Dynamic BIM Adoption Impact on Contract Cost Variance Factors Using PLS-SEM Techniques
Source abstract
Abstract
This paper investigates the Building Information Modeling (BIM) adoption impact on the factors of Contract Cost Variance (CCV) over time. The study considers qualitative and quantitative data to identify the most common causes of CCV through pre-tendering. A partial least square-structure model (PLS-SEM) procedure was used to develop a causal model and rank CCV factors based on their effect, partially based on prior survey raw data conducted in 2022 and the data from 94 projects. Construction industry experts assessed the prior five-year rate of BIM adoption on construction projects to infer the expected trend in BIM adoption in the future (until 2037). Based on the causal model of CCV factors and the future rates of BIM adoption, the dynamic impact of BIM on CCV factors over time was modeled and analyzed. The analysis shows that BIM reduces CCV over time by improving Estimator Performance (EP), Information Quality (IQ), and contractual procedure (CP). The results showed that the CP, EP, and EF have directly impacted CCV, and the PC and IQ indirectly affect the CCV. This paper considers the temporal aspect, examining how the impact of BIM on CCV factors evolves. This dynamic analysis is crucial for long-term strategic planning in construction management.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: BIM and Construction Integration · Construction Project Management and Performance · Organizational and Employee Performance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, King Saud University, P.O. Box 2454, Riyadh 11421, Saudi Arabia
Funding
King Saud University
References (59)
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1. An Assessment of the Impact of the Construction Sector on the Gross Domestic Product (Gdp) Of Nigeria
2. de Marco, G., Slongo, C., and Siegele, D. (2024). Enriching Building Information Modeling Models through Information Delivery Specification. Buildings, 14.10.3390/buildings14072206 ↗
3. Rashidi, A., Chan, D.W.M., Ravanshadnia, M., Sarvari, H., and Tajaddini, A. (2024). Applying Building Information Modelling (BIM) Technology in Pre-Tender Cost Estimation of Construction Projects: A Case Study in Iran. Buildings, 14.10.3390/buildings14051260 ↗
4. Chung, F., and Ashuri, B. (2022, January 9–12). Comparative Analysis of Right of Way Acquisition Processes by State DOTs. Proceedings of the Construction Research Congress 2022: Project Management and Delivery, Controls, and Design and Materials—Selected Papers from Construction Research Congress, Arlington, TX, USA.10.1061/9780784483978.098 ↗
5. Alsugair, A.M., Alsanabani, N.M., and Al-Gahtani, K.S. (2023). Forecasting the Final Contract Cost on the Basis of the Owner’s Cost Estimation Using an Artificial Neural Network. Buildings, 13.10.37247/PAENGG.1.23.13 ↗
6. Effects of project’s physical characteristics on cost deviation in road construction
7. Albtoush, F., Shu Ing, D., Rahman, R.A., and Aldiabat Al-Btoosh, J.A. (2020, January 9). Factors Affecting the Accuracy of Cost Estimate in Construction Projects: A Review. Proceedings of the National Conference for Postgraduate Research (NCON-PGR 2020), Virtual.10.34218/IJCIET.11.1.2020.011 ↗
8. Saqer, F., Malalla, Y., Suliman, P.S.M.A., and Al Jamal, O. (2020). Development of Cost Estimation Model for Ministry of Youth and Sports Affairs Construction Projects A Case study from Kingdom of Bahrain. [Master Thesis, University of Bahrain].
9. Survey of Procedures Adopted by A/E Firms in Accounting for Design Variables in Early Cost Estimates
10. Baek, M., Ph, D., Ashuri, B., and Ph, D. (2019, January 10). Assessing low bid deviation from engineer’s estimate in highway construction projects. Proceedings of the 55th Associated Schools of Construction Annual International Conference, Denver, CO, USA.
11. Alsugair, A.M. (2022). Cost Deviation Model of Construction Projects in Saudi Arabia Using PLS-SEM. Sustainability, 14.10.3390/su142416391 ↗
12. Discussing the influence of BIM for the construction project cost10.4028/www.scientific.net/AMM.357-360.2533 ↗
13. Wen, Z., and Zhang, H. (2021). Research on cost management of prefabricated construction based on BIM. IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd.10.1088/1755-1315/768/1/012110 ↗
14. Yang, S.W., Moon, S.W., Jang, H., Choo, S., and Kim, S.A. (2022). Parametric Method and Building Information Modeling-Based Cost Estimation Model for Construction Cost Prediction in Architectural Planning. Appl. Sci., 12.10.3390/app12199553 ↗
15. Comparative Analysis of Traditional Methods and Building Information Modeling (BIM) in Construction Project Cost Estimation10.22214/ijraset.2024.61705 ↗
16. Forecasting Ratio of Low Bid to Owner’s Estimate for Highway Construction10.1061/(ASCE)CO.1943-7862.0001970 ↗
17. Predicting Ratio of Low Bid to Owner’s Estimate Using Feedforward Neural Networks for Highway Construction
18. Factors affecting accuracy of pretender cost estimate: Studies of Saudi Arabia
19. Neural network method for control valve cost estimation on the EPC project bidding10.1051/shsconf/20184902004 ↗
20. Singh, A.K., Persad, K., Wu, J., Murphy, M., and Wu, H. (2016, January 10–14). Preliminary Analysis of Engineer’s Estimate and Winning Bid Price Using Quantile Regression. Proceedings of the Transportation Research Board 95th Annual Meeting, Washington, DC, USA. Available online: https://trid.trb.org/View/1394365.
21. Dijkstra, T.K. (2010). Latent variables and indices: Herman Wold’s basic design and partial least squares. Handbook of Partial Least Squares: Concepts, Methods and Applications, Springer.10.1007/978-3-540-32827-8_2 ↗
22. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration10.2307/20650284 ↗
23. PLS-SEM: Indeed a silver bullet10.2753/MTP1069-6679190202 ↗
24. Do right PLS and do PLS right: A critical review of the application of PLS-SEM in construction management research10.1007/s42524-021-0153-5 ↗
25. PLS-SEM Model of Integrated Stem Education Concept and Network Teaching Model of Architectural Engineering Course
26. Exploring the failure factors of Yemen construction industry using PLS-SEM approach10.1007/s42107-020-00253-z ↗
27. PLS–SEM in business promotion strategies. A multigroup analysis of mobile coupon users using MICOM10.1108/IMDS-12-2019-0726 ↗
28. PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model10.1007/s12553-020-00484-9 ↗
29. Kineber, A.F., Siddharth, S., Chileshe, N., Alsolami, B., and Hamed, M.M. (2022). Addressing of Value Management Implementation Barriers within the Indian Construction Industry: A PLS-SEM Approach. Sustainability, 14.10.3390/su142416602 ↗
30. Improving PLS-SEM use for business marketing research10.1016/j.indmarman.2023.03.010 ↗
31. Al-Mekhlafi, A.B.A., Othman, I., Kineber, A.F., Mousa, A.A., and Zamil, A.M.A. (2022). Modeling the Impact of Massive Open Online Courses (MOOC) Implementation Factors on Continuance Intention of Students: PLS-SEM Approach. Sustainability, 14.10.3390/su14095342 ↗
32. Su, C.H., and Cheng, T.W. (2019). A sustainability innovation experiential learning model for virtual reality chemistry laboratory: An empirical study with PLS-SEM and IPMA. Sustainability, 11.10.3390/su11041027 ↗
33. Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis10.1109/ACCESS.2023.3268037 ↗
34. Building Information Modelling (BIM) Adoption for Cost Engineering Consultant; Case Study of Southern China10.37934/araset.29.3.2136 ↗
35. Harty, J., and Laing, R. (2009, January 15–17). Drivers for change in construction procurement and its impact on management. Proceedings of the 2009 2nd International Conference in Visualisation, VIZ 2009, Barcelona, Spain.10.1109/VIZ.2009.53 ↗
36. Technology Acceptance Model for Building Information Modelling Based Virtual Reality (Bim-Vr) in Cost Estimation
37. Cai, X., Tao, B., Bosché, F., Huang, J., and Chen, H. (2022, January 24–26). Dynamic Occupational Safety Risk Assessment in Construction Project Unsing a Network-Based Modelling and Analysis Framework. Proceedings of the 2022 European Conference on Computing in Construction, Rhodes, Greece.10.35490/EC3.2022.166 ↗
38. A Framework for Project Risk Assessment in Dynamic Networks: A Case Study of Oil and Gas Megaproject Construction10.1109/ACCESS.2021.3084349 ↗
39. Al-Gahtani, K.S., Aldokhi, M.I., Alsanabani, N.M., Alotaibi, H.F., and Bin Mahmoud, A.A. (2024). The Impact of Dynamic Risk Interdependencies on the Saudi Precast Concrete Industry. Buildings, 14.10.3390/buildings14040875 ↗
40. Alsanabani, N.M., Al-Gahtani, K.S., Almohsen, A.S., and Alsharef, A. (2023). Risk Interdependency Network Model for the Cost and Time of Pile Installation in Saudi Arabia, Using Partial Least Squares Structural Equation Modeling. Appl. Sci., 13.10.3390/app131910886 ↗
41. Latan, H., and Noonan, R. (2017). Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications, Springer.10.1007/978-3-319-64069-3 ↗
42. Hair, J.F., Sarstedt, M., Hopkins, L., and Kuppelwieser, V.G. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool in Business Research, Emerald Group Publishing Ltd.10.1016/j.jfbs.2014.01.002 ↗
43. Walpole, R.E., Myers, R.H., Myers, S.L., and Ye, K. (2017). Probability & Statistics for Engineers & Scientists. Library of Congress. MyStatLab Update Book.
44. Jaadi, Z. (2023). A Step-by-Step Explanation of Principal Component Analysis (PCA). Built in, Macmillan.
45. Descriptive statistics and normality tests for statistical data10.4103/aca.ACA_157_18 ↗
46. Pituch, K.A. (2015). Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS, Routledge.10.4324/9781315814919 ↗
47. Critical Determinants of Public Construction Tendering Costs
48. Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry10.1061/(ASCE)LM.1943-5630.0000127 ↗
49. Comparing Bim Adoption around the World, Syria’s Current Status and Furture
50. Lin, X. (2023, January 24–26). Analysis on the Influencing Factors of BIM Technology Application in Engineering Cost Management. Proceedings of the 2nd International Conference on Engineering Management and Information Science, EMIS 2023, Chengdu, China.10.4108/eai.24-2-2023.2330689 ↗
51. BIM technologies’ effect on transformation of a property life cycle10.1051/e3sconf/20199108030 ↗
52. Bukhary, R., Taihairan, R., and Ismail, Z. (2024, July 16). BIM: Integrating Cost Estimates at Initial/Design Stage. Available online: http://penerbit.uthm.edu.my/ojs/index.php/IJSCET.
53. Calculation of Cost Estimation Based on Building Information Modeling in Construction Projects
54. Building Information Modeling (BIM)-Based Information Management Platform in the Construction Industry
55. Impact of BIM-related factors affecting construction project performance10.1108/IJBPA-05-2021-0068 ↗
56. Technology Ontology and BIM-enabled Estimating for Owners and Contractors
57. Ying, C. (2019, January 30–31). Application of BIM Technology in the Phase of Project Tendering and Bidding. Proceedings of the 2019 Scientific Workshop on Advanced in Social Sciences, Arts & Humanities (ASSAH 2019), Chengdu, China.
58. Role of BIM Contract Practices in Stakeholder BIM Implementation on AEC Projects10.1061/JLADAH.LADR-916 ↗
59. Maximizing project efficiency and collaboration in construction management through building information modeling (BIM)10.54254/2755-2721/72/20240986 ↗