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Time of adoption and intensity of technology transfer: an institutional analysis of offices of technology transfer in the United States


This paper considers the adoption of institutional innovations by not for profit organizations, an issue that can be considered in the context of the extensive literature on the adoption of technological innovation by firms. The specific institutional innovation considered is the offices of technology transfer (OTT)—the organization that assemble and disclose university innovations and negotiate and enforce licenses with users of these innovations. We propose a theoretical framework that depart from previous studies by focusing on the timing decision of institutions rather than on the percentage of institutions that adopt at each point in time. Our theoretical framework also incorporates organization theory via imitation effects on the timing of adoption. We find that number of adopters has an S-shape function of time, which may indicate a strong element of imitation led universities to create OTTs. We also find that universities with higher research incomes and rankings were earlier adopters of the OTT model and that universities with medical schools were generally late adopters. Finally, we find that the number of universities who have already adopted the OTT model increases the speed by which other non-adopters make their OTT adoption decisions and that the number of invention disclosures, a primary indicator of output of OTTs, increases with the size of research budget, is smaller for those with medical schools, and larger for those that were earlier adopters of OTT. Section 1 of the paper discusses the recent trends in technology transfer while Section 2 reviews the advent of OTTs as facilitators of technology transfer activities. Section 3 reviews the relevant technology and institutional innovation literature. Section 4 develops a conceptual framework that links Sections 2 and 3 to analyze the advent and timing of the establishment of OTTs. Section 5 estimates the time of adoption of the OTT working model on the part of major research universities in the US, and analyzes the impact of time of adoption of the OTT model on the intensity of the technology transfer process. Section 6 presents empirical results while the conclusions and policy implications are discussed in Section 7.

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Fig. 1


  1. 1.

    In this section the terms “University” and “Research Institution” are used interchangeably.

  2. 2.

    Other authors have suggested that what the Bayh–Dole Act has done is to add layers of administrators that have not necessarily resulted in an easier commercialization of ideas process (Litan et al. 2007).

  3. 3.

    Offices of technology transfer are also called offices of technology licensing (OTLs), sponsored project offices (SPOs) and office of industrial relations (OIRs) among others. In this analysis, we will refer to all of them as OTTs.

  4. 4.

    Mimetic Isomorphism has been defined as “Conformity through imitation” (Haveman 1993). Other types of isomorphism are Coercive—when organizations are compelled to adopt organizational structures and Normative—when organizations adopt certain behavior because leaders claim they are superior (DiMaggio and Powell 1983).

  5. 5.

    Our empirical analysis supports this argument by showing that the differences in quality measured by ranking of different fields explains the timing of adoption of OTT only for certain levels of ranking.

  6. 6.

    This modeling approach requires that faculty quality x be distributed across all N university such that \( \int\limits_{1}^{N} {f\left( x \right)dx} = 1 \).

  7. 7.

    Note that Eq. (3) was defined previously.

  8. 8.

    Note that in our statistical analysis we use year as our regressor. In essence, year of OTT establishment and age of OTT are equivalent. We use age in Table 3 because, in our view, summary statistics are easier to read for OTT age than for year of OTT establishment.

  9. 9.

    Not also that there is a long tradition of relationship between universities with medical schools and the private sector, which is another factor that may reduce the need for the establishment of an OTT in a formal way even tough technology transfer was taking place.


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The authors knowledge comments by Bruce McWilliams, Marcos Adamson and participants at the Seminar “Universities and Technology Transfer in Costa Rica” at the University of Costa Rica, March 2008 as well as attendees at a seminar talk by Federico Castillo at the Hebrew University in Rehovot, Israel in March, 2012. We also thank useful comments by the anonymous reviewers. Federico Castillo gratefully acknowledges financial support from the Ciriacy-Wantrup Post-Doctoral Fellowship at the University of California, Berkeley.

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Correspondence to Federico Castillo.

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Castillo, F., Gilless, J.K., Heiman, A. et al. Time of adoption and intensity of technology transfer: an institutional analysis of offices of technology transfer in the United States. J Technol Transf 43, 120–138 (2018).

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  • Adoption
  • Institutional innovation
  • Heterogeneity
  • Mimetic isomorphism

JEL Classification

  • O32
  • O33