### Methods: We used a comprehensive search strategy to identify research on teams that lived and worked together. Data on team dynamics were extracted where possible, and sources were coded for key contextual features. The data did not lend themselves to traditional meta-analysis. We used two approaches to summarize the data: a weighted averages approach when the study reported enough data to calculate an effect size, and descriptive figures when data across studies were directly comparable.

Results: Seventy-two sources met our inclusion criteria, yielding 253 effect sizes and 1,150 data points. Results from our weighted averages approach suggested that the team cohesion and performance relationship may be operating differently in isolated and confined environments than other teams that lived and worked together (e.g., military teams), and that, given the available data, we can say very little about the magnitude and direction of the relationship. Our descriptive figures revealed important trends: (a) team members in longer missions generally spent less social time together than shorter missions; (b) consistent team efficiency over time was typical, whereas decreased team efficiency over time was atypical; (c) by 40% of mission completion or 90 days, all teams reported at least one conflict, (d) commanders' written communication with mission control decreased in length over time, and (e) team mood dynamics did not consistently support the third-quarter phenomenon. Conclusions: There are inherent limitations to our study, given the nature of the analog research (e.g., correlational studies, small sample size). Even so, our systematic review provides key insights into team dynamics in LDSEM-analog environments. We discuss the implications of our research for managing future space crews. Importantly, we also provide guidance for future research.

Keywords: team dynamics/processes, space exploration, astronaut, conflict, small sample, analog, over time changes, teams and groups

#### INTRODUCTION

Extreme teams help to solve complex problems outside of traditional performance environments and have significant consequences associated with failure (Bell et al., 2018). As a type of extreme team, astronaut crews will be expected to live and work under psychologically and physically demanding conditions for future long-distance space exploration missions (LDSEMs), such as missions to Mars (Salas et al., 2015b). For example, LDSEM astronaut crews will be required to function effectively as a team in isolated and confined environments for up to 30 months (Human Exploration of Mars Design Reference Architecture [DRM] 5.0; Drake, 2009). LDSEMs will require crews to operate more autonomously as their communication with mission control (MC) will be delayed up to 22 min (DRM; Drake, 2009). Crewmembers will switch between periods of high and low workload, as well as between individual and team tasks. It will be necessary for the LDSEM crew to work together seamlessly for demanding team performance situations such as landing on Mars, keep conflicts manageable, and provide one another with social support as crewmembers deal with the stressors of prolonged space flight.

#### Rationale

The National Aeronautics and Space Administration (NASA) and other space agencies seek to optimize team performance to minimize the risk of mission failure, and work with researchers from various scientific disciplines to prepare for future LDSEM missions. While meta-analytic investigations of important team relationships exist (e.g., team cognition, cohesion, composition, and performance), these investigations include traditional work team samples and findings may not necessarily generalize to the LDSEM context (Beal et al., 2003; Bell, 2007; DeChurch and Mesmer-Magnus, 2010; Bell et al., 2011). As such, researchers collect data in spaceflight and Earth-based analog environments, which are thought to mimic the challenges crews will encounter in LDSEM, to best design, prepare, and support future LDSEM crews and mission teams. Research on natural analogs examines teams that exist outside of research purposes; examples include polar stations in the Antarctic, where teams conduct scientific research while living in an isolated and harsh environment (e.g., Leon et al., 2011). Research in controlled analogs includes teams that exist specifically for research purposes; examples include teams in HI-SEAS, Human Exploration Research Analog (HERA) at Johnson Space Center, and the NEK facility at the Russian Academy of Science's Institute of Biomedical Problems (e.g., Ushakov et al., 2014; Binsted, 2015; Roma, 2015).

Analog settings share similar characteristics of LDSEMs expected to challenge crews and possibly impinge on team dynamics. As examples, analog crews live in a confined space (i.e., small living and working spaces with minimal privacy, physical discomfort), are isolated from others (i.e., limited interaction with others outside the crew, difficulty in communicating with family), are surrounded by a harsh physical environment (i.e., an environment in which survival is not possible without special equipment), have variable workload (i.e., a high and low volume of work at different periods), and have longduration missions (i.e., the team works together for an extended period of time). Each analog may have its strengths and weaknesses given that not all of the environmental factors may be present in a particular analog. For example, crews in Antarctic stations experience physical confinement and isolation, but are typically isolated as smaller crews for shorter periods than is expected for LDSEMs. They also have environmental cues not available in spaceflight (e.g., daylight). Crews in space simulations (e.g., HUBES, SFINCSS) may experience isolation and confinement but are typically not surrounded by a harsh physical environment.

Research on teams in analog environments has a rich history. In fact, a number of factors (e.g., compatibility and cohesion, mood, communication, conflict, performance) have been investigated in natural analogs (e.g., Antarctic; Wood et al., 1999; Steel, 2001), space simulations (e.g., HUBES, Mars 105, SFINCSS; Gushin et al., 2001; Sandal, 2004; Nicolas et al., 2013), and isolated and confined laboratory settings (e.g., Emurian et al., 1984) dating back to at least the 1960s (e.g., Gunderson and Nelson, 1963; Altman and Haythorn, 1965; Gunderson and Ryman, 1967). This research suggests several dynamics unique to the LDSEM-analog settings.

As examples, while a meta-analysis of the traditional team literature suggests that the team cohesion and team performance relationship is generally small (Beal et al., 2003), team cohesion may be of particular significance when crewmembers live and work together and rely on one another for social support (Landon et al., 2015). Astronaut journals collected in the International Space Station (ISS) reveal a decreasing number of positive comments about team interaction over the course of a mission (Stuster, 2010). Further, problems associated with poor unitlevel team cohesion such as subgrouping and isolation can occur, which have implications for conflict, information sharing, and team performance (Kanas, 1998; Kanas et al., 2009).

The psychological health of the crew is likely to be important for LDSEMs as crews will be living and working in an extreme environment for an extended duration. Communication between space crews and MC is thought to provide information about the crew's psychological health and the crew's psychological climate. Analysis of a space crew's communication with MC is the standard operating procedure of the psychological support group in Russian MC and is used to examine crews' emotional status and the communicators' coping strategies (Gushin et al., 2012, 2016). Among other things, research by Gushin et al., 1997, 2012 indicated that crews decreased the scope and content of their communication to outside personnel over time—a phenomenon called psychological closing.

Some crews have reported changes in mood over time. The third quarter phenomenon is the tendency for positive mood levels to decrease while negative mood levels and conflict increase after the midpoint of the mission (Bechtel and Berning, 1991; Steel, 2001; Dion, 2004; Kanas, 2004; Wang et al., 2014). Though mood is typically measured in LDSEM-analog research as an individual-level variable, researchers sometimes use the team mean of individual-level mood scores to represent team mood. Team mood is important because it contributes to team emotion, which is defined as a team's affective state that arises from bottom-up components such as affective composition, and topdown components such as affective context (Kelly and Barsade, 2001). Team emotion starts with individual-level moods and emotions and is then shared with the team either implicitly through emotional contagion or explicitly through means such as affect management. Environmental context such as lighting and physical layout can affect moods (see Kelly and Barsade, 2001). Thus, a better understanding of how team mood changes over time is necessary, especially given the extreme conditions expected for LDSEMs, such as living in a small transit vehicle with no natural light. The aforementioned evidence on team cohesion, communication, and mood are examples of findings that may be unique to the LDSEM context; this underscores the importance of examining team phenomena in LDSEM-analog environments.

While a body of research examines teams in analog environments, to date, it has not been quantitatively summarized. A quantitative summary of the analog team research is important for several reasons. First, it summarizes what we know about teams in LDSEM analog environments, given the available data. Specifically, it can provide insights into how team dynamics may unfold over time for LDSEM teams, and be used to benchmark typical and atypical team dynamics in the LDSEM environment. It also can identify potential threats to LDSEM team dynamics and performance. Second, it can help guide future research in analog environments by identifying what areas are in need of more research, new areas for research, and strategies that aid with knowledge accumulation over time. Guidance for future research is particularly important given the expense and time required to collect analog research.

#### Objectives and Research Questions

The primary purpose of our research was to provide an overall picture of the available data on team dynamics and performance in LDSEM-analog environments. To do this, we systematically reviewed quantitative research conducted on teams in LDSEManalog environments. We answer two primary questions with our systematic review: (1) which factors have been examined with quantitative research, and (2) what do these studies reveal about team dynamics in LDSEM-analog environments?

## METHODS

## Study Design and Inclusion Criteria

Typically, meta-analysis is preferred for integrating estimates of the same relationship of interest across studies; it allows us to generate cumulative knowledge about a set of studies. The benefits of meta-analysis over narrative reviews have been widely noted (see Glass, 1976; Schmidt and Hunter, 2015). Early in our review process, however, we suspected that most studies conducted on teams in analog environments would not lend themselves to traditional meta-analysis. Frequentist metaanalytic techniques can be inappropriate when a limited number of studies have examined a particular relationship or when sample sizes or data do not permit the calculation of an effect size, for example, when data are only reported for a single team. Further, a review of the analog research at the individuallevel determined that traditional meta-analytic techniques were inappropriate (e.g., Shea et al., 2011). Given this, our general approach (e.g., search strategies, coding) was consistent with best practices in meta-analysis in organizational psychology (e.g., Schmidt and Hunter, 2015); however, we retained a broader set of studies and ultimately used alternative analytic approaches to summarizing the data. Our reporting is consistent with the PRISMA guidelines (Moher et al., 2009) to the extent that they apply to non-medical systematic reviews.

We sought to be as inclusive as possible while also striving to ensure that the data were relevant to understanding team dynamics in an LDSEM environment. We applied three general inclusion criteria. First, we retained sources that reported quantitative data from teams in LDSEM-analog environments, however, we excluded descriptive case studies and narrative reviews. Second, we identified and included only team-level data (as opposed to individual-level data). We excluded articles that reported individual-level data that were not tied to a particular team (e.g., Bartone et al., 2002), or that were tied to a large polar station (>40 people) but not to a team or a small station (e.g., Doll and Gunderson, 1971; Palinkas et al., 1989). Third, we included research in which members of the focal team (e.g., the "crew" analog) live and work together for a period. We provide more detail on this decision next.

Defining an LDSEM-analog environment has challenges because a particular extreme environment (e.g., Antarctic winterovers) may only share some of the same characteristics expected of LDSEM. All analogs are imperfect approximations of LDSEM, and researchers must weigh the importance of different features of the context in understanding the phenomena of interest. Because of this, we broadly defined LDSEM-analog research as research in which members of the focal team (e.g., the "crew" analog) live and work together for a period. We included military teams when they were expressly described as intact teams (e.g., combat teams; Ko, 2005; Lim and Klein, 2006) even if the research did not explicitly mention that the unit lived together. We did not include military or firefighter training exercises when it was unclear whether the team lived together either while at training or while not at training (e.g., Oser et al., 1989; Hirschfeld and Bernerth, 2008). We excluded sources that included data on children (e.g., Tyerman and Spencer, 1983). We coded features of the analog environment and sample characteristics as moderators, rather than excluding studies based on specific features of the analog (e.g., mission length, autonomy). We chose this approach so that we could make comparisons across different conditions (e.g., in isolated and confined setting, non-isolated confined settings; how phenomena change over time), as opposed to designating arbitrary cutoffs related to fidelity. It is important to note, however, that our decision criteria led to the inclusion of some missions in which teams lived and worked in an isolated and confined setting for shorter-durations (e.g., 6 and 10 days). We retained these in order to be able observe any potential changes over time, but note that they have lower fidelity in regards to duration.

#### Search Strategy

We used a comprehensive search strategy to obtain quantitative research on teams in LDSEM-analog environments. Our efforts included: (1) searches of 13 databases that ranged from general databases such as Google Scholar and EBSCOhost, specialized databases such as the Military and Government Collection and space agency databases and technical report repositories (e.g., NASA, ESA, JAXA); (2) searches of specific journals such as Acta Astronautica, Aerospace Medicine and Human Performance, Human Factors; (3) contacting 29 researchers that we identified through the NASA taskbook, our project contact at NASA, or because they frequently publish in the area (e.g., Vinokhodova, Leon); (4) posts to listservs (e.g., Science of Team Science, INGRoup, relevant Academy of Management area listservs); and (5) a review of reference lists of key articles, including those from which we were able to obtain an effect size (e.g., Gunderson and Ryman, 1967; Emurian and Brady, 1984), reviews of similar domains (e.g., Schmidt, 2015), and recent technical reports on team research funded by NASA (e.g., Bell et al., 2015b; Burke and Feitosa, 2015; DeChurch and Mesmer-Magnus, 2015; Gibson et al., 2015; Smith-Jentsch, 2015). The search process included research published until November 2016. Researchers were contacted in May 2015.

### Data Sources, Studies Sections, and Data Extraction

In total, we identified approximately 309 sources (e.g., books, technical reports, dissertations, journal articles, and conference papers) for possible inclusion. To better understand the nature of the available data, we sorted the 309 sources into three categories: (1) sources that included quantitative data with a team-level sample size of 5 or greater, for which a team-level effect size between a predictor and criteria related to team functioning could be generated; (2) sources that included quantitative data on fewer than 5 teams or only data for one variable over time; and (3) sources that did not provide relevant data for our quantitative review. Sources in the third category were excluded from further review. The decision to exclude an article was agreed upon by at least two members of the research team. Seventy-two sources were retained for inclusion and coded for fidelity characteristics and other moderators, and the quantitative data on team dynamics. Of these, 11 different sources (e.g., journal articles, technical reports) provided enough information to calculate effect sizes representing the relationship between a predictor and a criterion related to team functioning, and 61 different sources reported quantitative data on team dynamics over time in LDSEM-analog environments but did not include enough data to calculate an effect size.

To extract data, two coding forms were created: one for coding effect sizes and one for coding data (e.g., means and standard deviations) related to team dynamics over time. When a source reported data on 5 or more teams and a predictor and team outcome relationships, we coded or calculated an effect size, either r or Spearman's r. For sources with a team sample size <5 we coded quantitative data such as means (or another teamlevel representation) and within-team standard deviation, when available, for team dynamics across time. We included data that were presented numerically as well as those presented in figures, except when the approximate value reported in the figure could not be reasonably estimated (e.g., due to ambiguities in labeling of the axis).

Coding forms were similar in that both captured characteristics of the source, the sample, fidelity characteristics, and information about the predictor and/or criteria. In addition, a codebook with definitions of the variables and descriptions for the different categories for each variable was developed. We coded fidelity characteristics when they were described or could be reasonably assumed by two independent coders, given the descriptions provided in the sources. We used the Internet to locate information about specific simulations or Antarctic stations to complete missing fidelity information, where possible.

We coded study design as: (a) descriptive, (b) correlational, (c) quasi-experimental, and (d) experimental. We coded the degree of similarity between the sources' samples and the anticipated characteristics of LDSEM crews in terms of demographic differences (e.g., gender, national background). We coded the fidelity of the team to the characteristics expected for LDSEM crews. Studies were coded as occurring in dangerous environments when the setting had features that required individuals to use special equipment (e.g., winter-overs in Antarctic) or posed an imminent threat (e.g., polar bear threat). Studies were coded as isolated when team members were limited in physical interaction with outside parties for a substantial period of time during the study, and confined when they primarily operated in a highly restricted space. For example, winter-overs in small Antarctic stations or space simulations were coded as an isolated and confined environment. Autonomy was coded as high, moderate, low, or not reported. Many studies did not describe the level of autonomy in detail and were coded as "not reported." Mission length was coded as the total of number of days in the team's life span. Ongoing teams such as firehouses (e.g., Kniffin et al., 2015) were coded to the max of the distribution (e.g., 730 days). We also coded crew size, workload amount and variability, how the crew communicated with those outside of the focal crew (e.g., mission control) and whether there was a time delay in the communication.

### Coder Training and Agreement

The second and third authors served as coders for this study. The primary author trained the coders on the coding scheme described in the previous section. Coders first received a coding sheet and a codebook that provided descriptive information about each category of variables. All three authors then used the codebook and coding sheet to independently code three articles. The three authors met to discuss the coding, observe areas of agreement and disagreement, and make modifications to the coding sheet and codebook. Next, all three authors recoded the initial set of articles to help establish a frame of reference that incorporated the modifications made to the coding documents. Disagreements about the coding were resolved during a followup meeting using a consensus approach. After the second round of coding, a common set of 5 articles was coded to determine the efficacy of the coding process and to establish decision rules. When there was little disagreement (i.e., <3 disagreements across the variables coded in the studies), two coders coded the remaining articles. A randomly sampled common set of coded articles indicated that initial agreement, prior to the consensus meeting between coders, was relatively high (mean agreement of 87% on the variables that were coded). Discrepancies between the two coders were discussed and agreement was reached using a consensus approach. When consensus could not be reached with certainty between the two coders, the coders met with the primary author to discuss how the characteristic in question should be coded. After the coding was completed, we inspected the data sets to better understand the nature of the data, to determine the appropriateness of meta-analysis for summarizing the data, and to determine the best way to summarize the available evidence.

## Analytical Strategy

Although we were able to locate a relatively large amount of data for our review, the small sample sizes in most studies (e.g., <5 teams) and the variety of relationships examined in the effect size studies, suggested the majority of the data did not lend themselves to traditional meta-analytic techniques. Thus, we used the following approaches. First, when the team factor and team outcome relationship could be represented using an effect size, we calculated a weighted average of the effect size from the local (analog) population and the relevant meta-analytic

estimate from the traditional teams literature as a minimumvariance estimate. We used this approach as a means of balancing the precision that meta-analysis can provide in estimating a relationship across multiple settings with the high uncertainty (especially due to small sample sizes, etc.) but localness that a specific effect size generated in an LDSEM-analog environment can provide. We also calculated the average inaccuracy of the estimates and used these to create 95% credible intervals to quantify the uncertainty of the estimates.

We used equations 1, 2, 3, and A12 from Newman et al. (2007) in forming our weighted averages. We used estimates from metaanalyses in the extant literature (e.g., Beal et al., 2003; LePine et al., 2008; Bell et al., 2011) to inform the prior probability distribution. We only generated an estimated distribution of the true population local validity when there was a relevant metaanalytic effect reported in the extant literature that could inform our prior distribution. This limited the number of relationships we estimated and narrowed the effects to team performance as the outcome. Further, even with performance as the outcome, there were a number of relationships for which we could not locate relevant meta-analyses; the relationships between leadermember exchange—the idea that leaders have relationships with their followers that vary in quality (Graen and Uhl-Bien, 1995) and team performance, and between personality characteristics (e.g., conscientiousness) of the leader and team performance are examples. We also did not locate relevant meta-analyses for many of the personality and needs variables examined by Gunderson and Ryman (1967), such as wanted affection and nurturance personality. Finally, there were two estimates from military teams [e.g., shared mental models from Lim and Klein (2006) and collectivism from Ko (2005)] that were already included in meta-analyses that would have been used in the calculation of the weighted averages [(DeChurch and Mesmer-Magnus, 2010); and Bell (2007) respectively]; we did not estimate local validity of these two estimates. We corrected the observed correlations in a given analog study for unreliability of the predictor and criterion in order to match the corrections used in meta-analyses that were used to inform our prior distribution. Although we would have preferred not to correct the local validity estimates for unreliability because of the small sample sizes on which they were based, the majority of the variances used to inform the prior distributions were corrected for unreliability. Newman et al. (2007) indicate the importance of ensuring that the prior and local effects have the same corrections. When reliability was not reported, we used the closest estimation of reliability from the most similar research in our data set. When the correction resulted in an estimate >1, we did not compute a weighted average. This is because the weighted averages approach relied on the z transformation, which for values over 1 is undefined. Values exceeded 1 for correlations from Gunderson and Nelson (1963) and Gunderson and Ryman (1967), which were based on the same source data (e.g., selfreport cooperation and performance) and exceeded 0.90 prior to correction.

Second, when the number of teams included in the study was too few to generate an effect size, and when data across studies were comparable, we descriptively summarized the data on team dynamics over time via a series of figures. We plotted team dynamics over time when data were comparable (e.g., similar scales, similar response formats), and reported for at least three different teams across at least two different data sources (e.g., articles, conference presentations). We plotted team dynamics over time in terms of mission days and over relative time. Relative time was calculated as the mission day divided by the total mission length. Relative mission time was examined given that some effects for factors such as team cohesion and conflict, are thought to different because of the point of the team in the lifespan (e.g., third quarter phenomenon) rather than the mission day itself.

### RESULTS

### Flow Diagram of the Studies Retrieved for the Review

**Figure 1** depicts the flow diagram of the studies retrieved for review.

### Study Selection and Characteristics

Eleven sources (e.g., journal articles, technical reports) provided enough data (team n ≥ 5) to generate 253 team-level effect sizes that represent a team factor (e.g., team cohesion) and team outcome (e.g., team performance) relationship. We refer to this as our effect size data set. Sixty-one sources included data on team functioning from fewer than 5 teams; from these sources we were able to glean 1,150 data instances (i.e., data collected on one or more variable at a particular time point) to benchmark team dynamics in LDSEM-analog environments over time. We refer to this as our benchmarking data set. We provide a summary of the fidelity characteristics of our samples in **Supplementary Table 1**.

### Synthesized Findings

Our first research question asked: what factors related to team dynamics has quantitative research examined in analog environments? In the effect size data set, the majority of effects (i.e., 102 effects across 9 studies) represented the relationship between a predictor and team performance. Fortyseven effects across 6 studies represented the relationship between a predictor and cohesion or compatibility, and the remaining effects represented a variety of outcomes that differed across studies. The specific predictor and criterion relationship examined varied across studies. Predictors included inputs, emergent states, and team process variables (see Marks et al., 2001), personality (e.g., Gunderson and Ryman, 1967), values, leader-member exchange, and team-member exchange (e.g., Ko, 2005), compatibility and cohesion (e.g., Gunderson and Nelson, 1963), mental models (e.g., Lim and Klein, 2006), conflict (e.g., Seymour, 1970), leadership (e.g., Lim and Ployhart, 2004), ability, experience, mood, exploratory search, and planning (e.g., Knight, 2015). Outcome variables included performance effects (e.g., accomplishment, accuracy, time to completion, efficiency, and quality), emergent states, team processes, and other team dynamics such as cohesion, team mood, egalitarian atmosphere, viability, team-member exchange, leader-member exchange, exploratory search, and cooperation. The data were largely dependent (i.e., the 253 effects came from only 11 different sources), and a variety of predictor and outcome relationships were examined. Only the relationship between measures of cohesion (e.g., compatibility, spending time together) and team performance was examined in more than 3 independent samples (k = 6).

In the benchmarking data set, team factors included emergent states, team processes, outcomes, and additional team dynamics markers. For example, emergent states included team cohesion (e.g., Allison et al., 1991; Vinokhodova et al., 2012), and team processes included conflict and interpersonal relations (e.g., Leon et al., 2004; Šolcová et al., 2014). Outcomes included performance (e.g., Emurian and Brady, 1984) and more subjective outcomes such as satisfaction (e.g., Bhargava et al., 2000; Leon et al., 2004). Finally, other dynamics markers, such as team mood (e.g., Kahn and Leon, 2000; Steel, 2001; Bishop et al., 2010), were commonly reported in analog studies. A full list of all team factors examined for the effect size and benchmarking data sets is available in **Supplementary Table 2**.

Our second research question asked what quantitative research reveals about team functioning in LDSEM-analog environments. We discuss the results of the weighted averages approach, and descriptive figures benchmarking team dynamics next.

### Weighted Averages Approach

We used our weighted averages approach to provide the best possible estimate of the magnitude and direction of the relationships between team factors and team outcomes in the analog environments, given the available data. **Figure 2** summarizes the weighted averages results, the credible intervals around the estimates, and displays the forest plot. Specific information about the local validity information obtained from LDSEM-analog studies, the meta-analytic effects that we used in the calculation of the weighted averages, and the estimated posterior distributions are provided in **Supplementary Table 3**. Local validity estimates include team performance with cohesion, age homogeneity, education homogeneity, team learning, planning, team task-relevant experience, cooperation, and transformational leadership.

First, we discuss the team cohesion and team performance relationships. Studies 1, 3, and 5 (as noted in **Figure 2**) were conducted on teams in isolated and confined environments (ICE); each of these studies measured team cohesion and team performance with different operationalizations. Estimates 7 and 13 reflect the team cohesion and team performance relationships for teams that are sometimes used as LDSEM-analogs but which are not isolated or confined for extended periods (non-ICE).

That data suggest that with 95% certainty, we cannot speak to the direction or size of the team cohesion and team performance relationship in ICE. For example, estimate 1 reflects the estimated results for the team cohesion and team performance relationship for data collected in Antarctic stations where team cohesion was operationalized as self-rated compatibility of station members, and team performance was operationalized as self-rated station achievement. The mean estimated validity is −0.10, and with 95% certainty, we estimate that the true population validity falls between −0.46 and 0.28. This is rather imprecise, as the prediction interval includes large, moderate, and small negative effects, no effect, and small and moderate positive effects. Conversely, with 95% certainty, we can describe the team cohesion and team performance relationship in the firehouses studied as positive and small to moderate (i.e., estimate 7), and in the special operations military teams studied, as positive and moderate to large (estimate 13).

Data for a few additional relationships other than team cohesion and team performance were also available. The age homogeneity and team performance (**Figure 2**, Estimate 2) and the educational level homogeneity and team performance relationships (**Figure 2**, Estimate 4) in an ICE (e.g., Antarctic station winter-over) were estimated with a large degree of imprecision; the prediction interval included positive, negative and no effect. Conversely, with 95% certainty, the true population effect between cooperation and team performance is estimated to be positive and large (**Figure 2**, Estimates 11, 12) for firehouses and special operations teams. Finally, with 95% certainty, the true population effect between transformational leadership and team performance for special operations teams, and the true population effect between team task-relevant expertise and team performance for military training teams are positive and exceed a small effect (**Figure 2**, Estimates 9, 10).

Taken together, there is a high degree of imprecision associated with estimates of the true predictor and team performance relationships from studies with teams in ICEs. Specifically, unlike most of the estimated relationships from teams in non-ICE, given the current data, if we retain a 95% level of certainty, we have limited to no understanding of the size or direction of the relationship of team cohesion and team performance observed in multiple ICE, age homogeneity and team performance in an ICE, and educational homogeneity and team performance in an ICE.

### Benchmarking Team Functioning Over Time

Next, we benchmarked team dynamics over time in studies with sample sizes too small to generate a between-team effect size, but for which data were comparable (e.g., similar measures, similar response formats) on at least three different teams from at least two different data sources (e.g., articles, conference presentations). With this requirement, we were able to generate figures on cohesion, efficiency, team conflict, communication with MC, and team mood.

### Team Cohesion

While we identified several studies with cohesion data reported over time from 5 or fewer teams, these data were collected using a variety of cohesion operationalizations making it difficult to directly aggregate and make for meaningful comparisons across settings. We were able to benchmark a subset of this data by identifying 3 sources with data from 11 teams spending time together (e.g., social activities, eating meals). We classified these activities as evidence as social cohesion. **Figure 3A** illustrates team cohesion across mission days. **Figure 3B** plots team

chance that the true population predictor and team performance relationship (ρ) is between the first number and the second number. Number in the left column indicates the analog data source. 1. Gunderson and Nelson (1963), Outcome = self-report team achievement, Antarctic stations; 2 and 4 from Gunderson and Ryman (1967), Outcome = team accomplishment, mixed sources, Antarctic stations; 3. Emurian and Brady (1984), outcome = performance on lab task; 10-day isolated lad experiment; 5. Nelson (1964), outcome = supervisor ratings of individual performance aggregated within station, Antarctic station; 6, 8, and 9 Knight (2015), outcome = team's time and number of obstacles completed in a final challenge task, military training; 7 and 11 Kniffin et al. (2015), outcome = supervisor rating of performance, firehouses; 10, 12, 13. Ko (2005), outcomes = team performance, mixed sources, special operations teams.

cohesion over relative time (i.e., the mission day divided by the total mission length).

The data reported suggests some fluctuations in cohesion over time. However, two patterns are present. First, it appears team members spend more time together during shorter missions. The Concordia, Tara Drift, and Mars 500 missions lasted for 268, 507, and 520 days, respectively. In comparison to shorter missions [i.e., Emurian et al., 1978, 1985], which lasted for 6, 10 and 12 days, team members in longer simulations generally spent less social time together. There was one exception to this: time together increased sharply at certain points for a team at Concordia station. These instances could have been the result of significant events at the station during those periods (Tafforin et al., 2015). It is important to note that we included shorterduration missions to avoid an arbitrary cut off and to observe changes over time. The stark contrast between shorter-duration and longer-duration missions on time spent together suggest limited usefulness of shorter-duration studies in understanding team cohesion for LDSEM.

### Team Performance

Homeostat was used to collect data on team performance across a number of space simulations (e.g., HUBES, SFINCSS). Homeostat is a computer task in which, under time pressure, a team solves tasks that require the coordinated action of the whole team (Eskov, 2011). A number of metrics can be assessed using Homeostat, including an efficiency metric (Csh) and leadership tactics. **Figure 4A** is a plot of team efficiency across mission days. **Figure 4B** is a plot of team efficiency over relative time.

The data suggest that three teams (i.e., a team in EXEMSI and two of the teams in SFINCSS simulations) were relatively consistent in terms of efficiency over time. The HUBES team decreased steadily in efficiency over time. One of the SFINCSS teams (Group 3) had a sharp decline in efficiency early in the simulation and then steadily increased during the remainder of the simulation.

Descriptive information on team dynamics in the HUBES and SFINCSS simulations implicate ineffective role structure and conflict as possible triggers of the performance decrements of HUBES and SFINCSS–Group 3. Specifically, in addition to measures of efficiency, the Homeostat also collects information on leadership tactics by individual team members as a means of understanding the leadership structure used while completing the task. For SFINCSS group 3, Vinokhodova et al. (2002) indicated that the data did not suggest that a role distribution structure had sufficiently developed. Further, the SFINCSS simulation also included a New Year's Eve incident between a member of another group and a woman in Group 3 of the simulation, which led to tension between crews (Sandal, 2004). The sharp decrease in effectiveness in

the SFINCSS Group 3 depicted in **Figure 4A** also happened around this time. For HUBES, Sandal (2001) reports that there was evidence of an unstable crew structure; specifically, the commander's leadership was challenged during the first 8 to 10 weeks of the mission. Further, crew relations in the simulation were marked by interpersonal tension and alienation of one crew member during later parts of the experiment. Taken together, this may suggest ineffective role structure, conflict, and alienation as possible threats to team efficiency.

### Team Conflict

A few sources (k = 4) reported conflict scores over time for 8 different teams using 2 types of conflict metrics (e.g., total number of conflicts reported, Likert scale). **Figures 5A,B** summarize data that were comparable across multiple teams from different analog environments for the total number of conflicts reported within crews. Data do not show a consistent trend across teams. Some teams are more variable than others in the number of conflict incidents per month, while others are more stable. Some teams report conflict early on, while others do not. By 40% of the mission completion (with this data the equivalent of at least 90 days) all teams had reported a least one instance of conflict. No team had more than six instances of conflict per month with a given target (i.e., the crew or MC).

### Communication With Mission Control (MC)

Gushin et al. have examined crew communication with MC in several studies (e.g., Gushin et al., 1997, 2001; Gushin

and Yusupova, 2003) and have reported comparable data, which allowed us to plot the total duration of crew–MC audio-communication sessions (in seconds) over time (see **Figures 6A,B**), as well the average report length per week of the commander's end-of-day report to MC (see **Figures 7A,B**). For the SFINCSS, HUBES, and ECOPSY simulations, audio communication paralleled the standards of Mir in that 30 min were made available for audio communication every 90 min in the daily schedule but use of the time was not required. At the end of each day, the commander submitted a written report to MC on mission status and fulfillment of the daily schedule (Gushin et al., 1997, 2001). Data in Gushin and Yusupova (2003) was collected by researchers listening to crew-MC communication once a week (for ISS mission 1) and twice a week (for ISS mission 2).

As depicted in **Figures 6A,B**, patterns of average audiocommunication length between the commander and MC were inconsistent across teams. It is interesting to note, that the HUBES crew that had decreasing efficiency over time (**Figure 4A**) also had shorter audio communication with MC over time (**Figures 6A,B**). As depicted in **Figures 7A,B**, average mission report length to MC per week decreased over the course of the mission in SFINCSS, EXEMSI, and ECOPSY. Gushin et al. (2012) describe this as the closing of a communication channel, or psychological closing. Psychological closing can include a decrease of the communication volume throughout isolation, decrease in the issues discussed, and preference for communication partners.

It should be noted that there is a wealth of specific details (e.g., negative statements, jokes) that can be gleaned and assessed via content analysis of within- and between-group communications. Our figures here only reflect report length and total time for audio communication, which were reported in the same format across multiple teams. We refer the interested reader to Gushin

et al. (2012) and Tafforin (2015) for more detail on the range of communication parameters that have been examined.

#### Team Mood

Multiple studies reported the affect of team members using Profile of Mood States (POMS; Shacham, 1983; Curran et al., 1995). POMS captures individuals' mood via self-report ratings on six dimensions using a 5-point Likert scale. The dimensions are tension-anxiety, depression-dejection, angerhostility, fatigue-inertia, confusion-bewilderment, and vigoractivity. To arrive at an overall total mood disturbance score, the first five subscales listed are summed and then the vigoractivity subscale is subtracted.  **Figures 8A,B** show team mood over time and team mood over relative time, respectively. **Figure 8A** shows that the MARS 500 crew reported elevated total mood disturbance compared with teams in other LDSEM-analog environments, although it should be noted that the scaling reported for Scott Base was 0 to 4 instead of 1 to 5 as in the other simulations. Thus, the winter-over at Scott Base may have ratings more similar to Mars 500. Both studies that included teams in ICE for a year or more (e.g., Mars 500, an Antarctic winter-over) showed a spike in team total mood disturbance around the 1-year mark, and this was confirmed in the text of the studies reporting the data (e.g., Steel, 2001; Wang et al., 2014). **Figure 8B**, which shows total mood disturbance over time relative to the proportion of the mission complete, does not support a clear third-quarter phenomenon at the team level.

Team mood also has been operationalized in LDSEM-analog environments as the team mean of self-report ratings on the positive and negative mood components of the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988, see Leon et al., 2004, 2011, for examples). **Figures 9A,B**,**10A,B**, show the relationship between affect operationalized as the team

mean PANAS scores over time. **Figures 9A**, **10A** show team positive affect over time and relative time. **Figures 9B**, **10B** show team negative affect over time and relative time. For team negative affect over relative time, three of seven LDSEManalog teams show an increased negative affect during the third quarter.

#### Risk of Bias

There are two key risks of bias in our systematic review. First, publication bias may be a problem, especially given the small sample sizes associated with analog research. More extreme findings are more likely to be published. Small sample sizes compound the issue because the extreme findings are less likely to replicate. Given this, we made a focused effort to obtain unpublished research. Second, there were two potential biases associated with our weighted analyses approach. Some of the effect sizes used in our weighted averages approaches were based on very small sample sizes, which may influence the normality of the local validity distribution. We based our weighted averages approach on Newman et al. (2007) local validity Bayesian estimation approach. However, the local validity Bayesian approach is only regarded as Bayesian when the distribution of the local estimate is normally distributed. Because it is not possible for us to test this assumption without access to raw data, we referred to our approach as taking a weighted average.

Further, due to the limited amount of data in different analog conditions, we were unable to estimate potential bias due to certain moderators such as whether the analog study was conducted in an ICE or non-ICE environment. However, Newman et al. (2007) indicates that the accuracy of their local validity Bayesian estimation approach holds true even in the

presence of true moderators (e.g., teams that perform in ICE environments, for example, where the ICE/non-ICE context moderates the observed predictor and outcome relationship). Even so, we acknowledge that because we cannot assess or model the bias that may be present due to combining a local effect size from an ICE environment with a meta-analytic effect from non-ICE environments, we are trading an unknown amount of bias to generate a minimum variance estimate. If raw data were available, it would be better to do a full Bayesian analysis that takes into account sampling variability at the local level, as well as any bias in using a meta-analytic estimate based on the broader team literature as the prior distribution. Given the limitations of available data, however, we believe our weighted averages approach provides the best estimate of the team predictor and outcome relationships in the specific LDSEManalog environment. Further, given the limitations of the data from sources, which had fewer than 5 teams, we believe our descriptive figures best represent the data.

### DISCUSSION

LDSEMs such as human missions to Mars are of increasing interest to NASA, space agencies, and private sector organizations. Conducting research in analog environments provides a means for understanding team dynamics for a potential LDSEM mission as well as other teams operating in similar ICE environments (e.g., oil drilling teams). Analog research on team dynamics has a long history dating back to at least the 1960's, thus it is important for researchers and agencies to learn from the past analog research to inform future analog research and prepare for future space exploration. The primary goal of this research was to summarize the existing quantitative evidence on team dynamics in LDSEM-analog environments.

#### Summary of Main Findings

Our study has three key takeaways. First, there is an extensive research base on teams in LDSEM-analog environments. We

were able to locate 72 different sources reporting quantitative research. Although there are quite a few studies that have examined teams in LDSEM-analog environments, the major of the studies had too small of a sample size to generate a between team effect size. Inconsistency in how the same construct was measured across studies further limited the ability to make comparisons across studies. Second, team dynamics are dependent on specific aspects of the context. For example, the team cohesion and team performance relationship was positive and strong for teams that lived and worked together but not in isolation and confinement (e.g., special forces teams), while little could be said about the relationship between team cohesion and team performance for teams in isolation and confined environments—an important aspect of LDSEM. Further, team dynamics varied greatly over time, underscoring the importance of temporal considerations and fidelity in analog environments. Third, we were able to document and provide interesting insights into how team dynamics unfold over time. These benchmarking figures provide insights into how team dynamics may unfold over time for LDSEM teams, benchmark typical and atypical team dynamics in the LDSEM, and identify potential threats to LDSEM team dynamics and performance. More detail on specific findings is provided next.

Results from our weighted averages approach suggest that the team cohesion and team performance relationship may be operating differently in isolated and confined environments (e.g., Antarctic stations, laboratory research with ICE characteristics) than in traditional work team environments. While we can confidently state that the relationship between team cohesion and team performance in non-ICE studies (e.g., firehouses, special operations teams) is positive and small to large, and similar to previous meta-analytic estimates (Beal et al., 2003), we cannot draw any conclusions about the direction and magnitude of the relationship between team cohesion and team performance in isolated and confined environments. Despite the limitations of such results, our findings highlight the importance

of examining the effects of team cohesion on team performance in isolated and confined environments, and provide a cautionary note about generalizing findings from teams sometimes used as analogs that live and work together (non-ICE) to teams operating in isolated and confined environments. Similarly, limited information on other team factors (e.g., age homogeneity, education level homogeneity) and team performance inhibited us from estimating the true population validity of specific relationships in isolated and confined environments. Bringing further clarity to team cohesion for LDSEM, our figures that benchmarked team cohesion over time revealed that teams in shorter-duration missions spent more time with each other (an operationalization of team cohesion) than longer-duration teams. These results suggest limited usefulness of shorterduration studies in understanding team cohesion for LDSEM.

As part of our quantitative review of team dynamics in LDSEM-analog environments, we also explored our benchmarking data set for trends in team dynamics over time (i.e., team efficiency, team conflict, team communication, team mood). Beginning with team efficiency, crews must coordinate and complete mission tasks in an efficient manner in order to achieve mission success (Salas et al., 2015a). Based on the available data, team efficiency in LDSEM-analog settings was relatively consistent across time; it was atypical for team efficiency to decrease over time. In uncommon situations in which team efficiency decreased during missions (see Vinokhodova et al., 2001; Eskov, 2011), researchers implicate ineffective role structure and conflict as possible triggers of the performance decrements (Sandal, 2001, 2004; Vinokhodova et al., 2001), suggesting that such factors are key threats to team efficiency. Further, the primary focus of team performance in LDSEM-analog environments has been efficiency. LDSEM will likely have team performance demands beyond team efficiency. For example, the team may need to be creative in order to use scare resources effectively, which suggests an expanded view of team performance in analog research is needed.

In contrast to team efficiency, intrateam conflict data greatly varied over time in LDSEM-analog settings, such that data do not show a consistent trend across teams. However, all teams reported at least one conflict within the team or with mission control by 40% of the mission completion or 90 days. Given that all teams engage in at least some conflict in extended mission, and will likely have to resolve these conflict incident rather autonomously, it is important to better understand conflict and effective conflict management strategies in LDSEManalog settings.

With regard to team communication in LDSEM-analog settings, communication between crews and mission control is thought to provide valuable information about the psychological health of the crew and the interpersonal climate within the crew. It is interesting to note that one of the crews that demonstrated decreased efficiency over time (i.e., HUBES crew) also had shorter audio communication with mission control over time. Moreover, commanders' written communication with mission control across several missions were in line with the psychological closing phenomenon in that the length of commanders' reports to mission control decreased over time (Gushin et al., 1997, 2012). Analysis of communication is likely to provide a fruitful means for understanding team dynamics.

As for team mood—operationalized as total mood disturbance or positively affectivity—there was inconsistent support for the third quarter phenomenon (Steel, 2001; Dion, 2004; Kanas, 2004; Wang et al., 2014); however, three of seven LDSEM-analog teams reported an increase in negative affect in the third quarter of their missions. The two teams in particular that were studied for an extended period (i.e., greater than a year) both reported an increase in total mood disturbance approximately 1 year into the mission. These findings are important to note in light of the fact that team mood plays an instrumental role in team dynamics (e.g., Kahn and Leon, 2000;

are not displayed.

Steel, 2001). They suggests that it is prudent to better understand the effects of extended isolation on team mood for LDSEM.

### Limitations

The results described should be considered in light of the limitations of this research. In our attempt to quantitatively summarize team dynamics in LDSEM-analog environments, we were limited by the empirical research available within the extant literature (e.g., small sample size, correlational). The validity coefficients from the LDSEM-analog studies used in our analyses are based on small sample sizes. When weighted average analyses are based on smaller sample sizes, there is more uncertainty regarding how well an observed effect in a given sample reflects the true population validity. To help address this issue, based on the available data, we calculated improved estimates of the true population team predictor and team criterion relationships in an LDSEM-analog environment by inversely weighting the variances of the validity coefficients from the LDSEM-analog studies and the meta-analytic estimates of the same team predictor-criterion relationships from the extant literature. Additionally, we calculated the average inaccuracy of the estimates to generate 95% credible intervals regarding the uncertainty of the estimates. This approach afforded us the precision associated with meta-analytic estimates while accounting for the localness associated with a specific effect size from an LDSEM-analog environment.

Moreover, the studies included in our quantitative review were almost exclusively descriptive or correlational in design (see the work by Emurian and colleagues for a notable exception). With this is mind, we cannot make causal statements about the relationships examined in our review, nor can we disentangle the effects of one team predictor from another. Consequently, we encourage researchers to employ experimental and quasiexperimental designs to identify key threats to team dynamics and performance in LDSEM-analog settings. We acknowledge the limitations of this data (e.g., small sample size, correlational). Importantly, however, this is the data that we currently have for understanding team dynamics in LDSEM-analog environments.

### Future Directions for Research

Despite the limitations of this study, our findings provide insight into several potentially fruitful areas for research in regards to content, and research approaches related to extreme teams. In general, it seems that research should be prioritized when the nature of the relationship would be most likely to change as a function of the LDSEM context. One area in need of research is team affect. While most of the team mood data presented in this article were generated from aggregated individual-level data (for a notable exception see Šolcová et al., 2013), applying a teamlevel perspective and conducting investigations on team affect and team affect management could provide a more in-depth understanding of the role of affect in crew performance and crew member well-being. For example, team affect tends to become more homogenous through mechanisms such as emotional contagion (Totterdell et al., 1998), which could be magnified by specific characteristics of the LDSEM context (e.g., isolation and confinement). Also, crew composition factors (e.g., national diversity) could influence the emergence of team affect, norms for affective suppression or sharing, and the effectiveness of affect management approaches. Considering the unique features of ICE, further exploration of team affect, emotions, emotion regulation, and affect management in ICE across diverse crews and over time is warranted. Further, given that spikes in total mood disturbance were observed at the 1 year mark for studies in which teams both teams were in extended isolation, it is prudent to better understand the effects of extended isolation for LDSEM.

A second area in need of research is conflict management. LDSEMs provide a unique context in which conflict will need to be managed. Given the significant communication delays with those on Earth as teams travel into deep space, the teams will likely need to effectively manage conflict with at least some degree of autonomy. Our data suggest that at some point conflict is likely to occur between the crew, or between the crew and mission control. Indeed, LDSEMs are likely to be a situation where the crew will face competing or inconsistent priorities. For example, if more than one mission control is utilized for a particular mission, competing information may be given in regards to priorities (e.g., perform a function that requires the whole crew; require an individual adheres to a particular exercise schedule), which could create ambiguity in how crewmembers should allocate their time and resources. Crewmembers are likely to be diverse in a number of ways (e.g., professional, national background) which could also lead to misunderstandings or competing priorities (e.g., maintenance of the space vehicle, complete the science experiment) and potentially cause intrateam conflict (Bell et al., 2015a). The extent that crews effectively manage conflict will be of great importance given the expected durations of the space missions, the inability for crewmembers to leave, and the limited and delayed communication with mission control possibly compounding issues between the team and mission control. A better understanding of conflict and the conflict management cycle as teams live and work together in extended isolation and confinement is prudent.

In addition to their effects on team performance, conflict management and affect are important areas for future research because they will likely play a critical role in a team's resilience. While researchers are working diligently to mitigate all potential threats to team effectiveness, LDSEM crews will inevitably face challenges. A key aspect of correctly composing, training, and providing countermeasure support to crews will include consideration of the crew's resilience, defined as the capability to withstand and recover from stressors, pressure, or challenges (Alliger et al., 2015). Crewmembers' challenges may range from subtle changes that result in a less than ideal team state (e.g., the general decline in positive mood) to events that are more acute in nature (e.g., dispute related to the involvement of MC in conflict management). Regardless of the specific challenge, team resilience will likely be critical to the success of crews on LDSEMs. Future research should examine the effects of specific manipulations of stressors on crew resilience as well as the effects of subtle changes that occur during a team's life cycle on crew resilience.

We believe the decline in team efficiency during the HUBES simulation and the dip in team efficiency for one of the teams during the SFINCSS simulation provide interesting directions for future research in LDSEM-analog settings. Several researchers (e.g., Sandal, 2001; Vinokhodova et al., 2001) suggested that the decline might have been due to intra-team conflict and instability in or a lack of established leadership structure. Given the autonomy of the crew at long-distances from Earth, and the likelihood that crews will include individuals from both high and low power distance countries, a better understanding of the conditions needed for teams to establish a workable leadership structure, and the process for ensuring crews high in gender and cultural diversity can effectively resolve status conflict is necessary (Bendersky and Hays, 2012).

Finally, a number of methodological recommendations can be made for future research. First, sample sizes in high fidelity environments to LDSEM, particularly ICE, are likely to be small. Where possible, data should be collected in such a way that they can be aggregated and compared across multiple studies. Ideally, enough data should be collected to generate an effect size. The normality of the data could be reported (or even better, the raw data) to allow future summaries to ensure the data are being appropriately modeled. When the sample size is too small to allow an effect size to be generated, data on key team constructs (e.g., team efficiency, communication, mood, and cohesion) should be collected with a common set of measures. Analog research on mood has consistently relied on the PANAS and POMS which made comparisons across studies possible. Researchers at the Russian Academy of Science's Institute of Biomedical Problems and some individual researchers (e.g., Leon) have consistently collected data using the same measures, which allowed us to report many of the figures in this article. In addition, NASA's Human Research Program is adopting a standardized set of measures to be collected across NASA analogs that includes measures such as team conflict, team cohesion, and team mood as well as other constructs. For key constructs (e.g., conflict, mood, cohesion), it is essential that analog research use the same measures so that the data better lend itself to the eventual culmination of studies.

Second, continued research is needed on small sample sizes. As an example, some meta-analytic approaches (e.g., Bayesian, Fisherian) calculate sample variance as 1/n and others as 1/(n-3) (Brannick, 2001), and the Schmidt and Hunter (2015) method uses n-1 in the denominator of their random effects meta-analysis of correlations. As Brannick (2001) states, "if the sample is so small that the choice of n or n-3 is critical, then the researcher has a more serious issue to confront, namely, how to collect more data" (p. 469). Unfortunately for analog researchers, more data is not likely to be a feasible option for many studies. While differences in how sampling variance is calculated and the ability to calculate sampling variance at small sample sizes may generally be less of an issue in traditional meta-analyses, it is an important issue for the eventual culmination of team LDSEM-analog research. Future research may wish to explore the accuracy of the different meta-analytic approaches for use with extremely small sample sizes (e.g., correlations based on 3 to 7 teams) through simulations as well as develop alternative versions of quantitative aggregation for small sample sizes. Continued advances in analytics that can best represent small sample size data is likely to be important for space research as well as extreme teams in general (Bell et al., 2018).

### CONCLUSIONS

Future space exploration teams will be required to work effectively under complex and dangerous conditions to successfully accomplish their missions. With an understanding of team dynamics in LDSEM-analog environments, we can minimize potential threats to mission success while optimizing team performance. While an extensive research base exists that examines teams in LDSEM-analog environments, small sample sizes make traditional forms of meta-analysis inappropriate. Importantly, however, this is the data that we have for understanding team dynamics for future LDSEMs. Given this, we used a weighted averages approach to generate minimum variance estimates of team predictor and outcome relationships, and generated descriptive figures depicting team dynamics over time. Our systematic review of quantitative research on teams in LDSEM-analog settings summarizes what we know about team dynamics for future LDSEM, and provides guidance for future research.

## AUTHOR CONTRIBUTIONS

STB developed the analytic approach, designed the search process and coding forms, conducted data analyses, and wrote aspects of the manuscript. SGB and TM conducted extensive literature searches, coded articles included in the review, generated tables and figures, and wrote aspects of the manuscript.

## ACKNOWLEDGMENTS

This work was supported by contract NNJ15HK18P awarded to the first author from the National Aeronautics and Space Administration (NASA). There are some similarities between this article and our longer technical report submitted to the funding agency, NASA/TM-2016-219280. All opinions expressed herein are strictly those of the authors and not necessarily those of the sponsoring organization.

We thank Alan H. Feiveson for providing insight into potential risks of bias with our weighted averages approach. We thank Alla Vinokhodova and Gloria Leon for helping us obtain some limited circulation publications with valuable data, and Kim Binsted and Pete Roma for sharing unpublished data.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2019.00811/full#supplementary-material

### REFERENCES

Communication, and Psychosocial Adaptation Within a Team. Technical Report. Houston, TX: Johnson Space Center, National Aeronautics and Space Administration.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Bell, Brown and Mitchell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

<sup>∗</sup>References marked with one asterisk indicate effect size data set studies. ∗∗References marked with two asterisks indicate benchmark data set studies.

# A Bottom Up Perspective to Understanding the Dynamics of Team Roles in Mission Critical Teams

C. Shawn Burke<sup>1</sup> \*, Eleni Georganta1,2 and Shannon Marlow<sup>3</sup>

<sup>1</sup> The Institute for Simulation and Training, University of Central Florida, Orlando, FL, United States, <sup>2</sup> Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany, <sup>3</sup> Department of Management, The University of Texas at San Antonio, San Antonio, TX, United States

Edited by:

Marissa Shuffler, Clemson University, United States

#### Reviewed by:

Esther Sackett, Northwestern University, United States Suzanne Bell, DePaul University, United States

> \*Correspondence: C. Shawn Burke<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology

Received: 09 December 2018 Accepted: 21 May 2019 Published: 11 June 2019

#### Citation:

Burke CS, Georganta E and Marlow S (2019) A Bottom Up Perspective to Understanding the Dynamics of Team Roles in Mission Critical Teams. Front. Psychol. 10:1322. doi: 10.3389/fpsyg.2019.01322 There is a long history, dating back to the 50 s, which examines the manner in which team roles contribute to effective team performance. However, much of this work has been built on ad hoc teams working together for short periods of time under conditions of minimal stress. Additionally, research has been conducted with little attention paid to the importance of temporal factors, despite repeated calls for the importance of considering time in team research (e.g., Mohammed et al., 2009). To begin to understand team roles and how temporal aspects may impact the types of team roles employed when teams are working in extreme mission critical environments, the current manuscript uses a data-driven, bottom-up approach. Specifically, we employ the use of retrospective historical data as our input and a historiometric approach (Simonton, 2003). Source documents consist primarily of autobiographies, memoires, biographies, and first-hand accounts of crew interaction during spaceflight. Critical incidents regarding team interaction were extracted from these source documents and independently coded for team roles by two trained raters. Results of the study speak to the importance of task and social roles within teams that are predominantly intact and operating in extreme environments where mistakes can be life threatening. Evidence for the following task (i.e., coordinator, boundary spanner, team leader, evaluator, critic, information provider, team player, and innovator) and social roles (i.e., team builder, nurturer, harmonizer, entertainer, jokester, and the negative roles of attention seeker and negativist) were found. While it is often task roles that receive the greatest attention, results point to the importance of not neglecting the socioemotional health of the team (and the corresponding roles). Results also indicated that while some roles were consistently enacted independent of temporal considerations (e.g., mission length), the degree to which others were enacted varied across missions of differing lengths. Additionally, based on the current sample we see the following trends: (1) increased enactment of the team builder role as mission duration increases, (2) prominence of the entertainer role, and (3) increased emphasis on the visionary/problem solver role on missions over 2 years.

Keywords: teams and groups, team roles, team performance, time, temporal and contextual factors

### INTRODUCTION

fpsyg-10-01322 June 8, 2019 Time: 9:5 # 2

It has often been said that a team of experts does not make an expert team. Although different conceptualizations of teams have been introduced within the literature, one prevalent definition stipulates that teams consist of two or more individuals who interact dynamically, adaptively, and interdependently; share common goals or purposes; and have specific roles or functions to perform (Salas et al., 1992). Teams represent a prevalent approach to structuring work, with a majority of employees reporting spending at least some part of their day within a team setting (Ken Blanchard Companies, 2006). In this vein, there is a long history of research that has sought to examine the factors that contribute to team effectiveness within a variety of contexts and much has been learned (Mathieu et al., 2008).

Despite the long history of research on team effectiveness, much of this work has been built on ad hoc teams working together for short periods of time within laboratory or organizational settings. Additionally, much of this work is primarily static in nature despite repeated arguments for the importance of considering temporal factors in team research (e.g., Mohammed et al., 2009). This, in turn, has led to minimal guidance for those individuals tasked with staffing, developing, and assessing teams that operate over longer periods of time as intact teams or operate within mission critical, extreme contexts. Teams that operate in these environments are often referred to as "extreme teams." According to Bell et al. (2018), extreme teams are those which are embedded in environments whereby one or more contextual features exist that are atypical in level or kind.

While understanding the factors that facilitate team effectiveness and how these may change over time is an important and difficult endeavor due to the complexity of collecting longitudinal data on teams, facilitating this understanding is of even greater importance for teams operating in extreme contexts. Extreme teams are not only exposed to stressors that are atypical in level, but stressors often occur simultaneously and oscillate between chronic and acute duration levels (Bell et al., 2018). Teams operating under these conditions have been shown to be more likely to have decrements in performance due to the effects of stress on team process (and correspondingly performance, Driskell et al., 1999).

In seeking to understand the factors that facilitate the effectiveness of such teams and how these factors may change based on temporal factors (e.g., team duration), we focus on team roles. Research on team roles has a rich history dating back to Bales (1950). Roles have been defined as a "set of behaviors that are interrelated with the repetitive activities of others and characteristic of the person in a particular setting" (Stewart et al., 2005, p. 344). Throughout the years, many taxonomies have been created to delineate the roles that facilitate performance in teams (e.g., Bales, 1950; Belbin, 1981; Mumford et al., 2006). While there are differences in the taxonomies created throughout the years, nearly all argue for the importance of both task and social roles. However, not much is known regarding the types of team roles needed within mission critical, extreme contexts, or how team roles in this context vary based on temporal factors (e.g., team/mission duration).

Therefore, the goal of the current study is to move the literature forward in two thrusts: (1) understanding the team roles needed within extreme environments and (2) examining how the instrumentality of specific team roles may vary based on temporal factors in extreme environments. These advancements meet a critical need in better understanding the dynamic nature of teams and consequently the roles that are enacted, but also begin to highlight the importance of context.

To achieve our goals, we employ historiometry (Simonton, 2003) as a methodology to analyze archival documentation of crew interaction, with a particular emphasis on role enactment in extreme teams using spaceflight crews as an exemplar. In the following, we first present background on team roles, extreme teams, and highlight a set of hypotheses that serve to drive our approach. Next, we summarize our methodology including the nature of our sample and procedure. Finally, we describe our results, extract the implications for understanding the dynamic nature of team roles within the context of extreme teams, and highlight future research needs.

### TEAM ROLES

Team roles have been defined as different functions and responsibilities team members must assume to enable smooth team functioning (Stewart et al., 1999, 2005). In this vein, a number of taxonomies have been created that argue for those roles that must be enacted to facilitate team performance (Benne and Sheats, 1948; Belbin, 1993; Mathieu et al., 2015; Driskell et al., 2017). The manner in which taxonomies have described team roles has varied, ranging from descriptions involving: (1) high overarching categories consisting of 2– 3 dimensions, (2) nuanced categories consisting of 5–12 dimensions, and (3) those focusing on a set of core characteristics (see **Table 1** for exemplars). Early work tended to describe team roles primarily in terms of broad overarching roles (e.g., Bales, 1950). Evidence of this research stream can still be seen in work on team roles for despite many role taxonomies becoming more nuanced, there is now general agreement on two broad classes of team roles: task roles (those behaviors that further task completion and fulfillment of the team's objectives) and social roles (those behaviors that maintain the team's social environment and the socioemotional health of the team).

As the literature progressed, taxonomies began to become more nuanced, accounting for a more varied set of roles (e.g., Margerison and McCann, 1985; Belbin, 1993; Parker, 1994, 1996; DuBrin, 1995). Perhaps most recent in this steam of work are role taxonomies put forth by Mumford et al. (2006) and Mathieu et al. (2015). Mumford et al. (2006) synthesized the previous literature on roles and delineated a set of ten roles, five task roles (i.e., contractor, creator, contributor, completer, critic) and five social roles (i.e., communicator, cooperator, calibrator, consul, coordinator, see **Table 1**). Mathieu et al. (2015) suggest that one of the key theoretical contributions of this work is integrating Ancona and Caldwell's (1988, 1992) work on roles with additional theoretical frameworks to include the

#### TABLE 1 | Example team role taxonomies.

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notion of boundary spanning. Work by Mathieu et al. (2015) attempted to find a middle ground between high overarching taxonomies of team roles and those taxonomies with many nuanced team roles. Mathieu et al. (2015) proposed and validated the Team Role Experience and Orientation (TREO), that includes six team roles. The six roles consist of the organizer (i.e., structures the team and task to ensure goals are being met), doer (i.e., completes taskwork), challenger (i.e., challenges the team to question assumptions and approaches to the task), innovator (i.e., generates ideas and solutions), team builder (i.e., maintains a positive atmosphere within the team, establishes norms, and supports team decisions), and connector (i.e., connects the team with outside entities). Taken as a whole, the research provides compelling evidence to support the validity of the six roles introduced within this theoretical framework.

Representing the last category of role taxonomies is the work of Driskell et al. (2017). Building upon previous work, Driskell et al. (2017) delve deeper into roles and argue that there are three characteristics (i.e., dominance, sociability, task orientation, see **Table 1**) that can be used to describe all team roles based on the degree to which each characteristic is present. This threedimensional model is labeled TRIAD or Tracking Roles in and Across Domains. Its usefulness lies in helping to understand how team roles might covary with one another based on their underlying characteristics.

Each of these approaches has expanded an understanding of the team roles needed for successful teamwork. However, there remains a gap in the literature regarding the influence of context. Researchers have sought to create team role taxonomies that are comprehensive and generalize across samples and conditions. Yet, we suggest that the prevalence and necessity of team roles may be contingent upon the demands of the situation. Therefore, we draw from a taxonomy introduced to describe team roles in extreme environments to further understanding in this area. In particular, Burke et al. (2016) developed a taxonomy which utilized existing literature and interviews with domain experts to form an initial set of team roles grounded in the context of teams operating in extreme environments. The taxonomy depicts a set of eleven roles consisting of five social roles (three functional, two dysfunctional) and six functional task roles. Social roles include: contribution seeker, team builder, jokester/entertainer, attention seeker, and negativist. In contrast, task roles consist of the following: team player, evaluator, information provider, boundary spanner, visionary/innovator, coordinator (see **Table 2** for a full description of roles).

While the taxonomy put forth by Burke et al. (2016) provides initial input into the types of team roles that may appear, further research needs to be conducted to examine the degree to which these roles actually occur in teams operating in extreme contexts. Teams embedded within extreme environments are repeatedly faced with strong situations which present unique demands, and each demand may require a different team role. Consequently, a more precise theoretical model explicating the roles needed for success, depending upon the various demands of the situation, is required. To address this gap, we leverage the taxonomy described by Burke et al. (2016) along with the literature on extreme teams (below) to foster our understanding of how different conditions faced by spaceflight teams influence the necessity of specific team roles.

TABLE 2 | Team role taxonomy (Burke et al., 2016).

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### ROLE ENACTMENT IN EXTREME TEAMS

As the predominant amount of work on team roles has been conducted within the context of teams operating in non-extreme environments, those charged with composing, managing, or developing teams that operate in extreme environments have little guidance upon which to rely; this is despite the mission critical nature of the teams that operate within these types of environments. Extreme environments have been described as ones in which "one or more extreme events are occurring or are likely to occur that may exceed the organization's capacity to prevent and result in an extensive and intolerable magnitude of physical, psychological, or material consequences to – or in close physical or psycho-social proximity to – organization members" (Hannah et al., 2009, p. 898). Teams that operate within extreme environments often face stressors that are atypical in kind or level (Bell et al., 2018); this culmination of stressors may drive the instrumentality of the various task and social roles that have been argued for within the broader literature.

While there are a number of team types that operate in extreme environments, perhaps the most commonly referenced are those operating within the context of polar exploration, firefighting, spaceflight, and some military environments. In investigating role enactment within these more extreme teams, we utilize teams involved in space exploration/spaceflight. Teams operating within the context of spaceflight face a number of potential stressors that are atypical in terms of kind and level. For example, research has identified at least four different classes of stressors often present in this environment: physiological/physical, habitability, taskwork, and psychosocial (see Dietz et al., 2017). In terms of physiological/physical stressors the following have been identified: decreased exposure to sunlight, circadian rhythm disruption, and sleep deprivation. Stressors related to habitability have been argued to include things such as a lack of privacy, noise/vibrations, and cooking/eating restrictions. Crews also face task related stressors such as: scheduling, variations in task autonomy, periods of monotomy/boredom, shiftwork, time pressure, and high workload. Finally, there are a myriad of psychosocial stressors which may occur, including but not limited to family life disruption, multicultural issues, task and relationship conflict, communication delays, and isolation/confinement (Dietz et al., 2017). These stressors often occur in conjunction with one another and serve as a source of threat to the crews embedded within this environment. As such, space exploration, and the teams therein, provide an exemplar of teams that operate in extreme environments and can be categorized along the set of characteristics argued by Hannah et al. (2009) to define extreme environments (i.e., location in time, magnitude of consequences, probability of consequences, physical/psychosocial proximity, and form of threat).

In seeking to understand the team roles that must be enacted within extreme environments, such as spaceflight, we can leverage work conducted on how teams respond when under stress. In this vein, early work by Sorokin (1943) found that groups involved in catastrophic events tended to become overly aroused and emotional which consequently impacted the way they processed information and made decisions. Similarly, work conducted by Driskell and Salas (1991) found stress impacts the degree to which members are receptive to informaton offered by team members. Specifically, replicating previous findings (Foushee and Helmreich, 1988), Driskell and Salas (1991) found

that under stress low status members became more willing to defer to high status members. However, contrary to previous findings, results indicated that high status members were more likely to attend to the task contributions of others. In these cases the team is in a situation in which the high status member is willing to accept task input, yet lower status members may be less willing to provide such input. This drives a need for task related roles which seek to proactively elicit information from relevant team members. While this role primarily serves to facilitate task accomplishment, it does have a social component by providing a sense of meaning and value to team members indicating that their contributions are valued.

Extending this work are findings by researchers indicating that stress leads to a loss of team perspective whereby an individual member's breadth of attention narrows and they become more self-focused, less group identity is reported, and members have less of a collective representation of the task (Driskell et al., 1999). Similarly, stress has been argued to increase distraction and decrease attentional focus, increase team members' cognitive load, increase negative emotion (e.g., frustration, fear, anxiety), and increase social impairment (e.g., reduce back-up behavior, increased interpersonal conflict/aggression, failure to appropriately read social cues, and less cooperative behavior as seen through attentional narrowing) (Driskell et al., 2018). Given the impact that stress has on both task and psychosocial aspects of the team, in line with prior research, we would expect that both task and social roles would be present (Prichard and Stanton, 1999; Chong, 2007) and fairly equally distributed when looked at across the lifecycle of the team.

#### Hypothesis 1: The distribution of task and social roles will be fairly equally represented in extreme teams.

The taxonomy put forth by Hannah et al. (2009) along with the types of stressors often experienced within spaceflight can be used to further make predictions regarding the specific types of task and social role behaviors that might be evidenced. Hannah et al. (2009) delineates five dimensions of extreme environments: location in time/temporal ordering, magnitude of consequences, probability of consequences, form of threat, and physical or psychosocial proximity. For the current effort, the first four of these are perhaps the most relevant in delineating the types of roles needed within the context of spaceflight (and other teams operating in similar extreme contexts). As such, these will be briefly discussed next.

### Location in Time

The types of threat that are present within the predominant number of extreme environments are ones which oscillate over time (e.g., at certain times being more of a concern). The temporal cycle of the impact of such threats will vary across extreme contexts and as such will drive the nature of the type of team processes required for teams to be resilient within such environments. With regard to spaceflight, the threat is primarily located in the situation although some physiological effects can persist beyond the immediate situation. While there are always low intensity chronic stressors that exist within spaceflight due to the mission criticality of the environment and distance of the crew from earth, there are periods of high intensity, acute stressors which may occur in combination as unexpected or off-nominal events occur. In this vein, Hannah et al. (2009) argue for the importance of the management of transitions between these periods of nominal and off-nominal events. With regard to roles, this drives the need for the sets of behavioral activities which will facilitate team and leader transition phase behaviors as seen in the work of Marks et al. (2001) and Morgeson et al. (2010). More specifically, role behaviors that facilitate structuring and planning of coordinative activities and points of transition, such that member cognitive and behavioral capacities are taken into account in order to ensure the capacity of any one individual member is not exceeded. This would, in turn, point to the importance of the coordinator role, information provider which serves to facilitate the exchange and clarification of information, boundary spanner to push and pull information in from outside the immediate team for use in planning, as well as the enactment of the evaluator role.

### Magnitude/Probability of Consequences

The second and third factors that Hannah et al. (2009) argue as defining characteristics of extreme environments are the magnitude and probability of consequences. With respect to spaceflight, the magnitude and probability of consequences is high given the distance from earth, relative isolation, and the environmental characteristics of space. To better understand the impact on the crew and the roles that may be important, we leverage existing literature on the impact of stress on teams along with that on high reliability organizations. Extracting from the literature on stress and teams, stress has been shown to degrade team process by causing: a narrowing of attention, loss of team perspective, degradations in coordination, and tendency for groupthink with low status members more willing to defer to others and less likely to speak up (e.g., Janis, 1972; Callaway et al., 1985; Driskell and Salas, 1991; Burke et al., 2008; Ellis and Pearsall, 2011). This points to team roles such as the critic (to combat groupthink) and boundary spanner (to bring in alternative information from outside and serving to combat the narrowing of attention and in combination with the critic role serving to combat groupthink). The propensity for low status members to "go with the flow" and potentially not offer valuable information drives the need for the contribution seeker.

High reliability organizations (HROs) can be defined as organizations that operate within environments where the magnitude and probability of consequence of error is high, yet are able to minimize errors (Roberts, 1990). As such, HROs should provide some insight into the types of roles needed when magnitude and probability of consequence is high. Research has suggested that principles of collective mindfulness (i.e., preoccupation with failure, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience, and underspecification of structures, Weick et al., 1999) are the mechanisms that allow HROs to effectively operate. Moreover, work has attempted to translate the above

organizational practices to the team level (e.g., Wilson et al., 2005; Baker et al., 2006). Wilson et al. (2005) argue that at the team level, these processes may be manifested through the following actions: sensitivity to operations (e.g., cross-lagged communication, information exchange, maintaining shared situation awareness), commitment to resilience (e.g., backup/monitoring, shared mental models), deference to expertise (e.g., assertiveness, collective orientation, expertise), reluctance to simplify (e.g., adaptability, flexibility, and planning), and preoccupation with failure (e.g., error management, feedback/team self-correction).

An examination of the HRO principles can provide insight into the types of team roles needed. For example, many of the principles speak to ensuring that information is being transmitted throughout the team (i.e., sensitivity to operations, preoccupation with failure) to maintain shared mental models and situation awareness (i.e., sensitivity to operations, commitment to resilience). This speaks to the need for team roles such as the information provider and contribution seeker to ensure relevant input is being gained no matter the status of the individual team member. The importance of members backing one another up (i.e., commitment to resilience) and maintaining a collective orientation (i.e., deference to expertise) drives the need for the team player, jumping in wherever needed. Finally, the requirement to be adaptive and flexible (i.e., reluctance to simplify, preoccupation with failure) drives the need of the critic who can combat against groupthink as well as the boundary spanner role to ensure that the team is maintaining an awareness of events outside the team that may impact their mission.

> Hypothesis 2: The oscillations in stressor onset as well as the high magnitude and probability of consequences will drive the following task-orientated roles as being commonly seen: boundary spanner, team player, evaluator/critic, contribution seeker, and information provider.

### Form of Threat

The fourth characteristic along which extreme environments can be characterized is the form of the threat(s) presented to the teams. Hannah et al. (2009) argue that threats can be physical, psychological, or material. In the case of spaceflight, while threats can exist on any of the three aforementioned dimensions, they are most often physical and psychological. Factors such as isolation, confinement, and disruption of family life drive the increased need for team roles that are targeted at maintaining the psychosocial health of the team, in addition to the physical health. Therefore, we predict that the enactment of behavioral sets of activities that serve to reduce interpersonal conflict (e.g., harmonizer), maintain team morale, redirect crew attention from the negative aspects (e.g., team builder, entertainer), and ensure that personal physical and space needs are met (e.g., nurturer) are the key social roles that will be seen within extreme environments. The latter set of roles (e.g., nurturer) arise to fulfill the gap created based on the confinement and isolation from loved ones who might otherwise ensure these basic needs are met.

Hypothesis 3: Social roles that will be most prominently seen in extreme teams (e.g., spaceflight crews) include: the harmonizer, nurturer, team builder, and entertainer.

## ROLE ENACTMENT AND TEMPORAL CONSIDERATIONS

While the contextual nature of extreme teams is expected to drive the importance and/or frequency of enactment of particular roles as argued for above, it is also expected that team roles are dynamic and the degree to which specific roles are manifested within a team will vary based on several temporal factors. Below, we begin to set forth a series of propositions driven by the literature on team development, albeit manifested in two different ways. The literature on team development and team dynamics has a long history (e.g., Tuckman, 1965; Gersick, 1991; Salas et al., 1992; Hackman and Wageman, 2005; Kozlowski et al., 2009; Burke et al., 2017), yet in thinking about extreme teams we take a slightly different approach in that we couple team development with contextual factors due to their tightly linked nature in teams.

The context within which we are investigating extreme teams is one in which the team members tend to be task experts, colocated with fellow crew members, and highly driven individuals. These crews also tend to be intact, operate under varied stressors that occur simultaneously, and tend to have high level of isolation and confinement. Therefore, our propositions will touch less upon the team developmental needs as by the time the predominant number of these teams are on a mission, they have already been exposed to a wide variety of team building and training exercises and in most cases have prior knowledge of crew members (if not prior working experience with them). Instead, we focus predominantly on how team needs may change over time based on the temporal duration of the missions within which the team is operating.

Work by Salas et al. (1992) has argued, and later research has shown (e.g., Mathieu et al., 2008), that in order to be effective, teams must master two tracks of skills – taskwork and teamwork. Specifically, the taskwork track represents "taskorientated skills that members must understand and acquire for task performance" (Salas et al., 1992, p. 10). In contrast, the teamwork track refers to "the behavioral interaction and attitudinal responses that team members must develop before they can function effectively as a team" (p. 11). We expect that teams operating in extreme contexts are no different than most operational teams in this regard (i.e., both sets must be mastered, as indicated by Hypothesis 1). However, we do propose that teams operating in these extreme environments have different challenges that cause the instrumentality of roles related to the maintenance of these two tracks to differ over time.

Within the set of extreme teams under consideration, missions of shorter duration tend to be characterized by high operational tempo due to the high workload present as crew members strive to complete science payloads, engage in public outreach and educational efforts, adhere to exercise and diet schedules, and ensure the equipment in transport vehicles and the habitat are working properly. The degree of high operational tempo seen

in missions of short duration drives the crew into a very taskoriented mindset. Therefore, within these missions when the crew is together for shorter periods of time roles will tend to revolve around ensuring task needs are met. This is not to say that social roles are not important on the shorter duration missions, but the social stressors that the teams are exposed to on the short duration missions are not as salient as the taskorientated stressors. For these reasons, we would expect that in terms of frequency of enactment, there would be a greater proportion of task roles enacted on those missions that fall within the short duration category. The social stressors that the teams are presented with on short missions may be viewed as low level, while task stressors tend to be of higher levels and oscillate between acute and chronic in nature. Although not conducted with extreme teams, a review of team studies conducted by Bradley et al. (2003) revealed a pattern consistent with this expectation. They found that teams working on tasks of shorter duration, as compared to longer duration tasks, focused on "the task to the exclusion of efforts to form cohesive team norms that would only benefit the teams if they were going to remain together for the performance of future tasks" (p. 12). This evidence suggests that teams are less likely to invest in interpersonal relations and focus on fostering group norms via social roles when focused on tasks or missions of shorter duration (i.e., <=15 days).

> Hypothesis 4: In shorter duration missions, task roles will be the driving factor in facilitating team performance, particularly those roles which foster the self-regulatory capacity of the team and facilitate collective mindfulness (e.g., boundary spanning, evaluator/critic).

As the duration of the mission increases, and correspondingly the team is exposed to the extreme conditions for longer periods of time, we expect that the enactment of social roles will become more prominent. The task-based stressors do not disappear as many are defining features of the extreme environment; however, the perceptions of isolation and confinement increase and begin to take a socio-emotional toll on the team. This effect is commonly reported in literature with respect to teams that have been deployed within extreme conditions for long periods of time. This phenomena is known as the third-quarter effect whereby individuals within isolated extreme environments often experience a decrease in mood and affect during the third quarter of their deployment or mission (Evans et al., 1987; Bechtel and Berning, 1991; Steel, 2001). This, in turn, is expected to drive an increased focus on behaviors that are related to ensuring that the social needs of the team are being met as a way to combat this natural drop in affect and mood. Moreover, teams formed for a longer period of time, as compared to teams working on tasks of shorter duration, have been found to invest more effort in forming relationships with other team members because they are aware that the longer task duration makes it more beneficial to have these relationships (Bradley et al., 2003). In line with this evidence, we suggest this is another reason, in addition to contending with the extreme environment (e.g., Steel, 2001), that more social roles are likely to be enacted on longer duration missions. Team members may engage in more social roles with the underlying goal of forming close relationships with other team members due to the longer duration of the mission.

> Hypothesis 5: As team duration increases within extreme contexts the enactment of social roles become more frequent. Particularly, those roles that foster the socioemotional health of the team such as behaviors which provide an escape from the stressors present as well as behaviors which seek to maintain the emotional and physical health of the team (e.g., entertainer/jokester, nurturer).

## MATERIALS AND METHODS

In order to test our assumptions and to gain a better understanding of team roles in extreme teams, with an emphasis on spaceflight crews, a historiometric approach (Simonton, 2003) was applied. Historiometry describes the systematic analysis of the content of past events and is defined as the "collection of methods in which archival data concerning historic individuals and events are subjected to quantitative analyses in order to test nomothetic hypotheses about human thought, feeling, and action" (Simonton, 1998, p. 269). This method is especially useful for exploring a relatively new research area, such as examining the dynamic nature of team roles in extreme environments, because it depends on data that were not explicitly collected for the research question of interest, thus limiting some bias. Further benefits of this approach include the contextual richness of the data and the corresponding external validity (Crayne and Hunter, 2018). Historiometry also enables the examination of complex constructs as expressed in behavior (e.g., team roles) during real situations, and the investigation of how such (team) constructs may differ depending on the type of situation (Antonakis et al., 2003). Recent studies have similarly applied historiometric analysis to explore topics such as team leadership in mission critical/isolated environments, successfully providing insight into other relatively new team-level research areas (e.g., DeChurch et al., 2011; Burke et al., 2018).

### Sample

The final sample used to examine our hypotheses consisted of 525 roles extracted from 514 critical incidents describing collective team interaction within the context of spaceflight. The incidents and coded roles came from the following seven missions that varied in length, allowing an examination of how team roles may vary over time: Shuttle, Soyuz, Gemini, Skylab, Salyut, Mir, and Mars 500.

### Procedure

#### Sources

The first step was to identify historical events (i.e., missions) that documented team interaction within the context of spaceflight. Sources were identified through the following databases: EBSCOhost, Google, and Google Scholar. Sources were also identified by searching the following websites: Amazon,

#### TABLE 3 | Sources and the respective spaceflight context.

Biography of Skylab Astronaut Jerry Carr

and the Quest for Interplanetary Travel

Leaving Earth: Space Stations, Rival Superpowers,

Johnson Space Center, and European Space Agency. Both primary (e.g., diaries and autobiographies) and secondary sources (e.g., biographies and missions reports) (Simonton, 1990) were

Book Zimmerman R. 2003 Soyuz, Mir, and

collected (see **Table 3** for complete list of final sources used). Sources were examined for the extent to which they described team interaction and corresponding behaviors whereby critical

Salyut

incidents regarding team role enactment could be extracted. Of specific interest was task and social role enactment as evidenced within collaborative activities that occurred while members were engaged in their primary tasks (i.e., task execution) as well as those that occurred during off-task periods (i.e., downtime). Information related to duration of the spaceflight missions comprising our sample was also collected (**Table 4**). The missions identified fell into one of four durations: short (15 days or less), medium (greater than 15 days, maximum 6 months), long (greater than 6 months, maximum 11/<sup>2</sup> years), longest (longer than 11/<sup>2</sup> years, maximum 2 years).

#### Sampling

The initial search produced approximately 150 sources for further examination. Sources were then examined with respect to the following criteria: (a) sources must describe interdependent interaction among the crew/team; (b) sources must describe crew/team actions where team role behaviors (positive or negative) are present and described; (c) teams being described must be operating in a real or simulated spaceflight environment; and (d) source must be accessible. A group of psychologists with experience in team roles and historiometric analysis reviewed the suitability of all sources as described previously, while taking into consideration the representation of all different spaceflight contexts and missions durations. At the end of this stage, a set of 39 sources remained (i.e., 14 books and 25 diaries).

In order to systematically extract all relevant information from the final set of 39 sources, seven subject matter experts were trained on the critical incident technique and its application in the current context (Flanagan, 1954). The critical incident technique has been described as a set procedures that assist in the systematic extraction of human behavioral observation which may be ". . .adapted to meet the specific demands of the situation at hand" (p. 335). The first step in developing a critical incident is to understand the aim of the incident. For us, the aim is driven by our stated research questions. Therefore, the raters responsible for extraction of the critical incidents needed to understand what team roles were and how they manifest in teams. While all raters had a prior familiarity with team roles, ensuring their understanding was the initial part of our training. Next, training progressed to incident extraction. While the specific form a critical incident may take can vary based on the researcher's need, for the current project, extraction included a behavioral description of team interaction at a specific point in time during the team's mission as well as the consequence of that interaction (see **Table 5** for examples).

#### Coding

Once extracted, all incidents were double-coded by two SMEs with experience in teams (and more specifically team roles). The SMEs were asked to independently sort the identified roles into role type (i.e., social, task, or non-applicable), role category (e.g., team player, contribution seeker, or non-applicable), and if applicable into role subcategory. Raters utilized the Burke et al. (2016) taxonomy as a baseline for their coding, but were told not be restricted by the dimensions contained within that particular taxonomy. For some incidents, more than one role category was TABLE 4 | Differentiating of spaceflight context based on mission duration.

identified. For testing the interrater reliability among the SMEs, we calculated Krippendorff's alpha, a standard reliability measure regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data, by using the respective SPSS macro (Hayes and Krippendorff, 2007). The interrater agreement was excellent for role type (Krippendorff's α = 0.79), role category (Krippendorff's α = 0.77), and for role subcategory (Krippendorff's α = 0.75) (Cicchetti, 1994). In the final step, a meeting was held where both SMEs came to consensus regarding any discrepancies in their codes.

### Data Analysis

Data analysis consisted of two primary foci. First, to examine the set of propositions pertaining to team role enactment within extreme teams (Hypotheses 1–3), the roles that emerged from the card sort were rank-ordered by their frequency of occurrence. The frequency of each role type (i.e., task, social), role category (e.g., jokester, critic) and role subcategory (if applicable) was calculated.

To examine the dynamic nature of the identified team roles, we differentiated between spaceflight contexts in terms of the mission's duration (i.e., short, medium, long, and longer duration, see **Table 5**). Specifically, we adopted a comparative method (e.g., Gardner, 1993) by comparing and contrasting the illustrated team roles, in order to extract the common and differing role characteristics between the various temporal durations.

## RESULTS

### Team Roles

One of the primary questions posed within the current study was with regard to the types of task and social roles exhibited in teams operating within extreme contexts, using spaceflight crews as an exemplar. Closely related to this question was an examination of how temporal factors (i.e., mission duration) impact the nature of team roles exhibited. In this vein, five hypotheses were put forth regarding the team roles expected to be the most prevalent based on the defining features of spaceflight crews operating in extreme contexts and the frequency of specific role enactment based on mission duration.

With respect to Hypothesis 1, as predicted, results indicate that in terms of frequency both task and social roles were enacted in nearly equal proportions. Specifically, collapsing across missions, results indicated that 51% of the roles witnessed were social roles, while 49% of the roles were task-related (N = 267 and 258, respectively). Additionally, results indicated that many of

#### TABLE 5 | Example statements and categorization.

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the roles seen in previous taxonomies developed with respect to teams operating in more traditional, non-extreme environments also appeared in the current context (e.g., team builder, jokester, team player, information provider). However, at a global level there were some differences to note. First was the presence of the social role of "entertainer." While similar to the jokester role seen in many role taxonomies outside of extreme contexts, the entertainer role is broader. Specifically, we define it as behaviors which seek to maintain cohesion and emotional wellbeing of team members through humor and other active, public forms of artistic expression. Additionally, the role of "nurturer" was a prominent role that does not often appear outside this context. This role consists of behaviors primarily focused on the maintenance of the physical health and personal space of crew members. Finally, of note is the lack of enactment of what would traditionally be considered negative roles consisting of behaviors directed at fellow team members (e.g., attention seeking, social loafing, expression of negativity). While a negativist role was frequently seen in some contexts it tended to consist of negative affect (i.e., complaining) regarding environmental, contextual, or equipment difficulties; it did not tend to be directed toward fellow crew members. When it was directed at individuals, it was most often members of ground control.

Hypotheses 2–3 described the task and social roles that were believed to be the most critical to teams operating in extreme contexts, such as spaceflight. To examine the data in relation to the hypothesis presented herein, the team roles that emerged from the card sort were rank-ordered in terms of their frequency of occurrence with respect to task and social roles, respectively. With respect to the predictions set forth in TABLE 6 | Rank ordering of the top five task roles which emerged.

Hypothesis 2, findings were mixed. In line with predictions, the roles of boundary spanner, team player, and information provider emerged within the top five most frequently occuring task roles (see **Table 6**). The team player role is comprised of behaviors that reflect a willingness to pitch in wherever help is needed. Whereas, the information provider is comprised of behaviors serving to transmit and gather informaton within the team and create shared mental models. Finally, the boundary spanning role involves those behaviors which serve to maintain a link between the team and external entities and may involve the pulling and pushing of information. However, also occuring within the top five, but not predicted, were the coordinator role (encompassing subroles of team leader and project management) and the visionary/innovator role. The later role involving behaviors related to problem solving and thinking outside the box. Finally, contrary to predictions, behaviors related to the analysis and evaluation of ideas (e.g., critic) did not appear within the top five enacted task roles.

Hypothesis 3 pertained to the enactment of social roles. Similar to Hypothesis 2, results suggest partial support for this prediction. As expected, the team builder, entertainer, and nurturer roles were witnessed within the top five most enacted social roles (see **Table 7**). This reflects the importance of positive behaviors that improve the team's social structure and well-being. Specifically, the team builder reflects behaviors which seek to improve and maintain the social structure of the team, including behaviors that foster motivation and harmony. A subrole of this dimension is the nurturer role which primarily focuses on behaviors promoting the physical and emotional well-being of crew members, including personal space. However, the presence of behaviors reflecting an explicit negative outlook (i.e., the negativist) was unexpected. In further examining the results, these role behaviors primarily came from crews involved in the Skylab mission where relations between mission control and the crew degraded to such a point that the crew went on strike. Dropping the mission where the crew went on strike does drastically reduce the prevalence with which these behaviors are seen, but they would still appear within the top five. However, the focus then becomes negative comments related primarily to environmental and equipment conditions, with much less of a focus being on interpersonal negativity. **Table 8** contains a full listing of all team roles which emerged and the frequency with which emergence took place (both task and social).

Additionally, we conducted exploratory analyses to determine the five most commonly enacted roles when looking across the total set of task and social roles. As can be seen in **Tables 8**, **9**, results indicated the following five roles were the most frequently occurring, in order: boundary spanner, team builder, entertainer, negativist, and team player. This last role was closely followed by the presence of the visionary/innovator role. In essence this analysis pits social and task roles against one another to examine the most frequently occurring roles across the set of extreme contexts.

#### Roles Over Time

Another primary goal of our study was to investigate the degree to which roles may vary across spaceflight contexts in terms of mission duration. As is common with the exploration of phenomena on which there is not a large body of prior work upon which to build hypotheses (and one reason for the approach taken), the hypotheses concerning the specific task and social roles expected to be most prevalent based on temporal duration received mixed support. **Table 10** contains the full list of task

TABLE 9 | Rank ordering of the top five team roles enacted across task and social categories.

and social team roles, their frequency counts and percentages as delineated by temporal duration.

Results indicated that during short missions (i.e., less than 15 days), task team roles emerged twice as frequent (N = 84) as social roles (N = 48), while during medium duration missions (i.e., up to 6 months), the exact opposite role distribution was found between task (N = 44) and social (N = 80) team roles. During long (i.e., up to 1.5 years) and longer spaceflight missions (i.e., more than 2 years), the task (N = 98 in long missions, N = 32 in longer missions) and social (N = 99 in long missions, N = 40 in longer missions) team roles were evenly distributed. It seems that task roles are notably salient in very short missions, while social roles are gaining importance as the duration of the mission

<sup>4</sup>Percentages contained in table are based on the total task and social roles enacted for a given mission.

TABLE 11 | Frequencies of task and social roles identified for each mission duration.

increases. At the same time, when the duration of the spaceflight missions exceeds a duration of 6 months both task and social team roles become equally frequent (see **Table 11**). The above set of results tends to support the primary tenets put forth in Hypotheses 4 and 5. Specifically, that the enactment of task roles are the most prominent within missions of short duration, while social roles gain more prominence as mission duration increases.

However, in looking at the predictions as to what particular task and social roles would appear most prominently, we received mixed results (see **Table 10**). One of the top task role categories, similarly frequent in all mission durations, was the team player, highlighting the importance of being willing and prepared to contribute and help whenever and wherever needed. The boundary spanner role also emerged as one of the top task roles in all mission durations, gaining frequence with increasing duration up to long duration missions; during the longer duration missions, the frequence of the boundary spanner was lower compared to the other mission durations. The opposite trend emerged for the third top task role for all mission durations – visionary/innovator; this social role decreased in frequency as mission duration was increasing, demonstrating its lowest frequency during long duration missions. For the longer mission duration, the visionary/innovator role emerged more frequently than in any other mission duration. The task role of team leader, highlighting the importance of leadershiporiented behaviors focusing on directing the teams toward mission completion, was identified as one of the top social roles only in short duration missions.

The entertainer role was one of the top social roles that similarly emerged in all mission durations, demonstrating the

relevance of positive behaviors that serve to bring humor into the team. The team builder was identified as one more top team role in almost all spaceflight contexts, gaining frequence with increasing mission duration. During short duration missions, the frequency of the team builder role was noticeably lower compared to the other mission durations. The complainer team role, reflecting negative behaviors of complaining and whining about social team issues, emerged as one further top social role only for medium mission duration.

### DISCUSSION

The use of teams has become ubiquitous within organizations due to the potential for teams to accomplish complex and interdependent work within environments that are increasingly dynamic. A well coordinated team is not only a pleasure to watch, but can bring tremendous rewards to organizations by leveraging the combined intellectual strength of its individual members. However, more often is the case that teams are implemented, yet fail to fully capitalize on the potential synergy present in the team; when capitalized upon, this synergy allows teams to become more than the sum of their individual member contributions. In effort to facilitate the probability that teams can leverage this potential capacity, there has been a tremendous amount of research conducted on the factors that facilitate the ability for members to work in a coordinated and adaptive manner such that they are ready to respond to changes both internal and external to the team. Due to the tremendous growth in team research and the corresponding lessons learned, a great deal of guidance can be currently provided to organizations regarding team dynamics. However, as noted by the editors of this special issue, sorely lacking in the area of team research is guidance pertaining to how the instrumentality of processes, states, and facilitating factors seen in team effectiveness models and team taxonomic efforts may vary due to temporal factors.

Due to the complexity of teams there are a variety of ways that temporal factors could be operationalized within teams, including but not limited to: the moment to moment changes in team process dynamics, oscillations between transition and action phase while engaging in a performance episode, team developmental stage, and/or length of time the team has been together. Within the current study, we have begun to take initial steps to delineate how team roles may vary over time by examining teams operating within extreme environments over short, medium, and long durations. Given our interest in team roles and how they may change over time within extreme teams, we chose to initially investigate this phenomena at a more global level in terms of time. The path we chose was dictated by the fact that, while dynamic, the enactment and switching of roles is most likely not as dynamic as changes in team process, thereby pushing us initially toward a more global view of time. In addition, given the lack of research on team roles over time within teams operating in extreme conditions we did not feel the theory was yet there to begin to predict moment to moment changes at a finegrained level.

Results of the study speak to the importance of task and social roles within teams that are predominantly intact and operating in extreme environments where mistakes can be life threatening. Additionally, our findings begin to highlight areas of commonality and distinction between these environments and the more traditional organizational environments in which teams have been studied. In essence, while there were many commonalities between the team roles seen in the context of spaceflight and those which appear in the team role taxonomies which appear in the broader literature on teams, there were also differences. In terms of commonalities, task roles such as the team player, coordinator, evaluator/synthesizer, information provider/facilitator were seen. However, far less commonly seen were task related roles that may be considered dysfunctional (e.g., social loafer, power seeker). The decreased prevalence of these roles may be due to the mission critical environment in which the teams in this sample (and many teams in extreme environments) are embedded. Mistakes in these environments can often be extremely costly not only in terms of material, but personal resources – in some cases life threatening.

Many of the differences seen in terms of role enactment dealt with aspects of the social roles. Perhaps most prevalent was the expansion of the traditional jokester role to encompass a more inclusive entertainer role. This role reflects the elevation of mood and team member bonding not only through humor, but also through competitive activities and coming up with novel ways to occupy "down time." Additionally, the team builder role incorporated the notion not only of behaviors which serve to reduce conflict and promote harmony among team members, but behaviors that serve to keep the team motivated, and behaviors that are more "nurturing" by nature. This later aspect of the team building role is one that is not often explicitly mentioned in the team taxonomies that appear in the broader literature. Finally, it is interesting to note that results did suggest a prevalence of behaviors related to negative affect; however, the predominant amount of these affective remarks were not directed at the immediate crew, but were either directed outside the immediate crew, or expressed in relation to conditions or equipment. This points to the fact that the atypical stressors present in the environment do serve to impact the affect of teams within extreme contexts; being resilient in these environments does not mean that negative affect does not occur. Future research should further investigate the mechanisms through which the team deals with the negativity when expressed. It is likely that some of the other social roles seen may serve as a buffer against the negativist comments, but this needs to be further investigated.