TCI 2015 What Do Links Mean in Innovation Clusters? ‘Relational Dialectics’

What Do Links Mean in Innovation Clusters?:

‘Relational Dialectics’

Jang Hyun Kim

Parallel Session 2.1: Cluster Models Worldwide. Unique Features, Common Challenges

What Do Links Mean in Innovation Clusters?: ‘Relational Dialectics’

Jang Hyun KIM, Ph.D. [email protected] Sungkyunkwan Univ.* Some of the results here are unpublished yet. Please do not cite them.

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)

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?

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.

Retrieved from http://home.earthlink.net/~tebrister/realism_and_liberalism.htm

©All rights reserved. Contact [email protected] or +82-10-2389-2655 (Dr. Jang Hyun Kim) for getting a written permission for citing or using this material.

mailto:[email protected]

Predicting international conflict networks



• 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.

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.

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.

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.

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).

Semantic Networks - Industry

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

Semantic Networks - University


Semantic Networks - Government


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

• Nanotechnology inter-website network (Triple Helix Structure, Kim 2012, JCMC)

University, industry, and government websites are exchanging hyperlinks, but key actors are from industry.

• Noteworthy is that nanobot.blogspot.com, crnano.typepad.com, and nanotechlaw.blogspot.com showed the following transitivity: AB, BC, and AC. Here AC was weaker than AB and BC.

• This result indicates a series of relationships: industry public/government academic/university.

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

• 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.

• 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.

How about social networks among

cluster constituents?

출처 : N.A. Christakis & James Fowler (2010) Connected


Contagion networks of happiness/depression


출처 : N.A. Christakis & Fowler (2010) Connected


Contagion networks of overweight


출처 : N.A. Christakis & James Fowler (2010) Connected


Contagion networks of heavy-smoking


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)

Distribution of Responsibilitycould be unfair.

Distribution ofBenefits or Disadvantages

could be unfair.

In networking:Outcome not necessarily fair enough

• “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

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.”

• 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. 422Keohane & Nye (1989) 12-13 cited in Rogerson (2000) 421-422

Semantic Networks, as a network of memes,

actuallymeans a lot.

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.

Sony (Co-commented Semantic Analysis)

35

• Sony’s Facebook page is much more salient

• Positive words: ‘like’, ‘love’, ‘good’, ‘awesome’, ‘wow’, ‘great’, ‘best’, ‘nice’


F-- company (Co-commented Semantic Analysis)

36

• Negative cluster: ‘not’, ‘unfortunately’

• No other significant semantic structure found


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 BELOW 59 1.1 407 7.9 PERSONAL 50 1.0 329 6.4 LOG 58 1.1 368 7.1 PLEASE 212 4.1 1371 26.5 MEMBER 58 1.1 367 7.1 POLICY 67 1.3 408 7.9 ADDRESS 53 1.0 341 6.6 PRIVACY 53 1.0 322 6.2 FRAUD 53 1.0 331 6.4 PROTECT 64 1.2 417 8.1 PRIVACY 53 1.0 322 6.2 RECEIVE 43 0.8 294 5.7 ITEM 51 1.0 178 3.4 RECORDS 62 1.2 341 6.6 LOGIN 50 1.0 342 6.6 REFERENCES 49 0.9 335 6.5 PERSONAL 50 1.0 329 6.4 SECURITY 148 2.9 853 16.5 VERIFY 50 1.0 314 6.1 SERVICE 101 2.0 645 12.5 REFERENCES 49 0.9 335 6.5 SERVICES 44 0.9 282 5.5 INC 47 0.9 271 5.2 SYSTEM 41 0.8 246 4.8 HTTPS 44 0.9 308 6.0 THANK 87 1.7 591 11.4 SERVICES 44 0.9 282 5.5 UK 64 1.2 417 8.1 RECEIVE 43 0.8 294 5.7 UPDATE 153 3.0 869 16.8 CONFIRM 42 0.8 263 5.1 USER 68 1.3 449 8.7 HALIFAX 41 0.8 230 4.5 VERIFY 50 1.0 314 6.1 SYSTEM 41 0.8 246 4.8 WWW 121 2.3 770 14.9

Phishing message

Two word clusters: privacy & security

Kim & Kim (2013)



• Semantic network reveals intrapersonal communication with a constellation of numerous thoughts

https://athenakramos.wordpress.com/category/thoughts/

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.

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

TCI 2015 What Do Links Mean in Innovation Clusters? ‘Relational Dialectics’

Economy & Finance

Transcript of TCI 2015 What Do Links Mean in Innovation Clusters? ‘Relational Dialectics’