GAPS BETWEEN PATENT

APPLICATIONS AND GRANTS

IN JOINT LICENSING NETWORKS

IN A JAPANESE BIOCLUSTER

International Workshop:

Regional Innovation Capability and Technology Transfer in Biotechnology Clusters :

New Recipes in Japan and Europe?

Sep. 20, 2013

CEEJA, Kientzheim, France

Naoki Wakabayashi Graduate School of Management

Content

1. Introduction

2. Technology Transfer Networks in Bioclusters

3. Frameworks

4. Hypothesis

5. Research Context

6. Comparison of Application and Grant Networks

7. Factors for Successful Partnership

8. Conclusion

I. Introduction

• Big gaps between patent applications and grants in joint

licensing alliances between Industry and Academia

• What partnerships are successful?

• In R & D networks among universities, research institutes and firms

• Examine what Lead to Successful Partnerships

• Comparing two networks of patent applications and grants.

• Examine how firms have successful partnerships in industry

academia alliance networks, testing joint patenting networks

2. Technology Transfer Network Policies

for Biocluster Development • Technology transfer networks and regional innovation capability

• Technology transfer networks accelerate regional learning and

innovation between universities, labos and firms (Inkpen & Tsang,

2005; Pouder and St. John, 1996)

• Previous research focused on Best Practices but need to pay more

attention to Whole Network Change by Policy (Thune , 2007)

• Developed networks lead to regional innovation capabilities

• convey technological knowledge and managerial routines (Casper,

2007)

• facilitate development of regional innovation capabilities (Cooke, 2005).

• University-Industry Alliance Policies try network expansion

• Many efforts on policies for investment, funding and networking for

alliances

3. Networks is Innovation Resources

• Network Resources or “Social Capital” for Organizations

• For organizations, their networks make competitive advantages

when they help knowledge-sharing and value creation, they make

(Nahapit & Goshal, 1998)

• Specific social networks accelerating Interorganizational learning

(Ingram, 2000)

• Networking via research institutes faciltates knowledge transfer

(Powell et al., 1996; Owen-Smith & Powell, 2004)

Innovative Social Capital

• Main Two Types of Innovative Networks • Strength of Bridging Ties (Burt, 2004; Granovetter, 1974)

• Bridging ties: linking among isolated persons and groups (Burt, 2004)

• Covey novel, explicit and heterogeneous knowledge widely

=> Radical Innovation

• Features: Many bridging ties, wide linkages, structural holes

• Strength of Cohesive Ties (Krackhardt, 1992) • Cohesive ties: Narrow and dense structure and frequent interaction

• Share contextual, implicit and homogeneous knowledge and value

=> Incremental innovation

• Features: direct and strong linkages, narrow and dense network

• Observed network effects in Bioclusters • Bridging Tie

• Network development and effective linkages with research institutes (Owen-Smith et al., 2004)

• International bridging by academia for local bio-ventures (Al-Laham, et al., 2007)

• Ambidexterity of ventures and their dual linkages with business and academia and (Padget & Powell, 2012)

• Cohesive tie • Transfer of tacit knowledge in local strong networks (Cooke, 2005; Thune, 2007)

• Regional differences and research institute centered ties in Munich (Casper & Murrey, 2007)

4. Three Major Hypothesis

• H1: Bridging tie effects

• In regional industry academia networks, firms with bridging ties are

likely to meet high performing partners and succeed in patenting.

• H2: Cohesive tie effects

• In regional industry academia networks, firms with cohesive ties are

likely to intensively collaborate with high performing partners and

succeed in patenting.

• H3: Laboratory tie effects

• In regional industry academia networks, firms with many laboratory

ties are likely to have many successful partners in patenting

• I will attempt to check these network effects with my data.

5-1. University Industry Alliance Policies in

Japan • Network Dynamics and Institutional Policy

• New Japanese institutional policies accelerate formation of industry

and academia interfaces (Woolgar, 2007)

• Strong Government policy pushing for life science

industries in Japan in 2000’s (Motohashi ed., 2009)

• Increase of university industry alliances and bio-ventures from

universities

• Increase of patenting from academia

• Japanese Contexts in Life Science Industry

• Dominance of big companies of food, pharmaceutical, chemical

industry (Goto & Odagiri, eds, 2003)

• Higher value of licenses from industry than acadmia (Nakamura,

2009)

TLO Policy Changes during 2000’s

1. Partial Privatization: National University become State Agencies

• National University Corporation Act from 2004

• More contribution of national universities to industrial development

2. Organizational Intellectual Property Policy Changes

• IPO Policy Changes from 2004

• Reinforcement of IPO Management in University

• Change of IPO Ownership: From Researcher to University

3. Promotion of Industry Academia Alliances

• Rapid increase of alliances from universities

• Rapid growth of Joint R & D alliance networks in Bioclusters

Increase of Industry Academia Alliances in

National Universities

All Area Joint Research in Life Science

Source: http://www.mext.go.jp/a_menu/shinkou/sangaku/__icsFiles/afieldfile/2009/10/30/1282374_4_1.pdf

5-2. Kansai Biotechnology Cluster

• One of the Biggest Biotechnology Clusters

• Kansai Area: West Japan

• 200 firms, 36 universities, 14 institutions, 12 incubators in 9

prefectures (Ibata-Arens, 2005)

• Main Industries

• Pharmaceutical, Food, Medical Equipment

• National Development Program set by METI

• Kansai Bio Cluster Development Program in 2000’s

Kansai Bio Cluster in West Japan

Source: http://www.biobridge-kansai.com/

Kobe Biomedical Cluster

Saito Bio Hills [Northern

Osaka Bio Cluster]

Kyoto Techno Innovation Cluster

Source: http://www.biobridge-kansai.com/

Hot Combined

Clusters in Japan

Bio Tech Cluster Ranking in Japan

2004

1 Kobe City (Hyogo)

3 Ibaraki City (Osaka)

Three Main Players

14

1. Pharmaceutical firms

• Tanabe Mitsubishi, Dainihon Sumitomo, Shionogi, Takeda

Pharma

2. University

• Osaka University, Kyoto University, Kinki University

3. Local and National Governmental Research

Institutes

• The National Institute of Advanced Industrial Science and

Technology , Japan Science and Technology Agency

• Osaka Bio Science Institute

Policies made University Local hub

Patent Application Network of Major Organizations: 2000-2003

Patent Application Network of Major Organizations: 2004-2007

Tanabe-Mitsubishi

Astellas

Senju

Dainihon-

Sumitomo

Shionogi Shionogi

Tanabe-Mitsubishi

Dainihon-Sumitomo

Osaka Univ

Kyoto Univ

6. Comparison of Patent Grant and

Application Networks • Method and Data

• Method of Social Network Analysis

• Comparison of application and grant networks in joint patenting in Kansai Area in 2000’s

• Data

• Patent

• Joint patents: 1413 selected joint patents submitted from organizations in Kansai district in Pharmaceutical Category (A61K)

• Source: Patolis (Japanese License DB based on GovermentDB)

• Unit of analysis

• Selected Organizations over more than 4 applications

• Selected 195 Organizations (154 Business Firms / 41 Universities and Labos)

• 154 Firms (Kansai company: 98 / Outside company: 56 )

• Observation period

• Year: 2000-2007

• Category of Patents

• IPC: A61K

16

High Failure Rate

• Fewer granted patents

• Many applications but

low grant rate

• Only 7.5 %

• C.f. Japanese Average in

2005: 21.6%

• Alliances may very low

economic value patents

Gap between Grant and Application

Networks during 2000 to 2007

Application Network Grant Network

Fewer Partnerships Got Granted Patents!

Zoom-in in Grant Networks:

What are Successful Groups ? • Very few granted partnerships

• Divides of Big pharma groups and Big university group

Big Pharma

groups

Major University

group

Few Successful Partnerships

• Huge increase of

application partners

• But very fewer

successful partnerships

with granted patents

• Two major groups

• Big University Group

• Big Pharma Group

Who tends to have successful partnerships

• Probit Anaysis of Granted Partnership Ratio of Firms

• Target variable: Ratio of Grant Ties in Application Ties

(Granted (Successful) partnership percentage)

• Independent variable:

• Size

(1) Capital Volume (Logged)

• Network Variables

(2) Structural holes: Bridging indicator: H1

(3) Clustering coefficient: Cohesion indicator: H2

(4) Labo tie percentage: Ratio of linkages to Research Institutes in all linkages

• Categorical Variable: H3

(5) Locating in Kansai (98 firms) / outside Kansai (56 firms)

Results of the Probit Estimation of Effects of Capital Size,

Bridging and Cohesive Ties, Percentage of Lab Ties and

Location on Percentage of Granted Partnership in Application

Independent and controlsCapital (logged) -0.011 (0.078)

Structural Holes -0.725 ** (0.245) 0.522 (0.590) 0.784 (0.635)

Clustering Coefficient 0.699 ** (0.301) 1.136 * (0.581) 1.267 ** (0.625)

Labo Tie Percentage -1.047 * (0.610) -1.031 * (0.544) -1.187 * (0.649) -1.008 (0.640)

InterceptFirms locating in Kansai -1.107 *** (0.106) -0.537 *** (0.175) -1.498 *** (0.456) -1.665 *** (0.554)

Firms locating outside Kansai -1.294 *** (0.148) -0.691 *** (0.174) -1.661 *** (0.442) -1.776 *** (0.562)

NChi-square

Notes : *p<0.1; **p<0.05; ***p<0.01; Standard errors are i n parantheses .

Model 1 Model 2 Model 3 Model 4

122 153 122 115111.932 144.882 118.032 109.256

7. Factors for Successful Partnership

• Probit Analysis on Percentage of Successful Partnerships

• Capital size: Neutral

• Network position: Firms with dense linkages and without bridging

ties in applications may have more good partners.

• Linkages with labos were bad

• Results of Hypothesis Test

• H1 (Bridging tie effect): not accepted

• H2 (Cohesive tie effect): accepted

• H3 (Labo tie effect): bad effect rather than good

8. Discussion

• Industry academia alliance policy effect

• These policies formed university-centered technology transfer networks during 2000’s in Japan

• Expansion of joint-patenting partnerships between industry and academia

• But, many failing alliances between Industry and Academia

• Very few successes in joint-patenting (Only 7.5% for 21.7% of Japan)

• Devotion to narrow and cohesive linkages may not to lead to increase of innovation.

• Universities and, especially, labs, were bad partners in licensing during 2000’s

• Advises

• More widening firm’s networks in clusters, more with ventures

• Universities and especially, labs, more commitment to economic value of patents and more focus on high economic value expected patents

Je vous remercie de votre attention!

25

Major Reference

26

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Appendix 1. Descriptive Statistics of Major

Variables

Items Unit N Average S.D.

Granted Tie Ratio % 153 0.171 0.296

Applications Patents 154 9.747 9.867

Grants in 5 Years after

Application

Patents 154 0.812 1.292

Granted Patent Ratio % 154 0.078 0.102

Capital 10K JPY 144 2767589.568 11408430.989

Employee Persons 143 3207.105 12552.114

Sales 10K JPY 139 16193002.219 39506244.941

Net Profit 10K JPY 135 877329.717 3442469.140

Eigenvector

Centrality_Application

154 0.042 0.062

Structural Hole_Application 153 0.441 0.317

Clustering Coefficient (UCINet

Routine)_Application

122 0.144 0.288

Longest Length of Shortest Path Ties 154 6.058 1.948

Firm Tie Percentage 154 0.735 0.345

Labo Tie Percentage 153 0.072 0.198

Appendix 2. Pearson Correlation of Major

Variables

No Items 1 2 3 4 5 6

1 Granted Tie Ratio

2

Number of

Applications

-0.01

3 Captal Size Log -0.14 0.28 **

4

Eigenvector Centrality

in Application

Networks

-0.07 0.74 ** 0.29 **

5

Structural Hole in

Application Networks

-0.30 ** 0.47 ** 0.30 ** 0.39 **

6

Clustering Coefficient

in Application

Network

0.19 * -0.14 -0.18 0.02 -0.86 **

7

Labo Tie Percentage

in Application

Network

-0.17 * 0.03 -0.03 0.18 * -0.02 0.04

Notes: *p<0.1; **p<0.05; ***p<0.01.