The Competitiveness of Nations
in a Global Knowledge-Based Economy
May 2003
Nathan Rosenberg
Exploring the Black Box:
Technology, economics and history
8
Critical issues in science policy research
Cambridge
University Press
Cambridge,
U.K. 1994
pp.
139-158
Index
Impact of science on technology
Instrumentation as a production tool
HHC –
titling and index added
Everyone knows that the linear model of innovation is dead. That model represented the innovation process
as one in which technological change was closely
dependent upon, and generated by, prior scientific research. It was a model that, however flattering it may
have been to the scientist and the academic, was
economically naive and simplistic in the extreme. It has been accorded numerous decent burials,
and I do not intend to resurrect it only to arrange for it to be interred once
again.
However, in a world in which the economic role of science may
reasonably be expected to grow over time, and in which policy-making will need
to be based on a more sophisticated understanding of the ways in which science
and technology interact and influence one another, a better road-map of the
science/technology landscape is vitally necessary. I will therefore not be primarily examining
the determinants of innovation. Rather,
my main focus will be on some of the ways in which the two communities, of
scientists and technologists, exercise influences on one another.
Obviously, while my central focus will not be on the determinants of
innovation, what I say will, I hope, be highly relevant to that question. Indeed, I regard it as central to a more
useful framework for analyzing the innovation process that it should be based
on a more sharply delineated road-map of science/technology relationships. That road-map ought, at a minimum, to identify
the most influential traffic flows between science and technology. Obviously, such a map cannot at present be
drawn.
Consequently, this chapter offers no more than the preliminary findings
of a reconnaissance expedition, identifying some of the most significant
This chapter first appeared in Science
and Public Policy, 1991, volume 18, no.6, pp. 335-346. It is reprinted with omissions. The paper was first presented at the
celebration of the twenty-fifth anniversary of the Science Policy Research
Unit, University of Sussex, in July 1991. The author has incurred substantial
intellectual debts, in the preparation of this chapter, to Harvey Brooks, Ralph
Landau, David Mowery, Richard Nelson, and Ed Steinmueller.
139
features of the landscape - including some rather intriguing
features that have been surprisingly neglected - rather than providing a
detailed map. But perhaps that will be
sufficient to identify some of the major locations where research is most
urgently called for. If it is successful
in this, it will have achieved its major purpose of providing, in the
time-honored academic locution, “a guide to further research.”
It is, of course, a matter of definition that science/technology
interactions are most significant in the so-called high-technology industries. But it must be recognized, to begin with, that
there still remain crucial portions of high-technology industries where
attempts to advance the technological frontier are painstakingly slow and
expensive, because of the limited guidance that science is capable of
providing.
The development of new alloys with specific combinations of properties
proceeds very slowly because there is still no good theoretical basis for
predicting the behavior of new combinations of materials, although materials
science may now be getting closer to the point of developing models with
predictive powers. Many problems
connected with improved fuel efficiency are severely constrained by the limited
scientific understandng of something as basic as the
nature of the combustion process. The
development of synthetic fuels has been seriously hampered in recent years by
scientific ignorance of the relationship of the molecular structure of coal
(which is known) to its physical and chemical properties.
The requirements of computer architecture remain badly in need of an
improved scientific underpinning. The
design of aircraft and steam turbines are both hampered by the lack of a good
theory of turbulence. In the case of
aircraft, wind-tunnel tests are still subject to substantial margins of error
in terms of predicting the actual flight performance of a new prototype. Some of the functions of wind-tunnel testing
in generating data for aircraft design have been taken over in recent years by
computer simulation techniques, but by no means all of them.
It is noteworthy that the rapid growth in development costs in
industrial economies shows no sign of subsiding. The extremely high development costs that
prevail in most of the high-technology industries, and their rapid growth, are
due to the inability to draw more heavily on a predictive model in determining
the performance of specific new designs or materials.
More precisely, the true desideratum is a good predictive model that
will lead to a reduction in the cost of determining optimal design. It needs to be a computationally simplifying
model, which is not always the case. One
can learn a great deal about reaction mechanism in the computational chemistry
if one has unlimited access to a Cray computer, but Cray computers are
extremely expensive.
If science provided a cheaper predictive basis for moving to optimal
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design configurations, development costs, which constitute
about two-thirds of total R&D expenditures in the United States, wouldn’t
be nearly so high. They are as high as
they are because engineers and product designers continue to need to engage in
very extensive testing activities before they can be sufficiently confident in
the performance characteristics of a new product.
On the other hand, although there continue to be sharp limits on the extent
to which technology can draw on science, it is far less appreciated that
scientific progress has become increasingly dependent on technology. Indeed, it is tempting to say that an
alternative definition of a high-technology industry is one in which problems
that arise at the technological frontier exercise a major role in shaping the
research agenda of science. In these
industries, it is not enough to say that scientific knowledge is applied to the
productive process; rather, to a considerable extent, such knowledge is also
being generated there.
An important source of scientific progress, in advanced industrial
societies, has derived from the attempt to deal with difficulties, unexpected
problems, or anomalous observations that first arose in connection with new
product designs or novel productive processes. Additionally, the industrial context has
identified highly specific areas of research in which some expansion of
knowledge would make possible a significant improvment
in quality or in the performance characteristics of a material.
In the course of the twentieth century, that additional knowledge has
been produced to an increasing degree by scientists employed inside industrial
research labs of high-tech firms. This
has not been just some stroke of good fortune or act of a benign Providence. Scientists in industry are inevitably
confronted with specific observations or difficulties that are extremely
unlikely to present themselves in a university laboratory: premature corrosion
of an underwater cable, unidentifiable sources of interference in
electromagnetic communications systems, or extreme heat generated on the
surface of an aircraft as it attains supersonic speeds.
The fact is that industrial activity, especially, but not only, in
high-tech sectors, provides unique observational platforms from which to
observe unusual classes of natural phenomena. In this respect, the industrial research
laboratory may be said to have powerfully strengthened the feedback loop
running from the world of economic activity back to the scientific community. [1]
It must be added that observations are sometimes made in an industrial
1. See “How
Exogenous is Science?”, chapter 7 in Nathan Rosenberg, Inside the Black Box,
Cambridge University Press, Cambridge 1982, and Stephen J. Kline and Nathan
Rosenberg, “An Overview of Innovation,” in Ralph Landau and Nathan Rosenberg
(eds.), The Positive Sum Strategy, National Academy Press, Washington
(DC), 1986.
141
context by people who are not capable of appreciating their
potential significance, or who are simply uninterested in observations that
have no immediate practical relevance. In 1883 Edison observed the flow of electricity
across a gap, inside a vacuum, from a hot filament to a metal wire. Since he saw no practical application, he
merely described the phenomenon in his notebook and went on to other matters of
greater potential utility in his effort to enhance the performance of the
electric light bulb.
Edison was of course observing a flow of electrons, and the observation
has since come to be referred to as the Edison Effect. Had he been a patient scientist and less
preoccupied with matters of short-run utility, he might later have shared a
Nobel Prize with Owen Richardson who analyzed the behavior of electrons when
heated in a vacuum, or conceivably even with J.J. Thomson for the initial
discovery of the electron.
Edison’s inventive contributions were so great that it would be rank
ingratitude for later generations to chastise him for his “practical” orientation!
But, ironically enough, the Edison
Effect, together with other scientific discoveries, eventually had immensely
important practical consequences through the development of the vacuum tube and
the vast technology that was later associated with the emergence of modern
electronics. However, perhaps it was not
ironical after all. When one speaks of
someone as being “practical,” what is usually meant is that he or she is
interested in matters of short-run utility only. Science is, surely, a very practical activity
but, typically, only in the long run.
Impact of science on technology
These considerations suggest an important avenue through which the
technological realm has shaped the research agenda, and therefore the eventual
findings, of portions of the scientific community. In considering the flow of traffic in the opposite, and more “traditional” direction - the impact of
science on technology - it is useful to make two separate observations.
First, where scientific findings have indeed profoundly influenced technological
activities, these findings need not be derived from recent research at the
scientific frontier. Indeed, many points
of contention and dispute over the economic importance of science really derive
from the fact that the science that was essential to some technological
breakthrough was simply “old” science. Often this science was so old that it was no
longer considered by some to be science.
The problem is compounded by the fact that many spokesmen for the
economic importance of science are anxious to make a case for larger
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research budgets. In
order to strengthen the case it helps considerably to emphasize the benefits
that may be derived from what goes on at the research frontier, rather than the
continuing contribution of, say, nineteenth-century analytical chemistry to the
mining industry, or the economic contribution made by “old” science through the
current education of engineers. [2]
The fact is that technology draws on scientific knowledge and methodology
in highly unpredictable ways - and we are likely to cover up our ignorance by
invoking such shameless tautologies as “When the time is ripe.” The body of knowledge that is called “science”
consists of an immense pool to which small annual increments are made at the
“frontier.” The true significance of
science is diminished, rather than enhanced, by extreme emphasis on the
importance of the most recent “increment” to that pool.
The lags may be very long indeed, often because much essential complementary
technology needs to be developed before it can be said that “the time is ripe”
for some major invention. Consequently,
the perspective of the economist or the policy-maker needs to be distinctly
different from that of the historian of science or, for that matter, of
contemporary advocates of larger science budgets in the public sector.
Consider the laser. The first
lasers were developed around 1960, since when they have expanded into a
remarkably diverse range of uses, including the printer that produced the
manuscript of this chapter. But, from
the point of view of the historian of science, it could be argued that the
basic science underlying the laser was formulated by Einstein as long ago as
1916. [3] A
historian of science might say that everything of real interest had been
completed by 1916, and the rest was “just” engineering and product development.
At the same time, what is relatively
uninteresting to her may be the most essential part of the story from the point
of view of the technological innovation.
Amid this specialization of interests, it is essential to retain the
point that there may be lags of many decades between a given increment to
science and
2. On the significance of old science, see Nathan Rosenberg, “The
Commercialization of Science in American Industry,” in Kim B. Clark, Robert H.
Hayes, and Christopher Lorenz (eds.), The Uneasy Alliance: Managing the
Productivity-Technology Dilemma, Harvard Business School Press, Boston
(MA), 1985.
3. “The
underlying science involves an understanding of the energy levels of molecules
and solids, and the specific principle was that described by Einstein in his
1916 work on stimulated emission. Much
of the technology necessary for the laser emerged only during the Second World
War from work on microwave radar - including magnetrons and klystron sources,
semiconductor detectors, and wave-guiding networks.” John R. Whinnery,
“Interactions between the Science and Technology of Lasers,” in Jesse H. Ausubel and J. Dale Langford (eds.), Lasers: Invention
to Application, National Academy Press, Washington (DC), 1987, p. 124.
143
the useful application that may one day flow from it. This is one important reason (but only one)
why the commercial benefits of basic research need not be captured by firms in
the country where the basic research was performed. Perhaps equally important and equally
neglected, the development of sophisticated, high-performance technologies, such
as lasers and other complex electronics instrumentation, has generated much new
basic research that was recognized as essential to the further improvement of
the new technologies. [4]
The second major source of disjunction between an advance in science and
its eventual influence on technology and the economy has received little
attention. The problem is that, even
when scientific research opens up an entirely new field of technological
possibilities, it is usually a multi-stage process. The reason is that it is not ordinarily
possible to proceed directly from new scientific knowledge into production,
even when that new knowledge is actually of a specific final product, as
opposed to the discovery of some new piece of information about the natural
universe that may serve as an “input” into the eventual development of a new
product.
In fact, the emergence of the two disciplines of electrical and
chemical engineering, beginning in the late nineteenth-century, occurred for
precisely this reason. It was not
possible to move directly from the enlarged experimental and theoretical
understanding of the electromagnetic and synthetic organic chemical realms into
the production of goods that incorporated such new knowledge.
The reason was simple. The appropriate
technologies could in no way be derived from or deduced from the scientific
knowledge. On the contrary, distinctly
different bodies of knowledge had to be drawn upon or generated before
production could begin. Sometimes, this
required the development of entirely new disciplines.
Consider the synthetic dye industry that was launched by Perkin’s (accidental) synthesis of mauve, the first of the
synthetic aniline dyes, in 1856. The
subsequent growth of the synthetic organic chemicals industry did not occur
immediately after this dramatic laboratory breakthrough. So long as dyestuffs could be produced only be
enlarging the physical dimensions of the original laboratory apparatus, the
industry was destined to remain a small-scale batch operation of little
industrial consequence. [5]
The essential point is that the design and construction of plants
devoted to large-scale chemical processing activities involves an entirely
different set of
4. See Harvey Brooks, “Physics and the Polity,” Science (26 April
1968).
5. See W.K.
Lewis, “Chemical Engineering - A New Science.” in Lenox R. Lohr
(ed.), Centennial of Engineering, 1852-1952, Museum of Science and
Industry, Chicago, 1953 p. 697.
144
activities and capabilities than those that generated the
new chemical entities. To begin with,
the problems of mixing, heating, and contaminant control, which can be carried
out with great precision in the laboratory, are immensely more difficult to
handle in large-scale operations, especially if high degrees of precision are
required. Moreover, economic
considerations play a much larger role in the design process, since cost
considerations come to play a decisive role in an industrial context.
Thus, the discovery of a new chemical entity has commonly posed an
entirely new question, one that is remote from the scientific context of the
laboratory: how does one go about producing it? A chemical process plant is far from a
scaled-up version of the original laboratory equipment. Experimental equipment may have been made of
glass or porcelain. A manufacturing
plant will almost certainly have to be constructed of different materials.
Moreover, efficient manufacturing is, inherently, something very different
from a simple, multiple enlargement of small-scale experimental equipment. This is what accounts for the unique
importance of the pilot plant, which may be thought of as a device for
translating the findings of laboratory research into a technically feasible and
economically efficient production process. [6] The translation, however,
requires a kind of expertise that need not exist at the experimental research
level: a knowledge of mechanical engineering and a careful attention to the
underlying economics of the engineering alternatives.
Pilot plants have in the past been essential, and not only for the
purpose of the reduction of uncertainties with respect to scale. Until a pilot plant was built, the precise
characteristics of the output could not be determined. Test marketing could not proceed without the
availability of reliable samples. Other
essential features of the production process could not possibly be derived from
scientific knowledge alone.
Consider the recycle problem. Very
few chemical reactions are complete in the reaction stage. Therefore products of the reaction stage will
not only
6. “Often, in dealing with a complicated practical situation, the
engineer arbitrarily reduces the number of variables in his theory by combining
them into dimensionless groups, of which a well-known example is the Reynolds
number characterizing the flow of fluid through a pipe. Such dimensionless groups are evaluated in the
laboratory, and are used then for predicting the behavior in a large-scale
chemical plant. But this procedure
reduces somewhat our confidence in our predictions; though the group as a whole
may have varied widely in the laboratory experiments, one or more of the
variables within the group may have been virtually unchanged. Because of this reduced confidence in using
dimensionless groups in scaling-up predictions, the chemical engineer usually
builds a pilot plant, intermediate in size between the laboratory system and
the proposed full-scale production plant, so that he can check whether the
scaling-up predictions of his simplified theory are working sufficiently
accurately.” John T. Davies, “Chemical
Engineering: How Did it Begin and Develop?” in William F. Furter
(editor), History of Chemical Engineering, American Chemical Society,
1980, pp. 40-41.
145
include desired end products but also intermediates, unreacted feed, and trace impurities - some measurable and
some unmeasurable.
Impurities, in particular, are identified by the operation of the pilot
plant and methods of removing them devised to achieve a steady-state condition
on a continuing basis. [7]
In the twentieth century, a gap of several years has separated the
discovery under laboratory conditions of many of the most important new materials
from the industrial capability to manufacture them on a commercial basis. For instance the first polymers that W.H. Carothers had produced with his glass equipment at the Du Pont Laboratories; and polyethylene and terephthalic acid, an essential material in the production
of terylene, a major
synthetic fibre. [8]
Eventually, to manage the transition from test tubes to manufacture,
where output has to be measured in tons rather than ounces, an entirely new
methodology, totally distinct from the science of chemistry, had to be devised.
This new methodology involved exploiting
the central concept of unit operations. This term, coined by Arthur D. Little at MIT
in 1915, provided the essential basis for a rigorous, quantitative approach to
large-scale chemical manufacturing, and thus may be taken to mark the emergence
of chemical engineering as a unique discipline, not reducible to “applied
chemistry.” [9]
Moving from scientific breakthroughs to technologies ready for commercialization
is a highly complex, inherently interdisciplinary subject that is far from well
understood and far from adequately studied. Again in the realm of chemicals, the work of Staudinger and Mark in the 1920s on polymerization provided
an excellent scientific basis for the development of polyester fibres. But the
commercial introduction of such fibres required the
use of a new raw material - paraxylene - which was
not, at the time, an
7. In recent
years, computers have begun to displace the reliance on expensive and
time-consuming pilot plants. In the
hands of experienced designers, micropilot plant
data, combined with good analytical equipment, may yield workable commercial
designs.
8. It is
important to note that progress in the subdiscipline
of polymer chemistry has been primarily an achievement of research in
industrial laboratories.
9. In Little’s words: “Any chemical process,
on whatever scale conducted, may be resolved into a coordinated series of what
may be termed ‘unit actions,’ as pulverizing, mixing, heating, roasting,
absorbing, condensing, lixivating, precipitating,
crystallizing, filtering, dissolving, electrolyzing and so on. The number of these basic unit operations is
not very large and relatively few of them are involved in any particular
process... Chemical engineering research... is directed toward the improvement,
control and better coordination of these unit operations and the selection or
development of the equipment in which they are carried out. It is obviously concerned with the testing and
the provision of materials of construction which shall function safely, resist
corrosion, and withstand the indicated conditions of temperature and pressure. Its ultimate objective is so to provide and
organize the means for conducting a chemical process that the plant shall
operate safely, efficiently, and profitably.” Arthur D. Little, Twenty-five Years of
Chemical Engineering Progress, Silver Anniversary volume, American Institute
of Chemical Engineers, D. Van Nostrand Company, New
York, 1933, pp. 7-8.
146
article of commerce. It also required a new way of cheaply
converting paraxylene to terephthalic
acid, since the use of nitric acid was both too expensive and too messy - it
produced an unacceptably impure product.
Eventual success in this major breakthrough not only took many years
but owed little, if anything, to further scientific research. Here, as elsewhere, scientific breakthroughs
are, at best, only the first step in a very long sequence of knowledge
accumulation, if we think in terms of an economic perspective rather than that
of the historian of science.
Consider the present-day world-wide search for products in which to
embody the recently acquired knowledge of high-temperature superconductivity. The world may still be decades away from the
large-scale commercial exploitation of this knowledge, just as the great
breakthroughs in molecular biology of the 1950s are only now beginning to find
an embodiment in the products of an emerging biotechnology industry.
The complexity of the science - technology interface, and especially
the two-way movement of traffic across that interface, clearly calls for some
new institutional responses. Decision-makers
in both the public and private sectors will need to address the question of how
to improve the organizational conditions and incentive structures at the
science-technology interfaces. The
ability to improve the functioning of various specialists at that interface
will undoubtedly be an important determinant of future leadership in
high-technology industries. This is so
not only for the reasons that have already been suggested, but also because
important changes appear to be occurring on the science side of the interface
as well as on the technology side.
In particular, there is much evidence that scientific knowledge of a
kind that is most likely to be useful in high-technology industries has to be
pursued in an increasingly interdisciplinary fashion. Consider the realm of medicine, a truly
high-technology industry, as can be readily verified by a quick walk through
the intensive care unit of any major teaching hospital. In recent years, medical science has benefitted immensely, not only from such “nearby”
disciplines as biology, genetics, and chemistry, but from nuclear physics
(especially in diagnostic technologies such as magnetic resonance imaging,
radioactive tracers, and radioimmunoassays), electronics,
and materials science and engineering. Lasers are now a frequent instrument of choice
in extremely delicate surgery, and the availability of fibre-optic
technology has made possible the direct visualization of internal organs - as
in the esophagoscope, the flexible sigmoidoscope, and the bronchoscope.
An interesting index of the growing importance of electronics in
medicine
147
is exhibited by Sony Medical Electronics, a recently
established division of the giant consumer electronics company. This company is now promoting such new
products as remote surgical consultation systems and cardiac recording systems.
Other Japanese consumer-electronics
companies are in earlier stages of a similar transition into medical
applications. [10]
In pharmaceuticals, there have been remarkable advances drawing upon
findings in such fields as biochemistry, molecular and cell biology, immunology,
neurobiology, and scientific instrumentation. These advances are creating a situation in
which new pharmaceutical compounds, with specific properties, can be targeted
and perhaps eventually designed, in contrast with the randomized, exhaustive,
and expensive screening methods that have characterized pharmaceutical research
in the past. [11] The
essential point is that the newly emerging pattern of innovation is, by its
very nature, increasingly interdisciplinary. That is to say, success requires close cooperation
among a growing number of specialists.
In other fields, some of the most fundamental innovations of the postwar
world have been the product of interdisciplinary research. The transistor was the result of the combined
efforts of physicists, chemists, and metallurgists. Optical-fibre light
guides, now transforming the telecommunications industry, were essentially the
product of these same three disciplines. Moreover, materials science has now emerged as
an independent discipline, representing “a fusion of metallurgy, chemistry, and
ceramics engineering with aspects of condensed-matter physics.” [12]
The scientific breakthrough leading to the discovery of DNA was the
work of chemists, physicists, biologists, biochemists and, far from the least,
crystallographers. In agriculture, more
productive seed varieties, such as the high-yielding rice varieties developed
at the International Rice Research Institute in the Philippines, were the work
of geneticists, botanists, biochemists, entomologists, and soil agronomists. These new varieties have totally transformed
the food supply situation of Asia in the past twenty-five years.
In some cases, the continuing interdisciplinary nature of technological
progress has been underlined by quite unexpected shifts in the bodies of
scientific knowledge upon which progress has sometimes depended. The transition from the transistor to the
integrated circuit brought with it a shift from essentially mechanical and
metallurgical techniques of fabrication to chemical techniques. The increasing dependence of semiconductors on
10. Wall
Street Journal, 20 May 1991.
11. See Alfonso Gambardella, “Science and Innovation in the U.S.
Pharmaceutical Industry during the l980s,” Stanford University doctoral
dissertation, 1991.
12. Scientific
Interfaces and Technological Applications, Physics Through
the 1990s, National Academy Press, Washington (DC), 1986, page 6. See also chapter 4.
148
metallurgical inputs has played a major role in elevating
metallurgy to what is now called “materials science.”
More recently, the continued shrinkage in the size of electronic
devices has led to a situation where further technological progress may
eventually involve thinking in entirely different terms. Specifically, the unit of analysis for further
progress in miniaturization may no longer be solid materials, but, perhaps,
chains of molecules. If such a
transition were to take place, the required knowledge base would no longer be
the kind in which electronic engineers have been trained. It would become, rather, theoretical
chemistry.
Such a drastic shift in the underlying body of scientific
knowledge, on which a technology is based, is not uncommon, and continued
commercial viability may turn on the ease, or difficulty, that firms experience
in making such a transition. Consider
the shift in electronics from vacuum tubes to solid-state transistors to
integrated circuits, or from propeller-driven aircraft engines to jet engines. The possibilities opened up by such shifts,
and the potential difficulties of failing to make such transitions when a firm
is suddenly “blindsided” by an unexpected shift, is an important reason for
maintaining a substantial in-house scientific capability. Indeed, it may be a reason for maintaining a
portfolio of research capabilities in a range of scientific disciplines. Unfortunately, only a relatively small number
of firms have the resources for maintaining such a portfolio.
The increasing importance of interdisciplinary research has created
serious organizational problems. Such
research often runs counter to the traditional arrangements, training,
priorities, and incentive structures of the scientific professions,
particularly in the academic world where great emphasis is placed on working
within well-recognized disciplinary, and therefore departmental, boundary
lines.
The American university system in the past forty years or so has been
very successful in combining the performance of basic research at the
scientific frontiers with the training of future professionals. Nevertheless, the present organizational
structure of American universities along disciplinary lines, as reflected in
its departmental structures, poses some serious limitations as the solutions to
research problems become increasingly interdisciplinary in nature.
Another major strength of the American university system has been the
highly successful interface that it has developed with the industrial world. This relationship has not been without its
problems and dangers. Industrial
financing of university research runs the danger that universities will
149
increasingly have their research agendas set by their
external sources of finance. In so
doing, there is the threat that they will compromise their autonomy, focus on
short-term problems of immediate interest to industry, and thereby suffer a
loss of effectiveness as leaders in fundamental research.
Perhaps even more serious is the possibility that the potentially great
commercial value of scientific findings will lead to a loss of free and frank
communication among university faculty members, and a reluctance
to disclose research findings from which other faculty members or students
might derive great benefit. Such
developments could prove to be harmful to future progress in the realms of both
science and technology, as well as to education itself.
Nevertheless, as is usually the case, there has been another side of
the coin. Scientific autonomy is always
subject to the potential pressures of its funding sources. Federal funding of research since the Second
World War, which has been far greater than industrial funding, has given an
immense prominence to the needs and the priorities of the military and to its
notorious penchant for secrecy. But, at
the same time, Department of Defense funding has played a highly creative role
in the emergence of new specializations of great importance to high-technology
industries, such as computer science and materials science.
Moreover, although I say this with some sense of trepidation to an
academic audience, it is possible for the notion of autonomy to be carried to extreme
lengths. The dominant role of the
academic department in American universities has been, in some respects,
excessive. It has, in particular, been
very slow to provide professional career opportunities to those who have
identified research problems at the edges, or interstices, of traditional
academic disciplines. Indeed, it is
highly significant that there is no research institution in the United States
comparable to the Science Policy Research Unit in its commitment to
interdisciplinary research at the intersection between science, technology, and
economics.
Perhaps it should be added that there is nothing uniquely rigid about
the department structure of American universities. Departmental rigidity is probably the
inevitable price to be paid for the fact that, historically, scientific
progress has required a high degree of disciplinary specialization. It is therefore, a widespread phenomenon. It is doubtful, for example, whether the
department structures in German or Japanese universities are any less rigid. In fact, it may be suggested that the slow
pace of German entry into the realm of biotechnology owed much to the
inflexible role of German university departments.
The important role attached to the department as a unit of intellectual
organization has not prevented the American higher educational system from
being remarkably adaptive and responsive to changing social needs in
150
general, and the changing requirements of business and
industry in particular. Indeed, it has long been a recurrent criticism of
European visitors, especially from Britain, that American colleges and
universities have been excessively responsive to the changing dictates of the
marketplace and to vocational needs of all sorts.
The determination to improve the links between the academic and industrial
worlds has already led to a great deal of institutional innovation at many
research universities. And, as is often
the case, it has been the availability of new monies from external sources, and
the associated possibilities for new hiring, that has
generated a willingness to enlarge the traditional, single-discipline focus,
and to contemplate new institutional arrangements. Earlier in the twentieth century the
availability of funds from private philanthropies, such as the Carnegie and
Rockefeller Foundations and the Guggenheim Fund, served as powerful and highly
creative catalysts for intellectual and institutional innovation. It is fair to say that the financial
stringency of recent years has rendered the American academic community more
responsive to the introduction of new interdisciplinary arrangements.
The Center for Integrated Systems at Stanford is an interesting example
of university-based research with industrial financing. The Center receives financial support from
twenty corporations, and it is devoted primarily to developing methods for
designing and manufacturing large-scale integrated microelectronic circuits. Its research activities draw heavily on
computer science, integrated circuit engineering, solid-state physics, as well
as other disciplines.
MIT has a Whitehead Institute, with a huge private endowment, which is
devoted to biomedical research, as well as a ten-year contract with Exxon
Research and Engineering Company to support research in the field of
combustion. West Germany’s huge chemical
and pharmaceutical company, Hoechst AG, has given the Massachusetts General
Hospital, a teaching arm of the Harvard Medical School, $50 million for the
support of basic research in the area of molecular biology.
At the federal level, the National Science Foundation has been instrumental
in establishing Engineering Research Centers at a number of major universities.
These represent important institutional
departures of a multidisciplinary nature, typically involving a strong
underlying emphasis on computers and specialized engineering skills working in
close liaison with the rather more traditional scientific disciplines of
physics, chemistry, and biology.
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Columbia University has a center for telecommunications, Harvard
University for the application of advanced computers to the design of
communications systems, MIT for the improvement of manufacturing processes in
the biotechnology industry, and Purdue University for research on highly
automated manufacturing systems. The centers represent significant
multi-disciplinary innovations. It is too early to appraise their overall
effectiveness, although it may be noted that there have already been failures
as well as successes.
In some important respects, private industry in the United States has,
in the past at least, had a substantial advantage over universities in the
organization of multi-disciplinary research. It has not attached nearly the same
significance to the rigid, disciplinary boundary lines that have loomed so
large in the academic world.
It has proven easier to bring people from different disciplines
together in an industrial environment where research is not organized by
discipline but by problems, and where there has been a very different set of
incentives and criteria by which the contributions of scientists are evaluated.
In the best American industrial
laboratories, unlike the universities, a high value and considerable
recognition are likely to go to individuals who are useful in solving the
problems encountered by colleagues in fields other than their own. The most successful American research
laboratories in private industry have demonstrated that it is possible to
perform research of both a fundamental and an inter-disciplinary nature in a
commercial, “mission-oriented” context. The
most successful appear to have been those that have managed to create close
interactions, and exchanges of information, between those responsible for
performing the research, on the one hand, and those responsible for the
management of production and marketing, on the other. But it is far from clear exactly how this has
been accomplished, and precisely what organizational, managerial, and incentive
factors have differentiated successful from unsuccessful firms. The subject is one that deserves a high research
priority.
One point worth emphasizing is that the firms with the most outstanding
industrial laboratories - Bell Labs., IBM, General Electric, duPont, Eastman Kodak - have developed excellent interfaces
with university-based research precisely because they are known to be involved
in basic research of high quality. University scientists believe that they have
much to learn from industrial scientists from such laboratories. Since the flow of knowledge, in these cases,
is widely understood to move in both directions, industrial scientists from
distinguished laboratories are likely to be enthuse-
152
astically received on university
campuses, while university scientists welcome the opportunity to observe or
even to become directly involved, as consultants, in industrial research.
A related comment about the Japanese scene may be appropriate at this
juncture. It has been a common practice
to point to the low level of commitment of Japanese universities to scientific
research as a serious weakness, and as a potential threat to the prospects for
continued expansion of Japanese technological capabilities. Certainly, Japanese universities represent a
weak link within the Japanese science and technology systems.
Nevertheless, the concentration of scientific research in Japanese
private industry has certain positive, or at least
redeeming aspects. These include the
problem orientation that comes so naturally in the industrial context, and the consequent weakness of the barriers to
interdisciplinary research that can loom so large in the academic world. Equally important, perhaps, is the ease with
which knowledge can be transmitted between the potential “producers” of new
knowledge and those responsible for its eventual industrial application, and it
cannot be emphasized too strongly that knowledge is readily transmitted in both
directions.
This industrial context is not ideal for the pursuit of long-term basic
research. But basic research is not
always “the name of the game.” The
Japanese firm may be well-suited for producing and utilizing precisely the kind
of new knowledge that is most directly relevant to providing improved
industrial performance. [13]
A further, significant category of science/technology interactions is
closely related to the multi-disciplinary issues that have already been
discussed, but nevertheless is sufficiently distinctive to warrant separate
recognition. To put it in the most
general terms, an important determinant of both the rate and direction of
scientific progress in recent decades has involved the actual transfer of
concepts, methodologies, or instrumentation from one scientific discipline, or
specialty, to another.
In some cases the scale of these transfers has assumed almost the
appearance of an invasion of one discipline by another (perhaps “migration” is
a better term, since the transfer has often involved the permanent movement of
scientists from one discipline to another). The field of chemistry has for a long time benefitted immensely from the work of physicists, whose
interests in the fundamental nature of matter have
13. See Masahiko Aoki and Nathan Rosenberg, “The Japanese Firm as an
Innovating Institution,” in T. Shiraishi and S. Tsuru (eds.), Economic Institutions in a Dynamic
Society: Search for a New Frontier, Macmillan, London, 1989.
153
provided a natural intersection of common concerns between
physics and chemistry. In the last
couple of decades, the benefits to chemistry from such transfers from outside
have increased considerably, in part as a result of the availability of new
techniques of instrumentation. The
primary instrument, of course, has been the computer.
In addition, both analytical and synthetic chemistry have experienced
transformations in the very nature of their research as a result of new approaches
based on the contributions of physicists, mathematicians, statiticians,
laser experts, materials specialists, and a formidable arsenal of new
computer-controlled instruments. One
result, to which I have already referred, is the increasing capability for
“designing” new pharmaceuticals instead of achieving them through crude
empirical testing or experimentation.
The creative significance of these transfers to chemistry received
broad recognition when the 1985 Nobel Prize in Chemistry was awarded to Herbert
Hauptman (mathematician) and Jerome Karle (chemist). They were the developers of sophisticated
mathematical techniques that made it possible to deduce the three-dimensional
structure of natural substances from observations based on X-ray
crystallography. The feasibility of
their mathematical technique was, in turn, vastly improved by the availability
of the computer:
They developed ways of actually calculating structure
by analyzing the intensity of the points visible as dots in the X-ray pictures
and calculating the “phase” of the atoms in the structures. In this context, phase is an angular
measurement that can vary from zero to 360 degrees. The advent of powerful modern computers has
made it possible to use the two scientists’ mathematical formulations on
intensity and phase to determine quickly the three-dimensional structure of a
molecule under study. [14]
The 1986 Nobel Prize in Chemistry, awarded to Dudley Herschbach, Yuan Lee, and John Polanyi,
for their work on “reaction dynamics,” reflected some of these underlying
trends:
They invented a set of tools in the
1950’s and 1960’s that helped bring both the theory and the technology of
modern physics into chemistry. Among
them is the technique of using beams of molecules, fired at supersonic speeds,
to study chemical reactions molecule by molecule for the first time... Like much
of chemistry in the decades that followed, this work had a style that owed much
to physics and depended on a broad understanding of theory. [15]
Although modern physics is probably the main “exporter” of concepts and
methods to other scientific disciplines, it is by no means the only one.
14. New York Times, 17 October 1985.
15. New York Times, 16 October 1986, page 12.
154
The so-called “life sciences” of biology, genetics, and medicine have
been heavily dependent on chemistry. The
intellectual revolution that gave birth to molecular biology had diverse roots
that certainly included the contributions of scientists trained in physics,
such as Max Delbruck, Leo Szilard,
Francis Crick, Maurice Wilkins, and George Gamow. But essential contributions also came from Mendelian genetics, X-ray crystallography, physical
chemistry, and biochemistry. [16]
Within the realm of engineering disciplines, techniques developed in
one area frequently turn out to be useful in others. Sometimes, they turn out to be of much broader
significance and applicability. In
aircraft design, a standard problem involves calculations of air flow over
wings. In solving these problems in the
very early years of the industry, Ludwig Prandtl
devised what has come to be essentially a new branch of mathematics - known as
asymptotic perturbation theory. Applications
of that theory can be found in radar design and the study of the combustion
process, but also in astronomy, meteorology, and even in biology. Recently, asymptotic perturbation theory has
been used in designing pills so as to provide for optimal timing in the
controlled release of medication.
One of the most powerful intellectual tools that has
had extensive transfer experience in the past several decades has been
information theory. Claude Shannon, who
developed information theory at Bell Labs., actually provided a generalization
for calculating the maximum capacity of a communication system for transmitting
error-free information. [17] This
generalization has been of great, and obvious, utility to the telephone
industry, where a precise understanding of the determinants of channel capacity
is central to engineering design.
However the theory, once it had received a rigorous formulation, turned
out to be highly relevant in places very remote from the telephone system. For Shannon’s central notion, that it is
possible to give a quantitative expression to information content, had numerous
ramifications. Information theory
represented a distinctively new way of thinking about a range of problems that
occur in many places, and it has powerfully influenced the design of both
hardware and software. Eventually, information
theory grew into a family of models of wide generality, with applications in
the behavioral sciences as well as in the physical sciences and engineering.
16. See Horace F. Judson, The Eighth Day of Creation, Simon and
Schuster, New York, 1979, especially pages 605-613. For an account that emphasizes the
contribution of physics, see Donald Fleming, “Emigré
Physicists and the Biological Revolution,” in Donald Fleming and Bernard Bailyn, The Intellectual Migration: Europe and America,
1930-1960, The Belknap Press of Harvard University Press, Cambridge (MA),
1969, pp. 152-189.
17. Claude Shannon, “A Mathematical Theory of Communications,” Bell
System Technical Journal (July 1948).
155
It appears also that instrumentation requirements sometimes serve as a
powerful device for bringing together research scientists from separate
disciplines. X-ray crystallography
played such a role in the development of molecular biology, precisely because
it is, in effect, an instrument-embodied technique. In a very different way the increasing
reliance on supercomputers is serving to bring members of different disciplines
together. The impetus in this case is,
to a considerable degree, the high cost of the technology and, consequently,
the small number of locations where users need to convene.
Instrumentation as a production tool
Finally, there is another extremely important science/technology
interaction that receives virtually no attention. It involves movement of new instrumentation
technologies, not from one scientific discipline to another, but from the
status of a tool of basic research, often in universities, to the status of a
production tool, or capital good, in private industry. This is an “output” of basic research that has
been of great significance in specific sectors of the economy. In fact, instrumentation originating in the
world of academic research in the years since the Second World War has been
responsible for critical contributions to certain industrial technologies. In the electronics industry, this would
include instruments that are essential to the fabrication of semiconductors,
such as ion-implantation technology and the scanning electron microscope. [18]
It is far from clear why this particular economic contribution of
scientific research, including research of the most fundamental nature, has
been so badly neglected. In the academic
world, of course, high status is usually accorded on the basis of the “purity,”
or the abstractness, or the generality, of research findings. Conversely, matters involving “hardware,”
including techniques of instrumentation, are often
dismissed as constituting an inferior form of knowledge, some of which may even
(mirabile dictum!) turn out to be directly
useful.
This sort of academic snobbery should surely have been discarded long
ago, even by standards internal to this hierarchical manner of thinking, since
a number of Nobel Prizes have been awarded to scientists for contributions that
could be classed as hardware - the computer-aided tomography scanner, the
electron microscope, and the particle accelerator. Moreover, a casual glance at the award of
Nobel Prizes in science in recent years should make it apparent how crucial it
has become in the realm of scientific research to have access to the most
sophisticated instrumentation
18. For
further discussion, see chapter 13, pp. 255-257.
156
available. Much more
to the present point, when the context of discussion is the economic impact of
science, there is no obvious reason for failing to examine the hardware
consequences of even the most fundamental scientific research.
The purpose of this chapter has been to raise questions about the
science/ technology interface by examining specific patterns of interaction at
various points on that interface. No
assertion is being made that these are the most important of the existing
patterns. I merely call attention to
them as being important as well as neglected. Their significance compared to other
activities that have not been discussed here will obviously have to await the
results of much further research.
Several policy issues already emerge clearly:
how can
organizations and incentives be created that will be conducive to high quality
interdisciplinary research?
To what extent is it reasonable to expect
such research to be conducted inside individual firms, as contrasted with the
resort to collaborative linkages with other firms, or with universities, in
order to gain access to complementary skills and capabilities? [19]
How can fruitful interactions between
scientists and technologists, as well as among scientists from different
disciplines, be most effectively encouraged?
What measures can be taken to ensure that
valuable findings or methodologies from any point on the science/technology
interface will be transferred rapidly to other points?
It is essential that these issues are not approached in a piecemeal
fashion. The realms of science and
technology must be conceived of, not as disconnected bits and pieces of human
activity, but as parts of large and complex, interrelated systems.
Equally important, it is apparent that these systems differ very
significantly from one country to another. As a result, policies or institutional
arrangements that work well in one country may not be readily transferable to
other countries. Systemic differences
need to be taken into account, which brings us back to the importance of
mapping, with which this paper began.
Finally, far more attention needs to be devoted to what determines the
profitability of private spending on science and technology. Although the
19. For an analytical treatment of some of the underlying issues, see Ashish Arora and Alfonso
Gambardella, “Complementarity and External Linkages:
The Strategies of Large Firms in Biotechnology,” Journal of Industrial
Economics (June 1990).
157
linear model is no longer credible, the causal sequences
that it emphasized still remain dominant in a subtle yet highly significant
way, so that there is still a strong preoccupation with how research leads to
economic consequences, and little attention is given to how economic factors
influence the willingness to do research. In both the United States and the United
Kingdom, for example, much attention is given to the argument that a weakening
commitment to R&D may be responsible for the deterioration in the
competitive position of these countries in international markets.
Much less noticed is the possibility that causality may also be the
other way around. In the American case,
at least, her overwhelming dominance in international markets in the
twenty-five years or so after the Second World War surely provided a strong
incentive to commit private money to R&D, since the prospects were
excellent that American firms would receive the primary rents in international
markets from the development of new technologies. Surely it is reasonable to believe that the
private incentive to spend money on R&D in the United States weakened along
with the growth of international competitors and the declining prospect of
generating profits overseas, as well as at home, from larger R&D budgets.
In practice, far too much of the debate over R&D spending in the
United States and the United Kingdom has been over the size of the public
component of such spending and far too little on the determinants of private
spending and on how private incentives can be strengthened. This has been particularly unfortunate, in my view, because it is the “downstream” development spending
that plays a crucial role in determining who gets to capture the potential
rents generated by scientific research.
This is a point that appears to be well understood in Japan, where
approximately 80 percent of total R&D spending is financed by private
industry - a far higher percentage than in either the United States or United
Kingdom. This also suggests a conclusion
which, coming from an economist, will occasion no great surprise: prospects for
more rapid economic growth - surely a major though not exclusive goal of
science and technology policy - will depend on success in providing a structure
of economic incentives and rewards that are supportive of the rapid diffusion
of new technologies, once they have been developed.
Decisions to adopt new technologies are, typically, investment
decisions, involving the acquisition of new capital goods. Such decisions are therefore subject to the
same sort of economic calculus that attends all investment decisions. Indeed, precisely the same is true of the
decision to commit private resources to R&D in the first place. Science and technology policy, in this sense,
is simply an aspect of economic policy-making and not an entirely separate
subject. The wrong set of economic
policies can guarantee the failure of any specific set of policies directed
toward the realms of science and technology, no matter how ingeniously
conceived.
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The Competitiveness of Nations
in a Global Knowledge-Based Economy
May 2003