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11© Rothaermel & Hess, 2007
Building Dynamic Capabilities:
Innovation Driven by Individual, Firm, and Network
Level Effects
Paper forthcoming in Organization Science
Frank T. Rothaermel
Andrew M. Hess
Georgia Institute of Technology
The Biotechnology Revolution(s)
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33© Rothaermel & Hess, 2007
Global Pharmaceutical Industry
• R&D expenditures have grown from $6.8 billion in 1990 to $21.3 billion in 2000 (17% of sales)
• Development cost for new drugs have increased from $231 million to $802 million over the same period
• Average sales per patented product have fallen from $457 million in 1990 to $337 million in 2001
�Constant 1999 dollars.
44© Rothaermel & Hess, 2007
R&D Investments and New Drug Approvals
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55© Rothaermel & Hess, 2007
Value of Expiring Pharmaceutical Patents
0
1
2
3
4
5
6
7
8
1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 * 2 0 0 4 *
Bil
lio
n
$ $
Source: Warburg Dillon and Read
66© Rothaermel & Hess, 2007
Research Questions
• Where is the locus of innovation capabilities?
– Is it within the individual, firm, or network level of
analysis
– Is this a multilevel story of capability development
involving interactions across levels of analysis?
• If so, are the different innovation mechanisms
complements or substitutes?
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77© Rothaermel & Hess, 2007
Empirical research on capability development
Most research has focused on one level of analysis:– Network Level: Powell, Koput, Smith-Doerr (1996), Rothaermel (2001),
Higgins and Rodriguez (2006)
– Firm Level: Cohen and Levinthal (1989, 1990)
Tushman and Anderson (1986)
– Individual Level: Zucker and Darby studies
Such a focus makes two implicit assumptions:– Homogeneity within non-focal levels of analysis
– Independence between focal and non-focal levels of analysis
88© Rothaermel & Hess, 2007
Interactions Across Levels
• Complements vs. Substitutes
– Competing hypotheses are advanced to test the
interdependence across levels
• Two activities are complements (substitutes) if the
marginal benefit of each activity increases (decreases)
in the presence of the other activity:
– Complements: the interactions across levels are positive
– Substitutes: the interactions across levels are negative
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99© Rothaermel & Hess, 2007
Theoretical Model
Individual-Level:Intellectual Human Capital (H1a)
Star Scientists (H1b)
Firm-Level:R&D Capability (H2)
Network-Level:Biotech Alliances (H3a)
Biotech Acquisitions (H3b)
H4
: C
om
ple
men
ts H5
: Su
bstitu
tes
1010© Rothaermel & Hess, 2007
Methodology: Overview
• Developed a detailed & comprehensive panel dataset (1980-2004) documenting:
• 900 biotech acquisitions
• 4,000 biotech alliances
• 13,200 biotech patents
• 110,000 non-biotech patents
• 135,000 research scientists
• 480,000 journal publications of biotechnology research
• 9.2 million journal citations
• Last but not least:
– These data are complemented by qualitative fieldwork through interviews and direct observation before, during, and after completion of the study.
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1111© Rothaermel & Hess, 2007
Dependent Variable
• Innovation Output
Biotechnology patent applications granted:
• Externally validated measure of technological novelty
• Critical to success in pharmaceutical industry and
correlated with key performance measures
– Citation-weighted patents
– New product development
1212© Rothaermel & Hess, 2007
Independent Variables
• Intellectual Human Capital (IHC):
– Searched ISI Scientific Citation Index for journal
articles published between 1980 and 2004:
• An organization’s name corresponding to a
pharmaceutical firm
• A keyword related to scientific research
• Longer time period than study period– To address “rising star” effect
– To address right truncation
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1313© Rothaermel & Hess, 2007
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Pfize
r
Bayer
Pharm
acia
& U
pjoh
n
War
ner-La
mbe
rt
Shiono
gi
Sank
yo
Rhone
-Pou
lenc Ror
er
Astr
aZen
ecaAH
P
Hoe
chst
Mario
n Rou
ssel
Yam
anou
chi
Ajin
omoto
Nip
pon R
oche
Bristo
l-Mye
rs Squ
ibb
Sero
no
Alli
ed-S
igna
l
Mits
ubish
i Kas
ei
Moc
hida
Hoe
chst
Rouss
el
Toyo Bos
eki (
Toyob
o)
Hyb
ritec
h
Kaken
Jans
sen Ph
arm
Nip
pon Zeo
n
Interferon
Techn
icare
Kyo
wa M
edex
Carter-W
allac
e Inc
Alta
na
Armou
r
Ana
lytab
Pu
blicati
on
s 3
Pharmaceutical Firm Publications in Biotechnology
• Resulted in a population of over 480,000 articles and 135,000 authors.
• The average scientist published 3.8 papers that were
cited an average of 66.4 times
• The average firm employed 214 publishing research scientists per year
1414© Rothaermel & Hess, 2007
Independent Variables
Star Scientists:
• Number of publications & times cited
• Defined stars based on 3 standard deviations
above the mean in publications and citations
• Sample Statistics:
– Number of Stars @ st. dev > 3:
• By publication: 2,392 stars
• By citation: 1,570 stars
• Both: 851 stars
< 0.65% of total pop. is responsible for
15.2% of total pubs & 27.3% of total cites
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1515© Rothaermel & Hess, 2007
Distribution of Innovative Output
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76
Number of Publications/Patents per Individual
Co
un
t o
f P
ub
lica
tio
n A
uth
ors
Publication Count
Star scientists
• publish 25x more articles
• are cited 45x more
1616© Rothaermel & Hess, 2007
Distribution of Innovative Output
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76
Number of Publications/Patents per Individual
Co
un
t o
f P
ub
lica
tio
n A
uth
ors
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
Co
un
t o
f P
ate
nt
Inv
ento
rs
Publication Count
Patent Count
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The Role of IHC – Publication Count
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
Non-s
tar pubs
0
100
200
300
400
500
600
700
800
900
Sta
r Pubs
Non-star Authors
Star Authors
1818© Rothaermel & Hess, 2007
Independent Variables and Controls
Other IV’s:
• R&D Capability– R&D expenditures
• Biotech alliances and acquisitionsControls:
– Lagged biotech patents
– Non-biotech patents
– Time to Cohen-Boyer patent citation
– Diversified pharmaceutical firm
– Horizontal merger
– Firm size (total assets)*
– Firm performance (net income & revenues)
– Country Effects: U.S., European, Asian (Japanese) Firm
– Year Effects
* All financial data are in constant U.S. dollars
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Results
1.18
1.14
0.55
1.22
0.62
1.20
IRR
p < 0.001
p < 0.001
p < 0.001
p < 0.001
p < 0.001
p < 0.001
BPA
- 45%Time to Cohen-Boyer
Patent Citation
14%Non-Biotech Patents
22%Total Revenues
18%Lagged Biotech Patents
- 38%Total Assets
20%Firm Merged
Factor Change
Model 1: Controls Only
2020© Rothaermel & Hess, 2007
Results – Direct Effect Hypotheses
p < .001
BPA
1.15
IRR
15%Intellectual Human Capital
Factor Change
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2121© Rothaermel & Hess, 2007
Results – Direct Effect Hypotheses
-8%0.92p < .001R&D Expenditures Squared
p < .05
p < .001
BPA
1.32
1.15
IRR
32%R&D Expenditures
15%Intellectual Human Capital
Factor Change
2222© Rothaermel & Hess, 2007
Results – Direct Effect Hypotheses
-8%0.92p < .001R&D Expenditures Squared
NS
p < .05
p < .001
BPA
-
1.32
1.15
IRR
-Biotech Alliances
32%R&D Expenditures
15%Intellectual Human Capital
Factor Change
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2323© Rothaermel & Hess, 2007
Results – Direct Effect Hypotheses
-8%0.92p < .001R&D Expenditures Squared
p < .05
NS
p < .05
p < .001
BPA
1.04
-
1.32
1.15
IRR
-Biotech Alliances
4%Biotech Acquisitions
32%R&D Expenditures
15%Intellectual Human Capital
Factor Change
2424© Rothaermel & Hess, 2007
Results – Direct Effect Hypotheses
-8%0.92p < .001R&D Expenditures Squared
p < .01
NS
p < .05
p < .01
BPA
1.06
-
1.32
1.08
IRR
-Biotech Alliances
32%R&D Expenditures
6%Biotech Acquisitions
8%Star Scientists
Factor Change
• The effect of stars
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2525© Rothaermel & Hess, 2007
Results – Direct Effect Hypotheses
-8%0.92p < .001R&D Expenditures Squared
p < .05
NS
p < .05
p < .05
NS
BPA
1.05
-
1.32
1.10
-
IRR
-Biotech Alliances
32%R&D Expenditures
5%Biotech Acquisitions
10%Non-Star Scientists
-Star Scientists
Factor Change
• The effect of stars disappears while controlling for non-stars
• Unobserved heterogeneity
• Non-stars fully mediate any star effect
2626© Rothaermel & Hess, 2007
Results – Interaction Effect Hypotheses*
- 8%0.92p < .05Star Scientists x R&D Exp.
p < .05
BPA
0.91
IRR
- 9%IHC x R&D Expenditures
Factor ChangeIndividual x Firm Level
* only significant interactions are shown
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2727© Rothaermel & Hess, 2007
Results – Interaction Effect Hypotheses*
- 8%0.92p < .05Star Scientists x R&D Exp.
p < .05
BPA
0.91
IRR
- 9%IHC x R&D Expenditures
Factor ChangeIndividual x Firm Level
* only significant interactions are shown
Non-Stars x Bio Alliances
IHC x Bio Alliances
Individual x Network Level
p < .001
p < .001
BPA
0.91
0.94
IRR
- 9%
- 6%
Factor Change
2828© Rothaermel & Hess, 2007
Results – Interaction Effect Hypotheses*
- 8%0.92p < .05Star Scientists x R&D Exp.
p < .05
BPA
0.91
IRR
- 9%IHC x R&D Expenditures
Factor ChangeIndividual x Firm Level
* only significant interactions are shown
Non-Stars x Bio Alliances
IHC x Bio Alliances
Individual x Network Level
p < .001
p < .001
BPA
0.91
0.94
IRR
- 9%
- 6%
Factor Change
Factor ChangeIRRBPAFirm x Network Level
8%1.08p < .01R&D Exp. x Alliances
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Conclusions – Direct Effects
• Locus of innovation capabilities resides across different
levels
– In the intersection between individual, firm, and network-
level effects
• Significant amount of the variance in biotech patenting
is explained by individual-level factors
– Mediation of star effect on innovation by non-stars
• Stars close cognitive gap, while non-stars close operational gap (Lavie,
2006)
3030© Rothaermel & Hess, 2007
Conclusions – Direct Effects
• Firms are able to build, buy and access innovation
capabilities through
– Recruitment of IHC and star scientists,
– R&D spending,
– Acquisitions of new technology firms,
• But: Firms must already possess necessary R&D
capabilities to be a means by which firms can leverage
different innovation mechanisms
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Conclusions – Interaction Effects
When attempting to innovate:
• Individual-level effects appear to be substitutes to firm
or network-level antecedents
• In contrast, firm and network-level effects appear to be
complements