knowledge and collaboration networks

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?