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When two dogged researchers in IBM’s Swiss research lab demonstrated
high temperature superconductivity in 1986, they triggered an amazing burst
of scientific activity, government investment and press speculation. Stories
abounded of super-efficient power transmission, levitating trains and other
wonders to come. The announcement was the starting gun for a race to exploit
the discovery, with Japan, the US and Europe all vying for the lead. Academic
and industrial laboratories eagerly joined in. In Britain, universities
competed to host the first of the Science and Engineering Research Council’s
interdisciplinary research centres, dedicated to work on high temperature
superconductors. The following year’s Nobel prize award to the IBM researchers,
Georg Bednorz and Alex Muller, further heightened the enthusiasm for applying
their breakthrough.

What were all those who followed in the wake of Bednorz and Muller doing?
Some answers are obvious: they were seeking a share in the glory; trying
to understand the physics behind the phenomenon; trying to make money; trying
to boost their national economies. But there was also something else. They
were all proceeding from a set of shared assumptions about a linear model
of innovation.

Of all the ways of thinking about invention and innovation, the linear
model is the simplest. Most crudely, it assumes that scientists make unexpected
discoveries, technologists take them up and apply them, then engineers and
designers turn them into new products or processes. Thus, nuclear physics
spawned the atom bomb, solid-state physics led to the transistor and then
the microchip, and molecular biology will give us an AIDS vaccine.

Unfortunately, like most simple models, this one starts to come apart
as soon as you look at a few real cases, and most people who study how science
and technology interact now think that the linear model is wrong. But its
grip on policy making is still strong. It is an easy model to think with
and to incorporate in arguments to support your particular project. And
there is no replacement as simple or appealing.

All this complicates the relations between science policy researchers
on the one hand, and decision makers and pressure groups on the other. This
was only too clear at a conference held in July to celebrate the silver
jubilee of the Science Policy Research Unit (SPRU) at the University of
Sussex. Almost the opening words of the conference came from Nathan Rosenberg,
professor of economics at Stanford University in California and a leading
economist of technical change. ‘Everyone knows that the linear model of
innovation is dead,’ he said. There is no academic dissent from this proposition.
But one head of a research council was quick to assure me after the session
that the linear model is certainly still alive in the minds of members of
the Advisory Board for the Research Councils (ABRC), and many civil servants
and government ministers. The model may be dead, but it won’t lie down.

Listening to the rest of Rosenberg’s paper, it was easy to see why.
For while the linear model is now called into question, few other generalisations
emerge so strongly. In Rosenberg’s metaphor, what is needed now is a road
map to identify the most influential traffic flows between science and technology.
But Rosenberg immediately went on to say that no one can draw such a map
yet. Small wonder that the linear logic, often also dubbed a ‘science push’
model of innovation, still creeps back into so many discussions of science
policy.

We can best understand how science policy has arrived at this apparent
impasse by looking back at its history. The foundation of SPRU did not quite
mark the birth of science policy. Its first director, Chris Freeman, rightly
harks back to J. D. Bernal’s The Social Function of Science as the fountainhead.
But although the great Marxist crystallographer’s book was published on
the eve of the Second World War, many of the research questions it raised
were not taken up seriously until the early 1960s. So SPRU’s jubilee was
a good opportunity to take stock of the growth of academic science policy
research since then. The key question here is: what impact has this research
had on those wrestling with the twin problems at the heart of science policy
– how much to spend on scientific research, and what to spend it on?

The experience of the Second World War, and especially the development
of radar and the atomic bomb, shaped in two ways what Freeman now sees as
the initial phase of postwar science policy. First, it underlined the enormous
importance of science and technology, one of Bernal’s main arguments. But,
secondly, whereas Bernal argued that planning should be extended to the
heart of scientific research, the physicists who dominated science advice
to governments in the postwar years seemed to support an opposite conclusion.
They were firm believers in ‘science push’, which they had seen in action
in the way that the Manhattan Project to build the atom bomb followed on
from the basic research of physicists in the 1920s and 1930s. They argued
that you could plan large-scale technological developments, but science
could be left to take care of itself.

In fact, the guiding spirit was much more like that of Bernal’s arch
critic, the chemist and philosopher Michael Polanyi, who argued fiercely
that the ‘pursuit of science can be organised . . . in no manner (other)
than by granting complete independence to all mature scientists . . . The
function of public authorities is not to plan research, but only to provide
opportunities for its pursuit’. No need, then, for much else in the way
of a science policy. Certainly no need for science policy research.

The demise of this ‘hands-off’ view had many causes. When economies
were growing very rapidly it was easy to give scientists more cash, and
easy to believe that research fuelled economic growth. When SPRU started,
the research councils’ science budget in Britain was growing by more than
10 per cent a year, and the same was true in most other industrialised countries.
Yet it became apparent relatively soon that it was very hard to demonstrate
any clear correlation between research spending and the state of a country’s
economy. Even if you stick with the linear model, the links between an individual
discovery and a particular innovation can be hard to trace. As Rosenberg
pointed out at Sussex, the timescales involved can be very long. There is
no reason why the knowledge required to make a particular piece of technology
work need be new knowledge.

How far back should you look for the science underpinning the laser
in a CD player, for example? Further back than the early 1960s, when the
first lasers were developed, certainly. Further, even, than the Second World
War, although much of the technical know-how appeared then. In fact, you
should probably begin in 1916, when Einstein first wrote about stimulated
emission of radiation.

True, science today often gives the appearance of being closer to application
than in Einstein’s day, as in the current feverish excitement to develop
areas like biotechnology. Yet even this depends on the elucidation of the
structure of DNA nearly 40 years ago. You can always argue about where to
draw the baseline. And in general, as Rosenberg said, many who argue for
the economic importance of science overemphasise the significance of the
contemporary frontiers of science because they want larger research budgets.
They neglect old science because it has already been paid for.

All this implies an unpalatable conclusion for defenders of national
basic research spending. As Rosenberg said: ‘The commercial benefits of
basic research need not be captured by firms in the country where the basic
research was performed.’

At first, this realisation did not diminish enthusiasm for supporting
basic research. But policy makers’ concerns did start shifting to what came
after basic research. They began to think, for example, that the final product
had to be envisaged when judging the value of science. This process is dubbed
‘technology pull’, or nowadays ‘market pull’, in contrast to ‘science push’.
But how should these projects be chosen? The question became more urgent
when governments felt new pressures in the late 1960s and early 1970s to
mitigate the undesirable consequences of earlier economic growth, and to
face the reality of a decline in growth.

By the mid-1970s, it had become all too obvious that research could
not go on expanding exponentially. There seemed no slowdown in the growth
of scientific oppor-tunity, but increases in money to match were out of
reach. Researchers could (and still do) write proposals for more and more
expensive projects, but the idea that they would continue to spend an expanding
share of the national wealth was not practical politics. Polanyi’s view
that scientists should be left ‘to distribute themselves over the whole
field of possible discoveries’ was simply no longer plausible. Recognising
that the science could no longer be left to look after itself, science policy
advisory committees were forced to started choosing between disciplines.

And they had no obvious grounds for their choice, except to say that
some fields looked more likely to yield benefits than others. As John Kendrew’s
crucial report on high energy physics to the Advisory Board for the Research
Councils put it in 1985, ‘probabilistic assessments can be made . . . it
is not meaningless to assert that research on black holes is less likely
to provide utility by the turn of the century than research on self-organising
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Such arguments carried less and less conviction. On the one hand, anyone
could play this game, and have opinions about which science to support.
On the other hand, the attempt to emphasise particular fields as worthy
of support focused critical attention on the process by which the supposed
benefits of those fields arrived – and they generally became harder to identify
on closer examination.

At its sternest, such an examination can make a policy on basic research
seem almost incidental. Rosenberg closed his paper at the SPRU conference
by emphasising the influence of general economic conditions on research
spending. This is more or less an inversion of the proposition that science
spending leads to strong economies. Maybe it is strong economies which lead
to science spending? Considered as a series of investments, Rosenberg argued,
science and technology policy making is simply an aspect of economic policy
making. ‘The wrong set of economic policies can guarantee the failure of
any set of policies directed towards the realms of science and technology,
no matter how ingeniously conceived,’ he said.

The work of SPRU and others has now come to emphasise that basic research
is only one of the things a country needs in order to be successful in commercialising
new technology. In Technology and the Future of Europe, a collection of
essays published to mark the Sussex unit’s jubilee, Pari Patel and Keith
Pavitt discuss Europe’s technological performance as the collective product
of national systems of innovation which vary widely. Their archetypes are
‘myopic’ systems, exemplified by Britain, and ‘dynamic’ ones, exemplified
by Germany. The terms sum up wide differences: in financial systems, where
the Germans give greater weight to long-term performance and are much more
clued up about the value of companies’ intangible assets, like know-how;
in methods of management, where, in Britain, financial competence dominates
technical competence; and in education and training, where the German emphasis
on vocational training serves its engineering industries especially well,
although British strengths in higher education are good for sectors like
chemicals and pharmaceuticals. The overall implication is that, if one goal
of science policy is to increase national prosperity, there is little point
in spending heavily on basic research without also trying to reform some
complex social institutions which take a long time to change.

So does this mean the science policy researchers’ conclusions are largely
negative for basic research, or at best give it only a small place in a
policy framework which should include everything from industrial subsidies
to environmental regulation? Not quite. While news of the demise of the
linear model of innovation still comes fresh to some, in its wake there
is a growing body of work which enriches understanding of how sciences and
technologies interact. Although Nathan Rosenberg’s road map is not yet drawn,
some of the main routes are visible.

There are perhaps three strands in this kind of research which contribute
to an effort to replace the linear model with something that is more open
to the complexities of the real world, yet is still tidy enough to be of
some use as a model – which after all means there must be a certain amount
of simplification.

First, there are the economists, compiling and inspecting their statistics.
These cover national R&D spending (civil and military, public and private),
as well as patents, balances of trade, and performance of particular industries
or companies. Following such figures over time, and comparing countries
or industrial sectors, can tell you a number of things. Take, for example,
recent work by Pavitt of SPRU, who has long used US patents as an index
of technological innovation. With Patel, he is now quarrying a database
which profiles the technological strategies of almost 700 of the world’s
largest companies over the last 20 years. Between them, these concerns produce
about half the world’s technology, judging by the volume of patents filed.

Among other things, the two researchers find that, although there is
much talk of the ‘globalisation’ of production, around 90 per cent of the
innovative work of these companies takes place in their home countries.
It appears to be easier to manage the development and first introduction
of new technology in the country where the main centres of planning and
decision lie. Very large firms, in fact, tend to be strongly shaped by the
histories of their countries of origin, and by their patterns of natural
resources, consumer preferences and traditions of skilled work. Companies
accumulate a particular set of competences that enable them to innovate
in some sectors, and not others.

This rootedness of firms in a national environment is one element in
the case for maintaining a national scientific base – not for specific benefits,
which remain uncertain, but as a general capability. The results of research
may be an international resource, but the people trained in university and
polytechnic laboratories are largely a national resource.

A second strand in the study of science policy involves detailed examination
of individual innovations by economists and historians of technology. You
can call this storytelling, or be more academic and speak of case studies,
whose value lies in their illustration of the complexities of the whole
business.

In the hands of a scholar like Rosenberg, for example, such studies
can be made to reveal some of the many ways technology influences science,
rather than the other way round – technology pull in a different sense.
At the turn of the last decade, Rosenberg’s celebrated book Inside the Black
Box helped to persuade many people that technological advances often shape
the scientific agenda. At the Sussex conference, he returned to this theme
with a new emphasis, as part of his sketch of the ‘road map’ science policy
researchers now need to work towards.

Today, he suggests, one definition of a high-tech industry is that ‘it
is one in which problems that arise at the technological frontier exercise
a major role in shaping the research agenda of science’. An example of this,
he believes, is that industrial problems throw up observations or stumbling
blocks which are unlikely to crop up in a university laboratory – an underwater
cable which corrodes unexpectedly fast, perhaps, or the surface heating
of an aircraft wing as it breaks the sound barrier. ‘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
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On the other side of the interaction, Rosenberg points out that the
focus on scientific knowledge has led to a neglect of the importance of
scientific instruments. In a number of key industries, instruments which
were first developed as tools for laboratory investigation – as a by-product,
if you like, of the search for new knowledge – have become essential capital
goods, at the heart of production.

‘Much, if not most, of the equipment that one sees today in an up-to-date
electronics manufacturing plant had its origin in the university research
laboratory,’ he observes. For example, ion implantation, now used for ‘doping’
microchips, stems from expertise in manipulating particle beams first developed
in high energy physics machines. Synchrotron radiation, first encountered
as an irksome by-product of accelerator rings, became a research tool in
condensed matter physics and biology and is now poised to assume a new role
in submicron lithography for chips. And microelectronics products have long
been examined for quality under the scanning electron microscope, which
was originally a laboratory aid.

Similar tales could be told about industrial process control, robotic
sensing, cryogenic techniques for handling rocket fuel, or superconducting
magnets now used in medical scanners. Taken together, ‘they constitute part
of the benefits of basic research that are separate and distinct from the
benefits flowing from pure scientific knowledge and the eventual applications
of that knowledge’. In this respect, it may be that the linear model, in
tandem with traditional academic snobberies about the value of ‘pure’ knowledge
as opposed to hardware, actually undervalues science’s contribution to industry.

As these examples indicate, work like this can outline some landmarks
on the map, but it tends towards the anecdotal. We don’t know how such features
fit into a complete landscape. So is there any prospect of making the mapping
Rosenberg seeks a bit more, shall we say, systematic?

Perhaps there is, in a third strand of science and technology policy
research which is now beginning to bear fruit: the analysis of scientific
publications and citations, normally termed bibliometrics. The introduction
of computerised databases of papers in scientific journals, and the citations
they list to the rest of the literature – principally by Eugene Garfield’s
Institute for Scientific Information – was initially a boon for scientists
searching the archives to find out what was going on. But this kind of data
has increasingly been used by policy researchers to investigate the output
of laboratories that use up taxpayers’ money.

Bibliometrics has found two main uses: as an index of the productivity
and impact of countries, institutions or even research groups; and as a
probe of interactions between groups of research workers, and of the state
of different fields of research. By looking at who is citing whom, for example,
and how fast it is happening, you can get some notion of the way the patterns
of knowledge growth are changing over time. Dutch researchers have christened
this ‘science dynamics’.

These techniques are useful for making relatively crude productivity
assessments, and SPRU led the way in using bibliometrics to compare large
scientific facilities. The cash squeeze, combined with the general search
by the government in the 1980s for ‘performance indicators’ for publicly
funded work, meant this approach was adopted, with modifications, by both
the ABRC and the University Grants Committee (now the Universities Funding
Council). This kind of assessment, though imperfect in many ways, is now
entrenched in the bureaucracy of the UFC and the research councils.

However, while these productivity assessments naturally preoccupy many
academics, the other uses of bibliometrics may turn out to be more interesting.
Part of their appeal to policy makers at first was as a surrogate for knowledge
about areas of science which would lead to valuable technologies. Bibliometric
analysis can cut short the wait for the moment when benefits arrive according
to the linear model, and at least confirm quite quickly that something interesting
is happening – a useful aid when you have to decide allocation of funds
between fields. That this was a strong reason for looking seriously at such
techniques was plain in a paper I wrote in 1988 with the ABRC chairman,
David Phillips, summarising the advisory board’s experience until then.
As we said: ‘While ABRC has a strong interest in the economic and social
benefits of research . . . these commonly take many years to appear and
are hard to measure. The appeal of bibliometric techniques, despite their
restriction to scientific publication, is their relative immediacy and wide
applicability to the study of scientific outputs, whether in basic or strategic
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An added appeal is that they enable science policy researchers to test
some of the assumptions which too easily creep into debates on science policy.
The best example so far is the demonstration by Diana Hicks and Jim Skea
of SPRU that there is no link between the number of papers individual researchers
publish and the size of the departments where they work. This conclusion,
which holds good for chemistry, physics and the earth sciences in Britain,
runs directly counter to current funding which favours departments larger
than some supposed ‘critical mass’ for effective research.

Beyond this, work which brings in citations as well as publications
offers intriguing possibilities for illuminating the details of connections
in science – between fields, between people, between institutions, between
countries. If you bring in the more recent databases of patents and the
references they make to the journal literature, this extends to connections
between science and technology.

Take just two examples described at the SPRU conference. Ben Martin
and colleagues from Sussex are looking at the use large companies in Britain
and Japan make of outside researchers. Despite the obvious constraints of
commercial life, many companies publish widely in the open literature. ICI
in Britain, for example, produces more than 400 papers a year, 60 per cent
more than it did a decade ago, and more than many good-sized universities.
Martin’s study of the papers published by British companies reveals that
an increasing proportion are written in collaboration with outside authors,
most commonly from universities. More than 40 per cent of BP’s papers involve
outside co-authors, for example, as do 34 per cent of ICI’s papers. And
the early findings indicate that the largest proportion, just over 30 per
cent, of these corporate-academic links are made in Britain.

The project still has a way to go. There are plans to extend the analysis
to patents, and their citations to research, to compare the findings with
similar work on Japanese companies, and to put the raw numbers in context
with case studies and interviews with industrial research managers. The
result should be a very detailed picture of the interconnections between
corporate R&D and academic science.

The advent of patent databases has also been extensively exploited by
the American researcher Francis Narin, one of the pioneers of the analysis
of scientific publications in policy research. Narin, who founded the private
company CHI Research over 20 years ago, is now looking at the patterns of
citations to the academic literature found in patents approved by the US
authorities. Looking at trends between 1975 and 1989, he finds that patents
citating scientific papers are becoming more common. They are generally
subject-specific – that is, patents for drugs and medicines cite papers
in the life sciences, and so on. The time-lag between scientific publication
and patent citation is decreasing, and has virtually disappeared in some
fields.

All this is evidence for increasingly rapid interchange between science
and technology, and evidence that the interchange is fruitful. The science-intensive
areas of patenting, including electronics and biotechnology, are growing
more rapidly than others, such as heavy engineering. The citation of contemporary
science in patents would once have been used to support the idea that ‘lags’
in a linear scheme of discovery-then-application were reducing. But the
finding works just as well to support the contention that the kind of applications
of science that are sought shape the science that is being done. The genes
of the pig are being mapped today, for example, not because the creature
is scientifically more interesting than, say, the tapir, but because pig
business is big business. When pig papers start turning into patents, it
will be because that was the intention all along.

What work like Narin’s also suggests – and you can get very similar
results using the database built up from the European Patent Office – is
that there are still strong national ties in science and technology. This
chimes with Pavitt’s observations about the behaviour of large companies.
Although knowledge knows no national boundaries, patents filed in the US
which originate in Britain are significantly more likely to cite scientific
papers by British authors than Britain’s share of the world’s papers would
suggest (see Diagram). And the same is true for other countries.

As results like this mount up, it becomes plainer that a national science
base is a vital resource. But this general truth is still not underpinned
by much in the way of specific prescriptions for different fields. The policy
makers’ ideal of a clearer understanding of the relations between funding
and the growth of knowledge remains a tall order. Perhaps one day some of
the detailed questions which vex decision makers will be posed in bibliometric
terms. For example, if you are becoming increasingly selective in your funding
system, it would be very nice to know whether the best science, which you
want to keep, needs the results and insights of all the middle-ranking researchers
to keep it going. Can you just have the cream, or does it need some milk
to sit on? The answer is almost certainly that you can make do with just
the cream, but no one knows how much cream, or whether it differs in different
fields, or at different times, nor even whether the separation can be reliably
performed.

Spending on science, as on most other things, consists largely of buying
what you bought last year, plus a little bit extra if you are lucky. So
can the new analyses help pinpoint areas which no longer produce any cream
at all? Some areas must be pruned to make space for new opportunities,
but what indicators can you use?

So far, so frustrating. The demise of the linear model implies the need
to make choices in science, and to manage the whole innovation process.
But there are few clues about how a planned selection should be made, either
to maximise efficiency in the simple sense of getting the best science for
the money, or in the larger task of serving economy and society. Science
policy is still much more art than science.

Even so, the demise of linear thinking should be a welcome challenge
to the collective imagination. For the linear model was, at its heart, a
determinist scheme. Science set the agenda, developing according to its
own internal logic. Technology then appeared as a more or less inevitable
consequence of the boffins’ unguided discoveries. It seemed the rest of
the world had little choice in the matter.

Now it can be seen that a whole variety of people are involved in the
interactions between science and techno-logy. Product planners, designers,
marketing experts, consumers, regulators, forecasters and pressure groups
all intervene, influencing what innovation theorists call the ‘selection
environment’ which produces particular technologies from a spectrum of possibilities.
The choices they make penetrate science, fixing the direction of inquiry
if not its results. One implication of the decline in linear thinking is
that, through the sum of all these choices, we eventually get the technology,
and perhaps even the science, we deserve. It is a conclusion Bernal would
have approved of.

Jon Turney is features editor for The Times Higher Education Supplement.