project 5 final presentation dec 2016

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Jonathan Levie, Karen Bonner Enrico Vanino, Mark Hart E: [email protected] Team size and entrepreneurial ambition in the UK London - 13 th December 2016

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Page 1: Project 5 Final presentation Dec 2016

Jonathan Levie, Karen Bonner

Enrico Vanino, Mark Hart

E: [email protected]

Team size and entrepreneurial ambition in the UK

London - 13th December 2016

Page 2: Project 5 Final presentation Dec 2016

Main Goals of the Project

• To explore the potential impact of ambition and team size (the number of owner-managers in a business).

• To gather the current evidence on the effect of team size and team diversity on performance.

• To measure potential impact in a variety of ways, including realized organization size, export propensity and intensity, and innovativeness.

• To study the effect of owner-manager education level and business complexity on this relationship.

Page 3: Project 5 Final presentation Dec 2016

How we achieve this?

1. Meta-analysis of previous studies linking team size and diversity to business performance.

2. Econometric analysis using the combined 2002 to 2015 UK GEM database to test hypotheses that link ambition and team size, and team size and performance.

Page 4: Project 5 Final presentation Dec 2016

Previous research

• Team size usually treated as a control variable.

• Research on cognitive and affective conflict within teams generates different hypotheses on the effects of team size on performance.

• This issue has not been satisfactorily tested empirically.

• Most studies being conducted in the US and on high technology ventures.

Page 5: Project 5 Final presentation Dec 2016

I. Team size is a function of ambition.

II. Performance is a function of team size and diversity.

III. Business owners in complex environments are more likely to succeed if they are more highly educated and have larger teams to address these complexity issues.

Research Hypotheses

Page 6: Project 5 Final presentation Dec 2016

1. Meta-analysis

The effect of top managerial teams (TMT) size and diversity on the performance of new ventures and SMEs

• Disciplined search, statistical comparison of findings and summarizing of “effects” by size, direction and consistency (homogeneity)

• 47 studies conducted between 1990 and 2016 and 266 measured relationships

• 77% of the studies focus on new ventures, 60% on high-tech firms and 36% on SMEs

Page 7: Project 5 Final presentation Dec 2016

Summary of studies included

No. of

Obs.

% of

Obs.

No. of

Studies

% of

Studies

No. of

Obs.

% of

Obs.

No.

Studies

% of

Studies

SMEs 113 42.48% 17 36.17% Independent Variables

Young 212 79.7% 36 76.6% Age 13 4.87% 8 17.02%

High-Tech 127 47.57% 28 59.57% Diversity 20 7.49% 8 17.02%

Team Education 42 15.73% 17 36.17%

TMT 168 62.92% 28 59.57% Experience 88 32.96% 29 61.7%

Board 18 6.74% 5 10.64% Functional 22 8.24% 11 23.4%

Founding 81 30.34% 19 40.43% Network 21 7.87% 10 21.28%

Geographical Distribution Size 60 22.47% 30 63.83%

All 18 6.74% 1 2.13% Dependent Variables

Asia 19 7.12% 5 10.64% Employment 44 16.48% 12 25.53%

Australia 4 1.5% 1 2.13% Finance 47 17.6% 9 19.15%

EU 77 28.84% 18 38.3% Innovation 23 8.61% 7 14.89%

N. America 146 54.68% 24 51.06% International 39 14.61% 7 14.89%

S. America 3 1.12% 1 2.13% Performance 30 11.24% 8 17.02%

Interaction Effects Profit 31 11.61% 7 14.89%

None 200 74.91% 41 87.23% Sales 53 19.85% 15 31.91%

Environment 9 3.37% 1 2.13% Industrial sectors

Firm-level 39 14.61% 7 14.89% Service 135 50.75% 18 38.3%

Team-level 19 7.12% 5 10.64% Manufacturing 131 49.25% 29 61.7%

Page 8: Project 5 Final presentation Dec 2016

Mean Effect on Firm Performance

Page 9: Project 5 Final presentation Dec 2016

Mean effect by type of performance

Page 10: Project 5 Final presentation Dec 2016

Sources of Heterogeneity(1) (2) (3) (4) (5) (6)

GENERAL SIZE DIVERSITY SMEs YOUNG HIGH-TECH

Geography +* +* +*** +*** +**

Sample Size +** +*

SME -*** -** +*

Young -** -*

High-Tech +*** +*** +*

TMT/Board -**

Est. Method +*** +***

Mediating Eff. -**

Indep. Variable +** +*

Dep. Variable +**

Panel Period +*** +*** +** +*** +*** +***

No. Control Var. -*** -**

No. Obs. 266 60 206 113 194 126

Page 11: Project 5 Final presentation Dec 2016

Mean Effect on SMEs Performance

Page 12: Project 5 Final presentation Dec 2016

Mean Effect on New Ventures Performance

Page 13: Project 5 Final presentation Dec 2016

Mean Effect on HT Firms Performance

Page 14: Project 5 Final presentation Dec 2016

Key Findings from Meta-Analysis

These relationships are positive, significant and consistent across studies:

• Functional diversity sales & profit performance overall

• Team size innovation in new ventures (v. small effect)

• Education diversity valuations in hitech ventures

• Ethnic/gender diversity internationalization in SMEs

• Experience diversity internationalization in SMEs

• Experience diversity valuations in SMEs

• Functional diversity internationalization in SMEs & hitech

• Function diversity innovation in SMEs

Page 15: Project 5 Final presentation Dec 2016

Key Findings from Meta-Analysis

These relationships are positive and significant but not consistent across studies:

• Team size valuations in new and hitech ventures

• Team size innovation in hitech ventures

• Ethnic/gender diversity internationalization in SMEs

• Experience diversity valuations in new & hitech ventures

• Experience diversity innovation in new & hitech ventures

• Experience diversity internationalization in SMEs

• Functional diversity internationalization in SMEs & hitech

Page 16: Project 5 Final presentation Dec 2016

Summing up

• Team size is related to the financial value of a venture and innovativeness

• Different aspects of team diversity are related to financial value, internationalization and innovativeness

Page 17: Project 5 Final presentation Dec 2016

2. Econometric analysis

• We use the UK Global Entrepreneurship Monitor dataset pooled over 2002-15

• Analysis undertaken for early-stage businesses (nascent and new businesses up to 42 months old) and established businesses (over 42 months old)

• Two stages: Ambition team size performance

• OLS or Logistic regression depending on the dependent variable

• Four types of performance outcome considered: organisation size; export propensity, export intensity and innovation

Page 18: Project 5 Final presentation Dec 2016

Model Part 1

Owner/manager

Ambition

Owner/manager

team size

Control variables

Moderators:

Education

Business complexity

+

+

Page 19: Project 5 Final presentation Dec 2016

Ambition and Team size• Main predictor ambition measured in two ways:

• Owner-manager expects to employ at least 20 people in 5 years time

• Owner-manager expects to employ at least 10 people and at least double employment in 5 years time

• Education level used as measure of owner-manager quality; degree level or above considered highly educated

• Business complexity categorises medium- and high-tech manufacturing and knowledge-intensive services as complex

• Control variables include gender and age group; opportunity perception; established business owner ; expects to start a new business in the next three years.

Page 20: Project 5 Final presentation Dec 2016

Results: Early-stage Businesses (n=9,910)

Team size Team size Team size Team size

Ambition 1 (20+ emp .in 5yrs) 0.45*** 0.61**

Ambition 2 (10+ emp in 5 yrs) 0.30*** 0.29***

High business complexity 0.04* 0.03 0.04 -0.00

At least post-secondary

education 0.08*** 0.10*** 0.09*** 0.09**

Ambition and complexity 0.08 0.18**

Ambition and education -0.18 -0.02

Page 21: Project 5 Final presentation Dec 2016

Team size Team size Team size Team size

Ambition 1 (20+ emp .in 5yrs) 0.70*** 0.48**

Ambition 2 (10+ emp in 5 yrs) 0.41*** 0.44*

High business complexity 0.03 0.04 0.02 0.01

At least post-secondary

education 0.14*** 0.12*** 0.19*** 0.19**

Ambition and complexity -0.07 0.04

Ambition and education 0.25* -0.04

Results: Established Businesses (n=10,931)

Page 22: Project 5 Final presentation Dec 2016

Team size and performance• Main predictor team size split into four sub-categories:

• solo entrepreneur

• two owner-managers

• three or four owner-managers

• five or more owner-managers.

• Education level used as measure of owner-manager quality; degree level or above considered highly educated

• Business complexity categorises medium- and high-tech manufacturing and knowledge-intensive services as complex

• Control variables include gender and age group; opportunity perception; established business owner ; expects to start a new business in the next three years.

Page 23: Project 5 Final presentation Dec 2016

Model Part 2

Owner/manager

team size

Potential economic impact:

Organization size

Export propensity

Export intensity

InnovativenessControl variables

Education

Business complexity

++

Page 24: Project 5 Final presentation Dec 2016

Results: Early-stage Businesses (n=9,910)

No. of

employees

Export

Propensity

Export

Intensity Innovator

Predictors

Team size two owners +*** +*** +*** +**

Team size three-four owners +*** +**

Team size five plus owners +*** +* +*

High business complexity

Medium education

High education +*** +** +***

Page 25: Project 5 Final presentation Dec 2016

Results: Established Businesses (n=10,931)

No. of

employees

Export

Propensity

Export

Intensity Innovator

Predictors

Team size two owners +*** +**

Team size three-four owners +*** +* +*

Team size five plus owners +*** +*** +***

High business complexity -** +**

Medium education +** +**

High education +*** +*** +*** +***

Page 26: Project 5 Final presentation Dec 2016

Returning to hypotheses..

• H1: Business complexity positively moderates the relationship between team size and firm performance

• H2: The quality of the owner manager, as identified by education level, positively moderates the relationship between team size and firm performance

• H3: Business complexity and owner-manager quality positively moderate the relationship between team size and firm performance

Page 27: Project 5 Final presentation Dec 2016

Results: Early-stage Businesses (n=9,910)

Average marginal effects No. of

employees

Export

Propensity

Export

Intensity Innovator

Team size at least 3 owners 0.64** 0.14 -0.01 0.16*

High business complexity -0.04 0.04 0.04 0.14

At least post-secondary

education 0.02 0.05* 0.03 0.06**

Team size and complexity -0.89*** 3.34*** 0.68*** -0.63**

Team size and education -0.18 -0.00 0.09 -0.05

Complexity and education -0.07 0.06 0.03 -0.08

Team size, complexity and

education 1.31*** -3.28*** -0.60*** 0.74**

Page 28: Project 5 Final presentation Dec 2016

Results: Established Businesses (n=10,931)

Average marginal effects No. of

employees

Export

Propensity

Export

Intensity Innovator

Team size at least 3 owners 1.21*** 0.14** 0.07** -0.01

High business complexity .25 0.15 -0.05 0.16**

At least post-secondary education .17*** 0.10*** 0.04 0.03**

Team size and complexity .21 -0.30*** -0.06 -0.15

Team size and education .22 -0.01 -0.04 0.05

Complexity and education -.43* -0.05 0.10* -0.10

Team size, complexity and

education .21 0.41*** 0.15 0.17

Page 29: Project 5 Final presentation Dec 2016

Summary

• Team size is strongly associated with ambition

• Team size does have an effect on firm performance, this differs between early-stage and established businesses

• Positive 3-way effect of team size, business complexity and owner-manager quality for org size and innovation, negative for exporting in early-stage

• Positive 3-way effect for exporting in established stage

Page 30: Project 5 Final presentation Dec 2016

Possible Policy Implications

• Both the meta-analysis and the empirical UK study reach broadly similar conclusions:

• Team size has a positive effect on organization size and innovativeness in early-stage ventures

• Team size/diversity has a positive effect on internationalization in established SMEs

• Encouraging larger, diverse teams with different backgrounds and experience may boost innovation, firm growth and internationalization and generate higher returns

Page 31: Project 5 Final presentation Dec 2016

Contact us:

If you would like any more information about this research please contact

Jonathan Levie at [email protected]