1. Social network analysis in business and economics Marko Pahor

2. Agenda What is social network analysis Short overview of social network analysis techniques Applications of social network analysis in business and economics Learning networks Ownership relations

3. Why do we need (social) network analysis? Different types of data: Attribute (properties, opinions, behavior,…) Ideational (meanings, motives, definitions,…) Relational (contacts, ties, connections…) Different data needs different analysis Variable analysis for attribute data Typological analysis for ideational data Network analysis for relational data

4. What is social network analysis Network analysis is a series of techniques (mathematical, statistical,…) designed to analyze relation data Mathematically funded in graph theory Social network analysis is the application of network analysis in the social sciences context

5. What are networks Imagine a closed set of units, call them actors or nodes For example people, companies, web pages, Networks are a set of actors or nodes connected by one or more relations

6. Social networks Thinking: … well, yes, but not really what social network analysis is about Social networks is any network with social entities (persons, groups, companies, social events,…) as actors

7. Social network analysis techniques “Descriptive statistics” of networks Actors’ properties Network properties Statistical methods Blockmodelling Probability models One-time networks Dynamic networks

8. Descriptive statistics Properties of actors Measures, that describe the position and importance of individual actors in the network E.g. Degree, betweeness,... Properties of network Describe the entire network Centalization, degree distribution, triadic census,...

9. Statistical methods of social network analysis Blockmodelling A clustering methods Permutations of the adjacency matrix in order to find some apriori expected blocks Probability models for one-time networks Modeling the probability of existence of a tie given parameters Network parameters (e.g. reciprocity), covariates (e.g. gender) and dyadic covariates (e.g. other relation) Probability models for dynamic networks Modeling the probability of creation or dissolution of a tie given parameters

10. Applications of social network analysis in business and economics Example 1: Organizational learning and through learning networks Example 2: The evolution of the cross-ownership network in Slovenia

11. The Network Perspective to Organizational Learning – A Comparison of Two Companies

12. Organizational learning and learning networks Organizational learning: individuals’ acquisition of information and knowledge, analytical and communicative skills Twodivergent perspectives for organizational learning the acquisition perspective the participation perspective Elkjaer’s (2004) ‘third way’ - a synthesis of the participation perspective and communities of practice Critisim: too much emphasis on the participation perspective and neglects some vital aspects of the acquisition perspective

13. The learning network perspective The individual is recognized as the primary source and destination for learning Learning takes place primarily in social interaction The network perspective helps develop an organizational learning culture

14. Learning networks External an extended enterprise model and comprise relationships that a firm has with its customers, suppliers and other stakeholders Internal a set of internal relationships among individual members of the firm and other constituencies such as product/service divisions and geographical units Components of learning networks learning processes learning structures actors

15. Propositions P1: Learning in the network will mostly occur in relatively dense clusters. P2a: More experienced employees will be more sought after to learn from. P2b: More experienced employees will have less of a need to learn from others. P3a: People higher up the hierarchical ladder will be more sought after to learn from. P3b: People higher up the hierarchical ladder learn as much or even more than those on lower levels. P4a: An opportunity (working in the same location or in the same business unit) will increase the probability of learning. P4b: Homophily has an effect; it is more probable you will learn from those who are similar in terms of gender, position, tenure...

16. Data – first company a software company 93 employees in three geographical units 81 employees participated in the study 59 from Ljubljana (Slovenia), 11 in Zagreb (Croatia) and another 11 in Belgrade (Serbia) 56.7% of the respondents have a university degree or higher (even one PhD) 74% of the respondents are male average tenure 38.9 months

17. Learning network in the first company

18. Data – second company main business engineering and production of pre-fabricated buildings 860 employees, 470 of which on the main location One production and several sales subsidiaries 348 employees from the main location participated 59 % of respondents have finished high school, 29 % have a university degree 79% of the respondents are male, average tenure is 12.7 years

19. Learning network in the second company

20. Methodology Network analysis is concerned with the structure and patterning of these relationships Logistic model for social networks known as the exponential random graph model (Snijders, 2002, Snijders et al., 2004) What makes a learning tie more probable? Structural effects Actor covariate effects Dyadic covariate effects

21. Results – first company

22. Results – second company

23. Discussion of results findings offers support for the network perspective to organizational learning learning often occurs in project settings and mainly involves the transfer of tacit knowledge through participation a particular learning setting is dependent on corporate culture and it is hard to capture all of its parameters

24. Paths of Capital: The Creation and Dissolution of the Slovenian Corporate Network

25. Corporate networks Networks between corporate entities (companies) Different types of links Interlocking directorates Financial links Strategic alliances Cross-ownership Multilink …

26. Corporate networks configurations Corporate networks evolve through time Self-organized or guided The configuration of a network is a reflection of the current situation (and historical path) Some configurations: Groups around financial centers (US, Mizruchi, 1982) Pyramidal structures (Belgium, Renneboog, 1997 and 1998) Cross-owned groups (keiretsu system in Japan, Gerlach, 1992) Sparse (“dismanteled”) network (Hungary, Stark, 2001)

27. The Slovenian corporate network Basically no corporations before 1992 Socially (not state!) owned companies “Ownership allocation” (privatization) Began in 1992 Was over by 1998 “Voucher” privatization In 1998 almost no connection between (non- financial) corporation By the year 2000 a rather dense network and growing

28. Data Ownership relations Owns a share in a public limited company Only non-financial companies 476 public limited companies Companies that existed in 2000 and their legal successors Were connected at least once in the observed period 10 years (2000-2009), two observations per year

29. Changes in the network Network is evolving Changing links Changing composition Two distinct periods are visible Network creation period Dismantlement period

30. Network in 2000

31. Network in 2002

32. Network in 2004

33. Network in 2006

34. Network in 2008

35. Number of ties

36. Disconnected companies

37. Size of the largest strong component

38. How it happened? Some patterns can be observed Two phased process Preparatory phase Execution phase Coincides with changes in network Examples 2000 – 2004 comparisons for the first phase 2005 – 2009 comparisons for the second phase

39. First phase: Building a portfolio

40. Second phase: Cashing out

41. First phase: making a group

42. Second phase: Closing the deal

43. Findings Slovenian corporate network is dissolving after a rise early in the decade Reason: ownership changes Shows how networks are used to gain control

44. Conclusions Social network analysis is an emerging technique for the analysis of relations data Networks are everywhere Many possibilities for applications in business and economics Interpersonal relations Interorganizational relations Marketing applications: products and customers networks