Title: Exploring project manager commitment using machine learning on fuzzy big data

Authors: Kenneth David Strang; Narasimha Rao Vajjhala

Addresses: W3-Research, St. Thomas District, USVI 00802, USA; University of the Cumberlands, Williamsburg, KY 40769, USA ' Faculty of Engineering and Architecture, University of New York Tirana, Tirana, Albania

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.

Keywords: project management; big data analysis; talent retention; project failure rates; predictive modelling; unstructured data; behavioural analysis; human resources metrics; machine learning; ML; organisational commitment.

DOI: 10.1504/IJPOM.2025.146727

International Journal of Project Organisation and Management, 2025 Vol.17 No.2, pp.135 - 152

Received: 07 Jan 2024
Accepted: 09 Aug 2024
Published online: 16 Jun 2025 *

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