By Jang Hyun Kim, presented at the 18th TCI Global Conference, Daegu, 2015.

1. What Do Links Mean in Innovation
Clusters?:
‘Relational Dialectics’
Jang Hyun Kim
Parallel Session 2.1: Cluster Models Worldwide. Unique Features, Common Challenges

2. What Do Links Mean in Innovation Clusters?:
‘Relational Dialectics’
Jang Hyun KIM, Ph.D. jangkimuh@gmail.com Sungkyunkwan Univ.
* Some of the results here are unpublished yet. Please do not cite them.

3. What are you doing with links?
• As a social/semantic scholar we’re dealing
with many kinds of networks on the levels
of:
- Inter-websites
- Inter-personal, Inter-group, Inter-national
- Intra-personal (cognitive), intra-group,
- Inter-words or Inter-windows (semantic)

4. Our naïve assumption
• Links are a good thing.
• The more communicate or the more we
link with each other, the better the world is
becoming.
• Is it really so?

5. Kim & Barnett (2007)
• We hypothesized that:
“The more nations communicate, the less
conflict they would have.”
- An expanded version of liberalism position
of international relations theory.

6. Retrieved from http://home.earthlink.net/~tebrister/realism_and_liberalism.htm
©All rights reserved. Contact
alohakim@dgist.ac.kr or +82-10-2389-2655
(Dr. Jang Hyun Kim) for getting a written
permission for citing or using this material.

7. Predicting international conflict
networks
©All rights reserved. Contact
alohakim@dgist.ac.kr or +82-10-2389-2655
(Dr. Jang Hyun Kim) for getting a written
permission for citing or using this material.

8. • We found:
- The more nations communicate through
phone calls, sending air mails, hyperlinks,
exchange freights, the MORE they fight
each other.
But those conflicts are of little intensity such as
regional firing or verbal aggressions among
diplomats or ministries.
There were hardly any full-scale war.

9. Theory (1) – Triple Helix
• Leydesdorff and Meyer (2003) summarized
existing systematic approaches to the actors
of technological innovation.
• Triple helix model posits that three sectors
(helices), university-government-industry
(UGI), communicate with one another and
can occasionally take one another’s roles
because they share at least a portion of one
another’s roles.

12. Theory (2) - Convergence
• Convergence theory envisions the flow of
information through a communication
network, and the theory is useful for
examining interorganizational networks.
• Theoretically, UGI organizations in a social
system would converge over time to
similar value sets, beliefs, and institutional
arrangements if communication were to
continue indefinitely.

13. Theory (3) – Semantic and Social
Networks
• Hyperlink network analysis reveals social
connections and relationships in
cyberspace.
• Semantic network analysis explicates
cultural value sets shared among
organizational members.

14. Nanotechnology UGI Website
Analysis Results
• The most frequently used words in semantic data
were device, material, molecular, nanotechnology,
research, and science. The industry sector was
most likely to use these six words.
• Of the 52 words that were used at least 10 times
by at least one sector, there were eight that were
used at least 10 times by all three groups: control,
developing, device, material, molecular,
nanometer, nanotechnology, and science.
• These eight words represent the research subject
(control, material, device, nanotechnology, and
science) and the scale (nanometer and
molecular).

15. Semantic Networks - Industry
Legend. circle: academic organization/university; square: industry/firm; up-triangle: public/government organization; diamond: not available.

16. Semantic Networks - University
Legend. circle: academic organization/university; square: industry/firm; up-triangle: public/government organization; diamond: not available.

17. Semantic Networks - Government
Legend. circle: academic organization/university; square: industry/firm; up-triangle: public/government organization; diamond: not available.

18. • Overall, the semantic network of
government sector did not produce any
large clusters.

19. • Nanotechnology inter-website network
(Triple Helix Structure, Kim 2012, JCMC)
University, industry, and
government websites are
exchanging hyperlinks,
but key actors are from
industry.

20. • Noteworthy is that nanobot.blogspot.com,
crnano.typepad.com, and
nanotechlaw.blogspot.com showed the
following transitivity: AB, BC, and
AC. Here AC was weaker than AB
and BC.
• This result indicates a series of
relationships: industry 
public/government  academic/university.

21. Discussion
• The results of this study provide evidence
that in web-mediated environments,
hyperlinks can be an important proxy for
interorganizational linkages.
• In addition, from a convergence theory
perspective, bilateral linkages and shared
keywords in cyberspace can increase the
level of dyadic reciprocity during periods of
early technology development, particularly
through partnerships and alliances

22. • According to the semantic network diagram, the
clusters of university and industry websites were
very similar, particularly their major-word clusters.
The major agendas on industry and university
websites included research subjects such as
device and material; scale and nanoscale; and
nanometer.
• These results are consistent with the recent
“entrepreneurial university”: the university sector is
becoming more similar to the industry sector by
accepting the cultures, norms, and even
organizational systems of the industry sector.

23. • The results of the interlink analysis
indicate that industry
(nanobot.blogspot.com) and
public/government (crnano.typepad.com)
websites were central in the network. In
addition, there was the transitivity of
relationships among industry 
public/government  academic/university
websites.

24. How about social
networks among
cluster
constituents?

25. 출처: N.A.
Christakis & James
Fowler (2010)
Connected
©All rights reserved. Contact
alohakim@dgist.ac.kr or +82-10-2389-2655
(Dr. Jang Hyun Kim) for getting a written
permission for citing or using this material.
Contagion
networks of
happiness/
depression

26. 출처: N.A.
Christakis & Fowler
(2010) Connected
©All rights reserved. Contact
alohakim@dgist.ac.kr or +82-10-2389-2655
(Dr. Jang Hyun Kim) for getting a written
permission for citing or using this material.
Contagion
networks of
overweight

27. 출처: N.A.
Christakis & James
Fowler (2010)
Connected
©All rights reserved. Contact
alohakim@dgist.ac.kr or +82-10-2389-2655
(Dr. Jang Hyun Kim) for getting a written
permission for citing or using this material.
Contagion
networks of
heavy-smoking

28. Links are complicated than we think –
(1) outcome/cause: dependence
Compiled and restructured from Rogerson (2000). Information
interdependence. Information, Communication, & Society, 3(3),
p.423.
Targeting at a
satisfactory
outcome for
one actor
Perfect (near
perfect)
symmetry of
dependence
Dependence
ruled by
private/
community
governance
One party
(perfectly or
near-perfectly)
exploits the
other(s)

29. Distribution of
Responsibility
could be unfair.
Distribution of
Benefits or Disadvantages
could be unfair.
In networking:
Outcome not necessarily fair enough

30. • “Complex interdependence” (Keohane & Nye, 1989, 24-25).
Links are complicated than we
think – (2) multi-lateral
Many actors..
Many channels..
Many issues in a
hierarchical format (issue
salience)
Many non-violent ways
for negotiation..
Rogerson (2000), p. 421

31. Complicatedness applies to
interpersonal networks, too.
• The Relational Dialectics approach to
human communication (Baxter &
Montgomery 1996) argues that
- close interpersonal relationships necessarily
involve inherent and ongoing tensions,
struggles, and contradictions among opposite
tendencies
- such as integration-separation, stability-
change, expression-nonexpression, inclusion-
seclusion, conventionality-uniqueness, and
revelation-concealment. A close relation is
formed and maintained in continual tensions
like playing at a tug of war.
- “I’m leaving you because I love you.”

32. • Vulnerability:
the relative availability
and costliness of the
alternatives that
various actors face
• Sensitivity: the
degree of
responsiveness
Links are complicated than we
think – (3) a matter of
psychology
Rogerson (2000), p. 422
Keohane & Nye (1989) 12-13
cited in Rogerson (2000) 421-422

33. Semantic Networks,
as a network of memes,
actually
means a lot.

34. Google (Co-commented Semantic Analysis)
34
• Google’s Facebook page is very
active, despite the company’s lack
of active participation
• Positive words: ‘nice’, ‘good’,
‘like’, ‘love’
Hong & Kim, presented at
DISC 2014.

35. Sony (Co-commented Semantic Analysis)
35
• Sony’s Facebook page is much
more salient
• Positive words: ‘like’, ‘love’,
‘good’, ‘awesome’, ‘wow’, ‘great’,
‘best’, ‘nice’
Hong & Kim, presented at
DISC 2014.

36. F-- company (Co-commented Semantic Analysis)
36
• Negative cluster: ‘not’,
‘unfortunately’
• No other significant
semantic structure
found
Hong & Kim, presented at
DISC 2014.

37. Phishing message semantic
networks
Phishing Messages 1
Table 6. The Top 50 Frequent Words List
TOTAL WORDS 5171 THRESHOLD 0.000
TOTAL UNIQUE WORDS 50 RESTORING FORCE 0.100
TOTAL EPISODES 5165 CYCLES 1
TOTAL LINES 4838 FUNCTION Sigmoid
CLAMPING Yes
DESCENDING FREQUENCY LIST ALPHABETICALLY SORTED LIST
CASE CASE CASE CASE
WORD FREQ PCNT FREQ PCNT WORD FREQ PCNT FREQ PCNT
--------------- ---- ---- ---- ---- --------------- ---- ---- ---- ----
ACCOUNT 523 10.1 2308 44.7 ACCESS 112 2.2 632 12.2
EBAY 293 5.7 1105 21.4 ACCOUNT 523 10.1 2308 44.7
ONLINE 237 4.6 1263 24.5 ADDRESS 53 1.0 341 6.6
PLEASE 212 4.1 1371 26.5 AGREEMENT 60 1.2 399 7.7
HTTP 206 4.0 1113 21.5 BANK 136 2.6 752 14.6
COM 192 3.7 1096 21.2 BANKING 127 2.5 712 13.8
INFORMATION 187 3.6 1044 20.2 BELOW 59 1.1 407 7.9
PAYPAL 165 3.2 759 14.7 CLICK 96 1.9 630 12.2
UPDATE 153 3.0 869 16.8 CO 98 1.9 569 11.0
SECURITY 148 2.9 853 16.5 COM 192 3.7 1096 21.2
BANK 136 2.6 752 14.6 CONFIRM 42 0.8 263 5.1
CUSTOMER 136 2.6 769 14.9 CUSTOMER 136 2.6 769 14.9
BANKING 127 2.5 712 13.8 DEAR 102 2.0 708 13.7
WWW 121 2.3 770 14.9 EBAY 293 5.7 1105 21.4
ACCESS 112 2.2 632 12.2 EMAIL 98 1.9 623 12.1
DEAR 102 2.0 708 13.7 FRAUD 53 1.0 331 6.4
SERVICE 101 2.0 645 12.5 HALIFAX 41 0.8 230 4.5
CO 98 1.9 569 11.0 HTML 84 1.6 483 9.4
EMAIL 98 1.9 623 12.1 HTTP 206 4.0 1113 21.5
CLICK 96 1.9 630 12.2 HTTPS 44 0.9 308 6.0
LINK 88 1.7 610 11.8 INC 47 0.9 271 5.2
THANK 87 1.7 591 11.4 INFORMATION 187 3.6 1044 20.2
HTML 84 1.6 483 9.4 ITEM 51 1.0 178 3.4
NATIONWIDE 70 1.4 355 6.9 LINK 88 1.7 610 11.8
USER 68 1.3 449 8.7 LOG 58 1.1 368 7.1
POLICY 67 1.3 408 7.9 LOGIN 50 1.0 342 6.6
MAIL 66 1.3 321 6.2 MAIL 66 1.3 321 6.2
PROTECT 64 1.2 417 8.1 MEMBER 58 1.1 367 7.1
UK 64 1.2 417 8.1 MESSAGE 62 1.2 342 6.6
MESSAGE 62 1.2 342 6.6 NATIONWIDE 70 1.4 355 6.9
RECORDS 62 1.2 341 6.6 ONLINE 237 4.6 1263 24.5
AGREEMENT 60 1.2 399 7.7 PAYPAL 165 3.2 759 14.7
Phishing message
Two word clusters:
privacy & security
Kim & Kim (2013)
©All rights reserved. Contact
alohakim@dgist.ac.kr or +82-10-2389-2655
(Dr. Jang Hyun Kim) for getting a written
permission for citing or using this material.

38. • Semantic
network reveals
intrapersonal
communication
with a
constellation of
numerous
thoughts
https://athenakramos.wordpress.com/categ
ory/thoughts/

39. Conclusion
• The effect of communication networks
revealed by semantic and social network
analyses is not too simplistic. That is,
complicated.
• It’s important to avoid any presumptions of
‘networking is good’ and to scrutinize
what’s really happening in the world of
nodes and words.
• There are various kinds of dependency
through networks and we have to respect
each network’s own traits.

40. http://www.businessnewsdaily.com/5578-sample-thank-you-letters.html