r Academy of Management Journal
2021, Vol. 64, No. 2, 435–457.
https://doi.org/10.5465/amj.2019.0500
Cite Article
DEPLOYING HUMAN CAPITAL RESOURCES:
ACCENTUATING EFFECTS OF SITUATIONAL ALIGNMENT
AND SOCIAL CAPITAL RESOURCES
MIKHAIL A. WOLFSON
University of Kentucky
JOHN E. MATHIEU
University of Connecticut
Researchers and organizational leaders alike have long known that securing human
capital (HC) is critical for organizational success. The bulk of research in this domain
has addressed issues regarding the accrual of HC stocks. However, there has been a
dearth of research exploring the underlying mechanisms through which accrued HC
stocks translate to competitive advantage. We expand on Wolfson and Mathieu’s (2018)
human capital resource complementarity (HCRC) theory by incorporating the deployment of human capital resources (HCRs) as well as the role of social capital resources
(SCRs) in the form of members’ shared team task-specific experience in predicting
performance. We empirically test HCRC theory-derived hypotheses using a sample of
448 cyclists from 17 world teams who competed in 26 races during the 2015 season of the
Union Cycliste Internationale World Tour. Our results revealed that higher-level HCR
stocks indirectly led to deployed HCR performance through deployed HCR alignment
with situational characteristics. Furthermore, SCRs acted as a critical boundary condition for the value of aligning deployed HCRs with situational characteristics such that
the alignment of deployed HCRs was only advantageous when paired with relatively
high levels of SCRs. Implications for future theory building, research, and practice
related to HCR deployment are discussed.
Effectively securing, developing, and deploying
human capital (HC) are critical practices associated
with organizational performance. Ployhart and colleagues advanced the concept of human capital resource (HCRs) as a subset of HC that can be used for
unit-relevant purposes (Ployhart, Nyberg, Reilly, &
Maltarich, 2014) and exclaimed that converting HC
to HCRs is the key to reaping competitive advantages
from HC (Ployhart et al., 2014). Furthermore, “HCR
can be used to describe subparts of a firm’s peoplerelated resources (i.e., individual HCRs), the totality
of those resources (i.e., unit-level HCRs), or both”
(Moliterno & Nyberg, 2019: 5). Wolfson and Mathieu
(2018) recently called for research exploring the
nature of complementarity between team members’
competencies and how aligning their HCRs with
situational characteristics can lead to enhanced performance outcomes. Context can be particularly impactful
in dictating which team member competencies and behaviors may ultimately translate into beneficial processes and outcomes (Bell, Brown, & Weiss, 2018).
However, to date, this research has neither adequately accounted for the role of deployment nor empirically incorporated the social side of the story—i.e.,
the role that social capital (SC) plays when (and the
extent to which) HCRs lead to organizational performance. In fact, the mobilization and deployment of
HCRs, such as groups and teams, exist at the mesolevel, and “one cannot understand human capital resources without understanding groups and teams”
(Ployhart & Chen, 2019: 361). Others have noted that
“teams are the vehicle through which human capital
translates into important outcomes” (Porter, Amber, &
Wang, 2019: 315–316). Furthermore, accessing abilities like HCRs may be contingent on complementarities (Adegbesan, 2009; Milgrom & Roberts, 1995) such
as identifying those that can augment the focal HCR to
The authors would like to thank associate editor
Anthony Nyberg and the three anonymous reviewers for
their thoughtful engagement and constructive feedback
throughout the review process. Correspondence concerning this article should be addressed to Mikhail A. Wolfson
(Mikhail.Wolfson@uky.edu), Management Department,
Gatton College of Business and Economics, 550 South
Limestone, Lexington, KY 40506-0034
435
Copyright of the Academy of Management, all rights reserved. Contents may not be copied, emailed, posted to a listserv, or otherwise transmitted without the copyright holder’s express
written permission. Users may print, download, or email articles for individual use only. For permission to reuse AMJ content, please visit AMJ Permissions.
436
Academy of Management Journal
create unique value (Moliterno & Nyberg, 2019). One
such resource is SC, as it provides the basis for strategically relevant HCR complementarities (Brymer &
Hitt, 2019). SC represents the shared relations between
individuals (Adler & Kwon, 2002) and further facilitates the motivation, cooperation, and subsequent
outcomes of individuals and teams (Brymer & Hitt,
2019). Thus, it may be that the presence of HCRs alone
is not enough to lead to parity or competitive advantage, and that SC may act as the boundary condition
through which the deployment of HCRs is augmented
to lead to better performance.
Presently, we build on Wolfson and Mathieu’s
(2018) human capital resource complementarities
(HCRC) theory and make three theoretical contributions. First, we extend HCRC theory by highlighting
deployment influences and incorporating the role of
SC. Second, we elaborate upon the theory by demonstrating that SC represents a critical boundary
condition for aligning HCRs with situational characteristics. And finally, we conduct a first test of
the expanded HCRC theory, featuring deployment
practices and the joint influences of task characteristics with deployed HCRs and SC. We conclude
with discussions of generalizability and future directions for exploring HCR deployment.
THEORY AND HYPOTHESES
HCRC Theory and Extant Research
With the nature of the HC construct expanding
from an individual’s ability to capture value in the
marketplace to approximately all value associated
with employees (Nyberg & Wright, 2015), researchers
have sought to provide a framework that allows for
greater specificity and utility in both defining and
utilizing HC. Ployhart et al. (2014) distinguished
between traditional views of HC and their proposed
construct of HCRs. The former was described as “an
individual’s KSAOs [knowledge, skills, abilities and
other characteristics] that are relevant for achieving
economic outcomes,” whereas the latter captured
“individual- or unit-level capacities based on individual KSAOs that are accessible for unit-relevant
purposes” (Ployhart et al., 2014: 373). This clarification allows researchers to home in on the generic
and specific competencies that can be advantageous
for performance purposes.
Prior HC research has differentiated generic HC as
being “transferable across a variety of firms” (Barney
& Wright, 1998: 37) from HC being specific to a unit
and thus inimitable, non-substitutable, and therefore
April
more likely to create competitive advantage (Hatch &
Dyer, 2004; Ployhart, Van Iddekinge, & MacKenzie,
2011). However, both specific and generic HC can be
viewed at a finer granularity. Rather than focusing on
firm versus industry, one could instead (or additionally) focus on HCRs as differentiated by being
relevant for specific situations versus being relevant
across situations. The primary focus of macro research on HC as being unit-specific experience and
knowledge has largely ignored micro-level research
on individuals’ cognitive and non-cognitive KSAOs
(Ployhart & Moliterno, 2011). As such, Ployhart and
Moliterno’s (2011) conceptualization differed in context specificity and malleability, and continued emphasis is needed on the relative context specificity of
these KSAOs. Along those lines, Soda and Furlotti
(2017) emphasized that context ultimately determines
the nature of HCR complementarity because a set of
resources may be complementary in one context but
not in another.
Increased levels of HC allow organizations to accumulate and combine members’ competencies in
ways that create resources that can be valuable, rare,
inimitable, and non-substitutable (Barney, 1991). The
bulk of the research in this domain has addressed
issues regarding the accrual of HC stocks (Dierickx
& Cool, 1989; Nyberg & Wright, 2015; Ployhart,
Weekley, & Ramsey, 2009). Researchers have also
explored how HC emerges from the individual to the
team level (e.g., Ployhart et al., 2014; Ployhart &
Moliterno, 2011). However, the emphasis on the accrual of HC takes at face value that having greater
levels of these resources will, in fact, lead to improved performance without articulating the underlying mechanisms providing that linkage. The
process of combining various forms of HC can increase their value by transforming them into HCRs
(Barney & Felin, 2013; Campbell, Coff, & Kryscynski,
2012; Dierickx & Cool, 1989). Yet, exploring different
combinations of HC introduces greater complexity
and coordination demands as members must interact and combine their efforts; moreover, the extent to
which they do so effectively can further enhance the
utility of HCRs (Rusbult & Van Lange, 2003, 2008).
As Mathieu, Gallagher, Domingo, & Klock (2019:
18) noted, “given the modern-day hypercompetitive
and fluid environment, organizations have adopted
team-based designs to maximize the value of their
human capital [enabling] organizations the flexibility to compose and reconfigure their team memberships to align members’ competencies with task
demands”. Since teams can be viewed as HCRs, exploring team composition across various situations
2021
Wolfson and Mathieu
can shine a light on the value of aligning deployed
HCRs with SCRs and situational demands.
Wolfson and Mathieu (2018) recently advanced a
theory of HCRC that integrated work on HCR (Ployhart
et al., 2014) with work on resource complementarity
(Soda & Furlotti, 2017). They featured “(1) the potential
of human capital resources, (2) at multiple levels, (3) to
interact directly with task demands, as well as with
other human capital resources to predict individual,
unit, or organization-relevant outcomes” (Wolfson &
Mathieu, 2018: 1166). They proposed that the nature of
complementarity was ultimately driven by situational
demands and that it could take on the form of resources
complementing one another (complementarity) or a
similarity-based fit where more of the same resources
(supplementarity) are advantageous. Furthermore, the
form of complementarity among resources would be
situationally determined by contextual demands. With
a focus on dynamic situational characteristics, HCRC
theory is uniquely situated to address questions regarding how HCRs can be deployed. In fact, Wolfson
and Mathieu (2018) have called for future research to
explore the deployment of HCRs across situations in
concert with team members’ interrelationships.
Deploying HCR and Performance
For organizations to truly glean the benefits of
team-based structures, they must consider the value
of team composition as a means of enabling effective
teamwork and ultimately influencing team performance (Bell et al., 2018; Mathieu, Tannenbaum,
Donsbach, & Alliger, 2014). Furthermore,
there is a disconnect between micro-level research,
such as that focused on team composition, and macrolevel human resource management (HRM) literature
. . . As a result, there is a lack of clarity regarding how
team composition decisions can contribute to an organization’s effectiveness and competitive advantage. (Bell et al., 2018: 451)
In part, the utilization of teams allows organizations
increased flexibility in terms of reconfiguring and
redeploying resources through coordination flexibility (Bell et al., 2018; Chang, Gong, Way, & Jia,
2013; Wright & Snell, 1998). Team composition can,
therefore, effectively act as a bridge between these
literatures, as the makeup of a team can embody the
strategic decision associated with deploying HCR
into relevant situations.
These fluid team-based work structures can enable
organizations to utilize dynamic composition to
align their team-level competencies better with
437
situational demands (Mathieu et al., 2014). Organizations amass a wide range of HCRs in what could be
considered HCR stocks, or the relatively stable accumulation of HCRs. The size and nature of these
stocks afford greater or lesser strategic flexibility in
terms of deployment strategies. Researchers have
noted that there are a wide variety of ways of approaching team composition, particularly when one
considers either multiple member replacement or the
composition of an entirely different team (Mathieu
et al., 2014; Wolfson & Mathieu, 2017). There could be
a variety of uses and benefits for team member combination and recombination; organizations could seek
to rest individuals to avoid burnout or deploy them in
situations that would allow for greater employee development. As such, having greater capabilities available in terms of HCR stocks can afford organizations
greater flexibility in terms of their various deployments
of HCRs. However, the nature and ultimate utility of
these HCR stocks are largely dependent on the nature
of the tasks at hand.
Ployhart and Chen (2019: 366) posed the question
“how does a firm mobilize and deploy its human
capital, given that the groups and teams are the way
that human capital resources are structured in organizations?” In order to deploy HCRs effectively, the
nature of a given situation needs to be accounted for.
Soda and Furlotti (2017) called for the role of task
characteristics to be brought to the forefront, as they
may ultimately dictate the nature of complementarity. They argued that resource complementarity can
only exist to the extent that resource stocks can aid
in task completion and that the nature of complementarity ultimately resides in the nature of the task
at hand. Building on this work, with their HCRC
theory, Wolfson and Mathieu (2018: 1168) proposed
that “greater alignment of human capital resource
complementarity among individuals, teams, or organizations with dynamic situational factors is associated with higher levels of performance.” Thus,
there are critical recurring choices to consider in
terms of which specific HCRs should be deployed in
any given circumstance at any given time. Furthermore, to the extent that organizations deploy their
HCRs in a complementary fashion, as aligned with
situational demands, they should reap greater performance benefits. In short, having greater HC stocks
may matter little unless you are able to deploy your
HCRs differently, contingent on situational demands.
To make deployments that exploit complementarities, you must: (a) have a sufficient amount and diversity of HCR stocks to have options, (b) adequately
perceive the different circumstances, and (c) be
438
Academy of Management Journal
willing to make different deployment decisions over
time. Consequently, we propose:
Proposition 1. HCR stocks and situational characteristics jointly influence deployed members’ HCRs.
Proposition 2. Deployed HCR alignment with situational characteristics influences the performance of
deployed members.
Deployed SCRs Accentuate Deployed HCRs
In addition to deploying HCRs and aligning them
with situational demands, it is vital to consider the
underlying mechanisms that convert team composition into team performance (Bell et al., 2018). SC, or
“the sum of the actual and potential resources embedded within, available through, and derived from
the network of relationships possessed by an individual or social unit” (Nahapiet & Ghoshal, 1998:
243), has been viewed as a critical factor yielding
competitive advantage (Collins & Clark, 2003). In
fact, there may be a particular benefit to exploring
how intrateam factors such as SC may affect teams’
success. However, studies have conceptualized SC
in a variety of ways. For example, Adler and Kwon
(2002) discussed how there can be market relations,
hierarchical relations, and social relations as subsets
of SC. Meanwhile, some scholars have viewed SC as
an external factor, such as an individual’s personal or
institutional network (Belliveau, O’Reilly, & Wade,
1996), and some have viewed it as an internal factor
or an ability (Fukuyama, 1995). SC has been considered as access to resources through relationships
(Lin, 2001) and as a basis of shared norms and expectations (Coleman, 1988; Crocker, 2019). Although there
are many aspects of SC that could potentially hold
value, not all of these are necessarily accessible for
unit-relevant purposes and will therefore not be relevant for performance parity or competitive advantage
(Ployhart et al., 2014).
In the same way that Ployhart and colleagues
(2014) viewed HCRs as HC that is accessible for unitrelevant purposes, we propose that SC resources
(SCRs) represent a subset of individual or unit SC that
is accessible for unit-relevant purposes. Although SCRs
can be conceptualized in many ways, perhaps the
true value in SCRs lies in enabling the efficient or
effective use of resources. The teams literature refers
to this as maximizing process gains while minimizing process losses (Steiner, 1972). Access to information and opportunities (Burt, 1997) or shared
norms and expectations (Coleman, 1988) may be
advantageous; however, in order for SC to be relevant
April
for unit-relevant purposes and considered SCRs, the
organization or unit must be able to actually leverage
it. Individuals have myriad relationships and social
ties, but not all of these are ultimately salient for
performance parity or competitive advantage.
Nyberg and Wright (2015) called for research to
examine the precise mechanisms that link HCRs and
SC, and Soltis, Brass, and Lepak (2018) have called
for the integration of SC into human resource management. One way to do so is to explore the potential
of SCRs to augment HCRs. At the unit level, as
complexity increases due to interrelationships
among individuals (Crocker, 2019), unit-level properties regarding SCRs become more salient (Oh,
Labianca, & Chung, 2006; Ployhart & Moliterno,
2011). Consequently, SCRs become an essential
component for extracting value from HCRs as HCRs
and SCRs uniquely combine in units (Crocker, 2019).
Given that not all combinations of resources will
create complementarities (Somaya, Williamson, &
Zhang, 2008), SCRs become exceedingly important
and bolster how HCR complementarities lead to
improved performance (Hollenbeck & Jamieson,
2015; Labianca & Brass, 2006). As SCRs have the
potential to augment HCRs and their alignment with
situational factors, we propose the following:
Proposition 3. SCRs accentuate the positive relationship between HCRs and performance and further
bolster the positive effects of aligning HCRs with situational characteristics.
TESTING DEPLOYMENT HYPOTHESES DERIVED
FROM HCRC
To test hypotheses derived from our propositions,
we sought to sample an environment where different
organizations must acquire and deploy HCRs across
varying situations while having comparable outcomes and relatively objective measures of competencies and performance. There are a variety of
situations in which organizations must choose
which combination of HCRs to deploy based on situational demands and where interrelations among
these individuals may further accentuate the
deployed HCRs and subsequently predict their performance. For example, consulting teams routinely
choose which combination of consultants to assign
to a project based on their competencies related to
project demands and how well they work together.
Special forces teams are assembled according to the
demands of their unique missions. Surgical crews
are assembled based on the specializations of the
2021
Wolfson and Mathieu
surgeons, and they routinely seek out nurses and
anesthesiologists with whom they have shared experience (Luciano, Bartels, D’Innocenzo, Maynard,
& Mathieu, 2018). In all these contexts and more,
deploying HCRs that are better aligned with situational characteristics is key; furthermore, the more
interdependent the context, the greater the likelihood that complementarities with SCRs can lead to
further gains.
Johns (2006: 386) defined “context as situational
opportunities and constraints that affect the occurrence of meaning of organizational behavior as well as
functional relationships between variables.” The
need for context was exemplified in Soda and Furlotti’s
(2017) call to bring context to the forefront to ultimately
understand what constitutes HCR complementarity.
For instance, Wolfson and Mathieu (2018) sampled
a cycling context and modeled the relationships
between competitors’ competencies and dynamic situational characteristics across the stages of a single
race. In their work, they illustrated the importance of
differentiating specific types of cyclist competencies
(i.e., sprinting versus mountain) and their alignment
with varying situational demands. They illustrated
that examining such specificity and alignment yielded
greater insights than that provided by generic competencies. However, their study was limited in the sense
that they studied the performance of one deployed
team per organization over the duration of the centennial Tour de France. As such, they suggested that
future researchers should examine such relationships
over time and situations because
teams configure their composition explicitly for the
attributes of a certain race . . . teams have to make
decisions regarding how to best deploy their strategicHCR across races . . . consider[ing] the importance of
races with regard to aligning individual competencies, as well as teammate competences, while balancing overall attributes of any given event and the
effects of fatigue. (Wolfson & Mathieu, 2018: 1178)
Although they did explore temporal effects, their
deployments and team compositions were fixed, and
the value of complementarities beyond competencies was not incorporated.
The current work is in part a constructive replication of Wolfson and Mathieu’s (2018) in that we examine the alignment of varying team compositions
over time. While Wolfson and Mathieu (2018) illustrated competency complementarities and interactions with situations, we argue that such knowledge is
only valuable if one has sufficient HCR stocks available and can deploy them optimally across situations.
439
As such, we extend their work by featuring how teams
utilize their HCR stocks through deployments across
races based on situational characteristics. Moreover,
we incorporate complementarities among deployed
HCRs and SCRs as a further contributor to team performance. Bacharach (1989) highlighted the notion
that generalizability includes different levels of theorizing which span from grand theoretical statements
to detailed empirical generalizations. Furthermore,
he exclaimed that
on a more abstract level, propositions state the relationships among constructs, and on the more concrete
level, hypotheses (derived from the propositions) specify the relationships among variables. In this context,
theorists must be specific in how they use the notions
of constructs and variables. (Bacharach, 1989: 500)
Below, we feature hypotheses derived from our three
propositions and consequently provide contextually
grounded variables (i.e., team and start list competencies, shared team task-specific experience, and
race characteristics) to represent our theoretical constructs (HCRs, SCRs, and situational characteristics).
By theorizing specific competencies by situational
interactions, one necessarily needs to contextualize a
particular investigation (Johns, 2006; Mathieu, 2016).
Accordingly, we advance a cross-level, cross-classified
model designed to test hypotheses derived from our
extension of HCRC theory that are grounded in the
context of professional cycling. We feature the cycling
context as it allows us to leverage objective competencies, situational characteristics, interrelationships, and
measures of performance in a highly interdependent
environment that necessitates teamwork. In addition to
selecting contextually relevant variables, it is necessary
to determine the focal unit of analysis and relevant criteria (Mathieu & Chen, 2011). In our study, our focal level
is the repeated measures of deployed HCR (start list)
performance, which are cross-nested within cycling
teams and races. For clarity, cycling teams are akin to
organizations in our study, and start lists are the group of
specific team members that are deployed for any given
race. Start lists’ deployed HCRs represent the HCRs
possessed by a subset of the riders that are participating
from any given team and must remain intact for the
duration of a race.
We test models featuring both generic and specific
member competencies, along with situational (race)
characteristics, as related to the performance of team
start lists over the duration of a season. We propose
that dynamic features of the contexts place premiums on specific team competencies which give
rise to team-by-situation interactions over time. As
440
Academy of Management Journal
such, we introduce multilevel aspects of HCRC and
model how situational characteristics can influence
both the deployment of HCRs and deployed HCR
performance, which can be further bolstered by the
degree of complementarity among deployed members’ HCRs and SCRs. A summary of our hypothesized model is shown in Figure 1.
April
Although professional cycling may at first glance
appear to be an individual sport,
behind every jersey-winner is a team of tacticians who
are responsible for guiding their favourite to victory.
A team’s strategy depends on the strengths of its lead
rider and supporting “domestiques”, but any game
plan can be rejigged or unraveled as a race unfolds
and rival contenders emerge. (Cycle Surgery, 2015)
Hypothesis Development
Prior to each race, a team’s manager must choose
from their roster of 20–30 cyclists which seven to
nine members to deploy for a given race. In determining which group of riders or start list to deploy, a
team’s manager begins by choosing a captain whose
abilities would put them in the best position to
win based on the demands of the race. The team
is then completed with six to eight domestiques—
teammates whose roles are devoted primarily to
supporting the featured team rider(s): “The strategy
for the entire race is dictated by the strengths of the
team’s leader—if the leader excels in climbing, for
example, then the team will focus on winning the
mountain stages” (Cycle Surgery, 2015). Teammates
will assist the leader in a variety of ways, including providing an aerodynamic advantage by riding
ahead of them, dropping back to get supplies from
Wolfson and Mathieu (2018: 1169) stated that “the
context of professional cycling has several key features
that make it a relevant setting to explore performance
that could generalize to traditional work contexts”
(Wolfson & Mathieu, 2018: 1169). In terms of deployment decisions, cycling teams face issues similar to those
of many traditional organizations. They must balance
performance needs with the likelihood of burnout and
the need for development. Cyclists face a variety of
changing situational characteristics and perform highly
interdependent tasks. Ultimately, performing in this
context requires complementarity among individuals’
competencies and situations, as well as high levels of
coordination. To do so, cycling teams choose a group of
riders from their roster to deploy in a given race in the
form of a start list.
FIGURE 1
Hypothesized Model
Team HCR Stocks Level
Time Invariant
n = 17
Deployed HCRs Level
Time Varying
n = 442
Mountain n = 204
Sprint n = 187
HCR Stocks
(Mountain or Sprint)
Hypothesis 1: +
Deployed HCRs
(Mountain or Sprint)
Race Characteristics
(Mountain or Sprint)
Deployed HCR
Performance
(Mountain or Sprint Points)
Hypothesis 2: +
Hypothesis 3: +
Race Level
Time Varying
n = 26
Mountain n = 12
Sprint n = 11
Deployed SCRs
(STTE)
Notes: HCR(s) 5 human capital resource(s). SCRs 5 social capital resources. STTE 5 shared team task-specific experience. n 5 442
represents the deployed HCRs for each team across all 26 races, and n 5 204 and n 5 187 represent the number of deployed HCRs for which
mountain and sprint points, respectively, were available.
2021
Wolfson and Mathieu
441
the support car, or even giving up their own wheel in
the event of a puncture.
would be expected to be the likely beneficiary of this
type of route:
Domestiques take turns cycling in front of their leader
to minimise wind resistance and to maximise
aerodynamics—this is known as “drafting” or “slipstreaming”. Riders in the peloton [the large cluster of
competitors riding together during the race] get the
benefit of drafting too, as they take turns fronting the
pack and dropping back (sometimes, teams surge
ahead just to give their sponsors some air time). (Cycle
Surgery, 2015)
Chris Froome has, without a doubt, the strongest team
in the race . . . he’s amongst the best climbers in the
race. . . . With riders like Richie Porte, Leo König and
Wout Poels to support him on the climbs, Froome too
will be very difficult to beat. (Condé, 2015)
Team members set the pace for one another to help
conserve energy, and in sprint races, they form leadout trains which allow the leader to conserve energy
as they move to the front of the pack to form an attack.
In short, teams deploy different coordination strategies designed to maximize the potential points that
they are targeting and that are available.
Although a wide variety of HCRs can be valuable for
success, competitive cycling places a premium on
sprinting and mountain competencies (Wolfson &
Mathieu, 2018). Throughout the World Tour, which
consists of the main events (i.e., races) during the cycling
season, points are awarded toward sprint and mountain
classifications. Some races offer points in both categories,
whereas others only offer one classification type. In fact,
during races that consist of multiple stages, riders that are
leading their respective classification will ride in a special colored jersey to denote their first-place standing. In
concert with HCRC theory, races that have disproportionately higher points available (i.e., offer great competitive value for cycling teams) for either mountains or
sprints (or both) will favor teams that can deploy the
corresponding HCRs. Furthermore, as these dynamic
task features vary from race to race, the alignment or
misalignment of HCR complementarities will likely lead
to better or worse performance, respectively. Consistent
with Wolfson and Mathieu (2018), we also seek to demonstrate the value of aligning specific competencies beyond that of riders’ general competencies alone.
Below, we introduce examples from commentaries
regarding the 2015 season of the World Tour as they
pertain to our hypotheses. The excerpts were selected for
illustrative purposes ex post to demonstrate the underlying rationale and to provide greater contextual
grounding for our hypotheses. In previewing the Tour de
France, the premiere race of the cycling season, one analyst described its situational characteristics as “a race
that is celebrating the 40th anniversary of the red polka
dot King of the Mountains jersey, the emphasis of the
102nd Tour de France is very firmly on climbing”
(Cossins, 2015). Consequently, a specific set of HCRs
As these two quotes demonstrate, it is not enough to
simply have the best generic HCRs, but rather it is critical to deploy HCRs effectively to maximize alignment
with situational characteristics. As such, both the deployment of HCRs and the performance of the deployed
HCRs in the form of the start list chosen to ride a given
race are likely to be contingent on the characteristics of
the race at hand. In order to test proposition one, this
context yields two specific hypotheses, as follows:
Hypothesis 1a(b). Race-level mountain (sprint) characteristics moderate the positive relationship between teamlevel mountain (sprint) HCR stocks and deployed mountain
(sprint) HCRs, such that the relationship is stronger in races
with greater mountain (sprint) characteristics.
Hypothesis 2a(b). Race-level mountain (sprint) characteristics moderate the positive relationship between deployed mountain (sprint) HCRs and
mountain (sprint) performance, such that the relationship is stronger in races with greater mountain
(sprint) characteristics.1
In sum, Hypotheses 1 and 2 argue that situational
demands will influence the quality of deployed HCR
from team stocks and the performance of deployed
members. Although the alignment of competencies
is not an entirely novel notion, the bulk of the HC
literature obscures the relationship between the
acquisition of HCR stocks and performance by
overlooking the key aspect of HCR deployment.
Furthermore, while prior research has largely focused on the acquisition of HCR stocks, our emphasis on HCR deployment across varying situational
characteristics allows us to unpack the criterion
space and model team performance over time as
influenced by dynamic task characteristics to highlight the value of aligning HCR deployment.
Accentuating Effect of SCRs (Shared Team
Task-Specific Experience)
Wolfson and Mathieu’s (2018) HCRC theory was
based largely on the alignment of member and team
1
The (a) portion of hypotheses 1, 2, and 3 correspond to
mountains, whereas the (b) portion corresponds to sprints.
442
Academy of Management Journal
competencies relative to dynamic situational demands. Yet, team performance is driven not only by
team composition in terms of members’ KSAOs but
also in terms of how well those competencies can
be combined and coordinated (Mathieu et al., 2014).
Teamwork takes time to develop, and members
having shared experience may further accentuate
the value of their HCRs (Harrison, Mohammed,
McGrath, Florey, & Vanderstoep, 2003). Highlighting the interrelationships among individuals, we
feature shared team task-specific experience (STTE)
as a contextually relevant variable representing SCR
as an aggregate of dyadic STTE among deployed
start list members. Shared team experience can be
viewed in a variety of ways, ranging from simply
having worked together with someone (Kor, 2006), to
working on a similar task as someone else (Cooke,
Gorman, Duran, & Taylor, 2007), to working with
the same individual on the same task (Huckman &
Staats, 2011). STTE captures this latter notion of
working with the same individuals on a task similar
to the one at hand (Kor, 2006). Luciano et al. (2018)
explained that STTE can influence team functioning by generating social entrainment. Social entrainment theory suggests that when teammates
work closely together, they develop synchronized
rhythms for work activities that have performance
implications (Harrison et al., 2003; Moon et al.,
2004). Often these rhythms are related to an external
pacer, or specific task requirements, and enable
teammates to better coordinate their individual actions (Harrison et al., 2003; Luciano et al., 2018). This
conception suggests that members’ STTE is an SCR
that is inimitable and non-substitutable, and therefore is a particularly valuable source of competitive
advantage.
Given that teams can be viewed as “assemblies of
interdependent relations and activities organizing
shifting sets or subsets of participants” (Humphrey &
Aime, 2014: 450), understanding STTE among
teammates could shed additional light on such
mechanisms. STTE derived from shared previous
experiences may allow individuals to better reap the
benefits of their HCRs (Harrison et al., 2003; Luciano
et al., 2018, Nyberg & Wright, 2015) by facilitating the
“adjustment or moderation of one’s behavior either
to synchronize or be in cycle or rhythm with another’s behavior” (Ancona & Chong, 1992: 7).
Deploying the right member combinations and developing STTE among them may be the key to converting HC stocks to HCRs and ultimately capturing
competitive advantage (Weller, Hymer, Nyberg, &
Ebert, 2019).
April
The context of cycling is incredibly interdependent, and although it may appear to be a race between
individual riders, the sport entails a great deal of
teamwork (Clarke, 2015). Riders train and compete
in teams in varying events throughout the season and
often employ a variety of strategies throughout a
given race. For example, in sprint-heavy races, teams
will employ lead-out trains, which is a drafting
technique that allows a particularly capable sprinter
to gain aerodynamic advantages from the riders
ahead to conserve energy for a key sprint. In other
portions of races, domestiques will often chase down
attacks from competitors or drop back to a support
car to fetch supplies so that featured riders do not
lose time to competitors. Furthermore, the top riders
from their respective teams will often ride together in
a peloton to gain aerodynamic advantages and to
pace each other for large portions of a race. However,
for teammates to truly benefit from one another’s
expertise, they must establish coordination, which
takes time to develop through STTE. In fact, the
benefits of superior deployment often extend far
beyond simply having the most competent riders:
Richie Porte has been an important figure for Sky in
his four years at the team, weighing in with big victories and playing a key role in their Tour de France
success, and his switch to BMC does weaken the
team. Perhaps the loss will be felt most keenly by
Froome, whose two Tour victories have been heavily
underpinned by Porte’s efforts . . . there is no [better
partnership] anywhere in the world and the pair’s
close-knit relationship has been plain to see. Froome
is clearly not short of talented support riders but
that special bond is something rather more rarefied.
Fletcher (2015)
The STTE that Porte and Froome had developed over
many races together allowed Froome to win the more
than 3,300 km Tour de France by just over four
minutes in 2013 and by one minute and twelve seconds in 2015. Given the highly interdependent nature of this context, such member STTE can
accentuate the value of deployed HCRs. The benefits
of complementarities between deployed HCR and
STTE will accentuate performance. Although STTEs
are thought to generally accentuate the value of
HCRs, the magnitude and nature of these effects may
vary as a result of the nature of the context (Wolfson &
Mathieu, 2018). Although aligning deployed HCRs
with situational characteristics is likely to be a critical component to success, there needs to be a high
level of coordination for these effects to translate into
superior performance. Therefore, we anticipate that
2021
Wolfson and Mathieu
STTE will further accentuate the positive effect of
aligning race characteristics with deployed HCRs,
and to test Proposition 3, this context yields the following hypothesis:
Hypothesis 3a(b). Deployed STTE will interact with
the positive relationship between deployed mountain
(sprint) HCRs and changing race-level mountain
(sprint) characteristics as related to deployed HCR
mountain (sprint) performance. Specifically, the
positive relationship between deployed mountain
(sprint) HCRs and mountain (sprint) race characteristics strengthens in the presence of higher deployed
STTE.2
METHODS
Data Collection and Sample
We empirically tested our hypotheses using a
sample of professional male cyclists who competed
during the 2015 season of the Union Cycliste Internationale World Tour. We gathered data at three
levels: (a) race, (b) individual, and (c) team. We
gathered data on race characteristics, individual
demographics and competencies, team competencies, and salaries using three archival sources
(Cycling Weekly, 2015; Pro Cycling Stats, 2015;
Sportune, 2015). Our final sample consisted of the
448 riders from 17 World Teams who competed in 26
races (yielding 442 performances) during the 2015
season. For every given race, the 17 teams would
choose a unique deployment of HCR in the form of
seven to nine riders (the start list) from their HCR
stocks (team roster). The cyclists were on average
28.72 years old (SD 5 4.20), 1.81 m tall (SD 5 .06),
and had an average weight of 68.84 kg (SD 5 6.51).
Measures
Performance. The performance for each race was
indexed using the sum of points accrued by each
team’s deployed HCR per race (i.e., start list) for
mountain points (mean 5 27.66, SD 5 41.73) and
sprint points (mean 5 72.65, SD 5 93.14). Given the
differences in the races that comprise the World
Tour, of the 26 races, 12 only had mountain points
available, 11 only had sprint points, and 10 had both
mountain and sprint points available.
2
We thank one of our anonymous reviewers for suggestions that led to the development of this additional
hypothesis through the review process.
443
Race characteristics. The characteristics of each
race were indexed based on their contribution toward the overall points in their respective categories
(mountain, sprint, and general) for the entire season.
Mountain, sprinting, and general characteristics index the importance of the race to the season’s respective points total using the sum of mountain,
sprinting, or general classification points for the race
out of the total mountain, sprinting, or general classification points across the 26 races. The characteristics were calculated using data from Pro Cycling
Stats (2015), which is a premier cycling website that
extensively follows professional cycling and indexes
the associated statistics on riders, races, and teams.
Greater point values represent a greater emphasis
on either type of characteristic.
Previous competencies. In line with Wolfson and
Mathieu (2018), we indexed generic and specific
competencies of each cyclist as the sum of the total
points earned in each of the three categories (general
classification, mountain, and sprint) during the
prior two seasons, 2013 and 2014, leading up to the
2015 season. General competence, mountain competence, and sprint competence were indexed as the
sums of their respective points throughout the course
of the previous two seasons. As different races emphasize varying competencies and differ in terms of
their impact on the season, the races each offer different amounts of points in each category. The riders
over the span of 2013 and 2014 had an average of
148.79 general points (SD 5 188.32), 36.22 mountain
points (SD 5 51.65), and 82.02 (SD 5 113.28) sprint
points.
Deployed HCR. Deployed HCR was indexed as the
sum of the general classification, mountain, and
sprint points from 2013 and 2014 accrued by the
individuals deployed for each 2015 race in a start list.
Start lists had an average age of 28.55 years (SD 5
1.71), a height of 1.81 m (SD 5 .03), a weight of 68.96
kg (SD 5 3.54), 1,158.75 general classification points
(SD 5 610.06), 116.76 mountain points (SD 5 98.82),
and 298.67 sprint points (SD 5 168.81).
Deployed STTE. Deployed STTE was indexed as
the number of days individuals in a deployed start
list had ridden together during World Tour races in
the prior two seasons, essentially acting as a network
density measure of STTE. In other words, a score of
one on deployed STTE would represent a single
dyad within the deployed start list as having ridden
one day together over the span of the prior two seasons. Star lists’ STTE ranged from 0 to 1,144 days,
with the average start list having a 329.26 days for
deployed STTE (SD 5 190.42).
444
Academy of Management Journal
Team (HCR stocks) competencies. There were 17
World Teams in the 2015 season. The teams had
between 27 and 33 riders (mean 5 30, SD 5 1.93)
with an average age of 28.71 years (SD 5 .84), a height
of 1.81 m (SD 5 .02), and a weight of 68.86 kg (SD 5
2.07). Teams averaged 6,971.59 general points (SD 5
2,146.87), 637 mountain points (SD 5 326.34), and
1,833.35 sprint points (SD 5 618.46), and they
earned 13.06 million euros (SD 5 3.34) (Sportune,
2015) in salaries.
Analysis Overview
We employ a cross-classified mixed-model research
design. Start list composition and performance constituted within-team, repeated-measures of the predictor and criterion (i.e., Level 1, n 5 442) cross-nested
in 17 teams (Level 2A) across 26 races (Level 2B). Because not all races awarded both mountain points
and sprint points, analyses with mountain points
and sprint points have level 1 sample sizes of 204
and 187, respectively. Overall effect size estimates
are tenuous in multilevel models, so we report
pseudo effect size estimates (i.e., ;R2 [see Kreft & de
Leeuw, 1998]). We employ p , .05 as our significance
level throughout and have z-scored our variables at
their respective levels of analysis to roughly equate
scales of measurement.
Given our cross-classified design, we used a multistage model building approach to test our hypotheses (Raudenbush, Bryk, & Congdon, 2004). We
feature two sets of analyses, one using deployed HCR
as the criterion and the second using the two performances as criteria. For each analysis, we first fit a
baseline, or “null,” model to determine the percentage of criterion variance residing between start lists,
between races, and between teams. Second, we include temporal effects. Third, we include cross-level
team characteristics. Fourth, when predicting the
deployment of HCR, we next introduce race characteristics, and then we include two-way interactions
between race characteristics and team characteristics in a fifth step. For the fourth and fifth steps of
models predicting the performance of deployed
HCRs, we first introduce start list characteristics
and then two-way interactions between race
characteristics and start list characteristics, respectively. Finally, in a sixth step, we introduce
three-way interactions between race characteristics, start list characteristics, and STTE. We present
the results in a model-building fashion which reveals the relative contributions of different variables and interactions.
April
RESULTS
Table 1 reports the descriptive statistics and correlations among all variables in the model. Notably
for cross-level correlations, Level 2A and 2B variables were assigned to all Level 1 variables, and
therefore, standard errors are biased and significance
levels should be interpreted with caution. However,
hypotheses were tested utilizing multilevel modeling
and have unbiased standard errors. The first five rows
of Table 1 represent team-level variables at Level 2,
rows six through eight represent race characteristics at
Level 2B, and rows nine through 14 represent start list
characteristics and performance at Level 1. Notably,
there are no correlations among the Level 2A and 2B
variables as they represent the two classifications in
the cross-classified model. Team and race scores were
assigned down to performance episodes as constants
for these correlations, and as such, they would not
be expected to covary (Wolfson & Mathieu, 2018).
We first present the results for deployed mountain
HCRs, followed by deployed sprint HCRs, then
deployed mountain HCR performance, and then finally, deployed sprint HCR performance.
Hypothesis Tests
Models predicting deployed HCR. The baseline
model predicting deployed mountain HCR indicated
that there was significant variance attributable to
each level, with 45.08% of the total variance residing
at the deployment or start list level, 27.51% residing
between teams, and 27.41% residing between races.
To account for performance trajectories over time,
we regressed deployed mountain HCR onto a linear
trend (b 5 .04, standard error [SE] 5 .11, n.s.), which
was not significant and accounted for only .14% of
criterion variance. In Table 2, Model 2, we added
team-level HCR stocks to the equation in terms of
team salary (b 5 .00, SE 5 .08, n.s.) and general
(b 5 2.35, SE 5 .15, p , .05), mountain (b 5 .70, SE 5
.13, p , .001), and sprint (b 5 .08, SE 5 .10, n.s.)
competencies, which jointly explained an additional
22.08% of the variance. In Model 3, we added general (b 5 .13, SE 5 .31, n.s.), mountain (b 5 .20, SE 5
.21, n.s.), and sprint (b 5 2.11, SE 5 .32, n.s.) race
characteristics, which jointly predicted an additional 4.75% of the total variance in deployed
mountain HCR. In Model 4, we introduced team HCR
stocks by race characteristics interactions for general
(b 5 .06, SE 5 .06, n.s.), mountain (Hypothesis 1a:
b 5 .10, SE 5 .05, p , .05), and sprint (b 5 2.02, SE 5
.04, n.s.), which provided support for Hypothesis 1a
—
—
—
—
.83***
.71**
.44
2
—
—
—
—
.51*
.30
3
—
—
—
—
.54*
4
—
—
—
—
5
—
.87***
.94***
6
—
.86***
7
—
8
—
.75***
.69***
.43***
.23**
.34***
9
—
.47***
.25***
.26***
.22**
10
—
.44***
.11
.39***
11
—
.24**
.44***
12
—
.42***
13
Notes: GC 5 general competencies. MTN 5 mountain. SPR 5 sprint. STTE 5 shared team task-specific experience.
a
Level 2A HCR stock scores. These were assigned to start lists and correlated at the deployment and episodic performance level; therefore, standard errors are biased and
significance levels should be interpreted cautiously.
b
Level 2B race characteristics. As with Level 2A HCR stock scores, standard errors are biased and significance levels should be interpreted cautiously.
c
There are no correlations in rows five through seven among the first seven variables as the two levels of cross-nesting do not have meaningful correlations.
*p , .05
**p , .01
***p , .001
Level 1—Deployment and Episodic Performance (GC, n 5 442; MTN, n 5 204; SPR, n 5 187; Shared MTN & SPR, n 5 170)
9. GC HCRs
1,158.75
610.06
.14**
.46***
.38***
.30***
.17***
.24***
.21***
.23***
10. MTN HCRs
116.76
98.82
.20***
.43***
.53***
.26***
.14**
.23***
.25***
.21***
11. SPR HCRs
298.67
168.81
.15**
.34***
.25***
.45***
.24***
.24***
.19***
.24***
12. STTE
329.26
190.42
–.10*
–.05
–.04
.14**
.45***
.25***
.21***
.24**
13. MTN Points
27.66
41.73
.07
.28***
.32***
.15*
.12
.45***
.53***
.45***
14. SPR Points
72.65
93.14
.05
.11
.07
.18*
.12
.73***
.67***
.76***
—c
—
—
1
Level 2B—Race Characteristics (n 5 26)
.04
.02
6. GCb
.04
.06
7. MTNb
.04
.08
8. SPRb
SD
—
.44
.49*
.45
.06
Mean
Level 2A—HCR Stocks (n 5 17)
13.06
3.34
1. Salarya
6,971.59
2,146.87
2. GCa
637.06
326.34
3. MTNa
1,833.35
618.46
4. SPRa
3,134.06
1,004.52
5. STTEa
Variable
TABLE 1
Means, Standard Deviations, and Correlations Among Variables
2021
Wolfson and Mathieu
445
446
Academy of Management Journal
April
TABLE 2
Hierarchical Linear Model Results Predicting Deployed HCRs
Deployed Mountain HCRs as Outcome
Deployed Sprint HCRs as Outcome
Predictor
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Intercept
Linear
–.00 (.17)
.04 (.11)
–.00 (.12)
.04 (.11)
–.01 (.11)
.05 (.10)
–.01 (.11)
.05 (.10)
.00 (.14)
–.02 (.08)
.00 (.08)
–.02 (.08)
–.00 (.06)
–.06 (.07)
–.00 (.06)
–.06 (.07)
Team HCR Stocks
Salary
—
General
—
Mountain
—
Sprint
—
.00 (.08)
–.35 (.15)*
.70 (.13)***
.08 (.10)
.00 (.08)
–.35 (.15)*
.70 (.13)***
.08 (.10)
.00 (.08)
–.35 (.15)*
.69 (.13)***
.08 (.10)
—
—
—
—
–.08 (.05)
.04 (.09)
.02 (.08)
.46 (.06)***
–.08 (.05)
.04 (.09)
.02 (.08)
.46 (.06)***
–.08 (.05)
.04 (.09)
.02 (.07)
.46 (.06)***
Race Characteristics
General
—
Mountain
—
Sprint
—
—
—
—
.13 (.31)
.20 (.21)
–.11 (.32)
.13 (.31)
.20 (.21)
–.11 (.32)
—
—
—
—
—
—
.17 (.21)
–.14 (.14)
.20 (.21)
.17 (.21)
–.14 (.14)
.20 (.21)
—
—
—
26.97
4.75
.06 (.06)
.10 (.05)*
–.02 (.04)
28.73
1.76***
—
—
—
0.04
0.04
—
—
—
21.84
21.80***
Team HCR Stocks by Race Characteristics
General
—
—
Mountain
—
—
Sprint
—
—
0.14
22.22
;R2 (%)
0.14
22.08***
;ΔR2 (%)
—
—
—
27.95
6.11**
.03 (.07)
–.04 (.06)
.15 (.05)**
30.26
2.31**
Notes: n 5 442 HCR deployments for 17 teams across 26 races.
*p , .05
**p , .01
***p , .001
and explained a total of 28.73% of variance in
deployed mountain HCRs.
The baseline model predicting deployed sprint
HCRs revealed significant variance attributable to each
level, with 66.00% of the total variance residing at the
deployment or start list level, 20.58% between teams,
and 13.42% between races. To account for performance trajectories over time, we regressed deployed
sprint HCR onto a linear trend (b 5 2.02, SE 5 .08,
n.s.), which was not significant and accounted for only
.04% of the criterion variance. In Table 2, Model 6, we
introduced team-level HCR stocks to the equation to
test the effects of team salary (b 5 2.08, SE 5 .05, n.s.),
and general (b 5 .04, SE 5 .09, n.s.), mountain (b 5 .02,
SE 5 .08, n.s.), and sprint (b 5 .46, SE 5 .06, p , .001)
competencies, which collectively explained an additional 21.80% of criterion variance. In Model 7, we
added general (b 5 .17, SE 5 .21, n.s.), mountain
(b 5 2.14, SE 5 .14, n.s.) and sprint (b 5 .20, SE 5 .21,
n.s.) race characteristics which accounted for an additional 6.11% of the total variance in deployed sprint
HCRs. In Model 4 we introduced team HCR stocks by
race characteristics interactions for general (b 5 .03,
SE 5 .07, n.s.), mountain (b 5 2.04, SE 5 .06, n.s.), and
sprint (Hypothesis 1b: b 5 .15, SE 5 .05, p , .01), the
last of which provided support for Hypothesis 1b and
explained a total of 30.26% of criterion variance.
We plotted the interactions predicting deployed
HCRs by depicting their relations at the mean value
and plus or minus one standard deviation of the
team-level moderators. Additionally, we conducted
regions of significance tests (Preacher, Curran, &
Bauer, 2006) to illustrate the nature of the interaction
effect. The shaded areas in Figures 2 through 7 represent the observed range of our data, whereas the
red dashed and green cross-hatched subareas represent the regions of significance below and above the
mean levels of the moderator respectively, within
the observed range of our data. In other words, these
reveal the ranges within which our cross-level interactions were significant. Regions of significance
are a more precise depiction of the levels of the
moderator for which the linear predictor–criterion
relationships are significant as “there is nothing sacred about one standard deviation above or below
the mean of the moderator, and the combination
of the complete range of . . . values are what give
rise to the interactive effect” (Gardner, Harris, Li,
Kirkman, & Mathieu, 2017: 627).
As shown in Figure 2, race-level mountain characteristics exhibited an accentuating effect on the
positive relationship between team-level mountain
HCR stocks and deployed mountain HCRs and
are significant throughout the entire range of the
2021
Wolfson and Mathieu
FIGURE 2
Team Mountain HCR Stocks by Race Mountain
Characteristics as Related to Deployed Mountain
HCRs
FIGURE 3
Team Sprint HCR Stocks by Race Sprint
Characteristics as Related to Deployed Sprint HCRs
3 SD
3 SD
447
Race Mountain Characteristics
–1 SD
Race Sprint Characteristics
–1 SD
Mean
+1 SD
Deployed Mountain HCRs
Deployed Sprint HCRs
Mean
+1 SD
–1 SD
–1 SD
Low
Low
Team Mountain HCR Stocks
High
Notes: Team-level mountain HCR stocks by race mountain
characteristics as related to deployed mountain HCRs. Shaded
area in the figure represents the range of the observed sample
values. Red dashed and green cross-hatched subareas represent
the region of significance below and above the mean, respectively,
with a 5 .05.
distribution of race-level mountain characteristics,
consistent with Hypothesis 1a. In other words, teams
with greater accumulated mountain HCR stocks deploy greater mountain HCRs, particularly when accounting for the mountainous nature of the race.
Similarly, as shown in Figure 3, race-level sprint
characteristics exhibited an accentuating effect on
the positive relationship between team-level sprint
HCR stocks and deployed sprint HCRs, and the relationship was significant throughout the entire
range of the distribution of race-level sprint characteristics, consistent with Hypothesis 1b. Otherwise
stated, teams with greater sprint HCR stocks deploy
greater sprint HCRs, particularly when accounting
for the sprinting nature of the race.
Models predicting deployed HCR performance.
The baseline model predicting deployed HCR
mountain performance indicated that 68.40% of the
total variance resided between deployed HCR,
9.81% resided between teams, and the remaining
21.79% resided between races. Given that the composition of deployed HCRs varies from race to race,
the high deployment-level (Level 1) variance is
Team Sprint HCR Stocks
High
Notes: Team-level sprint HCR stocks by race sprint characteristics as related to deployed sprint HCRs. Shaded area in the figure
represents the range of the observed sample values. Green crosshatched subarea represents the region of significance, with a 5 .05.
understandable as it accounts for the variance of both
the time-varying deployed HCRs as well as the timevarying race characteristics. As shown in Table 3,
Model 1, regressing mountain performance onto the
linear trend (b 5 8.75, SE 5 6.52, n.s.) did not exhibit
a significant linear trend but accounted for 3.77% of
the variance in mountain points. Next, in Model 2,
we introduced deployed HCR characteristics for
general (b 5 2.98, SE 5 3.83, n.s.), mountain (b 5
8.37, SE 5 3.20, p , .01), sprint (b 5 25.42, SE 5
3.21, n.s.), and STTE (b 5 21.29, SE 5 2.93, n.s.), as
well as race characteristics for general (b 5 22.71,
SE 5 7.57, n.s.), mountain (b 5 22.46, SE 5 4.62, p ,
.001), and sprint (b 5 3.40, SE 5 8.69, n.s.), which
accounted for an additional 30.07% of criterion
variance. In Model 3, we introduced deployed HCR
by race characteristics interactions for general (b 5
4.48, SE 5 2.43, n.s.), mountain (Hypothesis 2a: b 5
8.16, SE 5 2.69, p , .01), and sprint (b 5 26.28,
SE 5 2.18, p , .01), which provided support for
Hypothesis 2a and accounted for an additional 6.28%
of the variance in mountain points. In Model 4, we
added the remaining two-way interactions for HCRs
by STTE for general (b 5 25.87, SE 5 3.59, n.s.),
mountain (b 5 2.93, SE 5 2.46, n.s.), and sprint (b 5
3.92, SE 5 3.00, n.s.) and race by STTE interactions
22.71 (7.57)
22.46 (4.62)***
3.40 (8.69)
2.98 (3.83)
8.37 (3.20)**
25.42 (3.21)
21.29 (2.93)
27.52 (5.74)***
21.11 (2.90)
Model 2
—
—
—
Race Characteristics by STTE
General
—
Mountain
—
Sprint
—
*p , .05
**p , .01
***p , .001
Deployed HCRs by Race Characteristics by STTE
General
—
—
Mountain
—
—
Sprint
—
—
3.77
33.84
;R2 (%)
3.77
30.07***
;ΔR2 (%)
—
—
—
Deployed HCRs by STTE
General
—
Mountain
—
Sprint
—
Deployed HCRs by Race Characteristics
General
—
—
Mountain
—
—
Sprint
—
—
Race Characteristics
General
—
Mountain
—
Sprint
—
—
—
—
—
30.03 (7.00)***
8.75 (6.52)
Intercept
Linear
Deployed HCRs
General
Mountain
Sprint
STTE
Model 1
Predictors
—
—
—
40.12
6.28***
—
—
—
—
—
—
4.48 (2.43)
8.16 (2.69)**
26.28 (2.18)**
1.76 (7.52)
23.00 (4.43)***
25.58 (9.14)
22.46 (3.99)
9.11 (3.07)**
23.59 (3.11)
.20 (2.82)
24.53 (5.71)***
–.38 (2.77)
Model 3
3.22 (2.09)
4.90 (2.03)*
24.72 (2.41)
44.08
3.96*
23.32 (7.08)
2.72 (4.67)
4.08 (7.88)
25.87 (3.59)
2.93 (2.46)
3.92 (3.00)
21.11 (4.05)
4.51 (3.13)
22.70 (4.35)
21.66 (7.66)
20.93 (4.39)***
–.30 (9.51)
–.40 (4.04)
8.61 (3.12)**
23.02 (3.42)
.95 (5.07)
25.51 (6.09)***
–.85 (2.69)
Model 4
Deployed Mountain HCR Performance as Outcome (n 5 204)
—
—
—
10.45
10.45
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
76.03 (19.54)***
30.86 (19.86)
Model 5
—
—
—
63.48
53.03***
—
—
—
—
—
—
—
—
—
.97 (13.22)
25.16 (9.73)
66.10 (16.33)***
–.96 (6.28)
.21 (5.20)
19.03 (5.19)***
3.83 (4.83)
71.17 (9.68)***
4.82 (4.82)
Model 6
—
—
—
70.79
7.31***
—
—
—
—
—
—
29.14 (4.11)*
25.57 (4.94)
23.82 (3.54)***
12.19 (12.83)
28.20 (9.00)
47.76 (17.78)**
6.72 (6.46)
–.11 (4.66)
14.31 (4.69)**
4.40 (4.35)
62.26 (9.69)***
5.50 (4.31)
Model 7
–.71 (3.25)
23.93 (3.51)
8.33 (3.84)*
76.39
5.60***
44.60 (11.61)***
238.79 (8.39)***
214.80 (13.98)
2.75 (5.76)
26.71 (3.85)
4.20 (4.52)
211.84 (86.24)
3.27 (5.56)
10.22 (6.77)
11.18 (13.06)
25.52 (8.56)
46.61 (19.22)*
7.97 (5.30)
.42 (4.53)
9.26 (5.26)
224.38 (8.92)**
61.05 (10.20)***
3.94 (3.99)
Model 8
Deployed Sprint HCR Performance as Outcome (n 5 187)
TABLE 3
Hierarchical Linear Model Results Predicting Episodic Deployed HCR Performance
448
Academy of Management Journal
April
Wolfson and Mathieu
for general (b 5 23.32, SE 5 7.08, n.s.), mountain
(b 5 2.72, SE 5 4.67, n.s.), and sprint (b 5 4.08, SE 5
7.88, n.s.), as well as our three-way interactions
between deployed HCRs, race characteristics, and
STTE for general (b 5 3.22, SE 5 2.09, n.s.),
mountain (Hypothesis 3a: b 5 4.90, SE 5 2.03, p ,
.05), and sprint (b 5 24.72, SE 5 2.41, n.s.), which
provided support for Hypothesis 3a and jointly
accounted for an additional 3.96% or a total of
44.08% of the variance in deployed HCR mountain
performance.
The baseline model predicting deployed HCR
sprint performance indicated that 43.84% of the total
variance resided between deployed HCRs, .68% resided between teams, and the remaining 55.48%
resided between races. As seen in Table 3, Model 5,
regressing performance onto the linear trend (b 5
30.86, SE 5 19.86, n.s.) did not exhibit a significant
effect, but it accounted for 10.45% of the variance in
sprint points. Next, in Model 6, we introduced
deployed HCR characteristics for general (b 5 2.96,
SE 5 6.28, n.s.), mountain (b 5 .21, SE 5 5.20, n.s.),
sprint (b 5 19.03, SE 5 5.19, p , .001), and STTE (b 5
3.83, SE 5 4.83, n.s.), as well as general (b 5 .97, SE 5
13.22, n.s.), mountain (b 5 25.16, SE 5 9.73, n.s.),
and sprint (b 5 66.10, SE 5 16.33, p , .001) race
characteristics, which accounted for an additional
53.03% of the variance in sprint performance. In
Model 7, we introduced deployed HCRs by race
characteristics interactions for general (b 5 29.14,
SE 5 4.11, p , .05), mountain (b 5 25.57, SE 5 4.94,
n.s.), and sprint (Hypothesis 2b: b 5 23.82, SE 5 3.54,
p , .001), which provided support for Hypothesis 2b
and jointly accounted for an additional 7.31% of the
variance in sprint performance. In Model 8, we introduced the remaining two-way interactions for
deployed HCRs by STTE for general (b 5 2.75, SE 5
5.76, n.s.), mountain (b 5 26.71, SE 5 3.85, n.s.), and
sprint (b 5 4.20, SE 5 4.52, n.s.) and race by STTE
interactions for general (b 5 44.60, SE 5 11.61, p ,
.001), mountain (b 5 238.79, SE 5 8.39, p , .001),
and sprint (b 5 214.80, SE 5 13.98, n.s.), as well as
our three-way interactions between deployed HCRs,
race characteristics, and STTE for general (b 5 2.71,
SE 5 3.25, n.s.), mountain (b 5 23.93, SE 5 3.51,
n.s.), and sprint (Hypothesis 3b: b 5 8.33, SE 5 3.84,
p , .05), which provided support for Hypothesis 3b
and jointly accounted for an additional 5.60%, or a
total of 76.39%, of the variance in deployed HCR
sprint performance.
We again plotted the regions of significance for the
significant hypothesized interactions noted above in
Figures 4 through 7. Consistent with Hypothesis 2a,
449
FIGURE 4
Race Mountain Characteristics by Deployed
Mountain HCRs as Related to Mountain Points
9 SD
Race Mountain Characteristics
–1 SD
Mean
+1 SD
Mountain Points
2021
–2 SD
Low
Deployed Mountain HCRs
High
Notes: Deployed mountain HCRs by race mountain characteristics as related to mountain points. Shaded area in the figure
represents the range of the observed sample values. Red dashed
and green cross-hatched subareas represent the region of significance below and above the mean, respectively, with a 5 .05.
Figure 4 shows that mountain race characteristics
exhibited a significant accentuating effect on the
positive relationship between deployed mountain
HCRs and mountain performance in the top 42% of
mountain races. In other words, aligning deployed
mountain HCRs with the mountain characteristics of
the race led to increased performance. To provide
meaningful context for an interpretation of these results, the difference between being a capable start
list—in terms of deployed mountain HCRs in a high
level mountain race as compared to a mean level
mountain race—was roughly equivalent to the difference between being the first-place and third-place
team in our 17-team sample of Grand Tour races.
Consistent with Hypothesis 3a, Figure 5A shows that
at relatively high levels of STTE, the positive effect of
aligning deployed mountain HCRs with race mountain characteristics is accentuated. Notably, as
shown in Figure 5B, this relationship is absent at
relatively low levels of STTE. In fact, this positive
effect was only present at high levels of STTE and
significant for the top 33% of mountain races. In
other words, aligning competences with situational
characteristics in mountain races had a generally
positive effect that was contingent on the level of
450
Academy of Management Journal
April
FIGURE 5
Deployed Mountain HCRs by Race Mountain Characteristics at High and Low Levels of STTE
as Related to Mountain Points
A
9 SD
High Shared Team Task-Specific
Experience
B
Race Mountain Characteristics
9 SD
–1 SD
Low Shared Team Task-Specific
Experience
Race Mountain Characteristics
–1 SD
Mean
+1 SD
Mountain Points
Mountain Points
Mean
+1 SD
–2 SD
–2 SD
Low
Deployed Mountain HCRs
High
Low
Deployed Mountain HCRs
High
Notes: Deployed mountain HCRs by race mountain characteristics as related to deployed HCR mountain points at high and low levels of
shared team task-specific experience. Shaded area in the figure represents the range of the observed sample values. Green cross-hatched subarea
represents the region of significance above the mean, with a 5 .05.
STTE, without which the benefits of aligned deployment were absent.
Consistent with Hypothesis 2b, Figure 6 shows
that sprint race characteristics exhibited a significant accentuating effect on the positive relationship
between deployed sprint HCRs and sprint performance in the top 28% of sprint races. In other
words, aligning deployed sprint HCRs with the
sprint characteristics of the race led to increased
performance. Again, to place this in context, a capable start list—in terms of deployed sprint HCRs in
a high level sprint race as compared to a mean level
sprint race—was roughly equivalent to the difference between being the first-place and fifth-place
team in our 17-team sample of Grand Tour races.
Finally, consistent with Hypothesis 3b, Figure 7
shows that the presence of greater STTE further
accentuates the positive effect of aligning deployed
sprint HCRs with race sprint characteristics. As
shown in Figure 7A, the positive effect was present
at relatively high levels of STTE and significant for
the top 27% of sprint races. Meanwhile, as shown in
Figure 7B, at relatively low STTE levels, aligning
deployed sprint HCR with race sprint characteristics provided no significant benefits. In other
words, aligning riders’ competences with situational characteristics only had a positive effect in
the top quarter or so of sprint races, and at relatively
high levels of STTE.3
3
The structure of our hypothesized model depicted in
Figure 1 implies cross-level moderated mediation relationships. In order to explore the downstream effects of
HCR stocks in terms of their impact on performance
through the deployment of HCR, we conducted 2-1-1
moderated mediation analyses using a Monte Carlo procedure with 20,000 bootstraps developed by Selig and
Preacher (2008). In these analyses, the relationship between team mountain (sprint) HCR stocks and deployed
mountain (sprint) HCR represents the X→M relationship
that is moderated by race mountain (sprint) characteristics;
whereas the relationship between deployed mountain
(sprint) HCR and mountain (sprint) performance represents that M→Y relationship that is also moderated by race
(sprint) characteristics. The moderated boot-strapped indirect effects were significant (i.e., excluded zero) within
the upper five out of 12 (i.e., 42%) of the mountain races
and within the upper five out of 11 (i.e., 45%) of the sprint
races. In other words, teams were able to deploy their HCR
by aligning member and race characteristics in such a way
as to gain competitive advantage in upwards of 42% of the
races. These findings are consistent with the implied mediation effects associated with our hypotheses. Further
details are available from the authors.
2021
Wolfson and Mathieu
FIGURE 6
Race Sprint Characteristics by Deployed Sprint
HCR as Related to Sprint Points
Race Sprint Characteristics
–1 SD
5.5 SD
Simple Points
Mean
+1 SD
–4.5 SD
Low
Deployed Sprint HCRs
High
Notes: Deployed sprint HCRs by race sprint characteristics as
related to sprint points. Shaded area in the figure represents the
range of the observed sample values. Red dashed and green crosshatched subareas represent the region of significance below and
above the mean, respectively, with a 5 .05.
DISCUSSION
Many organizations focus on acquiring the best talent with the assumption that greater levels of HCR
451
stocks will lead to greater performance (Ployhart et al.,
2014). However, the underlying mechanisms through
which accrued HCR stocks generate enhanced performance have been obfuscated (Bell et al., 2018; Ployhart
& Chen, 2019). We set out to demonstrate how a focus
on HCR deployment, particularly in concert with situational demands and SCRs among deployed HCRs,
can lead to greater team performance. We heeded
Wolfson and Mathieu’s (2018) call to explore the value
of HCRC over time, and advanced HCRC theory by
developing propositions aimed at uncovering the value
of SCR and HCR deployment. We were particularly
interested in the role of aligning deployed HCRs with
situational characteristics as the underlying mechanism
that explains how HCR stocks can drive temporally
varying performance. We also demonstrated the value of
SCRs among individuals, given the interdependent nature of teamwork whereby SCRs further accentuated the
value of deployed HCRs. By integrating theory from the
HC and teams literatures, we highlighted how deployed
HCRs interact with situational demands to predict team
performance over time. In so doing, we illustrate that
competitive advantage comes not only from accruing
greater HCR stocks, but also from deploying them more
effectively over time and varying situations.
Our analyses of deployed start lists’ performances
across a season of cycling races were consistent with
our hypotheses. We found that race-level characteristics moderated the positive relationship between
FIGURE 7
Deployed Sprint HCRs by Race Sprint Characteristics at High and Low Levels of STTE as Related to Sprint Points
A
High Shared Team Task-Specific
Experience
Race Sprint Characteristics
Low Shared Team Task-Specific
Experience
5.5 SD
Mean
+1 SD
Simple Points
–1 SD
Mean
+1 SD
–4.5 SD
–4.5 SD
Low
Race Sprint Characteristics
–1 SD
Simple Points
5.5 SD
B
Deployed Sprint HCRs
High
Low
Deployed Sprint HCRs
High
Notes: Deployed sprint HCRs by race sprint characteristics as related to deployed HCR sprint points at high and low levels of shared team
task-specific experience. Shaded area in the figure represents the range of the observed sample values. Green cross-hatched subarea represents
the region of significance above the mean, with a 5 .05.
452
Academy of Management Journal
corresponding team-level HCR stocks and deployed
HCRs such that the relationship strengthened to the
extent that corresponding race characteristics increased, and this effect was true for both mountains
and sprints. Furthermore, we found that corresponding race characteristics moderated the positive
relationship between deployed HCRs and performance as corresponding race characteristics increased. Interestingly, although not anticipated, we
obtained a negative interaction between sprint race
characteristics and deployed sprint competencies,
as related to mountain team performance. In other
words, when the competencies were misaligned with
focal demands, team performance suffered. Finally,
we found that deployed STTE further accentuated the
positive relationship between deployed HCRs and
corresponding race characteristics as related to performance. In fact, the significant benefits of aligning
deployed HCRs with race characteristics in predicting performance were contingent on the presence
of high STTE. The significant interactions were
present at high levels of STTE and non-significant at
low levels.
Although coordination manifested differently in
mountain versus sprint races, in both instances there
were benefits from STTE. Mountain races and climbs
during a given race require coordination in terms of
knowing when to chase down an attack or when to drop
back to the supply car. In these instances, STTE is critical
from a strategic as well as a situational awareness
standpoint. During downhill sprints riders go upwards
of 122 km per hour (Clarke, 2016), and must stay extremely close to one another to gain aerodynamic
advantages (Martin, Davidson, & Pardyjak, 2007). In
these instances, miscoordination of even a few centimeters, could literally mean the difference between
victory and a potentially life-threatening crash. Even
on flat surfaces, teammates orchestrate a finely tuned
sequence of rotating the lead rider to maximize
overall team speed while minimally taxing featured
riders. As seen in Figures 5 and 7, when the situation
is extreme, the relevant competencies are high, and
so is the STTE, the full benefits of HCR and SCR
complementarities are realized.
Contributions
Our work makes three primary theoretical contributions. First, we extend HCRC theory by incorporating
HCR deployment and by incorporating the construct of
SCRs. Furthermore, we address Ployhart and Chen’s
(2019) call to feature teams as a mechanism whereby
HCRs are deployed. By integrating team composition
April
(e.g., Mathieu et al., 2014) insights into the HCR literature (e.g., Nyberg & Wright, 2015) we proposed that
decisions concerning the composition of deployed
team members in different circumstances is a prime
mechanism by which HCR stocks manifest in performance. To date, the micro-level research on teams and
the macro-level strategic HRM literatures have largely
evolved independently, with the two exploring similar
areas, but often to the exclusion of one another (Bell
et al., 2018). We aimed to bridge the gaps in the literature by reconceptualizing team composition in terms of
deployed HCRs (Wolfson & Mathieu, 2018). We sought
to shine a light into the black box as to how accrued
HCR stocks ultimately generate competitive advantage.
Certainly, having amassed greater HC stocks places an
organization at a potential competitive advantage, but
how those resources are deployed is key to translating
them into HCRs. Our results revealed that organizations
can maximize their performance by comprising their
deployed teams to possess complementary competencies given their task environment. Thus, competitive
advantage accrues not simply from the accumulation of
valuable HCR stocks but rather from successfully
deploying HCRs. As such, knowledge of competency
by situational complementarities is maximized to the
extent that one accrues, and ultimately deploys, HCR
stocks accordingly.
Second, we elaborate on HCRC theory by demonstrating that SCRs represent a critical boundary
condition in terms of aligning HCRs with situational
characteristics for improved performance. As such,
we illustrated that given the interdependent nature
of teamwork, micro-processes—namely, deployed
members’ STTE—accentuate the value of deployed
HCR complementarities. Third, we conducted a first
test of the extended HCRC theory, particularly regarding deployment and the three-way interactions
of race characteristics with deployed HCRs and
STTE. By adopting a temporal scope that incorporates repeated HCR deployments in varying situations, we highlight the unique complexities and
opportunities faced by organizations seeking to
maximize the benefits of their HCR deployments
while simultaneously needing to consider their
members’ fatigue, burnout, and developmental
needs. Building on work by Harrison et al. (2003)
and Luciano et al. (2018), we incorporate the role of
SCRs (i.e., STTE) as a further accentuating force
that improves the utility of HCRs. By bringing a
SCR focus to HCRC theory, we contribute to the literature by showing that interrelationships among
deployed members have utility beyond that of their
HCRs alone.
2021
Wolfson and Mathieu
Limitations and Generalizability
Although our work makes several contributions to
the HCR literature and the teams literature, it has
several potential limitations. First, although sampling professional cycling across the 2015 World
Tour allowed us to explore cross-level complementarities and deployments among objectively measured HCRs with dynamic task characteristics,
STTE, and objective outcome measures, our use of a
professional sports context may potentially limit the
generalizability of our findings. In the context of
professional cycling, although the situational demands are changing both within and between races,
the challenges are relatively well known ahead of
time, and the HCR competencies are also well
known. Additionally, we indexed competencies using past performance. Although we acknowledge
that it would be ideal to be able to measure riders’
competencies using more direct strength and endurance tests, those were not available to us. Nevertheless, many HR information systems maintain
archives of members’ KSAOs, educational backgrounds, etc., which could prove to be incredibly
fruitful in future research endeavors. Given these
constraints, it should be noted that our findings may
be limited to contexts in which competencies and
situations are well defined. As such, further research
is needed to determine whether our findings would
hold for less transparent settings where situations
and individual competences are not as finely
indexed and their alignment demands cannot be as
easily anticipated.
Second, we were able to index SCRs by utilizing
shared experience among cyclists (STTE), both as
teammates on their current team as well as teammates in the past on other teams. Although our results highlighted the value of these STTEs in
bolstering performance among deployed HCRs,
STTE is still a proxy for coordination. Moreover, our
consideration of STTE does not account for potential
differences in practice and other nonrace efforts
among teammates. Future research should consider
capturing actual coordination processes and exploring their potential to mediate how these STTE
and other shared past experiences influence deployed
HCR–performance relationships. Third, we measured
relatively few predictors in terms of generic and specific HCRs as well as STTE. Future research should
consider additional predictors beyond prior competencies and STTE as there may be many other individual (e.g., members’ personalities) and team-focused
(e.g., leadership processes, training interventions)
453
variables that can drive team effectiveness (Mathieu
et al., 2014, 2019).
Future Research, Theory, and Application
Although we demonstrated the performance value
of considering HCR deployment, there are a slew of
other factors to consider, especially when taking a
temporal perspective. For example, future research
should explore additional costs and benefits associated with HCR deployments. For instance, might
some employees be underutilized between assignments while awaiting their next optimal deployment? What are the team developmental costs
associated with changing team members for different
circumstances, especially given the role that STTE
played in our results (Arrow & Crosson, 2003)? Furthermore, in contexts where leaders play a particularly pivotal role, future research should consider
whether teams are better served by pursuing superstars or enhancing the team’s overall competencies.
Ultimately, this may be a question of whether the
strategy is to maximize the deployment of an individual, a team, or both.
While not feasible in the current context, one might
also consider whether the use of multiple team
memberships provides a mechanism to more flexibly
deploy employees to maximize HCR complementarities (Rapp & Mathieu, 2019). Deploying individuals
for multiple teams may open a particularly fruitful
avenue of research, because multiple team memberships could better utilize combinations of HCRs, but
may also introduce a number of potential other issues
such as generating role stress. In a related vein, the
question of whether to deploy members across teams
to uniformly maximize collective performance versus
pursuing a strategy designed to maximize the performance of a handful of teams while balancing the
performance of others, for example, raises the conversation to a more strategic level. In any case, deployment decisions clearly play a role in maximizing
the potential benefits of HCRs.
There may be other benefits of HCR deployments
beyond performance. For example, might deployments be used to place individuals in situations where
they can learn by doing while avoiding costly mistakes (Katz-Navon, Naveh, & Stern, 2009) or otherwise
engage in informal field-based learning to develop
through observation, feedback, and experimentation
(Wolfson, Mathieu, Tannenbaum, & Maynard, 2019;
Wolfson, Tannenbaum, Mathieu, & Maynard, 2018)?
Perhaps HCR deployments can also be used to expose
employees to different aspects of the larger work
454
Academy of Management Journal
environment and build their HCRs and SCRs such that
they may be deployed in more varied and flexible
fashions in the future. All these options reify the fact
that HC acquisition and deployments should be done
in a strategic fashion to develop HCRs and SCRs that
will be valuable for competitive advantage.
Although we chronicle HCR deployments and
their outcomes over the course of an entire cycling
season, future research should consider employing
an even longer temporal scope. The strategic horizon
for developing HCRs is far greater in most industries
and also needs to consider the ebbs and flows of
collective turnover and thereby the quality of leavers
and replacements (Hausknecht & Holwerda, 2013;
Hausknecht & Trevor, 2011; Nyberg & Ployhart,
2013; Reilly, Nyberg, Maltarich, & Weller, 2014).
And while dynamic membership change offers the
opportunity for more creative team composition
reconfigurations (Bell & Outland, 2017; Wolfson &
Mathieu, 2017), some of those benefits may be offset
by undermining members’ STTE (Luciano et al.,
2018). Our findings suggest that the collective turnover literature would benefit from not only considering the relative knowledge and skills of leavers
and replacements but also the reconfiguration options and STTE of dynamic team memberships
(Arrow & Crosson, 2003; Mathieu et al., 2014).
Although greater levels of STTE among deployed
HCRs proved to be advantageous, not all patterns of
STTE may be equivalent, and consequently, future
research should explore patterns of STTE in addition
to density measures. In fact, there are often domestiques who will be deployed alongside particularly
elite riders from race to race, so the STTE could take on
a similar form in more important races. Furthermore,
future research should consider exploring the drivers
of STTE deployment, both in terms of the antecedents
and curvilinear relationships (Luciano et al., 2018).
Continuing to deploy a dominant grouping of individuals may lead to greater success in the interim, but
in order to exploit the downstream benefits associated
with STTE, leaders may need to sacrifice present performance to attain a sustained competitive advantage
via deployments that allow for development.
CONCLUSION
The purpose of this study was to investigate the
underlying dynamics of the deployment of HCRs as
the mechanism explaining how HCR stocks translate
to performance. Our results revealed that higherlevel HCR stocks indirectly lead to deployed HCR
performance through deployed HCR alignment with
April
situational characteristics. Furthermore, these effects were bolstered to the extent that deployed HCRs
had greater SCRs. Overall, these findings answer
the call to expand the HCRs space to focus on the
deployment of HCRs by focusing on multiple deployment decisions across varying situations over a
year-long period of time. We advanced HCRC theory
(Wolfson & Mathieu, 2018) by incorporating the role
of deployed HCRs and their SCRs. In closing, we
need continued research on not just why the accrual
of HCR stocks matters, but on how we can deploy our
HCRs more effectively while taking situations and
SCRs into consideration in order to acquire and leverage HCRs strategically and make the most of the
HCRs we already have.
REFERENCES
Adegbesan, J. A. 2009. On the origins of competitive advantage: Strategic factor markets and heterogeneous
resource complementarity. Academy of Management Review, 34: 463–475.
Adler, P. S., & Kwon, S. W. 2002. Social capital: Prospects
for a new construct. Academy of Management Review, 27: 17–40.
Ancona, D. G., & Chong, C. L. 1992. Entrainment: Cycles
and synergy in organizational behavior. Working paper 3443-92: 1–32. MIT Sloan School of Management.
Arrow, H., & Crosson, S. 2003. Musical chairs: Membership
dynamics in self-organized group formation. Small
Group Research, 34: 523–556.
Bacharach, S. B. 1989. Organizational theories: Some criteria for evaluation. Academy of Management Review, 14: 496–515.
Barney, J. B. 1991. Firm resources and sustained competitive advantage. Journal of Management, 17: 99–120.
Barney, J. B., & Wright, P. M. 1998. On becoming a strategic
partner: The role of human resources in gaining competitive advantage. Human Resource Management, 37: 31–46.
Barney, J., & Felin, T. 2013. What are microfoundations?
Academy of Management Perspectives, 27: 120–137.
Bell, S. T., & Outland, N. 2017. Team composition over time.
Research on Managing Groups and Teams. In E. Salas
(Ed.), Team dynamics over time: Advances in psychological theory, methods and practice, vol. 18: 3–27.
Bingley, U.K.: Emerald Group Publishing Limited.
Bell, S. T., Brown, S. G., & Weiss, J. A. 2018. A conceptual
framework for leveraging team composition decisions
to build human capital. Human Resource Management Review, 28: 450–463.
Belliveau, M. A., O’Reilly, C. A., III, & Wade, J. B. 1996.
Social capital at the top: Effects of social similarity and
2021
Wolfson and Mathieu
455
status on CEO compensation. Academy of Management Journal, 39: 1568–1593.
expert-advice-and-inspiration/training/teamwork-andtactics-in-pro-cycling-exlained.html.
Brymer, R. A., & Hitt, M. A. 2019. Agonistic relations, social
capital, and (dis) complementarity in the emergence
of human capital resources. In A. J. Nyberg & T. P.
Moliterno (Eds.), Handbook of research on strategic
human capital resources: 245–256. Cheltenham,
U.K.: Edward Elgar Publishing.
Cycling Weekly. 2015. Cycling weekly. Retrieved from
http://www.cyclingweekly.co.uk.
Burt, R. S. 1997. A note on social capital and network
content. Social networks, 19: 355–373.
Campbell, B. A., Coff, R., & Kryscynski, D. 2012. Rethinking
sustained competitive advantage from human capital.
Academy of Management Review, 37: 376–395.
Chang, S., Gong, Y., Way, S. A., & Jia, L. 2013. Flexibilityoriented HRM systems, absorptive capacity, and
market responsiveness and firm innovativeness.
Journal of Management, 39: 1924–1951.
Clarke, S. 2015. Eight examples of great teamwork at the Tour
de France. Retrieved from http://www.cyclingweekly.
co.uk/news/racing/tour-de-france/eight-examples-ofgreat-teamwork-at-thetour-de-france-181392.
Clarke, S. 2016. Only 122kph? Some riders went even
faster on the Tour de France stage nine. Retrieved from
https://www.cyclingweekly.com/news/racing/tourde-france/122kph-jeremy-roy-went-even-faster-tour-defrance-stage-nine-258371.
Coleman, J. S. 1988. Social capital in the creation of human
capital. American Journal of Sociology, 94: S95–S120.
Collins, C. J., & Clark, K. D. 2003. Strategic human resource
management practices, top management team social
networks, and firm performance: The role of human
resource practices in creating organizational competitive advantage. Academy of Management Journal,
46: 740–751.
Condé, M. 2015. Tour de France 2015—Overall preview
& favorites. Retrieved from http://www.c-cycling.com/
sites/Tour_de_France_2015/overall_preview.html.
Cooke, N. J., Gorman, J. C., Duran, J. L., & Taylor, A. R. 2007.
Team cognition in experienced command-and-control
teams. Journal of Experimental Psychology: Applied,
13: 146–157.
Cossins, P. 2015. Tour de France 2015 preview. Retrieved
from http://www.cyclingnews.com/races/tour-defrance-2015/preview/.
Dierickx, I., & Cool, K. 1989. Asset stock accumulation and
sustainability of competitive advantage. Management Science, 35: 1504–1511.
Fletcher, P. 2015. Team Sky: 2015 report card. http://www.
cyclingnews.com/features/team-sky-2015-report-card/.
Fukuyama, F. 1995. Trust: The social virtues and the
creation of prosperity. New York, NY: Free Press.
Gardner, R. G., Harris, T. B., Li, N., Kirkman, B. L., &
Mathieu, J. E. 2017. Understanding “it depends” in
organizational research: A theory-based taxonomy,
review, and future research agenda concerning interactive and quadratic relationships. Organizational
Research Methods, 20: 610–638.
Harrison, D. A., Mohammed, S., McGrath, J. E., Florey,
A. T., & Vanderstoep, S. W. 2003. Time matters in team
performance: Effects of member familiarity, entrainment, and task discontinuity on speed and quality.
Personnel Psychology, 56: 633–669.
Hatch, N. W., & Dyer, J. H. 2004. Human capital and learning
as a source of sustainable competitive advantage. Strategic Management Journal, 25: 1155–1178.
Hausknecht, J. P., & Holwerda, J. A. 2013. When does employee turnover matter? Dynamic member configurations, productive capacity, and collective performance.
Organization Science, 24: 210–225.
Hausknecht, J. P., & Trevor, C. O. 2011. Collective turnover
at the group, unit, and organizational levels: Evidence,
issues, and implications. Journal of Management, 37:
352–388.
Hollenbeck, J. R., & Jamieson, B. B. 2015. Human capital,
social capital, and social network analysis: Implications for strategic human resource management.
Academy of Management Perspectives, 29: 370–
385.
Huckman, R. S., & Staats, B. R. 2011. Fluid tasks and
fluid teams: The impact of diversity in experience
and team familiarity on team performance. Manufacturing & Service Operations Management, 13: 310–
328.
Humphrey, S. E., & Aime, F. 2014. Team microdynamics:
Toward an organizing approach to teamwork. Academy of Management Annals, 8: 443–503.
Crocker, A. 2019. Social capital and human capital coemergence: A socialized view of emergent human
capital resources. In A. J. Nyberg & T. P. Moliterno
(Eds.), Handbook of research on strategic human
capital resources: 215–235. Cheltenham, U.K.:
Edward Elgar Publishing.
Johns, G. 2006. The essential impact of context on organizational behavior. Academy of Management Review,
31: 386–408.
Cycle Surgery. 2015. Teamwork and tactics in pro cycling
explained. Retrieved from https://www.cyclesurgery. com/
Katz-Navon, T., Naveh, E., & Stern, Z. 2009. Active learning: When is more better? The case of resident
456
Academy of Management Journal
physicians’ medical errors. Journal of Applied Psychology, 94: 1200–1209.
Kor, Y. Y. 2006. Direct and interaction effects of top management team and board compositions on R&D investment strategy. Strategic Management Journal,
27: 1081–1099.
Kreft, I. G., & de Leeuw, J. 1998. Introducing multilevel
modeling. Atlanta, GA: Sage.
Labianca, G., & Brass, D. J. 2006. Exploring the social ledger: negative relationships and negative asymmetry in
social networks in organizations. Academy of Management Review, 31: 596–614.
Lin, N. 2001. Social capital: A theory of social structure
and action. Cambridge, U.K.: Cambridge University
Press.
Luciano, M. M., Bartels, A. L., D’Innocenzo, L., Maynard,
M. T., & Mathieu, J. E. 2018. Shared team experiences
and team effectiveness: Unpacking the contingent effects of entrained rhythms and task characteristics.
Academy of Management Journal, 61: 1403–1430.
Martin, J. C., Davidson, C. J., & Pardyjak, E. R. 2007. Understanding sprint-cycling performance: The integration of muscle power, resistance, and modeling.
International Journal of Sports Physiology and
Performance, 2: 5–21.
Mathieu, J. E. 2016. The problem with [in] management theory.
Journal of Organizational Behavior, 37: 1132–1141.
Mathieu, J. E., & Chen, G. 2011. The etiology of the multilevel paradigm in management research. Journal of
Management, 37: 610–641.
Mathieu, J. E., Gallagher, P. T., Domingo, M. A., & Klock,
E. A. 2019. Embracing complexity: Reviewing the past
decade of team effectiveness research. Annual Review of Organizational Psychology and Organizational Behavior, 6: 17–46.
Mathieu, J. E., Tannenbaum, S. I., Donsbach, J. S., & Alliger,
G. M. 2014. A review and integration of team composition models: Moving toward a dynamic and temporal framework. Journal of Management, 40: 130–160.
Milgrom, P., & Roberts, J. 1995. Complementarities and fit
strategy, structure, and organizational change in
manufacturing. Journal of Accounting and Economics, 19: 179–208.
April
Asymmetric adaptability: Dynamic team structures as
one-way streets. Academy of Management Journal, 47:
681–695.
Nahapiet, J., & Ghoshal, S. 1998. Social capital, intellectual
capital, and the organizational advantage. Academy
of Management Review, 23: 242–266.
Nyberg, A. J., & Ployhart, R. E. 2013. Context-emergent
turnover (CET) theory: A theory of collective turnover.
Academy of Management Review, 38: 109–131.
Nyberg, A. J., & Wright, P. M. 2015. 50 years of human
capital research: Assessing what we know, exploring
where we go. The Academy of Management Perspectives, 29: 287–295.
Oh, H., Labianca, G., & Chung, M. H. 2006. A multilevel
model of group social capital. Academy of Management Review, 31: 569–582.
Ployhart, R. E., & Chen, G. 2019. The vital role of teams
in the mobilization of strategic human capital resources. In A. J. Nyberg & T. P. Moliterno (Eds.),
Handbook of research on strategic human capital
resources: 354–369. Cheltenham, U.K.: Edward
Elgar Publishing.
Ployhart, R. E., & Moliterno, T. P. 2011. Emergence of the
human capital resource: A multilevel model. Academy of Management Review, 36: 127–150.
Ployhart, R. E., Nyberg, A. J., Reilly, G., & Maltarich, M. A.
2014. Human capital is dead; long live human capital
resources! Journal of Management, 40: 371–398.
Ployhart, R. E., Van Iddekinge, C. H., & MacKenzie, W. I., Jr.
2011. Acquiring and developing human capital in
service contexts: The interconnectedness of human
capital resources. Academy of Management Journal,
54: 353–368.
Ployhart, R. E., Weekley, J. A., & Ramsey, J. 2009. The
consequences of human resource stocks and flows: A
longitudinal examination of unit service orientation
and unit effectiveness. Academy of Management
Journal, 52: 996–1015.
Porter, C. O. L. H., Amber, B., & Wang, E. 2019. Team motivation and goal (mis)alignment: The missing link in
human capital resources research. In A. J. Nyberg &
T. P. Moliterno (Eds.), Handbook of research on
strategic human capital resources: 315–337. Cheltenham, U.K.: Edward Elgar Publishing.
Moliterno, T. P., & Nyberg, A. J. 2019. Strategic human
capital resources: A brief history, construct definition,
and introduction to the handbook of research on
strategic human capital resources. In A. J. Nyberg &
T. P. Moliterno (Eds.), Handbook of research on
strategic human capital resources: 2–14. Cheltenham, U.K.: Edward Elgar Publishing.
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 Educational and Behavioral
Statistics, 31: 437–448.
Moon, H., Hollenbeck, J. R., Humphrey, S. E., Ilgen, D. R.,
West, B., Ellis, A. P. J., & Porter, C. O. L. H. 2004.
Rapp, T. L., & Mathieu, J. E. 2019. Team and individual
influences on members’ identification and performance
Pro Cycling Stats. 2015. Pro cycling stats. Retrieved from
http://www.procyclingstats.com.
2021
Wolfson and Mathieu
per membership in multiple team membership arrangements. Journal of Applied Psychology, 104:
303–320.
457
capital resources research. Academy of Management
Annals, 13: 188–214.
Raudenbush, S. W., Bryk, A. S., & Congdon, R. 2004. HLM 6
for Windows. Lincolnwood, IL: Scientific Software
International, [Computer software].
Wolfson, M. A., & Mathieu, J. E. 2017. Team composition. In
E. Salas, R. Ramon, & J. Passmore (Eds.), The psychology of team working and collaborative processes,
vol. 1: 129–149. Hoboken, NJ: Wiley Blackwell.
Reilly, G., Nyberg, A. J., Maltarich, M., & Weller, I. 2014.
Human capital flows: Using context-emergent turnover
(CET) theory to explore the process by which turnover,
hiring, and job demands affect patient satisfaction.
Academy of Management Journal, 57: 766–790.
Wolfson, M. A., & Mathieu, J. E. 2018. Sprinting to the
finish: Toward a theory of human capital resource
complementarity. Journal of Applied Psychology,
103: 1165–1180.
Rusbult, C. E., & Van Lange, P. A. 2003. Interdependence,
interaction, and relationships. Annual Review of
Psychology, 54: 351–375.
Wolfson, M. A., Mathieu, J. E., Tannenbaum, S. I., &
Maynard, M. T. 2019. Informal field-based learning
and work design. Journal of Applied Psychology,
104: 1283–1295.
Rusbult, C. E., & Van Lange, P. A. 2008. Why we need interdependence theory. Social and Personality Psychology Compass, 2: 2049–2070.
Selig, J. P., & Preacher, K. J. 2008. Monte Carlo method for
assessing mediation: An interactive tool for creating
confidence intervals for indirect effects [Computer
software]. Retrieved from http://quantpsy.org/.
Soda, G., & Furlotti, M. 2017. Bringing tasks back in: An
organizational theory of resource complementarity
and partner selection. Journal of Management, 43:
348–375.
Soltis, S. M., Brass, D. J., & Lepak, D. P. 2018. Social resource management: Integrating social network theory
and human resource management. Academy of
Management Annals, 12: 537–573.
Somaya, D., Williamson, I. O., & Lorinkova, N. 2008.
Gone but not lost: The different performance impacts of employee mobility between cooperators
versus competitors. Academy of Management
Journal, 51: 936–953.
Sportune. 2015. Tour de France 2015: Tous les budgets des
équipes du Tour. Retrieved from http://www.sportune.
fr/sport-business/tour-de-france-2015-tous-les-budgetsdes-equipes-du-tour-116309/2.
Steiner, I. D. 1972. Group process and productivity. New
York, NY: Academic Press.
Weller, I., Hymer, C. B., Nyberg, A. J., & Ebert, J. 2019. How
matching creates value: Cogs and wheels for human
Wolfson, M. A., Tannenbaum, S. I., Mathieu, J. E., &
Maynard, M. T. 2018. A cross-level investigation
of informal field-based learning and performance
improvements. Journal of Applied Psychology, 103:
14–36.
Wright, P. M., & Snell, S. A. 1998. Toward a unifying
framework for exploring fit and flexibility in strategic
human resource management. Academy of Management Review, 23: 756–772.
Mikhail A. Wolfson (Mikhail.Wolfson@uky.edu) is an assistant professor of management in the Gatton College of
Business and Economics at the University of Kentucky. He
received his PhD from the University of Connecticut. His
research focuses on teams, human and social capital resources, and informal learning.
John E. Mathieu (John.Mathieu@uconn.edu) is a Board of
Trustees Distinguished Professor at the University of
Connecticut and holds the Friar Chair in Leadership and
Teams. His interests include models of team and multiteam effectiveness, the development and deployment of
human and social capital resources, and cross-level
models of organizational behavior.
Copyright of Academy of Management Journal is the property of Academy of Management
and its content may not be copied or emailed to multiple sites or posted to a listserv without
the copyright holder's express written permission. However, users may print, download, or
email articles for individual use.