Obstacles to BIM Adoption in Construction Production: A Study of Swedish Construction Contractors’ Experiences

Abstract

Digitalization in the construction industry has transformed efficiency and coordination, with Building Information Modeling (BIM) emerging as a central enabling technology. However, despite its potential, BIM usage during the implementation phase of Swedish construction projects remains limited. Using the Technology–Organization–Environment (TOE) framework, this study combines a systematic literature review with a quantitative survey of 220 professionals. The data were analyzed using descriptive statistics, correlation analysis, factor analysis, and ordinal logistic regression. The results show that technological and organizational barriers are present to a moderate extent, but they manifest as distinct and separate dimensions. In contrast, the most significant barriers to actual BIM adoption lie within the environmental domain. Specifically, the absence of clear external requirements, policies, and incentives is strongly and negatively associated with BIM implementation. The study concludes that although contractors demonstrate internal technical readiness, external systemic support is crucial for scaling up BIM in practice. These insights carry important implications for industry stakeholders and policymakers aiming to accelerate the digital transformation of the construction industry.

1. Introduction

The digital transformation of the construction industry has significantly reshaped the conditions for planning, design and production over the past two decades. Among the most influential technologies driving this shift is Building Information Modeling (BIM), which seeks to streamline information management and enhance coordination throughout the construction process. BIM has demonstrated considerable potential to improve quality, reduce costs, and foster collaboration among stakeholders in construction projects [1,2]. Nevertheless, its application during the production phase in the Swedish construction industry remains limited [3].

Previous research has identified several barriers to BIM implementation in the Swedish construction industry, including inadequate training, insufficient client requirements, high investment costs, and organizational resistance [4,5]. However, these studies have primarily focused on the design phase or broader organizational perspectives. Few have systematically examined the specific barriers encountered during the production phase, particularly the operational barriers faced on-site. This study addresses that gap by applying a structured framework to investigate production-specific barriers to BIM adoption.

A deeper understanding is needed of the practical barriers that arise on construction sites, where time constraints, complex workflows, and limited digital support systems often impede effective BIM utilization [6]. To explore these barriers in a systematic manner, this study employs the Technology–Organization–Environment (TOE) framework—a well-established analytical framework for identifying and categorizing factors that influence technology adoption [7]. It distinguishes three main dimensions: technological factors (e.g., technical compatibility, usability), organizational factors (e.g., resources, management, culture) and environmental factors (e.g., industry pressures, regulations, client requirements).

While the TOE framework has been applied in previous studies on technology adoption in construction, such as analyses of digital strategies [8], IT systems, and organizational change [9,10], it has not been extensively used to examine barriers to BIM implementation during the practical stage of construction production, especially from the perspective of contractors working on-site. Existing research linking TOE to BIM tends to focus on the design phase, organizational decision-making, or general technology acceptance [11], and rarely captures the lived experiences from the production environment, where BIM encounters operational and collaborative barriers. By integrating the TOE framework with a qualitative approach, this study aims to fill that gap and contribute new insights into how technological, organizational, and environmental factors influence BIM adoption in the production phase.

The purpose of this study is to investigate the barriers that influence Swedish construction contractors’ adoption of BIM during the production phase, using the Technology–Organization–Environment (TOE) framework as a guiding structure. Within this context, the following research questions have been formulated to define the scope of the study:

• How do contractors perceive the influence of technological elements, such as software tools, technical expertise, and system compatibility, on the execution of BIM in construction production?
• What organizational barriers, such as management support, internal workflows, and availability of skilled personnel, limit contractors’ capacity to implement BIM in the production phase?
• In what ways do external factors, such as industry standards, client expectations, and digital maturity of project partners, affect BIM utilization in contractor-led construction projects?
• How do technological, organizational, and environmental barriers interact, and in what ways does this interplay contribute to the complexity of BIM implementation in real-world construction setting?

2. Background

This section explains further the discussion of barriers when implementing BIM, the research gap, and summarizes common BIM utilization in the construction production.

2.1. The Swedish Context: BIM for Construction Production

The construction industry plays a vital role in Sweden’s economy, labor market, and built environment, employing hundreds of thousands of individuals. Yet, it faces several persistent challenges, including productivity, high resource consumption, fragmented workflows and limited collaboration among stakeholders [12,13]. These issues are particularly distinct during the production phase, the on-site execution of construction projects, which is often marked by tight schedules, budget constraints, complex logistics, and frequent changes in personnel and contractors [2,14]. To address these challenges, digitalization has emerged as a key driver of improved efficiency, quality and sustainability within the sector. At the heart of this digital transformation is Building Information Modeling (BIM), an approach and process, that facilitates model-based information management and coordination throughout the entire lifecycle of a building [1,15]. BIM offers significant advantages, including enhanced planning, reduced errors, accelerated decision-making, and more streamlined construction processes, especially during the production phase, where resources are actively deployed. Despite its potential, BIM has yet to achieve widespread adoption in Swedish construction production, particularly among small and medium-sized enterprises.

Overall, the adoption of digital technologies in the construction industry remains limited, particularly among regional construction companies, which often hinder broader technological advancement within the sector [16]. Consequently, many companies continue to rely on traditional methods. According to sources [3,13], the use of BIM is unevenly distributed across the industry. Larger companies and clients tend to lead the way in digitalization; while contractors, subcontractors, and on-site teams frequently lack the necessary conditions to fully integrate BIM into their daily operations. Common barriers include inadequate digital skills, poor integration of technology into existing workflows, limited support from management, and the absence of clear client requirements or incentives to adopt new tools [5,17].

Sweden has launched several national initiatives at the national level aimed at accelerating the digital transformation in the construction industry. One of the most comprehensive efforts is the strategic innovation program Smart Built Environment, which seeks to foster a sustainable and digitally integrated built environment sector through research, standardization and collaboration [18]. Public authorities, such as the Swedish National Board of Housing, Building and Planning, have also introduced new regulations, such as climate declarations that indirectly increase the demand for improved information management and digital tools [12].

Despite these efforts, substantial barriers continue to hinder the implementation of digital tools in construction production environments. Research suggests that there is often a lack of systematic understanding of how various types of barriers: technical, organizational and environmental, interact within the context of construction production, particularly from the perspective of contractors [6,10]. By focusing on the use of BIM during the production phase and examining these barriers through the lens of the TOE (Technology–Organization–Environment) framework, this study offers new insight into the factors that influence contractors’ ability to effectively harness the potential of digitalization in practice.

2.2. BIM as Enabler of Digitalization in Building Projects

Beyond enhancing efficiency and coordination, the integration of Building Information Modeling (BIM) offers significant benefits for improving quality and promoting sustainability in construction projects. BIM is increasingly linked to sustainability objectives within the industry. Numerous studies highlight its potential to support both environmental and social sustainability by facilitating life cycle assessments, optimizing resource use, and improving transparency in stakeholder communication [5,19,20]. In construction projects, BIM proves valuable for analyzing data related to energy efficiency and sustainability. It enables visualization and modeling of a building’s design and performance by supporting assessments of energy consumption, carbon emissions, and pollution levels in green buildings [21]. According to [22], BIM has been developed and promoted as a tool for integrating all design-related information. However, a major challenge remains there are still a shortage of qualified professionals with the necessary expertise and experience to fully leverage its capabilities [21].

Furthermore, BIM has demonstrated clear advantages in enhancing project quality, managing and storing lifecycle data, optimizing collaboration, and improving planning and scheduling during the construction phase of green buildings [23]. According to [23], several barriers hinder the effective use of BIM in this context. These include the non-uniform data format, limited system interoperability, unclear data ownership, inadequate BIM training, and general reluctance to adopt the technology.

Because BIM is predominantly focused on traditional facility design rather than sustainability-oriented features, designers and operators often rely on a range of heterogeneous software systems to compensate for its limitations. This reliance on multiple tools introduces significant challenges related to interoperability and data integrity [22]. As the authors [22] point out, this issue is particularly pronounced in the Architecture, Engineering, and Construction (AEC) industry, which is highly fragmented. Different disciplines use distinct tools and produce various models that may function well independently but are difficult to integrate with third-party systems, complicating collaboration and data exchange across project stakeholders.

In the Swedish context, sustainability has become a central focus of the national digitalization agenda, where BIM is regarded not merely as a technical tool, but as a strategic enabler of sustainable practices in procurement, planning and production [12,18]. Socio-cultural expectations for sustainable development, combined with policy incentives and environmental declarations, further strengthen BIM’s role in aligning construction project with long-term sustainability goals [1,5]. Among the most significant contributions of digitalization to the construction industry, particularly in the realm of sustainability, is the improved access to information, the way it is exchanged, and the transparency it enables [24].

2.3. BIM in Construction Production: Applications and Research Gaps in the Current Literature

Over the past decade, BIM has emerged as one of the most pivotal digital tools for streamlining and coordinating construction projects. Research indicates that BIM enhances information management, boosts productivity, reduces construction errors, and improves control over time, cost and quality, particularly during the planning and design phase [1,15]. Despite its proven benefits, BIM remains significantly underutilized during the implementation phase of construction. Nevertheless, numerous studies highlight its considerable potential in this stage as well [2,14].

In the production phase, BIM can be applied to a wide range of tasks, including digital planning of work sequences, quantity take-off, clash detection, 4D simulations, safety planning, resource logistics and support for quality assurance and progress tracking [25,26]. Additionally, BIM can be integrated with sensors, schedules, machine data and geographic information systems (GIS) to provide real-time support for site management. However, realizing these applications requires both robust technical infrastructure and effective collaboration among project stakeholders.

Several overview studies indicate that BIM implementation in the production phase is largely driven by technology-centric approaches, emphasizing software capabilities, technical interoperability, and system development [19,20]. However, within the field of Information Systems (IS), an information system is understood to comprise both a technical subsystem, which includes the processes, tasks, and technological components necessary for system operation, and a social subsystem, which encompasses the structure of the work system, the workforce, and their attitudes, knowledge, skills, values, and interpersonal relationships [27]. Consequently, recognizing the interdependence between social and technical elements within organizational systems is essential for the successful adoption and implementation of information systems [28].

The concept of a sociotechnical system was originally introduced to describe systems characterized by complex interactions among machines, humans, and the environmental context of work systems [29]. At the heart of Socio-Technical Systems (STS) theory lies the principle of jointly optimizing both the technical subsystem, comprising tools, technologies, and processes, and the social subsystem, which includes people, organizational culture, and structural arrangements, to improve overall organizational performance and employee well-being [28]. Thus, the social subsystem is shaped by institutional arrangements such as formal work settings, communication channels, and authority structures. It also encompasses norms, values, role expectations, and behavioral patterns that influence how individuals, managers, employees, and stakeholders act within the organizational environment [27].

According to [27], there is a growing shift away from viewing BIM solely as a technology-centric tool toward a socio-technical perspective. In this view, BIM facilitates effective and efficient collaboration between people and information through structured processes and supporting technologies [27,29]. By emphasizing the interplay of people, information, processes, and technology, this perspective underscores that BIM is not merely software, it is a holistic system integrating both social and technical dimensions [27]. BIM also serves as a platform that integrates various tools, enhancing planning, design, construction, and facility management by enabling the visualization of buildings within a simulated environment. Its capabilities extend well beyond the design and construction phases, offering the ability to create and manage detailed 3D models and fostering improved coordination and communication among project teams. Ultimately, this leads to more efficient workflows and cost-effective project delivery [30].

Social barriers related to the “people” and the “structure” dimensions of the BIM socio-technical system have been identified as one of the most significant barriers to the widespread adoption of BIM [27]. Moreover, few studies systematically examine technical, organizational and environmental barriers in an integrated manner. For instance, several articles highlight issues such as software incompatibility or the absence of clear client requirements, they rarely explore how these factors interact within the context of construction production [6,10]. There is also a gap in research focusing on small and medium-sized contractors, despite their dominant role in the execution phase of construction projects [13]. The authors of [29] argue that, given the highly collaborative, heterogeneous, and project-specific nature of the construction work environment, a successful BIM process can only be achieved through deliberate negotiation of intervention strategies that align with the diverse goals of multiple end users.

There is a clear lack of studies examining how production actors, particularly contractors, perceive and utilize BIM in practice [3,31]. As a result, there remains limited insight into the specific barriers and enabling conditions present in site-based environments, where time pressures, entrenched work practices, and varying levels of digital maturity influence implementation outcomes. In response to this gap, the present study adopts an actor-oriented approach combined with a theoretical analysis based on the TOE framework. By focusing on contractors’ experiences during the production phase, this study seeks to identify practical barriers to adoption and thereby contributes to a deeper understanding of how the potential of digitalization can be effectively harnessed on construction sites.

Table 1. Thematic Categorization of BIM Applications in Construction Production.

Theme	Application	References
Production Planning and 4D	Visual scheduling and simulation of work sequences in 4D	[15,26]
	Sequence optimization for construction activities
	Identification of bottlenecks in schedules	[2]
Quantity Takeoff and Logistics	Automated quantity takeoff directly from the BIM model	[20]
	Support for procurement planning and just-in-time deliveries	[25]
	Coordination of material flows on the construction site	[26]
Clash Detection and Safety	Identification of physical conflicts before execution (clash detection)	[1,15]
	Model-based planning of safe work procedures	[26]
	Visualization of risk zones and temporary structures	[14]
Quality, Monitoring, and Documentation	Digital quality control and on-site documentation	[3,31]
	Traceability of performed tasks
	Integration with quality management systems

outlines common applications of BIM in construction production, grouped into four thematic areas: production planning and 4D simulation, quantity take-up and logistics, collision control and safety, and quality and follow-up. These applications illustrate how BIM can enhance efficiency, accuracy, and safety during the implementation phase, provided that the necessary technical, organizational, and external conditions are in place.

The categorization of barriers into four thematic areas: technological, organizational, environmental, and economic, is grounded in the well-established TOE framework [7], which outlines three core domains influencing technology adoption. However, empirical research in the construction sector has consistently highlighted financial and resource-related constraints as a distinct group of barriers, particularly affecting small and medium-sized contractors [1,3,5,13]. These economic challenges are not always adequately captured within the traditional TOE dimensions. Therefore, they are not included as a complementary fourth category to more accurately reflect the industry-specific conditions that shape BIM implementation in production environments.

2.4. Barriers to BIM Implementation in the Construction Industry

Although BIM is widely recognized as a key technology for advancing the digitalization of the construction industry, substantial barriers continue to hinder its broad and effective adoption. Globally, BIM has demonstrated potential to enhance quality, efficiency, information management and coordination in construction projects. However, its implementation remains uneven and is often influenced by a combination of technical, organizational and external factors. In many cases, practical challenges emerge during the transition from design to production, where BIM is inconsistently applied or frequently not integrated with established workflows and practices [1,14,19].

In Sweden, the structural conditions for digitalization are relatively favorable. Nevertheless, BIM usage still varies significantly across company types, professional roles and project phases. As noted by [3], BIM is employed far more extensively during planning and design than in production phase, where contractors often face barriers such as low internal demand, limited management support, insufficient collaboration requirements and constrained resources for training and technology development. A report by [13] highlights that small and medium-sized contractors frequently lack the capacity to implement BIM at scale, resulting in a disconnect between strategic digitalization ambitions and operational execution on construction sites. Importantly, these barriers are not solely technical or economic. Several studies emphasize that organizational structures, entrenched work practices, and a limited culture of change represent equally significant obstacles to BIM adoption in production environments [10,11]. In the context of BIM implementation, ref. [32] identified a comprehensive set of barriers, categorizing them into fifteen distinct areas: cost, expertise, legal frameworks, interoperability, awareness, organizational culture, processes, management, market demand, project scale, technology, skills, training, contractual arrangements, and BIM standards. Further, the author of [33], highlighted in a review five particularly impactful barriers: (1) traditional methods of contracting, (2) social and habitual resistance to change, (3) lack of awareness about BIM, (4) unavailability of proper training on BIM, and (5) lack of BIM experts. Similarly, ref. [34] classified BIM implementation into five overarching categories, technology, cost, management, personnel, and legal.

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Technological factors refer to limitations associated with BIM tools themselves, such as immature or underdeveloped software, lack of standardized protocols, and insufficient interoperability.

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Costrelated factors include expenses associated with acquiring BIM software and hardware, ongoing service fees, and the costs of training and upskilling personnel.

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Management factors encompass organizational and processrelated challenges. These include negative attitudes toward BIM adoption, absence of successful reference cases and management standards, the fragmented nature of the construction industry, unsuitable business models, and limited cooperation among industry stakeholders.

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Personnel factors highlight the shortage of skilled professionals with practical BIM experience. The widespread and effective use of BIM requires substantial investment in training and education to build a competent workforce.

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Legal factors stem from the underdeveloped contractual and regulatory landscape. Issues such as software imperfections can lead to legal and insurance complications, including potential litigation.

A study [35] highlights that personnel-related barriers were ranked as the most significant, while legal barriers were considered the least impactful. Similar conclusions have been drawn in other research, which suggests that most BIM implementation barriers are linked to organizational dynamics and human factors as the training of employees [34,36]. Also, findings by [35] showed that emphasized that key obstacles to BIM adoption include the need for substantial cultural change within organizations, lack of senior management support, insufficient knowledge of BIM implementation strategies, limited staff experience and skills, and the necessity to modify existing workflows to align with new digital requirements. These findings suggest that companies face greater internal resistance to BIM implementation than individual projects do, underscoring the importance of organizational readiness and leadership commitment in driving successful adoption.

In summary, numerous global studies have identified key barriers to BIM implementation. These include the absence of common standards, limited interoperability between different BIM tools, high software complexity, and fragmented data and information flows [10,37]. Additional recurring challenges include insufficient training, high implementation costs, legal liability uncertainties, and low digital maturity among the project stakeholders [25,31]. At the systemic level, weak or ambiguous client requirements, low level of standardization, and a lack of incentives within the procurement frameworks further contribute to BIM being deprioritized during the implementation phase [5,17].

To address these fragmentation issues, new delivery models such as Integrated Project Delivery (IPD) have been proposed since a well-structured delivery approach is vital for construction project success. Research highlights that early stakeholder involvement and a collaborative environment significantly boost performance [38,39]. IPD is a modern method that enhances efficiency by promoting teamwork, sharing accurate data, and leveraging new technologies. IPD improves cost, time, and quality outcomes by aligning team incentives, sharing risks and rewards, and enabling fast information exchange through multiparty contracts [38]. Studies show that IPD outperforms traditional methods in areas like quality, change management, and communication [40].

Integrated Project Delivery (IPD) also supports the implementation of Building Information Modeling (BIM). According to the American Institute of Architects [41], IPD facilitates the adoption of BIM by promoting collaborative workflows [42]. Ref. [39] highlights the importance of collaborative practices in optimizing BIM for sustainability. The contractual framework of IPD enhances BIM integration, particularly in achieving green building objectives. By fostering trust, encouraging early stakeholder involvement, and promoting collaboration, IPD helps overcome common barriers to BIM adoption [42].

Moreover, BIM can serve as a powerful tool for advancing social sustainability in green building projects. It can promote equal opportunities through inclusive design processes; raise awareness that encourages alternative consumption habits; support broad participation by involving diverse groups in decision-making; and strengthen community cohesion through integrated planning and engagement [39].

In summary, these models aim to promote more collaborative and transparent processes that support BIM integration across all project phases [19,43]. This literature review identifies 18 recurring barriers to BIM implementation (see

Table 2. Thematic Overview of Challenges to BIM Implementation in the Construction Industry.

Category	Barriers	References
Technical barriers	Lack of interoperability between BIM tools and systems	[10,15]
	Fragmented data storage and poor system integration	[19,31]
	Complex and unintuitive user interfaces	[2,20]
	Absence of common standards and file formats	[11,37]
	Inadequate technical support at the construction site	[3,10]
Organizational barriers	Lack of management support and strategic prioritization	[5,19]
	Insufficient in-house competence and training	[3,10]
	Weak project integration of BIM in the production phase	[2,14]
	Unclear responsibilities and role definitions	[20,31]
	Low internal demand from site-level personnel	[3,17]
Economic barriers	High upfront investment costs for software and equipment	[1,10]
	Limited resources for education and skill development	[3,13]
	Difficulty demonstrating short-term financial benefits, especially in smaller projects	[5,11]
Industry and cultural barriers	Reliance on traditional work methods and resistance to change	[17,19]
	Low digital maturity among subcontractors	[13,31]
	Lack of BIM requirements from clients	[3,5]
	Unclear regulations and legal uncertainty	[10,37]
	Absence of standardized procurement requirements for BIM	[11,14]

), organized into four thematic categories: technical, organizational, economic, and industry and culture-based obstacles. Together, these barriers underscore the multifaceted nature of BIM adoption, particularly in the production phase, and highlight the need for coordinated effort across multiple levels, including system integration and workforce training, governance, requirements setting and change management. Table 2 provides an overview of the main barriers identified in the literature on BIM implementation in practice, especially for contractors operating in the production phase.

Barriers to BIM implementation vary significantly across countries, as each nation approaches these challenges from distinct perspectives shaped by its unique socio-economic, regulatory, and technological context [32]. Differences in time periods, regional practices, and even individual construction projects can result in varying limiting factors [34]. Moreover, a clear digital divide exists in BIM adoption, both between small and medium-sized enterprises (SMEs) and large firms, and between developed and developing countries. This divide reflects disparities in resources, technical capacity, and institutional support, which influence the pace and effectiveness of BIM implementation [44].

3. Materials and Methods

3.1. Research Approach and Design

The purpose of this study is to examine the factors that hinder BIM implementation during execution phase of construction production, with a particular focus on contractors’ experiences within the Swedish construction industry. To support a comprehensive and in-depth analysis, the study employs a mixed-methods approach, combining a systematic literature review, a quantitative survey, and semi-structured interviews (see Figure 1). This methodological triangulation enhances the validity and reliability of the findings by integrating multiple data sources and perspectives [45,46].

The literature review aimed to map existing research on BIM usage in construction production, with a particular emphasis on implementation barriers, stakeholder perspectives and the Swedish context. Systematic searches were conducted using the databases Scopus, Web of Science and Google Scholar. Search strings included combination such as (“BIM” OR “Building Information Modeling”) AND (“construction production” OR “contractor” OR “construction phase”) AND (“barriers” OR “adoption” OR “implementation”). Only peer-reviewed articles published between 2012 and 2024 were considered. Google Scholar was used to identify industry reports and grey literature with practical significance [31].

Primary data collection was conducted through a quantitative survey targeting professionals involved in Swedish construction production. The respondent group included site managers, supervisors, project managers and BIM coordinators with hands-on experience using BIM in the production phase. The survey was structured using the TOE framework [7], which served as the analytical foundation. Three core dimensions were operationalized:

• Technological factors, e.g., software support, system integration, technical expertise.
• Organizational factors, e.g., management support, internal procedures, skills development.
• Environmental factors, e.g., customer requirements, industry practices, legal and procurement-related aspects.

Each survey item was presented as a statement and measured using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). The scale items and phrasing were adapted from previously validated instruments in technology adoption research [5,46].

The survey underwent pilot testing with two senior researchers specializing in digital transformation. They evaluated the survey for clarity, content validity, and theoretical alignment. Based on their feedback, revisions were made to the wording, logical flow, and question order. This process aligns with the established best practice for survey design and validation [47].

The final version of the survey was distributed digitally via email and professional networks, including industry forums and LinkedIn groups. A purposive sampling strategy was employed to ensure participation from individuals with relevant expertise and experience. The sample included professionals from both small and large construction companies across various regions of Sweden.

To enrich the analysis, the survey was complemented by semi-structured interviews with key industry stakeholders. The interviews were conducted by telephone and targeted individuals with strategic or operational responsibility for digital practices in the production phase. The interview questions were guided by the TOE framework and aimed to capture context-specific insights into barriers and opportunities associated with BIM adoption consisted with recommendation from previous studies [10,11]. Although the semi-structured interviews were not analyzed using a separate coding scheme, they played a vital interpretive role within the study’s mixed-methods design. Specifically, the interview responses served as a qualitative triangulation tool to enrich the interpretation of the survey findings, in accordance with established practices in construction digitalization research in construction [10]. The interviews provided contextual insights into how and why certain barriers were encountered in practice, particularly those related to management support, unclear role definitions, and external regulatory uncertainty. This qualitative input deepened the understanding of the quantitative results without constituting a standalone dataset.

The analysis of the questionnaire responses was designed to directly address the study’s four research questions, with a particular focus on the three dimensions of the TOE framework: technological, organizational and environmental barriers. To achieve this, a combination of descriptive statistics, reliability testing, correlation analysis, group comparisons, factor analysis, and regression analysis was employed.

To answer research question 1, concerning the impact of technological factors on BIM usage in the production phase, descriptive statistics were used to analyze responses to each item within technological barrier category (T1–T5). Means and standard deviations were calculated to provide an overview of how these barriers are perceived. Cronbach’s alpha was calculated to assess the internal consistency of the items within this dimension. To explore the relationship between technological barriers and actual BIM usage, Spearman correlation was conducted between each technological barrier and the item measuring frequency of use (Q6). Additionally, ANOVA or Kruskal–Wallis tests were performed to determine whether perceptions of technological barriers differ across professional roles (Q1) or company sizes (Q3).

To address research question 2, which focuses on organizational barriers, the same statistical procedures used for technological factors were applied to items O1–O5. Descriptive statistics, Cronbach’s alpha for internal consistency, and Spearman correlation tests with BIM usage (Q6) were conducted. To examine whether perceptions of organizational barriers vary based on background variables such as professional role, company size, or level of experience, group comparisons were performed using ANOVA or non-parametric equivalents.

For research question 3, which investigates environmental factors, descriptive statistics were used to analyze response patterns for items E1–E6. Internal reliability within this dimension was assessed using Cronbach’s alpha. To determine whether perceived environmental barriers differ across geographical regions (Q5) or project types (Q4), group comparisons were conducted using ANOVA or Kruskal–Wallis tests. Additionally, Spearman correlation analyses were performed between each environmental item and BIM usage (Q6).

To address research question 4, which explores how technological, organizational, and environmental barriers interact to create complexity in BIM implementation, a multi-step analytical approach was employed. First, an exploratory factor analysis (using PCA with varimax rotation) was conducted to assess whether the survey items align with the TOE framework, i.e., whether they cluster into technological, organizational, and environmental dimensions, or whether alternative groupings emerge. Next, the relationships among the three TOE dimensions were examined through correlation analysis to explore potential interdependencies. Finally, an ordinal logistic regression analysis was performed, with self-reported BIM usage (Q6) as the dependent variable and the composite indices for each TOE dimension as independent variables. The objective was to identify which categories of barriers most significantly influence low levels of BIM adoption in practice. While technological and organizational challenges were evident, they did not fully account for the variation in BIM usage. Instead, external systemic factors—such as regulatory requirements and client-driven demands appeared to function as critical gatekeepers in the implementation process.

All analyses were conducted using the statistical software programs SPSS 30 and R 4.5.0. A significant level of p < 0.05 was applied throughout. For the TOE indices, mean values were calculated for each group of items (T1–T5 for technological, O1–O5 for organizational, and E1–E6 for environmental factors). The Likert scale responses (1–5) were treated as interval data for descriptive statistics and index construction, while non-parametric methods were also employed where appropriate to ensure robustness. By combining insights from the literature review with data from the questionnaire survey, this study enabled a comprehensive analysis of the potential of Big Data to support sustainability in the construction industry. This methodological approach integrates theoretical perspectives with empirical evidence, providing a solid foundation for future research and practical development in the field [48,49].

3.2. Survey Design Grounded in the TOE Framework

To examine the factors that hinder BIM usage in construction production in Sweden, a survey instrument (see Supplementary Material S1) was developed based on the well-established TOE framework [7]. The TOE model is widely used to explain technology adoption in industrial and technically complex settings [10,50]. It is particularly relevant to the construction industry, where the effect of digitalization remains unevenly distributed, especially in the production phase, and are shaped by both internal and external influences [3,5].

The TOE framework categorizes the key factors affecting organizational technology adoption into three overarching domains:

• Technological factors: including system compatibility, usability and technical availability.
• Organizational factors: such as management support, internal procedures and training.
• Environmental factors: encompassing external customer requirements, legislation, industry practices and policies.

To ensure a strong alignment between theoretical constructs and empirical data, each survey item was explicitly designed to reflect one of the three dimensions in the TOE framework. Questions related to technological factors addressed issues such as technical deficiencies, low user-friendliness, and limited access to digital support [20]. Items under organizational factors focused on management prioritization of BIM, availability of training, and the distribution of responsibilities related to BIM functions [10,19]. Environmental factors were explored through questions on the perceived influence of client requirements, the absence of structured collaboration with external stakeholders, and the clarity and application of national and EU-level guidelines in production settings [11,31]. This approach enabled the operationalization of theoretical concepts into practical, experience-based survey questions grounded in industry realities.

The final survey instrument consisted of 21 statements organized into three thematic blocks:

• 5 technological barriers (T1–T5).
• 5 organizational barriers (O1–O5).
• 6 environmental barriers (E1–E6).

In addition to the core survey items, background questions were included to capture respondents’ professional roles, level of experience, company size, project type, geographic region of work, and self-assessed frequency of BIM use in the production phase.

Each item was rated on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree), enabling quantitative analysis of both individual indicators and composite index values for each TOE dimension. The estimated completion time was 5–7 min.

Although BIM has demonstrated potential to streamline information flows, reduce construction errors, and enhance coordination, its adoption in the production phase remains limited [14,15]. By applying the TOE model, a structured approach is created to identify both technical, organizational, and external barriers that affect implementation in practice.

Linking each survey item to the TOE dimensions enables a robust, theoretically grounded analysis of how contractors perceive barriers to BIM use in the execution phase of construction. This structure also facilitates the identification of systematic patterns across the three domains, technology, organization and the environment, and thereby supporting the study’s four research questions.

To ensure reliability and validity, the survey instrument, a two-step review process was conducted. First, face validity was evaluated through expert reviews involving academic researchers and industry professionals with extensive experience in BIM. Based on their feedback, several items were revised for clarity and relevance. Second, internal consistency was assessed using Cronbach’s alpha for each TOE dimension. While the technological and organizational constructs yielded relatively low alpha values, suggesting heterogeneity within these dimensions, the environmental index demonstrated acceptable internal consistency. These findings are further elaborated in the Results and Limitations sections.

3.3. Data Collection and Selection Strategy

This study explores the barriers to BIM implementation in construction production, with a particular emphasis on contractors’ experiences and perceptions within the Swedish construction industry. To ensure high validity and generalizability, a strategic and systematic data collection method was employed. This study targets Swedish construction companies of varying sizes and project types, aiming to capture the full spectrum of how BIM-related barriers are encountered in practice [10,31].

Construction production is one of the most complex and resource-intensive phases of the building process and remains significantly less digitalized compared to the planning and design stages [3]. Yet, BIM is widely recognized as a key technology for enhancing information flow, coordination, and quality during implementation [15]. By focusing on contractors in Sweden, this study offers context-specific insights into how technical, organizational, and environmental factors interact with national policy frameworks, industry norms, and varying levels of digital maturity [5].

The sample consists of professionals involved in construction production who have direct or indirect experience with BIM-related tasks, including site managers, supervisors, project managers, production engineers, and BIM coordinators. These roles span various operational levels and carry differing responsibilities related to planning, coordination, and digital work practices. By incorporating multiple professional perspectives, the study offers a comprehensive view of the barriers to BIM adoption, both from practical and strategic standpoints, which is essential for understanding why BIM usage often remains confined to the design phase and fails to be fully integrated into production workflows [19,20]. Though, as stated by [38,40,41], project delivery methods may have impacts on the contractor involvement in BIM implementation and hence impact on the project result and its success.

Participant selection was conducted through purposive sampling, with recruitment carried out via professional networks, industry platforms, and direct outreach to companies across different regions of Sweden. Potential respondents were identified through industry contacts and networks, including organizations such as Byggföretagen and the strategic innovation program Smart Built Environment. Additional recruitment was conducted through industry-specific forums and social media platforms targeting professionals in the construction sector.

It is important to note that subcontractors are underrepresented in this study, which primarily reflects the perspectives of main contractors. Future research should aim to include subcontractors to better capture their unique barriers and opportunities related to BIM use in the production phase.

The questionnaire was distributed to 385 professionals in Swedish construction production, including site managers, project managers, production engineers, foremen, and BIM coordinators. In total, 220 responses were received, yielding a response rate of 57%.

As previously outlined, the sample was focused on contractors actively engaged in construction production, including site managers, supervisors, project managers, production engineers and BIM coordinators. These roles span different operational levels and offer direct insight into both technical and organizational challenges. Respondents reported varying levels of BIM experience, ranging from less than one year to over ten years, with the majority indicating between three and seven years of practical use. This diversity in experience and role enhances the representativeness of the sample and strengthens the generalizability of the findings.

BIM managers were not included as a separate category, as in many small and medium-sized projects in Sweden, BIM management responsibilities are often integrated into the BIM coordinator role or distributed across the project team. By incorporating multiple contractor-level roles, the study provides a broad and nuanced understanding of the barriers to BIM adoption, both from practical and strategic perspectives, which is essential for explaining why BIM use frequently stalls at the design stage and is not fully embedded in production workflows [19,20].

Data collection was conducted through a digital questionnaire survey, complemented by semi-structured interviews with key industry professionals. The survey was distributed via email and professional networks, while the interviews were conducted through video conferencing or telephone calls. The purpose of the interviews was to deepen the understanding of the respondents’ experiences and enhance the analytical depth of the survey findings. To strengthen the representativeness of the sample and clarify respondents’ levels of expertise, the survey included questions specifically targeting practical BIM experience during the construction phase. While 89% of respondents reported having participated in at least one project where BIM was used in production, only 66% indicated having direct access to the BIM model on site. This reveals that formal project involvement does not necessarily equate to full system access or hands-on use, a distinction that is critical when interpreting perceived barriers.

To explore this further, BIM experience was analyzed across professional roles. Site managers and BIM coordinators were most likely to report both extensive project experience (more than five years) and frequent access to BIM models. In contrast, supervisors and production engineers often participated in BIM-enabled projects but lacked full model access or editing rights. Respondents categorized as “Other”, including cost estimators and logistics planners, typically reported the least experience and lowest levels of access. These differences suggest that perceptions of BIM-related barriers are shaped not only by professional role but also by the degree of practical engagement with the model. This underscores the importance of distinguishing between individuals with decision-making authority and technical responsibility for BIM, and those involved in production processes who may operate at a distance from the digital tools. The findings highlight the need for future research to more clearly differentiate types of BIM involvement (e.g., viewer-only vs. editor access) to better understand how technical and organizational barriers are experienced in practice.

3.4. Ethical Considerations in Data Collection

This study investigates barriers to BIM usage in construction production using the TOE framework as its theoretical foundation. It was conducted in accordance with established research ethics guidelines, the EU General Data Protection Regulation (GDPR) [50] and the Swedish Research Council’s guidelines for good research practice [51]. Prior to participation, respondents were informed about the study’s purpose, research design, data handling procedures, and their rights as participants. They were made aware that participation was voluntary and that they could withdraw at any time without providing a reason [52]. To safeguard participant privacy, no personally identifiable information, such as names, contact details, or company affiliations, was collected. All survey responses were analyzed in aggregate form, ensuring that individual participants or organizations could not be identified in the presentation of results. Informed consent was obtained at the point of survey distribution, where respondents actively agreed to the terms and conditions before proceeding [51]. Although direct identifiers (e.g., names, email addresses) were not collected, the study acknowledged that combinations of role, company size, and geographical region could potentially lead to indirect identification. To mitigate this risk, all data were analyzed and reported exclusively in aggregated form, in compliance with GDPR principles of data minimization and privacy protection.

Given that several survey items addressed potentially sensitive topics, such as organizational support, technical limitations, and external pressures, the questions were carefully phrased to focus on general experiences rather than company-specific shortcomings, thereby reducing the likelihood of respondents feeling exposed [52].

All data were stored in a secure digital environment with restricted access limited to the responsible research team. Upon completion of the analysis phase, the data will be permanently deleted in accordance with GDPR’s principles of storage minimization and purpose limitation [50].

This study is independent and free from commercial or political affiliations. Participants were informed that the research is conducted for non-profit purposes and that the results will not be used for marketing or internal company evaluations. By adhering to these ethical principles, the study ensures respect for participants’ rights, integrity, and autonomy, while maintaining the credibility and transparency of the research process [51].

3.5. Measurement Scales and Statistical Analysis Methods

To examine how technological, organizational and environmental factors influence the use of BIM in construction production, a survey study was conducted using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). Likert scales are widely recognized in quantitative research and are particularly effective for capturing subjective perceptions, attitudes, and perceived barriers [52]. The use of a consistent scale enables both nuanced analysis of individual responses and systematic comparisons across different respondent groups and barrier types within the three dimensions of the TOE framework: technology, organization and environment. Descriptive statistics were first employed to map response patterns within each question group: T1–T5 (technical factors), O1–O5 (organizational factors) and E1–E6 (environmental factors). For each item, the mean, standard deviation and frequency distribution were calculated. These results were visualized using tables and charts, forming the foundation for further interpretation. To assess the internal consistency within each TOE dimension, Cronbach’s alpha was calculated. Values above 0.7 were considered indicators of good internal reliability [53].

Next, Spearman’s rank correlation analysis was conducted to explore the relationship between perceived barriers in each TOE category and the self-reported frequency of BIM use in production (question Q6). Spearman’s rho is particularly suitable for ordinal data and allows for the identification of nonlinear associations between variables [53,54]. Additionally, one-way analysis of variance (ANOVA) or the non-parametric Kruskal–Wallis test when assumptions of normality and homogeneity of variance were not met, was used to examine differences between respondent groups. These tests were applied to determine whether, for example, site managers and project managers, or employees from small versus large companies, perceive BIM-related barriers differently. A significance level of p < 0.05 was used throughout the analysis.

To uncover underlying patterns in how respondents perceive BIM-related barriers, an exploratory factor analysis (Principal Component Analysis, PCA) with varimax rotation was performed. The aim was to determine whether the 16 survey indicators could be grouped into broader latent constructs. This method was chosen because the survey items were conceptually related but not independent. Factor analysis reduces the large set of observed indicators into latent constructs, enabling both data reduction and construct validation. It allows us to test whether the empirical grouping of items aligns with the theoretical TOE framework, thereby strengthening the study’s internal validity. The results revealed a natural clustering of items into three distinct factors that closely align with the TOE framework: (1) technical/practical barrier, (2) organizational structure barrier, and (3) external/regulatory barrier.

To further examine how these barrier categories interact and influence BIM usage in practice, an ordinal logistic regression analysis was conducted. In this model, self-reported BIM use (Q6) was treated as the dependent variable, while the composite index scores for each TOE dimension served as independent variables. This regression method was selected due to the ordinal nature of the BIM usage variable, which is ranked but not continuous. It allows for interpretation of the likelihood of increased BIM use based on the intensity of perceived barriers [55]. Ordinal regression is appropriate because the dependent variable was measured on a five-point Likert scale, which represents an inherent order but not equal distances between categories. Unlike linear regression, this method accommodates the ordinal nature of the data and enables interpretation of the likelihood of higher BIM adoption levels given different intensities of perceived barriers.

Indexes for each TOE dimension were constructed by calculating the mean of the respective question group (T1–T5, O1–O5, E1–E6). Although Likert scales are technically ordinal, it is widely accepted practice to treat them as interval scales for index construction and certain statistical analyses [56].

To maintain a systematic connection between the study’s purpose and its research questions, each analysis step was anchored in the TOE framework. The first research question, which explores how technological factors such as system compatibility, hardware availability, and usability affect BIM usage, was addressed using descriptive statistics, Cronbach’s alpha, Spearman correlations and group comparisons. The second research question, focused on organizational barriers to implementation, was examined using similar methods, with particular attention to differences across roles and company sizes. The third research question, concerning external influences such as industry standards and client expectations, was analyzed through responses to items E1–E6, including correlation with BIM usage and comparisons across project types and geographic regions. The fourth and final research question, which investigates how barriers interact and contribute to implementation complexity, was addressed through PCA, index-level correlation analysis, and ordinal logistic regression.

Together, this analytical framework provides a robust, theory-driven, and multi-layered approach to understanding both isolated and interrelated barriers to BIM implementation in Swedish construction production. By integrating the TOE dimensions with clearly defined analytical procedures, the study ensures that its findings are both statistically sound and directly aligned with its research objectives. Importantly, the results raise critical questions about the applicability of the TOE framework in the context of construction production. The assumption of equal relevance among technological, organizational, and environmental factors may not fully reflect the realities of a project-based, fragmented industry where external mandates and procurement systems heavily influence implementation decisions. Future research may benefit from expanding the framework to incorporate institutional and market-level drivers that more accurately capture the dynamics of BIM adoption in practice.

4. Results

4.1. Professional Roles, Experience and BIM Use Among Respondents

To establish a foundational understanding of the respondents’ background, a descriptive analysis was carried out on their professional roles, experience and use of BIM in construction production. The findings are presented numerically in

Table 3. Descriptive statistics of respondents’ professional roles, experience, and BIM use in construction production.

Background Variable	Percentage (%)
Site Manager	26
Project Manager	23
Foreman	17
Production Engineer	19
BIM Coordinator	10
Other	5
Less than 2 years	6
2–5 years	14
6–10 years	33
11–20 years	27
More than 20 years	20
Small (1–49 employees)	18
Medium (50–249 employees)	43
Large (250+ employees)	39
Residential Buildings	28
Commercial Buildings	17
Public Buildings	21
Infrastructure	18
Mixed	16
Other	0
Northern Sweden	12
Central Sweden	30
Southern Sweden	48
Nationwide/Multiple	10
Daily	26
Weekly	32
Monthly	18
Rarely	15
Never	9

and illustrated visually in Figure 2.

As shown in the table, site managers (26%) represent the largest professional group in the sample, followed closely by project managers (23%) and production engineers (19%). These roles are pivotal to the practical execution of construction production, encompassing responsibilities such as planning, managing and coordinating activities on site. Their predominance in the sample aligns with the study’s objective to explore BIM implementation from a production-oriented contractor perspective [3,19]. Supervisors account for a substantial portion of the respondents (17%). and, given their close collaboration with both tradespeople and project management, they offer valuable insights into the practical applicability of BIM. BIM coordinators and digital coordinators make up 10% of the sample, contributing a crucial digital and strategic dimension that complements previous research on digital roles in construction projects [19].

To establish a clear context for the respondent selection, survey responses to questions Q1–Q6 were analyzed. These questions addressed professional role, industry experience, company size, project type, geographical location and frequency of BIM use in construction production. This background analysis provides a crucial foundation for interpreting the study’s broader findings. Regarding industry experience, the data reveal that most respondents possess substantial professional expertise: 33% have 6–10 years of experience, 27% have 11–20 years, and 20% have over 20 years in the field. Only 6% have less than two years of experience. This indicates that the vast majority bring deep practical insight into production-related barriers and digitalization issues [15].

In terms of company size, 43% of respondents are employed by medium-sized companies (50–249 employees), 39% by large companies (250+ employees), and 18% by small companies (1–49 employees). This distribution enables meaningful comparisons of BIM-related barriers across different organizational scales, considering variations in resources and operational complexity [9,10]. The analysis of project types (Q4) shows a diverse range of construction environments: 28% of respondents primarily work on residential projects, 21% on public buildings, 18% in infrastructure, and 17% in commercial construction. Additionally, 16% report on involvement in mixed project environments. This diversity enhances the generalizability of the study’s findings.

The respondents are geographically distributed across Sweden (Q5), with the majority located in southern Sweden (48%), followed by central Sweden (30%) and northern Sweden (12%). An additional 10% work nationally or across multiple regions. This distribution mirrors the actual regional concentration of construction activity in Sweden, where the southern region, particularly in the housing sector, dominates production. Given that nearly half of the respondents are based in southern Sweden, the findings may reflect region-specific conditions related to digital maturity and policy frameworks. While regional differences were not the focus of this study, they represent a valuable avenue for future research.

BIM usage in production (Q6) varies in frequency: 26% of respondents use BIM daily, 32% weekly, 18% monthly, 15% rarely, and 9% never. As illustrated in Figure 2, more than 75% of respondents engage with BIM in production on a regular basis. This indicates that the sample includes both highly experienced users and those with limited exposure, thereby enhancing the study’s ability to identify barriers across different levels of BIM adoption [1,14].

Overall, this background analysis demonstrates that the respondent sample is broad and well-distributed across professional role, experience level, company size, project type, geographic regions, and BIM usage. It comprises professionals with direct, hands-on experience in construction production and reflects the real-world diversity of the Swedish construction industry. The relatively high frequency of BIM use further reinforces the relevance of this study and provides a strong foundation for examining perceived obstacles in practice.

4.2. Technological Barriers to the Use of BIM in Construction Production

To explore respondents’ perceptions of technological barriers to BIM use in construction production, a comprehensive analysis was conducted. This included descriptive statistics, reliability testing with Cronbach’s alpha, Spearman correlations with general attitude towards BIM (O1), and group comparisons based on frequency of use. The five indicators T1–T5 represent core technological barriers such as software compatibility, technical infrastructure, limited access to models, user-friendliness, and availability of technical support. These factors have also been noted in previous studies as significant barriers to BIM adoption in production settings [14,19].

The descriptive analysis of the technological indicators (T1–T5) reveals that respondents generally perceive technological barriers as moderately prevalent, though experiences vary depending on the specific type of barrier (see

Table 4. Descriptive Statistics of Technological Barriers (T1–T5).

Code	Technological Barrier	Mean (M)	Standard Deviation (SD)
T1	Compatibility with other systems	3.05	1.39
T2	Insufficient on-site infrastructure	3.10	1.36
T3	Access to BIM models on-site is problematic	3.13	1.37
T4	Tools not user-friendly for site staff	2.95	1.41
T5	Technical issues disrupt BIM use	2.92	1.40

). The highest average score was recorded for T3: Problems accessing BIM models on the construction site (M = 3.13; SD = 1.37). This indicates that many respondents still encounter difficulties in accessing updated and properly integrated models in field environments, an issue that is critical for operational efficiency and coordination during production. The second highest score was observed for T2: Limitations in technical infrastructure (M = 3.10; SD = 1.36), suggesting that factors such as network stability, hardware availability, and system capacity continue to hinder BIM implementation in some organizations. These limitations may include, for example, poor internet connectivity on construction sites, insufficient mobile devices for model access, or incompatible technical systems that obstruct seamless use of BIM tools.

The lowest mean was observed for T5: Insufficient technical support (M = 2.92; SD = 1.40), indicating that while access to support services and technical assistance is perceived as a barrier, it is less prominent than more structural or infrastructure-related challenges. However, the relatively high standard deviations across all indicators suggest considerable variation in how respondents experience technological barriers. This variability is likely influenced by contextual factors such as company size, project complexity, and prior with digital tools.

Overall, the results presented in Table 4 indicate that technological barriers should not be viewed as a uniform issue, but rather as a complex and multifaceted challenge. Some barriers appear to be more widespread, while others are highly context dependent. These findings align with previous research that underscore the importance of differentiating between various types of technical barriers, such as system compatibility, on-site functionality, and user support, when examining barriers to BIM implementation in construction production [37,57].

To evaluate whether the five technological indicators (T1–T5) collectively represent a coherent underlying dimension, namely a unified technological barrier index, Cronbach’s alpha was calculated. The resulting value of 0.10 is notably low and falls well below the commonly accepted threshold of 0.70 for satisfactory internal reliability [58]. This outcome suggests that the indicators do not exhibit sufficient covariance to be interpreted as manifestations of a single latent construct. Instead, they appear to capture distinct and independent aspects of technological barriers. In practical terms, this means that each indicator, such as limited model access (T3), technical infrastructure (T2) or lack of technical support (T5), should be treated as a standalone barrier rather than as a subcomponent of a unified barrier framework. The very low, and in some cases negative, Cronbach’s alpha values further reinforce the conclusion that the five technological (and organizational) indicators do not form a unidimensional construct. This implies that each type of barrier may be perceived independently, reflecting the nuanced and context-specific nature of BIM-related barriers in construction production.

This interpretation is also supported by the results in Table 4, where both the mean values and standard deviations vary significantly across indicators, highlighting the diverse ways in which respondents experience these barriers.

The finding is methodologically important, as it supports the decision to analyze each indicator individually rather than as part combined technological index. It also underscores the complexity highlighted in previous studies regarding technology-related barriers in the construction industry, where technical challenges are seldom uniform. Instead, they are shaped by contextual factors such as project environment, technical competence and system selection [15,43]. Rather than conceptualizing technology as a singular barrier, future analyses should differentiate between specific types of technological obstacles, such as system compatibility, hardware limitations, and access to technical support, in order to more accurately reflect the diversity and practical relevance of these barriers within construction production.

To examine whether the perceptions of technological barriers are associated with the respondents’ overall attitude towards BIM use, a series of Spearman correlations analyses were conducted between each technological indicator (T1–T5) and the variable O1, which measures general BIM attitude. As shown in

Table 5. Spearman Correlations Between Technological Barriers (T1–T5) and General BIM Attitude (Q6).

Technological Barrier	Spearman’s ρ with Q6	Significance (p)	Interpretation
T1. Compatibility with other systems	<0.10 (ns)	>0.05	Very weak, non-significant
T2. On-site infrastructure	<0.10 (ns)	>0.05	Very weak, non-significant
T3. Access to BIM models	<0.10 (ns)	>0.05	Very weak, non-significant
T4. User-friendliness of BIM tools	<0.10 (ns)	>0.05	Very weak, non-significant
T5. Technical disruptions in BIM use	<0.10 (ns)	>0.05	Very weak, non-significant

Note: ns = not significant; |ρ| < 0.10 for all items.

, the results consistently revealed very low and statistically non-significant correlations. The values ranged from weakly positive to weakly negative, with all absolute coefficient below 0.10 (|ρ| < 0.10), indicating no clear direction or systematic relationships. This lack of correlation suggests that respondents who report strong technological barriers, such as limited model access (T3) or inadequate technical infrastructure (T2), do not necessarily hold more negative attitudes towards BIM. Likewise, a generally positive attitude towards BIM does not appear to correspond with a reduced perception of technical challenges. These findings imply that perceived technological barriers and attitudes toward BIM are analytically distinct dimensions, not easily connected through simple linear associations. One possible interpretation is that general attitudes towards BIM are shaped by factors beyond the immediate technical context. Previous research has shown that elements like organizational support structures, educational background, prior experience with digital projects and management commitment may exert a stronger influence on individuals’ attitudes towards new technology than the technical limitations themselves [59,60]. It is also plausible that technical barriers are viewed as a routine aspect of digitalization and therefore do not significantly affect attitudes, particularly among respondents who are already familiar with digital tools.

Overall, the results presented in Table 5 indicate that there is no direct linear relationship between technical experiences and general attitude towards BIM. This suggests that technological barriers should be examined independently from attitudinal factors. To gain a deeper understanding of how attitudes toward BIM are formed, future analyses should incorporate additional explanatory variables, such as organizational culture, policy expectations, and prior experience with digital technologies.

To explore how the perceptions of technological barriers vary according to the extent of actual BIM use, a comparative analysis was conducted across five user groups: respondents who reported using BIM daily, weekly, monthly, rarely or never. The results, presented in

Table 6. Mean Ratings of Technological Barriers (T1–T5) by BIM Usage Frequency.

BIM Usage Frequency	T1. Compatibility	T2. Infrastructure	T3. Model Access	T4. User-friendliness	T5. Tech Support
Daily	3.45	3.50	3.60	3.30	3.35
Weekly	3.10	3.15	3.25	2.95	3.00
Monthly	2.90	3.05	3.10	2.85	2.90
Rarely	2.85	2.95	3.00	2.80	2.85
Never	2.70	2.85	2.75	2.70	2.65

, reveal clear and systematic patterns in the reported technological barriers (T1–T5) across these groups.

Daily BIM users consistently reported higher levels of perceived technological barriers compared to less frequent users. The differences were especially pronounced for T1: Lack of compatibility between systems and software, and T3: Problems accessing models on the construction site, both of which were rated as more problematic by frequent users. These findings suggest that increased BIM usage tends to heighten awareness of technical challenges, rather than diminishing them through familiarity or routine. In contrast, respondents who rarely or never use BIM reported lower levels of perceived technological barriers. This pattern likely reflects limited exposure rather than the actual absence of problems, if the tools are not actively used, their limitations remain unexperienced. Similar trends have been observed in previous research, where technological constraints were more frequently reported by practitioners engaged in hands-on work than by staff operating at a strategic or managerial level [20,61].

This finding is methodologically significant, as it suggests that technological barriers are not solely linked to the implementation of BIM but also emerge as usage-related challenges. In other words, these barriers are primarily encountered in day-to-day operational work rather than during theoretical planning or strategic discussions. The visual summary in Figure 1 reinforces this interpretation, showing that average ratings for indicators T1–T5 increase with the frequency of BIM use.

Overall, the results in Table 6 indicate that perceptions of technological barriers are strongly shaped by practical experience. This implies that individuals who are more advanced in their digital implementation journey do not necessarily experience fewer obstacles, rather, they may be more attuned to the limitations of the systems they use. These insights carry important implications for the design of strategies: promoting the adoption of BIM tools is not sufficient on its own, ongoing support is essential to address the challenges that arise as usage intensifies.

In conclusion, the analysis demonstrates that technological barriers to BIM use in construction production should not be viewed as a uniform or monolithic phenomenon. Findings from the descriptive statistics, the reliability testing, the correlation analysis, and group comparisons consistently reveal that various technological barriers, such as system incompatibility, inadequate infrastructure, limited model access, and lack of technical support, are experienced differently across user groups and exhibit distinct characteristics.

The very low reliability score (Cronbach’s alpha = 0.10) clearly indicates that the indicators do not constitute a coherent latent construct and should therefore be examined individually. This heterogeneity in the barrier structure is further supported by the varying estimates across the indicators in Table 4, suggesting that certain technological barriers (e.g., field functionality) are more prevalent than others (e.g., technical support). Consequently, constructing a general BIM barrier index risks obscuring meaningful distinctions between different types of technological challenges.

Moreover, the correlation analysis (Table 5) revealed that there are no significant relationships between perceived technical barriers and the respondents’ overall attitude towards BIM. This suggests that these two dimensions are analytically distinct: it is entirely possible for individuals to hold a positive view of BIM conceptually while simultaneously encountering practical technical limitations in their daily work, and vice versa. This finding underscores the importance of not overestimating the explanatory power of attitudes when assessing actual technology use in production contexts.

The group comparison in Table 6 further revealed that respondents who use BIM daily reported higher levels of technological barriers than those who rarely or never engaged with the technology. This finding suggests that some barriers only become apparent during operational use, highlighting that implementation barriers often emerge through practical application rather than during initial planning. It challenges the assumption that barriers diminish with experience and instead emphasizes the need for continuous support and adaptation beyond the point of implementation.

Overall, the results demonstrate that technological barriers in construction production are complex, varied, and highly dependent on usage. To ensure the effective and sustainable application of BIM in practice, targeted interventions are needed to address each type of barrier individually. These may include improving system compatibility, enhancing technical infrastructure, developing mobile solutions for on-site access, and providing fast, user-centered technical support. Without such tailored adaptations, the practical utility of BIM may remain constrained, regardless of its theoretical appeal.

The analysis clearly shows that technological barriers to BIM use in production should not be treated as a uniform phenomenon. The technical challenges vary both in perceived severity and in relation to usage frequency. The low internal reliability among indicators and the absence of significant correlations with general BIM attitudes suggest that each barrier must be examined and addressed independently. The fact that daily users report higher levels of perceived barriers underscores the importance of technical standardization, interoperability solutions, and responsive support systems to enable effective and enduring BIM integration in construction production.

4.3. Organizational Barriers to the Use of BIM in Construction Production

To identify key organizational barriers to BIM use in production, five indicators (O1–O5) were analyzed, focusing on management, resource allocation, communication, division of responsibilities, and corporate culture. These dimensions have been recognized in prior research as crucial factors that can impede the progress of digitalization within the construction industry [14,37,43].

The descriptive analysis of the organizational barriers (O1–O5) reveals that they are perceived as moderately prevalent among respondents, with average scores ranging from 2.89 to 3.06 (see

Table 7. Descriptive Statistics of Organizational Barriers (O1–O5).

Organizational Barrier		Mean (M)	Standard Deviation (SD)
O1	BIM is not prioritized by management	2.91	1.44
O2	Insufficient resources for BIM implementation	3.06	1.47
O3	Poor internal communication regarding BIM	3.04	1.45
O4	Weak digital leadership within the organization	2.89	1.43
O5	Unclear responsibility allocation for BIM tasks	2.98	1.46

). This suggests that while organizational barriers are present across many projects, they are not viewed as overwhelming or structurally dominant in day-to-day operations. However, the variation in mean values and standard deviations indicates that certain barriers are encountered more frequently and perceived as more severe than others. The most prominent barrier was O2: Insufficient resources for BIM (M = 3.06; SD = 1.47), highlighting that many organizations continue to struggle with inadequate staffing, limited time, and constrained financial resources for the full implementation and maintenance of BIM processes. This challenge is particularly acute in smaller companies or project-driven environments, where digital initiatives often take a back seat to immediate delivery demands. Previous studies have similarly identified resource limitations as a key factor contributing to the slow pace of technology integration in the construction industry [37,62].

The second highest-rated barrier was O3: Insufficient communication within the organization (M = 3.04; SD = 1.45), indicating that internal coordination, role clarity, and information flow remain significant challenges. Given that BIM implementation requires collaboration across multiple internal functions, including technology, project management and production, communication deficiencies can result in fragmented digital processes and diminished effectiveness. This finding aligns with previous research emphasizing that organizational integration is a critical factor in realizing the full value of BIM [14,63].

The lowest mean score was recorded for O4: Weak digital leadership (M = 2.89; SD = 1.43), though this still reflects a clear presence of the barrier. While not rated as severely as resource constraints or communication issues, the wide variation in responses suggests that some organizations face substantial challenges related to management commitment to digital initiatives. This is concerning, as leadership support is consistently identified in digital transformation literature as a key driver of successful implementation [57,64].

The relatively high standard deviation for all indicators suggests that the experience of organizational barriers varies significantly between organizations. These differences are likely influenced by contextual factors such as company size, project complexity, geographical distribution, and client type. Prior studies have shown that organizational structures and levels of digital maturity in the construction industry are highly context-dependent [57], supporting the interpretation for these variations as a reflection of the sector’s fragmented nature.

Overall, the results in Table 7 clearly indicate that organizational barriers are a present but unevenly distributed challenge within Swedish construction production. The three most critical areas, resource availability, communication gaps, and leadership commitment, should be prioritized when developing organizational strategies aimed at fostering more effective and embedded BIM implementation.

To assess whether indicators O1–O5 reflect a common underlying construct, namely a collective measure of organizational barriers to BIM adoption, internal consistency was calculated using Cronbach’s alpha. The analysis yielded a very low and negative value (α = −0.075), indicating a lack of covariation among the indicators and, in some cases, even opposing response patterns.

A negative alpha value is rare in applied research and suggests that certain indicators may be inversely related or conceptually disconnected. This finding rules out the possibility of combining the indicators into a reliable index or scale representing a coherent latent construct of organizational barrier. Instead, it implies that the organizational dimensions being assessed, such as resources constraints, weak digital leadership, poor communication, ambiguous responsibility structures, and low prioritization of digitalization, are perceived by respondents as distinct and context-dependent challenge.

This result aligns with previous studies highlighting the complexity and variability of organizational conditions for technology adoption in the construction sector, which are often influenced by factors such as company size, leadership culture, project organization, and the degree of decentralization [15,64]. The lack of covariation among the indicators reinforces the argument that organizational barriers should be examined and addressed individually, rather than treated as a unified or monolithic issue.

From a methodological standpoint, the findings indicate that constructing a combined organizational index is not statistically justified. Instead, interpretations should be based on each individual indicator. Practically, this means that initiatives aimed at promoting BIM adoption must be tailored to address specific organizational barriers, rather than relying on broad or generalized assumptions about organizational behavior.

In summary, the very low internal reliability confirms that organizational barriers in construction production are multifaceted and should be treated as distinct dimensions. This conclusion aligns well with previous theoretical and empirical research on the complexity of organizational dynamics in digital transformation [57,64].

To explore whether perceived organizational barriers are associated with respondents’ general attitudes towards BIM use in construction production, a Spearman correlation analysis was conducted between indicators O2–O5 and the variable Q6, which operationalizes overall BIM attitude. As shown in

Table 8. Spearman Correlations Between Organizational Barriers (O2–O5) and General BIM Usage (Q6).

Organizational Barrier		Spearman’s ρ with Q6	Significance (p)	Interpretation
O2	Insufficient resources for BIM implementation	<0.10 (ns)	>0.05	Very weak, not significant
O3	Poor internal communication regarding BIM	<0.10 (ns)	>0.05	Very weak, not significant
O4	Weak digital leadership within the organization	<0.10 (ns)	>0.05	Very weak, not significant
O5	Unclear responsibility allocation for BIM tasks	<0.10 (ns)	>0.05	Very weak, not significant

Note: ns = not significant. All correlations are below |ρ| < 0.10.

, all correlations were very weak (|ρ| < 0.10) and statistically non-significant. Neither positive nor negative correlation values reached a level of analytical significance. This indicates that there is no clear evidence to suggest that organizational barriers, such as insufficient resources, poor communication, weak digital leadership, or unclear division of responsibilities, directly influence how positively or negatively a respondent views BIM as a concept or working method. In other words, individuals may hold favorable attitudes towards BIM even within organizations facing substantial structural challenges, or conversely, express skepticism despite well-supported environments. This lack of correlation suggests that attitudes towards BIM in construction production are shaped by factors beyond the internal organizational structure.

Previous research has shown that elements such as perceived usefulness, ease of use, educational background, and hand-on experience with digital tools tend to have a stronger influence on the individual attitudes than organizational culture or internal processes [20,59]. It is also plausible that attitudes are relatively stable and evolve gradually, making them less sensitive to short-term organizational changes, particularly in cross-sectional data.

Another possible interpretation is that respondents differentiate between personal beliefs and organizational realities. For instance, they may view BIM as valuable and necessary, while simultaneously perceiving their organization as ill-equipped to implement it effectively. In such cases, the lack of correlation between attitude and perceived barriers reflects the distinct nature of these dimensions, each shaped by different underlying logics.

Finally, the results presented in Table 8 suggest that organizational barriers and individual attitudes towards BIM are relatively independent phenomena within the analyzed data. For researchers, this implies that attitude-based analyses should be complemented by contextual variables, rather than focusing solely on internal structural factors. For practitioners, the findings highlight that a positive attitudinal climate does not necessarily indicate the absence of organizational barriers. Structural changes, improved communication, and strengthened leadership require dedicated strategies, regardless of how the technology is perceived by individuals.

To examine whether perception of organizational barriers differs based on the type of organization or the respondent’s professional role, a series of Kruskal–Wallis tests were conducted for each indicator within the organizational domain (O2–O5). The analysis compared groups by company size (small, medium, and large companies) and professional category (e.g., project manager, site manager, production engineer, digital coordinator). As shown in

Table 9. Kruskal–Wallis Tests of Organizational Barriers (O1–O5) by Company Size and Role.

Organizational Barrier		p-Value (Company Size)	p-Value (Role)	Interpretation
O1	BIM is not prioritized by management	>0.29	>0.29	No significant difference
O2	Insufficient resources for BIM implementation	>0.29	>0.29	No significant difference
O3	Poor internal communication regarding BIM	>0.29	>0.29	No significant difference
O4	Weak digital leadership within the organization	>0.29	>0.29	No significant difference
O5	Unclear responsibility allocation for BIM tasks	>0.29	>0.29	No significant difference

, the results revealed no statistically significant differences between the groups: all p-values exceeded 0.29. This indicates that there is no evidence to support the notion that different actors within the industry systematically perceive organizational barriers differently. This absence of significant variation is particularly noteworthy given prior research, which often emphasizes organizational size as a key determinant in managing digital transformation. Larger firms, for instance, are typically assumed to possess greater resources, more formalized processes, and stronger strategic leadership [23,63], which theoretically should result in lower perceived barriers. However, the present analysis challenges this assumption, suggesting instead that organizational barriers are pervasive and commonly experienced across roles and organizational scales.

A similar pattern emerged when analyzing differences across professional roles. As illustrated in Figure 2, the variation in mean values between different professional groups are minimal, even among roles that, in theory, should have different perspectives on BIM-related issues. For example, the comparison between technology specialists (closely aligned with digitalization) and production managers (primarily focused on operational control) revealed only marginal differences. This convergence in perceptions may indicate that digitalization has evolved into a broad organizational concern, rather than a specialized domain confined to specific roles. It could also reflect growing organizational maturity within the industry, where various functions increasingly share a common understanding of the barriers to BIM implementation.

An alternative interpretation is that the lack of variation reflects a standardized level of organizational support for BIM across the industry. In this view, many actors, regardless of company size or professional role, may be operating at a similar stage of digital development, which would explain the consistently small differences observed both statistically and visually.

Overall, the results presented in Table 9 indicate that organizational barriers are perceived as relatively uniform across various types of organizations and professional roles. This suggests that broad, industry-wide initiatives, rather than narrowly targeted interventions—may be effective in addressing these challenges. It also implies that strategies aimed at enhancing organizational conditions for BIM adoption should be designed for wide applicability, rather than customized for specific stakeholder groups.

In conclusion, the analysis reveals that organizational barriers to BIM implementation in construction production are moderately prevalent, yet they represent conceptually and practically distinct challenges rather than a single unified phenomenon. The indicators examined, insufficient resources (O2), lack of communication (O3), weak digital leadership (O4), and unclear division of responsibilities (O5), exhibit variation in both mean values and response distribution (see Table 7), suggesting that they correspond to different dimensions of organizational capacity. This interpretation is statistically supported by the low and even negative reliability score (Cronbach’s alpha = –0.075), confirming that the indicators do not co-vary and should not be aggregated into a single index.

Furthermore, the correlations between these organizational barriers and respondents’ general attitudes toward BIM (Q6) were consistently weak and statistically insignificant (see Table 8). This indicates that perceived organizational challenges are not closely linked to individual attitudes toward digital tools. Instead, such attitudes are likely shaped by other factors, such as technical functionality, educational background, or prior experience, rather than by the organizational context itself [20,59]. Group comparisons also showed no significant variation in the perception of organizational barriers based on company size or professional role (Table 9). This finding is particularly noteworthy, as it contrasts with earlier research that emphasized these variables as key determinants in the digitalization process [37,64]. The results suggest that organizational barriers are largely industry-wide, and that the perception of these barriers remains relatively consistent across different roles and organizational contexts.

Taken together, the findings underscore the need for differentiated, role-specific strategies to address organizational barriers, rather than relying on generic change models or standardized policy instruments. Each indicator reflects a distinct organizational issue, such as resource limitations, leadership deficiencies, or coordination challenges, and therefore demands tailored interventions. Prioritized areas for organizations seeking to foster more robust and sustainable BIM adoption include strengthening resource allocation, enhancing internal communication, and clarifying roles and responsibilities.

Overall, the analysis demonstrates that organizational barriers are widespread, heterogeneous and relatively independent of both individual attitudes and organizational characteristics. This reinforces the case for a more nuanced, context-sensitive approach to organizational change in the digitalization of construction production.

4.4. Environmental Barriers and External Factors Affecting BIM Use

To examine how external and environmental factors influence BIM use in construction production, six indicators (E1–E6) were analyzed, focusing on regulations, industry requirements, public incentives, climate targets, certification systems and societal sustainability expectations. Previous research has identified these factors as critical for establishing the conditions necessary for successful technology adoption in the construction industry [65,66].

The analysis of the environmental and external barriers reveals that respondents perceive these factors with varying degrees of intensity. As shown in

Table 10. Descriptive Statistics of Environmental Barriers (E1–E6).

	Environmental Barrier	Mean (M)	Standard Deviation (SD)
E1	Clients rarely demand BIM use during production	3.20	1.33
E2	Lack of clear external requirements or industry standards	3.32	1.34
E3	Collaboration with external actors is difficult	3.05	1.31
E4	Legal and contractual uncertainties hinder BIM use	2.88	1.29
E5	BIM-related policy is poorly communicated to site personnel	2.94	1.28
E6	Lack of societal pressure for sustainability	2.49	1.30

, the average values for indicators E1–E6 range from moderately high to relatively low, suggesting that external influences are not experienced as uniform or consistently impactful barriers The highest-rated barrier was E2: Lack of clear external requirements (M = 3.32; SD = 1.34), indicating that many respondents feel there is an absence of well-defined guidelines, standards, and regulations mandating BIM use in production. This finding aligns with previous studies that have identified the lack of external pressure, such as public procurement mandates, industry-wide standards, or certification frameworks, as a key obstacle. In such cases, BIM is often perceived as a voluntary or optional initiative rather than an integrated and essential component of the construction process [37,65].

Several indicators, such as E1: Low customer demand (M = 3.12; SD = 1.32) and E3: Lack of digital maturity of partners (M = 3.08; SD = 1.31), also received relatively high scores. These findings indicate that market dynamics and supply chain conditions continue to pose substantial challenges to broader BIM adoption. This is especially pertinent in the construction industry, which is characterized by its project-based structure and fragmentation. Effective BIM implementation requires coordinated digital collaboration among numerous stakeholders. However, disparities in digital maturity and expectations across actors can lead to inertia in the adoption process, even when individual companies demonstrate strong internal commitment to BIM integration [67,68].

In contrast, E6: Lack of societal pressure around sustainability received the lowest rating (M = 2.49; SD = 1.30). This finding is noteworthy, as it suggests that societal norms and public discourse on sustainability have yet to emerge as strong drivers of digitalization in construction production. Despite the growing focus on climate targets and energy efficiency within the industry, the results imply that sustainability rhetoric has not yet translated into concrete operational requirements or regulatory signals within the practical building context. Furthermore, this may reflect a prevailing perception of BIM as primarily a technical tool, rather than a strategic enabler of sustainability outcomes, an interpretation that diverges from the role BIM is often assigned in governmental initiatives and policy frameworks [12]. The relatively high standard deviations across several indicators also suggest considerable variation in how environmental barriers are experiences across different projects, companies and regions. This variability points to the context-dependent nature of these barriers, influenced by factors such as the structure of the demand-setting entity, geographical location, contract type, and client profile. For instance, public clients involved in large-scale projects may impose different requirements than private actors in smaller assignments, thereby shaping the perceived strength of external expectations.

Overall, the results presented in Table 10 indicate that environmental and external barriers to BIM adoption are not uniform. However, the absence of clear requirements and externally driven incentives emerges as the most prominent barriers. This underscores the need for BIM implementation strategies to extend beyond internal resources and technical capabilities, and to actively foster a robust external framework for digitalization in construction production, through mechanisms such as regulated procurement processes, standardized guidelines, and coordinated efforts among industry stakeholders.

To evaluate whether the indicators E1–E6, representing various environmental and external barriers, could be aggregated into a single index, internal consistency was assessed using Cronbach’s alpha. The resulting value of α = 0.659, which according to [58]. moderate internal consistency, which is considered acceptable for exploratory analysis, though not sufficient to guarantee a fully uniform measure. In contexts where indicators reflect different but interrelated dimensions of a broader concept, such as policy, regulation, and market influence, a threshold above 0.6 is generally deemed adequate [69,70].

This level of co-variation suggests that respondents experience some overlap in how they perceive external barriers, while each indicator still captures distinct aspects of the broader phenomenon. For instance, E1 addresses customer expectations, E2 highlights the absence of formal requirements, E3 focuses on the digital maturity of partners, and E4–E6 pertain to various sustainability and policy-related deficiencies. This diversity reflects the multifaceted and institutionally embedded nature of external influences on technology adoption [71].

Compared to the technological and organizational indicators in this study, which demonstrated very low and even negative reliability, the environmental indicators show greater potential for aggregation into a coherent index. This finding aligns with previous research suggesting that external factors, such as policy pressures and market demands, tend to be perceived as more consistent barriers than internal organizational structures [37,72].

In summary, the Cronbach’s alpha result supports the use of an environmental index (Env_Index) for further quantitative analysis, such as regression modeling, provided that the indicators are recognized as not entirely homogeneous. The moderate reliability offers sufficient statistical justification for constructing an exploratory composite index, while also reflecting the inherent heterogeneity of the institutional landscape surrounding BIM adoption in the construction industry.

To assess the extent to which perceived environmental and external barriers are associated with the individual’s general attitudes towards BIM use in production, a Spearman correlation analysis was conducted between the indicators E1–E6 and the variable Q6 (operationalized as BIM_Use). As shown in

Table 11. Spearman Correlations Between Environmental Barriers (E1–E6) and General BIM Usage (Q6).

	Environmental Barrier	Spearman’s ρ with Q6	Significance (p)	Interpretation
E1	Clients rarely demand BIM use during production	≤0.10 (ns)	>0.13	Very weak, not significant
E2	Lack of clear external requirements or industry standards	≤0.10 (ns)	>0.13	Very weak, not significant
E3	Collaboration with external actors is difficult	≤0.10 (ns)	>0.13	Very weak, not significant
E4	Legal and contractual uncertainties hinder BIM use	≤0.10 (ns)	>0.13	Very weak, not significant
E5	BIM-related policy is poorly communicated to site personnel	≤0.10 (ns)	>0.13	Very weak, not significant
E6	Lack of societal pressure for sustainability	≤0.10 (ns)	>0.13	Very weak, not significant

Note: ns = not significant. All correlations are |ρ| ≤ 0.10 and p > 0.13.

, all correlations coefficients fell within the range |ρ| ≤ 0.10, indicating very weak relationships. Moreover, all correlations were statistically non-significant (p > 0.13), confirming that there is no systematic association between perceived environmental barriers and the respondents’ attitude towards BIM.

This is a theoretically significant finding. Although factors such as lack of customer demand (E1), absence of regulatory guidance (E2), and low digital maturity across the industry (E3) are frequently cited in prior research as major barriers to BIM adoption [37,65], the results of this study suggest that external conditions do not directly shape individual attitudes toward technology. Instead, attitudes appear to be influenced by more immediate and experiential factors—such as ease of use, prior exposure, and perceived internal benefits [20,59].

One possible interpretation is that environmental barriers act as structural constraints than psychological impediments. Institutional theory suggests that such frameworks, comprising rules, norms, and industry practices, shape organizational behavior without necessarily affecting individual perceptions [71]. In this context, external factors may represent objective barriers to implementation, but respondents perceive them as beyond their personal control. As a result, these factors are not internalized in their individual attitudes toward BIM.

An alternative explanation is that individuals with a strong positive attitude toward BIM remain optimistic about technology regardless of external challenges. They may have developed a cognitive distinction between their belief in BIM’s potential and the limitations imposed by the external environment. This could account for why some respondents who clearly recognize environmental barriers still rate BIM highly on the attitude scale.

In summary, Table 11 indicates that the environmental obstacles reported by respondents do not significantly influence their underlying attitudes toward BIM. For practitioners, this suggests that fostering a positive attitudinal climate alone is insufficient to overcome external barriers. Structural interventions, such as regulatory mandates or changes in procurement practices, are likely necessary for BIM to achieve broader practical adoption. For researchers, these findings reinforce the importance of distinguishing between individual-level and institutional-level influences, aligning with prior studies on technology adoption in the construction sector [68,69].

The analysis of the environmental and external factors reveals that they represent a significant yet multifaceted, obstacle to a broader and more consistent adoption of BIM in Swedish construction production. Among the indicators in this category, the lack of clear external requirements (E2) emerged as the most prominent barrier, receiving the highest mean score in the descriptive analysis (M = 3.32; see Table 10). This suggests that respondents perceive the absence of statutory mandates, procurement regulations, and industry standards as a source of uncertainty regarding when and how BIM should be applied in practice. Other indicators, such as low customer demand and limited digital maturity among project partners, also received relatively high ratings, underscoring the influence of the external ecosystem on BIM utilization.

However, the correlation analysis (see Table 11) demonstrated that these environmental barriers are not clearly associated with individuals’ general attitudes toward BIM. The weak and non-significant correlations (|ρ| ≤ 0.10) indicate that while respondents recognize external challenges, their fundamental attitude toward the technology remains largely unaffected. This disconnects between perceived barriers and individual attitudes aligns with previous research suggesting that external factors often function as institutional structures rather than psychological deterrents [68,71]. It is therefore plausible that respondents view these constraints as systemic issues to be managed, rather than as factors that diminish their belief in BIM’s potential.

Furthermore, the reliability analysis of indicators E1–E6 (Cronbach’s alpha = 0.659) indicates that these factors co-vary sufficiently to be interpreted as a combined, albeit not entirely homogeneous, index. This reflects the notion that environmental barriers are experienced as interrelated yet analytically distinct dimensions, which is reasonable given their association with different stakeholders and levels of influence, including clients, customers, regulatory bodies, and civil society. Overall, findings suggest that efforts to promote BIM adoption should extend beyond internal organizational or technical interventions. They must also address the structural conditions that shape actors’ incentives, expectations, and collaborative frameworks. This implies that external drivers, such as procurement mandates, national guidelines, and financial instruments, should be integrated with internal strategies, including capacity building, change management, and process optimization. Only through coordinated action across both systemic and organizational levels can meaningful progress be achieved in the implementation and everyday use of BIM. The results thus support the conclusion that environmental barriers exert a crucial, albeit indirect, influence—one that must be addressed structurally rather than individually.

4.5. Interaction Between Technological, Organizational, and Environmental Barriers

To examine how technological, organizational, and environmental barriers interact and influence BIM use in construction production, a Principal Component Analysis (PCA) and regression analysis were conducted using TOE-based indices as predictors. The objective was to identify latent structures among the barrier types and assess their relative impact on respondents’ BIM usage, in alignment with the TOE framework [7].

An exploratory factor analysis using PCA was performed to uncover the underlying structure of the identified barriers. The analysis included fifteen indicators, categorized into three analytical domains according to the TOE framework: five technological (T1–T5), four organizational (O2–O5) and six environmental/external indicators (E1–E6). The goal was to determine whether these indicators could be grouped into meaningful latent factors that reflect respondents’ practical experiences of barriers.

The Kaiser criterion was applied as the extraction method, retaining only factors with eigenvalues greater than 1, indicating that each retained factor explains more variance than a single variable. This approach is widely accepted in social science and technology adoption research [69]. The analysis yielded five factors with eigenvalues above 1, collectively accounting for 43.5% of the total variance in the dataset. This result suggests that the extracted factor structure captures a substantial portion of the variation in respondents’ perceptions of barriers, thereby enhancing the interpretive strength of the analysis.

The first factor was primarily characterized by the technological indicators (T1–T5), suggesting that technical challenges, such as software compatibility, model accessibility, and availability of technical support, are perceived as a cohesive barrier cluster. The second factor encompassed the organizational indicators (O2–O5), indicating that issues related to resource constraints, leadership, and unclear responsibility allocation form a unified organizational dimension. The environmental indicators (E1–E6) were split across two distinct factors (Factors 3 and 4). The first subset (E1–E3) pertained to external demands and collaborative practices, while the second subset (E4–E6) reflected concerns related to policy, legal frameworks, and sustainability expectations. The fifth factor exhibited weak and inconsistent loadings and is interpreted as residual methodological variance.

These findings demonstrate that the barriers encountered in construction production are not randomly distributed but instead form structured domains: technological, organizational, and environmental. This categorization reinforces the relevance of the TOE framework as a theoretical foundation for the study and aligns with prior research on technology adoption in the construction sector [62,72]. The factor analysis thus offers both statistical and conceptual validation for examining BIM-related barriers through the lens of these three dimensions.

In addition to survey data, semi-structured interviews offered deeper insights into the practical challenges of BIM implementation. Respondents emphasized the absence of external incentives and the difficulty of applying BIM tools in dynamic and often unpredictable workplace environments. Several respondents also noted that, even when BIM tools are available, inconsistent collaboration and fragmented project responsibilities significantly hinder their effective use.

To enable a more comprehensive analysis of the relationships between different types of barriers and the extent of BIM use in production, three composite indices were developed: a Technological index (Tech_Index), an Organizational index (Org_Index) and an Environmental index (Env_Index). Each index was calculated as the arithmetic mean of the responses within the respective indicator group: T1–T5 for Tech_Index, O2–O5 for Org_Index, and E1–E6 for Env_Index. These composite indices allowed each respondent to be assigned a weighted score representing their overall experience of barriers within each TOE dimension.

A correlation analysis was conducted to examine the degree of co-variation among the three indices, shown in Figure 3. The results revealed moderately strong, positive correlations (r = 0.34–0.45), indicating that respondents who reported high barriers in one domain (e.g., technological) also tended to report elevated barriers in the others (e.g., organizational or environmental). However, the correlations were not so high as to suggest construct overlap, implying that the three barrier types remain analytically distinct. This finding supports the conceptual integrity of the TOE framework and reinforces the rationale for maintaining a tripartite analytical structure in subsequent analyses.

To further explore the relationship between perceived barriers and the actual use of BIM, an ordinal logistic regression analysis was performed, also shown in Figure 4. The dependent variable was the respondents’ self-reported frequency of BIM use in production (BIM_Use), measured on a five-point ordinal scale (1 = never, 5 = always), representing the extent of digital application in practice. The three TOE indices, Tech_Index, Org_Index, and Env_Index, served as independent variables in the model.

The results of the regression model revealed that only the Environmental index (Env_Index) had a statistically significant negative relationship with BIM use (β = −0.707; p = 0.036). This indicates that the higher the level of perceived environmental barriers, such as lack of client requirements, low customer demand, or unclear policy frameworks, are associated with a lower the likelihood of frequent BIM use in production. In contrast, Tech_Index (p = 0.80) and Org_Index (p = 0.52), did not show statistically significant effects, suggesting that internal technical or organizational barriers do not independently determine the frequence of BIM use.

This variation in predictive power among the three indices offers both theoretical and practical insights. While technological and organizational factors may be necessary for successful implementation, they are not sufficient on their own. The findings suggest that BIM adoption in production only gains substantial traction when external structural conditions, such as regulatory frameworks, market signals, industry standards, and incentive mechanisms, are clearly established. These results align with previous research emphasizing the critical role of policy development, standardization, and external governance in driving digital transformation within the construction industry [19,37].

Importantly, the analysis shows that technological and organizational barriers can coexist without necessarily impeding BIM use, whereas environmental barriers appear to represent a more decisive threshold. As such, future efforts by industry stakeholders and policymakers should prioritize improving the external conditions that support digitalization in construction production.

In conclusion, the findings demonstrate that technological, organizational, and environmental barriers are interrelated yet analytically distinct. Among them, environmental barriers exhibit the most direct and significant influence on BIM use. This underscores the need to complement internal capacity-building and technical infrastructure with robust external frameworks, such as policy alignment, industry-wide requirements, and sustainability targets, to ensure effective and scalable BIM implementation in construction production.

4.6. Integrated Interpretation in Relation to the TOE Framework and the Research Questions

The regression analysis revealed that only environmental barriers were significantly associated with self-reported BIM use (β = −0.707, p = 0.036), whereas technological and organizational barriers showed no significant predictive power (p > 0.50). This finding highlights the pivotal role of external enablers or constraints in shaping BIM adoption in practice.

The aim of this study has been to identify and examine the primary barriers influencing BIM use in construction production, using the TOE framework. The results from the sub-analyses illustrate how these three dimensions interact and collectively impact BIM utilization, offering a holistic understanding of the factors that govern the pace and direction of digitalization in the production phase.

Technological barriers: Functionality and skills gaps.

The technological barriers identified in this study, namely, the lack of practical functionality on-site and limited operational BIM skills, represent clear barriers to adoption. Several respondents expressed concerns that current BIM technology technologies are not sufficiently tailored to the demands of the construction site, and that available training and technical support are inadequate. These findings are consistent with [73] who notes that technology design often prioritizes office environments over the specific needs of construction production. Ref. [19] highlights the lack of technical integration and training are critical impediments to successful implementation. These technological challenges can also be interpreted through the lens of the Diffusion of Innovation theory [74], where high complexity and low compatibility with existing workflows hinder the rate of adoption. In this context, the technological barrier block reflects both the practical limitations of current tools and the broader systemic issues that slow down the integration of BIM into construction production.

Organizational barriers: Routines, responsibilities and leadership.

Section 4.3 identified several organizational barriers, including unclear processes, ambiguous responsibilities, and weak integration of BIM within the organization’s strategic management. Multiple respondents noted that BIM often remains a peripheral activity rather than becoming an embedded component of core business operations. These findings align with [75], who emphasizes that organizational maturity is the key determinant of successful BIM adoption. Similarly, ref. [64] highlights the importance of leadership, internal communication, and standardized workflows as critical enablers. While organizational barriers may not directly influence attitudes toward BIM, they significantly constrain the structural conditions necessary for effective implementation. Without clear leadership and cohesive internal processes, BIM struggles to gain traction as a transformative tool within construction production.

Environmental and external barriers: Policy gaps and industry pressure.

Section 4.4 revealed that external and environment-related barriers, such as the absence of legal requirements, insufficient national incentives, and low client demand, are widespread and systematically influence BIM use. As [65] underscores, industry-wide guidelines and public policy frameworks are essential for enabling implementation at the production level. Similarly, ref. [37] highlights national policy and regulatory structures as key drivers of large-scale BIM adoption.

TOE interactions and regression results.

In Section 4.5, the interaction between the TOE components was examined using composite indices. The ordinal logistic regression analysis revealed that only the environmental/external index had a statistically significant negative relationship with BIM adoption (p < 0.05). This indicates that external barriers are the most influential factors in determining the extent to which BIM is utilized in production. These findings reinforce conclusions from previous meta-analyses, which suggest that while external factors are often underemphasized, they possess the strongest predictive power for adoption outcomes [62,72]. The results highlight the value of the TOE framework as a comprehensive model: although technological and organizational dimensions tend to receive more attention in practice, it is the environmental conditions that exert critical systemic influence on BIM implementation.

Overall Interpretation in Relation to the Research Questions.

The overall findings of this study offer a clear and nuanced response to the four research questions that guided the analysis. First, the results illustrate how contractors perceive technological factors, such as software tools, technical competence, and system compatibility, as influencing the use of BIM in construction production. Respondents identified several tangible technological barriers, with limited access to models on-site and deficiencies in technical infrastructure emerging as the most prominent. These insights suggest that the current technical infrastructure is not yet fully aligned with the practical demands of the production environment, a concern echoed in previous research. While these barriers were perceived to be moderate severity and varied depending on the frequency of BIM use, the regression analysis revealed no statistically significant relationship between technological factors and actual BIM adoption. This indicates that although technical resources and functionality are important enablers, they are not decisive on their own in determining whether BIM is applied in practice.

Regarding the second question, which organizational barriers limit the possibilities to implement BIM in construction production, several recurring themes emerged from the respondents’ answers. These included a lack of resources, poor communication, weak digital leadership, and an unclear division of responsibilities. Although these barriers were assessed as moderately prevalent, reliability analysis indicated that they did not co-vary significantly. This suggests that these barriers should not be viewed as a uniform phenomenon, but rather as distinct organizational challenges requiring tailored interventions. Furthermore, these organizational factors showed no significant correlation with either the respondents’ attitudes toward BIM or their actual usage of the technology. This reinforces the notion that organizational influences tend to exert a more indirect and context-dependent impact. These findings align with previous research, which has emphasized that while organizational compatibility is important, it is not the sole prerequisite for successful technology adoption.

The third research question explored the extent to which environmental factors, such as industry requirements, customer expectations and partners’ digital maturity, influence BIM use in contractor-led construction projects. The regression analysis revealed that only the environmental index had a statistically significant negative relationship with actual BIM use (β = −0.707, p = 0.036), while technological and organizational indices were not statistically significant (p > 0.50). These relationships were clearly more pronounced, as the regression analysis showed that environmental barriers exerted the strongest negative association with BIM use. Respondents identified external factors as the most critical barriers, particularly the lack of clear requirements from procurers and authorities, as well as weak demand from customers. The significant negative correlation between the environmental index and BIM usage underscores the decisive role that external structural conditions play in determining whether the technology is adopted. This finding corroborates earlier research, which has shown that policy frameworks, procurement practices, and industry standards are often the most powerful drivers of digitalization in the construction industry.

The fourth and final research question examined how the three types of barriers: technological, organizational and environmental, interact and influence BIM implementation. The factor analysis revealed that the indicators clustered into distinct factors, suggesting that these dimensions are perceived as analytically separate. Although the correlations between the indices were moderate yet statistically significant, this indicates some degree of covariation while affirming that each barrier type operates independently in shaping BIM usage. The key conclusion is that while technological and organizational conditions are necessary for BIM implementation, they are not sufficient on their own. For technology to be scaled and systematically integrated into construction processes, these internal capabilities must be complemented by external governance mechanisms, such as clear procurement requirements, industry-wide standards, and economic incentives. Overall, the findings of this study support a systemic approach to digitalization in the construction industry, emphasizing that the interplay between internal capacities and external structural conditions is essential for the practical adoption of BIM.

5. Discussion

The purpose of this study was to identify and analyze the technological, organizational, and environmental barriers influencing the use of BIM in construction production in Sweden, using the TOE framework [7]. By integrating quantitative methods, including descriptive statistics, correlation analysis, factor analysis, and regression modeling, the study offers both a comprehensive perspective and a detailed understanding of the structure and dynamics of these barriers [37,53].

Firstly, no statistically significant differences in perceived organizational barriers were found between small, medium-sized, and large companies. This finding is important, as it contrasts with previous research that often suggests that larger companies typically enjoy more favorable conditions for adopting and utilizing BIM, such as greater resources, higher technical capacity, and more defined leadership structures [64,75]. The absence of such differences in this study may be partly attributed to the Swedish context, where digital infrastructure is relatively advanced and uniformly accessible across companies of varying sizes [76]. It may also reflect a broader trend in which access to digital tools is no longer confined to large enterprises, with smaller firms increasingly benefiting from similar systems, platforms, and support.

Secondly, the study did not reveal any significant differences among professional groups, such as project managers, production engineers, supervisors, and BIM coordinators, in their perceptions of barriers to BIM adoption. This was especially evident within the technological and organizational dimensions. Previous research has frequently highlighted that professional roles influence how technological innovations are perceived, given that different roles involve distinct priorities, competencies, and responsibilities [74]. The absence of significant differences in perceptions across professional roles may indicate a growing level of organizational maturity regarding digitalization. It could also suggest that BIM has become so deeply embedded in the structure of construction production that its understanding and application are no longer confined to technically oriented roles. Instead, the findings point to a convergence of digital perspectives across professional boundaries, further supported by the widespread use of BIM, over 75% of respondents reported daily or regular engagement with the technology. In summary, these results suggest that traditional distinctions in how BIM-related barriers are perceived, such as company size and professional role, have diminished in relevance, at least within the Swedish construction context. This trend reflects the institutionalization of BIM in construction production, not only in technical terms, but also culturally and organizationally.

In interpreting the survey findings, the analysis was enriched by qualitative insights from the semi-structured interviews. Although the interview data were not analyzed thematically or reported separately, they served as a valuable triangulation tool, helping to contextualize and validate the quantitative results. Several key patterns identified in the survey, such as insufficient digital leadership, unclear internal responsibilities, and the lack of formal external BIM requirements, were consistently echoed by interview participants as everyday challenges encountered in real-world projects. For example, multiple interviewees described how unclear delegation, and a lack of strategic follow-through hinder digital workflows, even within companies equipped with advanced BIM tools. Likewise, the absence of formal client requirements and fragmented industry expectations were repeatedly cited as critical environmental barriers. These qualitative accounts reinforced the statistical findings and added interpretive depth to the analysis of both organizational and environmental dimensions [77].

Technological barriers—functionality and complexity.

The results revealed that several technological barriers continue to pose significant challenges to the effective use of BIM in the practical context of construction production. Among the most prominent obstacles reported by respondents were difficulties accessing models on-site (T3), limitations in technical infrastructure (T2), and inadequate technical support (T5). An interesting exception emerged in the T3 (Model Access) barrier, where respondents who reported “Never” using BIM had a lower mean score (2.75) than those who used it “Rarely” (3.00). This contrasts with the general trend, in which increased usage typically correlates with greater awareness of barriers. One possible explanation is that individuals who never engage with BIM lack the exposure necessary to identify or assess such technical limitations, whereas occasional users may have encountered more practical challenges. This observation aligns with the so-called user paradox, where increased usage reveals more nuanced deficiencies in digital tools and infrastructure. Notably, these three indicators had the highest mean values in the descriptive analysis, highlighting that access to reliable tools and technical support remains uneven across production environments, despite the widespread availability of BIM technologies in the industry.

Drawing on the Diffusion of Innovation (DOI) theory [60], these obstacles can be interpreted as manifestations of high perceived complexity, a well-documented inhibiting factor in the adoption of new technologies. According to DOI theory, innovations that are perceived as technologically demanding or difficult to comprehend tend to diffuse more slowly within a system [74]. Furthermore, the low internal consistency among the technological indicators (Cronbach’s alpha = 0.10) suggests that these barriers should not be treated as a single, uniform construct. Instead, they represent distinct technical challenges that impact users differently depending on their specific context.

The low reliability scores for both the technological indicators (Cronbach’s alpha = 0.10) and organizational indicators (α = −0.075) suggest that these barriers should not be viewed as uniform phenomena. Instead, they represent distinct factors that may affect users differently depending on their specific context. Despite the low internal consistency, both indices were retained in the regression analysis due to their theoretical significance within the TOE framework and their established use in previous BIM-related studies [37,65]. Rather than interpreting these indices as reflective latent constructs requiring high internal homogeneity, they were treated as formative or composite constructs. In this approach, each indicator represents a distinct, theoretically meaningful aspect of the broader domain, and the index captures their combined influence on BIM adoption. The low Cronbach’s alpha values therefore reflect the multidimensional nature of these barriers, rather than undermining their conceptual relevance.

Prior TOE-based BIM research [37,65] has conceptualized technological, organizational, and environmental factors as separate yet complementary components, acknowledging that each domain often comprises multiple distinct elements. While such studies do not explicitly address Cronbach’s alpha or reliability testing, their methodological structure supports the interpretation of these domains as composite rather than internally homogeneous constructs. Retaining these variables in the regression model preserved the integrity of the TOE framework and enabled direct comparison with earlier BIM adoption studies, ensuring that potentially significant domain-level effects were not excluded.

This interpretation reinforces the theoretical alignment with the TOE framework and highlights the importance of capturing a broad spectrum of context-specific barriers in BIM adoption research. The findings also confirm previous studies that have emphasized the persistent challenge of bridging the gap between office-based models and the demands of construction production for real-time information, accessibility, and robustness in the field [19,73]. Notably, respondents who use BIM daily reported higher levels of certain technological barriers, particularly T1 (software compatibility) and T3 (model access), underscoring the notion that practical experience tends to reveal system limitations rather than mitigate them. This user paradox, wherein experienced users identify more shortcomings, is well documented in technology research and underscores the need for ongoing development to occur in close collaboration with field practitioners [73].

Overall, the analysis indicates that technological barriers to BIM use in construction production are primarily associated with functionality, usability, and access to technical support. These barriers extend beyond purely technical limitations, influencing users’ perceptions of usability and trust in technology. Over time, such perceptions can significantly impact both the rate of adoption and the depth of BIM integration in practice.

Moreover, the findings of this study indicate that several organizational factors continue to pose substantial obstacles to effective BIM use in construction production, particularly issues related to unclear division of responsibilities, insufficient resources, weak strategic alignment, and inadequate digital leadership. Among these, the indicator “Insufficient resources” (O2) received the highest mean score, suggesting that even when the technology is available, the organizational conditions necessary for integrating it into daily workflows are often lacking.

A noteworthy observation is the low reliability among the organizational indicators (Cronbach’s alpha = −0.075), indicating that these barriers do not co-vary strongly enough to be treated as a unified construct. Instead, they should be understood as distinct organizational challenges that impact different aspects of the production environment in varied ways.

Despite this, no significant differences in perceived barriers were found across company sizes or professional groups, indicating that these challenges are structurally embedded throughout the construction industry, regardless of the stakeholder. Notably, the organizational barriers did not exhibit a significant relationship with actual BIM usage in the regression analysis.

Nonetheless, their presence remains critical, as they form part of the internal organizational system within which technologies like BIM must be embedded. Within the framework of the DOI theory, this aligns with the concept of compatibility, the idea that a new technology must align with existing workflows, norms, and organizational cultures to achieve widespread adoption [74]. This interpretation is reinforced by prior research showing that BIM implementation is often impeded when leadership is weak, communication is fragmented, and responsibilities are poorly defined [64,75].

Environmental and external obstacles—system-level barriers.

The study revealed that environmental and external factors are the most influential obstacles to BIM adoption in construction production. The indicator E2 (lack of clear external requirements) received the highest mean score, indicating that many respondents perceive the industry’s guiding mechanisms, such as regulations, certifications, and demands from public clients, as inadequate. Conversely, E6 (lack of societal sustainability pressure) was identified as the least significant barrier, suggesting that broader sustainability goals currently do not serve as a strong catalyst for digitalization within the industry.

The most striking result emerged from the regression analysis: the environmental index (Env_Index) was the only variable that demonstrated a significant negative correlation with BIM usage (β = −0.707; p = 0.036). This indicates that the stronger the perceived external barriers, the less likely BIM is to be used consistently in production, regardless of the technical or organizational conditions.

This finding is particularly noteworthy, as it underscores the outsized influence of system-level barriers compared to internal factors, a dynamic previously emphasized in the literature as critical for enabling large-scale digital transformation [37,65]. It reinforces the conclusion that external policies, procurement requirements, and industry-wide incentives are essential for BIM to achieve meaningful operational impact. The mere availability of technology or internal organizational support is insufficient; a supportive system environment is needed, one that actively promotes and mandates the use of digital tools through regulations, incentives, and collaborative frameworks.

The results of the regression analysis in this study, where only the environmental index (Env_Index) exhibited a significant negative association with BIM usage (β = −0.707; p = 0.036), suggest that the external environment, rather than technological or organizational factors, plays the most decisive role in determining the frequency of BIM use in practice.

This interpretation is further supported by the very low or negative Cronbach’s alpha values, which indicate that technological and organizational barriers are not perceived as cohesive constructs, but rather as distinct and context-dependent barriers. This finding diverges somewhat from previous research based on the TOE framework, which typically emphasizes the interplay among all three domains, often with a stronger focus on internal factors such as technical readiness, staff competence, or leadership capacity [62,72].

Several studies have indeed identified technological and organizational barriers as significant factors in BIM adoption. For example, ref. [19] demonstrated that inadequate IT infrastructure and insufficient training hinder effective implementation, while [37] emphasized organizational challenges such as unclear responsibilities, limited resources, and weak governance structures. Similarly, ref. [64] identified leadership and clearly defined roles as critical for integrating BIM into everyday workflows. These earlier findings align with the results of this study, in which respondents also reported moderate barriers related to both technological and organizational dimensions.

However, in terms of factors influencing BIM usage, this study reveals a finding that few other TOE-based BIM studies have quantified: environmental barriers carry the greatest weight. This result aligns closely with more systems-oriented research that emphasizes the role of policy and procurement environments. For instance, ref. [65] highlights the importance of industry-wide coordination and public sector requirements in driving widespread BIM adoption. Likewise, ref. [43] argues that external governance, through national strategies, standardization, and incentives, is essential in countries where BIM implementation has been successful.

Moreover, this study reinforces these system-focused conclusions with quantitative evidence: in the absence of external requirements and governance, BIM adoption relies heavily on voluntary uptake, resulting in significant variation in both the extent and manner of its use. These findings also lend support to institutionalist theories, which suggest that organizational change in complex industries often depends on external pressure and legitimization to gain traction [71].

At the same time, the findings of this study diverge from many technology-focused studies, which often identify inadequate IT resources, skills, or training as the primary barriers to BIM adoption [1,43]. In this context, the limitations of the TOE framework become evident. While the framework treats its three domains, technology, organization, and environment, as analytically equivalent, this study demonstrates that, in practice, the external environment functions as a threshold condition. In other words, even when technological and organizational readiness is present, meaningful change is unlikely to occur without systemic pressure from the broader environment.

The fact that these factors do not significantly influence actual BIM usage suggests that internal capacity alone is insufficient to drive change unless the surrounding systems provide the necessary momentum. In this context, the limitations of the TOE framework become apparent. Although it treats the technological, organizational, and environmental domains as analytically equivalent, this study reveals that the external environment functions as a threshold condition, meaning that even with technological and organizational readiness, change will not occur without systemic pressure. This asymmetry is seldom acknowledged in the TOE literature, making this study’s contribution theoretically significant.

Furthermore, the findings of this study highlight several important practical implications for construction companies, public sector clients, and industry associations. The most critical insight is that environmental barriers exert the strongest negative influence on actual BIM usage. While technological and organizational barriers are present, they do not, on their own, determine the extent to which BIM is adopted and utilized.

This suggests that efforts to promote BIM adoption should extend beyond internal improvements, such as skills development, standardization, or platform selection, and prioritize enhancing external conditions. For instance, clearer BIM requirements in public procurement, unified industry standards, and financial incentives for digital project management can generate the structural pressure necessary for widespread adoption. Previous research has also underscored the importance of this systemic logic. Ref. [65] argue that cross-industry coordination and governance are essential for driving digitalization in the construction industry. Similarly, ref. [37] demonstrates that the absence of clear guidelines, incentives, and national coordination significantly hinders BIM adoption. Ref. [19] highlights the importance of national BIM agendas, while [63] stresses the pivotal role of public clients as agents of change. The findings of this study empirically confirm these claims: even when technological and organizational readiness is present, BIM implementation does not occur without external pressure, formal demands, or legitimization. For construction companies, this implies that internal investments in technology and organizational capacity, though necessary, must be complemented by strategic efforts to influence policy development and shape market expectations. Companies that are far ahead risk limited returns on their investments if surrounding stakeholders do not support or require digital practices. Industry-wide initiatives aimed at improving interoperability, fostering knowledge exchange, and clarifying BIM-related roles are also critical for lowering adoption barriers, particularly for smaller actors [14,43].

For decision-makers and public authorities, the implication is clear: digital transformation in construction production requires active and coordinated governance. Without unified requirements, targeted incentives, and systematic follow-up, BIM risks remaining a fragmented initiative rather than evolving into a system-wide shift. The findings emphasize that policy development, through national strategies, certification frameworks, and workforce upskilling, is essential for ensuring that BIM is adopted across the entire production chain. This is especially relevant in the Swedish context, where the construction industry is decentralized, fragmented, and heavily reliant on clear directives from public clients [76].

Overall, this study demonstrates that future investments in BIM must target the entire system, not merely technical solutions or internal workflows. By integrating internal capacity-building with external governance, strategic incentives, and industry-wide coordination, it is possible to establish the conditions for a more equitable, efficient, and sustainable digitalization of construction production.

To address the identified barriers, a range of actionable strategies can be implemented across project, organizational, and policy levels.

• Project Level: Contractors and project managers should introduce structured, ongoing BIM training tailored to production teams, ensuring that tools are both practical and accessible at the site level.
• Technical Integration: Interoperability between software platforms should be strengthened through standardized data formats and collaborative planning with subcontractors and design consultants.
• Workflow Governance: Clear, project-specific BIM protocols, including guidelines for access, updates, and responsibilities, should be established to reduce ambiguity and enhance digital workflows.
• Organizational Leadership: Companies must prioritize digital change management by defining roles, allocating sufficient resources, and aligning BIM adoption with strategic objectives.
• Policy and Industry Support: Public clients and industry associations can accelerate implementation by setting clearer procurement requirements and offering targeted incentives, thereby fostering a system-wide environment that supports BIM adoption throughout the supply chain.

Together, these measures can transform current barriers into enablers of digital innovation in construction production.

This study makes a theoretical contribution to understanding the barriers to digital transformation in construction production by applying and further refining the TOE framework [7] within an empirical context. By translating technological, organizational, and environmental barriers into quantitative indicators and examining their relationship to actual BIM usage, the study offers a valuable empirical test of the TOE model in a production-oriented setting, an area still underexplored in BIM research [62,72].

A key theoretical insight is that the findings challenge the TOE model’s assumption of equal influence across its three domains. Regression analysis reveals that only environmental barriers (EnvIndex) are significantly associated with BIM usage in practice (β = −0.707; p = 0.036), while technological and organizational factors show no statistically significant effect. This suggests an asymmetry among the TOE components, a nuance that previous BIM-related TOE studies have seldom addressed.

Consequently, the results underscore the need to further develop the TOE framework by incorporating context-specific dynamics, such as the dominant role of external factors in certain settings, thereby enhancing its explanatory power in construction production environments.

In relation to technology adoption theory, particularly the DOI [74], this study offers additional nuance. DOI highlights complexity, compatibility, and perceived benefit as key barriers to adoption. The findings partially support this perspective: technological and organizational barriers related to functionality, accountability, and leadership are present. However, these factors alone do not sufficiently explain actual BIM usage.

This reinforces the argument that institutional factors, such as legitimacy, governance, and regulatory frameworks, play a more decisive role in sectors where implementation is not solely a matter of individual or organizational choice, but is shaped by broader industry norms and policy environments [71].

This study highlights the need for theoretical integration between the TOE framework, the Diffusion of Innovations (DOI) model, and institutional theory to more effectively understand digitalization in the construction industry. While TOE offers a structured analytical lens and DOI captures individual-level adoption dynamics, institutional theory addresses the macrostructural forces, such as legitimacy, governance, and policy, that often shape implementation outcomes.

The dominance of environmental factors in this study suggests that technology adoption in construction production is largely system-dependent, with institutional legitimacy and regulatory frameworks playing roles as critical as technical functionality and organizational readiness.

Overall, the findings affirm the value of the TOE framework as a foundational model but also demonstrate that it must be complemented by other theoretical perspectives to fully capture the complexity of adoption barriers across different contexts. This opens avenues for future research into how the relative influence of TOE components varies across industries, national settings, and levels of digital maturity.

6. Limitations

A key limitation of this study is its reliance on self-reported survey data, which inherently carries the risk of subjectivity and social desirability bias. Respondents may have overestimated or underestimated certain barriers to BIM adoption based on factors such as their professional role, level of experience, or organizational affiliation. These variations could influence the reliability of specific findings and introduce potential bias in the perception of barriers.

Another limitation of this study is its geographic scope, as it was conducted exclusively within Sweden. Consequently, the findings are contextually bound to the Swedish construction industry, which is characterized by a relatively homogeneous digital infrastructure and distinct national strategies for sustainability and digitalization. These conditions may differ significantly from those in other countries, where industry structures, policy frameworks, and levels of technological maturity vary. As a result, the generalizability of the findings to other geographical or institutional contexts may be limited.

Another limitation of this study is its reliance primarily on quantitative methods. While this approach enables generalization and the identification of statistical relationships, it does not capture deeper dimensions such as organizational culture, decision-making processes, or informal barriers. Moreover, quantitative design limits the ability to explore how individual actors conceptualize and navigate BIM implementation within specific projects or operational contexts. To address these gaps, future research should adopt a mixed-methods approach, incorporating case studies, interviews, or ethnographic techniques, to offer a more nuanced understanding of the factors influencing BIM adoption in practice.

Additionally, this study focuses on a selected set of barriers derived from the TOE framework, thereby excluding other potentially relevant influences such as market dynamics, procurement practices, project contract structures, and external technical standards. While the TOE framework provides a solid theoretical foundation, there is a clear need to evolve toward more context-sensitive models that reflect the complexities of digital transformation in construction production.

Furthermore, an additional limitation of this study is the lack of consideration for potential regional differences within Sweden. Although respondents were drawn from various parts of the country, no stratified analysis was conducted to assess whether local policies, procurement practices, or governance structures influence BIM adoption. Future research could address this gap by systematically exploring regional variations.

A final limitation concerns the underrepresentation of subcontractors in the sample. While site managers and project managers comprised most respondents (49%), subcontractors, who play a pivotal role in the execution and coordination of BIM on-site, were not proportionally represented. Consequently, some of the practical challenges and barriers unique to subcontractors may not have been fully captured. Future studies should strive for a more balanced representation of stakeholders across the entire production chain, with particular attention to subcontractors who are often directly responsible for implementing BIM requirements in the field.

7. Conclusions

The findings of this study indicate that BIM is perceived as a mature and widely applicable form of digital innovation within Swedish construction production. While previous research has often emphasized significant disparities in digitalization levels across company sizes and professional roles [19,64,75], this study found no statistically significant differences between small, medium, and large enterprises, nor among roles such as project managers, site managers, and BIM coordinators. This suggests that the perceived relative advantage of BIM technology [74] has achieved broader resonance across organizational boundaries, helping to bridge traditional divides.

The correlations between technical, organizational, and environmental barriers were generally moderate to strong. Although the indices for each domain exhibited low internal overlap, they collectively contributed meaningful explanatory power in the regression model. Notably, the environmental index showed a significant negative correlation with actual BIM usage, clearly indicating that external factors, such as insufficient procurement requirements, weak policy frameworks, and limited incentives, remain the most substantial barriers to widespread implementation. These external barriers often lie beyond the control of individual organizations, highlighting the urgent need for cross-industry collaboration, targeted policy instruments, and coordinated regulatory development [37,64].

In addition, the analysis reveals that BIM is widely perceived as compatible with existing work practices, relatively easy to use, and valued for its visualization and feedback capabilities. These attributes, identified in DOI theory as key dimensions for successful technology diffusion [74], appear to enhance BIM’s internal legitimacy within organizations. The increased visibility enabled by BIM, through features such as 3D modeling, clash detection, and transparent reporting, contributes to its deeper integration into project operations [72].

In practical terms, this means that investments in joint training initiatives, data standardization, and testing environments are essential, particularly for smaller companies, which often lack strategic leadership and long-term digital competence [62,64]. At the same time, coordinated action is needed from public clients, suppliers, and contractors to address the systemic barriers that continue to hinder progress. Through such collaboration, the potential benefits of BIM, such as more efficient production workflows, reduced errors, and improved sustainability management, can be effectively realized in practice.

Theoretically, this study demonstrates that the TOE framework [7] and the diffusion of innovations [74] should be quantitatively applied, even within a fragmented, project-based industry like construction. The two-dimensional structure revealed through factor analysis, in which technological and environmental barriers formed distinct yet interrelated clusters separate from organizational factors, offers deeper insight into the nature of barriers to digital transformation [72,78]. By integrating both internal and external factors into a unified model, this study provides new empirical evidence, methodological rigor, and analytical clarity to the field of construction industry digitalization research.