# Project success and failure: evidence on project managers, cognitive bias and team transparency

Research synthesis · Project management & project success
Canonical page: https://www.narasimharao.net/research/focus-areas/project-management-success-failure/
Author: Narasimha Rao Vajjhala, Professor and Chair of Computer Science, American University in Bulgaria (ORCID 0000-0002-8260-2392)
Updated: 2026-09-25

> Strang and Vajjhala show, with machine learning on project big data and controlled experiments, that IT project outcomes depend on the project manager — experience, risk competency, resistance to incentive bias and commitment — and on team transparency during procurement.

## Synthesis

Kenneth David Strang and Narasimha Rao Vajjhala have combined machine learning on project big data with controlled experiments to ask why IT projects succeed or fail. Their central finding is that outcomes depend heavily on the project manager: experience, risk competency, resistance to biased incentives and organizational commitment.

Mining big data on about 17,430 US government IT projects, [Strang and Vajjhala (2023a)](https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/) used random forest classification to identify seven failure indicators with 79.9% precision, 81% recall and a ROC area of 0.849; post-hoc logistic regression confirmed project manager experience, project budget and outsourced versus in-house project managers as significant predictors. Turning to successful projects, analyses of 439 US government IT projects found that linear regression best predicted project manager commitment tenure (R² = 0.238), with line of business, years of experience and team network collaboration among the strongest predictors ([Strang & Vajjhala, 2025b](https://www.narasimharao.net/research/project-manager-commitment-machine-learning-big-data/); [Vajjhala & Strang, 2024a](https://www.narasimharao.net/research/machine-learning-project-manager-organizational-commitment-big-data/)).

Two controlled experiments isolated the role of cognitive bias. When 16 experienced US IT project managers were offered a 400% bonus to continue a simulated failing military software project, their likelihood of cancelling it fell from 3.438 to 2.438 on a 1–5 scale (p = .005, Cohen’s d = 0.826), and risk competency — not certification — was the significant protective factor ([Strang & Vajjhala, 2022a](https://www.narasimharao.net/research/project-manager-bias-failing-project-termination-experiment/)). With 24 project managers facing a COVID-19 risk event, bias again lowered decision quality, and certification and competency were the only individual factors significantly related to good decisions ([Strang & Vajjhala, 2022b](https://www.narasimharao.net/research/project-manager-risk-decision-bias-crisis-experiment/)).

Team transparency also matters. In Fintech projects, [Strang and Vajjhala (2025a)](https://www.narasimharao.net/research/project-team-past-performance-disclosure-fintech-project-success/) found that team members’ willingness to disclose past performance evaluations during procurement significantly improved project success after controlling for budget, end-user community size and certification, raising explained variance from 9.6% to 18.8%. Vajjhala and Strang also edited a volume on risk and contingency management during COVID-19 and other crises across nations and industries ([Vajjhala & Strang, 2022](https://www.narasimharao.net/research/global-risk-contingency-management-research-times-of-crisis/)).

For practitioners, the evidence points to selecting and developing project managers for risk competency and experience, designing incentives that do not reward persisting with failing projects, and building transparency about past performance into team selection.

## Key claims with sources

1. Random forest analysis of about 17,430 US government IT projects identified seven failure indicators, led by project manager experience ([Strang & Vajjhala, 2023a](https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/)).
2. A 400% bonus incentive significantly lowered experienced project managers’ likelihood of cancelling a failing project (Cohen’s d = 0.826) ([Strang & Vajjhala, 2022a](https://www.narasimharao.net/research/project-manager-bias-failing-project-termination-experiment/)).
3. Willingness to disclose past performance raised the explained variance in project success from 9.6% to 18.8% ([Strang & Vajjhala, 2025a](https://www.narasimharao.net/research/project-team-past-performance-disclosure-fintech-project-success/)).

## References

- Strang, K. D., & Vajjhala, N. R. (2022a). How Bias Impacted the Project Manager Decision to Not Terminate a Failing Project. International Journal of Information Technology Project Management, 13(1), 1–17. https://doi.org/10.4018/IJITPM.304059 — summary and key findings: https://www.narasimharao.net/research/project-manager-bias-failing-project-termination-experiment/
- Strang, K. D., & Vajjhala, N. R. (2022b). Testing Risk Management Decision Making Competency of Project Managers in a Crisis. The Journal of Modern Project Management, 10(1), 52–71. https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/JMPM02904 — summary and key findings: https://www.narasimharao.net/research/project-manager-risk-decision-bias-crisis-experiment/
- Strang, K. D., & Vajjhala, N. R. (2023a). Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods. International Journal of Information Technology Project Management, 14(1), 1–24. https://doi.org/10.4018/IJITPM.317221 — summary and key findings: https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/
- Strang, K. D., & Vajjhala, N. R. (2025a). Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success?. Information, 16(11), 955. https://doi.org/10.3390/info16110955 — summary and key findings: https://www.narasimharao.net/research/project-team-past-performance-disclosure-fintech-project-success/
- Strang, K. D., & Vajjhala, N. R. (2025b). 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 — summary and key findings: https://www.narasimharao.net/research/project-manager-commitment-machine-learning-big-data/
- Vajjhala, N. R., & Strang, K. D. (2024a). An Exploratory Big Data Approach to Understanding Commitment in Projects. In Á. Rocha (Ed.), WorldCIST 2024 (World Conference on Information Systems and Technologies) (pp. 66–75). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-60227-6_6 — summary and key findings: https://www.narasimharao.net/research/machine-learning-project-manager-organizational-commitment-big-data/
- Vajjhala, N. R., & Strang, K. D. (Eds.). (2022). Global Risk and Contingency Management Research in Times of Crisis. IGI Global. https://doi.org/10.4018/978-1-6684-5279-0 — summary and key findings: https://www.narasimharao.net/research/global-risk-contingency-management-research-times-of-crisis/