knowledge and collaboration networks
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Knowledge and Collaboration Networks. CS 8803 – Networks and Enterprises. Agenda. Basic overview Open Vs. Closed networks Collaborative networks in universities A resource based view on the interactions of university researchers – Rjinsoever , Hessels , Vandeberg - PowerPoint PPT PresentationTRANSCRIPT
Knowledge and Collaboration NetworksCS 8803 – Networks and Enterprises
Agenda Basic overview Open Vs. Closed networks Collaborative networks in universities
A resource based view on the interactions of university researchers – Rjinsoever, Hessels, Vandeberg
Collaborative networks in firms Evolution of R&D Capabilities: The Role of Knowledge
Networks Within a Firm - Nerkar, Paruchuri
Spillovers and collaboration in Biotech firms Knowledge Networks as Channels and Conduits: The
Effects of Spillovers in the Boston Biotechnology Community – Owen –Smith, Powell
Comparison of collaborative networks in Universities Vs. Industries
Collaborative networks What are collaborative networks ? Is this pertinent to any of us ? What do we gain in understanding the
dynamics of these networks?
The process
Proposition
Draw inspiration
from existing
work
Device a model,
determine variables
Collect the data
Inferences from data
Conclusions
Open Vs. Closed
Breaking it down What is open / closed?
Who can contribute What is hierarchical / flat?
Who decides what to work on and which solution to choose
Which one is best?
Case studies Alexi furniture firm Linux IBM Innocentive.com iPhone app
Takeaways Choose the model based on –
Problem domain Availability of experts
Combine models when appropriate Change models as problem / firm
evolves
Collaborative networks in Universities
Paper discussion Isn’t this field old, why write a paper
about it in 2008? How is this different from old papers?
What were the contributions ? What is the main motivating factor?
How does it affect scientists ? What was their method of data
collection ?
Research model
Thoughts Was their method of data collection
successful ? Did they cover all the possible data sets? How did the variables influence each
other ? Some findings were intuitive, did you
find any that was not ? What were the limitations of the paper?
Takeaways Increase Academic rank by faculty and
external networking Matthew effect is present in networks Help younger faculty establish networks
and ensure older faculty maintain theirs Hire both adapters and innovators
Collaboration in industries
Paper discussion What was their method of data
collection ? What factors affect the selection of an
idea? How did they model the data ? Was this
the right approach ?
Hypotheses Hypothesis 1 : Centrality of an inventor in an
intraorganization knowledge network will be positively associated with the likelihood of his knowledge being selected by other inventors.
Hypothesis 2 : The extent of structural holes spanned by an inventor in an intraorganizational knowledge network will be positively associated with the likelihood of their knowledge being selected by other inventors.
Hypotheses Hypothesis 3 : The relationship between the
centrality of an inventor in an intraorganizational knowledge network and the likelihood of her knowledge being used by other inventors is positively moderated by the extent to which this inventor spans structural holes in the network.
Independent, Control variables Centrality Spanning
structural holes
Calendar Age Patent Age Scope of Patent Claims Age of prior art Self citation Number of patent References Academic references Team size International presence Time to grant Year effects Technological controls
Thoughts / Takeaways Centrality and spanning of structural
hole has positive effect on propagation of an individual’s idea
Inventors shape the capabilities of the firm
Socioeconomic view of R&D capabilities of a firm
Possible limitations ?
Spillovers and collaboration in Biotech firms
Spillovers Why map knowledge sharing to
plumbing? How do spillovers help a community ? Conduits Vs. Leaks
The “wh” questions Why was the biotech industry chosen? Was there prior work which was based
on the biotech industry, did they yield concrete results?
What was this paper’s distinguishing factor ?
Why Boston ? Where did they get the data from ?
Propositions Proposition 1: Membership in a geographically
colocated network will positively effect innovation, but centrality in the same network will have no effect.
Proposition 2: Centrality in a geographically dispersed network will positively effect innovation, but membership per se will have no effect.
Proposition 3: In networks dominated by PROs, membership will positively effect innovation, but centrality will have no effect.
Proposition 4: In networks dominated by commercial entities, centrality will positively effect innovation, but membership will have no effect per se.
Independent/ control variables Membership Position (Centrality) Time periord
Public Age Age(square) Log(size) R&D ties - PRO Ties to NIH PRO x NIH ties
Takeaways Geographic propinquity and institutional
characteristic of key members of network transforms the way in which an organization's position translates into it’s advantage
Flow of information depends on density of network and the presence of “leaks”
Legal arrangements/ disclosure terms are a consequence of the network’s characteristic (open / closed)
Proprietary arrangements dominate once the networks stabilize
Comparing the papers
Which paper did you like the most ? Which method of data collection was most
accurate ? How did the authors select the variables? Did
they add new variables ? How are collaborative networks in universities
different from those in industries? Which have better innovation? Are these results pertinent to today’s
landscape?