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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
A Resource Model of Team Resilience Capacity and Learning
Kyle M. Brykman1 and Danielle D. King2
1 Odette School of Business, University of Windsor
2 Department of Psychological Sciences, Rice University
Author Note
Kyle M. Brykman https://orcid.org/0000-0002-4268-1129
Danielle D. King https://orcid.org/0000-0002-1277-5669
We have no known conflict of interest to disclose. 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 researchh. 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. An earlier version of this paper was accepted as part of a Showcase Symposium for the
Academy of Management 2020 annual conference.
Correspondence concerning this article should be addressed to Dr. Kyle M. Brykman, Odette
School of Business, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4. Email:
kbrykman@uwindsor.ca. Phone: 519 253 3000 x4214.
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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
towards 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, multi-source 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).
Keywords: team resilience, conservation of resources theory, voice climate, team learning,
learning goal orientation, information elaboration
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
A Resource Model of Team Resilience Capacity and Learning
As organizations increasingly structure work in teams (Bell et al., 2012; Kozlowski &
Ilgen, 2006), and teams encounter challenges that impair coordination and performance (Alliger
et al., 2015; King et al., 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’s capacity to bounce back from adversities or
setbacks (Stoverink et al., 2020), offers a valuable multilevel foundation to bridge insights from
the individual and organizational paradigms, thereby developing a more complete understanding
of resilience at work (Hartmann et al., 2020b; Hartwig et al., 2020). Although scholarly interest
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 et al.,
2020b; 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 explanatory framework to understand how team resilience capacity
is a linking pin between team inputs and outputs (Bowers et al., 2017; Mathieu et al., 2008)
because it describes the acquisition (input) and deployment (output) of core team resources to
achieve team goals and proactively buffer against threats (Bardoel et al., 2014; Stoverink et al.,
2020)
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. Indeed, several scholars have emphasized the utility of COR for explaining the
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
emergence and function of team resilience (see Hartmann et al., 2020b; 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
antecedents, outcomes, and boundary conditions of team resilience capacity.
------------------------------------------
Insert Figure 1 about here.
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More specifically, our model proposes voice climate (shared perceptions within a team of
the extent to which voice is encouraged; Morrison et al., 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 orientation (LGO; a
goal orientation focused on developing new skills and increasing competence; Dweck, 1986)
activates and amplifies this relationship by orienting team discourse towards 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 et al., 2019).
Finally, we posit that information elaboration (an iterative team process of exchanging,
discussing, and integrating ideas and information; Homan et al., 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 continuity 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,
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and outputs (Bowers et al., 2017; Hartmann et al., 2020b; Stoverink et al., 2020). We assessed
this model with a multi-source, multi-wave field study involving 48 teams from five Canadian
technology start-ups.
Altogether, our research addresses several calls in the literature by empirically 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 et al., 2020b; 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 research that has examined whether,
why, or how team resilience relates to team learning.
Second, we focus on expanding the nomological network of team resilience capacity to
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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).
Moreover, 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 sensemaking and promote resilience
in teams (Alliger et al., 2015; Williams et al, 2017).
Theoretical Framework and Hypotheses
A Conservation of Resources 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
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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 et al., 2015; Hobfoll et al., 2018), and thus people are motivated
to acquire resources to protect themselves 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 and colleagues (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 et al., 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 environments 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 resource that builds team resilience capacity by encouraging open communication, 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 towards adversity and orienting
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team discourse towards 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 teams resilience capacity for learning via efficient interpersonal
exchange (i.e., “crossover”; Bolger et al., 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 et al., 2020a; 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 setbacks, (b) a dynamic social process that enables
positive adaptation to collectively-experienced threats or challenges, or (c) the demonstration of
resilience as manifested in positive outcomes (e.g., recovery, 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 conceptualization to ensure a unified
approach to understanding this phenomenon. 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
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
“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 et al., 2013; Stoverink et al., 2020). Operationally, it reflects team
members’ shared beliefs in, or perceptions of, their collective capacity to overcome future
adversities or setbacks (Carmeli et al., 2013; Hartmann et al., 2020b; Vera et al., 2017).
It is also important to clarify the role of adversity within this conceptualization. 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, insufficient 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 coordination 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 and
colleagues (2020a, 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 et al., 2020a;
Stoverink et al., 2020). With this foundation in place, we elaborate on each proposition in greater
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detail below.
Voice Climate and Team Resilience Capacity
Employee voice – discretionary communication of information, ideas, or issues that may
be challenging in nature but are 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 encouraged on the team (Morrison et al., 2011) – for stimulating voice, thereby
ensuring that organizations reap its collective benefits (Frazier & Bowler, 2015). In line with
individual-level research (Ashford et al., 1998), the primary beliefs that underlie voice climate
are: (a) voice safety – shared belief about whether speaking up is safe versus dangerous, and (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 impending
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 responses 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
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necessary resources that enable teams to manage adversities by vocalizing problems and forming
shared understandings. Similarly, Bowers et al. (2017) modeled team resilience as a second-
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 combination of safety and efficacy beliefs, and focuses on intrateam
communications, 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 communications. For example, Vera
et al. (2017) found that teamwork (e.g., respectful interactions) builds team resilience capacity.
Related research also demonstrates the importance of team communication for shaping team
resilience, such as by generating new ideas and creating alignment within the team (Carmeli et
al., 2013; Gomes et al., 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 organizational 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 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
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events (Maynard & Kennedy, 2016; Sutcliffe & Vogus, 2003). As Alliger and colleagues (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 Learning Goal Orientation
As noted above, in applying COR theory to the study of resilience, Hobfoll and
colleagues (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 learning goal orientation (LGO), for activating and amplifying the constructive effects of
voice climate on team resilience capacity by orienting voice climate towards 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 environmental 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
development) and resource protection (offering latitude for team members to make mistakes).
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LGO is characterized by investment in goal-directed efforts with the intention 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 et al., 2019). As leaders hold power and prominence in team
hierarchies, with a primary function of guiding their team towards 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 opportunity, 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., modelling) 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 et al., 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 towards developing competencies through challenging work
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(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 capacity via
encouragement to speak up, and LGO amplifies this effect by orienting team communications
towards learning from mistakes, thereby activating 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 resilience capacity effects via leaders converting crises into developmental
challenges (Sommer et al., 2016; Vera et al., 2017). Here, we 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 and colleagues (2019), we define team learning as team members’
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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, 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 capacity 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 towards learning because it is a means to acquire more
resources (e.g., new knowledge, shared understanding; Maynard & Kennedy, 2016; Sutcliffe &
Vogus, 2003). Resilient teams do not strictly seek feedback and experiment during (e.g., manage,
coping) or after adversity occurs (e.g., mend, adaptation), but also before adversity strikes (e.g.,
minimize, 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) argued that a core outcome of
individual resilience is the capacity to learn from life’s setbacks. This insight likely explains
Seery and colleagues’ (2010; 2013) findings that individuals who experienced some lifetime
adversity reported being more resilient than those who experienced no or high adversity, as they
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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 difficulties 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 and colleagues (2020) argued that resilient teams
engage in thoughtful reflection, knowledge crystallization, and information integration when
adversity 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 and
colleagues (2017) argued that resilient teams learn from prior challenges because it prepares
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 resources, 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 resource exchange enable resilient teams to fully capitalize on their abundant pool of
resources (Hobfoll et al., 2018). Accordingly, we position team information elaboration as a
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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
clarification on these perspectives, and integrating this information, which helps teams capitalize
on individual members’ discrete knowledge and skills (Homan et al., 2007; Resick et al., 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 information
(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 et al. (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 and colleagues (2016)
demonstrated that closed-loop communication, whereby team members exchanged information
and coordinated activities through a feedback process, was integral to team adaptation and
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performance during a crisis. Therefore, we propose:
Hypothesis 4: Team information elaboration amplifies the positive relationship 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; Liu et al., 2017), although the specific
mechanism(s) linking these constructs is largely unspecified. One possibility is that voice 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). Alternatively, 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 transfers to the team and
amplifies 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
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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, multi-source 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 particularly important for
their teams to develop resilience capacity. At the same time, as noted above, these start-ups were
fairly established and of considerable 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 administered the leader survey, which
included measures for leader LGO, team information elaboration, and team learning
3
. This multi-
source approach enabled us to proactively address concerns of common-method bias, particularly
between our independent and dependent variables (Podsakoff et al., 2003; Podsakoff et al.,
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 perceptions of themselves overall.
As team membership has become increasingly fluid in modern organizations, we
20
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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
et al., 2007; O’Neill et al., 2018). Consequently, several recent studies advocate against such
deletion methods because, among other reasons, it reduces statistical power and distorts effect
sizes (Hirschfeld et al., 2013; Stanley et al., 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
21
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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). 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 organizational tenure was 3.75 (SD = 2.34) years, 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 recommendations 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 et al., 2020b). Importantly, this operationalization matches our
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
conceptualization of team resilience capacity as reflecting team members’ shared beliefs in their
collective 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 et al.’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 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 rwg(J) values above suggested cut-
offs (James et al., 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 requirement 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
23
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
organizational membership did not significantly influence any of these variables: 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.
------------------------------------------
Insert Tables 1 and 2 about here.
-------------------------------------------
Hypotheses Testing
We centered all variables that defined a product term to clarify the regression coefficients
and interpretation of the interactions (Dawson, 2014) and proceeded with hypothesis testing. In
support of Hypothesis 1, we found that voice climate was positively related to team resilience
capacity (β = .60, 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 capacity 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.
------------------------------------------
Insert Table 3 about here.
-------------------------------------------
To visualize these interactions, we plotted the values of the independent variables at one
standard deviation above and below the mean of the moderators, as per convention (Aiken &
24
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
West, 1991), as seen in Figures 2A and 3A. We also probed the significance of these conditional
effects using the Johnson-Neyman technique (i.e., J-N; Hayes & Matthes, 2009; Preacher et al.,
2006), as seen in Figures 2B and 3B. 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
differs from zero, as opposed to arbitrarily assigning a cut-off value. As illustrated in Figure 2B,
the effect of voice climate on team resilience capacity is statistically different from zero when
leader LGO exceeds 4.19. As well, as illustrated in Figure 3B, 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 a high resilience capacity engaged in even more learning activities as information
elaboration increased.
------------------------------------------
Insert Figures 2A, 2B, 3A, and 3B about here.
-------------------------------------------
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, n.s.). Results from the bias-
corrected bootstrapping procedure for the indirect effect with 20,000 resamples at a 95%
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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 in Table 4, 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 2A and 3A. 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).
------------------------------------------
Insert Table 4 about here.
-------------------------------------------
Discussion
This research responds to several recent calls for greater clarity on how resilience
capacity develops in teams and what teams with a high capacity for resilience do (e.g., Duchek,
2020; Hartmann et al., 2020b; Stoverink et al., 2020), along with the need to delineate specific
26
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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. Altogether,
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 (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, leader LGO) that are essential for overcoming adversity. In turn,
resilient teams expend their stocks of resources to engage in learning activities, and information
elaboration enhances this effect by facilitating resource exchange via “crossover”. COR theory
aligns with the dominant conceptualization of team resilience as a capacity to overcome
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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 et al., 2020a; 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 resilience, which has instead largely focused on well-being and performance
outcomes (Gucciardi et al., 2018; Hartmann et al., 2020b). Our research highlights that resilient
teams engage in learning activities presumably because it is 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
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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 elaboration, and
learning. Each of these relationships adds to our understanding 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 aggerate
operationalizations (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 variables 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 a 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
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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 et al., 2016a). Such structures may be especially important today, due to
shifts towards 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; 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 et al., 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 consequences; 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
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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 papers 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 et al., 2020b; Hartwig et al., 2020; Maynard &
Kennedy, 2016; Stoverink et al., 2020). We also encourage researchers 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
supports the discrimination of these constructs, as a model with both factors separated (X2[26] =
77.21, CFI = .95, RMSEA = .10, SRMR = .04) fit significantly better than a model with factors
combined (X2[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 et al., 2010). The second issue is that we cannot establish that
voice climate causes team resilience capacity because temporal precedence is a necessary
precondition for causal conclusions (Mathieu et al., 2008; Spector, 2019). However, as described
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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 interpretations, 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 time points 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
capacity, 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.
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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 beforehand (i.e., minimize, anticipate) and evaluating challenges afterwards via
debriefs (i.e., mend, adapt; Alliger et al., 2015; Duchek, 2020); however, we focus on the
direction from resilience to learning in this manuscript to provide an initial empirical perspective
on their relationship.
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
especially 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 resilience capacity. For example, voice climate
may also affect team resilience capacity by reducing fears of punishment (e.g., Kish-Gephart et
al., 2009). Indeed, several scholars have described the links between team affect and resilience
(e.g., Meneghel et al., 2016b; Stephens et al., 2013). Thus, we encourage future research to
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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 empirically
uncover states and resources that facilitate team resilience, demonstrate key outcomes of team
resilience, and detail boundary conditions of resilience effects. It is our hope that the precision
employed in the conceptualization 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.
1
While similar to the Input-Mediator-Output-Input model of teamwork (IMOI; Ilgen et al., 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 two-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 regarding these behaviors (Kozlowski & Klein, 2000). Moreover, our measure
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of team learning (Edmondson, 1999) was developed specifically for observer ratings, 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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Table 1
Aggregation Statistics for Team-Level Variables
Variable
F
rwg(J) mean
ICC(1)
ICC(2)
1. Voice climate
2.64**
0.94
0.26
0.61
2. Team resilience capacity
3.12**
0.92
0.15
0.44
Note. n = 215. ** p < .01.
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Table 2
Descriptive Statistics and Correlations
Variable
Mean
SD
1
2
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
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A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
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 X leader
LGO
.31*
Team resilience capacity X
information elaboration
.29*
R2
.00
.36**
.37
.46*
.02
.27**
.38*
.44*
ΔR2
.00
.36**
.00
.09*
.02
.25**
.10*
.07*
F
.09
24.69**
25.32
32.37*
.56
15.73**
22.85*
27.80*
ΔF2
.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.
38
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Table 4a
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; ** p < .001.
Table 4b
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; ** p < .001. * p < .01.
Table 4c
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; ** p < .001. * p < .01.
39
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Figure 1
Resource Model of Team Resilience Capacity
Team Information
Elaboration
Team Resilience
Capacity
Leader’s Learning
Goal Orientation
Voice Climate Team Learning
40
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Figure 2A
Team Resilience Capacity as a Function of Voice Climate and Leader Learning Goal
Orientation (LGO)
Figure 2B
John-Neyman Regions of Significance for the Conditional Effect of Voice Climate at Values of
Leader Learning Goal Orientation
3
3.5
4
4.5
5
Low Voice Climate High Voice Climate
Team Resilience Capacity
Low Leader LGO
High Leader LGO
Leader Learning Goal Orientation
4.19
Conditional Effects of Voice Climate on Team
Resilience Capacity
95% ULCI
95% LLCI
Point Estimate
41
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Figure 3A
Team Learning as a Function of Team Resilience Capacity and Information Elaboration
Figure 3B
John-Neyman Regions of Significance for the Conditional Effect of Team Resilience Capacity at
Values of Information Elaboration
3
3.5
4
4.5
5
Low Resilience Capacity High Resilience Capacity
Team Learning
Low Information
Elaboration
High Information
Elaboration
Team Information Elaboration
3.60
Conditional Effects of Team Resilience Capacity
on Team Learning
95% ULCI
95% LLCI
Point Estimate
42
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
References
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions.
Sage Publications, Inc.
Allen, N. J., Stanley, D. J., Williams, H. M., & Ross, S. J. (2007). Assessing the impact of
nonresponse on work group diversity effects. Organizational Research Methods, 10(2),
262–286. https://doi.org/10.1177/1094428106/294731
Alliger, G. M., Cerasoli, C. P., Tannenbaum, S. I., & Vessey, W. B. (2015). Team resilience:
How teams flourish under pressure. Organizational Dynamics, 44(3), 176–184.
https://doi.org/10.1016/j.orgdyn.2015.05.003
Ashford, S. J., Rothbard, N. P., Piderit, S. K., & Dutton, J. E. (1998). Out on a limb: The role of
context and impression management in selling gender-equity issues. Administrative
Science Quarterly, 43(1), 23–57. https://doi.org/10.2307/2393590
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral
change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-
295x.84.2.191
Bardoel, E. A., Pettit, T. M., De Cieri, H., & McMillan, L. (2014). Employee resilience: An
emerging challenge for HRM. Asia Pacific Journal of Human Resources, 52(3), 279–
297. https://doi.org/10.1111/1744-7941.12033
Barton, M. A., & Kahn, W. A. (2018). Group resilience: The place and meaning of relational
pauses. Organization Studies, 40(9), 1409–1429.
https://doi.org/10.1177/0170840618782294
Bell, B. S., Kozlowski, S. W. J., & Blawath, S. (2012). Team learning: A theoretical integration
and review. In S. W. J. Kozlowski (Ed.), The Oxford Handbook of Organizational
43
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Psychology (Vol. 2, pp. 859–909). Oxford University Press.
https://doi.org/10.1093/oxfordhb/9780199928286.013.0026
Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications
for data aggregation and analysis. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel
theory, research, and methods in organizations: Foundations, extensions, and new
directions (pp. 349–381). Jossey-Bass.
Bolger, N., DeLongis, A., Kessler, R. C., & Wethington, E. (1989). The contagion of stress
across multiple roles. Journal of Marriage and the Family, 51(1), 175–183.
https://doi.org/10.2307/352378
Bowers, C., Kreutzer, C., Cannon-Bowers, J., & Lamb, J. (2017). Team resilience as a second-
order emergent state: A theoretical model and research directions. Frontiers in
Psychology, 8, 1–14. https://doi.org/10.3389/fpsyg.2017.01360
Brynjolfsson, E., Horton, J., Ozimek, A., Rock, D., Sharma, G., & Ye, H. Y. T. (2020). COVID-
19 and remote work: An early look at US data. Available at:
http://ide.mit.edu/publications/covid-19-and-remote-workearly-look-us-data.
Carmeli, A., Friedman, Y., & Tishler, A. (2013). Cultivating a resilient top management team:
The importance of relational connections and strategic decision comprehensiveness.
Safety Science, 51(1), 148–159. https://doi.org/10.1016/j.ssci.2012.06.002
Chadwick, I. C., & Raver, J. L. (2015). Motivating organizations to learn: Goal orientation and
its influence. Journal of Management, 41(3), 957–986.
https://doi.org/10.1177/0149206312443558
Chan, D. (1998). Functional relations among constructs in the same content domain at different
levels of analysis: A typology of composition models. Journal of Applied Psychology,
44
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
83(2), 234–246. https://doi.org/10.1037/0021-9010.83.2.234
Chen, S., Westman, M., & Hobfoll, S. E. (2015). The commerce and crossover of resources:
Resource conservation in the service of resilience. Stress and Health, 31(2), 95–105.
https://doi.org/10.1002/smi.2574
Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal
of Business and Psychology, 29(1), 1–19. https://doi.org/10.1007/s10869-013-9308-7
Detert, J. R., & Edmondson, A. C. (2011). Implicit voice theories: Taken-for-granted rules of
self-censorship at work. Academy of Management Journal, 54(3), 461–488.
https://doi.org/10.5465/amj.2011.61967925
Dormann, C., & Griffin, M. A. (2015). Optimal time lags in panel studies. Psychological
Methods, 20(4), 489–505. https://doi.org/10.1037/met0000041
Dragoni, L. (2005). Understanding the emergence of state goal orientation in organizational
work groups: The role of leadership and multilevel climate perceptions. Journal of
Applied Psychology, 90(6), 1084–1095. https://doi.org/10.1037/0021-9010.90.6.1084
Dragoni, L., & Kuenzi, M. (2012). Better understanding work unit goal orientation: Its
emergence and impact under different types of work unit structure. Journal of Applied
Psychology, 97(5), 1032–1048. https://doi.org/10.1037/a0028405
Duchek, S. (2020). Organizational resilience: A capability-based conceptualization. Business
Research, 13(1), 215–246. https://doi.org/10.1007/s40685-019-0085-7
Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10),
1040–1048. https://doi.org/10.1037/0003-066X.41.10.1040
Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams.
Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
45
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Frazier, M. L., & Bowler, Wm. M. (2015). Voice climate, supervisor undermining, and work
outcomes: A group-level examination. Journal of Management, 41(3), 841–863.
https://doi.org/10.1177/0149206311434533
George, J. M. (1990). Personality, affect, and behavior in groups. Journal of Applied Psychology,
75(2), 107–116. https://doi.org/10.1037/0021-9010.75.2.107
Gomes, J. O., Borges, M. R. S., Huber, G. J., & Carvalho, P. V. R. (2014). Analysis of the
resilience of team performance during a nuclear emergency response exercise. Applied
Ergonomics, 45(3), 780–788. https://doi.org/10.1016/j.apergo.2013.10.009
Gucciardi, D. F., Crane, M., Ntoumanis, N., Parker, S. K., Thøgersen-Ntoumani, C., Ducker, K.
J., Peeling, P., Chapman, M. T., Quested, E., & Temby, P. (2018). The emergence of
team resilience: A multilevel conceptual model of facilitating factors. Journal of
Occupational and Organizational Psychology, 91(4), 729–768.
https://doi.org/10.1111/joop.12237
Guzman, F. A., & Espejo, A. (2019). Introducing changes at work: How voice behavior relates to
management innovation. Journal of Organizational Behavior, 40(1), 73–90.
https://doi.org/10.1002/job.2319
Halbesleben, J. R. B., Neveu, J.-P., Paustian-Underdahl, S. C., & Westman, M. (2014). Getting
to the “COR”: Understanding the role of resources in conservation of resources theory.
Journal of Management, 40(5), 1334–1364. https://doi.org/10.1177/0149206314527130
Hartmann, S., Weiss, M., & Hoegl, M. (2020a). Team resilience in organizations: A conceptual
and theoretical discussion of a team-level concept. In E. H. Powley, B. B. Caza, & A.
Caza (Eds.), Research Handbook on Organizational Resilience (pp. 39-52). Cheltenham,
UK: Edward Elgar Publishing.
46
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Hartmann, S., Weiss, M., Newman, A., & Hoegl, M. (2020b). Resilience in the workplace: A
multilevel review and synthesis. Applied Psychology: An International Review, 1–47.
https://doi.org/10.1111/apps.12191
Hartwig, A., Clarke, S., Johnson, S., & Willis, S. (2020). Workplace team resilience: A
systematic review and conceptual development. Organizational Psychology Review.
https://doi.org/10.1177/2041386620919476
Harvey, J.-F., Leblanc, P.-M., & Cronin, M. A. (2019). Beyond separate emergence: A systems
view of team learning climate. Frontiers in Psychology, 10, 1–12.
https://doi.org/10.3389/fpsyg.2019.01441
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A
regression-based approach (2nd ed.). Guilford.
Hayes, A. F., & Matthes, J. (2009). Computational procedures for probing interactions in OLS
and logistic regression: SPSS and SAS implementations. Behavior Research Methods,
41(3), 924–936. https://doi.org/10.3758/BRM.41.3.924
Headd, B. (2003). Redefining business success: Distinguishing between closure and failure.
Small Business Economics, 21, 51-61.
Hirschfeld, R. R., Cole, M. S., Bernerth, J. B., & Rizzuto, T. E. (2013). Voluntary survey
completion among team members: Implications of noncompliance and missing data for
multilevel research. Journal of Applied Psychology, 98(3), 454–468.
https://doi.org/10.1037/a0031909
Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress.
American Psychologist, 44(3), 513–524. https://doi.org/10.1037/0003-066X.44.3.513
Hobfoll, S. E. (2001). The influence of culture, community, and the nested‐self in the stress
47
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
process: Advancing conservation of resources theory. Applied Psychology: An
International Review, 50(3), 337–421. https://doi.org/10.1111/1464-0597.00062
Hobfoll, S. E., Halbesleben, J., Neveu, J.-P., & Westman, M. (2018). Conservation of resources
in the organizational context: The reality of resources and their consequences. Annual
Review of Organizational Psychology and Organizational Behavior, 5(1), 103–128.
https://doi.org/10.1146/annurev-orgpsych-032117-104640
Hobfoll, S. E., Stevens, N. R., & Zalta, A. K. (2015). Expanding the science of resilience:
Conserving resources in the aid of adaptation. Psychological Inquiry, 26(2), 174–180.
https://doi.org/10.1080/1047840X.2015.1002377
Homan, A. C., van Knippenberg, D., Van Kleef, G. A., & De Dreu, C. K. W. (2007). Bridging
faultlines by valuing diversity: Diversity beliefs, information elaboration, and
performance in diverse work groups. Journal of Applied Psychology, 92(5), 1189–1199.
https://doi.org/10.1037/0021-9010.92.5.1189
Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005). Teams in organizations: From
input-process-output models to IMOI models. Annual Review of Psychology, 56(1), 517–
543. https://doi.org/10.1146/annurev.psych.56.091103.070250
James, L. R., Demaree, R. G., & Wolf, G. (1984). Estimating within-group interrater reliability
with and without response bias. Journal of Applied Psychology, 69(1), 85–98.
https://doi.org/10.1037/0021-9010.69.1.85
Johns, G. (2006). The essential impact of context on organizational behavior. Academy of
Management Review, 31(2), 386–408.
Johnson, R. E., King, D. D., Lin, S.-H. J., Scott, B. A., Walker, E. M. J., & Wang, M. (2017).
Regulatory focus trickle-down: How leader regulatory focus and behavior shape follower
48
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
regulatory focus. Organizational Behavior and Human Decision Processes, 140, 29–45.
https://doi.org/10.1016/j.obhdp.2017.03.002
King, D. D., Newman, A., & Luthans, F. (2016). Not if, but when we need resilience in the
workplace. Journal of Organizational Behavior, 37(5), 782–786.
https://doi.org/10.1002/job.2063
King, D. D., Ryan, A. M., & Van Dyne, L. (2019). Voice resilience: Fostering future voice after
non‐endorsement of suggestions. Journal of Occupational and Organizational
Psychology, 92(3), 535–565. https://doi.org/10.1111/joop.12275
Kish-Gephart, J. J., Detert, J. R., Treviño, L. K., & Edmondson, A. C. (2009). Silenced by fear:
The nature, sources, and consequences of fear at work. Research in Organizational
Behavior, 29, 163–193. https://doi.org/10.1016/j.riob.2009.07.002
Kossek, E. E., & Perrigino, M. B. (2016) Resilience: A review using a grounded integrated
occupational approach. Academy of Management Annals, 10(1), 729–797.
DOI:10.1080/19416520.2016.1159878
Kozlowski, S. W. J., & Doherty, M. L. (1989). Integration of climate and leadership:
Examination of a neglected issue. Journal of Applied Psychology, 74(4), 546–553.
https://doi.org/10.1037//0021-9010.74.4.546
Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the effectiveness of work groups and
teams. Psychological Science in the Public Interest, 7(3), 77–124.
https://doi.org/10.1111/j.1529-1006.2006.00030.x
Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to theory and research in
organizations: Contextual, temporal, and emergent processes. In K.J. Klein & S.W.J.
Kozlowski (Eds.), Multilevel theory, research and methods in organizations (pp. 3-90).
49
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
San Francisco, CA: Jossey-Bass.
Li, A. N., Liao, H., Tangirala, S., & Firth, B. M. (2017). The content of the message matters: The
differential effects of promotive and prohibitive team voice on team productivity and
safety performance gains. Journal of Applied Psychology, 102(8), 1259–1270.
https://doi.org/10.1037/apl0000215
Li, A. N., and Tangirala, S. (forthcoming). How employees’ voice helps teams remain resilient in
the face of exogenous change. Journal of Applied Psychology
Mathieu, J. E., Maynard, M. T., Rapp, T., & Gilson, L. (2008). Team effectiveness 1997-2007: A
review of recent advancements and a glimpse into the future. Journal of Management,
34(3), 410–476. https://doi.org/10.1177/0149206308316061
Mayer, D. M., Kuenzi, M., Greenbaum, R., Bardes, M., & Salvador, R. B. (2009). How low does
ethical leadership flow? Test of a trickle-down model. Organizational Behavior and
Human Decision Processes, 108(1), 1–13. https://doi.org/10.1016/j.obhdp.2008.04.002
Maynard, M. T., & Kennedy, D. M. (2016). Team adaptation and resilience: What do we know
and what can be applied to long-duration isolated, confined, and extreme contexts.
National Aeronautics and Space Administration.
Meneghel, I., Martínez, I. M., & Salanova, M. (2016a). Job-related antecedents of team
resilience and improved team performance. Personnel Review, 45(3), 505–522.
https://doi.org/10.1108/PR-04-2014-0094
Meneghel, I., Salanova, M., & Martínez, I. M. (2016b). Feeling good makes us stronger: How
team resilience mediates the effect of positive emotions on team performance. Journal of
Happiness Studies, 17(1), 239–255. https://doi.org/10.1007/s10902-014-9592-6
Mjelde, F. V., Smith, K., Lunde, P., & Espevik, R. (2016). Military teams – A demand for
50
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
resilience. Work, 54(2), 283–294. https://doi.org/10.3233/WOR-162298
Morrison, E. W. (2011). Employee voice behavior: Integration and directions for future research.
Academy of Management Annals, 5(1), 373–412.
https://doi.org/10.5465/19416520.2011.574506
Morrison, E. W., Wheeler-Smith, S. L., & Kamdar, D. (2011). Speaking up in groups: A cross-
level study of group voice climate and voice. Journal of Applied Psychology, 96(1), 183–
191. https://doi.org/10.1037/a0020744
Northouse, P. G. (2021). Leadership: Theory and practice. SAGE Publications, Incorporated.
O’Neill, T. A., McLarnon, M. J. W., Hoffart, G. C., Woodley, H. J. R., & Allen, N. J. (2018).
The structure and function of team conflict state profiles. Journal of Management, 44(2),
811–836. https://doi.org/10.1177/0149206315581662
Piccolo, R. F., & Buengeler, C. (2013). Leadership and goal setting. In E. A. Locke, & G. P.
Latham (Eds.), New Developments in Goal Setting and Task Performance (pp. 357-374).
New York: Routledge.
Podsakoff, P. M., MacKenzie, S. B., Lee, J-Y., & Podsakoff, N. P. (2003). Common method
biases in behavioral research: A critical review of the literature and recommended
remedies. Journal of Applied Psychology, 88(5), 879–903.
http://dx.doi.org/10.1037/0021-9010.88.5.879
Podsakoff, P.M., MacKenzie, S.B. & Podsakoff, N.P. (2012). Sources of method bias in social
science research and recommendations on how to control it. Annual Review of
Psychology, 63, 539–569.
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions
in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of
51
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Educational and Behavioral Statistics, 31(3), 437–448.
https://doi.org/10.3102/10769986031004437
Rauter, S., Weiss, M., & Hoegl, M. (2018). Team learning from setbacks: A study in the context
of start-up teams. Journal of Organizational Behavior, 39(6), 783–795.
https://doi.org/10.1002/job.2278
Resick, C. J., Murase, T., Randall, K. R., & DeChurch, L. A. (2014). Information elaboration and
team performance: Examining the psychological origins and environmental
contingencies. Organizational Behavior and Human Decision Processes, 124(2), 165–
176. https://doi.org/10.1016/j.obhdp.2014.03.005
Sanner, B., & Bunderson, J. S. (2015). When feeling safe isn’t enough: Contextualizing models
of safety and learning in teams. Organizational Psychology Review, 5(3), 224–243.
https://doi.org/10.1177/2041386614565145
Seery, M. D., Holman, E. A., & Silver, R. C. (2010). Whatever does not kill us: Cumulative
lifetime adversity, vulnerability, and resilience. Journal of Personality and Social
Psychology, 99(6), 1025–1041. https://doi.org/10.1037/a0021344
Seery, M. D., Leo, R. J., Lupien, S. P., Kondrak, C. L., & Almonte, J. L. (2013). An upside to
adversity? Moderate cumulative lifetime adversity is associated with resilient responses
in the face of controlled stressors. Psychological Science, 24(7), 1181–1189.
https://doi.org/10.1177/0956797612469210
Siemsen, E., Roth, A., & Oliveira, P. (2010). Common method bias in regression models with
linear, quadratic, and interaction effects. Organizational Research Methods, 13(3), 456–
476. https://doi.org/10.1177/1094428109351241
Sommer, S. A., Howell, J. M., & Hadley, C. N. (2016). Keeping positive and building strength:
52
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
The role of affect and team leadership in developing resilience during an organizational
crisis. Group & Organization Management, 41(2), 172–202.
https://doi.org/10.1177/1059601115578027
Spector, P. E. (2019). Do not cross me: Optimizing the use of cross-sectional designs. Journal of
Business and Psychology, 34(2), 125–137. https://doi.org/10.1007/s10869-018-09613-8
Stanley, D. J., Allen, N. J., Williams, H. M., & Ross, S. J. (2011). Examining workgroup
diversity effects: Does playing by the (group-retention) rules help or hinder? Behavior
Research Methods, 43(2), 508–521. https://doi.org/10.3758/s13428-010-0053-9
Stephens, J. P., Heaphy, E. D., Spreitzer, G. M., & Dutton, J. E. (2013). Relationship quality and
virtuousness: Emotional carrying capacity as a source of individual and team resilience.
The Journal of Applied Behavioral Science, 49(1), 13–41.
https://doi.org/10.1177/0021886312471193
Stoverink, A. C., Kirkman, B. L., Mistry, S., & Rosen, B. (2020). Bouncing back together:
Toward a theoretical model of work team resilience. Academy of Management Review,
45(2), 395–422. https://doi.org/10.5465/amr.2017.0005
Sutcliffe, K. M., & Vogus, T. J. (2003). Organizing for resilience. In K. S. Cameron, J. E.
Dutton, & R. E. Quinn (Eds.), Positive organizational scholarship: Foundations of a new
discipline (pp. 94–110). Berrett-Koehler.
Tangirala, S., & Ramanujam, R. (2008). Exploring nonlinearity in employee voice: The effects
of personal control and organizational identification. Academy of Management Journal,
51(6), 1189–1203. https://doi.org/10.5465/amj.2008.35732719
Tedeschi, R. G., & Calhoun, L. G. (2004). Posttraumatic growth: Conceptual foundations and
empirical evidence. Psychological Inquiry, 15(1), 1–18.
53
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
https://doi.org/10.1207/s15327965pli1501_01
Tugade, M. M., & Fredrickson, B. L. (2004). Resilient individuals use positive emotions to
bounce back from negative emotional experiences. Journal of Personality and Social
Psychology, 86(2), 320–333. https://doi.org/10.1037/0022-3514.86.2.320
van Dick, R., van Knippenberg, D., Hägele, S., Guillaume, Y. R. F., & Brodbeck, F. C. (2008).
Group diversity and group identification: The moderating role of diversity beliefs.
Human Relations, 61(10), 1463–1492. https://doi.org/10.1177/0018726708095711
Vandewalle, D. (1997). Development and validation of a work domain goal orientation
instrument. Educational and Psychological Measurement, 57(6), 995–1015.
https://doi.org/10.1177/0013164497057006009
Vandewalle, D., Nerstad, C. G. L., & Dysvik, A. (2019). Goal orientation: A review of the miles
traveled and the miles to go. Annual Review of Organizational Psychology and
Organizational Behavior, 6, 115–144. https://doi.org/10.1146/annurev-orgpsych-041015-
062547
Vera, M., Rodríguez-Sánchez, A. M., & Salanova, M. (2017). May the force be with you:
Looking for resources that build team resilience. Journal of Workplace Behavioral
Health, 32(2), 119–138. https://doi.org/10.1080/15555240.2017.1329629
Waller, M. J. (1999). The timing of adaptive group responses to nonroutine events. Academy of
Management Journal, 42(2), 127–137. https://doi.org/10.5465/257088
Williams, T. A., Gruber, D. A., Sutcliffe, K. M., Shepherd, D. A., & Zhao, E. Y. (2017).
Organizational response to adversity: Fusing crisis management and resilience research
streams. Academy of Management Annals, 11(2), 733–769.
https://doi.org/10.5465/annals.2015.0134
54
A RESOURCE MODEL OF TEAM RESILIENCE CAPACITY
Zhu, Y., & Akhtar, S. (2019). Leader trait learning goal orientation and employee voice
behavior: the mediating role of managerial openness and the moderating role of felt
obligation. The International Journal of Human Resource Management, 30(20), 1–25.
https://doi.org/10.1080/09585192.2017.1335338
