Cutting Your Teeth: Building on the Micro-
Foundations for Dynamic Capabilities
(with Edward B. Roberts)
Charles E. Eesley
cee@stanford.edu
Stanford University
Department of Management Science & Engineering
Sept. 10-12th, 2009
West Coast Research Symposium
Univ. of Washington

Where Do Firm Differences Come From?
Resource-based view (RBV)  heterogeneity in firm performance
Superior information / luck  Resources (Barney 1991; Peteraf 1993;
Wernerfelt 1984)
When organizations are putting together new bundles of routines
and capabilities, what determines who puts together the more
valuable and difficult to imitate bundles?
Charles Eesley – Cutting Your Teeth

Bounded Rationality
Psychological Bias
•Impossibly large set of information bounded rationality (Simon, 1955)
• Varied career experiences more (or less) biased
•Specialization via (Daniels et al., experiential route of discovery (Herriott,
representationslocal search,1994; Hodgkinson& Johnson, 1994; Reger, 1990)Levinthal and
March, 1985)
• Availability heuristic
•Imperfect cognitive representations (Gavetti and Levinthal 2000, Levinthal& March, 1993)
• Instances easily recalled (Tversky and Kahneman, 1973; Fischhoff, Slovic and Lichtenstein,
• “universal” decision biases (Camerer, 1995, Nisbett and Ross, 1980, Tversky and
1978, Russo and Kolzow, 1994)
• Kahneman, knowledge  and of recollecting instances
Less domain1992, Kahneman easeLovallo, 1993, Kahneman and Tversky, 1979)
• Experts  number of cases that come to mind (Ofir, 2000)
•Representations = mental models – beliefs
• Partition dependence(Fox and Clemen, 2005)about action-outcome linkages
(Gavetti&Levinthal, 2000; Simon, 1955; Porac, Thomas and Baden-Fuller, 1989)
• Framing of issues and
Career •Diversity of partitions
Experiences information • Partition
Dependence • Strategic search for
•Feedback Cognitive Capability
(span of clusters of
signals from Representations • Availability Development
decision competitive capabilities/resources
heuristic • Decision making
rights) environment
Charles Eesley – Cutting Your Teeth

Partition dependence
Car will not
Business
failed
start
Engine
Engineering Out of gas
Marketing Battery
Finance (?%) Alternator
Sales (?%) Other (?%)
(?%) (?%) Other (7%)
(15%) (20%) (50%) (8%)
Technical
reason 1 (%)
Car will not
Technical Carstart not
will
reason 2 (%) start
Engine Out of gas Battery Other
(15%)
Technical
Engine (20%)
Out of gas (50%)
Battery (15%)
reason 3 (%) Other (9%)
(17%) (22%) (52%)
Charles Eesley – Cutting Your Teeth

Existing picture
Strategic groups
• Cognitive inertia (Porac& Thomas, 1990;
Hodgkinson, 1997, 2005; Haleblian& Finkelstein 1999)
• Success hubris
<switch industries or fail>
Successful  stay in functional role
Why? Talking to similar people
about the same things.
Charles Eesley – Cutting Your Teeth

New view
• Left out of the picture  Cross-functional role
• Avoid pitfalls of cog. inertia and success hubris
• Less optimized for particular role
• More likely to deviate Bunderson& Sutcliffe, (2002)
Geletkanycz and Black (2001)
Talk to new people about different things
F.E. cross-func. exper.
Charles Eesley – Cutting Your Teeth

More Accurate Representations Drive Location of Valuable Resources
• Framing of issues and
Career •Diversity of partitions
Experiences information • Partition
Dependence • Strategic search for
•Feedback Cognitive Capability
(span of clusters of
signals from Representations • Availability Development
decision competitive capabilities/resources
heuristic • Decision making
rights) environment
H1: More cross-functional experience will lead to higher
performance via more accurate industry representations.
H2: Cross-functional experience will lead to less cognitive inertia
from remaining in similar contexts and higher performance for those
remaining in the same industry.
H3: Cross-functional experience will lead individuals who have
experienced success to continue higher performance.
Charles Eesley – Cutting Your Teeth

Ideal Experiment
Performance of subsequent
Functional work entrepreneurial firm
exp. representations
t
Not founder of
entrepreneurial firm Exploit shift Accuracy of
representations
(performance)
Cross-functional
work exp. representations
t
Founder of
Performance of subsequent
entrepreneurial firm
entrepreneurial firm
Charles Eesley – Cutting Your Teeth

Experiment
Levels of cross-
functional work Exploit
exp. major shift
t
Performance of subsequent
entrepreneurial firm
Charles Eesley – Cutting Your Teeth

MIT Data
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)
Charles Eesley – Cutting Your Teeth

Methods
OLS
Y = F(α + 1‟θi + 2‟*(cross-func.) + 3‟*(cross-func.)*(ind. disruption) + ‟Xi + τt+ ηj + φa+εi)
Cox Hazard Rate Model (robust to logit)
Prob (Y= 1) = F(α + 1‟*(cross-func.) + ‟Xi + τt+ ηj + φa+εi)
• Dependent variable: Revenues, employees, acquisition, IPO, survival
• # cross-functional experiences, # acquired, # IPO, same/different industry
• Xi = Set of controls for firm age, external funding, num. cofounders,
functional diversity, initial capital
• Include (τ + η + φ) grad. year, region and Bachelor‟s academic dept. fixed
effects
• Before and after significant industry disruption
11
Proportional Hazards Test

Revenues
Independent (N=964)
variables
Age at founding -0.015* -0.016* -0.001
(0.009) (0.009) (0.005)
# of cross-func.
0.296*** 0.195** 0.119***
experiences
(0.066) (0.087) (0.033)
Bachelor‟s deg. 0.298 0.361 -0.027
(0.254) (0.253) (0.132)
Master‟s degree 0.402 0.036 0.054
(0.252) (0.330) (0.132)
R-squared 0.353 0.311 0.330
Controls for firm age, num. cofounders, functional diversity, industry, year,
initial capital, VC funding are controlled.
(more)
Charles Eesley – Cutting Your Teeth

Large Industry Disruption
VARIABLES Pr(Acquired) Pr(Public) Ln(employees) Ln(revenues)
-1.208*** -1.581** -0.896 -0.302
Postxcross-func. (0.524) (0.826) (0.568) (0.840)
Cross-functional 0.281 0.277 0.760* 1.333**
experience (0.289) (0.387) (0.393) (0.596)
Post 1997 0.532 1.089 -3.578 -7.316
internet boom (0.907) (0.945) (2.956) (2.989)
Standard errors in parentheses *** p<0.01, ** p<0.05, *
p<0.1
Software firms only; N=205
Controls: age founded, num. cofounders, Bachelor‟s, Master‟s, initial capital, firm
age
Charles Eesley – Cutting Your Teeth

Industry Technology Shift
VARIABLES Ln(Revenues)
Pre-Internet Post-Internet
Prior IPO -0.456 1.288
(1.217) (1.043)
Prior Acquired 1.470** 0.396
(0.713) (0.527)
Cross-func.
experience 1.301* 0.690
(0.731) (0.532)
R-squared 0.639 0.647 0.391 0.398
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Software firms only. N=80; Pre-internet is 1985-1996. Post-internet is 1997-2003
Controls: age founded, num. cofounders, Bachelor‟s, Master‟s, initial capital, firm age
Charles Eesley – 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 (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

Conclusion and Implications
Hypothesis: Supported?
Cross-functional experience leads to greater performance via more
H1 YES
accurate cognitive representations
Cross-functional experience reduces the negative impact of remaining
H2 YES
in similar industry contexts.
Cross-functional experience reduces the negative impact of prior
H3 YES
success.
Individual Level
• Cross-func. experience overcome impediments to learning
• Variation in work experience may lead to variation in cog. representations
• All psychological biases may not be universal
Strategy
• Micro-foundations of competitive advantage, dynamic capabilities
• Active view on identification of valuable resources
• Level playing field after disruptions
• Large firm new business/new ventures
• Challenge of sectors with more serial entrepreneurs
Institutions
• Fostering this type of experience
16

Index of Backup Slides
• Panel Data
• Individual Fixed Effects
• Learning about start-up
process
• Descriptive statistics
• Response Bias
Charles Eesley – 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

Probability of Acquisition
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.
Charles Eesley – 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.
Charles Eesley – 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

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.
Charles Eesley – 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)

Industry Context / Network
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 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

Descriptive Statistics
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

Initial Evidence
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 Your Teeth

Characteristics of Non-respondents
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 Your Teeth