Strategies to Mitigate Risks in Building Information Modelling Implementation: A Techno-Organizational Perspective
Abstract
1. Introduction
1.1. Literature Review
1.2. Theoretical Framework
2. Methodology
2.1. Research Design
2.2. Paper Retrieval Process (Step 1–3)
2.3. Online Survey (Step 4)
- Variables and Scales: Nominal (N) Gender and Age; Ordinal (O) Level of BIM experience, BIM-RBS as the outcome variable and shares the same properties as the dependent variables; String (S) BIM-RBS-MS as the intervening variable that transmit the effect of an independent variable on a dependent variable.
- Cronbach’s Alpha (α) test (CA): was employed to measure internal consistency/reliability of the survey scale. It assessed the reliability of multiple Likert-scale questions determining the magnitude of risk factors (BIM-RBS). Various thresholds were applied and the test procedure involved [Click Analyze > Scale > Reliability Analysis > select the variables > ensuring the model says ‘Alpha’ > then click OK]. The results are shown in Section 3.2.
- Simple linear regression (SLR): attempts to predict the outcome variable (BIM-RBS) using the predictor variable (Level of BIM experience). The test procedure involved [Click Analyze > Regression > Linear > select outcome variable and move to the ‘Dependent’ box > select predictor variable and move to the ‘Block 1 of 1’ box > Select Statistics > click Estimates, Confidence Intervals, Model Fit, R Squared Change, and Descriptives]. See results in Section 3.2.
2.4. Mix-Method Analysis (Step 5)
3. Results
3.1. Secondary Data Analysis (Literature Review)
3.2. Primary Data Analysis (Online Survey)
- Analyzing for risk magnitude (BIM-RBS) and management strategies (BIM-RBS-MS)
3.3. Findings
4. Discussions
4.1. Conclusions
4.2. Recommendations
- Socio-organizational Aspect
- Eco-financial perspective
- Legal-contractual perspective
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Techno-Organisational Aspect | Magnitude of Risk Factors | ||||
---|---|---|---|---|---|
Severity→ | Very Low Risk | Low Risk | Medium Risk | High Risk | Very High Risk |
Difficult to implement due to fragmented structure of construction industry and also BIM | 4 | 4 | 21 | 40 | 30 |
Lack of strategies for integration and exchanging information among BIM components | 14 | 6 | 16 | 24 | 39 |
Lack of alignment between the IT strategy and organizational strategy | 6 | 8 | 23 | 29 | 33 |
Privacy constraints associated with external storage | 7 | 7 | 14 | 27 | 45 |
Average (%) | 7.75 | 6.25 | 18.5 | 30 | 36.75 |
LSTM | Equilibrium state | <<<<<<<< >>>>>>>> | Disequilibrium state |
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Dogonyaro, I.; Elnokaly, A. Strategies to Mitigate Risks in Building Information Modelling Implementation: A Techno-Organizational Perspective. Intell. Infrastruct. Constr. 2025, 1, 5. https://doi.org/10.3390/iic1020005
Dogonyaro I, Elnokaly A. Strategies to Mitigate Risks in Building Information Modelling Implementation: A Techno-Organizational Perspective. Intelligent Infrastructure and Construction. 2025; 1(2):5. https://doi.org/10.3390/iic1020005
Chicago/Turabian StyleDogonyaro, Ibrahim, and Amira Elnokaly. 2025. "Strategies to Mitigate Risks in Building Information Modelling Implementation: A Techno-Organizational Perspective" Intelligent Infrastructure and Construction 1, no. 2: 5. https://doi.org/10.3390/iic1020005
APA StyleDogonyaro, I., & Elnokaly, A. (2025). Strategies to Mitigate Risks in Building Information Modelling Implementation: A Techno-Organizational Perspective. Intelligent Infrastructure and Construction, 1(2), 5. https://doi.org/10.3390/iic1020005