building dynamic capabilities - massachusetts institute of...

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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)

2


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.


R&D Investments and New Drug Approvals

3


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


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?

4


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


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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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


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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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


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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0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

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Bayer

Pharm

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oche

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Techn

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blicati

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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


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

8


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


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


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


Results – Direct Effect Hypotheses

p < .001

BPA

1.15

IRR

15%Intellectual Human Capital

Factor Change

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-8%0.92p < .001R&D Expenditures Squared

p < .05

p < .001

BPA

1.32

1.15

IRR

32%R&D Expenditures


Factor Change




NS

p < .05

p < .001

BPA

-

1.32

1.15

IRR

-Biotech Alliances

32%R&D Expenditures


Factor Change

12




p < .05

NS

p < .05

p < .001

BPA

1.04

-

1.32

1.15

IRR

-Biotech Alliances

4%Biotech Acquisitions

32%R&D Expenditures


Factor Change




p < .01

NS

p < .05

p < .01

BPA

1.06

-

1.32

1.08

IRR

-Biotech Alliances

32%R&D Expenditures


8%Star Scientists

Factor Change

• The effect of stars

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p < .05

NS

p < .05

p < .05

NS

BPA

1.05

-

1.32

1.10

-

IRR

-Biotech Alliances

32%R&D Expenditures


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


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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p < .05

BPA

0.91

IRR




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




p < .05

BPA

0.91

IRR




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)


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

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