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This paper examines the relationship between time and performance in the commercialisation of university-originated inventions. Focusing on the commercialisation time, defined as the time taken for a lab-scale invention to be developed for commercial sale, we investigate whether there is any systematic association between commercialisation time and the licensing revenue generated by university-originated inventions. Analysing a sample of inventions from Stanford University, we find that both pre- and postlicensing time of commercialisation are negatively associated with its licensing revenue. We also find that the negative relationship between the postlicensing time of commercialisation and licensing revenue is contingent on the inventor's orientation towards university-industry collaboration. We discuss theoretical and practical implications of our findings as well as future avenues of research in technology commercialisation. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the timing of patent rights on licensing performance in the context of university technology licensing. We provide an argument that the timing of patent allowance, i.e., a legal event that clarifies patent rights, in the technology licensing phase can alter licensing performance by shifting technology valuation, as well as behavioral dynamics, in the process of commercialization. By analyzing inventions disclosed from Stanford University, we find that preallowance licensing (i.e., entering a licensing arrangement prior to patent allowance), compared to postallowance licensing, is negatively associated with licensing performance by increasing the chances of licensing underperformance. However, licensing underperformance associated with preallowance licensing is only significant when the inventor and licensee do not have prior collaboration experience. This study emphasizes the importance of understanding the temporal condition of appropriability in the route for technology commercialization by revealing the patent‐licensing timing sequence as a source of variation in university technology licensing. [ABSTRACT FROM AUTHOR]

Experts have difficulty assessing the economic value of university-originated technologies due to the high level of uncertainty associated with the commercialization of early stage and basic technologies. This article proposes a random forest approach to the valuation of university-originated technologies that integrates monetary value and patent value models for technology valuation. First, a technological characteristics-value matrix was constructed after defining a total of 23 indicators from the U.S. Patent and Trademark and Scopus databases and extracting the value of university-originated technologies from technology transaction databases. Second, a random forest model, an ensemble machine learning model based on a multitude of decision trees, was employed to assess the economic value of university-originated technologies. Finally, the performance of our approach was assessed using quantitative metrics. A case study of the technologies registered in the Office of Technology Licensing of Stanford University confirms, with statistically significant outcomes, that our method is valuable as a complementary tool for the valuation of university-originated technologies. [ABSTRACT FROM AUTHOR]