating BIM capability
Mahamadu et al. (2019a); Mahamadu et al. (2019b);
Mahamadu et al. (2017); McCuen et al. (2012); Interview
FBIM5
Company has sufficient resources to implement BIM demand
Mahamadu et al. (2019a); Mahamadu et al. (2019b); Ahuja
et al. (2018); Mahamadu et al. (2017); Succar et al. (2012);
Succar et al. (2013)
FBIM6
Company has the necessary infrastructure (software &
hardware) to implement BIM
Mahamadu et al. (2019a); Mahamadu et al. (2019b); Succar
et al. (2012); Wang et al. (2019); Interview
FBIM7
Company has a good history of implementing BIM
Mahamadu et al. (2019a); Mahamadu et al. (2019b);
Mahamadu et al. (2017)
FBIM8
Staffs can design specific model using BIM
Mahamadu et al. (2019a); Mahamadu et al. (2019b);
Mahamadu et al. (2017)
FBIM9
Company has specific roles for staffs
McCuen et al. (2012)
FBIM10
Company and staffs have the same goals
Succar et al. (2013)
FBIM11
Company can provide a good cost structure
Mahamadu et al. (2019a); Mahamadu et al. (2019b); Oraee
et al. (2019)
FBIM12
Company has a standard performance benchmarked
Succar et al. (2012); Succar et al. (2013); Mahamadu et al.
(2017)
FBIM13
Staffs receive guidance and supervision by BIM experts
Succar et al. (2012); Succar et al. (2013); Mirhosseini et al.
(2020); Interview
FBIM14
Company has a good attitude towards new technology
Ahuja et al. (2018); Lattuch and Hickey (2020)
FBIM15
Company can provide an example with rich BIM data
McCuen et al. (2012)
FBIM16
Company can provide the best products and services
Succar et al. (2012)
FBIM17
Company has official standard contracts and agreements for
BIM
Succar et al. (2012); Wong et al. (2010)
FBIM18
Company has a research and development (R&D) department
/team for BIM
Succar et al. (2013)
FBIM19
Company understands its expertise
McCuen et al. (2012); Succar et al. (2013)
Table Click here to access/download;Table;JMEBIM_Tables.docx
Table 3. Respondent profile
Characteristics
Categories
Frequency
Percentage (%)
Years of experience in construction
industry
Less than 2 years
36
29.8
2 - 5 years
63
52.1
6 - 9 years
16
13.2
10 years and above
6
5.0
Type of organization
Clients
8
6.6
Contractors
34
28.1
Consultants
79
65.3
Types of projects that used BIM
Infrastructure construction
26
21.5
Building construction (residential)
47
38.8
Building construction (non-residential)
37
30.6
Industrial construction
11
9.1
Number of projects you have
experienced in using BIM
technology:
1 to 5 projects
84
69.4
6 to 10 projects
23
19.0
More than 10 projects
14
11.6
Table 4. Results of the normalization method
Code
Factors affecting organizational BIM capabilities
Mean
Standard
deviation
Normalization
value
FBIM6
Company has the necessary infrastructure (software & hardware) to implement BIM
4.430
0.825
1.000*
FBIM14
Company has a good attitude towards new technology
4.372
0.848
0.929*
FBIM19
Company understands its expertise
4.306
0.835
0.848*
FBIM17
Company has official standard contracts and agreements for BIM
4.248
0.888
0.778*
FBIM10
Company and staffs have the same goals
4.248
0.897
0.778*
FBIM13
Staffs receive guidance and supervision by BIM experts
4.248
1.059
0.778*
FBIM16
Company can provide the best products and services
4.240
0.827
0.768*
FBIM9
Company has specific roles for staffs
4.240
0.866
0.768*
FBIM8
Staffs can design specific model using BIM
4.165
0.850
0.677*
FBIM1
Staffs have enough BIM experience
4.165
0.860
0.677*
FBIM12
Company has a standard performance benchmarked
4.157
0.922
0.667*
FBIM11
Company can provide a good cost structure
4.149
0.919
0.657*
FBIM5
Company has sufficient resources to implement BIM demand
4.149
0.972
0.657*
FBIM4
Company has a standard process of evaluating BIM capability
4.066
0.964
0.556*
FBIM3
Company has sufficient BIM experience
4.025
0.944
0.505*
FBIM15
Company can provide an example with rich BIM data
3.975
1.028
0.444
FBIM2
Staffs have adequate academic qualifications
3.752
0.869
0.172
FBIM18
Company has a research and development (R&D) department /team for BIM
3.636
1.169
0.030
FBIM7
Company has a good history of implementing BIM
3.612
1.060
0.000
Note: *significant capability factors
Table 5. Results of FA on strategies for improving organizational BIM capabilities
Constructs
Code
Strategies for improving organizational BIM
capabilities
Factor
loadings
Variance
explained (%)
Cronbach’s
alpha
Capability
requirements
SBIM6
Hire competent supervisors to provide
guidance
0.801
43.207
0.836
SBIM10
Hire BIM experts into the company
0.797
SBIM7
Ensure database is sufficient for BIM-based
projects
0.693
SBIM14
Prepare staffs for the demanding BIM-based
construction projects
0.649
SBIM13
Have top management providing clear
company direction
0.602
Organization culture
SBIM2
Encourage creativity among staffs
0.804
13.217
0.716
SBIM1
Change staff attitude towards new
technology
0.773
SBIM3
Motivate staffs in helping each other
0.671
Organization
competitiveness
SBIM8
Create a partnership with BIM expert
companies
0.821
10.913
0.721
SBIM11
Ensure good company history
0.748
SBIM9
Establish strategies to cater to client’s
demand on BIM
0.700
Table 6. Results of FA on factors affecting organizational BIM capabilities
Constructs
Code
Factors affecting organizational BIM
capabilities
Factor
loadings
Variance
explained (%)
Cronbach
alpha
Organizational BIM
capabilities
FBIM5
Company has sufficient resources to
implement BIM demand
0.858
53.753
0.877
FBIM4
Company has a standard process of
evaluating BIM capability
0.819
FBIM3
Company has sufficient BIM experience
0.805
FBIM17
Company has official standard contracts and
agreements for BIM
0.637
FBIM6
Company has the necessary infrastructure
(software & hardware) to implement BIM
0.634
FBIM1
Staffs have enough BIM experience
0.634
Organizational
capabilities
FBIM9
Company has specific roles for staffs
0.804
10.328
0.853
FBIM16
Company can provide the best products and
services
0.725
FBIM14
Company has a good attitude towards new
technology
0.716
FBIM11
Company can provide a good cost structure
0.686
FBIM19
Company understands its expertise
0.667
Table 7. Measurement model evaluation
Constructs
Indicators
Loadings
AVE
CR
CA
Capability requirements
SBIM10
0.589
0.611
0.885
0.846
SBIM13
0.863
SBIM14
0.900
SBIM6
0.654
SBIM7
0.853
Organization culture
SBIM1
0.793
0.633
0.837
0.715
SBIM2
0.878
SBIM3
0.706
Organization competitiveness
SBIM11
0.733
0.631
0.835
0.733
SBIM8
0.719
SBIM9
0.916
Organizational BIM capabilities
FBIM1
0.710
0.619
0.906
0.874
FBIM17
0.748
FBIM3
0.852
FBIM4
0.835
FBIM5
0.872
FBIM6
0.683
Organizational capabilities
FBIM11
0.796
0.632
0.895
0.855
FBIM14
0.791
FBIM16
0.859
FBIM19
0.807
FBIM9
0.713
Note: AVE = Average variance extracted; CR = Composite reliability; CA = Cronbach’s alpha;
Table 8. Discriminant validity
Constructs
Organizational BIM
capabilities
Organizational
capabilities
Capability
requirements
Organization
culture
Organization
competitiveness
Organizational BIM
capabilities
0.787
-
-
-
-
Organizational
capabilities
0.696
0.795
-
-
-
Capability requirements
0.353
0.286
0.782
-
Organization culture
0.363
0.383
0.473
0.796
-
Organization
competitiveness
0.244
0.368
0.632
0.391
0.795
Table 9. Cross loadings
Code
Organizational BIM
capabilities
Organizational
capabilities
Capability
requirements
Organization
culture
Organization
competitiveness
FBIM1
0.710
0.466
0.303
0.326
0.210
FBIM17
0.748
0.644
0.234
0.264
0.262
FBIM3
0.852
0.588
0.288
0.286
0.191
FBIM4
0.835
0.559
0.280
0.196
0.228
FBIM5
0.872
0.580
0.271
0.287
0.096
FBIM6
0.683
0.437
0.269
0.327
0.148
FBIM11
0.537
0.796
0.306
0.302
0.373
FBIM14
0.572
0.791
0.192
0.258
0.203
FBIM16
0.637
0.859
0.214
0.389
0.336
FBIM19
0.698
0.807
0.182
0.261
0.203
FBIM9
0.341
0.713
0.214
0.282
0.290
SBIM10
0.147
0.077
0.589
0.080
0.330
SBIM13
0.331
0.294
0.863
0.382
0.542
SBIM14
0.354
0.273
0.900
0.528
0.560
SBIM6
0.172
0.079
0.654
0.300
0.290
SBIM7
0.280
0.262
0.853
0.404
0.631
SBIM1
0.307
0.280
0.279
0.793
0.247
SBIM2
0.336
0.391
0.380
0.878
0.347
SBIM3
0.198
0.207
0.538
0.706
0.359
SBIM11
0.069
0.270
0.379
0.324
0.733
SBIM8
0.049
0.167
0.365
0.177
0.719
SBIM9
0.325
0.371
0.655
0.373
0.916
Table 10. Structural model evaluation
Hypothesis
Path
Path coefficient
t-Value
Decision
H1
Capability requirements → Organizational BIM capabilities
0.235
1.671
Not Supported
H2
Capability requirements → Organizational capabilities
-0.020
0.169
Not supported
H3
Organization culture → Organizational BIM capabilities
0.253
2.619**
Supported
H4
Organization culture → Organizational capabilities
0.288
3.466**
Supported
H5
Organization competitiveness → Organizational BIM capabilities
-0.004
0.035
Not supported
H6
Organization competitiveness → Organizational capabilities
0.268
2.645**
Supported
Note: **p<0.01
