[
  {
    "id": "10.1007/978-3-031-60227-6_6",
    "type": "paper-conference",
    "title": "An Exploratory Big Data Approach to Understanding Commitment in Projects",
    "author": [
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      },
      {
        "family": "Strang",
        "given": "Kenneth David"
      }
    ],
    "editor": [
      {
        "literal": "Á. Rocha"
      }
    ],
    "container-title": "WorldCIST 2024 (World Conference on Information Systems and Technologies)",
    "collection-title": "Lecture Notes in Networks and Systems",
    "issued": {
      "date-parts": [
        [
          2024
        ]
      ]
    },
    "volume": "989",
    "page": "66-75",
    "publisher": "Springer Nature Switzerland AG",
    "DOI": "10.1007/978-3-031-60227-6_6",
    "URL": "https://www.narasimharao.net/research/machine-learning-project-manager-organizational-commitment-big-data/",
    "abstract": "This study addresses the twin challenges of talent retention and high project failure rates (40–70%) by harnessing machine learning (ML) techniques to analyze retrospective big data. The study’s objective was to ascertain whether project performance indicators can be a reliable gauge of project manager (PM) organizational commitment. This approach sidesteps the inherent bias and small effect sizes associated with survey self-report responses. Our innovative methodology leverages secondary big data, transforming the values into structured features that predict PM organizational commitment. This study proposes a novel conceptual framework, focusing on actual behavioral evidence rather than traditional, self-reported attitudes to assess the fuzzy predictors of organizational commitment. Among the three developed ML models, one demonstrated a significant 24% effect size, uncovering key features correlating PM tenure and organizational commitment with success. The insights gained from this research have broad implications for global stakeholders in projects and programs, offering a more objective and big data-driven understanding of PM commitment.",
    "keyword": "Project Management, Organizational Commitment, Machine Learning, Big Data Analysis, Talent Retention, Project Failure Rates, Data-Driven Management, Predictive Modeling, Stakeholder Implications, Program Management, Performance Indicators, machine learning, big data, project management, organizational commitment, project manager tenure, talent retention, project success, IT projects, U.S. military projects, linear regression, random forest, support vector machine, multi-dimensional scaling, secondary data",
    "language": "en"
  }
]