Innovation speed: Transferring university technology to market

Research Policy 34 (2005) 1058–1075

Innovation speed: Transferring university technology to market Gideon D. Markman a,∗ , Peter T. Gianiodis a , Phillip H. Phan b , David B. Balkin c b

a Terry College of Business, University of Georgia, Athens, GA 30602-6265, USA Lally School of Management and Technology, Rensselaer Polytechnic Institute, 110 8th St., Troy, NY 12180, USA c Leeds School of Business, University of Colorado at Boulder, Boulder, CO 80309, USA

Available online 7 July 2005

Abstract This study extends innovation speed theory by empirically linking the antecedents and outcomes of technology commercialization at universities. Assessing university technology transfer offices (UTTOs) in the U.S., we found that the faster UTTOs can commercialize patent-protected technologies, the greater their licensing revenues streams and the more new ventures they spin off. Furthermore, using commercialization time as a surrogate for innovation speed, we identify several determinants of speed. That is, UTTO resources and the competency in identifying licensees are related to commercialization time. Also, the participation of faculty-inventors in the licensing process is a critical determinant of commercialization time. Illustrating that innovation speed is an antecedent of performance as well as a desired outcome in and of itself, provides support for innovation speed theory. © 2005 Elsevier B.V. All rights reserved. Keywords: Innovation speed; Technology commercialization

1. Introduction In the literature on innovation, the elapsed time between an initial discovery and its commercialization is defined as innovation speed (Kessler and Chakrabarti, 1996). According to Sonnenberg (1993), innovation speed is a capability that, when combined with core processes, can yield significant competitive advantage for a firm. The purpose of this study is to extend innovation speed theory by: (a) assessing links ∗ Corresponding author. Tel.: +1 706 542 3751; fax: +1 706 542 3743. E-mail address: [email protected] (G.D. Markman).

0048-7333/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.respol.2005.05.007

between commercialization time of patent-protected technologies and organizational-level outcomes such as licensing revenues and new-venture creation; and (b) identifying various determinants of innovation speed. Innovation speed theory is inherently related to time. For example, because innovation is subjected to rapid depreciation, time is regarded as a scarce resource (Lawless and Anderson, 1996; Parkinson, 1957; Taylor, 1911). Therefore, by accelerating the pace of successful innovation, organizations are able to fully leverage research-related assets, amortize the costs of research projects across more successful introductions of new products, and thus maximize profit (McEvily et al., 2004). Unfortunately, and despite the intuitive

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importance of innovation speed, much of the research on this topic has relied on case studies and anecdotes. For example, the concept of the innovation “life cycle” is relatively under-developed, which makes it difficult for researchers to operationalize ‘speed’ in a consistent manner (Brown and Karagozoglu, 1993). Further while some research has touted the benefits of time compression for cost and product quality (Meyer, 1993; Rosenthal, 1992; Takeuchi and Nonaka, 1986; Wheelwright and Clark, 1996), others report opposite effects (Carmel, 1995; Crawford, 1992; Smith and Reinertsen, 1991). In short, “so far we have seen too much specious reasoning and hoopla and not enough hard data” (Crawford, 1992: 97). In part, such assessments are due to under-developed theory, insufficient understanding of the factors that explain and predict differences in innovation, imprecise conditions under which speed jeopardizes quality, and underspecified factors that differentiate fast from slow innovation processes. More fundamentally, the operationalization of innovation speed—ow to measure the construct and its antecedents (Kessler and Chakrabarti, 1996)—is unclear, which can result in imprecise theoretical frameworks. For instance, in exploring the competitive repercussions of speed, many models have ignored the impact of environmental and organizational factors (Kessler and Chakrabarti, 1996). Research on innovation and speed is grounded in two theoretical streams, economics and management. Economic perspectives have examined innovation patterns, spillovers, and dispersions across nations, industries, and sectors (Dosi, 1988; Nelson and Winter, 1977). Here, speed captures the rate at which innovation is diffused throughout populations of organizations, regions, and nations (Rogers, 1983). Management perspectives have examined innovation patterns in the context of organizational structures, processes, and competitive behaviors (Eisenhardt, 1989; Kessler and Chakrabarti, 1996; Lawless and Anderson, 1996). Here, speed refers to the rate at which discoveries are converted into rent-producing assets (Stalk and Hout, 1990). We follow a management perspective because we are interested in the association between antecedents to innovation speed and organizational outcomes. Using a sample of 91 university technology transfer offices (UTTOs) in the U.S. and viewing commercialization time of patent-protected technologies as a sur-

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rogate for innovation speed, we posit that speed would be positively related to licensing royalties and the number of university-based new ventures. We then explore the determinants and impediments of innovation speed in the context of technology commercialization processes. Our paper is organized in the following way. Section 2 explains the importance of speed in the UTTO technology commercialization process and explains why speed is important in such context. In Section 3, we extend the theory on innovation speed in our discussion of the hypothesis. Section 4 presents the methodology, Section 5 the results, and finally Section 6 concludes with a discussion of the findings, their limitations, and implications for policy and future research.

2. Literature review Because research universities function as creators and consumers of new knowledge, their societal role in value creation has become an important policy issue. According to the Coase theorem, when transactions costs are kept to a minimum, fees from licensed technologies will rise to their socially efficient levels—even in the presence of externalities. Thus, patent policies seek to encourage innovation by granting temporary, exclusive property rights to inventors and their sponsoring universities. Research has shown that royalties and the number of startups resulting from licensed technology vary significantly across universities (Markman et al., 2004b, 2005b). A university’s academic eminence, equity involvement in their startups, and royalty rewards to faculty may explain some of the variation in technology licensing outcomes (Di Gregorio and Shane, 2003). Others explanations focus on the quality of the disclosed discoveries (Jensen et al., 2004). Still others attribute the variation in royalties and the formation of new ventures to specific licensing strategies, UTTO organizational structures, and incentive systems (Markman et al. (2005a). While these and other studies1 have provided crucial insights on technology commercialization, to the best of our knowledge, the 1 See the 2002 special issue of Management Science on technology transfer and papers by Siegel et al. (2003, 2004) for more examples. Additionally, the Journal of Technology Transfer publishes diverse research topics on these issues.

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extant research has yet to address the role of time compression in commercialization processes. Addressing the issue of speed is important for theoretical and practical reasons. Theoretically, accelerated innovation speed—in our case, commercialization time—is presumed to be reciprocally tied to new wealth creation. More specifically, real options theory suggests that the value of an investment option is inversely proportional to the elapsed time between an initial investment and the start of positive cash flows (McGrath, 1999). Thus, if innovation speed can be accelerated, the option value of the R&D investment increases. Furthermore, an accelerated innovation process allows an organization to experiment with a greater number of new technologies and product features, thus spreading the costs of errors over many attempts while increasing the likelihood of successful innovations. In practice, the ability of entrepreneurs to found new firms based on university technology, as well as the proclivity of universities to license out their discoveries depend heavily on such public policies as the 1980 Bayh-Dole Act and its subsequent derivatives. Di Gregorio and Shane (2003) found that only about 12% of university-assigned inventions are transferred to private new ventures. If the purpose of such legislation is also to accelerate the yield from research assets by reducing commercialization cycle time, then empirical research on this issue should receive greater attention. Kessler and Chakrabarti (1996) indicate that policies, procedures, and protocols can systematically accelerate or decelerate knowledge and technology diffusion. For example, incentives for university departments to disclose discoveries can provide a positive impetus to the process of innovation. On the other hand, policies that restrict licensing reduce the value of a technology option and thus impede its commercialization (Markman et al., 2005a). In addition, Shane and Stuart (2002) suggest that startups based on university technology (such as Genentech, Cirrus Logic, and Lycos) tend to survive longer and are more likely to achieve Initial Public Offering (IPO) status. These examples point to the continuing importance of incubation (Clarysse et al., 2005), a notion that embeds time, a construct that has not been sufficiently explicated in the extant research models. In summary, while research has provided valuable insights on the antecedents and determinants of technology commercialization, the function and theory of

speed in innovation contexts are not yet well understood. Partly, this is because the construct of speed and its function in innovation are frequently underspecified. The problem, naturally, is that in the absence of empirical assessment of innovation speed, theory remains under-developed and measures of speed in innovation noisy. Thus, the lack of theoretical development, incomplete models, and a dearth of empirical work motivated us to explore innovation speed. We do so by conceptualizing commercialization time of patentprotected technologies as a proxy for innovation speed. We then assess a path-dependent model, where innovation speed mediates the relationships between various determinants of speed and certain organizational outcomes. The path model linking the antecedents of speed and its consequences is presented in Fig. 1.

3. A theory of innovation speed Research suggests that the ability to accelerate innovation processes can confer strategic advantage (Eisenhardt and Martin, 2000). Speed is essential as any given window for exploiting technological discoveries is constantly shrinking due to knowledge spillovers, competitors’ replication of processes and operations, and technological obsolescence that render most advantages temporary. Therefore, the ability to compress time is a unique capability that may confer a sustainable competitive advantage (Kessler and Chakrabarti, 1996; Stalk et al., 1992).2 New discoveries and technological innovations may yield shorter product life cycles and the need for accelerated technology commercialization. When speed becomes the basis for competition, rivals are compelled to seek newer sources of technological knowledge and rely on accelerated innovation to drive product differentiation and competitive advantage (Porter, 1980). The need for new sources of technology to accelerate product development may explain why organizations are increasingly turning to licensing agreements with research universities and federal labs. Such 2 Speed is not an unconditionally desirable objective. In certain industries, such as pharmaceuticals, biotechnology, genetics, and agribusiness (to name a few), innovation speed is purposely curtailed through regulatory testing and approval processes in order to ensure safety, quality and efficacy of products and services.

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Fig. 1. Path analysis

licenses confer access to technology at the point of discovery and may increase the potential for the development of newer products (George et al., 2002). For new ventures, licensing rather than internal R&D can be a more efficient way to mine and harvest a new technology; licenses can shortcut the process of discovery, reduce technology risk, and compress innovation time. Furthermore, proprietary technologies in niche markets or product categories may buffer new ventures from direct competition with large and resourceful incumbents (Gans and Stern, 2003; Markman et al., 2004a). 3.1. The consequence of innovation speed The extant research on R&D, innovation, and productivity highlights three interlocking conclusions: (a) increases in R&D yield more inventions; (b) larger numbers of inventions have a positive effect on future productivity growth; and (c) productivity growth

leads to long term economic well-being (Lawless and Anderson, 1996). The theoretical implication of including the notion of time compression is that markets tend to value earlier technological improvements at a higher level than later ones (Espina and Markman, 2005; Merges and Nelson, 1990). This holds true because, given the time value of money, earlier technological improvements result in earlier cost savings and hence economic efficiencies that can be reaped over a longer period. Also, because incremental technological innovations are based on earlier discoveries, early innovations—whether by small startups or large incumbents—will accelerate the pace at which secondand third-order innovations can be exploited. Given that innovators frequently compete on time, we hypothesize that, ceteris paribus, faster technology commercialization will be associate with more positive outcomes. Innovators that can simultaneously contain operational costs and compete on time improve their

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chances of being first to market and thus reap earlier and hence higher returns to innovation (Kessler and Chakrabarti, 1996). Innovators can gain firstmover advantages by increasing the ‘technology distance’ between themselves and subsequent entrants. Early windows to breakthrough innovations grant first movers added time to extract more profit from new products, develop next generation products and service extensions, and avoid price competition (Eisenhardt and Martin, 2000; Porter, 1980). In short, fast innovators can develop new offerings at compressed time and reap pioneering advantages (Merges and Nelson, 1990). In addition, U.S. patent laws tend to confer more value to recent, basic and novel discoveries than later, derivative ones (Espina and Markman, 2005). For instance, the ability to patent quickly and comprehensively can shield a discovery from the rentdestroying effects of imitation and work-around solutions (Shapiro, 2001). Thus, expedited licensing opportunities of patent-protected discoveries are associated with early reduction-to-practice and the creation of pioneering prototypes in innovation processes. If we conceptualize innovation speed as the elapsed time between the disclosure of a discovery and the licensing of that discovery (commercialization time), then the relationships between speed and organizational outcomes—here captured as licensing revenues and new-venture creation—might adhere to the following predictions: Hypothesis 1. Commercialization time will be negatively related to licensing revenues. Hypothesis 2. Commercialization time will be negatively related to new ventures. 3.2. Speed-related factors Although innovation is frequently described as a non-discrete and non-sequential process (Schroeder et al., 1989), if we can theoretically attach it to a specific organizational or industrial context, it can be unpacked into a collection of tasks for the purposes of empirical study and theory testing (King, 1992; Zaltman et al., 1973). For example, by examining organizational policies and processes we can pinpoint how much importance organizations attach to speed. In time-based competition, such organizational policies may include the

nurturing of a pro-speed culture, expanded scope in new product development, increased levels of investment in radical technologies, extensive use of external technology partners, adoption of time-based goals, use of a speed-oriented reward system, and so on. Thus, organizational policies and arrangements necessary to innovation are important determinants of speed. These may comprise capabilities and assets, including management skills, routines and protocols, and the collective learning that allow organizations to coordinate tasks and activities in order to increase responsiveness and flexibility (Prahalad and Hamel, 1990; Zahra and George, 2002). It is important to recognize upfront that universitybased technology commercialization processes include discovery, disclosure of discoveries to an UTTO, assessment for patentability, and eventual attempts to transfer and license IP to industry. As such, UTTOs, acting as agents for their institutions, evaluate discoveries, seek patent protection for promising technology, identify potential licensees, and monitor the licensees’ use of the technology. Each constituency in this ecosystem—faculty, UTTO, and firms—plays a different and ever changing role during this process. For instance, at the discovery and disclosure stage, research universities rely on employment contracts and an honor system that call for faculty to disclose discoveries to their UTTOs in a timely manner. This suggests that (a) disclosure and subsequent engagements with licensees depend on faculty who self-select into this process; and (b) faculty who self-select to disclose and support commercialization efforts represent only a small subset of the research faculty population. To advance theory, we study various determinants of commercialization speed, at different commercialization stages. Because during the discovery and disclosure stage UTTOs interact mostly with faculty, our discussion focuses on commercialization impediments related to faculty and UTTO’s resources. In contrast, because during advanced licensing stages UTTOs interact with both faculty-inventors and firms, we focus on the relative importance of inventors and firms, the degree of collaboration among inventors, and the competency of a UTTO to match its patent-protected technology with clients. We also assess whether speed is subject to who initiates the licensing process: facultyinventors versus UTTOs and firms versus UTTO. The following section focuses on the discovery and dis-

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closure stage, and subsequent sections focus on the transfer stage. 3.2.1. Discovery and disclosure stage: the role of faculty and UTTOs Earlier work with UTTOs suggests that during the discovery and disclosure stage both faculty and UTTOs play a key role (Markman et al., 2005a). For example, factors related to faculty such as the resistance to disclosing discoveries, indifference to licensing opportunities, and disclosing relatively poor-quality discoveries can slow down licensing process. Alternatively, lack of resources at the UTTO level such as limited budgets and administrative support may also impede commercialization speed. Which of these factors might impede commercialization more severely? Institutional theory cites faculty disengagement as a stronger impediment to technology commercialization than the resource factors. Indeed, empirical research suggests that faculty resist working with UTTOs because university policies are geared towards scholarly work, whereas licensing activity is ranked as ‘service’, which does not weigh heavily in tenure and promotion decisions (Jensen et al., 2004). This is exemplified by the 8:1 ratio between publications and patents reported by mechanical and electrical engineering departments (Agrawal and Henderson, 1991). Many licensing agreements also include ‘delay-ofpublication’ clauses, which explain why faculty might resist disclosure before their findings are published (Thursby and Thursby, 2002). As such, faculty may disengage from technology licensing processes because it conflicts with tenure and promotion policies, delays publication, and hinders professional advancement. In contrast, resource dependence theory argues that UTTO’s work may be hindered by resource constrains. For example, UTTO personnel have to evaluate many disclosures, negotiate licensing agreements with representatives of prospective and existing clients, and interact with IP attorneys and university administrators. To manage these functions well, UTTOs must rely on service-for-fee providers such as patent attorneys, technology consultants, and other external contractors. The inability to access such high-quality external resources when needed may reduce the effectiveness of UTTO’s technology management. Valid arguments, then, may be marshaled in support of each paradigm. However, because discoveries

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are the fundamental feedstock for UTTOs to begin the technology commercialization process, resource dependence theory complements the view advocated by institutional theory. That is, during early stage, limited availability of resources—in this case, quality discoveries—is a strong impediment to commercialization. Given that licensing hinges on faculty discoveries and their willingness to disclose (Owen-Smith and Powell, 2001), faculty-related factors—resistance to disclosures, indifference to licensing opportunities, and poor-quality discoveries—represent a greater impediment to accelerated commercialization than the more surmountable resource constraints such as budgets and administrative support. Hence: Hypothesis 3. During the discovery and disclosure stages, faculty-based factors will increase commercialization time more than UTTO resource-based factors. 3.2.2. Advanced commercialization stages: faculty-inventors and companies Hypothesis 3 refers to the discovery and disclosure stage of the commercialization process. Once the process advances, faculty-inventors (i.e. those who subsequently become involved with their UTTOs), can actually play a positive role in accelerating the commercialization process. This argument relates to the selfselection process discussed above; faculty-inventors who opt to participate tend to be passionate about their discoveries and enthusiastic about the likely commercial impact. This is especially salient in particular technological domains, where patenting prevents externally driven lockout from disrupting a scientist’s research program (Owen-Smith and Powell, 2001). Further, faculty-inventors can often help their UTTOs to identify and contact companies that might have a need for the new technology (Jensen et al., 2004).3 In addition to the role that faculty-inventors play, corporations that require university-based technologies also fulfill an important role in accelerating the licensing process. Firms seek university-based IP because such technology can represent the bleeding edge of 3 We use the term faculty-inventors in the context of advancedstage commercialization in order to differentiate between this group of faculty from the population of faculty at large.

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discovery and thus, the option value of the technology has the potential to increase more quickly when it can be brought to market or integrated with the firm’s own technological platform. However, early fieldwork indicates that it is challenging for UTTOs to identify and secure contacts with company representatives who might act as champions for university-based technologies.4 The reason for such difficulties is probably the asymmetric knowledge about and capability to scrutinize novel technologies between companies and universities. First, because breakthrough discoveries are untested, companies are disinclined to lock themselves into licensing agreements, some of which hinge on a commitment for additional resources to mature the technology (George et al., 2002). Second, researchintensive firms can access new technology through various modes, including internal R&D, cooperative R&D such as joint ventures or technology-based alliances and consortiums, minority investments in other firms, mergers and acquisitions, and technology licensing from commercial partners (Gans and Stern, 2003). In the language of real-options theory, university technology is merely a single ‘option’ in a company’s portfolio of technology alternatives. The larger and more diverse the portfolio, the smaller the value of a single option and therefore, the less likely a company will risk expending resources on a nascent discovery. In sum, our model suggests that faculty-inventors who are focused on their discoveries have intimate knowledge of the research surrounding the discovery but are usually less clear on commercializability. Companies, on the other hand, have a clearer understanding of commercializability but are less sure of the economic value of discoveries originating from outside their R&D labs. The uncertainty experienced by companies is increased by the fact that many discoveries require substantial additional R&D resources for further development before they can be commercialized or integrated with existing core technologies (Schilling, 1998). As such, we predict that, at least when it comes to UTTOs, faculty-inventors will play a more positive role in accelerating the licensing process when compared to the role of companies: 4

One of the authors of this paper observed how five teams of students working closely with an UTTO to identify and inform firms of ‘hot new technology’ failed to secure even a single face-to-face appointment with corporate partners after several months of legwork.

Hypothesis 4. During advanced commercialization stages, faculty-inventors will play a stronger role in reducing commercialization time than companies. 3.2.3. Advanced commercialization stages: licensing complexity and innovation speed Disclosures made to an UTTO may originate from the collaborative work of scientists who work in different institutions. Because scientific and technological discoveries co-evolve (Murray, 2002), cross-university collaborations can increase the chance for new insights resulting from spillover effects. Though useful to a discovery, such collaborations may introduce another layer of complexity to licensing and thus add time to the transfer process. This raises a question about the tradeoffs between the value of insights derived from inter-university collaboration and the costs incurred due to licensing complexity. Complexities increase when processes and protocols span organizational boundaries (Thompson, 1967). Also, the efficiency of a cooperative alliance is limited by the complexities of coordination (Galbraith, 1973). These complexities contribute to the commercialization time of innovation and increase its costs. As such, we predict that innovation speed is higher for discoveries made within the boundaries of a single institution rather than across organizational boundaries. Licensing of inter-university discoveries requires UTTOs to coordinate among a greater number of parties, with potentially divergent goals, expectations, protocols, and levels of engagement. In addition, past research warns that contracts involving multiple constituencies might adversely impact the quality of the collaboration and the subsequence performance of the technology (George et al., 2002). Taken together, we predict that the contractual complexity arising from inventors working in different institutions might slow down commercialization processes: Hypothesis 5. Inventions made by faculty-inventors from multiple universities will be associated with greater commercialization time than those invented by faculty-inventors from a single university 3.2.4. Advanced commercialization stages: UTTO competency in identifying licensees The resource-based view of the firm (RBV) suggests that competitive advantage is achieved when compe-

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tencies are context specific, socially complex, embedded in history and culture, and hinging on tacit knowledge (Barney, 1991; Wright and McMahan, 1992). For the purposes of this paper, and consistent with existing literature (Lado and Wilson, 1994), competencies are specific capabilities that enable organizations to develop and deploy value-enhancing licensing strategies. Such competencies may include institutionspecific assets, knowledge, skills, and capabilities embedded in organizational structure, protocols, and relationships with industry. In the context of the technology transfer stage undertaken by UTTOs these competencies are brought to bear on reducing commercialization time through the identification of viable licensees. In RBV parlance, the ability to accurately and rapidly identify suitable licensees for a technology is a competency because the process is embedded in core and architectural activities and stems from the roles and skills that personnel must possess to perform their work. This is perhaps the reason why UTTOs actively recruit licensing representatives with experience in an industry to which they are trying to license (OwenSmith and Powell, 2003). In the absence of the necessary knowledge assets to identify suitable licensees, UTTOs must rely on costly and time-consuming parties to ‘connect’ them with networks of industry representatives (chief scientists, corporate R&D departments, technology park directors, etc.). In sum, a foundational UTTO competency is connecting the right discoveries with the right companies at the right time. To be the basis for a competitive advantage, a competency has to be of economic value. The ex-ante knowledge of what and when firms desire to procure (and can pay licensing fees for) patent-protected technology is critical to future revenue streams. In this context, UTTOs that are particularly adept and effective at identifying firms that need university-based technologies are more likely to sign licensing agreements at a faster pace and hence generate longer term cash flows. Thus, we predict that having a competency to match technology with companies is related to an accelerated licensing speed. Thus:

3.2.5. Advanced commercialization stages: who initiates licensing proposals Novel discoveries are inherently idiosyncratic, pathdependent, and context specific (Schilling, 1998). First, idiosyncrasy is a function of novelty itself in that the newer the technology the greater its idiosyncrasy vis-`a-vis the market in which it is deployed. Second, because firm-level competitive advantage flows from difficult-to-replicate knowledge, firms actively seek to obscure the precise nature and usability of their technologies (Barney, 1991; Teece, 1998). To an UTTO, the idiosyncratic character of new technology increases the complexity of identifying promising licensees, as companies may be unwilling to reveal their ‘technological bottlenecks’ and therefore the value that a university technology might bring to those projects. Moreover, because technology is continuously evolving, discerning which discovery might be useful to the innovation trajectory of a particular company may involve careful screening of many companies, a slow and lengthy process indeed.5 The internal exchange market (UTTO interactions with faculty) is more efficient than the external market (UTTO interaction with industry). This is because shared organizational culture and physical proximity may facilitate an UTTO’s dealings with faculty but would generally not with companies. Therefore, the commercialization time resulting from faculty or inventor-initiated licenses will likely be shorter than those associated with company initiated licenses. Consistent with the discussions leading to Hypotheses 4 and 6, inventors who actively participate in licensing processes are more likely to take the initiative to link their UTTOs with companies that need the technology (Markman et al., 2005a). This occurs because academics, scientists, company technologists, and technology consultants often interact in conferences, seminars, company and government sponsored forums, and as reviewers of each other’s research papers. This network can be the basis for identifying commercial applications and research opportunities. As such, faculty-inventors may accelerate the licensing process by simply eliminat-

Hypothesis 6. UTTOs that are more effective in identifying licensees are more likely to reduce commercialization time.

5 As one UTTO director put it, “this is certainly not the norm, but we might prospect hundreds of potential licensees before we finally secure a single licensing agreement”.

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ing much of the guesswork that UTTOs face when attempting to identify licensees for their patent portfolio. Along the same logic, firms that actively initiate licensing contacts with UTTOs will probably enjoy an accelerated process relative to firms that take a more passive approach. However, compared to inventor- or UTTO-initiated licensing contracts, the commercialization speed to firm-initiated licensing agreement is normally slower. This is so because most firm-initiated contacts are in the form of sponsored research, whereby a company ‘leases’ university resources—labs, instruments, faculty, postdoctoral and graduate students—in order to pursue further research and development. Such arrangements, coupled with the need for ongoing interactions between faculty-inventors and company scientists and the use of university assets, is legally and logistically more complicated than a straight licensing deal in which a patented discovery is encoded in a document: Hypothesis 7a. As compared with UTTO-initiated licensing, inventor-initiated licensing will be associated with shorter commercialization time. Hypothesis 7b. As compared with UTTO-initiated licensing, firm-initiated licensing will be associated with longer commercialization time.

4. Methods The UTTO is our unit of analysis and field interviews our primary data collecting method. UTTO directors interact with university administration, faculty, and industry representatives, and therefore are best positioned in the university technology transfer process to have a deep understanding of the licensing process. We collected data through structured phone interviews with 91 UTTO directors from a sample of 138 U.S. universities (65.5% response rate). The sample was drawn from the population of Association of University Technology Managers (AUTM). The sample represents research institutions and accounts for over 50% of federal and industry research support, and over 60% of licenses executed, inventions disclosures, and new patent applications. Thus, the sample approximates the general population of research universities

that engage in technology commercialization in the U.S. To adequately triangulate innovation speed and avoid common method bias, data were collected from four sources including: (a) the AUTM Licensing Surveys, (b) phone interviews with 91 UTTO directors, and for convergent validity (c) searches of each UTTO web site, and (d) the UTTOs’ patent data set (U.S. Patent and Trademark Office). Data derived from interviews might raise concerns regarding external validity. However, because our sample captures the most active UTTOs and is representative of the population, we believe that threats to external validity and statistical generalizability are minimal (Giddens, 1996; Glaser, 1992). In the following sections we briefly outline how we operationalized the variables. 4.1. Dependent variables 4.1.1. Outcomes: licensing revenues and entrepreneurial activity Data on licensing revenues came from AUTM (2000, 2002) and it was measured by actual dollar amounts UTTOs obtained from licensees. We measured entrepreneurial activity by counting the number of startup ventures that are based on university technology. Data on this variable were collected from the AUTM report and validated by the structured interviews. In order to abate concerns regarding heteroscedasticity, non-linearity, and non-normality, we log-transformed the dependent measures—licensing revenues and entrepreneurial activity (Tabachnick and Fidell, 1996). 4.1.2. Innovation speed Two different variables of commercialization time served as surrogates for innovation speed. The first variable captures the average time in years it takes to license university-based technology to industry (based upon the AUTM three-entity designation: large, small, and entrepreneurial firms). The second variable captures the average time in years it takes to license universitybased technology to new ventures only. Again, innovation speed is seen here as the time from disclosing a discovery to an UTTO until a licensing contract is signed to transfer the new technology to a licensee. These variables function as independent variables in Table 2, but as dependent variables in Table 3.

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4.2. Independent variables To assess the impediments for securing discoveries from faculty we asked UTTO directors to list, in order of importance, the top impediments. A content analysis by two raters suggested that these impediments should be grouped into two categories. First, inventor-related impediments included faculty indifference, ideological resistance, or poor discoveries. Second, resourcebased impediments included limited UTTO budget, bad UTTO reputation, or university bureaucracy. Consequently, inventor-related impediments were coded as 1 whereas resource-based impediments were coded as 0. It is important to note that this two-dimensional variable captures general impediments for obtaining discoveries, not for licensing per se. 4.2.1. Importance of inventors versus firms To assess the importance of faculty-inventors versus firms, who seek university-based technology, in accelerating the UTTO licensing process, we asked UTTO directors the following question: “On a scale from 0 to 100 percent, how important are (a) facultyinventors and (b) firms seeking university IP to the acceleration of licensing processes?” We then compared the score given to each and created a dummy variable that combined the ranking of both, facultyinventors and firms. The dummy variable was coded as 1 when faculty-inventors were ranked as more important (higher score); and 0 when firms attracted higher score. 4.2.2. Complexity: inventors collaboration To assess licensing complexity we measured intrauniversity and inter-university collaboration by asking UTTO directors to list the average yearly number of collaborations among inventors from the same institution and from different institutions. The final variable was the ratio of outside to inside inventors on disclosed discoveries. Large ratios suggest a higher percentage of collaboration with university’s outsiders and thus greater licensing complexity. 4.2.3. UTTO competency in identifying licensees To assess UTTOs competency in identifying appropriate licensees, directors were asked the following question: “Based on your record, what is the average number of industry contacts that your office makes until

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a suitable and willing party for licensing is identified?” Because high quantities of contacts suggest fruitless tries, it is an indication that an UTTO’s competency in identifying licensees is low. Conversely, when UTTOs secure licensees with only few contacts, it is an indication of a strong competency in identifying appropriate licensees accurately and swiftly. 4.2.4. Licensing initiation: inventors and companies vis-`a-vis UTTOs To assess the extent to which faculty-inventors, UTTOs, and firms initiate the licensing process, we asked directors to rank UTTOs, faculty-inventors, and firms as initiators of successful licenses. To disentangle the impact of faculty-inventors and companies as they initiate licensing, we used UTTOs as a benchmark. Thus we created two contrast codes, one comparing inventors with UTTOs the other comparing companies with UTTOs. 4.3. Control variables 4.3.1. UTTO experience and size One factor that may impact the adoption of a university-based technology is the accumulated experience and size of UTTOs. For example, it is reasonable to expect that more experienced and larger UTTOs sign a greater number and more lucrative licensing agreements. Hence we controlled for organization experience and size by measuring, respectively, UTTO age and number of employees. 4.3.2. UTTO structure Consistent with Markman et al. (2005a), UTTOs were categorized into three distinct organizational structures, including traditional, non-profit, and forprofit. We validated the three organizational forms through web searches of each institution. Consistent with past research, the three UTTO quasi archetypes reflect different levels of autonomy vis-`a-vis the parent university—the for-profit private extension enjoys the highest level of operational autonomy whereas the traditional UTTO structure is least autonomous. The use of structure as a control variable is important because UTTO autonomy may be related to licensing speed. We used two contrast codes with the non-profit structure as a comparison. Thus, first we compared the traditional UTTO structure (coded as 1) with the non-profit

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foundation (coded as 0), and second, we compared the for-profit structure (coded as 1) with the non-profit foundation (coded as 0). These two contrast codes were labeled, respectively, traditional structure and for-profit structure. 4.3.3. Public versus private UTTO Because private universities might be more competitive and resourceful in terms of their licensing efforts, our last control variable contrasts public UTTOs (1) with private UTTOs (0).

variables—UTTO experience, size, structure (traditional and for-profit), and public versus private. Then we entered the hypothesized predictors—early impediments (inventor = 1; resources = 0); relative importance (firm = 0; inventor = 1); inventors’ collaboration (outside/inside); UTTO competency (in identifying licensees); and the contrast codes regarding who initiates successful licensing (comparing first inventors to UTTOs and then comparing firms to UTTOs). We report the results and findings below.

4.4. Analyses

5. Results

We used path analysis to test whether commercializing speed is a full or partial mediator to performance outcomes—licensing revenues and new ventures (Pedhazur, 1997; Schumaker and Lomax, 1996). Path analysis is especially useful because it investigates a model with the determinants of innovation speed as the exogenous variables, and whether innovation speed is a mediating variable to the dependent outcomes of licensing revenues and new venture creation. That is, we tested the direct and indirect effects of the determinants of speed on licensing revenues and number of new ventures. We also tested at that juncture a quadratic term of innovation speed in order to detect non-linear effects. This variable tests the possibility of a curvilinear association between innovation speed and licensing revenues and new ventures, on the supposition that extreme speed (“rushed commercialization”) can lead to dysfunctional outcomes.6 As part of the path analysis, we performed several hierarchical regressions to test the hypothesis. Controlling for UTTO experience, size, structure, and public versus private, we regressed the two speed variables—overall commercialization time and commercialization time to new ventures only—on two measures of UTTO outcomes, namely, licensing revenues and number of new ventures. Given the significant relationships between commercialization time and UTTO outcomes, our second set of regressions used commercialization time as the dependent variables. As with the previous regressions, we first entered the control

Results from the path analysis show that the antecedents to speed used here have neither significant direct effects on licensing revenues nor on number of new ventures. This explains why Fig. 1 shows only the predicted and significant paths between the antecedents, mediating, and dependent variables (Pedhazur, 1997). The path analysis illustrates that when the determinants of speed are significant, their only association with licensing revenues and number of new ventures is through the speed construct—commercialization time. Similarly, the quadratic term was not significant, suggesting no support for a curvilinear association between innovation speed and licensing revenues and new ventures. That is, a concern that rushed commercialization might be negatively related to either licensing revenues or new-venture formation was unfounded. Table 1 summarizes the descriptive statistics and the correlation matrix of variables used in the analyses. The mean licensing income and number of startups was $4.86 million and 2.5 firms (Table 1 reports these variables after they have been transformed). The average commercialization time from university to industry is 4.17 and 4.27 years to new ventures. On average an UTTO had 15 years of experience (age) and enjoyed the expertise of more than six licensing professionals and support personnel in the organization (size). Finally, almost 71% of our sample was made of public universities. The main results of the regressions are summarized in Tables 2 and 3. We found support for Hypotheses 1 and 2; commercialization time is significantly related to UTTO outcomes. As shown in Table 3, shorter commercialization time to all firms is significantly related to

6 We thank the anonymous reviewers for pointing out this possibility.

Table 1 Means, standard deviations, and correlations

11. 12. 13. 14. 15.

Log licensing income Log new ventures Innovation speed to all firms Innovation speed to new ventures UTTO experience (age) UTTO size (personnel) Traditional UTTO structure For-profit UTTO structure Public (1) vs. private (0) Early-stage impediments (UTTO resources = 0; faculty = 1) Later-stage impediments: (firm = 0; inventor = 1) Complexity: inventors’ collaboration (outside/inside) UTTO competency in identifying licensees Who initiates licensing (comparing inventors to UTTOs) Who initiates licensing (comparing firms to UTTOs)

Means

S.D.

1

2

3

4

5

6

7

8

13.55 1.79 4.17 4.27 1986 6.15 .52 .06 .71 .73

2.19 1.05 1.83 .55 11.53 9.16 .50 .23 .46 .44

9

10

11

12

13

.60 −.54 −.45 −.40 .52 −.18 .07 −.26 .03

−.47 −.41 −.47 .53 −.09 .20 −.17 −.02

.87 .39 −.42 .22 −.21 .22 .05

.44 −.31 .24 −.25 .26 .14

−.32 .17 −.08 .15 −.02

.03 −.02 −.06 .10

−.26 −.25 −.14

−.18 .16

.02

.53

.50

−.05

−.06

.06

−.03

.01

−.01

.00

−.02

−.06

.04

.53

.19

.27

.34

−.07

.11

−.25

.33

.02

−.03

.03

.01

−.05

6.46

2.07

−.33

−.39

.40

.38

.22

−.15

.03

−.05

.21

.12

.21

−.08

.66

.48

−.06

−.28

.04

.04

.19

−.20

.17

.00

−.20

−.07

−.06

−.03

.16

.65

.48

−.18

−.37

.33

.37

.32

−.32

.15

−.08

−.06

.13

.01

−.01

.32

14

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G.D. Markman et al. / Research Policy 34 (2005) 1058–1075

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Correlations greater than .18 are significant at the p < .05. Correlations greater than .30 are significant at the p < .01. N = 91 UTTOs (University Technology Transfer Offices).

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Table 2 Regression results for the determinants of university-based licensing revenues and new venture creation Dependent variables

Independent variables UTTO experience (age) UTTO size (personnel) Traditional UTTO structure For-profit UTTO structure Public (1) vs. private (0) Commercialization time to all firms Commercialization time to new ventures Adj. R2 F † p < .10. * p < .05; ** p < .01.

Log licensing revenues

Log new ventures

−.01 .53** −.24** −.10 −.24**

.16† .46** −.16† .08 −.12

−.23** −.16† .55** 17.35**

.45 11.07**

N = 91 UTTOs

licensing revenues, and shorter commercialization time to new ventures is a marginally significant predictor of the number of new venture starts. Thus, Hypotheses 1 and 2 were supported. The regressions obtained large F statistics and explained a large portion of the variance (11.07 ≤ F ≤ 17.35; .45 ≤ adjusted R2 ≤ .55). Using G*Power we conducted a power analysis.7 Power tests of the regressions in Table 2 report coefficients for the regressions at 1.00, which indicates an adequate sample size for the variance explained and number of predictors in the model (Cohen, 1988, 1992). Consistent with past research, UTTO size was a consistent and positive predictor of UTTO outcomes. Traditional UTTO structure and public universities were inversely related to licensing revenues. Hypothesis 3 predicted that during the discovery and disclosure stage, limited cooperation from faculty would increase commercialization time more significantly than UTTOs’ limited resources. As shown in Table 3, faculty disengagement early in the licensing process was associated with slower commercialization to new ventures, but this relationship did not hold for commercialization time to all firms. Hypotheses 4–7 focused on advanced stages of the commercialization process. Hypothesis 4 predicted that after self selection occurs—where faculty disclose their invention and 7 A description of the program and relevant algorithm is published in Erdfelder et al. (1996).

work with their UTTOs—engaged faculty-inventors would be associated with decreased commercialization time. This relationship was marginally significant for commercialization time to new ventures but not to all firms. Hypothesis 5 predicted that licensing complexity would be associated with decelerate commercialization. As Table 3 reports, higher levels of complexity (as measured by the ratio of inventors collaboration with outsiders to insiders) were associated with increased commercialization time to all firms (p < .10) and to new ventures (p < .001). Hypothesis 6 predicted that UTTO competency in matching technologies to clients would be related to commercialization time. Results support this prediction. Finally, Hypotheses 7a and 7b predicted that licensing initiated by faculty would be associated with reduced commercialization time whereas licensing initiated by firms would be associated with increased commercialization time. Hypotheses 7a and 7b received, respectively, marginal and strong support for commercialization time to new ventures. Again, a power test of the full regression models in Table 3 report coefficients of 0.997 to 1.00, indicating adequate sample size for the number of predictors and variance explained. The control variables illustrate that commercialization time to new ventures is somewhat shorter among more experienced, larger, for-profit UTTOs, and private universities. On the other hand, in the context of commercialization time to all firms, UTTO size was the only significant control variable. Independently, the control variables account for 24 and 26% of the variance in commercialization time to all firms and to new ventures respectively. Once all predictors were included, the model explained 38 and 54% of the variance in commercialization time to all firms and to new ventures, respectively.

6. Discussion The results provide several contributions to the theory on innovation speed and to future research on technology commercialization. First, we provided explicit definitions of innovation speed and testable measures of the construct. Overall, our findings provide support for innovation speed theory by demonstrating a positive link between commercialization time and licensing rev-

G.D. Markman et al. / Research Policy 34 (2005) 1058–1075

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Table 3 Regression results for the determinants of speed to new venture creation Independent variables

Dependent variables: commercialization time to All firms .15 −.28** .18 −.09 .26**

UTTO experience (age) UTTO size (personnel) Traditional UTTO structure For-profit UTTO structure Public (1) vs. private (0)

† p < .10; * p < .05; ** p < .01.

.10 −.31** .14 −.12 .15

.31** −.07 .18† −.13 .28**

.11 −.07 .16† .33** −.15 .15

Early-stage impediments (UTTO resources = 0; faculty = 1) Later-stage impediments: (firm = 0; inventor = 1) Complexity: Inventors’ collaboration (outside/inside) UTTO competency in identifying licensees Who initiates licensing (inventor and UTTO) Who initiates licensing (firm and UTTO) .24

Adj. R2 Change in adj. R2 F

New ventures

5.92**

.38 .14** 5.37**

.26** −.17† .12 −.16† .17† .18* −.15† .34** .30** −.15† .21*

.26 6.54

.54 .28** 9.29**

N = 91 UTTOs.

enues and new venture creation. A second contribution hinges on identifying discrete determinants of innovation speed. This contribution is particularly important once one recognizes how the current study explicitly acknowledges that technology commercialization is a process in which the antecedents of speed operate differently during different stages of the process. Together, these contributions provide an opportunity to connect and extend the research streams on technology commercialization and innovation speed. We found that early in the process, during the discovery and disclosure stage, UTTO’s resources—lack of time, capital, or poor central administration support for licensing activity—are less of a hindrance to speedy commercialization than the limitations posed by inventor-related impediments such as resistance, indifference, and poor-quality disclosures. However, during advanced commercialization stages to new ventures, faculty-inventors seem to play a more positive role in accelerating the process. It could be that some faculty-inventors are the founders of these technologybased startups. Therefore, their interest in the new venture extends beyond the licensing process, involving the management of the commercialization process itself. Our data may be capturing the technology commercialization process at different points of the maturation cycle of the technology itself. Specifically, when we compare faculty disengagement with UTTO

resources, we are measuring their involvement during early stages of the commercialization process. For faculty, involvement in the licensing process before a discovery demonstrates commercial promise represents opportunity costs in time and effort. In the context of the ‘publish or perish’ exigencies of research institutions, such irrecoverable opportunity costs may prove too high to justify deeper involvement. When we compare the faculty-inventors with firms, however, we are looking at the post-disclosure stage of the technology maturation cycle. At this point, the potential for a commercial application is clearer and indeed the opportunity costs for further involvement may fall as the inventor now takes a keener interest in helping the UTTO find commercial licensees. Interestingly the strongest predictors of innovation speed are complexity and UTTO competency. That is, licensing complexity is related to increase commercialization time, whereas UTTO competency in matching technology with companies is significantly associated with reduced commercialization time. We also found that more experienced and larger UTTOs are better at accelerating licensing to new ventures, suggesting that they may have more developed organizational routines to transfer technologies. Finally, we found that technology commercialization is faster when faculty-inventors initiate the licensing process than when firms take the lead.

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6.1. Implications As noted earlier, in certain industries speed is deliberately curtailed through a regulatory process of testing and approval, in order to improve the quality and efficacy of products, services, or technologies. However, our findings support the notion that a shrinking window of IP (e.g., patents give only temporary monopoly) for technology exploitation puts a premium on faster licensing deals. In the context of our research sample, this means that to be successful, UTTOs might want to strive for more rapid disclosures of faculty discoveries in order to drive innovation speed. Our findings are generally consistent with the research on new product development and innovation. For example, faster product development is associated with higher rates of learning (e.g. Eisenhardt, 1989; Patterson and Lightman, 1993) and with the development of competencies related to new product development (Sonnenberg, 1993). Shorter turnaround time may not allow for ideas to be tested completely, but it compresses the feedback loop between cause and effect and makes it explicit, all of which accelerate the learning process. This attenuation of uncertainty over the causes of errors and positive outcomes improves learning, early detection and correction, and provides for greater knowledge accumulation, which in turn leads to a competency to manage innovation speed, as our data suggest (Meyer, 1993). Speed can lead to higher quality outputs because it facilitates a greater focus and commitment among employees on learning (e.g. Clark, 1989; Dumaine, 1989). Competencies that accelerate innovation processes increase licensing revenues by allowing UTTOs to access licensees earlier and to extend the reach of their technological portfolios. In turn, the early realization of licensing revenues increases the resources available for further basic research, which increases the feedstock of technologies into the UTTO’s portfolio. In addition, the fast and efficient allocation of research dollars allows institutions to support more research with the same pool of funds, effectively leveraging existing research assets (laboratories, scientists, graduate assistants and the like). One implication of this conclusion is that such a system puts greater pressure on UTTOs, and universities in general, to recruit and retain competent licensing officers. As noted in prior research, the limited options for advancement for

licensing officers within the university structure are a significant impediment to positive outcome, whether they are commercialization time or other outcomes (Markman et al., 2005a). 6.2. Limitations As with all research, ours is subject to limitations. For example, to adequately address concerns about common method variance, we collected secondary data from other sources and verified our interview data with archival data. In verifying the data, we did not find evidence of systematic bias from our informants. A more serious limitation is the use of concurrent measures in operationalizing the speed construct. An implied assumption in this approach is that university policies and UTTO structures remain stable across time, and if this assumption is correct, then the associations reported here might be relatively stable. Finally, the specification of our model is constrained in that we chose to concentrate only on the antecedents and effects of speed. While it allowed us to test our hypothesis unambiguously, such constrained models do not allow us to build normative theory. More can and has to be done to explicate the role of innovation speed in the ecology of the technology commercialization process. Therefore, we suggest some directions for future research in the conclusion section. 6.3. Conclusion Despite the apparently increasing role that research universities play in innovation by transferring new knowledge and technologies to industry, it is still unclear what drives their effectiveness. In this research, we specifically examined the amount of time that UTTOs take to license their patent-protected technologies. We argued that because time is a valuable resource, it could be combined with other capabilities to confer advantage. As such, one should expect to find those UTTOs best able to accelerate the commercialization process in a stronger position to reap higher licensing revenues and spawn new venture creation. Indeed, we found support for this conjecture. More importantly, we found that the active participation of faculty-inventors is an important determinant of innovation speed. In a university context, faculty are the primary providers of discoveries to UTTOs and there-

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fore, whether they choose to actively contribute to or ignore the licensing process, will determine UTTO’s effectiveness (Owen-Smith and Powell, 2001). This finding in itself is not surprising, but the contribution that we make is by answering the question, ‘how do inventors hinder or help the licensing process?’ Our answer is, ‘by extending or reducing the time to commercialization.’ Future research can move in various directions. One can elaborate the path model we have suggested by exploring other determinants of innovation speed (e.g., industry type, technological life cycle, and localize public policy effects). Similarly, while the current study focuses on innovation speed to all firms and new ventures, future research can assess more discrete categories of licensees such as faculty-inventors themselves, independent or international licensees, and local and national firms. Comparing speed outcomes with different licensing strategies vis-`a-vis different licensing types (e.g., sponsored research, exclusive, non-exclusive licensing) is also a viable topic for future research. Another path for future research is to further explicate the roles that various participants play in the technology commercialization ecosystem. For example, the relationship between external institutions such as economic development offices and even incubators and science parks and innovation speed can be further explored. The extant literature suggests that such agencies help reduce commercialization time. However, this link is theoretical unexplored, even though it has been empirically demonstrated. Our research shows that commercializing inventions originating from research done by scientists from multiple universities tend to take longer than those invented by scientists from a single university. Because, however, we did not control for the quality of inventions, another direction for future research is to assess the quality of discoveries made by local versus more national or even international groups of scientists. This empirical question is, to the best of our knowledge, not been fully addressed. Finally, there is an opportunity to investigate other competencies of UTTOs and their relationships with innovation speed. To reiterate our findings, innovation speed is associated with greater licensing revenues and, to an extent, more new ventures. Findings also illustrate that UTTO

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experience, size, for-profit UTTO structure, and private universities predict innovation speed, and that this relationship is particularly strong in the context of licensing time to new ventures. Although during the discovery and disclosure stages of the licensing process, the impact of faculty involvement is negatively associated with licensing outcomes, support from faculty-inventors during the technology commercialization stage is associated with reduced licensing time. As expected, licensing complexity is associated with decreased innovation speed while UTTO effectiveness in matching technology and firms is associated with increased innovation speed. Finally, facultyinitiated licensing to new ventures is associated with greater speed whereas those initiated by firms with lower. Acknowledgements We are grateful to three anonymous reviewers for their insightful comments and suggestions. We thank Robert R. Fincher and Charles F. Rancourt for their insights on UTTO work. We also acknowledge the financial support of the Broadbent Endowment for Entrepreneurship Research at RPI. Opinions are the authors alone and not those of their sponsoring organizations. References Agrawal, A., Henderson, R., 1991. Putting patents in context: exploring knowledge transfer from MIT. Management Science 48, 44–60. Association of University Technology Managers, 2000. The AUTM Licensing Survey: Fiscal Year 1999. Association of University Technology Managers, Norwalk, CT. Association of University Technology Managers, 2002. The AUTM Licensing Survey: Fiscal Year 2000. Association of University Technology Managers, Norwalk, CT. Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management 17, 99–120. Brown, W., Karagozoglu, N., 1993. Leading the way to faster new product development. Academy of Management Executive 7, 36–47. Carmel, E., 1995. Cycle time in packaged software firms. Journal of Product Innovation Management 12, 110–123. Clark, K., 1989. What strategy can do for technology. Harvard Business Review 67, 94–98. Clarysse, B., Wright, M., Lockett, A., de Velde, E.V., Vohora, A., 2005. Spinning out new ventures: a typology of incubation strate-

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