Emergence of Communication Networks: A Self-organizing Systems Perspective

1. Emergence of Communication Networks:A Self-organizing Systems Perspective Noshir S. Contractor Depts. of Speech Communication & Psychology University of Illinois at Urbana-Champaign nosh@uiuc.edu Viestintä, viisaus ja vastuu Lume-mediakeskuksessa Hämeentie 135 C, 00560 Helsinki February 4, 2000

2. OUTLINE • Examples of self-organizing entities • The role of technologies in facilitating self-organizing systems • The new role of communication research in studying self-organizing systems

3. Self-Organizing Entities • FAA initiative for “free flight” • Hollywood production teams • Organizational consulting firms • Linux • Internet

4. Stages of Technology Use Substitution

5. Substitution • Adoption based on relative advantage, observability, adaptability, compatibility, trialability • Examples: Automobiles, Telephone, Videoconferencing, Arpanet/Internet, WWW

6. Substitution Effects • U.S. Conference Board estimates National secretarial pool has shrunk by more than half a million in the past decade

7. Substitution Effects ? • The Hollywood Syndrome versus the Shakespeare Syndrome? • Media shape the nature of arguments, which in turn shape the nature of decisions • Media shape the nature of coalitions, which in turn shape the nature of decisions

8. Substitution Effects ?

9. Stages of Technology Use Enlargement Substitution

10. Enlargement • If the automobile were invented in 1970 and dropped in price accordingly, while increasing features, a car would cost less than $5 and drive 25,000 miles/gallon (Economist, 1998) • To which the president of GM replied: "Yes, but would you want your car to crash every time you tried to open a window?"

11. Enlargement • 1996: Total volume of email greater than snail mail; total sales of PC greater than TV sets • 1999: Total volume of data traffic greater than voice; 10 fold increase in U.S. e-commerce in 10 months • Moore’s Law: Computational power doubles every 18 months • Metcalfe’s Law: The value of a network is proportional to the number of users squared

12. Enlargement: Information Gap • Emerging technologies improve the amount of information among the “haves” and the “have-nots” • But the “haves” are much better informed than the “have-nots” resulting in an increase in the Information Gap

13. Information Gap

14. Stages of Technology Use Reconfiguration Enlargement Substitution

15. WORK BY BID?

16. Coordination Theory

17. Transaction costs of coordination mechanisms • Hierarchies (Low) • Markets (Medium) • Networks (High)

18. Organizational Forms Hierarchy Matrix Network

19. Fedex and cookies Firm A Firm B Corporate level Business unit level Group level Individual level Interdependencies in the virtual organization can occur both internally and externally and at various levels of the firm.

20. Surge of Network Organizations • More than 20,000 alliances formed worldwide in 1996-98, accounting for 21% of the revenue of America’s 1000 largest firms in 1997 (Harbison & Pekar, 1999)

21. Reconfiguration Examples : Put your money where your mouse is • Amazon.com, Priceline.com:. Lowest price for me. • Ebay.com, Guru.com: Auction. Highest price for me. • Mercata.com, Accompany.com: Lowest price for us

22. Dawn of the E-lance Economy • The fundamental unit of such an economy is not the corporation but the individual. Electronically connected free lancers or e-lancers join together into fluid and temporary nets to provide and sell goods and services (Malone & Laubacher, Harvard Business Review, 1998).

23. Reconfiguring relationships:Brokering information • Info-mediaries (John Hagel & Marc Siegel) • Importance of leveraging knowledge capital via social capital - The case of the Lovegety

24. Social and Knowledge Capital • Social networks and supporting tools • Cognitive social structures and supporting tools • Knowledge networks and supporting tools • Cognitive knowledge networks and supporting tools

25. Social Networks • It’s not what you know, it’s who you know.

26. Social Networks Nodes represent people. Links represent who knows who.

27. Tools to Assist Social Networks • Tools (such as Ph, WhoIs, Four11) can help reduce disparities in social networks • Example: How can I get in touch with person X?

28. Cognitive Social Structures • It’s not who you know, it’s who they think you know.

29. Tools to Assist Cognitive Social Structures • Collaboration filtering tools (such as SixDegrees) can help individuals answer the “Who knows who knows who” question -- to find out how one may be connected to those identified as knowledge experts. • Example: I understand that X is an expert in topic A. Whom do I know who knows X, and can introduce me to X?

30. Knowledge Networks • Who knows what? • Nodes represent the individuals, project teams, organizations, physical locations. • Links representing the shared knowledge could be (i) skills, (ii) expertise, (iii) activities, (iv) interest sets, (v) interpretations of project goals and/or missions, (vi) work flow information.

31. Knowledge Networks Nodes represent people. Links represent shared knowledge.

32. Tools to Assist Knowledge Networks • Data bases and traditional search engines such as Alta Vista. • Example: I need to find out something about topic X. Where do I get this information?

33. Cognitive Knowledge Networks • Who knows who knows what? • Example: I need to know more about topic X. Who in my extended (direct or indirect) network can tell me more about topic X?

34. Summary • Social Structures are based on “who knows who.” • Cognitive Social Structures are based on “who knows who knows who.” • Knowledge Networks are based on “Who knows what.” • Cognitive Knowledge Networks are based on “who knows who knows what.”

35. The Answer to these Questions . . IKNOW !!!! http://iknow.spcomm.uiuc.edu

36. Goal of IKNOW http://iknow.spcomm.uiuc.edu

37. Data Used in IKNOW • Based on organizational members’ Web pages: • Links between Web pages • Common external links from Web pages • Content on the Web pages http://iknow.spcomm.uiuc.edu

38. Data Used in IKNOW (cont’d) • Based on organizational members volunteering information about social and knowledge resources • Content: inventory of skills, expertise, etc. • Links: inventory of social networks • Incentives for volunteering information tied to performance appraisal and evaluation of help provided. http://iknow.spcomm.uiuc.edu

39. So why would one want to use IKNOW? • Makes the virtual visible. • Adds social capital to knowledge capital by adding contacts to content. • While collaboration tools help improve the process of collaboration in knowledge networks … IKNOW helps one effectively identify collaboration partners and grow the knowledge network. http://iknow.spcomm.uiuc.edu

40. The New Role of Communication Research

41. Exchange theories Contagion theories Cognitive theories Consistency theories Homophily theories Theories of social capital Proximity theories Uncertainty reduction theories Social support theories Collective action theories Coordination theories of organizational forms Self-organizing Networks:Why do actors create, maintain, and dissolve network links? Source: Monge & Contractor, in press

42. Examples • Collective Action: Public Goods Theory • Cognitive Theory: Transactive Memory Theory • Cognitive Consistency Theory • Affect Theory • Social Capital Theory

43. Cognitive Theory: Transitivity Mechanism: Increase balance A + B B C C

44. + Affective Theory:Group Cohesion Mechanism: Attraction to group

45. _ Social Capital Theory:Structural holes Mechanism: Increase autonomy, effective network size A D B B C C

46. Summary • Technologies enable reconfigurable networks • Reconfigurable networks lead to self-organizing systems • New theory and methods needed to study the emergence – creation, maintenance, and dissolution – of these self-organizing networks FOR FUTHER INFORMATION EMAIL NOSH@UIUC.EDU