Construction competitive bidding has been studied by many researchers; however, their focus was mainly on certain bidding aspects. Thus, despite their value, existing bidding models have limited applicability for various reasons including the non-inclusion of some real-life factors that impact bidding-related decisions. As such, there is a need for identifying a comprehensive list of bidding factors that impact construction bidding-related decisions, studying their associations, and identifying understudied factors to direct future research efforts. This paper fills this knowledge gap. First, the authors conducted a systematic selection of 124 construction bidding-related articles published within the past 40 years. Second, the authors identified a list of 43 bidding factors from the contractor's perspective and divided them into four groups (project, bidding environment, economy, and company) based on their characteristics and relevance. Thereafter, the authors implemented cluster analysis and social network analysis (SNA) to quantitatively analyze the level of inclusion, co-occurrence, and interconnectivity among the bidding factors and identify the gaps in the literature. Results of cluster analysis revealed that previous studies focused mainly on company-related factors, while economy-related factors were the least studied. Also, SNA outcomes highlighted that experience in similar project and availability and costs of rental labor/equipment/material are the most studied factors in theoretical discussions, while number of competitors and size of the project are the most investigated factors in developed models. Consequently, this paper identified understudied bidding factors, including company reputation level in the market, bidding method, bidding stages, and contractor's risk attitude. Thus, this paper contributes to the body of knowledge by promoting a comprehensive understanding of bidding-related factors and providing a road map for incorporating critical understudied bidding-related factors. This would minimize redundancies and maximize the effectiveness of future bidding-related research. Ultimately, this should provide better decision-making for all associated stakeholders.