This document summarizes a study that measured Korea's triple helix model of university-industry-government relations using web data from 1999-2009. It analyzed five web sources: webpages, blogs, online communities, question/answer sites, and media sites. It found the relationships varied over time and between sources. Blogs showed the strongest long-term university-industry-government linkage. Relationships also depended on the political administration and policies. While web data captured more variation than traditional sources like publications, both have limitations in fully representing the knowledge-based infrastructure. The study provided evidence that the web can be a useful alternative data source for measuring these relationships.

1. Measuring Triple Helix (TH) on Web Presented by: Dr. Junghoon Moon Authors: GoharFeroz Khan, Junghoon Moon, & Han Woo Park Prepared for: Triple Helix 9 International Conference (Stanford University, 11-14 July 2011)‏

2. Measuring knowledge-based infrastructure : Methods There have been many studies to measure knowledge-based infrastructure Several models and approaches have been proposed for measuring knowledge-based infrastructure, for example: National Innovation System (Freeman, 1987, 1988; Lundvall; 1988). Mode 1 and Mode 2 knowledge creation Mechanism (Gibbon, 1994), and Triple Helix Model (Etzkowitz & Leydesdorff, 1995, 2000)

3. Measuring knowledge-based infrastructure : Sources 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, usually, well-documented database or formal written communications, such as, patent and publications, are mainly used (e.g. Science Citation Index, which is commercial)

4. Approach of The Study Method: TH Model Data Source: Web (Korean) using WeboMetrics Method, etc. for data collecting Does Web indicate UIG relations using Triple helix indicators well as an alternative approach? Tuig Using SCI

5. Method We employed Co-word analysis technique and Triple Helix Indicators (Leydesdorff, 2003) We analyzed the data by using the TH indicators developed by Leydesdorff (2003) based on Shannon’s information theory (Shannon, 1948; Shannon & Weaver, 1949) and T values by using a standard technique in the TH program available at http://www.leydesdorff.net/th2/index.htm.

6. Data Data was collected from Naver.com (the most popular portal/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 Search Terms with Boolean operators: “대학(dae-hawg: Univeristy)” “기업(ghi-oeup: Industry)” “정부(Jeong-bu: Government)”

7. Data Sources We collected data from five different Web sources through Naver.com: WebPages (personal web sites, commercial, etc.) Blogs Online Café (e-communities) Knowledge-In (comparable to Yahoo answers) Media sites (News services, Broadcasting services, etc.)

8. Data Collection Data Collection Results, Overall: The number of hits for TH components from 1999 to 2009

9. Results Longitudinal Trends in the UIG Relationship by Category Key points: Blogs indicated the strongest trilateral relationship since 2004, reaching T(-0.400) in 2008 Webpagesshowed large variations in the trilateral relationship, indicating several ups and downs in the relationship News sites indicated a consistently improving trilateral relationship since 2002, reaching to its highest point in 2009, as indicated by T values as shown in figure 1 Figure 1 Rho’s Gov Lee’s Gov

10. Figure 3 Strength of the bilateral and trilateral relationship in WebPages Results: Web pages Rho’s Gov Lee’s Gov Figure 2 Occurrence of UIG in WebPages Key points (Figure3): The bilateral T values for U and I were the highest, indicating the important role played by Webpages in the UI relationship (Figure 3) The IG relationship was weakest between 2003 and 2007. This may be due to President Roh’s preference for the UI relationship over the IG/UIG relationships Evidence of some tension in the longitudinal UIG relationship in Korea are visible. For example, between 1999 and 2009, the strengthening of the bilateral UI relationship was always accompanied by the weakening of the bilateral UG and IG relationships and vice versa. Lack of coordination

11. Results:Knowledge-In Figure 5 Longitudinal trends in bilateral and trilateral UIG relationships forKnowledge-In Rho’s Gov Lee’s Gov Figure 4 Longitudinal trends in the occurrence of U, I, and G in titles of Knowledge–In documents Key points: Effect of dot-com crisis is visible on UIG relations as indicated by the T values since mid-2002. In addition, the government has been implementing policies to improve this relationship, which is supported by the slight improvement in the UIG relationship and the bilateral UI relationship in 2007, when President Lee was in office Key point: Only the term U increased noticeably since Naver started the Knowledge-In service in October 2002.

12. Results:Blogs Figure 6.1 Longitudinal trends in bilateral and trilateral UIG relationships for Blogs Rho’s Gov Lee’s Gov Figure 6 Longitudinal trends in the occurrence of U, I, and G in blog titles Key points: Blogs showed the strongest trilateral relationship. The trilateral relationship remained steady throughout the 2003-2009 period. Noteworthy is the conflicting behavior of the T(ui) and T(ug) relationships. An increase (decrease) in T(ui) values was accompanied by a decrease (increase) in T(ug) values. Key point: Noteworthy is that the occurrence of U and I increased at almost the same rate since the blog service started in 2003. The occurrence of G also increased from 2003 to 2008, the last year of the Roh administration

13. ResultsCafe Figure 7.1 Longitudinal trends in bilateral and trilateral UIG relationships for Cafes (BBS) Rho’s Gov Lee’s Gov Figure 7 Longitudinal trends in the occurrence of U I and G in titles of café (BBS) documents Key points: Cafés/BBS provided the highest T(ui) values. The bilateral UI relationship peaked in 2007. The T(ui) and T (ug) values diverged beginning in 2004. An improvement in the UI relationship weakened the UG relationship and vice versa. On the other hand, the IG relationship was almost nonexistent. Finally, Cafés/BBC showed a strong trilateral relationship, but here were no large variations in the relationship. Key point: Noteworthy is that there were nearly 70,000 hits for IG between 2008 and 2009 and approximately 50,000 hits for UI, which were the highest numbers of hits across the categories for the co-occurrence of IG and UI. This may be due to the professional nature of cafes and the BBS and users’ interest in the country’s affairs and business

14. ResultsNews Figure 8.1 Longitudinal trends in bilateral and trilateral UIG relationships for News sites Rho’s Gov Lee’s Gov Figure 8 Longitudinal trends in the occurrence of U, I, and G in titles of documents on online News sites Key points: News sites showed the strongest bilateral IG relationship in terms of the T value Noteworthy is that an improvement in the bilateral IG relationship was accompanied by a decline in the bilateral UI relationship and vice versa Key point: Noteworthy is that the titles of documents from online news sites, unlike those of documents from other categories, provided the highest number of hits for I, followed by G.

15. Results: Comparison Figure 9 A Comparison between web-based T(uig) and SCI-based T(uig) values Key point: It is clear from Figure 9 that web-based T(uig) values shows much more variation in the UIG relationship than SCI-based T(uig) values, which, to some extent, remained steady throughout the sample period. This striking difference may be because internet resources are more diverse than SCI-based indicators, which are strictly codified and available commercially only to a restricted number of users. Lee’s Gov Rho’s Gov

16. Findings and Discussion Evidence of some tension in the longitudinal UIG relationship in Korea. The UIG relationship seems to be associated with Government Policy The results from four different Web source, except for Knowledge-In shows similar change patterns: Partial evidence of Web as a reliable source for knowledge-based infrastructure measure Results from the analysis using Web sources shows more fluctuant changes than those using SCI Which one is more relevant? Every source has its own limitations Web: e.g. Government supported IT industry vs. Government declined IT industry’s plea for deregulation SCI/Patent: Only formulated results, partial and somewhat biased,

17. Q&AThank you! moonj@snu.ac.kr