Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36426
Title: Do licensors learn from out-licensing? Empirical evidence from the pharmaceutical industry
Authors: KELCHTERMANS, Stijn 
LETEN, Bart 
Rabijns, Maarten
Riccaboni, Massimo
Issue Date: 2022
Publisher: ELSEVIER
Source: Technovation (Print), , p. 102405 (Art N° 102405)
Abstract: This paper starts by observing that many licensing contracts contain explicit organizational arrangements for transferring the licensed technology, involving repeated and close interaction between the licensing partners. We argue that these interactions provide opportunities for the licensor to learn from the licensee. Using data on 1861 licensing deals of 254 pharmaceutical and biotech firms between 1995 and 2015, we show that licensors are more likely to cite the inventions from their licensing partner after an out-licensing deal than matched control firm-pairs that do not engage in licensing. The paper makes the following contributions: first, it demonstrates that not only licensees but also licensors can learn from licensing and that this reverse learning stems from the licensor-licensee relation. Second, it shows that firms can learn from directed outward knowledge transfers rather than non-deliberate knowledge spill-outs. Third, we show that the licensor's post-licensing behavior vis-à-vis the licensee reflects targeted learning by tapping into the most valuable components of the licensee's technology portfolio and those new to the licensor. Finally, the paper extends the theoretical framework behind strategic out-licensing decisions. We show that learning from out-licensing is an additional (positive) element in the trade-off faced by licensors in addition to short-term revenue generation and the risk of long-term rent dissipation.
Keywords: Licensing;Markets for technology;Firm learning;Pharmaceutical industry
Document URI: http://hdl.handle.net/1942/36426
ISSN: 0166-4972
e-ISSN: 1879-2383
DOI: 10.1016/j.technovation.2021.102405
ISI #: 000784359200011
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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