[
  {
    "id": "10.1504/IJPOM.2025.146727",
    "type": "article-journal",
    "title": "Exploring project manager commitment using machine learning on fuzzy big data",
    "author": [
      {
        "family": "Strang",
        "given": "Kenneth David"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      }
    ],
    "container-title": "International Journal of Project Organisation and Management",
    "issued": {
      "date-parts": [
        [
          2025
        ]
      ]
    },
    "volume": "17",
    "issue": "2",
    "page": "135-152",
    "publisher": "Inderscience",
    "ISSN": "1740-2891",
    "DOI": "10.1504/IJPOM.2025.146727",
    "URL": "https://www.narasimharao.net/research/project-manager-commitment-machine-learning-big-data/",
    "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.",
    "keyword": "project management, big data analysis, talent retention, project failure rates, predictive modelling, unstructured data, behavioural analysis, human resources metrics, machine learning, ML, organisational commitment, organizational commitment, project manager tenure, project failure, big data, IT projects, US military projects, linear regression, random forest, support vector machine, multi-dimensional scaling, predictive analytics",
    "language": "en"
  }
]