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

reciprocity alternating out-k-stars, par. 2 alternating in-k-stars, par. 2 direct + indirect connections alternating k-triangles, par. 2 alternating independent twopaths, par. 2 location (centered) sector (centered) tenure ego tenure alter hierarchy ego hierarchy alter gender ego gender alter tenure similarity hierarchy similarity gender identity

Model 1 1.32 (0.24) -0.63 (0.23) 0.46 (0.13) 1.24 (0.13) Model 2 Model 3 0.75 (0.29) 0.53 (0.26) -0.6 (0.2) -0.74 (0.22) -0.83 (0.26)

0.23 (0.15) 0.23 (0.14) 0.21 (0.16) 0.15 (0.17) 0.2 (0.18)

Model 4

0.54 (0.3) 0.26 (0.21)

1.14 (0.11) 0.8 (0.12) -0.2 (0.03) 0.76 (0.14) -0.19 (0.02) -0.21 (0.03) 1.68 (0.26) 0.62 (0.11) 1.63 (0.26) 0.81 (0.13)

0 (0.02) 0.02 (0.01) -0.04 (0.06)

-0.12 (0.04) Model 5

0.54 (0.31)

-0.8 (0.21)

0.22 (0.17) 0.17 (0.2)

0.79 (0.14) -0.2 (0.03) 1.72 (0.23) 0.72 (0.11)

-0.02 (0.02)

0.05 (0.02)

-0.06 (0.07)

-0.15 (0.06)

0.26 (0.15) 0.07 (0.13)

0.47 (0.16)

0.78 (0.41)

0.47 (0.22)

Results – second company

Effect

reciprocity alternating out-k-stars, par. 2 alternating in-k-stars, par. 2 direct + indirect connections alternating k-triangles, par. 2 alternating independent twopaths, par. 2

Model 1 1.38 (0.27) -0.47 (0.12) 0.7 (0.08) 1.63 (0.11) Model 2 0.79 (0.08) Model 3 1.4 (0.26) 1.33 (0.34) -0.33 (0.12) -0.55 (0.11) 0.86 (0.09) 0.79 (0.3) 0.93 (0.24) 0.77 (0.24) 0.73 (0.18) -0.13 (0.02) -0.17 (0.03) Model 4 1.27 (0.31) -0.57 (0.13) 0.74 (0.09) 0.92 (0.31) 0.66 (0.26) -0.17 (0.02) Model 5 1.22 (0.35) -0.6 (0.13) 0.72 (0.09) 0.75 (0.26) 0.82 (0.18) -0.16 (0.02)

Opportunity sector (centered) tenure ego tenure alter hierarchy ego hierarchy alter gender ego gender alter education similarity hierarchy similarity gender identity

1.14 (0.11) 1.23 (0.11)

0.1 (0.09) -0.05 (0.04) 0.16 (0.21) -0.02 (0.13)

1.18 (0.12)

0.08 (0.1) -0.03 (0.07) 1.09 (0.93) 0.86 (0.89) -0.31 (0.18) 0.02 (0.09) 0.04 (0.27) -1.84 (1.8) 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

500 400 300 200 100 0 1000 900 800 700 600 Number of ties ties

Disconnected companies 350 300 250 200 150 100 50 0

Size of the largest strong component 45 40 35 10 5 0 30 25 20 15

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