UNIVERSITY OF CALGARY
The Temporal Transition of Team Exploratory and Exploitative Learning
by
Nicole Lynn Larson
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
GRADUATE PROGRAM IN PSYCHOLOGY
CALGARY, ALBERTA
SEPTEMBER, 2015
© Nicole Lynn Larson 2015
Abstract
This study investigated exploratory and exploitative actions, an organizational learning theory,
and conceptualized it at the level of the team. Results indicated that teams tend to pursue both
exploratory and exploitative actions simultaneously and exponentially over time, however these
learning behaviours were not related to subsequent performance. Additionally, team goal
orientations were found to be differentially related to each of the learning behaviours.
Supplemental analyses revealed that intra-team relationship conflict had a moderating effect on
the degree to which teams engaged in exploratory learning over time. Furthermore, task conflict
measured at the project midpoint, predicted earlier initial levels of team exploratory and
exploitative learning behaviours. The findings contribute to the growing body of research on
team learning, and offer unique insight into how learning behaviour unfolds over time and the
factors which impact these behaviours in teams.
Keywords: Team learning, exploratory learning, exploitative learning, innovation, team goal
orientation, team conflict.
ii
Acknowledgements
I would like to thank my supervisor Dr. Thomas O’Neill for his support and guidance
throughout the past two years. Dr. O’Neill was instrumental in helping me build and refine the
theoretical rationale for this study. Additionally, he taught me much of the specialized statistical
knowledge required to analyze my data. I would also like to thank my committee members
Joshua Bourdage, Susan Boon, and Chris Macnab for their feedback and insights. I would like to
extend a special thank you to my cohort and peers in the Individual and Team Performance Lab
for acting as a motivational force during this process. I would also like to acknowledge and thank
Lisa Nguyen for her assistance with data collection and management. I extend my sincere
gratitude to the aforementioned individuals, without their support, this project could not have
been accomplished.
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Table of Contents
Abstract ............................................................................................................................... ii!
Acknowledgements ............................................................................................................ iii!
Table of Contents ............................................................................................................... iv!
List of Tables ..................................................................................................................... vi!
List of Figures ................................................................................................................... vii!
CHAPTER ONE: INTRODUCTION ..................................................................................1!
1.1 Team Learning and Innovation ..................................................................................3!
1.2 Foundations of Exploration and Exploitation ............................................................4!
1.3 Exploration and Exploitation in a Team Context ......................................................7!
1.3.1 Team exploratory learning over time. ...............................................................8!
1.3.2 Team exploitative learning over time. .............................................................10!
1.4 Goal Orientation ......................................................................................................12!
1.4.1 Learning orientation and exploratory behaviour. ............................................14!
1.4.2 Performance orientation and exploitative behaviour. ......................................15!
CHAPTER TWO: METHOD ............................................................................................17!
2.1 Participants...............................................................................................................17!
2.2 Procedure .................................................................................................................18!
2.3 Measures ..................................................................................................................18!
2.3.1 Exploratory and exploitative learning. ............................................................18!
2.3.2 Goal orientation. ..............................................................................................19!
2.3.3 Innovation. .......................................................................................................20!
2.4 Measures for Supplemental Analyses ......................................................................21!
2.4.1 Work intensity. ................................................................................................21!
2.4.2 Team conflict. ..................................................................................................21!
CHAPTER THREE: ANALYSIS .....................................................................................23!
3.1 Preliminary Analyses ...............................................................................................23!
3.2 RCM Analyses .........................................................................................................23!
3.2.1 Power. ..............................................................................................................24!
3.3 Planned Analyses .....................................................................................................24!
3.3.1 Exploratory learning trajectory........................................................................24!
3.3.2 Predictors of exploratory learning. ..................................................................28!
3.3.3 Exploitative learning trajectory. ......................................................................30!
3.3.4 Predictors of exploitative learning...................................................................31!
3.4 Supplemental Analyses ............................................................................................32!
3.4.1 Work intensity. ................................................................................................32!
3.4.2 Team dispersion in goal orientation. ...............................................................34!
3.4.3 Team conflict. ..................................................................................................35!
CHAPTER FOUR: DISCUSSION ....................................................................................38!
4.1 Theoretical Contributions ........................................................................................38!
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4.1.1 Exploratory and exploitative learning. ............................................................38!
4.1.2 Team learning behaviours and innovation. .....................................................40!
4.1.3 Goal orientation and team learning. ................................................................41!
4.1.4 Team conflict and team learning. ....................................................................43!
4.2 Limitations and Future Directions ...........................................................................44!
4.3 Practical Implications ..............................................................................................46!
4.4 Conclusion ...............................................................................................................47!
REFERENCES ..................................................................................................................48!
APPENDIX A: EXPLORATORY AND EXPLOITATIVE LEARNING MEASURE....60!
APPENDIX B: INNOVATION RATING TRAINING MATERIAL...............................61!
APPENDIX C: INNOVATION RATING FORM ............................................................62!
APPENDIX D: GOAL ORIENTATION SURVEY..........................................................63!
APPENDIX E: WORK INTENSITY AND TEAM CONFLICT SCALES......................64!
APPENDIX F: DESIGN PROJECT OUTLINE ..............................................................65!
APPENDIX G: DELIVERABLES AND DATA COLLECTION DETAILS ..................67!
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List of Tables
Table 1. Means, Standard Deviations, and Correlations for Study Variables .............................. 26!
Table 2. Growth Model Parameter Estimates for Exploratory Learning ...................................... 29!
Table 3. Growth Model Parameter Estimates for Exploitative Learning ..................................... 32!
Table 4. Exploratory and Exploitative Learning with Work Intensity as Covariate .................... 33!
Table 5. Team Conflict as Predictors of Exploratory and Exploitative Learning ........................ 36!
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List of Figures
Figure 1. Hypothesized multilevel model of relationships. ............................................................ 8!
Figure 2. Graphic representation of Gersick’s punctuated equilibrium model. ............................. 9!
Figure 3. Predicted team exploratory learning trajectory over time ............................................. 10!
Figure 4. Predicted team exploitative learning trajectory over time ............................................ 12!
Figure 5. Exploratory learning and learning goal orientation. ..................................................... 15!
Figure 6. Exploitative learning and performance orientation. ...................................................... 17!
Figure 7. A Lattice Plot of the relationship between time and exploratory learning ................... 27!
Figure 8. Effect of team learning goal orientation on exploratory learning ................................. 29!
Figure 9. A Lattice Plot of the relationship between time and exploitative learning ................... 31!
Figure 10. Effect of relationship conflict on exploratory learning ............................................... 37!
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Chapter One: Introduction
The notion of organizational learning has been an important topic of consideration over the
past several decades (e.g., Garvin, 2000; Levitt & March, 1988; March & Simon, 1958).
Traditionally, the study of organizational learning has taken a macro-level approach, often
neglecting to develop and integrate micro-level systems of learning (Gupta, Smith, & Shalley,
2006). This is problematic given that modern organizations have moved toward a team-based
structure (Kozlowski & Bell, 2003), and teams are now considered “fundamental learning units”
through which organizational objectives are realized (Edmondson & Nembhard, 2009). As Senge
stated, “this is where the rubber meets the road; unless teams can learn, the organization cannot
learn” (1990, p. 10). This shift toward team-based work necessitates a modification to, and
extension of, traditional organizational learning theories. Exploratory and exploitative learning,
originally advanced by March (1991), is one such macro-level concept that is particularly
relevant in the context of team learning. Indeed, numerous scholars have called for more
empirical research on exploratory and exploitative learning at a micro-level (Kang & Snell,
2009; Raisch, Birkinshaw, Probst, & Tushman, 2009; Gupta et al., 2006); however, research in
this area remains limited (e.g., Kostopoulos & Bozionelos, 2011; Li, Chu, & Lin, 2010).
Exploration and exploitation are seen as dominant underlying issues in organizational
learning (e.g., March, 1991; March, 1996; Levinthal & March, 1993). Exploratory learning
involves flexibility and discovery that leads to the development of new capabilities, while
exploitative learning is associated with efficiency-enhancement that leads to skill refinement
(March, 1991). Organizations often utilize teamwork with the expectation that it will result in
mutual learning and the rapid development of knowledge (Devine, Clayton, Phillips, Dunford, &
Melner, 1999); however, there remains a paucity of research examining the processes through
1
which team learning occurs (Kozlowski & Bell, 2008). This is an important gap because if we
can understand how teams learn, we can structure team tasks in a way that best facilitates
learning. Additionally, we can train teams to learn more rapidly and, ultimately, innovate and
accomplish more. I argue that the exploration-exploitation paradigm has the potential to offer
new insights that will deepen our understanding of team learning.
Team goal orientation reflects the extent to which a team emphasizes learning or
performance goals (Bunderson & Sutcliffe, 2003; Dragoni, 2005). A team’s goal orientation is
expected to facilitate coordination and decision making, directing group actions toward learning
or performance (DeShon & Gillespie, 2005). Thus, team goal orientation is imposed by
individual members’ preferences for learning or performance (Porter, 2005). Team goal
orientation may have important implications for how a team approaches learning. A team’s
primary goal orientation may dictate the degree to which they focus on exploring new
possibilities or exploiting old certainties. Given the conceptual overlap, I integrate goal
orientation literature as it relates to exploratory and exploitative learning.
There is a prolific amount of research on exploratory and exploitative learning at the
organizational-level (Lavie, Stettner, & Tushman, 2010). However, as team-based work has
become ubiquitous in organizations, there exists a need to extend current knowledge to
incorporate a team-level perspective. Understanding the role of team-level exploratory and
exploitative learning is important because it can offer new insights into how teams, and
ultimately organizations, learn. Accordingly, I adopt an exploration-exploitation framework for
examining how team learning behaviours relate to team innovation. In doing so, this thesis will
attempt to answer calls for both a longitudinal examination of team learning behaviours
(Mathieu, Maynard, Rapp, & Gilson, 2008) and for a more nuanced understanding of the
2
exploration-exploitation paradigm at the level of the team (Kostopoulos et al., 2011). In addition,
this paper will examine how team goal orientation (learning vs. performance) is expected to
impact the degree to which teams engage in the aforementioned learning behaviours. Thus, my
thesis will advance several predictions regarding the role of team learning and goal orientation
on innovation.
1.1 Team Learning and Innovation
As mentioned earlier, fast acting project teams have become common within many
organizations and contribute to an organization’s knowledge base and innovation capacity.
Learning plays a central role in achieving creativity and innovation (Edmondson, Bohmer, &
Pisano, 2001). Understanding team learning, therefore, is of chief concern (Edmonson et al.,
2009). Previous research has found that team learning results in an ability to identify and
generate novel solutions to problems (e.g., Hirokawa, 1990; Maier & Solem, 1962). Further
research on this topic has found team learning mediates the relationship between team
heterogeneity and innovation (Drach-Zahavy & Somech, 2001). Moreover, Van Offenbeek and
Koopman (1996) suggested that learning is a critical interaction process in teams that promotes
innovation. Taken together, teams must work toward a continuous learning process (Allred,
Snow, & Miles, 1996), and increasing the effectiveness of this process could offer an advantage
to organizations in competitive marketplaces (Stata & Almond, 1989). In other words, learning
in teams promotes flexibility and efficiency which offers organizations a competitive advantage
in today’s turbulent markets.
Edmondson (2002) defines team learning as “a process in which a team takes action,
obtains and reflects upon feedback, and makes changes to adapt or improve” (pg. 129). This
definition is consistent with the notion of exploratory and exploitative learning, which acts as the
3
guiding framework in the current study. Specifically, I conceptualize team learning as a process
variable consisting of two primary types of actions (i.e., exploration and exploitation) and I relate
the temporal pattern of these behaviours to the prediction of team innovation. In contrast, the
majority of studies examining this topic view learning as an outcome variable and study factors
that promote team learning. For example, psychological safety (e.g., Edmondson, 2002), stable
team membership (e.g., Moreland, Argote, & Krishnan, 1996), and performance feedback (e.g.,
Lant, 1992) were identified as antecedents of team learning.
Although team learning is an important criterion variable, consideration of team innovation
takes past work one step forward. Team learning involves information gathering, sharing, and
manipulation, and I believe this process should translate into tangible team outcomes. Innovation
contains two stages: the generation of new ideas and their implementation (Amabile, 1996; West
& Farr, 1990). Additionally, West (1990) identified four events involved in innovation:
recognizing opportunity, initiating a process, implementation, and stabilizing. Such phases of
innovation map on to the learning constructs examined in the current study. In other words,
exploration may lead to the recognition of previously unforeseen opportunity and result in the
initiation of a process to capitalize on the discovery. Whereas, exploitation entails refining ideas
and implementing them in an effective manner that stabilizes the innovation as a viable solution.
Accordingly, innovation is considered an appropriate team outcome variable in the context of the
current study.
1.2 Foundations of Exploration and Exploitation
March’s (1991) seminal article classifies exploration and exploitation into two qualitatively
distinct organizational learning activities. March defines exploration as “experimentation with
new alternatives [having] returns that are uncertain, distant, and often negative” and exploitation
4
as “the refinement and extension of existing competencies, technologies, and paradigms [with]
returns that are positive, proximate, and predictable” (p. 85). Exploratory learning includes
flexibility, variation, experimentation, and discovery. Although March discusses the potential
risks involved in exploration due to its distal relation with performance, this behaviour is
generally associated with the development of new capabilities. On the other hand, exploitative
learning involves refinement, efficiency, implementation, and execution of work which results in
enhanced knowledge and skills. Levinthal et al. (1993) further developed these definitions by
stating that exploration involves the “pursuit of new knowledge”, whereas exploitation involves
the “use and development of things already known” (p. 105).
Numerous authors have discussed exploration and exploitation in relation to different
phenomena at the organizational level. For instance, these concepts have been applied to product
development (e.g., Katila & Ahuja, 2002), process management (e.g., Benner & Tushman,
2003), environmental dynamism (e.g., Kim & Rhee, 2009), financial performance (e.g., Uotila,
Maula, Keil, & Zahra, 2009), knowledge management (e.g., Brown & Duguid, 2001), and
innovation (e.g., Jansen, Van den Bosch, & Volberda, 2006). However, the generalizability of
the antecedents and outcomes of exploration and exploitation are difficult to synthesize due to
the varied nature of the studies’ contexts (Lavie et al., 2010). Research has established that both
activities lead to better organizational performance (He & Wong, 2004), yet they entail inherent
contradictions (March, 1991; O’Reill & Tushman, 2008). Although a full discussion of the
interplay between exploration-exploitation at the organizational-level is beyond the scope of this
paper, a key takeaway is that scholars typically view exploratory and exploitative behaviour as a
choice between flexibility and stability (Lavie et al., 2010). This takeaway is relevant to the
5
current study because the choice or tension between these activities may be experienced in a
different way at the team-level, and I will discuss this issue in the following section.
A central challenge of the exploration-exploitation framework relevant to the current study
is the question of whether exploratory and exploitative learning should be viewed as opposing
ends of a continuum or as discrete constructs (Gupta et al., 2006; Raisch et al., 2009). Lavie et al.
(2010) argue that studies which conceptualize exploration and exploitation as discrete constructs
underestimate the inherent trade-offs between these activities. In other words, resource allocation
constraints and the opposing nature of the activities suggest that organizations make deliberate
choices to support either exploration or exploitation. Although this may be an accurate
assumption at the organizational-level, there are numerous reasons to believe this argument is not
transferable to a team-level perspective. First, the nature of team work allows individual
members to participate in various tasks at any given time that may be either exploratory or
exploitative in nature (Jansen et al., 2006; Raisch & Birkinshaw, 2008). Therefore, teams have
the flexibility to simultaneously engage in both behaviours, avoiding potential tradeoffs. Second,
upon reviewing extant literature Lavie et al. (2010) acknowledged that, currently, there is no
convincing rationale for selecting one measurement approach over the other (i.e., distinct
constructs vs. a continuum). Third, if exploration and exploitation are opposite ends of a
continuum and involve distinct behaviours they should be inversely related. At the organizational
level some studies have reported a negative correlation between the constructs (e.g., Park, Chen,
& Gallagher, 2002; Van Deusen & Mueller, 1999); however, Kostopoulos et al. (2011) found a
positive correlation between exploration and exploitation at the team-level. In line with the above
evidence, the current study conceptualizes and measures exploration and exploitation as distinct
constructs.
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1.3 Exploration and Exploitation in a Team Context
To my knowledge, only one study has adopted March’s (1991) exploration-exploitation
framework to understand how learning activities at the team-level relate to team outcomes. In
this research Kostopoulos et al. (2011) developed a team-based measure of exploratory and
exploitative learning and collected data from 146 project teams. The findings established
exploration and exploitation as distinct learning activities, supported through factor analysis,
which operate at the team level. The authors found exploratory and exploitative learning to be
additively related to team performance meaning both activities demonstrated a positive
correlation with team performance.
Kostopoulos et al. (2011) suggested teams should pursue both exploration and exploitation
in order to maximize performance. This research, however, neglects the possibility that
exploratory and exploitative activities may unfold at various stages in a team’s life cycle.
Although, Kostopoulos et al.’s results offer useful insight regarding the positive effects of team
exploratory and exploitative learning, numerous scholars emphasize the need for more
temporally-based views of team processes (Cronin, Weingart, & Todorova, 2011; Kozlowski,
Chao, Grand, Braun, & Kuljanin, 2013). Accordingly, in the following sections I build on
Kostopoulos et al.’s work by adopting a temporal view of the relationship between team learning
processes and team innovation. The hypothesized relationships, which are depicted in Figure 1,
incorporate temporal aspects by adopting a multilevel model of exploratory and exploitative
learning activities in teams.
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Figure 1. Hypothesized multilevel model of relationships. Note: Time indicates the
point at which the measures were taken. Time 0 indicates that the measure was taken before
the team’s project start date.
1.3.1 Team exploratory learning over time. Theoretical support for different patterns of
exploratory and exploitative team learning over time can be extrapolated from Gersick’s (1988)
punctuated equilibrium model. Gersick’s model describes a team’s developmental process as
involving a punctuated equilibrium; that is, teams show very little progress during the initial
project phase but experience a major transition in their approach to work at the calendar
midpoint (see Figure 2). Exploratory learning entails play, discovery, and search, all of which are
often associated with ambiguity and distal outcomes. Thus, what Gersick describes as a period of
inertia may actually represent a period of idea incubation, where team members are searching
for, encoding, storing, and synthesizing information. Marks, Mathieu, and Zaccaro (2001) posit
8
that team performance is enabled through the sequencing and timing of member actions;
therefore, the initial project phase should ideally be characterized by high levels of exploratory
learning behaviour leading to idea generation.
Figure 2. Graphic representation of Gersick’s punctuated equilibrium model.
Indeed, high performing teams are often described as those that explore novel possibilities
and entertain numerous ideas (Alper, Tjosvold, & Law, 1998). However, increased variation in
the reliability of performance is regularly cited as an inherent risk associated with exploratory
behaviour (March, 1991). In the context of project teams, it seems reasonable to expect that
exploratory behaviour is necessary for high levels of innovation. Therefore, risk associated with
exploratory learning may only be evident when this behaviour is enacted, inappropriately, during
the teams’ performance or implementation phase. In other words, high degrees of exploration
past the project midpoint may be harmful to overall team performance because it may create
uncertainty and excessive variation. In contrast, a team that does not sufficiently engage in
9
exploratory learning and therefore lacks exploration during the initial idea generation phase of a
project may be more likely to make suboptimal decisions, leading to worse performance and
innovation. Accordingly, the following hypotheses are proposed:
H1a: The average trajectory of exploratory learning will be negative over time. There
will be a negative relationship between time and exploratory learning (see Figure 1).
H1b: There will be an interaction between team innovation and time in the prediction of
exploratory learning such that the slope will be more negative for high innovation
teams and less negative for low innovation teams.
Exploratory Learning Behaviour
80
70
60
50
40
30
20
10
0
StartTime 1
Project
Time
2 Midpoint
Time 3
Finish
Time
4
Figure 3. Predicted team exploratory learning trajectory over time (hypothesis 1a).
1.3.2 Team exploitative learning over time. Gersick’s (1988) punctuated equilibrium
model predicts that teams experience a crucial transition point around the project midpoint. This
midpoint is interpreted as an alerting mechanism that causes team members to attend to and
reevaluate their progress and then shift their approach to the work accordingly. Past this temporal
midpoint teams pay explicit attention to task requirements and the team moves from generating
10
new ideas to editing and preparing existing ideas (Gersick, 1988). Exploitative learning entails
refinement, efficiency, and implementation and is associated with certainty and proximal
outcomes. Thus, exploitative learning maps onto the later phases of Gersick’s model.
Additionally, Marks et al. (2001) suggested that teams experience action phases that are
triggered by a temporal change in task requirements where team members engage in monitoring,
tracking, and performing task work. Consequently, later team phases should be characterized by
high levels of exploitative behaviour aimed at idea implementation.
Exploitative learning is expected to have positive effects on team innovation (Katila et al.,
2002). Specifically, in contrast to exploration, exploitative learning behaviours are variancereducing activities that exploit the team’s current knowledge base. These behaviours contribute
to innovation by assuring the effective implementation and stabilization of an idea. Exploitative
learning, which also acts as a coordination mechanism, aligns team members’ common memory
systems and task related knowledge (Liang, Moreland, and Argote, 1995). Regardless of these
advantages, high levels of exploitative behaviour during early phases in a team’s project may be
detrimental to team performance. An overemphasis on exploitative learning at the beginning of a
team’s life cycle may result in both insufficient experimentation and inadequate incorporation of
novel concepts and materials, thereby leading to suboptimal solutions. However, as teams move
past the project midpoint, exploitative learning becomes increasingly important for completing
the team project and achieving a high level of innovation. In other words, the advantages of
exploitative learning becomes more salient during later phases of a team’s work. Accordingly, I
made the following predictions:
H2a: The average trajectory of exploitative learning will be positive over time. There
will be a positive relationship between time and exploitative learning (see Figure 2).
11
H2b: There will be an interaction between team innovation and time in the prediction of
exploitative learning such that the slope will be more positive for high innovation teams
and less positive for low innovation teams.
Exploitative Learning Behaviour
80
70
60
50
40
30
20
10
0
Time 1
Start
Time
2
Time 3
Project
Midpoint
Time
4
Finish
Figure 4. Predicted team exploitative learning trajectory over time (hypothesis 2a).
1.4 Goal Orientation
Goal orientation has been described as a motivational orientation that directs an
individual’s approach to achievement situations (Dweck & Leggett, 1988). Two distinct goal
orientations have been identified in extant literature: learning goal orientation and performance
goal orientation (Button, Mathieu, & Zajac, 1996; DeShon et al., 2005; Payne, Youngcourt, &
Beaubien, 2007). Learning goal orientation is characterized by a drive to explore new topics or
techniques with an emphasis on gaining skills and expertise. Performance orientation, on the
other hand, is described as a concern for executing and accomplishing task work in order to
receive external rewards and demonstrate ability (e.g., grades, promotion, monetary incentive,
12
praise). Hence the primary distinction is that performance-oriented individuals seek to
demonstrate competence, whereas learning-oriented individuals seek to build competence.
Previous research supports the notion that dispositional and situational aspects of goal
orientation are distinguishable and may be treated as either a trait or situational characteristic
(Button, et al., 1996). For the purpose of the current study, goal orientation will be considered
from a trait perspective, in which an individual’s disposition prompts him or her toward a
particular pattern of responses (see DeShon et al., 2005). It is important to note that learning and
performance orientations are not mutually exclusive, nor do they represent opposite ends of a
single continuum as evidenced by a non-significant correlation between them (Button, et al.,
1996). Therefore, individuals may hold one, none, or both orientations (Heymen & Dweck,
1992). Similarly, Nicholls, Cheung, Lauer, and Patashnick (1989) contend that learning goal and
performance goal orientation are largely independent of one another. In sum, the proposed study
treats goal orientation as being a dispositional and multi-dimensional construct.
Barrick, Stewart, Neubert, and Mount (1998) suggested team members’ traits can be
averaged to represent the team’s aggregate trait level, which can be predictive of team processes
and performance (see also LePine, 2003; O’Neill & Allen, 2011, 2013, 2014; Stewart, 2003).
Thus, dispositional goal orientation at the individual-level can be combined, as a team member
input, to form a team-level trait. Goal orientation as a collective group construct is increasingly
prevalent in organizational research (e.g., Bunderson et al., 2003; DeShon, Kozlowski, Schmidt,
Milner, & Wiechmann, 2004; Gully & Phillips, 2005; Porter, 2005), and empirical evidence
supports its use as a team composition variable that can predict team behaviour and outcomes.
For example, Porter (2005) found mean team goal orientation was related to backup behaviour,
commitment, and efficacy. Taken together, goal orientation has the potential to predict critical
13
team behaviours. In the following section I propose links between team goal orientations (i.e.,
learning and performance) and team exploration and exploitation behaviour.
1.4.1 Learning orientation and exploratory behaviour. As mentioned previously,
learning goal orientation fosters an interest in skill and knowledge acquisition. Therefore, teams
high on this orientation are likely to value exploratory behaviour as it may lead to the
development of new knowledge. Previous research has found a positive relation between
learning orientation and innovative behaviour (Janssen & Van Yperen, 2004). Idea generation,
an element of early stages in the innovation process, involves activities which closely parallel
exploratory learning behaviour. Specifically, idea generation involves investigation, flexibility in
thinking, and skill acquisition (McAdam & McClelland, 2002). Team learning orientation is
particularly relevant in this regard because it should relate positively to exploratory behaviour. In
other words, teams high on learning orientation should spend more time exploring several
solutions and searching for new ideas early in their lifecycle. Given this line of reasoning, the
following hypothesis was made (see Figure 5):
H3a: Team learning orientation will relate to the team-specific intercept (i.e.,
starting point) for exploratory learning, such that teams high on learning orientation
will have a higher intercept for exploratory learning.
Although having a strong team learning goal orientation is likely advantageous during
early project stages, it may not be uniformly beneficial. Specifically, learning oriented teams
may continue to engage in exploratory behaviour beyond a point where it is advantageous.
Exploration late in the project may produce excessive variation, misalign shared mental models,
and reduce team members’ confidence in the existing course of action. Levinthal et al. (1993)
have grappled with this question, stating that determining the optimum level of learning-related
14
behaviour for a given situation is difficult to specify. Teams with a strong learning orientation
are predisposed to prefer exploration and thus are likely to engage in more exploratory behaviour
throughout the entire course of the project. However, teams with a weak learning orientation will
engage in less exploratory behaviour over time, following the predicted (negative) trajectory
from hypothesis 1a (see Figure 5).
H3b: There will be an interaction between team learning goal orientation and time
in the prediction of exploratory learning. Specifically, the slope will be flatter
between time and exploratory learning for high learning orientation teams compared
to low.
Intercept (H3a)
Exploratory Learning Behaviour
80
High LO
Low LO
70
60
50
40
30
20
10
0
Time 1
Start
Time
2 Midpoint
Time 3
Project
TimeFinish
4
Figure 5. Exploratory learning and learning goal orientation. Prediction of teamspecific intercepts for exploratory learning (H3a), and the expected interaction of team
learning orientation with time, in the prediction of exploratory learning (H3b).
1.4.2 Performance orientation and exploitative behaviour. Individuals holding a strong
performance goal orientation strive to demonstrate competence and, therefore, task performance
15
becomes a salient outcome for these individuals (Farr, Hofmann, & Ringenbach, 1993). Previous
research found performance orientation leads individuals to practice and master familiar task
components and strategies (Fisher & Ford, 1998) and devote attention to surface processing
(Elliot & McGregor, 2001; Elliot, 1999). It follows that teams composed of individuals with a
performance orientation may engage in particular learning behaviours (exploitative) that, at face
value, appear to have a stronger connection to task completion. Given the uncertain nature of the
returns from exploratory behaviour (e.g., the cost of experimentation), performance oriented
teams may be more likely to focus on exploitative learning and decide to practice, refine, and
build on existing certainties. Additionally, this proposition is aligned with previous research
which found performance-oriented individuals prefer task work that is less complex and
ambiguous (Fisher et al., 1998). Taken together, I made the following prediction (see Figure 6):
H4a: Team performance orientation will relate to the team-specific intercept (i.e.,
starting point) for exploitative learning, such that teams high on performance
orientation will have a higher intercept for exploitative learning.
Following the above line of reasoning, performance oriented teams are likely to engage in
more exploitative learning throughout the entire project. Additionally, teams low in performance
orientation are expected to follow the predicted (positive) trajectory of exploitative behaviour
indicated in hypothesis 2a. Put differently, the situation will exert influence on the team
behaviour, meaning the temporal (situational) characteristics of the project timeline will result in
low performance oriented teams following the predicted trajectory of exploitative behaviour (see
Figure 6).
H4b: There will be an interaction between team-level performance goal orientation
and time in the prediction of exploitative learning. Specifically, the slope will be
16
flatter between time and exploitative learning for high performance orientation
teams compared to low.
Intercept (H4a)
Exploitative Learning Behaviour
80
High PO
Low PO
70
60
50
40
30
20
10
0
Time 1
Start
Time
2 Midpoint
Time 3
Project
TimeFinish
4
Figure 6. Exploitative learning and performance orientation. Prediction of team-specific
intercepts for exploitative learning (H4a), and the expected interaction of team
performance orientation with time, in the prediction of exploitative learning (H3b).
Chapter Two: Method
2.1 Participants
The sample consisted of 569 students (29% female) enrolled in Engineering 200, a firstyear design and communications course from the Schulich School of Engineering at the
University of Calgary. Students were placed into 4-person teams at the beginning of the Fall
2014 semester and completed a sequence of four team-based design projects over the semester.
Data was collected from the participants each week during their fourth and final project. Project
4 required students to build a functioning prototype of a rover with a robotic component capable
17
of picking up and displacing an object simulating a rock (see Appendix F). This project was
worth 20% of the students’ overall course grade and was graded at the team level.
2.2 Procedure
Students were invited to participate in the study at the beginning of Project 3, the semester
midpoint, and were provided with a consent form. Only students consenting to share their data
were included in the thesis analyses and results. Team learning behaviours (i.e., exploratory and
exploitative) were collected from the participants near the end of each regular weekly scheduled
lab time for the duration of the project. More specifically, I visited each team weekly and had
them discuss and record the degree to which they engaged in exploratory and exploitative
learning that week.
Students completed the goal orientation scale a few weeks prior to the start of Project 4,
and completed the learning behaviour scales three times during the project. Data collection took
less than five minutes for each team and was conducted at three time points over the span of five
weeks (see Appendix G for data collection information and schedule).
Each lab section was supervised by an experienced teaching assistant (TA) for the duration
of the semester. There were 23 TAs and each individual was responsible for teams in their lab
section (this ranged from 3 to 9 teams each; see Appendix G). TAs were provided with rater
training (Appendix B), and completed ratings of innovation for their teams during the final week
of the project, after teams presented their functioning prototype. These ratings were not shared
with the teams and were used for research purposes only.
2.3 Measures
2.3.1 Exploratory and exploitative learning. Learning behaviours were measured
using a definitions adapted from Kostopoulos et al., 2011, see Appendix A. The Relative
18
Percentile Method formed the basis for the rating scale used to assess team exploratory and
exploitative learning (cf. Goffin, Gellatly, Paunonen, Jackson, & Meyer, 1996). Teams, as a
collective group, responded to two RPM items: (1) measuring exploratory learning and (2)
measuring exploitative learning. Each item provided a detailed description of the learning
behaviour, and teams indicated, in percentile units, the degree to which they engaged in each of
the behaviours over the past week. The scale midpoint indicates the average, and teams made
their first rating based on their comparison to other teams in their class. Teams placed a dash on
the line at the point that best represented their weekly learning behaviour on the specific
dimension and marked it as Week 1. In following weeks, teams were provided with their
completed scale from the previous weeks and again marked the position best corresponding to
their learning behaviours.
The RPM capitalizes on social comparison theory, in which predictive validity is
strengthened by anchoring (comparing) one individual’s performance against others’
performance. Although this method is typically used in the context of performance assessment
and measurement, it has also been successfully employed in the measurement of personality and
attitudes (e.g., Goffin et al., 1996; Goffin, Jelley, Powell, & Johnson, 2009; Goffin & Olson,
2011). Accordingly, the RPM has the potential to improve the validly and accuracy of the
proposed team learning measure. Specifically, it may be difficult for teams to determine the
absolute amount of learning behaviour occurring each week. The RPM provided teams with a
meaningful reference point, thus refining the accuracy of the ratings, as responses were relative
to and judged against learning behaviours occurring in previous weeks.
2.3.2 Goal orientation. Goal orientation was measured at the individual-level using a
16-item scale developed by Button et al. (1996). This scale consists of 8-items measuring
19
learning goal orientation (e.g., “I prefer to work on tasks that force me to learn new things.”; ! =
.85) and 8-items measuring performance goal orientation (e.g., “I like to work on tasks that I
have done well on in the past.”; ! = .84). Responses were based on a 5-point Likert scale ranging
from (1) ‘strongly disagree’ to (5) ‘strongly agree’ (see Appendix D). Previous research has
supported the factor structure and reliability of this scale (e.g., Gully et al., 1997). To
operationalize learning and performance orientation at the team-level, I will compute the mean
from individual member’s responses. As mentioned previously, mean scores on dispositional
variables are theoretically meaningful when conceptualized at the team-level (e.g., Stewart,
2003; Barrick et al., 1998). Additionally, because goal orientation is an individual-level variable,
intra-team agreement calculations (ICCs) were not necessary.
2.3.3 Innovation. Following Amabile’s (1993) recommendations, ratings of innovation
were collected from experienced domain-relevant experts who were all graduate-level TAs
familiar with the course and the project requirements (cf. O’Neill & Allen, 2014). Because raters
were not rating the same teams, inter-rater reliability cannot be calculated (cf. O’Neill et al.,
2011). However, to help calibrate responses, raters were provided with training before collecting
the innovation ratings (see Appendix B). The Relative Percentile Method, described above,
formed the basis for the rating scale employed to measure innovation (see Goffin et al., 1996).
Team innovation was defined for the raters as the extent to which the team’s prototype embodies
both a) the existence of a novel, unique, and original idea, and b) the effective implementation of
the idea and functionality of the prototype (see Appendix C). The above definition is consistent
with the dominant conception that innovation consists of both idea generation and successful
execution of the idea (Amabile, 1996; West et al., 1990). Additionally, innovation ratings were Z
scored within each rater.
20
2.4 Measures for Supplemental Analyses
I considered several variables for supplemental analyses. First, work intensity was
measured at each time point as a potential covariate of team learning behaviours. Temporal
motivation due to impending deadlines could influence the amount of effort team’s exert (Locke
& Latham, 1990), potentially resulting in greater exploratory and exploitative actions. Second,
team conflict was measured because it has conceivable influence on the degree to which teams
engage in learning behaviours. Indeed, previous research has found team dynamics to be
significantly related to team learning behaviour (e.g., Kostopoulos et al., 2011; Wong, 2004;
Edmondson, 1999). Team conflict was measured at the project midpoint.
2.4.1 Work intensity. Team work intensity was measured using an adapted version of
Brown and Leigh’s (1996) work intensity scale (see Appendix E). Specifically, I adapted 3 items
to reflect a referent-shift from the individual to the team, and added a time component. For
example, “Over the past week, we worked on the project at our full capacity.” The items were
rated on a 5-point scale from “strongly disagree” to “strongly agree.” Cronbach’s alpha ranged
from .95 to .96 for each time period, ICC(1) ranged from .52 to .40, and ICC(2) was .81 to .73.
2.4.2 Team conflict. I adopted Jehn’s (1995) taxonomy of team conflict and included
measures of task conflict, relationship conflict, and process conflict (see Appendix E). Conflict
was measured at the individual-level and aggregated to the team level using the within-team
mean (Kozlowski & Klein, 2000). All conflict items were rated on a 5-point Likert scale with
options ranging from “very small amount” to “a lot.”
Task Conflict was measured using 4-items from O’Neill et al. (2015). These items were
adapted from Behfar, Mannix, Peterson, and Trochim’s (2011) task conflict scale to better
21
represent the task conflicts engineering teams may face. Thus the four items were presented as
follows: “To what extent are different opinions, viewpoints, and perspectives discussed while
settling on your team’s (1) problem definition; (2) design concept; (3) prototypes; and (4)
presentation?” Unlike relationship and process conflict, task conflict captures a constructive team
process as it is related to debates regarding the context of the work. The internal consistency and
ICCs for this measure were all acceptable (! = .83; ICC(1) = .10; ICC(2) = .31).
Relationship conflict was measured using 4-items from Jehn (1995) which capture the
degree to which the team is experiencing personal tensions. An example item is “How much
friction is there among members of your team?” This scale had a Cronbach’s alpha of .91,
ICC(1) of .41, and ICC(2) of .73.
Process conflict was measured using Behfar et al.’s (2011) 3-item scale, which measures
the team’s logistical tensions with items such as “How often do members of your team disagree
about who should do what?” Cronbach’s alpha, ICC(1), and ICC(2) for this scale were .80, .30,
and .63 respectively.
22
Chapter Three: Analysis
3.1 Preliminary Analyses
Outlier analyses were conducted to investigate the multivariate normality of the data.
Mahalanobis distance was used to measure how much each case’s values on the study variables
(i.e., multi-dimensional mean) differ from the average of all other cases. A large Mahalanobis
distance indicates that a particular case has extreme values relative to the multivariate mean.
Mahalanobis distance values are evaluated with a chi-square (!2) distribution where the degrees
of freedom equal the number of variables. The input variables used in the calculation were
exploratory and exploitative learning at each measurement period, goal orientation, and
innovation ratings. Thus, the critical cut-off value of 27.88 was determined from calculating the
chi-square value with 9 degrees of freedom. Because I wanted to be conservative in my decision
to exclude outliers from the analyses, the alpha level was set to p =.001. There were eight
significant Mahalanobis distance values that met the criteria for exclusion, as they exceeded the
value of 27.88. The eight cases were removed from the dataset and the study hypotheses were
tested. Additionally, I ran the analyses again to determine if the outlier affected the results, and
found that removing the 8 cases had a slight impact on the overall findings. Specifically, when
outliers were included in the analyses, the amount of slope variance in exploitative learning was
found to be non-significant, but with outliers removed the slope variance was significant.
3.2 RCM Analyses
I applied random coefficient models (RCM) to examine relationships between goal
orientation, innovation, and exploratory and exploitative learning trajectories in teams using
Bliese and Ployhart’s (2002) model estimation procedures. Longitudinal random coefficient
models handle non-independence of observations by including a parameter that estimates the
23
correlation of observations over time rather than ignoring this non-independence (Singer &
Willett, 2003). In other words, each week teams responded to questions regarding their learning
behaviours; naturally, the team’s responses will correlate (to some degree) with each time point,
thus violating the assumption of independence. Violating this assumption can result in inflated
Type I error rates (Barcikowski, 1981). Thus, random coefficient modeling is an appropriate
approach for testing the study hypotheses. I estimated RCM’s separately for exploratory and
exploitative learning behaviours. Analyses were conducted using the statistical program R and
the Nonlinear and Linear Mixed Effects package (Pinheiro, Bates, DebRoy, & Sarkar, 2007;
nlme).
3.2.1 Power. Sample size determination for estimating statistical power is a complex
issue when working with multilevel data (Bickel, 2012). Hox (2010) has provided guidelines for
suitable sample sizes in multilevel research, and a commonly cited rule of thumb is 30 groups
and 30 observations per group. Such recommendations, however, are heavily qualified and may
not be applicable for longitudinal designs, which often have fewer level-1 variables (repeated
measurement waves) and a greater number of level-2 variables. In such cases, a large number of
higher level units (i.e., the number of teams in the current study) can compensate for a lack of
level-1 units, resulting in increased statistical power for detecting effects (Bosker, Snijders, &
Guldemond, 2003). Therefore, the current sample size should be sufficient to appropriately test
the study hypotheses. Muthén and Curran (1997) and Raudenbush and Xiao-Feng (2001) provide
further detail on power and random coefficient growth models.
3.3 Planned Analyses
3.3.1 Exploratory learning trajectory. Following Bliese et al’s (2002)
recommendations, I first tested an intercept-only model to assess the amount of variance in
24
exploratory learning residing between and within teams. The between-group variance ("00) was
166.05 and the residual within-group variance (#2) was 188.96. The intraclass correlation (ICC)
was calculated as "00 /("00 + #2), and the ICC obtained from this model indicated a nontrivial
degree of non-interdependence, 166.05 /(166.05 + 188.96) = .467, for exploratory learning (cf.
Bliese, 2002). In other words, 46.7% of the total variance in exploratory learning resided
between teams.
To test Hypothesis 1a, that exploratory learning would have a negative slope over time, a
second model was examined using RCM where a time factor (designated as 0, 1, and 2 for times
1, 2, and 3) was set to predict the exploratory learning scores at the three time points.
Conceptually, this model involved regressing exploratory learning on to time, and thus the
intercept indicated initial levels of exploratory learning (i.e., time 1 exploration) and the
parameter estimate for the time factor reflected the extent to which, on average, exploratory
learning changed over time. Results indicated that the average exploratory learning intercept was
66.92, which, as expected, closely matched the mean value obtained for time 1 exploratory
learning (see Table 1). Contrary to Hypothesis 1a, results indicated the rate of exploratory
learning was positive and significant rather than negative (!10 = 6.24, p <.001), suggesting that,
exploratory learning increased by 6.24 points at each consecutive time point.
25
Table 1
Means, Standard Deviations, Correlations, and Reliabilities for Study Variables
Variables
Mean
SD
n
1
2
3
4
5
6
Time 1
1. Exploratory Learning
2. Exploitative Learning
3. Work Intensity
67.02
66.13
3.43
18.11
17.40
0.74
134
134
133
.44*
.33*
.37*
(.95)
Time 2
4. Exploratory Learning
5. Exploitative Learning
6. Work Intensity
73.02
72.99
3.57
16.91
15.72
0.71
133
133
134
.64*
.31*
.09
.25*
.55*
.04
Time 3
7. Exploratory Learning
8. Exploitative Learning
9. Work Intensity
79.48
79.25
4.04
19.39
17.18
0.73
132
133
135
.46*
.18*
.20*
Predictors
10. Innovation Rating
11. Learning GO
12. Performance GO
13. Task Conflict
14. Relationship Conflict
15. Process Conflict
61.55
4.15
3.90
3.58
1.91
2.31
22.33
0.29
0.35
0.46
0.78
0.70
133
135
135
135
135
135
.11
.34*
.14
.21*
-.06
.08
7
8
9
.07
.08
.25*
.63*
.37*
.37*
(.95)
.33*
.36*
.37*
.10
.08
.18*
.55*
.44*
.27*
.36*
.63*
.34*
.16
.27*
.29*
.60*
.54*
.57*
(.96)
.16
.21*
.22*
.12
-.01
.03
.09
.33*
.18*
.18*
-.21*
-.07
.05
.27*
.06
.22*
.03
.08
.07
.18*
.09
.20*
-.05
.00
.00
.24*
.07
.13
-.15
-.09
.08
.18*
.12
.03
.18*
.17+
.06
.10
.23*
-.03
.08
.09
.17+
.14
.11
-.03
-.01
.10
10
11
12
13
14
15
.13
.11
.17*
.02
.01
(.85)
.03
.18*
-17+
-.14
(.84)
-.05
.00
.01
(.83)
-.36*
-34*
(.91)
.73*
(.80)
* p < .05., and +p =.05. Note. GO = Goal Orientation. Values on the diagonals indicate Cronbach’s alpha reliabilities. Because innovation, exploratory learning, and
exploitative learning were measured with one item (RPM), it is not possible to calculate Cronbach’s alpha reliabilities for these measures.
26
I used polynomials to test the possibility that exploratory learning may follow a quadratic
trend. Results from this analyses were non-significant t(263) = .234, SE = 12.31, p = .815 and
therefore subsequent models assumed a linear trend. Finally, I assessed a third model, in which
exploratory learning intercepts and slopes were allowed to vary across teams. A comparison of
the fit of the second model (in which the slopes were set to be equal across teams) with the fit of
the third model yielded a log-likelihood ratio of 10.20 (p = .006), indicating that there is a
significant amount of slope variance. That is, teams differed significantly from each other on
their magnitude and direction of exploratory learning change (See Figure 7). Additionally, the
time factor (i.e., slope variance) accounted for 12.30% of the within-team exploratory learning
variance.
Figure 7. A Lattice Plot of the relationship between time and exploratory learning for a
random sample of 32 teams.
27
Before building the final level-2 model, I tested whether the fit of the model could be
improved by accounting for within-team error structures. The results failed to support the
presence of autocorrelation ($2diff(1) = 0.49, ns), and therefore subsequent models excluded this
specification.
3.3.2 Predictors of exploratory learning. Hypothesis 1b was that team innovation ratings
would predict slope variance in exploratory learning, and hypotheses 3a and 3b suggested
learning goal orientation would predict intercept and slope variance in exploratory learning. To
test these hypotheses, I conducted RCM analyses in which initial exploratory learning and
learning change were treated as level-1 outcomes, and team innovation and learning goal
orientation were treated as level-2 predictors of exploratory learning intercept and slope.
Hypothesis 1b was not supported, as seen in Table 2. Team innovation did not significantly
predict slope variance in exploratory learning, t(258) = -.023, p = .815. Hypothesis 3a was
supported, as team learning goal orientation significantly predicted the intercept or starting point
of team exploratory learning, t(129) = 4.37, p <.001. This suggests that teams higher on learning
goal orientation have greater initial levels of exploratory learning behaviours. Hypothesis 3b was
partially supported, as learning goal orientation was trending in the predicted direction and was
marginally significant, t(258) = -1.77, p <.077. In other words, teams higher on learning goal
orientation had a flatter exploratory learning slope (See Figure 8).
28
Table 2
Growth Model Parameter Estimates of Predictors of Exploratory Learning
Variable
Estimate
SE
df
t
p
66.92
6.24
1.53
0.85
264
264
43.74
7.34
.000
.000
Final Level 2 model
Intercept
- 23.38
Time
27.88
Team Innovation
- 0.34
Learning Goal Orientation
21.67
Team Innovation % Time
- 0.36
Learning Goal Orientation % Time
- 5.12
Note: n = 405 observations nested in 135 teams
20.64
12.26
1.56
4.95
0.92
2.94
258
258
129
129
258
258
- 1.13
2.27
- 0.22
4.38
- 0.39
- 1.77
.258
.024
.830
.000
.700
.078
Final Level 1 model
Intercept
Time
90
80
Exploratory Learning
70
60
50
High LGO
40
Low LGO
30
20
10
0
Time 0
Time 2
Figure 8. Effect of team learning goal orientation on exploratory learning behaviours.
29
3.3.3 Exploitative learning trajectory. Consistent with the previous analyses, I first tested
an intercept-only model using RCM to determine the strength of the non-independence for
exploitative learning. The between-group variance ("00) was 128.39 and the residual withingroup variance (#2) was 181.26. The ICC was calculated as "00 /("00 + #2), and the value
obtained from this model indicated a nontrivial degree of non-independence, 128.39 /(128.39 +
181.26) = .414, for exploratory learning. Thus, 41.4% of the total variance in exploitative
learning resided between teams.
Hypothesis 2a was that exploitative learning would have a positive slope over time. I tested
this prediction using a second model with the inclusion of a designated time factor, as explained
previously. Results support hypothesis 2a as they reveal a positive, linear trend in exploitative
learning across the three time periods, with an average intercept value of 66.23 and a 6.56
increase at each consecutive time point.
I used polynomials to test for a quadratic trend in exploitative learning, and results did not
support the presence of a quadratic trend, t(264) = -.226, SE = 11.81, p = .822. Consequently,
subsequent models assumed a linear trend. I assessed a third model that allowed for random
intercepts and slopes and compared it against a model with fixed intercepts and slopes. The
model allowing for random intercepts and slopes fit the data significantly better with loglikelihood ratio of 20.83 (p < .001). As with exploratory learning, the finding indicate that teams
significantly differed from one another in the magnitude and direction of change in their
exploitative learning behaviours (See Figure 9). The time factor accounted for 17.94 % of the
within-team exploitative learning variance.
30
Additionally, I modeled an autoregressive error structure and results indicated that
specifying an autoregressive error term did not significantly improve the model ($2diff(1) = 4.21,
ns). Thus, subsequent models did not include this specification.
Figure 9. A Lattice Plot of the relationship between time and exploitative learning for a
sample of 32 teams.
3.3.4 Predictors of exploitative learning. Hypothesis 2b was that team innovation ratings
would predict slope variance in exploitative learning, and hypotheses 4a and 4b suggested
performance goal orientation would predict intercept and slope variance in exploitative learning.
To test these hypotheses, I conducted RCM analyses in which initial exploitative learning and
learning change were treated as level-1 outcomes, and team innovation and performance goal
orientation were treated as level-2 predictors of exploitative learning intercept and slope.
Hypothesis 2b was not supported, as seen in Table 3. Team innovation did not significantly
31
predict slope variance in exploitative learning, t(259) = -1.26, p = .208. Hypothesis 4a was
partially supported, as performance goal orientation was a marginally significant predictor of the
exploitative learning intercept, t(129) = 1.91, p = .059. This suggests that teams higher on
performance goal orientation have greater initial levels of exploitative learning behaviours.
Hypothesis 4b was not supported, as performance goal orientation did not predict the slope of
exploitative learning behaviours, t(259) = .22, p =.830.
Table 3
Growth Model Parameter Estimates of Predictors of Exploitative Learning
Variable
Estimate
SE
df
t
p
66.22
6.55
1.45
0.85
265
265
45.69
7.73
.000
.000
Final Level 2 model
Intercept
35.19
Time
4.44
Team Innovation
1.80
Performance Goal Orientation
7.97
Team Innovation % Time
- 1.17
Performance Goal Orientation % Time
0.54
Note: n = 405 observations nested in 135 teams
16.36
9.73
1.55
4.18
0.92
2.49
259
259
129
129
259
259
2.15
0.46
1.16
1.91
- 1.26
- 0.22
.032
.648
.249
.059
.208
.830
Final Level 1 model
Intercept
Time
3.4 Supplemental Analyses
3.4.1 Work intensity. It is possible that the observed positive trend in exploratory and
exploitative learning is due to an increase in motivation as the project deadline approaches. To
account for this possibility, work intensity was considered as a time-varying covariate of
exploratory and exploitative learning. Naturally, effort and learning behaviours are expected to
correlate as a team cannot engage in learning behaviours without also exerting effort, and as seen
in Table 1, this is indeed the case. However, to evaluate whether or not exploratory and
32
exploitative learning maintain a significant positive trend after accounting for work effort, I
added effort as a time-varying covariate. The results from these analyses indicate that both
exploratory and exploitative learning maintained a significant positive trend over time after
accounting for work effort (see Table 4). For interest sake, I ran the study hypotheses again
adding work effort as a covariate. Doing so altered two of the initial study findings. First, adding
work effort as a time-varying covariate resulted in a non-significant interaction between time and
learning goal orientation in the prediction of exploratory learning. Second, it resulted in
performance goal orientation becoming a non-significant predictor of exploitative learning
intercept. All other results remain consistent with original findings.
Table 4
Model Estimates of Exploratory and Exploitative Learning with Work Intensity as a Covariate
Models
Level 1 Model – Exploratory
Intercept
Time
Work Intensity
Estimate
SE
df
t
p
39.16
3.85
8.29
4.32
0.83
1.17
260
260
260
9.07
4.63
7.09
.000
.000
.000
4.26
.85
1.13
261
261
261
9.29
5.49
6.93
.000
.000
.000
Level 1 Model – Exploitative
Intercept
39.54
Time
4.65
Team Innovation
7.81
Note: n = 405 observations nested in 135 teams.
To examine how closely work effort paralleled the trajectory of learning behaviours, I
subjected work effort to the same analyses described above: estimated the ICC, determined the
fixed function for time, determined the variability in growth parameter, and determined the error
structure. From these analyses, two noteworthy findings emerged. First, there was a significant
quadratic trend for work effort, t(265) = 2.41, SE = .63, p = .017. This finding aligns with
33
Gersick’s (1988) punctuated equilibrium model; a sharp increase followed by a plateau past the
project midpoint. Recall that this trend was absent in the learning behaviour trajectories. Second,
there was no significant variability in the growth parameter, as a model with random slopes did
not fit the data significantly better, log-likelihood ratio of .665 (p = .713). These results also help
to differentiate learning behaviours from work intensity, as teams did not significantly differ
from each other in their effort trajectory, whereas teams were extremely variable in their learning
behaviour trajectories.
3.4.2 Team dispersion in goal orientation. In the study hypotheses, goal orientation was
treated as additive, meaning the emergent phenomenon (i.e., team goal orientation) was simply
the mean of the lower-level units (i.e., individual team member traits). However, goal orientation
may also be examined from a dispersion perspective. In other words, goal orientation can be
represented by the variability or dispersion among the lower-level units by calculating the
within-team standard deviation (Chan, 1998, 2011; DeRue, Hollenbeck, Ilgen & Feltz, 2010;
Kozlowski et al., 2000).
Given the possibility that team goal orientation dispersion may impact the degree to which
a team engages in each learning behaviour, I conducted an RCM analyses in which goal
orientation dispersion was treated as a predictor of exploratory and exploitative learning. For
these analyses, I calculated the within-team standard deviation for learning goal orientation and
performance goal orientation. First, I ran a model with learning goal dispersion as a predictor of
exploratory learning. Second, I ran a model with performance goal dispersion as a predictor of
exploitative learning. The results from both these analyses were non-significant. More
specifically, goal orientation dispersion did not significantly predict the intercept or slope of
exploratory and exploitative learning.
34
3.4.3 Team conflict. Task conflict, relationship conflict, and process conflict were tested
as predictors of exploratory and exploitative learning intercept and slopes. In these RCM
analyses, the direct effects of the level-2 predictors (i.e., task, relationship, and process conflict)
on exploratory and exploitative learning intercepts capture their relationships with initial learning
behaviour, whereas the cross-level interaction between the level-2 predictors and the level-1 time
variable reflect the relationships of predictors with learning behaviour change (i.e., slope).
As shown in Table 5, task conflict and process conflict positively predicted initial
exploratory learning behaviours, but relationship conflict did not. Interestingly, task conflict and
process conflict did not predict exploratory learning change, however, relationship conflict
significantly predicted exploratory learning growth. Inspection of the interaction between time
and relationship conflict in the prediction of exploratory learning (see Figure 10) suggests that
teams low on relationship conflict appeared to maintain similar levels of exploratory learning
throughout the project, but teams with high relationship conflict had a steeper positive slope as
they increased exploratory actions over time.
Regarding the effects of team conflict on exploitative learning, task conflict was a
significant predictor of exploitative learning intercepts, whereas relationship conflict and process
conflict did not predict initial levels of exploitative learning behaviours. Additionally, task
conflict, relationship conflict, and process conflict did not predict exploitative learning change.
35
Table 5
Team Conflict as Predictors of Exploratory and Exploitative Learning
Model
Estimate
SE
df
t
p
20.58
13.13
10.60
-4.42
7.34
-2.44
4.13
-2.62
15.06
8.44
3.48
2.81
3.11
1.96
1.58
1.76
261
261
130
130
130
261
261
261
1.37
1.56
3.04
-1.57
2.36
-1.25
2.61
-1.49
0.173
0.121
0.003
0.118
0.020
0.214
0.009
0.138
Exploitative Learning Level-2
Intercept
37.48
Time
15.95
Task Conflict
6.98
Relationship Conflict
-1.10
Process Conflict
2.57
Task Conflict % Time
-2.72
Relationship Conflict % Time
0.72
Process Conflict % Time
-0.46
Note: n = 405 observations nested in 135 teams.
14.74
8.69
3.41
2.75
3.04
2.02
1.62
1.80
262
262
130
130
130
262
262
262
2.54
1.84
2.05
-0.40
0.84
-1.35
0.44
-0.26
0.012
0.068
0.043
0.690
0.400
0.180
0.657
0.799
Exploratory Learning Level-2
Intercept
Time
Task Conflict
Relationship Conflict
Process Conflict
Task Conflict % Time
Relationship Conflict % Time
Process Conflict % Time
36
100
90
Exploratory Learning
80
70
60
50
Low RC
40
High RC
30
20
10
0
Time 0
Time 2
Figure 10. Effect of relationship conflict on exploratory learning behaviours.
37
Chapter Four: Discussion
To my knowledge, this is the first study to model team-level exploratory and exploitative
learning longitudinally, and to examine predictors of these learning behaviours in teams.
Extending the work of Kostopoulos et al. (2011), this research makes two primary contributions.
First, it provides insight into how team learning behaviours unfold over time, and examines the
relationship between exploratory-exploitative learning and team innovation. Second, it revealed
associations between team characteristics and dynamics (i.e., goal orientation and team conflict)
and team learning behaviour trajectories. Due to the widespread use of teams in organizations
and the increasing complexity of their tasks, an understanding of team learning processes is
essential to remain competitive in world markets. To date, team learning literature has paid scant
attention to the dynamic and changing nature of learning processes; therefore, the current study
makes meaningful contributions to this field. The following sections discuss theoretical and
practical contributions of this study, and suggest fruitful avenues for future research.
4.1 Theoretical Contributions
4.1.1 Exploratory and exploitative learning. The primary goal of this study was to
investigate the temporal nature of team learning behaviours. It was expected that exploratory
learning would follow a negative trend and exploitative learning a positive trend, over time.
However, the pattern that emerged suggested that both exploratory and exploitative behaviours
increase over the duration of a project. A potential explanation for these findings is that teams
engaged in both exploratory and exploitative behaviours for each task component involved in
completing the project. For example, teams built a rover capable of picking up and transporting a
rock to a designated position on a table. Accordingly, the project required them to design a
propulsion, steering, and lifting mechanism. It is plausible that teams engaged in both
38
exploratory and exploitative behaviours for each mechanism at different points during the
project, which could explain the observed trends.
It may be the case that exploratory and exploitative actions, in a team setting, have a closer
temporal rhythm than expected, and cycle in a reciprocal pattern for each task within a broader
project. Indeed, Marks and colleagues (2001) reoccurring phase model proposes that teams are
multitasking units that engage in multiple processes simultaneously and sequentially. Marks et
al. argued that team processes can be categorized into action or transition phases. Action phases
are characterized as periods when a team engages in behaviours that are directly aligned with
goal accomplishment, and transition phases are described as periods when the team is focusing
on planning or evaluating work in order to guide their team toward accomplishing a goal (Marks
et al., 2001). This model offers a conceptual basis for classifying exploratory and exploitative
learning behaviours in terms of ongoing cyclical processes. Specifically, exploratory learning
may be more likely to occur in the transition phase, as teams are evaluating their past
accomplishment and planning future work. In planning work, teams may engage in exploratory
actions such as searching for new possibilities and experimenting with different ways of
accomplishing work. On the other hand, exploitative learning behaviours may be more likely to
occur in action phases when the team is focusing on the execution of activities toward goal
accomplishment. Given this logic, combined with temporal pressures of an approaching
deadline, the observed positive learning trends may be due to smaller reciprocal processes of
exploratory and exploitative activities occurring within a broader deliverable.
Despite the unexpected positive trend for exploratory learning, there are several
meaningful takeaways from these results. First, we need to consider the possibility that
exploratory and exploitative activities happen at several points during a teams’ work, most likely
39
for each subtask in a larger goal or project. Additionally, the outcomes of exploratory behaviours
in a transition phase may act as inputs for action phases, and this pattern is likely to occur in a
cyclical fashion. Second, the findings bolster evidence supporting exploratory and exploitative
learning as distinct team actions that are positively related to one another. Because of inherent
tensions and tradeoffs related to the exploratory-exploitative paradigm, there is ongoing debate
as to whether or not these actions can and do occur together (cf. Benner et al., 2003; Lavie et al.,
2010; Gupta et al., 2006). This study found that teams engage in both behaviours as there was a
significant positive correlation, ranging between .44 to .63, between exploratory and exploitative
actions. This aligns with the above notion that teams can explore and exploit for several tasks
simultaneously.
4.1.2 Team learning behaviours and innovation. An unexpected finding was the
absence of a relationship between team learning behaviours and innovation. These findings are
puzzling given theoretical (e.g., Van Offenbeek et al., 1996; Stata et al., 1989, West et al., 1990),
and empirical evidence that points to a positive relationship between team learning and
performance and innovation (e.g., Zellmer-Bruhn & Gibson, 2006; Hirokawa, 1990; DrachZahavy et al., 2001). A theoretical explanation for the findings is that there needs to be more
time to encode learning actions, and that learning during one performance episode may only
predict outcomes in subsequent performance cycles. More specifically, receiving feedback helps
teams adopt and encode the processes and learning actions that lead to successful components of
their performance (Gibson & Vermeulen, 2003). That is, teams acquire shared mental models
which allow them to adopt proven patterns of interaction when similar conditions arise (Mathieu,
Heffner, Goodwin, Salas, & Cannon-Bowers, 2000), thus learning actions may only transfer to
subsequent performance episodes.
40
A methodological explanation for the findings may be unreliability in innovation ratings.
Innovation ratings were collected from 24 TAs and because each TA rated only the teams in their
individual lab section, I was unable to calculate inter-rater reliability and agreement. Thus, it is
possible that innovation is a difficult construct to calibrate raters on (Downs & Mohr, 1976), and
a lack of agreement may have masked relationships between learning and innovation. However,
innovation ratings did show a positive and significant relationship with two other study variables,
task conflict and work intensity. Task conflict was positively related to innovation (r = .17, p =
.05), a finding consistent with previous research (De Dreu, & West, 2001; De Dreu, 2006).
Second, work intensity also showed a positive relationship with innovation, r = .17, p = .05.
Another possible explanation may be related to the grading rubric used for the project.
Specifically, for teams to receive a high grade on the project, they did not necessarily have to
create a novel solution, as teams were scored based on whether or not their device met specific
requirements and was able to successfully transfer the rock. For these reasons, the relationship
between team learning and innovation may have been masked.
4.1.3 Goal orientation and team learning. An important contribution of this research
involves the relationship between goal orientation and team learning. Specifically, learning goal
orientation predicted the starting point (i.e., average levels) of exploratory behaviour and
demonstrated a marginally significant interaction with time. Teams with a weak learning
orientation engaged in less exploratory behaviours at the start of the project; however, as the
deadline approached, these teams substantially increased their exploratory behaviours. In
contrast, teams with a strong learning goal orientation maintained high levels of exploratory
actions over the span of the project. As such, team learning orientation may prompt teams to
engage in and maintain exploratory learning behaviours over time, whereas it appears that
41
deadlines are needed to motivate low learning orientation teams to engage in exploratory
learning activities.
Temporal motivation theory emphasizes time as a critical motivational force (Steel &
Konig, 2006), where the perceived utility of action increases as deadlines approach (Lord,
Diefendorff, Schmidt, & Hall, 2010). Drawing on this theory, it seems plausible that the effect of
temporal pressure on exploratory actions is stronger in teams that are less orientated toward
learning. More specifically, these teams may be driven to engage in exploratory actions due to
time related pressures, whereas teams with strong learning orientations may be more intrinsically
motivated to engage in high levels of exploratory behaviour and to maintain these actions over
time. For teams lacking a natural inclination toward learning, more frequent and regular goals
and deadlines may be needed.
One noteworthy finding was that performance goal orientation predicted the intercept or
starting point for exploitative behaviour but did not predict the trajectory. Performance
orientation involves a concern for demonstrating ability (Farr, 1993), and because exploration
involves risk and uncertainty, performance-oriented teams may be more inclined to stick with
what they know (Fisher et al., 1998). As predicted, performance-oriented teams spent more time
engaged in exploitative actions, such as building on existing certainties, during the first week of
the project. Although performance orientation did not predict the trajectory of exploitative
behaviour, looking at Table 1, we can see that it correlated with Time 3 exploitative behaviour.
Interestingly, performance orientation was not related to exploratory actions at any time point,
but learning orientation was related to both exploratory and exploitative learning at almost every
time point (with the exception of Time 3 exploitative behaviour). Taken together, these findings
42
support the notation that a team’s goal orientation can be an influential factor in determining how
teams pursue learning activities.
4.1.4 Team conflict and team learning. Given that healthy team dynamics are
considered a prerequisite for team learning (cf. Kasl, Marsick, & Dechant, 1997), we examined
how task, relationship, and process conflict related to exploratory and exploitative learning.
Predictions were not specified a priori and therefore the results should be interpreted with
caution; however, interesting patterns did emerge that warrant discussion. First, task conflict was
related to the intercept of exploratory and exploitative learning. Task conflict involves debate
regarding incompatible views and ideas related to a teams’ task (Jehn, 1995; Behfar et al., 2011).
Exploratory learning will likely produce a variety of ideas and options, resulting in teams
needing to discuss the merits of each possibility. Thus, it is not surprising that exploratory
learning and task conflict were found to be related, what is less clear is the relationship between
exploitative learning and task conflict. Results also suggest that process conflict is related to
initial levels of exploratory learning. That is, exploratory activities at the beginning of a project
may lead to greater disagreement about how work should be executed.
Relationship conflict interacted with time in the prediction of exploratory learning but was
unrelated to the intercept of explore and exploit. This is a particularly interesting finding, and
there are two explanations worth consideration. One interpretation may be that teams with high
levels of relationship conflict did not sufficiently explore near the start of the project, as their
interpersonal tensions drew resources away from the critical analysis and processing of divergent
perspectives (Shaw et al., 2011). Consequently, these teams may have increased their levels of
exploration in an attempt to catch up. Given that conflict was measured at the project midpoint, a
second explanation for these findings is that high levels of exploratory learning spurred
43
relationship conflict. Previous research found that task conflict has the potential to spark
relationship conflict (Simons & Peterson, 2000). Thus, teams that continue to engage in high
levels of exploratory activity have more instances of debating each others perspectives, and this
may lead to greater interpersonal tension.
4.2 Limitations and Future Directions
Although this study is the first to examine exploratory-exploitive team learning
longitudinally, one of its limitations is the single-level conceptualization of team learning. More
specifically, this study did not consider the complex interplay of individual members’ knowledge
and actions that likely contribute to the emergence of learning as a group-level phenomenon (cf.
Morgeson & Hofmann, 1999; Kozlowski et al., 2008). Conceptualizing and measuring team
learning poses distinct challenges that future research will need to address. First, research should
take a multilevel perspective and consider how individual members’ learning plays into grouplevel learning phenomena (Wilson, Goodman, & Cronin, 2007). For example, a pertinent
question would be whether or not exploratory and exploitative actions are equally likely to occur,
and have similar or distinct functions, at the team and individual level. Second, much of the
research on team learning has been conducted using cross-sectional designs, and thus fails to
provide an account for how learning processes unfold and influence each other over time.
Accordingly, future research should include multilevel perspectives of team learning while also
examining longitudinal patterns of team exploratory and exploitative activities. A longitudinal
design that is able to integrate both individual learning processes with group-level experiences
would provide a more dynamic understanding of team learning, and would allow for stronger
recommendations of how to best optimize learning-related behaviour in teams.
44
Due to the nature of the project and the potential unreliability of innovation ratings, it is
still unclear as to whether or not exploratory and exploitative activities help or harm team
performance depending on when they occur. Future research should examine this possibility in
teams that have more discretion over their choice of learning activities. Given that this study
used student teams, course learning objectives and weekly project deliverables may have
influenced team learning activities. Additionally, student teams may engage in higher levels of
exploratory learning than organizational teams as a result of being immersed in a learning
environment. Consequently, future research should examine how team exploratory and
exploitative learning is related to innovation under different team conditions and within various
organizational contexts.
Another limitation of the study is the generalizability of the findings. In this study,
exploratory and exploitative learning behaviours were examined in project teams; however,
findings may differ for other types of functional teams. For example, production teams may
engage in more exploitative learning and less exploratory learning, as their primary function is to
produce tangible products in an efficient manner. Future research should examine team types as
a potential boundary condition of the current study findings.
There are two additional research questions that warrant further consideration. First, future
research should continue to broaden our understanding of the relationship between goal
orientation and team learning. Although our results suggest that dispersion in goal orientation
does not impact team learning behaviour, this may not hold true for all teams. Additionally, we
did not examine the effect of individual team members’ minimum and maximum scores on their
team’s learning behaviour (Steiner, 1972). An important question would be whether a single
member’s goal orientation can drive how their team engages in learning activities. Second, given
45
the post hoc nature of the team conflict and team learning results, future research should attempt
to replicate these findings to gain a better understanding of how team conflict is related to
exploratory and exploitative learning activities.
4.3 Practical Implications
From a practical standpoint, this research highlights the importance of considering
temporal aspects of team learning. The ability to balance exploration and exploitation has lead to
positive organizational outcomes (He et al., 2004), and teams may also be more successful if
they are able to weave between exploratory and exploitative activities (Raisch et al., 2009). Thus,
managers, team leaders, and team members should be aware of the most suitable times to
endorse and encourage exploratory and exploitative learning. Supporting exploratory learning
during transition phases and exploitative learning during action phases of a team’s work could
prevent the tensions and tradeoffs typically associated with this paradigm. In fact, as discussed
above, outcomes from exploratory learning may act as valuable inputs for later exploitive
learning.
A second practical implication stems from the findings related to team goal orientation.
Specifically, goal orientation was measured at the individual-level and averaged to represent a
team-level construct (cf. Bunderson et al., 2003; DeShon et al., 2004; Gully et al., 2005). Results
indicated that team members’ goal orientations influenced the team’s learning behaviour. This
finding speaks to the importance of considering team composition variables when forming and
managing teams. Indeed previous research has found personality traits, cognitive ability, and
experience and skills to be meaningful composition variables related to important team outcomes
(cf. Mathieu, Tannenbaum, Donsbach, & Alliger, 2014). Given that team members’ goal
orientations have the potential to influence their team’s learning behaviours, this characteristic
46
should be taken into consideration when selecting members for a team. For example, research
and development teams may be more suitable for members with strong learning orientations, as
this type of team has an exploratory purpose. Functional teams, on the other hand, may benefit
from having more members with strong performance orientations. Given that today’s modern
team faces complex tasks that require learning, problem solving, flexibility, and rapid execution
of work, it is important to consider how individual member’s goal orientations align with their
team’s purpose in order to facilitate effective teamwork.
4.4 Conclusion
Due to the widespread use of teams in organizations, an understanding of team learning
processes is critical. This research bolstered support for the utility of the exploration-exploitation
learning paradigm at the level of the team. Findings support the notion that teams practice both
exploratory and exploitative learning actions concurrently, and increase the degree to which they
engage in these actions over time. Additionally, goal orientation and team conflict demonstrated
meaningful relationships with team learning. Thus, it is recommended that future research
continue to examine the nomological network of team exploratory-exploitative learning.
Moreover, practitioners should be aware of these consequential team learning activities so that
they are able to monitor the suitability of these activities in accordance with a teams’ work
responsibilities.
47
References
Allred, B. B., Snow, C. C., & Miles, R. E. (1996). Characteristics of managerial careers in the
21st century. The Academy of Management Executive, 10, 17-27.
Alper, S., Tjosvold, D., & Law, K. S. (1998). Interdependence and controversy in group decision
making: Antecedents to effective self-managing teams. Organizational behavior and
human decision processes, 74, 33-52.
Amabile, T. M. (1996). Creativity and innovation in organizations (Vol. 5). Boston: Harvard
Business School.
Amabile, T. M. (1993). What does a theory of creativity require? Psychological Inquiry, 4, 179181.
Barcikowski, R. S. (1981). Statistical power with group mean as the unit of analysis. Journal of
Educational and Behavioral Statistics, 6, 267-285.
Barrick, M. R., Stewart, G. L., Neubert, M. J., & Mount, M. K. (1998). Relating member ability
and personality to work-team processes and team effectiveness. Journal of applied
psychology, 83, 377-391.
Behfar, K. J., Mannix, E. A., Peterson, R. S., & Trochim, W. M. (2011). Conflict in small
groups: The meaning and consequences of process conflict. Small Group Research, 42,
127-176.
Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management:
The productivity dilemma revisited. Academy of management review, 28, 238-256.
Bickel, R. (2012). Multilevel analysis for applied research: It's just regression! Guilford Press.
Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models:
Model building, testing, and illustrations. Organizational Research Methods, 5, 362-387.
48
Bosker, R. J., Snijders, T. A. B., & Guldemond, H. (2003). PINT (Power IN Two-level designs)
user’s manual, version 2.1.
Brown, J. S., & Duguid, P. (2001). Knowledge and organization: A social-practice
perspective. Organization science, 12, 198-213.
Brown, S. P., & Leigh, T. W. (1996). A new look at psychological climate and its relationship to
job involvement, effort, and performance. Journal of applied psychology, 81, 358.
Bunderson, J. S., & Sutcliffe, K. M. (2003). Management team learning orientation and business
unit performance. Journal of Applied Psychology, 88, 552-560.
Button, S. B., Mathieu, J. E., & Zajac, D. M. (1996). Goal orientation in organizational research:
A conceptual and empirical foundation. Organizational behavior and human decision
processes, 67, 26-48.
Chan, D. (1998). Functional relations among constructs in the same content domain at different
levels of analysis: A typology of composition models. Journal of applied psychology, 83,
234.
Chan, D. (2011). Advances in analytical strategies. In S. Zedeck (Ed.), APA Handbooks in
Psychology. APA handbook of industrial and organizational psychology, Vol. 1. Building
and developing the organization: 85-113.
Cronin, M. A., Weingart, L. R., & Todorova, G. (2011). Dynamics in groups: Are we there
yet? The Academy of Management Annals, 5, 571-612.
De Dreu, C. K. (2006). When too little or too much hurts: Evidence for a curvilinear relationship
between task conflict and innovation in teams. Journal of Management, 32, 83-107.
De Dreu, C. K., & West, M. A. (2001). Minority dissent and team innovation: the importance of
participation in decision making. Journal of applied Psychology, 86, 1191.
49
DeRue, D. S., Hollenbeck, J., Ilgen, D., & Feltz, D. (2010). Efficacy dispersion in teams:
Moving beyond agreement and aggregation. Personnel Psychology, 63, 1-40.
DeShon, R. P., & Gillespie, J. Z. (2005). A motivated action theory account of goal
orientation. Journal of Applied Psychology, 90, 1096-1127.
DeShon, R. P., Kozlowski, S. W., Schmidt, A. M., Milner, K. R., & Wiechmann, D. (2004). A
multiple-goal, multilevel model of feedback effects on the regulation of individual and
team performance. Journal of applied psychology, 89, 1035-1056.
Devine, D. J., Clayton, L. D., Philips, J. L., Dunford, B. B., & Melner, S. B. (1999). Teams in
organizations prevalence, characteristics, and effectiveness. Small Group Research, 30,
678-711.
Downs, G. W., & Mohr, L. B. (1976). Conceptual issues in the study of
innovation. Administrative Science Quarterly, 700-714.
Drach-Zahavy, A., & Somech, A. (2001). Understanding team innovation: The role of team
processes and structures. Group Dynamics: Theory, Research, and Practice, 5, 111-123.
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and
personality. Psychological review, 95, 256-273.
Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams.
Administrative Science Quarterly, 44, 350-383.
Edmondson, A. C. (2002). The local and variegated nature of learning in organizations: A grouplevel perspective. Organization Science, 13, 128-146.
Edmondson, A. C., Bohmer, R. M., & Pisano, G. P. (2001). Disrupted routines: Team learning
and new technology implementation in hospitals. Administrative Science Quarterly, 46,
685-716.
50
Edmondson, A. C., & Nembhard, I. M. (2009). Product development and learning in project
teams: the challenges are the benefits. Journal of Product Innovation Management, 26,
123-138.
Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals. Educational
psychologist, 34, 169-189.
Elliot, A. J., & McGregor, H. A. (2001). A 2% 2 achievement goal framework. Journal of
personality and social psychology, 80, 501-519.
Farr, J. L., Hofmann, D. A., & Ringenbach, K. L. (1993). Goal orientation and action control
theory: Implications for industrial and organizational psychology. International review of
industrial and organizational psychology, 8, 193-232.
Fisher, S. L., & Ford, J. K. (1998). Differential effects of learner effort and goal orientation on
two learning outcomes. Personnel Psychology, 51, 397-420.
Garvin, D. A. (2000). Learning in action: A guide to putting the learning organization to work.
Harvard Business Press.
Gersick, C. J. (1988). Time and transition in work teams: Toward a new model of group
development. Academy of Management journal, 31, 9-41.
Gibson, C., & Vermeulen, F. (2003). A healthy divide: Subgroups as a stimulus for team
learning behavior. Administrative Science Quarterly, 48, 202-239.
Goffin, R. D., Gellatly, I. R., Paunonen, S. V., Jackson, D. N., & Meyer, J. P. (1996). Criterion
validation of two approaches to performance appraisal: The behavioral observation scale
and the relative percentile method. Journal of Business and Psychology, 11, 23-33.
51
Goffin, R. D., Jelley, R. B., Powell, D. M., & Johnston, N. G. (2009). Taking advantage of social
comparisons in performance appraisal: The relative percentile method. Human Resource
Management, 48, 251-268.
Goffin, R. D., & Olson, J. M. (2011). Is it all relative? Comparative judgments and the possible
improvement of self-ratings and ratings of others. Perspectives on Psychological
Science, 6, 48-60.
Gully, S. M., & Phillips, J. M. (2005). A multilevel application of learning and performance
orientations to individual, group, and organizational outcomes. Research in personnel
and human resources management, 24, 1-51.
Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and
exploitation. The Academy of Management Journal, 49, 693-706.
He, Z. L., & Wong, P. K. (2004). Exploration vs. exploitation: An empirical test of the
ambidexterity hypothesis. Organization science, 15, 481-494.
Heyman, G. D., & Dweck, C. S. (1992). Achievement goals and intrinsic motivation: Their
relation and their role in adaptive motivation. Motivation and emotion, 16, 231-247.
Hirokawa, R. Y. (1990). The Role of Communication in Group Decision-Making Efficacy A
Task-Contingency Perspective. Small group research, 21, 190-204.
Hox, J. J., Maas, C. J., & Brinkhuis, M. J. (2010). The effect of estimation method and sample
size in multilevel structural equation modeling. Statistical Neerlandica, 64, 157-170.
Jansen, J. J., Van Den Bosch, F. A., & Volberda, H. W. (2006). Exploratory innovation,
exploitative innovation, and performance: Effects of organizational antecedents and
environmental moderators. Management science, 52, 1661-1674.
52
Janssen, O., & Van Yperen, N. W. (2004). Employees' goal orientations, the quality of leadermember exchange, and the outcomes of job performance and job satisfaction. Academy of
management journal, 47, 368-384.
Jehn, K. A. (1995). A multimethod examination of the benefits and detriments of intragroup
conflict. Administrative Science Quarterly, 40, 256-282.
Kang, S. C., & Snell, S. A. (2009). Intellectual capital architectures and ambidextrous learning: a
framework for human resource management. Journal of Management Studies, 46, 65-92.
Kasl, E., Marsick, V. J., & Dechant, K. (1997). Teams as learners a research-based model of
team learning. The Journal of Applied Behavioral Science, 33, 227-246.
Katila, R., & Ahuja, G. (2002). Something old, something new: A longitudinal study of search
behavior and new product introduction. Academy of management journal, 45, 1183-1194.
Kim, T., & Rhee, M. (2009). Exploration and exploitation: Internal variety and environmental
dynamism. Strategic Organization, 7, 11-41.
Kostopoulos, K. C., & Bozionelos, N. (2011). Team exploratory and exploitative learning:
psychological safety, task conflict, and team performance. Group & Organization
Management, 36, 385-415.
Kozlowski, S. W., & Bell, B. S. (2008). Team learning, development, and adaptation. Group
learning, 15-44.
Kozlowski, S. W., & Bell, B. S. (2003). Work groups and teams in organizations. Handbook of
psychology.
Kozlowski, S. W., Chao, G. T., Grand, J. A., Braun, M. T., & Kuljanin, G. (2013). Advancing
Multilevel Research Design Capturing the Dynamics of Emergence. Organizational
Research Methods, 16, 581-615.
53
Kozlowski, S. W. J., & Klein, K. J. 2000. A multilevel approach to theory and research in
organizations: Contextual, temporal, and emergent processes. In K. J. Klein & S. W. J.
Kozlowski (Eds.), Multilevel, theory, research, and methods in organizations:
Foundations, extensions, and new directions: 3-90. San Francisco: Jossey-Bass.
Lant, T. K. (1992). Aspiration level adaptation: An empirical exploration. Management Science,
38, 1-22.
Lavie, D., Stettner, U., & Tushman, M. L. (2010). Exploration and exploitation within and across
organizations. The Academy of Management Annals, 4, 109-155.
LePine, J. A. (2003). Team adaptation and postchange performance: effects of team composition
in terms of members' cognitive ability and personality. Journal of Applied
Psychology, 88, 27-39.
Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic management
journal, 14, 95-112.
Levitt, B., & March, J. G. (1988). Organizational learning. Annual Reviews Sociology, 14, 319340.
Li, C. R., Chu, C. P., & Lin, C. J. (2010). The contingent value of exploratory and exploitative
learning for new product development performance. Industrial Marketing
Management, 39, 1186-1197.
Liang, D. W., Moreland, R., & Argote, L. (1995). Group versus individual training and group
performance: The mediating role of transactive memory. Personality and Social
Psychology Bulletin, 21, 384-393.
Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance.
Englewood Cliffs, NJ: Prentice-Hall.
54
Lord, R. G., Diefendorff, J. M., Schmidt, A. M., & Hall, R. J. (2010). Self-regulation at
work. Annual review of psychology, 61, 543-568.
Maier, N. R., & Solem, A. R. (1962). Improving solutions by turning choice situations into
problems. Personnel Psychology, 15, 151-157.
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization
Science, 2, 71-87.
March, J. G. (1996). Exploration and exploitation in organizational learning. Organizational
learning, 101-123.
March, J. G., & Simon, H. A. (1958). Organizations. New York: Wiley.
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and
taxonomy of team processes. Academy of Management Review, 26, 356-376.
Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. (2000). The
influence of shared mental models on team process and performance. Journal of applied
psychology, 85, 273.
Mathieu, J., Maynard, M. T., Rapp, T., & Gilson, L. (2008). Team effectiveness 1997-2007: A
review of recent advancements and a glimpse into the future. Journal of Management, 34,
410-476.
Mathieu, J. E., Tannenbaum, S. I., Donsbach, J. S., & Alliger, G. M. (2014). A review and
integration of team composition models moving toward a dynamic and temporal
framework. Journal of Management, 40, 130-160.
McAdam, R., & McClelland, J. (2002). Individual and team-based idea generation within
innovation management: organizational and research agendas. European Journal of
Innovation Management, 5, 86-97.
55
Moreland, R. L., Argote, L., & Krishnan, R. (1996). Socially shared cognition at work:
Transactive memory and group performance. In J. L. Nye & A. M. Brower (Eds.), What’s
so social about social cognition? Social cognition research in small groups: 57–84.
Thousand Oaks, CA: Sage.
Morgeson, F. P., & Hofmann, D. A. 1999. The structure and function of collective constructs:
Implications for multilevel research and theory development. Academy of Management
Review, 24, 249-265.
Muthén, B. O., & Curran, P. J. (1997). General longitudinal modeling of individual differences
in experimental designs: A latent variable framework for analysis and power
estimation. Psychological Methods, 2, 371-402.
Nicholls, J. G., Cheung, P. C., Lauer, J., & Patashnick, M. (1989). Individual differences in
academic motivation: Perceived ability, goals, beliefs, and values. Learning and
Individual differences, 1, 63-84.
O'Neill, T. A., & Allen, N. J. (2011). Personality and the prediction of team
performance. European Journal of Personality, 25, 31-42.
O’Neill, T. A., & Allen, N. J. (2014). Team task conflict resolution: An examination of its
linkages to team personality composition and team effectiveness outcomes. Group
Dynamics: Theory, Research, and Practice, 18, 159-173.
O’Reilly III, C. A., & Tushman, M. L. (2008). Ambidexterity as a dynamic capability: Resolving
the innovator's dilemma. Research in organizational behavior, 28, 185-206.
Park, S. H., Chen, R. R., & Gallagher, S. (2002). Firm resources as moderators of the
relationship between market growth and strategic alliances in semiconductor startups. Academy of management Journal, 45, 527-545.
56
Payne, S. C., Youngcourt, S. S., & Beaubien, J. M. (2007). A meta-analytic examination of the
goal orientation nomological net. Journal of Applied Psychology, 92, 128-150.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2007). Linear and nonlinear mixed effects
models. R package version 3.1-117.
Porter, C. O. (2005). Goal orientation: effects on backing up behavior, performance, efficacy,
and commitment in teams. Journal if Applied Psychology, 90, 811-818.
Phillips, J. M., & Gully, S. M. (1997). Role of goal orientation, ability, need for achievement,
and locus of control in the self-efficacy and goal--setting process. Journal of Applied
Psychology, 82, 792-802.
Raisch, S., & Birkinshaw, J. (2008). Organizational ambidexterity: Antecedents, outcomes, and
moderators. Journal of Management, 34, 375-409.
Raisch, S., Birkinshaw, J., Probst, G., & Tushman, M. L. (2009). Organizational ambidexterity:
Balancing exploitation and exploration for sustained performance. Organization Science,
20, 685-695.
Raudenbush, S. W., & Xiao-Feng, L. (2001). Effects of study duration, frequency of observation,
and sample size on power in studies of group differences in polynomial
change. Psychological methods, 6, 387-401.
Senge, M. (1990). The leader’s new work: Building learning organizations. Sloan Management
Review, 32, 7-23.
Shaw, J. D., Zhu, J., Duffy, M., Scott, K. L., Shih, H., & Susanto, E. 2011. A contingency model
of conflict and team effectiveness. Journal of Applied Psychology, 96, 391-400.
57
Simons, T. L., & Peterson, R. S. (2000). Task conflict and relationship conflict in top
management teams: the pivotal role of intragroup trust. Journal of applied
psychology, 85, 102.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and
event occurrence. Oxford university press.
Stata, R., & Almond, P. (1989). Organizational learning: The key to management
innovation. The training and development sourcebook, 2.
Steel, P., & König, C. J. (2006). Integrating theories of motivation. Academy of Management
Review, 31, 889-913.
Steiner, I. D. 1972. Group processes and productivity. New York: Academic Press.
Stewart, G. L. 2003. Toward an understanding of the multilevel role of personality in teams. In
M. R. Barrick & A. M. Ryan (Eds.), Personality and work: Reconsidering the role of
personality in organizations:183-204. San Francisco: Jossey-Bass.
Uotila, J., Maula, M., Keil, T., & Zahra, S. A. (2009). Exploration, exploitation, and financial
performance: analysis of S&P 500 corporations. Strategic Management Journal, 30, 221231.
Van Deusen, C. A., & Mueller, C. B. (1999). Learning in acquisitions: understanding the
relationship between exploration, exploitation and performance. Learning Organization,
The, 6, 186-193.
Van Offenbeek, M., & Koopman, P. (1996). Interaction and decision making in project
teams. Handbook of work group psychology, 159, C187.
58
West, M. A. (1990). The social psychology of innovation in groups. In M. A. West & J. L. Farr
(Eds.), Innovation and creativity at work: Psychological and organizational strategies:
309-333. Chichester, England: Wiley.
West, M. A., & Farr, J. L. (Eds.). (1990). Innovation and creativity at work: Psychological and
organizational strategies. Chichester, England: Wiley.
Wilson, J. M., Goodman, P. S., & Cronin, M. A. 2007. Group learning. Academy of Management
Review, 31, 1041-1059.
Wong, S. S. (2004). Distal and local group learning: Performance trade-offs and tensions.
Organization Science, 15, 645-656.
Zellmer-Bruhn, M., & Gibson, C. (2006). Multinational organization context: Implications for
team learning and performance. Academy of management journal, 49, 501-518.
59
!"#$%&'()*+#,*+-%#./%&'()*0,#,01"%2"3#10*4+#)%54+1"-%%
Appendix
A: Exploratory and Exploitative Learning Measure
Room Number: ___________________
%
Team Member Name: __________________________________
Team Number: ___________________
Team Member Name: __________________________________
Project Number: __________________
Team Member Name: __________________________________
Section Number: __________________
Team Member Name: __________________________________
Directions:
You will be asked to report on the degree to which your team engaged in exploratory and exploitative learning
over the past week, up to and including todays lab. Please take a minute to discuss the answer as a team, and
reflect back on how much your group engaged in these behaviours over the past week.
Next, place a mark on the line best representing your teams answer, and label the line with todays date. Please
note that there is no correct or best answer, and teams will likely display a wide range of these behaviors at
various points in the project. Most important, the midpoint marked 50 does not mean 50% or half but, instead,
signifies the average amount of the behavior has occurred in the team in comparison to all other teams. When
making future ratings you will compare each weeks rating to your previous weeks ratings.
Exploratory Behaviour
Exploration, in the current context, involves experimentation, flexibility in thinking, searching for new
information, seeking variation, testing novel ideas and concepts, and discovering new information. Often these
behaviors create new capabilities and the acquisition of new knowledge.
Please rate the extent to which your team engaged in these exploratory behaviours over the past week, up to and
including today’s lab. Place a vertical dash on the line that best represents your team’s answer and label it with
todays date.
|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|
0
Below Average
50
Average
100
Above Average
Exploitative Behaviour
Exploitation, in the current context, involves refining existing knowledge and skills, executing task work,
working toward efficiency, practicing existing skills, production, implementation of ideas, and utilizing
standard knowledge. Often these behaviours lead to enhanced efficiency and reinforce existing capabilities.
Please rate the extent to which your team engaged in these exploratory behaviours over the past week, up to and
including today’s lab. Place a vertical dash on the line that best represents your team’s answer and label it with
todays date.
|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|
0
Below Average
50
Average
60
100
Above Average
Appendix B: Innovation Rating Training Material
Definition of Innovation
Innovation encompasses two components:
1. The existence of a novel, unique, or original idea(s).
2. The effective implementation of the idea(s) or functionality of the prototype.
Scale Directions
•
•
•
•
•
•
In principal, if all the teams could be measured by one rater, each team could be ranked
from most effective to least effective.
A team project of average innovativeness would be ranked in the middle of the scale.
If you rank a team at the 50 mark this indicated that half of all the other projects would be
more innovative than the team and the other half of all team projects would be less
innovative than the team.
You are being asked to make a relative judgment about where the teams in your lab fall
on the scale relative to all the teams in ENGG 200.
On the next page you will fill in the information about each team. When you place a
vertical dash on the scale label it with the team number assigned to each team.
Important: When making your ratings of how innovative each team was you must
consider both aspects of innovation: existence of novel and original ideas and the
effective implementation of the idea or functionality of the project.
61
Appendix C: Innovation Rating Form
Your Name: _________________________ Today’s Date: ________________________
Lab Room Number: ___________________ Friday or Monday Lab: _______________
Team Number: ______
Team Number: ______
Team Number: ______
Name 1: _________________
Name 1: _________________
Name 1: _________________
Name 2: _________________
Name 2: _________________
Name 2: _________________
Name 3: _________________
Name 3: _________________
Name 3: _________________
Name 4: _________________
Name 4: _________________
Name 4: _________________
Team Number: ______
Team Number: ______
Team Number: ______
Name 1: _________________
Name 1: _________________
Name 1: _________________
Name 2: _________________
Name 2: _________________
Name 2: _________________
Name 3: _________________
Name 3: _________________
Name 3: _________________
Name 4: _________________
Name 4: _________________
Name 4: _________________
Team Number: ______
Team Number: ______
Team Number: ______
Name 1: _________________
Name 1: _________________
Name 1: _________________
Name 2: _________________
Name 2: _________________
Name 2: _________________
Name 3: _________________
Name 3: _________________
Name 3: _________________
Name 4: _________________
Name 4: _________________
Name 4: _________________
Team Innovation
Please rate team projects relative to one another based on innovativeness. First consider the rank
order of the projects, and further refine the assigned rating by considering the project relative to
the average of all other team projects in ENG200.
Please place a vertical dash on the line which best represents the standing of each team project
on the measure of innovation. Innovation includes both the existence of novel and original
ideas and the effective implementation of the idea or functionality of the project.
|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|
0
50
Below Average
Average
62
100
Above Average
Appendix
D: Goal Orientation Survey
!"#$%$#&'()*+'(),-$."/'/$+")0&-%.1)
Room Number: ___________________
)
Team Member Name: __________________________________
Team Number: ___________________
Section Number: __________________
Please rate the extent to which you agree or disagree with the following statements about
YOU.
Strongly
Disagree
Disagree
Neutral
Agree
Strongly
Agree
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
The things I enjoy the most are the things I do
the best.
1
2
3
4
5
The opinions others have about how well I
can do certain things are important to me.
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
The opportunity to do challenging work is
important to me.
When I fail to complete a difficult task, I plan
to try harder the next time I work on it.
I prefer to work on tasks that force me to
learn new things.
The opportunity to learn new things is
important to me.
I do my best when I’m working on a fairly
difficult task.
I try hard to improve on my past
performance.
The opportunity to extend the range of my
abilities is important to me.
When I have difficulty solving a problem, I
enjoy trying different approaches to see
which one will work.
I prefer to do things that I can do well rather
than things that I do poorly.
I’m happiest at work when I perform tasks on
which I know that I won’t make any errors.
I feel smart when I do something without
making any mistakes.
I like to be fairly confident that I can
successfully perform a task before I attempt
it.
I like to work on tasks that I have done well
on in the past.
I feel smart when I can do something better
than most other people.
63
Appendix E: Work Intensity and Team Conflict Scales
Work Intensity Measure
1 – Strongly Disagree, 2 – Disagree, 3 – Neutral , 4 – Agree, 5 – Strongly Agree
1. Over the past week, we devoted a lot of energy to the project.
2. Over the past week, we worked intensely on the project.
3. Over the past week, we worked on the project at full capacity.
Task Conflict Measure
1 – A Very Small Amount, 2 – A Little , 3 – Some, 4 – A Considerable Amount, 5 – A lot
1. To what extent are different opinions, viewpoints, and perspectives discussed while
settling on your team’s problem definition?
2. To what extent are different opinions, viewpoints, and perspectives discussed while
settling on your team’s design concept?
3. To what extent are different opinions, viewpoints, and perspectives discussed while
settling on your team’s prototypes?
4. To what extent are different opinions, viewpoints, and perspectives discussed while
settling on your team’s presentation?
Relationship Conflict Measure
1 – A Very Small Amount, 2 – A Little , 3 – Some, 4 – A Considerable Amount, 5 – A lot
1.
2.
3.
4.
How much emotional conflict was there among the members of your group?
How much anger was there among the members of the group?
How much personal friction was there in the group during decisions?
How much were personality clashes between members of the group evident?
Process Conflict Measure
1 – A Very Small Amount, 2 – A Little , 3 – Some, 4 – A Considerable Amount, 5 – A lot
1. How frequently do your team members disagree about the optimal amount of time to
spend on different parts of teamwork?
2. How frequently do your team members disagree about the optimal amount of time to
spend in meetings?
3. How often do your team members disagree about who should do what?
64
Appendix F: Design Project Outline
65
66
Appendix G: Project Deliverables and Data Collection Details
Project Deliverables
Week 1:
Oct 31 &
Nov 3
Nov 7 & 10
Week 2:
Nov 14 & 17
Before the end of lab period, show your TA an objective tree and have a
problem statement written that precisely describes the functional
requirements, constraints, and your assumptions (if any).
NO LAB
A short presentation is due that includes your potential designs, a model of a
steering, propulsion, or lifting mechanism. No text allowed in the
presentations; only labeled drawings, pictures, and tables allowed.
Week 3:
Before the end of the lab period, you must show your TA a physical model
Nov 21 & 24 and demonstration of a device that is capable of picking up the rock. This
does not need to be your final prototype or be constructed out of your final
materials, and does not need to fulfill all of the design requirements.
Week 4:
Nov 29 &
Dec 1
At the end of the lab period, a demonstration of the device will be evaluated
by your TA, as well as a brief summary presentation.
Data Collection Details
Room
Friday 8AM
Friday 2PM
Monday 12pm
Monday 3pm
1
216
(24)
6 teams
(24)
6 teams
(12)
3 teams
(24)
6 teams
2
215
(24)
6 teams
(24)
6 teams
(28)
7 teams
(24)
6 teams
3
224 (A)
N/A
(24)
6 teams
(32)
8 teams
(28)
7 teams
4
221 (B)
(20)
5 teams
(24)
6 teams
(32)
8 teams
(24)
6 teams
5
220 (C)
(21)
5 teams
*1 team of 5
(24)
6 teams
(26)
7 teams
*2 teams of 3
(24)
6 teams
6
219 (D)
(23)
6 teams
*1 team of 3
(24)
6 teams
(35)
9 teams
*1 team of 3
(28)
7 teams
573 Students
144 Teams
112 Students
28 Teams
144 Students
36 Teams
165 Students
42 Teams
152 Students
38 Teams
67