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assigned to single patents, then the knowledge had been successfully integrated and
was applicable afterward without further collaboration. Used in conjunction with
this procedure, the binary variable TransKnowledge indicates whether knowledge
has been exchanged in prior collaborations. This variable takes the value 1 if either
partner has gained new knowledge; otherwise it takes the value zero. That is, the
variable captures both symmetric and asymmetric learning.
Our three measures of cognitive proximity—RelOverlap, ReciPot, and
TransKnowledge—do not develop independently of each other. Their changes over
time go hand in hand. Figure 16.1 illustrates the dynamics of these three variables.
Two actors, I and II, hold specific knowledge portfolios before cooperating with
each other (precollaboration). Actor I’s portfolio comprises ABCDEF; actor II’s,
ABGH. The knowledge overlap in t-1 is given by AB and amounts to .2, relative to
the overall knowledge. The reciprocal potential equals .5 because actor II possesses
two knowledge units that actor I can gain as opposed to four knowledge units that
actor II might be able to acquire from actor I. In other words, actor I can gain at most
only half the amount of knowledge that actor II, the partner, stands to gain.
Formulated differently, actor II can earn twice the amount of new knowledge that is
being offered to actor I. In this example, the potential gains are unequal. Assume
that collaboration then leads to symmetric learning in that C and G are exchanged.
Actor I’s postcollaboration portfolio is thereby enlarged to ABCDEFG; actor II’s, to
ABCGH. As a result, the overlap has increased to ABCEG and amounts now to .3
in relation to the overall knowledge possessed by the two firms. In turn, the ratio
between the potential knowledge gains has decreased to .3 because actor II now
offers only one new knowledge unit to actor I, whereas actor I now offers three
knowledge units to actor II. The potential for knowledge flows has thus decreased
and become more uneven. The attractiveness of this fictive alliance and the likeli-
hood that it will continue have therefore declined. This example illustrates the case
of knowledge having been efficiently exchanged. When actors collaborate but are
unable to integrate new knowledge into their stock, then knowledge has only been
shared and the collaboration is more likely to continue than if they are able to inte-
grate the new knowledge. In this sense, a continuation of collaboration can be inter-
preted as a failure to learn (Hamel, 1991).
Social Proximity Between the Cooperation Partners
To test whether the probability for the creation or re-creation of a link increases with
the social proximity between the partners (hypothesis 2), we included a variable for
common experience, CoopExp, as a proxy for social proximity. CoopExp measures
how often the pair was cooperating prior to the cooperation in question. The number
of prior research projects with the partner is commonly used as a measure of the
strength of the tie and is assumed to capture the trust and ease of communication
between the partners (Cantner & Meder, 2007).
16 Coevolution of Innovative Ties, Proximity, and Competencies
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book Knowledge and Networks"
Knowledge and Networks
- Title
- Knowledge and Networks
- Authors
- Johannes Glückler
- Emmanuel Lazega
- Ingmar Hammer
- Publisher
- Springer Open
- Location
- Cham
- Date
- 2017
- Language
- German
- License
- CC BY 4.0
- ISBN
- 978-3-319-45023-0
- Size
- 15.5 x 24.1 cm
- Pages
- 390
- Keywords
- Human Geography, Innovation/Technology Management, Economic Geography, Knowledge, Discourse
- Category
- Technik