Journal article · 2025
Exploring project manager commitment using machine learning on fuzzy big data
International Journal of Project Organisation and Management, 17(2), pp. 135–152 · Published
Summary
Strang and Vajjhala (2025) analysed retrospective secondary big data on 439 successful US government IT projects with 1,230 features, in an exploratory machine learning study of which project attributes predict project manager organisational commitment, measured as tenure with the same employer. Linear regression outperformed random forest and SVM in predicting commitment tenure. Line of business, years of experience, team network collaboration type, cross-industry subject matter and contract status were the strongest predictors.
Practical implication. Organisational decision-makers can use behavioural big data to predict project manager commitment tenure and to support data-driven talent retention strategies.
- The Problem
- Project manager organizational commitment is usually measured with self-report surveys, and little is known about which project attributes predict it in real project data.
- The Methodology
- Exploratory machine learning study of retrospective secondary big data (439 successful US government IT projects, 1,230 features): multidimensional scaling, silhouette scores, and linear regression, random forest and SVM regression with ten-fold cross-validation.
- The Core Finding
- Across 439 successful US government IT projects with 1,230 features, linear regression outperformed random forest and SVM in predicting project manager commitment tenure (R² = 0.238 versus 0.108 and 0.047). Line of business, years of experience, team network collaboration type, cross-industry subject matter and contract status were the strongest predictors.
- The Citation
- Strang, K. D., & Vajjhala, N. R. (2025). Exploring project manager commitment using machine learning on fuzzy big data. International Journal of Project Organisation and Management, 17(2), 135–152. https://doi.org/10.1504/IJPOM.2025.146727
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The study analysed a final sample of 439 successful US government IT projects, drawn from 1,230 features collected on over 500 projects during 2015–2023, using project manager tenure with the same employer as a proxy for organisational commitment.
- Finding 2. Average project manager commitment tenure in the sample was 11.08 years (median 11), ranging from 1 to 25 years; the average project team size was 24 (SD = 11.4) and the mean final project budget was $29,919,153.
- Finding 3. Among the machine learning regression models, linear regression performed best with an R² of 0.238 (MSE = 40.957, RMSE = 6.3998, MAE = 5.3051), compared with random forest (R² = 0.108) and SVM (R² = 0.047), against a baseline R² of –0.001.
- Finding 4. Classification-based clustering of the ten best features produced weak results, with silhouette scores ranging only from 0.18 to 0.2, which justified moving to machine learning regression.
- Finding 5. The linear regression model identified 18 features as significant indicators (p < 0.05) of project manager commitment tenure, led by line of business (relative relief coefficient 0.36), experience (0.29), network collaboration type (0.28), cross-industry project subject matter (0.27) and contract status (0.22).
- Finding 6. Scatter-plot analysis showed more Jira/kanban collaboration in US Air Force projects where project managers had lower commitment tenure, while other lines of business collaborated more through SharePoint document sharing than virtual communications or email.
- Finding 7. The authors conclude that project manager commitment tenure in successful projects is related to line of business, years of experience, team network collaboration type, cross-industry subject matter and contract versus on-staff status, with about a 25% effect size in the best model.
Source: Strang & Vajjhala (2025), International Journal of Project Organisation and Management, 17(2), pp. 135–152. DOI: 10.1504/IJPOM.2025.146727
The study in detail
What question does this paper answer?
Is a project manager’s organisational commitment (measured as tenure with the same employer) in successful projects related to other project attributes, and which attributes predict that commitment tenure?
What did the study find, in detail?
Analysing 439 successful US government (military) IT projects from 2015–2023 with 1,230 features, the authors found that linear regression was the best of three machine learning models, explaining 23.8% of the variance in project manager commitment tenure (MSE = 40.957, MAE = 5.3051), versus 10.8% for random forest and 4.7% for SVM. Line of business (relative relief 0.36), years of experience (0.29), team network collaboration type (0.28), cross-industry subject matter (0.27) and contract status (0.22) were the strongest predictors.
Why does it matter?
The study shows that secondary, unstructured big data about actual behaviour can be turned into predictive factors of project manager commitment, bypassing speculative survey-based perceptions. The authors argue this offers organisational decision-makers robust, data-driven strategies to improve talent retention and project success rates.
This study is part of the evidence overview What predicts IT project failure and success? Evidence on project managers, bias and team transparency from machine learning and experiments
Methods and data
| Research question | RQ1: Is project managers’ organisational commitment in successful projects related to other project attributes? RQ2: Which attributes predict project manager commitment tenure? |
|---|---|
| Design | Exploratory, pragmatic machine learning study of retrospective secondary big data; 90% confidence level set for statistical tests |
| Data | Declassified US government big data repository of IT projects (mainly Army, Navy and Air Force); 1,230 features from over 500 projects in 2015–2023, cleaned to 439 successful projects |
| Methods | Radial exploratory analysis, multi-dimensional scaling, silhouette scores, linear regression, random forest and SVM regression with ten-fold cross-validation, built in R, R Studio and Python |
| Main result | Linear regression performed best (R² = 0.238, MAE = 5.3051); random forest R² = 0.108; SVM R² = 0.047; 18 features were significant predictors (p < 0.05) |
| Implication | Behavioural big data can predict project manager commitment tenure and support data-driven talent retention and project success strategies |
| Citation | Strang & Vajjhala (2025) · DOI 10.1504/IJPOM.2025.146727 |
Definitions and key terms
- Organisational commitment
- An employee’s psychological attachment and loyalty to the organisation (Meyer and Allen, 1991), comprising affective, continuance and normative commitment. — as used in Strang & Vajjhala (2025)
- Commitment tenure
- In this study, the number of years a project manager worked for the same employer, used as a proxy for organisational commitment. — as used in Strang & Vajjhala (2025)
- Coefficient of determination (R²)
- The proportion of variance in the target variable explained by a model’s input features, used here as the effect size for comparing machine learning models. — as used in Strang & Vajjhala (2025)
Abstract
This study addresses two critical organisational challenges: retaining human talent and reducing high project failure rates. Our approach diverges from traditional methods by employing machine learning (ML) to analyse retrospective big data. This study’s innovation lies in utilising secondary, unstructured data to derive predictive factors of a project manager’s (PM) commitment, moving away from the speculative nature and limited impact of survey-based perceptions. We developed a new conceptual framework that focuses on actual behaviour rather than espoused theories to identify fuzzy predictors of organisational commitment. Based on three distinct ML models, our findings reveal that one model showed a notable 25% effect size, highlighting various features connected to a PM’s tenure and organisational commitment. These insights have broad implications, offering valuable global knowledge for stakeholders in projects and programs. This study underscores the significance of non-traditional data sources in understanding and predicting critical human resource metrics, opening new avenues for organisational research and decision-making.
Abstract as published in International Journal of Project Organisation and Management.
Keywords: project management; big data analysis; talent retention; project failure rates; predictive modelling; unstructured data; behavioural analysis; human resources metrics; machine learning; ML; organisational commitment
Limitations and open questions
- There was no reliable way to know that 100% of the IT projects were carried out by military employees or subcontractors.
- More than one project could exist in the data for the same project manager and this could not be checked, so the data had to be treated as sampling with replacement.
- Only successful projects were examined in this exploratory phase; future studies could examine project manager commitment in failed projects.
- The MDS clusters suggest additional, unobserved features act together, and much more research would be needed to explore the collaboration-type patterns.
Open questions
- How does project manager commitment tenure behave in failed projects, since only successful projects were examined?
Open questions from the evidence overview Project management and project success.
Practical implications
- Organisational decision-makers can turn secondary, unstructured big data about actual behaviour into predictive factors of project manager commitment, rather than relying on survey-based perceptions, to inform talent retention and project success strategies.
- For organisational decision-makers, project manager commitment tenure in 439 successful US government IT projects was related to line of business, years of experience, team network collaboration type, cross-industry subject matter and contract versus on-staff status.
- For researchers comparing linear and ensemble regression on high-dimensional organisational data, linear regression performed best on 439 successful US government IT projects, ahead of random forest and SVM.
- For researchers studying project manager retention and tenure, only successful projects were examined in the exploratory phase, and future studies could examine project manager commitment in failed projects.
Drawn from Strang & Vajjhala (2025), International Journal of Project Organisation and Management, 17(2), pp. 135–152.
When this research may be relevant
This article may be relevant to researchers studying project manager organisational commitment, retention and tenure, the use of machine learning and big data in project management research, predictors of project success in government or defence IT projects, and comparisons of linear versus ensemble regression on high-dimensional organisational data.
Research topics addressed: machine learning; project management; organizational commitment; project manager tenure; talent retention; project failure; big data; IT projects; US military projects; linear regression; random forest; support vector machine; multi-dimensional scaling; predictive analytics
How to cite
- APA 7
- Strang, K. D., & Vajjhala, N. R. (2025). Exploring project manager commitment using machine learning on fuzzy big data. International Journal of Project Organisation and Management, 17(2), 135–152. https://doi.org/10.1504/IJPOM.2025.146727
- MLA 9
- Strang, Kenneth David, and Narasimha Rao Vajjhala. “Exploring project manager commitment using machine learning on fuzzy big data.” International Journal of Project Organisation and Management, vol. 17, no. 2, 2025, pp. 135–52. https://doi.org/10.1504/IJPOM.2025.146727.
- IEEE
- K. D. Strang and N. R. Vajjhala, “Exploring project manager commitment using machine learning on fuzzy big data,” International Journal of Project Organisation and Management, vol. 17, no. 2, pp. 135–152, 2025, doi: 10.1504/IJPOM.2025.146727.
BibTeX
@article{strang2025projectmanager,
title = {Exploring project manager commitment using machine learning on fuzzy big data},
author = {Strang, Kenneth David and Vajjhala, Narasimha Rao},
journal = {International Journal of Project Organisation and Management},
volume = {17},
number = {2},
pages = {135--152},
year = {2025},
publisher = {Inderscience},
doi = {10.1504/IJPOM.2025.146727},
url = {https://doi.org/10.1504/IJPOM.2025.146727}
}RIS
TY - JOUR TI - Exploring project manager commitment using machine learning on fuzzy big data AU - Strang, Kenneth David AU - Vajjhala, Narasimha Rao JO - International Journal of Project Organisation and Management PY - 2025 DA - 2025 VL - 17 IS - 2 SP - 135 EP - 152 PB - Inderscience SN - 1740-2891 DO - 10.1504/IJPOM.2025.146727 UR - https://www.narasimharao.net/research/project-manager-commitment-machine-learning-big-data/ LA - en ER -
Links and files
Related work
Related work: the literature this study cites
The work frames commitment through Meyer and Allen’s affective, continuance and normative components[1] and positions itself after earlier predictive uses of machine learning in project control, such as Wauters and Vanhoucke’s support vector machine regression for forecasting[2].
- [1] Meyer, J.P. and Allen, N.J. (1991) ‘A three-component conceptualization of organizational commitment’, Human Resource Management Review, Vol. 1, No. 1, pp.61–89.
- [2] Wauters, M. and Vanhoucke, M. (2014) ‘Support vector machine regression for project control forecasting’, Automation in Construction, Vol. 47, No. 1, pp.92–106.
From the evidence overview What predicts IT project failure and success? Evidence on project managers, bias and team transparency from machine learning and experiments, which cites only works that appear in the studies’ own reference lists. This is a selection, not the full reference list of Strang & Vajjhala (2025).
Related research
Related publications on project management and project success
Supervised machine learning on 439 successful US government IT projects showed linear regression best predicted project manager commitment tenure (r² = 0.238), with line of business, experience, network collaboration tools and cross-industry team membership as the strongest features — supporting an association between higher commitment and project success.
Random forest analysis of about 17,430 US government IT projects identified seven failure indicators with 81% recall and a ROC area of 0.849.
A 400% bonus incentive significantly lowered 16 experienced IT project managers’ likelihood of cancelling a failing project (from 3.438 to 2.438 on a 1–5 scale; p = .005, Cohen’s d = 0.826).
Team members’ willingness to disclose their past performance evaluations during procurement significantly improved project success after controlling for budget, end-user community size and certification, roughly doubling the explained variance in project success from 9.6% to 18.8%.
In a controlled experiment with 24 experienced US project managers, a biasing incentive significantly lowered decision quality, and certification and risk competency were the only individual factors significantly related to good decisions (rho = 0.549 to 0.712).