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Learning horizon and optimal alliance formation

Hans T. W. Frankort (), John Hagedoorn () and Wilko Letterie ()
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Hans T. W. Frankort: City University London
John Hagedoorn: Royal Holloway University of London

Computational and Mathematical Organization Theory, 2016, vol. 22, issue 2, No 4, 212-236

Abstract: Abstract We develop a theoretical Bayesian learning model to examine how a firm’s learning horizon, defined as the maximum distance in a network of alliances across which the firm learns from other firms, conditions its optimal number of direct alliance partners under technological uncertainty. We compare theoretical optima for a ‘close’ learning horizon, where a firm learns only from direct alliance partners, and a ‘distant’ learning horizon, where a firm learns both from direct and indirect alliance partners. Our theory implies that in high tech industries, a distant learning horizon allows a firm to substitute indirect for direct partners, while in low tech industries indirect partners complement direct partners. Moreover, in high tech industries, optimal alliance formation is less sensitive to changes in structural model parameters when a firm’s learning horizon is distant rather than close. Our contribution lies in offering a formal theory of the role of indirect partners in optimal alliance portfolio design that generates normative propositions amenable to future empirical refutation.

Keywords: Technological uncertainty; Alliance formation; Bayesian learning; Learning horizon; Indirect partners (search for similar items in EconPapers)
JEL-codes: D85 L14 O32 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10588-015-9203-z

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