Measuring project performance by applying social network analyses

It is often argued that the core of organizational success is efficient collaboration. Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual skills or expertise. However, the lack of efficient models to manage collaboration properly is a major constraint for organizations to profit from internal and external collaborative initiatives. Currently, much of the collaboration in organizations occurs through virtual network channels, such as e-mail, Yammer, Jabber, Microsoft Teams, Skype, and Zoom. These are even more important in situations where different time zones and even threats of a pandemic constrain face-to-face human interactions. This work introduces a multidisciplinary heuristic model developed based on project risk management and social network analysis centrality metrics graph-theory to quantitatively measure dynamic organizational collaboration in the project environment. A case study illustrates the proposed model's implementation and application in a real virtual project organizational context. The major benefit of applying this proposed model is that it enables organizations to quantitatively measure different collaborative, organizational, and dynamic behavioral patterns, which can later correlate with organizational outcomes. The model analyzes three collaborative project dimensions: network collaboration cohesion evolution, network collaboration degree evolution, and network team set variability evolution. This provides organizations an innovative approach to understand and manage possible collaborative project risks that may emerge as projects are delivered. Organizations can use the proposed model to identify projects' critical success factors by comparing successful and unsuccessful delivered projects' dynamic behaviors if a substantial number of both project types are analyzed. The proposed model also enables organizations to make decisions with more information regarding the support for changes in observed collaborative patterns as demonstrated by statistical models in general, and linear regressions in particular. Further, the proposed model provides organizations with a completely bias-free data-collection process that eliminates organizational downtime. Finally, applying the proposed model in organizations will reduce or eliminate the risks associated with virtual collaborative dynamics, leading to the optimized use of resources; this will transform organizations to become more lean-oriented and significantly contribute to economic, social, and environmental global sustainability.