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Frimpong, S, Sunindijo, R Y, Wang, C C, Boadu, E F, Dansoh, A, Hon, C K H and Yiu, T W (2024) Promoting positive mental health among young construction workers: the role of theory. Construction Management and Economics, 42(04), 366–85.
Hamerski, D C, Saurin, T A, Formoso, C T and Isatto, E L (2024) The contributions of the Last Planner System to resilient performance in construction projects. Construction Management and Economics, 42(04), 328–45.
Hickey, P J and Cui, Q (2024) Tracing the career trajectories of architecture, engineering and construction (AEC) women leaders. Construction Management and Economics, 42(04), 289–306.
Morland, K V and Breslin, D (2024) Resolving learning paradoxes within a UK new-build housebuilder. Construction Management and Economics, 42(04), 307–27.
Nyqvist, R, Peltokorpi, A and Seppänen, O (2024) Uncertainty network modeling method for construction risk management. Construction Management and Economics, 42(04), 346–65.
- Type: Journal Article
- Keywords: Uncertainty; risk management; project management; networks; modeling; visual management; collaboration; complexity; systems thinking;
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/01446193.2023.2266760
- Abstract:
In recent decades, uncertainty management has increasingly elicited attention in construction management research due to increasing project complexity. However, existing management methods have not been able to solve the issues around risk and uncertainty, and regardless of the proposed network-based risk modeling approaches, there are insufficiencies in contemporary methods, such as their practical applicability. This study examined the current state and issues of uncertainty and risk management and proposed a novel uncertainty network model (UNM) as a solution. The uncertainty network model was designed and validated using design science methodology (DSM), drawing on literature and empirical data from interviews, questionnaires, case observations, and case testing. The UNM visually presents project risks, uncertainties, and their interconnections and criticality transforming project stakeholders’ tacit knowledge into an explicit, systematic representation of a project’s uncertainty and risk architecture. Applied to a real-world construction project, the model received positive feedback, demonstrating its effectiveness in enhancing practitioners’ understanding of networked risks and the potential to guide cost-effective risk-control activities by applying a systemic lens to project management. This practical validation showcases the model’s potential in addressing the shortcomings of existing methods and improving construction project risk management.