1

Evolutionary theories of technological diffusion

and their policy implications

Petr Hanel Professor of Economics

University of Sherbrooke Canada

and

Jorge Niosi Professor of Management

University of Québec at Montreal Canada

To be

Presented to the Annual meeting of EAEPE Porto

Portugal

November 1-3, 2007

Not to be reproduced or cited without the authors approval

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Table of contents

1. Introduction 2. Birth of adoption and diffusion theories 3. Economic research

3.1 Evolutionary theories on adoption 3.2 Evolutionary approaches to development

4. Policy implications 5. Conclusion

Bibliography

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1. Introduction

Technological innovation has an impact on economic growth only when it is widely adopted

and diffused. In this paper, we call “adoption” the actual implementation of a new technology

at firm level, and “diffusion” the spread of the new technology across the economy. As the

proportion of potential users that have adopted an innovation increases, the new technology is

diffused and its economic impact grows. Ruttan (2001) proposed that the economic literature

on adoption and diffusion be split into two different strands: equilibrium and evolutionary

models. However, even though the equilibrium and evolutionary approaches were developed

in parallel for two decades, they began to converge lately in empirical studies. This paper

concentrates on evolutionary models and their policy implications.

Evolutionary models of diffusion are based on the work by Richard Nelson (1968) and Nelson

and Winter (1982). A new strand of evolutionary models is based on complexity and systems

dynamics inspired by the work of Jay Forrester and John Sterman at MIT and W. B. Arthur

and David Lane at the Santa Fe Institute (Sterman, 2000; Lane, 2006). In evolutionary

models, technology adoption takes place in a context of uncertainty and limited information.

In these models, learning is a central feature of the adoption process, and it represents costs

that cannot easily be recovered by the would-be adopter1. Other costs include the disposal of

the previous technology. In these conditions, many potential users prefer to delay the adoption

of new technology in order to reduce uncertainty, and to estimate the population of adopters,

the size of which would most often add to the economic value of the novelty, due to network

externalities, and multiple learning processes. A Rosenberg-type “learning by using” process

occurs in the adopters, as well as a “learning by searching” progression takes places in the

innovating firms together with an Arrow-type learning by doing one. Also, in the evolutionary

approach the original innovation changes during the process of diffusion as learning by

different types of users creates feedback effects that enhance the original innovation.2

1 To be profitable, the adopter has to recover the learning and other related cost sooner or later- otherwise he would not survive. Taking the learning costs into consideration may help to explain the slow speed of diffusion of some technologies. There are two types of learning about the new technology, its suitability, potential problems and advantages: 1) before adoption and (2) after adoption learning to use the new technology. The evolutionary perspective stresses the necessity and implications of “post adoption learning”, including the effect learning may have on further improvement of the technology. 2 The operational concept of innovation used in Oslo innovation survey guidelines takes this evolution into account. The new and improved products and processes introduced by a firm

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A major theme in the evolutionary literature on adoption concerns the role of increasing

returns and the frequency of “lock-in“ situations. While these are supposed to be aberrant and

extreme temporary situations in neoclassical economics (Liebowitz and Margolis, 1998),

evolutionary ideas on development give them a central role, from Gunnar Myrdal (1957) to

W. Brian Arthur (1999). Also, central elements of explanation are information contagion, path

dependent processes, increasing returns and technology choice under uncertainty.

2. Theories

2.1 Birth of adoption and diffusion theories

It is widely accepted that adoption and diffusion theories were born in the traditions of rural

and medical sociology (epidemic models), anthropology and education in the 1920s, 1930s

and 1940s, mainly in the United States (Rogers, 1995). Rural sociology dominated the field,

and up to 1964 it represented over 45% of all publications in the area of diffusion research, all

disciplines combined (Ibid, p. 52). A paper by Ryan and Gross (1943) in rural sociology on

the diffusion of hybrid corn seeds provided the method and model for hundreds of others;

Ryan and Gross developed the S-shaped curve of diffusion, as well as a first classification of

adopters on the basis of how early to adopt an innovation is adopted.

By the late 1950s and mid-1960s, economists became aware of adoption and diffusion

literatures, and started contributing to it, through the works of Zvi Griliches and Edwin

Mansfield. Not by chance, Griliches (1957) wrote his first seminal paper on the diffusion of

hybrid corn, giving its letters patent to the epidemic models he had found in the sociological

literature. Mansfield (1963a, 1963b), instead, devoted himself to the analysis of the diffusion

of industrial innovations. Working often with detailed time series data of a small sample of

firms (panel data) he integrated economic perspective in the epidemic models. The driving

force behind technology diffusion in these models is information diffusion, complemented by

are considered as innovations even though they may not be new to the market or the world. According to a more traditional view these “innovations” would have been considered as “imitations” in the process of diffusion of the original innovation. In the age of politically correct statements everybody may declare to innovate while nobody has to admit to the less glorious act of imitation. Yet, imitation with the associated learning and incremental innovations is at the core of adoption and diffusion of new technology and essential to its economic impact.

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effects of expected profitability and cost, while diffusion is slowed by uncertainty associated

with the use new technology, and influenced by economic characteristics of the adopter.

Models inspired by the epidemic approach provided a useful “macro” synthesis of the

diffusion process for a whole set of potential users. Yet, these macro-economic studies already

address the issues of the “micro” determinants of adoption.

A decade later, management science became also active in the field, and focused more on the

firm-level characteristics of early as opposed to late adopters. In the meantime, the frontiers

between the different disciplines involved in adoption and diffusion research began to fade,

and concepts and methods flowed from one discipline to the next.

The first economic studies of technology diffusion (Griliches, 1957; (Mansfield, 1963a and

1963b) were inspired by epidemic models.

2.2 Current research

Since Nathan Rosenberg made his observation in 1972, the literature on technology adoption

and diffusion has exploded and counts thousands of papers, all disciplines combined. In the

1970s though, economics and management took the lead previously held by sociology in the

area of diffusion.

Some of the major characteristics of the adoption process have been studied in depth. Yet,

putting order in this literature is a complex task because it encompasses economics,

management, policy sciences, geography and sociology. In his major book, Everett Rogers

(Rogers, 1995) has summarized the contribution of the social sciences other than economics

and management, while Richard Nelson and others (Nelson et al, 2004) have tried to organize

the multifarious economic literature. Other authors (Baptista, 1999; Lissoni and Metcalfe,

1994) also tried to paint a broad picture. Some key concepts follow along the disciplinary

paths.

Equilibrium models. In the first generation of these models, technology is basically given

from the start of the diffusion process, and adopters differ in the amount of information they

possess about it. All adopters have similar characteristics. Once the original innovation

introduced the new technology is divisible, explicit, codified and transparent and unchanging.

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Only lack of information about the new technology can preclude or retard its adoption. There

is no uncertainty with regards to its practical application, further evolution and use. In this

simplified framework the rational profit maximizing firms attain a unique, competitive,

welfare maximizing equilibrium. Depending on their access to information and effect of

various market failures, some of them tend to be early adopters; others are laggards resulting

in the time-honoured diffusion curves of the epidemic model. The diffusion process is

characterized by a sequence of shifting static equilibria in which agents are perfectly adjusted

at each point in time (Sankar, 1998, p. 151).

The time path of the proportion of potential users of new technology follows the typical S-

shaped curve (logistic or sigmoid), albeit with shapes and slopes (i.e. speed of diffusion)

varying widely among innovations, industries, regions and countries. (Figure 1 in Annex

shows the widely different patterns of diffusion of selected consumer products and services).

In the 1970s and 1980s, the equilibrium approach changed some of the assumptions inherited

from the rural sociology tradition. Imperfect information of potential adopters ceased to be the

main reason given to explain the evident slowness of the diffusion process. Instead, it was

assumed that the first generation of the new technology was not necessarily superior to the

existing ones. Davies’ book (Davies, 1979) represented a new equilibrium-based approach,

comparable to Griliches seminal article twenty years before. Davies introduced probit models

where potential adopters differed from each other on some important dimension.

Stoneman and Ireland (1983) added learning-by-doing processes taking place at the suppliers,

which translated into price decreases of the new technology over time.

After Mansfield, the equilibrium literature focused not so much on competition and

substitution among technologies, but more on the decision about when to adopt. Companies

are supposed to adopt any new and superior technology, yet they may defer the adoption due

to the high costs of the new technology, due to uncertainty about suitability and performance,

and/or due to sunk costs invested in previous ones. Also, the actual profitability of the

innovation may be low at the beginnings of its diffusion, particularly when early adopters

have to bear the cost of training of existing workers, and assume the cost of complementary

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technologies. In sum, firms continuously update their information on the new technology in

order to optimize the time of their adoption (Jensen, 1982; McCardle, 1985)3.

The equilibrium models were criticized on several topics. They assumed that learning by both

users and producers of the innovation was marginal; they often treated all potential users as

similar in resources and competencies; firms are supposed to behave optimally: they are just

waiting for the new technology to become profitable for them; the number of potential

adopters is also given. Probit and Logit models treat adoption as detached both from diffusion

and from time. In spite of such limitative assumptions, the economic mainstream has

produced important insights into the process of adoption. For instance, firm size was

confirmed as a key determinant of the speed of adoption. However, as McWilliams and

Zilberman (1996) show, probit and logit can incorporate diffusion and time. There is no

reason other than the lack of information on the extent of diffusion –hence time- that prevents

inclusion of these elements in the Probit or logit models. The authors used a sample of

adopters compared to one of non-adopters, and estimated the chances that a given firm adopts

the technology at a certain time on the basis of its size. This allowed the authors to predict the

diffusion pattern. Several diffusion curves were found in different markets, and in the

diffusion of several products and processes. Their study also introduced explicitly the Arrow-

type learning-by-doing within suppliers) was introduced.

The work by Nelson (1968) and Nelson and Winter (1982) launched evolutionary models. In

these models, contrary to the equilibrium ones, adoption and diffusion take place in an

environment of uncertainty and limited information. Rational maximizing behaviour, the

fundamental assumption underlying the neoclassical equilibrium approach is impossible in the

world of uncertain future states of technology. Learning is a central feature of the adoption

process, and it represents costs that cannot easily be recovered by the would-be adopter. Other

costs include the disposal of the previous technology. In these conditions, many potential

users prefer to delay the adoption of new technology in order to reduce uncertainty and to

estimate the population of adopters. Larger number of adopters adds to the economic value of

the novelty, due to network externalities and multiple learning processes. Also, in the

evolutionary approach, the original innovation is changed during the process of diffusion, as

learning by different types of users creates feedback effects that enhance the original novelty

3 McCardle (1985) recognizes the limitations of his model: linearity of the profit function, constancy of profits over time, the one-shot exogenous nature of the innovation, and lack of competition (Ibid, p. 1386).

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(Hall, 2005). A Rosenberg-type “learning-by-using” process occurs in the adopters, and a

“learning-by-searching” progression (Dutton and Thomas, 1985) takes places in the

innovating firms together with the Arrow-type learning-by-doing already indicated. Finally,

evolutionary economics has emphasized the industry-specific and even firm-specific character

of knowledge. In evolutionary economics and management, information is most often

“sticky” (partially tacit and difficult to transfer) rather than “leaky” (explicit, codified,

universally available) (von Hippel, 1994).

Yet, evolutionary theories are difficult to test: micro-economic learning, uncertainty,

technological trajectories, and path dependency are often difficult and costly to measure in

empirical surveys. The “sticky” or “leaky” debate on the character of information will not be

solved with a few easy empirical studies.

A major debate in the adoption literature between the two economic strands concerned the

role of increasing returns and the frequency of “lock-in“ situations. At one extreme, we find

positions such as those taken by P. David (1985), involving the QWERTY keyboard case,

where adopters have chosen a suboptimal novelty due to the early entrance of a particular

solution. At the other extreme are authors such as Liebowitz and Margolis (1998), arguing

that such lock-in situations in inferior technologies are aberrant and uncommon cases, and

that, in the course of adoption, markets are more rational than argued by David. Network

externalities, predominant in later evolutionary models, suggest that these lock-in situations

are likely to be abundant today, and will probably multiply in future. Inefficient standards

may be adopted and persist through time due to sunk costs, information limitation and similar

obstacles to perfect rational adoption decisions (Katz and Shapiro, 1986; Clements, 2005).

The evolutionary approach is based on a “micro” perspective of technology adoption. Viewed

from the point of view of an individual, firm or other organization, models of this type

estimate the factors (characteristics of firms, industries and technologies) that increase or

decrease the probability of adoption of the new technology by the observed unit. These

probabilistic models allow different potential adopters to give different values to the same

technological novelty, and thus allow for heterogeneous behaviors of would be adopters

(Geroski, 2000). Heterogeneous agents, exploiting different learning capabilities may give

different values to technology irrespective of the number of users.

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The speed of the diffusion of innovations thus depends on a variety of micro-level factors,

including the amount of sunk costs incurred in previous adoptions, network externalities, the

response of the providers of the older, competing technologies, the quality and reliability of

information sources about the costs and benefits of adoption, the importance of

complementary inputs, and the market structures of both the adopters and the suppliers of

technological innovation. As one might expect, the greater the sunk costs, and particularly the

sunk cost of learning, the slower will be the rate of adoption of an innovation (Astebro, 2004).

Network externalities are also crucial; they are defined as benefits (or cost) related to the

number of adopters of an innovation. The larger the number of adopters, the larger the

benefits for each particular adopter; the cases of the computer and the dynamo are well known

examples of such network goods in which adopters only benefit when the number of adopters

is substantial (David, 1990). Suppliers also shape the curve of adoption. Suppliers may either

favor fast adoption of innovation, as in the case of agricultural innovations provided by

governmental agricultural stations (Griliches, 1957); or they can delay adoption and try to

extract monopoly rents from their novelty through patenting and/or special arrangements with

particular users (Nelson et al. , 2004).

Also, in the evolutionary approach, technology is not given from the start, but its trajectory

may be affected by the type and number of users. Technology changes as it is adopted,

because the innovators adapt the technology to different markets (industries, countries, size

and capabilities of users). More awkward, some adopters abandon the new technology and

revert to previous ones (McGuckin, Streitweiser and Doms 1998).

The evolutionary models were more inspired by the substitution approach pioneered by

Mansfield. This kind of perspective became more general and substitution replaced simple

adoption, as in the case of the substitution of horses by cars in the United States between 1905

and 1955 (see Figure 2 in Annex) and successive waves in the replacement of raw steel

processes, from crucible to the Bessemer process, to open hearth furnaces to basic oxygen to

the electric processes (Grübler, 1991). The substitution theory brought new forms to the S-

shaped curve: logistic growth is followed by saturation and logistic decline (Nakicenovic,

1997).

In spite of its novel concepts, and its promising synergy between economics and management,

evolutionary approaches of technological diffusion are not easily testable. Bounded

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rationality, learning and uncertainty as micro-foundations in economic agents, whatever their

realism, are difficult to measure; technological trajectories and path dependencies are more

easily mapped ex-post than ex-ante; lock-in situations in inferior technologies are evident

after the adoption process has taken place, but difficult to predict, particularly because the

adopted technological paths tend to evolve, whereas the abandoned ones tend to stagnate.

Since evolutionary approach stresses the importance of the economic and technological

context in which a particular technology is adopted, it is less amenable to statistical analysis

than models based on unifying, albeit simplistic assumptions proper to the equilibrium

approach. The evolutionary theorizing about adoption and diffusion of technology does not

yet – and probably will not in the future – provide a simple model that could be tested. It has,

however, enriched the empirical studies of adoption of new technologies. Increasingly, the

probabilistic models of technology adoption include features suggested by the evolutionary

approach to technological change. As a result, the econometric studies of technology adoption

are becoming more eclectic and realistic.

System dynamics has provided evolutionary economics with a set of tools for modelling

(Arthur, 1994; Sterman, 2000). Case studies (Consoli, 2005; Shi and Gill, 2005) and

conceptual models (Weil and Utterback, 2005) are now appearing using dynamic simulation

methods. Yet, in spite of the advancement that these models provides, particularly through a

more rigorous treatment of the interaction through time of multiple variables, the basic

obstacle remains, namely, how to obtain high quality data through time. These data are

difficult and expensive to obtain, even if the models and methods are far more sophisticated

than previous simple statistical analysis.

4. Policy implication of theories and empirical findings

Both the neoclassical equilibrium approach and the evolutionary theory are in agreement that

government role today4 is to ensure (1) well functioning markets with well defined property

rights, basic economic freedoms including foreign trade and investment and adequate

infrastructure. (2) Government policies are also responsible for health, education and training

of human resources – the development of human capital indispensable for continuous

4 The market consensus defined in the present era of globalization.

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technological change. (3) Last, but not least, Government is to arbitrate in the cases of

conflict of interest, seek to reduce or eliminate major market failures and redistribute income.

All currents have stressed the importance of healthy and open economic environment for

investment in general and adoption of new technologies in particular. For instance, the rate of

adoption of AMTs by Canadian manufacturing firms increased notably with renewed

economic growth after the beginning of the 1990s (Baldwin, Rama and Sabourin, 1999).

International trade and international investment barriers act as an impediment to technology

adoption (Daly and Globerman, 1976)

Taxation is also very important. International differences in effective tax rates on the returns

to technology are, according to Parente and Prescott (1991), fundamental to understand

differences in adoption of new technologies and the resulting diversity in per capita incomes.

Since most new technology originates abroad, policies ensuring openness to international

trade, investment and skilled labour migration are another set of measures contributing to a

favourable environment for diffusion and adoption of new technologies (Comin and Hobijn,

2004; Herrendorf and Teixeira, 2002; Caselli and Coleman 2001, and Coleman, 2004).

However, the market consensus and the usual macroeconomic solutions do not seem enough

in order to catch up with world technological leaders.

Major differences in policy approach appear with regard to promotion of technological

change. The neoclassical equilibrium model of technological change is not concerned with

diffusion of new technologies. The diffusion is believed to happen through profit maximizing

behaviour in a well functioning market. The only need for policy intervention is to eliminate

or reduce market failures affecting the appropriability of innovation related economic

benefits, access to financing and uninsurable risk (Arrow, 1962).

In contrast to the neoclassical doctrine, the evolutionary theory shows that the government

policy can not be limited to ensuring well functioning markets. According to Lipsey, 2002;

(based on Lipsey and Carlaw, 1998- OECD –STI review no.22), not only in industrialised but

also in developing countries the main reasons for additional government intervention are :

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(1) R&D and innovation creates positive externalities and therefore should be supported5 (2)

since technology changes endogenously, it is possible to change a country’s comparative

advantage through policy intervention. (3) Far from being unique, universally available and

free, technological change depends on the local context. (4) Given the uncertainty about

technological development and its acceptance by the market there is no unique, optimal

allocation of resources (R&D and related activities) for innovation and diffusion. Lipsey

argues that policy with respect to these matters has to be based on a mixture of theory,

measurement and subjective judgement.

As pointed out by Canepa and Stoneman (2002), given the diversity of diffusion experiences

of different countries across various technologies, international comparisons may not be a

valid means by which to judge the need for policy intervention in the diffusion process. There

is just not yet sufficient reliable information on the adoption and diffusion process.6 Instead,

the attention should be given to reduce reasons for the divergence from the optimal, i.e. to

address the likely market failures.

The literature has emphasized a major market failure based on information deficits in the

potential adopters; other market imperfections were related to the minimum optimal size of

the firm and the uninsurable uncertainty that contribute to suboptimal investment in

innovation. As our overview of adoption and diffusion studies above shows, these market

failures directly affect the decision to adopt new technology. Policies to subsidize adoption of

new technologies by small and medium size firms aim primarily at reducing these barriers to

the adoption of new technology. It should be noted that small firms and firms in mid and low

tech industries use significantly less R&D tax credit policies (the presumably neutral preferred

neoclassical policy instrument) than larger and high-tech firms. Besides, tax credits aim

creation of new technologies rather than diffusion of the existing ones. Providing information

to firms at low or no cost and help to develop their R&D capabilities is one policy response.

In this direction, Canada created the IRAP program and has spanned agricultural and

industrial national laboratories across the country in order to support technology diffusion.

The evaluation of the IRAP program provides a textbook example of the contrast (difference) 5 Other authors, (Cohen and Levinthal, 1989), have underlined the fact that early adopters are most often R&D executants, as they need to scan the world for novelties in order to solve specific process and product obstacles, and thus learn about existing alternative technologies: “innovation and learning are the two phases of R&D”. 6 As illustrated by the study of technology adoption in Canadian food industries (Baldwin, Sabourin and Smith, 1999) discussed in Section 3.5 above. .

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between the neoclassical and evolutionary approach to diffusion policies. According to the

neoclassical assessment the program was wasteful and the money would have been better

spent on a framework policy such as R&D tax credits. In contrast, according to the

evolutionary perspective (Lipsey and Carlaw, 1998), the program was very successful because

it has placed particular emphasis on changing research and technological capabilities within

firms and on creating new channels of information flows between private industry, university

and government researchers and succeeded to do so.

Agricultural R&D and extension has for decades been the showcase of the usefulness of

government laboratories in the production and diffusion of plant and animal technologies in

Canada, the United States as well as in Western Europe (Ruttan, 1982, 2001).

In Canada and the United States, both the federal level and all the constituent provinces and

states have applied manufacturing extension policies that are the centre of their diffusion

policies (Georghiu and Roessner, 2000). Public laboratories and technology institutes may act

as “windows” of new technology for local firms. They may also be conducive to industrial

technology diffusion if their standard-promotion practices are carefully orchestrated (Link,

1996; Tassey, 2000).

The “lack of information” explanation for the usually slow diffusion of new technology,

consistent with both the equilibrium and the evolutionary approach models, has generated

other policy responses. Human capital building in firms is among them. In Germany,

vocational education has aimed at developing industry specific skills. Industries and their

cooperative associations direct technical change. There is a tradition of long-term

commitment of employees to firms and close links between firms7 and the financial system.

Canada may consider some kind of similar industry-education linkage in order to promote the

permanent upgrading of the labour force. Given Canada’s industrial structure dominated by

small and medium size firms and their position of technological follower rather than leader in

most industries, more emphasis on human capital policies may be called for smaller firms.

7 A great proportion of German firms is made of small and medium size and at the same time export-oriented enterprises.

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Policy emphasis on vocational training could be beneficial in alleviating the chronic lack of

skills- one of the most frequently cited obstacles to adoption of AMTs by SMEs8.

In the equilibrium as in the product life cycle perspectives, there is an overseas corollary. The

solution to slow international adoption of new technology is fairly straightforward: policies

should promote the accumulation of human capital, in order to allow backward nations to

understand, adopt and use superior process technologies and openness to international trade

and direct investment (Comin and Hobijn, 2004; Herrendorf and Teixeira, 2002; Caselli and

Coleman 2001 and Coleman (2004). This type of policy may be called increasing the human

capital pool. This policy implication applies to Canadian backward provinces, such as the

Maritimes. They receive little human capital from abroad, and are less involved in research

and university-industry collaboration, than other provinces (Doutriaux and Barker, 1995).

Both federal and some provincial governments should make additional efforts to attract more

skilled immigrants, develop incentives to insert them in the labour force, and help to convert

their universities into important cradles of human capital by developing scientific and

engineering research. Adoption, or the lack of it, may be the result not so much of attributes of

the firm, but of the structural failures of the “system” of innovation in which they operate.

In the evolutionary perspective, policy implications go further than just correcting imperfect

information and low levels of human capital. Knowledge is seen as partially explicit, and

partially tacit (“know-how”). Even the explicit part of it is not readily available, as firms make

efforts to keep it within the walls of the company, and protect them as industrial secrets. As

such, technical knowledge is often firm and industry specific. As a result, technology transfer

and diffusion are not always costless undertakings. Besides, superior technologies are not

8 German-speaking countries have been applying vocational training schemes with some success. Originally aimed at the chemical and electrical engineering industries, these have extended to ICTs. As the competencies of firms are extremely diverse, the adoption of ICT (and related AMTs) depends on human capital deficiencies at the level of the firm. Several authors (Arvanitis and Hollenstein, 2001a and 2001b) suggest that activist vocational education and retraining institutions may help to increase the absorptive capacity of the firms and should be at the heart of diffusion policies. After realizing that there were deficiencies of AMT-specific knowledge among small and medium size firms, Switzerland introduced AMT-specific training programs that, according to evaluation, had significant diffusion effects (Arvanitis, Hollenstein and Lenz, 1998). On the other hand, the vocational training system may be less appropriate than a more general education system for a continuous adaptation to rapidly changing demand for labour skills.

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always successful.9 Sunk costs in previous technical solutions may also preclude or delay the

adoption of superior technologies, as it happened with electricity and the computer (David,

1990).

In this approach, technology adoption is associated with different degrees of uncertainty.

When there is uncertainty about the future value of different new technologies (the C-series

plane, different high-definition DVD standards today) evolutionary policy may be aimed at

maintaining diversity in technology competition as to permit the unfolding of different

technical trajectories over time (Bryant, 2001). Yet, public policy may be more effective if it

is implemented at the beginnings of the technological competition between different solutions.

Thus, policies aimed at keeping open diversity may be preferable to any early public decision

that would preclude some solution in favor of others. To avoid irreversible sub-optimal

choices, appropriate policies should favor experimenting with alternative technologies. For

instance, in the present debate about future energy technology in Canada, surrounded by much

uncertainty, the evolutionary approach would suggest to keep all alternatives (hydro, thermal,

nuclear and wind) open. National champion technology policies would thus be at odds with

the evolutionary approach, and technical pluralism much indicated. If uncertainty is pervasive

due to accelerated technical change, information and diversification policies may become

permanent elements of government intervention (Metcalfe, 1995; Lipsey and Carlaw, 1998).

Governments may influence diffusion of new technologies by supporting or creating

demonstration projects and by government procurement programs (Rosenberg, 1972;

Mowery, 1993). These government initiatives may, however, carry the risks of “government

failure” implicit in the National champion technology policies.

The patent system may speed up but also retard diffusion of new technologies. This is

especially important consideration as regards diffusion of new technologies to developing

countries. Since the effect of patents on diffusion depends on the breadth of protected claims,

the assessment of the patent system on technology diffusion must be context specific.

Excessively broad patent rights impede adaptation of new technologies to local conditions and

constitute an obstacle to adoption and diffusion of new technologies.

9 The diffusion, prevalence and lock-in in inferior technologies may occur due to historical events, such as the QWERTY keyboard becoming widespread with no specific ergonomic advantage over other types of keyboards, or VHS dislodging from the market the technically superior Betamax video technology (Arthur, 1989).

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Policies supporting adoption and diffusion of new technologies must take into consideration

whether the object of the policy- country, industry or even firms are close to the leading edge

or trying to catch up more advanced competitors. Another important consideration is that

different technologies are at different stages in their life cycle and the policy response has to

take this aspect in consideration. Even though the intra-firm transfer of technology in

multinational firms is very efficient, some of the most successful adopter countries such as

Japan, Taiwan, and South Korea succeeded to catch up the leading edge technologies without

relying on TT via the multinational channel (Lipsey, 2002). In this respect the Japan’s MITI is

a good example of very successful diffusion policies helping the country to catch up and

further develop its technological capabilities.

Evolutionary economics, as pointed up before, has fallen short of empirical research other

than case studies. Its public policy implications are scanty. Besides, when network

externalities exist, and there are several different possible trajectories, public policy

implications are less evident. For instance, when there is uncertainty about the final outcome

where several technologies are competing, second-mover advantages may occur. It is not

evident that faster is better, or that policy should create incentives to rapid adoption as it was

the case in hybrid corn (Hoppe, 2000). Yet, in the evolutionary approach, policy should be

aimed at preventing the generalized adoption of inferior technologies and the subsequent lock-

in of the economic system in these types of technologies.

An important point is that as new technologies are adopted and diffused, they evolve both in

terms of technical characteristics and market applications. Therefore, diffusion policies can

have the greatest impact at the beginning of their life cycle. However, it is at that stage that

the uncertainty is greatest. One way of characterizing evolutionary policy is to provide

government assistance in helping private sector to pick up and push potential winners (Lipsey,

2002).

In sum, in order to benefit from the evolutionary perspective, and draw more precise policy

implications, multiple panel studies would be needed to better understand learning processes

of economic agents, the waning of uncertainty in different technological paths, as well as the

profitability and the specific trajectories (improvements) followed by both the adopted and the

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rejected technologies. Such empirical studies are still to be made10. Also, systems dynamics

has become an increasingly used method in evolutionary economics and management.

Dynamic simulation of adoption processes could shed a different light on diffusion, inasmuch

as data are available to calibrate the models.

10 But we are not entirely devoid of some tentative econometric studies in some industries (see Bonaccorsi and Giuri, 2001)

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5. Conclusions

Several disciplines (chronologically sociology, economics, geography, and management) have

contributed towards an increasingly convergent set of hypothesis and empirical generalizations.

Technology adoption and diffusion have been theorized and hundreds of empirical studies have

been conducted using these theories and different methods. Some general conclusions are already

evident. Technology diffusion is slow and uneven. First adopters are most often large R&D-

intensive firms, and those located in main urban agglomerations in advanced countries.

Conversely, slow and partial diffusion is particularly evident in disadvantaged regions in

industrial nations, as well as in developing countries. Industrial patterns of adoption are also

apparent; industries such as aircraft, automobile and information technology are eager adopters of

AMTs, while more traditional ones, such as pulp and paper, textiles and clothing are less

advanced. Several credible hypotheses have been advanced to explain these delays and

incomplete adoption, including inadequate and reduced information, a meager stock of human

capital, and poor managerial practices.

Yet, many issues remain fairly opaque, due to methodological problems: several types of

externalities (among which knowledge spillovers are the most important) are difficult to measure,

even using the most expensive and sophisticated panel-generated data. If diffusion is affected by

externalities, which is possible, these remain to be proven. Also, learning processes in economic

agents are difficult but not impossible to grasp, requiring panel studies where the stock of

knowledge is measured through time, as well as the processes through which new knowledge is

acquired and integrated into adoption practices. Also, the adoption process is a complex one, with

different agents putting forward diverse strategies; system dynamics, again a costly and time

consuming method, may in the future shed some light on these complex patterns.

Some policy implications are fairly straightforward. They are twofold. First, from a public policy

point of view, governments may diffuse information about new technologies through government

laboratories or other channels; training may increase the private-firm personnel able to

understand, assess and operate new technologies; promoting a variety of technological solutions

may be preferable to one-fits-all strategies and national champions. Helping disadvantaged

19

provinces to increase the influx of skilled immigrants, reducing barriers to integration of

immigrants in the labour market and creating incentives to train their manpower (either in private

firms, colleges and universities) may also provide a fertile soil for technology adoption. Also, as

Geroski (2000) has suggested, diffusion policies assisting small and medium-sized firms to move

closer to the technology frontier may be more advisable than big-science projects pushing the

frontier farther away from them. This latter suggestion seems particularly advisable for smaller

countries such as Canada, Sweden or Switzerland, which may represent between one and two

percent of the world R&D effort.

Second, and from the perspective of collecting useful information, industry studies at a more

disaggregated level, increasingly using panel data, and based on samples valid at the regional

level seem appropriate. More in-depth analysis of specific technologies in which the public purse

has heavily invested (and may invest in the future) also seem suitable; they include nuclear

energy, agricultural biotechnology and nanotechnology.

20

Table 2.1 Theories about diffusion. Representative authors Author Year Theory/model Method/Data Parameters Predictions B. Ryan and N. Gross

1943 Epidemic model

Survey interviews

Innovation, time, social structure, communication channels

Hybrid corn diffused in Iowa through an S-shaped curve. Neighbor imitation is key channel

T. Hagerstrand 1953 Simulation models

Regional data Proximity, personal communication, subsidy

Probability of diffusion a negative function of distance

Z. Griliches 1957 Epidemic Model, logistic curves

Econometric analysis of time series

Profitability Hybrid corn will diffuse if brings higher returns than natural ones. Social returns high on public R&D

E. Mansfield 1963 Epidemic model

Time series of adoption

Information diffusion, cost profitability

Information drives adoption, uncertainty a barrier

R. Vernon 1966 Product life cycle model

Industry studies

Time, standardization, exports, FDI

Innovation diffuses across borders as it standardizes

F. M. Bass 1969 S-shaped marketing model

Case and industry studies

Communication channels, innovator efforts

Media key at beginning, personal communication later

P. David 1985 Evolution, path dependency

Product and industry cases

Information lags, sunk costs

Innovation spreads slowly and best technology is not always adopted

W. Bijker et.al. 1987 Social models Case studies Social structure, culture Social structure affects adoption W. Cohen and D. Levinthal

1989 1990

Evolutionary learning model

Case studies R&D activities, ability to assess technology

R&D executants are early adopters

M. Feldman 1994 Economic geography

Economic data Proximity, spillovers, metropolitan size

Spillovers and competition in cities affect probability of innovation

E. Rogers 1995 Sociological Case studies Superiority, complexity, observability

Parameters affect speed of diffusion

B. Mc Williams & D. Zilberman

1996 Probit & logit models

Samples, industry cases

Size and industry Large firms adopt early, different patterns according to industry

21

Table 2: Public policy about diffusion. Representative authors Author Year

publication Years studied

Country Technologies Impediment Results

T. Hagerstrand 1953 1930s Sweden Agricultural conversion

Distance, lack of information

Personal communication by government carrier was key

W. Ruttan 1982 2001

1850-1980

USA Agricultural technologies

Lack of information, small size of

farms

Government laboratory system an effective way of technology diffusion; but little R&D on soil

degradation

H. Ergas 1987 1945-1985

Germany Switzerland

Sweden

All types System geared to existing

industry and trajectories

Reinforces patterns of specialization; reduced emphasis on new industry

A. Link and G. Tassey; A. Link G. Tassey

1988

1996 2000

1980s USA Advanced industrial

technologies

Lack of product standards

Public standards developed at NIST often had a positive effect on network externalities and

diffusion. Too much or too early standardization may have the opposite effect

R. Lipsey and K. Carlaw

1998 1962-1990s

Canada All types Lack of information

Technology counsellors an effective way of information

G. Klaassen et al. 2003 19780s/ 1990s

Denmark, Germany,

UK

Wind turbines R&D and adoption

Cost, uncertainty

Danish public R&D and demonstration for small turbines was most successful

D. Krueger and K. Kumar

2003 1960s /1990

Germany Vocational training

Absorptive capability

Technical training worked well only under slow technical change in the 1960s

22

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