Our research examines human decision making and behavioral prediction as well as how social networks impact goals in complex systems. Given the breadth and applicability of our methods, our applications include topics at the individual, team, group, organizational, and international levels.
Click below to see some of our Our Current Research or Sample Publications for more information.
The following sample references are related to our work in the Dynamic Network Lab.
Westaby, J. D., Pfaff, D. L., & Redding, N. (2014). Psychology and social networks: A dynamic network theory perspective. American Psychologist, 69, 269-284. (Good overview article of our theory - flagship journal of the APA) (Note: Our newer conceptualizing modifies system reactance (R), given the new focus on constructive reactions to conflict, such as illustrated in following Westaby and Parr, 2020, article).
Westaby, J. D., & Parr, A. K. (2020). The network goal analysis of social and organizational systems: Testing dynamic network theory in complex social networks. Journal of Applied Behavioral Science, 56(1), 107-129. Click here for copy: Network goal analysis paper (Westaby & Parr, 2020)(Good article for researchers wanting to conduct and visualize network analytics using our advanced NGA tools)
Westaby, J. D. (2012). Dynamic network theory: How social networks influence goal pursuit. Washington, DC: American Psychological Association. (The original theory - good for examining many details about DNT's multidisciplinary grounding - group researchers can also examine dedicated chapter to the topic that's not addressed in the American Psychologist article). (Note: Our newer conceptualizing modifies system reactance (R), given the new focus on constructive reactions to conflict).
Westaby, J. D., Woods, N., & Pfaff, D. L. (2016). Extending dynamic network theory to group and social interaction analysis: Uncovering key behavioral elements, cycles, and emergent states. Organizational Psychology Review, 6, 34-62. (Good article to understand live social interactions without need for self-report survey)(Note: Our newer conceptualizing modifies system reactance (R), given the new focus on constructive reactions to conflict).
Westaby, J. D. (2020). Modeling massive social network problem solving via network goal analysis vs social network analysis. Proceedings form the Academy of Management Conference. Publication submission version in progress with Eccho Yu. (Good for examining online behavior and Big Data from a DNT perspective; no self-report surveys are needed).
Westaby, J. D., & Parr, A. K. (2018). Using network goal analysis to explain complex systems: New advances in visualization and analytics. Paper Accepted for Presentation at the North American Social Network Conference, Washington DC, November 30, 2018.
Lace, A., Westaby, J. D., & Coleman, P. T. (2019). Using network goal analysis to explain complex systems: New advances in visualization and analytics. Paper Accepted for Presentation at International Association for Conflict Management.
(If interested in manual graphing): Westaby, J.D. (2012):The following online resource, published with the main book above, uses old labeling from the book: G' is now P in our charts. S' is now V. R' is now N: Click here to go to the online supplemental resource A from APA. Also, it is important to note that system reactance (R) is now turned into representing constructive reactions to others if there is conflict.
Topical Applications of DNT
Westaby, J. D., & Shon, D. (2017). Simulating the Social Networks in Human Goal Striving. In R. R. Vallacher, S. J., Read, & A. Nowak (Eds.), Computational models in social psychology (1st ed.). pp. 231-257. New York, NY: Psychology Press (Frontiers of Psychology series). Recommendation: Unless you're interested in technical computional example methods, you can skip the middle section to see broader new theoretical implications (i.e., focus on the intro and latter parts of the paper).
Westaby, J. D., & Echtenkamp, A. (2017). Humor and Organizational Networks: Functions and Dysfunctions. In C. Robert (Ed.), Humor in the workplace (1st ed.). pp. 45-59. Routledge.
Westaby, J. D., & Redding, N. (2014). Social networks, social media, and conflict resolution. In P.T. Coleman, M. Deutsch, & E.C. Marcus (Eds.), The handbook of conflict resolution: Theory and practice (3rd ed.). pp. 998-1022. San Francisco, CA: Jossey-Bass.
These articles are relevant for those interested in micro behavioral intention models to predict specific behaviors. (These are not as systems-oriented):
Westaby, J. D. (2005). Behavioral Reasoning Theory: Identifying New Linkages Underlying Intentions and Behavior. Organizational Behavior and Human Decision Processes, 98, 97-120. (This is the scientific paper that introduced the theory that received considerable application across numerous behavioral contexts by external researchers).
Westaby, J. D. Probst, T. M., & Lee, B.C. (2010). Leadership Decision-Making: A Behavioral Reasoning Theory Analysis. Leadership Quarterly, 21, 481-495. This paper aimed to empirically test and validate BRT.
Wagner, M., & Westaby, J. D. (2020). Changing Pay Systems in Organizations: Using Behavioral Reasoning Theory to Understand Employee Support for Pay-for-Performance (or Not). Journal of Applied Behavioral Science, 56(3), 301-321. Click here for copy: Wagner and Westaby (2020) Winner, McGregor Best Paper Award
Westaby, J. D., Versenyi, A., & Hausmann, R. C. (2005). Intentions to Work During Terminal Illness: An Exploratory Study of Antecedent Conditions. Journal of Applied Psychology, 90, 1297-1305.
Westaby, J. D., & Lowe, J. K. (2005). Risk taking orientation and injury among youth workers: Examining the social influence of supervisors, coworkers, and parents. Journal of Applied Psychology, 90, 1297-1305.
Lee, B. C., Westaby, J. D., & Berg, D. (2004). Impact of a National Rural Youth Health and Safety Initiative: Results from a Randomized Controlled Trial. American Journal of Public Health, 94, 1743 -1749.
Order of authorship may vary depending on total writing and methodological contributions in each study. Additional author inclusions may apply.
Network goal analysis in work settings
Westaby and Parr
Multiple goal striving and feedback linkages in dynamic network theory
Parr and Westaby
Personality and network goal pursuit
Rosemarino and Westaby
New advances in behavioral reasoning theory: Predicting behavior, decision quality, and regret
Westaby and colleagues
Methodological application of network goal analysis
Parr and Westaby
The big 5 personality constructs and leadership decision making: Explaining mediational mechanisms through behavioral reasoning theory
Rosemarino and Westaby
The network goal analysis of massive online communities: A multiple goal node analysis
Westaby and Yu
Individual and group decision making: A dynamic network and behavioral reasoning approach
Mah and Westaby
Behavioral reasoning theory and remote working during the pandemic
Zlupko and Westaby
The network dynamics of unethical behavior: The Bernie Madoff case
Rosemarino and Westaby
Case study analysis of international conflict: Using network goal analysis to examine the Northern Ireland conflict
Lace, Westaby, Coleman, and DNL staff
Teacher goal striving in complex social networks: Applying network goal analysis in dynamic network theory
Hibbard, Westaby, and DNL Staff
The dynamic networks involved in coping with depression: A new network goal survey approach
Westaby, Sardana, Verdeli, and Parr
Counseling interventions among refugees: Using network goal analysis to examine outcome change
Verdeli, Sardana, Westaby, and Parr
Ph.D. level researchers in full-time Professorial or Director-level positions familiar with our theorizing and network goal approach can contact us to inquire about our process for potential collaboration in research projects or case studies using our surveys, computer visualizations, and advanced statistics. Basic costs typically apply to cover research time, processing fees, and the use of our computer program tools (e.g., Qualtrics, R-shiny, etc.).
For those seriously interested in potential research collaboration, please contact us through the link shown below and provide the requested information.
This will be used to gauge the initial feasibility of the Lab's involvement. If there is a potential fit and availability, we would further discuss the study's design and focus, the ideal integration of our surveys and analyses, and the likely base cost involved for deliverable outputs. If these communications continue to show a potential fit, a budget and contract would be drafted and agreements confirmed.
Our goal in such collaboration is to advance knowledge, publish impactful articles in rigorous scientific journals, and/or generate insights that help create positive change in people's lives. All collaborations would adhere to APA ethical guidelines.
The number of collaboration partners may be limited by staff availability.