Knowledge-based national innovation system

- A case of South KoreaDr. Han Woo PARK

Visiting Research Fellow Oxford Internet Institute, UK

Assistant ProfessorDepartment of Media & CommunicationYeungNam University214-1 Dae-dong, Gyeongsan-si, Gyeongsangbuk-do 712-749Republic of Koreahanpark2020@gmail.com http://www.hanpark.net This is in collaboration with Min-Ho So, L. Leydesdorff, and M. Thelwall

Virtual Knowledge Studio (VKS)

Knowledge-based innovation

• There are probably three ways to measure knowledge-based innovation system in terms of networked communication

- Journal articles: Traditional knowledge indicator; Scientometric

- Patent registration: Innovation indicator; Technometric

- Website links: Digital (proxy) indicator; Webometric

Knowledge-based systemness

• A surprising growth of SCI publications with a Korean address

- Korea increases its percentage world share of publications with approximately 0.20 percent point each year (r 2> 0.99). In terms of the number of SCI papers, Korea occupied the 14th position in the year 2005

- Korea obtained even the 6th position during the first eight months of the year 2004 in the field of nanotechnology

Long-term trend of the percentage publications with a Korean address in the SCI

Percentage World Share of Korea (articles, reviews, letters)

R2 > 0.99

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Increasing Korean SCI journals

Korea’s portfolio becomes strong?

• The answer is both yes and no

• Why YES?

- Research evaluation and scholarly practice become internationalized and national R&D budget is growing

- WCU (World Class University) project

- YES part is not the focus of this presentation

Critical points and frustrating aspects

• Only three scientists working in Korean institutions were included in the 2008 list of ISI’s Highly Cited Researchers

• Korean SCI journals are being neglected by domestic/international scientists as a reference journal

- These journals function more like publication places, neither research channels nor information sources

Position of Korean SCI journals?

Networked innovation system

• A knowledge-based innovation can no longer be attributed to single nodes in a society

• New technologies enable individual and institutional actors to collaborate in new modes

• Is Korea national R&D system strong in terms of networkedness?

Gov’t policy and TH

• The networked knowledge infrastructure can be measured in terms of a Triple-Helix of University-Industry-Government relations; L. Leydesdorff

• Does Korea governmental policy facilitate the development of cooperation among individual/institutional actors comprising of national research system?

Government Characteristics of R&D programs related to the TH indicators

Park, Jung-Hee(1970-1979)

Government’s strong push to run governmental institutes and joint research between universities and public organizations

Chun, Doo-Hwan

(1980-1987)

Merger and acquisition among government-sponsored research institutes; e.g., the integration of the KAIS (Korea Advanced Institute of Science) university and the KIST (Korea Institute of Science and Technology) into the KAIST (Korea Advanced Institute of Science and Technology)

Roh, Tae-Woo(1988-1992)

The gradual opening of research organizations in both private and public sectors; e.g., KIST became independent from KAIST in 1989

Kim, Young-Sam

(1993-1997)

Dominance of governmental agencies from early 1990 to 1997 when Korea started to be subject to International Monetary Fund (IMF) conditions

Kim, Dae-Jung(1998-2002)

BK21 project started in 1999 to increase the research capacity of universities through large central government subsidies, thus decreasing UIG joint research

Roh, Moo-Hyun(2003-2007)

Continual promotion of the BK21 and internationalization of R&D, particularly in the academic sector

Characteristics of R&D programs according to government

Number of papers by Korean authors in the Science Citation Index and bi- and trilateral

relations between TH-sectors within the economy

R2 > 0.99

0

5,000

10,000

15,000

20,000

25,000

30,000

1970 1975 1980 1985 1990 1995 2000 2005 2010

Nr

of

Ko

rea

n p

ap

ers

in

th

e SC

I

Total

University

Industry

Government

UI

UG

IG

UIG

Mutual information in bilateral relations between the TH sectors in Korea

0

100

200

300

400

500

600

700

800

900

1970 1975 1980 1985 1990 1995 2000 2005 2010

T(u

ig)

in m

bit

s o

f in

form

ati

on

T(ui)

T(ug)

T(ig)

The Mutual Information in Two Dimensions:

Tij = Hi + Hj - Hij

Tij ≥ 0The Mutual Information in Three Dimensions:

TUIG = HU + HI + HG – HUI – HIG – HUG + HUIG

TUIG is potentially negativeA negative entropy can be a consequence of the mutual relations at the network level. The configuration then reduces the uncertainty.

Mutual information in trilateral Triple Helix relations in Korea

-140

-120

-100

-80

-60

-40

-20

0

20

1970 1975 1980 1985 1990 1995 2000 2005 2010

T(u

ig)

in m

bit

s o

f in

form

ati

on T(uig)

2-year moving average

Have changes in government research policies affected longitudinal changes?

First, there is the reduction of uncertainty among academic, public, and industrial research actors in the Korean publication system from 1970 to 1990.

Second, the mutual information among the three TH agencies (Tuig) is relatively stable during the 1990s but begins to decrease over the last ten years. The trend line shows that the TH dynamics of UIG relations have varied considerably; this variation generally accords with changes in Korean government research policies.

Publication patterns in and between TH sectors using the A&HCI Index

R2 = 0.844

0

10

20

30

40

50

60

70

80

90

100

1970 1975 1980 1985 1990 1995 2000 2005 2010

Nr.

of

Ko

rea

n p

ap

ers

in

th

e A&

HC

I

University

Industry

Government

UG

UI

Linear (1997-2006)

Mutual information measured in bilateral relations between TH sectors using the SSCI

-200

0

200

400

600

800

1000

1200

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Tra

nsm

issio

ns in

mb

its

UI

UG

IG

3 per. Mov. Avg. (UG)

3 per. Mov. Avg. (UI)

Publication rates of Korean papers and synergy effects among TH sectors on the

basis of coauthorship relations in the SSCI

-400

-300

-200

-100

0

100

200

300

400

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

T(u

ig)

in m

bit

s

0

100

200

300

400

500

600

700

800

900

1,000

Nr

of

Ko

rea

n p

ap

ers

in

th

e S

SC

I

T(uig)

Number of papers

2-yr moving average T(uig); r > 0.99

exponential fit (1986-2006) for the nr of papers; r > 0.99

SCI 2002 Number UI UG IG UIG Univers Industry Govt

All 683222 17095 116782 4626 5664 556370 41840 234843

USA 238676 7274 40650 1777 2732 206813 18193 68835

EU 250395 4586 54617 1400 2187 204531 11011 99830

UK 66544 1569 14263 360 763 53972 3617 26673

Germany 59630 1181 14986 405 703 50319 2925 24364

France 39973 431 12214 422 585 26663 1826 25721

Scand 30437 592 8757 170 411 26283 1431 13064

Italy 29795 374 7609 79 321 26680 956 10863

Netherlands 17865 328 4663 78 307 15927 859 6762

S. Korea 14931 533 3115 118 183 13163 996 4904

Japan 68338 4303 13297 1113 1481 57345 9892 22776

PR China 28913 381 6408 111 173 24328 728 11103

Taiwan 9572 183 2772 15 59 8608 295 3757

Singapore 3411 110 622 16 53 2978 202 1085

Russia 20723 81 6637 134 157 11486 443 15960

India 12570 109 2180 92 67 7140 459 7486

Brazil 10888 189 2054 45 81 9584 386 3368

Relative position of Korea TH

Source: Science Citation Index 2000

2002-70.7

-71.0-45.3

-54.0-39.6-42.5-32.5-27.6-32.8

-82.4-11.0-18.0-28.6-33.7

-18.9-67.7-26.8

Top 68 title words with cosine ≥ 0.1 for South-KoreaScience Citation Index 2002

bio

materials

organic

control medical

Co-word network in Korea

Top 49 words with cosine ≥ 0.1 for The NetherlandsScience Citation Index 2002

cancer

biotech

Co-word network in the Netherlands

chemistry

flowers medical systems

electro-technical

cars

coating

energy

Cosine normalized map of 105 co-occurring words in patents (in 2002) with a Dutch address among the assignees or inventors (N Patents = 2,824; Word frequency > 22; cosine ≥ 0.1).

Cosine normalized map of 103 co-occurring words in patents (2002) with a Korean address among the assignees or inventors (N Patents is 4,200; Word frequency > 40; cosine ≥ 0.1).

Cosine normalized map of 103 co-occurring words in patents (2002) with a Korean address among the assignees or inventors (N Patents is 4,200; Word frequency > 40; cosine ≥ 0.1).

info devices

coating

chipsdisplay

printing

Inter-regional collaboration

• Three kinds of data set among 16 Korean regions were constructed: Total number of publications, citations, cited articles

• Network measures- Centrality: sum of connections- Density: cohesive properties- Fragmentation: to identify the key actor

whose replacement is extremely urgent if the actor is excluded from the network

Seoul’s (normalized) centralities and overall network centralization

Summary of centrality analysis

• In the last 10 years, the centrality of Seoul in the co-authorship network was clearly decreasing. peaked in 1990 at 34.38 and then decreased to a plateau

• Attributed to the emergence of Dae-jeon and Kyeong-gi

• The re-structuring of the two regions was obviously a result of government interventions

Density values 1974-2006 for all categories, SCI-only, SSCI-only

Summary of density analysis

• The co-authorship networks of Korean provincials became more cohesive over the last three decades (0.004 in 1974 to 48.779 in 2006), revealing the rapidly increasing collaboration among geographically dispersed researchers and institutions.

• Need to look at longitudinal trends of ‘clustering coefficient’ besides densities

Fragmentation value when one key player, Seoul, is removed

Summary of fragmentation analysis

• Seoul was identified as the key player over the decades but NOT selected as the most important key player from 1992 to 1994

• Dae-jeon was identified as the key player in 1992 and 1993 and Busan in 1994 in the co-authorship network

• In the cited article network, Dae-jeon was chosen as the most powerful key player in 1992 and 1993 and Kwang-ju in 1994.

Web indicator for knowledge and information networks

• Links between sites might not provide for actual knowledge/information flow

• But one university receives more links from another, this can be because it is more productivein terms of scholarly performance (e.g., journal article publications, class materials, pre-prints etc.)

• Or two universities are more collaborative than ..

• Indicator for quantity, not quality??

• Hyperlinks tend to reveal both existing and emerging socio-communicational network

Universities in Eurasia (at least 100 hyperlinks)

Universities in Asia (at least 20 hyperlinks)

Universities in Asia (at least 50 hyperlinks)

Summary of ASEM links

• Clear geographic trends are visible, with most universities connecting mainly to other universities from the same country

• A closed-network among China and Singaporean universities: Collaboration

• Academic digital divide

- European universities (e.g., UK) have more incoming links than Asian ones

Discussion issuesDiscussion issues

• Role of hyperlinks as an indicator of what?

- Innovation activity- Scientific activity- Communicational

activity- Proxy VS Actual- Comparable to citation

Configurations may constrain the further TH developments at national system level by generating more uncertainty than can be managed by three TH actors- L. Leydesdorff

The endThank you for listening, and thank you to my

assistants (Ae-Jin Bae) and collaborators (Loet Leydesdorff, Min-Ho So, Mike Thelwall, Hyo Kim)

Han-Woo Park, Ph.D.Email: hanpark2020@gmail.comWebsite: www.hanpark.net

Partially supported by a Korea Research Foundation Grant