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
 Collaborative

networks in firms
Evolution of R&D Capabilities: The Role of Knowledge
Networks Within a Firm - Nerkar, Paruchuri
 Spillovers
firms

and collaboration in Biotech
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
Device a
model,
determine
variables
Proposition
Draw
inspiration
from existing
work
Inferences
from data
Collect the
data
Conclusions
Open Vs. Closed
Breaking it down
 What

Who can contribute
 What

is open / closed?
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?