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Social network analysis in business and economics

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					Social network analysis in
business and economics


Marko Pahor
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
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
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
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
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
Social network analysis techniques

• “Descriptive statistics” of networks
   • Actors’ properties
   • Network properties
• Statistical methods
   • Blockmodelling
   • Probability models
       • One-time networks
       • Dynamic networks
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,...
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
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
The Network Perspective to
Organizational Learning – A
Comparison of Two
Companies
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
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
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
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...
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
Learning network in the first company
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
Learning network in the second
company
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
   Results – first company

              Effect                                     Model 1        Model 2       Model 3       Model 4        Model 5
              reciprocity                                1.32 (0.24)    0.75 (0.29)   0.53 (0.26)   0.54 (0.3)     0.54 (0.31)
 Structural
  effects


              alternating out-k-stars, par. 2            -0.63 (0.23)   -0.6 (0.2)    -0.74 (0.22) -0.83 (0.26)    -0.8 (0.21)
              alternating in-k-stars, par. 2             0.46 (0.13)    0.23 (0.15)   0.23 (0.14)   0.21 (0.16)    0.22 (0.17)
              direct + indirect connections              1.24 (0.13)    0.15 (0.17)   0.2 (0.18)    0.26 (0.21)    0.17 (0.2)
              alternating k-triangles, par. 2                           1.14 (0.11)   0.8 (0.12)    0.76 (0.14)    0.79 (0.14)
steri
Clu

 ng




              alternating independent twopaths, par. 2                  -0.2 (0.03)   -0.19 (0.02) -0.21 (0.03)    -0.2 (0.03)
              location (centered)                                                     1.68 (0.26)   1.63 (0.26)    1.72 (0.23)
Opp
ortu
nity




              sector (centered)                                                       0.62 (0.11)   0.81 (0.13)    0.72 (0.11)
              tenure ego                                                                            0 (0.02)       -0.02 (0.02)
Exp
erie
nce




              tenure alter                                                                          0.02 (0.01)    0.05 (0.02)
              hierarchy ego                                                                         -0.04 (0.06)   -0.06 (0.07)
Seni
orit
 y




              hierarchy alter                                                                       -0.12 (0.04)   -0.15 (0.06)
              gender ego                                                                                           0.26 (0.15)
 Gen
 der




              gender alter                                                                                         0.07 (0.13)
              tenure similarity                                                                                    0.47 (0.16)
 Homop
  hily




              hierarchy similarity                                                                                 0.78 (0.41)
              gender identity                                                                                      0.47 (0.22)
    Results – second company
                Effect                                     Model 1        Model 2        Model 3        Model 4        Model 5
                reciprocity                                1.38 (0.27)    1.4 (0.26)     1.33 (0.34)    1.27 (0.31)    1.22 (0.35)
   Structural
    effects
                alternating out-k-stars, par. 2            -0.47 (0.12)   -0.33 (0.12)   -0.55 (0.11)   -0.57 (0.13)   -0.6 (0.13)
                alternating in-k-stars, par. 2             0.7 (0.08)     0.79 (0.08)    0.86 (0.09)    0.74 (0.09)    0.72 (0.09)
                direct + indirect connections              1.63 (0.11)    0.79 (0.3)     0.77 (0.24)    0.92 (0.31)    0.75 (0.26)
                alternating k-triangles, par. 2                           0.93 (0.24)    0.73 (0.18)    0.66 (0.26)    0.82 (0.18)
  Clus
  terin
    g




                alternating independent twopaths, par. 2                  -0.13 (0.02)   -0.17 (0.03)   -0.17 (0.02)   -0.16 (0.02)



Opportunity sector (centered)

                                                                                         1.14 (0.11)    1.23 (0.11)    1.18 (0.12)
                tenure ego                                                                              0.1 (0.09)     0.08 (0.1)
  Exp
  erie
  nce




                tenure alter                                                                            -0.05 (0.04)   -0.03 (0.07)
                hierarchy ego                                                                           0.16 (0.21)    1.09 (0.93)
  Seni
  orit
   y




                hierarchy alter                                                                         -0.02 (0.13)   0.86 (0.89)
                gender ego                                                                                             -0.31 (0.18)
   Gen
   der




                gender alter                                                                                           0.02 (0.09)
                education similarity                                                                                   0.04 (0.27)
   Homop
    hily




                hierarchy similarity                                                                                   -1.84 (1.8)
                gender identity                                                                                        0.05 (0.15)
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
Paths of Capital: The Creation and
Dissolution of the Slovenian
Corporate Network
Corporate networks

• Networks between corporate entities
  (companies)
• Different types of links
   •   Interlocking directorates
   •   Financial links
   •   Strategic alliances
   •   Cross-ownership
   •   Multilink
   •   …
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)
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
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
Changes in the network

• Network is evolving
   • Changing links
   • Changing composition
• Two distinct periods are visible
   • Network creation period
   • Dismantlement period
Network in 2000
Network in 2002
Network in 2004
Network in 2006
Network in 2008
 Number of ties
                  ties




1000


900


800


700


600


500


400


300


200


100


  0
Disconnected companies
 350



 300



 250



 200



 150



 100



  50



  0
Size of the largest strong component
 45


 40


 35


 30


 25


 20


 15


 10


 5


 0
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
First phase: Building a portfolio
Second phase: Cashing out
First phase: making a group
Second phase: Closing the deal
Findings

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

				
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posted:9/10/2012
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