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Many firms increasingly collaborate with universities to create innovation through the commercialization of scientific knowledge. However, a variety of mismatches between a firm's interests and the value delivered by university partners (e.g., common knowledge base, strategic goals, agreements on intellectual property management, etc.) can cause rough interactions and relationships. Therefore, managers demand an effective practice to support the management of university-industry collaborations (UICs). This article builds on the previous work on a matching quality assessment model by extending its applicability to propose an analytical framework for developing a strategic roadmap to guide relationship development toward effective UIC. [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]