Back to Search Start Over

Technology licensing contract design considering royalty transparency and demand information asymmetry with downstream co-opetition.

Authors :
Li, Xiufeng
Li, Bo
Xing, Ruxiao
Source :
Expert Systems with Applications. Mar2024:Part D, Vol. 238, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

This paper studies how technology suppliers can optimally design licensing contracts when there is incomplete information on the licensee's demand potential for the product in a high-technology supply chain. A licensing contract typically contains an up-front payment and royalties on product sales. The licensor decides to license one technology to a licensee, which designs products based on the supplier's technology and outsources the product manufacturing to a competitive downstream manufacturer. We apply principal-agent models to formulate the licensor's contracting problem while considering the transparency of the licensing royalty and find that under demand information asymmetry, the optimal contract structure changes with the demand potential. More specifically, both the separating and pooling strategies can be the optimal strategies for the licensor under the observable contract case. Conversely, under the unobservable case, only the separating strategy is the optimal licensing strategy. And the licensor can always charge zero royalty to a high-type design firm regardless of the menu strategy or the single contract. Furthermore, we examine the effect of licensing royalty transparency on licensing decisions and firms' profits and highlight the effect of downstream competition on the licensor's profit. Our results show that the licensor can be better off in the observable contract case. By contrast, the design firm may be better off in the unobservable contract case. We provide a rationale for the licensing contract design under demand information asymmetry and further shed light on the choice between public and secret licensing terms. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09574174
Volume :
238
Database :
Academic Search Index
Journal :
Expert Systems with Applications
Publication Type :
Academic Journal
Accession number :
173706143
Full Text :
https://doi.org/10.1016/j.eswa.2023.122183