Collective learning in clusters: Mechanisms and biases

Although collective learning has long been considered a core feature of successful clusters, many researchers have treated the concept of learning more as a metaphor than a construct that requires an understanding of the various processes and mechanisms involved. I draw on the cultural-evolutionary...

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Bibliographic Details
Published in:Entrepreneurship and regional development 2009-09, Vol.21 (5-6), p.553-573
Main Author: Staber, Udo
Format: Article
Language:English
Subjects:
Autonomy
Beliefs
Bias
Business management
cluster
Cluster analysis
Clustering
Cognitive style
Collaboration
Collective behaviour
collective learning
Cultural evolutionism
Data analysis
Enterprises
evolutionary theory
Germany
Imitation
Learning
Organizational behaviour
Organizational learning
Owners
Small and medium sized enterprises
Small business
Studies
Western Europe
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Summary:Although collective learning has long been considered a core feature of successful clusters, many researchers have treated the concept of learning more as a metaphor than a construct that requires an understanding of the various processes and mechanisms involved. I draw on the cultural-evolutionary perspective to argue that learning in clusters is an inherently biased process, with outcomes that can be both functional and dysfunctional. The cultural-evolutionary approach views learning as a process of imitation, treats beliefs as the unit of selection, and considers individuals as agents who are limited in their cognitive capabilities and social autonomy. Using interview data on 62 small business owners and 34 institutional actors in a textile and a surgical instruments cluster in South-west Germany, I show that the learning process can involve social biases which, in these cases, have the effect of reproducing a collective mindset built on distrust and rivalry. The findings provide an explanation for the fact that many studies of clusters have not been able to document the high levels of interfirm collaboration that cluster theory predicts.
ISSN:0898-5626
1464-5114
DOI:10.1080/08985620802529526