Group & Organization Management
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Archetypes: Toward a
of Team Performance
Narda R. Quigley1, Catherine G. Collins2,
Cristina B. Gibson3, and Sharon K. Parker4
We examine the concept of team performance and propose a framework
to understand patterns of change over time. Following a literature review
on team performance (focusing on empirical articles published between
2007 and 2017) and drawing on Greek and Roman mythology, we identify
five team performance trajectories: “Jupiter” (consistently high performing),
“Neptune” (relatively steady, average performance), “Pluto” (low
performing), “Icarus” (initially high performing, with a downward spiral), and
“Odysseus” (initially low to midrange performing, with an upward spiral),
which we refer to as “team performance archetypes.” We discuss how
they might be used in conjunction with growth modeling methodology to
help facilitate theory building and data collection/analysis with respect to
team performance. In addition, we discuss the future research implications
associated with using the archetypes to help conceptualize patterns of team
performance over time.
1Villanova University, PA, USA
2University of New South Wales, Sydney, Australia
3University of Western Australia, Perth, Australia
4Curtin University, Perth, Western Australia, Australia
Narda R. Quigley, Professor of Management, Villanova School of Business, Villanova
University, 2083 Bartley Hall, 800 Lancaster Ave., Villanova, PA 19085, USA.
794344GOMXXX10.1177/1059601118794344Group & Organization ManagementQuigley et al.
2 Group & Organization Management 00(0)
group or team effectiveness or performance, group or team development,
group or team dynamics/processes, longitudinal, theory or theory building
Although a decade has passed since Mathieu, Maynard, Rapp, and Gilson’s
(2008) recommendation that teams researchers “embrace the complexity” of
team dynamics (p. 461), relatively few studies have attempted to understand
the ways in which teams change over time (Mathieu, Hollenbeck, van
Knippenberg, & Ilgen, 2017). As Mathieu et al. (2017) wrote,
[S]ignificant changes are needed if we are to advance our science of teamwork.
These include more formally incorporating temporal issues. Nearly every
variable in team effectiveness models may change over time, and for a variety
of reasons relationships may wax and wane over time. (p. 460)
We agree with Mathieu et al. (2017; see also Cronin, Weingart, & Todorova,
2011, and Shuffler, Diazgranados, Maynard, & Salas, 2018). One issue within
the team literature may be the prevalence of theories that advance a set con-
ception regarding the developmental path teams must take to perform (e.g.,
Tuckman & Jensen, 1977); another issue may be the myriad difficulties asso-
ciated with conceptualizing and implementing longitudinal studies that accu-
rately reflect the dynamic phenomenon of interest (cf. Luciano, Mathieu,
Park, & Tannenbaum, 2018). In our practical and empirical experience, how-
ever, we have observed considerable variety in how team performance, in
particular, changes over time within and across teams. The recent use of
growth modeling techniques to examine teams over time has begun to move
the literature in this direction (see Collins, Gibson, Quigley, & Parker, 2016).
Our goal is to develop a new conceptual understanding of patterns of team
performance over time to spur more future research that incorporates the tem-
poral dimension of work team performance.1 We begin with a brief review of
the last decade of the team performance literature to assess whether and how
the dimension of time is being taken into account. We find that previous
research has focused on the prediction of levels of performance at the end of a
team’s life span or at the end of a performance episode (or set of episodes),
without much consideration of the patterns of change in performance over
time. We suggest that a new conceptual understanding of team performance
over time—using team performance archetypes, with metaphorical nomencla-
ture borrowed from ancient Greek and Roman mythology—will be helpful in
spurring future research on team performance that takes into consideration
temporal issues. As we define them, these archetypes are typical examples of
Quigley et al. 3
what patterns of change in team performance look like over three or more
performance episodes (three is the minimum number required to constitute an
empirical pattern; Chan, 1998). We then draw from the logic of growth model-
ing methodology to help illustrate the potential value of the archetypes for
theory development and empirical advances. Last, we propose an agenda for
future research, focusing on (a) the verification of the archetypes, (b) the
exploration of facilitating conditions for the archetypes, and (c) the consider-
ation of whether and how teams may shift or change archetypes.
We work within the bounds of three conditions. First, we define teams as
two or more individuals who (a) socially interact; (b) possess one or more
common goals; (c) are brought together to perform organizationally relevant
tasks; (d) exhibit interdependencies with respect to workflow, goals, and out-
comes; (e) have different roles and responsibilities; and (f) are embedded in
an encompassing organizational system, with boundaries and linkages to the
broader context (Kozlowski & Ilgen, 2006). Second, we focus on the team
level of analysis, defining team performance as the task-related outcomes
achieved by the team (e.g., Hackman, 1987). Last, our ideas are intended to
apply to teams that have multiple, distinct performance episodes over time
and that receive feedback on these episodes in real time. These episodes are
“distinguishable periods of time over which performance accrues and feed-
back is available . . . They constitute the rhythms of task performance for
teams” (Marks, Mathieu, & Zaccaro, 2001, p. 359). For example, a relevant
performance episode for a sports team might be a game; for a consulting
team, a project; and for a sales team, the completion of a major sales transac-
tion. As we will discuss, these performance episodes will differ in their objec-
tive time spans; we argue, however, that the patterns that constitute archetypal
team performance trajectories can be revealed irrespective of the objective
length of the episode. Taking this longer term perspective means that we
develop insights about team performance across episodes; we return to a
more complete discussion of this issue following the review.
Team Performance Literature Review (2007-2017)
The last published review of the team performance literature, to our knowl-
edge, is Mathieu et al.’s (2008) review of team effectiveness. Using McGrath’s
(1964) classic input–process–outcome (IPO) model and Ilgen, Hollenbeck,
Johnson, and Jundt’s (2005) input–mediator–output–input (IMOI) model,
Mathieu et al. reviewed literature that touches on each of the linkages in the
models, making two observations that help guide our review of the last 10
years of team performance literature. First, they found that team performance
as a construct has not been as systematically addressed, as the focus has been
4 Group & Organization Management 00(0)
on “the left hand side of the equation (i.e., antecedents and mediating influ-
ences) . . . [resulting] in criterion measures, and in particular performance
indices, [that] are often idiosyncratically and organizationally specific”
(Mathieu et al., 2008, p. 415). Second, Mathieu et al. noted that numerous
teams researchers, prior to their review, had called for a better understanding
of temporal dynamics in teams (e.g., Ancona & Chong, 1999; Kozlowski,
Gully, Nason, & Smith, 1999; Marks et al., 2001; McGrath, 1991); yet, still
there was insufficient consideration of this in the literature. Below, we assess
whether—since the publication of Mathieu et al.—these issues still apply,
that is, (a) whether the “left-hand side of the equation” with respect to team
performance has continued to dominate research and (b) whether recent stud-
ies continue to have underdeveloped theoretical conceptualizations of how
team performance changes over time.
In terms of selection criteria for the review, we focused specifically on
published manuscripts from 2007 to 2017 in major management and organi-
zational behavior journals that include measures of performance (e.g.,
Courtright, McCormick, Mistry, & Wang, 2017) at the team level of analysis
(see the appendix for a complete list of these journals). Using various data-
bases (ABI/Inform, ProQuest, etc.), we searched for empirical articles that
included the term “team performance” in the abstract within each of the jour-
nals during the decade in question. Notably, some articles included multiple
studies. We scanned through each study within each article to make sure that
the criterion variable examined was, indeed, a team-level performance mea-
sure provided by a source that was not the team itself (i.e., simulation scores,
sales numbers, manager/leader/supervisor ratings). For parsimony, we
excluded studies that focused on top management teams; we also excluded
studies that considered creativity/innovation performance outcomes. This
yielded 222 articles, 22 of which were meta-analyses. Within the 200
non–meta-analytic articles, we found 221 studies that met the above criteria
for inclusion in the review.
We coded these studies for a variety of methodological details. To under-
stand the dynamics and causality associated with team performance, we
coded for (a) the timing of the data collection (i.e., whether the study was
cross sectional or included lagged team performance outcome[s] collected
after the independent variables; if a lag existed, whether multiple waves of
lagged performance outcomes were captured; if multiple waves of perfor-
mance were collected, whether growth modeling was used with respect to
performance outcomes) and (b) the research design of the study (i.e., whether
it was correlational, quasi-experimental, or experimental in nature; whether it
included mixed methods). We also coded the studies for the type of team
performance data used. Our categories included (a) objective performance
Quigley et al. 5
data (e.g., sales/financial performance indicators, scores on a simulation gen-
erated by the simulation itself, scores as compared with correct answers on
decision-making tasks), (b) customer/client ratings of team performance, (c)
supervisor/manager/leader ratings of team performance, (d) other external
ratings of team performance (e.g., external judges or industry experts), and
(e) instructor/professor grade or ratings. Last, we coded for the types of par-
ticipants (students, employees, or athletes) and the types of teams using
Sundstrom, McIntyre, Halfhill, and Richards’ (2000) typology (production,
service, management, project, action/performing, and advisory). Team
types—and team tasks—were quite varied in the sample, and did not always
fit clearly within the Sundstrom et al. (2000) typology. To account for
instances where there was not enough information provided, or the author(s)
deliberately chose a range of different types of teams working on different
types of tasks, we classified teams as “mixed/unclear.”
Table 1 provides a high-level, numeric summary of the included literature.
The appendix provides greater detail on sample studies, methodologies, team
types, and predictors of performance. Although this review is not intended to
be exhaustive, two themes that address the issues identified by Mathieu et al.
(2008) emerged from our examination of these articles.
Theme 1: Continuing Dominance of the IPO and IMOI
The IPO and IMOI models are the most popular foundational structure for the
studies we reviewed, leading us to conclude that they are alive and well in the
team literature. The bulk of theoretical attention has continued to be on what
Mathieu et al. (2008) refer to as the “left-hand side of the equation.” Only
24% (54 total) of the included studies were cross sectional in nature (i.e.,
inputs, processes, emergent states, etc. collected at the same time as perfor-
mance indicators). A notable trend among the included studies is that lagged
outcome designs (wherein performance indicators are collected after other
variables) have been most prevalent, with 76% of the scholarship we reviewed
utilizing this approach to unpack the causal ordering of antecedents to perfor-
mance (167 studies, as noted in Table 1). One example of this basic lagged
outcome approach is Courtright et al. (2017), which considers the impacts of
team charter quality, team conscientiousness, and task cohesion on team per-
formance in undergraduate student project teams working through a 14-week
semester. We found many other studies that use similar designs and analytic
approaches (see the appendix). What is especially noteworthy is that most
studies with lagged designs did not use multiwave or growth modeling
approaches. Of the total studies included, just 11% incorporated a multiwave
design, and only 2% used growth modeling.
6 Group & Organization Management 00(0)
Table 1. Methodological Themes in Empirical Team Performance Literature 2007
Number of studiesaPercentage of studies
Timing of data collection
Cross sectional 54 24
- Of these lagged
- Of these multiwave
studies, five use
- Of these lagged studies,
15.5% have multiwave
performance (11% of the
- Of these multiwave studies,
19% use growth modeling
(2% of the overall total)
Correlational 173 78
Quasi-experimental 5 2
Experimental 43 19
Mixed methodc6 3
Objective 92 42
Customer/client ratings 9 4
Manager ratings 81 37
Other external ratings 18 8
Instructor/professor rating 24 11
Students 91 41
Employees 116 52
Athletes 14 6
Production 9 4
Service 41 19
Management 10 5
Project 64 29
Action and performing 66 30
Advisory 5 2
Unclear/mix/other 26 12
aThe total number of included studies (n = 221) is greater than the number of empirical articles reviewed
(200), as several articles included two or more studies.
bLagged outcome studies include all studies where at least one performance point is collected after
independent variables. Multiwave and growth modeling studies are included in the overall count of lagged
outcome studies. Multiwave studies include growth modeling. Both multiwave and growth modeling coding
refer explicitly to the consideration of team performance within the relevant study.
cStudies coded as “mixed method” were also coded as correlational, quasi, or experimental, depending on
the nature of the quantitative research included.
dThe total number of performance outcomes (n = 224) is greater than the number of empirical papers
reviewed (200) and total studies (221), as three studies include two performance outcomes.
eThe team type categorization is based on the typology of teams presented in Sundstrom, McIntyre, Halfhill,
and Richards (2000).
Quigley et al. 7
Indeed, the IPO–IMOI model serves as such a strong theoretical underpin-
ning for the temporal ordering of variables that researchers have frequently
relied on correlational research to provide support for hypotheses about the
antecedents of performance. Seventy-eight percent of the included studies
(173 studies, as noted in Table 1) were correlational in nature; only 22% of
studies explored causality (43 studies were experimental, five were quasi-
experimental). This is also illustrated by published meta-analyses included in
our review that consider team performance; these also exhibit the emphasis
on antecedents, mediators, and moderators that lead to team performance,
rather than unpacking anything specifically about team performance itself
(see the appendix for sample references).
A strength of the IPO–IMOI research stream is multisource data collec-
tion, with a focus on team performance from external sources. Researchers
have collected performance data from a rater who is external to the team in
49% of the studies we reviewed; 37%, 8%, and 4% of the total studies
included in the review featured manager ratings, other external ratings, and
client/customer ratings, respectively (Table 1). Objective performance data
(sales numbers, for example) have been included as the criterion performance
variable in 42% of the reviewed studies. However, there is little research that
has explored team performance with different measures simultaneously. Less
than 2% of the included studies combined team performance measures from
different sources (not including self-ratings of perceived performance) within
the same study. This is notable, given Mathieu et al.’s (2008) point that the
conceptualization (and subsequent measurement) of team performance may,
indeed, matter. Similarly, meta-analyses have generally not focused on this
issue. Only Castaño, Watts, and Tekleab (2013) unpacked the possible ways
in which performance measurement (among other factors) might have
affected the results of the cohesion literature. Although they found that mea-
surement differences (outcome vs. behavioral, subjective vs. objective) did
not affect the relationships between different types of cohesion (task and
social) and team performance, there may be other crucial differences. For
example, are some measurement forms more sensitive to team dynamics
across different time frames?
Theme 2: A Small, but Growing, Set of Studies With Novel
Approaches to Temporal Issues
As noted above, Mathieu et al. (2008) called for a more deliberate approach
in terms of considering temporal dynamics with respect to team performance.
Although a majority (167, 76%) of the included studies used a lagged design
8 Group & Organization Management 00(0)
(i.e., predictors and mediators collected prior to performance data), far fewer
studies considered any temporal dimensions with respect to the measurement
of performance itself. As noted above, we found that only 11% (26 studies)
of the included studies measured team performance at multiple points in
time. We divided these into two groupings to better understand the state of the
included literature: (a) novel approaches with finer grained consideration of
cause-and-effect relating to team performance (21 studies) and (b) growth
modeling approaches that examine how patterns in team performance develop
over time (five studies). These are summarized in the appendix and described
Novel approaches with finer grained consideration of cause and effect. As noted
above, only 21 studies during the last decade included in this review have
adopted more novel approaches in their data collection and analytic strategies
to help unpack cause and effect in temporal dynamics with respect to team
performance. As one example, Gardner (2012) conducted a multimethod
field study of 78 audit and consulting teams, including both survey results
and longitudinal qualitative case studies of six project teams (with life spans
of 3-10 weeks). She examined the conditions under which pressure leads to
(or detracts from) team performance, finding that four limiting team pro-
cesses are prevalent when pressure is high, and these detract from perfor-
mance. Although team performance was still considered at a single point in
time at the end of the teams’ task (and lining up with the ability of clients to
provide ratings for team performance), this study deliberately sought to
understand the temporal cause and effect of team member communication at
a fine-grained level. As another example, Murtha (2013) also sought to exam-
ine temporal cause and effect in an exploration of “peaking at the right
time”—the idea that timing high levels of performance for teams is itself an
aspect of performance. A third example is Knight (2013), who studied how
shared team mood shapes the exploratory search process in military teams,
finding that mood drives search processes differently before the midpoint, at
the midpoint, and in the second half of team life spans as a deadline
approaches; the trajectory of search processes, in turn, drives team perfor-
mance at the end of the task.
Unusual and intriguing approaches occurred in at least four other studies
that sought to shed light on the question “what should teams be doing when?”
to positively affect performance over time (e.g., Cheng, Chua, Morris, & Lee,
2012; Lei, Waller, Hagen, & Kaplan, 2016; Pearsall, Ellis, & Bell, 2010;
Villado & Arthur, 2013). Villado and Arthur (2013), for example, explored
after-action reviews in work teams. The authors examined the impact of
training participants in distinct types of after-action review to compare how
Quigley et al. 9
four-person teams interacting on a computer-based battlefield simulation
game perform with and without the training. The study unfolded over five
hours and included six sessions (during which teams received feedback on
their performance). Villado and Arthur (2013) plotted out the mean team per-
formance scores over time by training condition to demonstrate the point at
which the training conditions began to differ on performance. Teams in dif-
ferent training conditions, on average, had different performance trajectories
over time—those that underwent the training experienced performance
improvement, whereas those that did not undergo training had some stagna-
tion in team performance.
As another example of an unusual fine-grained approach, Lei et al. (2016)
examined how “teams working in dynamic settings successfully transition
across routine and non-routine situations,” using data from 11 two-person
flight crews engaged in flight simulation sessions (p. 491). The authors used
expert evaluations of crew adaptive performance as the focal dependent vari-
able. This study stands out due to its emphasis on an episodic, event-oriented
approach targeted at changes within the team over time and across levels;
they sought to understand the relationship between team interaction patterns
at a more microlevel in dynamic settings and team adaptive performance. It
is notable that Lei et al. (2016) used theory (i.e., Marks et al., 2001) to con-
sider what kinds of team interactions might “match” with the situation (con-
ceptualized in an unfolding way over time) to affect performance over three
phases of the flight (“en route,” “descending,” and “landing”).
Growth modeling approaches. In the last decade, the team performance litera-
ture we reviewed has also included five studies (2% of the included studies)
that use growth modeling methodologies. These approaches are notable, in
that, they begin to consider patterns in team performance as it unfolds over
time in a dynamic manner, acknowledging that performance is not a static
concept. Mathieu and Rapp (2009), for example, discussed the importance of
“foundational activities” for teams (i.e., taking the time to lay down a solid
foundation, cf. Ericksen & Dyer, 2004), and then considered how teams
might manage taskwork and teamwork over time (Ilgen, 1999; Marks et al.,
2001; McIntyre & Salas, 1995) as an outgrowth of that foundation. They
emphasized the use and importance of longitudinal criterion measures of per-
formance to enable analyses of dynamic effects. The growth modeling
approach that Mathieu and Rapp (2009) used allows for a very precise con-
sideration of what happens to team performance across a series of perfor-
mance episodes, and so, the criterion variable received a much more complete,
10 Group & Organization Management 00(0)
Mathieu, Kukenberger, D’Innocenzo, and Reilly (2015) examined the
relationship between team cohesion and performance in two growth model-
ing studies based off samples of teams of undergraduate and graduate stu-
dents engaged in a 10-week-long management computer simulation. They
considered the reciprocal influence of these two variables, finding that the
strength of the cohesion–performance relationship is stronger than that of the
performance–cohesion relationship; moreover, the strength of the cohesion–
performance relationship increases over time, whereas the strength of the
performance–cohesion relationship remains relatively stable. Both concepts
evidenced time-related mean changes; for team performance, this meant that
“[a]verage team performance declined initially until around midway through
the simulation, at which time, team mean performance levels rose until the
end” (Mathieu et al., 2015, p. 728). The authors noted that this is a perfor-
mance pattern over time that is to be expected, given the nature of the simula-
tion task—the sample of teams experienced investment–performance cycles,
much like entrepreneurial start-ups. The consideration of the patterns in team
performance, a deep understanding of the team task, and the use of theory
from Kozlowski et al. (1999) and Marks et al. (2001) allowed Mathieu et al.
(2015) to make nuanced observations about the reciprocal relationship
between cohesion and performance and further unpack the underlying pro-
cesses behind how and why cohesion and performance are related.
Another example of the use of growth modeling to examine team perfor-
mance is Dierdorff, Bell, and Beelohlav (2011), who combined the IMOI
theoretical approach with a consideration of the patterns in team performance
over time. The authors examined collectivism and how it related to initial
team performance. Given that teams are, at the start of their life spans, collec-
tions of individuals, this strategy makes theoretical sense. They also theo-
rized about which facets might lead to change in team performance, in
addition to examining the influence of team member exchange (an emergent
state that likely reflects patterns of processes, behaviors, and interactions on
the team) on performance, given the team’s composition in terms of collec-
tivism. Dierdorff et al. (2011) used a multilevel growth modeling analytic
strategy to tease out exactly when these variables influence team performance
and patterns in team performance change as they examined 66 student teams
completing a business simulation over a 5-week period. In addition, they
used Kozlowski et al.’s (1999) theory of team compilation and performance
as a “conceptual backdrop for the temporal hypotheses,” which provided the
rationale for why they considered the average level of team member collec-
tivism as a team input (p. 257). Like Mathieu and Rapp (2009) and Mathieu
et al. (2015), this study’s strong connection to theory allows for a nuanced
examination of factors influencing team performance over time. This included
Quigley et al. 11
the classic IMOI approach and the Kozlowski et al.’s (1999) developmental
approach to teams over time.
Lorinkova, Pearsall, and Sims (2013) also used growth modeling to
explore the teams in their sample. In particular, they focused on how team
leadership styles might have differential effects on team processes and per-
formance over time. Teams made up of undergraduate students participated
in a 3-hr-long computer simulation task that focused on leadership develop-
ment; team leaders were both selected (using a leadership assessment tool)
and trained to exhibit a certain leadership style as part of the style manipula-
tion within the experimental design. Like Mathieu et al. (2015), Lorinkova
et al. intended to unpack the relationship between two variables—empower-
ing leadership and team performance—by considering patterns in team per-
formance over time, and how these patterns might be influenced by the type
of leadership to which team members were exposed. The growth modeling
analysis revealed that “although teams with directive leaders started per-
forming well more quickly, their performance plateaued, whereas the emer-
gent cognitions and improved learning and coordination capabilities of
empowered teams allowed them to improve over time” (Lorinkova et al.,
2013, p. 589). The use of growth modeling to consider patterns of change in
team performance, along with Kozlowski et al.’s (1999) theoretical frame-
work of team development, enabled these authors to reexamine an enduring
question in the leadership and teams literatures with a more thorough, fresh
Conclusions From the Last Decade of Team Performance
Research: Looking Ahead
Evident from this review, issues relating to time—and timing—are still sur-
prisingly scarce in scholarship on team performance (see Cronin et al., 2011,
for more on this topic). Consistent with the conclusions of Mathieu et al.’s
(2008) review, team performance research in the last decade has continued to
draw heavily on the IMO/IMOI models, with team performance itself being
mostly considered in a relatively static way. Although 76% of the research
has investigated lagged relationships, only 11% includes multiwave designs
with respect to team performance. Only in 2% of the included studies have
scholars examined team performance as a variable that changes and develops
over time, investigating the patterns of team performance with growth mod-
One of the complexities associated with incorporating time as a concept in
organizational research is that there has been a “lack of coherence in the
field” (Ancona, Okhuysen, & Perlow, 2001), and this is also true within the
12 Group & Organization Management 00(0)
teams literature. In addition to classics such as Tuckman and Jensen (1977)
and Gersick (1988), a few more recent key theoretical frameworks within the
team literature are helping to sort out some of the confusion about unpacking
the timing of team dynamics (e.g., Kozlowski et al., 1999; Marks et al., 2001;
McGrath, 1991). Marks et al. (2001) focused on the temporal commonalities
that all work teams experience, emphasizing,
No work-related tasks are performed in a vacuum, unaffected by deadlines,
time limits, or schedules. Work teams strive toward collective goals that
incorporate time as a component (Locke & Latham, 1990). Time factors such
as project deadlines, synchronization of schedules, alignment of coordination
efforts, and so forth dictate many aspects of team functioning, including the
strategies that are employed, the pace of activities, and role assignments that
develop in order for the teams to perform successfully. (pp. 358-359)
Marks et al. (2001) call attention to the concept of team performance trajec-
tories, noting that they “most commonly consist of several I-P-O cycles that
run sequentially and simultaneously” (p. 359). Performance trajectories are
rooted in temporal cycles of goal-directed activity, called episodes—
“distinguishable periods of time over which performance accrues and feed-
back is available (Mathieu & Button, 1992, p. 359).” Yet, as our review above
explained, only five studies (2%) have examined how performance is embed-
ded in multiple episodes over time. Each of these studies starts to unpack
facilitating conditions for patterns of change in team performance rather than
a specific level of performance at one point in time.
In considering the body of research from the last decade, promising seeds
are emerging for the dynamic examination of team performance with growth
modeling; it is an opportune occasion to develop a conceptual framework
about how team performance changes over time. Overall, we seek to move
the literature away from a focus on “snapshot” levels of team performance,
and toward the consideration and appreciation of the study of patterns of
change in work team performance (i.e., team performance trajectories). In the
next sections, we advance conceptual, theoretical, and practical ideas regard-
ing the consideration of patterns in team performance over time, and we dis-
cuss the future research implications associated with these patterns.
Team Performance Archetypes
How a given team has performed over time (Cyert & March, 1963; Greve,
2003)—and how it is performing in the present moment (Carp, 2003; Lehman
& Hahn, 2013)—are inextricably linked. The English language has a host of
Quigley et al. 13
idioms to describe this concept of performance momentum: Teams may be “on
a roll,” “unstoppable,” “perennially high performing,” “unbeatable,” and “rid-
ing a wave”; they can “stay the course,” experience a “losing streak” or a “cold
streak,” and can “break” the streak. Interestingly, labels for teams typically
categorize them into “groups that work (and those that don’t)” (Hackman,
1990, p. vi), a dichotomous divide that may become a self-fulfilling prophecy
for teams. Such stigmas may not be practically or theoretically helpful, and
perhaps has led to our literature’s myopic focus on “high-performing” teams.
Because research to date focuses on what predicts high team performance at a
single point in time, it implies that teams are either high performing or low
performing, with this dichotomy being fairly static.
To move beyond this, we suggest a focus on patterns of performance
across multiple episodes. Our framework, therefore, applies to teams that
have multiple, distinct performance cycles over time and that receive feed-
back on these episodes in real time. Although the length of an episode might
differ in different contexts, we expect the patterns that we describe below as
archetypes to unfold regardless. For example, in some teams (i.e., a consult-
ing team), projects that are months in duration might delineate episodes, and,
hence, examining performance across episodes might capture their perfor-
mance over several years. For other teams (i.e., an emergency response team),
an event that is only hours in duration might constitute an episode, and so
examining performance across episodes might capture performance in the
space of a week. The important delineating factor for what constitutes the
episode is the completion of goal-directed activity with receipt of feedback in
some form about that activity (Marks et al., 2001), and so, we believe it
essential to remain open to these episodes being of different lengths in differ-
ent contexts. Importantly, we argue that the archetypes are useful in concep-
tualizing the patterns irrespective of the length of time they cover in these
different contexts, and the point in the team’s life span that we might engage
with the team. Simply put, the archetypes serve to conceptualize the patterns
of change in team performance over time, as teams work across multiple
tasks and multiple context-specific performance episodes.
To assist in the conceptualization of patterns of team performance over time,
we use metaphors from the mythology of the ancient Greek and Roman world
to introduce and describe team performance archetypes. The use of meta-
phors in theory construction has been long advocated by organizational sci-
entists (cf. Weick, 1989), because they are helpful in expressing complex and
abstract organizational phenomena (Cornelissen, 2006). Existing metaphors
14 Group & Organization Management 00(0)
in the teams’ literature are insufficiently intricate to capture trajectories that
team performance may take over time (Collins et al., 2016). The mythology
of the ancient world provides an accessible, relevant, and appropriately com-
plex set of stories to be used as an illustrative tool that sheds light on these
possible patterns. We introduce the five team performance archetypes,
explore features of each performance pattern, and describe connections to
current teams’ research. Table 2 summarizes these metaphors and archetypes,
which we now describe.
Team Jupiter: High-performing teams. The “Jupiter” archetype includes what
have been classically referred to as “high-performing teams” (Katzenbach &
Smith, 1993) and “groups that work” (Hackman, 1990, p. vi), although our
focus on patterns of performance means that we emphasize the idea that high
performance is sustained over time. These teams can be described as “on a
roll,” “unstoppable,” “perennially high performing,” and “unbeatable.” In
these teams, initial high performance and goal achievement are maintained,
with no major fluctuations in positive or negative momentum thereafter. In
Roman mythology, Jupiter is the all-powerful king of the gods and serves as
the god of the sky and thunder. Despite minor fluctuations (see Table 2 for
visual examples), ultimately Jupiter teams are high performing: starting and
continuing as highly effective teams, meeting or exceeding lofty goals, and
exhibiting high reliability within and across performance episodes (e.g.,
flight crews; Hackman, 1993; Salas, Burke, Bowers, & Wilson, 2001). As
one empirical example, we observed the Jupiter archetype in Mathieu and
Rapp’s (2009) work. In this study, teams that developed high-quality team
charters and performance strategies at team formation exhibited a high level
of performance at the start of a business simulation, and over time, the trend
in their trajectories was slightly positive, indicating that these teams started
strong and continued to exhibit high levels of performance over time.
Team Neptune: Midrange teams, with relatively steady, average performance.
“Neptune” teams “stay the course,” delivering the necessities. These teams
start and continue with moderate performance; their output is relatively sta-
ble with no major fluctuations in positive or negative momentum. According
to the Romans, Neptune is the god of freshwater and of the sea. He plays an
important but somewhat background role in the pantheon; classicists note
that only one temple in Rome was dedicated to him. Neptune teams’ mid-
range performance is evident from the start, and this has little positive or
negative momentum thereafter; these teams are known for consistently deliv-
ering sufficient performance. Performance goals are achieved, but they may
not be especially lofty or impactful. These teams are proficient, albeit not
Quigley et al. 15
necessary innovative or proactive in their performance. Again, there may be
minor fluctuations, but there is an overall relatively flat team performance
trajectory. Ultimately, Neptune teams meet goals, get the job done, and are,
thus, vital for organizations to achieve their goals (e.g., Locke & Latham,
1990). Mathieu and Rapp’s (2009) work includes an empirical example of
Table 2. Team Performance Archetypes.
Archetype name Illustrative graph
Description of team performance
1. Jupiter High level of initial performance
intercept. Change over time
could be linear, quadratic, cubic,
or otherwise nonlinear—team
maintains high performance
level across life span.
2. Neptune Midrange initial performance.
Change over time could be
linear, quadratic, cubic, or
maintains average performance
level across life span.
3. Pluto Low level of initial performance
intercept. Change over time
could be linear, quadratic, cubic,
or otherwise nonlinear—team
maintains low performance level
across life span.
4. Icarus High level of initial performance
intercept. Downward spiral
of change over time could
be linear, quadratic, cubic, or
otherwise nonlinear—team is at
low performance level by end of
team life span.
5. Odysseus Low to midrange initial
performance intercept. Upward
spiral of change over time could
be linear, quadratic, cubic, or
attains high level of performance
at end of life span.
Note. In the above graphs, the x axis is time and the y axis is team performance.
16 Group & Organization Management 00(0)
Neptune teams: Teams that developed low-quality charters, but used high-
quality performance strategies, were able to sustain an average level of per-
formance over time relative to other teams in the context of their study.
Team Pluto: Low-performing teams. “Pluto” teams do not meet performance
expectations—they are low-performing teams on a “losing streak” or “cold
streak.” These teams start and continue with low performance; their output
never achieves any positive momentum from their initial inferior perfor-
mance. The dark, brooding, and sometimes violent Roman god Pluto presides
over the underworld with his three-headed guard dog, Cerberus, and abducted
bride, Persephone. The metaphor suggests that Pluto teams are trapped in a
low-performing state, whereby their slow start on team performance never
truly shows any momentum that enables improvement. These teams gener-
ally do not meet performance goals and may miss deadlines and budgeting
goals, perhaps because of extreme process loss (and/or coordination failures;
e.g., Staats, Milkman, & Fox, 2012). Like Neptune and Jupiter, the Pluto
team trajectory could have minor fluctuations, but any short bursts of positive
momentum are overtaken by countervailing negative momentum. This inef-
fective ebb and flow in performance reflects the struggles Pluto has in the
gloom of the underworld. Empirical examples of Pluto teams may be more
difficult to find in the organizational literature, because the focus tends to be
on successful teams. In the Mathieu and Rapp (2009) paper, the team perfor-
mance trajectory of low-quality charters and low-quality performance strate-
gies may be a Pluto archetype. However, we suspect that teams susceptible to
the Pluto archetype are more likely to emerge when working in weak situa-
tions (e.g., lack an overarching organizational strategy, structure, or tight
team task cycles), given that context provides fewer of the necessary condi-
tions needed for team success (e.g., compelling direction, enabling structure,
supportive context; Hackman, 2012). Illustrations of these teams could be
communities of professionals who work across organizations on short-term
projects, such as the documentary film teams consisting of independent con-
tractors in Gibson and Dibble (2013).
Team Icarus: Initially high-performing teams, with a downward spiral. The “Ica-
rus” archetype is emblematic of teams that start off with impressive perfor-
mance but then suffer a “losing streak.” That is, the team’s initially
high-performance levels are followed by a “downward spiral” of negative
momentum in team performance. In Greek mythology, Icarus’ father is Dae-
dalus, a talented inventor who builds two flying contraptions. He warns his
son not to fly too high, as it would be too close to the sun; initially, Icarus is
in perfect control of his wings. As he becomes overconfident and approaches
Quigley et al. 17
the sun, however, the wax that holds his feathers begins to melt, and he falls
to his death in the sea below. The metaphor of Icarus teams suggests that they
are initially high-performing teams that seem to have their tasks and pro-
cesses under control. Early levels of team efficacy (Bandura, 1997) may be
unrealistically high, however. These teams quickly fall apart and may suc-
cumb to a deviation-amplifying downward spiral of negative momentum
(e.g., Lindsley, Brass, & Thomas, 1995), resulting ultimately in a low level of
continuing performance. Once again, Mathieu and Rapp (2009) provide an
empirical example of this archetype; teams that developed high-quality char-
ters and low-quality performance strategies started off with typical team per-
formance, but quickly spiraled into declining performance. The overall
performance trend for these teams over time was declining, without any
bursts of positive momentum—indicative of the Icarus archetype.
Team Odysseus: Initially low- to midrange-performing teams with an upward spi-
ral. “Odysseus” teams “ride the wave” and “break the streak.” These teams
begin with low levels of initial performance, but then seem to surge miracu-
lously ahead, gathering positive momentum and attaining increasingly higher
levels of team performance over time. The trials and tribulations of Odys-
seus, the hero of Homer’s epic The Odyssey, are legendary. After fighting in
the Trojan War, Odysseus tries to return home. He is met with rough seas and
dangerous detours. After 10 long years of overcoming challenge after chal-
lenge, Odysseus finally makes it home to Ithaca again to be reunited with his
wife and son. In keeping with the legend, Odysseus teams are initially
swamped with challenges. With each small victory and goal attainment, how-
ever, these teams gain positive momentum and increase their levels of perfor-
mance (e.g., Amabile & Kramer, 2011; Chen & Kanfer, 2006; Locke &
Latham, 1990), perhaps with deviation-amplifying positive spirals (e.g.,
Lindsley et al., 1995). Ultimately, these teams end with an elevated level of
performance (i.e., Odysseus’ ultimate goal was to return home and reunite
with his wife and son). In terms of their performance pattern, these teams
begin with a low level of performance, and may have minor fluctuations in
performance thereafter, but ultimately their positive performance trajectory
carries through to the conclusion of the team’s life span. Empirical evidence
of the Odysseus archetype exists in Lorinkova et al.’s (2013) work. In consid-
ering how teams with different leadership styles performed over time, they
found that both directive and empowering team leaders result in a positive
performance trend over time for teams. Notably, the teams with empowering
leaders took more time than the teams with directive leaders to achieve posi-
tive trajectories of team performance, but when they did, the positive change
in performance over time was more pronounced (i.e., steeper positive slope
in performance trajectory).
18 Group & Organization Management 00(0)
Although it is premature for us to draw any conclusions about exactly
when and where (and why) teams may exhibit these performance archetypes,
it is worth noting that we do not expect all studies examining team perfor-
mance over time to include empirical examples of all five trajectories. In fact,
the presence or absence of different trajectories may serve as a clue regarding
what concepts might be most relevant to consider as leverage points for
improving team performance over time. As an example, in the study by
Lorinkova et al. (2013), leaders were specifically trained to exhibit directive
and empowering styles. As noted above, teams exposed to both types of lead-
ership could be considered examples of the Odysseus archetype. It may be
that strong leadership (whether it be directive or empowering) is a key driver
of this archetype.2 Clearly, we are in need of much more conceptual and theo-
retical development to explain the emergence of the archetypes and unpack
the numerous antecedents; the few exemplars from existing teams literature
suggest this will be a fruitful avenue for future research.
On the Use of the Archetypes, in Conjunction With Growth
The fundamental purpose of the archetypes is to help researchers conceptual-
ize patterns of change in work team performance over time. As such, the
archetypes can be used to think about latent trajectories of work team perfor-
mance (i.e., the description of the average shape of scores in team perfor-
mance over time for the population of teams under investigation). As Collins
et al. (2016) note,
One or more latent trajectories may be theorized to exist within a population.
In the scenario where multiple latent trajectories are theorized, there are
subpopulations that change in different ways . . . Predictors can be used to
investigate why a team emerges within one class rather than another. (p. 71)
Growth modeling techniques can be used to test for these latent trajectories
and tease out why and how they exist, given the theoretical frame of interest.
A basic understanding of the procedural logic of growth modeling, therefore,
complements the use of the archetypes and greatly helps in terms of develop-
ing testable theory. In the next few paragraphs, we provide an introduction to
this logic as it relates to the archetypes; we encourage interested readers to
reference some of the recent outstanding extant management/organizational
behavior articles that delve more deeply into this topic (cf. Bliese & Ployhart,
2002; Collins et al., 2016; Ployhart & Hakel, 1998). In particular, as noted
above, the Collins et al.’s (2016) piece is explicit in terms of how growth
Quigley et al. 19
modeling can help researchers unpack the temporal dynamics of work groups/
teams; others have also started to explore the implications of growth model-
ing for teams research (Kozlowski, 2015; Shuffler et al., 2018).
Growth modeling is an exceptionally powerful way to consider patterns of
change in any given variable across time, while accounting for issues inherent in
longitudinal data (e.g., nonindependence; Bliese & Ployhart, 2002). Given the
theoretical framework or research questions of interest, researchers might
choose to collect data on possible independent variables that might influence
how the team performs at the beginning of the measurement period—which
may or may not coincide with the start of the team’s life span, as we noted
above. Researchers might also consider, based on theory, possible factors that
might account for any change in performance over time, and collect data on
these mediators and moderators. As they approach these questions, the arche-
types can provide a conceptual map to help researchers consider what they
expect initial performance levels to look like; whether they expect performance
to change or not over time; and if so, what that change might look like a priori.
When collecting data on team performance over time, the team perfor-
mance variable is nested within each team—so it is nonindependent over
time, and needs to be accounted for as such. Collecting data at three or more
points in time is essential (Chan, 1998); patterns of team performance can
only be detected when there are multiple points in time, and the patterns that
can be detected are limited by the number of data points that a researcher col-
lects. For example, if only three data points are collected, researchers can
only test for the presence of linear and quadratic trends; cubic trends (or other
more complicated nonlinear trends) cannot be identified without more data
points. Kozlowski, Chao, Grand, Braun, and Kuljanin (2013) discuss using
growth modeling to study emergent team processes and states over time, and
they note that emergence takes time to unfold. Depending on the theoretical
questions being examined, 30 or more repeated measurements over time
could be helpful. Although thus far, team performance has not been theoreti-
cally considered as an emergent state, it potentially could be studied as such
in the future. In other words, at what point does a level of performance emerge
from the team, perhaps such that the team’s expectations change associated
with that particular level of performance—how do teams “raise the bar”?
Furthermore, how might teams fitting different performance archetypes
experience the emergence of performance differently?
It is clear that the decision regarding the number of repeated measure-
ments of team performance, and the interval between them, should be based
on theory (and/or the research questions in consideration). So, for example, is
there an intervention of theoretical interest to the researchers, and would it be
advantageous to develop growth models reflecting team performance
20 Group & Organization Management 00(0)
patterns before and after the intervention? If so, the researchers might think
about ways in which to collect performance data 3 to 4 times before the inter-
vention, and then again 3 to 4 times after. It is worth noting that all five of the
studies in our review that used growth modeling used performance scores
from rounds of different computer simulations. There was no additional bur-
den on the team or other stakeholders with respect to data collection; supervi-
sors, managers, external observers, and clients were not asked to provide
performance data over multiple survey periods. The need for more data points
will limit, in practice, what kinds of team performance data may be available
for consideration. Simulation, archival, or other unobtrusively available team
performance data will be helpful here. Depending on the theoretical ques-
tions in play, and the timing of the collection of performance data, the research
team would potentially be able to ascertain whether teams simply change
performance momentum—or actually change overall archetypes—as each
team performance episode is completed.
Once the data have been collected (no easy feat; e.g., Luciano et al., 2018),
the statistical steps of growth modeling can begin. As noted above, research-
ers may have a sense of what the latent trajectories of the teams in the sample/
subsamples of interest are a priori. With this in mind, one first fits temporal
patterns to the performance data.3 Using a model-testing approach (see Bliese
& Ployhart, 2002), it is possible to determine whether team performance (a
Level 1, within-team variable) progresses over time for a given sample (or
subsample) of teams in a linear, quadratic, cubic, or other, more complicated
nonlinear manner. The next steps involve introducing higher level, between-
team predictors to the model that may predict initial levels of performance
and/or variables that may serve to predict the rate of change in performance
over time. The importance of theory here is critical; we cannot overempha-
size how much potential this technique has to unlock critical questions about
team performance over time and to make new theoretical leaps (see Collins
et al., 2016, for a detailed discussion of the importance of theory in growth
modeling when studying team dynamics). Our work here complements this
existing work; we provide teams’ researchers with a concrete conceptual
model for a variety of abstract patterns of change in team performance that
could be tested using growth modeling methodologies.
Theoretical Implications and Future Research Avenues
The team archetypes conceptual framework aims, in part, to inspire team
researchers to utilize growth modeling to investigate team performance
dynamically. A challenge from our theory-driven hypothetico-deductive
Quigley et al. 21
research tradition is that it does not yet provide us with sufficient tools to
unpack patterns of team performance over time (Cronin et al., 2011;
Kozlowski, 2015). It is notable that qualitative, contextual information was
critical in early studies of team development (e.g., Gersick, 1988; Tuckman
& Jensen, 1977). Ideally, in future teams’ research, the precision of growth
modeling will be complemented with more qualitative research methodolo-
gies for developing a deep understanding of context (Gibson, 2017).
Researchers might also consider an abductive approach, which seeks to solve
real-world challenges and simultaneously advance theory (Mathieu, 2016); it
promotes “the accumulation of knowledge through grafting together and
repurposing insights from different theories and contextualizing them to lend
insights for any particular application” (p. 1138). With these options in mind,
we suggest three more specific future research directions: (a) verifying the
archetypes, (b) exploring facilitating conditions for the archetypes, and (c)
considering shifting archetypes.
Verifying the archetypes. An important initial research question is to explore
whether there are indeed categories of team performance trajectories—that
is, the team archetypes we propose. Are five archetypes sufficiently encom-
passing, yet also parsimonious enough, to capture team performance trajecto-
ries in many organizations? The rise of methods of coping with big data will
be beneficial in this endeavor, and growth mixture modeling (see Collins
et al., 2016) can identify whether the five team archetypes emerge. This
future research avenue will also need to consider how context, such as indus-
try type, enables or restrains the emergence of team archetypes. For example,
perhaps in the military, emergency services, and high-reliability organiza-
tions, the more constrained context may result in fewer archetypes. In statisti-
cal terms, growth mixture modeling team performance trajectories in these
contexts may only reveal two archetypes—Jupiter and Icarus—given that
only high-performing teams are released from training, and if team perfor-
mance starts to significantly drop, these teams go back into training. In con-
trast, for contexts with more autonomy (e.g., business consulting), growth
mixture modeling may reveal all five proposed team archetypes. Mathieu and
Rapp (2009) provide an illustration with student teams in which all our team
archetypes emerged except Odysseus; as noted above, Lorinkova et al. (2013)
provide evidence of the Odysseus archetype.
A critical component of unpacking the existence of the proposed five
archetypes is the contextual features that enable or restrain their emergence.
Is it the rhythm of performance deadlines and feedback from industry that
influences the emergence of archetypes, or are the archetypes more inherent
in the team/task type? That is, taking a systems approach to understanding
team performance dynamics (Cronin et al., 2011), which system—the
22 Group & Organization Management 00(0)
industry, organization, and/or task cycle—is the more powerful driver of the
team archetype? These research questions require context to be substantively
incorporated into future teams’ research, rather than the current trend of
including these issues as control variables (Cronin et al., 2011).
Similarly, different types of performance outcomes need to be substan-
tively incorporated into the research agenda. The sensitivity of the team per-
formance outcomes utilized in the research may also determine whether a
variety of team archetypes emerge. For example, in the high-reliability teams
identified above, investigation of the archetypes with objective performance
measures (e.g., near miss rates for air-traffic control teams or emergency
department medics) may show insignificant variation in team performance.
Whereas, manager or team member ratings may be more sensitive when there
are safety and legal implications of team performance.
Clearly, future field research would be helpful in verifying the existence
and frequency of the five archetypes. Whether these team archetypes exist is
essentially a descriptive research question, with the focus on systematically
mapping patterns of team performance from big data sources to develop
benchmarks to guide research design about the occurrence of team arche-
types for theoretical contingencies for contextual features such as team types,
tasks, and deadlines (Kozlowski, 2015). Knowing the archetypes would
enable team researchers to better consider path dependency in a key depen-
dent variable in our field, team performance. As Cronin and colleagues (2011)
highlighted, “[F]ailing to account for path dependence can mis-specify the
strength of found effects” (p. 596), because boundary conditions and illusory
contradictions across research are not considered.
Facilitating conditions for archetypes. A second arena ripe for future research is
building a greater understanding of what promotes specific archetypes. Some
prior research has focused on the enabling conditions at the onset of a team’s
existence, such as team composition, team charters, or team training/coach-
ing (cf. Hackman, 2012). However, given our approach does not assume
engagement with the team at the point of formation, and we are interested in
patterns that constitute archetypes, which may be identifiable across perfor-
mance episodes at any point in the team’s life, we recommend, instead, a
focus on how a team’s performance changes over time and how positive
momentum is created or sustained.
For example, recall that Odysseus teams are characterized by overcoming
ongoing struggles to achieve positive momentum and ultimately high perfor-
mance. What may differentiate Odysseus teams from other teams is how they
take advantage of the continual and varied opportunities to learn and act on
feedback, with this being an iterative process across multiple performance
Quigley et al. 23
episodes. These principles are the premise of “agile teams” in software devel-
opment and consulting, which are structured to work through relatively short
iterations of multiple performance deliverables (Dybå & Dingsøyr, 2008).
These teams create momentum from the team task cycles. This involves set-
ting project goals and plans, and then during and after completing tasks,
obtaining feedback from multiple end users as well as conducting frequent
short meetings for debriefs and handoffs. The continuous feedback cycle con-
sistently reevaluates whether team outputs are “performing,” thus enabling
iterative learning to improve performance (Edmondson, Dillon, & Roloff,
2008; Marks et al., 2001). The short task cycles provide just enough con-
trol—in the form of goals, deadlines, and systematic feedback—that guides
Odysseus teams in the completion of team tasks. Task requirements are clear
and create a way forward, stimulating momentum. As teams learn from the
feedback obtained, this is likely to enhance team performance across epi-
sodes. Effective team leadership that facilitates team learning may also influ-
ence Odysseus teams (e.g., Lorinkova et al., 2013); the literature on the
benefits of after-action reviews in teams also touches on this issue (e.g.,
DeRue, Nahrgang, Hollenbeck, & Workman, 2012; Eddy, Tannenbaum, &
Mathieu, 2013; Ellis & Davidi, 2005).
Interestingly, many theories of team development coincide with this
Odysseus pattern of increasing positive momentum, arguing that most teams
progress linearly through a series of phases or stages, such as the emergence
and resolution of conflict, with the goal to become high-performing teams
(Arrow, 1997). However, we know that conflict frequently derails teams (see
De, Dreu, & Weingart, 2003), and only a minority of teams overcome this
conflict in a way that enables them to flourish with a positive team perfor-
mance trajectory (Behfar, Peterson, Mannix, & Trochim, 2008; Thiel, Harvey,
Courtright, & Bradley, 2017). In particular, three strategies seem to differen-
tiate those teams that resolve conflict: (a) To address interpersonal difficul-
ties, focus on the content of interpersonal interactions rather than delivery
style; (b) to counteract conflict around assignment of work, discuss reasons
behind it; and (c) to determine roles, use relevant task expertise rather than
volunteering, default, or convenience (Behfar et al., 2008). We suspect that
these processes are characteristic of the Odysseus archetype, and encourage
future research to examine performance trajectories where these are in place
to verify this is the case.
A second illustration of exploring ways of maintaining positive momen-
tum in team performance pertains to regulatory mechanisms. For example, is
learning the most crucial team regulatory mechanism? Perhaps, it is for the
two most successful team archetypes, Jupiter and Odysseus, because they
have ways to manage adverse performance feedback. However, for teams
24 Group & Organization Management 00(0)
with long-term performance slumps such as Icarus, perhaps emotional regu-
lation is most important, so that these teams are open to incoming feedback
about what improves team performance. In addition, understanding how to
foster team resilience (being able to bounce back from challenges; Alliger,
Cerasoli, Tannenbaum, & Vessey, 2015) might be a fruitful avenue for iden-
tifying antecedents of positive team trajectories over time. Although most of
the research on team resilience has unfortunately relied on cross-sectional
designs, an array of antecedents of team resilience have been identified, such
as the team having a supportive climate (Meneghel, Martínez, & Salanova,
2016). Similarly, a wide range of team emergent states and processes have
been advocated as important mechanisms for overcoming difficulties as
teams strive to be high performing (e.g., team learning, affect, efficacy, goal
setting, cohesion). We have yet to understand which are most important for
fostering long-term team performance across multiple episodes.
Shifting archetypes. Knowing more about how to create meta-change that
shifts teams from one team archetype to another is also of vital importance.
For example, what enables a shift from a low-performing archetype (i.e.,
Icarus or Pluto) to a high-performing archetype (i.e., Jupiter or Odysseus)?
This is a question of “turning around teams with stagnating or poor perfor-
mance” (Collins & Gibson, 2014). Ultimately, this shift in archetypes con-
ceptually describes a change in the rate and/or direction of momentum of
There are likely multilevel issues at play in terms of how teams move from
one archetype to another. For example, team performance feedback has an
impact on the effort and motivation individual team members expend in the
next team performance cycle. So, prior to turning around team performance,
it may be important to understand and address the emotional toll that the low-
performing archetype likely has on individual team members. The double-
edged sword of performance pressure (Gardner, 2012) or individual team
members’ satisfaction with the team, may be the trigger of static or negative
momentum. These reactions will affect the effort team members put forward
in the next performance cycle, and, thus, the future team performance trajec-
tory. For example, Jupiter teams may have grand expectations that induce
stress, derailing team performance; Neptune teams may have burnout from
constantly seeking to overcome challenges; and the low performance of Pluto
teams may become a self-fulfilling spiral. Perhaps, it is only once such indi-
vidual issues have been addressed that the team can effectively approach
shortcomings in their task accomplishment. More generally, it is important to
recognize that teams need time out from their tasks to attend to team viability
issues, which may create momentary downturns in team performance
Quigley et al. 25
patterns. Ultimately, however, these activities will likely enable the teams to
become more viable in the longer term (McGrath, 1991).
Empirical evidence suggests that interventions such as team building can
positively affect team member satisfaction, even when satisfaction is low as
a result of ongoing poor team performance (cf. Klein et al., 2009). Perhaps, it
is only after individual team member needs are recognized that performance
on team tasks can be improved. For example, if the team’s shortcomings are
a result of a lack of individual skill development, team training interventions
could help, or if the team’s challenge is problem solving, an intervention on
conflict management could help (Behfar et al., 2008).
Another option is for the team to look outward, and consider reimagining
its boundaries. Recent research calls for attention to the permeability of team
boundaries, suggesting that when members move easily in and out of the
team, then external activity is enhanced, providing access to resources and
increasing the team’s options for moving forward (Dibble & Gibson, 2018).
This increase in externally focused activity may be critical for both identify-
ing and taking advantage of opportunities to turn the team around (Collins &
Gibson, 2014). Thus, like Shuffler et al. (2018), we call for future research to
explore combining team interventions. Albeit, we highlight here that a neces-
sary first step is to understand the archetypal performance trajectory to iden-
tify when and how teams became derailed, so that the conditions instigating
the negative or static momentum can be addressed to pull the teams out of
their performance slump and turn the team around. In sum, we suspect that
different interventions will be needed along the way, depending on the arche-
type; therefore, understanding which trajectory characterizes the team (i.e.,
which archetype best describes it) will help to focus relevant interventions on
the specific issues that are most troublesome.
Perhaps, the most important implication of the conceptual approach pre-
sented here for practitioners is that knowledge of team performance at any
one point in time does not provide a holistic picture of where teams have been
or where they are going. Rather, it is important to shift our thinking from
predicting “levels of performance” to “patterns of performance.” The
approach to the examination of team performance over time that we advocate
is fine grained enough such that future researchers will be able to hypothesize
and test very specific recommendations for interventions based on empirical
evidence. With knowledge of the likely team archetype, a more appropriate
intervention is likely to be chosen. For example, a Pluto team will need assis-
tance to overcome challenges; perhaps, these teams initially need an
26 Group & Organization Management 00(0)
intervention that acknowledges emotional reactions to poor performance
such as coaching and/or team building (Shuffler et al., 2018). A Jupiter team
needs to manage performance pressure (Gardner, 2012), so interventions that
provide support via leadership and/or work design may be optimal. Thus, the
archetypes underscore the need for a customized rather than a blanket
approach to the implementation of interventions. Part of this customization of
team interventions is also likely to also be contingent on the team’s context
(including industry, organization, and task). Bresman and Zellmer-Bruhn’s
(2013) framework could be a useful starting point to understand these con-
straints and enablers of team archetypes.
A second practical implication is that our archetypes extend the key char-
acteristics of “high-performing teams” (Katzenbach & Smith, 1993), to
include as a key characteristic the ability to change the team’s performance
trajectory when warranted. Prior literature on team performance and develop-
ment has addressed enabling conditions for setting up high-performing teams
at the outset of the team life span (e.g., Hackman, 2012). We suggest this lit-
erature needs to be recast. Are enabling conditions at the outset of a new
team, or new performance episode, the same enabling conditions that main-
tain momentum (i.e., consistent or increasing performance over time), and/or
help to change the team archetype (i.e., turning around poor performing
teams)? That is, in addition to knowing how to set themselves up for success
such as Jupiter teams, we argue that teams (and their leaders) should also
know how to increase performance from low levels of performance such as
the Odysseus archetype, and turn around the stagnating/poor performance of
the Pluto and Icarus archetypes. The ability to see patterns of performance
over time—thus identifying the archetype of the team—is a critical, practical
first step for those who wish to improve team performance.
Just as we advocate that teams have important patterns of change in perfor-
mance over time, the literature on team performance has its own trajectory. It
is our hope that this trajectory has more in common with the story of Odysseus
than with the story of Icarus! More seriously, to reflect and incorporate the
dynamic and complex modern world of organizations and work, researchers
will need to break free from the constraints of prior models of team effective-
ness and development to consider other ways of characterizing change in
performance over time. Our aim was to facilitate this journey with a concep-
tual model of team performance archetypes. We hope to see further creative
and inspiring developments in the consideration of patterns of team perfor-
mance over time in the upcoming decade and beyond.
Journals Included in Literature Review.
Academy of Management Journal Journal of Business Ethics Leadership Quarterly
Academy of Management Learning and Education Journal of International Business Studies Management Science
Administrative Science Quarterly Journal of Management Organization Science
Group and Organization Management Journal of Management Studies Organizational Behavior and Human Decision
Human Performance Journal of Occupational and Organizational
Human Relations Journal of Organizational Behavior
Journal of Applied Psychology Journal of Vocational Behavior
Themes in Empirical Team Performance Literature, 2007 to 2017.
Theme Sample studies Methodology Sample predictors of team performance
1. Traditional IPO/IMOI
is static. Predictors
Primary studies: Algesheimer, Dholakia, and Gurau
(2011); Balkundi, Kilduff, and Harrison (2011); Bradley,
Klotz, Postlethwaite, and Brown (2013); Bresman
(2010); Carton and Cummings (2013); Chi and Huang
(2014); Cole, Bedeian, and Bruch (2011); Courtright,
McCormick, Mistry, and Wang (2017); Cropanzano,
Li, and Benson (2011); J. P. De Jong, Curseu, and
Leenders (2014); B. A. De Jong and Elfring (2010);
Dietz, van Knippenberg, Hirst, and Restubog (2015);
Gonzalez-Mulé, Courtright, DeGeest, Seong, and
Hong (2016); González-Romá and Hernández (2014);
Griffith and Sawyer (2010); Hu and Judge (2017);
Humphrey, Aime, Cushenberry, Hill, and Fairchild
(2017); Kostopoulos and Bozionelos (2011); Lehmann-
Willenbrock and Allen (2014); Mohammed, Alipour,
Martinez, Livert, and Fitzgerald (2017); Mohammed
and Nadkarni (2011); Palanski, Kahai, and Yammarino
(2011); Pearsall, Christian, and Ellis (2010); Peters and
Karren (2009); Schaubroeck, Carmeli, Bhatia, and Paz
(2016); Schaubroeck, Lam, and Peng (2011); Schippers
(2014); Stewart and Johnson (2009); ten Brummelhuis,
Oosterwaal, and Bakker (2012); Tsai, Joe, Chen, Lin,
Ma, and Du (2016); Zhang and Peterson (2011); Zhang,
Waldman, and Wang (2012)
Meta-analyses examples: Bell, Villado, Lukasik, Belau,
and Briggs (2011); Castaño, Watts, and Tekleab
(2013); D’Innocenzo, Mathieu, and Kukenberger
(2016); Marlow, Lacerenza, Paoletti, Burke, and Salas
(2018); B. A. De Jong, Dirks, and Gillespie (2016);
Marlow et al. (2018); Mesmer-Magnus and DeChurch
(2009); Salas, Cooke, and Rosen (2008); Stahl,
Maznevski, Voigt, and Jonsen (2010)
Methodology is quantitative albeit
• Multisource field studies (e.g.,
employee IVs and supervisor
• Multimethod field studies
(qualitative and survey research)
• Multiple-study designs (lab,
student, field samples)
• Simulations and lab studies
• Quantitative survey data
sometimes collected across
time periods; inputs, mediator
deliberately sometimes collected
at different times prior to
Team types varied: Examples include
student project, marketing, HR,
manufacturing, customer service,
logistics, engineering, finance, R&D,
sales, drug development, project,
front-line public safety services,
technology sales, problem solving,
audit/tax, management, police
tactical/special unit, military, online
gaming, entrepreneurial venture,
virtual, anesthesia, sports
Inputs: team personality composition, goal
orientation, cultural diversity, leader
humility, temporal leadership, shared
temporal cognitions, cross-functional
understanding, conflict perceptions,
personality, individual knowledge, social
loafing, group conflict asymmetry,
diversity of temporal orientations,
expert inclusion, team charters,
transactive memory, task routineness,
collective orientation, demographic
diversity, cultural diversity, team training
Processes/mediators: team member
monitoring, speaking up, providing
assistance, giving instructions,
information exchange, conflict
management, team learning, backing-up,
team building, collaborative planning,
team adaption and team implicit
coordination, self-managing behaviors,
boundary-spanning behaviors, task
debate, team goal monitoring,
information sharing, communication
Emergent states/mediators: group
atmosphere, relationship and task
conflict, trust, commitment, team
potency, team efficacy, cohesion,
psychological safety, team adaptiveness,
shared leadership, task cohesion, task
and relationship conflict, temporal
conflict, humor patterns, collective
humility, team trustworthiness
Theme Sample studies Methodology Sample predictors of team performance
2. Novel approaches to
Team performance is
considered in a more
dynamic way. Finer
of cause and effect
as it relates to team
modeling used to
focus on patterns of
Cordery, Morrison, Wright, and Wall (2010); Dierdorff,
Bell, and Belohlav (2011); Gardner (2012); Lehmann-
Willenbrock, Chiu, Lei, and Kauffeld (2017); Waller,
Hagen, and Kaplan (2016); Lorinkova, Pearsall, and
Sims (2013); Mathieu, Kukenberger, D’Innocenzo,
and Reilly (2015); Mathieu and Rapp (2009); Murtha
(2013); Villado and Arthur (2013)
Methodology is quantitative and
• Multimethod field study
• Qualitative case studies
• Statistical discourse analysis
• Multilevel SEM, snapshots of
performance predictors at early,
middle, late stages of team life span
• Bayesian estimation
• Pooled interrupted time series
• Training and after-action reviews
• Audio recordings coded for
• Random coefficient growth
modeling in HMLM, multilevel
Team types: problem solving, audit,
consulting, student “new venture,”
wastewater treatment, airline flight
crews, student simulation teams,
student project teams
Context: contextual performance
Inputs: team declarative knowledge,
task uncertainty, autonomy, team
charters, team performance strategies,
Processes/mediators: team patterns
of interpersonal interaction and
communication, task debate, task
conflict, in-process planning, backing-up
behavior, performance monitoring
Emergent states/mediators: dynamic
positivity, team efficacy, openness
of communication, cohesion, team
adaptiveness, interaction patterns,
Note. IPO = input–process–outcome; IMOI = input–mediator–output–input; IV = independent variable; HR = human resources; R&D = research and development;
SEM = structural equations modeling; HMLM = hierarchical multivariate linear modeling.
30 Group & Organization Management 00(0)
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publi-
cation of this article.
1. We gratefully acknowledge an anonymous reviewer’s suggestion here.
2. We were amused to discover evidence of the existence of the Odysseus arche-
type in a study on leadership in teams (Lorinkova, Pearsall, & Sims, 2013), as
Odysseus himself was a legendary heroic leader.
3. We want to thank an anonymous reviewer for suggesting we include the basic
logic behind the growth modeling process.
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Associate Editor: Lucy Gilson
Submitted Date: December 11, 2017
Revised Submission Date: July 16, 2018
Acceptance Date: July 23, 2018
Narda R. Quigley is a professor of management and department chair of Management
and Operations at the Villanova School of Business (Villanova University,
Pennsylvania, USA). Her research interests include groups and teams in organiza-
tions, leadership, motivation, and multilevel issues.
Catherine G. Collins is a Senior Lecturer in the School of Management at the
University of New South Wales, Australia. Her research focuses on team dynamics
and effectiveness, organizational ambidexterity, team and self-efficacy, proactive
behavior, and employee well-bring.
Cristina B. Gibson is Woodside Chair in Leadership and Management at University
of Western Australia School of Business. Her area of expertise is the nexus of
38 Group & Organization Management 00(0)
organizational behavior, international management, and cross-cultural psychology,
with a focus on increasing performance, sustainability, and quality of work life for
team members across cultures.
Sharon K. Parker is an ARC Laureate Fellow, Director of the Centre for
Transformative Work Design, and a professor of management at Curtin University.
Research interests include job and work design, team work, job performance, proac-
tive behavior, and employee development. Her Ph.D. was from the University of