juan alcacer: local r&d strategies and multi-location firms: the role of internal linkages

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


Patent families instead of patents

Empirical approach


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


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


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


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


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*


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


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

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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

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sibutramine (31) Psychiatric; Cardiovascular

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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;

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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



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 *


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 +


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

juan alcacer: local r&d strategies and multi-location firms: the role of internal linkages

Education

focal firms firms

global firms

knowledge outflows

appropriate knowledge

valuable knowledge

outward knowledge flows

terms of knowledge flows

global competitors