Technology Licensing Performance and Strategy of US Research Institutions

Jisun Kim, Tuğrul Daim () and João Ricardo Lavoie

Chapter Chapter 20 in R&D Management in the Knowledge Era, 2019, pp 531-549 from Springer

Abstract: Abstract This study aims to develop institutional strategies improving licensing practice of academic research institutions based on understanding licensing performance and influencing institutional characteristics. The study resulted in a new approach that integrated the steps of identification of time lags in licensing, efficiency change analysis, and exploration of the influence of organizational characteristics on the efficiency change. A super-efficiency variable returns-to-scale DEA model was applied to the time-lag neutralized licensing data. This model measured the efficiency of US research institutions’ licensing performance over time. The study also included an innovative approach to resolve issues with the super efficiency DEA model, including mathematical infeasibility and zero data considerations. The results that are grounded on the comprehensive observations over multiple time durations provide an insight into the licensing practices of US research institutions. The recommendations for the research institutions are built on the relationships identified among academic prestige, research intensity, organizational characteristics of the technology licensing office, and licensing performance.

Keywords: Licensing performance; Licensing strategy; Technology transfer; Time-lag; Data envelopment analysis (DEA); Malmquist index; Benchmarking (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:innchp:978-3-030-15409-7_20

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DOI: 10.1007/978-3-030-15409-7_20

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