Factors Influencing Building Information Modeling (BIM) Adoption Intention Among Multiple Stakeholders to Promote the Sustainable Development of the Construction Industry: Insights from the Technology–Organization–Environment (TOE) Theoretical Framework

Abstract

BIM is a key technology for the digital transformation and sustainable development of the construction industry through enhanced productivity, transparency, and fostered innovation. Although scholars have investigated the constructs driving BIM adoption intention, a comprehensive framework has seldom been adopted, and thus some vital factors have been overlooked, such as collaboration partner pressure. Meanwhile, the targeted group is usually practitioners of a certain type of company while a construction project requires the participation of multiple types of companies. To address these research gaps, the aim of this study is to explore the factors driving various stakeholders’ intention to adopt BIM by applying the TOE framework, considering nine factors across three dimensions. A total of 512 valid responses from owners, consulting firms, design firms, construction companies, suppliers, engineering surveying firms, and universities or research institutes were collected and analyzed through the structural equation modeling (SEM) method. The SEM results indicated that six factors were positively related to the intention to employ BIM, among which management commitment (β = 0.182, p < 0.001) and perceived ease of use (β = 0.180, p < 0.001) exhibited the strongest effects. However, three factors (perceived usefulness, supporting technical facilities, and mimetic pressure) exerted no significant influence. The findings of this study may provide a valuable reference for promoting the application of BIM technology in the construction industry.

1. Introduction

In recent years, the application of BIM in the construction industry has expanded significantly because of its potential for improving efficiency and reducing errors [1]. BIM is a technology-enabled methodology for generating, communicating, and analyzing digital representations of construction projects [2]. These models not only accurately capture the geometric properties of structures but also encompass comprehensive information covering prefabrication, procurement, construction, and even operation and maintenance. Serving as a collaborative platform for information exchange, BIM eliminates interdisciplinary barriers and enables comprehensive management of project quality, schedule, cost, and human resources [3]. Therefore, this approach delivers higher quality, reduced costs, shorter construction duration, and optimized resource utilization, which is of great significance in promoting the sustainable development of the construction industry. Furthermore, it serves as a foundation for building lifecycle management and the integration of other emerging technologies. Although existing research shows that the potential of BIM remains largely untapped [4], it is evident that BIM appears to be an indispensable technology to achieve the goal of sustainable development.

Considering the important role of BIM technology, numerous studies have devoted considerable attention to factors influencing BIM adoption intention to formulate effective implementation strategies. The facilitating or inhibiting effects of different factors may vary across different countries [5], industries [6], enterprise types [7], and objectives [8].

Table 1. Determinants of the BIM adoption intention and behavior by stakeholders.

Theory	Methodology	Dependent Variable (DV)	Independent Variable (IV)	The Target Population for the Study	Year of Publication	Source
Theory of Reasoned Action (TRA)	SEM	Intention of BIM utilization	Motivation Technical defects of BIM BIM capability Management support Knowledge structure	Architects in Shenzhen, China	2015	[9]
Technological adoption framework (TAF)	PLS-SEM	Intention to Use	Perceived Usefulness Perceived Ease of Use Technological Quality Personal Competency Organizational Commitment	Stakeholders in Malaysia’s construction industry, including consultants, contractors, and clients	2023	[14]
Modified Technology Acceptance Model (TAM)	SEM	Actual System Use	Perceived Usefulness Perceived Ease of Use Attitude Towards Using Behavioral Intention to Use	Contractors in Lima, Peru	2023	[10]
Literature Review	PLS-SEM	Intention to Adopt the BIM	Policy Process Technology People Environment	Professionals in Yemen’s construction industry	2022	[15]
TAM and Social Network Analysis (SNA)	SEM	BIM adoption	Standards Information Sharing Retrofit Tools Culture of the organization Clients’ expectations	New Zealand construction professionals who have participated in a refurbishment project and have BIM experience	2019	[11]
Dimensions of technology, research, and development	Mixed methods (Constant Comparative Method and SEM)	BIM adoption	Perceived Benefits External Influences Internal Organizational Readiness Individual Innovativeness	BIM specialists, engineers, and managers within Indonesia’s AEC (Architecture, Engineering, Construction) sector	2024	[16]
Literature Review	PLS-SEM	BIM Implementation	Knowledge Barriers Technical Barriers Creativity Barriers Functioning Barriers Supervision Barriers	Construction professionals within Malaysia’s oil and gas sector (including safety managers, contractors, consultants, etc.)	2023	[17]
Literature Review	SEM	BIM adoption intention	Organizations’ needs Potential Benefits Knowledge support Ease of operation Downtime Potential Benefits Potential Challenges	Small and medium-sized construction enterprises in Australia and China	2020	[5]
BIM technical features and the strength of COVID-19	SEM	BIM Adoption Intention	Relative Advantage Compatibility Complexity Perceived usefulness Event criticality	Respondents included owners, designers, architects, engineers, contractors, and other project participants in China	2021	[22]
Theory of Planned Behavior (TPB)	SEM	BIM adoption behavior	Technical feasibility Economic viability Industrial environment Governmental supervision Attitude towards behavior Subjective norm Perceived behavioral control	Developers, contractors, and research institutions in China’s construction industry	2021	[12]
Literature Review	PLS-SEM	BIM Implementation	Knowledge Creativity Evaluation Function Normalization Regulation	Participants with work experience in Malaysia’s construction sector	2023	[18]
Literature Review	SEM	BIM Adoption	Adoption motivation Potential benefits Potential challenge Knowledge support Down time Staff’s BIM capability	Small and medium-sized construction contractors in Australia	2018	[23]
Systematic Literature Review (SLR)	SEM	BIM adoption in infrastructure projects	Application Environment Project Organization Information Management	Specialist departments across various organizations in Ethiopia	2021	[19]
TAM and Information System Success Model (ISSM)	SEM	Individual Usage Acceptance Organizational Usage Acceptance	Perceived Usefulness Perceived Ease of Use Financial Aspects Organizational Efficiency Innovativeness Total Quality Operational Efficiency	Project managers, architects, engineers, contractors, BIM specialists, suppliers, etc., within Qatar’s construction industry	2024	[6]
Institutional Theory	PLS-SEM and Questionnaire surveys	BIM Adoption BIM awareness	Coercive Pressure Mimetic Pressure Normative Pressure Size of the SME Organization Type Years of Establishment	Small and medium-sized enterprises in Nigeria’s construction sector	2024	[7]
Institutional Theory	PLS-SEM	Level of BIM Adoption	Normative Pressure Mimetic Pressure Coercive Pressure Top Management Support	BIM managers from AEC organizations in the United States	2022	[20]
TAM + TOE	SEM	BIM Adoption Behavior	Technical Features Government Policies Social Influence Organizational Support Perceived Usefulness Perceived Ease of Use	Project owners within China’s construction sector	2019	[13]
TAM	Questionnaires + Interviews	Actual BIM use	Organizational Factors Personal Factors Technology Quality Financial Factors Environmental Factors Perceived Ease of Use Consensus Perceived Usefulness Individua Intention Organizational Intention	Architects and civil engineers with overseas experience working in contractors’ technical departments	2023	[21]
TAM	SEM	Individual intention Organizational intention	Organizational Competency Technology Quality Personal Competency Behavior Control Perceived Ease of Use Perceived Usefulness Consensus on Appropriation	Contractors, architects, and engineers within the South Korean	2020	[24]
Knowledge Coupling Theory + Knowledge Governance	PLS-SEM	BIM Integration Intention	Procedural inertia Learning inertia Experience inertia Formal knowledge governance Informal knowledge governance	Managers with BIM integration experience in Chinese construction enterprises	2022	[25]
TAM	SEM	Architects’ behavioral intentions towards BIM	Top Management Support Subjective Norm Compatibility Perceived Usefulness Behavioral Intention Facilitating Conditions Perceived Ease of Use Computer Self-Efficacy	Architects from three South Korean design firms	2015	[26]
TOE	SEM	BIM Adoption	Environment People Policy Technology Process Organization	Professionals in Bahrain	2020	[27]
Literature Review	SEM	OSC and BIM Integration	Integrated Knowledge Deficit Competency and Preparedness Deficit External Support and Policy Regulation Implementation Cost Integration Facilitators	Participants in engineering, construction, planning, and project management across New Zealand regions	2024	[28]
TAM	SEM	Behavioral Intention to Use	Attitude Knowledge Acquisition Lean Organizational Learning Organizational Support Process Training Social Factors Technical Training User Competency	Professionals in the architecture, engineering, and construction sectors	2021	[29]
TAM + IDT	SEM	BIM Adoption	Perceived ease of use Perceived Usefulness BIM standards Compatibility Interoperability Monitoring Visualization Advantage Complexity Support Professionals Training Willingness Interest Perceived cost	China’s construction industry encompassing owners, design firms, construction companies, contractors, software companies, and operators	2014	[30]
Literature Review	SEM	BlM Drivers	Construction Process digitalization and economics BlM Drivers Sustainability and efficiency Visualization and productivity	Professionals within the construction industry	2021	[31]
Literature Review	PLS-SEM	BlM Usage and Awareness	Technology and business Training and people Cost and standards Overcoming BlM barriers Process and economic	Specialists in Nigeria’s construction sector	2022	[32]
TOE	SEM	Adoption of BIM-AR	External Support Competitive pressure Trading Partners Readiness Subjective Norms Size of firm Demographic Composition Scope of Business Operation ICT Infrastructures Technical know-how Perceived Compatibility Perceived Values Security	Senior management of Ghanaian construction firms	2023	[33]
TOE + TAM	SEM	BIM Behavior	Government BIM Policies Organization Supports BIM Technical Features Perceived Usefulness Perceived Ease of Usefulness Attitude toward BIM Adoption Behavioral Intention	AEC professionals in China	2022	[34]
Literature Review	SEM	BIM implementation	Conflict & Risk Management Communication & Safety Practices Planning & Technical Safety Management Resource & Facility Management	BIM specialists, project managers, and civil engineers in Pakistan	2024	[35]
TPB + TAM	SEM	Actual behavior	Perceptual behavior control Perceived usefulness Perceived ease of use Behavior attitude Behavior intention Subjective norm	BIM practitioners within China’s construction sector	2020	[36]
Literature Review	SEM	Implementation of BIM in Small Construction Projects	Material Selection & Life Cycle Assessment Waste Reduction & Prefabrication Energy Efficiency & Performance Analysis Early-stage Design optimization	Practitioners involved in small-scale construction projects in Perak, Malaysia	2023	[37]
Literature Review	PLS-SEM	BIM implementation drivers	Standards Knowledge Software Legalization Management Training	Professionals in Nigeria’s construction industry	2023	[38]
TAM	SEM	Continuous use intention	Perceived ease of use Perceived usefulness Confirmation Satisfaction	Designers who have been directly involved in BIM usage	2021	[39]
Literature Review	SEM	BIM implementation	Awareness of technological benefits Organizational readiness and competitive advantages Motivation of management regarding BIM	Architects and civil engineers working for companies employing BIM technology in Turkey	2022	[40]
TAM	SEM	Intention to Use	System and Display Quality Organizational Support Perceived Usefulness Perceived Control Perceived Ease of Use Perceived Cost Compatibility	Construction site personnel in South Korea with BIM application experience	2018	[41]
Unified Theory of Acceptance and Use of Technology (UTAUT)	SEM	Use Behavior	Performance Expectancy Effort Expectancy Perceived Cost User Trust Facilitating Conditions Task-technology Fit Behavioral Intention	BIM practitioners in China’s construction industry	2023	[42]

Note: Dependent variable (DV) for these previous studies generally refers to either the intention to adopt BIM or actual behavior.

summarizes previous studies, including theories, methodologies, dependent variables, independent variables, and target populations. Ding et al. employed the Theory of Reasoned Action (TRA) to explore the factors influencing architects’ intention to apply BIM [9], and results showed that motivation, technical flaws, and BIM capability significantly affected participants’ BIM adoption. Except for TRA, the Modified Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) are the most common theories adopted by researchers in this field [10,11,12,13]. Wong et al. proposed a quantitative research study using a new conceptual framework to identify the factors influencing stakeholders in Malaysia to use BIM technology [14]. The results showed that perceived usefulness, influenced by technological quality, personal competency, and organizational commitment, was a key facilitator of BIM adoption in Malaysia’s construction industry. Meanwhile, many other previous studies selected a series of factors based on the literature review and explored their influence on multiple construction professionals’ intention to adopt BIM technology [15,16,17,18]. A previous study proposed a modified Technology Acceptance Model (TAM) model to investigate contractors’ BIM adoption and validated the important role of participant attitude in predicting their intention [10]. What is more, researchers from Yemen also highlighted the influence of the environment on the professionals’ BIM adoption intention, in addition to the individual perceptions [15]. This environment includes both internal and external organizational contexts, including facility management and risk management within the organization, as well as external economic conditions, and some previous studies also confirmed this conclusion [7,16,19]. Okakpu et al. found that positive organizational culture contributed to BIM adoption intentions among construction professionals who had participated in refurbishment projects [11], indicating that promoting emerging technology is not merely a matter of personal choice but requires organizational efforts, and this finding has been validated by many previous studies [6,13,20,21]. In summary, numerous scholars worldwide have employed multiple theoretical frameworks to determine drivers and barriers to BIM implementation in recent decades, and the conclusions have significantly contributed to overcoming obstacles to BIM acceptance and advancing the digital transformation and sustainable development of the construction industry.

Although the body of current literature is extensive, several research gaps persist. Firstly, many studies have adopted individual-centered theoretical frameworks such as TRA, TAM, and TPB, which fail to cover all dimensions comprehensively. For example, TAM pays attention to individual perceptions (perceived usefulness and ease of use) of emerging technology, whereas organizational-level factors also influence technology adoption intention, such as organizational support. So, a comprehensive framework is required to cover potential factors in different aspects. Furthermore, numerous studies have grouped various items derived from literature reviews into key factors using exploratory factor analysis; however, certain critical factors remain unexplored. For instance, few studies have examined pressure from collaboration partners, ignoring the fact that construction projects require participation from multiple parties. On the other hand, previous studies have typically focused on limited target populations. As BIM adoption among all stakeholders is essential for the construction industry’s digital transformation and sustainable development, expanding the target population to include diverse organizational types is imperative to ensure the generality of conclusions. Lastly, it is crucial to determine the relative importance of factors influencing BIM adoption intention across different stages of technology diffusion, given that the significance of these factors may vary across stages. That is to say, findings from previous studies conducted at earlier stages may differ from those in the present context, and thus, the major drivers at present should be explored to formulate targeted measures. Hence, a comprehensive study is needed to assess the impact of potential factors currently, providing empirical insights for advancing BIM implementation at the present stage.

To address the gaps mentioned above, this study employs the comprehensive TOE framework by integrating a set of factors across three dimensions: technological, organizational, and environmental. The purpose of this study is to empirically examine the impact of these factors on multiple stakeholders’ intentions to adopt BIM technology, particularly those that have been underexplored in previous studies, such as pressure from collaborators. Consequently, this study complements previous studies by considering more comprehensive factors and determining the influence of potential factors at current stage. This study includes diverse stakeholders across the construction industry (N = 512 valid responses from owners, consultants, designers, contractors, suppliers, and other participants), which enhances the generalizability of the findings. Data were analyzed using SEM, and based on the results, strategies tailored for the current BIM diffusion stage were identified.

The paper is organized as follows: Section 2 proposes the research hypotheses based on the existing literature; Section 3 presents a discussion of the research methods; Section 4 shows the results of data analysis; Section 5 presents the discussions of the findings, practical contributions, and limitations of this study; and Section 6 presents the conclusions of the study.

2. Literature Review and Research Model

2.1. Technical Factors

2.1.1. Perceived Usefulness

The concept of perceived usefulness was originally proposed by Davis et al. [43] and constitutes a core construct of TAM. Perceived usefulness reflects the extent to which an individual believes that adopting specific technology can improve work performance. Many previous studies have already validated the significant effects of perceived usefulness on behavioral intentions, particularly in the computer-related fields. In a longitudinal study conducted in Peru from 2017 to 2020, the findings showed perceived usefulness exerted a significant impact on contractors’ attitude toward BIM and adoption intention [10]. Wang et al. developed an integrated model incorporating TPB with TAM to investigate BIM adoption behavior and revealed that the perceived usefulness by participants was the most critical driver influencing their adoption intentions [44]. Another previous research concluded that perceived usefulness exerted a direct and strong influence on satisfaction with BIM, thereby enhancing perceived performance [45]. Although the finding does not directly show that perceived usefulness is a significant driver of BIM adoption intention, satisfaction and performance improvement undoubtedly stimulate the enthusiasm for adoption [46]. It is widely recognized that BIM enables related tasks to be completed more efficiently compared with traditional techniques [34]. From the perspective of the user, they may receive extra benefits such as accelerated career development, which also contributes to promoting BIM technology. Therefore, we propose this hypothesis:

H1. 

Perceived usefulness positively influences the intention to adopt BIM.

2.1.2. Perceived Ease of Use

Perceived ease of use, representing the extent to which a user finds a particular system or technology effortless and straightforward to use, also originates from the TAM [43] and has been widely acknowledged as a key factor influencing system usage and intention [47]. Evidently, it is more likely for users to adopt an emerging technology if this technology involves shorter processes, is easier for users to comprehend and master, and minimizes errors [48]. Cui et al. combined the TAM with the expectation confirmation theory (ECT) to study the factors influencing the willingness of architectural designers to continue to use BIM technology [39]. The results showed that improving BIM’s ease of use and user satisfaction is crucial to promoting its continuous application, thereby facilitating the deep integration of BIM technology in the architectural design stage. Son et al. proposed a model exploring key factors influencing architects’ adoption of BIM based on the TAM, and found that while both perceived usefulness and perceived ease of use significantly influenced behavioral intentions, perceived ease of use had a stronger effect [26]. Therefore, the following hypothesis is proposed:

H2. 

Perceived ease of use positively influences intention to adopt BIM technology.

2.1.3. Compatibility

In the diffusion of innovation theory, compatibility reflects the extent to which an emerging technology is regarded as aligned with users’ core beliefs, backgrounds, and immediate requirements [49]. Numerous studies have shown that compatibility is significantly related to users’ intention to adopt digital technologies within fields such as cloud computing [50], electronic logistics information systems [51], and online shopping systems [52]. In the construction industry, BIM compatibility is manifested in two aspects: the ability to operate smoothly without requiring significant changes to existing software or programs, and improved efficiency in employee collaboration [22]. Wang et al. concluded that the relative advantage and compatibility influence adoption intention through perceived usefulness [22], and Xu et al. also validated this conclusion [30]. Up to now, few studies have examined whether compatibility is a direct predictor of BIM adoption intention among construction industry stakeholders. However, it is reasonable that the adoption rate and users’ acceptance increase significantly if BIM integrates well with existing software and hardware and workflows. Consequently, the following hypothesis is proposed:

H3. 

Compatibility positively influences the intention to adopt BIM technology.

2.2. Organizational Factors

2.2.1. Management Commitment

Management commitment manifests as engaging in and sustaining behaviors that support subordinates in achieving organizational goals [53]. In the context of BIM adoption, the management commitment refers to the relevant policies, resources, and institutional support provided by the management of the organization [13]. Previous studies explored the impact of management commitment or organizational support on professionals’ intentions to adopt BIM. A previous study found that senior management support not only directly affected BIM adoption but also exerted an indirect influence on it through two mediating variables [54]. Abbasnejad et al. aimed to identify enabling factors for BIM implementation and found BIM leadership and senior management support emerged as the strongest driving forces during the initial stages of BIM promotion [55]. Tavallaei et al. revealed that top management support shaped the cognition pattern of the members within the organization and significantly promoted the levels of BIM adoption among various samples [20]. Consequently, we put forward the hypothesis:

H4. 

Management commitment positively influences the intention to adopt BIM technology.

2.2.2. Training

Training can be defined as the efforts made by the enterprise to help employees cultivate understanding and practical skills related to a new technology, and it appears to be a significant factor influencing the acceptance of that technology [56]. Besides imparting necessary knowledge, training influences workers’ shared beliefs, which in turn contribute to technology adoption [56]. Several studies have validated this finding in the context of BIM technology. A previous study identified four dominant factors for BIM adoption: BIM training (or capability development) for people without a BIM technical background, software efficiency and simplicity, organizational top management support, and initial investment cost [57]. Kineber et al. also found that training and people were listed as one primary obstacle to BIM implementation within Egypt’s construction sector, and argued that overcoming implementation hurdles was critical for achieving breakthroughs in BIM adoption [58]. In the practical implementation of BIM, the purpose of training is to provide opportunities for trial and error, enabling participants to become proficient and drive the process forward quickly. Therefore, we put forward the hypothesis:

H5. 

Training positively influences intention to adopt BIM technology.

2.2.3. Supporting Technical Facilities

Supporting technical facilities refer to the hardware equipment, software tools, and network resources provided by an organization to facilitate the adoption of new information technology. Facilitating conditions, a key construct in the UTAUT [59], encompass supporting technical facilities—including hardware, software, and network resources—as merely one component. Unlike the broader facilitating conditions, supporting technical facilities excludes other forms of support, such as guidance, policies, and organizational culture. Previous studies have validated the significant impact of facilitating conditions on BIM adoption intention. Wang et al. concluded that the influence of facilitating conditions was stronger in studies with smaller sample sizes conducted in individualistic cultures [60]. Howard et al. extended the UTAUT framework to explore the key factors influencing adoption intentions for BIM, and concluded that facilitating conditions exerted the strongest influence on actual user behavior [61]. In addition to these findings, software [38] and ICT infrastructure [33] are also key determinants for BIM adoption intention. These results underscore the critical importance of supporting technical facilities—a core component of facilitating conditions—in BIM implementation. Therefore, we propose this hypothesis:

H6. 

Supporting technical facilities positively influence intention to adopt BIM technology.

2.3. Environmental Factors

2.3.1. Government Policies

In the context of BIM promotion, government policies refer to formal principles, guidelines, and regulations issued by national or local governments to promote BIM adoption in the construction industry [62]. These policies typically employ various instruments, including mandatory requirements, subsidies, technical standards, and demonstration projects, which are recognized as important drivers for new technology adoption [63]. Consistently, Wang et al. found that government policy pressure and innovation strategy are important driving factors for BIM application, especially for state-owned enterprises, which are more susceptible to policy influences [64]. In the context of Chinese construction enterprises, Zhang et al. combined the TOE and TAM frameworks to investigate the key drivers of BIM application, and results showed that government policies, particularly external incentives, could significantly accelerate BIM diffusion [65]. Hany Omar et al. used a combination of quantitative and qualitative methods to discuss the reasons that hindered the development of BIM [66]. They concluded that the lack of mandatory BIM requirements from the government or clients constitutes the primary obstacle to BIM diffusion. Therefore, we propose this hypothesis:

H7. 

Government policies positively influence intention to adopt BIM technology.

2.3.2. Mimetic Pressure

Mimetic pressure refers to organizations’ imitation of legitimized or high-performing peers under conditions of uncertainty [67]. Intense competition often triggers mimetic responses, as organizations imitate successful practices to avoid competitive disadvantages [68]. Previous research has concluded that there was a positive relationship between mimetic pressure and the implementation of BIM in small and medium-sized enterprises, while normative and coercive pressure exerted a significant influence on the implementation and organizational cognition of BIM technology [7]. Similarly, Gao et al. reached the conclusion that mimetic pressure exerted the strongest influence on BIM acceptance and perceived usefulness [69]. Therefore, it is reasonable to assume that organizations are likely to adopt BIM when their competitors do so [33], especially when the competitors gain a great advantage. Therefore, we propose this hypothesis:

H8. 

Mimetic pressure correlates positively with the intention to adopt BIM technology.

2.3.3. Collaboration Partner Pressure

In addition to mimetic pressure, professionals may also face collaborative pressure from external stakeholders to adopt BIM, which few scholars have examined. Previous studies have focused primarily on coercive pressure, which originates from regulatory agencies and is mandatory in nature, differing from collaborative pressure [70]. Few enterprises can complete a construction project alone, and thus multiple stakeholders, including clients, designers, contractors, consultants, and others, take part in the whole process of the project [71]. During data exchange among project parties, using the same software and file format enhances interoperability and reduces errors. To achieve this smooth coordination, collaboration partners may require the use of BIM, which provides significant advantages for multi-party collaboration. Therefore, although the pressure from collaboration partners is not coercive, this pressure cannot be neglected, and it is reasonable to assume that collaboration partner pressure promotes the adoption intention of BIM, considering the advantages of this technology. Therefore, we propose this hypothesis:

H9. 

Collaboration partner pressure correlates positively with the intention to adopt BIM technology.

To summarize, nine potential determinants of BIM technology adoption intention were identified and grouped into three categories: technological, organizational, and environmental. Figure 1 illustrates the proposed model of this study.

3. Methods

3.1. Sample and Data Collection

To identify drivers of BIM adoption intention, a survey was carried out to collect self-reported data from practitioners in the construction industry. Different types of firms have potential BIM users, and thus, this study aims to include practitioners from various enterprises across the industry. Given the heterogeneity of the construction industry, a large sample size was required to adequately cover firms from diverse sectors. It should be noted that the diversity of the sample in this study enhances the scope of applicability relative to studies limited to specific firm categories [39]. The survey went through two rounds in September 2025 using the Questionnaire Star system, which is a popular platform to gather data from questionnaires. In the first round, convenience sampling was adopted via Wenjuanxing to send an online link to construction industry practitioners without special requirements for the type of organization. Then, we collected data from researchers’ acquaintances in the construction sector. Finally, 563 responses were collected in total. Before filling out the questionnaire, all subjects signed informed consent forms. In addition, they were assured that the collected data would be kept confidential and used for academic research only. To ensure data quality, responses were excluded based on three criteria: (a) incomplete submissions, (b) unreasonably short completion duration, and (c) responses with pattern responding. Finally, 512 valid responses were retained for data analysis. The protocol received ethics committee approval at Sichuan Normal University.

Respondents’ demographic characteristics are summarized in

Table 2. Demographic parameters of respondents. (N = 512).

Demographic Parameter	Distribution	Frequency	Percentage
Gender
	Male	324	63.3%
	Female	188	36.7%
Age
	22–25	92	18.0%
	26–35	162	31.6%
	36–44	116	22.7%
	>45	142	27.7%
Type of organization
	Owner/Consulting company	117	22.9%
	Design company	93	18.2%
	Construction contractor	166	32.4%
	Supplier/Engineering Surveying company	90	17.6%
	Universities or research institutes	46	9.0%
Educational Background
	College degree or below	155	30.3%
	Undergraduate	252	49.2%
	Graduate student or above	105	20.5%
Working experience
	0–3 years	152	29.7%
	4–5 years	151	29.5%
	6–10 years	156	30.5%
	Over 10 years	53	10.4%

. Most were male (63.3%), consistent with the gender distribution in the construction industry [41]. Regarding age, 31.6% were 26–35 years old, 27.7% were older than 45, 22.7% were aged from 36 to 44, and 18.0% were younger than 25. This study includes five types of enterprises. Most participants (32.4%) were from construction contractors, 22.9% from owners or consulting companies, 18.2% from design companies, 17.6% from suppliers or engineering surveying companies, and 8.9% from universities or research institutes. Among the participants, the majority (49.2%) held a bachelor’s degree, 30.3% had an associate degree or below, and the remaining 20.5% had graduate degrees. Participants with 0–3 years and 4–5 years of work experience accounted for 29.7% and 29.5%, respectively; 30.5% had 6–10 years, and 10.4% had over 10 years of experience. The rounding principle, which the respondents were informed of by the researchers, is applied for work experience.

3.2. Measurements

This study adopts a quantitative approach to determine the influence of factors on the intention to employ BIM. The questionnaire encompasses two parts: (1) participants’ demographic information; (2) their perceptions of factors which may exert a significant impact on BIM adoption intention. At first, all the respondents were required to provide their demographic information, including gender, age, type of employing organization, educational background, and years of work experience. The second part consists of nine factors and one outcome variable: perceived usefulness (PU), perceived ease of use (PEU), compatibility (CO), management commitment (MC), training (TR), supporting technical facilities (STF), government policies (GP), mimetic pressure (MP), collaboration partner pressure (CPP), and BIM adoption intention (INT). The questions for each construct were drawn from or adapted from previous research. The measuring items for PU and PEU were adapted from Zhao et al.’s work [34], while those for CO were adapted from Park et al.’s study [41]. The questionnaire on three factors from an organizational perspective was based on some previous studies [13,23,38,41]. The scales for GP and MP were adapted from two studies [13,20]. As limited research has examined CPP, we developed the questionnaire for CPP by referring to some related studies [13,72]. The items for the outcome variable were adapted from Hong et al.’s work [23]. Two items were adopted to measure the dependent variable. The details of the second part are shown in

Table A1. Description of measurable variables.

Constructs	Variable Code	Measurement Item
Perceived Usefulness
	PU1	Using BIM improves my work productivity
	PU2	Using BIM enhances the quality of project deliverables
	PU3	Using BIM is beneficial to my professional development
Perceived Ease of Use
	PEU1	Learning to operate BIM software is easy for me
	PEU2	BIM software is user-friendly
	PEU3	I find it easy to use BIM to complete my tasks
Compatibility
	CO1	BIM software works with my present workflow
	CO2	BIM software is compatible with my other software and hardware
Management Commitment
	MC1	My organization understands the benefits of BIM
	MC2	Top management in my organization strongly supports the use of BIM
	MC3	My organization provides sufficient resources for BIM adoption
	MC4	My organization has given high priority to BIM
Training
	TR1	My organization provides proper education and training for BIM adoption and implementation.
	TR2	My organization provides proper technical guidance for using BIM
	TR3	My organization utilizes a specific person, group, or external consultants to solve difficulties in using BIM
Supporting technical facilities
	STF1	My organization possesses hardware capable of supporting BIM
	STF2	My organization has full access to BIM software
	STF3	In my organization, there are no barriers in terms of technical facilities hindering the collaboration among the staff using BIM
Government Policies
	GP1	The government has issued clear policies encouraging BIM adoption
	GP2	The government provides financial incentives for BIM implementation
	GP3	The government is trying to promote industry standards and references relevant to BIM
Mimetic pressure
	CP1	My company thinks that BIM technology influences competition in the industry
	CP2	My company is under pressure from competitors to adopt BIM technology
Collaboration partner pressure
	CPP1	My company’s major partners encouraged the implementation of BIM technology
	CPP2	My company’s major partners recommended the implementation of BIM technology
	CPP3	My company’s major partners requested the implementation of BIM technology
BIM adoption intention
	INT1	I intend to adopt BIM technology
	INT2	I will recommend BIM technology to other technicians or organizations

, where a five-point Likert scale (strongly disagree = 1, strongly agree = 5) was employed.

Although most of the measurement items are adapted from previous studies, their validity was further ensured through pilot testing. Therefore, once the questionnaire was developed, five professionals in this field were invited to review the items for content validity. The questionnaire was modified based on feedback, and some items were rephrased to make it easier to understand.

3.3. Data Analysis Approach

Data analysis was carried out in two stages by employing SPSS 24 and SmartPLS 3.2.9. In the first stage, SPSS 24 was used to analyze the frequency distribution of each item in the first part of the questionnaire. In the second stage, SmartPLS 3.2.9 was adopted to examine the causal relationships between the nine factors and the outcome variable, and the statistical distribution of the responses for all variables was analyzed using SPSS 24. SmartPLS is on the basis of Partial Least Squares Structural Equation Modeling (PLS-SEM) and employs a component-based approach. Compared with covariance-based methods, PLS-SEM is advantageous when sample sizes are small and distributional assumptions fail [73]. Furthermore, PLS-SEM is also suitable for the research models which encompass many latent variables and aim to identify key antecedents of the outcome variable and assess their predictive capacity. However, Covariance-based SEM prioritizes the evaluation of the proposed model’s goodness-of-fit, which does not align with the research aim of this study. Therefore, this study selected PLS-SEM as the statistical analysis method. There are some requirements for the minimum number of cases for PLS-SEM. The sample size should be greater than the larger of (a) the maximum number of indicators for any single construct or (b) the maximum number of structural paths pointing to any construct in the proposed model [73], which is satisfied in this research.

4. Results

4.1. Assessment of the Measurement Model

Before evaluating the measurement model, it is important to address potential Common Method Variance (CMV). CMV, also called common method bias, represents the systematic error variance attributable to methodological approach rather than theoretical variables, and poses a critical threat to the internal validity of research conclusions [74]. To assess the potential CMV, we conducted Harman’s single-factor test. The analysis revealed that the first factor accounted for 32.9% of the variance, which was below the threshold [75]. The findings indicate that CMV does not appear to be a significant concern in this study.

The evaluation of the measurement model involves three steps: (1) convergent validity, (2) internal consistency or construct reliability, and (3) discriminant validity. For convergent validity, the loadings of indicators on their related constructs should exceed 0.70 and be statistically significant [76]. The bootstrapping procedure was employed to examine the statistical significance of the loadings, which were all found to be significant in this study. Furthermore,

Table 3. Reliability and convergent validity test.

Constructs	Measuring Items	Outer Loading	Cronbach’s α	CR	AVE
Perceived Usefulness
	PU1	0.798	0.774	0.813	0.593
	PU2	0.694
	PU3	0.812
Perceived Ease of Use
	PEU1	0.709	0.832	0.858	0.670
	PEU2	0.891
	PEU3	0.844
Compatibility
	CO1	0.743	0.752	0.785	0.648
	CO2	0.862
Management Commitment
	MC1	0.843	0.814	0.857	0.602
	MC2	0.817
	MC3	0.742
	MC4	0.692
Training
	TR1	0.765	0.837	0.863	0.678
	TR2	0.816
	TR3	0.885
Supporting technical facilities
	STF1	0.886	0.876	0.903	0.756
	STF2	0.854
	STF3	0.868
Government Policies
	GP1	0.832	0.812	0.830	0.620
	GP2	0.788
	GP3	0.739
Mimetic pressure
	CP1	0.821	0.763	0.781	0.641
	CP2	0.780
Collaboration partner pressure
	CPP1	0.767	0.802	0.838	0.633
	CPP2	0.808
	CPP3	0.812
BIM adoption intention
	INT1	0.838	0.744	0.777	0.636
	INT2	0.755

shows that most outer loadings exceeded 0.70, with a few exceptions ranging from 0.65 to 0.70. Hair et al. concluded that it is unnecessary to delete measurement items whose loadings are slightly below the threshold because the deletion deteriorates other validity indices, such as composite reliability [77]. If the average variance extracted (AVE), representing the variance explained by the construct, exceeds 0.50, convergent validity is established. As shown in Table 3, values of all constructs’ AVE were larger than 0.50, and thus convergent validity was established. Cronbach’s α and the composite reliability (CR) are recommended to be higher than 0.70 [76]. Table 3 shows that values of Cronbach’s α and CR for all latent constructs were larger than the threshold of 0.70, satisfying the requirements for internal consistency reliability. To establish discriminant validity, indicators must exhibit stronger correlations with their assigned constructs relative to other constructs. In terms of evaluating discriminant validity, the Fornell–Larcker criterion and the HTMT (Heterotrait–Monotrait ratio) are two popular methods. For the Fornell–Larcker criterion [78], the square root of each construct’s AVE is supposed to exceed the correlations with other latent constructs, as confirmed in

Table 4. Fornell–Larcker criterion results.

Construct	PU	PEU	CO	MC	TR	STF	GP	MP	CPP	INT
PU	0.770
PEU	0.616	0.819
CO	0.558	0.628	0.805
MC	0.447	0.522	0.571	0.776
TR	0.577	0.605	0.657	0.453	0.823
STF	0.462	0.536	0.478	0.677	0.469	0.869
GP	0.552	0.619	0.672	0.467	0.559	0.587	0.787
MP	0.671	0.547	0.488	0.473	0.329	0.394	0.479	0.801
CPP	0.439	0.416	0.366	0.553	0.441	0.477	0.437	0.377	0.796
INT	0.346	0.632	0.557	0.569	0.605	0.382	0.616	0.376	0.556	0.797

. Furthermore, the HTMT values should be lower than 0.85 [74]. As shown in

Table 5. HTMT results.

Construct	PU	PEU	CO	MC	TR	STF	GP	MP	CPP	INT
PU
PEU	0.753
CO	0.641	0.784
MC	0.539	0.672	0.661
TR	0.617	0.642	0.783	0.742
STF	0.533	0.422	0.701	0.773	0.475
GP	0.638	0.561	0.505	0.483	0.566	0.641
MP	0.559	0.629	0.643	0.566	0.481	0.735	0.807
CPP	0.447	0.746	0.628	0.525	0.534	0.783	0.633	0.572
INT	0.729	0.653	0.782	0.652	0.722	0.673	0.448	0.623	0.755

, the highest HTMT value in this study was 0.807, supporting discriminant validity.

4.2. Assessment of the Structural Model

As for the assessment of the structural model, the coefficient of determination (R²) is adopted to determine the in-sample explanatory power, while the cross-validated redundancy index (Q²) assesses the predictive relevance [77]. The R² value is supposed to range from 0 to 1, and a previous study recommended that this value exceed 0.10 to indicate sufficient explanatory power [73]. In this study, the R² value for the sole endogenous construct (INT) was 0.629, reflecting substantial explanatory power. In addition, the value of Q² for INT (blindfolding distance = 7) exceeded the threshold of zero, indicating predictive relevance of the proposed model [79].

Then, the significance and value of the path coefficients were assessed using bootstrapping, and 5000 subsamples were considered sufficient to determine reasonable standard error estimates [79].

Table 6. Results of Hypothesis Testing (significance level = 0.05).

Hypothesis	Path	Path Coefficient	Standard Deviation	t-Value	p-Value
H1	PU -> INT	0.050	0.056	0.891	0.373
H2	PEU -> INT	0.180	0.048	3.761	0.000 ***
H3	CO -> INT	0.143	0.043	3.302	0.001 ***
H4	MC -> INT	0.182	0.052	3.510	0.000 ***
H5	TR -> INT	0.090	0.045	2.005	0.045 *
H6	STF -> INT	0.083	0.048	1.740	0.082
H7	GP -> INT	0.113	0.048	2.379	0.017 *
H8	MP -> INT	0.026	0.038	0.704	0.482
H9	CPP -> INT	0.103	0.052	1.991	0.047 *

Note: * denotes p < 0.05; ** denotes p < 0.01; *** denotes p < 0.001.

presents the testing results for all hypotheses. Six hypotheses were supported, whereas three (H1, H6, and H8) were rejected. Among the nine factors, PEU (β = 0.180, p < 0.001) and MC (β = 0.182, p < 0.001) had the two strongest effects on the intention to adopt BIM. In addition, CO (β = 0.143, p < 0.001), TR (β = 0.090, p < 0.05), GP (β = 0.113, p < 0.05), and CPP (β = 0.103, p < 0.05) were significant predictors of BIM adoption intention. Notably, the impacts of CPP and GP were comparable. What is more, PU (β = 0.050, p = 0.373), STF (β = 0.083, p = 0.082), and MP (β = 0.026, p = 0.482) were insignificantly related to BIM adoption intention, as shown in Figure 2.

5. Discussion

This paper proposes a comprehensive framework to explore the influence of nine factors on the BIM adoption intention from three dimensions: technological, organizational, and environmental. 512 valid responses from multiple stakeholders, including owners, consulting firms, design firms, construction companies, suppliers, engineering surveying firms, and universities or research institutes, have been collected and retained for data analysis. The SEM results show that six factors exerted a significant impact on the BIM adoption intention, while three factors were not antecedents of the intention.

Among the three technical factors, perceived ease of use and compatibility are two important antecedents of BIM adoption intention, while perceived usefulness has no significant impact. The insignificant relationship between perceived usefulness and intention is contrary to the findings of previous studies, which have confirmed the significant impact of participants’ perception of usefulness regarding BIM technology [80,81]. The possible reasons for this conclusion are as follows. On the one hand, the advantages of BIM have been increasingly recognized after so many years of promotion [39]. Existing users may have experienced great benefits, such as enhanced visualization and interdisciplinary coordination, when applying BIM technology in their work, whereas potential users may have been frequently informed of these advantages due to the wide promotion. Therefore, the usefulness of BIM is supposed to be widely recognized among multiple stakeholders, and thus, this factor may not be a bottleneck for the wide application of BIM anymore. On the other hand, many emerging technologies are expected to be integrated with BIM, placing higher demands on BIM capabilities, which may indicate that using BIM technology alone cannot fully leverage its potential [82,83]. This study confirms that perceived ease of use significantly promotes the adoption intention of BIM, which is in line with extant literature, and represents the second most important predictor. In a previous study, Son et al. expanded the TAM and identified perceived ease of use as a key factor directly influencing willingness to adopt BIM [26]. If workers find it easy to use some technology, the cognitive load will be reduced. Jiang et al. argue that if the user interface is intuitive and the operation is smooth, users do not need to spend a lot of time and energy learning the tool, allowing them to focus on the task [84]. Furthermore, technological complexity may lead to black-box anxiety, which can be alleviated by perceived ease of use. It needs to be pointed out that many technologies are being applied in a coordinated manner in the construction industry, which undoubtedly increases the difficulty of learning and adopting BIM for the users and potential users. Higher usability of BIM, especially when integrated with other emerging technologies, will lower the barriers to BIM adoption intention. Existing evidence suggests that compatibility influences respondents’ intention to use BIM through the mediating factor of perceived usefulness [22], and a recent study also demonstrates that cognitive level mediates the relationship between the features of BIM (including compatibility) and its adoption [85]. The results of this study show that compatibility directly influences the adoption intention of BIM. BIM relies on specialized software and hardware to be effective. When the implementation of BIM is compatible with existing IT infrastructure, it yields favorable results. If technological challenges have been overcome, it may also be important to consider whether BIM is compatible with the current practices of professionals. However, a previous study revealed the insignificant relationship between perceived compatibility and BIM-Augmented Reality (AR) adoption [33]. This contradiction likely stems from differences in the technological scope between BIM-AR and BIM. First, BIM-AR applications are developed to solve context-specific problems (e.g., on-site visualization). Second, BIM serves as an interoperable tool requiring integration with multiple other technologies, imposing stricter compatibility demands.

According to the results of this study, management commitment and training exert a significant influence on BIM adoption intention, with management commitment being the greatest antecedent, while supporting technical facilities are not a key antecedent. A study has also demonstrated the significant impact of management commitment on the adoption intention of BIM [20]. Caglayan et al. have built a systematic evaluation framework and revealed that senior management support directly affected the implementation of BIM [86]. Open communication and proactive engagement of the management could effectively reduce resistance to adopting new technology and enhance employee engagement [87]. And a previous study found that management’s motivation plays a key role in driving innovation in the construction industry [40]. In addition to high priority, management commitment also means the guarantee of various resources such as funds and time, which may be one of the key reasons for the BIM promotion. The application of new technology is not a matter of an individual and requires efforts from many members within the organization. Sufficient input, including money and time, can also enhance the confidence of the whole team and send positive signals, which is of great strategic significance [88]. The statements above align with the findings of this study that management commitment plays a dominant role in removing inevitable barriers and ensuring the smooth implementation of BIM within the organization. Furthermore, according to the results of this study, training also significantly influences BIM adoption intention, which is consistent with previous studies. Ding et al. argued that the BIM capability of a team was an important predictor of architects’ intention to use BIM [9]. This highlighted the opportunities for BIM training provided by the companies and project managers and showed that enriching relevant experience promoted architects’ capability and intention. Seman et al. also revealed that technical training significantly affected user capabilities and helped users to understand potential construction logic and processes more comprehensively, and had become a key factor in accelerating the application of BIM [29]. Unskilled workers are a key barrier to the application of new technology. Training may not only directly improve the abilities of workers but also play a key role in the process of transforming abstract theories into concrete strategies. With the emergence of new technologies [82,83,89], integrating BIM with these technologies may have become a critical challenge for BIM users, which necessitates sufficient training support. The results show that supporting technical facilities do not appear to have a significant impact on the adoption intention of BIM, which contrasts with the conclusions of many previous studies. The implementation of BIM requires relevant software and hardware facilities to be effective [90]. Pan et al. found that favorable conditions directly affect the learning behavior of BIM, among which institutional resources (such as hardware facilities and software) and teacher support are the key factors [91]. Zhang et al. observed that favorable conditions exerted a continuous positive influence on behavioral intention and usage behavior, with hardware, software, and technical support being the key ones for BIM adoption intention [42]. This contradiction can be explained by two factors. First, BIM does not impose high hardware requirements, so the impact of hardware, software, and other facilities may be less significant than other factors, such as training provided by the management. Second, these technical resources are now readily available to enterprises after years of BIM promotion. Consequently, the lack of such facilities may no longer constitute a major obstacle to BIM implementation.

As for environmental factors, mimetic pressure is not an important predictor of BIM adoption intention, whereas government policies and collaboration partner pressure are both significant predictors and similar in terms of influence. This study demonstrates that government policies are an enabler for BIM implementation, which is in line with many previous studies. Algahtany et al. confirmed government initiatives, such as clarifying the direction of development and establishing standards, had a profound impact on the application of BIM [92]. Furthermore, government financial support and guidelines are also critical for overcoming barriers faced by enterprises during the initial stage of BIM adoption [90]. Qin et al. took Chinese construction enterprises as the research subject and also found that government policies were the strongest driving factors affecting the application of BIM in enterprises [93]. The state-owned enterprises in China are more sensitive to government policies because they will receive more government subsidies and media reputation as compensation mechanisms when assuming heavier policy burdens [94]. In the institutional environment with Chinese characteristics, state-owned construction enterprises function simultaneously as policy implementers and instruments of state capacity. Meanwhile, non-state-owned construction enterprises would benefit from market-oriented incentive policies and proactively adopt BIM technology to enhance their competitive advantage. Standardized construction norms and other mandatory policies may also be conducive to the increase in the adoption rate of BIM. This study fails to identify the significant correlation between mimetic pressure and the BIM adoption intention. In previous studies, the relationship between mimetic pressure and BIM adoption intention is controversial. For example, Ma et al. found that mimetic pressure positively affected the intention to adopt BIM [95], while Tavallaei et al. applied the institutional theoretical framework and concluded that mimetic pressure had no significant impact on the BIM adoption [20]. The reasons for this conclusion in this study are as follows. First, the completion of projects requires numerous technologies, and BIM is only one of them and may not be the decisive one. So, the pressure posed by competitors using BIM technology may still be limited. Second, there may be relatively little communication between competitors in the construction industry. Consequently, the weak ties between enterprises may further explain why mimetic pressure does not significantly increase BIM adoption intention. Unlike mimetic pressure, collaboration partner pressure significantly affects the adoption intention of BIM in this study. Few scholars have paid attention to the pressure from collaboration partners, yet such pressure may be important because construction projects require multi-party cooperation. Yuan et al. argued that social influence significantly promoted the perceived ease of use, which in turn predicted attitudes toward BIM and indirectly influenced adoption intention [13]. This social influence stems from colleagues, top management, and cooperative partners. Although this previous study did not directly examine the impact of pressure from collaboration partners, the findings still revealed the multifaceted collaborative nature of the construction industry, indicating that smooth collaboration among stakeholders may remain important. Similarly, Papadonikolaki et al. found that contractors tended to select partners who use BIM [96], which suggests that BIM adoption serves as a criterion for partner selection. BIM technology is widely recognized for facilitating collaboration among all participants, as it features enhanced information sharing, better communication, and real-time collaboration. The advantage of BIM, ensuring smooth coordination among different parties, may be the reason for this finding in this study.

5.1. Practical Implications

This study adopts a comprehensive framework to determine key predictors of BIM adoption intention. Specifically, we examine how nine factors, derived from previous literature across three dimensions, impact stakeholders’ intention to adopt BIM. The conclusions of this study have practical value for two reasons. First, a comprehensive range of factors is taken into consideration in this study, offering practical recommendations from multiple perspectives. Second, survey participants include owners, consulting firms, design firms, construction companies, suppliers, engineering surveying firms, and universities or research institutes. This diversity makes the results more generalizable than those of previous studies limited to one type of enterprise. The results show that the influence of three factors on BIM adoption intention is not significant. The insignificant relationship between two factors (perceived usefulness and supporting technical facilities) and BIM adoption intention is inconsistent with the conclusions of much previous research, reflecting the new situation of promoting BIM technology. After years of effort, it has been widely recognized that BIM is useful in the construction industry, while concerns about supporting technical facilities have lessened as an obstacle to BIM adoption intention. Regarding the impact of mimetic pressure, previous studies have yielded inconsistent conclusions. In this study, the insignificant influence of mimetic pressure is attributed to both the non-decisive role of BIM technology and the lack of communication among competitors. Regarding the other six key predictors of BIM adoption intention, practical suggestions are provided below for three groups: BIM technology service providers, enterprises in the construction industry, and regulatory agencies (including government and industry associations).

(1)

BIM technology service providers should pay attention to the ease of use of the relevant software and reduce the operational difficulty. Meanwhile, being compatible with existing software, hardware, and workflow is another concern for providers. With the emergence of many new technologies for the construction industry, new requirements have emerged for the service providers. From the perspectives of usability and compatibility, ensuring smooth collaboration between BIM and other new technologies has become imperative.

(2)

For the enterprises, a series of management measures could be implemented to promote BIM technology. First and foremost, the management should give high priority to BIM, setting an example for staff to follow. Apart from the visible commitment, enterprises should also allocate necessary resources, including funding and technical support. Training is also an efficient way, especially when other new technologies appear, bringing stricter requirements for professionals’ technical capabilities. As a result, training with up-to-date content, provided by enterprises, is necessary.

(3)

The government plays a vital role in facilitating new technology diffusion through policy instruments such as incentives, regulatory standards, and reference cases. Furthermore, the government and industry associations could organize seminars and invite all types of construction enterprises to participate. How to use BIM to enhance effective coordination among all parties is an important topic, as BIM has been widely accepted as the cornerstone of the industry’s digital transformation.

5.2. Limitations

Despite some innovative and useful findings, several limitations still exist in this study which necessitate future research. First, this study relied on self-reported questionnaire data to examine the influence of various factors on BIM adoption intention. Although this approach is widely employed in new technology adoption studies, it is susceptible to subjective biases. To reduce the negative impact, we assured participants of anonymity. However, objective data could be introduced to complement these findings in future research. In addition, common method variance (CMV) should be addressed using stronger statistical tests other than Harman’s single-factor test in future research. Second, this study relied on cross-sectional survey data. A future study would benefit from longitudinal data to establish causal relationships. As the impact of drivers and barriers on BIM adoption intention may vary over time, examining such temporal heterogeneity would contribute to more tailored recommendations for BIM promotion. Furthermore, multi-group analysis or an examination of mediation/moderation effects could be conducted to enhance the robustness of the findings. Third, the outcome variable in this study is BIM adoption intention, which is not equivalent to actual use of BIM technology. Therefore, future studies may target the actual use, like records of logging into relevant software, and adoption intention as the dependent variables to address the intention–behavior gap to effectively translate the intention into actual use.

6. Conclusions

Considering that BIM provides significant advantages in enhancing interdisciplinary coordination and optimizing the whole lifecycle management of projects, identifying antecedents of the intention to adopt BIM technology is essential for driving digital transformation and sustainable development in the construction industry. This study aims to explore how the factors influence BIM adoption intention among multiple stakeholders in the construction industry. The diverse sample and comprehensive factor coverage in this study provide valuable theoretical and practical insights for promoting BIM adoption intention. The major findings are as follows:

(1)

From a technological point of view, perceived usefulness no longer poses a challenge because prolonged advocacy has successfully established its value proposition among practitioners. By identifying the significant impact of perceived ease of use, this study highlights the importance of reducing the operational difficulty for BIM users. Compatibility with existing facilities and workflows further facilitates adoption. These findings direct service providers to take user-centric design seriously, particularly as emerging technologies that work together with BIM introduce new usability challenges at the current stage.

(2)

For enterprises, the management should give priority to BIM adoption, as this study validates the dominant role of management commitment. Once BIM’s value is recognized by the management, additional resources should also be allocated to implementation. Training improves professionals’ BIM capabilities and facilitates BIM adoption intention. This underscores the necessity of enterprise-sponsored training programs. Furthermore, emerging technologies pose new challenges for technical training. Due to long-term promotion efforts, supporting technical facilities are no longer a bottleneck for BIM adoption.

(3)

From the perspective of the environment, mimetic pressure is not a major concern, while collaborators’ opinions play an important role in predicting the intention to adopt BIM technology. Given BIM’s effectiveness in promoting interdisciplinary cooperation, practitioners adopting BIM expect their collaborators to adopt BIM technology to facilitate information sharing. Policies issued by the government are a key driver of new technology diffusion, and it is recommended that the government and industry associations organize BIM technical exchange seminars in addition to conventional policy instruments.