My primary work focuses on understanding how individuals and teams build knowledge, make decisions, and enact behaviors to accomplish personal and collective goals. My research philosophy is heavily inspired by the science of complex systems, which suggests that the psychological and social phenomena we observe emerge from how actions, relationships, perceptions, events, and contexts unfold within and between people over time in an environment.
Examples of my specific research interests include how individuals working within teams gather, share, and interpret information to make decisions and accomplish tasks; how the behaviors, communication, and regulatory efforts (e.g., leadership, influence) among individuals affects individual and collective outcomes; and how judgment/information processing influence individual learning and assessment outcomes. I rely on a variety of methodologies and data sources to study these topics, including behavioral observation, experimental studies, and computational modeling/simulation.
Beyond my professional interests, I love spending time with my wife and our two kids, hiking and outdoor activities, watching and remembering when I used to actually be able to play sports, playing board games, and learning about computers, space, and other nerdy stuff.
PhD in Organizational Psychology, 2012
Michigan State University
MA in Organizational Psychology, 2008
Michigan State University
BA in Psychology (Summa Cum Laude, Minor in Business Administration), 2006
Auburn University, Auburn AL
I explore how the judgments, choices, and behaviors of individuals unfold over time and give rise to unique patterns of psychological, social, and organizational outcomes.
I teach and present on topics related to organizational psychology, judgment & decision-making, and research methods for the social and organizational sciences.
The recognition of teams as complex dynamic systems was a hallmark and among the earliest considerations of research on team functioning. However, the popularization of conceptual heuristics such as the input-process-outcome (IPO) framework and the accessibility of methodological, analytical, and meta-theoretical principles from multilevel theory (MLT) have resulted in a disconnect between contemporary theory and empirical research on teams and this foundational perspective. Thus, the primary motivation for the present paper is to facilitate and stimulate future research on team phenomena that embraces systems thinking. To do so, we describe key concepts, terminology, and ideas from specific branches of the systems sciences—namely open systems theory, dynamical systems, and agent-based systems—that have direct relevance for researching team phenomena as complex systems. Additionally, a comparison between two example models of team performance that are rooted in an IPO+MLT versus a systems-oriented perspective is offered to highlight the difference in foci, applications, and inferences these approaches offer. The paper concludes with a summary of key advantages as well as potential obstacles for reintroducing systems-thinking back into team science.
Situational judgment tests (SJTs) have emerged as a staple of assessment methodologies for organizational practitioners and researchers. Despite their prevalence, many questions regarding how to interpret respondent choices or how variations in item construction and instruction influence the nature of observed responses remain. Existing conceptual and empirical efforts to explore these questions have largely been rooted in reflexive psychometric measurement models that describe participant responses as indicative of (usually multiple) latent constructs. However, some have suggested that a key to better understanding SJT responses lies in unpacking the judgment and decision-making processes employed by respondents and the psychological and contextual factors that shape how those processes play out. To this end, the present paper advances an integrative and generalizable process-oriented theory of SJT responding. The framework, labeled Situated Reasoning and Judgment (SiRJ), proposes that respondents engage in a series of conditional reasoning, similarity, and preference accumulation judgments when deciding how to evaluate and respond to an SJT item. To evaluate the theory’s plausibility and utility, the SiRJ framework is translated into a formal computational model and results from a simulation study are analyzed using neural network and Bayesian survival analytic techniques that demonstrate its capability to replicate existing and new empirical effects, suggest insights into SJT interpretation and development, and stimulate new directions for future research. An interactive web application that allows users to explore the computational model developed for SiRJ https://grandjam.shinyapps.io/sirj as well as all reported data and the full model/simulation code https://osf.io/uwdfm/ are also provided.
Team cognition has been identified as a critical component of team performance and decision-making. However, theory and research in this domain continues to remain largely static; articulation and examination of the dynamic processes through which collectively held knowledge emerges from the individual- to the team-level is lacking. To address this gap, we advance and systematically evaluate a process-oriented theory of team knowledge emergence. First, we summarize the core concepts and dynamic mechanisms that underlie team knowledge-building and represent our theory of team knowledge emergence (Step 1). We then translate this narrative theory into a formal computational model that provides an explicit specification of how these core concepts and mechanisms interact to produce emergent team knowledge (Step 2). The computational model is next instantiated into an agent-based simulation to explore how the key generative process mechanisms described in our theory contribute to improved knowledge emergence in teams (Step 3). Results from the simulations demonstrate that agent teams generate collectively shared knowledge more effectively when members are capable of processing information more efficiently and when teams follow communication strategies that promote equal rates of information sharing across members. Lastly, we conduct an empirical experiment with real teams participating in a collective knowledge-building task to verify that promoting these processes in human teams also leads to improved team knowledge emergence (Step 4). Discussion focuses on implications of the theory for examining team cognition processes and dynamics as well as directions for future research.