Description
During in this such a detailed breakdown explores effects of teaching and team composition on success in an entrepreneurship education.
1
EFFECTS OF TEACHING AND TEAM COMPOSITION ON SUCCESS
IN AN ENTREPRENEURSHIP EDUCATION COURSE
CHRISTIAN SCHULTZ
TAMARA ALMEYDA
KATHARINA HÖLZLE
Abstract
We analyse the effects of teaching and team composition on the success of student teams who
attended the course “Entrepreneurship and Business Plan” in the period 2007-2011 at the
University of Potsdam and participated in a popular business plan competition. External jurors
rank the business plans in three different stages of the competition. Each stage focuses on
different aspects of the overall plan e.g. business idea or market analysis.
A logistic regression model is performed to calculate the effects. While no effect of team
composition is found either in the case of service- and technology-oriented business plans the
contextual variable course focus emerges as highly significant to predict the rank bracket of the
participating teams.
Different implications of the findings are discussed and avenues for future research are framed.
2
1. PRINCIPLE TOPIC
Innovation and entrepreneurship have emerged as important topics on the political and
academic agenda. They have been identified as key factors for economic growth in small and
medium enterprises. Government initiatives have been formed to promote and support
entrepreneurship and entrepreneurial education. In Germany the federal Ministry of Economics
and Technology supports the EXIST program “aimed at improving the entrepreneurial
environment at universities and research institutions and at increasing the number of technology
and knowledge based business start-ups” (EXIST 2012). Some researchers emphasize that
individual personality and talent make a successful entrepreneur and that technique-based
training, i.e. entrepreneurial education can only teach skills and provide tools that may help the
entrepreneur but won´t make a mayor differences in success (Thompson 2004). But the majority
of scholars and researchers in the field of entrepreneurial education state that both the
characteristics and skills of entrepreneurship can be trained or learned and that subsequently „the
question of whether entrepreneurship can be taught is obsolete“ (Kuratko 2005, 580).
Business plan courses and business plan competitions have emerged as an extraordinarily
popular part of the entrepreneurship education curriculum in higher education institutions (Honig
2004; Kuratko 2005; Russell et al. 2008; Müller 2011). Business plan competitions although
originally aimed at stimulating start-up activities have been recognized to provide a range of
benefits to participants, such as the access to mentors, access to workshops and events such as
team building or networking and judges’ advice. On the educational level an increase of both
general economic as well as entrepreneurship specific knowledge has been generally observed in
the curricula (Russell et al. 2008). Another benefit for participants is the development of social
skills such as team competences, confidence in dealing with risks, problem solving or dealing
with pressure (Russell et al. 2008). The combination of these different learning objectives,
entrepreneurial spirit, economic knowledge and social skills, is both a challenge and a great
opportunity to business plan courses. So in translating their theoretical knowledge to a hands-on
experience such as the creation of a business plan, participants might gain valuable knowledge
and skills. These benefits prevail despite the fact that the importance of a business plan from the
decision-making process of venture capitalists is sometimes challenged (Kirsch et al. 2009) and
participants of business plan courses or competitions at the university level might not
immediately decide to start their own businesses.
On the institutional level of academic education business plan courses are cross-
disciplinary courses, combining the knowledge of different business school disciplines such as
marketing and management in one course (Bowers and Scherpereel 2008). The goal is to translate
3
an idea into a business plan, that is „to understand and fill the gaps between their idea and a
commercially viable business plan [and to understand] the knowledge gap between their idea and
a judge’s or the market’s view of a business plan“ (Russell et al. 2008, 127). To enhance the
inclusion of the market perspective, the University of Potsdam has opted to combine the business
plan course in the curriculum with the regional business plan competition Berlin-Brandenburg.
The business plan course at the University of Potsdam is designed as an elective for
undergraduate business school students. It is open to undergraduate and graduate students of all
other departments of the university. Students attend lectures on the components of a business plan
by faculty members and guest speakers. Every participant has to develop a business idea
throughout the semester, either on his own or in a team. It is obligatory that everybody
participates in the Business Plan Competition Berlin-Brandenburg (BPW). The accompanying
lecture is aligned to the 3 milestones of the BPW, which are described in more detail in the next
section.
The course design combines a number of different teaching methods. The course starts with
introductory classes on entrepreneurship and business idea generation followed by a “Market of
Ideas” where idea posters for the potential businesses are presented to fellow students and faculty
members. Students may revise their business ideas based on the early feedback they received
before starting the business plan process. During the semester students attend lectures on the
components of a business plan by faculty members and guest speakers. There are also three peer
review sessions with fellow student tutors or faculty members prior to submitting the business
plan documents. The business plans are graded at the university and simultaneously ranked and
feedback is given by the jurors of the BPW. So while students can earn credit they also get
feedback from outside the academic world.
The course´s goal depend on the target group – whether they are business students or a non-
business audience with presumably fewer knowledge of market evaluation, business processes,
marketing and finance. For business students it is more relevant to practice knowledge acquired in
different courses during their studies and apply those to an original idea. Students of other
university-departments are in a greater need of basic business-related knowledge to develop an
idea in a marketable business.
An important factor in the process of composing a business plan is team diversity (Sanjib
2005; Der Foo et al. 2005). Der Foo et al. (2005) argue that task related diversity of team
members might enhance team effectiveness, while non-task diversity such as age might have
negative effects. Sanjib (2005) also include the gender perspective and find that demographic
diversity is not important for an entrepreneurial team. With the emergence of entrepreneurial
4
education specifically aimed at women (e.g. the Plan B(usiness) at the Technical University of
Berlin or simulation games funded by the EXIST program), the gender component of
entrepreneurial teams is in the focus of university level education.
After outlining the principle topic the basic research question arises, what determines the
success of the business plans?
The described setting enables the study of two influencing factors on the quality of the
business plan first the team composition and second the impact of the presented content to the
students.
5
2. METHODOLOGY
The sample consists of 256 participating teams (technology: 49, service: 121) and single
persons (technology: 16, service: 70) in the BPW in the period 2007-2011, who attended the
course “Entrepreneurship and Business Plan” at the University of Potsdam.
The BPW is by far the largest public business plan competition in Germany. Regularly
more than 3.000 persons in about 600 teams participate in this competition and turn in their
business plans. The BPW is divided into the two sub segments service and technology. In the first
the service-oriented business ideas are evaluated and in the second the more technology-oriented.
A distinct document outline is obligatory to participate in the BPW (1. Executive Summary,
2. Business Idea, 3. Team, 4. Market Analysis, 5. Marketing, 6. Legal Issues and 7. Finance).
Starting in October of each year the competition is divided in 3 milestones, which require the
delivery of specific parts of the business plan (1
st
Phase: 1-3; 2
nd
: 1-5; 3
rd
Phase: 1-7). Two
anonymous referees evaluate the documents on a standardized point scheme and also qualitative
feedback is provided. According to the (cumulative) points reached in each step the teams are
ranked and the best10 participants win up to 10.000!.
Model and Variables
The dependent variable is the rank range of the teams in the BPW. For this purpose we
coded the ranks in winner (Rank: 1-50), good, bad and loser. We choose different rank codes for
the segments technology and service because a larger proportion of teams is located in the service
sector than in the technology sector. For both segments a different ranking is published.
We include two independent variables in our overall model, one individual and two
contextual. In the course every participant can choose if he wants to turn in a business plan by
himself or if he wants to join a team with up to 3 persons.
We add to the model a contextual independent variable the phase (categorical: 1, 2, 3) and
the team composition (categorical: 1, 2, 3, 4, 5)
The used dependent and independent variables are summarized in table 1 below.
6
Table 1: Dependent and independent Variables
In order to examine the group differences with existing independent variables, on principle
the discrimination analysis and the logistic regression can be used as quantitative statistical
procedures. In general, the logistic regression is considered as the more robust method because of
less stringent conditions. No normally distributed independent variables and equal variance-
covariance matrices are required (Backhaus 2010; Brosius 2004). Altogether, in the case of
service 191 observations have been included in the regression calculation. For assessing the
quality of the comprehensive model, three pseudo-r-square measures are available. The Cox and
Snell amount to ,213, Nagelkerke to ,231 and McFadden R
2
to ,093. The Cox and Snell is
considered as good for values bigger than ,40 the Nagelkerke value is to be interpreted also as
good from ,40 and as very good from ,50 the McFaddens R
2
is considered as acceptable from
values bigger than ,20 and as good from ,40.
According to all represented common measurements, all in all the comprehensive model
shows goodness of fit ranging between rather poor and bad. The likelihood-ratio test determines
the significance of the model and tests the assignability of the results to the main unit (see Table
3). The significance of the used independent variables varies. The aspired significance level of "
I. Dependent Variables
Name Rank (service, technology)
Winner 1-50 (service), 1-10 (technology)
Good 50-140 (service), 20-45 (technology)
Bad 141-219 (service), 46-75(technology)
Loser 220-500 (service), 76-300 (technology9
II. Independent Variables
A. Team Composition
Name Description
A Team mixed
B Team, strictly male
C Team, strictly female
D Women, single
E Man, single
B. Course Focus
Name Description
Creativity 1
st
stage of BPW, focused on the business idea
Market 2
nd
stage of BPW, focused on business idea and market analysis
Final 3
rd
stage of BPW, complete business plan
7
1% is reached for the area of course focus while team composition doesn´t show the slightest
significance.
Likelihood-Ratio Test
Effect
-2 Log-Likelihood
for reduced model
Chi-squared df. Sig.
Constant Term 94,856
a
,000 0 .
Team Composition 99,934 5,078 12 ,955
Course Focus 133,778 38,921 6 ,000
Table 1: Likelihood-Ratio Test, Service
Altogether, in the case of technology-oriented business plans 65 observations have been
included in the regression calculation. The Cox and Snell reaches ,484, Nagelkerke to ,516 and
McFadden R
2
to ,239. In contrast to the service model these indicators show a general good
model fit. The likelihood-ratio test shows (Table 4) that team composition although not
statistically significant is by far more promising than in the first model while “course” focus is
significant.
Likelihood-Ratio Test
Effect
-2 Log-Likelihood
for reduced model
Chi-squared df. Sig.
Constant Term 56,664 ,000 0 .
Team Composition 70,612 13,949 12 ,304
Course Focus 85,581 28,917 6 ,000
Table 4: Likelihood-ratio test, technology
In conclusion, the results of the classification matrix can be used for the assessment of the
overall quality of the model (see Table 4). Here, for each group the lines show the observed group
belonging and the columns the estimated group belonging. For Group A, the success rate of the
prognosis amounts to 52.3%, for Group B 56.1% and for Group C 52%. All in all, 61% of the
observations have been correctly classified. This value can be interpreted in its quality when one
compares it with the hit rate of a random distribution taking into account the group volumes. The
groups consist of 15 (Group A), 18 (Group B) and 16 (Group C) 16 observations (altogether: 65
observations). The result is an incidental hit rate of 27,69%. The model results in a rate of correct
classifications of 52,3% which is about 88,87% better than the incidental hit rate.
8
Observed
Valid
Winner Good Bad Loser Total
Winner
9 2 3 1 60,0%
Good
3 9 2 4 50,0%
Bad
3 7 5 1 31,2%
Loser
3 2 0 11 68,8%
Total 27,7% 30,8% 15,4% 26,2% 52,3%
Table 5: Classification matrix, technology
To look in more detail how the team ranks in the different course stages differ, we use simple
cross tables. Table 6 shows the ranks in the technology area and it gets clear that in the course of
the competition the placements improve. While about 50% of the teams place as “Loser” in the
“Creativity” segment, in the following stages (“Market” and “Final”) no team gets ranked in this
bracket. Just as the proportion of better placed teams (“Winner” and “Good” column) rises in the
2
nd
and 3
rd
stage.
Rank
Winner Good Bad Loser Total
Course Focus
Creativity 5 7 4 16 32
Market 7 8 9 0 24
Final 3 3 3 0 9
Total 15 18 16 16 65
Table 6: Cross table, technology
In the service segment a different situation exists (table 7). While also about 50% of the teams
place as “Loser” in the “Creativity” segment in the following stages the rank improvement isn´t
as large as in the technology segment. In the “Market” stage still 12,1% place as “Loser” and
about 48,5% as “Bad.”
Rank
Winner Good Bad Loser Total
Course Focus
Creativity 10 12 36 57 115
Market 10 16 32 8 66
Final 3 4 3 0 10
Total 23 32 71 65 191
Table 7: Cross table, service
9
3. RESULTS AND IMPLICATIONS
Overall the contextual variable course focus has a significant impact on the success of the
observed business plans while team composition does not have a significant impact either on the
service or the technology segment. In the case of the technology segment there are hints through
the likelihood-ratio test that team composition might have an impact on the dependent variable
but nevertheless a relationship isn´t proven. From a theoretical perspective it makes sense that in
a technology context where the business model is innovative and R&D has to be conducted that
the start-up team has a (larger) impact on success than in the service segment where the business
model is rather proven and the context variables are considerably more important than the
individual team composition.
Overall, the empirical result might seem contradictory to those who interpret
entrepreneurship as a highly individual process where the team is of uttermost importance for the
outcome. While this holds true in the “real” business world for the controlled environment of a
business plan competition this doesn´t seem to be the case. Another interpretation is, that a
typology of teams according to number (team or single) and gender (male, female, mixed) just
cannot capture the influencing factors like capacities, resources work experience and networks
which are independent from sex or team characteristics.
We can derive from the cross tables that the rankings are improving throughout the course
of the competition. While in the first stage, where participants have to creatively develop a
business idea their ranks are considerably lower than in comparison to the following stages of the
competition, where more and more hard facts e.g. market analysis and financial planning are
focused. Some critics might consider this effect some kind of a self-fulfilling prophecy, because
academics, who receive a special training to develop business plans might certainly do well or
considerably better in comparison to non-academics in a business plan competition. In
retrospective this might seem as a simple fact but you could also argue that practical experience is
more relevant to success in a practice-oriented competition.
There is obviously potential to improve the effectiveness of the creative development of a
business idea in the analysed course. By increasing the use of traditional creativity techniques and
the more innovative approach of design thinking the observable results might advance.
In future research the presented research design offers the opportunity to study effects of
changes in the course content on success. Through an accompanying questionnaire it will be
possible to gather more meaningful data on the capabilities and character of the participating
students and to use more independent variables for the proposed model. Especially those teams
who combine high formal education and practical experience might place considerably better than
teams with strictly theoretical knowledge.
10
REFERENCES
Backhaus, K. (2010) (in German). Multivariate Analysemethoden: Eine anwendungs-
orientierte Einführung, Berlin.
Bell, S.T. et al. (2011). Getting Specific about Demographic Diversity Variable and Team
Performance Relationships: A Meta-Analysis. Journal of Management, 37 (3), 709-743.
Brosius, F. (2004) (in German). SPSS 12: Das mitp-Standardwerk, Bonn.
Bowers, M.Y. & Scherpereel, C. M. (2008). BizBlock: A cross-disciplinary teaching and learning
experience. Business Communication Quarterly 71 (2), 221–226.
Chandler, G.N.; Lyon, D.W. (2001). Entrepreneurial teams in new ventures: Composition,
turnover and performance. Academy of Management Proceedings 2001 ENT: A3.
Der Foo, M., Kam 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. Journal of
Business Venturing 20 (3), 385–402.
EXIST (2012) (in German). EXIST - Existenzgründungen aus der Wissenschaft Summary
English. Bundesministerium für Wirtschaft und Technologie. Onlinehttp://www.exist.de/englische_version/index.php, (16.02.2012).
Honig, B. (2004). Entrepreneurship education: Toward a model of contingency-based business
planning. Academy of Management Learning & Education 3 (3), 258–273.
Horwitz, S.K., Horwitz, I.B. (2007). The effects of team diversity on team outcomes: A meta-
analytic review of team demography. Journal of Management, 33, 987-1015
Kirsch, D., Goldfarb, B. & Gera, A. (2009). Form or substance: the role of business plans in
venture capital decision making. Strategic Management Journal 30 (5), 487–515.
Kuratko, D.F. (2005). The emergence of entrepreneurship education: Development, trends, and
challenges. Entrepreneurship Theory and Practice 29 (5), 577–598.
Mueller, S. (2011). Increasing entrepreneurial intention: Effective entrepreneurship course
characteristics. International Journal of Entrepreneurship and Small Business 13 (1), 55–
74.
Russell, R., Atchison, M. & Brooks, R. (2008). Business plan competitions in tertiary institutions:
encouraging entrepreneurship education. Journal of Higher Education Policy &
Management 30 (2), 123–138.
Sanjib, C. (2005). Demographic diversity for building an effective entrepreneurial team: is it
important? Journal of Business Venturing 20 (6), 727–746.
Thompson, J.L. (2004). The facets of the entrepreneur: identifying entrepreneurial potential.”
Management Decision 42 (2), 243–258.
doc_609685576.pdf
During in this such a detailed breakdown explores effects of teaching and team composition on success in an entrepreneurship education.
1
EFFECTS OF TEACHING AND TEAM COMPOSITION ON SUCCESS
IN AN ENTREPRENEURSHIP EDUCATION COURSE
CHRISTIAN SCHULTZ
TAMARA ALMEYDA
KATHARINA HÖLZLE
Abstract
We analyse the effects of teaching and team composition on the success of student teams who
attended the course “Entrepreneurship and Business Plan” in the period 2007-2011 at the
University of Potsdam and participated in a popular business plan competition. External jurors
rank the business plans in three different stages of the competition. Each stage focuses on
different aspects of the overall plan e.g. business idea or market analysis.
A logistic regression model is performed to calculate the effects. While no effect of team
composition is found either in the case of service- and technology-oriented business plans the
contextual variable course focus emerges as highly significant to predict the rank bracket of the
participating teams.
Different implications of the findings are discussed and avenues for future research are framed.
2
1. PRINCIPLE TOPIC
Innovation and entrepreneurship have emerged as important topics on the political and
academic agenda. They have been identified as key factors for economic growth in small and
medium enterprises. Government initiatives have been formed to promote and support
entrepreneurship and entrepreneurial education. In Germany the federal Ministry of Economics
and Technology supports the EXIST program “aimed at improving the entrepreneurial
environment at universities and research institutions and at increasing the number of technology
and knowledge based business start-ups” (EXIST 2012). Some researchers emphasize that
individual personality and talent make a successful entrepreneur and that technique-based
training, i.e. entrepreneurial education can only teach skills and provide tools that may help the
entrepreneur but won´t make a mayor differences in success (Thompson 2004). But the majority
of scholars and researchers in the field of entrepreneurial education state that both the
characteristics and skills of entrepreneurship can be trained or learned and that subsequently „the
question of whether entrepreneurship can be taught is obsolete“ (Kuratko 2005, 580).
Business plan courses and business plan competitions have emerged as an extraordinarily
popular part of the entrepreneurship education curriculum in higher education institutions (Honig
2004; Kuratko 2005; Russell et al. 2008; Müller 2011). Business plan competitions although
originally aimed at stimulating start-up activities have been recognized to provide a range of
benefits to participants, such as the access to mentors, access to workshops and events such as
team building or networking and judges’ advice. On the educational level an increase of both
general economic as well as entrepreneurship specific knowledge has been generally observed in
the curricula (Russell et al. 2008). Another benefit for participants is the development of social
skills such as team competences, confidence in dealing with risks, problem solving or dealing
with pressure (Russell et al. 2008). The combination of these different learning objectives,
entrepreneurial spirit, economic knowledge and social skills, is both a challenge and a great
opportunity to business plan courses. So in translating their theoretical knowledge to a hands-on
experience such as the creation of a business plan, participants might gain valuable knowledge
and skills. These benefits prevail despite the fact that the importance of a business plan from the
decision-making process of venture capitalists is sometimes challenged (Kirsch et al. 2009) and
participants of business plan courses or competitions at the university level might not
immediately decide to start their own businesses.
On the institutional level of academic education business plan courses are cross-
disciplinary courses, combining the knowledge of different business school disciplines such as
marketing and management in one course (Bowers and Scherpereel 2008). The goal is to translate
3
an idea into a business plan, that is „to understand and fill the gaps between their idea and a
commercially viable business plan [and to understand] the knowledge gap between their idea and
a judge’s or the market’s view of a business plan“ (Russell et al. 2008, 127). To enhance the
inclusion of the market perspective, the University of Potsdam has opted to combine the business
plan course in the curriculum with the regional business plan competition Berlin-Brandenburg.
The business plan course at the University of Potsdam is designed as an elective for
undergraduate business school students. It is open to undergraduate and graduate students of all
other departments of the university. Students attend lectures on the components of a business plan
by faculty members and guest speakers. Every participant has to develop a business idea
throughout the semester, either on his own or in a team. It is obligatory that everybody
participates in the Business Plan Competition Berlin-Brandenburg (BPW). The accompanying
lecture is aligned to the 3 milestones of the BPW, which are described in more detail in the next
section.
The course design combines a number of different teaching methods. The course starts with
introductory classes on entrepreneurship and business idea generation followed by a “Market of
Ideas” where idea posters for the potential businesses are presented to fellow students and faculty
members. Students may revise their business ideas based on the early feedback they received
before starting the business plan process. During the semester students attend lectures on the
components of a business plan by faculty members and guest speakers. There are also three peer
review sessions with fellow student tutors or faculty members prior to submitting the business
plan documents. The business plans are graded at the university and simultaneously ranked and
feedback is given by the jurors of the BPW. So while students can earn credit they also get
feedback from outside the academic world.
The course´s goal depend on the target group – whether they are business students or a non-
business audience with presumably fewer knowledge of market evaluation, business processes,
marketing and finance. For business students it is more relevant to practice knowledge acquired in
different courses during their studies and apply those to an original idea. Students of other
university-departments are in a greater need of basic business-related knowledge to develop an
idea in a marketable business.
An important factor in the process of composing a business plan is team diversity (Sanjib
2005; Der Foo et al. 2005). Der Foo et al. (2005) argue that task related diversity of team
members might enhance team effectiveness, while non-task diversity such as age might have
negative effects. Sanjib (2005) also include the gender perspective and find that demographic
diversity is not important for an entrepreneurial team. With the emergence of entrepreneurial
4
education specifically aimed at women (e.g. the Plan B(usiness) at the Technical University of
Berlin or simulation games funded by the EXIST program), the gender component of
entrepreneurial teams is in the focus of university level education.
After outlining the principle topic the basic research question arises, what determines the
success of the business plans?
The described setting enables the study of two influencing factors on the quality of the
business plan first the team composition and second the impact of the presented content to the
students.
5
2. METHODOLOGY
The sample consists of 256 participating teams (technology: 49, service: 121) and single
persons (technology: 16, service: 70) in the BPW in the period 2007-2011, who attended the
course “Entrepreneurship and Business Plan” at the University of Potsdam.
The BPW is by far the largest public business plan competition in Germany. Regularly
more than 3.000 persons in about 600 teams participate in this competition and turn in their
business plans. The BPW is divided into the two sub segments service and technology. In the first
the service-oriented business ideas are evaluated and in the second the more technology-oriented.
A distinct document outline is obligatory to participate in the BPW (1. Executive Summary,
2. Business Idea, 3. Team, 4. Market Analysis, 5. Marketing, 6. Legal Issues and 7. Finance).
Starting in October of each year the competition is divided in 3 milestones, which require the
delivery of specific parts of the business plan (1
st
Phase: 1-3; 2
nd
: 1-5; 3
rd
Phase: 1-7). Two
anonymous referees evaluate the documents on a standardized point scheme and also qualitative
feedback is provided. According to the (cumulative) points reached in each step the teams are
ranked and the best10 participants win up to 10.000!.
Model and Variables
The dependent variable is the rank range of the teams in the BPW. For this purpose we
coded the ranks in winner (Rank: 1-50), good, bad and loser. We choose different rank codes for
the segments technology and service because a larger proportion of teams is located in the service
sector than in the technology sector. For both segments a different ranking is published.
We include two independent variables in our overall model, one individual and two
contextual. In the course every participant can choose if he wants to turn in a business plan by
himself or if he wants to join a team with up to 3 persons.
We add to the model a contextual independent variable the phase (categorical: 1, 2, 3) and
the team composition (categorical: 1, 2, 3, 4, 5)
The used dependent and independent variables are summarized in table 1 below.
6
Table 1: Dependent and independent Variables
In order to examine the group differences with existing independent variables, on principle
the discrimination analysis and the logistic regression can be used as quantitative statistical
procedures. In general, the logistic regression is considered as the more robust method because of
less stringent conditions. No normally distributed independent variables and equal variance-
covariance matrices are required (Backhaus 2010; Brosius 2004). Altogether, in the case of
service 191 observations have been included in the regression calculation. For assessing the
quality of the comprehensive model, three pseudo-r-square measures are available. The Cox and
Snell amount to ,213, Nagelkerke to ,231 and McFadden R
2
to ,093. The Cox and Snell is
considered as good for values bigger than ,40 the Nagelkerke value is to be interpreted also as
good from ,40 and as very good from ,50 the McFaddens R
2
is considered as acceptable from
values bigger than ,20 and as good from ,40.
According to all represented common measurements, all in all the comprehensive model
shows goodness of fit ranging between rather poor and bad. The likelihood-ratio test determines
the significance of the model and tests the assignability of the results to the main unit (see Table
3). The significance of the used independent variables varies. The aspired significance level of "
I. Dependent Variables
Name Rank (service, technology)
Winner 1-50 (service), 1-10 (technology)
Good 50-140 (service), 20-45 (technology)
Bad 141-219 (service), 46-75(technology)
Loser 220-500 (service), 76-300 (technology9
II. Independent Variables
A. Team Composition
Name Description
A Team mixed
B Team, strictly male
C Team, strictly female
D Women, single
E Man, single
B. Course Focus
Name Description
Creativity 1
st
stage of BPW, focused on the business idea
Market 2
nd
stage of BPW, focused on business idea and market analysis
Final 3
rd
stage of BPW, complete business plan
7
1% is reached for the area of course focus while team composition doesn´t show the slightest
significance.
Likelihood-Ratio Test
Effect
-2 Log-Likelihood
for reduced model
Chi-squared df. Sig.
Constant Term 94,856
a
,000 0 .
Team Composition 99,934 5,078 12 ,955
Course Focus 133,778 38,921 6 ,000
Table 1: Likelihood-Ratio Test, Service
Altogether, in the case of technology-oriented business plans 65 observations have been
included in the regression calculation. The Cox and Snell reaches ,484, Nagelkerke to ,516 and
McFadden R
2
to ,239. In contrast to the service model these indicators show a general good
model fit. The likelihood-ratio test shows (Table 4) that team composition although not
statistically significant is by far more promising than in the first model while “course” focus is
significant.
Likelihood-Ratio Test
Effect
-2 Log-Likelihood
for reduced model
Chi-squared df. Sig.
Constant Term 56,664 ,000 0 .
Team Composition 70,612 13,949 12 ,304
Course Focus 85,581 28,917 6 ,000
Table 4: Likelihood-ratio test, technology
In conclusion, the results of the classification matrix can be used for the assessment of the
overall quality of the model (see Table 4). Here, for each group the lines show the observed group
belonging and the columns the estimated group belonging. For Group A, the success rate of the
prognosis amounts to 52.3%, for Group B 56.1% and for Group C 52%. All in all, 61% of the
observations have been correctly classified. This value can be interpreted in its quality when one
compares it with the hit rate of a random distribution taking into account the group volumes. The
groups consist of 15 (Group A), 18 (Group B) and 16 (Group C) 16 observations (altogether: 65
observations). The result is an incidental hit rate of 27,69%. The model results in a rate of correct
classifications of 52,3% which is about 88,87% better than the incidental hit rate.
8
Observed
Valid
Winner Good Bad Loser Total
Winner
9 2 3 1 60,0%
Good
3 9 2 4 50,0%
Bad
3 7 5 1 31,2%
Loser
3 2 0 11 68,8%
Total 27,7% 30,8% 15,4% 26,2% 52,3%
Table 5: Classification matrix, technology
To look in more detail how the team ranks in the different course stages differ, we use simple
cross tables. Table 6 shows the ranks in the technology area and it gets clear that in the course of
the competition the placements improve. While about 50% of the teams place as “Loser” in the
“Creativity” segment, in the following stages (“Market” and “Final”) no team gets ranked in this
bracket. Just as the proportion of better placed teams (“Winner” and “Good” column) rises in the
2
nd
and 3
rd
stage.
Rank
Winner Good Bad Loser Total
Course Focus
Creativity 5 7 4 16 32
Market 7 8 9 0 24
Final 3 3 3 0 9
Total 15 18 16 16 65
Table 6: Cross table, technology
In the service segment a different situation exists (table 7). While also about 50% of the teams
place as “Loser” in the “Creativity” segment in the following stages the rank improvement isn´t
as large as in the technology segment. In the “Market” stage still 12,1% place as “Loser” and
about 48,5% as “Bad.”
Rank
Winner Good Bad Loser Total
Course Focus
Creativity 10 12 36 57 115
Market 10 16 32 8 66
Final 3 4 3 0 10
Total 23 32 71 65 191
Table 7: Cross table, service
9
3. RESULTS AND IMPLICATIONS
Overall the contextual variable course focus has a significant impact on the success of the
observed business plans while team composition does not have a significant impact either on the
service or the technology segment. In the case of the technology segment there are hints through
the likelihood-ratio test that team composition might have an impact on the dependent variable
but nevertheless a relationship isn´t proven. From a theoretical perspective it makes sense that in
a technology context where the business model is innovative and R&D has to be conducted that
the start-up team has a (larger) impact on success than in the service segment where the business
model is rather proven and the context variables are considerably more important than the
individual team composition.
Overall, the empirical result might seem contradictory to those who interpret
entrepreneurship as a highly individual process where the team is of uttermost importance for the
outcome. While this holds true in the “real” business world for the controlled environment of a
business plan competition this doesn´t seem to be the case. Another interpretation is, that a
typology of teams according to number (team or single) and gender (male, female, mixed) just
cannot capture the influencing factors like capacities, resources work experience and networks
which are independent from sex or team characteristics.
We can derive from the cross tables that the rankings are improving throughout the course
of the competition. While in the first stage, where participants have to creatively develop a
business idea their ranks are considerably lower than in comparison to the following stages of the
competition, where more and more hard facts e.g. market analysis and financial planning are
focused. Some critics might consider this effect some kind of a self-fulfilling prophecy, because
academics, who receive a special training to develop business plans might certainly do well or
considerably better in comparison to non-academics in a business plan competition. In
retrospective this might seem as a simple fact but you could also argue that practical experience is
more relevant to success in a practice-oriented competition.
There is obviously potential to improve the effectiveness of the creative development of a
business idea in the analysed course. By increasing the use of traditional creativity techniques and
the more innovative approach of design thinking the observable results might advance.
In future research the presented research design offers the opportunity to study effects of
changes in the course content on success. Through an accompanying questionnaire it will be
possible to gather more meaningful data on the capabilities and character of the participating
students and to use more independent variables for the proposed model. Especially those teams
who combine high formal education and practical experience might place considerably better than
teams with strictly theoretical knowledge.
10
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