Organizational scholars commonly refer to organizations as complex systems unfolding as a function of work processes. Consequently, the direct study of work processes necessitates our attention. However, organizational scholars tend not to study work processes directly. Instead, organizational scholars commonly develop theories about relationships among psychological construct phenomena that indirectly reference people’s affective, behavioral, cognitive, and/or social processes as underlying explanations. Specifically, construct-oriented theories summarize processes in operation across actors, time, and contexts, and thus, provide limited insights into how focal phenomena manifest directly as a function of process operations. Construct theories remain one-step removed from articulating sequences of actions and two-steps removed from describing generative mechanisms responsible for observed actions. By “missing the action,” construct theories offer incomplete explanatory accounts and imprecise interventions. We assert that researchers in organizational science can make progress towards addressing these concerns by directing greater attention to developing computational process theories. We begin by presenting a framework for differentiating theories based on their focus (constructs versus processes) and modality (narrative versus computational). We use the framework to contrast narrative construct theories to computational process theories. We then describe key design principles for developing computational process theories and explain those principles using a leadership example. We use simulated data, from the computational process model we develop, to explicitly demonstrate the differences between construct and process thinking. We then discuss how computational process theories advance theory development. We conclude with a discussion of the long-term benefits of computational process theories for organizational science.

Computational modeling holds significant promise as a tool for improving how theory is developed, expressed, and used to inform empirical research and evaluation efforts. However, the knowledge and skillsets needed to build computational models are rarely developed in the training received by social and organizational scientists. The purpose of this manuscript is to provide an accessible introduction to and reference for building computational models to represent theory. We first discuss important principles and recommendations for “thinking about” theory and developing explanatory accounts in ways that facilitate translating their core assumptions, specifications, and ideas into a computational model. Next, we address some frequently asked questions related to building computational models that introduce several fundamental tasks/concepts involved in building models to represent theory and demonstrate how they can be implemented in the R programming language to produce executable model code. The accompanying supplemental materials describes additional considerations relevant to building and using computational models, provides multiple examples of complete computational model code written in R, and an interactive application offering guided practice on key model-building tasks/concepts in R.

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.

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.