Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods

Kenneth David Strang and Narasimha Vajjhala
Additional contact information
Kenneth David Strang: RMIT, Australia & W3 Research, USA

International Journal of Information Technology Project Management (IJITPM), 2023, vol. 14, issue 1, 1-24

Abstract: The literature revealed approximately 50% of IT-related projects around the world fail, which must frustrate a sponsor or decision maker since their ability to forecast success is statistically about the same as guessing with a random coin toss. Nonetheless, some project success/failure factors have been identified, but often the effect sizes were statistically negligible. A pragmatic mixed methods recursive approach was applied, using structured programming, machine learning (ML), and statistical software to mine a large data source for probable project success/failure indicators. Seven feature indicators were detected from ML, producing an accuracy of 79.9%, a recall rate of 81%, an F1 score of 0.798, and a ROCa of 0.849. A post-hoc regression model confirmed three indicators were significant with a 27% effect size. The contributions made to the body of knowledge included: A conceptual model comparing ML methods by artificial intelligence capability and research decision making goal, a mixed methods recursive pragmatic research design, application of the random forest ML technique with post hoc statistical methods, and a preliminary list of IT project failure indicators analyzed from big data.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITPM.317221 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jitpm0:v:14:y:2023:i:1:p:1-24

Access Statistics for this article

International Journal of Information Technology Project Management (IJITPM) is currently edited by John Wang

More articles in International Journal of Information Technology Project Management (IJITPM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().