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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

Personal Innovativeness and Initial Trust in AI-Enabled BIM

Adoption Using UTAUT in Malaysia

Shiau-Yoon Choong

, Zahir Osman

Faculty of Business and Management, Open University Malaysia, Kuala Lumpur, Malaysia

DOI: https://doi.org/10.51583/IJLTEMAS.2026.150600007

Received: 11 June 2026; Accepted: 16 June 2026; Published: 02 July 2026

ABSTRACT

The construction industry is undergoing a significant digital transformation driven by Construction 4.0

technologies, with Building Information Modeling (BIM) and Artificial Intelligence (AI) emerging as key

enablers of improved productivity, collaboration, and decision-making. The integration of AI with BIM has given

rise to AI-enabled BIM systems, which combine BIM's data-rich environment with advanced capabilities such

as machine learning, predictive analytics, natural language processing, and intelligent automation. Despite the

potential benefits of AI-enabled BIM, its adoption within the Malaysian construction industry remains limited,

and empirical evidence on the factors influencing its acceptance is scarce.

This study examines the determinants of behavioral intention to adopt AI-enabled BIM systems among

construction professionals in Malaysia. Grounded in the Unified Theory of Acceptance and Use of Technology

(UTAUT), the study extends the model by incorporating personal innovativeness and initial trust as additional

predictors. Data were collected from 261 professionals employed by CIDB Grade 7 (G7) construction firms

randomly selected using probability sampling and analyzed using Partial Least Squares Structural Equation

Modeling (PLS-SEM).

The results indicate that performance expectancy, effort expectancy, social influence, and personal

innovativeness have significant positive effects on behavioral intention to adopt AI-enabled BIM systems.

Personal innovativeness was a particularly important predictor, exerting both direct effects on behavioral

intention and indirect effects through performance expectancy and effort expectancy. Initial trust did not directly

influence behavioral intention; however, it significantly enhanced perceptions of usefulness and ease of use,

which in turn indirectly affected adoption intention through these mediating constructs. The model demonstrated

satisfactory explanatory and predictive power.

This study contributes to the literature by extending UTAUT to the emerging context of AI-enabled BIM

adoption and by providing one of the first empirical investigations of this technology in Malaysia. The findings

offer practical implications for construction firms, policymakers, and software developers seeking to accelerate

AI-enabled BIM adoption through user-centered design, digital skills development, organizational support, and

targeted policy interventions.

Keywords: AI, Building Information Modeling, Personal Innovativeness, Initial Trust, UTAUT.

INTRODUCTION

The global construction industry is undergoing a profound transformation as it responds to increasing project

complexity, fragmented workflows, productivity pressures, sustainability demands, and the need for more

integrated decision-making (Datta et al., 2024; Dagou et al., 2024; Egwim et al., 2023; Nnaji et al., 2023; Rahim,

Ismail et al., 2023). Historically reliant on manual labor and conventional project delivery methods, the sector is

now moving toward Construction 4.0, where digital technologies such as Building Information Modeling (BIM),

artificial intelligence (AI), cloud computing, big data analytics, the Internet of Things, drones, laser scanning,

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blockchain, augmented reality, and 3D printing are reshaping how built assets are designed, constructed, operated,

and maintained (Faraji et al., 2024; García de Soto et al., 2022; Srivastava et al., 2022). Among these

technologies, BIM has emerged as one of the most widely promoted and strategically important tools because it

enables the creation, management, and exchange of reliable digital information across the entire lifecycle of a

built asset.

BIM represents a shift from traditional two-dimensional computer-aided design toward an intelligent,

collaborative, and data-rich model-based process. It supports design coordination, clash detection, cost

estimation, schedule planning, construction monitoring, facility management, and lifecycle asset maintenance

(Heidari et al., 2024; Lee et al., 2022; Pan & Zhang, 2023; Zawada et al., 2024). By integrating geometric and

non-geometric project information into a shared digital environment, BIM enhances communication, improves

coordination among stakeholders, reduces errors and rework, supports more accurate cost and time estimation,

and contributes to greater productivity and sustainability. Nevertheless, BIM adoption remains uneven across

countries and regions, shaped largely by institutional support, economic capacity, government policy,

technological readiness, and user acceptance (Oyuga et al., 2023; Yizing et al., 2025).

Recent advances in AI have further expanded the potential of BIM. AI technologies, including machine learning,

natural language processing, predictive analytics, computer vision, and generative AI, enable construction

systems to learn from data, identify patterns, automate complex tasks, and support intelligent decision-making

(Adebayo et al., 2025; Ghimire et al., 2024; Omotayo et al., 2025; Pan & Zhang, 2023). When integrated with

BIM, AI transforms BIM from a descriptive digital representation into an intelligent, predictive, and prescriptive

decision-support system. AI-enabled BIM systems can facilitate generative design, automated documentation,

real-time project monitoring, predictive risk and safety management, natural-language interaction with BIM data,

issue detection, conflict resolution, stakeholder training, and cross-disciplinary collaboration (Dagou et al., 2024;

Heidari et al., 2024; Li et al., 2024; Zawada et al., 2024). This convergence offers significant potential to improve

project performance, enhance sustainability, reduce uncertainty, and strengthen knowledge management

throughout the construction project lifecycle.

In Malaysia, the construction sector is central to national socio-economic development and has been increasingly

encouraged to adopt digital technologies through policy initiatives such as the Construction Industry

Transformation Program and CIDB’s Construction 4.0 Strategic Plan (2021–2025). These initiatives position

BIM and other IR 4.0 technologies as key enablers of productivity, competitiveness, and modernization.

However, despite policy support and the availability of BIM implementation guidelines, BIM adoption in

Malaysia remains lower than in several developed economies, with reported adoption at approximately 55% as

of 2021 (Yizing et al., 2025). More importantly, empirical evidence on AI-enabled BIM adoption in Malaysia is

extremely limited, and no substantial research has yet examined the factors influencing construction

professionals’ intention to adopt this integrated technology.

This gap is significant because the successful implementation of AI-enabled BIM depends not only on

technological availability but also on users’ perceptions, organizational readiness, trust, skills, innovativeness,

and social influence. Existing studies have examined BIM or AI adoption separately, yet have given limited

attention to their convergence as an integrated system, particularly in Malaysia’s construction context.

Addressing this gap is essential for guiding policymakers, construction firms, software developers, and

technology vendors in promoting effective AI-enabled BIM adoption in Malaysia.

Therefore, the objective of this study is to apply the Unified Theory of Acceptance and Use of Technology

(UTAUT) model to the context of AI-enabled BIM among Malaysian construction professionals and to test the

relationships specified in the established theoretical framework. To achieve this goal, we conducted a nationwide

online survey to assess the attitudes of construction professionals working in CIDB’s G7 construction firms

towards AI-enabled BIM. Based on the UTAUT model, we extended the framework by incorporating two

additional variables: personal innovativeness and initial trust to better capture the unique dynamics of AI

adoption in the construction sector. Specifically, we analyzed the impact of performance expectancy, effort

expectancy, social influence, personal innovativeness, and initial trust on construction professionals’ intention

to use AI-enabled BIM.

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This study validated the core UTAUT pathways and elucidated the pivotal roles of drivers and barriers

influencing AI-enabled BIM adoption among Malaysian construction professionals. The findings provide

actionable insights for policymakers, construction firms, and technology developers to formulate targeted

strategies that promote AI integration with BIM.

This report is structured as follows. Next, in Section 2, we present a review of the extant literature on technology

acceptance models, constructs, and the hypothesis for this study. Section 3 describes research methodology and

data collection. Section 4 presents statistical analysis, and Section 5 discusses the major findings. In Section 6,

we reflect on the theoretical contributions and practical implications. Section 7 presents limitations and

suggestions for future research. Section 8 presents the overall conclusions of the study.

LITERATURE REVIEW AND HYPOTHESES

Since its inception, the UTAUT framework has gained widespread use for understanding technology acceptance

in various fields (Wang et al., 2021). Recent bibliometric analyses confirm that the UTAUT model and its

variants are widely used to study intentions to adopt new technologies across organizational settings, including

consumer health tech, E-health, higher education, and mobile learning (Wang et al., 2021). However, since 2021,

there has been limited empirical research on technology adoption in the construction industry using UTAUT or

its extensions. Instead, models like TAM (Lu & Deng, 2022; Mei et al., 2023 on 3D Digital Technology;

Obidallah et al. on Blockchain technology, 2024), TAM combined with Task Technology Fit (Cai et al., 2023

on off-site construction technology), and TAM with TOE (Wang et al., 2022 on Blockchain technology) are

more prevalent. Although TAM-based models are popular among researchers, they may not fully capture the

complex and autonomous nature of phenomena such as AI-enabled BIM adoption (Min & Qu, 2008; Venkatesh

et al., 2003). Additionally, UTAUT2 (Venkatesh et al., 2012) and its extension (Farooq et al., 2017) were

designed to address post-adoption and voluntary consumer usage scenarios, making them less appropriate for

studying construction professionals’ adoption of AI-enabled BIM, as their technology use is typically driven by

professional duties rather than personal enjoyment or cost factors. Consequently, this study employs the original

UTAUT model to investigate the main factors influencing construction professionals’ adoption of AI-enabled

BIM.

For construction professionals, performance expectancy indicates how much they believe that using AI-powered

BIM will improve their efficiency and skills. Effort expectancy relates to how easy they perceive learning and

using AI-enabled BIM tools to be. Social influence refers to the effect of peers, supervisors, and others on

individuals' intention to adopt AI. Facilitating conditions refer to the availability of technical support, training,

and resources needed for AI implementation and usage. Research from other fields shows these factors generally

boost individuals’ willingness to adopt new technologies. This study excluded facilitating conditions and the

four moderators because the goal is to identify factors affecting behavioral intention. According to Venkatesh et

al. (2003), facilitating conditions predict actual usage behavior, and the four moderators indicate demographic

differences rather than psychological or contextual influences. This study does not aim to analyze how the effects

of these constructs vary across demographic groups.

Empirical findings supporting the influence of performance expectancy on behavioral intention, include, Abbad

(2021) on e-Learning, Alvi (2021) on a social network tool, Handa and Kaur (2025) on online insurance, Heo

and Na (2025) on construction AI technology, Lee et al. (2024) on ChatGPT, Rana et al. (2024) on AI, Rahim,

Bakri et al. (2023) on Islamic fintech, Tai et al. (2025) on smart city technology, Tuyen and Nguyen (2025) on

microlearning, and Wu et al. (2022) on AI-assisted learning. Therefore, the study hypothesizes that:

H1: Performance expectancy positively influences construction professionals’ behavioral intention to adopt AI-

enabled BIM.

Empirical findings supporting the influence of effort expectancy on behavioral intention, include, Arfi et al.

(2021) on IoT in e-Health, Handa and Kaur (2025) on online insurance, Heo and Na (2025) on construction AI

technology, Kim et al. (2024) on generative AI, Lee et al. (2024) on ChatGPT, Rana et al. (2024) on AI, Tai et

al. (2025) on smart city technology, Tuyen and Nguyen (2025) on microlearning, Wu et al. (2022) AI-assisted

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learning, Wu et al. (2025) on AI in the construction industry, and Yizing et al. (2025) on BIM. Therefore, the

study hypothesizes that:

H2: Effort expectancy positively influences construction professionals’ behavioral intention to adopt AI-enabled

BIM.

Empirical findings supporting the influence of social influence on behavioral intention, include, Alvi (2021) on

social networking tool, Arfi et al. (2021) on IoT in e-Health, Handa and Kaur (2025) on online insurance, Heo

and Na (2025) on construction AI technology, Kim et al. (2024) on generative AI, Lee et al. (2024) on ChatGPT,

Rana et al. (2024) on AI, Tai et al. (2025) on smart city technology, Wu et al. (2022) AI-assisted learning, Wu et

al. (2025) on AI in the construction industry, and Yizing et al. (2025) on BIM. Therefore, the study hypothesizes

that:

H3: Social influence positively impacts construction professionals’ behavioral intention to adopt AI-enabled

BIM.

However, even though UTAUT overcomes the limitations of eight prominent theories and models, Venkatesh

(2022) acknowledges the need to extend UTAUT to include AI-related factors, as the three predictors of

behavioral intention do not capture all factors influencing the decision to adopt and use AI technologies. In

addition, Nnaji et al. (2023) proposed that acceptance models for studying the adoption and use of integrated

technologies, such as AI-enabled BIM, must be extended to include domain-specific factors. Wang et al. (2021)

noted that numerous empirical studies have extended the UTAUT model by incorporating additional predictive

factors.

The adoption of AI and AI-enabled BIM tools is still in its early stages (Pan & Zhang, 2023). It faces challenges

arising from a complex mix of technological, organizational, social, and human factors, particularly from the

perspective of construction professionals (Adebayo et al., 2025). While UTAUT can effectively assess how

technological, organizational, and social factors influence traditional technologies, it needs to be expanded to

address the human element when studying AI or AI-enabled tools. Human factors include personal

innovativeness—defined as an individual’s willingness to adopt and evaluate new information technologies

(Agarwal & Prasad, 1998; Rogers, 2003)—and trust, which is the confidence that information technologies are

reliable, capable, and acting in the user’s best interest (Rousseau et al., 1998; McKnight et al., 2011).

The Diffusion of Innovation (DOI) framework suggests that individuals vary in their response to adopting

innovations (Rogers, 2003). Those with high IT innovativeness are generally better at managing new technology

and tend to view it as less complex and less difficult (Eren et al., 2025; Fan et al., 2020). Personal innovativeness,

as a key factor, helps focus on potential users’ willingness to adopt (Senali et al., 2023). Numerous empirical

studies on AI and related technologies recognize personal innovativeness as a predictor of behavioral intention.

For example, Lee et al. (2021) found that it influences how users evaluate and adopt AI-enabled voice assistants.

Research by Ramos Salazar and Peeples (2025) on ChatGPT in higher education shows that more innovative

teachers are more likely to use ChatGPT in their teaching. Similarly, Bhadauria and Chennamaneni (2022),

studying IoT adoption in the USA, identified a positive link between personal innovativeness and IoT device

use. Gokcearslan et al. (2024) observed that more innovative pre-service teachers tend to use IoT technologies

more frequently in education. Innovative construction professionals, naturally inclined to experiment with new

tools, also strongly predict the intention to adopt technology. Based on this analysis, we propose the following

hypothesis:

H4: Personal innovativeness positively impacts construction professionals’ behavioral intention to adopt AI-

enabled BIM.

Venkatesh et al. (2003) define effort expectancy as the perceived difficulty or complexity felt when using a

technology. Numerous studies suggest that users with high IT innovativeness are more likely to view new

technologies as less complex and easier to use (Eren et al., 2025; Fan et al., 2020). Empirical evidence linking

personal innovativeness to effort expectancy includes Fan et al. (2020), who examined an AI-enabled medical

diagnosis support system (AIMDSS), and Wu et al. (2011), who studied healthcare professionals’ adoption of

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mobile healthcare. Additionally, Basak et al. (2015), in their research on PDA use physicians, and Vitente et al.

(2024), in their study on BIM adoption, both found that personal innovativeness positively affects perceived ease

of use.

Mata et al. (2024) suggested that personal innovativeness boosts an individual’s perception of usefulness and

effort, as it reflects their ability to adopt and adapt to new technologies. Their research on BIM adoption

confirmed the links between personal innovativeness and both perceived usefulness (performance expectancy)

and perceived ease of use (effort expectancy). Alkawsi et al. (2021) studied smart meter technology acceptance

and found that the influence of performance expectancy and effort expectancy on behavioral intention is stronger

among innovative individuals. Similarly, Senali et al. (2023) noted that the usefulness and ease of use of e-

wallets have less impact on adoption decisions among highly innovative individuals than among those with

lower innovativeness. Based on this analysis, we propose the following hypothesis:

H5: Personal innovativeness (PI) is positively correlated with performance expectancy (PE).

H6: Personal innovativeness (PI) is positively correlated with effort expectancy (EE).

In situations where outcomes are unpredictable or could go wrong, trust becomes especially important

(McKnight et al. 2011). In AI-enabled BIM, trust represents construction professionals’ confidence that the

technology will improve project quality without causing harm. Currently, there is limited empirical evidence on

how trust influences the adoption of BIM and other digital construction tools. Therefore, insights from other

fields are used to highlight the significance of trust in the adoption of AI-enabled BIM.

Fan et al. (2020) highlight that initial trust (IT) is crucial for adopting new technology. They identify three key

factors influencing initial trust: personal traits, perceptions of the technology, and the organizational context.

These factors represent the trustor's views from personal, technical, and managerial perspectives. They also

suggest that higher trust in a technology increases the likelihood of adoption or of considering adoption. Prakash

and Das (2021) recognized trust as essential for the intention to use intelligent clinical diagnostic decision-

support systems. Shahzad et al. (2025) showed that trust significantly influences Pakistani university students'

actual use of ChatGPT. Furthermore, studies by Al-Haraizah et al. (2025) on smart city technology acceptance,

Arfi et al. (2021) on IoT adoption in e-Health, and Chan and Lee (2021) on autonomous vehicle acceptance all

found that trust affects behavioral intentions. Based on these findings, the following hypotheses are proposed.

H7: Initial trust positively impacts construction professionals’ behavioral intention to adopt AI-enabled BIM.

There are apparent connections between trust, performance expectancy, and effort expectancy. In the context of

UTAUT, Guo and Barnes (2007) suggested that trust could influence both performance and effort expectations.

Similarly, McLeod et al. (2008) found that trust in software logic and performance expectancy loaded onto the

same factor. Regarding TAM, studies have indicated that trust positively impacts perceived usefulness (Pavlou,

2003; Thiesse, 2007) and perceived ease of use (Pavlou, 2003).

Additional empirical evidence indicates that trust significantly influences performance expectancy, effort

expectancy, and intention to use various technologies, including the adoption of a new technology service by

Lee & Song (2013), the use of internet websites by Al-Gahtani (2011), the adoption of AI-health assistants for

mobile payments by Su et al. (2025), the adoption of mobile internet by Alalwan et al. (2018), information

systems by Söllner et al. (2016), and e-commerce by Al-Gahtani (2011). Thus, the following hypotheses are

formulated:

H8: Initial Trust is positively correlated with performance expectancy.

H9: Initial Trust is positively correlated with effort expectancy.

The empirical evidence in the existing literature indicates that personal innovativeness and initial trust

individually correlate with performance expectancy and effort expectancy, respectively. Consequently, both

performance expectancy and effort expectancy act as mediators between personal innovativeness and behavioral

intention and between initial trust and behavioral intention. Chen and Aklikokou (2020), in examining the

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determinants of E-government adoption, found that this proposition was supported for initial trust. Chong and

Go (2024) explored the actual adoption of mobile wallets and found that this proposition was not supported for

personal innovativeness. Thus, the following hypotheses are formulated:

H10: Performance expectancy mediates the relationship between personal innovativeness and behavioral

intention.

H11: Performance expectancy mediates the relationship between Initial trust and behavioral intention.

H12: Effort expectancy mediates the relationship between personal innovativeness and behavioral intention.

H13: Effort expectancy mediates the relationship between Initial trust and behavioral intention.

RESEARCH METHODOLOGY AND DATA COLLECTION

Measurements

In the present study, we adopted the measurement items from previous studies that used UTAUT as the founding

theory and research framework. Four constructs in the UTAUT model were adopted from Venkatesh et al. (2003),

including Performance Expectancy (5 items), Effort Expectancy (5 items), Social Influence (5 items), and

Behavioral Intention (5 items). In addition, the Personal Innovativeness construct has seven items and was

adapted from Argawal and Prasad (1998) and Hurt et al. (1997). The Initial Trust construct has six items and was

adapted from Choi et al. (2023) and Fan et al. (2020).

Sample and Data Collection

A self-administered online survey was conducted targeting construction professionals at CIDB’s G7 firms across

Malaysia. The total number of registered G7 construction firms under CIDB was known, and a sample of 385

was selected using Yamane's (1967) formula. The survey was sent via WhatsApp and email to the 385 randomly

selected CIDB G7 companies, with a link to the questionnaire. Responses were recorded on a 5-point Likert

scale. The response rate is detailed in Table 1.

Table 1: Response Rate

Response

Frequency

No. of distributed questionnaires

385

Returned questionnaires

269

Rejected questionnaires

None

Retained questionnaires

269

Response rate

69.87%

According to Lindner and Wingenbach (2002) and Lund (2023), a minimum response rate of 50% is adequate

for the survey. Therefore, this study’s response rate of 69.87% is acceptable for preliminary data analysis.

Statistical Analysis

Descriptive Statistics

The 269 responses were examined in IBM SPSS version 29 for outliers using Mahalanobis Distance. Eight

outliers, respondents 13, 20, 184, 185, 197, 216, 220, and 249, were identified and removed from the dataset.

The remaining 261 responses were then assessed for normality using skewness and kurtosis tests [Hair et al.

(2021) criterion of between +1 and -1 as normal] and for multicollinearity using the variance inflation factor

(VIF: >0.2 and <5.0). The normality test and multicollinearity (VIF) scores are both within the thresholds; hence,

there are no normality or multicollinearity issues.

Given the risk of common method bias (CMB) in single-source self-reported data (Kock et al., 2021), we

conducted Harman’s single-factor test (Fuller et al., 2016; Podsakoff et al., 2003) and a full multicollinearity test

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(VIF) (Kock, 2015; Kock & Lynn, 2012) for statistical evaluation before computing descriptive statistics. The

test results did not reveal common method bias in either Harman’s single-factor test or the common method bias

test.

Table 2 presents descriptive statistics computed in IBM SPSS V29 to summarize participants' basic

characteristics and provide an overview of the sample composition.

Table 2: Demographic Profiles of Respondents

Frequency

Percent (%)

1. Year of experience in industry:

- Less than 2 years

3.80%

- 2-5 years

26.40%

- 6-10 years

116

44.40%

- 11-15 years

18.40%

- More than 15 years

6.90%

Total

261

2. Level of experience with construction digital tools:

- No experience and no knowledge of it

1.50%

- Beginner (Theoretical knowledge only or Limited practical use)

11.50%

- Intermediate (Regular user for specific tasks)

120

46.00%

- Advanced (Proficient user, capable of managing BIM projects)

35.60%

- Expert (Can develop and implement BIM protocols)

5.40%

Total

261

3. Using AI for:

- Curious explorer (General enquiries like travel ideas and health matters)

7.30%

- Convenience users (Also use for translation, letters, and general report

writing)

138

52.90%

- Task optimizers (Use to generate spreadsheet formulas, meeting notes,

and professional report outlines)

28.40%

- Workflow integrators (Use for analysis, drafting, testing, and ideation)

9.60%

- Strategic power users (Use to design systems and products, decision

making, and training)

1.90%

Total

261

4. Your Gender:

- Male

194

74.30%

- Female

25.70%

Total

261

5. Your Age Group: *

- Under 25 years

0.40%

- 26 - 35 years

109

41.80%

- 36 - 45 years

114

43.70%

- 46 - 55 years

12.30%

- Over 55 years

1.90%

Total

261

6. Your Highest Academic Qualification:

- Diploma

3.40%

- Bachelor's Degree

4.60%

- Master's Degree

184

70.50%

- Doctorate's Degree

21.10%

- Other

0.40%

Total

261

7. Your Current Position/Role:

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- Project Manager

21.10%

- BIM Manager/Coordinator

16.10%

- Architect

8.40%

- Engineer (Civil, Structural, MEP)

109

41.80%

- Quantity Surveyor

7.70%

- Site Supervisor/Foreman

3.40%

- Other

1.50%

Total

261

The respondents were predominantly mid-career and experienced construction professionals, with most having

6–10 years of industry experience (44.4%), followed by 2–5 years (26.4%) and 11–15 years (18.4%). The sample

was largely male (74.3%) and concentrated within the 26–45 age range, indicating a professionally active group.

Respondents were also highly educated, with the majority holding master’s degrees (70.5%) or doctorates

(21.1%). In terms of roles, engineers formed the largest group (41.8%), followed by project managers (21.1%)

and BIM managers/coordinators (16.1%), suggesting strong representation from technical and managerial

professionals directly involved in construction project delivery and digital implementation.

The respondents also demonstrated substantial familiarity with construction digital tools, with 46.0% identifying

as intermediate users and 35.6% as advanced users, while only 1.5% reported no experience. AI usage was

evident but mostly remained at a basic-to-intermediate level: 52.9% were convenience users who used AI for

translation, writing, and routine documentation, while 28.4% were task optimizers using AI for more structured

professional tasks. Only a small proportion were workflow integrators (9.6%) or strategic power users (1.9%).

Overall, the demographic profile indicates that the sample comprises experienced, highly educated, and digitally

aware construction professionals, making them well-positioned to provide informed views on AI-enabled BIM

adoption, although the strategic use of AI within construction workflows remains limited.

Structural Equation Modeling (SEM)

Structural equation modeling (SEM) was used to examine relationships among latent and observed variables

(Anderson & Gerbing, 1988). Confirmatory factor analysis (CFA) was conducted in SmartPLS 4 to assess the

fit of the data to the specified measurement model. The specified model registered three indicators, EE2 (0.680),

IT2 (0.675), and SI (0.673), with loadings below the 0.70 threshold. Hence, they are deleted. After deleting all

three, SI2 fell below 0.70 and was also deleted. The validity and reliability of the re-specified measurement

model were rigorously evaluated using analyses of construct reliability, convergent validity, and discriminant

validity (Hair et al., 2021). The results are summarized in Tables 3 (construct reliability and convergent validity),

4 (Cross-Loadings), 5 (Fornell-Larcker Criterion), and 6 (Heterotrait-Monotrait Ratio).

Table 3 shows that all constructs exhibited robust internal consistency, with composite reliability and Cronbach’s

alpha ranging from 0.744 to 0.859, exceeding the recommended threshold of 0.70 and thereby confirming the

reliability of the measurement scales. Convergent validity was assessed through factor loadings (FL) and average

variance extracted (AVE). The FLs for all items ranged from 0.704 to 0.872, surpassing the minimum acceptable

value of 0.70, indicating that each item effectively captured the essence of its corresponding latent construct.

The AVE values for all constructs ranged from 0.541 to 0.707, consistently exceeding the critical threshold of

0.50, demonstrating that the latent variables accounted for a sufficient proportion of the variance in their

respective indicators.

Table 3: Construct Reliability and Convergent Validity

Construct

Item

Factor

Cronbach’s

alpha

Composite

reliability

AVE

Performance Expectation:

0.819

0.823

0.581

PE1

0.791

PE2

0.716

PE3

0.752

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PE4

0.731

PE5

0.818

Effort Expectancy:

0.744

0.565

EE1

0.741

EE3

0.765

EE4

0.742

EE5

0.759

Social Influence:

0.793

0.797

0.707

SI3

0.872

SI4

0.809

SI5

0.841

Personal Innovativeness

0.859

0.541

PI1

0.726

PI2

0.728

PI3

0.729

PI4

0.773

PI5

0.704

PI6

0.722

PI7

0.767

Initial Trust

0.808

0.811

0.565

IT1

0.778

IT3

0.769

IT4

0.729

IT5

0.755

IT6

0.724

Behavioral Intention:

0.843

0.848

0.615

BI1

0.832

BI2

0.707

BI3

0.798

BI4

0.769

BI5

0.808

Discriminant validity was assessed using cross-loadings (Radomir & Moisescu, 2020). The results in Table 4

indicate that all indicators load higher on their respective constructs than on other constructs. This confirms that

each construct is distinct and measures a unique concept. Although some constructs, particularly initial trust and

self-efficacy, exhibit relatively higher cross-loadings, all indicators still load highest on their intended constructs.

Therefore, discriminant validity is established.

Table 4: Discriminant Validity – Cross-loading

BI1

0.832

0.485

0.467

0.542

0.552

0.547

BI2

0.707

0.374

0.414

0.455

0.487

0.426

BI3

0.798

0.500

0.465

0.469

0.557

0.527

BI4

0.769

0.461

0.446

0.463

0.540

0.485

BI5

0.808

0.505

0.526

0.530

0.582

0.533

EE1

0.470

0.741

0.349

0.297

0.398

0.399

EE3

0.431

0.765

0.413

0.372

0.506

0.358

EE4

0.431

0.742

0.351

0.338

0.456

0.354

EE5

0.461

0.759

0.469

0.345

0.461

0.351

IT1

0.499

0.437

0.778

0.494

0.481

0.469

IT3

0.447

0.392

0.769

0.581

0.565

0.526

IT4

0.450

0.394

0.729

0.425

0.518

0.464

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IT5

0.435

0.409

0.755

0.528

0.523

0.469

IT6

0.390

0.342

0.724

0.403

0.498

0.456

PE1

0.487

0.378

0.518

0.791

0.565

0.517

PE2

0.473

0.357

0.444

0.716

0.504

0.454

PE3

0.495

0.342

0.527

0.752

0.553

0.525

PE4

0.408

0.328

0.485

0.731

0.495

0.488

PE5

0.524

0.314

0.513

0.818

0.580

0.534

PI1

0.507

0.478

0.527

0.481

0.726

0.522

PI2

0.502

0.416

0.520

0.508

0.728

0.533

PI3

0.489

0.433

0.501

0.484

0.729

0.461

PI4

0.526

0.465

0.515

0.540

0.773

0.518

PI5

0.525

0.463

0.546

0.583

0.704

0.484

PI6

0.506

0.452

0.466

0.512

0.722

0.555

PI7

0.517

0.413

0.465

0.535

0.767

0.542

SI3

0.580

0.390

0.545

0.597

0.557

0.872

SI4

0.517

0.410

0.538

0.504

0.619

0.809

SI5

0.527

0.428

0.521

0.565

0.601

0.841

Discriminant validity was further assessed using the Fornell–Larcker criterion (Henseler et al., 2015). A construct

should share more variance with its own indicators than with other constructs. The results in Table 5 show that

all constructs meet the required threshold, with the square root of the AVE exceeding the inter-construct

correlations. Discriminant validity is established.

Table 5: Discriminant Validity – Fornell-Larcker Criterion

0.784

0.596

0.752

0.593

0.528

0.751

0.628

0.450

0.652

0.763

0.694

0.607

0.688

0.709

0.736

0.645

0.486

0.635

0.661

0.702

0.841

Heterotrait–Monotrait (HTMT) ratio is another discriminant validity test (Henseler et al., 2015; Roemer et al.,

2021). For the constructs to meet the discriminant validity criteria, the HTMT ratio, according to Franke and

Sarstedt (2019) and Kline (2023), should not exceed 0.85; according to Gold et al. (2001), it should not exceed

0.90. The results in Table 6 indicate that all HTMT values are below the recommended threshold of 0.90,

confirming acceptable discriminant validity. Most values are below the stricter threshold of 0.85, indicating

strong discriminant validity.

Table 6: Discriminant Validity – Heterorait-Monotrait Ratio (HTMT)

0.750

0.715

0.676

0.754

0.577

0.795

0.815

0.757

0.825

0.842

0.786

0.635

0.794

0.819

0.854

In summary, the reliability and validity parameters of our study met the recommended standards, providing a

solid foundation for structural model analysis.

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The structural model after bootstrapping is presented in Figure 1.

Figure 1: Structural Model

The results show that behavioral intention (BI) achieved an R

(coefficient of determination) of 0.588, indicating

that approximately 58.8% of the variance in behavioral intention toward AI-enabled BIM systems is explained

by the model's predictor constructs. This suggests a moderate to substantial explanatory power. Effort expectancy

(EE) had an R

of 0.391, indicating that 39.1% of the variance in EE is explained by its antecedent variables,

representing a moderate level of explanatory power. Similarly, performance expectancy (PE) demonstrated an

of 0.554, indicating that the model explains 55.4% of the variance in PE, a substantial amount.

The study then computes the effect size (f²) to evaluate the model's predictive accuracy (Henseler, 2020; Hair et

al., 2019). Following Cohen's (1962) standards, f² values of 0.02, 0.15, and 0.35 represent small, medium, and

large effects, respectively (Hair et al., 2019). Accordingly, the impact of exogenous constructs on their

corresponding endogenous constructs was assessed individually for each effect size, as detailed in Table 7.

Table 7: f-Square (f²)

0.086

0.003

0.039

0.115

0.032

0.045

0.184

0.289

0.046

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Based on Table 7, effort expectancy (EE) had a small effect on behavioral intention (BI) with an f² value of 0.086,

while performance expectancy (PE), personal innovativeness (PI), and social influence (SI) also demonstrated

small effects on BI, with f² values of 0.032, 0.045, and 0.046, respectively. Initial trust (IT) had a negligible

effect on BI (f² = 0.003), indicating that it contributed minimally to the explanation of behavioral intention.

Regarding the determinants of EE and PE, personal innovativeness (PI) exerted the strongest influence, showing

a medium effect on EE (f² = 0.184) and a moderate-to-large effect on PE (f² = 0.289). In contrast, IT demonstrated

only a small effect on EE (f² = 0.039) and PE (f² = 0.115). Overall, the findings suggest that personal

innovativeness is the most influential predictor in the model, particularly in shaping respondents’ perceptions of

effort expectancy and performance expectancy toward AI-enabled BIM systems.

Next, the study assesses the Predictive Relevance (Q²) and Power (RMSE) of the structural model. If the cross-

validated redundancy measure (Q²) for an endogenous construct exceeds zero (Q

>0), it indicates that the latent

constructs are predictive. The predictive power can be assessed by comparing the RMSE (root mean square error)

values of the PLS-SEM and LM (multiple linear regression) models. The comparison is to determine whether

this study model is better than a simple regression model. SmartPLS 4 has generated the Q²predict values, PLS-

SEM RMSE, and LM RMSE as shown in Table 8.

Table 8: Predictive Relevance and Predictive Power Level

Q²predict

PLS-

SEM_RMSE

LM_RMSE

PLS-LM

EE1

0.158

0.930

0.950

-0.020

EE3

0.254

0.856

0.888

-0.032

EE4

0.197

0.866

0.894

-0.028

EE5

0.237

0.811

0.824

-0.013

PE1

0.345

0.746

0.749

-0.003

PE2

0.265

0.920

0.946

-0.026

PE3

0.338

0.847

0.845

0.002

PE4

0.275

0.839

0.825

0.014

PE5

0.351

0.851

0.858

-0.007

BI1

0.341

0.923

0.941

-0.018

BI2

0.247

0.878

0.882

-0.004

BI3

0.338

0.883

0.903

-0.020

BI4

0.308

0.928

0.958

-0.030

BI5

0.373

0.824

0.862

-0.038

As shown in Table 8, all indicators for effort expectancy (EE), performance expectancy (PE), and behavioral

intention (BI) recorded positive Q

predict values ranging from 0.158 to 0.373. This indicates that the model

possesses adequate predictive relevance for all endogenous constructs. Among the indicators, BI5

predict=0.373) and PE5 (Q

predict=0.351) demonstrated the strongest predictive relevance, while EE1

predict=0.158) showed the lowest but still acceptable predictive capability.

The table also compares the Root Mean Squared Error (RMSE) values from the PLS-SEM and linear (LM)

models. Most indicators in Table 8 showed negative “PLS-LM” values, suggesting superior predictive

performance of the PLS-SEM model. Although two indicators (PE3 and PE4) recorded very small positive

differences, the values were minimal, indicating only marginally weaker performance compared to the linear

model. Overall, the findings indicate that the model demonstrates medium predictive power and satisfactory out-

of-sample predictive relevance for explaining behavioral intention, effort expectancy, and performance

expectancy toward AI-enabled BIM systems.

Hair et al. (2021) proposed including the Cross-Validated Predictive Ability Test (CVPAT) as an additional test

to evaluate the model's predictive performance. The results of the CVPAT generated using PLSpredict of

SmartPLS 4 are presented in Table 9.

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Table 9: Cross-Validated Predictive Ability Test (CVPAT)

PLS loss

LM loss

Average loss difference

t value

p value

0.751

0.792

-0.041

3.153

0.002

0.710

0.717

-0.007

0.439

0.661

0.788

0.828

-0.040

2.756

0.006

Overall

0.750

0.778

-0.028

2.880

0.004

The findings show that for EE, the PLS-SEM loss value (0.751) was lower than the LM loss value (0.792), with

a significant negative average loss difference of -0.041 (t = 3.153, p = 0.002). Similarly, for BI, the PLS-SEM

loss value (0.788) was lower than the LM loss value (0.828), with a significant average loss difference of -0.040

(t = 2.756, p = 0.006). These results indicate that the PLS-SEM model predicts EE and BI more accurately than

the benchmark linear model.

For performance expectancy (PE), although the PLS-SEM model yielded a slightly lower loss value (0.710) than

the linear model (0.717), the difference was very small and not statistically significant (t = 0.439, p = 0.661).

This suggests that the predictive performance of the PLS-SEM model for PE was comparable to that of the linear

model rather than significantly better.

Overall, the model demonstrated a significantly lower overall prediction loss (0.750) than the linear model

(0.778), with a statistically significant average loss difference of -0.028 (t = 2.880, p = 0.004). This indicates that

the proposed PLS-SEM model possesses satisfactory out-of-sample predictive ability and performs better overall

than the benchmark linear model in predicting the adoption-related constructs of AI-enabled BIM systems. The

findings therefore support the predictive robustness and practical applicability of the proposed research model

in the Malaysian construction industry.

Finally, an importance-performance map analysis (IPMA) was performed. Importance is assessed on a scale

from 0 to 1, while performance is evaluated on a scale from 0 to 100. This study’s IPMA statistics are presented

in Table 10.

Table 10: Importance-Performance Map Analysis (IPMA)

Importance

Performance

0.242

68.975

0.159

69.732

0.177

69.784

0.439

69.044

0.209

67.959

The results indicate that personal innovativeness (PI) had the highest importance value (0.439), making it the

most influential predictor of behavioral intention toward AI-enabled BIM systems. However, its performance

score (69.044) was only moderate compared to other constructs. This suggests that improving construction

professionals’ innovativeness, openness to new technologies, and willingness to experiment with AI-enabled

BIM systems could substantially enhance adoption intentions. Effort expectancy (EE) also demonstrated

relatively high importance (0.242) with a performance score of 68.975, indicating that ease of use and user-

friendliness remain important drivers of behavioral intention and should continue to be enhanced through

training, intuitive system design, and technical support. Social influence (SI) showed moderate importance

(0.209) but the lowest performance score (67.959), suggesting that organizational encouragement, peer influence,

and management support for AI-enabled BIM adoption remain relatively weak and warrant greater attention.

Performance expectancy (PE) had moderate importance (0.177) and one of the highest performance scores

(69.784), suggesting that respondents generally perceive AI-enabled BIM systems as useful for improving job

performance. Similarly, initial trust (IT) demonstrated lower importance (0.159) but relatively high performance

(69.732), indicating that respondents already possess a reasonable level of trust in AI-enabled BIM systems,

although its influence on behavioral intention is comparatively weaker. Overall, the IPMA findings highlight

personal innovativeness as the most critical area for strategic improvement due to its strong influence on

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behavioral intention and only moderate performance level. Enhancing users’ readiness to embrace innovation,

improving effort expectancy, and strengthening social influence could significantly increase the adoption and

use of AI-enabled BIM systems in the Malaysian construction industry.

The final step involved testing the hypothesized relationships by running a bootstrapping algorithm in SmartPLS

4. Tables 11 and 12 present the results of the hypothesis testing for this study.

Table 11: Hypotheses Testing - Direct Effect

Path Coefficient

T statistics

P values

Decision

PE -> BI

0.177

3.001

0.003

Accepted

EE -> BI

0.242

5.047

0.000

Accepted

SI -> BI

0.209

3.480

0.001

Accepted

PI -> BI

0.239

3.463

0.001

Accepted

PI -> PE

0.495

8.322

0.000

Accepted

PI -> EE

0.461

6.278

0.000

Accepted

IT -> BI

0.052

0.899

0.369

Rejected

IT -> PE

0.312

5.577

0.000

Accepted

IT -> EE

0.211

3.117

0.002

Accepted

Table 12: Hypotheses Testing - Mediating Effect

Path Coefficient

T statistics

P values

Decision

H10

PI -> PE -> BI

0.088

2.833

0.005

Accepted

H11

IT -> PE -> BI

0.055

2.411

0.016

Accepted

H12

PI -> EE -> BI

0.112

3.878

0.000

Accepted

H13

IT -> EE -> BI

0.051

2.610

0.009

Accepted

DISCUSSION OF MAJOR FINDINGS

The main purpose of this study was to investigate, using an extended UATAU framework, the impact of

performance expectancy, effort expectancy, social influence, personal innovativeness, and initial trust on

construction professionals’ intention to use AI-enabled BIM, with performance expectancy and effort

expectancy also acting as mediators. Tables 11 and 12 present the results for the direct and mediating effects of

the proposed hypotheses on the adoption of AI-enabled BIM systems among Malaysian G7 construction firms.

The findings provide important insights into the factors influencing construction professionals’ behavioral

intention (BI) to adopt AI-enabled BIM technologies.

The results of the direct effects analysis in Table 11 indicate that performance expectancy (PE), effort expectancy

(EE), social influence (SI), and personal innovativeness (PI) significantly influence behavioral intention (BI).

Specifically, PE had a significant positive effect on BI ( = 0.177, p = 0.003), supporting H1 as found by Abbad

(2021) on e-Learning, Alvi (2021) on a social network tool, Handa and Kaur (2025) on online insurance, Heo

and Na (2025) on construction AI technology, Lee et al. (2024) on ChatGPT, Rana et al. (2024) on AI, Rahim et

al. (2023) on Islamic fintech, Tai et al. (2025) on smart city technology, Tuyen and Nguyen (2025) on

microlearning, and Wu et al. (2022) on AI-assisted learning. This suggests that construction professionals are

more likely to adopt AI-enabled BIM systems when they perceive that the technology can improve work

performance, productivity, and project outcomes.

Effort expectancy (EE) also significantly influenced BI ( = 0.242, p < 0.001), supporting H2 that aligned with

the empirical study by Arfi et al. (2021) on IoT in e-Health, Handa and Kaur (2025) on online insurance, Heo

and Na (2025) on construction AI technology, Kim, Blaznez, and Oh (2024) on generative AI, Lee et al. (2024)

on ChatGPT, Rana et al. (2024) on AI, Tai et al. (2025) on smart city technology, Tuyen and Nguyen (2025) on

microlearning, Wu et al. (2022) AI-assisted learning, Wu et al. (2025) on AI in the construction industry, and

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Yizing et al. (2025) on BIM. This indicates that ease of use and perceived simplicity of AI-enabled BIM systems

are important determinants of adoption intention. Among the UTAUT-related constructs, EE had the strongest

direct influence on BI, indicating that user-friendliness is critical to encouraging adoption in the Malaysian

construction context.

Similarly, social influence (SI) positively affected BI ( = 0.209, p = 0.001), supporting H3 that was consistent

with the findings of Alvi (2021) on social networking tool, Arfi et al. (2021) on IoT in e-Health, Handa and Kaur

(2025) on online insurance, Heo and Na (2025) on construction AI technology, Kim et al. (2024) on generative

AI, Lee et al. (2024) on ChatGPT, Rana et al. (2024) on AI, Tai et al. (2025) on smart city technology, Wu et al.

(2022) AI-assisted learning, Wu et al. (2025) on AI in the construction industry, and Yizing et al. (2025) on BIM.

This finding implies that organizational encouragement, peer opinions, industry expectations, and management

support significantly shape professionals’ willingness to adopt AI-enabled BIM systems.

Personal innovativeness (PI) also significantly influenced BI ( = 0.239, p = 0.001), supporting H4, similar to

the empirical results of Lee et al. (2021) on the adoption of AI-enabled voice assistants, Ramos Salazar and

Peeples (2025) on ChatGPT in higher education, Bhadauria and Chennamaneni (2022) on IoT adoption in the

USA, and Gokcearslan et al. (2024) on the use of IoT technologies more frequently in education. This suggests

that individuals who are more open to experimenting with new technologies are more likely to embrace AI-

enabled BIM. Furthermore, PI significantly influenced both PE ( = 0.495, p < 0.001) and EE ( = 0.461, p <

0.001), supporting H5 and H6. These findings indicate that innovative individuals tend to perceive AI-enabled

BIM systems as both more useful and easier to use.

Interestingly, initial trust (IT) did not significantly influence BI directly ( = 0.052, p = 0.369), leading to the

rejection of H7. This was contrary to the findings of Prakash and Das (2021) on the use of intelligent clinical

diagnostic decision-support systems, Shahzad et al. (2025) on university students' actual use of ChatGPT, Al-

Haraizah et al. (2025) on smart city technology acceptance, Arfi et al. (2021) on IoT adoption in e-Health, and

Chan and Lee (2021) on autonomous vehicle acceptance. This suggests that although trust in AI-enabled BIM

systems exists, it does not directly motivate Malaysian construction professionals to adopt them. One possible

explanation is that users may prioritize practical benefits and ease of use over trust considerations when

evaluating new digital technologies. Nevertheless, IT significantly influenced both PE ( = 0.312, p < 0.001)

and EE ( = 0.211, p = 0.002), supporting H8 and H9. These findings are in line with the findings of Lee & Song

(2013) on the adoption of a new technology service, Al-Gahtani (2011) on the use of internet websites, Su et al.

(2025) on the adoption of AI-health assistants for mobile payments, Alalwan et al. (2018) on the adoption of

mobile internet, Söllner et al. (2016) on the use of information systems, and Al-Gahtani (2011) on the use of e-

commerce. This indicates that higher trust in AI-enabled BIM systems improves users’ perceptions of usefulness

and ease of use, even though trust alone is insufficient to directly drive behavioral intention.

The mediation analysis presented in Table 12 further clarifies the indirect mechanisms influencing AI-enabled

BIM adoption. The results show that PE significantly mediated the relationship between PI and BI ( = 0.088, p

= 0.005), supporting H10, and between IT and BI ( = 0.055, p = 0.016), supporting H11. These findings are

consistent with those of Chen and Aklikokou (2020) on the determinants of E-government adoption. This implies

that innovative individuals and users with higher initial trust are more likely to adopt AI-enabled BIM systems

because they perceive the systems as beneficial and performance-enhancing. Similarly, EE significantly

mediated the relationship between PI and BI ( = 0.112, p < 0.001), supporting H12, and between IT and BI (

= 0.051, p = 0.009), supporting H13. These findings also resonated with those of Chen and Aklikokou (2020)

on the determinants of E-government adoption. This suggests that innovative and trusting users are more inclined

to adopt AI-enabled BIM systems when they perceive the technology as easy to learn and operate.

Overall, the findings indicate that the adoption of AI-enabled BIM systems in Malaysia is primarily driven by

perceived usefulness, ease of use, social influence, and personal innovativeness. Personal innovativeness

emerged as one of the strongest predictors in the model, both directly and indirectly influencing adoption

intention through PE and EE. Although initial trust did not directly predict behavioral intention, it played a

significant indirect role by enhancing users’ perceptions of system usefulness and ease of use. These findings

highlight the importance of developing user-friendly AI-enabled BIM platforms, strengthening organizational

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support, and cultivating innovative mindsets among construction professionals to accelerate digital

transformation in Malaysia’s construction industry.

Theoretical Contribution and Practical Implications

This study contributes to the growing body of knowledge on digital transformation and technology adoption in

the construction industry by extending the original Unified Theory of Acceptance and Use of Technology

(UTAUT) framework to AI-enabled BIM systems. While previous studies have largely examined BIM and AI

adoption separately, this research integrates these technologies into a single adoption framework, thereby

addressing an important gap in construction technology literature. The study demonstrates that the convergence

of AI and BIM introduces new behavioral and cognitive considerations beyond those traditionally associated

with conventional BIM adoption. A major theoretical contribution of this study is the incorporation of personal

innovativeness (PI) and initial trust (IT) into the UTAUT model. The findings reveal that PI is a strong predictor

of behavioral intention both directly and indirectly through performance expectancy (PE) and effort expectancy

(EE), highlighting the importance of individual innovativeness in the adoption of emerging intelligent

construction technologies. Additionally, although IT did not directly influence behavioral intention, it

significantly affected PE and EE, exerting indirect effects through these mediators. This finding contributes to

the literature by demonstrating that trust in AI-enabled systems may operate indirectly through users’ perceptions

of usefulness and ease of use rather than directly influencing adoption intention. The study also contributes

empirically by offering one of the earliest investigations into AI-enabled BIM adoption in the Malaysian

construction industry, particularly among CIDB Grade 7 (G7) firms. Given the limited global empirical research

on AI-enabled BIM systems and the near-absence of studies in Malaysia, the findings provide valuable evidence

on the determinants of behavioral intention toward next-generation construction technologies in a developing-

country context. Furthermore, the study validates the applicability of the extended UTAUT model in explaining

AI-enabled BIM adoption, thereby strengthening the theoretical robustness of technology acceptance models in

Construction 4.0 environments. Another important theoretical contribution lies in demonstrating the mediating

roles of PE and EE. The findings show that users’ perceptions of usefulness and ease of use are key psychological

mechanisms through which personal innovativeness and initial trust shape behavioral intention. These advances

in understanding how cognitive evaluations influence technology adoption in highly technical, AI-driven

construction systems.

The findings suggest that construction firms should prioritize strategies that improve employees’ perceptions of

the usefulness and ease of use of AI-enabled BIM systems. Since effort expectancy emerged as one of the

strongest predictors of behavioral intention, firms should invest in user-friendly platforms, structured training

programs, technical support systems, and continuous professional development to reduce the perceived

complexity of AI-enabled BIM technologies. The strong influence of personal innovativeness indicates that firms

should cultivate a culture of innovation and digital experimentation within their organizations. Encouraging

employees to explore new technologies, participate in pilot projects, and engage in digital innovation initiatives

can significantly enhance AI-enabled BIM adoption. Firms may also benefit from recruiting digitally competent

professionals and establishing internal champions or BIM innovation teams to accelerate organizational

transformation. Furthermore, the significant role of social influence suggests that leadership support and

organizational commitment are critical. Senior management should actively promote AI-enabled BIM adoption

through clear strategic direction, incentives, awareness programs, and integration of digital technologies into

standard operating procedures. This can help foster positive peer influence and strengthen organizational

readiness for the implementation of Construction 4.0.

For policymakers, particularly the Construction Industry Development Board (CIDB) and related government

agencies, the findings highlight the need for stronger institutional support to accelerate AI-enabled BIM adoption

in Malaysia. Existing BIM policies and Construction 4.0 initiatives should be expanded to include AI integration

frameworks, AI-enabled BIM implementation guidelines, and standardized best practices. The results also

suggest that policymakers should focus on improving digital competencies within the construction workforce.

This can be achieved through industry-wide certification programs, subsidized training, university-industry

collaboration, and national digital upskilling initiatives related to AI, BIM, data analytics, and automation

technologies. Additionally, financial incentives such as tax deductions, grants, technology adoption funds, and

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pilot project support could help reduce barriers to implementing AI-enabled BIM systems, particularly for firms

facing high setup costs and infrastructure limitations. Policymakers may also consider establishing regulatory

frameworks and ethical guidelines addressing AI reliability, data governance, cybersecurity, and interoperability

to strengthen industry trust in AI-enabled construction technologies.

The findings provide important guidance for AI-enabled BIM software developers and vendors. Since effort

expectancy significantly influences adoption intention, developers should focus on designing intuitive, user-

friendly, and accessible systems that minimize technical complexity. Simplified interfaces, conversational AI

functions, automation tools, and seamless interoperability with existing BIM platforms can improve user

acceptance. The importance of performance expectancy suggests that software vendors should clearly

demonstrate the practical benefits of AI-enabled BIM systems, including improved project efficiency, automated

clash detection, predictive analytics, real-time monitoring, and enhanced decision-making. Providing

measurable evidence of productivity gains and cost savings can strengthen users’ perceptions of usefulness.

Moreover, because initial trust indirectly affects adoption, developers must emphasize system reliability,

transparency, data security, and explainability of AI outputs. Features such as audit trails, error validation

mechanisms, transparent AI recommendations, and cybersecurity safeguards can improve user confidence in AI-

enabled BIM technologies. Finally, the significant role of personal innovativeness implies that software vendors

should actively engage users through interactive demonstrations, pilot testing opportunities, hands-on workshops,

and continuous customer support. Encouraging experimentation and providing practical exposure to AI-enabled

BIM applications can help construction professionals become more comfortable and confident with these

emerging technologies.

Limitations and Future Research

Despite providing valuable insights into the adoption of AI-enabled BIM systems among Malaysian construction

firms, this study has several limitations that should be acknowledged. First, the study focused exclusively on

CIDB Grade 7 (G7) construction firms in Malaysia. These firms generally possess greater financial resources,

technological infrastructure, and organizational capabilities compared to small and medium-sized construction

firms. Consequently, the findings may not fully reflect the perceptions and adoption behaviors of smaller firms

facing distinct technological, financial, and operational constraints. The generalizability of the findings beyond

large construction firms may therefore be limited. Second, this study employed a cross-sectional research design

in which data were collected at a single point in time. As a result, the study captures respondents’ perceptions

and behavioral intentions only within a specific period and cannot fully explain changes in attitudes or

technology adoption behavior over time. Since AI-enabled BIM technologies continue to evolve rapidly, users’

perceptions and acceptance levels may also change as familiarity, organizational exposure, and technological

maturity increase. Third, the study relied on self-reported data from a questionnaire administered to construction

professionals. Although statistical procedures were conducted to minimize common method bias, self-reported

responses may still be influenced by social desirability bias, subjective interpretation, and respondents’ personal

perceptions. Additionally, behavioral intention was measured rather than actual usage, which means the study

examines adoption intention rather than the implementation and ongoing use of AI-enabled BIM systems. Fourth,

the research adopted an extended UTAUT framework incorporating personal innovativeness and initial trust.

While the model demonstrated satisfactory explanatory and predictive power, other potentially influential factors

were not examined. Variables such as organizational readiness, facilitating conditions, top management support,

perceived risk, technology anxiety, data security concerns, AI explainability, organizational culture, and

regulatory support may also significantly influence AI-enabled BIM adoption, but were beyond the scope of this

study. Fifth, the study was conducted in the Malaysian construction industry, which has unique institutional,

cultural, economic, and technological characteristics. Therefore, the findings may not be directly transferable to

construction industries in other countries with different levels of digital maturity, regulatory frameworks, or

technological adoption environments.

Future research should expand the scope of investigation to include small- and medium-sized construction firms,

consultants, developers, subcontractors, and public-sector organizations to provide a more comprehensive

understanding of AI-enabled BIM adoption across the entire construction ecosystem. Comparative studies

between large and small firms may also reveal differences in adoption barriers, technological readiness, and

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organizational capabilities. Longitudinal studies are recommended to examine how perceptions, trust, and

adoption behaviors evolve over time as AI-enabled BIM technologies mature and become more widely

implemented. Such studies could provide deeper insights into the transition from behavioral intention to actual

use, continued use, and organizational integration. Future researchers may also extend the theoretical model by

incorporating additional constructs, including facilitating conditions, organizational support, perceived risk, AI

transparency, cybersecurity concerns, ethical considerations, technology anxiety, resistance to change, and data

governance. Integrating theories such as the Technology–Organization–Environment (TOE) framework,

Diffusion of Innovation (DOI) theory, or trust-based AI adoption models may further enrich understanding of

AI-enabled BIM adoption behavior. In addition, future studies should investigate the actual implementation

outcomes of AI-enabled BIM systems, including project performance, productivity improvements, cost

reductions, sustainability improvements, safety management, and decision-making effectiveness. Examining

how AI-enabled BIM contributes to circular economy practices and sustainable construction objectives would

also provide valuable practical and theoretical contributions. Cross-country comparative research is another

promising avenue for future investigation. Comparing AI-enabled BIM adoption across developed and

developing countries could help identify how institutional support, digital infrastructure, national policies, and

cultural factors influence adoption behavior. Finally, future research could employ mixed-method or qualitative

approaches, such as interviews, case studies, and ethnographic investigations, to gain deeper insights into

organizational experiences, implementation challenges, employee resistance, and strategic transformation

processes associated with AI-enabled BIM adoption in real-world construction environments.

OVERALL CONCLUSION

This study examined the factors influencing the adoption of AI-enabled Building Information Modeling (AI-

BIM) systems among CIDB Grade 7 (G7) construction firms in Malaysia, using an extended Unified Theory of

Acceptance and Use of Technology (UTAUT) framework. Specifically, the study investigated the direct and

indirect effects of performance expectancy (PE), effort expectancy (EE), social influence (SI), personal

innovativeness (PI), and initial trust (IT) on behavioral intention (BI) to use AI-enabled BIM systems. The

findings revealed that performance expectancy, effort expectancy, social influence, and personal innovativeness

significantly influenced behavioral intention to adopt AI-enabled BIM systems. Among these factors, effort

expectancy and personal innovativeness emerged as particularly important determinants, indicating that

construction professionals are more likely to adopt AI-enabled BIM systems when they perceive them as easy

to use and when they possess a stronger inclination toward innovation and experimentation with new

technologies. Social influence also played a significant role, demonstrating the importance of organizational

encouragement, management support, and industry influence in shaping adoption behavior. The study further

found that personal innovativeness and initial trust significantly enhanced both performance expectancy and

effort expectancy. However, initial trust did not directly affect behavioral intention, suggesting that trust alone

may not be sufficient to drive adoption unless users also perceive the technology as beneficial and user-friendly.

The mediation analysis confirmed that performance expectancy and effort expectancy are important mechanisms

by which personal innovativeness and initial trust indirectly shape behavioral intention toward AI-enabled BIM

adoption.

Overall, the study demonstrates that the successful adoption of AI-enabled BIM systems in Malaysia depends

not only on technological capabilities but also on human, organizational, and psychological factors. The findings

contribute theoretically by extending the UTAUT framework to the emerging context of AI-enabled BIM systems

and empirically by providing one of the earliest studies on AI-enabled BIM adoption in Malaysia. Practically,

the study offers valuable insights for construction firms, policymakers, and software developers in designing

strategies, policies, training initiatives, and user-centered systems that can accelerate digital transformation in

the Malaysian construction industry. As the construction sector continues to transition toward Construction 4.0,

the integration of AI and BIM technologies is expected to become increasingly important for enhancing

productivity, sustainability, collaboration, and intelligent decision-making throughout the project lifecycle.

Therefore, strengthening digital readiness, fostering innovative mindsets, and improving user acceptance of AI-

enabled BIM systems will be essential for achieving a more competitive, efficient, and technologically advanced

construction industry in Malaysia.

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ACKNOWLEDGEMENT

We would like to express our gratitude to the faculty who participated in this study. We would also like to thank

the reviewers and the editor for their supportive recommendations during the writing process of this manuscript.

Author contributions

Shiau-Yoon Choong conceptualized the whole research. Zahir Osman supervised and reviewed the research.

Ethical approval

Ethics approval has been obtained from the first author’s institution.

Competing interests

The authors declare that they have no competing interests.

Data availability

The data that support the findings of this study are available from the corresponding author upon

reasonable request.

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