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Local R&D Strategies and Multi-location Firms: The Role of Internal Linkages
Juan Alcacer, HBS
Minyuan Zhao, Univ. of Michigan
Dec 12 2010
Benefits and costs from collocating
Firms co-locate to reap benefits of agglomeration in a given location (Marshall,1922)
Localized knowledge spillovers is force behind co-location in high-tech industries & R&D function
. . .But co-location also have costs – and these are likely to be most pronounced for leading firms (Shaver & Flyer, 2000)
Knowledge flows in both directions Leading firms may lose valuable knowledge if they co-locate with
competitors
How can leading global firms reduce their cost from outward spillovers to competitors when geographic distance is not an option?
Our contribution We explore alternative strategies that MNEs can follow to reduce
outward knowledge flows when they co-locate with competitors Internal linkages
We find that innovation generated in clusters with high competitor density are more likely to be associated to cross-cluster teams
are more self-cited by other locations (more internalized) are less cited locally by competitors (less knowledge outflows)
We bring together the local competitive environment and the global element of MNEs to better explain how firms can benefit from location-specific advantages without compromising their ability to profit from innovation
How do firms appropriate knowledge?
Raising barriers for imitation Geographic distance Rules, routines that encourage secrecy Patents & trade-marks Legal reputation
Reducing incentives for imitation Complementary assets
Physical assets Intangible assets (marketing skills, managerial skills)
Internal linkages
However, knowledge flow is not location free; the actual spillovers – hence the actions to prevent it – mostly happen at specific locations
Internal linkages…
Field work Interviews with IP managers, R&D managers in 7
semiconductor firms Numerous ways to raise barriers for imitation Use of complementary assets
Fabs: most process innovation not useful to competitors Managerial skills: time is money
Internal linkages: Cross-cluster teams Rotation of engineers Enhanced communication channels
Internal linkages dual use…
For knowledge appropriation but also…
For knowledge sourcing… Strong internal linkages lead to external knowledge absorption
(Lahiri, 2010)
Knowledge flows within MNEs (Frost and Zhou, 2005)
Internal linkages for knowledge appropriation…
Strong internal linkages increase control (Nobel and Birkinshaw, 1998; Edstrom and Galbraith 1977)
and coordination within MNEs To the extent that MNEs are able to integrate and internalize
knowledge on a global basis they can build on new technologies faster than imitators (Buckley & Casson, 1976; Bartlett & Ghoshal, 1990 )
Strong internal linkages increase internal interdependence (Liebeskind, 1996; Zhao, 2006)
Propositions
We expect to observe
more cross-cluster teams at locations with higher appropriation risks, e.g. in clusters with a large number of direct competitors
more intensive intra-firm knowledge flows across clusters with the presence of cross-cluster teams
less knowledge outflow to local competitors with the presence of cross-cluster teams
Data and Sample The global semiconductor industry, from 1998 to 2001
Agglomeration benefits are well documented Innovations easily traced through patents Technology used across different industries
Derwent as main source for innovation
Characterize clusters with comprehensive profile: Basic science: 50,387 publications – ISI Web of Knowledge Innovation: 60,880 patents (28,334 US patents) belonging to 2,217
organizations Development
974 plants – World Fab Watch Production:
549 fabless firms – Directory of Fabless Semiconductor Companies
…but 16 leading innovators of the industry as focal firms
Firms in sample
Sample FirmsCountries with
Innovation
ADVANCED MICRO DEVICES 6INTEL 6IBM 16TEXAS INSTRUMENTS 10HITACHI 6MATSUSHITA 2NEC 3SIEMENS (including INFINEON) 13TOSHIBA 5MITSUBISHI 5SAMSUNG 4MICRON TECHNOLOGY, INC. 8FUJITSU 3TAIWAN SEMICONDUCTOR MANUFACTURING CO., LTD. 4HYUNDAI 3STMICROELECTRONICS 12
Empirical approach
Test whether innovations generated in clusters with high density of competitors differ as predicted in terms of Presence of cross-cluster teams Cross-cluster self-citations (internalization) Citations by local competitors
Empirical approach
Test whether innovations generated in clusters with high density of competitors differ as predicted in terms of Presence of cross-cluster teams Cross-cluster self-citations (internalization) Citations by local competitors
Patent families instead of patents
Empirical approach
Test whether innovations generated in clusters with high density of competitors differ as predicted in terms of Presence of cross-cluster teams Cross-cluster self-citations (internalization) Citations by local competitors
Organic algorithm instead of administrative units
Identifying cluster through administrative units
Administrative units:• Cities• Metropolitan Areas• States• Countries
+ Data normally collected in admin. units
- Admin. unit varies across countries
- Less precise, more so in highly dense areas that span borders
Identifying clusters organically
High ZeroInnovation density
Data for clusters
Obtain latitude and longitude for all locations International: Geonet Names Server (GNS) by the National
Geospatial Intelligence Agency Dataset with 5.5 million names of locations worldwide Alternative spelling
USA: Geographic Names Information System (GNIS) by the US Geological Survey (USGS)
Initial clustering based on inventor locations, then add plants, fabless, publications
Semiconductors patents: Europe 2001-2004
Empirical approach
Test whether innovations generated in clusters with high density of competitors differ as predicted in terms of Presence of cross-cluster teams Cross-cluster self-citations (internalization) Citations by local competitors
Alternative definitions
Identifying actors in cluster
Universities
Gov’t entities
Other non-profit
Based on names
Industry
No industry
Segment
No segment
Competitor
No competitor
Based on Hoovers
Profit
Non-Profit
Organizations in cluster(2,217)
Based on names, USPTO,internet
Based on USPTO
Small Firms
Large Firms
Empirical approach
Test whether innovations generated in clusters with high density of competitors differ as predicted in terms of Presence of cross-cluster teams Cross-cluster self-citations (internalization) Citations by local competitors
Based on patent family data
Dependent Variables Cross-cluster teams
Binary level indicating of family is associated to a cross-cluster team
Internalized value Cross-cluster self-citations (Trajtenberg et al., 1997; Hall et al., 2005) Cross-cluster self-citations per patent family =∑cross-cluster self-
citations of US patents Main analysis using only assignee citations
Citations by local competitors Competitors according characterization of competition in cluster: all
assignees, for profit assignees, firms in industry, firms in segment and competitors.
Empirical models Similar specifications across dependent variables
Dependent_variable = Cict + Xf + Yict + Zct + ζt +υi + τctry + γtech + εfict
Competitive environment faced by firm i, in cluster c at time t
Firm fixed effects
Error term
Location specific traits for cluster c at time t
Country fixed effects
Year fixed effects
Firm specific traits for firm i in cluster c at time t
Different estimation technique: Cross cluster teams fict Logit cross_cluster_self_citationfict local_citations_by_innovatorsfct Negative binomial
with exposure
Family specific traits
Technology fixed effects
(1) (2) (3) (4) (5) (6)
assignees 1.007+
profit 1.0760**
small firms 0.99678
large firms 1.0766 *
in industry 1.0723 **
no-industry 1.0032
in segment 1.0727 *
no-segment 1.0032
competitor 1.081**
no-competitor 1.001
non-profit 0.9988 0.9729 0.9911 0.8960
universities 1.002571
gov’t institutions 1.064322
others 1.167388
+ significant at 10%; * significant at 5%; ** significant at 1%
cross_clusterfict = Cict + Xf + Yict + Zct + ζt +υi + τctry + γtech + εfict
(1) (2) (3) (4) (5) (6)
cross-cluster team 0.5179** 0.5062** 0.5034** 0.4958** 0.4922** 0.4985**
assignees 0.0003
profit 0.0037+
small firms 0.0077
large firms -0.0028
in industry 0.035 **
no-industry -0.0064
in segment 0.0288 **
no-segment 0.0007
competitor 0.0298**
no-competitor -0.0025
non-profit 0.0415 0.045 0.0379 0.004
universities 0.0325
gov’t institutions -0.0223
others -0.0641
+ significant at 10%; * significant at 5%; ** significant at 1%
cross_cluster_self_citationfict = Cict + Xf + Yict + Zct + ζt +υi + τctry + γtech + εfict
(1) (2) (3) (4) (5) (6)
cross-cluster team -0.544** -0.5488** -0.5563** -0.5524** -0.5692** -0.5607**
assignees 0.1174**
profit 0.1196**
small firms 0.0155
large firms 0.1344*
in industry 0.0168 **
no-industry 0.0029
in segment 0.0105*
no-segment 0.0079
competitor 0.0215
no-competitor -0.001
non-profit 0.0184 0.0024 0.0016 0.0015
universities 0.0098
gov’t institutions 0.035
others 0.0713
+ significant at 10%; * significant at 5%; ** significant at 1%
local_citations_by_ENTITYfct = Cict + Xf + Yict + Zct + ζt +υi + τctry + γtech + εfict
Summarizing results
Innovations generated by MNEs in clusters with high competitor density are different More likely to be associated to a cross-cluster team More cross-cluster self-cited (beyond effect of cross-
cluster teams)
Innovations associated to cross-cluster teams are different More cross-cluster self-cited (cross-cluster teams increase
internalization) Less cited by local competitors (cross-cluster teams as
appropriability)
Robustness checks Hierarchical clusters vs. organic clusters Percentages instead of counts for self-citations and
citations by local competitors Adding examiner self-citations and citations: weaker
results Analysis for top 5% of multi-location companies in
semiconductors Analysis for more than 25 clusters Collinearity:
Orthogonalized variables 1 variable to characterize clusters Ratio variables All locations Including 1 variable at a time
Contributions Explain how MNEs can still benefit from innovating in
clusters where competitors are also present
Offer a richer picture of clusters combining two elements Local environment: organizations innovating across
technological space Global actors: deciding what to allocate to specific clusters
depending on global competition
Bring location to the appropriability literature
Bring internal organization to cluster literature
Contributions Definition of clusters A dataset with latitude and longitude data for all patents
since 1969 Identifying results by differentiating technology space vs.
product market space
Number of family patents with inventors across-clusters Differentiating between core and peripheral
Measuring control
Core cluster Peripheral cluster
Co-location in R&D is the norm
# of patents in cluster with% Mean Min Max % Mean Min Max
At least one assignee? 98% 29.83 1 1577 2% 2.61 1 16At least one assignee in the same industry? 96% 36.08 1 1577 4% 2.87 1 38At least one assignee within the same segment? 96% 37.71 1 1577 4% 2.86 1 58At least one assignee that is direct competitor? 94% 37.17 1 1577 6% 4.11 1 214
NoYes
USPTO Technology class: a moving target
Figure 1. Number of patets, classification orders, and new classes established, 1980-2002
0
100
200
300
400
500
600
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
Total Classification Orders(LH scale)
Total Established Classes(LH Scale)
Total patents (RH scale)
1 product = 1 innovation = 1 patent?
Source: Dolphin Database
Drug name Therapy areas Com
pone
nt o
f C
ombi
natio
n Fo
rmul
atio
n
Gen
eral
inte
rest
N
ew u
se
Proc
ess
Proc
ess
(inte
rmed
iate
) Pr
oduc
t (d
eriv
ativ
e)
File
d w
ithin
the
last
4 y
ears
sibutramine (31) Psychiatric; Cardiovascular
2 1 28
lopinavir (9) Infection 2 6 1 5ritonavir (8) Infection 2 5 1 3valproate semisodium (6) Neurological 1 5 3lansoprazole (4) Gastrointestinal 4 4adalimumab (4) Immune;
Musculoskeletal1 1 1 1 4
clarithromycin (2) Infection 1 1 tiagabine (2) Neurological 2 1neuronal nAChR ligands Neurological 1 1 1paricalcitol (2) Genitourinary;
Immune 1 1 1
cefditoren pivoxil (1) Infection 1 1ABT-239 (1) Neurological 1 1erythromycin A derivatives, Infection 1 BTS-79018 (1) Psychiatric 1 ICAM anticancer, ICOS/ Immune;
Cardiovascular 1
itopride (1) Gastrointestinal 1
Examiner vs. assignee
41%
12%
10%
41%
63%
21%
18%
57%
0% 10% 20% 30% 40% 50% 60% 70%
US Patents
Foreign Patents
Non-patent prior art
Self-Citations
Dyad Level Patent Level
Patents across countries
Patent 6172911 generates:• 3 European patents• 4 Japanese patents• 2 Korean patents• 9 US patents
Patent 1 Patent 2Application Patent 3
Claims 1 to 10
Claims 1, 3, 5
Claims 2, 4, 6
Claim 11
Patent 1 Patent 2Innovation Patent 3Use 1 Production Use 2
Family of Patents from 1 innovation
1 patent, 1 innovation?
Is this prevalent?...at least non-trivial
# of Patents in Family Freq. % Freq. % Freq. %
1 26,342 57% 23,705 68% 1,202 56%[2,5] 17,380 38% 9,581 27% 791 37%(5,10] 1,562 3% 833 2% 106 5%(10,.) 907 2% 818 2% 54 3%
46,191 34,937 2,153
Semiconductors (69-06) Wireless (69-06) Pharma (69-06)
Econometric issues: dependent observations Many observations may not be new information
World patents (Inpadoc) around 60% American patents around 15
Issues
Solution Following Gittelman & Kogut, 2003, we use patent families instead of patents
Data on patent families from Derwent
Internalization: alternative explanation
1 2 3 1 2 3Ave.
citations 3.57 3.25 3.40Ave.
citations 1.72 1.09 0.732 3.25 -0.320 2 1.09 -0.625 *
0.367 0.2343 3.40 -0.168 0.152 3 0.73 -0.988 * -0.363 *
0.378 0.376 0.229 0.2504 3.20 -0.371 -0.051 -0.203 4 0.52 -1.200 * -0.575 * -0.212
0.388 0.386 0.396 0.241 0.261 0.256
Quantiles for competitive enviroment
Quantiles for competitive enviroment
Quantiles for competitive enviroment
Citations Self-Citations
Knowledge seeking & agglomeration
International expansion & competition
Location & Strategy
Knowledge Seeking and FDI Location, Mgt Sci ‘02
Location Strategies & Knowledge Flows, forthcoming Mgt Sci
Location Strategies and Agglomeration Economies
Strategic Interaction in International Strategies, under review AMR
Competition and Dynamic International Strategies
Location Choices Across the Value Chain, Mgt Sci 06
The Impact of Firm Rivalry on Location Choices
Patents & MNEs
Patent Citations as a Measure of Knowledge Flows, REStat ’06
Patent Quality, R&R Research Policy
International Patenting Strategies
Transferring intangibles across border
Global Competitors as Next-Door Neighbors: Competition and Geographic Co-location in the Semiconductor Industry
Global value chain fragmentationThe geography of drug development and clinical trials, NBER
Fragmentation in the Value Chain in the Wireless Telecommunication Industry
GlobalizationThe Intergovernmental Network and the Governance of FDI
How firms can use their location decisions to…
…acquire new capabilities? …compete across geographic markets?
Knowledge Seeking & FDI Location, Mgt Sci ‘02
Location Strategies & Knowledge Flows, Mgt Sci forthcoming
Location Strategies & Agglomeration Economies
Strategic Interaction in International Strategies, under review AMR
Competition and Dynamic International Strategies
Location Choices Across the Value Chain, Mgt Sci ‘06
What do patent data mean?
Patent Citations as a Measure of Knowledge Flows, REStat ’06
Patent Quality, R&R Research Policy
Global Competitors as Next-Door Neighbors: Competition & Geographic Co-location
Location in drug industry, NBER chapter
Transferring intangibles overseas
Technological distance to minimize outward flows
MNEs can differentiate across multiple dimensions
For an outward knowledge spillover to occur, the receiving firm must be able to identify and absorb knowledge Technological distance affects competitors’ scope of
search (Stuart & Podolny 1996) and their capacity to absorb knowledge (Cohen & Levinthal 1990)
Leading firms are more likely to generate innovations that are technologically distant from competitors’ innovations in clusters with high competitor density
Does technological distance mean no learning?
Jacobs-Porter vs. Marshall-Arrow-Romer clusters Different actors across value chain in same industry Same technology used in other industries
Automobiles Aerospace Defense Electronics Telecommunications
Groups within the cluster with semiconductor MNEs as centers or small groups
Still plenty of opportunities for knowledge seeking
Propositions
In clusters with high competitor density, MNEs will generate innovations that are technologically distant from innovations generated by competitors
(1) (2) (3) (4) (5) (6)
assignees 0.0001
profit 0.001*
small firms -0.0002
large firms 0.0017 *
in industry 0.0062 **
no-industry 0.0001
in segment 0.0031 *
no-segment 0.0003
competitor 0.0072 **
no-competitor 0.0004
non-profit -0.0086 ** -0.0068 * -0.0072 * -0.0074 **
universities -0.0081 *
gov’t institutions -0.0044
others -0.0255 +
+ significant at 10%; * significant at 5%; ** significant at 1%
average_technological_distanceict = Cict +Xict + Yct + ζt + υi + τctry+εict
IBM
MOTOROLA
ATMEL
EMTEC MAGNETICS GMBH
CYMER, INC.
ISOVOLTA OSTERREICHISCHE ISOLIERSTOFFWERKE AKTIENGESELLSCHAF
LSI LOGIC CORPORATION
ANGEWANDTE SOLARENERGIE--ASE GMBH
PATENT-TREUHAND-GESELLSCHAFT FUR ELEKTRISCHE GLUHLAMPEN MBH
BAYERISCHE MOTOREN WERKE AG
DAIMLER-BENZ AKTIENGESELLSCHAFT
NATIONAL SEMICONDUCTOR
ROBERT BOSCH
WACKER-CHEMIE GMBH
THOMSON-CSF
DEUTSCHES ZENTRUM FUR LUFT- UND RAUMFAHRT E.V.
ROHDE & SCHWARZ GMBH & CO.
WEBASTO VEHICLE SYSTEMS INTERNATIONAL GMBH
TRW INC.
MICRONAS (formerly ITT INDUSTRIES)
UNIVERSITAET STUTTGART INSTITUT FUER STRAHLWERKZEUGE
BAYER CORPORATION (INCLUDING AGFA-GEVAERT)
DIALOG SEMICONDUCTOR GMBH
SEMICONDUCTOR COMPONENTS INDUSTRIES, LLC
DIEHL LUFTFAHRT ELEKTRONIK GMBH
ASTRIUM GMBH
ADVANCED TECHNOLOGY MATERIALS, INC.
TEXAS INSTRUMENTS
ISTITUTO NAZIONALE DI FISICA NUCLEARE
MAX-PLANCK-INSTITUTE FUR MIKROSTRUKTURPHYSIK
FRAUNHOFER INSTITUTE
STMICROELECTRONICS
SYMETRIX CORPORATION
SIEMENS (INCLUDING INFINEON)
0 .1 .2 .3 .4Technological Distance
Munich, DE
ADVANCED MICRO DEVICES
NTU VENTURES PTE LTD
IBM
SEAGATE TECHNOLOGY, LLC
UCT CORPORATION
ARIZONA BOARD OF REGENTS
SINGAPORE TECHNOLOGIES ELECTRONICS, LTD.
VISHAY SA
HITACHI
E20 COMMUNICATIONS, INC.
NATIONAL SEMICONDUCTOR
INSTITUTE OF MATERIALS RESEARCH & ENGINEERING
SIEMENS (INCLUDING INFINEON)
CREATIVE TECHNOLOGY LIMITED
SEMICONDUCTOR TECHNOLOGIES & INSTRUMENTS, INC.
TEXAS INSTRUMENTS
PENN STATE RESEARCH FOUNDATION, INC.
APPLIED MATERIALS, INC.
OKI ELECTRONIC INDUSTRY CO., LTD.
SUN MICROSYSTEMS, INC.
CIRRUS LOGIC, INC.
MARVELL INTERNATIONAL LTD.
NATIONAL UNIVERSITY OF SINGAPORE
INSTITUTE OF MICROELECTRONICS
NANYANG TECHNOLOGICAL UNIVERSITY
MICRON TECHNOLOGY, INC.
NANO SILICON PTE. LTD.
AGILENT TECHNOLOGIES, INC.
CHARTERED SEMICONDUCTOR
AGERE SYSTEMS
0 .1 .2 .3 .4 .5Technological Distance
Singapore, SG