In the last decade, it has been increasingly recognized that efforts to address project delays without understanding the relationships among risk factors (RFs) could be futile. In light of the limitations of previous research that has addressed this issue, this study seeks to contribute to the literature by developing a novel weighted fuzzy social network analysis (SNA)-based approach, which accounts for the likelihood of occurrence and the impact of RFs on one another. To validate its practicality, the approach is applied to a demonstrative study in which the causes of delays in real-world electrical installation projects were modeled and analyzed. In addition to providing a holistic view of relationships among RFs, the proposed approach enables engineering managers to identify the root causes of delays and their corresponding critical propagation paths. Compared to other approaches in the literature, the value of the approach lies in its simplicity and utility for supporting engineering managers in developing effective risk-mitigation plans to minimize the severity of project delays