[
  {
    "id": "10.4018/IJITPM.317221",
    "type": "article-journal",
    "title": "Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods",
    "author": [
      {
        "family": "Strang",
        "given": "Kenneth David"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      }
    ],
    "container-title": "International Journal of Information Technology Project Management",
    "issued": {
      "date-parts": [
        [
          2023,
          2,
          3
        ]
      ]
    },
    "volume": "14",
    "issue": "1",
    "page": "1-24",
    "publisher": "IGI Global",
    "DOI": "10.4018/IJITPM.317221",
    "URL": "https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/",
    "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.",
    "keyword": "Big Data, Information Technology, Machine Learning, Model, Prediction, Project Failure, Project Management, Random Forest, machine learning, random forest, project failure, IT project management, big data, mixed methods, logistic regression, project success factors, project manager experience, U.S. government projects, defense projects, data mining, predictive analytics",
    "language": "en"
  }
]