Digital twin and BIM adoption in construction project management: a quantitative expert-based study

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This study investigates the role of Digital Twin (DT) services in facilitating the adoption of Building Information Modeling (BIM) in construction project management. Despite growing interest in digital transformation within the Architecture, Engineering, and Construction (AEC) industry, empirical evidence on how DT influences BIM implementation remains limited. To address this gap, a structured questionnaire was developed through an extensive literature review and distributed to 53 professionals actively engaged in BIM and DT applications, including contractors, consultants, and academics. The collected data were analyzed using SPSS with reliability tests (Cronbach’s Alpha), Pearson correlation, independent-samples t-tests, and one-way ANOVA with post-hoc analysis. The results revealed strong internal consistency of the survey instrument (Cronbach’s Alpha = 0.944), confirming the robustness of the measurement scale. Correlation analysis showed significant positive associations between DT service factors and BIM adoption (p < 0.01). Group comparisons demonstrated that perceptions of DT’s contribution to BIM adoption varied across organizational roles, with notable differences between contractors, consultants, and research institutions (p < 0.05). These findings highlight the synergistic relationship between DT and BIM, suggesting that integrating DT services can enhance BIM utilization and overall project performance. The study contributes to academic knowledge and professional practice by providing empirical evidence of DT’s enabling role in digital transformation. Practical implications include guiding policymakers, project managers, and technology providers in making informed decisions regarding DT-enabled BIM adoption. Although limited by its sample size and geographic scope, this research lays the groundwork for future studies employing larger international datasets and advanced statistical modeling. The results confirm the critical importance of DT services in accelerating successful BIM implementation across the construction sector.

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Introduction Over the past decade, the construction industry has undergone a significant digital transformation, with Building Information Modeling (BIM) emerging as a key methodology to enhance project coordination, cost estimation, scheduling, and lifecycle decision-making [1; 2]. Despite these recognized benefits, BIM adoption faces substantial challenges, including limited interoperability, high implementation costs, resistance to organizational change, and shortages of skilled personnel - especially in developing regions [3; 4]. Simultaneously, the emergence of Digital Twin (DT) technologies has offered opportunities for real-time connectivity between physical assets and their digital counterparts, enabling advanced simulation, predictive analytics, and operational control [5; 6]. Although previous reviews have outlined the enabling technologies and conceptual synergies between DT and BIM [7; 8], empirical studies exploring how DT implementation affects BIM uptake in real construction practice remain limited. Existing literature is dominated by conceptual models and theoretical discussions, with a notable lack of quantitative validation from practitioners’ perspectives [9; 10]. This gap is particularly relevant as organizations increasingly invest in digital transformation initiatives without clear evidence on return or implementation effectiveness. To address this gap, the current study investigates the perceptions of industry professionals regarding how DT services influence BIM adoption in construction project management. The study’s objectives are to: · Quantitatively assess the perceived impact of DT services on BIM adoption. · Examine the correlations between specific DT capabilities and BIM utilization. · Identify organizational factors affecting perceptions of DT-BIM integration. · Provide empirical evidence to support strategic digital technology investments. Purpose of the study is to investigate how DT services influence the adoption of BIM in construction project management. By capturing the perspectives of industry professionals, this research aims to provide empirical evidence on the relationship between specific DT capabilities and the extent of BIM utilization. Furthermore, the study seeks to identify how different organizational roles, such as contractors, consultants, and researchers, perceive the value of DT-enabled tools in enhancing BIM implementation. Ultimately, the findings are intended to support strategic decision-making among stakeholders in the Architecture, Engineering, and Construction (AEC) sector regarding digital transformation investments. Materials and methods This study employs a quantitative survey-based methodology to evaluate industry experts’ perceptions of how DT services influence the adoption of BIM in construction project management. The research design comprised the following stages: Survey instrument design: The questionnaire was developed based on a thorough review of existing BIM-DT integration literature, using validated constructs from studies such as Deng et al. [9] and Omrany et al. [10]. Sample and data collection: A purposive sampling strategy targeted 53 professionals engaged in BIM and/or DT implementation, including contractors, consultants, and academic/research institutes. Invitations were sent electronically, and responses were collected online between March and May, 2025. Reliability and validity assessment: Cronbach’s Alpha was computed to assess the internal consistency of each construct. An alpha coefficient surpassing 0.90 confirmed high reliability, consistent with established quantitative research standards [11]. Descriptive and inferential statistical analysis: Descriptive statistics (mean, standard deviation) characterized respondents’ demographic and organizational profiles. Pearson correlation analysis examined relationships between DT-related variables and BIM adoption levels. Data normality and diagnostic tests: Normality of responses was verified using skewness and kurtosis metrics. Homogeneity of variances was checked via Levene’s test. These tests ensured the validity of parametric inferential procedures. This methodology integrates best practices from empirical research in AEC digital technology adoption [12], enabling robust interpretation of data that bridges theory and practice. By leveraging statistical rigor, this study aims to provide defensible evidence on the impact of DT services on BIM uptake within the construction industry, operationalized through practitioners’ perceptions. Results A total of 53 industry professionals participated in the survey. The majority were civil engineers, followed by architects and other related specialists. The demographic breakdown of the respondents is summarized in Figure, which presents key characteristics such as professional roles, specialization areas, education levels, and years of experience. The internal consistency of the survey items measuring DT services’ influence on BIM adoption was tested using Cronbach’s Alpha. The result yielded a value of 0.944, exceeding the commonly accepted threshold of 0.70 for reliability, and demonstrating strong internal consistency among the items. Table 1 presents the item-total statistics, including each item’s correlation with the overall scale and the effect on Cronbach’s Alpha if the item were deleted, thereby confirming the robustness of the measurement construct. Demographic characteristics of survey respondents Source: made by L.M. Nguyen, K.V. Vu with the use of MS Word. Table 1 Cronbach’s Alpha and item-total statistics assess the internal consistency of survey items related to the DT services’ influence on BIM adoption Item-Total Statistics N Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach›s Alpha if Item Deleted Y1 37.00 45.269 0.769 0.778 0.938 Y2 37.04 44.691 0.789 0.775 0.938 Y3 36.85 44.631 0.773 0.781 0.938 Y4 37.11 43.141 0.806 0.711 0.937 Y5 37.19 45.156 0.741 0.748 0.940 Y6 37.02 44.865 0.777 0.748 0.938 Y7 36.96 45.691 0.768 0.799 0.939 Y8 36.98 45.057 0.747 0.820 0.939 Y9 37.19 46.964 0.633 0.660 0.943 Y10 37.13 44.617 0.800 0.846 0.937 Y11 37.08 45.994 0.700 0.667 0.941 0.944 Source: developed by L.M. Nguyen, L.V. Nguyen. Table 2 Results of normality testing for survey items, showing means, standard deviations, skewness, and kurtosis values to assess the distributional characteristics of the data Descriptive Statistics N N Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error Y1 53 2 5 3.75 0.806 0.025 0.327 -0.667 0.644 Y2 53 2 5 3.72 0.841 -0.020 0.327 -0.661 0.644 Y3 53 2 5 3.91 0.861 -0.378 0.327 -0.492 0.644 Y4 53 1 5 3.64 0.963 -0.420 0.327 -0.112 0.644 Y5 53 1 5 3.57 0.844 -0.215 0.327 0.569 0.644 Y6 53 1 5 3.74 0.836 -0.488 0.327 0.959 0.644 Y7 53 2 5 3.79 0.769 -0.150 0.327 -0.327 0.644 Y8 53 2 5 3.77 0.847 -0.331 0.327 -0.356 0.644 Y9 53 2 5 3.57 0.772 0.421 0.327 -0.456 0.644 Y10 53 2 5 3.62 0.837 0.004 0.327 -0.548 0.644 Y11 53 2 5 3.68 0.803 -0.037 0.327 -0.459 0.644 Valid N (listwise) 53 Source: developed by L.M. Nguyen, L.V. Nguyen. Pearson’s correlation test revealed substantial and statistically significant positive relationships (p < 0.01) among the DT-related variables (Y1-Y11). Correlation coefficients ranged from 0.342 to 0.799, suggesting a consistent perception of DT services’ positive contribution to BIM adoption, as reflected in the correlation matrix presented in Table 3. Independent-samples t-tests comparing respondents from organizations with and without BIM adoption showed no statistically significant differences across most items (p > 0.05). At the same time, one-way ANOVA was performed to assess variations across different organizational roles. Descriptive group statistics for the BIM adoption comparison are provided in Table 4, with detailed t-test results in Table 5. The outcomes of the ANOVA, highlighting differences in perceptions across organizational roles, are summarized in Table 6. Table 3 The Pearson correlation matrix shows statistically significant relationships concerning BIM adoption among DT-related variables (Y1-Y11) Correlations N Corellation Y١ Y٢ Y٣ Y٤ Y٥ Y٦ Y٧ Y٨ Y٩ Y١٠ Y١١ Y1 Correlation 1 0.747 0.714 0.653 0.575 0.587 0.537 0.593 0.505 0.686 0.559 Y2 Correlation 0.747 1 0.653 0.609 0.583 0.685 0.711 0.665 0.488 0.610 0.575 Y3 Correlation 0.714 0.653 1 0.724 0.605 0.633 0.580 0.683 0.342 0.617 0.623 Y4 Correlation 0.653 0.609 0.724 1 0.728 0.645 0.599 0.630 0.537 0.664 0.644 Y5 Correlation 0.575 0.583 0.605 0.728 1 0.680 0.511 0.452 0.620 0.608 0.585 Y6 Correlation 0.587 0.685 0.633 0.645 0.680 1 0.751 0.512 0.594 0.542 0.616 Y7 Correlation 0.537 0.711 0.580 0.599 0.511 0.751 1 0.695 0.655 0.653 0.482 Y8 Correlation 0.593 0.665 0.683 0.630 0.452 0.512 0.695 1 0.494 0.799 0.485 Y9 Correlation 0.505 0.488 0.342 0.537 0.620 0.594 0.655 0.494 1 0.545 0.391 Y10 Correlation 0.686 0.610 0.617 0.664 0.608 0.542 0.653 0.799 0.545 1 0.674 Y11 Correlation 0.559 0.575 0.623 0.644 0.585 0.616 0.482 0.485 0.391 0.674 1 Source: developed by L.M. Nguyen, L.V. Nguyen. Table 4 Group statistics comparing mean scores of DT-related items (Y1-Y11) between respondents from companies implementing BIM and those not implementing BIM Group Statistics N Is your company implementing BIM? N Mean Std. Deviation Std. Error Mean Y1 Yes 17 3.76 0.970 0.235 No 36 3.75 0.732 0.122 Y2 Yes 17 3.71 0.849 0.206 No 36 3.72 0.849 0.141 Y3 Yes 17 4.06 0.899 0.218 No 36 3.83 0.845 0.141 Y4 Yes 17 3.53 1.231 0.298 No 36 3.69 0.822 0.137 Y5 Yes 17 3.65 0.702 0.170 No 36 3.53 0.910 0.152 Y6 Yes 17 3.65 0.786 0.191 No 36 3.78 0.866 0.144 Y7 Yes 17 3.76 0.831 0.202 No 36 3.81 0.749 0.125 Y8 Yes 17 3.59 1.004 0.243 No 36 3.86 0.762 0.127 Y9 Yes 17 3.53 0.717 0.174 No 36 3.58 0.806 0.134 Y10 Yes 17 3.47 1.007 0.244 No 36 3.69 0.749 0.125 Y11 Yes 17 3.53 0.717 0.174 No 36 3.75 0.841 0.140 Source: developed by L.M. Nguyen, L.V. Nguyen. Table 5 Independent-samples t-test results comparing the means of DT-related items (Y1-Y11) between organizations implementing and not implementing BIM Independent Samples Test N Assumed/Not Levene’s Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (٢-tailed) Mean Difference Std. Error Difference ٩٥ ٪ Confidence Interval of the Difference Lower Upper Y1 Assumed 3.753 0.058 0.061 51 0.951 0.015 0.240 -0.466 0.496 Not assumed. 0.055 24.934 0.956 0.015 0.265 -0.531 0.561 Y2 Assumed 0.011 0.915 .065 51 0.948 -0.016 0.250 -0.518 0.485 Not assumed. -0.065 31.472 .0948 -0.016 0.250 -0.526 0.493 Y3 Assumed 0.000 0.988 0.888 51 0.379 0.225 0.254 -0.284 0.735 Not assumed. 0.868 29.761 0.392 0.225 0.260 -0.305 0.756 Y4 Assumed 5.652 0.021 -0.579 51 0.565 -0.165 0.285 -0.737 0.407 Not assumed. -0.503 22.982 0.620 -0.165 0.328 -0.844 0.514 Y5 Assumed 1.137 0.291 0.477 51 0.636 0.119 0.250 -0.383 0.622 Not assumed. 0.523 39.962 0.604 0.119 0.228 -0.341 0.580 Y6 Assumed 0.023 0.879 -0.528 51 0.600 -0.131 0.248 -0.628 0.366 Not assumed. -0.547 34.417 0.588 -0.131 0.239 -0.616 0.355 Y7 Assumed 0.231 0.633 -0.179 51 0.859 -0.041 0.228 -0.499 0.418 Not assumed. -0.172 28.691 0.864 -0.041 0.237 -0.526 0.444 Y8 Assumed 3.123 0.083 -1.097 51 0.278 -0.273 0.249 -0.772 0.226 Not assumed. -0.994 25.040 0.330 -0.273 .275 -0.838 0.293 Y9 Assumed 0.430 0.515 -0.235 51 0.815 -0.054 0.229 -0.514 0.407 Not assumed. -0.245 35.072 0.808 -0.054 0.220 -0.500 0.392 Y10 Assumed 3.055 0.087 -0.907 51 0.369 -0.224 0.247 -0.719 0.272 Not assumed. -0.816 24.678 .422 -0.224 0.274 -0.789 0.342 Y11 Assumed 0.396 0.532 -0.932 51 0.356 -0.221 0.237 -0.696 0.255 Not assumed. -0.987 36.477 0.330 -0.221 0.223 -0.674 0.232 Source: developed by L.M. Nguyen, L.V. Nguyen. Table 6 One-way ANOVA results comparing DT-related item scores (Y1-Y11) across different organizational roles ANOVA N Dispersion Sum of Squares df Mean Square F Sig. Y1 Between Groups 3.366 4 0.841 1.327 0.274 Within Groups 30.445 48 0.634 Total 33.811 52 Y2 Between Groups 4.409 4 1.102 1.636 0.181 Within Groups 32.345 48 0.674 Total 36.755 52 Y3 Between Groups 5.343 4 1.336 1.932 0.120 Within Groups 33.185 48 0.691 Total 38.528 52 Y4 Between Groups 6.379 4 1.595 1.831 0.138 Within Groups 41.810 48 0.871 Total 48.189 52 Y5 Between Groups 8.762 4 2.190 3.721 0.010 Within Groups 28.257 48 0.589 Total 37.019 52 Y6 Between Groups 5.126 4 1.282 1.973 0.114 Within Groups 31.176 48 0.649 Total 36.302 52 Y7 Between Groups 1.235 4 0.309 .503 0.734 Within Groups 29.482 48 0.614 Total 30.717 52 Y8 Between Groups 1.824 4 0.456 .617 0.652 Within Groups 35.459 48 0.739 Total 37.283 52 Y9 Between Groups 2.522 4 0.631 1.062 0.386 Within Groups 28.497 48 0.594 Total 31.019 52 Y10 Between Groups 3.613 4 0.903 1.320 0.276 Within Groups 32.839 48 0.684 Total 36.453 52 Y11 Between Groups 8.477 4 2.119 4.057 0.007 Within Groups 25.071 48 0.522 Total 33.547 52 Source: developed by L.M. Nguyen, L.V. Nguyen. Conclusion This study explored how DT services influence the adoption of BIM in construction project management by surveying a diverse group of professionals and applying rigorous statistical analysis. The research provides empirical evidence that DT technologies play a meaningful role in strengthening BIM-based practices. Beyond validating the connection between DT and BIM, the study uncovers nuanced differences in perception among different organizational roles - consultancy firms. In particular, they appear to recognize greater benefits from DT-enabled workflows than contractors and research institutions. The results contribute to the broader discourse on digital transformation in the Architecture, Engineering, and Construction sector. Despite its contributions. The study has limitations that open avenues for future research, such as the relatively small sample size.
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About the authors

Luan M. Nguyen

Thuyloi University

Email: minhluan1102@gmail.com
ORCID iD: 0009-0004-3547-7375

Master student

175 Tay Son st., Hanoi, 100000, Vietnam

Luat V. Nguyen

Ton Duc Thang University

Email: ng.vuluat1910@gmail.com
ORCID iD: 0000-0001-5569-874X

Researcher, Intelligent Civil Computing Group

19 Nguyen Huu Tho st., Ho Chi Minh, 700000, Vietnam

Kien V Vu

Thuyloi University

Email: kienvuvan@tlu.edu.vn
ORCID iD: 0009-0002-7535-4519

Master Student

175 Tay Son st., Hanoi, 100000, Vietnam

Ngoc T. Pham

Thuyloi University

Author for correspondence.
Email: thinhtls@tlu.edu.vn
ORCID iD: 0000-0002-4928-6236

Doctor of Engineering Sciences, Professor

175 Tay Son st., Hanoi, 100000, Vietnam

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