Progress Report:Triple Helix (TH) on Web

&Types of TH

Gohar Feroz Khan

Dept. of Media & Communication, YeungNam University,

South Korea.

Prepared for: SSK (Social Science Korea) Project Workshop Busan

Introduction

Progress Report

Article 1:Measuring Triple Helix on the web.Longitudinal trends of among University (U), Industry

(I), and Government (G)

Article 2:Broadening TH by proposing 5 types of TH That may exist under certain conditions from

evolutionally economic point of viewWe will use Social network analysis concepts such as

strong and week ties and information theory.

Article 1Measuring Triple-Helix on the Web:

Longitudinal Trends of Relationship among University-Industry-Government (UIG) in Korea

Article 1: Measuring TH on the web

Several models and approaches have been proposed for measuring knowledge-based system of innovation, for example

National Innovation System (Freeman, 1987, 1988; Lundvall; 1988).

Mode 1 and Mode 2 knowledge creation Machanisim (Gibbon, 1994)

Triple Helix Model (Etzkowitz & Leydesdorff, 1995, 2000)

However,

majority of the studies that measure this infra are conducted in non-Asian (i.e. English) context (LEE and JEONG (2008)

And limited only to analyzing contents of written communication in English.

In addition, they use well documented and formal written communications, such as, patent and publications, for example PARK et al. (2005).

Furthermore, most of the knowledge-based innovation indicators, such as, Science Citation Index are available commercially and accessibly only to subscribers

Thus

In this research, we try to measure the strength of relations among TH components in Korean context using World Wide Web (WWW).

We argue that WWW and advanced search engines on the internet can indicate UIG relations using Triple helix indicators (Leydesdorff and Curran, 2000;) in Korean context

Method

We employed webometrics combined with content analysis, and co-word analysis technique in this research.

Data: Naver.com (the most popular search engine in South Korea) using

WeboNaver in March 2010 Naver started its service in 1998, thus we harvested the data from

1999 to 2009 (see table 1) We analyzed a verity of sources—web, documents, blogs, online café,

knowledge in, and media Search Terms with Boolean operators: “ 대학 (dae-hawg)” “기업 (ghi-oeup)” and “정부 (Jeong-bu)” were

used.

Year U I G UI UG IG UIG

1999 21 23 8 0 0 0 0

2000 2210 13836 4712 47 10 187 1

2001 3024 17687 5977 30 7 184 0

2002 4537 28529 5984 80 24 170 2

2003 21767 36352 13947 308 147 567 81

2004 69717 55637 33825 2249 2399 4631 1088

2005 90899 68210 42696 2724 2939 5323 1475

2006 233,768 138,193 91,181 9,918 9,099 16,621 4,703

2007 496227 295467 151214 22063 15346 30305 7615

2008 677336 392342 271605 30568 21062 50311 10255

2009 814746 502035 276756 38755 24734 56293 12086

Table 1 Number of hits for TH components from 1999 to 2009

Results

Figure 1 A longitudinal trend of Web-based T(uig) across categories

Results

Figure 2 Occurrence of UIG in Web documents Figure 3 Strength of the bilateral and trilateral relationship in web docs

President Noh—2003 to 2007

Results

Figure 4 and 6 Titles of Knowledge In

Results

Figure 6. A longitudinal trend of occurrence of U, I, G in the titles of all five categories

Conclusion

The results indicate that the UIG relationship varied according to the government’s policies and that there was some tension in the longitudinal UIG relationship.

Further, websites/documents and blogs were the most reliable sources for examining the strength of and variations in the UIG bilateral and trilateral relationships on the Web.

In addition, web-based T(uig) values showed a stronger trilateral relationship and larger variations in the UIG relationship than SCI-based T(uig) values.

Article 2Broadening the Triple Helix

Introduction

We have several modelsNational Innovation System (Freeman, 1987, 1988;

Lundvall; 1988). Mode 1 and Mode 2 knowledge creation Machanisim

(Gibbon, 1994)Triple Helix Model (Etzkowitz & Leydesdorff, 1995, 2000)

According to Etzkowitz & Leydesdorff (2000) knowledge-based innovations system can be operationalized in terms of network of relationships between university, industry, and government

Introduction

However, TH fails to capture the dynamic of nature of knowledge-based innovation (KBI) system that may prevail in different economies (particularly developing countries) under the pressure of economic condition from evolutionary perspective.

In this article, we argue that interaction among the actors of knowledge based innovation system is a complex phenomenon & may take different forms due to economic pressure, prevailing circumstances, diversity in the linkages, and complex nature of KBI system

Social Network Ties

Strength of Week ties (Granovetter,1973)Bridging ties (Harary, et al, 1965;)Structural holes (Burt, 1992)

5 types of TH

Type 1:

Government (G)

University (U)

Industry (I)

Lack of network ties; KBI system may exist but no collaborative novelty production is taking place, thus it is inefficient

Let us assume that U represents the novelty produced by university, expressed in terms of SCI publications, I by industry, and G by government independently from one another. The total output of the knowledge based innovation system “T” can be expressed as T= U+I+G

Type 1 TH may exist in situation where the fundamental components (university, industry, government) of a knowledge-based economy produce novelty independently from one another and networked ties are not yet established.

5 types of TH

Type 2:

The total output of the knowledge based innovation system “T” can be expressed as T= U+I+G-ug

Type 2 TH exists when two of the components of KBI system established bilateral ties for join novelty production. However the third component is not yet part of network as shown in the figure 2. Here U and G are collaborating in novelty production whereas; I is not part of network and may produce novelty independently.

Government (G)

University (U) Industry

(I)

Network ties established; KBI exist but not efficient due “I” not participating in the systems.

Missing ties. One of the fundamental components is not participating in the novelty production

Figure 2 Type 2 Triple Helix

5 types of TH

Type 3:

The total output of the knowledge based innovation system “T” can be expressed as T= U+I+G-ug-ig

Type 3 TH exists when one of the players, normally government, plays a central role in KBI system by facilitating the novelty production and different types of ties exist among the players, such as, combination of strong and week ties as shown in figure 3.

Government (G)

University (U)

Industry (I)

Bridging , week ties, and structural hole

KBI system established with high efficiency due to existence of combination of strong ties, week ties, and bridging ties.

5 types of TH

Type 4:

The total output of the knowledge based innovation system “T” can be expressed as T= U+I+G-ug-ig-ui

When all the components of KBI system establish bilateral network ties among each other it forms type 4 TH as shown in the figure 4.

Government (G)

University (U)

Industry (I)

Figure 4 Type 4 Triple Helix

Network ties established; KBI exist but not efficient due to too many strong ties and information redundancy. Too costly to maintain this system

Bridging , week ties, and structural holes missing

5 types of TH

Type 5:

The total output of the knowledge based innovation system “T” can be expressed as T= U+I+G-ug-ig-ui+uig

When all the components of KBI system establish bilateral and trilateral network ties among each other it forms type 5 TH as shown in the figure 5.

Government (G)

University (U)

Industry (I)

Redundant network ties not efficient; may give birth to negative network externality. The KBI is complex and too costly to operate.

Figure 5 Type 5 Triple Helix

Bridging , week ties, and structural holes missing

Empirical validation of TH types

In future, we plan to use WoS data to show the existence of TH types. For exam-ple, we can use SCI publication longitudinal data of Korea and some other countries?

 

Thank you