Dr Rick Albers, Radboud University, the Netherlands, presented this seminar entitled Organizing Intra-Organizational Networks for Innovation: introducing the basic concepts as part of the Network Data Analytics workshop hosted by the Social Sciences Compuing Hub at the Whitaker Insitute, NUI Galway on 26th February 2014

1. NUI Galway 2014 Workshop on network analytics
Part 1: Organizing Intra-Organizational Networks for
Innovation: introducing the basic concepts
Hendrik Leendert (Rick) Aalbers* PhD
(*) Assistant Professor Strategy & Innovation Radboud University - Institute for Management
Research // Centre for Organization Restructuring
r.aalbers@fm.ru.nl

2. Objective of today
• Introduction to social network analysis,
including:
• Relevance
• Core concepts
• Core methodology
• Main tools and visualization (Ucinet)
• Large online networks
• Reflection on future research possibilities
• Wrap up
2

3. Rick Aalbers, Phd
3
Research agenda:
Reorganization
&
Innovation

4. Introducing a network view of the world
4
• People are represented as
nodes.
• Relationships are represented
as edges (or ties)
• (Relationships may be
acquaintanceship, friendship,
co-authorship, etc.)
• Allows analysis using tools of
mathematical graph theory

5. 5
Timeline / history of networks
(based on Freeman, 2000)
• 1736: Euler's paper on “Seven Bridges of Königsberg” ?
• 1937: J.L. Moreno pioneered sociometry
• Sociogram
• 1948: A. Bavelas established the group networks laboratory at MIT
• Centrality
• 1949: A. Rapaport developed a probability based model of information flow
• 50s and 60s: Social Networks studied by researchers in graph theory
• Cohesion, power, cooperation, triads (a.o. Harary et al. 1950s).
• 70s: Field of social network analysis emerged.
• New features in graph theory – more general structural models
• Better computer power – analysis of complex relational data sets

6. What is an industry or interfirm network?
• repeated, enduring exchange
relations with one another and, at the same time, lack a legitimate
organizational authority to arbitrate and resolve disputes that may
arise during the exchange.
Podolny and Page (1998: 59)

7. What is a business or intrafirm network?
A collection of individuals, teams or business units
repeated, enduring exchange relations with one another.
Knowledge exchanged trough a shared social context. Intra
organizational networks facilitate the creation of new
knowledge within organizations (e.g., Kogut &
Zander, 1992; Tsai, 2000; Tsai, 2001)

8. 8

9. 9

10. Source: Aalbers and Dolfsma 2014
Networks of innovation (intra firm)

11. An example of a modern network:
9-11 Hijackers Network
SOURCE: Valdis Krebs
http://www.orgnet.com/

12. 12
Building blocks of an inter/ intra firm network
• Abstract level:
- Nodes
- Ties
• Interorganizational network (between firms)
- Firm level
- Examples: alliances, long-term buyer-supplier relationships
- Relationship is a connection between two firms that can be used
to transfer both tangible and intangible resources such as assets,
knowledge, money, and information.
• Intraorganizational network (within a firm)
- Employees
- Formal, informal
- Advice relationships, innovation, gossip, daily routines/ tasks
- Mandated, unmandated

13. Transaction cost economics (Williamson)
Adopts an undersocialized view of human being
• Human being as an atomistic entity
• Human beings are bounded rational
• Risk of moral hazard
• Risk of opportunistic behavior
Sociology
Adopts an oversocialized view of human being
• Environment determines human behavior
• No room for individual discretion
Economic Sociology (Granovetter, Uzzi)
Adopts an embeddedness perspective
• Economic relationships are embedded in social relationships
• Environment constrains humans but there is room for agency
Networks as alternative lens to the firm

14. Comparing markets, hierarchies and
networks (Powell,1990)
Governance forms
Key features Market Hierarchy Network
Normative basis Contracts / property
rights
Employment
relationship /
authority
Complementary
strengths
Means of
communication
Prices Routines Relational
Degree of
flexibility
High Low Medium
Commitment Low Medium to high Medium to high
Actor choices Independent Dependent Interdependent

15. 15
A network perspective to the firm
• Roots in graph theory
• Network is stored in a
matrix
A B C D E F G H I J
A 1 0 0 1 1 1 1 0 0 1
B 0 0 1 1 1 1 0 1 0 1
C 1 1 0 0 1 1 1 0 0 1
D 0 1 0 0 1 1 1 1 1 0
E 0 0 0 0 0 0 1 0 0 1
F 1 0 0 1 0 1 1 0 0 0
G 1 0 1 0 1 1 0 0 0 0
H 0 1 0 1 1 0 0 1 0 0
I 0 0 0 0 1 1 1 0 0 0
J 0 1 1 1 0 0 1 0 0 0

16. In general, a relation can be: (1) Binary or Valued (2) Directed or Undirected
a
b
c e
d
Undirected, binary Directed, binary
a
b
c e
d
a
b
c e
d
Undirected, Valued Directed, Valued
a
b
c e
d
1 3
4
21
Alright, so where to start?
The value (and direction) of a tie

17. 17
Why does it matter?
Different perspectives to study a network
• Structural embeddedness
- Looks at the quantity and configuration of interfirm relationships
-
(Structure – Conduct – Performance)
- Ignores firm/ individual characteristics
• Relational embeddedness
- Looks at the quality and contents of interfirm relationships
- Interfirm relationships are viewed as source of competitive
advantage/ intra firm relationships as source of innovation
- Invisible
- Causal ambiguous
- Inimitable

18. 18

19. Structural embeddedness terminology
• Network structure: the collection of actors and their relationships at any
given point in time.
• Network position: the pattern of relations to and from an actor within a
network structure.
Burt (1980: 893)

20. Degree: most likely to influence and be influenced directly
Closeness: most likely to find out first
Betweenness: most likely to broker and synthesize diverse info
Bonachich power: When your centrality depends on your neighbors’
centrality
20
indegree
In each of the following networks, X has higher centrality than Y according to
a particular measure:
outdegree betweenness closeness
Centrality measures

21. 21

22. 22
When degree is not everything
In what ways does degree fail to capture centrality in the
following graphs?
• ability to broker between groups
• likelihood that information originating anywhere in the network reaches you

23. 23
Betweenness
• Intuition: how many pairs of individuals would have to go through you in order
to reach one another in the minimum number of steps?
• who has higher betweenness, X or Y?
XY

24. 24
• degree
- number of connections
- denoted by size
• closeness
- length of shortest path to all
others
- denoted by color
How closely do degree and betweenness
correspond to closeness?

25. Extreme diversity
channel of broad and
diverse information
Combination
diverse ties provide the
perspective at which
knowledge held in
specialized parts
can be interpreted
Extreme similarity
repository of high-
quality, specialized
information
Relational embeddedness
Diversity vs. similarity (ter Wal 2013)

26. 26
Brokerage
• Centrality only captures part of knowledge brokering
• Centrality does not take division membership of the nodes into account
• Different brokerage roles exist . . .
Gould + Fernandez (1989):
(1) coördinator (2) gatekeeper (3) representative (4) itinerant broker (5) Liaison
Same centrality, different roles

27. Conducting a social network analysis in
the context of the firm
• Identify a Strategically Important Community
– Channeling creative ideas towards market ready innovations
– Integrating networks that cross core processes
– Facilitating post-merger integration and large-scale
organizational change
– Supporting communities of practice
– Identifying change agents for a reorganization to come
– Forming strategic partnerships and alliances
– Improving learning and decision making in top leadership
networks
– Crowd sourcing
– Building political cloud
... Each benefits from a particular form of network configuration

28. Assess meaningful relationships and network
constructs that connect and define these communities
• Relationships that reveal collaboration in a network
e.g., Communication, Information, Problem solving, Innovation
• Relationships that reveal information sharing potential
e.g., access, blockades
• Relationships that reveal rigidity in a network
e.g., decision making, influence, interdependencies
• Relationships that reveal well-being and supportiveness in a network
e.g., liking, friendship, trust

29. How to get to this kind of data
Network survey procedure
• Roster vs snowball method
• Snowball sampling method:
- Useful when boundaries of the network cannot be determined a priori /
particularly relevant for knowledge sharing
- Initial round of 8-10 ‘seeds’ (with different backgrounds); network
measures collected via interviews
- All contacts mentioned by first-round respondents become ‘targets’ for
the second round
- electronic network survey asking them about their network contacts
- Second-round targets: same thing until boundary is reached
- Y-round targets already included or only peripherally involved in the
theme)
29

30. Possible name generator questions (individual level)
30
Source: Aalbers, H.L., Dolfsma. W. Koppius, O. (2013). Rich Ties and Innovative Knowledge Transfer within a Firm. British Journal of Management, DOI:10.1111/1467-8551.12040
Business unit level example: Which units provide your unit with new knowledge or expertise
when your unit is seeking technical advice inside your organization?" (Tsai 2001)

31. The innovation network
Source: Aalbers, Dolfsma & Koppius 2014
So we got the network(s) and the key concepts...
Now what?

32. Combining network methodology into
relevant propositions
Possible angles
Hierarchy
Diversity
Multiplexity
• Actor attributes
• Brokerage
• Longitudinality
• Interventions
• Multi level networks
32

33. Horizontal cross unit ties
• The ties that team members have directly with other organization members
across unit boundaries.
Advantages
• Access to alternative ideas and insights relevant for a firm’s existing strategy, goals,
interests, time horizon, core values and emotional tone (Sethia 1995; Floyd and
Lane 2000).
• Creativity (Burt 2004).
• Complementary functional expertise (Aalbers et al. 2013; Haas 2010; March 1991).
• Team anticipation and prevention of potential weaknesses in technical and
marketing solutions (Leenders et al. 2003).
• Project performance (Cohen and Levinthal 1990; Obstfeld 2005; Tortoriello and
Krackhardt 2010).
33

34. Vertical cross hierarchical ties
• The ties that the team maintains with organization members at higher
hierarchical levels (Jaworski and Kohli 1993; Sheremata 2000).
- Received limited attention – with focus on the project team leader specifically
(Shim and Lee 2001)
Advantages
• Access to higher status positions brings:
- Desirable resources (e.g. funding, prestige, power) (Pfeffer & Salancik, 1978)
- Positive publicity
- Managerial attention & championing (Markham 1998)
- Legitimacy (Brass, 1984; Cross, Rice & Parker 2001; Feldman & March,
1981).
- Blocking off competing projects (Kijkuit & Van den Ende 2007).
- Perspective of how the team output fits in the overall firms objectives and goals
- Stocktaking of what is relevant within the rest of the organization (Hansen et al.
2001; Subramaniam and Youndt 2005; Mom et al. 2009).
34

35. Fosteringdiversity
Fostering support
Horizontal cross ties
Verticalcrossties
Delivery of innovative
project outcomes
Source: JPIM - Aalbers, Dolfsma & Leenders 2015

36. Figure 1: Horizontal cross-ties
Figure 1A (under-performing) Figure 1B (performing)
= Conceptual
projectteam
composition
Source: JPIM - Aalbers, Dolfsma & Leenders 2015

37. Figure 2: Hierarchical cross-ties
Figure 2A (under-performing) Figure 2B (performing)
= Conceptual
projectteam
composition
Source: JPIM - Aalbers, Dolfsma & Leenders 2015

38. Informal ties matter for knowledge sharing
39
• Informal networks: “interpersonal relationships in the
organization that affect decisions within it, but are either
omitted from the formal scheme or are not consistent with
that scheme”(Simon, 1976, p.148)
- Informal ties are discretionary and emergent (Monge & Contractor,
2001)
- Affective component stronger than instrumental component
(Ibarra, 1993)
- Primary basis for formation of interpersonal trust, which is
necessary for knowledge transfer (Szulanski et al., 2004)
Source: BJoM - Aalbers, Dolfsma & Koppius 2013

39. 40
Formal ties matter for knowledge sharing
• Formal networks: “the planned structure for an
organization”(Simon, 1976, p.147)
- Formal ties are designed or mandated by corporate management
(Monge & Contractor, 2001)
- Not just the org chart, also includes ‘quasi-structures’ such as
committees, task forces, teams and other workflow relations
mandated by the firm (Schoonhoven & Jellinek, 1990)
- Instrumental component stronger than affective component (Ibarra,
1993)
- Builds shared understanding (Gabarro, 1990; Tiwari, Koppius & van Heck, 2011)
and relative absorptive capacity (Lane & Lubatkin, 1998) as basis for more
complex knowledge transfer
Source: BJoM - Aalbers, Dolfsma & Koppius 2013

40. 41
Multiplex ties matter for knowledge sharing
• Multiplexity: Combination of multiple relational contents in a
single tie (Burt 1983; Ibarra, 1993; Rank et al., 2010)
- Ties in an organization are not either formal or informal, many
are a combination of the two, i.e. multiplex ties. (Gulati & Puranam,
2009)
- Multiplex ties are qualitatively different: more intimate (Minor,
1983), more stable (Ibarra, 1995), reduce uncertainty (Albrecht & Ropp,
1984), more supportive (McAllister, 1995) and improve performance
(Roberts & O’Reilly, 1989)
- Multiplex ties create transfer synergy between willingness and
ability: shared understanding from formal ties (ability) and trust
from informal ties (willingness) (Hansen, 2001)
Source: BJoM - Aalbers, Dolfsma & Koppius 2013

41. When studying networks in knowledge sharing, we
need to be aware about what is really driving the
results...
• Formal networks matter at least as much as informal
networks
• Multiplex ties matter much more than just formal or
informal ties
• Most results ascribed to informal networks should
probably be ascribed to multiplex networks instead
42
M
Pure
F
Pure
I
Source: BJoM - Aalbers, Dolfsma & Koppius 2013

42. Measurement 1 – Summer time
Innovation Innovation
Measurement 2 – Winter time
Source: Aalbers 2012
An example of network intervention – network
expansion at a financial services firm

43. Wrap up part 1 – core concepts and relevance
» Social network analysis is a different way of looking at
organization structures
» Networks exist on different levels, which intertwine – thereby
creating different layers to analyse and influence an
organisations performance
» Network analysis can help in multiple contexts, including
R&D/ innovation, process redesign and reorganisations
» Network modeling helps in simplifying complex
relations
» Different modes of analysis can be identified;
including roles, behavior, clustering, and affiliation
» Measuring the behavior of a network requires both statistic as
well as organisational process knowledge
» A common methodology is needed to secure an objective analysis
» Networks can be altered – governing is an option
» SNA is fun!

44. r.aalbers@fm.ru.nl

45. Objective of today – Part 2
• Introduction to social network analysis,
including:
• Relevance
• Core concepts
• Core methodology
• Main tools and visualization (Ucinet)
• Large online networks
• Reflection on future research possibilities
• Wrap up
46

46. Propositions for discussion
47
Certain network positions offer an advantageous opportunity
structure, but whether this opportunity is seized, depends on the
motivation of the actor (Burt, 2010)