Leadership Strategy
Veedish
TeamsDevBus_ByFoo.pdf
Teams developing business ideas: how member characteristics and conflict affect member-rated team effectiveness
Maw-Der Foo
Accepted: 29 January 2009 / Published online: 25 February 2009
� Springer Science+Business Media, LLC. 2009
Abstract Team researchers have found that the
diversity to effectiveness ratings are mediated by
team conflict. Using a sample of 73 teams developing
their business ideas, I found direct effects of diversity
and conflict on member-rated team effectiveness.
Here, I explain how the circumstances under which
these teams operate can lead to these findings. For
these teams, task conflict was found to relate
negatively to member-rated team effectiveness. This
finding contrasts with research on organizational
teams, where task conflict usually relates positively to
team effectiveness ratings. I also found that both
diversity and average member experience influence
member-rated effectiveness. These findings imply
that diversity, conflict, and ratings of team effective-
ness may differ for teams developing business ideas
as compared to organizational teams. Thus, findings
from organizational team research should be applied
with caution to teams developing business ideas and
possibly to new venture teams in general.
Keywords Business ideas � Conflict � Diversity � Ratings of team effectiveness
JEL Classifications L26 � M12 � M13
It is important to study venture teams because high-
growth ventures are usually started by a team instead
of an individual entrepreneur (Friar and Meyer 2001).
Researchers have argued for relationships among
diversity, conflict, and ratings of team effectiveness
(Milliken and Martins 1996; Pelled et al. 1999).
Member diversity of a non-task nature, such as race
and age, may result in non-task conflict, while
member diversity of a task nature, such as work
function and education, may lead to task conflict
(Lankau et al. 2007; Milliken and Martins 1996;
Pelled et al. 1999). Non-task conflict that distracts
members from the team’s task can hurt team effec-
tiveness ratings; while task conflict that increases the
information that the team considers may enhance the
ratings of team effectiveness (Bantel and Jackson
1989; Pelled et al. 1999). That is, diversity to team
effectiveness ratings are mediated by team conflict.
For teams developing business ideas, a type of new
venture team, I examined the relationships among
diversity, conflict, and member-rated team effective-
ness. At the early venture stage, how members
evaluate their team may be important. If members
rate their team negatively, the team may dissolve
before it can move to later stages of venture
development. Consistent with organizational team
research, I propose that diversity and conflict result in
higher, member-rated team effectiveness. Departing
from organizational team research, I also propose that
both diversity and conflict result in direct effects on
member-rated team effectiveness—that is, conflict
M.-D. Foo (&) University of Colorado, Boulder, USA
e-mail: [email protected]
123
Small Bus Econ (2011) 36:33–46
DOI 10.1007/s11187-009-9176-8
does not mediate the diversity to effectiveness rating
relationships. Mediating effects are not expected
because founding team members can often choose
who they want to be on the team. Likewise, potential
members, even if they come from different back-
grounds or experiences, are only likely to join the
team if they agree to the team’s goals and expecta-
tions. Thus, diversity is unlikely to lead to team
conflict.
Studies on organizational teams indicate that
experience diversity, rather than the experience level,
influences team effectiveness ratings (Tsui et al.
1995). Another way that this study departs from
studies of organizational teams is the proposal that,
for new venture teams, the members’ average expe-
rience should relate positively to member-rated team
effectiveness. This argument is based on the fact that
members of new venture teams often have little
experience, which contrasts with organizational
teams where members are usually selected based on
their skills, knowledge, and job experience.
Taken together, this study contributes to the
entrepreneurship literature in three ways. First, the
findings indicate that studies from the organizational
team literature should be extrapolated with caution
for teams at the early stages of venture development.
Second, they also indicate direct effects of both
diversity and conflict on team effectiveness ratings;
significantly, conflict does not mediate the diversity
and effectiveness ratings relationships. Third, the
findings show that assertions by some organizational
team researchers that the average experience is
unimportant should perhaps be taken more tentatively
for new ventures.
1 Theoretical development
1.1 Member-rated team effectiveness
Before the hypotheses are developed, I first explain
the dependent variable, member-rated team effec-
tiveness. It comprises member ratings of the number
of innovations or new ideas introduced, reputation for
work excellence, efficiency of team operations, and
overall performance. As Foo et al. (2006) noted, for
new ventures, the team is still fragile and may not
survive unless the team manages to establish mem-
bership, identity, and commitment. During this phase,
objective performance measures, such as sales, cash
flow, and profits, may not yet be relevant as the team
is unlikely to have substantial sales when the main
focus is to establish the venture. Therefore, what is
important is that the team stays together and remains
excited about its ideas.
Subjective measures of team effectiveness are
frequently used in team studies. For organizational
team research, Jehn et al. (1999) used member ratings
of how well the unit was performing and the
effectiveness of the work unit. Ancona and Caldwell
(1992b) also used member ratings in terms of
efficiency, quality, technical innovation, adherence
to schedules, adherence to budgets, and work excel-
lence. Pelled et al. (1999) used manager ratings of
efficiency and number of innovations or new ideas
introduced in the team. For early stage ventures, Foo
et al. (2006) used member ratings of the team,
including the extent to which members of the team
care about it and work together to make it one of the
best. Chowdhury (2005) used new venture member
ratings of the team’s knowledge of tasks, quality of
work, quantity of work, initiative, interpersonal skills,
planning and allocation, and overall performance.
Ensley and Hmieleski (2005) defined team effective-
ness of new ventures as the degree of collective
efficacy within a group toward achieving its goals.
Although these are subjective team effectiveness
measures, Ancona and Caldwell (1992b) noted that
objective measures may not necessarily be preferred
since information is often interpreted through sub-
jective lenses. They also noted that in organizational
teams, subjective ratings often determine promotions,
future job assignments, and performance evaluations.
For entrepreneurship studies, Ensley and Hmieleski
(2005) noted that subjective measures of team effec-
tiveness may be related to future venture performance.
In this paper, the questions on team effectiveness
follow closely team studies, both organizational and
new venture teams (Ancona and Caldwell 1992b;
Chowdhury 2005; Ensley and Hmieleski 2005; Pelled
et al. 1999). By using measures related to past
studies, researchers can compare the results of this
study with those with these past studies. Importantly,
in this paper, the members rated team effectiveness,
not the team’s business ideas. Since business ideas
and venture goals may change as the venture
develops, it may be more valuable that members
evaluate the effectiveness of the team as a whole.
34 M.-D. Foo
123
1.2 Diversity and conflict
In this section, I compare the relationships between
diversity and conflict on ratings of team effectiveness
proposed in organizational team research versus the
relationships in new venture teams. Diversity and
conflict relationships are premised on the fact that
individuals tend to categorize others based on
observable characteristics (Barsade et al. 2000; Tajfel
1982). The self-categorization processes lead to in-
group and out-groups, with members viewing in-
group members more favorably (Barsade et al. 2000;
Tajfel 1982). Even trivial differences can evoke the
categorization of individuals into different subgroups
(Barsade et al. 2000). Members tend to be attracted to
others with similar backgrounds since they tend to
share similar values, attitudes, and interests. More-
over, people are motivated to maintain high self-
esteem by comparing their subgroup favorably to
other subgroups, leading to biases against other
subgroups (Eisenhardt et al. 1998; Tajfel and Turner
1986).
Although diversity may lead to differences, not all
diversity types hurt the team (for a review, see
Williams and O’Reilly 1998). The literature differ-
entiates task diversity from non-task diversity (Pelled
et al. 1999). Task diversities are differences in task-
related areas, such as work experience and job
function (Williams and O’Reilly 1998). Such diver-
sities can lead to work-related conflicts that may
improve ratings of team effectiveness. For example,
Eisenhardt et al. (1998) noted that individuals from
the engineering department and those from the
marketing department approach issues differently.
This divergent focus may not be pleasant for team
members but can increase the team’s knowledge base
(Jehn et al. 1999). In contrast, non-task diversities are
unrelated to the team’s work (Simons and Peterson
2000). This diversity type can lead to non-task
conflict that distracts the team from its task, thus
hurting the team effectiveness ratings (Williams and
O’Reilly 1998). The relationships among diversity,
conflict, and team effectiveness ratings in the orga-
nizational team literature are shown in Fig. 1.
Although diversity and the resulting differences in
perspectives can lead to conflicts, such a relationship
may not hold for teams developing business ideas.
Members of these teams have some choice of which
members to admit, and there is little reason to expect
that they will include individuals whom they view
negatively. Moreover, for these teams, participation
is voluntary and rewards are uncertain. In such
circumstances, potential members should have some
buy-in of team goals before they decide to join the
team, and such team identification can counteract the
negative effects of diversity on conflict (Van der Vegt
and Bunderson 2005). Diversity and conflict, how-
ever, should still affect team effectiveness ratings,
albeit direct effects, and not mediating effects. The
rest of this section hypothesizes how conflict type,
namely task or non-task, leads to positive or negative
effects on member-rated team effectiveness. The
section also explores how diversity increases the
knowledge base available to the team, which may
lead to higher member-ratings. The proposed rela-
tionships among diversity, conflict, and team
effectiveness for new venture teams are shown in
Fig. 2.
Task diversity
Nontask Diversity
Nontask conflict
Task conflict
Ratings of Team Effectiveness
+
+
+
-
Fig. 1 Relationships among diversity, conflict and team effectiveness ratings in organizational team research
Teams developing business ideas 35
123
Non-task conflict can hurt team effectiveness
ratings as time and energies are wasted on non-task
disagreements instead of centering efforts on tasks
(e.g., Pelled et al. 1999; Simons et al. 1999). Non-
task conflict, by contrast, can lower member capacity
to accept the ideas of other team members and restrict
information exchange among members, since conflict
can lead to distrust and avoidance of contact (Zenger
and Lawrence 1989). Conflict can also lead to anxiety
and frustration; these feelings can cause members to
lose perspective and work less effectively with one
another (Amason and Sapienza 1997). In contrast,
task conflicts result in critical appraisals that facilitate
information exchange among team members and
enhance understanding of the tasks to be performed
(Jehn 1995). Task conflicts can result in higher-rated
teams as members search for information to resolve
differences, generate a broader range of options
(Eisenhardt et al. 1998), increase decision compre-
hensiveness (Simons et al. 1999), and reduce group-
think (Jehn 1995).
H1a For teams developing business ideas, non-task
conflict relates negatively to member-rated team
effectiveness.
H1b For teams developing business ideas, task
conflict relates positively to member-rated team
effectiveness.
1.3 Diversity and member-rated team
effectiveness
Let us turn to diversity and member-rated team
effectiveness. Diversity of gender, age, and race are
considered to be less task-related because they usually
do not contribute work-related skills and knowledge
(Williams and O’Reilly 1998). In contrast, functional
and educational diversities are task-related because
they capture experiences, information, and perspec-
tives relevant to the team’s work (Dahlin et al. 2005;
Williams and O’Reilly 1998). Task diversity increases
the breadth and depth of information considered
(Brunninge et al. 2007; Dahlin et al. 2005) and can
benefit the team since they may enable members to
determine what is important, how things are done, and
how to entice important contacts to assist the team.
Empirical evidence supports the benefits of task
diversity; for example, Bantel and Jackson (1989)
found that task-diverse teams received higher mana-
gerial ratings for innovation.
H2a For teams developing business ideas, task
diversity relates positively to member-rated team
effectiveness.
Age and race, commonly examined non-task
diversity types, are often found to be detrimental to
team effectiveness ratings because they can trigger
Task conflict
Non-task conflict
Non-task diversity
Task diversity
Ratings of Team Effectiveness
+
+
-
+
Average member experience
+
Fig. 2 Proposed relationships among
diversity, conflict, and team
effectiveness ratings for
new venture teams
36 M.-D. Foo
123
differences in interests and perspectives unrelated to
team tasks (Simons et al. 1999). In contrast, the
results of research on organizational teams age and
race diversities are expected to relate positively to
member-rated team effectiveness. These differences
can make members aware of new markets, market
needs, and ways to reach the target segment. Some
evidence of the benefits of race diversity for teams
engaged in a novel task is provided by Watson et al.
(1993), who found that racially diverse teams are able
to overcome process differences and become more
effective than more homogeneous teams.
H2b For teams developing business ideas, non-task
diversity relates positively to member-rated team
effectiveness.
Researchers have used diversity to proxy skills and
information available to the team (Jehn et al. 1999).
In organizational teams, the average is seldom used,
probably because members are selected based on
skills that they can contribute to the team. Therefore,
skill diversity (rather than the average skill level)
should influence team effectiveness ratings. For
teams developing business ideas, the average, in
addition to diversity, can represent skills available to
the team. Members of these teams can be selected on
factors such as the ability to work well with others,
likeability, mutual enjoyment of each other’s com-
pany (Bird 1989), and similarity of beliefs and
interests (Kamm and Nurick 1993). These factors
have little to do with task experiences. Since
experience can lead to the recognition of business
opportunities (Shane 2000; Shane and Venkataraman
2000; Wong et al. 2008) and is linked to the creation
of high-growth ventures (Friar and Meyer 2001), it
should relate positively to member-rated team
effectiveness.
H3 For teams developing business ideas, the aver-
age experience relates positively to member-rated
team effectiveness.
2 Methods
The sample comprises teams developing business
ideas as part of a business plan competition organized
by a university in the northeastern part of the United
States. Such competitions may provide opportunities
for people with ideas and those involved with startups
(e.g., business angels, venture capitalists, and entre-
preneurs) to network, discover, and exploit business
ideas (cf. Foo et al. 2005; Huffman and Quigley
2002). For this study, each team’s output was a three-
to-five page description of a new product or service,
and the advantage(s) of the offering relative to its
competitors.
Participation in the competition was voluntary and
did not form part of a course assignment. Only one
team member had to be a student in the university
organizing the competition and there were no entry
restrictions for the other team members. On average,
each person in the competition spent 61 hours on the
business idea during the last week of the competition.
Many of the participants with whom the organizers of
the competition interacted freely communicated that
they participated in the competition to gain publicity
for their potential ventures.
Some 82 teams comprising 310 individuals par-
ticipated in the competition. Of these individuals,
77% had, or were pursuing, graduate degrees. A
questionnaire was sent to every participant immedi-
ately after the competition and before the competition
results were announced, so that the results would not
bias the responses. Some 84% of the participants
were men and 16% women. The questionnaire
included questions on member characteristics, per-
ceptions of how the team functioned, and team
outcomes. The respondents were assured that their
responses would not be shown to the competition
judges. A total of 257 participants (83%) responded
to the questionnaire. With the exception of one team,
all teams turned in at least one questionnaire. In four
teams, fewer than 50% of the members returned the
questionnaire; these teams were omitted from the
final sample. The final sample used to analyze the
findings comprised 73 teams, or 94% of the teams
that participated in the competition for that year.
2.1 Measures of team characteristics
The measure of non-task diversity was based on race
diversity and age diversity. Race diversity was
calculated using Blau’s index of heterogeneity for
categorical variables (Blau 1977) and calculated
by (1 - P
pi2), where p is the proportion of group
members in a category and I is the number of different
categories represented on the team. This index is
Teams developing business ideas 37
123
frequently used to calculate heterogeneity of categor-
ical variables, including in papers by Bantel and
Jackson (1989), Barsade et al. (2000), and Richard
et al. (2004). In this study, 56% were Caucasians, 5%
African-American, 32% Asians, and 7% others. Race
diversity was 0.40 with a standard deviation of 0.31.
2.1.1 Age diversity
The age of the individuals ranged from 18 to 56 years,
with a mean of 26 years, a median of 26 years, and a
standard deviation of 5. At the team level, the average
age ranged from 20 to 41 years, with a mean of 27
years and a standard deviation of 4 years. Age diversity
was calculated using the coefficient of variation, which
is the standard deviation divided by the mean. This
method is preferred to the standard deviation for
measuring diversity because it is scale invariant
(Allison 1978). The advantage of such a measure is
that it reflects relative differences, rather than absolute
differences (Allison 1978). The average age diversity
score was 0.14, with a standard deviation of 0.12.
2.1.2 Task diversity
Task diversity was measured by the diversity of work
specialization and calculated using Blau’s index of
heterogeneity for categorical variables. The work
specialization categories represented were computer
(39%), engineering (30%), management (15%), and
others (16%). Task diversity was 0.62 with a standard
deviation of 0.20.
2.1.3 Experience diversity
The work experience of individuals ranged from no
experience to 26 years of experience, with a mean of
45 months and a standard deviation of 45 months.
Experience diversity was calculated using the coef-
ficient of variation, with an average of 0.79 and
standard deviation of 0.46.
2.1.4 Average experience
To calculate average experience, the members’ work
experience was totaled and divided by team size.
Average experience of the teams ranged from none to
14 years, with a mean of 45 months and standard
deviation of 33 months.
2.1.5 Non-task conflict
The conflict scale developed by Jehn and associates
(e.g., Jehn et al. 1999) was used to measure conflict.
The non-task conflict items were: ‘‘How much
personality conflict was there among team mem-
bers?’’, ‘‘How much tension was there among team
members?’’, and ‘‘How much emotional conflict was
there among team members?’’
A scale of 1–7 was used in which a higher number
means higher conflict levels. Following past work on
teams, internal reliability was tested using Cron-
bach’s Alpha on the responses at the individual level
(Bunderson and Sutcliffe 2002; Ensley et al. 2002).
The scale was reliable with an alpha of 0.87, which is
above the traditional cut-off of 0.70 (Nunally 1978).
The rwg was also calculated in order to determine
the interteam level of agreement. In this study,
member-ratings were collected at the individual level,
but the analyses were conducted at the team level.
Individual responses should not be aggregated unless
team members have provided relatively similar ratings
(James et al. 1984). James et al. (1984) developed
such a measure of agreement among raters called the
rwg, and each group gets an rwg value. Typically, rwg
values of 0.70 and above are taken as evidence of
agreement among raters [for recent reviews on the use
of rwg, see papers by Newman and Sin (2009) and
Cohen et al. (2001)]. In this study, the average level
rwg was 0.90. Since this is above the cutoff of 0.7
(James et al. 1984), individual responses were aggre-
gated to the team level. At the team level, non-task
conflict ranged from 1 to 5.5, with an average of 1.92
and a standard deviation of 0.95.
2.1.6 Task conflict
The task conflict items in the scale were: ‘‘How often
do people in your team disagree regarding the work
being done?’’, ‘‘To what extent are there differences
of opinion in your team?’’, ‘‘How much conflict was
there about the work you do on your team?’’, and
‘‘How frequently are there conflicts about ideas in
your team?’’
Cronbach’s Alpha was 0.80. The average rwg was
0.79, which suggests member agreement on the level
of task conflict in the team. At the team level, task
conflict ranged from 1 to 5.5, with a mean of 2.79 and
a standard deviation of 0.97.
38 M.-D. Foo
123
2.1.7 Member-rated team effectiveness
Member-rated team effectiveness was measured
using four items adapted from Ancona and Caldwell
(1992a). Team members responded on a 7-point scale
that ranged from 1, meaning the team was deeply
disappointing, to 7, meaning the team exceeded
expectations. The four items were: (1) number of
innovations or new ideas introduced by the team; (2)
our reputation for work excellence; (3) efficiency of
team operations; (4) our overall performance. The
measure was reliable with an a of 0.80. The mean rwg value was 0.88. Subsequently, individual scores
for all members of the team were averaged to form a
team-level construct. Member-rated team effective-
ness at the team level ranged from 2 to 7, with an
average of 5.03 and a standard deviation of 0.81.
Since measures of conflict (task and non-task) and
the outcome variable (member-rated team effective-
ness) were taken from the same source, I followed the
procedures outlined by Podsakoff and Organ (1986)
and used by team researchers such as Ensley et al.
(2002) and Amason and Sapienza (1997) to control for
common method variance. For each team, half the
responses were randomly assigned to one subgroup
and the other half to another subgroup. For teams with
odd-numbered responses, the remaining response was
randomly assigned to one of the two subgroups.
Responses for task conflict and non-task conflict in one
subgroup were used to calculate the values of these
variables (taking the mean value of task conflict and
mean value of non-task conflict). The responses for
member-rated team effectiveness were calculated
from the mean value of this variable from the other
subgroup. Since the dependent variables and the
independent variables were taken from different
sources, the relationships between them were free
from response–response bias, such as common method
variance (Podsakoff and Organ 1986).
2.2 Control variables
2.2.1 Team size
Team size was controlled for because the coefficient
of variation was used to calculate age diversity and
experience diversity. The magnitude of the coeffi-
cient of variation can be influenced by team size
(cf. Ancona and Caldwell 1992a). Team size was
calculated using the number of participants listed on
the team’s entry. Size ranged from two to ten
participants, in which 17 teams comprised two
members; 15 teams, three members; 14 teams, four
members; 15 teams, five members; four teams, six
members; three teams, eight members; one team, ten
members.
2.2.2 Industry
Industry was controlled for because industries have
different success rates, and some sectors face more
competition than others (Cooper et al. 1994). Teams
that had technology-based ideas were coded as 1; all
others, as 0. Some 76% of the teams did a plan on a
technology-based venture. The small sample size
limited the ability to include more fine-grained
industry measures.
2.2.3 Entrepreneurial experience
Entrepreneurial experience was controlled for since
this experience can affect how the team functions
(Ucbasaran et al. 2001; Westhead and Wright 1998).
For example, having a founder in a team may
provoke conflict between members because the
founder can claim to know how things are supposed
to be done and expect other members to follow his or
her opinions. Teams with at least one member with
business-founding experience were coded as 1; those
without were coded 0.
3 Results
The correlation statistics in Table 1 show that mem-
ber-rated team effectiveness correlated negatively
with both non-task conflict (r = -0.36, p \ 0.01) and task conflict (r = -0.31, p \ 0.01). It also correlated negatively with race diversity (r =
-0.27, p \ 0.01), task diversity (r = -0.26, p \ 0.01) and experience diversity (r = -0.30, p \ 0.01). Average experience correlated positively with member-rated team effectiveness (r = 0.43,
p \ 0.01). The highest correlation of 0.6 (p \ 0.01) was between that of task conflict and non-task
conflict. This was expected, since task conflict can
spill over to non-task conflicts. Also, disagreements
can degenerate into interpersonal disagreements,
Teams developing business ideas 39
123
T a
b le
1 P
e a rs
o n
’s c o
rr e la
ti o
n s,
m e a n
s, st
a n
d a rd
d e v
ia ti
o n
, a n
d v
a ri
a n
c e
in fl
a ti
o n
fa c to
r
V a ri
a b
le s
M e a n
S ta
n d
a rd
d e v
ia ti
o n
V IF
1 2
3 4
5 6
7 8
9 1
0
M e m
b e r-
ra te
d te
a m
e ff
e c ti
v e n
e ss
5 .0
3 0
.8 1
S iz
e a
4 .1
9 1
.8 0
1 .3
5 -
0 .1
7
In d
u st
ry a
0 .7
6 0
.4 3
1 .1
9 -
0 .0
7 0
.0 6
E n
tr e p
re n
e u
ri a l
e x
p e ri
e n
c e
a 0
.1 3
0 .3
4 1
.1 5
- 0
.1 9
0 .1
8 0
.0 2
N o
n -t
a sk
c o
n fl
ic t
1 .9
2 0
.9 5
1 .8
6 -
0 .3
6 *
* 0
.0 5
- 0
.1 7
0 .1
1
T a sk
c o
n fl
ic t
2 .7
9 0
.9 7
1 .7
3 -
0 .3
1 *
* -
0 .0
3 0
.0 3
0 .0
6 0
.6 0
* *
R a c e
d iv
e rs
it y
0 .3
9 0
.3 0
1 .3
4 -
0 .2
7 *
* 0
.4 2
* *
- 0
.0 8
0 .1
1 0
.0 9
- 0
.0 2
A g
e d
iv e rs
it y
0 .1
4 0
.1 2
1 .2
2 0
.0 8
0 .1
7 0
.0 3
0 .0
1 0
.0 9
0 .2
0 0
.1 5
T a sk
d iv
e rs
it y
0 .6
2 0
.2 0
1 .8
2 -
0 .2
6 *
* 0
.3 3
* *
0 .0
2 -
0 .1
6 0
.1 3
- 0
.0 2
0 .3
5 *
* 0
.1 2
E x
p e ri
e n
c e
d iv
e rs
it y
0 .7
9 0
.4 6
1 .6
3 -
0 .3
0 *
* 0
.2 1
0 .2
0 0
.0 3
0 .1
9 0
.0 5
0 .2
6 *
0 .2
1 0
.5 6
* *
A v
e ra
g e
e x
p e ri
e n
c e
4 5
.3 6
3 3
.4 6
1 .5
0 0
.4 3
* *
- 0
.1 5
- 0
.1 6
- 0
.0 4
- 0
.2 2
- 0
.0 3
- 0
.1 4
0 .1
5 -
0 .4
2 *
* -
0 .4
2 *
*
V IR
, V
a ri
a n
c e
in fl
a ti
o n
fa c to
r
* p \
.0 5
, *
* p \
.0 1
n =
7 3
te a m
s; a ll
c o
rr e la
ti o
n s
a re
tw o
-t a il
e d
a C
o n
tr o
l v
a ri
a b
le
40 M.-D. Foo
123
since no one likes to be criticized or contradicted (e.g.,
Ensley et al. 2002). Before doing the regressions, we
checked for multicollinearity problems by examining
the Variance Inflation Factor (VIF) of each indepen-
dent variable. The largest VIF in our regression was
less than three, suggesting that multicollinearity was
unlikely to be an issue (Guo et al. 1996).
Several relationships must be shown to demon-
strate mediating effects (Baron and Kenny 1986).
First, the diversity factors should predict the level of
conflict. Second, conflict level should predict member
ratings of effectiveness. Third, when both diversity
and conflict factors are included into the regressions,
the b’s of the diversity measures become insignificant (suggesting full mediation) or fall (suggesting partial
mediation).
The results include an intercept term. There are
two advantages to reporting b coefficients: first, b coefficients can be included in meta-analysis to
compare effect sizes across studies; second, b coef- ficients can be used to compare the relative effects of
the independent variables on the dependent variables.
For more details on the use of b coefficients, see Allison (1999, p. 59) and Hunter and Hamilton
(2002). Models 1 and 2 tested the effects of diversity
on task and non-task conflicts on member-rated team
effectiveness. Neither model was significant. Since
the first condition of mediation was not supported
(i.e., diversity factors should predict conflict), medi-
ation was not supported.
Models 3 through 7 tested the effects of the
dependent variables on member-rated team effective-
ness. Model 3, the baseline model, included the
control variables of team size and industry type (1 for
technology based and 0 otherwise). The results in
Table 2 show that the model did not relate to
member-rated team effectiveness. Models 1–3 have
been retained in Table 2, even though the models did
not support mediated relationships. Model 4 added
the conflict variables to the baseline model. DR2 of 0.13 was statistically significant at p \ 0.01 as compared to the baseline model, showing that conflict
was related to member-rated team effectiveness.
However, only the coefficient for non-task conflict
(b = -0.27, p \ 0.05) was significant, thereby pro- viding support for H1a.
Model 5 added the diversity variables to the
baseline model. The DR2 of 0.14 was statistically
significant at p \ .05, as compared to the baseline model. Thus, diversity adds to the explanation of the
level of member-rated team effectiveness. As hypoth-
esized by H2b, age diversity related positively to
member-rated team effectiveness (b = 0.17, p \ 0.10). However, contrary to H2b, race diversity related negatively to member-rated team effective-
ness (b = -0.18, p \ 0.10). Model 6 includes the control, conflict, and diversity variables. The DR2
compared to the baseline model was 0.13 (p \ 0.05). H1b was not supported as task conflict (b = -0.24, p \ 0.05) related negatively to member-rated team effectiveness. Partial support was found for H2b, as
age diversity related positively to member-rated team
effectiveness (b = 0.23, p \ 0.05), but race diversity (b = -0.19, p \ 0.10) related negatively to member- rated team effectiveness.
Taken together, although some of the variables
were significant only at the p \ 0.10 level, Model 6, which examines how conflict and diversity relate to
member-rated team effectiveness, was significant at
p \ 0.01 level. This model explains 32% of the variance of the dependent variable (or 22% of the
variance for adjusted R2). A usefulness analysis
carried out to determine whether conflict or diversity
contributed more to the prediction of member-rated
team effectiveness revealed that the conflict variables
explained an additional 13% of the variance over and
above the diversity variables (significant at p \ 0.01) (Table 3) and that the diversity variables explained
an additional variance of 13% over and above the
conflict variables (significant at p \ 0.01). The relative effects of diversity and conflict appear to be
equally important in explaining member-rated team
effectiveness. The results of Models 3–6 and the
usefulness analysis indicate that conflict and diversity
had separate, but direct, effects on member-rated
team effectiveness.
Model 7 tested whether average experience and
diversity of experience were associated with member-
rated team effectiveness. The results showed that
average experience (b = 0.28, p \ 0.05) was related to member-rated team effectiveness but that experi-
ence diversity was not (b = -0.10, n.s.). The DR2 of Model 7 (0.05), as compared to that of Model 6, was
significant at p \ 0.05. Task conflict related nega- tively to member-rated team effectiveness (b = -0.26, p \ 0.05).
Teams developing business ideas 41
123
T a
b le
2 H
ie ra
rc h
ic a l
re g
re ss
io n
a n
a ly
si s
o f
te a m
c h
a ra
c te
ri st
ic s
o n
c o
n fl
ic t
a n
d m
e m
b e r-
ra te
d te
a m
e ff
e c ti
v e n
e ss
M o
d e l
1 M
o d
e l
2 M
o d
e l
3 M
o d
e l
4 M
o d
e l
5 M
o d
e l
6 M
o d
e l
7
D V
= N
o n
-
ta sk
c o
n fl
ic t
D V
= T
a sk
c o
n fl
ic t
D V
= M
e m
b e r-
ra te
d
te a m
e ff
e c ti
v e n
e ss
D V
= M
e m
b e r-
ra te
d
te a m
e ff
e c ti
v e n
e ss
D V
= M
e m
b e r-
ra te
d
te a m
e ff
e c ti
v e n
e ss
D V
= M
e m
b e r-
ra te
d
te a m
e ff
e c ti
v e n
e ss
D V
= M
e m
b e r-
ra te
d
te a m
e ff
e c ti
v e n
e ss
b b
b b
b b
b
T e a m
si z e
a -
0 .0
2 -
0 .0
7 -
0 .1
3 -
0 .1
3 0
.0 0
- 0
.0 2
- 0
.0 1
In d
u st
ry a
- 0
.2 0
? 0
.0 2
- 0
.0 6
- 0
.1 0
- 0
.0 4
- 0
.0 7
- 0
.0 3
E n
tr e p
re n
e u
ri a l
e x
p e ri
e n
c e
0 .1
2 0
.0 8
- 0
.1 6
? -
0 .1
3 -
0 .1
9 ?
- 0
.1 5
? -
0 .1
3
N o
n -t
a sk
c o
n fl
ic t
- 0
.2 7
* -
0 .1
6 -
0 .1
0
T a sk
c o
n fl
ic t
- 0
.1 4
- 0
.2 4
* -
0 .2
6 *
R a c e
d iv
e rs
it y
- 0
.0 1
- 0
.0 3
- 0
.1 8
? -
0 .1
9 ?
- 0
.1 9
?
A g
e d
iv e rs
it y
0 .0
5 0
.2 1
? 0
.1 7
? 0
.0 3
* 0
.1 6
?
T a sk
d iv
e rs
it y
0 .0
6 -
0 .0
1 -
0 .1
4 -
0 .1
3 -
0 .0
5
E x
p e ri
e n
c e
d iv
e rs
it y
0 .1
9 0
.0 2
- 0
.2 0
? -
0 .1
6 -
0 .1
0
A v
e ra
g e
e x
p e ri
e n
c e
0 .2
8 *
M o
d e l
F
st a ti
st ic
s
0 .8
9 0
.4 8
1 .3
3 2
.9 9
* 2
.1 4
? 3
.1 2
* *
3 .4
6 *
*
R 2
0 .1
0 0
.0 5
0 .0
6 0
.1 9
0 .1
9 0
.3 2
0 .3
7
A d
jR 2
0 0
0 .0
1 0
.1 3
0 .1
0 .2
2 .0
2 6
D R
2 0
.1 3
* *
0 .1
4 *
0 .1
3 *
0 .0
5 *
C o
m p
a re
d to
M o
d e l
3 C
o m
p a re
d to
M o
d e l
3 C
o m
p a re
d to
M o
d e l
4 C
o m
p a re
d to
M o
d e l
6
? p \
0 .1
, *
p \
.0 5
, *
* p \
.0 1
D V
, D
e p
e n
d e n
t v
a ri
a b
le
n =
7 3
te a m
s; a ll
te st
s a re
1 -t
a il
e d
a C
o n
tr o
l v
a ri
a b
le
42 M.-D. Foo
123
4 Discussion
This study examined the relationships among diver-
sity, conflict, and member-rated team effectiveness.
The results indicate that diversity and conflict directly
influenced the member-rated team effectiveness and,
as such, they differ in important ways from organi-
zational team studies. Notably, task conflict related
negatively to member-rated team effectiveness, while
age diversity related positively to member-rated team
effectiveness. While some researchers have used
conflict as a mediator of diversity to ratings of team
effectiveness (Bantel and Jackson 1989; Ensley et al.
2002; Pelled et al. 1999), my study found that conflict
and diversity directly influenced member-rated team
effectiveness.
I reasoned that for teams developing business
ideas, the ability of members to select potential
members and the ability of potential members to
decide whether to join the team, together with the
uncertainty of rewards, make it likely that members
share goals and aspirations. Interestingly, age diver-
sity, which is often used to proxy non-task diversity
in organizational teams, was found to relate posi-
tively to member-rated team effectiveness. I reasoned
that in high age diversity teams, members bring
different bundles of experiences, which can lead to
the discovery of business opportunities (Shane and
Venkataraman 2000). Importantly, task conflict
related negatively to member-rated team effective-
ness. This was contrary to my expectations that such
conflicts make teams consider a greater range of
information to resolve differences and, subsequently,
that these teams will have higher effectiveness
ratings. Possibly, at the early venturing stages, teams
experiencing task conflict can be overwhelmed with
conflicting information.
Team researchers have used diversity as a measure
of skills and information available to the team. This
study found that for teams developing business ideas,
the average should also be included. For these teams,
it cannot be assumed that members have the required
skills and knowledge to do the team’s task because
having this knowledge may not be a requirement to
join the team. This contrasts with organizational
teams, where member skills and knowledge are
usually not called into question. In fact, this study
found mixed effects of diversity on member-rated
team effectiveness, but positive relationships of
average experience on member-rated team effective-
ness. Collectively, the implications of the findings
described in this section are clear; that is, findings
from organizational team research must be extrapo-
lated with caution when applied to new venture
teams.
4.1 Limitations and future directions
In interpreting the findings, the specific circum-
stances faced by the teams, i.e., developing a business
idea, must be considered. Future studies can deter-
mine if team characteristics that relate to
effectiveness ratings at this stage continue to be
important at later stages of the entrepreneurial
journey. For example, at later stages, task conflict
may be needed for effective teams, since the teams
could be more settled, and task conflict can ensure
that the team continues to critically evaluate and
improve its business proposition. Moreover, this
study focused on the influence of diversity on internal
team factors. Team composition also affects the
extent to which team members interact with contacts
outside of the venture, and such interactions can
impact team success (Ancona and Caldwell 1992b).
Table 3 Alternative hierarchical regressions of diversity and conflict variables on member-rated team effectiveness (use-
fulness analysis)
Regressions and standardized b’s Member-rated team effectiveness
Controls
R 2
0.06
Diversity first, then conflict
Step 1: Diversities of race, age, task,
and experience
R2 0.19
Step 2: Conflicts of non-task and task
R2 0.32
Conflict beyond diversity 0.13**
Conflict first, then diversity
Step 1: Conflicts of non-task and task
R2 0.19
Step 2: Diversities of race, age, task
and experience
R 2
0.32
Diversity beyond conflict 0.13**
* p \ .05, ** p \ .01
Teams developing business ideas 43
123
Future studies can examine how internal factors and
interactions with individuals outside of the venture
affect team functioning.
A limitation of this study is the subjective measure
of team effectiveness. Significantly, many of the
studies reviewed in this paper were based on
subjective team effectiveness measures (e.g., Ancona
and Caldwell 1992b; Chowdhury 2005; Ensley and
Hmieleski 2005; Foo et al. 2006; Jehn et al. 1999;
Pelled et al. 1999). While it is uncertain the extent to
which subjective measures reflect reality, past studies
in organizational teams and in new venture teams,
using measures relatively similar to this study, have
shown or argued for positive relationships between
subjective and objective team effectiveness measures
(Ensley and Hmieleski 2005). Whether this statement
holds for this study can not be determined, but Foo
et al. (2006) suggested that unless members posi-
tively evaluate their teams, the teams may dissolve
before members have a chance to implement their
business ideas. Therefore, member-ratings are critical
for new venture teams. At later stages of venture
development, when the team is stable, objective
performance measures, such as sales, positive cash
flow, and profits, can be more relevant.
Another limitation of the study is that it was
conducted in one university in the USA. In particular,
a university located in a northeast region that is a hot-
bed of entrepreneurship. Future studies can examine
if this study’s findings apply to other regions with a
less developed venture community. For instance,
teams may be less important for regions that do not
focus on high-growth entrepreneurship. Despite this
limitation, the findings may be generalized to other
hot-beds of entrepreneurship, such as those in Silicon
Valley in California, the North Carolina Research
Triangle, Silicon Alley in New York, Hsinchu High
Technology Park in Taiwan, and the high technology
region in Bangalore, India.
4.2 Practical implications
Some researchers suggest promoting task conflict
while limiting non-task conflict (e.g., Ensley et al.
2002; Pelled et al. 1999). One way to increase task
conflict is to use a devil’s advocate to promote
debate. This study indicates that this advice may be
misplaced for new venture teams, since task conflict
can relate to lower member-rated team effectiveness.
Further, new venture teams should attract members
with working experience, as these members may
increase the skillsets available to the team. The
findings in this study may inform entrepreneurship
education. Entrepreneurship education has been the
focus of substantial interest in business schools
(Finkle and Deeds 2001), and students often work
in teams to develop business ideas. The results in this
study caution the student to be careful when extrap-
olating findings from the organizational team
literature to new venture teams. For example, the
advantages of task conflict and the mediating effects
of conflict on the diversity-to-team-effectiveness
relationship can operate differently for new venture
teams. This study, together with other studies in
entrepreneurship, enable students and instructors to
have theoretically and empirically grounded prescrip-
tions for developing effective new venture teams.
References
Allison, P. (1978). Measures of inequality. American Socio- logical Review, 43, 865–879. doi:10.2307/2094626.
Allison, P. (1999). Multiple regression: A primer. Thousand Oaks: Pine Forge Press.
Amason, A., & Sapienza, H. (1997). The effects of top man-
agement team size and interaction norms on cognitive and
affective conflict. Journal of Management, 23, 495–516. doi:10.1016/S0149-2063(97)90045-3.
Ancona, D., & Caldwell, D. (1992a). Demography and design.
Organization Science, 3, 321–341. doi:10.1287/orsc.3.3. 321.
Ancona, D., & Caldwell, D. (1992b). Bridging the boundary:
External activity and performance in organizational
teams. Administrative Science Quarterly, 37, 634–665. doi:10.2307/2393475.
Bantel, K., & Jackson, S. (1989). Top management and inno-
vations in banking: Does the composition of the top team
make a difference? Strategic Management Journal, 10, 107–124. doi:10.1002/smj.4250100709.
Baron, R., & Kenny, D. (1986). The moderator-mediator var-
iable distinction in social psychological research:
Conceptual, strategic, and statistical considerations.
Journal of Personality and Social Psychology, 51, 1173– 1182. doi:10.1037/0022-3514.51.6.1173.
Barsade, S., Ward, A., Turner, J., & Sonnenfeld, J. (2000). To
your heart’s content: A model of affective diversity in top
management teams. Administrative Science Quarterly, 45, 802–836. doi:10.2307/2667020.
Bird, B. (1989). Entrepreneurial behavior. Glenview, Illinois: Scott, Foresman.
Blau, P. (1977). Inequality and heterogeneity. New York: Free Press.
44 M.-D. Foo
123
Brunninge, O., Nordqvist, M., & Wiklund, J. (2007). Corporate
governance and strategic change in SMEs: The effects of
ownership, board composition and top management teams.
Small Business Economics, 29, 295–308. doi:10.1007/ s11187-006-9021-2.
Bunderson, J., & Sutcliffe, K. (2002). Comparing alternative
conceptualizations of functional diversity in management
teams. Academy of Management Journal, 45, 875–893. doi:10.2307/3069319.
Chowdhury, S. (2005). Demographic diversity for building an
effective entrepreneurial team: Is it important? Journal of Business Venturing, 20, 727–746. doi:10.1016/j.jbusvent. 2004.07.001.
Cohen, A., Doveh, E., & Eick, U. (2001). Statistical properties
of the rwg(j) index of agreement. Psychological Methods, 6, 297–310. doi:10.1037/1082-989X.6.3.297.
Cooper, A., Gimeno-Gascon, F., & Woo, C. (1994). Initial
human and financial capital as predictors of new venture
performance. Journal of Business Venturing, 9, 371–395. doi:10.1016/0883-9026(94)90013-2.
Dahlin, K., Weingart, L., & Hinds, P. (2005). Team diversity
and information use. Academy of Management Journal, 48, 1107–1123.
Eisenhardt, K., Kahwajy, J., Bourgeois, L., & II, I. (1998).
Conflict and strategic choice: How top management teams
disagree. In D. C. Hambrick, D. A. Nadler, & M. L.
Tushman (Eds.), Navigating change (pp. 141–169). Bos- ton, Massachusetts: Harvard Business School Press.
Ensley, M., & Hmieleski, K. (2005). A comparative study of
new venture top management team composition, dynam-
ics and performance between university-based and
independent start-ups. Research Policy, 34, 1091–1105. doi:10.1016/j.respol.2005.05.008.
Ensley, M., Pearson, A., & Amason, A. (2002). Understanding
the dynamics of new venture top management teams.
Cohesion, conflict, and new venture development. Journal of Business Venturing, 17, 365–386. doi:10.1016/ S0883-9026(00)00065-3.
Finkle, T., & Deeds, D. (2001). Trends in the market for
entrepreneurship faculty, 1989–1998. Journal of Business Venturing, 16, 613–663. doi:10.1016/S0883-9026(99) 00051-8.
Foo, M., Sin, H., & Yiong, L. (2006). Effects of team inputs
and intrateam processes on new venture team effective-
ness. Strategic Management Journal, 27, 389–399. doi: 10.1002/smj.514.
Foo, M., Wong, P., & Ong, A. (2005). Do others think you
have a viable business idea? Team diversity and judges’
evaluation of ideas in a business plan competition. Jour- nal of Business Venturing, 20, 385–402. doi:10.1016/ j.jbusvent.2004.04.001.
Friar, J., & Meyer, M. (2001). Entrepreneurship and start-ups
in the Boston Region: Factors differentiating high-growth
ventures and micro-ventures. Small Business Economics, 21, 145–152. doi:10.1023/A:1025045828202.
Guo, S., Chumlea, W., & Cockram, (1996). Use of statistical
methods to estimate body composition. The American Journal of Clinical Nutrition, 64, 428S–435S.
Huffman, D., & Quigley, J. (2002). The role of university in
attracting high-tech entrepreneurship: A Silicon Valley
tale. The Annals of Regional Science, 36, 403–419. doi: 10.1007/s001680200104.
Hunter, J., & Hamilton, M. (2002). The advantages of using
standardized scores in causal analysis. Human Commu- nication Research, 28, 552–561. doi:10.1111/j.1468-2958. 2002.tb00823.x.
James, L., Demaree, R., & Wolf, G. (1984). Estimating within-
group interrater reliability with and without response bias.
The Journal of Applied Psychology, 69, 85–98. doi: 10.1037/0021-9010.69.1.85.
Jehn, K. (1995). A multimethod examination of the benefits
and detriments of intragroup conflict. Administrative Sci- ence Quarterly, 40, 256–282. doi:10.2307/2393638.
Jehn, K., Northcraft, G., & Neale, M. (1999). Why differences
make a difference: A field study of diversity, conflict, and
performance in workgroups. Administrative Science Quarterly, 44, 741–763. doi:10.2307/2667054.
Kamm, J., & Nurick, A. (1993). The stages of team venture
formation: A decision-making model. Entrepreneurship: Theory and Practice, 17, 17–28.
Lankau, M., Ward, A., Amason, A., Ng, T., Sonnenfeld, J., &
Agle, B. (2007). Examining the impact of organizational
value dissimilarity in top management teams. Journal of Managerial Issues, 19, 11–34.
Milliken, F., & Martins, L. (1996). Searching for common
threads: Understanding the multiple effects of diversity in
organizational groups. Academy of Management Review, 21, 402–433. doi:10.2307/258667.
Newman, D., & Sin, H. (2009). How do missing data bias
estimates of within-group agreement? Sensitivity of
SDWG, CVWG, r, r*, and ICC to systematic nonresponse.
Organizational Research Methods, 12, 113–147. Nunally, J. (1978). Psychometric theory. New York: McGraw-
Hill.
Pelled, L., Eisenhardt, K., & Xin, K. (1999). Exploring the
black box: An analysis of work group diversity, conflict,
and performance. Administrative Science Quarterly, 44, 1–28. doi:10.2307/2667029.
Podsakoff, P., & Organ, D. (1986). Self-reports in organizational
research: Problems and prospects. Journal of Management, 12, 531–544. doi:10.1177/014920638601200408.
Richard, O., Barnett, T., Dwyer, S., & Chadwick, K. (2004).
Cultural diversity in management, firm performance, and
the moderating role of entrepreneurial orientation
dimensions. Academy of Management Journal, 47, 255–266.
Shane, S. (2000). Prior knowledge and the discovery of
entrepreneurial opportunities. Organization Science, 11, 448–469. doi:10.1287/orsc.11.4.448.14602.
Shane, S., & Venkataraman, S. (2000). The promise of entre-
preneurship as a field of research. Academy of Management Review, 25, 217–226. doi:10.2307/259271.
Simons, T., Pelled, L., & Smith, K. (1999). Making use of
differences: Diversity, debate, and decision comprehen-
siveness in top management teams. Academy of Management Journal, 42, 662–673. doi:10.2307/256987.
Simons, T., & Peterson, R. (2000). Task conflict and non-task
conflict in top management teams: The pivotal role of
intragroup trust. The Journal of Applied Psychology, 85, 102–111. doi:10.1037/0021-9010.85.1.102.
Teams developing business ideas 45
123
Tajfel, H. (1982). Social identity and intergroup relations. Cambridge: Cambridge University Press.
Tajfel, H., & Turner, J. (1986). The social identity theory in
intergroup behavior. In S. Worchel & W. G. Austin (Eds.),
Psychology of intergroup relations. Chicago: Nelson- Hall.
Tsui, A., Egan, T., & Xin, K. (1995). Diversity in organiza-
tions: Lessons from demography research. In M. M.
Chemers, S. Oskamp, & M. A. Costanzo (Eds.), Diversity in organizations: New perspectives for a changing work- place (pp. 191–219). Thousand Oaks, CA: Sage Publications.
Ucbasaran, D., Westhead, P., & Wright, M. (2001). The focus
of entrepreneurial research: Contextual and process
issues. Entrepreneurship: theory & practice, 25, 57–80. Van der Vegt, G., & Bunderson, J. (2005). Learning and per-
formance in multidisciplinary teams: The importance of
team identification. Academy of Management Journal, 48, 532–547.
Watson, W., Kumar, K., & Michaelsen, L. (1993). Cultural
diversity’s impact on interaction process and
performance: Comparing homogeneous and diverse task
groups. Academy of Management Journal, 36, 590–602. doi:10.2307/256593.
Westhead, P., & Wright, M. (1998). Novice, portfolio, and
serial founders in rural and urban areas. Entrepreneurship: Theory & Practice, 22, 63. 10.
Williams, K., & O’Reilly, C. (1998). Demography and diver-
sity in organizations. In B. M. Staw & R. M. Sutton
(Eds.), Research in Organizational Behavior (Vol. 20, pp. 77–140). Greenwich, CT: JAI Press.
Wong, P., Lee, L., & Foo, M. (2008). Occupational choice: The
influence of product vs. process innovation. Small Busi- ness Economics, 30, 267–281. doi:10.1007/s11187- 006-9044-8.
Zenger, T., & Lawrence, B. (1989). Organizational demogra-
phy: The differential effects of age and tenure
distributions on technical communication. Academy of Management Journal, 32, 353–376. doi:10.2307/256366.
46 M.-D. Foo
123
Copyright of Small Business Economics is the property of Springer Science & Business Media B.V. and its
content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's
express written permission. However, users may print, download, or email articles for individual use.
