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Better Together: Member Proactivity Is Better for Team
Performance When Aligned with Conscientiousness
Journal:
Academy of Management Discoveries
Manuscript ID
AMD-2021-0208.R2
Manuscript Type:
Revision
Keywords:
Team Composition < Interpersonal & Team Processes, Personality <
Individual Differences, Proaction < Individual Differences, Team
Development & Building < Interpersonal & Team Processes
Abstract:
Proactivity, the tendency to create change in the work environment,
typically improves team performance. This relationship is far from
perfect, however. We explore inconsistencies in the team proactivity
literature to shed light on an important question – when is member
proactivity beneficial or dysfunctional for teams? First, we consider the
composition of member proactivity at the team level and whether a
simple ‘more is better’ heuristic neglects a more complex relationship
linking member proactivity to team coordination and performance.
Second, we explore whether proactivity is better when aligned with
another individual difference focused on the propensity to plan and
coordinate with others (i.e., conscientiousness). In two studies, we
compare traditional additive and configurational compositional
approaches to these two attributes with a new attribute alignment
approach, allowing us to examine the co-occurrence of proactivity and
conscientiousness within some team members relative to others. First,
we find that team member proactivity-conscientiousness alignment (P-C
alignment) predicts the performance of MBA consulting teams better
than the other team composition models we considered. Then, we
replicate this finding in a laboratory simulation, finding that it occurs
because P-C alignment improves team coordination. Our results
demonstrate that member proactivity is most effective for the team
when it aligns with conscientiousness.
Academy of Management Discoveries
Better Together: Member Proactivity Is Better for Team
Performance When Aligned with Conscientiousness
Kyle J. Emich*
University of Delaware
Newark, DE 19716
kemich@udel.edu
Li Lu
West Chester University
West Chester, PA 19383
Amanda Ferguson
Northern Illinois University
DeKalb, IL 60115
Randall Peterson
London Business School
Marylebone, London, UK
Michael McCourt
Intel
2200 Mission College Blvd, Santa Clara, CA, 95054
Sean Martin
University of Virginia
Charlottesville, VA, 22903
Elizabeth McClean
Cornell University
Ithaca, NY, 14853
Col. Todd Woodruff
United States Military Academy
West Point, NY, 10996
*Please direct all correspondence to Dr. Kyle J. Emich
Page 1 of 55 Academy of Management Discoveries
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BETTER TOGETHER: MEMBER PROACTIVITY IS BETTER FOR TEAM
PERFORMANCE WHEN ALIGNED WITH CONSCIENTIOUSNESS
ABSTRACT
Proactivity, the tendency to create change in the work environment, typically improves team
performance. This relationship is far from perfect, however. We explore inconsistencies in the
team proactivity literature to shed light on an important question – when is member proactivity
beneficial or dysfunctional for teams? First, we consider the composition of member proactivity
at the team level and whether a simple ‘more is better’ heuristic neglects a more complex
relationship linking member proactivity to team coordination and performance. Second, we
explore whether proactivity is better when aligned with another individual difference focused on
the propensity to plan and coordinate with others (i.e., conscientiousness). In two studies, we
compare traditional additive and configurational compositional approaches to these two
attributes with a new attribute alignment approach, allowing us to examine the co-occurrence of
proactivity and conscientiousness within some team members relative to others. First, we find
that team member proactivity-conscientiousness alignment (P-C alignment) predicts the
performance of MBA consulting teams better than the other team composition models we
considered. Then, we replicate this finding in a laboratory simulation, finding that it occurs
because P-C alignment improves team coordination. Our results demonstrate that member
proactivity is most effective for the team when it aligns with conscientiousness.
Teams in organizations perform their work in increasingly dynamic and uncertain
contexts (Mathieu, Gallagher, Domingo, & Klock, 2019; Wageman, Gardner, & Mortensen,
2012). Particularly important for team effectiveness, therefore, is the question of how to create
teams that are proactive in anticipating and solving problems, rather than simply being reactive
or responsive only when directed (Harris & Kirkman, 2017). The construct of proactivity has
emerged to explain this distinction, referring to teams and their members who have the tendency
to bring about self-initiated and future-focused change in the work environment (Bateman &
Crant, 1993; Parker, Bindl, & Strauss, 2010; Williams, Parker, & Turner, 2010).
In highly dynamic environments, however, the distinction of initiating versus responding
to change is not enough. Teams must not only initiate change-focused activity, but also
coordinate that activity with few process losses (e.g., Steiner, 1972). After all, simply adding
more future-focused activity in a team is not helpful if it is not coordinated. As Williams et al.,
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(2010) argue, “individuals within a team might behave proactively, such as by introducing new
methods, but unless this effort is coordinated, the team itself might not be proactive” (p. 302).
For that reason, we question the assumption supporting much of the existing literature:
that effective team proactivity is best understood as the simple total of individual members’
proactivity (cf. Zhang, Li, & Gong, 2021). In this paper, we explore alternatives to this ‘more is
better’ approach for considering how the composition of individual team members’ proactivity
affects team performance through the coordination of the team’s resources. Particularly, this
approach neglects the complex and multilevel nature of teams themselves (Mathieu et al., 2019),
resulting in mixed findings regarding the utility of member proactivity. While member
proactivity is generally beneficial for organizational teams (e.g., Lam, Lee, Taylor, & Zhao,
2018; Strauss, Griffin, & Rafferty, 2009; Williams et al., 2010), there are anomalies in that a
significant number of studies have found no relationship between member proactivity and team
performance (e.g., Chiu, Owens, & Tesluk, 2016), while others have found having too many
proactive members does not help team performance (e.g., Zhang et al., 2021).
Intriguingly, recent research considering this complexity finds that the positive effects of
member proactivity on team effectiveness depend on the diversity of proactivity within the team
rather than the mean level (Zhang et al., 2021). These scholars suggest that a complementarity
approach to member proactivity, where highly proactive members define strategy while less
proactive members follow that strategy, may help teams coordinate to enact constructive change.
However, there is a significant and unexplored assumption supporting this complementarity
argument; specifically, that the team members who are more proactive will also engage in the
planning and organization necessary to coordinate with others. This assumption may or may not
be true, and the degree to which it is or is not the case could help account for the differing
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findings considering how proactivity relates to team performance. After all, careful planning
helps individuals to use their proactivity “wisely” in social contexts (Parker et al., 2019). For that
reason, if there is an absence of planning, proactive team members are likely to cause team
coordination failures by engaging in behaviors that are considered inappropriate, ill-timed, and
ineffective (Chan, 2006; Grant & Ashford, 2008).
We decided to explore this assumption considering team members’ tendencies to plan as
well as their proactivity to help us better understand when and why member proactivity benefits
teams. To capture team member tendencies for planning, we turn to conscientiousness, one of the
Big Five traits in the Five Factor Model of personality (Digman, 1990). Conscientiousness
describes individuals who are purposeful, prepared, organized, and deliberate in accomplishing
goals (McCrae & Costa, 2010). Conscientiousness positively predicts job performance and
leadership across a wide variety of contexts (Barrick & Mount, 1991; Judge, Bono, Ilies, &
Gerhardt, 2002), and is positively associated with situational judgment and contextual
performance in individual and team settings (Cabrera & Nguyen, 2001; Chan & Schmitt, 2002;
Morgeson, Reider, & Campion, 2005; Whetzel & McDaniel, 2009). As such, member
conscientiousness should help teams better engage in a comprehensive and coordinated process
of proactivity, which includes both anticipation of future change and planning to coordinate
efforts to implement actions that generate that change (Grant & Ashford, 2008).
The team composition literature contains several plausible conjectures as to how
proactivity and conscientiousness might best come together among team members (e.g., Chan,
1998; Emich, Lu, Ferguson, Peterson, & McCourt, 2022; Mathieu, Tannenbaum, Donsbach, &
Alliger, 2014). We explore the four most widely adopted team composition models here, where
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each model contains distinct theoretical assumptions about the ideal configurations of proactivity
and conscientiousness.
First, we explore the most frequently used model of team composition, the additive
model (Chan, 1998), which assumes that having a large reserve of proactivity and
conscientiousness, usually in the form of respective mean levels, is enough to develop effective
team-level coordination and performance. Second, we explore whether simply having one highly
proactive and one highly conscientious member, in the form of respective maximum values, is
enough for a team to leverage their complementary benefits. Third, we ask whether having
dispersed proactivity and conscientiousness on a team, in the form of respective variances, is
enough for its proactive members to dictate strategy while others follow, improving coordination
and performance. Fourth, we ask if teams coordinate and perform better when proactivity and
conscientiousness coexist within some team members relative to others, creating attribute
alignment within members across the team (Emich et al., 2022). We examine these conjectures
abductively, using a combination of exploratory and confirmatory tests over multiple studies to
select the best explanation from several plausible but competing explanations (e.g., Mantere &
Ketokivi, 2013; Martin, Harrison, Hoopes, Schroeder, & Belmi, 2022; Mueller, 2018; Sætre &
Van de Ven, 2021).
Specifically, to examine the independent and simultaneous effects of proactivity and
conscientiousness on team coordination and performance, we conduct two studies: a field study
of MBA consulting teams and a controlled laboratory simulation. In both, we examine the
theoretical assumptions of each of the four team composition models suggesting how member
proactivity and conscientiousness combine to create effective team-level coordination and
performance. Our findings point to the value of considering proactivity in combination with
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conscientiousness, while recognizing that the configuration of these attributes matters.
Specifically, we find that teams perform better when their members who are more proactive are
also more conscientious, while their less conscientious members are also less proactive, and that
this alignment matters significantly more than the amount of proactivity or conscientiousness
present in a team.
Along the way, we explore different ‘patterns’ of alignment versus unalignment and map
them across team members, providing a new way of visualizing team composition. Additionally,
our second study demonstrates that P-C aligned teams outperform P-C unaligned teams because
they are better at team-level coordination, offering evidence for a key process criterion to
proactivity research (Lu et al,. 2023; Williams et al., 2010; Wu, Parker, Wu, & Lee, 2018; Zhang
et al., 2021). Taken together, we discover that teams where proactivity and conscientiousness
align are more coordinated and perform better than teams where these attributes do not align. We
conclude by discussing how these emergent findings inform scholarly research on team
composition and processes, while also surfacing practically important implications for teams.
PROACTIVITY AND CONSCIENTIOUSNESS AMONG TEAM MEMBERS
We focus on proactivity and conscientiousness as stable individual differences that affect
individual behavior and may vary across the individuals who comprise teams. Proactivity in this
sense, or proactive personality, is defined as “the relatively stable tendency to effect
environmental change” (Bateman & Crant, 1993, p. 103). People high in proactivity scan for
opportunities and show initiative in bringing about change (Bateman & Crant, 1993), which can
include behaviors such as feedback seeking (Ashford & Cummings, 1983), building social
networks (Morrison, 1993; Thompson, 2005), exchanging information (Gong, Cheung, Wang, &
Huang, 2012), innovating, gaining political knowledge, taking career initiative (Seibert, Kraimer,
& Crant, 2001) and championing issues (Dutton & Ashford, 1993; Dutton, Ashford, O'Neill, &
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Lawrence, 2001). Proactivity also extends across multiple contexts, which can include efforts
directed toward an individual’s work unit or team. For example, proactive individuals may
suggest new work methods for their teams, anticipate future problems rather than reacting to
them, or identify opportunities for the team (Williams et al., 2010).
Despite some evidence of a generally positive relationship between proactive personality
and effectiveness for both individuals (Fuller & Marler, 2009) and teams (Lam et al., 2018;
Williams et al., 2010), one concern with proactivity from a team composition standpoint is that
having too many proactive people on a team is likely to result in lack of coordination. This is a
logical outcome when every team member is proactive, as the actions of proactive teammates are
often directed towards implementing different changes (Harris & Kirkman, 2017; Williams et al.,
2010; Zhang et al., 2021). Moreover, proactive team members are likely to pursue these different
directions even in the face of teammate opposition (Bateman & Crant, 1993). As such, recent
work suggests that having some proactive team members and some team members who are not
proactive may constitute an ideal team proactivity composition (Zhang et al., 2021).
Although diversity in proactivity may help teams, coordination problems may persist if
those who are proactive are unable to effectively plan their change initiatives. For example,
proactive individuals are focused on “making things happen”, but this may not always be
beneficial without carefully considering task and strategic elements of the situation (Parker et al.,
2019). When individuals do not consider these situational demands, their proactivity can be
ineffective because it is not pursued realistically (Chan, 2006; Sun & van Emmerik, 2015). As
such, the emphasis on order, dutifulness, and planning associated with conscientiousness
(McCrae & Costa, 2010) may help to orient proactivity to the situational context. It may also
enable the planning and organization necessary to coordinate any anticipated changes with
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teammates, as coordinated action is positively associated with conscientiousness in teams
(Gevers & Peeters, 2009).
Although proactivity and conscientiousness tend to be modestly correlated, they are
conceptually distinct and predict different outcomes (Bateman & Crant, 1993; Crant, 1995;
Major, Turner, & Fletcher, 2006; Neal, Yeo, Koy, & Xiao, 2012). These attributes are similar in
that both proactivity and conscientiousness are characterized by self-directed activity towards
achieving goals (Barrick, Mount, & Strauss, 1993; Parker, Williams, & Turner, 2006). However,
proactivity centers on bringing about change, and involves envisioning possible future events
and outcomes (Grant & Ashford, 2008), whereas conscientiousness centers on bringing
organization and structure to situations generally, which may or may not involve change
(McCrae & Costa, 2010; Neuberg & Newsom, 1993). Indeed, conscientious members that are
not proactive may also limit their teams, especially in dynamic environments, for example if they
are inflexible or narrow-minded when it comes to adopting new team strategies (Bradley, Klotz,
Postlethwaite, & Brown, 2013). Proactivity and conscientiousness may thus complement one
another such that the emphasis on change and foreseeing future opportunities gained from
proactivity provides a compelling focus for the planning and preparation inherent in
conscientiousness.
At the team level, however, it is unclear how member proactivity and conscientiousness
should come together to allow the team to effectively coordinate and thus perform well, as
different theoretical and empirical approaches provide different recommendations. To explore
this, we describe conjectures from four different team composition approaches that could be used
to answer this question, outlining their unique theoretical assumptions and methodological
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operationalizations. Then, we examine how these different configurations of member proactivity
and conscientiousness relate to team coordination and performance across two empirical studies.
DIFFERENT THEORETICAL CONFIGURATIONS OF MEMBER PROACTIVITY
AND CONSCIENTIOUSNESS
Our arguments above articulate the theoretical idea that, to be effective, teams need
members who are both proactive and conscientious. Now, we consider conjectures from several
different team composition models to explore the most effective way to arrange these constructs
within teams – including traditional approaches, which comprise team-level assessments of
attributes, and a new attribute alignment approach to team composition, which integrates
individual-level and team-level assessments of attributes (Emich et al., 2022).
Traditional Team Composition Approaches
Mathieu et al. (2014) explain that team composition models have traditionally focused on
either individual attributes that are aggregated to the team level (i.e., personnel models that
consider individual knowledge, skills, or abilities that would benefit teamwork) or on assessing
patterns or configurations of individual attributes (i.e., team profile models or relative
contribution models: e.g., team diversity or minimum/maximum scores on attributes). The first
approach commonly emphasizes additive models of attributes of interest (e.g., mean levels of
proactivity) whereas the second considers other configurations of attributes of interest (e.g.,
proactivity dispersion or the team maximum score). Both approaches, however, are inherently
variable-centered where each attribute is considered first as a team-level distribution in some
form (Emich et al., 2022).
The most widely applied composition approach is the additive approach suggesting
“more is better” when it comes to desirable knowledge, skills, and abilities, and thus it is more
valuable for a team to contain higher overall levels of desirable attributes (Mathieu et al., 2014).
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From this perspective, proactivity and conscientiousness are both considered desirable attributes
for teams (e.g., Barrick, Stewart, Neubert, & Mount, 1998; Chiu et al., 2016; Hyatt & Ruddy,
1997; Neuman & Wright, 1999; Williams et al., 2010). Thus, this approach assumes that teams
benefit from generally higher levels of proactivity and conscientiousness among team members
rather than low levels of these attributes. And, although magnitudes of these attributes
considered independently might benefit teams, their joint consideration may illuminate their
complementarity, i.e., this approach may theoretically assume that teams with higher amounts of
both conscientiousness and proactivity perform better than teams with high amounts of only one
or the other.
A second well-established team composition approach involves considering patterns of
team member attributes at the team level, such as their variability or the impact of one very high
or very low scoring member, on team processes or outcomes (e.g., Barrick et al., 1998; Emich &
Lu, 2017; Ferguson & Peterson, 2015; Humphrey, Hollenbeck, Meyer, & Ilgen, 2007; Neuman,
Wagner, & Christiansen, 1999). For example, we could examine whether having at least one
person who is proactive will benefit the team because that person fulfills the need to initiate
constructive change and whether having at least one person who is conscientious will benefit the
team because that person fulfills the need to plan and organize activities related to implementing
that change. Alternatively, we could examine relative scores across team members by
considering the dispersion of these attributes. For example, in line with findings from previous
research, complementarity of member proactivity and similarity in conscientiousness may
positively influence team effectiveness (e.g., Barrick et al., 1998; Gevers & Peeters, 2009; Grant,
Gino, & Hofmann, 2011; Zhang et al., 2021). These approaches are consistent with relative
contribution models of team composition (Mathieu et al., 2014) and previous research
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highlighting the tendency for personality traits to correspond to task and process-related roles in
group settings (Barry & Stewart, 1997; Stewart, Fulmer, & Barrick, 2005).
These traditional approaches to team composition carry certain assumptions. First, the
additive approach and the maximum configurational approach suggest that it does not matter
who is conscientious or proactive on the team (or how these attributes are distributed relative to
each other) as long as those attributes are present. Additionally, both assume that “more is
better”, whether all team members contribute to high attribute levels (e.g., via mean levels) or
specific individuals do (e.g., via maximum scores). Configurational approaches using dispersion
do acknowledge the importance of complementarity or similarity among members, and therefore
the potential value of low scores on these attributes. However, like other traditional approaches,
these only consider one attribute across team members at a time (e.g., a team-level dispersion
score on proactivity and a team-level dispersion score on conscientiousness).
Yet, there is reason to believe that it may matter whether multiple attributes occur within
team members in the context of their teammates (e.g., whether team members score high or low
on both proactivity and conscientiousness, relative to the rest of the team). To explore this
possibility, we turn to a new team composition approach that allows us to model multiple
attributes both within and across team members – the attribute alignment approach to team
composition (Emich et al., 2022).
Attribute Alignment Approach to Team Composition
The alignment approach allows us to examine the possibility that the alignment of
proactivity and conscientiousness may be important, such that the members of the team who are
proactive are also conscientious (and the team members who are not conscientious are not
proactive). This approach generally assumes that the expression of individual team member
attributes changes as a function of other within-person attributes, and thus simultaneously
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examining the configuration of member attributes before aggregating them to the team level may
offer additional insight into how multiple compositional attributes influence team processes
(Emich et al., 2022; in press). Specifically, when team members who are proactive are also
conscientious, they may be more likely to propose actions to bring about change and engage in
careful planning with respect to the actions they propose. Alternatively, members who score low
on these attributes may be flexible in accepting the proposed changes and planning initiated by
others, allowing for effective team coordination. Indeed, people who score low on both attributes
should be reliant on others to take initiative (Bateman & Crant, 1993) and be easy-going in
accepting direction from others (McCrae & Costa, 2010).
Instead, if some team members are proactive but not conscientious, while others are
conscientious but not proactive, there may be difficulties in coordinating anticipated changes.
For example, team members who are proactive but not conscientious may drive risk-taking to
bring about change, but a lack of planning and situational judgment may make their proposed
changes ill-advised, poorly timed, or ineffective (Chan, 2006; Grant & Ashford, 2008; Parker et
al., 2019). Similarly, team members who are conscientiousness but not proactive may use their
dutiful planning to improve and implement existing team processes, instead of seeing a different
future-oriented state which may include risk taking, innovating, or rule-breaking to create
necessary change (Miron-Spektor et al., 2022; Morrison, 2006; Robert & Cheung, 2010). Thus,
when high levels of conscientiousness and proactivity exist in different team members (i.e., there
is unalignment of these attributes), team members may work against each other as they focus on
different activities. Because of the potential for conscientiousness to influence the expression of
proactivity within individual team members, considering P-C alignment may help to explain
when team member proactivity is beneficial.
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OVERVIEW OF STUDIES
We explored how these different team composition models of member proactivity and
conscientiousness influence team coordination and performance in two studies. First, we
conducted an exploratory study of MBA teams completing a consulting project in which they
gathered and analyzed data, proposed recommendations, and presented their findings to client
organizations. The ambiguity and uncertainty associated with consulting for real-world clients
provided an ideal context in which to examine the proposed benefits of member proactivity and
conscientiousness for team performance. Second, we conducted a confirmatory study of teams
completing a decision-making simulation in which they exchanged information, balanced
individual and collective goals, and adapted to changing task conditions in a laboratory setting.
This study provided more situational control in terms of team tasks and greater opportunity to
observe team behaviors including coordination as well as team performance.
STUDY 1: MBA CONSULTING TEAMS
Participants and Procedure
Our sample comes from an ongoing data collection effort to examine team composition
and outcomes among MBA students at a graduate business school in the United Kingdom. Our
sample includes 610 individuals assigned to 92 teams of between five and eight members (M =
6.63, SD = .60). Of these, 539 (88%) provided usable data on all measures. Individuals were
assigned to teams by the MBA program office with the intent to maximize diversity in functional
expertise and nationality. They represented 64 countries, led by the United States (103), India
(68), and the United Kingdom (65), and 467 companies across 16 industries. Their average age
was 28.41 years (SD = 2.33), 76% were male, and they had average work experience of 5.55
years (SD = 2.07). Teams were assigned at the start of the students’ first academic term in the
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MBA program and remained intact for one calendar year. Students completed all group project
work across courses in these assigned teams.
The first week of the academic term was devoted to leadership assessment and training,
where students received feedback on a personality inventory and 360-degree feedback ratings
that were collected prior to the start of the MBA program. During this week, they completed
several activities with their newly assigned teams (e.g., a team agreement exercise and a day-
long business simulation). They then completed the first term of academic courses, and in the
second academic term the teams sourced and completed major consulting projects for external
organizations. Team grades on this group project serve as team performance data and were
assigned by the professors of the course, neither of whom were a part of this study, nor were they
aware of our research questions.
Measures
Proactivity. Members of the students’ former organizations provided ratings of
proactivity in the form of 360-degree feedback. In the summer prior to beginning the MBA
program, each student asks members of his or her former organization to provide structured
feedback via a 360-degree survey administered by an outside provider. Through this process,
former peers, supervisors, and subordinates provide feedback, of which four items were
identified as an appropriate measure for proactivity (described below). Although we were unable
to collect self-report ratings of proactivity, ratings of personality attributes by others who are
familiar with the person being rated (e.g., through observations and past experiences) tend to
have high agreement with self-ratings, both generally (Funder & Colvin, 1988) and for
proactivity in particular (Seibert, Crant, & Kraimer, 1999). Five hundred thirty-nine of the 610
team members had usable 360-degree feedback evaluations from 2,931 external raters (a rate of
88%), which represents an average of 5.44 raters per person.
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Four items from the 360-degree feedback survey were used to measure proactivity: “Is
adaptable and responsive to new situations”, “Is able to motivate and energize others”, “Has a
high degree of personal energy”, “and “Enjoys change”. These items were rated on a 1 (Very
ineffective, one of his/her least developed skills. A definite gap in his/her skill set), to 5 (Very
effective, one of his/her most successful skills. He/she acts as a role model for others) scale (α =
.79). These items are consistent with the definition of proactive individuals as those who “scan
for opportunities, show initiative, take action, and persevere until they reach closure by bringing
about change” (Bateman & Crant, 1993, p. 105). However, we further validated these items in
two ways. First, we followed other recent studies to establish content validity (e.g.,
Nurmohamed, 2020) by sharing these four items with six well-published personality scholars for
review. All rated these four items combined as embodying proactive personality more than other
plausibly-related personality constructs including openness, extraversion, self-monitoring, and
narcissism. We also followed procedures by Schaumberg and Flynn (2012) and Jones and Shah
(2016) to show convergent validity with Seibert, Crant, and Kraimer’s (1999) established
measure of proactive personality, which is a shortened version of Bateman and Crant’s (1993)
measure of proactive personality (example items: “Has been a powerful voice for constructive
change”, and “Is always looking for better ways to do things”). We surveyed 143 participants on
Amazon’s Mechanical Turk (73% Male; 60% White, 36% Asian, 4% Other Ethnicity).
Participants were asked to consider a current or recent team leader (e.g., at work, at a recreational
league, or another project) and answer questions about that person. They completed our 360-
feedback measure and Seibert and colleagues’ (1999) measure with their chosen leader in mind,
as well as a series of filler measures. Our four-item measure strongly and positively correlated
with Seibert, Crant and Kraimer’s (1999) proactivity items (r = .80, p < .01). This correlation is
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comparable with those in other recent studies that follow this method to provide convergent
validity (Jones & Shah, 2016; Schaumberg & Flynn, 2012). These procedures gave us
confidence in using the 360-degree feedback ratings on these four items as a reasonable measure
of proactivity (Individual-level: M = 3.95, SD = .37; Team-level: M = 3.93, SD = .15).
Conscientiousness. Prior to beginning the MBA program, students completed the 240-
item NEO-PIR Inventory (McCrae & Costa, 2010), of which 48 items measured
conscientiousness (α = .89) (Chan, 1998). Individual-level: M = 3.66, SD = 0.37; Team-level M
= 3.66, SD = 0.16.
Team performance. We assessed team performance as team grades on the major
consulting project for the core Organizational Behavior course, completed after nine months of
working together. In this project, teams worked with outside organizations to design and
implement a consulting project that would benefit the focal organization. This task required
teams to work interdependently to complete multiple activities associated with the consulting
engagement (e.g., meeting with the client, formulating plans, conducting research and analysis,
and presenting their results and recommendations). Examples of consulting projects include
assessing the performance of local bus service to the school, advising a local start-up how to
establish a culture of motivation in their employees, and working with a moderate-size division
of a large multinational company to understand why employee turnover was so high for mid-
level managers but not for others. The final deliverable was a presentation to the client
organization and a culminating project report, graded by the professor of the course with
feedback from the client organizations – none of whom were involved in or aware of this study.
Grades were assigned out of 50 possible points, which represented one-third of the students’
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overall grades in the course (M = 41.33, SD = 2.85, Range = 34-48). A Shapiro-Wilk test
indicated that these performance scores were normally distributed, W(92) = .98, p = .21.
Control variables. Because teams were not randomly assigned, we included two control
variables that could potentially influence their grades on the consulting project: mean GMAT
score, indicating additive collective cognitive ability, and class year (i.e., dummy coded) since
different faculty members taught the course each year of our study1.
Analytical Approaches
Additive and configurational approaches to conscientiousness-proactivity composition.
Commensurate with the four plausible configurations of team proactivity and conscientiousness,
we aggregated member proactivity and conscientiousness to the team level using team means,
maximum scores, and standard deviations (Chan, 1998; Mathieu et al., 2014; Zhang et al., 2021).
We also consider the interaction terms between team means, standard deviations, and maximums
in our analysis.
Alignment approach to proactivity-conscientiousness composition. We followed Emich
et al. (2022) by calculating the Euclidian distance between proactivity and conscientiousness
vectors (comprising attribute scores across all team members), accounting for team size.
Specifically, we calculated P-C alignment using the equation below, where K indicates the
alignment of two attributes x and z, and d indicates team size. This resultant term is referred to as
a vector norm distance. For more details of this method, see Emich et al. (2022).
1/2
2
1
| |
( , )
d
i i
i
x z
K x z d
1 Note that our results are the same in terms of signs and significance levels with and without these control variables.
In robustness checks, we also ran all models controlling for mean age, gender diversity, and ethnic diversity and our
results were unchanged.
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In this study, proactivity and conscientiousness had equivalent variances as measured, F
= .97, p = .74, so no transformation was needed prior to calculating alignment (Emich et al.,
2022).
Results
Descriptive statistics and correlations between individual and team-level proactivity,
conscientiousness, and performance can be seen in Table 1. We tested the different
compositional approaches to considering these attributes using a series of linear regressions (see
Tables 2 and 3).
---Insert Tables 1 and 2 about here---
First, in Model 1, we considered a traditional additive model by exploring whether a
team’s mean level of proactivity and conscientiousness relate to its performance. Mean
proactivity negatively related to team performance (B = -4.53, SE = 2.00, t = -2.27, p = .03), and
there were no effects of mean conscientiousness (B = 0.86, SE = 1.89, t = .45, p = .65; Model 1).
This indicates that, in general, teams with members who had a greater tendency to make self-
initiated efforts to bring about change performed worse than teams whose members had less of
this tendency. Model 2 shows that the interaction between mean level proactivity and
conscientiousness did not impact team performance (B = 18.24, SE = 11.23, t = 1.63, p = .11),
however the negative effect of mean proactivity remained significant (B = -4.15, SE = 2.00, t = -
2.08, p = .04).
Next, we explored a configurational explanation of team composition by examining
whether having high member proactivity and conscientiousness within the team (but not
necessarily in the same team member), measured using maximum scores, related to team
performance. Model 3 reveals that neither maximum proactivity (B = -1.88, SE = 1.27, t = -1.49,
p = .14) nor maximum conscientious (B = 1.72, SE = 1.46, t = 1.18, p = .24) related to team
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performance. Their interaction also did not relate to team performance (B = -3.58, SE = 5.79, t =
-.62, p = .54; Model 4). Finally, exploring configurations using diversity in these attributes
shows that neither the standard deviation of proactivity (Model 5: B = -1.04, SE = 2.54, t = -.41 p
= .68) nor conscientiousness (Model 5: B = 1.97, SE = 2.43, t = .81, p = .42) nor their interaction
(Model 6: B = 6.85, SE = 19.70, t = .35, p = .73) significantly accounted for team performance.
Models 1-6 also indicate that although the additive model did account for additional variance
over the control model (Δ R2 = .09, p = .049) standard deviations and maximums did not, as the
change in R2 for these models was not significant. Finally, we explored the alignment of these
attributes (i.e., the distance between the vector norms of proactivity and conscientiousness in
each team: P-C alignment) in Model 7. We found that P-C alignment alone negatively relates to
team performance (B = -12.52, SE = 3.98, t = -3.14, p < .01), increasing the predictive capability
of the control model by 10%. The negative coefficient in this model indicates that P-C alignment
is positively related to team performance (where smaller distances indicate greater alignment).
---Insert Table 3 about here---
Models 8-11, displayed in Table 3, test the relative influence of P-C alignment on team
performance over the other approaches tested. Model 8 indicates that the relationship between P-
C alignment and team performance held when controlling for mean proactivity and
conscientiousness and their interaction term – only P-C alignment significantly related to team
performance (B = -10.92, SE = 5.44, t = -2.01, p = .048). Model 9 reveals that P-C alignment
accounted for variance in team performance above maximum proactivity or conscientiousness
and their interaction (B = -12.52, SE = 4.47, t = -2.80, p < .01), while Model 10 shows that this
relationship held while controlling for the standard deviations of these attributes and their
interaction (B = -17.92, SE = 4.57, t = -3.92, p < .01. Finally, Model 11 demonstrates that the
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relationship between P-C alignment and team performance holds when all other team
composition models are considered (B = -18.87, SE = 7.18, t = -2.63, p = .01), explaining 7% of
the variance in team performance above the aggregate effects of these models.
These results reveal that P-C alignment best predicts team performance, and does so
beyond the other approaches considered. Teams in which proactive members are also high in
conscientiousness (and conversely, in which less conscientious members are also less proactive),
performed better than teams in which these attributes were unaligned. That said, an outstanding
question about the alignment of these attributes is how much the effects of alignment are
dependent upon overall magnitudes of proactivity and conscientiousness in teams and/or whether
these effects are being driven by specific individual(s).
To address these questions, we further explored our data in three ways. First, we assessed
whether alignment interacts with mean or maximum levels of these attributes, or their standard
deviations, to influence team performance. Neither the interaction between P-C alignment and
mean proactivity (t = -.48, p = .64), nor the interaction between P-C alignment and maximum
proactivity (t = -1.14, p = .26) significantly influenced team performance. Similarly, neither the
interaction between P-C alignment and mean conscientiousness (t = .70, p = .49), nor the
interaction between P-C alignment and maximum conscientiousness (t = -1.25, p = .14)
significantly influenced team performance. Finally, we considered the interaction of the standard
deviations, finding no effects on performance (P-C alignment and proactivity SD: t = .01, p =
.99; P-C alignment and conscientiousness SD: t = -1.67, p = .10). This indicates that mean and
maximum levels of team proactivity and conscientiousness, and their standard deviations, do not
affect the relationship between P-C alignment and team performance in this sample.
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Second, we assessed whether alignment within a subgroup or single individual within the
team could account for our finding that global P-C alignment helps team performance. We ran a
linear regression model regressing four team subcomponents: 1) conscientiousness of most
proactive member, 2) conscientiousness of least proactive member, 3) P-C alignment of high
proactivity subgroup (as determined by team proactivity mean split), and 4) P-C alignment of
low proactivity subgroup, on team performance. We found that none of these team
subcomponents significantly predicted team performance (1: t = .61, p = .54; 2: t = -1.43, p =
.16; 3: t = -1.79, p = .08; 4: t = -.89, p = .38). Further, despite high covariance, when team P-C
alignment was added as a fifth predictor, only it significantly predicted team performance (t =
2.49, p = .02). These analyses indicate that P-C alignment across the team as a whole seems to
drive its relationship to team performance in our sample.
---Insert Figure 1 about here---
Finally, we graphed the specific P-C alignment patterns of the 10 most aligned and 10
least aligned teams in this sample in Figure 1. This helped us to discover how different patterns
of alignment and unalignment of proactivity and conscientiousness can contribute to similar
levels of team performance. In each graph, we highlight two teams to show how alignment or
unalignment may take on different patterns. The blue lines represent teams close to an ‘ideal’
pattern of alignment or unalignment (e.g., for alignment: team members who have higher
proactivity also have higher conscientiousness and those with lower proactivity have lower
conscientiousness; for unalignment: team members who have higher proactivity have lower
conscientiousness and those with lower proactivity have higher conscientiousness). The red lines
represent teams that may appear different from this ‘ideal’ pattern but are still aligned or
unaligned and perform similarly to ‘ideal’ teams.
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For example, Team 61 (blue line, left panel) exemplifies an ideal pattern of alignment:
the least proactive member (2.98) is also the least conscientious (3.21) and the most proactive
member (4.19) is the most conscientious (4.25), while those members in between these extremes
are moderately proactive and conscientious (team P-C alignment score = .14). This team scored a
43 on their consulting project, putting them above 75% of the teams in our sample. Team 2 (red
line) has a similar P-C alignment score (.15) and team performance score (42) and nearly follows
this ideal pattern, but has an obvious exception in that the least conscientious member has
relatively high proactivity (the second highest on the team). This indicates that, in this case, the
exception to P-C alignment within one team member did not negate the effects of the more
global P-C alignment observed across the team.
The right panel has a similar display of unaligned teams. Team 57’s (blue line) two least
proactive members are the most conscientious, while the three most proactive members are the
least conscientious (team P-C alignment score = .43; 40th percentile for team performance).
Team 50 (red line) indicates a slightly different pattern of unalignment (team P-C alignment
score = .33). The four least conscientious members are reverse ordered in terms of their
proactivity (less conscientious members have higher proactivity), but there is one member who is
high in conscientiousness and proactivity. This team performed slightly better than Team 57
(57th percentile), although it seems the one high P-high C member was not able to overcome the
lack of alignment across the rest of her team. In short, these graphs and examples demonstrate
that our observed P-C alignment effect on performance comes from alignment across the whole
team, rather than the scores of any one or a few individual members.
Discussion
In summary, Study 1 discovers evidence that it is important to consider conscientiousness
in conjunction with proactivity to better understand how to create teams that perform well. It also
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reveals that team-level alignment of these attributes within members considered across the team
is the most predictive of team performance of all the compositional models we considered.
Further, had we only used traditional composition models of team conscientiousness and
proactivity, we would have concluded that having higher mean levels of team proactivity
negatively influences team performance. However, the alignment approach provides greater
insight into this finding, suggesting that it is important to consider proactivity and
conscientiousness simultaneously and as they coexist within team members.
However, while useful for examining our emergent ideas, Study 1 also has several
limitations. First, although the sample of MBA consulting teams has the advantage of showing
that these effects are relevant to real-world teamwork benefitting client organizations, it lacks
control over contextual variables like the nature of the team tasks (i.e., although grading would
have been similar across teams, consulting projects may have differed based on specific client
needs and deliverables). Second, this data collection effort did not include an established
measure of proactive personality. Although we took additional steps to verify the validity of the
proactivity measure we used, we cannot rule out the possibility that our observed effects might
have been different had we used a more traditional measure. Finally, we did not assess
coordination of team member efforts during the consulting projects, limiting our ability to
understand the processes by which P-C alignment affects team performance. We therefore
conducted a more controlled, confirmatory study that addresses each of these limitations in Study
2 and provides greater confidence in the emergent findings.
STUDY 2: LABORATORY TEAMS
Participants and Procedure
Six hundred seventy-five undergraduate business students from an East Coast university
were randomly assigned to 135 five-member teams to participate in Harvard’s Everest
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Leadership and Team Simulation (V2) (Roberto & Edmondson, 2011) for lab credit (51% male,
84% White, Mage = 20.12 years). The Everest Simulation consists of a simulated six-day climb of
Mount Everest that takes approximately 90 minutes to complete. Team members are randomly
assigned to team roles (Environmentalist, Leader, Marathoner, Photographer, and Physician) that
include unique information which they can choose to share with their teammates. Each day (i.e.,
one simulation round), teams must share and analyze information on weather, health, supplies,
and hiking speed to decide whether to move to the next camp or stay at their current location.
They also complete three decision-making challenges: a medical challenge where participants
must discern that the Environmentalist needs an inhaler, a weather challenge where participants
must accurately predict the weather at their next camp, and an oxygen challenge where
participants must decide how many oxygen tanks each member needs for the summit ascent.
After entering the behavioral research laboratory, each participant completed the
proactivity and conscientiousness measures described below. Then, participants were randomly
assigned to teams and simulation roles, and given detailed instructions about the task. Teams
were co-located in the same room, but communicated only through a computer-mediated chat,
which allowed us to track their interactions. Although participants generally find this task
intrinsically engaging (Roberto & Edmondson, 2011), we also awarded $100 cash ($20 per
member) to the two highest performing teams. This increased the importance of performing well
on the task beyond receiving course credit.
Measures
Proactivity. We measured proactivity using Seibert, Crant, and Kraimer’s (1999)
proactive personality measure, which is a shortened version of Bateman and Crant’s (1993)
measure. It has been used extensively in the organizational literature to measure proactive
personality and has shown good internal consistency, test-retest reliability, and discriminant,
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convergent, and criterion validity (Bateman & Crant, 1993; Seibert et al., 1999; Seibert et al.,
2001). To complete this measure, participants rate the extent to which they agree that ten
statements describe them from 1 (Strongly Disagree) to 7 (Strongly Agree) (example items: “I
am constantly on the lookout for new ways to improve my life” and “Wherever I have been, I
have been a powerful force for constructive change”) (α = .89; Individual-level: M = 3.74, SD =
.58; Team-level: M = 3.74, SD = .23).
Conscientiousness. We measured conscientiousness using Goldberg’s (1992)
International Personality Item Pool (IPIP). The IPIP consists of 50 items, ten for each Big Five
personality trait. Subscales have been shown to have good internal consistency and appropriate
convergent and discriminant validity (Lim & Ployhart, 2006). Participants rated the extent to
which a series of statements described them compared to their peers from 1 (Very Inaccurate) to
5 (Very Accurate) (example items: “Am always prepared”, “Pay attention to details”) (α = .80;
Individual-level: M = 3.58, SD = .60; Team-level: M = 3.58, SD = .25).
Team coordination. We used the team chat logs to unobtrusively measure team
coordination during the task. Teams communicated exclusively through the chat logs, so these
logs recorded the entirety of their task interactions. Two research assistants who were blind to
the study purpose read the team chat logs independently and rated each team on four team
coordination survey items taken from Lewis (2003) and Mathieu et al. (2020): “The team worked
together in a well-coordinated fashion”, “The team communicated well with each other”, “The
team smoothly integrated their work efforts” and “The team re-established coordination when
things went wrong” (rated on a 1 (not at all) to 7 (to a very great extent) scale). The research
assistants assessed the first 20 teams independently, then met to resolve any discrepancies and
build a shared understanding of how to consistently assess task interactions in the chat logs
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according to these items. Then, they rated the next 20 team transcripts independently. This
second set showed sufficient agreement (ICCs (2,1) = .75-.83; Shrout & Fleiss, 1979), so the
research assistants went on to rate the remainder of the transcripts independently. Their final
ratings of coordination for all transcripts also showed sufficient agreement: Item 1, ICC (2,1) =
.83, Item 2, ICC (2,1) = .80, Item 3, ICC (2,1) = .82, Item 4, ICC (2,1) = .76, full scale, ICC (2,1)
= .83, Cronbach’s α = .94. For example, Team 63 was rated as having good coordination
(7.00/7.00) as they got input from each member before making team decisions, consistently
checked on each other’s health, and gathered information on each team member after setbacks.
Alternatively, Team 117 was rated as having poor coordination (1.63/7.00) as they barely
communicated over the first two days of the simulation, did not ask for input from each other,
and did not incorporate others’ information when responding back to the group.
Team performance. In the Everest Simulation, team performance is calculated by
considering the percentage of individual and team goals each team achieves. Each team member
has between two and six individual goals, which often overlap. For example, each team member
is tasked with surviving the climb, however only three members are tasked with staying an extra
day at a particular camp and only two of these members may overlap on the same day. In all,
individual goals account for 39 points. Collective goals, which include the team’s ability to
complete each of the three decision-making challenges, count for 20 points plus an additional 15-
42 points depending on simulation-defined factors (e.g., team members’ locations during the
weather decision-making challenge) (see Roberto & Edmondson, 2011). Overall, the percentage
of total possible points earned (0-100%) is a standard comparative measure of team performance
(Pearsall & Venkataramani, 2015; Tost, Gino, & Larrick, 2013). As in Study 1, a Shapiro-Wilk
test indicated that these performance scores were normally distributed, W(135) = .99, p = .13.
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Analytical Approaches
Additive and configurational approaches to proactivity-conscientiousness composition.
As in Study 1, we used the team mean, maximum scores, and standard deviations of proactivity
and conscientiousness, as well as their interaction terms, to examine additive and configurational
team composition models in predicting team performance.
Alignment approach to proactivity-conscientiousness composition. We calculated P-C
alignment as in Study 1. To account for the differences in response scales (5 vs. 7 points), we
linearly transformed the proactivity scale to the conscientiousness scale by multiplying
proactivity scores by . This equated scale variances, F = .93, p = .53, confirming that both
5
7
attributes contributed to the alignment score equally (Emich et al., 2022).
Results
Descriptive statistics and correlations between individual and aggregated proactivity,
conscientiousness, coordination, and performance are presented in Table 4. As in Study 1, we
examined the different team composition models using a series of linear regressions. Results are
presented in Tables 5 and 6.
---Insert Tables 4, 5, and 6 about here---
First, we considered a traditional additive model by exploring whether a team’s mean
level of proactivity and conscientiousness relate to its performance. Results revealed no effects
for mean proactivity (Model 1: B = -6.13, SE = 6.16, t = -.99, p = .32), mean conscientiousness
(Model 1: B = 1.25, SE = 5.66, t = .22, p = .83), or their interaction (Model 2: B = -14.37, SE =
22.36, t = -.64, p = .52). Next, Models 3 and 4 indicated no effects for maximum proactivity
(Model 3: B = 1.13, SE = 4.23, t = .27, p = .79), maximum conscientiousness (Model 3: B =
1.03, SE = 3.68, t = .28, p = .78) or their interaction (Model 4: B = 2.43, SE = 10.16, t = .24, p =
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.81). Finally, Models 5 and 6 show no effects of standard deviation in proactivity (Model 5: B =
7.11, SE = 6.47, t = 1.10, p = .27), standard deviation in conscientiousness (Model 5: B = 1.37,
SE = 6.76, t = .20, p = .84), or their interaction (Model 6: B = 23.65, SE = 32.86, t = .72, p =
.47). Similar to the results of Study 1, these initial analyses indicate that traditional additive and
configurational models of team proactivity and conscientiousness did not directly explain much
variance in team performance.
We examine P-C alignment in Model 7. As in Study 1, P-C alignment across team
members negatively and significantly related to team performance (B = -42.03, SE = 13.12, t = -
3.20, p < .01). Again, this indicates that having smaller distance between proactivity and
conscientiousness (i.e., greater alignment) positively influences team performance. Models 8-10
indicate that this relationship holds even when controlling for additive and configurational
explanations of how these attributes impact team performance. Finally, Model 11 shows that this
relationship holds when considering all composition models simultaneously (B = -71.80, SE =
16.75, t = -4.29, p < .01). Further, it indicates that P-C alignment accounts for 13% of the
variance in team performance above these other explanations.
Next, we assessed whether team coordination, as rated from teams’ communication logs,
helped to explain the relationship between P-C alignment and team performance. Since our
alignment measure exists at the team level, we tested this using Hayes’s PROCESS macro to
conduct a bootstrapped mediation analysis with 5,000 resamples (Hayes, 2017). Results
indicated that P-C alignment led to greater team coordination (B = -3.91, SE = 1.40, t = -2.79, p
< .01), team coordination led to greater team performance (B = 2.35, SE .86, t = 2.73, p < .01),
and the indirect path from P-C alignment to team performance through team coordination was
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significant (95% CI [-22,69, -.98]) (R2 = .15). This indicates that a significant portion of P-C
alignment’s impact on team performance occurs because it allows teams to coordinate better.
Because we used a different team task and measure of proactivity in Study 2, we also
wondered whether the patterns of alignment we discovered in Study 1 would be similar or
different in this sample. As in Study 1, we examined the influence of the interactions between P-
C alignment and mean or maximum proactivity and conscientiousness, or their standard
deviations, on team performance. None were significant. Similarly, the conscientiousness of the
most proactive and least proactive team members, and the P-C alignment of the high and low
proactivity subgroups (as determined by mean split) did not independently influence team
performance2.
---Insert Figure 2 about here---
Next, we graphed the top 10 aligned and unaligned teams in Figure 2. Figure 2 reveals
similar patterns of alignment and unalignment, relative to means and individual team members,
as we observed in Study 1. For example, Team 63 (blue line) has an ‘ideal’ pattern of alignment
– each team member with a higher proactivity also has higher conscientiousness (P-C alignment
score = .12). They scored a 67 in the simulation, which was the second highest score of any
team, and they were rated as a perfect seven out of seven on coordination. The team
communication log revealed consistent information seeking and input, primarily driven by the
second and third most proactive members (who were also the second and third most
conscientious). For example, one of these members consistently checked in regarding future-
oriented actions (e.g., “does everyone wanna go to camp 1 right now?”; “everyone wants to stay
for the day?”). In addition, the least proactive and conscientious member only provided
2 Results are available from first author upon request.
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information when asked (e.g., making only 9% of the comments over the first two days of the
climb, but providing important information after the weather equipment failed on Day 3). Team
20 (red line) shows similar metrics on alignment, performance, and coordination (P-C alignment
score = .12; team performance = 47; team coordination = 5.25); however, the most conscientious
member is less proactive than three others on the team. This again indicates the team nature of
attribute alignment. The two members with higher proactivity and slightly lower (but still high)
conscientiousness helped to coordinate the team by sharing information about team goals (e.g.,
“for my role, I have to stay in camp 1 and 2 an extra day to take photos”) and asking specific
questions about the team members’ health to assess readiness to move forward. Importantly,
during this process members lower in proactivity and conscientiousness did not interrupt, which
allowed the team to effectively coordinate their efforts.
We also see similar trends to Study 1 for the unaligned teams in Figure 2. Team 117 (blue
line) is highly unaligned except for their centrally conscientious member (P-C alignment = .58).
This team had the lowest score of any in our sample and scored in the 11th percentile of
coordination (1.63). The team communication log revealed that the team did not communicate
often, but when they did the conversation was dominated by the two members with high
proactivity and low conscientiousness. This pair dictated the team strategy without consulting
anyone other than each other. The most conscientious member, who was the least proactive,
almost never spoke up. Team 28 (red line) shows similar metrics but with a slightly different
pattern (P-C alignment = .52; team performance = 24th percentile; team coordination = 2.00, 16th
percentile). The team is relatively unaligned except the two most conscientious members are
moderately proactive. Still, the least conscientious member was one of the most proactive people
in our entire sample and the second least conscientious member was also highly proactive. The
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team communication log indicated that proactivity on the part of these non-conscientious
members overwhelmed any effort by the team’s more conscientious members to coordinate. For
example, these team members were eager to take action (e.g., “the weather looks good, so I think
we should go”; “G-O”) without considering important information from others that required
coordination (e.g., that they needed to ration their oxygen canisters to attempt the summit).
Discussion
Study 2 replicates and extends the discovery that P-C alignment is the best predictor of
how member proactivity and conscientiousness can come together to improve team performance.
Teams perform best when they have some members who are proactive and conscientious and
some members who are not. Importantly, we also find that this happens because of the team’s
ability to achieve effective coordination, offering evidence for a key process criterion to team
proactivity research (Lu et al., 2023; Williams et al., 2010; Wu, Parker, Wu, & Lee, 2018; Zhang
et al., 2021). We also note that this study provides initial evidence that highly proactive team
members who are not conscientious may also present a threat to team coordination by pushing
ahead without the information or skills that are needed to effectively do so. As such, we find that
the proactivity of less conscientious members can dampen the participation of other potentially
valuable team members.
GENERAL DISCUSSION
At the outset of this paper, we asked how organizations should create teams that are
proactive, but that also engage in the planning necessary to coordinate that proactivity and
perform well. Taken together, our studies reveal that teams in which member proactivity and
conscientiousness align within team members across the team are more coordinated and perform
better than teams in which these attributes are unaligned. Further, we find evidence that
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considering P-C alignment is more useful in predicting how well a team will coordinate and
perform than other plausible team composition models of these attributes.
Theoretical and Practical Implications of Proactivity-Conscientiousness Alignment Effects
The discovery of the importance of P-C alignment in influencing team coordination and
performance is meaningful for several reasons. First, we provide further insight into the
anomalies in the literature concerning whether and when team member proactivity is beneficial,
detrimental, or does not matter for team performance. We confirm, for example, that it is not
necessary to have high mean levels of proactivity for team performance (e.g., Chiu et al., 2016),
and that a complementarity perspective is warranted (e.g., Zhang et al., 2021). Yet, we also find
that complementarity in proactivity alone is not sufficient. Instead, proactivity should be
considered in light of another individual difference that helps members plan and organize their
activities: conscientiousness. Considering the coexistence of these attributes allowed us to
address how organizations should compose teams that are proactive yet coordinate well, adding
coordination as a key process criterion in the team proactivity literature (Williams et al., 2010;
Wu et al., 2018; Zhang et al., 2021).
Second, our study amplifies discussion of the complexity and multilevel nature of teams
and their members (Mathieu et al., 2019) by revealing the importance of the alignment of
multiple traits within individual team members, specifically proactivity and conscientiousness.
We not only explored several of the most widely used compositional models of these attributes
(e.g., means, maximum scores, standard deviations), but we also employed the alignment
approach to modeling teams as matrices of members and their attributes (Emich et al., 2022). We
discovered that the alignment approach provides the best path to understand how proactivity and
conscientiousness coexisting at the individual-level may be considered across the team, allowing
us to better understand how to create high performing teams. This answers calls to consider the
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complexities of team composition in general (Bell, Brown, Colaneri, & Outland, 2018; Emich &
Lu, 2020; Emich et al., 2022) as well as to consider unique team composition models of
proactivity in particular (Harris & Kirkman, 2017). It also echoes previous literature that
considers the relative importance of team member attributes (e.g., social estimation schemes or
social decision modeling) (Bonner, Sillito, & Baumann, 2007; Davis, 1973; Yetton & Bottger,
1983), but extends it by relating the coexistence of multiple within-person attributes to team
processes and outcomes.
Practically, our findings can help managers appropriately staff teams and consider
interventions aimed at increasing their coordination. For example, Woolley et al. (2008) find that
teams that discuss collaborative planning for a few minutes before completing a task tend to
coordinate better during the task. However, these authors are careful to note that high
performance “requires both task-appropriate expertise and collaborative planning to identify
strategies for optimally using that expertise” (p. 352). We echo this integrative approach.
Interventions aimed to increase coordination should be considered in the context of team
composition. Having a P-C unaligned team meet to discuss strategy may not be effective because
proactive members who are not conscientious could still dominate that discussion with
deleterious effects (e.g., as we observed in our second study). As Bell et al. (2018) summarize:
“in teams, some combinations of people tend to work better together than others. Team
composition research provides insights into why as well as the optimal combinations of team
members. Importantly, the research allows for the evidence-based staffing and management of
teams” (p. 360).
That said, however, managers could also direct effort towards targeted interventions that
align these two facets of personality within individual team members. For example, it may be
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possible to coach highly proactive but less conscientious team members to engage in more
coordination-related activities, or more duty-oriented versus achievement-oriented displays of
conscientiousness (Marinova, Moon, & Kamdar, 2013). Alternatively, highly proactive team
members with lower conscientiousness could be directed to training or other development in
project management or collaborative planning to ensure that, in the context of team and project-
based work, they are able to create systems that maximize the value of their proactivity.
Similarly, our findings suggest managers may do well to encourage and support highly
conscientious yet less proactive team members to engage in more proactive ways of contributing
to the team. For example, managers could actively solicit input from those team members, which
can increase their perceived influence and willingness to engage in prosocial behavior (Martin &
Harrison, 2022; Tangirala & Ramanujam, 2012). In effect, our findings encourage managers to
consider both compositional and processual ways of aligning proactivity and conscientiousness
to enhance team performance.
Future Directions for Further Exploration
Here we offer several other ideas for further exploration, regarding both P-C alignment
and alignment effects in general. First, although we find that P-C alignment improves team
coordination and performance, it may also result in some drawbacks, which we were unable to
observe in our samples. For example, P-C alignment may somehow undermine learning for the
low P-low C members who follow the lead of their high P-high C counterparts instead of
contributing to the team in a more active role. Over the long term, this might result in entrenched
social dynamics which paradoxically may lead to reduced team flexibility. We also wonder
whether P-C alignment might be particularly helpful for achieving coordination in teams acting
in even more dynamic environments than those we reported; for example, in virtual teams,
multiteam systems, or teams that experience frequent membership change (e.g., De Vries,
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Hollenbeck, Davison, Walter, & Van Der Vegt, 2016; Lanaj, Hollenbeck, Ilgen, Barnes, &
Harmon, 2013). P-C alignment may result in more efficient processing when teams face even
greater uncertainty from challenges in team design (e.g., shifting roles).
Additionally, one could combine work on social estimation schemes, specifically social
permutation models (Bonner, 2004; Bonner et al., 2007), with work on attribute alignment to ask
whether all members should contribute to alignment values equally. While we did not find that
the conscientiousness of the most or least proactive member predicted team performance above
global alignment, it is possible that attributes such as expertise, extraversion, and status may
affect whether other member attributes are considered by the group (Baumann & Bonner, 2004;
Bonner, 2004). For example, such attributes could be used to weight the importance of other
attributes to teams (e.g., the proactivity and conscientiousness of the leader is weighted more
than other members), or as separate vectors which may align with other attributes to predict team
behavior (e.g., extraversion is added as a third attribute in addition to proactivity and
conscientiousness. See Emich et al., in press).
Beyond examining future questions related to proactivity and conscientiousness, our
exploration uncovered a number of insights about the nature of attribute alignment more broadly.
First, a theoretically critical question is whether the attributes that align matter; or, can we simply
assume that any positively-valenced attributes that coexist within a subset of team members
relative to others will improve team coordination and performance? While the alignment
approach may be applied to any set of team member traits, thoughts, emotions, or behaviors
(Emich et al, 2022), we believe the attributes under consideration do matter because of the
specific team processes they would be expected to influence. For example, we observed initial
evidence that P-C aligned teams had more effective coordination because when some team
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members had high proactivity and high conscientiousness they anticipated helpful changes and
planned and organized them with respect to their team context, while other team members who
were low on both traits let others initiate changes and were open to their direction. In contrast, P-
C unaligned teams had members who sometimes worked against each other, e.g., proactive
members who were not conscientious tried to direct changes that were ill-planned, and those who
were conscientious did not offer corrections because they let others take the initiative.
However, alignment of other positively-valenced traits may not affect coordination, or
may even affect it negatively. For example, if optimism and openness align such that some team
members are high on openness and optimism, and others are low on openness and optimism,
they may have difficulty coordinating their task strategies (e.g. highly open and optimistic
members may have confidence in adopting innovative task strategies whereas closed and
pessimistic members cling to more established strategies). Similarly, alignment that includes one
or more negatively-valenced traits could result in dysfunctional processes such as conflict (e.g.,
neuroticism-agreeableness alignment increased relationship conflict as reported by Emich et al.,
2022). As such, the theoretical properties of the attributes of interest should drive the cognitive,
affective, and behavioral manifestations of their alignments.
A second critical question is how attribute alignment generally relates to, and may
complement, more traditional ways of aggregating individual attributes to the team level. In our
study we did not find robust effects for traditional composition models using means, maximum
values, or standard deviations of proactivity and conscientiousness, and we did not find that these
traditional measures interacted with P-C alignment to influence team behavior. However, it is
important to consider why this occurred; and in doing so, consider situations in which traditional
approaches may better relate to team processes and outcomes than attribute alignment, or may
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interact with the alignment of team member attributes. To explore how attribute alignment
complements other team compositional models conceptually, we created Figure 3, which maps
theoretical patterns of P-C alignment, unalignment, and misalignment in nine hypothetical teams.
In these graphs, each line represents a team, and each point represents a team member. Teams 1,
2 and 3 are aligned; Teams 4, 5, and 6 are unaligned, and Teams 7, 8, and 9 are misaligned (i.e.,
there is no relationship between member proactivity and conscientiousness). These nine teams
also have different distributional properties of proactivity and conscientiousness in terms of their
respective means, maximum values, and variances.
---Insert Figure 3 about here---
The graphs of these nine hypothetical teams show that it is theoretically plausible for
alignment to explain team processes and outcomes that means, maximum values, or standard
deviations cannot explain (as we found). For instance, Teams 1 and 4, 2 and 5, and 3 and 6 have
the same mean levels and standard deviations of both proactivity and conscientiousness – only
their alignment differs. If alignment matters independently of other distribution properties of
these attributes we would expect the lower numbered teams (e.g., Team 1) to perform better than
their higher-numbered counterparts (e.g., Team 4). We would also expect this effect to become
particularly pronounced when considering teams with greater within-team variance (e.g., Team 2
and Team 5) as the conscientiousness associated with such teams proactive members varies
greatly. Thus, whenever there is moderate to high within-team variance (team lines slope
severely), we would expect C-P alignment to predict team coordination and performance above
mean levels of these attributes. This should also occur when there is little difference between
team means (i.e., low between-team variance – team lines overlap). Indeed, this is what we
observed in our samples (e.g., Figures 1 and 2, which show teams with similar means and
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standard deviations of proactivity and conscientiousness, but different alignments of these
attributes). These comparisons illustrate why we found effects of P-C alignment on team
performance, but did not find effects for means or the interactions between P-C alignment and
means, maximum scores, and standard deviations.
However, the mean level of any given attribute may interact with alignment to predict
how a team behaves. For example, there may be a threshold that exists below which alignment
does not matter. Using P-C alignment as an illustration, if a team does not have any proactivity
and thereby is unable to identify opportunities for change or does not have any conscientiousness
and therefore is completely unable to plan, the fact that these attributes are aligned should not
matter to team performance. In Figure 3, Team 3 may represent such a ‘below threshold’ case,
where P-C alignment may not be relevant because the team has such low levels of proactivity
and conscientiousness overall. If we had observed a range of teams including those like Teams 1,
3, 4, and 6 in our sample, we likely would have found a significant interaction between mean
levels of these attributes and their alignment on team performance, or a simple additive mean
effect. In addition, there may also be cases in which, as the levels of a given attribute increase,
the importance of its concurrent attributes also increases. This may also pertain to variance – as
variances in particular attributes increase, their alignment becomes more important – or other
distribution properties such as minimum or maximum levels.
These speculations raise a broader point regarding the role of alignment in curvilinear
team effects. Many sophisticated papers are tackling questions of curvilinearity (e.g. De Dreu,
2006), because too much of a good thing can turn into a bad thing (or a not as good thing) a lot
of the time. Alignment may be one way to approach these theoretical puzzles as it provides a
sense of closeness between attributes across the team, so it not only assesses whether attributes
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exist at high levels within some team members, but also whether they exist at low levels within
others. As we found, considering teams as matrices of their members may provide more insight
into complex problems than simply recommending that teams have more “good things”.
Moreover, this can extend from team composition problems to literatures investigating team
member roles (Mathieu et al., 2015), leadership emergence in organizational contexts (Martin et
al., 2022), and strategic governance (Hambrick, Misangyi, & Park, 2015). In each of these
literatures, scholars have described the tradeoffs inherent in examples like sharing versus
specializing in roles or team leadership, or initiating change versus maintaining the status quo in
strategic decisions and governance. The attribute alignment framework could help explore how
such tradeoffs are made.
Conclusion
In closing, this paper presents a new way to think about the composition of team member
proactivity and how it relates to team coordination and performance. It also reminds us that
proactivity, in the absence of the planning and organization necessary for coordination, (i.e.,
conscientiousness), may not be the positive force it is often argued to be. Indeed, via both
exploratory and confirmatory studies in different contexts, we demonstrate the key role of
conscientiousness in unlocking the full potential of member proactivity in teams. We also
explore new ways of theorizing about the role of individual attributes in teams, particularly their
alignment, as well as how those attributes relate to collective outcomes. We believe this to be an
exciting new direction for the broader literature on team composition and performance.
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Table 1. Means, standard deviations, and correlations among Study 1 variables
Variable
Mean
SD
1
2
3
4
5
6
7
8
9
10
1. Class Year Dummy
---
---
-----
.09
-.13
-.17
.11
-.21*
.30**
-.07
.16
.03
2. GMAT Score
677.66
42.76
.03
-----
-.13
.06
-.04
.07
.08
.06
-.09
.16
3. Proactivity
3.95
0.37
-.07
<.01
-----
-.02
.55**
-.10
-.22*
-.13
.34**
-.25*
4. Conscientiousness
3.66
0.37
-.06
-.11*
-----
-.06
.62**
-.10
-.24*
-.47**
.06
5. Max Proactivity
4.41
0.24
-----
-.13
.58**
.-18
.41**
-.17
6. Max Conscientiousness
4.11
0.21
-----
-.15
.49**
-.17
.14
7. SD Proactivity
0.36
0.12
-----
-.08
.24*
-.03
8. SD Conscientiousness
0.35
0.12
-----
.36**
.10
9. P-C Vector Norm Distance
0.24
0.07
-----
-.32**
10. Performance
41.30
2.85
-----
aVariables below the diagonal are at the individual level; variables above the diagonal are at the group level. Descriptive statistics are at the individual level for
variables 2-4 and the team level for variables 5-10. *p < .05 ** p < .01.
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Table 2. Study 1: The Impact of the Alignment of Member Proactivity and Conscientiousness on Team Performance
Variables
Null Model
Model 1:
Additive
Model with
Mean C,P
Model 2:
Additive
Model with
Mean
Interaction of
C,P
Model 3:
Max. Model
of P,C
Model 4:
Max. Model
of P,C with
Interaction
Model 5:
Variance
Model of P,C
Model 6:
Variance
Model of P,C
with
Interaction
Model 7:
Alignment of
P,C
Intercept
17.96 (15.91)
37.73 (19.46)
36.32 (19.30)
21.84 (17.73)
21.09 (17.84)
18.07 (16.02)
18.97 (16.30)
25.66 (15.37)
Data Year
.07 (.60)
-0.05 (.60)
0.01 (.59)
0.33 (.61)
0.37 (.61)
.18 (.63)
.20 (.63)
.38 (.58)
Mean GMAT Score
.03 (.02)
0.03 (.02)
0.03 (.02)
0.03 (.02)
0.03 (.02)
.03 (.02)
.03 (.02)
0.03 (.02)
Team Proactivity Mean
-4.53 (2.00)*
-4.15 (2.00)*
Team Conscientiousness Mean
0.86 (1.89)
0.97 (1.88)
Team Proactivity Mean x Team
Conscientiousness Mean
18.24 (11.23)
Team Proactivity Max
-1.88 (1.27)
-2.00 (1.28)
Team Conscientiousness Max
1.72 (1.46)
1.91 (1.49)
Team Proactivity Max x Team
Conscientiousness Max
-3.58 (5.79)
Team Proactivity SD
-1.04 (2.54)
-3.24 (6.82)
Team Conscientiousness SD
1.97 (2.43)
-.43 (7.34)
Team Proactivity SD x Team
Conscientiousness SD
6.85 (19.70)
Team P-C Alignment
-12.52
(3.98)**
F
1.10
1.93
2.10
1.57
1.32
0.76
0.63
4.10**
R2
0.02
0.08
0.11
0.07
0.08
0.03
0.04
0.12
∆R
2
0.02
0.06
0.08*
0.04
0.06
0.01
0.02
.10**
*p < .05 ** p < .01. DV = Team Performance. P = Proactivity. C = Conscientiousness.
Page 47 of 55 Academy of Management Discoveries
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Table 3. Study 1: Comparison of P-C Alignment to Other Team Composition Approaches
Variables
Null Model
Model 8:
Comparison to
Additive
Approach
Model 9:
Comparison to
Max Approach
Model 10:
Comparison to
Variance
Approach
Model 11: Full
Model with All
Predictors
Intercept
17.96 (15.91)
41.69 (19.16)*
22.10 (17.17)
27.57 (15.25)
36.89 (19.46)
Data Year
.07 (.60)
0.19 (.59)
0.61 (.60)
.49 (.59)
.56 (.63)
Mean GMAT Score
.03 (.02)
0.03 (.02)
0.03 (.02)
.02 (.02)
.02 (.02)
Team Proactivity Mean
-2.57 (2.11)
-3.18 (3.88)
Team Conscientiousness Mean
-1.31 (2.17)
-2.76 (4.13)
Team Proactivity Mean x Team
Conscientiousness Mean
6.83 (12.41)
1.95 (13.86)
Team Proactivity Max
-0.65 (1.33)
2.17 (2.86)
Team Conscientiousness Max
1.68 (1.44)
1.33 (3.50)
Team Proactivity Max x Team
Conscientiousness Max
-7.15 (5.72)
-7.56 (6.38)
Team Proactivity SD
6.12 (6.75)
-.73 (8.87)
Team Conscientiousness SD
11.20 (7.41)
6.15 (9.51)
Team Proactivity SD x Team
Conscientiousness SD
-14.47 (19.03)
-4.99 (21.26)
Team P-C Alignment
-10.92 (5.44)*
-12.52 (4.47)**
-17.92 (4.57)**
-18.87 (7.18)**
F
1.10
2.48
2.49
3.17**
1.77
R2
0.02
0.15
0.15
0.18
0.21
∆R
2
0.02
.04*
.08**
.14**
.07**
*p < .05 ** p < .01. DV = Team Performance. P = Proactivity. C = Conscientiousness.
Page 48 of 55Academy of Management Discoveries
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Table 4. Means, standard deviations, and correlations among Study 2 variables
Variable
Mean
SD
1
2
3
4
5
6
7
8
1. Proactivity
3.74
0.58
-----
.30**
.58**
.33**
-.10
.12
.24**
-0.08
2. Conscientiousness
3.58
0.60
.29**
-----
.10
.56**
-.15
-.07
-.22*
-.01
3. Max Proactivity
4.40
0.33
-----
0.24**
.63**
.25**
0.38**
0.03
4. Max Conscientiousness
4.28
0.38
-----
-.02
.67**
.19*
0.03
5. SD Proactivity
0.56
0.21
-----
.16
.29**
.10
6. SD Conscientiousness
0.57
0.20
-----
.45**
.03
7. P-C Vector Norm Distance
0.31
0.10
-----
-.27**
8. Performance
37.81
15.73
-----
Note. Variables below the diagonal are at the individual level; variables above the diagonal are at the group level. Descriptive statistics are at the individual level
for variables 1-2 and the team level for variables 3-8. *p < .05 ** p < .01.
Page 49 of 55 Academy of Management Discoveries
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Table 5. Study 2: The Impact of the Alignment of Member Proactivity and Conscientiousness on Team Performance
Variables
Model 1:
Additive
Model with
Mean P,C
Model 2:
Additive
Model with
Mean
Interaction of
P,C
Model 3:
Max.
Model of
P,C
Model 4:
Max.
Model of
P,C with
Interaction
Model 5:
Variance
Model of
P,C
Model 6:
Variance
Model of
P,C with
Interaction
Model 7:
Alignment of
P,C
Intercept
56.29
(25.76)*
-135.58
(299.62)
28.45
(21.30)
73.68
(190.39)
33.07
(5.02)**
41.09
(12.22)
50.77 (4.25)**
Team Proactivity Mean
-6.13 (6.16)
45.03 (79.83)
Team Conscientiousness Mean
1.25 (5.66)
55.22 (84.16)
Team Proactivity Mean x Team
Conscientiousness Mean
-14.37 (22.36)
Team Proactivity Max
1.13 (4.23)
-9.25
(43.60)
Team Conscientiousness Max
1.03 (3.68)
-9.58
(44.53)
Team Proactivity Max x Team
Conscientiousness Max
2.43 (10.16)
Team Proactivity SD
7.11 (6.47)
-7.30
(21.04)
Team Conscientiousness SD
1.37 (6.76)
-12.08
(19.87)
Team Proactivity SD x Team
Conscientiousness SD
23.65
(32.86)
Team P-C Alignment
-42.03
(13.12)**
F
0.50
0.47
0.10
0.08
0.68
0.62
10.26**
R2
0.01
0.01
0.01
0.01
0.01
0.01
0.07
*p < .05 ** p < .01. DV = Team Performance. P = Proactivity. C = Conscientiousness
Page 50 of 55Academy of Management Discoveries
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Table 6. Study 1: Comparison of P-C Alignment to Other Team Composition Approaches
Variables
Model 8:
Comparison to
Additive
Approach
Model 9:
Comparison to
Team Max
Approach
Model 10:
Comparison to
Variance
Approach
Model 11: Full
Model with All
Predictors
Intercept
-293.07 (292.92)
-22.53 (183.84)
40.08 (11.51)**
-361.37 (301.80)
Team Proactivity Mean
96.63 (78.58)
133.15 (88.38)
Team Conscientiousness Mean
96.42 (82.14)
131.65 (91.76)
Team Proactivity Mean x Team
Conscientiousness Mean
-26.93 (21.90)
-37.54 (24.42)
Team Proactivity Max
15.03 (42.19)
-18.33 (49.77)
Team Conscientiousness Max
11.08 (42.93)
-17.45 (49.87)
Team Proactivity Max x Team
Conscientiousness Max
-1.96 (9.79)
5.14 (11.22)
Team Proactivity SD
16.66 (20.61)
18.24 (24.49)
Team Conscientiousness SD
17.05 (19.94)
11.73 (23.97)
Team Proactivity SD x Team
Conscientiousness SD
-4.78 (31.67)
-9.82 (33.59)
Team P-C Alignment
-47.66 (14.49)**
-52.53 (14.34)**
-63.86 (15.15)**
-71.80 (16.75)**
F
3.08*
3.42**
4.97**
2.30*
R2
0.09
0.10
0.13
0.16
∆R
2
.08**
.09**
.12**
.13**
*p < .05 ** p < .01. DV = Team Performance. P = Proactivity. C = Conscientiousness
Page 51 of 55 Academy of Management Discoveries
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Figure 1. Top 10 Aligned and Unaligned Teams in Study 1.
Page 52 of 55Academy of Management Discoveries
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Figure 2. Top 10 Aligned and Unaligned Teams in Study 2.
Page 53 of 55 Academy of Management Discoveries
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Figure 3. Theoretical alignment types at different mean levels of proactivity and conscientiousness.
Page 54 of 55Academy of Management Discoveries
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Kyle Emich (Ph.D., Cornell University; kemich@udel.edu) is a Professor of Business
Administration at the University of Delaware’s Alfred Lerner College of Business and
Economics. His research explores the role of individual attributes, particularly perceptions and
emotions, in team dynamics. It appears in journals including Academy of Management Journal
and Psychological Science and media outlets such as TIME and the New York Times.
Li Lu (Ph.D. University of Southern California; llu@wcupa.edu) is an Associate Professor of
Management at West Chester University. Her research focuses on team decision-making and
performance. Her research has appeared in outlets including MIS Quarterly, Personality and
Social Psychology Review, and Journal of Organizational Behavior.
Amanda J. Ferguson (Ph.D. London Business School; amanda.j.ferguson@niu.edu) is an
Associate Professor of Management at Northern Illinois University. Her research focuses on
team composition, team conflict, and intergroup relationships and appears in outlets including
Administrative Science Quarterly, Academy of Management Journal, and Journal of Applied
Psychology.
Randall S. Peterson (Ph.D. University of California, Berkeley; rpeterson@london.edu) is
Professor of Organisational Behaviour and Director of the Leadership Institute at London
Business School. His research focuses on boards, top management teams, team conflict, and
team leadership and appears in outlets including Administrative Science Quarterly, Journal of
Applied Psychology, and Harvard Business Review.
Michael McCourt (Ph.D., Cornell University) is the General Manager of SigOpt. His research
involves computational statistics and Bayesian machine learning for efficient decision making in
optimization, search, and learning. His work has appeared in SIAM journals, Optica, and
Proceedings of Machine Learning Research. He also cowrote a text on kernel-based
approximation methods for World Scientific Press.
Sean R. Martin (Ph.D., Cornell University; martins@darden.virginia.edu) is the Don & Lauren
Morel Associate Professor at the Darden School of Business at the University of Virginia. His
research interests include leadership, social class, values, and communication.
Elizabeth J McClean (Ph.D Cornell University; ejm45@cornell.edu) is an Associate Professor
at the S.C. Johnson Graduate School of Management at Cornell University. Her research focuses
on voice, gender, leadership, and strategic human resource management.
Col. Todd Woodruff (Ph.D. University of North Carolina; todd.woodruff@westpoint.edu) is a
Professor of Management at the United States Military Academy, West Point, and the Director of
the West Point Leadership Center, developing the world’s preeminent leaders of character. He
also directs the Eisenhower (graduate) and Benavidez (executive) Leader Development Programs
cooperatively with Columbia University.
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