HARVEY BROOKS
Professor of Technology and Public Policy, Emeritus,
John F. Kennedy School of Government, Harvard University

Technology transfer is a way of linking knowledge to need. In fact, a continuum exists between the assimilation and creation of new knowledge, although in practice it is customary to think of technology transfer and research and development as separate activities. Such a continuum is important, because there is a tendency, especially in the developing countries, to think of technology transfer in minimal terms-for example, the transfer of the minimum amount of know-how needed to enable a specific manufacturing plant to produce certain products. Too many politicians and managers in developing countries view technology as a package that can be bought “off the shelf” and become immediately useful. But rightly conceived, technology transfer is a process of cumulative learning, in the same sense that research and development (R&D) are a form of cumulative learning.

Thus neither technology transfer nor R&D are synonymous with innovation. Each is just one element in a much more complex process that is better described as “social learning,” or, more accurately, as “sociotechnical learning” since technical knowledge, organizational knowledge, and new relationships among people inside and outside an organization have to be absorbed and “internalized” in groups of people before an innovation or even a production plant can be sustainable. In this sense, there is much less of a difference than generally supposed between a production system that is new to the world and one that is merely new in a particular sociotechnical context characterizing a particular manufacturing site and market.

For this reason, the process of creating a production system at a new site always can be considered, at least in part, an innovation because, though not new in the world, it is new in its particular social-economic-political context. The process itself is thus fundamentally similar in developed and developing countries, whether one is replicating something that has largely been done before elsewhere, or doing something that has never been done before anywhere. It is the absorption of knowledge into a system of design, production, and interaction with clients or customers that is critical; the novelty of the knowledge in context is really the critical variable in the process.¹

One implication of this viewpoint is that “know-why” is often as important as “know-how” in the process of absorption because only if the reasons for particular technological choices are understood can the transferred knowledge be built on in a cumulative manner and the processes continually improved and made more efficient. Thus Richard Nelson defines innovation as “the processes by which firms master and get into practice product designs and manufacturing processes that are new to them, whether or not they are new to the universe, or even to the nation.” Furthermore, he defines technology not only as “specific designs and practices” but also as “generic understanding . . . that provides knowledge of how [and why] things work” and of “what are the most promising approaches to further advances, including the nature of currently binding constraints.”²

This view of technology transfer begins therefore with the premise that the conditions for successful technology transfer are basically similar in developed and developing countries and differ only in the fact that in developed countries much of the transfer takes place between research and development organizations, whether inside or outside the firm, and other parts of the firm, while in the developing countries technology is usually transferred from outside both the country and the firm. In each case, a “culture gap” must be bridged, although in the case of developing countries it is likely to be much larger than within or among developed countries. Nevertheless, the basic challenges of organizational absorption are quite similar in kind if not in degree.

THE BASICS OF TECHNOLOGY TRANSFER

The process of technological innovation can be described as one of matching solutions in search of problems to problems in search of solutions. Solutions in search of problems are mostly produced by corporate and academic research laboratories and other forms of organized research and development, or, in the case of developing countries, are available somewhere in the industrialized world. Problems in search of solutions are what industry, society, and design engineers encounter in practice. Solutions in search of problems usually have proved to be the most efficient way to create and package knowledge for ready communication, but in that form it is usually not most easily used by appliers of knowledge. Improving the “impedance” match between these two forms of knowledge is a primary task of technology transfer.

Until quite recently, one problem of U.S. technology policy was its implicit overemphasis on R&D as synonymous with technological innovation and as the key focus of technology policy. The mission of technology policy was thought to be to get R&D right, on the assumption that everything else would take care of itself more or less automatically. Some of this same attitude also has tended to govern development strategy: the initial technology choice is the only critical factor rather than the entire process by which the results of this choice are ultimately internalized in the overall organization of the firm and its work force.³

But even in the developed world R&D represents only a small fraction of the total investment needed to get a new technology to market-about 10-15 percent on average according to the famous Charpie report. ⁴ The other 85-90 percent is usually referred to as “downstream” investment in design, manufacturing, applications engineering, and human resource development-the last involving a great deal of hands-on training in the context of actual operations. Although not all of these activities are purely technical (they are better described as “sociotechnical” because they involve changes in organization and human relations as well as technology), they do require a heavy commitment of experienced technical personnel. Indeed, about 65 percent of all professional scientists and engineers in the national work force in a typical industrialized country are not engaged in R&D at all but in a broad spectrum of downstream activities. Much of the technical activity from which “economic rents” may be derived resides not in the R&D but in these downstream activities, and this becomes increasingly so as technology becomes more “science based.” But because of the way in which statistics on technical activity in the United States have been collected, much more is known about the nature, quality, and content of R&D than about the quality and value-added of the downstream technical activities. The same is even truer in most of the developing world, where these downstream elements of the “technical capacity” of a firm represent an even larger fraction of the total effort.

Over the last 30 years, much research has been conducted on the features of the innovation process that lead to commercially or operationally successful technological innovation. This research began with the chemical and scientific instrument industries in the 1960s and since then has been extended to the machinery and electronics industries. According to Christopher Freeman, ⁵ who summarized the main conclusions of this work at a 1990 Montreal conference on networks of innovation, there are six sources of innovative success:

1. Understanding user needs and establishing user-producer networks, as well as attempting to understand the special circumstances and needs of potential users of products and processes. Strong and continuing user-producer linkages are vitally important.
2. Coupling development, production, and marketing activities, and considering manufacturing and marketing requirements at an early stage of development. Ongoing technology assessment should be carried out based on such considerations and on monitoring technological developments outside the firm.
3. Linking up with sources of scientific and technical information and advice outside the innovating organization. Outside networking is essential even with strong in-house R&D. Inside and outside sources of information are complementary, not alternatives. Internal R&D should tap into external science and technology networks in order to facilitate the generation of new knowledge internally.
4. Concentrating high-quality R&D resources on the innovative project. A critical size of R&D effort is necessary to realize internal goals and to match the activities and investments of competitors. A strong in-house technical effort is essential for understanding the significance of new technical developments outside the firm.
5. Seeking a relatively high level of performance of some relevant basic research within the firm, which usually correlates strongly with successful and timely innovation largely because it tends to enhance early awareness of technical developments that might affect the evolution of the innovation.
6. In the firm, an entrepreneur/innovator is generally characterized by high status, wide experience, and seniority within the organization. Top management has a high degree of commitment to the success of the innovation and performs a network coordination function both inside and outside the firm, serving as its principal innovation champion.

Almost all of these items are applicable to developing countries. Although R&D resources (item 4) as such are less important, the equivalent of R&D is high-quality training applicable to the particular products and processes involved. Such training, both generic and specific, should be an important part of any technology transfer package. ⁶ The entrepreneur and the entrepreneurial spirit (item 6) are equally essential in developing countries' but they are often more foreign to the indigenous culture than is the case in industrialized countries.

One of the major trends in the developed countries over the last 30 years has been the increased importance over time of the sources of technical information and ideas that originate outside firms, including the growth of institutional alliances and “innovation networks,” frequently crossing national boundaries. Many of these structures are ad hoc and temporary, formed for particular innovations or production plants. This results in a complex intermingling of competition and cooperation, with some firms cooperating in selected projects while at the same time competing in other areas. Some of the varieties of institutional interdependence and cooperation are:

1. Joint ventures and joint research corporations
2. Joint R&D agreements among firms
3. Technology exchange agreements
4. Direct investment motivated by technology factors
5. Licensing and second-sourcing agreements
6. Subcontracting, production-sharing, and supplier networks
7. Research associations
8. Government-sponsored joint research programs
9. Computerized data banks and value-added networks for S&T exchange
10. Informal and only partially sanctioned information sharing among technical people in competitive firms.

The kind of exchange described in item 10 is often not strongly opposed by management because there is an implicit expectation of future reciprocity that makes the joint gains from such cooperation exceed the possible competitive losses in the long run. ⁷

In short, as product design and production technology have become increasingly science-based, know-how has tended to diffuse more and more rapidly throughout the world technical community. As a result, the competitive advantage from which economic rents can be derived depends more on the downstream details of implementation and less on the novelty and originality of the basic technical idea or generic design. Moreover, more rapid diffusion greatly narrows the window of opportunity available for getting into the market with competitive technology and products, thereby reducing the chances of recovering the initial costs of innovation or investment in new products and production systems within this window. At the same time, the chances of being outclassed by competitors' innovations also are increased simply because of the increased volume of innovative activity worldwide.

TYPOLOGIES OF TECHNOLOGICAL INNOVATION

Freeman has proposed four categories of technological innovation:

1. Incremental innovations are those concerned only with improvements in existing products, processes, organizations, and production systems. They are closely linked to actual or potential market demand or experience in use and are driven by the user-producer and learning-by-doing relationships of producers and users. Such innovations follow a well-defined or relatively predictable techno-economic trajectory, and while they may not be dramatic individually, they have a large cumulative impact. Moreover, they often are essential to realizing the potential payoff from radical innovations.
2. Radical innovations are those that produce discontinuities in the techno-economic trajectory. They do not arise from incremental improvement of an existing product, process, or system. Indeed, one of their most important impacts is that they change the parameters for cost-effective incremental innovation.
3. New technological systems are constellations of innovations that are closely interrelated both technically and economically. Clusters of innovations form “natural trajectories” that are gradually consolidated into a system. Thus over time they become increasingly incremental as the interdependencies deepen and are assimilated into the economic, social, and educational structure.
4. Changes of technoeconomic paradigm correspond most closely to the “creative gales of destruction” in Schumpeter's theory of economic growth under capitalism. ⁸ Such innovation is accompanied by several clusters of radical and incremental innovations and embodies several different technological systems. But most important are the pervasive effects throughout the entire economy, which include the organizational and social changes and the widespread acceptance of technical and management practices that are necessary to fully realize the impacts of the technical changes.

Most technological innovation is incremental in nature. Here the intimate interaction between users and producers of technology is the key to success in the market. Moreover, it is the cumulative effect of many apparently minor incremental innovations that is primarily responsible for steady growth in productivity and for the expansion of both the size and technological scope of markets. Small, incremental innovations are even more important to economic success in developing countries than in developed countries. The conditions for such incremental innovation are optimized when the technology transfer from vendors is deliberately planned as a learning vehicle for the entire work force of the recipient firm. This will require a negotiating strategy to ensure that engineers and technicians in the recipient firm are involved in all the activities of the suppliers in order to promote the transfer of not only specific know-how but also related generic and systemic knowledge of the relevant technologies. In this way, the firm's own personnel can begin to contribute added value at the earliest possible moment to the information transferred. ⁹

The difficulty with such categories of innovation is that they overlap and are complementary so that, for example, radical innovations and, even more, technological paradigm shifts can realize their economic impact only through intensified incremental innovation efforts made after they first appear. Thus the role of radical innovations is largely to open up opportunities for new kinds of incremental innovations by increasing the reward/cost ratio of such innovations, much as the discovery of a new vein of ore in mining expands the economic rewards possible from prospecting that particular lode. But such novel efforts also are open to greater competition since the new paradigm tends to erode the competitive advantage derived from cumulative experience with older paradigms and thereby narrows the window of opportunity for competitive success in incremental innovation. This is an important consideration for developing countries because it implies that the work force must experience continual cumulative learning, both from experience and from formal training, in order to remain competitive in a world market where intense, continual, incremental improvement is increasingly essential to sustained competitiveness.

Similarly, new technological systems generate demand for all kinds of synergistic collateral innovations, some of which may be radical innovations in new fields. If the range of such collateral innovations becomes broad enough and affects enough sectors of economic activity, the whole can grow into a new technoeconomic paradigm, fundamentally altering the structure of relative factor costs and demanding far-reaching social and institutional innovations.

The transition from a single radical innovation to a new technoeconomic paradigm through the evolution of technological systems is seldom anticipated at the beginning of the process; it only becomes apparent gradually over time. Thus information technology and microelectronics, taken together, are a prime example of a new technoeconomic paradigm. Yet it all began with the invention of the transistor in 1947 and the earliest electronic computers that preceded the first use of transistors. Both the computer and the transistor were seen as radical innovations, but initially both were perceived as merely radically improved substitutes for existing technologies-the transistor for the vacuum tube and the computer for the mechanical calculator or tabulating machine. The new technoeconomic paradigm was recognized only gradually as numerous other innovations in materials, solid-state devices, mathematical programming and higher-level computer languages, information theory, and signal processing, among others, emerged at first more or less separately and then merged and became much more widely diffused. Sometimes, a particular radical innovation was recognized in retrospect to have been a precipitating event. The integrated circuit and the microprocessor were such events in the case of the information technology paradigm; the first higher-level computer language was as well. But such events appear to have been precipitory only in light of the availability of many other ancillary technologies.

A new technoeconomic paradigm can be recognized by three features:

1. Its clearly perceived low and rapidly falling cost.
2. Its apparently almost unlimited supply, available for long periods.
3. Its clear potential for use or incorporation in many products and processes throughout the economy, either directly or through related organizational and technical innovations that reduce the cost and enhance the quality of capital, labor, and material to virtually all sociotechnical systems.

Two examples are the mass-production paradigm of the early twentieth century (Fordism) and the “information society” paradigm that the world is still in the midst of (or perhaps only on the threshold of) today. Time has shown, however, that often decades pass before the enhanced productivity potential inherent in paradigm shifts is realized. ¹⁰

One of the aspects of technology transfer that has not been fully sorted out is the relationship between the criteria for successful innovation and the types of innovation. In particular, it is not entirely clear which features are most important for success in which types of innovation and how these features and types interact.

TECHNOLOGY TRAJECTORIES AND LIFE CYCLES

Another important concept is that of the technology trajectory or technology life cycle. Each type of innovation goes through such a life history, and the higher categories of innovation are in some sense the result of the superposition of life cycles associated with components from the lower categories of innovation. ¹¹ The lower categories usually go through their life cycles more rapidly than the higher categories, and the highest category, the technoeconomic paradigm, is often hypothesized to be with the Kondratiev long waves of economic speculation. ¹²

Technology historian Thomas P. Hughes distinguishes between two types of radical innovation: those that are radical in a technological sense but fit rather readily into existing institutional structures, and those that can only be realized on a significant scale after a substantial restructuring of institutions or social innovation accompanying technical innovation. ¹³ Put more crudely, one type of innovation favors existing power structures; the other tends to disrupt them.

A second subcategorization that applies mainly to incremental and radical innovations distinguishes between innovations for which a single patent or group of patents or trade secrets held by a single owner or inventor provides a relatively unassailable proprietary position (“simple” technologies), and innovations whose practical realization depends on a series of interdependent patents or trade secrets not controlled by any single company or organizational entity, thereby requiring extensive cross-licensing or alliances (“complex” technologies). ¹⁴ Specific chemical compounds and pharmaceuticals are examples of simple technologies, while most electronic innovations are examples of complex technologies.

Why should radical innovation and the emergence of new technoeconomic paradigms be relevant to problems of development, despite the fact that these types of innovation are likely to be created only by industrialized countries that possess the highly capable scientific and technological infrastructures necessary for such creation? The answer is that both of these types of innovation, the radical and the systemic, can generate many new niches for incremental innovation that do not necessarily require the advanced knowledge and experience needed to create and manage the technological system as a whole. Developing countries with minimum levels of basic education in their work force and little industrial experience may be able to fill these niches quite successfully, often at lower cost than developed countries. This already has been dramatically demonstrated by the success of the Asian “tigers” in numerous niches opened up by the revolution in information technology (see “Information Technology for Development” by John S. Mayo in this volume). Similar niches may be opening in the field of energy efficiency and certain kinds of decentralized generating systems (see “Technology Innovations in Energy” by Richard E. Balzhiser). This development may stem from the so-called Leontief paradox, which asserts that the early stages of a radical innovation are often labor-intensive rather than capital-intensive. ¹⁵ Although Leontief's theory was originally advanced to explain the trade advantage of developed countries in labor-intensive, high-technology trade, his argument can be modified to suggest that developing countries also can develop a competitive cost advantage when they use their own well-trained engineers and technicians to implement incremental innovations at a much lower cost than if such innovations were implemented by the more expensive engineers and technicians in the developed countries who can generate more value relative to their salaries in more sophisticated fields. Examples are disk drives for personal computers and certain types of software developments that have migrated to the newly industrialized countries where they incur much lower engineering manhour costs than in the developed countries.

In this kind of competition, timing is critical. Advantage accrues to well-prepared developing countries at a phase in the technology life cycle when demand is expanding rapidly (including the demand for ancillary and supporting technologies, which are the essential components of an emerging technological system), rather than at the earlier critical stage of innovation and discovery that produced the new technology in the first place.

Another kind of opportunity for developing countries which is more speculative arises from the “lock-in” effect that often develops in rapidly expanding technological trajectories. ¹⁶ At a certain stage the originators of the cycle may have accumulated large sunk costs in process-specific capital and process- and product-specific training that they are hesitant to write off before the costs are fully offset by the accumulated volume of sales. At this point, there is room for a new entrant with a different approach provided the market is still growing fast enough. But the opportunities for new entrants are usually small in fields where a single player holds a single patent or group of related patents. Where no one player has an impregnable position without access to intellectual property held by others, the opportunities for new players are greater.

In all of the niche-type opportunities just discussed the aspiring entrant must have a fairly thorough generic understanding of the technological system in which a potential new niche may lie. This is one reason why imitation can be said to be the first step toward innovation-but only to the extent that it provides a real window into an entire technological system.

MODELS OF INNOVATION AND TECHNOLOGY TRANSFER WITHIN FIRMS

Since World War II until recently, much of the thinking about technology policy and strategy in the United States was in terms of a linear-sequential type model of innovation (see Figure 1), in which innovation originates in a scientific discovery, which then leads to applied research, followed by development, design, manufacturing, and marketing in an orderly, unidirectional sequence. In this “supply-side” model of technology development, the economic rewards derivable from new technology are limited primarily by its supply. This model is not an unreasonable description of what happened, for example, in the case of nuclear fission, the laser, and the discoveries in molecular biology that led to the emerging biotechnology industry. And as Mayo points out, it represents a fairly good model for technological innovation in telecommunications as it has occurred up until quite recently. But such a linear sequence is occurring less and less frequently as a growing supply of technology accompanies the worldwide growth in the volume of R&D and in the population of scientists and engineers. And when the linear sequence does occur, it is, at the beginning at least, unanticipated. But more important, the model overlooks the two-way or iterative interaction that occurs between the successive stages of technology development, which is significant even in the case of radical innovations that otherwise conform to the linear model more closely than the other types of innovations listed earlier.

FIGURE 1 Linear-sequential model of innovation. SOURCE: Stephen J. Kline and Nathan Rosenberg, “An Overview of Innovation,” in The Positive Sum Strategy: Harnessing Technology for Economic Growth, ed. Ralph Landau and Nathan Rosenberg (Washington, D.C.: National Academy Press, 1986), 286.

A more realistic representation is provided by the so-called “chain-linked'' model (see Figure 2). In this model, the early stages in the chain have to be revisited frequently in light of new questions raised when insights are developed only after the downstream phases have been undertaken, including many issues that do not become apparent until early versions of a product or process already have entered the marketplace.

Ken-ichi Imai, one of the leading philosophers of the Japanese theory of innovation, has presented the same general idea in a different way (Figure 3). Imai distinguishes between three strategies of innovation. Type A essentially corresponds to the old linear model in which each phase is completed and then thrown “over the transom” to the next phase with a largely different set of actors taking over. Type B probably corresponds most closely to the current average U.S. practice in which there is substantial overlap between successive phases. Type C represents the predominant Japanese practice in which all three phases overlap much more extensively. Manufacturing and marketing considerations enter into the planning of upstream activities much earlier, and substantial research continues even after first introduction to the market, guided by feedback from customer experience. The best practice of the most successful U.S. companies also has become steadily closer to type C than to type B.

Comparison of the Japanese and American strategies (at least until recently) for the engineering development of a new automobile model (Figure 4) reveals that the Japanese new model development requires about half the number of engineering man-hours needed for the corresponding U.S. development. Furthermore, for Japanese manufacturers the basic features of the overall product and process design are “frozen” much earlier in the cycle. The difference in the Japanese and U.S. patterns is believed to stem in part from the much better job the Japanese do in documenting and codifying their previous model development experiences so that much more experience is transferred from one model development to the next. Similar comparisons have been constructed by Xerox for comparing the Japanese and U.S. development cycles for new photocopier models. Recent evidence indicates, at least for copiers and automobiles, that the gap in practice has narrowed in recent years and that in a few instances product design for manufacturability early in the cycle has gone further in the United States than in Japan (for example, the Chrysler Neon). ¹⁷

FIGURE 2 Chain-linked model of innovation. Symbols on arrows: C = central-chain of-innovation; f = feedback loops; F = particularly important feedback.
K-R: Links through knowledge to research and return paths. If problem solved at node K, link 3 to R not activated. Return from research (link 4) is problematic - therefore dashed line.
D: Direct link to and from research from problems in invention and design.
I: Support of scientific research by instruments, machines, tools, and procedures of technology
S: Support of research in sciences underlying product area to gain information directly and by monitoring outside work. The information obtained may apply anywhere along the chain.
SOURCE: Stephen J. Kline and Nathan Rosenberg, “An Overview of Innovation,” in The Positive SUm Strategy: Harnessing Technology for Economic Growth, ed. Ralph Landau and Nathan Rosenberg (Washington, D.C.: National Academy Press, 1986), 289.

FIGURE 3 Japan's national system of innovation. Sequential (A) vs. overlapping (B and C) phases of development. SOURCE: Ken-ichi Imai, “Japan's National System of Innovation,” paper prepared for the NISTEP Conference, Shimoda Tokyo Hotel, Tokyo, February 2-4, 1990.

These three representations of innovation all underline the importance of a fully integrated innovation process, which also is consistent with the criteria for success listed earlier. Such integration includes overlapping participation by the actual personnel in the various phases. Thus marketing and manufacturing people might participate in early R&D, and some R&D people might interact with the final customers in the market to obtain feedback for the ongoing improvement (kaizen) process that is fundamental to the Japanese system.

The separation between the various stages of the production system generally tends to be a more difficult problem in developing countries than in industrialized countries, in part because of the education gap in developing countries between the engineers and the rest of the work force and often because of a cultural tradition in which “hands-on” work has lower social status than intellectual work, making the integration of the stages of production more problematic. This may be partially counteracted in countries that have a strong artisan and craft tradition that has not yet been extinguished by the spread of the older mass-production paradigm of the West. It also is less of a problem in the countries, such as the East Asian “tigers,” that have a strong indigenous tradition of universal, high-quality education at the elementary level. The difficulty can be overcome as well by an emphasis on high-quality, on-thejob training that supports some generic training in parallel with highly job-specific know-how, including inputs from sources other than the exporter or vendor of the technology. ¹⁸

FIGURE 4 Rate of issuance of design changes - patterns of U.S. and Japanese auto manufacturers. Reprinted, with permission, from L. P. Sullivan, “QFD: The Beginning, End, and Problem in Between,” in Quality Function Deployment, A Collection of Presentations and Case Studies (Dearborn, Mich.: American Supplier Institute, 1987). © 1987 by American Supplier Institute, Inc., Allen Park, Michigan (USA).

It is important to emphasize that the present theory of innovation is very incomplete and impressionistic. Despite a growing volume of detailed case histories, it has proven difficult to develop robust generalizations, and especially to decide the degree to which they apply across many different types of innovation (such as those proposed by Freeman and described earlier) and many different industrial sectors. The generalizations about tight coupling between successive stages in the innovation process and about the importance of awareness of changes in technology and market conditions in the competitive environment are robust when stated at that level of generality, but what this coupling and this awareness actually entail in terms of the actions of managers and workers and technical experts in particular circumstances is far from self-evident. In any given case history, it is often difficult to distinguish between what is idiosyncratic to that particular case and what can be generalized to other similar cases or even what factors are most salient in defining what is meant by “similarity.” This is really a part of the unfinished business of innovation research implied in the subtitle of this paper, “Linking Knowledge to Action.”

NOTES

1. Nit Chantramonklasri, “Managing Technology Transfer for Acquiring Technological Capabilities in the Context of Developing Countries,” draft paper, 1991. Also see by the same author, “The Development of Technological and Managerial Capability in Developing Countries,” in Technology Transfer in the Developing Countries (New York: Macmillan, 1990).
2. Richard R. Nelson, ea., National Innovation: Systems: A Comparative Analysis (New York: Oxford University Press, 1993), 4, 6.
3. Chantramonklasri, “Managing Technology Transfer,” and “The Development of Technological and Managerial Capability.”
4. Robert A. Charpie, chair, Panel on Invention and Innovation, Technological Innovation: Its Environment and Management (Washington, D.C.: U.S. Department of Commerce, January 1961).
5. Christopher Freeman, “Networks of Innovators: A Synthesis of Research Issues,” Research Policy 20, no. 6 (1991), originally presented as a paper at the International Workshop on Networks of Innovators, Montreal, May 1990. Also republished as Chapter 5 in Christopher Freeman, The Economics of Hope: Essays on Technical Change, Economic Growth and the Environment (London and New York: Pinter, 1992). For a more recent summary discussion of the history of research on innovation and economic growth, see Richard R. Nelson, “An Agenda for Formal Growth Theory,” Working Paper WP-94-85, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria, September 1994 (a contribution to lIASA's research program on Systems Analysis of Technological and Economic Dynamics).
6. Chantramonklasri, “Managing Technology Transfer.”
7. Eric von Hippel, “Cooperation between Rivals: Informal Know-How Trading,” Research Policy 16 (1987): 291-302.
8. For a discussion of this and other aspects of Schumpeter's theories of innovation, see Freeman, Economics of Hope, chap. 5.
9. Chantramonklasri, “Managing Technology Transfer.”
10. Paul A. David, “Computer and Dynamo: The Modern Productivity Paradox in a Not-TooDistant Mirror,” in Technology and Productivity (Paris: Organization for Economic Cooperation and Development, 1991).
11. T. Vasko, R. Ayres, and L. Fontvielle, eds., Life Cycles and Long Waves, vol. 340 of Lecture Notes in Economics and Mathematical Systems Series, ed. M. Beckmann and W. Krelle (New York: Springer-Verlag, 1990). See especially chapter by Harvey Brooks, as well as summary observations by Brooks.
12. Carlotta Perez, “Micro-electronics, Long Waves and World Structural Change,” World De- velopment 13 (1985): 441-463.
13. S. B. Lundstedt and E. W. Colglazier, Jr., eds., Managing Innovation (New York: Pergamon Press, 1981). See chapter by Thomas P. Hughes.
14. Don E. Kash and Robert W. Rycroft, “Two Streams of Technological Innovation: Implications for Policy,” draft paper, January 1992.
15. W. Leontief, “Domestic Production and Foreign Trade: The American Capital Position Examined,” Proceedings of the American Philosophical Society (September 1953).
16. Paul A. David, “Clio and the Economics of QWERTY,” American Economic Review 75(2): 332-337; and Brian Arthur, “Competing Technologies: An Overview,” in Technical Change and Economic Theory, ed. G. Dosi et al. (London: Pinter, 1988), chap. 26.
17. Economist, October 15, 1994.
18. Chantramonklasri, “Managing Technology Transfer.”