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Previous research asserts that teams working in routine situations pass through performance episodes characterized by action and transition phases, while other evidence suggests that certain team behaviors significantly influence team effectiveness during nonroutine situations. We integrate these two areas of research—one focusing on the temporal n...
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... patterns may also be extremely difficult to detect with alternative procedures such as Markov chains (see Magnusson, 2000). Similarly, techniques such as log-linear modeling (Olson, Herbsleb, & Rueter, 1994), while powerful tools for analyzing thousands of cells repre- senting sequential interactions, do not ignore intervening behaviors not included in patterns (see Figure 1). The THEME algorithm identifies patterns in sequential data by using three steps. ...
Citations
... Considering that extreme action teams are often formed ad hoc, a long-term view is often very difficult to adopt due to the high rotation that such teams often encounter (Grote et al., 2018). Research has uncovered positive impacts of a variety of team processes and adaptations on performance in aviation, healthcare, police, and firefighter teams (Grote, Kolbe, Zala-Mezo, Bienefeld-Seall, & Künzle, 2010;Lei, Waller, Hagen, & Kaplan, 2016;Marques-Quinteiro, Curral, Passos, & Lewis, 2013;Schmutz, Meier, & Manser, 2019). In contrast to the definitions provided previously, studies investigating extreme action teams, such as emergency teams or disaster teams, do not explicitly include a hostile environment at all in their definitions (Klein et al., 2006;Power, 2018;Schmutz et al., 2018). ...
... If we consider a crew of astronauts and researchers tasked to undertake a mission to Mars, we could assume that the creation of ideas in team problem-solving would be facilitated by as many different ideas and perceptions as possible (Friedman, Friedman, & Leverton, 2016). However, too many inter-individual differences may lead to conflict or coordination breakdowns as uncertainty increases and the team experiences stress under conditions of high team extremeness, such as would be experienced during a Mars mission (Landon et al., 2018;Lei et al., 2016). ...
Work teams increasingly face unprecedented challenges in volatile, uncertain, complex, and often ambiguous environments. In response, team researchers have begun to focus more on teams whose work revolves around mitigating risks in these dynamic environments. Some highly insightful contributions to team research and organizational studies have originated from investigating teams that face unconventional or extreme events. Despite this increased attention to extreme teams, however, a comprehensive theoretical framework is missing. We introduce such a framework that envisions team extremeness as a continuous, multidimensional variable consisting of environmental extremeness (i.e., external team context) and task extremeness (i.e., internal team context). The proposed framework allows every team to be placed on the team extremeness continuum, bridging the gap between literature on extreme and more traditional teams. Furthermore, we present six propositions addressing how team extremeness may interact with team processes, emergent states, and outcomes using core variables for team effectiveness and the well-established input–mediator–output–input model to structure our theorizing. Finally, we outline some potential directions for future research by elaborating on temporal considerations (i.e., patterns and trajectories), measurement approaches, and consideration of multilevel relationships involving team extremeness. We hope that our theoretical framework and theorizing can create a path forward, stimulating future research within the organizational team literature to further examine the impact of team extremeness on team dynamics and effectiveness.
... In the context of entrepreneurship research, the effects of planning at the organizational level were found to be heterogeneous, indicating no uniform direction [10,11]. Studies in other contexts also provide further evidence that there can indeed be negative relationships between team planning and team performance (e.g., [12,13]). Consequently, the third aim of this study is to advance understanding of team planning by identifying classes of positive and non-positive effects and specifying the conditions under which they occur. ...
... Although team planning has been suggested to have a positive effect on task completion, team processes, emergent states, and performance [3,7,19], researchers have found there to be substantial heterogeneity in team planning effects and have also shown planning to have some detrimental effects (e.g., [12,13]). This suggests unobserved heterogeneity in the sample, where different patterns of effect sizes may belong to different subpopulations and in classes not captured by observable characteristics [43]. ...
... In fact, the time-consuming and resource-intensive nature of planning is often seen to be at odds with enhancing performance; planning is regarded as a distraction from goal-directed actions and as a reason why resources become depleted. Lei and colleagues [12] provided empirical evidence of this; they showed that planning activities initially increased team performance, but too much planning had adverse effects on task work and subsequent performance. This implies that different patterns of relationships might be expected. ...
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.
... When responding to complex and changing task demands, control crews often need to change their coordination-behavior patterns, which refer to stable sets of team coordination behaviors that co-occur repeatedly in the process of accomplishing collective goals (Kolbe et al., 2014;Lei et al., 2016;Wang et al., 2020). The most commonly used method to study coordination-behavior patterns is behavior observation and analysis (Manser et al., 2008;Wang et al., 2017). ...
... Coordination-behavior patterns vary substantially from industrial fields and performance levels (Kolbe et al., 2014;Lei et al., 2016). For instance, in the aviation domain, Lei et al. (2016) found that high-performing aviation pilot teams had higher complexity of coordination-behavior patterns and more shifts in directions of team interactions; in the healthcare domain, high-performing teams were found to exhibit more information-exchanging and cooperative behaviors, as well as more adaptive behaviors to meet others' needs (Kolbe et al., 2009;Manser et al., 2008;Riethmüller et al., 2012). ...
... Coordination-behavior patterns vary substantially from industrial fields and performance levels (Kolbe et al., 2014;Lei et al., 2016). For instance, in the aviation domain, Lei et al. (2016) found that high-performing aviation pilot teams had higher complexity of coordination-behavior patterns and more shifts in directions of team interactions; in the healthcare domain, high-performing teams were found to exhibit more information-exchanging and cooperative behaviors, as well as more adaptive behaviors to meet others' needs (Kolbe et al., 2009;Manser et al., 2008;Riethmüller et al., 2012). ...
This study aims to investigate the coordination‐behavior patterns of control crews in digital nuclear power plants (NPPs) during emergencies from a network perspective. We observed and coded 12 coordination processes (each from one crew) of handling simulated emergencies on a full‐scope dynamic simulator in Tianwan NPP of China. By calculating the proportion of coordination breakdowns and referring to the subjective evaluation of instructors, these control crews (all male) were classified into two performance levels (high and low). To compare the coordination‐behavior patterns between high‐ and low‐performing crews, we conducted social network analysis based on the number and direction of coordination behaviors. By examining intracrew relations in team coordination, this study indicated that high‐performing crews exhibited higher cohesion and more balanced behavioral patterns in team coordination than low‐performing ones. By investigating coordination‐behavior patterns from the hierarchical structure of relations in the team, this study revealed that high‐performing crews exhibited higher degree of team autonomy and self‐management among junior operators, as well as reactor operator (RO)‐centered pattern, whereas the low‐performing crews exhibited senior reactor operator‐centered pattern. For researchers, the results advance the understanding of the team coordination mechanism in NPP control rooms during emergencies and enrich team coordination theory in process control industries from the network perspective. In practice, this study suggested that the team coordination training of NPP control crews might be centered around junior operators (especially RO), with focuses on facilitating information sharing and mutual assistance between junior operators and enhancing the awareness of active cooperation of crew members. The results of the study provide nuclear instructors with practical reference to team coordination training and processes optimization.
... Even though the two musical tasks given to Group 2 had different levels of demand, there may have been a sense in which from the second rehearsal both became more 'routine' , as the group knew what to expect, whereas Group 1 were operating in what could be described as a more knowledgeintensive, non-routine environment as they were consistently exposed to new experiences and material. In such a context, there is a demand for more sharing and interpretation of complex information amongst team members (Kozlowski and Bell, 2003), with greater need for exchange of ideas and development of a shared understanding of the changing task environment (Lei et al., 2016). Looking at complexity of tasks in teams, Hoogeboom and Wilderom (2019) found a stronger effect of participative team interaction patterns in a nonroutine (versus routine) task context. ...
... Interaction patterns generally increased in complexity over time. This is consistent with research in other dynamic work situations, where teams demonstrated increasing pattern complexity (Lei et al., 2016;Uitdewilligen et al., 2018;Hoogeboom and Wilderom, 2019). This emergent behaviour may have been impacted by the setting; whilst the groups were rehearsing independently, they were working within the framework provided by a higher education institution. ...
Ensemble rehearsal in the European classical music tradition has a relatively homogenised format in which play-through, discussion, and practice of excerpts are employed to establish and agree on performance parameters of notated music. This research analyses patterns in such verbal communication during rehearsals and their development over time. Analysing two newly established ensembles that work over several months to a performance, it investigates the interaction dynamics of two closely collaborating groups and adaptation depending on task demands, familiarity with each other and an upcoming deadline. A case study approach with two groups of five singers allowed in-depth exploration of individual behaviours and contributions; results are reported descriptively and supported by qualitative data. The results highlight changes over time that reflect the development of implicit (faster decisions) interactions from explicit (slower decisions). They show a trajectory of opening up and closing down in terms of interactional flexibility, enabling members to significantly contribute to the group, followed by tightening the interaction to establish stability for performance. These findings and novel employment of T-pattern analysis contribute to the understanding of human group behaviour and interaction patterns leading to expert team performance.
... As we contend, intelligent teams engage in (adaptive) collective behaviors by matching their interaction patterns to fit the nature of the environment (Waller, 1999;Lei et al., 2016), orwhere feasible -actively shape the environment to develop a match with the collective behavior (Ancona, 1990;Marks et al., 2005). We note that we conceptualize "environment" broadly and consider both internal and external environmental demands: the team needs to deal with 'challenges' of what happens either outside or inside the team boundary (Maloney et al., 2016;Johns, 2018). ...
... The team environment not only shapes which collective behavior is more or less intelligent, but teams can often shape their environment as well. Although most studies consider environmental demands as requiring modified collective behavior from the team (e.g., Gersick, 1988;Waller, 1999;Lei et al., 2016), some studies consider how the team reaches out to its environment to potentially modify external and/or internal needs (Ancona, 1990;Marks et al., 2005). Teams can reduce uncertainty by negotiating malleable environmental conditions, for example proactively increasing resources by lobbying for additional human capital to manage the team's workload. ...
Collective intelligence (CI) in organizational teams has been predominantly understood and explained in terms of the quality of the outcomes that the team produces. This manuscript aims to extend the understanding of CI in teams, by disentangling the core of actual collective intelligent team behavior that unfolds over time during a collaboration period. We posit that outcomes do support the presence of CI, but that collective intelligence itself resides in the interaction processes within the team. Teams behave collectively intelligent when the collective behaviors during the collaboration period are in line with the requirements of the (cognitive) tasks the team is assigned to and the (changing) environment. This perspective results in a challenging, but promising research agenda armed with new research questions that call for unraveling longitudinal fine-grained interactional processes over time. We conclude with exploring methodological considerations that assist researchers to align concept and methodology. In sum, this manuscript proposes a more direct, thorough, and nuanced understanding of collective intelligence in teams, by disentangling micro-level team behaviors over the course of a collaboration period. With this in mind, the field of CI will get a more fine-grained understanding of what really happens at what point in time: when teams behave more or less intelligently. Additionally, when we understand collectively intelligent processes in teams, we can organize targeted interventions to improve or maintain collective intelligence in teams.
... Like preplanning, in-process planning encompasses the iterative process of recognizing problems, gathering data, generating ideas, and evaluating and choosing a course of action (Lei et al., 2016). What distinguishes in-process planning is that it: (1) allows teams who do not have a preexisting plan to develop a plan (Weingart, 1992); (2) allows teams with no or little task familiarity to allocate time during task performance for planning (Weingart, 1992); (3) occurs during task performance in action phases (DeChurch & Haas, 2008;Faludi, 1973;Lei et al., 2016;Weingart, 1992); and (4) provides teams the opportunity to make immediate progress on a task (Weingart, 1992). ...
... Like preplanning, in-process planning encompasses the iterative process of recognizing problems, gathering data, generating ideas, and evaluating and choosing a course of action (Lei et al., 2016). What distinguishes in-process planning is that it: (1) allows teams who do not have a preexisting plan to develop a plan (Weingart, 1992); (2) allows teams with no or little task familiarity to allocate time during task performance for planning (Weingart, 1992); (3) occurs during task performance in action phases (DeChurch & Haas, 2008;Faludi, 1973;Lei et al., 2016;Weingart, 1992); and (4) provides teams the opportunity to make immediate progress on a task (Weingart, 1992). Because in-process planning reduces up-front knowledge requirements, and thereby encourages the gathering of data, we assert that information front-loading during task performance is an aspect of inprocess planning (Weingart, 1992). ...
... Team scientists argue that in-process planning occurs during a transition phase brought into existence through a period where low or no taskwork is being accomplished during an action phase (Lei et al., 2016;Marks et al., 2001). However, in highly-dynamic environments, we argue, times of low or no taskwork are not often encountered, such that waiting for a transition phase could be disastrous for team outcomes (Lei et al., 2016). ...
The current study examines the effects of teams front-loading information and planning ahead through team-level communication during action phases of taskwork on team performance across all-human and human-autonomy teams (HATs) in a Remotely Piloted Aircraft System-Synthetic Task Environment (RPAS-STE). Twenty-one three-member teams (two participants teaming with either a trained experimenter or autonomous agent) flew an RPA with the goal of photographing target waypoints. Basing action phases on Information-Negotiation-Feedback (I-N-F) loops, we used the time difference between F-I as an indication of a team front-loading information. Planning ahead was hypothesized to occur in teams with longer F-I times. We found that all-human teams performed better than HATs while engaging in less front-loading. This indicates that F-I might have been measuring an aspect of team coordination related to optimal timing of action phases and flow of performing taskwork. Effective teamwork may require the right person (agent) get the right information at the right time rather than front-loading information as much as possible.
... The Team Adaptability Theory proposed by Burke et al. (2006) shows that the discussion or communication on the team's tasks and others' feedback reflects the team's response to the external environment, and the implementation of the team's plans is crucial to the implementation of innovative behaviors. Relying on previous research on team adaptiveness, Lei et al. (2015) emphasized that team interaction behaviors and processes were the reflections of how teams constantly transited behaviors under different workloads (routine or nonroutine situations) to quickly match and effectively respond to the dynamic work context. ...
Despite a wealth of research on the interaction behavior patterns among team members from different angles, few studies focus on the combination of innovation management and innovation team. With the “Input-Process-Output” theoretical framework, this study takes the coding analysis to explore the differences in the interaction behavior patterns of members caused by the cognitive differences in the higher and lower innovative-performing teams. An innovation experiment was conducted in 12 innovation teams based on an experimental paradigm proposed for team innovation tasks. Subsequently, team members’ 1,754 behaviors were coded to analyze the similarities and differences in the interaction behavior patterns between higher and lower innovative-performing teams with lag sequential analysis. The results revealed that both higher and lower innovative-performing teams showed some same interaction behavior patterns. More specifically, the probability of idea facilitation behaviors being followed by team spirit facilitation behaviors was significantly higher than expected, while the probability of idea facilitation behaviors recurring was significantly lower than expected. However, in lower innovative-performing teams, there were some special interaction behavior patterns, such as “the probability of idea facilitation behaviors being followed by neutral interaction or idea inhibition behaviors was significantly lower than expected.” These phenomena may reflect some realistic situations in our life, such as “One echoes the other,” “Sitting on the sidelines” and “A gentleman is ready to die for his bosom friends” in the members’ interaction after cognitive differences happen. This paper provides opinions and suggestions for the research on the interaction behavior observation and coding analysis among members of innovation teams, as well as theoretical contributions to the research on the behavior observation of innovation teams.
... The contribution of this research is threefold: First, using the contingency theory of conflict management, team sensemaking is deemed to be a crucial activity that has a central role in predicting the success of organizations (Lei et al., 2016), and it is critical to understand its boundary conditions to enable higher team performance. The study further explores the relationship between TMS and team sensemaking in the presence of task conflict and reward interdependence. ...
Purpose
This research aims to examine the relationship between transactive memory systems and team sensemaking in the presence of critical boundary conditions, namely, task conflict and reward interdependence.
Design/methodology/approach
The data for Study 1 was collected from 304 team members who worked in 87 organizations in the Information, Communication and Technology sector of Pakistan. Study 2 is based on team-level data that was collected from 180 teams working in the New Product Development sector, with four to seven members in each team. The data tested the three-way interaction effect of the transactive memory systems, task conflict and reward interdependence on team sensemaking.
Findings
Results have shown that transactive memory systems have a positive relationship with team sensemaking, particularly when both task conflict and reward interdependence were perceived to be high.
Practical implications
To reap synergies, human resource managers should avoid disrupting team structures, assigning new members to a team or rotating team members very frequently. Moreover, if a team is experiencing high task conflict, reward interdependence may encourage conflict to remain constructive.
Originality/value
The current study is one of the first few attempts that examine the pivotal role of task conflict and reward interdependence as boundary conditions on transactive memory systems and team sensemaking. This research, therefore, highlights the role of transactive memory systems in enhancing team sensemaking at higher levels of task conflict and reward interdependence.
... The lack of any empirical investigation is surprising, given that the dynamics of team processes depicted in the rhythm of teamwork have increasingly come to the attention of researchers in recent years (e.g., Kozlowski & Chao, 2018;Lehmann-Willenbrock, 2017;Mohammed et al., 2009). In addition, team research has already shown that both team process patterns and team cycles differentiate high-performing from low-performing teams (e.g., Lei et al., 2016;Tschan, 2002;Zijlstra et al., 2012). Nevertheless, it is still unclear whether different teamwork rhythms can be observed and generalized, and whether they vary across tasks. ...
... These studies are usually based on observational data relating to team members' behavior or communication, which are examined using techniques such as sequential analysis . The findings from various studies generally suggest that the sequence of team processes varies across teams (e.g., Zijlstra et al., 2012) and is related to team performance (e.g., Lei et al., 2016). ...
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 changing-focus 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.
... Second, critical situations are particularly dynamic in terms of the amounts of information and events they present to the teams struggling to respond over time; these situations ebb and flow in their episodic velocity and criticality, and changing with them are the patterns of teams' interactions (Lei et al., 2016). Depending on the nature of these changing situations, teams may engage in different patterns of interaction over time, or perhaps few patterns at all. ...
In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.
