The Relationship Between Team Dynamics, Team efficacy, and Team Performance

Abstract

The goal of this study was to assess how team dynamics and team efficacy affected the performance of teams making video games over time. Surveys were administered to three software product development teams over six iterative development cycles. The study found a significant relationship between the team dynamics and team efficacy. Additionally, there was a significant relationship between team dynamics and team performance, as well as team efficacy and team performance.

Full text

The Relationship Between Five Key Dynamics
of Successful Teams, Team-Efficacy, and Team
Performance
Abstract
— The
goal of
this
study
was to
generate
tools to
measure
team
dynamics and team-efficacy. Surveys were administered to
three software product development teams over six iterative
development cycles to assess five team dynamics and team-
efficacy. Data were gathered, analyzed, and examined to test
the relationship between the measures of team dynamics, team-
efficacy, and team performance. The study found a significant
relationship between the five team dynamics and team-efficacy.
Additionally, there was a significant relationship between team
dynamics and team performance, as well as team-efficacy and
team performance.
Keywords-component; team dynamics, team-efficacy,
psychological safety, dependability, structure and clarity,
meaning, impact, effectiveness, performance, game development
I. INTRODUCTION
In his 1997 book, Self-Efficacy: The Exercise of Control,
Albert Bandura analyzed the nature, merit and consequence
efficacy has on the effectiveness of individuals and teams
[1]. Bandura defines team-efficacy as the confidence
individuals have in their team’s ability to accomplish tasks
and solve problems given the necessary amount of effort to
achieve its goals [1]. According to Bandura, understanding
efficacy in teams is critical to improve the functions of a
team, because their perception of the work of the group
affects their performance [1].
Teams are defined as a group of people with
interdependent tasks that gather with common goals and
engage socially [2]. Research has shown that teams perform
better when the team-efficacy is high [1]. For example,
while
the
composition of a student body may have an influence on
academic achievement, Bandura found the most direct
impact comes from the team-efficacy of the faculty [1].
Specifically, when faculty members’ beliefs about their
team-efficacy improved, they motivated and educated their
students more effectively [1]. This also increased efficacy of
students which resulted in increased academic performance
[1]. While Bandura’s research focuses on the educational
domain, efficacy has been studied in various social systems,
such as healthcare, corporations, and software product
development teams [1, 3, 4].
Other predictors of team performance were studied by tech
giant Google. In 2016, Google performed an internal
research study of 180 teams and discovered that high
performing teams shared five key team dynamics [5]. They
identified the team dynamics as: psychological safety,
dependability, structure and clarity, meaning, and impact
[5]. Google noted that a team deficient in two or more of
these dynamics would not perform as well as one that
measured high in all categories [5]. However, if a team
scored low on a single dynamic, the lack of that dynamic
alone was not impactful enough to lower the performance of
the entire team [5]. Google’s research was selected for this
paper since much of their organizational structure is relevant
to software product development and design driven teams
[6].
The purpose of this study was to look for a relationship
between team-efficacy and team dynamics in relation to
team performance. The researchers measured and analyzed
Conor P. Dalton (Author)
Production
SMU Guildhall
Plano, TX
Elizabeth Stringer (Author)
Professor
SMU Guildhall
Plano, TX
John Slocum (Author)
Professor
SMU Guildhall
Plano, TX
Mark Nausha (Author)
Professor
SMU Guildhall
Plano, TX

the performance of cross-disciplinary software product
development teams with both team dynamics and team-
efficacy measures. The teams in this study were organized
with the purpose of designing a computer game. Game
teams were comprised of artists, designers, programmers,
and team leads working together to realize the team’s
creative vision [8]. Qualitative data was gathered through
participant interviews and observations to give context to
the analysis.
II. SITUATING THE STUDY
A. The Five Key Team Dynamics
The five key team dynamics are measures of the group
internal dynamics [5]. They are: psychological safety,
dependability, structure and clarity, meaning, and impact
[5]. Google’s research determined that high scores in the
five team dynamics are markers of highly successful teams
[5].
Google’s organization measures success using
observations of three levels of the corporate hierarchy:
managerial, team leadership, and team members [5].
Managers measure success through the number of product
launches and increase in profits [5]. Team membership
reports that the culture of teams measures success [5]. Team
leadership determines success by the combination of both
culture and number of product launches [5]. By improving
each dynamic, teams can achieve their goals and be more
effective [5].
1) Psychological Safety:
Psychological safety refers to an environment in which a
team member can contribute ideas and constructive
criticisms without fear of reprimand [5]. A climate of
psychological safety affects a person’s belief about
interpersonal risk [9]. In a climate of safety, members give
suggestions without worry of seeming ignorant, foolish, or
incompetent [5]. Lack of psychological safety often comes
from general distrust of those in authority [6]. For example,
to create trust, Google removed common signifiers of
authority, such as giving senior staff members exclusive
benefits and resources [6]. When the common signifiers of
authority were balanced, team members felt they could
openly discuss issues and solutions with their leaders [6].
Team members could fix and discuss issues they reported
without fear of repercussion [6]. These strategies resulted in
more effective and higher performing teams [6].
2) Dependability:
Dependability is defined as team members completing
tasks according to expectations set by the team [5].
Dependable team members also complete tasks on time and
meet deadlines [5]. For example, two members in a team at
Google were known for undermining each other and
refusing to work together [6]. After productivity fell and the
team lagged behind on meeting goals, the team was asked to
rate each other on their helpfulness [6]. Even though the
results were anonymized, the two individuals who were not
performing to the expectations of the team saw themselves
at the bottom of the scale and self-corrected [6]. They
realized they were less dependable because of their
behavior, and thus the team was not meeting expectations
[6]. Once they corrected their behaviors, the performance of
the team and their ability to meet deadlines improved [6].
3) Structure and Clarity:
Structure and clarity refer not only to goals that are well-
defined, but also an understanding of the expectations a
team has for the individual [5]. For structure, a team with
well-defined goals helps members understand how to meet
expectations through pipelines and workflows [5]. Team
members also understand the consequences of their work
[5]. For clarity, goals that are attainable, specific, and
challenging increase performance and motivation [10].
Without structure and clarity, a team member has a difficult
time finding the meaning and value in their work on a team
[6]. For example, Google discovered that half their team
members were dissatisfied with the company’s performance
evaluation system because it lacked transparency and took
more time than necessary [6]. After receiving feedback from
their employees, Google altered their performance
management structure to make teams’ goals visible to
everyone in the company and defined performance
evaluation time as an opportunity for goal creation and
team-evaluation against those goals [6]. After raising the
standard for goals and performance, team members reported
increased clarity of the internal structure of the company
and the teams were more in alignment with their peers [6].
4) Meaning:
Meaning is a measurement of the personal significance
of the individual work to each team member [1]. Meaning is
connected to individual purpose, and financial security or
self-expression can result in meaning [5]. People generally
desire to do work that has personal meaning to them, and
this desire constitutes a large part of their motivation [1].
For example, Google found that only 86% of their
salespeople strongly agreed that there was a clear link
between their work and the company’s objectives [6]. To
improve the meaning for its sales forces, sales teams were
given customer accounts of individuals and businesses that
were impacted and helped by their efforts [6]. Team
members were inspired by their influence in their
customer’s lives and reported an increase in meaningfulness
that positively affected performance [6].
5) Impact:
Impact is a measurement of the individual’s perception
of whether their work is part of something greater than their
individual tasks [5]. When a team member perceives that
their work influenced their team or organization and helped
complete its goals, they consider their work impactful [1].
For example, Google employees found suggesting changes
to the company difficult. The CFO introduced a program

that allowed team members to vent frustrations, talk about
new ideas, and suggest fixes for problems [6]. Team
members took advantage of those opportunities and the
company implemented those changes [6]. After team
members saw their ideas impact the company’s success,
employees reported increased happiness and productivity
[6].
B. Team-Efficacy
Team-efficacy is a psychological dynamic that exists at
the group level [1]. Team-efficacy is the belief that teams
can attain the goals they value and have the means to do so
[1]. It is not always the total sum of the perceived
effectiveness of the individuals in a team but rather refers to
the collective belief that a team can accomplish its goals [1].
Team-efficacy is the result of team members collecting
information about themselves, each other, and their tasks
within the context of the team [11].
Research has found that modeling constructive
behaviors, offering strategies to manage complex issues
(such as communication pipelines), and giving resources for
coping with work related stress can improve the efficacy of
teams [4]. Other factors that increase team-efficacy include
the combination of knowledge and competencies in the
team, the structure of the group, the effectiveness of the
leadership, the team strategies, and if the team members are
collaborative [1].
Team-efficacy influences the vision a team plans to
achieve, how their resources are managed, the workflows
they create, the effort placed in the goals of the team, and
their resistance to failure or obstacles [1]. When teams have
high efficacy, they take advantage of opportunities and
resources needed to complete their goals which results in
high performance [1].
III. HYPOTHESIS DEVELOPMENT
C. Team Dynamics and Team-Efficacy
In this study, the researchers proposed a framework to
measure team dynamics using a modified tool, created by
Google’s internal research team as well as a modified tool to
measure team-efficacy, originally made for self-efficacy
based on Bandura’s studies on efficacy in teams. The team
dynamics and team-efficacy have not been compared to one
another in prior research studies. Because both measure
effectiveness of the group, this study posited they would
have a positive relationship to each other. In this study, we
propose the following hypothesis:
Hypothesis 1: Team scores in the team dynamics are
positively related to team-efficacy scores.
D. Team Dynamics and Team Performance
In testing Hypothesis 1, team performance was a
dependent variable for team dynamics and team-efficacy.
When Google measured the effectiveness and performance
of teams, they developed tools to improve team success [6].
Because their teams were collaborative, they polled team
managers on interpersonal relationships, skill sets, and goal
structures to understand their teams’ effectiveness [6]. Their
research pulled together common denominators for success
[6]. The Google researchers looked for specific indicators
that impacted outcome metrics (both qualitatively and
quantitatively), were consistent across different teams with
different goals, and had statistical significance [6]. In this
study, we propose the following hypothesis:
Hypothesis 2: Team dynamics are positively related to team
performance.
E. Team-Efficacy and Team Performance
Bandura’s research centers on the study of
understanding the efficacy in teams [1]. If a team believes
collectively in their ability to accomplish certain tasks or
goals they have set, then their efficacy is high [1]. Factors
that contribute to team efficacy are the varied backgrounds
and experiences of group members, the competency of the
group, the methodologies the team adopts to solve
problems, and the behavioral interactions between team
members [1]. The common actions of teams with high
efficacy are 1) set meaningful goals, 2) exert the amount of
effort necessary to complete those goals, and 3) accomplish
those goals despite setbacks [1]. In this study we propose
the following hypothesis:
Hypothesis 3: Team-efficacy is positively related to team
performance.
IV. METHOD
F. Overview of Team Structures
Over four months, 47 students were divided into three
cross-disciplinary digital game development teams. Teams
one and three had 17 people each and team two had 13.
After a time period of two to four weeks, each team
completed a clearly defined milestone goal and presented
their progress to a group of three faculty stakeholders for
review. After each of the total six milestone presentations,
teams were given two surveys: team dynamics and team-
efficacy. The researchers also gathered team performance
scores given by the faculty stakeholders at the end of the
game development project.
G. Procedures and Sample
The measure for each of the five team dynamics was
assessed using a five-item survey. As seen in Figure 1, each
question was scored on a five-point Likert scale based on

Google’s tools for raising team effectiveness [5]. An
example item is: “We were able to take risks within the
goals of the sprint, voice our opinions and ask judgment-
free questions.” The response options were Strongly Agree,
Agree, Neutral, Disagree, or Strongly Disagree. The
dependability question is on a percentage scale from 1% to
100%.
Figure 1: Team Dynamics Survey
The measure for team-efficacy was assessed using a ten-
item survey. As seen in Figure 2, each question was rated on
a five-point Likert scale. An example item is “My team
always manages to solve difficult problems if they try hard
enough.” The response options were Strongly Agree, Agree,
Neutral, Disagree, or Strongly Disagree.
Figure 2: Team-Efficacy Survey
To alleviate student concerns about grading their peers
and to remove potential student bias in the results from the
participants, no grades were associated with completing the
surveys.
H. Statistical Analysis Tools
Data gathered from survey results were analyzed using
Statistical Package for the Social Sciences (SPSS) and
organized using Microsoft Excel. The results generated from
the analysis were then used to determine correlations
between team dynamics and team-efficacy. Pearson product-
moment correlation tests of mean comparisons were
computed to observe the correlation between team dynamics
and team-efficacy. Pearson product-moment correlation
tests were also used to measure the correlation between both
metrics and team performance. Pearson correlations that

result in scores around .50 and above are considered
moderately positive relationships, whereas scores around .
70 are considered strong positive relationships. Regression
tests were also performed to understand the relationships
between the two metrics and to explain internal variances in
them in connection with team performance. Alpha
coefficients were used to check the internal consistency
reliability of the survey tools using Cronbach’s Alpha. If
internal consistency reliability outputs results around or
above .70, then the tool is considered acceptably reliable or
consistent. If the results are between .60 and .70 then the
tool is considered questionable and may result from outliers
such as low number of test cases. Kendall’s W coefficient of
concordance was used to measure the inter-rater reliability
for the team performance scores. Kendalls W scores
between .61 and .80 indicate that the raters have good
agreement.
V. ANALYSIS RESULTS
I. Team Dynamics Survey Instrument Analysis
For the team dynamics, the internal consistency
reliability of the scale was assessed using coefficient Alpha.
The data indicate that  = .69 on Team 1,  = .74 on Team
2, and  = .78 on Team 3. These are the average alpha
coefficients over the six milestones, and these indicate that
the survey questions were consistent and thus the tool was
reliable.
J. Team-Efficacy Survey Instrument Analysis
For team-efficacy, the internal consistency reliability of
the scale was assessed using coefficient Alpha. The data
indicate that  = .88 on Team 1,  = .91 on Team 2, and  =
.93 on Team 3. These are the average alpha coefficients over
the six milestones, and these indicate that the survey
questions were consistent and thus the tool was reliable.
K. Team Performance Survey Instrument Analysis
Kendall’s W coefficient of concordance was calculated to
determine the inter-rater reliability of each team’s
performance. Results (w = .74, p < .05) indicate a high
degree of inter-rater agreement between the three raters over
the 18 trials.
L. Participant Observations
To establish context of the relationships between the
metrics discussed, the researchers interviewed the team
leaders. Team 1 included 13 interdisciplinary student
developers. Team 2 included 17 interdisciplinary student
developers. Team 3 included 17 interdisciplinary student
developers. One of the researchers was the team lead on
Team 2. This researcher contributes as a participant with
observations from journal entries.
M. Hypothesis Results Analysis
Hypothesis 1 posited a positive relationship between the
team dynamics and team-efficacy. A Pearson product-
moment correlation coefficient test was used to test this
hypothesis. The relationship between team-efficacy and the
team dynamics was (r = .96, p < .001) indicating that the
team dynamics and team-efficacy are strongly related to
each other.
Hypothesis 2 posited a positive relationship between the
team dynamics and team performance. A Pearson product-
moment correlation coefficient test was used to test this
hypothesis. The results (r = .69, p < .002) indicate that the
team dynamics and team performance are positively related.
To further understand this relationship, a multiple regression
test was performed. All teams’ performances over the 18
unique time periods were entered as the dependent variable
and all team dynamic scores were entered as the
independent variable in statistical software. The results (r2
= .47, p < .002) indicated that 47% of the variance in team
performance is explained by the team’s dynamic scores. In
other words, by knowing the measure of team dynamics,
team leads can accurately predict the performance of a team
47% of the time.
Hypothesis 3 posited a positive relationship between
team-efficacy and team performance. A Pearson product-
moment correlation coefficient test was used to test this
hypothesis. The results (r = .75, p < .001) indicate that team
efficacy and team performance are positively related. A
multiple regression test was performed. All teams’
performances over the 18 unique time periods were entered
as the dependent variable and all team-efficacy scores were
entered as the independent variable in statistical software.
The results (r2 = .54, p<.001) indicated that 54% of the
variance in team performance is explained by team-efficacy
scores. In other words, by knowing the measure of team-
efficacy, team leads can accurately predict the performance
of a team 54% of the time.
TABLE I. TEAM DYNAMICS AND TEAM-EFFICACY MEANS FOR TEAM
1
Measures
Team Dynamics and Team-Efficacy Means for each
milestone measured
M1 M2 M3 M4 M5 M6
Team
Dynamics 4.22 3.92 4.04 4.32 4.46 4.58
Team-
Efficacy 4.29 4.12 4.19 4.38 4.51 4.63
Performance 4.33 4.00 4.67 4.67 4.67 5.00
TABLE II. TEAM DYNAMICS AND TEAM-EFFICACY MEANS FOR
TEAM 2

Measures
Team Dynamics and Team-Efficacy Means for each
milestone measured
M1 M2 M3 M4 M5 M6
Team
Dynamics 4.22 4.24 4.42 4.08 3.68 3.44
Team-
Efficacy 4.37 4.32 4.49 4.19 3.69 3.57
Performance 4.67 3.00 2.67 3.33 1.33 1.00
TABLE III. TEAM DYNAMICS AND TEAM-EFFICACY MEANS FOR
TEAM 3
Measures
Team Dynamics and Team-Efficacy Means for each
milestone measured
M1 M2 M3 M4 M5 M6
Team
Dynamics 3.72 3.98 3.73 4.00 3.86 4.17
Team-
Efficacy 3.72 3.83 3.79 4.08 3.98 4.40
Performance 3.00 3.00 2.33 3.67 4.33 5.00
Table 1, shows the means for team dynamics and team-
efficacy for Team 1. The results indicated a trend of
increasing performance along with the increase of team
dynamics and team-efficacy. The data in Table 3 illustrate
team dynamics and team-efficacy means for Team 3 and
shows the same trends as in Table 1. However, in Table 2,
the team dynamics and team-efficacy means for Team 2
show there was a disparity in the third milestone. Team
dynamics and team-efficacy improved, but the performance
decreased.
N. Hypothesis Results Discussion
Per Hypothesis 1, the significant correlation between the
team dynamics and team-efficacy indicates that a team lead
can use either the team dynamics or team-efficacy to
influence the performance of their teams. Additionally, the
benefits of improving team-efficacy can be acquired through
the improvement of the team dynamics, and vice versa. This
strong correlation could be a result of team dynamics and
team-efficacy aggregating measures at the group level.
Despite the significant relationship between team
dynamics and team-efficacy, one may not be arbitrarily used
as a substitute for the other. Since each metric gathers data
on different aspects, team leads should measure that which
applies to their team’s situation. A team lead would use the
team dynamics for specific internal dynamic issues, such as
lack of psychological safety or team members reporting a
lack of structure and clarity. Alternately, a team lead would
measure team-efficacy to look specifically at how team
members are motivated, such as their belief in the team and
the quality of work done by their peers [12]. In either case,
the results indicate both are strong predictors for team
performance. Team leads should thus pick the measure that
is most prudent for their team.
Per Hypothesis 2, the results of this study show that,
team dynamics have a strong relationship with team
performance. Team dynamics are focused on the internal
structure of a team [5]. Teams that score high in each of the
team dynamics variables are more successful than teams
that score lower [5]. Each dynamic has a different effect on
team performance [5]. For example, psychological safety
affects team performance because if team members believe
they are safe to present ideas and discuss issues openly with
their team, higher quality ideas rise to the top [5].
Dependability directly improves performance because teams
are meeting their deadlines and quality expectations [5]. The
structure and clarity within the team is how schedule and
quality expectations are understood [5]. If team members
have established communication channels, they have the
structure to understanding the expectations [5]. Establishing
clear team expectations provides the vision of the project
[5]. Knowing the goals of the team as a whole allows team
members to understand the big picture and helps team
members stay aligned to those goals [5]. Meaning affects
performance because people are motivated to meet their
goals within a project when they feel their teams work has
value to them [5]. Internally, the tasks that they complete as
part of the team has personal worth to them. Impact is more
externally focused [5]. People are motivated when they their
work affects the world around them because they see the
significance of the work they perform [5]. Collectively, the
team dynamics determine the performance of teams.
Per Hypothesis 3, the results of this study show a strong
positive relationship between team-efficacy and team
performance [12]. Team-efficacy focuses on how members’
aspirations affects their team’s performance. Teams with
high efficacy believe: 1) they have the ability needed to
perform the task, 2) they can deliver the required effort
needed to undertake the task, and 3) they believe that few
outside events will keep them from performing their tasks.
First, high-efficacy teams believe that they can reach the
goals they set for themselves [12]. They set challenging
goals that require all members to work interdependently.
Challenging goals are used to keep members focused on the
team’s self-defined vision. Second, team-efficacy influences
the amount of effort that team members exert toward
reaching the team’s performance goal [12]. Teams with high
efficacy work diligently to learn new skills and are
confident that their efforts will be rewarded with high
performance [12]. Third, high-efficacy teams persist on
tasks even when the team encounters temporary obstacles.
In low-efficacy teams, obstacles derail team performance
[12].
O. Team Dynamics, Team-Efficacy and Team Performance
Data Results Discussion with Participant Observations
6) Team 1:
The researchers analyzed the results from Table 1 and
highlighted the improvement in scores from M2 - M3 after
the decrease in scores from M1 - M2 to discuss with the
team lead. A review of the separate measurement data

suggested impact and psychological safety were the
categories that affected team performance during this time
period.
Impact scores were 4.33 in M1, 3.92 in M2, and 4.11 in
M3. The team lead observed individual team members were
reticent to suggest initial ideas for the game during pre-
production. According to discussions, they believed their
ideas would not be implemented. During the production of
two milestones, they actually implemented several of the
team’s ideas. Team members were able to see the impact of
their effort on the project, and the team measured higher
performance. The final impact score in M6 was 4.56.
The psychological safety scores were 4.78 in M1, 4.33
in M2, and 4.11 in M3, indicating a downward trend. The
team lead reported that one department did raise an issue
with their heavy workload. Too many tasks spread effort too
thinly to meet the project quality standards. The team lead
decided to address the climate of psychological safety by
holding open meetings for the whole team to discuss
problems with the workload. The team prioritized design
decisions affording the opportunity to complete work on
time and to the project quality standard expected. The team
performance increased overall and the team successfully
delivered their game.
7) Team 2:
The researchers analyzed the results from Table 2 and
highlighted the decrease in performance scores from M2 -
M3 and subsequent decrease in team dynamic and team-
efficacy scores from M3 - M6 to research in the journals of
the team lead. A review of the separate measurement data
suggested meaning and team-efficacy were the categories
that affected team performance during this time period.
The meaning scores were 4.12 in M1, 4.06 in M2, and
4.46 in M3, indicating a downward trend with a spike at
M3. The team lead observed that the goals of the project
were unclear until late in the third milestone. This made it
difficult for the team members to contribute effectively and
develop personal value in their work. Per the observations
of the team lead, the lack of meaning hampered the team
members ability to accomplish their goals. The mean
meaning score was 3.87 out of 5, the lowest compared to
the other dynamics. The team lead observed low meaning
was due to an open form of design brainstorming without
hard decisions on the vision being made. Because the vision
was not clearly defined until M3, work was thrown out and
extra time was used to catch up, ultimately resulting in the
team losing personal value in the project. The brief spike in
meaning during M3 was due to the team believing their
work had personal value at that time, but because their
performance was still low, the team stopped believing their
work had meaning to their personal portfolios during M4
and continued to the end of the project.
The team-efficacy scores were 4.37 at M1, 4.32 at M2,
and 4.49 at M3. At the end of M3, the team-efficacy and
team dynamics were high, but their performance was low.
The team lead observed that the team members were caught
unaware by their low performance, perhaps due to over-
confidence. The low performance resulted in their team-
efficacy dropping over the next sprint, because team-
efficacy was tied to the groups perception of their work
quality. Team members stopped believing they could make a
professional quality game and missed the goals of their
milestones as a result. Ultimately, the lead observed that the
lack of meaning and decreasing team-efficacy lowered
team performance in the final two milestones and the team
was unable to recover.
A possible reason Team 2’s scores differed during M3
may have been due to forgetfulness of previous failures and
successes. Team 2 had high confidence in their ability to
complete their tasks successfully, but did not deliver the
product to the specifications of the stakeholders. Team
members may have overestimated their ability because of
selective memory of their successes in the past on similar
tasks, discounting their previous failures [1]. Another reason
the team did not succeed despite a high score in team-
efficacy may be that team members did not have the
experience yet on how to deliver at a professional quality
level. The members were unable to track their progress
against team goals accurately, creating a gap between the
quality of their work and the expectations of the
stakeholders [1].
8) Team 3:
The team lead was asked to discuss the results from
Table 3, specifically the decrease in team dynamic and
team-efficacy scores from M2 - M3 and the increase in team
performance scores from M4 - M5 despite a slight decrease
in team dynamic and team-efficacy scores. A review of the
separate measurement data suggested meaning and
structure and clarity were the categories that affected the
changes to the scores during this time period.
The meaning scores were 3.85 at M2 and 3.44 at M3.
The team lead reported the team struggled to figure out how
the gameplay matched the overall vision and goals of the
project. Team commitment was difficult to encourage, since
only one person held the vision of the project. This vacuum
led to lack of meaning for individual team members. The
mean meaning score was 3.79 of 5, the lowest score
compared to the other dynamics. To garner meaning, the
team lead encouraged the other members to iterate
repeatedly using paper prototypes and roleplays to engage
the entire team to “live” the vision. This put the team behind
on production, but they worked hard to get back on
schedule.
However, after M3, the performance of the team
decreased along with the team’s dynamics and team-

efficacy. Therefore, during the planning of M4, the team
leads aligned team members’ tasks with individual strengths
and framed the work within the context of the game for the
remainder of the project.
The structure and clarity scores were 4.22 at M4 and 4.40
at M5. The reframing of the individual team members
strengths caused them to see their project goals more
clearly, and they experienced structure and clarity. The
mean structure and clarity score was 4.14 of 5, the highest
score compared to the other dynamics. The team lead
reported team-efficacy increased, along with team
performance, in the final stages of the product development,
and the team launched their product successfully.
P. Limitations
The largest limitations of this research are the small
number of teams and the short study duration. The
relationship of team performance in a larger sample size
over a longer duration may show a stronger relationship
because the data would have more variance. In Bandura’s
studies of team-efficacy, there is ample evidence that
improving efficacy improves the team performance of the
teams and suggests there were other factors at play in the
three teams for this study [1]. In their research, Google
identified other dynamics of teams that they noted were not
significantly correlated to team performance, such as
consensus-driven decision making, extroversion of team
members, individual performance of team members,
workload size, team size, and experience [5].
Another factor may be demographics ratios on the teams.
No demographic data was collected, specifically gender
identification. Previous research demonstrated a positive
relationship between the ratio of females to males and team
performance [13]. Teams that had a more balanced ratio or
higher number of females to males showed increased team
performance [13]. Observationally, the ratio of males to
females on the teams in this study were not balanced. Thus,
it is proposed that research in the future include
demographic data gathering, specifically the data on gender,
to analyze this factor.
Another limitation may be the very close proximity of
the milestone presentations to the stakeholders and the
completion of the team dynamics and team-efficacy
surveys. These presentations may have influenced team-
efficacy, since the surveys were assessed on days milestones
were presented but before the grade was distributed back to
the team. If a team correctly or incorrectly believed their
performance was low based on their perceptions of the just
the presentation, it may have positively or negatively
specifically affected their team-efficacy scores in that
survey.
Q. Future Research
The data reflected a positive relationship between team
performance, team dynamics and team-efficacy. The
researchers propose the measure of team performance be
revised because the measure only captured task completion
and lacked sufficient variance. Other team analysis studies
used Liden, Wayne, and Stilwell’s four-item task
performance metric and Van Scotter and Motowidlo’s
thirteen-item contextual performance metric to generate
tools capturing performance data [2]. The internal
consistency reliability was high ( = .95) [2]. This metric
captures data on both task completion and contextual
behaviors [2]. Given the goals to capture team performance
and demographic data, a modified version of these three
tools could provide a more precise analysis and contextual
information for future research.
Future research should also be completed with teams of
more varied sizes. In this study the teams were similar in
size. Previous studies indicate that, in some instances, team
size can influence the team effectiveness and performance
[5]. Google’s researchers admit that while they focused on
five dynamics, there were outliers in their research,
including team size [5]. They concluded that the effects of
team size were not large enough to affect their data, but still
warranted consideration [5].
Finally, the researchers propose that future research
should measure and analyze the relationship between the
five team dynamics and the five dysfunctions of a team. The
five dysfunctions of a team are: absence of trust, fear of
conflict, lack of commitment, avoidance of accountability,
and inattention to results [15]. Studying the relationship
between the five dynamics and five dysfunctions may give
further context to the rise and fall in team performance
scores. For example, lack of commitment may be directly
correlated to meaning, and if so, team leads would gain
more understanding of how to improve their internal team
structure.
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