2014.02.26 Network Data Analytics ..Organizing intra-organizational networks for innovation
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2014.02.26 Network Data Analytics ..Organizing intra-organizational networks for innovation

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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 ...

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

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2014.02.26 Network Data Analytics ..Organizing intra-organizational networks for innovation 2014.02.26 Network Data Analytics ..Organizing intra-organizational networks for innovation Presentation Transcript

  • 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
  • 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
  • Rick Aalbers, Phd 3 Research agenda: Reorganization & Innovation
  • 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 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
  • 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)
  • 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)
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  • Source: Aalbers and Dolfsma 2014 Networks of innovation (intra firm)
  • An example of a modern network: 9-11 Hijackers Network SOURCE: Valdis Krebs http://www.orgnet.com/
  • 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
  • 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
  • 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 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
  • 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 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
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  • 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)
  • 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
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  • 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 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 • 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?
  • 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 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
  • 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
  • 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
  • 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
  • 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)
  • The innovation network Source: Aalbers, Dolfsma & Koppius 2014 So we got the network(s) and the key concepts... Now what?
  • Combining network methodology into relevant propositions Possible angles Hierarchy Diversity Multiplexity • Actor attributes • Brokerage • Longitudinality • Interventions • Multi level networks 32
  • 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
  • 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
  • Fosteringdiversity Fostering support Horizontal cross ties Verticalcrossties Delivery of innovative project outcomes Source: JPIM - Aalbers, Dolfsma & Leenders 2015
  • Figure 1: Horizontal cross-ties Figure 1A (under-performing) Figure 1B (performing) = Conceptual projectteam composition Source: JPIM - Aalbers, Dolfsma & Leenders 2015
  • Figure 2: Hierarchical cross-ties Figure 2A (under-performing) Figure 2B (performing) = Conceptual projectteam composition Source: JPIM - Aalbers, Dolfsma & Leenders 2015
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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!
  • r.aalbers@fm.ru.nl
  • 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
  • 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)