Our work has culminated in the development of Dynamic Network Theory (DNT) (Westaby, 2012; Westaby & Parr, 2020; Westaby, Pfaff, & Redding, 2014; etc.). The theory not only describes and explains the dynamics underlying complex systems, it also aims to predict and understand the way social networks impact goal pursuit, performance, emotional reactions, and climates in various social, organizational, and international domains, including those experiencing serious conflict. Our network goal analyses provide a new way to visualize how social networks impact goal pursuits in specific cases. To compare how this approach differs from traditional organizational charts and social network analysis, click here.
Individuals have used the theory to understand their general life networks, work networks, or specific goals, such as getting jobs, starting businesses, losing weight, running marathons, quitting tobacco, and improving performance in various contexts. The areas of potential application are nearly limitless, as network goal analysis can be applied to any goal or behavior.
In contrast to the classic study of social network structure alone, dynamic network theory examines how a taxonomy of only 8 social network roles can explain the complex ways in which networks wield their influence on our goals, dreams, and aspirations. What is unique about the approach is that goal nodes are directly inserted into social networks so see the connection to the goal. This can be visualized through our creation of network goal analysis (Westaby & Parr, 2020; Westaby & Shon, 2017). This terminology is synonymous or interchangeable with our original use of dynamic network chart terminology (Westaby, 2012; Westaby et al., 2014), but helps further clarify that our approach explicitly examines social networks in relation to target goals, as compared to traditional social network analysis that describes the relationship between social network entities alone, which is also important, depending on the research and practice question.
Theoretically, the 8 social network roles show how people are involved in goal or behavioral pursuits, such as by helping or hindering the process, which in turn is predicted to impact emergent outcomes, such as performance, climates, and system well-being. Our analyses demonstrate how these links function through our computer visualization tools. These visualizations are generated after participants engage in our network goal surveys online, or through our algorithms that apply DNT concepts to massive online behavior.
The 8 social network roles:
See our publications for more details that provide the empirical evidence that supports each role (e.g., Chapter 2, Westaby, 2012).
The following figure visually illustrates some of the presumed underlying causal processes in the theory, many of which have scientific support. This figure builds from the recent publication in the American Psychologist, the flagship journal of the American Psychological Association: Dynamic network theory (Westaby, Pfaff, & Redding, 2014). This figure represents more recent theorizing in our Lab.
What Activates the Roles?
Dynamic network theory states that people use various judgement and decision-making processes that activate each social network role (or multiplex combination of the roles). This then impacts important emergent outcomes in dynamic network systems, such as performance, emotional reactions, and climates.
If you are interested in learning more about validated psychological models that can be used to predict each social network role (e.g., why someone decides to goal strive, goal prevent, or support others in their goals), please see our section on reasoning underlying the roles in DNT for a brief overview of behavioral reasoning theory (Westaby, 2005; Westaby et al., 2010). This section demonstrates the reasoning, attitudinal, and intention processes theorized to underlie social network role activations. This is a contemporary behavioral intention theory that inserts reasons and justifications into the process.
What Else Does DNT Explain?
Our American Psychologist article provides a good overview: Dynamic network theory (Westaby, Pfaff, & Redding, 2014) . However, please note that our current conceptualization of system reactance (R) in our surveys and visualization tools have been modified to examine constructive ways to try to resolve conflicts. Moreover, we now use color-coded links with letters to denote the roles in our new analytics, instead of the solid black or dashed paths shown in this article and in Westaby's (2012) original formulation of dynamic network theory. These changes improve our computer visualizations of complex systems, making complex systems easier to visualize and understand. These more recent applications can be seen in our recent empirical tests of the theory: Network goal analysis paper (Westaby & Parr, 2020).
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