Abstract
Original language | English |
---|---|
Pages (from-to) | 212-236 |
Number of pages | 25 |
Journal | Computational and Mathematical Organization Theory |
Volume | 22 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 2016 |
JEL classifications
- g34 - "Mergers; Acquisitions; Restructuring; Voting; Proxy Contests; Corporate Governance"
- o32 - Management of Technological Innovation and R&D
Keywords
- Alliance formation
- Bayesian learning
- Indirect partners
- Learning horizon
- Technological uncertainty
- Distance education
- Engineering education
- RESOURCE-BASED VIEW
- STRATEGIC ALLIANCES
- RESEARCH-AND-DEVELOPMENT
- MARKET VALUE
- NETWORK STRUCTURE
- ENTREPRENEURIAL FIRMS
- TECHNOLOGY ALLIANCES
- DOMINANT DESIGNS
- INTERORGANIZATIONAL COLLABORATION
- KNOWLEDGE TRANSFER
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In: Computational and Mathematical Organization Theory, Vol. 22, No. 2, 06.2016, p. 212-236.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Learning horizon and optimal alliance formation
AU - Frankort, H.T.W.
AU - Hagedoorn, J.
AU - Letterie, W.
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PY - 2016/6
Y1 - 2016/6
N2 - 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. © 2015, The Author(s).
AB - 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. © 2015, The Author(s).
KW - Alliance formation
KW - Bayesian learning
KW - Indirect partners
KW - Learning horizon
KW - Technological uncertainty
KW - Distance education
KW - Engineering education
KW - RESOURCE-BASED VIEW
KW - STRATEGIC ALLIANCES
KW - RESEARCH-AND-DEVELOPMENT
KW - MARKET VALUE
KW - NETWORK STRUCTURE
KW - ENTREPRENEURIAL FIRMS
KW - TECHNOLOGY ALLIANCES
KW - DOMINANT DESIGNS
KW - INTERORGANIZATIONAL COLLABORATION
KW - KNOWLEDGE TRANSFER
U2 - 10.1007/s10588-015-9203-z
DO - 10.1007/s10588-015-9203-z
M3 - Article
SN - 1381-298X
VL - 22
SP - 212
EP - 236
JO - Computational and Mathematical Organization Theory
JF - Computational and Mathematical Organization Theory
IS - 2
ER -