2026-09-19T10:28:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/285932022-01-31T20:11:14Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Fiona E. Murray and R. Rox Anderson.
Wehby, Richard George, 1957-
Harvard University--MIT Division of Health Sciences and Technology.
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Engineering Systems Division
Technology and Policy Program
2005-09-27T17:12:40Z
2005-09-27T17:12:40Z
2004
2004
http://hdl.handle.net/1721.1/28593
57509147
Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2004.
Vita.
Includes bibliographical references (leaf 72).
This thesis is part of a larger body of research being undertaken by Dr. Fiona Murray and colleagues examining value creation and sharing between and among the three principal players in the commercialization of academic biomedical research: universities, biotech firms, and big pharma. The Recombinant Capital database provided access to contracts for biomedical technology licensed from academe to biotech, and also subsequent contracts that included that same technology from biotech to big pharma. These two contracts comprise a contract "pair". Importantly, these contract "pairs" were unredacted, that is., all parts of the contracts, including the commercial terms, were available. This thesis will lay the foundation for later work by examining the contracts between university and biotech, from the University's point of view. The goal is to identify factors that give the university more power in a pricing negotiation, and that predict higher economic value for the contract. The Specific Aim is to determine if certain University factors have a significant effect on predicting the economic value of the university-biotech licensing agreement. Four groups of readily quantifiable factors that contain attributes that might add power to the University in its pricing negotiation with the Biotech firm were identified: Institutional factors, Single Inventor factors, Aggregate factors, and Invention factors. The hypothesis is that at least one of these factors will have a significant effect on predicting the value of the licensing agreement, as determined using ordinary- and multiple-linear regression models. In formulistic terms, the null- and test-hypotheses are: (HO) no factor has a significant effect on predicting economic value, and (HI) at least one
(cont.) one factor has a significant effect on predicting economic value. A multiple regression model of the factors as explanatory variables for the economic value of the license revealed that two independent university factors significantly predict economic value of the contract. These combined factors account for 64% of the variance of the dependent variable (in excess of control), and have coefficients that are significant (p < 0.001). The results are discussed in the context of its importance to university technology transfer officers, biotech firms and venture capitalists.
by Richard George Wehby.
S.M.
74 leaves
4039582 bytes
4047132 bytes
application/pdf
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en_US
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Technology and Policy Program.
Harvard University--MIT Division of Health Sciences and Technology.
Patents and licensing and the commercialization of academic biomedical research
Thesis
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<Abstract>(cont.) one factor has a significant effect on predicting economic value. A multiple regression model of the factors as explanatory variables for the economic value of the license revealed that two independent university factors significantly predict economic value of the contract. These combined factors account for 64% of the variance of the dependent variable (in excess of control), and have coefficients that are significant (p < 0.001). The results are discussed in the context of its importance to university technology transfer officers, biotech firms and venture capitalists.</Abstract>
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