Academic stars and licensing experience in university technology commercialization

Abstract

Abstract Research Summary We examine the process by which university technology transfer offices (TTOs) allocate internal resources, which provides insight into technologies offered for commercialization to the private sector. Using detailed administrative records of patenting decisions and outcomes by one prominent U.S. research‐based university's TTO over a 30‐year period, we analyze the performance of invention disclosures by academic stars and by inventors with prior licensing experience. We find that the lead inventor's academic prominence (but not licensing experience) predicts patent application filing, while licensing experience (but not academic prominence) predicts patent impact and commercialization success. We supplement this descriptive analysis with follow‐up interviews and empirical evaluation of possible mechanisms for this seemingly outsized role of academic stars. Managerial Summary Increasingly prevalent hybrid organizations are expected to fulfill multiple objectives. University technology transfer offices (TTOs) are an example of a hybrid organization and are charged with disseminating academia‐originated technology for the public good as well as for economic benefit. We study how one TTO allocates internal resources, using comprehensive in‐house administrative data over a 30‐year span. We find that while the TTO tends to put resources behind inventions by academic stars, the commercial returns from licensing inventions from such individuals are no different than inventions by non‐stars. By contrast, inventors' prior licensing experience highly predicts commercial returns. These results illustrate the challenges inherent in internal resource allocation inside a complex hybrid organization.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 35 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.