Global Networks & Regional Characteristics

on Innovation Performance in the Medical Devices Sector

Pieter Ellerd Stek

Parallel Session 3.1: Cluster dynamic research. Implications on Cluster Performance and Business Competitiveness

1

The Influence of Global Networks &Regional Characteristicson Innovation Performance in the Medical Devices Sector

Pieter E. StekDoctoral Student, Economics of Technology & InnovationDelft University of Technology, the Netherlands

p.e.stek@tudelft.nl | +82 10 7220 9937

2

Content

1.Introduction: medical devices

2.Research goals

3.Methodological approach

4.Indicators

5.Preliminary results

3

Introduction: medical biotechnology

● Knowledge intensive & dynamic– combining “medical” with “technology” in surgical devices,

imaging, diagnostics, monitoring, implants, etc.

● Extensive collaboration between industry, universities, public research institutions, hospitals – and globally

● Socially relevant, especially in reducing medical costs and improving the quality of life of an aging population

● US$325 billion in revenue (2014)

4

Research goals

● Bring together different theoretical perspectives on innovation performance– Regional: specialization/diversity, agglomeration, regional

scientific base, regional university-industry-government collaboration (Triple Helix), etc.

– Global Network: collaboration between individuals/institutions & directed knowledge flows (e.g. branch → headquarters, foreign university → local company)

● To eventually explore sectoral differences and similarities

5

Methodological approach

● Mixed approach: economic geography + economics of knowledge + social network analysis + scientometrics + GIS

● Scientometrics (patents and scientific publications)– Used for the extraction of knowledge, social networks and regional

indicators– Sources: PATSTAT (EPO), Scopus (Elsevier) & OECD regional patent

statistics

● Estimation of a regression model● Validation & interpretation with case studies and expert interviews

(planned)

6

Indicators: defining the region

● OECD's Territorial Level 3 (TL3)● Offers link to the OECD regional patent database

– OECD countries, BRICS, Romania, Taiwan & Singapore

● Scale issues: Shandong vs. Gyeongsangbuk-do● Region's threshold for inclusion:

– regional sectoral patent share > global sectoral patent share– size: min. 5 patent, 10 papers and 5 citation*

● In this study n = 136

7

Indicators: defining the sector

● PATENTS: 977,187 patent familiesPatent classifications based on a keyword analysis of ANZSIC classes:– 2411: Photographic, optical and ophthalmic equipment– 2412: Medical and surgical equipment manufacturing

from an Australian Department of Industry study (2014)

● ACADEMIC PAPERS: 75,599 publicationsScopus subject areas of “Engineering” AND “Medicine” (and excluding social sciences, economics, etc.)

8

Indicators: dependent variable

● INNOVATION PERFORMANCEof the region(1) average patent family size per researcher(2) average patent citations per researcher

9

Indicators: regional

● REGIONAL SPECIALIZATION“Location indicator”: relative concentration / global share (McCann 2014)

● REGIONAL AGGLOMERATIONtotal number of patents with inventors from the region

● REGIONAL SCIENTIFIC BASEtotal number of citations received by academic papers from the region in the field

● REGIONAL TRIPLE HELIX SHAREpercentage of patents with assignees from different institution types (university, industry, government)– Institution type based on EEE-PPAT dataset from KU Leuven

10

Indicators: networks

● COLLABORATION NETWORKS (“CLUBS”) as:– undirected networks based on co-authorship– between inventors, assignees (institutions), scientists– weighted and unweighted

● TRANSFER NETWORKS (“TEAMS”) as:– directed networks based on co-authorship or inventor-assignee relationship– from university/government → industry (Triple Helix)

or branch → headquarters (Multinational Corporation)– weighted and unweighted– inbound and outbound

11

Indicators: correlation (working draft)

12

Model setup (working draft)

● Dependent variables: patent family size + patent citations● Independent/explanatory variables:

– Regional specialization– Inventor “teams” network– Researchers (regional sectoral agglomeration)– Scientific citatations– Triple Helix collaboration (patents)– Scientific “community” network

13

Model estimation (working draft)Call:lm(formula = log10(pfsz/resr + 10^patc/resr) ~ spec + netw_inv + resr + scic + thps + netw_sci, data = data)

Residuals: Min 1Q Median 3Q Max -0.52006 -0.05507 0.00696 0.09849 0.32080

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.84444 0.21090 4.004 0.000106 ***spec -0.15644 0.04081 -3.834 0.000198 ***netw_inv 0.28963 0.06338 4.570 1.15e-05 ***resr 0.51801 0.05761 8.992 3.06e-15 ***scic -0.08043 0.04169 -1.929 0.055933 . thps 0.02416 0.03288 0.735 0.463793 netw_sci 0.06779 0.04386 1.546 0.124712 ---Signif. codes: 0 ?**?0.001 ?*?0.01 ??0.05 ??0.1 ??1

Residual standard error: 0.1469 on 126 degrees of freedomMultiple R-squared: 0.9411, Adjusted R-squared: 0.9383 F-statistic: 335.7 on 6 and 126 DF, p-value: < 2.2e-16

14

Plot of input & output (working draft)

15

Largest regions (patents)

1)San Jose-San Francisco-Oakland, CA

2)New York-Newark-Bridgeport, NY-NJ-CT-PA

3)Los Angeles-Long Beach, CA

4)Minneapolis-St. Paul-St. Cloud, MN-WI

5)Boston-Worcester-Manchester, MA-NH

6)Taiwan

7)Gyeonggi-do

16

Largest regions (science citations)

1)Boston-Worcester-Manchester, MA-NH

2)New York-Newark-Bridgeport, NY-NJ-CT-PA

3)San Jose-San Francisco-Oakland, CA

4)Los Angeles-Long Beach, CA

5)Chicago-Naperville-Michigan City, IL-IN-WI

6)Inner London - West

7)Singapore

17

Most specialized regions

1)Lubbock-Levelland, TX

2)Bremerhaven

3)Greenville, NC

4)Bangor, ME

5)Uckermark-Barnmin

6)Fargo-Wahpeton, ND-MN

7)Frontenac, ON

18

Most “efficient” innovators

1)San Jose-San Francisco-Oakland, CA

2)Los Angeles-Long Beach, CA

3)Minneapolis-St.Paul-St. Cloud, MN-WI

4)New York-Newark-Bridgeport, NY-NJ-CT-PA

5)Boston-Worcester-Manchester, MA-NH

6)San Diego-Carlsbad-San Marcos, CA

7)Taiwan

19

Conclusions (tentative)

● Benefits of Marshallian agglomeration (technological diversity)

● Benefits of local scale● Benefits of inventor networks (international

collaboration and branch → headquarter relations)● “Insignificance” of basic research?● “Insignificance” of industry-university co-patenting?

20

Further applications

● “Big” data set usable for benchmarking, activity and network mapping

● Methodology applicable to all patent-intensive technologies, globally

● Interested in case studies that are backed up by interviews to validate and understand “why”

21

Thank you

● for listening and your questions, comments and suggestions!

● contact:Pieter E. Stekp.e.stek@tudelft.nl | +82 10 7220 9937