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Perceived frequency of the sources of uncertainty_
| Source of uncertainty | Variable | Mean* | SD |
|---|---|---|---|
| Organisational | Inherent complexity of the construction project | 4.62 | 1.30 |
| Ambiguity in selection criteria | 3.85 | 1.59 | |
| Experts’ consultation | 4.31 | 1.26 | |
| Risk-taking willingness of decision makers | 3.96 | 1.18 | |
| Activity durations | Activity duration differing from actual duration | 4.12 | 1.58 |
| Resource use | Inaccurate resource estimation | 3.81 | 1.52 |
| Requirement changes and quality Issues | Changes in project requirements | 3.77 | 1.27 |
| Resource availability | Inflexible resource availability | 3.54 | 1.36 |
| Logistics | Safety issues | 3.77 | 1.21 |
| Site access conditions | 2.88 | 1.18 | |
| Supply availability fluctuations | 3.58 | 1.21 | |
| Environmental | Inconsistent weather | 4.00 | 1.41 |
| Adverse geographic conditions | 3.65 | 1.60 | |
| Sociopolitical | Policies and regulations | 3.50 | 1.39 |
| Social conditions | 3.27 | 1.48 | |
| Market | Market conditions | 4.27 | 1.56 |
| Technological | Equipment reliability and construction methods | 3.42 | 1.33 |
Summary of the most influential features and dominant classification rules associated with higher perceived uncertainty across domains_
| Uncertainty source | Feature importance | CT rules for a higher level of uncertainty | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Most important feature | MDI | Most important signal | MDI | Number of activities | Number of state projects | Months in service | Origin | Signal | Highest% companies classified in a leaf node (%) | |
| Organisational | Number of activities | 0.217 | Subjective expert information | 0.185 | - | ≤327 | - | Subjective expert information | 70.7 | |
| Activity durations | Months ¡n service | 0.388 | Leader decision timing | 0.044 | - | - | - | Outside of Valle del Cauca and Huila | - | 50 |
| Resource use | Months in service | 0.341 | Inflexible cost estimation | 0.087 | ≤3 | - | ≥60 | Outside of Cauca | 43.9 | |
| Requirement changes and Quality issues | Months in service | 0.379 | Design changes | 0.042 | ≥l | - | ≥67 and ≤276 | - | - | 34.1 |
| Resource availability | Months in service | 0.315 | Limited availability of capable workers in the area | 0.066 | ≤3 | - | ≤I4I | Outside Valle del Cauca | - | 37.8 |
| Logistics | Material acquisition | 0.212 | Material acquisition | 0.212 | - | - | Other signals different to Material Acquisition and supply chain structure | 57.3 | ||
| Environmental | Months in service | 0.406 | Heavy rains | 0.030 | ≤2 | - | ≤327 | - | Heavy rains | 42.7 |
| Sociopolitical | Months in service | 0.202 | Worker social discontent | 0.127 | ≤2 | - | - | Other signals different to worker social discontent and non-working days granted | 62.2 | |
| Market | Months in service | 0.414 | Supply prices | 0.082 | - | - | ≥29 | - | - | 82.9 |
| Technological | number of states where the company has projects | 0.314 | Renewable resource efficiency | 0.048 | ≤2 | ≤3 | - | - | 74.4 | |
Descriptive statistics of the surveyed companies_
| Variable meaning | Sub-variables | Mean | SD | Min | Max |
|---|---|---|---|---|---|
| Origin state of the company | Cauca | 0.35 | 0.49 | 0 | 1 |
| Nariño | 0.19 | 0.40 | 0 | 1 | |
| Valle del Cauca | 0.35 | 0.49 | 0 | 1 | |
| Huila | 0.12 | 0.33 | 0 | 1 | |
| Number of states where the company has projects | - | 1.62 | 0.98 | 1 | 4 |
| Number of months since the commercial registration date of the company | - | 92.31 | 81.7 | 15 | 363 |
| Size of the company | Micro | 0.92 | 0.27 | 0 | 1 |
| Small | 0.04 | 0.20 | 0 | 1 | |
| Medium | 0.04 | 0.20 | 0 | 1 | |
| Number of ISIC activities the company executes | - | 2.00 | 0.94 | 1 | 4 |
| Economic activities carried out by companies | Construction of residential buildings | 0.35 | 0.49 | 0 | 1 |
| Construction of non-residential buildings | 0.23 | 0.43 | 0 | 1 | |
| Construction of roads and railways | 0.12 | 0.33 | 0 | 1 | |
| Construction of utility projects | 0.23 | 0.43 | 0 | 1 | |
| Construction of other civil engineering works | 0.42 | 0.50 | 0 | 1 | |
| Other specialised activities | 0.27 | 0.45 | 0 | 1 | |
| Real estate activities | 0.04 | 0.20 | 0 | 1 | |
| Architectural activities | 0.15 | 0.37 | 0 | 1 | |
| Technical consultancy | 0.19 | 0.40 | 0 | 1 |
Validity of controlled data expansion strategy across internal and external sources of uncertainty_
| Uncertainty source | Lower uncertainty class | Higher uncertainty class | Total observations | ||||
|---|---|---|---|---|---|---|---|
| Original sample number | Augmented sample number | Lowest p-value across all variables | Original sample number | Augmented sample number | Lowest p-value across all variables | ||
| Organisational | 2 | 8 | 0.62 | 23 | 95 | 0.45 | 103 |
| Activity durations | 5 | 21 | 0.29 | 20 | 82 | 0.68 | |
| Resource use | 7 | 29 | 0.69 | 18 | 74 | 0.52 | |
| Requirement changes and quality issues | 13 | 54 | 0.54 | 12 | 49 | 0.56 | |
| Resource availability | 8 | 33 | 0.47 | 17 | 70 | 0.40 | |
| Logistics | 2 | 8 | 0.62 | 23 | 95 | 0.29 | |
| Environmental | 4 | 16 | 0.32 | 21 | 87 | 0.52 | |
| Sociopolitical | 5 | 21 | 0.29 | 20 | 82 | 0.47 | |
| Market | 1 | 4 | 1.00 | 24 | 99 | 0.56 | |
| Technological | 4 | 16 | 0.37 | 21 | 87 | 0.27 | |
Perceived magnitude of the sources of uncertainty (Likert scale: 1–4)_
| Source of uncertainty | Variable | Mean* | SD |
|---|---|---|---|
| Organisational | Inherent complexity of the construction project | 3.15 | 0.78 |
| Ambiguity in selection criteria | 2.77 | 0.95 | |
| Experts’ consultation | 2.69 | 0.88 | |
| Risk-taking willingness of decision makers | 2.81 | 1.06 | |
| Activity durations | Activity duration differing from actual duration | 2.92 | 0.80 |
| Resource use | Inaccurate resource estimation | 2.92 | 1.06 |
| Requirement changes and quality Issues | Changes in project requirements | 2.38 | 1.02 |
| Resource availability | Inflexible resource availability | 2.85 | 0.97 |
| Logistics | Safety issues | 3.15 | 0.88 |
| Site access conditions | 2.19 | 0.98 | |
| Supply availability fluctuations | 3.12 | 0.95 | |
| Environmental | Inconsistent weather | 3.08 | 0.74 |
| Adverse geographic conditions | 2.92 | 0.98 | |
| Sociopolitical | Policies and regulations | 2.85 | 0.88 |
| Social conditions | 2.81 | 1.02 | |
| Market | Market conditions | 3.35 | 0.75 |
| Technological | Equipment reliability and construction methods | 3.04 | 0.82 |
Comparison with previous approaches to uncertainty assessment in construction projects and this study’s contribution_
| Study | Approach/method | Project scale | Data nature | Limitation | Study contribution |
|---|---|---|---|---|---|
| Ali et al. (2018) | Expert-based RII | Public infrastructure | Five-item Likert scale from domain experts | Subjective weighting; limited empirical validation | Provides baseline prioritisation for public-sector risk budgeting; relies on expert weighting rather than empirical inference |
| Shabani et al. (2023) | Narrative search and semi-structured expert interviews | Public road projects | Expert-informed categorisation | Subjective categorisation; limited replicability across contexts | Enhanced understanding of contextual, operational and strategic uncertainty through expert narratives |
| Erol et al. (2022) | ANP model with a two-round Delphi process | Mega construction projects | Domain experts weighting | Subjective weighting and limited applicability to MSMEs | Risk quantification model for mega construction projects |
| Ulupui et al. (2024) | Partial Least Squares and RA for ARI | Multisector MSMEs from Indonesia | Five-item Likert scale from MSME representatives | Applied to MSMEs, but not the construction industry explicitly | Framework for quantifying the interactions of technological, organisational and environmental risk dimensions among MSMEs |
| Our approach | RF feature importance and CTs | Construction MSMEs from Colombia | Empirical survey data combined with class-preserving synthetic augmentation for small-sample modelling | Strategic-level focus; does not capture operational dynamics | First interpretable, machine-learning framework modelling ten internal and external uncertainty sources in construction MSMEs |