Science, technology and innovation each represent a successively larger category of activities which are highly interdependent but distinct. Science contributes to technology in at least six ways: (1) new knowledge which serves as a direct source of ideas for new technological possibilities; (2) source of tools and techniques for more efficient engineering design and a knowledge base for evaluation of feasibility of designs; (3) research instrumentation, laboratory techniques and analytical methods used in research that eventually find their way into design or industrial practices, often through intermediate disciplines; (4) practice of research as a source for development and assimilation of new human skills and capabilities eventually useful for technology; (5) creation of a knowledge base that becomes increasingly important in the assessment of technology in terms of its wider social and environmental impacts; (6) knowledge base that enables more efficient strategies of applied research, development, and refinement of new technologies.
The converse impact of technology on science is of at least equal importance: (1) through providing a fertile source of novel scientific questions and thereby also helping to justify the allocation of resources needed to address these questions in an efficient and timely manner, extending the agenda of science; (2) as a source of otherwise unavailable instrumentation and techniques needed to address novel and more difficult scientific questions more efficiently.
1: Introduction
Much public debate about science and technology policy has been implicitly dominated by a ‘pipeline’ model of the innovation process in which new technological ideas emerge as a result of new discoveries in science and move through a progression from applied research, design, manufacturing and, finally, commercialization and marketing. This model seemed to correspond with some of the most visible success stories of World War II, such as the atomic bomb, radar, and the proximity fuze, and appeared to be further exemplified by developments such as the transistor, the laser, the computer, and, most recently, the nascent biotechnology industry arising out of the discovery of recombinant DNA techniques. The model was also, perhaps inadvertently, legitimated by the influential Bush report, Science, the Endless Frontier, which over time came to be interpreted as saying that if the nation supported scientists to carry out research according to their own sense of what was important and interesting, technologies useful to health, national security, and the economy would follow almost automatically once the potential opportunities opened up by new scientific discoveries became widely known to the military, the health professions, and the private entrepreneurs operating in the national economy. (See United States Office of Scientific Research and Development (1945) for a recent account of the political context and general intellectual climate in which this report originated; see also Frederickson, 1993.) The body of research knowledge was thought of as a kind of intellectual bank account on which society as a whole would be able to draw almost automatically as required to fulfil its aspirations and needs.
The public may be forgiven its confusions, as indeed the relationships between science and technology are very complex, though interactive, and are often different in different fields and at different phases of a technological ‘life cycle’. Nelson (1992) has given a definition of technology both as “ . . , specific designs and practices” and as “generic knowledge.. . that provides understanding of how [and why] things work.. . ” and what are the most promising approaches to further advances, including “. . . the nature of currently binding constraints.” It is important here to note that technology is not just things, but also embodies
a degree of generic understanding, which makes it seem more like science, and yet it is understanding that relates to a specific artifact, which distinguishes it from normal scientific understanding, although there may be a close correspondence.
2.1: Science as a direct source of new technological ideas
In this case, opportunities for meeting new social needs or previously identified social needs in new ways are conceived as a direct sequel to a scientific discovery made in the course of an exploration of natural phenomena undertaken with no potential application in mind. The discovery of uranium fission leading to the concept of a nuclear chain reaction and the atomic bomb and nuclear power is, perhaps, the cleanest example of this. Other examples include the laser and its numerous embodiments and applications, the discoveries of X-rays and of artificial radioactivity and their subsequent applications in medicine and industry, the discovery of nuclear magnetic resonance (NMR) and its subsequent manifold applications in chemical analysis, biomedical research, and ultimately medical diagnosis, and maser amplifiers and their applications in radioastronomy and communications. These do exemplify most of the features of the pipeline model of innovation described above. Yet, they are the rarest, but therefore also the most dramatic cases, which may account for the persistence of the pipeline model of public discussions. It also suits the purpose of basic scientists arguing for government support of their research in a pragmatically oriented culture.
2.2: Science as a source of engineering design tools and techniques
While the process of design is quite distinct from the process of developing new knowledge of natural phenomena, the two processes are very intimately related. This relationship has become more and more important as the cost of empirically testing and evaluating complex prototype technological systems has mounted. Theoretical prediction, modeling, and simulation of large systems, often accompanied by measurement and empirical testing of subsystems and components, has increasingly substituted for full scale empirical testing of complete systems, and this requires design tools and analytical methods grounded in
phenomenological understanding. This is particularly important for anticipating failure modes under
extreme but conceivable conditions of service of complex technological systems. (See Alit et al.,
1992, Chapter 4). For a discussion of technical knowledge underlying the engineering design
process, cf. Chapter 2 (pp. 39-341.)
Much of the technical knowledge used in design and the comparative analytical evaluation of alternative designs is actually developed as ‘engineering science’ by engineers, and is in fact the major activity comprising engineering research in academic engineering departments. This research is very much in the style of other basic research in the ‘pure’ sciences and is supported in a similar manner by the Engineering Division of the National Science Foundation, i.e. as unsolicited, investigator-originated project research. Even though it is generally labelled as ‘engineering’ rather than ‘science’, such research is really another
example of basic research whose agenda happens to be motivated primarily by potential applications in design ‘downstream’ though its theoretical interest and its mathematical sophistication are comparable with that of pure science.