At the turn of the century, Arrow, McGrath, and Berdahl portrayed teams as complex adaptive systems (CAS). And yet, despite broad agreement that this approach facilitates a better understanding of teams, it has only now been timidly incorporated into team research. To help fully incorporate the logic of teams as CAS in the science of teams, we review extant research on teams approached from a nonlinear dynamical system theory. Using a systematic review approach, we selected 92 articles published over the last 17 years to integrate what we know about teams as CAS. Our review reveals the evidence supporting teams as CAS, and the set of analytical techniques to analyze team data from this perspective. This review contributes to teams’ theory and practice by offering ways to identify both research methods and managing techniques that scholars and practitioners may apply to study and manage teams as CAS.

As a result of the growing interest in studying employee well‐being as a complex process that portrays high levels of within‐individual variability and evolves over time, this present study considers the experience of flow in the workplace from a nonlinear dynamical systems approach. Our goal is to offer new ways to move the study of employee well‐being beyond linear approaches. With nonlinear dynamical systems theory as the backdrop, we conducted a longitudinal study using the experience sampling method and qualitative semi‐structured interviews for data collection; 6981 registers of data were collected from a sample of 60 employees. The obtained time series were analyzed using various techniques derived from the nonlinear dynamical systems theory (i.e., recurrence analysis and surrogate data) and multiple correspondence analyses. The results revealed the following: 1) flow in the workplace presents a high degree of within‐individual variability; this variability is characterized as chaotic for most of the cases (75%); 2) high levels of flow are associated with chaos; and 3) different dimensions of the flow experience (e.g., merging of action and awareness) as well as individual (e.g., age) and job characteristics (e.g., job tenure) are associated with the emergence of different dynamic patterns (chaotic, linear and random). Copyright © 2011 John Wiley & Sons, Ltd.

Work-related flow is defined as a sudden and enjoyable merging of action and awareness that represents a peak experience in the daily lives of workers. Employees’ perceptions of challenge and skill and their subjective experiences in terms of enjoyment, interest and absorption were measured using the experience sampling method, yielding a total of 6981 observations from a sample of 60 employees. Linear and nonlinear approaches were applied in order to model both continuous and sudden changes. According to the R², AICc and BIC indexes, the nonlinear dynamical systems model (i.e. cusp catastrophe model) fit the data better than the linear and logistic regression models. Likewise, the cusp catastrophe model appears to be especially powerful for modelling those cases of high levels of flow. Overall, flow represents a nonequilibrium condition that combines continuous and abrupt changes across time. Research and intervention efforts concerned with this process should focus on the variable of challenge, which, according to our study, appears to play a key role in the abrupt changes observed in work-related flow.

This manifesto presents 10 recommendations for a sustainable future for the field of Work and Organizational Psychology. The manifesto is the result of an emerging movement around the Future of WOP (see www.futureofwop.com), which aims to bring together WOP-scholars committed to actively contribute to building a better future for our field. Our recommendations are intended to support both individuals and collectives to become actively engaged in co-creating the future of WOP together with us. Therefore, this manifesto is open and never “finished.” It should continuously evolve, based on an ongoing debate around our professional values and behavior. This manifesto is meant, first of all, for ourselves as an academic community. Furthermore, it is also important for managers, decision makers, and other stakeholders and interested parties, such as students, governments and organizations, as we envision what the future of WOP could look like, and it is only through our collective efforts that we will be able to realize a sustainable future for all of us.

Workgroup diversity can be conceptualized as variety, separation, or disparity. Thus, the proper operationalization of diversity depends on how a diversity dimension has been defined. Analytically, the minimal diversity must be obtained when there are no differences on an attribute among the members of a group, however maximal diversity has a different shape for each conceptualization of diversity. Previous work on diversity indexes indicated maximum values for variety (e.g., Blau's index and Teachman's index), separation (e.g., standard deviation and mean Euclidean distance), and disparity (e.g., coefficient of variation and the Gird coefficient of concentration), although these maximum values are not valid for all group characteristics (i.e., group size and group size parity) and attribute scales (i.e., number of categories). We demonstrate analytically appropriate upper boundaries for conditional diversity determined by some specific group characteristics, avoiding the bias related to absolute diversity. This will allow applied researchers to make better interpretations regarding the relationship between group diversity and group outcomes.

The aims of this study are to consider the experience of flow from a nonlinear dynamics perspective. The processes and temporal nature of intrinsic motivation and flow, would suggest that flow experiences fluctuate over time in a dynamical fashion. Thus it can be argued that the potential for chaos is strong. The sample was composed of 20 employees (both full and part time) recruited from a number of different organizations and work backgrounds. The Experience Sampling Method (ESM) was used for data collection. Once obtained the temporal series, they were subjected to various analyses proper to the complexity theory (Visual Recurrence Analysis and Surrogate Data Analysis). Results showed that in 80% of the cases, flow presented a chaotic dynamic, in that, flow experiences delineated a complex dynamic whose patterns of change were not easy to predict. Implications of the study, its limitations and future research are discussed.