Teams in Space Exploration: A New Frontier for the Science of Team Effectiveness

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Teams in Space Exploration: A New
Frontier for the Science of Team
Effectiveness
Eduardo Salas1, Scott I. Tannenbaum2, Steve W. J. Kozlowski3,
Christopher A. Miller4, John E. Mathieu5, and
William B. Vessey6
1
Department of Psychology, Rice University; 2
Group for Organizational Effectiveness, Albany, New
York; 3
Department of Psychology, Michigan State University; 4
Smart Information Flow Technologies,
Minneapolis, Minnesota; 5
Department of Management, University of Connecticut; and 6
Wyle Science,
Technology, and Engineering Group, NASA Johnson Space Center, Houston, Texas
Abstract
Researchers from a variety of disciplines are currently working with NASA to prepare for human exploration of Mars in
the next decades. Such exploration will take scientific discovery to new heights, providing unprecedented information
about the geology, atmosphere, and potential for life on Mars, including previous life, current life, and perhaps even
our own lives in the future. To make these unparalleled discoveries, however, astronauts will need to undertake a
novel and unprecedented journey. Moreover, the mission to Mars will require a team of crew members who will have
to endure and sustain team performance requirements never seen before. Multidisciplinary teams of scientists have
begun to provide the needed steps to address this challenge. The purpose of this article is (a) to illustrate the kinds of
new conceptual frameworks and paradigms needed for teams in space exploration, (b) to delineate promising research
paths to ensure that a robust team science can emerge for long-duration space exploration (LDSE), (c) to showcase
initial findings and insights from studying astronauts now, and (d) to outline a plan of action for team-effectiveness
research in LDSE.
Keywords
teams, teamwork, team training, team cohesion
Space Teams 201
day, you cannot simply go out for a walk or call in sick.
That is the challenge of long-duration space exploration
(LDSE). That is the challenge for NASA.
The aim of the science of team effectiveness is to better understand how to manage and sustain crew cohesion, coordination, and teamwork so as to help crew
members succeed, be safe, and thrive during such a mission. To this end, multidisciplinary teams of scientists
have begun to provide the needed steps to address this
challenge. The purpose of this article is fourfold—first, to
illustrate the kinds of new conceptual frameworks and
paradigms needed for teams in space exploration; second, to delineate promising research paths to ensure that
a robust team science can emerge for LDSE; third, to
showcase initial findings and insights from studying
astronauts now; and fourth, to outline a plan of action for
team-effectiveness research in LDSE.
The Science-of-Team-Effectiveness
Challenge
If we go to Mars, flight crew members and ground control engaging in LDSE will be required to operate under
unique conditions that pose both physical and psychological challenges. Specifically, team members will be
required to communicate, coordinate, and cooperate for
extended periods of time, under complex conditions
(e.g., extreme isolation, confinement; Kanas, 2011).
Further, such teams will largely operate autonomously, as
communication delays will hinder the capacity for immediate input from leaders or other outside members (Kanas
& Manzey, 2008). Overlaying these issues are the physical
demands involved in spaceflight, putting added stress on
crew members, and the inherent risk involved in longduration missions, which leaves little room for performance lapses (Ball & Evans, 2001; McPhee & Charles,
2009). And finally, add to the mix long durations of boring, monotonous inactivity. All of this creates a highpressure, high-stress environment that can be harmful for
team cohesion, teamwork, and performance (Schmidt,
Keeton, Slack, Leveton, & Shea, 2009). LDSE crews thus
are not regular teams that can be studied the regular
way—the unique conditions they face require us to
approach the study of these teams in new and different
ways.
The science of team effectiveness has made substantial progress in the last decades (for recent reviews, see
Kozlowski & Bell, 2003, 2013; Mathieu, Maynard, Rapp, &
Gilson, 2008; Salas, Stagl & Burke, 2004). This science has
generated evidence-based findings that NASA can draw
from to secure mission success (see Salas, Tannenbaum,
Cohen, & Latham, 2013; Tannenbaum, Mathieu, Salas, &
Cohen, 2012). Furthermore, team science in spaceflight
analog environments such as polar stations, submarines,
oil rigs, and other isolated, confined, and extreme environments has led to important insights into the dynamics
of teams operating in these types of environments (e.g.,
Lugg, 2005; Palinkas, 2003; Schmidt, Wood, & Lugg,
2004).
However, there are significant gaps in our understanding of team dynamics under the conditions noted above.
For example, while team cohesion has been studied in
many settings and several meta-analyses have been produced (e.g., Gully, Devine, & Whitney, 1995), none of
these have captured the LDSE conditions under which
spaceflight crew members will perform. Even in those
environments in which some aspects of LDSE are found
(e.g., polar stations, submarines), not all of the environmental factors involved in LDSE are present. For example, while Antarctic stations are physically confining and
involve some levels of danger, crews stay at the stations
for much shorter durations than those that will be experienced during LDSE. Similarly, naval submariners are
exposed to a complex, dangerous, isolated, and confined
environment, but crews are rotated regularly.
Moreover, the vast majority of the research foundation
is based on cross-sectional data; it is static. We need
“intensive” longitudinal research on long-term team functioning under isolated, confined, and extreme conditions
that are similar to those of LDSE. Team composition,
teamwork and team dynamics, individual and team selfregulation, and team adaptation and development are,
indeed, challenging to study in these environments, so
new ways of studying these teams are of paramount
urgency. For example, how can these team members
monitor or gauge, unobtrusively, their coordination, communication, and cooperation? How can data be captured
in real time as they perform? How do these teams selfcorrect (i.e., get better) without an outside intervention?
These are just a sample of key research questions. The
good news: Some progress has been made. We elaborate
next.
Emerging conceptual paradigms in
the science of team effectiveness
Space exploration is really a team sport. It is teams that
launch, monitor, operate, and return the space vehicle
safely to Earth. Clearly, in LDSE, team cohesion will be
paramount to mission success. However, teams are not
created equal, and certainly those performing LDSE are
not an exception. So, we need a richer and deeper conceptualization (and frameworks) of team cohesion,
beyond the historical view of it as consisting of (a) commitment to the team task and (b) a desire to belong to
the team or complete the task and interpersonal
202 Salas et al.
commitment to the team (Festinger, 1950; Forsyth, 2010;
Gully et al., 1995). Team cohesion is more than that for
LDSE. We submit that team cohesion is a dynamic, multifaceted, and multilevel phenomenon, with emerging
states and stable properties, that is dependent on the task
and teammate characteristics.
Our conceptualization of team cohesion as a phenomenon—a dynamic system, rather than a stable construct—
treats it as a complex system of causally linked
characteristics that self-reinforce (cf. Borsboom & Cramer,
2013). Cohesion emerges over time, cycles, and fluctuates
(Kozlowski, in press; Kozlowski, Chao, Chang, &
Fernandez, in press; Schmidt et al., 2009). Team cohesion
is affected by variables that are established both before
(e.g., team composition, prior experiences) and during
the mission (e.g., interpersonal conflicts). What we know
today is not enough to help in LDSE. Consider, for example, what we know about time. There are very few studies
that have analyzed team cohesion for weeks and months
(Cronin, Weingart, & Todorova, 2011; Kozlowski, in press).
Most studies have assessed cohesion for a few hours at
best. There is an urgent need for more studies that examine teams for long durations (e.g., months, years), particularly when those teams must remain together during that
time period.
Our conceptualization of cohesion must also be
expanded to include not only aspects of task work and
teamwork but psychological factors (i.e., team wellbeing) as well. LDSE is different from any other job
assignment. It is even different (e.g., in its length of time,
isolation, and limited space) from all prior space missions
(Galarza & Holland, 1999). These differences have implications for the selection of astronauts and the composition of LDSE flight and ground crews, as well as for how
to manage the collective performance of all involved in
the mission.
Recently, we have also begun exploring the construct
of team resilience (Alliger, Cerasoli, Tannenbaum, &
Vessey, in press), which refers to the capacity of teams to
handle and respond to stressors and challenges. Prior
research has examined individual (Friborg, Barlaug,
Martinussen, Rosenvinge, & Hjemdal, 2005) and organizational resilience (Somers, 2009), but little work has focused
on resilience at the team level. Our initial work has been
to define the construct, identify the behaviors resilient
teams demonstrate, and establish ways to measure it. We
have also been gathering daily measures of team resilience from teams in isolated, confined environments, with
early results suggesting that team resilience differs from
cohesion and other constructs such as psychological
safety. The ability to handle both chronic and acute stressors will be essential if we want LDSE crews to thrive, so
further research related to team resilience is needed.
Measurement of team dynamics
The measurement of team dynamics and effectiveness is
challenging. There is a long history of research examining team measurement issues (Brannick, Salas, & Prince,
1997; Rosen, Wildman, Salas, & Rayne, 2012), but there is
still no standard measurement approach. There are, however, significant advances in capturing what teams think,
feel, and believe (Salas et al., 2012). But for LDSE, the
assessment and measurement of team dynamics must
look and feel different, yet still be valid and diagnostic.
The isolation and delayed communication capabilities
that LDSE teams will experience means that they must
self-regulate and correct themselves. Team dynamics will
need to be continually assessed in an unobtrusive manner so that countermeasures (i.e., steps taken to minimize
the likelihood of problems or to address existing problems) can be deployed as needed.
Our initial research on the dynamics of team cohesion
(and related indicators of this phenomenon) is utilizing
experiencing-sampling methods (ESM), which are frequent but concise assessments (Beal, in press) of team
processes or perceptions experienced by team members.
Given the LDSE focus of the research, it is being conducted in a range of environments across a variety of durations. For example, we collect ESM data from teams that
camp on the ice during the Antarctic summer (6 weeks)
and station teams that winter-over (9–12 months). We also
collect ESM data in the Human Exploration Research
Analog (HERA), a 1- to 2-week asteroid transit simulation;
the NASA Extreme Environment Mission Operations
(NEEMO) habitat, a 1- to 2-week “weightless” exploration
simulation; and the Hawai‘i Space Exploration Analog
and Simulation (HI-SEAS), a series of Mars-surface-analog
exploration missions with durations of 4, 8, and 12 months.
We sample at least daily in all settings and several times
daily in some. This research is providing “benchmark”
data that is being used to establish expected normative
patterns of team-cohesion variation within and between
persons and within and between teams. Such data will
help to establish standards for detecting anomalies (i.e.,
when cohesion varies beyond normative patterns). One
interesting observation from ongoing work is that each
team has unique features and sensitivities with respect to
cohesion variability. For example, some teams are highly
sensitive to external (e.g., weather, workload) and internal (e.g., interpersonal conflict, unbalanced workload)
shocks, whereas others are buffered. Thus, although the
benchmark data are useful for establishing general expectations about team cohesion, each team is also its own
ecosystem. Therefore, it is likely that benchmarks will
have to be developed for specific teams prior to the onset
of an LDSE. Such “tailored” benchmarks would be used
Space Teams 203
to help teams initiate self-correction episodes or to trigger targeted interventions to guide cohesion recovery
(Kozlowski et al., in press).
The benchmark data are also useful for validating new
technologies to assess cohesion at very high sampling
frequencies (Kozlowski et al., in press). There is an
emerging array of developing technologies for unobtrusively assessing physiological indicators and social interactions that have the potential to capture the dynamics of
team cohesion and psychosocial health (e.g., Olguin,
Gloor, & Pentland, 2009; Quwaider & Biswas, 2010). For
example, one multidisciplinary research team, composed
of organizational psychologists and electrical engineers,
is working to develop a high-precision wearable sensor
array (Baard, Braun, et al., 2012; Baard, Kozlowski, et al.,
2012) that can capture multimodal data indicative of team
interaction dynamics, fuse the data to draw inferences
about the state of team cohesion, and—when needed—
direct interventions to support team-member and team
regulation (Kozlowski, Chang, & Biswas, 2013; Kozlowski
et al., in press).
In its current state of development, the wearable sensor technology is capable of capturing dynamic teammember interactions through several modalities, including
face time (face-to-face interaction), physical movement
(motion), vocal activity (duration, interval, and intensity
of vocalization), and heart rate (beats per minute and
variability). These multimodal data streams are sampled
at high frequencies and transmitted wirelessly to a computer server or to the “cloud.” Real-time data streams can
be displayed on a dashboard (e.g., computer display, tablet, or smartphone). Ultimately, the goal is to develop
analytic algorithms that allow the state of team members
and the team as a collective to be inferred from the multimodal data.
Consider that a collaborative interaction is patterned
by a sequence of dynamic indicators. One team member
approaches another, with corresponding indicators of
physical movement, distance closure, slight elevation in
heart rate, face time, vocalization, and finally, disengagement by one member or the other. Deviations from established interaction baselines—large spikes in vocal
intensity, heart rate variability, rapid disengagement, and
increased social isolation—signal an anomaly in teammember and team functioning. With appropriate algorithms applied across interaction patterns, inferences
about the status of the team can be drawn. When anomalies are detected by the system, countermeasures in the
form of feedback, advice, and guidance can then be
directed to specific individuals, the team, the team leader,
and other relevant parties to support team regulation,
cohesion, and effectiveness (Kozlowski et al., 2013).
Thus, the approach being advanced is designed to measure and model the unfolding of team process dynamics,
capture reciprocal relations between team processes and
team effectiveness over time, and intervene to regulate
team functioning (Kozlowski & Chao, 2012; Kozlowski
et al., in press).
Most existing measures of team effectiveness, particularly those that are survey based, require the interruption
of ongoing task work. LDSE crew (and ground-support
personnel) are engaged in complex, demanding, highcriticality tasks and are resistant to such interruption,
especially if they deem it counter to “productive work”
toward their mission. On the other hand, vast quantities
of data about human work-based interactions are already
available in NASA contexts—in the form of audio, video,
and textual records of crew behavior and communications. Since it is through observing interaction and communication behaviors that humans naturally infer
elements of psychological and social states of their fellows, it ought to be possible to automate those inference
processes to some extent. Methods for rapidly and easily
processing these data and inferring information would be
ideal in that they would entail no burden beyond that
already acceptable to dedicated astronauts.
Recent research by ourselves and others has suggested
that such automated processing has the potential to provide insight into team relationships, cohesion, and performance, as well as individual team members’ affective
and cognitive states. We have been identifying candidate
combinations of assessment techniques, data sources,
and psychosocial states of interest to NASA and have
focused on automated processing of two types of textual
or audio records. First, using variations on techniques
pioneered by Pennebaker (Tausczik & Pennebaker, 2010)
along with latent-semantic-analysis approaches to sentiment analysis (Landauer, Foltz, & Laham, 1998; SchmerGalunder & Sikström, 2007), we have been able to assess
many different emotional and attitudinal variations in
individual astronauts’ log data, even when these materials were prepared for NASA’s publicity or educational
purposes. Second, primarily using work we had developed previously based on interactive chat patterns (Miller
& Rye, 2012), we are able to assess aspects of team
dynamics such as power relationships and their shifts
over time and whether the team is experiencing a more
or less “comfortable” and “routine” time period. In both
cases, these assessments were accomplished simply by
automated examination and scoring of team communications during task performance.
In initial promising results from historical archives of
NASA data (Wu, Rye, Miller, Schmer-Galunder, & Ott,
2013), we were able to identify and track the emotional
states of astronauts keeping blogs or journals as well as
find correlations between topics the astronauts wrote
about and their positive and negative shifts in emotional
states (e.g., one astronaut’s use of terms pertaining to the
204 Salas et al.
crew correlated significantly with his or her use of anxiety terms across journal entries, suggesting anxiety about
crew members). Our team-interaction analysis techniques
were able to detect substantial leadership and comfort/
routine differences between the transcribed radio communications of the Apollo 13 mission (with its nearly
disastrous oxygen-tank explosion) and the other Apollo
missions and to track fluctuations in both parameters
over time. In all cases, however, our results could be validated only anecdotally because we lacked any true measure of “ground truth”—that is, an objective measurement
of these parameters that fluctuated over the same period.
This problem is being addressed in ongoing research
comparing automated assessment of crew writing or dialog with concurrent survey data.
There are, as well, promising approaches to capturing
team dynamics unobtrusively and in real time. For example, Stevens and colleagues (Stevens, Galloway, Wang, &
Berka, 2011) have used neurophysiologic indicators to
complement communication metrics of team cognition
with promising results. Similarly, Guastello, Gorman,
Cooke and colleagues (Gorman, Cooke, Amazeen, &
Fouse, 2011; Guastello, 2010) have applied nonlinear
dynamics and real-time communication pattern analysis
to a variety of teams with some success. Taken together,
these are encouraging methods for capturing team
dynamics—however, better and more robust approaches
are needed.
Enhancing and sustaining team
effectiveness: Countermeasures
Once we capture and understand team cohesion and
dynamics in LDSE, we can do something about it: deploy
countermeasures. Considering that space crews will be
isolated from other individuals who may serve as mediators or external problem solvers, it is vital that crew members be equipped with the means and tools to engage in
self-correction and regulation. Team training strategies,
then, must be designed, developed, and validated to
ameliorate the effects of stress on team cohesion. Current
preparation for spaceflight is extensive, with astronauts
spending a total of 5 to 10 years training before participating in their first spaceflight mission (Kanas & Manzey,
2008). However, much of this training is done individually or in ad hoc teams, with little training done together
by the full team that will engage in the mission (Vessey,
2014). Furthermore, the majority of this training is based
around the development of technical skills specific to
crew-member roles and tasks, with limited time devoted
to the development of more generalizable teamwork
skills. There are many promising team-development
interventions (see Salas & Frush, 2012; Shuffler,
DiazGranados, & Salas, 2011) that can be applied for
these purposes. One that shows much promise is a strategy pioneered by Smith-Jentsch and colleagues (SmithJentsch, Cannon-Bowers, Tannenbaum, & Salas, 2008)
called Team Dimensional Training. This strategy comprises a structured debriefing guide to focus on teamwork processes and to encourage, promote, and reinforce
teams’ monitoring and awareness of learned behavior
over time. Training based on this approach is beginning
to be deployed at NASA.
Clearly, team debriefings, in which teams reflect on
their experience and reach agreements about how to
work together going forward, are a potentially powerful
countermeasure. We conducted a meta-analysis on the
efficacy of debriefs that showed that, on average, teams
that debrief perform 20% better (Tannenbaum & Cerasoli,
2013). But traditionally, debriefs are often led by a trainer
or facilitator. For LDSE, teams will need to be self-reliant,
and temporal communication lags with ground control
will preclude real-time facilitation from Earth. Therefore,
teams must be able to self-debrief and adapt on their
own. Automated debrief-guidance countermeasures are
required.
We have been experimenting with one such tool with
teams living and working together in confined environments, including crews in NASA’s land-based HERA and
undersea NEEMO habitats. The tool gathers and analyzes
crew input to produce a customized debrief guide for
each team, with a focus not only on team- and task work
but also on factors that might affect team resilience. Data
analysis is underway, but initial findings have been positive, and crews consistently reported that this technique
surfaced important teamwork challenges and helped
them to self-adjust. A recent study conducted in a more
controlled environment showed that teams using this
guided debrief approach demonstrated better teamwork
behaviors and, subsequently, better team performance
and attitudes (Eddy, Tannenbaum, & Mathieu, 2013).
Team composition
During LDSE, crew members cannot take a day off to get
away from their coworkers, and no changes can be made
to crew membership. Therefore, mission success is contingent upon selecting the right team members who can
not only perform their respective mission roles effectively
but also work well together. Team-composition researchers have shown that the traditional personnel selection
model, which focuses on selecting the most qualified person to perform each designated role, will not necessarily
yield the most effective team when collaboration and
teamwork are at a premium. Therefore, we have been
developing a more complete framework for understanding team composition (Mathieu, Tannenbaum, Donsbach,
& Alliger, 2014). NASA-funded research is examining the
Space Teams 205
mix or complementarity of crew-member attributes,
including social team-role preferences and readiness,
backup abilities, collective orientation, and living-style
preferences, in an effort to optimize crew composition.
Fortunately, there is a very large applicant pool of
individuals who aspire to be astronauts, so it is possible
to select highly competent individuals. However, although
the applicant pool is large, the number of individuals
selected to be part of the astronaut program is quite
small, and it takes many years to prepare them to be mission ready. So, for any given mission, there are very few
potential crew members to choose from, greatly limiting
the number of possible team configurations. In addition,
when international partners in missions can each choose
their own crew members, the flexibility to compose a
crew with an optimal, complementary team profile is
reduced even further.
Recently, researchers have begun to emphasize that
team composition should be considered as a foundational
context for understanding how a team is likely to work
together, even when membership is unchangeable
(Mathieu, Tannenbaum, Donsbach, & Alliger, 2013). Team
composition provides the crew with certain strengths and
advantages, while also presenting certain liabilities or
areas of vulnerability. Therefore, NASA research is beginning to examine how composition can predict specific
teamwork challenges and how to use those insights to
trigger targeted team countermeasures.
The Way Forward
LDSE presents formidable challenges for the science of
team effectiveness, but they can be overcome as opportunities for testing and validating findings and countermeasures emerge.
Members of NASA’s research community are moving
forward on several fronts to help prepare for future missions. For example, researchers are conducting studies in
realistic analog environments such as HERA, NEEMO,
and the Antarctic to learn more about team dynamics in
isolated, confined, and extreme environments (Stuster,
2011; Vessey, Palinkas, & Leveton, 2013). In addition, significant work is ongoing to determine how best to conceptualize and measure team cohesiveness in a
meaningful way and capture affective states over time
(e.g., Kozlowski, 2012; Wu et al., 2013). Further, work is
going on to clarify the “social roles” that crew members
need to fulfill in addition to their technical roles (e.g.,
Mathieu et al., 2014; Roma et al., 2013).
In terms of team development interventions, some
researchers are studying how various countermeasures
such as team training and debriefing can enhance team
effectiveness, with an emphasis on enhancing team
self-sufficiency, adaptiveness, and resilience (e.g., Eddy
et al., 2013; Salas et al., in press; Smith-Jentsch et al.,
2008; Tannenbaum & Cerasoli, 2013). Team-effectiveness
scientists are also exploring how unobtrusive measures
such as team-member proximity, naturally occurring
communications, and even videotaped team interactions
can reveal insights about team dynamics (e.g., Kozlowski,
DeShon, Biswas, & Chang, 2012). Still others are seeking
to understand the risks associated with a team’s composition profile so that team-specific countermeasures can be
evoked, akin to “personalized medicine” for teams (e.g.,
Tannenbaum, Mathieu, Alliger, & Donsbach, 2013; Wu
et al., 2013).
Progress in these areas will help NASA prepare for
future space missions, and it is also likely to yield tools
and techniques of interest to other team researchers, as
well as insights that can be applied to teams in various
settings. Space is indeed a new frontier for energizing the
science of team effectiveness.
Recommended Reading
Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005).
Teams in organizations: From input-process-output models
to IMOI models. Annual Review of Psychology, 56, 517–
543. A review of the team-effectiveness literature.
Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the
effectiveness of work groups and teams (Monograph).
Psychological Science in the Public Interest, 7, 77–124. An
integration and review of team-effectiveness theories and
research.
Mathieu, J. E., Maynard, M. T., Rapp, T. L., & Gilson, L. L.
(2008). (See References). A review and integration of work
conducted from 1997 to 2007 and recommendations for
future investigations.
Salas, E., Goodwin, G., & Burke, C. S. (Eds.). (2010). Team effectiveness in complex organizations: Cross-disciplinary perspectives and approaches. Hillsdale, NJ: Erlbaum. An edited
book with chapters on the science of team effectiveness.
Salas, E., Tannenbaum, S., Cohen, D., & Latham, G. (Eds.).
(2013). (See References). An edited book that contains evidence-based best practices and advice for ensuring team
effectiveness.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.
Funding
This work was supported by National Aeronautics and Space
Administration (NASA) Grants NNX09AK486 and NNX14AM73G
awarded to the University of Central Florida, NASA Grant
NNX11AR22G provided to the Group for Organizational
Effectiveness, and NASA Grants NNX09AK476, NNX12AR15G,
and NNX13AM77G provided to Michigan State University. The
206 Salas et al.
views expressed in this work are those of the authors and do
not necessarily reflect the organization with which they are
affiliated or their sponsoring institutions or agencies. This work
was done while the first author was employed at the University
of Central Florida.
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