Measuring the Social Value of Innovation: A Link in the University Technology Transfer and Entrepreneurship Equation: Volume 19

Cover of Measuring the Social Value of Innovation: A Link in the University Technology Transfer and Entrepreneurship Equation

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(16 chapters)

List of contributors

Pages vii-viii
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This volume centers on introducing and exploring issues of and approaches to demonstrating the potential nonexclusively commercial values of individual university innovations, such as social, ecological, and economic values of innovation that may be realized through movement to the private marketplace. The volume papers comprised the basis for discussion and formation of a 2008 colloquium that was conceptualized and organized by the McGuire Center for Entrepreneurship and co-hosted by the McGuire Center and the Office of Technology Transfer at The University of Arizona. The goal of the colloquium and conference volume was to increase university-based ability to advance innovation by conducting technology-specific assessments of the social (and other noncommercial) gains of the individual innovation. New technologies and knowledge sets generated by university faculty are most frequently measured for potential academic value by the innovating faculty member, department, and general peer structure, and for potential commercial value when appropriate by technology transfer offices and other market-oriented units that are charged with the management of the intellectual property of the institution. A formal, institutionally supported valuation of the social, ecological, and/or economic potential of individual innovations may provide evidence of gain needed to motivate more parties to become engaged in academic entrepreneurship. Thus, the capacities of colleges and universities to more confidently forecast the noncommercial value of new knowledge and discovery has the potential to act as an additional link in the often disconnected value proposition of university innovation.

Colleges and universities play a vital role in the creation and dissemination of the innovations that feed the knowledge economy. First, universities carryout a significant portion of the basic research that is conducted in the United States. In 2006, the National Science Foundation reported having awarded $30 B in research-based funding to colleges and universities (National Science Foundation, 2007). While this figure is not an indicator of innovation output, the number helps to demonstrate the scope of research activity that is occurring within the academy. Mansfield (1995) noted that government funding for university research is bent toward science that holds commercial potential and highlighted that such research is likely to produce a high amount of social benefits. Mansfield also concluded that measuring the social returns of university-born (and federally funded) innovations though difficult, is important. Second, universities are instrumental in the production of economically relevant human capital, including students trained in key science and technology disciplines (Leslie & Brinkman, 1988). Audretsch (2007) indicates a highly educated workforce that is capable of creating and moving innovative technologies into the marketplace is a critical component of the current entrepreneurial economy. Also, faculty who intersect industry through consulting and other commercial-related activities make valuable contributions to the economic growth and prosperity of communities, regions, and beyond. In short, colleges and universities are key contributors to the production and function of the innovations that largely drive the knowledge economy.

An entrepreneurial mode is the latest stage in the evolution of the scientific role. Earlier phases included the differentiation of the modern experimental scientific role from natural philosophy in the mid 17th century. Indeed, the creation of the scientific role preceded the invention of the term “scientist” by Cambridge philosopher of science, William Whewell, in 1834 to describe Mary Somerville, a unique researcher. A transition from amateur to professional scientist followed in the mid 19th century, exemplified by another female scientist, Maria Mitchell, who carried out her astronomic investigations at home until she was appointed to an academic post in middle age (Bergland, 2008). A transition from basic researcher to entrepreneurial scientist is currently underway as part of a broader reconstruction of innovation systems from double helix (government–university or government–industry) to a university–industry–government triple helix (Etzkowitz, 2008). Each transformation in the scientific role reflected a change in the role of knowledge in the political economy. Thus, experimental science provided the instrumentation that allowed ocean commerce to be carried on in a secure fashion; professional science discoveries that were scaled up to provide the basis for the chemical and dye industries. Entrepreneurial academic scientists in collaboration with venture capitalists, building upon a substrate of government-funded research, created the biotechnology industry.

In the wake of growing pressures to make scholarly knowledge commercially relevant via translation into intellectual property, various techno-scientific communities have mobilized to create open access/open source experiments. These efforts are based on the ideas and success of free and open source software, and generally try to exploit two salient features: increased openness and circulation, and distributed collective innovation. Transferring these ideas from software to science often involves unforeseen challenges, one of which is that these movements can be deemed, often incorrectly, as heretical by university administrators and technology transfer officers who valorize metrics such as number of patents filed and granted, spin-off companies created, and revenue generated. In this paper, we discuss nascent efforts to foster an open source movement in nanotechnology and provide an illustrative case of an arsenic removal invention. We discuss challenges facing the open source nano movement that include making a technology widely accessible and the associated politics of metrics.

Contemporary life is replete with all manner of rankings, metrics, and benchmarks (Power, 1997; Espeland & Stevens, 1998). From J.D. Power evaluations of cars to Zagat restaurant reviews to US News and World Report ratings of colleges and universities, modern life seems to be deep in the grip of assessment and evaluation. In the early decades of the twentieth century, the introduction of scientific management transformed the workplace, altering relations between labor and capital, and embedding control over the nature and pace of work into the technical organization of production (Edwards, 1979; Shenhav, 1995). In a similar fashion, the current embrace of rankings may reflect a new “Taylorism,” as metrics have the capacity to not only reorder the social institutions they are purported to assess, but also provide a patina of objectivity, especially for the uninitiated.

Universities have a long history of training students to work in industry, and in recent years the number and percentage of students, especially those trained in science and engineering, who go to work in industry has grown. Today, three-eights of all PhDs with a degree in science and engineering (S&E) work in the private sector. These placements provide a major means for universities to participate in technology transfer. Students are not only up-to-date in terms of codified knowledge; they also possess tacit knowledge that can only be transferred by face-to-face interaction. They may also have participated as research assistants or as postdocs in the development of a technology that has been licensed by the firm where they are employed. Despite the important role that alumni play in technology transfer, universities rarely track the placements of graduate students in industry. Universities do not also systematically keep information on the contributions that alums make to innovations after graduating. Moreover, few programs socialize students to think of careers in the private sector as a top choice. Instead, many programs, especially in the biomedical sciences, socialize students to aspire to research careers in academe, with industry seen as a distinct second choice. Indeed, many PhDs only take jobs in industry after failing to find an academic position after serving as a postdoc for four or five years.

This paper examines recent placements of doctoral students in industry, using the verbatim records from the Survey of Earned Doctorates (SED) for 1997–2002. An advantage of this data is that we know the name of the firm (and the location of the firm) where the individual plans to work. This permits an exploration of several interesting dimensions regarding technology transfer not explored elsewhere, such as (1) sources (in terms of universities) educating students going to industry; (2) the R&D intensity of the firms where newly trained PhDs go to work and the industrial classification of the firms; (3) the role that proximity plays in facilitating these knowledge spillovers; and (4) the degree to which universities make placements with firms licensing their technologies.

The paper also examines the amount of information that universities provide regarding the placements of their PhDs. We find that although students are ready and willing to provide information regarding work plans after graduation, universities seldom provide information on placements. We conclude with a suggestion regarding the procedures universities could follow to create and make placement data available.

Measuring the economic and social impact of innovation is a nontrivial act. Using an embedded analysis method this paper examines the Grameen Bank and Microsoft Corporation as examples of social and commercial entrepreneurship. Both organizations embraced radical innovation that was scaleable and created wealth. They also both had profound economic and social impact on the world.

A fundamental problem in articulating the societal benefits of technology transfer is the lack of hard empirical evidence on the economic gains associated with this activity. To fill this gap, we apply the framework and methods developed by Griliches and Mansfield et al. to assess the social returns to university-based inventions. This methodology can be used to derive explicit measures of key metrics, such as social rates of return and benefit-to-cost ratios characteristic of specific new technologies. A case study is used to illustrate the application of this method.

An innovation's social value depends on various factors that are independent of how the particular innovation is used with other technologies. Examples of such factors are the size of the market the innovation will serve and the manner in which the innovation is managed. However, an innovation must often be implemented with complementary inventions whenever it is exercised and its benefits are realized. In such cases, an innovation's value depends, in part, on the ownership structure of the related inventions. This paper makes its contribution by examining how an innovation's social value is affected when it must be applied in concert with other essential inventions. In this paper, I propose a measure that helps predict an innovation's social value. I also suggest a practical procedure to implement this measure and I evaluate a key feature of this procedure.

In many states, legislators have serious concerns about American competitiveness in the global economy. Based on Thomas Friedman's The World Is Flat – perhaps the most highly read book in policy communities over the past decade – legislators are aware the United States is falling behind other countries on many indicators of educational attainment. Although the United States was once a leader in higher education access, with 60 percent of its population attending at least some college, nine countries now exceed this level of participation (Wagner, 2006). Our educational attainment is predicted to increase in the future, not because of increasing participation rates, but because of the expanding college-going population. In production of bachelor's degrees, the United States is now merely average among the 20 most prosperous countries. On a per capita basis, one could argue that the United States no longer has the best higher education system in the world.

Drawing on examples from the more developed realms of technology transfer and other “managerial professions” (Rhoades, 1998; Rhoades & Sporn, 2002) in the academy, this paper explores possible organizational sites for housing protocols for the measurement of the social value of individual innovations in higher education (that may enter the market or and augment or precede commercial valuation), and the possible implications of those different settings for the academy (particularly in terms of motivating more faculty to engage in more innovative and entrepreneurial activities). Organizational location matters. Organizational site is related to professional perspective and to the institutional outlook that attaches to various sorts of work in the academy. Five possible sites are explored, analyzing the dimensions of such locations from the experience of other “new” activities in universities. One type of site consists of an interstitial (Slaughter & Rhoades, 2004), nonacademic, support unit of managerial professionals (neither faculty nor senior level administrators), as in an Office of Technology Transfer or an Office of Institutional Research. A second type of site would be an academic unit in which measurement tasks could be performed by faculty members. A third type of site would be a hybrid model that combines elements of the first two models, as in the case of entrepreneurship units in many universities. A fourth possible type of site would be to situate such activity in an intermediating association (Slaughter & Rhoades, 2004) outside of the university, which mediates between public and private sectors, and that promotes various sorts of innovation and measurement as in the case of Educause and innovative information technologies. A fifth type of site would consist of establishing university extension units in the community, to provide services more directly to constituents, as traditionally was the model for agricultural extension in land grant universities. Each of the models has its owns benefits and challenges, its implications for what sorts of professionals would be doing the work and what they would see their principal function as being, and for the impact they would have on the academic workforce and the institution's direction.

Cover of Measuring the Social Value of Innovation: A Link in the University Technology Transfer and Entrepreneurship Equation
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Advances in the Study of Entrepreneurship, Innovation and Economic Growth
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Emerald Publishing Limited
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