Presentation on serial entrepreneurs for the West Coast Research Symposium in Seattle, WA at the Univ. of Washington.

1. CUTTING YOUR TEETH
Cutting Your Teeth: Learning from
Entrepreneurial Experience
Chuck Eesley (Stanford), Edward B. Roberts (MIT)
Organization Science Winter Conference
Feb. 3-7th, 2010
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S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

2. Motivation
CUTTING YOUR TEETH
 When do new ventures benefit from the prior entrepreneurial
experience of their founders?
 Under what conditions does organizational learning get
transferred by individuals to new organizations (what type of
learning in the case of entrepreneurial experience)?
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3. Organizational Learning
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Gruber, 1994; Rapping, 1965; Thornton & Thompson, 2001
Strategic Contexts:
acquisitions (Finkelstein &Haleblian, 2002; Haleblian& Finkelstein, 1999; Hayward, 2002;
Vermeulen&Barkema, 2001), alliances (Anand, B. and T. Khanna, 2000; Hoang
&Rothaermel, 2005) and internationalization (Bingham, Eisenhardt, &
Davis, 2009), innovation (Katila&Ahuja, 2002)
Type of Experience
Successes/failures (Sitkin, 1992), variety (Schilling, Vidal,
Ployhart, &Marangoni, 2003), complexity, voluntary or not
(Haunschild& Sullivan, 2002; Haunschild& Rhee, 2004)
Transfer of learning across organizations (Ingram & Baum,
1997; Kim & Miner, 2007; Miner &Haunschild, 1995) Vicarious
(Haunschild& Miner, 1997; Huber, 1991)
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4. Motivation: Types of learning experiences
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Organization or industry-level phenomenon (Cyert& March, 1963; Baum &
Ingram, 1998) Individual level?
Simon (1991):
1) by ‘ingesting’ new members who have knowledge not previously in the
organization, or
2) by its members learning
Huckmanand Pisano (2006) - experience within particular organization
Hire employees / management to access internally (rather than externally)
the accumulated learning – strategy/OT (Beckman & Burton, 2008;
Ahuja&Katila, 2001)
Hypothesis 1: The benefit from learning transferred by an individual will be
higher with greater levels of prior experience.
Hypothesis 2: The benefit from learning transferred by an individual will be
higher with prior successful experience.
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5. CUTTING YOUR TEETH
Transfer effects - loss of performance if skill is wrongly applied in a different
context (Haleblian& Finkelstein, 1999; March, 1991)
Hypothesis 3a: The benefit from learning transferred by an individual will
be higher with prior experience in the same industry.
Industry evolution - automobiles (Abernathy, 1978) typesetters (Tripsas,
1997)
Major disruptions - learning in the previous environment no longer relevant
Find the right causal relationships and models to fit the changed environment
(Kaplan &Tripsas, 2008)
Hypothesis 3b: The benefit from learning transferred by an individual
with prior experience will be lower after a significant shift in the industry.
Hypothesis 4: The benefit from learning transferred by an individual will
be higher with more recent prior experience.
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6. Shedding light on what the individual is learning
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Existing work mainly argues that processes and routines are learned from
operating experience (Nelson & Winter, 1982; Winter, 2000)
Bingham, Eisenhardt, and Davis (2009) - rather than routines, increasingly
sophisticated and refined portfolios of heuristics to guide actions
Content knowledge as important or more so than process knowledge
Hypothesis 5: The benefit from learning transferred by an individual will
come from content learning about the industry gained from prior
experience rather than about process learning.
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7. MIT Data
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Long time horizon in the cross section (1930s-2003)
Note: not MIT-originated technology
 Alumni: 105,000 surveyed; 42,930 records in 2001
– Date of birth, country of citizenship, gender, major at MIT, highest
attained degree
– 7,798 indicated founding at least one company
 Survey of self-identified MIT alumni entrepreneurs in 2003
– 2,067 respondents (r.r. 27%)
– More detailed info; new venture founding history (multi-founder r.r. of
30.4%, 1.79 vs. 2.13 reported)
 80% of the company names D&B database (obtain a credit rating) no bias
towards larger firms (Aldrich, Kalleberg, Marsden and Cassell, 1989)
 VEIC  SIC codes (Dushnitsky& Lenox, 2005)
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8. Univariatet-tests of means
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Panel A No prior founding experience At least 1 prior founding experience
Revenues 13.957 14.219*
Exits 0.205 0.240**
Prior founding exper. in a Prior founding exper. in the same
Panel B different industry industry
Revenues 14.042 14.854**
Exits 0.060 0.173***
Before dotcom boom, no prior Before dotcom boom, has prior
Panel C+ founding exper. founding exper.
Revenues 14.614 15.227*
Exits 0.275 0.391*
After dotcom boom, no prior After dotcom boom, has prior
Panel D+ founding experience founding experience
Revenues 12.810 13.375
Exits 0.182 0.061**
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9. Methods
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OLS
Y = F(α + 1’θi + 2’*(exper.) + 3’*(exper.)*(ind. disruption) + ’Xi + τt+ ηj + εi)
Cox Hazard Rate Model (robust to logit)
Prob (Y= 1) = F(α + 1’*(experience) + ’Xi + τt+ ηj + εi)
 Dependent variable: Revenues, exit (acquisition, IPO)
 # prior experiences, # exits (acquired, IPO), same/different industry
 Xi = Set of controls for firm age, external funding, num. cofounders,
functional diversity, initial capital
 Include (τ + η) year, industry sector fixed effects
 Before and after significant industry disruption
Proportional Hazards Test 9
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10. Revenues
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Pr(Exited) Ln(Rev) Pr(Exited) Ln(Rev) Pr(Exited) Ln(Rev) Pr(Exited) Ln(Rev)
(4-1) (4-2) (4-3) (4-4) (4-5) (4-6) (4-7) (4-8)
Exper.
Founder 1.568*** 0.453**
(0.219) (0.181)
Num. Prior
Exper. 1.167** 0.272***
(0.083) (0.064)
Prior exits 1.382*** 0.401***
(0.116) (0.137)
Same SIC 1.166 1.167***
(0.356) (0.429)
N=1106, 911. Controls for firm age, num. cofounders, functional diversity,
industry, year, initial capital, VC funding are controlled.
(more)
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11. Large Industry Disruption
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Pr(Exite Ln(Revenu Ln(Revenu Ln(Revenu Ln(Revenu Ln(Revenu
VARIABLES d) es) es) es) es) es) Pr(Exited)
Before Before After After
(5-1) (5-2) (5-3) (5-4) (5-5) (5-6) (5-7)
Exper. founder 0.407 1.333** 2.139*** 0.440
(0.287) (0.596) (0.697) (0.527)
Post-
1997*Experien
ced founder -1.214** -0.302
(0.522) (0.840)
Prior exits 1.173** 0.545
(0.473) (0.438)
Lag 25-50th
quartile 2.453**
(1.085)
Lag 50-75th
quartile 1.633
(1.407)
Lag 75th+
quartile -6.608***
(2.549)
Age founded -0.021* -0.029 -0.0537* -0.0663* 0.024 0.022 -0.015
Software firms only; N=205 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Controls: age founded, num. cofounders, Bachelor’s, Master’s, initial capital, firm age
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

12. Content vs. process
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VARIABLES Log(Rev) Pr(Exited) Log(Rev) Log(Rev) Log(Rev) Log(Rev)
Priorexper. 0.368*** 1.263** 0.388*** 0.356***
(0.072) (0.131) (0.087) (0.074)
Prior
exper.*External
funding -0.416*** 1.098
(0.149 (0.183)
Prior exper.*angel -0.553**
(0.273)
Prior exper.*VC -0.387**
(0.176)
Same SIC 1.124***
(0.392)
Same SIC*VC -2.399***
(0.904)
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Different SIC 0.265***
(0.066)
Different SIC*VC -0.489
(0.592)
VC 1.016** 0.496 0.462
1063 firms, 234 events and 16,068 years at risk. All models include controls for Master’s and Doctorate
degrees, the number of cofounders, log(firm age), functional diversity and constant terms, but the coefficients
are not shown to save space. Model (6-3) excludes firms that were VC funded. Models (6-4), (6-5) and (6-6)
exclude firms that were funded by angel investors. The results are robust to leaving these firms in as well.
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

13. Alternative stories/Robustness checks
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1. Unique to particular measures of “performance”
2. More talented or persistent individuals select into serial
entrepreneurship (individual fixed effects)
3. Learning about the start-up process (evidence on industries)
4. Increased social network size (evidence on location)
- most communication is with those in closer physical proximity
5. Wealth
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14. Conclusion and Implications
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Hypothesis: Supported?
The benefit from learning transferred by an individual will be higher
H1 Y
with greater levels of prior experience.
H2 “… higher with prior successful experience Y
H3a “… higher with prior experience in the same industry Y
H3b “… lower after a significant shift in the industry Y
H4 “… higher with more recent prior experience Y
“… will come from content learning about the industry gained from prior
H5 Y
experience rather than about process learning.
Organizational Learning and Entrepreneurship Literatures:
Individual Level
• Learning by ingesting new members
Strategy
• Micro-foundations of competitive advantage – content vs. process (Haliblian&
Finkelstein, 2002) diversification
• Active view on identification of valuable resources (routines)
• Level playing field after disruptions
• Challenge of sectors with more serial entrepreneurs
Institutions
• Fostering this type of experience (exit events, non-competes) 14
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15. Relationship to Broader Research Stream
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Drivers of entrepreneurial entry and performance (different contexts)
Developed economy
 Entrepreneurs from Technology-Based Universities - with David Hsu
(Wharton), Ed Roberts (MIT)
 Bringing Ideas to Life – Conditions when types of assets  performance
Developing economy
 The Right Stuff
– Role of institutional environment in selection of high human capital
entrepreneurs
 Entrepreneurial Performance in a Developing Country: Evidence from
China
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16. CUTTING YOUR TEETH
Thank you!
Chuck Eesley
Stanford University
Management Science & Engineering (MS&E)
cee@stanford.edu
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

17. Index of Backup Slides
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 Panel Data
 Individual Fixed Effects
 Learning about start-up
process
 Descriptive statistics
 Response Bias
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18. Alternative stories/Robustness checks
CUTTING YOUR TEETH
1. Unique to particular measures of “performance”
2. More talented or persistent individuals select into cross-functional
roles or attempt 2nd start-ups (individual fixed effects)
3. Learning about the start-up process (evidence on industries)
More difficult to rule out
1. Increased social network size (evidence on location, could be
mechanism)
2. Wealth
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

19. Probability of Acquisition
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Dep. Variable = Acquisition year
(subjects start being at risk at year of founding)
Note: reported coefficients are hazard ratios
Independent variables Model 7-1 Model 7-2 Model 7-3 Model 7-4
Age at founding 0.989 0.955** 0.969 0.965
(0.034) (0.021) (0.020) (0.029)
# of start-ups 2.224** -- -- --
founded (1.444)
Number of 1.551 1.563 1.489 1.578
Cofounders (0.492) (0.527) (0.470) (0.928)
Prior acquisitions -- 2.011*** -- --
(0.370)
Prior IPOs -- 1.777 -- --
(0.759)
# Same State -- -- 1.255** --
(0.171)
# Different State -- -- 1.333** --
(0.234)
Same Industry -- -- -- 37.621**
(56.90)
Different Industry -- -- -- 3.675
(3.015)
Firm age, indiv. degree, Industry, year, initial capital, VC funding
are controlled.
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20. CUTTING YOUR TEETH
Panel Data
PR(ACQUIRED) PR(IPO) LN(EMPL) LN(SURVIVAL)
Num. of start-
ups founded 0.040 (0.051) 0.002 (0.069) 0.066 (0.057) -0.028* (0.016)
Num. prior
acquired 0.396*** (0.087) 0.084 (0.116) 0.160 (0.103) 0.058 (0.024)
Num. same 2
digit SIC -0.239* (0.125) -0.014 (0.161) 0.442*** (0.143) 0.014 (0.034)
Age at founding
year -0.012*** (0.004) 0.001 (0.005) -0.012*** (0.004) 0.006 (0.001)
Gender
(1=male) 0.404** (0.202) 0.372 (0.289) 0.582*** (0.153) 0.059 (0.052)
Masters -0.016 (0.076) 0.170* (0.103) 0.305*** (0.086) 0.040 (0.028)
Doctorate -0.192* (0.102) 0.117 (0.130) 0.181 (0.121) 0.111 (0.036)
ln(emp) 0.055*** (0.019) 0.188*** (0.025)
ln(firm age) 0.173*** (0.057) 0.358*** (0.097) 0.532*** (0.074)
MA 0.330*** (0.081) 0.260*** (0.104) 0.214** (0.098) -0.021 (0.030)
CA 0.389*** (0.092) 0.440*** (0.123) -0.030 (0.102) 0.010 (0.033)
Constant -1.422 (1.347) -2.543*** (0.994) -3.290*** (0.626) 1.412*** (0.198)
Year F.E. YES YES YES YES
SIC F.E. YES YES YES YES
Individual F.E. NO NO NO NO
R-squared 0.160 0.228 0.150 0.622
Num. of obs. 1997 1760 2092 2217
***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors in parentheses.
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21. Alternative stories/Robustness checks
CUTTING YOUR TEETH
1. Unique to particular measures of “performance”
2. More talented or persistent individuals select into cross-functional
roles or attempt 2nd start-ups (individual fixed effects)
3. Learning about the start-up process (evidence on industries)
More difficult to rule out
1. Increased social network size (evidence on location , could be
mechanism)
2. Wealth
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22. CUTTING YOUR TEETH
Controls for individual characteristics
PR(ACQUIRED) PR(IPO) LN(EMPLOYEES) LN(SURVIVAL)
Num. of start-
ups founded 2.326*** (0.181) -0.099 (0.074) 0.029 (0.129) 0.161*** (0.043)
Num. prior
acquired -5.105*** (0.221) 0.331*** (0.114) 0.078 (0.186) -0.119** (0.060)
Num. same 2
digit SIC -0.298 (0.248) 0.090 (0.154) -0.034 (0.208) 0.010 (0.064)
Age at
founding year -0.103*** (0.010) 0.000 (0.005) -0.016 (0.011) 0.013 (0.013)
ln(emp) -0.099** (0.045) 0.158*** (0.025)
ln(firm age) 0.359** (0.157) 0.394*** (0.093) 0.322** (0.145)
Year F.E. YES YES YES YES
SIC F.E. YES YES YES YES
Individual F.E. YES YES YES YES
R-squared 0.750 0.206 0.750 0.884
Num. of obs. 463 1771 2135 2231
***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors in parentheses.
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23. CUTTING YOUR TEETH
Alternative stories/Robustness checks
1. Unique to particular measures of “performance”
2. More talented or persistent individuals select into cross-functional
roles or attempt 2nd start-ups (individual fixed effects)
3. Learning about the start-up process (evidence on industries)
More difficult to rule out
1. Increased social network size (evidence on location , could be
mechanism)
2. Wealth (no strong effects of prior IPO)
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

24. Industry Context / Network
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Independent Model 6-2 Model 6-3 Model 6-4
Model 7-3
variables Revenues Acq.
(N=964) (N=648) (N=964)
0.969
Founder char.
Age at founding -0.013 -0.019 -0.012
(0.020)
(0.009) (0.012) (0.009) --
Bachelor’s deg. 0.298 0.586+ 0.346
(0.256 (0.335) (0.255) 1.489
Master’s degree 0.402 0.508 0.434+ (0.470)
(0.255) (0.334) (0.254)
# Same State 0.238* 1.255**
(0.096) (0.171)
# Different State 0.125 1.333**
(0.104) (0.234)
Same 2 digit SIC 1.675** --
(0.614)
Different 2 digit SIC 0.153
--
(0.623)
Prior acquisitions 0.445**
(0.189)
Prior IPOs 0.408
(0.341)
R-squared 0.291 0.362 0.346
Firm age, functional diversity, Industry, year, initial capital, VC
funding are controlled.
Charles Eesley – Cutting
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

25. Alternative stories/Robustness checks
CUTTING YOUR TEETH
1. Unique to particular measures of “performance”
2. More talented or persistent individuals select into cross-functional
roles or attempt 2nd start-ups (individual fixed effects)
3. Learning about the start-up process (evidence on industries)
More difficult to rule out
1. Increased social network size (evidence on location , could be
mechanism)
2. Wealth
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

26. Descriptive Statistics
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Variable Obs. Mean Std. Dev. Min Max
Log revenues 1264 14.05 3.08 0.03 21.66
Acquired 1840 0.19 0.39 0 1
IPO 1790 0.11 0.32 0 1
Lag between 1502 12.11 9.41 0 50
Number of Firms 2058 1.61 1.30 1 11
Prior Acquisitions 2067 0.13 0.42 0 3
Prior IPOs 2067 0.04 0.23 0 3
Prior Same SIC 1473 0.02 0.14 0 2
Prior Different SIC 1473 0.02 0.18 0 3
Prior Foundings in 2067 0.38 0.90 0 8
the Same State
Prior Foundings in 2067 0.23 0.79 0 7
a Different State
Age Founded 1807 39.65 10.59 18 83
Bachelor's degree 2000 0.43 0.49 0 1
Master's Degree 2000 0.41 0.49 0 1
Operating Years 1837 14.34 11.30 0 74
Industry 1600 9.77 4.34 1 16
Number of 2056 1.05 1.22 0 4
Cofounders
VC funded 1691 0.13 0.34 0 1
Log initial capital 1264 11.91 2.72 0.28 21.02
Functional 1964 1.23 0.48 1 3
Diversity of Team
Charles Eesley
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

27. Initial Evidence
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Panel A – Likelihood of Exit Events and Revenues (in 2001 dollars)
5th firms and
1st firms 2nd firms 3rd firms 4th firms higher
Firm Rank (N=556) (N=182) (N=84) (N=21) (N=36)
Median
Revenues
(‘000s) 836 1,784 924 1,181 7,274
Standard Dev.
(‘000s) 153,000 117,000 130,000 10,800 21,200
Revenues adjusted to constant 2006 dollars
Charles Eesley – Cutting
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28. Characteristics of Non-respondents
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Variable Responded to 2001 survey Did not respond to 2001 survey t-stat for equal means
(N=43,668) (N=62,260)
Male 0.83 0.86 10.11
Engineering major 0.48 0.47 -4.49
Management major 0.16 0.15 -5.75
Science major 0.23 0.23 0.37
Social sciences major 0.05 0.06 4.07
Architecture major 0.06 0.08 11.82
Non-US citizen 0.81 0.82 3.77
North American (not US) citizen 0.13 0.11 -4.14
Latin American citizen 0.13 0.12 -1.44
Asian citizen 0.33 0.34 1.45
European citizen 0.30 0.26 -5.08
Middle Eastern citizen 0.05 0.08 6.32
African citizen 0.03 0.05 6.25
Variable Responded to 2003 survey Did not respond to 2003 survey t-stat for equal means
(N=2,111) (N=6,131)
Male 0.92 0.92 0.12
Engineering major 0.52 0.47 -3.63
Management major 0.17 0.21 4.17
Science major 0.17 0.18 1.09
Social sciences major 0.06 0.05 1.18
Architecture major 0.09 0.09 1.06
Non-US citizen 0.82 0.81 -1.36
North American (not US) citizen 0.17 0.14 -1.34
Latin American citizen 0.19 0.19 0.13
Asian citizen 0.22 0.24 0.73
European citizen 0.31 0.32 0.38
Middle Eastern citizen 0.08 0.07 -0.59
African citizen 0.04 0.04 0.17
Charles Eesley – Cutting
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering

29. CUTTING YOUR TEETH
Thank you!
Chuck Eesley
Stanford University
Management Science & Engineering (MS&E)
cee@stanford.edu
S T A N F O R D U N I V E R S I T Y • Management Science & Engineering