In this study, we distinguished between quantity and quality aspects of team reflection and examine how they relate to team performance improvement. We hypothesized that teams that reflect little but deeply and thoroughly show greater performance improvements than teams that reflect a great deal but on a superficial level. In addition, we examined the extent to which team performance will improve if teams engage in both quality and quantity in reflection in different extents, and whether implementation explained additional variance in team performance. We examined these issues in a sample of 46 three-person teams in a labbased hidden profile setting, using a repeated measure design. The results from Bayesian structural equation modeling confirmed our hypotheses. In addition, polynomial regression revealed that performance improved most when teams focused exclusively on the quality of team reflection and weakest when teams tried to engage in quality and quantity of reflection in the same extent.

Using an event‐triggered multi‐stage framework, this random‐effects meta‐analysis examined the changes in applicants' perceptions of fairness between consecutive stages and throughout the entire personnel selection process. We integrated findings of studies with at least two measurement points, resulting in 45 effect sizes (overall N = 3,038). Trajectories of perceptions of fairness decreased nonlinearly across the process, with a steeper decrease for people who held high levels of initial fairness expectations. Unjust treatment produced a decrease in perceptions of fairness from pretest to posttest and an increase from posttest to postdecision. Furthermore, the length of the time interval moderated the changes in fairness perceptions between the posttest and postdecision stage. Practical implications and an agenda for future research are discussed.

Abstract. Although there has long been consensus in team research that planning generally has a positive impact on performance, very little is known about how input factors (e. g., situational factors) affect the planning behavior of teams. In addition, the various dimensions of planning remain largely unexplored. In this study, we examine the effects of time pressure, task routine, and decision importance on team planning. We suggest that planning consists of four dimensions: exploration, strategic planning, detailed planning, and prognosis. In two policy-capturing studies, undergraduates and employees were presented with a series of hypothetical scenarios and asked to indicate in each case how they might plan for these if working as part of a team. Results from our Bayesian multilevel analyses revealed that teams overall used less planning when they were under acute time pressure, when tasks were very routine, and when the decisions involved were of little importance.

Objective: The recurring phase model of team processes suggests the existence of a rhythm of team task accomplishment, which refers to a repeated sequence of transition and action phases over time. Drawing on this model, we provide the first empirical investigation of whether different types of teamwork rhythm emerge, whether the rhythm varies according to the type of task, and whether the rhythm is related to team performance. Method: We observed and videoed student teams (N = 48) working on two different tasks (a creative task and a construction task) in a laboratory setting. Team processes were coded and assigned to transition or action phases using a custom algorithm. The rhythm of teamwork for each team was determined using the four parameters of tempo, regularity of tempo, focus (transition vs. action), and variability of focus. Results: Latent profile analysis revealed three distinct rhythms of teamwork across both tasks: a slow and action-oriented rhythm, a fast and regular rhythm, and a changingfocus rhythm. The results also show that the majority of the teams (63.04%) changed rhythm type between the tasks. Moreover, for the creative task, a changing-focus rhythm was predictive of lower performance (g = 0.25-0.48), whereas for the construction task, no association was found between rhythm and performance. Conclusions: The study provides a methodological procedure for analyzing the rhythm of teamwork and offers some initial insights into the types of teamwork rhythms and their association with type of tasks and levels of performance. Highlights and Implications• We establish a methodological procedure for capturing the rhythm of teamwork, which emerges as a repeated sequence of transition and action phases when teams are working on a task.• Teams do not generally follow a single rhythm of transition and action phases but adapt teamwork rhythm according to the task they are working on.• Teamwork rhythm and team performance seem to be related in creative tasks.• Teams should be made aware of the importance of different rhythms of teamwork and trained to use different rhythms according to the demands of the particular task and situation.

We meta-analyzed the relationship between team planning and performance moderated by task, team, context, and methodological factors. For testing our hypothesized model, we used a meta-analytic structural equation modeling approach. Based on K = 33 independent samples (N = 1,885 teams), a mixed-effects model indicated a non‐zero moderate positive effect size (ρ = .31, 95% CI [.20, .42]). Methodological quality, generally rated as adequate, was unrelated to effect size. Sensitivity analyses suggest that effect sizes were robust to exclusion of any individual study and publication bias. The statistical power of the studies was generally low and significantly moderated the relationship, with a large positive relationship for studies with high-powered (k = 42, ρ = .40, 95% CI [.27, .54]) and a smaller, significant relationship for low-powered studies (k = 54, ρ = .16, 95% CI [.01, .30]). The effect size was robust and generally not qualified by a large number of moderators, but was more pronounced for less interdependent tasks, less specialized team members, and assessment of quality rather than quantity of planning. Latent class analysis revealed no qualitatively different subgroups within populations. We recommend large‐scale collaboration to overcome several methodological weaknesses of the current literature, which is severely underpowered, potentially biased by self-reporting data, and lacks long-term follow-ups.