Due to the complex and dynamic atmosphere in the construction sector, different types of challenges are faced by project managers (PMs). These challenges potentially have negative impacts on the PM’s managerial performance, which mostly leads to budget and schedule contingencies. In this vein, scrutinizing the main challenges in a construction project and identifying the cause-and-effect relationships among these challenges is a crucially important process. In the literature, a considerable number of papers have tried to determine construction PM challenges, mainly using statistical methods. These methods do not consider the cause-and-effect relationship among variables. To enhance the existing methods, this paper applies social network analysis (SNA) principles in order to rank a group of variables based on cause-and-effect relationships. To demonstrate the proposed idea, a data set is constructed that includes different types of challenges acquired from the literature comprehended with the forward-chaining approach. In total, 49 critical challenges were identified and subsequently categorized into 12 groups. Two questionnaires were designed to assist in ranking the challenges. 108 construction experts and 20 panelists participated in this study, and the acquired data were used to evaluate the proposed SNA-based method. By applying the proposed method to the obtained data, a complex weighted and directed network is constructed and examined by three metrics: weighted in-degree centrality, betweenness centrality, and closeness centrality. The results revealed that poor planning, contractors’/subcontractors’ financial difficulties, and poor decision making are the main challenges that occur in the construction environment. Moreover, it was figured out that considering the cause-and-effect relationship among variables resulted in a highly different ranking of challenges, much closer to the real situation. This model could be used in quantitative-analytical research conducted in the construction project knowledge area in order to obtain more interpretable answers.

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