Ce diaporama a bien été signalé.
Le téléchargement de votre SlideShare est en cours. ×

Social Network Analysis for Competitive Intelligence

Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Chargement dans…3
×

Consultez-les par la suite

1 sur 18 Publicité

Social Network Analysis for Competitive Intelligence

Télécharger pour lire hors ligne

How can CI teams apply the concepts of social network analysis to gain insight into the capabilities and plans of their competitors? Presented by Jim Richardson and August Jackson in April 2007 at the Society of Competitive Intelligence Professionals annual conference in New York City.

How can CI teams apply the concepts of social network analysis to gain insight into the capabilities and plans of their competitors? Presented by Jim Richardson and August Jackson in April 2007 at the Society of Competitive Intelligence Professionals annual conference in New York City.

Publicité
Publicité

Plus De Contenu Connexe

Diaporamas pour vous (20)

Les utilisateurs ont également aimé (19)

Publicité

Similaire à Social Network Analysis for Competitive Intelligence (20)

Plus par August Jackson (16)

Publicité

Plus récents (20)

Social Network Analysis for Competitive Intelligence

  1. 1. Social Network Analysis for CI Jim Richardson Trident Systems August J. Jackson Verizon Business
  2. 2. SNA Maps Relationships for Predictive Analysis A SOCIAL NETWORK is a map of the relationships between individuals, organizations and other relevant NODES indicating the ways in which they are connected by financial, social or professional LINKS. SOCIAL NETWORK ANALYSIS applies visual and statistical analysis of mapped social networks to evaluate the structure and nature of the system. SOCIAL NETWORK ANALYSIS delivers value to Competitive Intelligence, though modification of concepts from fields of sociology and knowledge management is needed.
  3. 3. All Networks Are Collections of Nodes and Links Nodes Links
  4. 4. Traditional SNA SNA for CI IPO Only “cognitive agents” can be nodes: People Organizations (maybe) Nodes can include technologies, products, competencies, markets, etc
  5. 5. SNA for CI Requires Defined Relationships Supplier Distributor Joint Venture Equity Relationships Financing R&D Partnership Marketing Partnership Pending M&A Executive Employment Board Service Major Equity Ownership Education Membership Familial Direct Professional Co-authors, Co-Presenters Identifiable direct social connections Defining relevant magnitudes, frequencies or other attributes of linkages is important.
  6. 6. Traditional SNA SNA for CI Tolerant of subjective inter-personal linkages Dependent upon formalized relationships We’re best friends. I can’t stand that guy. CEO Former Coworkers
  7. 7. Traditional SNA SNA for CI Value is based on statistical analysis of linkages to understand nature of interactions among groups of people Value is based on deriving business “so what” from linkages to drive decisions
  8. 8. CEO CEO Acquired Acquired Board Member Distributor CEO Board Member IPTV Competency Competency Competency Product Service Provider WHAT COULD COME OF THIS?
  9. 9. “So What?” of SNA Concepts: Connectedness <ul><li>An individual with a broad professional network-- Gladwell’s “connector” </li></ul><ul><li>An individual researcher connected to multiple patents </li></ul>
  10. 10. “ So What?” of SNA Concepts: Reachability <ul><li>How strong is a firm’s distribution network? </li></ul><ul><li>An individual’s professional network? </li></ul>3 2 1
  11. 11. “ So What?” of SNA Concepts: Betweenness <ul><li>An industry bottleneck </li></ul><ul><li>An individual who connects entrepreneurs with financing </li></ul>
  12. 12. “So What?” of SNA Concepts: Density <ul><li>A high degree of “coopetition” </li></ul><ul><li>Employees regularly move among firms </li></ul>High density low density
  13. 13. Mapping Social and Economic Capital in the UK <ul><li>Hypothesis: interlocking board memberships were a mechanism to neutralize potential threats from suppliers, distributors, etc. to reduce constraints on the firm when dealing with monopoly or oligopoly firms </li></ul><ul><li>Survey of interlocking board memberships in 250 largest UK companies in late 1970s </li></ul><ul><li>Highlighted the importance and attraction of interlocking relationships with sources of finance </li></ul>Burt, R.S. (1979) ‘A Structural Theory of Interlocking Corporate Directorates’, Social Networks, 1.
  14. 14. Traditional SNA SNA for CI Collection method is surveys, record of phone or e-mail communications Collection is based primarily on secondary research
  15. 15. Rudimentary Social Network Generated Using Text Analysis
  16. 16. Steps to Implementing SNA <ul><li>Have a thorough understanding of your analytical goal </li></ul><ul><li>Simplify to focus on most relevant and obtainable data </li></ul><ul><li>Clearly identify relevant notes and relationships </li></ul><ul><li>Develop a data schema that reflects relevant attributes </li></ul><ul><li>Create a data collection plan </li></ul><ul><ul><li>Identify surrogate data for that you need but cannot capture directly </li></ul></ul>
  17. 17. Steps to Implementing SNA <ul><li>Determine the technical methods for </li></ul><ul><ul><li>Data collection </li></ul></ul><ul><ul><li>Data storage </li></ul></ul><ul><ul><li>Visualization </li></ul></ul><ul><ul><li>Statistical analysis </li></ul></ul><ul><li>Design visualization criteria which will convey the appropriate conclusions based on analytical goals </li></ul><ul><li>Engage customers of analysis in interim review of efforts to maximize comprehension </li></ul>
  18. 18. Contact Details V. Jim Richardson [email_address] August J. Jackson [email_address]

Notes de l'éditeur

  • Connectedness: The number of connections that any node in a network has to other nodes
  • Reachability: the ease with which one node can connect to another through intermediate nodes.
  • Betweenness: the degree to which a single node is an interim step connecting some other number of nodes. The red node has a high degree of betweeness – this is a measure of constraint as much as a measure of connectedness
  • Density: the degree to which each node in the network is connected to every other node in the network.

×