The New Visible Hand: Understanding Today’s R&D Management
The New Visible Hand: Understanding Today’s R&D Management
Recent decades have seen dramatic if not revolutionary changes in the organization and management of knowledge creation and technology development in U.S. universities. Market demands and public values conjointly influence and in many cases supersede the disciplinary interests of academic researchers in guiding scientific and technological inquiry toward social and economic ends. The nation is developing new institutions to convene diverse sets of actors, including scientists and engineers from different disciplines, institutions, and economic sectors, to focus attention and resources on scientific and technological innovation (STI). These new institutions have materialized in a number of organizational forms, including but not limited to national technology initiatives, science parks, technology incubators, cooperative research centers, proof-of-concept centers, innovation networks, and any number of what the innovation ecosystems literature refers to generically (and in most cases secondarily) as “bridging institutions.”
The proliferation of bridging institutions on U.S. campuses has been met with a somewhat bifurcated response. Critics worry that this new purpose will detract from the educational mission of universities; advocates see an opportunity for universities to make an additional contribution to the nation’s well-being. The evidence so far indicates that bridging institutions on U.S. campuses have not diminished either the educational or knowledge-creation activities. Bridging institutions on U.S. campuses complement rather than substitute for traditional university missions and over time may prove critical pivot points in the U.S. innovation ecosystem.
The growth of bridging institutions is a manifestation of two larger societal trends. The first is that the source of U.S. global competitive advantage in STI is moving away from a simple superiority in certain types of R&D to a need to effectively and strategically manage the output of R&D and integrate it more rapidly into the economy through bridging institutions. The second is the need to move beyond the perennial research policy question of whether or not the STI process is linear, to tackle the more complex problem of how to manage the interweaving of all aspects of STI.
The visible hand
This article’s title harkens back to Alfred Chandler’s landmark book The Visible Hand: The Managerial Revolution in U.S. Business. In that book, Chandler makes the case that the proliferation of the modern multiunit business enterprise was an institutional response to the rapid pace of technological innovation that came with industrialization and increased consumer demand. For Chandler, what was revolutionary was the emergence of management as a key factor of production for U.S. businesses.
Similarly, the proliferation of bridging institutions on U.S. campuses has been an institutional response to the increasing complexity of STI and also to public demand for problem-focused R&D with tangible returns on public research investments. As a result, U.S. departments and agencies supporting intramural and extramural R&D are now very much focused on establishing bridging institutions—and in the case of proof-of-concept centers, bridging institutions for bridging institutions—involving experts from numerous scientific and engineering disciplines from academia, business, and government.
All we know for certain is that some bridging institutions on U.S. campuses are wildly successful and others are not, with little systematic explanation as to why.
To name just a few, the National Science Foundation (NSF) has created multiple cooperative research center programs and recently added the I-Corps program for establishing regional networks for STI. The Department of Energy (DOE) has its Energy Frontier Research Centers and Energy Innovation Hubs. The National Institutes of Health (NIH) have Translational Research Centers and also what they refer to as “team science.” The Obama administration has its Institutes for Manufacturing Innovation. But this is only a tiny sample. The Research Centers Directory counts more than 8,000 organized research units for STI in the United States and Canada, and over 16,000 worldwide. This total includes many traditional departmental labs, where management is not as critical a factor, but a very large number are bridging institutions created to address management concerns.
The analogy between Chandler’s observations about U.S. business practices and the proliferation of bridging institutions on U.S. campuses is not perfect. Whereas Chandler’s emphasis on management in business had more to do with the efficient production and distribution of routine and standard consumer goods and services, the proliferation of bridging institutions on U.S. campuses has had more to do with effective and commercially viable (versus efficient) knowledge creation and technology development, which cannot be routinized by way of management in the same way as can, say, automobile manufacturing.
Nevertheless, management—albeit a less formal kind of management than that Chandler examines—is now undeniably a key factor of production for STI on U.S. campuses. Many nations are catching up with the United States in the percentage of their gross domestic product devoted to R&D, so that R&D alone will not be sufficient to sustain U.S. leadership. The promotion of organizational cultures enabling bridging institutions to strategically manage social network ties among diverse sets of scientists and engineers toward coordinated problem-solving is what will help the United States maintain global competitive advantage in STI.
Historically, U.S. research policy has focused on two things with regard to universities to help ensure the U.S. status as the global STI hegemon. First, it has made sure that U.S. universities have had all the usual “factors of production” for STI, e.g., funding, technology, critical materials, infrastructure, and the best and the brightest in terms of human capital. Second, U.S. research policy has encouraged university R&D in applied fields by, for example, allowing universities to obtain intellectual property rights emerging from publicly funded R&D. In the past, then, an underlying assumption of U.S. research policy was that universities are capable of and willing to conduct problem-focused R&D and to bring the fruits of that research to market if given the funds and capital to do the R&D, as well as ownership of any commercial outputs.
But U.S. research policy regarding universities has been imitated abroad, and for this reason, among others, many countries have closed the STI gap with the United States, at least in particular technology areas. One need read only one or both of the National Academies’ Gathering Storm volumes to learn that the U.S. is now on a more level playing field with China, Japan, South Korea, and the European Union in terms of R&D spending in universities, academic publications and publication quality, academic patents and patent quality, doctorate production, and market share in particular technology areas. Quibbles with the evidentiary bases of the Gathering Storm volumes notwithstanding, there is little arguing that the United States faces increased competition in STI from abroad.
Although the usual factors of production for STI and property rights should remain components of U.S. research policy, these are no longer adequate to sustain U.S. competitive advantage. Current and future U.S. research policy for universities must emphasize factors of production for STI that are less easily imitated, namely organizational cultures in bridging institutions that are conducive to coordinated problem-solving. An underlying assumption of U.S. research should be that universities for the most part cannot or will not go it alone commercially even if given the funds, capital, and property rights to do so (there are exceptions, of course), but rather that they are more likely to navigate the “valley of death” in conjunction with businesses, government, and other universities.
Encouraging cross-sector, inter-institutional R&D in the national interest must become a major component of U.S. research policy for universities, and bridging institutions must play a central role. Anecdotal reports suggest that bridging institutions differ widely in their effectiveness, but one of the challenges facing the nation is to better understand the role that management plays in the success of bridging institutions. Calling something a bridging institution does not guarantee that it will make a significant contribution to meeting STI goals.
The edge of the future
The difference between the historic factors of production for STI discussed above and organizational cultures in bridging institutions is that the former are static, simple, and easy to imitate, whereas the latter are dynamic, complex, and difficult to observe, much less copy. This is no original insight. The business literature made this case originally in the 1980s and 1990s. A firm’s intangible assets, its organizational culture and the tacit norms and expectations for organizational behavior that this entails, can be and oftentimes are a source of competitive advantage because they are difficult to measure and thus hard for competing firms to emulate.
University leaders and scholars have recognized that bridging institutions on U.S. campuses can be challenging to organize and manage and that the ingredients for an effective organizational culture are still a mystery. There is probably as much literature on the management challenges of bridging institutions as there is on their performance. Whereas the management of university faculty in traditional academic departments is commonly referred to as “herding cats,” coordinating faculty from different disciplines and universities, over whom bridging institutions have no line authority, to work together and also to cooperate with industry and government is akin to herding feral cats.
But beyond this we know next to nothing about the organizational cultures of bridging institutions. The cooperative research centers and other types of bridging institutions established by the NSF, DOE, NIH, and other agencies are most often evaluated for their knowledge and technology outcomes and, increasingly, for their social and economic impact, but seldom have research and evaluation focused on what’s inside the black box. All we know for certain is that some bridging institutions on U.S. campuses are wildly successful and others are not, with little systematic explanation as to why.
Developing an understanding of organizational cultures in bridging institutions is important not just because these can be relatively tacit and difficult to imitate, but additionally because other, more formal aspects of the management of bridging institutions are less manipulable. Unlike Chandler’s emphasis on formal structures and authorities in U.S. businesses, bridging institutions do not have many layers of hierarchy, nor do they have centralized decisionmaking. As organizations focused on new knowledge creation and technology development, bridging institutions typically are flat and decentralized, and therefore vary much more culturally and informally than structurally.
There are frameworks for deducing the organizational cultures of bridging institutions. One is the competing values framework developed by Kim Cameron and Robert Quinn. Another is organizational economics’ emphasis on informal mechanisms such as resource interdependencies and goal congruence. A third framework is the organizational capital approach from strategic human resources management. These frameworks have been applied in the business literature to explore the differences between Silicon Valley and Route 128 microcomputer companies, and they can be adapted for use in comparing the less formal structures of bridging institutions.
What’s more, U.S. research policy must take into account how organizational cultures in bridging institutions interact with “best practices.” We know that in some instances, specific formalized practices are associated with successful STI in bridging institutions, but in many other cases, these same practices are followed in unsuccessful institutions. For bridging institutions, best practices may be best only in combination with particular types of organizational culture.
Inside the black box
The overarching question that research policy scholars and practitioners should address is what organizational cultures lead to different types of STI in different types of bridging institutions. Most research on bridging institutions emphasizes management challenges and best practices, and the literature on organizational culture is limited. We need to address in a systematic fashion how organizational cultures operate to coordinate diverse sets of scientists and engineers toward coordinated problem-solving.
Specifically, research policy scholars and practitioners should address variation across the “clan” type of organizational culture in bridging institutions. To the general organizational scholar, all bridging institutions have the same culture: decentralized and nonhierarchical. But to research policy scholars and practitioners, there are important differences in the organization and management of what essentially amounts to collectives of highly educated volunteers. How is it that some bridging institutions elicit tremendous contributions from academic faculty and industry researchers, whereas others do not? What aspects of bridging institutions explain what enables academic researchers to work with private companies, spin off their own companies, and/or patent?
These questions point to related questions about different types of bridging institutions. There are research centers emphasizing university/industry interactions for new and existing industries, university technology incubators and proof-of-concept centers focused on business model development and venture capital, regional network nodes for STI, and university science parks co-locating startups and university faculty. Which of these bridging institutions are most appropriate for which sorts of STI? When should bridging institutions be interdisciplinary, cross-sectoral, or both? Are the different types of bridging institutions complements or substitutes for navigating the “valley of death?”
Research policy scholars and practitioners have their work cut out for them. There are no general data tracking cultural heterogeneity across bridging institutions. What data do exist, such as the Research Centers Directory compiled by Gale Research/Cengage Learning, track only the most basic organizational features. Other approaches such as the science of team science hold more promise, though much of this work emphasizes best practices and does not address organizational culture systematically. Research policy scholars and practitioners must develop new data sets that track the intangible cultural aspects of bridging institutions and connect these data to publicly available outcomes data for new knowledge creation, technology development, and workforce development.
Developing systematic understanding of bridging institutions is fundamental to U.S. competitiveness in STI. It is fundamental because bridging institutions are where the rubber hits the road in the U.S. innovation ecosystem. Bridging institutions provide forums for our nation’s top research universities, firms, and government agencies to exchange ideas, engage in coordinated problem solving, and in turn create new knowledge and develop new technologies addressing social and economic problems.
Developing systematic understanding of bridging institutions will be challenging because they are similar on the surface but different in important ways that are difficult to detect. During the 1980s, scholars identified striking differences in the organizational cultures of Silicon Valley and Route 128 microcomputing companies. Today, most bridging institutions follow a similar decentralized model for decisionmaking, with few formalized structures and authorities, yet they can differ widely in performance.
The most important variation across bridging institutions is to be found in the intangible, difficult–to-imitate qualities that allow for (or preclude) the coordination of diverse sets of scientists and engineers from across disciplines, institutions, and sectors. But this does not mean that scholars and practitioners should ignore the structural aspects of bridging institutions. In some cases, bridging institutions may exercise line authority over academic faculty (such as faculty with joint appointments), and these organizations may (or may not) outperform similar bridging institutions that do not exercise line authority.
Craig Boardman ([email protected]) is associate director of the Battelle Center for Science and Technology Policy in the John Glenn School of Public Affairs at Ohio State University.