To use social network, motivation, and decision science to improve individual, social, and organizational systems.
The lab combines social network analysis, motivation, and decision-making approaches to more fully explain how social networks wield their power on important targets: goals, important decisions, specific behaviors, performance, climates, and system well-being. Largely grounded in dynamic network theory (DNT), network goal analysis (NGA) aims to portray these system dynamics at individual, group, organizational, or international levels, including those with conflict. NGA can also be used with other theoretical approaches, not just DNT, whenever social networks and goals (or target behaviors) are the focus. In contrast, we also use behavioral reasoning theory (BRT) to examine how people's reasoning and counter-argument processes within the network help trigger motivated goal striving and behavior at the individual levels. Utilizing both DNT and BRT together is presumed to provide a richer, yet highly operational understanding of system behavior with direct implications for strategic change to improve system functioning.
Our approach inserts goal nodes into social networks to provide new insights about system functioning (Westaby and colleagues). Goals nodes are inherently motivational and can represent many important things, such as goals, missions, objectives, important decisions, intentions, wishes, needs, dreams, or target behaviors. Thus, inserting goal nodes into network structures has the capacity to help explain system functioning across diverse human experiences.
Network goal analyis (NGA) examines how social networks pivot around important goals with theoretically determined motivational linkages. In contrast, traditional social network analysis (SNA) focuses on social structural linkages without explict links to goal nodes. However, the approaches are complementary, depending on the scientific or practice question.
The Lab's surveys, computer visualizations, and reports allow each sub-network to be examined separately to better understand complex systems. It breaks down the complexity into meaningful chunks to maximize meaning and understanding. We also combine the concepts to provide overall system-level summaries of the networks, such as their positive focus ratio and network affirmation ratio. Out decision tools also provide feedback as to how people may be leaning in their decisions, based upon specific reasoning, counter-arguments, confidence, and information search.
Researchers engaged in rigorous projects examining important target goals, decisions, behaviors, or general life or work networks can contact us to inquire about using our decision-making calculators and/or network goal surveys and computer visualizations.
Fall, 2020, Columbia University, New York, NY
Summer, 2020, Academcy of Management
Click here for general theory paper: Dynamic network theory (Westaby, Pfaff, & Redding, 2014)
Click here for recent network goal analysis (NGA) publication: Network goal analysis paper (Westaby & Parr, 2020)
Click here for more publications: Research