A Resource Model of Team Resilience Capacity and Learning

Abstract

A team’s capacity to bounce back from adversities or setbacks (i.e., team resilience capacity) is increasingly valuable in today’s complex business environment. To enhance our understanding of the antecedents and consequences of team resilience capacity, we develop and empirically test a resource-based model that delineates critical team inputs and outputs of resilience capacity. Drawing from conservation of resources theory, we propose that voice climate is a critical resource that builds team resilience capacity by encouraging intrateam communication and that leader learning goal orientation (LGO) amplifies this relationship by orienting team discourse toward understanding and growing from challenges. In turn, we propose that team resilience capacity is positively related to team learning behaviors, as teams with a higher resilience capacity are well-positioned to invest their resources into learning activities, and that team information elaboration amplifies this relationship by facilitating resource exchange. Results of a time-lagged, multisource field study involving 48 teams from five Canadian technology start-ups supported this moderated-mediated model. Specifically, voice climate was positively related to team resilience capacity, with leader LGO amplifying this effect. Further, team resilience capacity was positively related to team learning behaviors, with information elaboration amplifying this effect. Altogether, we advance theory and practice on team resilience by offering empirical support on what builds team resilience capacity (voice climate) and what teams with a high resilience capacity do (learning), along with the conditions under which these relationships are enhanced (higher leader LGO and team information elaboration).

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Group & Organization Management
2021, Vol. 46(4) 737–772
© The Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/10596011211018008
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A Resource Model of
Team Resilience
Capacity and Learning
Kyle M. Brykman
1
and
Danielle D. King
2
Abstract
A team’s capacity to bounce back from adversities or setbacks (i.e., team
resilience capacity) is increasingly valuable in today’s complex business en-
vironment. To enhance our understanding of the antecedents and con-
sequences of team resilience capacity, we develop and empirically test
a resource-based model that delineates critical team inputs and outputs of
resilience capacity. Drawing from conservation of resources theory, we
propose that voice climate is a critical resource that builds team resilience
capacity by encouraging intrateam communication and that leader learning
goal orientation (LGO) amplifies this relationship by orienting team discourse
toward understanding and growing from challenges. In turn, we propose that
team resilience capacity is positively related to team learning behaviors, as
teams with a higher resilience capacity are well-positioned to invest their
resources into learning activities, and that team information elaboration
amplifies this relationship by facilitating resource exchange. Results of a time-
lagged, multisource field study involving 48 teams from five Canadian tech-
nology start-ups supported this moderated-mediated model. Specifically,
voice climate was positively related to team resilience capacity, with leader
LGO amplifying this effect. Further, team resilience capacity was positively
related to team learning behaviors, with information elaboration amplifying
this effect. Altogether, we advance theory and practice on team resilience by
1
Odette School of Business, University of Windsor, Windsor, ON, Canada
2
Rice University, Houston, TX, USA
Corresponding Author:
Kyle M. Brykman, Odette School of Business, University of Windsor, 401 Sunset Ave,
Windsor, ON N9B 3P4, Canada.
Email: kbrykman@uwindsor.ca

offering empirical support on what builds team resilience capacity (voice
climate) and what teams with a high resilience capacity do (learning), along
with the conditions under which these relationships are enhanced (higher
leader LGO and team information elaboration).
Keywords
team resilience, conservation of resources theory, voice climate, team
learning, learning goal orientation, information elaboration
As organizations increasingly structure work in teams (Bell, Kozlowski, &
Blawath, 2012;Kozlowski & Ilgen, 2006), and teams encounter challenges
that impair coordination and performance (Alliger, Cerasoli, Tannenbaum,
& Vessey, 2015;King, Newman, & Luthans, 2016), it is important for
scholars to explore and explain how teams develop a capacity needed to
overcome the inevitable adversities that they face. Team resilience, defined
as an emergent state reflecting a team’scapacity to bounce back from
adversities or setbacks (Stoverink, Kirkman, Mistry, & Rosen, 2020), offers
a valuable multilevel foundation to bridge insights from the individual and
organizational paradigms, thereby developing a more complete un-
derstanding of resilience at work (Hartmann, Weiss, Newman, & Hoegl,
2020;Hartwig,Clarke,Johnson,&Willis,2020). Although scholarly in-
terest in team resilience is rapidly growing, current research is largely
conceptual or restricted to extreme teams; thus, we still know surprisingly
little about what builds resilience capacity in typical work teams, as well as
the outcomes of this capacity (Duchek, 2020;Hartmann, Weiss, Newman,
et al., 2020;King et al., 2016;Stoverink et al., 2020). Accordingly, the
objective of this research was to answer three pressing questions: (a) what
factors build team resilience capacity? (b) how does team resilience capacity
relate to team learning behaviors? and (c) what leadership characteristics
and/or team behaviors amplify these relationships?
We address these questions by drawing from conservation of resources
theory (COR; Hobfoll, 1989,2001) to develop and test a resource-based
model of team resilience capacity (see Figure 1). COR is a valuable ex-
planatory framework to understand how team resilience capacity is a linking
pin between team inputs and outputs (Bowers, Kreutzer, Cannon-Bowers, &
Lamb, 2017;Mathieu, Maynard, Rapp, & Gilson, 2008) because it describes
the acquisition (input) and deployment (output) of core team resources to
achieve team goals and proactively buffer against threats (Bardoel, Pettit, De
Cieri, & McMillan, 2014;Stoverink et al., 2020).
1
Indeed, several scholars
738 Group & Organization Management 46(4)

have emphasized the utility of COR for explaining the emergence and
function of team resilience (see Hartmann, Weiss, Newman, et al., 2020;
Hartwig et al., 2020;King et al., 2016;Stoverink et al., 2020). Accordingly,
we present a model that connects a specific team resource (voice climate) to
a critical team output (learning) via team resilience capacity, along with
moderators that qualify these effects, thereby elucidating some of the ante-
cedents, outcomes, and boundary conditions of team resilience capacity.
More specifically, our model proposes voice climate (shared perceptions
within a team of the extent to which voice is encouraged; Morrison, Wheeler-
Smith, & Kamdar, 2011) as a resource that builds team resilience capacity by
encouraging open discourse, which is essential for helping teams manage and
overcome future adversities. We further posit that leader learning goal ori-
entation (LGO; a goal orientation focused on developing new skills and
increasing competence; Dweck, 1986) activates and amplifies this relationship
by orienting team discourse toward learning and growing from challenges and
mistakes, thereby increasing the positive effects of voice climate on team
resilience capacity. In turn, we argue that resilient teams—those with a high
capacity for resilience—are well-positioned to invest their stocks of resources
into learning activities (team members’knowledge processing behaviors that
enable team improvements; Harvey, Leblanc, & Cronin, 2019). Finally, we
posit that information elaboration (an iterative team process of exchanging,
discussing, and integrating ideas and information; Homan, van Knippenberg,
Van Kleef, & De Dreu, 2007) amplifies this relationship by enhancing the
mobilization of team resilience capacity via fluid information exchange and
integration. Leveraging COR in this framework offers much-needed conti-
nuity to the field because it aligns to the dominant conceptualization of team
resilience as an emergent capacity that is theorized to mediate the relationship
between other team characteristics, states, behaviors, and outputs (Bowers
et al., 2017;Hartmann, Weiss, Newman, et al., 2020;Stoverink et al., 2020).
We assessed this model with a multisource, multi-wave field study involving
48 teams from five Canadian technology start-ups.
Figure 1. Resource model of team resilience capacity.
Brykman and King 739

Altogether, our research addresses several calls in the literature by em-
pirically evaluating the mechanisms of COR theory to explain what builds
team resilience capacity (voice climate) and what teams with a high resilience
capacity do (learning), along with leadership characteristics (LGO) and team
behaviors (information elaboration) that enhance these relationships (Duchek,
2020;Hartmann, Weiss, Newman, et al., 2020;Stoverink et al., 2020).
Accordingly, we advance theory and research on team resilience in several
important ways. First, although scholars have theorized that learning is a core
antecedent, component, and/or outcome of team resilience (e.g., Bowers et al.,
2017;Stoverink et al., 2020;Sutcliffe & Vogus, 2003), we are unaware of any
empirical research that has actually linked team resilience to learning. Rather,
existing empirical research has primarily focused on performance and well-
being outcomes (Gucciardi et al., 2018). This is surprising considering that
learning is a more proximal outcome of team resilience than performance
(Bell et al., 2012;Mathieu et al., 2008) and thus may help to explain why
resilient teams tend to achieve positive adaptation and stronger performance.
It is also problematic, as scant empirical attention to the links between team
resilience and learning has helped to perpetuate the assumption that teams
primarily learn from adversity, which fits the narrative of resilience as a
process, but overlooks how resilient teams may be equally likely to engage in
learning behaviors to anticipate and prepare for future challenges (Alliger
et al., 2015;Duchek, 2020). Overall, we are unaware of any empirical re-
search that has examined whether, why, or how team resilience relates to
team learning.
Second, we focus on expanding the nomological network of team resil-
ience capacity to include voice climate, leader LGO, team learning, and
information elaboration. In doing so, we offer precision on the nuanced ways
that specific team states (voice climate) affect team resilience capacity,
juxtaposed to general contextual factors (e.g., psychological safety). More-
over, we focus on two moderators involving the emergence (leader LGO) and
function (team information elaboration) of team resilience capacity, thereby
clarifying the conditions under which (a) teams are more likely to develop
a high resilience capacity and (b) teams with a high resilience capacity are
more likely to engage in learning activities. As scholars have called for greater
consideration of context in organizational behavior research (see Johns,
2006), we believe this integrated consideration of climate, leadership, and
team processes and outcomes offers important advancements to the field.
Finally, we also offer a multilevel perspective on team resilience, along with
practical insights for leaders on how to build team resilience capacity, by
accounting for the fundamental role of leadership in this model, thereby
addressing calls for research that clarifies how leaders can facilitate productive
740 Group & Organization Management 46(4)

sensemaking and promote resilience in teams (Alliger et al., 2015;Williams,
Gruber, Sutcliffe, Shepherd, & Zhao, 2017).
Theoretical Framework and Hypotheses
A COR Model of Team Resilience
The fundamental principle of COR theory is that people strive to obtain and
retain valued resources to assist with goal achievement (Hobfoll, 1989,2001).
Resources denote “objects, personal characteristics, conditions, or energies
that are valued in their own right, or that are valued because they act as
conduits to the achievement or protection of valued resources”(Hobfoll,
2001, p. 339). Hobfoll (1989) and colleagues (Halbesleben, Neveu, Paustian-
Underdahl, & Westman, 2014;Hobfoll, Halbesleben, Neveu, & Westman,
2018) further assert that resources travel in packs or “resource caravans,”
which denote pools of resources that come from the same environment—an
important feature that we return to later. Another principle of COR theory is
that those with more resources are less vulnerable to resource loss and more
capable of resource gain (Chen, Westman, & Hobfoll, 2015;Hobfoll et al.,
2018) and thus people are motivated to acquire resources to protect them-
selves against resource threats. This principle reflects the notion of “rich
getting richer,”or “resource-gain spirals,”as people with more resources are
able to invest their greater stocks of resources into activities that increase their
available pool of resources (Bardoel et al., 2014). In sum, Hobfoll et al. (2018,
p. 107) assert that “resource possession and lack thereof are integral to
vulnerability and resilience.”
As noted earlier, COR theory has frequently been discussed as a potentially
fitting and useful framework to understand the antecedents and consequences
of team resilience (e.g., Hobfoll, Stevens, & Zalta, 2015;King et al., 2016;
Stoverink et al., 2020). COR is especially applicable to understanding team
resilience capacity because this conceptualization frames resilience as a team
property that develops from other team experiences (inputs) to subsequently
influence team behaviors (outputs; cf. Stoverink et al., 2020). Specifically,
COR theory suggests that team resilience capacity emerges from environ-
ments that are “(a) rich in personal, social, materials, and energy resources, (b)
allow access to those resources, and (c) provide safety and protection against
resource loss and promote resource growth”(Hobfoll et al., 2015, p. 176).
Thus, we draw from COR theory to develop a resource model that connects
a caravan of important protective and promotive team resources, voice climate
and leader LGO, to team learning via resilience capacity. That is, as described
in greater detail below, we position voice climate as an important social
Brykman and King 741

resource that builds team resilience capacity by encouraging open commu-
nication and leader LGO as an additional team resource that amplifies the
positive effect of voice climate on team resilience capacity by activating
growth-oriented attitudes toward adversity and orienting team discourse to-
ward positive views of mistakes intended for growth. Next, we argue that team
resilience capacity affects team learning such that teams high on resilience
capacity invest their abundant stocks of resources into learning activities to
further enhance and protect their resources and specify team information
elaboration as a resource mobilization mechanism that augments the benefits
of team resilience capacity for learning via efficient interpersonal exchange
(i.e., “crossover”;Bolger, DeLongis, Kessler, & Wethington, 1989;Hobfoll
et al., 2018;Stoverink et al., 2020). Before elaborating on this model, we first
describe our conceptualization of team resilience capacity to clarify our
perspective.
Conceptualizing Team Resilience Capacity
While research on team resilience is rapidly growing, different scholars have
adopted different conceptualizations, which has resulted in a somewhat
fragmented body of research. As has been noted elsewhere (Duchek, 2020;
Hartmann, Weiss, & Hoegl, 2020;Hartwig et al., 2020;Stoverink et al., 2020),
team resilience is commonly conceptualized as either a capacity, process, or
outcome. That is, scholars have either defined team resilience as (a) an
emergent state denoting a team’s capacity to bounce back from future set-
backs, (b) a dynamic social process that enables positive adaptation to col-
lectively experienced threats or challenges, or (c) the demonstration of
resilience as manifested in positive outcomes (e.g., recovery and growth) after
an adversity.
Our perspective is that all of these approaches are appropriate. However, it
is critical for scholars to be clear and precise regarding their chosen con-
ceptualization to ensure a unified approach to understanding this phenome-
non. To follow this advice, we explicitly conceptualize and define team
resilience as a team capacity to bounce back from adversities or setbacks and
reserve the term “resilient teams”to denote teams with a high capacity for
resilience. We also use the term “team resilience capacity”throughout to
clarify this focus, whereas we use the term “team resilience”to refer to the
literature and/or phenomenon more broadly. This conceptualization of team
resilience as an emergent capacity has become a dominant approach in the
literature, especially for quantitative research (e.g., Bowers et al., 2017;
Maynard & Kennedy, 2016;Stephens, Heaphy, Carmeli, Spreitzer, & Dutton,
2013;Stoverink et al., 2020). Operationally, it reflects team members’shared
742 Group & Organization Management 46(4)

beliefs in, or perceptions of, their collective capacity to overcome future
adversities or setbacks (Carmeli, Friedman, & Tishler, 2013;Hartmann,
Weiss, Newman, et al., 2020;Vera, Rodr´
ıguez-S´
anchez, & Salanova, 2017).
It is also important to clarify the role of adversity within this conceptu-
alization. We define adversity as “challenging events and circumstances [that]
place stress on individuals and on team processes”(Alliger et al., 2015,
p. 177), including, for example, difficult assignments, time pressure, in-
sufficient resources, and conflict. Adversity can range from chronic to acute,
short to extended, and sudden to gradual onset (Williams et al., 2017). It has
the potential to harm team performance because it often impairs team co-
ordination and goal attainment (Stoverink et al., 2020). Although adversity is
an essential component for teams to demonstrate resilience (by overcoming
the adversity), several scholars have explained why it is not a prerequisite for
teams to develop a high capacity for resilience. For example, Hartmann,
Weiss, and Hoegl (2020, p. 45) argue: “Team resilience capacity describes the
potential of a team to show positive adaptation if and when the team faces
adverse circumstances…Teams may hold this capacity regardless of whether
they have ever faced or will ever face a setback or adversity”(see also
Stoverink et al., 2020). Thus, while an adversity experience is a defining
element of the team resilience process, and a necessary precondition for a team
to demonstrate resilience, teams need not experience adversity to develop
a capacity to overcome future adversities, nor to harness this capacity to
engage in proactive learning behaviors (Hartmann, Weiss, & Hoegl, 2020;
Stoverink et al., 2020). With this foundation in place, we elaborate on each
proposition in greater detail below.
Voice Climate and Team Resilience Capacity
Employee voice—discretionary communication of information, ideas, or
issues that may be challenging in nature but is intended for improvement
(Morrison, 2011)—is a valuable team behavior that is positively related to
team performance (e.g., Frazier & Bowler, 2015), learning (e.g., Edmondson,
1999), and innovation (e.g., Guzman & Espejo, 2019). Given that employees
are generally reluctant to speak up with ideas and concerns (Detert &
Edmondson, 2011), recent research has emphasized the importance of
voice climate—shared team perceptions of the extent to which voice is en-
couraged on the team (Morrison et al., 2011)—for stimulating voice, thereby
ensuring that organizations reap their collective benefits (Frazier & Bowler,
2015). In line with individual-level research (Ashford, Rothbard, Piderit, &
Dutton, 1998), the primary beliefs that underlie voice climate are (a) voice
safety—shared belief about whether speaking up is safe versus dangerous—and
Brykman and King 743

(b) voice efficacy—shared belief about whether group members are able to
speak up effectively and their input is taken seriously (Morrison et al., 2011).
In the present research, we position voice climate as a critical resource that
builds team resilience capacity by ensuring that team members feel safe and
capable of vocalizing pertinent information, high-quality ideas, and im-
pending concerns, which is integral to build their capacity to navigate and
overcome future adversities.
Theoretical models and empirical insights support the potential for voice
climate to foster team resilience capacity. For example, Gucciardi et al. (2018)
highlight the role of supportive team norms for building team resilience, as
norms provide important information that guide team approaches and re-
sponses to adversity. Of particular relevance to our model, Stoverink et al.
(2020) drew from COR theory to identify several factors that build team
resilience capacity, including team potency and psychological safety. They
theorized that these states provide necessary resources that enable teams to
manage adversities by vocalizing problems and forming shared under-
standings. Similarly, Bowers et al. (2017) modeled team resilience as a sec-
ond-order emergent state that results from inputs such as psychological safety
and collective efficacy. Voice climate shares similar features with these
constructs; however, it is a more specific team state that involves the com-
bination of safety and efficacy beliefs, and focuses on intrateam communi-
cations, rather than other risky behaviors (Morrison et al., 2011).
2
Thus, in line
with COR theory, voice climate is particularly relevant for facilitating team
resilience capacity because it facilitates resource acquisition (efficacy) and
protects against resource loss (safety).
Empirical findings also support the potential for voice climate to influence
team resilience capacity based on the value of open, trusting team commu-
nications. For example, Vera et al. (2017) found that teamwork (e.g., re-
spectful interactions) builds team resilience capacity. Related research also
demonstrates the importance of team communication for shaping team re-
silience, such as by generating new ideas and creating alignment within the
team (Carmeli et al., 2013;Gomes, Borges, Huber, & Carvalho, 2014). More
recently, Li and Tangirala (forthcoming) found that team promotive and
prohibitive voice, proximal outcomes of voice climate (Frazier & Bowler,
2015;Morrison et al., 2011), enable process innovation (resource acquisition)
and error management (resource protection) in response to major organiza-
tional change events. Altogether, prior research suggests that voice climate is
a central mechanism for building team resilience capacity.
Drawing from COR theory (Hobfoll, 1989), we propose that voice climate
provides teams with a necessary environmental resource of safety and efficacy
to voice, which increases their capacity to overcome future challenges via
744 Group & Organization Management 46(4)

resource acquisition and protection. For example, voice climate can help
prevent rigid responses to difficulties by encouraging open discourse before
adversity strikes, thereby preparing teams to manage difficult and unexpected
events (Maynard & Kennedy, 2016;Sutcliffe & Vogus, 2003). As Alliger et al.
(2015, p. 179) elaborate, resilient teams “vocalize concerns and give one
another a ‘heads-up’when they see a challenge looming. They are particularly
good at attending to unfavorable information and are careful not to dismiss
concerns prematurely.”Thus, relative to teams with a low voice climate, we
expect teams with a high voice climate will develop a greater capacity to
overcome future adversities. Therefore, we propose
Hypothesis 1: Voice climate is positively related to team resilience
capacity.
The Moderating Effects of Leader LGO
As noted above, in applying COR theory to the study of resilience, Hobfoll
et al. (2015, p. 176) argue that the capacity for resilience emerges from
“resource rich”environments that “provide safety and protection against
resource loss and promote resource growth.”They further assert that resources
exist in “caravans”from the same environment, such as how teams with
a positive climate tend to also have supportive leadership and decision-making
autonomy, and that each additional personal, social, and/or material resource
further augments an entity’s capacity for resilience (Hobfoll et al., 2015,2018;
see also Halbesleben et al., 2014). To account for this dynamic, we consider
the role of an additional team resource, leader LGO, for activating and
amplifying the constructive effects of voice climate on team resilience ca-
pacity by orienting voice climate toward discussions of challenges and
learning from mistakes, as opposed to other voice content (e.g., novel ideas)
less relevant to resilience. Importantly, leader LGO also aligns to environ-
mental properties conducive for the development of team resilience capacity
based on COR theory (Hobfoll et al., 2015) in that it has the potential to
facilitate both resource acquisition (focusing goals on competency de-
velopment) and resource protection (offering latitude for team members to
make mistakes).
LGO is characterized by investment in goal-directed efforts with the in-
tention of developing new skills and increasing competence (Dweck, 1986).
Individuals with a high LGO thus tend to feel energized by challenges and
hold constructive views of mistakes as a means for growth (Dweck, 1986;
Vandewalle, Nerstad, & Dysvik, 2019). As leaders hold power and promi-
nence in team hierarchies, with a primary function of guiding their team
Brykman and King 745

toward shared goals (Northouse, 2021;Piccolo & Buengeler, 2013), their
personal goal orientation has the potential to dramatically shape their teams’
expectations and subsequent behaviors (Kozlowski & Ilgen, 2006;Mathieu
et al., 2008). For example, leaders with a high LGO would tend to encourage
team members to pursue difficult tasks, frame challenges as a learning op-
portunity, and foster discussions focused on skill development (Dragoni,
2005;Dragoni & Kuenzi, 2012). Indeed, broader research documents how
leaders’personal characteristics tend to affect team members’behaviors via
indirect (e.g., modeling) and direct (e.g., goal-setting) mechanisms that signal
collective expectations (cf. Dragoni & Kuenzi, 2012;Piccolo & Buengeler,
2013). In particular, the trickle-down effect of leadership (Johnson et al.,
2017;Mayer, Kuenzi, Greenbaum, Bardes, & Salvador, 2009), which is
grounded in social learning theory (Bandura, 1977), details that leader
characteristics often “trickle-down”to influence followers’cognitions and
behaviors. In support of this phenomenon, Dragoni and Kuenzi (2012) found
that leader goal orientation indirectly affects team performance via unit goal
orientation, and Zhu and Akhtar (2019) found that leader LGO indirectly
affects employee behavior via leader openness, both of which show how
leader characteristics, specifically goal orientation, trickle-down to affect
team behaviors.
Accordingly, in shaping team members’work approaches, we argue that
leader LGO activates and amplifies the constructive effects of voice climate on
team resilience capacity by orienting team communications toward de-
veloping competencies through challenging work (resource acquisition) and
learning from mistakes in the pursuit of growth (resource protection). Stated
otherwise, voice climate is a social resource that builds team resilience ca-
pacity via encouragement to speak up, and LGO amplifies this effect by
orienting team communications toward learning from mistakes, thereby ac-
tivating the potential for voice climate to build team resilience capacity. By
contrast, leaders with a low LGO are threatened by challenges and the
prospect of failure, and thus, the positive effects of voice climate would be
mitigated for their teams because their followers would be less likely to
engage in open discourse focused on understanding and growing from
mistakes, which would otherwise help them to fully leverage voice climate for
building resilience capacity.
Theoretical and empirical insights indirectly support this argument. For
example, Barton and Kahn (2018) noted that team members look to leaders to
frame adversity experiences and model appropriate responses. Prior work has
also demonstrated a link between transformational leadership and team re-
silience capacity effects via leaders converting crises into developmental
challenges (Sommer, Howell, & Hadley, 2016;Vera et al., 2017). Here, we
746 Group & Organization Management 46(4)

assert that leader LGO enhances the positive effect of voice climate on team
resilience capacity by setting the tone for team members to view adversity as
a challenge, rather than a hindrance, and by encouraging open discussions of
growth-oriented perspectives to setbacks. Voice climate equips teams with
a necessary condition to overcome potential future adversities via open
discourse, and leader LGO combines with this climate to further build team
resilience capacity by demonstrating that events requiring resilience are
opportunities for learning and growth. Therefore, we propose
Hypothesis 2: Leader learning goal orientation amplifies the positive
relationship between voice climate and team resilience capacity.
Team Resilience Capacity and Team Learning
Drawing from Harvey et al. (2019),wedefine team learning as team
members’behaviors related to knowledge processing, which enables team
improvements. Edmondson (1999) identified several core learning behaviors,
including “asking questions, seeking feedback, experimenting, reflecting on
results, and discussing errors or unexpected outcomes of action”(p. 353).
Edmondson (1999) further specified that learning behaviors consume valuable
resources (e.g., time and energy) without assurances of positive results, and
thus, teams will only invest these resources into learning activities under
positive team conditions (see also Harvey et al., 2019).
Therefore, in line with COR, we propose that teams with a high resilience
capacity will engage in more learning than teams with a low resilience ca-
pacity because they have greater stocks of resources to invest into learning
activities and can preserve and/or acquire more resources via learning. As
Maynard and Kennedy (2016, p. 22) elaborate, “team resilience can provide
adaptability to future threats by creating resources that can be drawn upon,
combined, or molded to new situations as needed.”In that sense, teams with
a high capacity for resilience are well-positioned to invest resources toward
learning because it is a means to acquire more resources (e.g., new knowledge
and shared understanding; Maynard & Kennedy, 2016;Sutcliffe & Vogus,
2003). Resilient teams do not strictly seek feedback and experiment during
(e.g., manage and coping) or after adversity occurs (e.g., mend and adap-
tation), but also before adversity strikes (e.g., minimize and anticipation),
thereby building resilient resources that enable them to overcome future
challenges (Alliger et al., 2015;Duchek, 2020;Stoverink et al., 2020;
Williams et al., 2017).
The importance of resilience for learning is deeply embedded within the
broader resilience literature. For example, Tugade and Fredrickson (2004)
Brykman and King 747

argued that a core outcome of individual resilience is the capacity to learn from
life’s setbacks. This insight likely explains Seery, Holman, and Silver’s (2010)
and Seery, Leo, Lupien, Kondrak, and Almonte’s (2013) findings that in-
dividuals who experienced some lifetime adversity reported being more re-
silient than those who experienced no or high adversity, as they theorized
that experiencing some adversity enabled individuals to learn effective
coping skills, develop support networks, and feel a sense of mastery. This
sentiment is further reflected in the post-traumatic growth concept, such that
some individuals emerge stronger after trauma because they channeled dif-
ficulties into learning activities, including reflection, problem-focused coping,
meaning-making, and changing worldviews (Tedeschi & Calhoun, 2004).
Several organizational scholars have also described learning as an outcome
of team resilience capacity. For example, Barton and Kahn (2018) argued that
resilient teams engage in “relational pauses,”a type of learning behavior
focused on improving information processing and goal coordination. As well,
Stoverink et al. (2020) argued that resilient teams engage in thoughtful re-
flection, knowledge crystallization, and information integration when ad-
versity strikes. Sutcliffe and Vogus (2003) also suggested that resilient teams
are more likely to accumulate knowledge and develop competencies because
they are willing to make mistakes for developmental purposes and view
setbacks as growth opportunities. Similarly, Bowers et al. (2017) argued that
resilient teams learn from prior challenges because they prepare them to adapt
to future ones. Therefore, we propose
Hypothesis 3: Team resilience capacity is positively related to team
learning.
The Moderating Effects of Team Information Elaboration
In addition to explaining how individuals protect, acquire, and preserve re-
sources, COR theory elaborates on how resources are exchanged within teams
via “crossover,”such that individual members’experiences, emotions, and
resources transfer within the social environment (Bolger et al., 1989;Hobfoll
et al., 2018). This crossover model proposes that these mechanisms of re-
source exchange enable resilient teams to fully capitalize on their abundant
pool of resources (Hobfoll et al., 2018). Accordingly, we position team in-
formation elaboration as a central mechanism that amplifies the effects of team
resilience capacity on team learning by specifying the extent to which resilient
teams mobilize their resources. Information elaboration denotes an iterative
process of exchanging information and ideas, discussing and seeking clari-
fication on these perspectives, and integrating this information, which helps
748 Group & Organization Management 46(4)

teams capitalize on individual members’discrete knowledge and skills
(Homan et al., 2007;Resick, Murase, Randall, & DeChurch, 2014). Thus, it
extends beyond information sharing to also capture the extent to which team
members deeply reflect on and integrate each other’s perspectives and ideas.
Accordingly, we expect information elaboration to enhance the positive
effects of team resilience capacity on learning, such that resilient teams engage
in even more learning to the extent that they integrate diverse opinions and
seek clarifications. This perspective maintains that even teams with a high
capacity for resilience will struggle to learn before, during, or after adversity
strikes if they fail to effectively exchange or elaborate on distributed in-
formation (Stoverink et al., 2020). Information elaboration is especially
important for leveraging a team’s resilience capacity because adversity tends
to narrow information processing and trigger anxiety, thereby undermining
team coordination and communication (Barton & Kahn, 2018;Sutcliffe &
Vogus, 2003;Waller, 1999). Empirical research also supports the amplifying
role of information elaboration on the relationship between team resilience
capacity and learning. For example, Rauter, Weiss, and Hoegl (2018) found
that team reflexivity moderated the effects of team affective reactions to
a setback on team learning, which suggests that the extent to which resilient
teams engage in learning depends on whether team members reflexively share
information. As well, using a scenario-based simulator training with military
teams, Mjelde, Smith, Lunde, and Espevik (2016) demonstrated that closed-
loop communication, whereby team members exchanged information and
coordinated activities through a feedback process, was integral to team ad-
aptation and performance during a crisis. Therefore, we propose
Hypothesis 4: Team information elaboration amplifies the positive re-
lationship between team resilience capacity and team learning.
Overall Moderated-Mediated Model
As described earlier, several scholars have conceptualized team resilience as
a team-centric capacity that is theorized to mediate the relationship between
other team states and outcomes (Bowers et al., 2017;Stoverink et al., 2020).
Accordingly, our overall model suggests that team resilience capacity is
a critical team resource that explains why voice climate relates to team
learning and that leader LGO and team information elaboration sequentially
moderate this mediated relationship. It is important to note that empirical
research also supports a link between voice climate and learning (Edmondson,
1999;Li, Liao, Tangirala, & Firth, 2017), although the specific mechanism(s)
linking these constructs is largely unspecified. One possibility is that voice
Brykman and King 749

climate facilitates voice behaviors (Frazier & Bowler, 2015;Morrison et al.,
2011), which affects team learning by encouraging team members to seek
clarifications and admit mistakes (e.g., Tangirala & Ramanujan, 2008). Al-
ternatively, our model argues that team resilience capacity relates voice
climate to team learning, such that voice climate builds a team’s capacity to
overcome future adversities, which motives and enables team members to
expend resources in the pursuit of learning (Alliger et al., 2015;Stoverink
et al., 2020). Our model also proposes that leader LGO amplifies the positive
effects of voice climate on team resilience capacity via trickle-down effects
through which leaders’positive views of challenges and growth transfer to the
team and amplify the beneficial role of voice climate in enhancing team
resilience capacity. In turn, we propose that information elaboration amplifies
the positive effects of team resilience capacity on team learning via efficient
resource mobilization. Therefore, we propose
Hypothesis 5: Team resilience capacity mediates the positive effects of
voice climate on team learning.
Hypothesis 6: The mediated relationship between voice climate, team
resilience capacity, and team learning is amplified by leader learning goal
orientation (stage 1) and team information elaboration (stage 2).
Method
Sample and Procedures
We assessed the proposed model with a time-lagged, multisource field study
involving 48 teams from five established Canadian technology start-ups.
These organizations were in existence for at least 4 years (μ= 6 years) and
ranged in size from 20 to 350 employees at the time of data collection. These
teams worked in various functions, including engineering, marketing, and
customer service. This context was relevant to our research because start-ups
experience heightened failure rates (Headd, 2003), in which case it is par-
ticularly important for their teams to develop resilience capacity. At the same
time, as noted above, these start-ups were fairly established and of consid-
erable size and thus are more similar to typical organizations than emerging
start-ups. Thus, teams in our sample likely faced similar challenges as teams in
conventional organizations (e.g., member change; Alliger et al., 2015).
We first administered the team member survey, which included measures
for voice climate and team resilience capacity. Two weeks later, we ad-
ministered the leader survey, which included measures for leader LGO, team
information elaboration, and team learning.
3
This multisource approach
750 Group & Organization Management 46(4)

enabled us to proactively address concerns of common-method bias, par-
ticularly between our independent and dependent variables (Podsakoff,
MacKenzie, Lee, & Podsakoff, 2003;Podsakoff, MacKenzie, & Podsakoff,
2012). It also helped to increase confidence in the robustness of our results,
such that they are not spurious artifacts due to teams holding positive per-
ceptions of themselves overall.
As team membership has become increasingly fluid in modern organ-
izations, we explicitly defined the boundaries for teams in this study through
discussions with our partners, on the basis that the team members regularly
interacted with each other, had shared goals, and reported to the same
leader(s), who was responsible for managing team goals and performance
(Chan, 1998;George, 1990). To ensure that participants reflected on their
experiences with the appropriate team, we identified team membership at the
beginning of each survey (with an organizational chart) and asked participants
to complete the survey with this team in mind. Participants’average team
tenure was one and a half years, suggesting that they had ample shared
experiences to develop resilience capacity.
To be eligible for the study, each team was required to consist of at least
three members in addition to the team leader, as otherwise they more closely
resemble a dyad than a team. At the same time, we included teams in our
analysis who met this qualification, but in which only two members and a
leader completed the surveys (n= 9). Although some scholars advocate for
removing teams that fail to reach a prespecified number or proportion of
responding team members due to issues of interrater agreement, removing
teams on this basis introduces new problems because these teams may be
different for important reasons related to our research questions, such as low
engagement in voice climate, thereby creating a biased sample (cf. Allen,
Stanley, Williams, & Ross, 2007;O’Neill, McLarnon, Hoffart, Woodley, &
Allen, 2018). Consequently, several recent studies advocate against such
deletion methods because, among other reasons, they reduce statistical power
and distort effect sizes (Hirschfeld, Cole, Bernerth, & Rizzuto, 2013;Stanley,
Allen, Williams, & Ross, 2011). Nevertheless, we conducted ANOVAs to
compare data between these nine teams in which only two members and a
leader completed the survey and teams in which three or more members and
a leader responded (n= 39) and did not observe any discernable or significant
differences.
In total, we distributed surveys to 72 teams, which were comprised of 462
team members and 74 team leaders. We received responses from 308 team
members (67%) and 50 team leaders (69%). We removed 24 teams from our
analysis because of insufficient data—either because less than two members
participated (n= 2), the team leader did not participate (n= 20), or both (n= 2).
Brykman and King 751

Accordingly, our analysis was based on data from 48 teams, which were
comprised of 215 team members and 50 team leaders.
4
Of these team
members, 48% identified as male. The dominant ethnicities were Caucasian
(53%) and Asian (35%). Their average age was 30 years old (SD = 5.44), and
86% had at minimum a university degree. Their average organizational tenure
was 2 years (SD = 1.72) and average team tenure was 1.5 years (SD = 1.15). Of
these team leaders, 70% identified as male. The dominant ethnicities were
Caucasian (58%) and Asian (26%). Their average age was 36 years old (SD =
6.11), and 72% had at minimum a university degree. Their average orga-
nizational tenure was 3.75 years (SD = 2.34), average team tenure was
2.15 years (SD = 1.95), and average managerial experience was 5.70 years
(SD = 4.50).
Measures
Team member survey. We measured voice climate with Frazier and Bowler’s
(2015) 6-item scale. The scale prompt states, “Members of my team are
encouraged to…” followed by the items, such as “develop and make rec-
ommendations concerning issues that affect the team”and “speak up and
encourage others on the team to get involved in issues that affect the team.”
We measured team resilience capacity with Stephens et al.’s (2013) 3-item
measure by replacing the phrase “this TMT”(top management team) with “my
team.”Example items include “my team knows how to cope with challenges”
and “my team is able to cope with difficult periods of time.”Thus, both scales
used a referent-shift approach (Chan, 1998), consistent with best practices on
assessing shared team constructs (Hartmann, Weiss, Newman, et al., 2020).
Importantly, this operationalization matches our conceptualization of team
resilience capacity as reflecting team members’shared beliefs in their col-
lective capacity to overcome future adversities or setbacks.
Leader survey. We measured leader’s LGO with Vandewalle’s (1997) 4-item
scale. Example items include “I am willing to select a challenging work
assignment that I can learn a lot from”and “I enjoy challenging and difficult
tasks at work where I’ll learn new skills.”We measured team information
elaboration with van Dick, van Knippenberg, H¨
agele, Guillaume, and
Brodbeck’s (2008) 7-item scale. Example items include “members of my
team exchange a lot of information about our tasks”and “members of my team
often say things that lead each other to learn something new.”Finally, we
measured team learning with Edmondson’s (1999)
5
7-item scale. Example
items include “my team actively reviews its own progress and performance”
and “my team relies on outdated information or ideas (reverse).”We anchored
752 Group & Organization Management 46(4)

all team member and leader measures on 5-point Likert scales ranging from
“Strongly Disagree”(1) to “Strongly Agree”(5).
Analyses and Results
Preliminary Testing
To begin, we evaluated the appropriateness of aggregating voice climate and
team resilience to the team level. Both constructs exhibited ICC and r
wg(J)
values above suggested cutoffs (James, Demaree, & Wolf, 1984; see Table 1),
which implies high levels of within-team agreement and thus that these are
suitable team-level variables. We also examined whether there were any
nesting effects due to organizational membership to determine the re-
quirement for multilevel modeling. That is, although all of the relationships
were conceptualized at the team level, we collected data from five different
organizations and it is possible that the constructs vary due to overarching
organizational differences (Bliese, 2000). ANOVA results revealed that or-
ganizational membership did not significantly influence any of these varia-
bles: voice climate (F= .10, n.s.), team resilience capacity (F= 1.27, n.s.),
leader LGO (F= .96, n.s.), team information elaboration (F= 1.69, n.s.), and
team learning (F= .96, n.s.). As a result, we assessed all hypotheses at the
team level, though we controlled for organizational membership to account for
potential effects in our model. We also controlled for team size because prior
research suggests that it can significantly affect team resilience capacity (e.g.,
Gomes et al., 2014). Table 2 lists means, standard deviations, and correlations
for all variables in the model.
Hypotheses Testing
We centered all variables that defined a product term to clarify the regression
coefficients and interpretation of the interactions (Dawson, 2014) and pro-
ceeded with hypothesis testing. In support of Hypothesis 1, we found
that voice climate was positively related to team resilience capacity (β= .60,
Table 1. Aggregation Statistics for Team-Level Variables.
Variable F r
wg(J)
mean ICC (1) ICC (2)
1. Voice climate 2.64

.94 .26 .61
2. Team resilience capacity 3.12

.92 .15 .44
Note. n = 215.

p< .01. ICC = intraclass correlation coefficient.
Brykman and King 753

p< .01). As well, we found support for Hypothesis 2, as leader’s LGO
moderated the effects of voice climate on team resilience capacity (β= .31, p<
.05). We also found support for Hypotheses 3 and 4, as team resilience ca-
pacity was positively related to team learning (β= .50, p< .01) and team
information elaboration moderated the effects of team resilience capacity on
team learning (β= .29, p< .01). Hierarchical regression results are reported in
Table 3.
To visualize these interactions, we plotted the values of the independent
variables at one standard deviation above and below the mean of the mod-
erators, as per convention (Aiken & West, 1991), as seen in Figures 2(a) and
3(a). We also probed the significance of these conditional effects using the
Johnson–Neyman technique (i.e., J–N; Hayes & Matthes, 2009;Preacher,
Curran, & Bauer, 2006), as seen in Figures 2(b) and 3(b). The J–N technique
has become a preferred method for assessing the significance of interactions
because it identifies points along the range of the moderator where the effects
of the independent variable on the dependent variable significantly differ from
zero, as opposed to arbitrarily assigning a cutoff value. As illustrated in
Figure 2(b), the effect of voice climate on team resilience capacity is sta-
tistically different from zero when leader LGO exceeds 4.19. As well, as
illustrated in Figure 3(b), the effect of team resilience capacity on team
learning is statistically different from zero when team information elaboration
exceeds 3.60. That is, as we predicted, leader LGO interacted with voice
climate such that teams with a high voice climate perceived themselves as
even more resilient as leader LGO increased. Likewise, team information
elaboration interacted with team resilience capacity such that teams with
Table 2. Descriptive Statistics and Correlations.
Variable Mean SD 12 3 4 5 6 7
1. Organization
(dummy code)
3.00 2.28 —
2. Team size 6.44 5.20 .25 —
3. Voice climate 4.38 .41 .06 .09 (.87)
4. Leader LGO 4.46 .46 .27 .24 .13 (.72)
5. Team resilience
capacity
4.12 .51 .06 .02 .60

.15 (.91)
6. Information
elaboration
3.83 .63 .11 .10 .22 .16 .19 (.88)
7. Team learning 4.11 .36 .06 .16 .50

.22 .50

.39

(.67)
Note. n = 48. SD = standard deviation; LGO = learning goal orientation. Scale reliabilities are
reported on the diagonal in parentheses.

p< .01.
754 Group & Organization Management 46(4)

Table 3. Hierarchical Regression Results for Team Resilience Capacity and Team Learning.
Team resilience capacity Team learning
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Control variables
Organization .06 .01 .03 .07 .02 .05 .08 .15
Team size .01 .08 .09 .11 .15 .15 .18 .17
Independent variables
Voice climate .60

.59

.53

Team resilience capacity .50

.44

.55
Moderators
Leader LGO .10 .17
Information elaboration .33.32
Interaction effects
Voice climate × leader LGO .31
Team resilience capacity × information elaboration .29
R
2
.00 .36

.37 .46.02 .27

.38.44
ΔR
2
.00 .36

.00 .09.02 .25

.10.07
F .09 24.69

25.32 32.37.56 15.73

22.8527.80
ΔF
2
.09 24.61

.62 7.06.56 15.17

7.124.96
Note. n = 48. Coefficients are standardized betas. LGO = learning goal orientation.

p<.01,
p< .05.
Brykman and King 755

a high resilience capacity engaged in even more learning activities as in-
formation elaboration increased.
Next, we assessed Hypothesis 5 using the PROCESS macro for SPSS
(Model 4; Hayes, 2017). This model estimated the indirect effects of voice
climate on team learning via team resilience capacity. Results support this
hypothesis, as we found that voice climate was significantly positively related
to team resilience capacity (β= .60, p< .01) and, in turn, team resilience
capacity was significantly positively related to team learning (β= .32, p<
.05), though voice climate was not directly related to team learning (β= .30,
Figure 2. (a) Team resilience capacity as a function of voice climate and leader
learning goal orientation (LGO). (b) John–Neyman regions of significance for the
conditional effect of voice climate at values of leader learning goal orientation.
756 Group & Organization Management 46(4)

n.s.). Results from the bias-corrected bootstrapping procedure for the indirect
effect with 20,000 resamples at a 95% confidence interval did not include zero
(β= .20; 95% CI: [.02–.46]), which suggests that voice climate is related to
team learning because of its effect on team resilience capacity.
Finally, we assessed the overall model (Hypothesis 6) using PROCESS
Model 21 (Hayes, 2017). In particular, we estimated the conditional indirect
effect of voice climate on team learning through team resilience capacity at
high and low levels of the leader LGO (stage 1) and team information
elaboration (stage 2) using the bias-corrected bootstrapping procedure for the
indirect effect with 20,000 resamples at a 95% confidence interval. As shown
Figure 3. (a) Team learning as a function of team resilience capacity and
information elaboration. (b) John–Neyman regions of significance for the conditional
effect of team resilience capacity at values of information elaboration.
Brykman and King 757

in Table 4–6, we found a significant interaction between voice climate and
leader LGO on team resilience capacity (b = .70, 95% CI: [.41–1.00]), as well
as between team resilience capacity and information elaboration on team
learning (b = .29, 95% CI: [.08–.51]). Altogether, we found support for the full
moderated-mediation model, in that both moderators amplified the effects of
the independent and mediating variables along the casual chain at mean and
high-levels of the moderators, consistent with Figures 2(a) and 3(a). That is,
voice climate was positively related to team resilience capacity (b = .67, p<
.01) and leader LGO moderated the effects of voice climate on team resilience
capacity (b = .10, p= .01), while team resilience capacity was positively
related to team learning (b = .28, p< .01) and team information elaboration
moderated the effects of team resilience capacity on learning (b = .35, p<.05).
Discussion
This research responds to several recent calls for greater clarity on how re-
silience capacity develops in teams and what teams with a high capacity for
Table 5. Moderated-Mediation Results (H4: Team Resilience Capacity →Team
Learning).
Level of LGO Level of team IE Conditional indirect effect SE LLCI ULCI
—Low .04 .12 .21 .29
—Med .29.11 .08 .51
—High .55.17 .21 .90
Note. n = 48. Coefficients are unstandardized betas. LGO = learning goal orientation; team
IE = team information elaboration, LLCI = lower-level confidence interval. ULCI = upper level
confidence interval;

p< .001. p< .01.
Table 4. Moderated-Mediation Results (H2: Voice Climate →Team Resilience
Capacity).
Level of LGO Level of team IE Conditional indirect effect SE LLCI ULCI
Low —.20 .25 .31 .70
Med —.70

.15 .41 1.00
High —1.21

.23 .75 1.67
Note. n = 48. Coefficients are unstandardized betas. LGO = learning goal orientation; team
IE = team information elaboration, LLCI = lower-level confidence interval. ULCI = upper level
confidence interval;

p< .001.
758 Group & Organization Management 46(4)

resilience do (e.g., Duchek, 2020;Hartmann, Weiss, Newman, et al., 2020;
Stoverink et al., 2020), along with the need to delineate specific boundary
conditions that moderate these relationships. Results support our resource-
based perspective of team resilience capacity. Specifically, we found that voice
climate was positively related to team resilience capacity, and that leader LGO
amplified its effect, such that teams with high voice climate, in which
members felt encouraged to express voice, perceived themselves as even more
capable of overcoming adversity (i.e., resilient) when they reported to a leader
with a higher LGO, who pursues challenging work for personal growth. In
turn, we found that team resilience capacity was positively related to team
learning, and information elaboration amplified its effect, such that team with
a high resilience capacity engaged in even more learning to the extent that
team members shared, discussed, and integrated diverse perspectives. Al-
together, reflecting back on our research questions, our results suggest that (a)
voice climate builds team resilience capacity, (b) teams with higher resilience
capacity engage in more learning than teams with lower resilience capacity,
and (c) leader LGO amplifies the positive effects of voice climate on team
resilience capacity, while team information elaboration amplifies the positive
effects of team resilience capacity on team learning.
Theoretical Implications
Our study contributes to research and theory on team resilience in several
ways. First, we offer empirical support for the tenets underlying COR theory
Table 6. Moderated-Mediation Results (H6: Voice Climate →Team Resilience
Capacity →Team Learning).
Level of LGO Level of team IE Conditional indirect effect SE LLCI ULCI
Low Low .01 .05 .07 .12
Low Med .06 .11 .11 .33
Low High .11 .20 .19 .60
Med Low .03 .09 .14 .20
Med Med .21 .12 .04 .49
Med High .39 .23 .08 .94
High Low .05 .15 .24 .34
High Med .35 .17 .08 .75
High High .67 .34 .15 1.45
Note. n = 48. Coefficients are unstandardized betas. LGO = learning goal orientation; team
IE = team information elaboration, LLCI = lower-level confidence interval. ULCI = upper level
confidence interval;

p< .001. p< .01.
Brykman and King 759

(Hobfoll, 1989) as a guiding framework to understand the emergence and
function of team resilience capacity. Specifically, our results suggest that team
resilience capacity develops from a caravan of critical team resources (voice
climate and leader LGO) that are essential for overcoming adversity. In turn,
resilient teams expend their stocks of resources to engage in learning ac-
tivities, and information elaboration enhances this effect by facilitating re-
source exchange via “crossover.”COR theory aligns with the dominant
conceptualization of team resilience as a capacity to overcome adversity, as
opposed to a process or outcome of triumphing over adversity, and thus can
help to unite the emerging literature on team resilience by describing how
teams build a capacity for resilience through interactions that boost their
reservoir of resources, which they can subsequently deploy to achieve team
goals, such as by engaging in learning before, during, and after adversity
strikes (Duchek, 2020;Maynard & Kennedy, 2016;Stoverink et al., 2020). As
COR is grounded in stress theory (and has only recently been applied beyond
that domain; see Hobfoll et al., 2018), we are excited by its potential to offer
a multilevel foundation for research on team resilience in terms of how teams
acquire and deploy social, cognitive, and emotional resources (Hartmann,
Weiss, & Hoegl, 2020;Hobfoll et al., 2018;Stoverink et al., 2020).
We also advance research on team resilience by demonstrating its empirical
links with team learning. Although learning is deeply embedded within the
broader resilience literature (e.g., Sutcliffe & Vogus, 2003;Tugade &
Fredrickson, 2004), it has been neglected in empirical research on team re-
silience, which has instead largely focused on well-being and performance
outcomes (Gucciardi et al., 2018;Hartmann, Weiss, Newman, et al., 2020).
Our research highlights that resilient teams engage in learning activities
presumably because they are a resource-enhancing activity that helps them
prepare for future challenges (e.g., minimize, Alliger et al., 2015; anticipate,
Duchek, 2020). That is, our results suggest that teams with a high resilience
capacity are well-positioned to engage in learning due to their abundant pool
of resources. Relatedly, our finding concerning the moderating effect of team
information elaboration highlights how team resilience capacity is not a
panacea for all team challenges. Rather, for teams to fully capitalize on their
resilience capacity, they also need social structures in place that help to
mobilize team resources via efficient communication and coordination
(Duchek, 2020;Hartwig et al., 2020). Coupled with the findings pertaining to
leader LGO, we offer important theoretical advancements by identifying the
conditions under which teams are more likely to develop resilience capacity
and leverage this capacity to achieve positive outcomes.
Finally, we expand the nomological network of team resilience capacity by
demonstrating its positive links with voice climate, leader LGO, information
760 Group & Organization Management 46(4)

elaboration, and learning. Each of these relationships adds to our un-
derstanding of team resilience and points to intriguing future directions. First,
our finding that voice climate relates to team resilience capacity supports
recent evidence on how open communication and supportive environments
are integral for the development of team resilience (Carmeli et al., 2013;
Gomes et al., 2014;Vera et al., 2017). Furthermore, it supports the critical role
of leadership in building team resilience capacity (cf. Alliger et al., 2015;
Gucciardi et al., 2018) via fostering a supportive voice climate (Frazier &
Bowler, 2015), which is further amplified when leaders hold a high LGO. We
also advance research on voice climate by demonstrating how it facilitates
important team outcomes beyond voice behavior and thus deserves greater
consideration in teams research. Similarly, we advance research on goal
orientation in teams, which has largely focused on team aggregate oper-
ationalizations (e.g., Chadwick & Raver, 2015), by instead illustrating how
leader LGO enhances the positive relationship between voice climate and
team resilience capacity.
Practical Implications
We also offer important practical contributions by establishing which vari-
ables are essential to help teams develop the capacity needed to overcome
adversity. In particular, we provide evidence suggesting how leaders can build
their team’s resilience capacity by (a) creating a positive voice climate through
active solicitation and encouragement of voice and (b) embracing an LGO
focused on tackling challenging work with the goal of personal development.
Overall, our results support Li and Tangirala’s (forthcoming, p. 21) contention
that “voice can separate resilient teams from brittle ones,”and thus, leaders
should strive to create climates that encourage voice.
Additionally, our finding regarding the moderating effect of information
elaboration highlights the importance for resilient teams to leverage each
member’s unique perspectives. This finding suggests that leaders would
benefit from creating structures for resource mobilization and exchange to
help their team fully capitalize on their resilience capacity (Chen et al., 2015;
Meneghel, Mart´
ınez, & Salanova, 2016). Such structures may be especially
important today, due to shifts toward distributed work triggered by COVID-
19, which has created new challenges for smooth team communication and
coordination (Brynjolfsson et al., 2020). In sum, we encourage organizational
leaders to build team resilience capacity by emphasizing open communication
and embracing an LGO through training, HRM practices, or structural
changes that enable team members to freely express opinions and seamlessly
integrate diverse perspectives (Bardoel et al., 2014;Bowers et al., 2017;
Brykman and King 761

Hobfoll et al., 2018). For example, organizations can educate leaders on the
potential benefits of LGO for team resilience purposes (e.g., Vandewalle et al.,
2019) or emphasize the importance of providing adequate responses when
team members express voice (e.g., King, Ryan, & Van Dyne, 2019).
Strengths, Limitations, and Future Directions
Despite these valuable contributions, our research also contains several
limitations that we hope to address in future research. First, as with any model,
we focused on a specific subset of antecedents, moderators, and con-
sequences; thus, it is possible that we omitted other important variables. For
example, we examined the effects of voice climate on team resilience capacity
because it has been shown to facilitate information and idea sharing in teams
(e.g., Frazier & Bowler, 2015) and fits our theoretical focus on team resources
that enable resource acquisition and protect against resource loss (Hobfoll
et al., 2015); however, it is possible that other unidentified variables, such as
psychological safety, would have had a stronger influence on team resilience
capacity. We encourage future research to continue exploring the nomological
network of team resilience so that we can build a body of evidence concerning
the relative importance of different variables. Several recent high-quality
conceptual articles have identified other potential variables to explore, which
we urge scholars to consider (see Alliger et al., 2015;Bowers et al., 2017;
Duchek, 2020;Hartmann, Weiss, Newman, et al., 2020;Hartwig et al., 2020;
Maynard & Kennedy, 2016;Stoverink et al., 2020). We also encourage re-
searchers to explicitly define and delineate their conceptualization of team
resilience to offer greater precision as to whether they are examining team
resilience as a capacity, process, or outcome.
Second, we measured variables across time and with different respondents
to proactively address concerns of common-method bias by introducing
temporal precedence. One exception, however, is that we assessed team
members’perceptions of voice climate and team resilience capacity at the
same timepoint, which introduces two potential issues. First, this relationship
may be inflated by a common method. Results of a supplemental confirmatory
factor analysis support the discrimination of these constructs, as a model with
both factors separated (X
2
[26] = 77.21, CFI = .95, RMSEA = .10, SRMR =
.04) fits significantly better than a model with factors combined (X
2
[27] =
351.39, CFI = .71, RMSEA = .24, SRMR = .10). Nevertheless, we cannot rule
out the possibility that common-method bias affected their relationship. At the
same time, it is important to clarify that common-method bias is a linear
phenomenon, and thus, it does not affect moderation results (Siemsen, Roth,
& Oliveira, 2010). The second issue is that we cannot establish that voice
762 Group & Organization Management 46(4)

climate causes team resilience capacity because temporal precedence is a
necessary precondition for causal conclusions (Mathieu et al., 2008;Spector,
2019). However, as described below, this issue is endemic to all survey
methods examining conditions or experiences with teams that existed prior to
data collection, in which case temporally separating the measurement of voice
climate and team resilience capacity would still not enable causal inter-
pretations, even if it offers “face validity.”Finally, it is important to clarify that
this cross-sectional approach only affects part of our model and is regarded as
appropriate when theory supports the predicted relationship (Mathieu et al.,
2008), particularly for relationships that have not been identified in prior
research (Spector, 2019). Nevertheless, we encourage future research to
continue to explore, expand, and refine our model to offer greater evidence of
causality, such as by measuring the variables at multiple timepoints and
probing for potential alternative explanations.
Relatedly, we positioned team resilience capacity as antecedent to
learning based on the notion that team states precede behaviors (Mathieu
et al., 2008). Although we measured team resilience capacity prior to team
learning, our results do not infer causality because we cannot speak to the
team conditions that existed prior to measurement, as discussed above
(Spector, 2019). We also noted that resilience capacity is a dynamic team
property, but we measured it at one timepoint, and thus cannot assess
changes in team resilience capacity over time. Therefore, it is possible that
some teams felt more resilient because they previously engaged in learning
activities, which our methods could not assess. Future research would
benefit from measuring these constructs longitudinally, with new teams, or
with experimental methods to tease apart causal effects. For example, given
the likely reciprocal links between team resilience and learning, it would be
interesting for scholars to experimentally manipulate team resilience ca-
pacity, such as by providing teams with negative feedback such that it
diminishes their collective perceptions of resilience, and subsequently chart
their capacity to engage in learning. Overall, we suspect that team resilience
exhibits a recursive relationship with learning, such that resilient teams are
well-suited to engage in learning to prepare for challenges, just as learning
provides resilient teams with resources to overcome future adversities. This
perspective aligns to recent considerations in the literature regarding how
resilient teams respond before, during, and after adversity strikes, such as by
monitoring and exchanging information about potential challenges be-
forehand (i.e., minimize and anticipate) and evaluating challenges afterward
via debriefs (i.e., mend and adapt; Alliger et al., 2015;Duchek, 2020);
however, we focus on the direction from resilience to learning in this article
to provide an initial empirical perspective on their relationship.
Brykman and King 763

Our results may also have been influenced by the unique sample of
knowledge-intensive and task-interdependent teams operating in emerging
start-ups. For example, Sanner and Bunderson (2015) provide meta-analytic
evidence that knowledge-intensity moderates the effects of psychological
safety on team learning. Thus, it is possible that voice climate may be es-
pecially relevant for building team resilience capacity, and team resilience
capacity for mediating its effects on team learning, for teams working on
complex and creative tasks that require knowledge exchanges, as was typical
of our sample. At the same time, it is important to note that resilience is
relevant and useful to any occupation (Kossek & Perrigino, 2016); thus, these
mechanisms seem relevant to a wide range of teams. Finally, our model details
a primarily cognitive process of resilience and thus overlooks how emotions
spread within teams to influence the development and consequences of re-
silience capacity. For example, voice climate may also affect team resilience
capacity by reducing fears of punishment (e.g., Kish-Gephart, Detert, Treviño,
& Edmondson, 2009). Indeed, several scholars have described the links
between team affect and resilience (e.g., Meneghel, Salanova, & Mart´
ınez,
2016;Stephens et al., 2013). Thus, we encourage future research to consider
cognitive and affective mechanisms in tandem to fully understand how team
resilience capacity develops and subsequently affects team functioning.
Overall, we view our study as a launching point for research and theory on
team resilience as we clarify some of the foundations and consequences of
team resilience capacity, along with the boundary conditions under which we
are more likely to observe these effects.
Conclusion
Grounded in COR theory, we present and demonstrate support for a model that
links a specific team resource, voice climate, to a critical team output, learning
behaviors, via team resilience capacity. In addition, we identify leader LGO as
an important mechanism that activates and amplifies the role of voice climate
in facilitating team resilience capacity and team information elaboration as
a critical mechanism that enhances the positive effect of team resilience
capacity on team learning. This work answers the calls of scholars to em-
pirically uncover states and resources that facilitate team resilience, dem-
onstrate key outcomes of team resilience, and detail boundary conditions
of resilience effects. It is our hope that the precision employed in the con-
ceptualization and operationalization of team resilience capacity in our
work contributes to clarity within this domain and that future work will
continue to build upon the team resilience nomological network extensions
offered here.
764 Group & Organization Management 46(4)

Authors’Note
An earlier version of this article was accepted as part of a Showcase Symposium for the
Academy of Management 2020 annual conference.
Acknowledgments
We would like to thank our anonymous research partners for their involvement in this
study and Jana Raver for her guidance on earlier versions of this research.
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.
Funding
The author(s) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: This research was supported by funding
provided by Smith School of Business at Queen’s University and the Social Sciences
and Humanities Research Council of Canada.
ORCID iD
Kyle M. Brykman https://orcid.org/0000-0002-4268-1129
Notes
1. While similar to the Input-Mediator-Output-Input model of teamwork (IMOI;
Ilgen, Hollenbeck, Johnson, & Jundt, 2005), COR is an explanatory theory that
describes how and why teams acquire and subsequently expend resources to
achieve goals. By contrast, IMOI is a general organizing framework intended to
guide research on the mechanisms linking team inputs to outputs and thus does
not offer the same theoretical precision.
2. Morrison et al. (2011) also conducted factor analysis to demonstrate that voice
climate is empirically distinct from psychological safety.
3. We determined that a 2-week time lag was appropriate to create a temporal
separation between the constructs, but not too long that it masks true relationships
(Dormann & Griffin, 2015;Podsakoff et al., 2003,2012). We were also worried
that a longer lag would introduce irreparable logistical issues (e.g., changes to
team membership).
4. Team leaders were not included as team members. None of the teams in our study
reported to the same leader, though two teams had two leaders, in which case we
computed leader ratings as a mean between both leaders.
5. We determined that leaders could accurately evaluate their team’s learning and
information elaboration behaviors because they have unique knowledge re-
garding these behaviors (Kozlowski & Klein, 2000). Moreover, our measure of
Brykman and King 765

team learning (Edmondson, 1999) was developed specifically for observer rat-
ings, while research on team information elaboration generally relies on observer
ratings (e.g., Homan et al., 2007;Resick et al., 2014).
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Associate Editor: Sebastian Raetze
Submitted Date: May 12, 2020
Revised Submission Date: April 1, 2021
Acceptance Date: April 26, 2021
Author Biographies
Kyle Brykman (Ph.D. Queen’s University) is an Assistant Professor of Management
at the Odette School of Business, University of Windsor, Canada. His research focuses
on employee voice and interpersonal team dynamics, including team resilience and
conflict.
Danielle D. King (Dr.) is an assistant professor of Industrial and Organizational
Psychology at Rice University and the principal investigator of the WorKing Resilience
Research Laboratory. Her research centers on the topics of employee resilience and
identity. She received her B.A. in Psychology from Spelman College and her M.A. and
Ph.D. in Organizational Psychology from Michigan State University.
772 Group & Organization Management 46(4)