Sources and patterns of uncertainty in construction MSMEs: A machine learning study in southwestern Colombia

Cristian David Tobar Montilla; Mariela Muñoz-Añasco; Adriana M. Nieto-Muñoz; Elvia Ruiz-Beltran

doi:10.2478/otmcj-2026-0005

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Sources and patterns of uncertainty in construction MSMEs: A machine learning study in southwestern Colombia

Organization, Technology and Management in Construction: an International Journal

Volume 18 (2026): Issue 1 (January 2026)

By: Cristian David Tobar Montilla, Mariela Muñoz-Añasco, Adriana M. Nieto-Muñoz and Elvia Ruiz-Beltran

Open Access

|May 2026

Figures & Tables

Classification of uncertainty sources in construction projects.
*Source:* (Hazr and Ulusoy 2020).

Data processing pipeline.
*Source:* Own 2025. CT, classification tree.

Tab. 1:

Validity of controlled data expansion strategy across internal and external sources of uncertainty.

Uncertainty source	Lower uncertainty class			Higher uncertainty class			Total observations
Uncertainty source	Original sample number	Augmented sample number	Lowest p-value across all variables	Original sample number	Augmented sample number	Lowest p-value across all variables	Total observations
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

Bootstrap-based feature importances (1,000 iterations) for internal sources of uncertainty in construction projects (a) Feature importance of organisational Unc. (b) Feature importance of activity durations Unc. (c) Feature importance of resource use Unc. (d) Feature importance of changes in Req. & Qual. Unc. (e) Feature importance of resource availability Unc.
*Source:* Own, 2025.

CT with the highest frequency across bootstrap samples for internal uncertainty sources in construction projects (a) Most frequent CT for organisational uncer. (frequency: 200/1000) (b) Most frequent CT for activity durations uncer. (frequency: 230/1000) (c) Most frequent CT for resource use uncer. (frequency: 42/1000) (d) Most frequent CT for changes in Req. & Qual. Uncer. (frequency: 139/1000) (e) Most frequent CT for resource availability uncer. (frequency: 35/1000).
*Source:* Own, 2025. CT, classification tree.

Tab. 2:

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

1 ISIC, international standard industrial classification of all economic activities; SD, standard deviation.

Tab. 3:

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
Environmental	Adverse geographic conditions	3.65	1.60
Sociopolitical	Policies and regulations	3.50	1.39
Sociopolitical	Social conditions	3.27	1.48
Market	Market conditions	4.27	1.56
Technological	Equipment reliability and construction methods	3.42	1.33

1* Likert scale from 1–6: 1 – never, 2 – very rarely, 3 – rarely, 4 – often, 5 – very often, 6 – always.

1 SD, standard deviation.

Tab. 4:

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
Environmental	Adverse geographic conditions	2.92	0.98
Sociopolitical	Policies and regulations	2.85	0.88
Sociopolitical	Social conditions	2.81	1.02
Market	Market conditions	3.35	0.75
Technological	Equipment reliability and construction methods	3.04	0.82

1* Likert scale 1–4: 1 – very small, 2 – minor, 3 – moderate, 4 – significant.

1 SD, standard deviation.

Bootstrap-based feature importances (1,000 iterations) for external sources of uncertainty in construction projects (a) Feature importance of logistics Unc. (b) Feature importance of environmental Unc. (c) Feature importance of sociopolitical Unc. (d) Feature importance of market Unc. (e) Feature importance of technological Unc.
*Source:* Own, 2025.

CT with the highest frequency across bootstrap samples for external uncertainty sources in construction projects (a) Most frequent CT for logistics uncer. (frequency: 268/1,000). (b) Most frequent CT for environmental uncer. (frequency: 83/1,000). (c) Most frequent CT for sociopolitical uncer. (frequency: 71/1,000). (d) Most frequent CT for market uncer. (frequency: 626/1,000). (e) Most frequent CT for technological uncer. (frequency: 241/1,000).
*Source:* Own, 2025. CT, classification tree.

Tab. 5:

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
Uncertainty source	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

1 Rules indicate conditions leading to the leaf node with the highest percentage of companies classified as experiencing high uncertainty.

1 CT, classification trees; NIDI, mean decrease in impurity.

Tab. 6:

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

1 ANP, analytic network process; ARI, adoption risk identification; CTs, classification trees; MSMEs, micro, small and medium-sized enterprises; RA, relative advantage; RF, random forest; RII, relative importance index.

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DOI: https://doi.org/10.2478/otmcj-2026-0005 | Journal eISSN: 1847-6228 | Journal ISSN: 1847-5450

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Language: English

Page range: 64 - 81

Submitted on: Sep 9, 2025

Accepted on: Jan 7, 2026

Published on: May 26, 2026

Published by: University of Zagreb

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

uncertainty,

MSMEs,

construction projects,

machine learning

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2026 Cristian David Tobar Montilla, Mariela Muñoz-Añasco, Adriana M. Nieto-Muñoz, Elvia Ruiz-Beltran, published by University of Zagreb
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 18 (2026): Issue 1 (January 2026)

Sources and patterns of uncertainty in construction MSMEs: A machine learning study in southwestern Colombia

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Paradigm

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