TECHNOLOGICAL DIFFUSION:
ALTERNATIVE THEORIES AND HISTORICAL
EVIDENCE
Jayati Sarkar
Indira Gandhi Institute of Development Research
Abstract. This paper presents an interpretive survey of the neoclassical and
evolutionary approaches to modeling the process of technological diffusion, with
an orientation that is distinct in two important respects from existing surveys.
First, the present survey is designed to provide a comparative overview of the
alternative approaches within a unified framework of analysis. The objective is to
bring out the areas of convergence as well as divergence between the approaches,
and address the issue of whether the approaches could be considered as
complementary rather than as alternatives. Second, the survey attempts to link the
theoretical methodologies to the variety of empirical and historical evidence, and
evaluate how the theories best fit the evidence on the dynamics of the
technological diffusion process.
Keywords. Technological diffusion; epidemic models; neoclassical equilibrium
models; evolutionary disequilibrium models.
1. Introduction
Technological diffusion can be defined as a mechanism that spreads ‘successful’
varieties of products and processes through an economic structure and displaces
wholly or partly the existing ‘inferior’ varieties. While the processes of invention
and innovation are necessary preconditions for the development of a new
technology, it is the process of diffusion that determines the extent to which the
new technology is being put to productive use, which in turn, determines the level
of technological dynamism in a firm, industry or an economy.
This paper presents an interpretive survey of existing literature on the
characterisation of technological diffusion. It also evaluates the extent to which
alternative theoretical modeling approaches fit with existing empirical and
historical evidence on the process of diffusion.
The theoretical and empirical literature on technological diffusion is
voluminous and diverse. The starting point of this literature can be traced back to
the pioneering work of Joseph Schumpeter (1912; English edition, 1934)
outlining a linear progression from invention to innovation to imitation/diffusion.
Schumpeter’s linear model notwithstanding, the analysis of technological
diffusion in the ensuing years received relatively less attention compared to the
processes of invention and innovation. This was perhaps because inventors and
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innovators were considered ‘the heroes’ in the process of technological change,
while adopters or imitators were ‘ambiguous figures’, ‘somehow unseemly and
underserving’ despite their obvious necessity to the process (Silverberg, 1990).
Such a perception, however, began to increasingly change since the 1950s, and
diffusion research became a ‘prominent’ analytical issue in its own right, with
economists and sociologists now studying diffusion more than ever (Freeman,
1994).
Formal theoretical and empirical research on diffusion took off in the fifties
with the epidemic models of diffusion These models were based on an analogy
between the spread of technological innovation and that of contagious diseases.
However, notwithstanding the popularity of epidemic models in empirical studies,
especially in such subject areas as geography, marketing and sociology, their
theoretical foundations were considered by many economists to be rather weak so
as to ‘really necessitate a starting point somewhat divorced’ from these models
(Karshenas and Stoneman, 1995).
In this regard, two distinct starting points can be identified in the theoretical
literature on diffusion since the seventies, both of which have placed an increasing
emphasis on modeling the decision making process of adopters and in determining the microeconomic foundations of the dynamics of diffusion that were
missing in the epidemic models. Following Metcalfe (1988), the distinct division
in this literature is based on two theoretical issues: (i) how to characterise the
mechanics of the diffusion process, and (ii) how to characterise the decision
making procedures driving the diffusion process.
The issue under (i) relates to whether the diffusion process should be
formalised as an equilibrium process with diffusion patterns reflecting a sequence
of shifting equilibria over time in which agents are fully adjusted, or as a
disequilibrium process reflecting a sequence of disequilibria lagging behind the
development of a final equilibrium position. Closely related to this question are
two more questions: whether the diffusion process should be modeled as being
driven by changes in exogenous factors or being driven by endogenous changes,
and whether the process should be modeled as continuous or discontinuous.
The issue defined under (ii) relates to whether potential adopters, in making
their decisions about the innovation, should be modeled as being infinitely rational
and fully informed or as being boundedly rational and limitedly informed. In
other words, the issue relates to whether the diffusion process should be modeled
as being constrained by lack of information or understanding on the part of
adopters about the worth of an innovation.
Combining these two issues, four classes of diffusion models can be logically
constructed, namely (i) full-information equilibrium models (ii) limited
information equilibrium models (iii) full-information disequilibrium models, and
(iv) limited-information disequilibrium models.
Full-information equilibrium models, due to the matching of fully-informed
rational action with an equilibrium mode of analysis are representative of
neoclassical theory. The limited-information disequilibrium models, on the other
hand, due to a more open-ended treatment of decision making processes and
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diffusion dynamics, have their roots in evolutionary theory. Most of the recent
theoretical analyses on the mechanics of diffusion can be categorised in terms of
either the neoclassical equilibrium (NE) models or the evolutionary disequilibrium (ED) models. There is also a significant volume of work in the industrial
organisation and game-theoretic literature which spans across all the four classes
of models.
Alongside the existing theoretical literature is a whole range of empirical and
historical studies that have provided evidence on the dynamic characteristics of
the technological diffusion process. However, as with the theoretical studies,
distinct divisions, often conflicting, can be identified in existing empirical
evidence, and in the technological historians’ interpretation of the dynamics of
technological diffusion. While some evidence seems to be consistent with the NE
conception of technological diffusion, some others seem to be consistent with the
ED concept.
The present survey is designed to overview and critique the salient features of
the alternative decision-theoretic approaches to modeling diffusion mostly at the
two extremes enumerated above, namely the NE and ED approaches, and how the
characterisation of the diffusion process under each is consistent with selected
evidence. The intermediate cases, not discussed at length, can be looked on as
extensions of these extreme cases. This is also true of some of the more advanced
theoretical work on epidemic models which are considered as being ‘on the
borderline between evolutionary and (neo)classical economics and the marketing
hybrid of the two’ (see Ziesemer, 1994, for a short survey of this strand of work).
The discussion of the NE and ED approaches will of course have as its prelude a
basic overview of epidemic models with which the theoretical and empirical
research on technological diffusion began in earnest.
The orientation of the present survey is distinct from the more recent surveys
on the subject of technological diffusion, notably by Karshenas and Stoneman
(1995), and Metcalfe (1988), in two important respects. First, while existing
surveys are loaded more in favour of one approach to the near exclusion of the
other, (the one by Karshenas and Stoneman concentrating on NE models and the
one by Metcalfe, concentrating on the ED models), 1 the present survey attempts
to provide a broader overview of both approaches within a unified framework of
analysis. The survey is designed to bring out the areas of convergence as well as
divergence between the two approaches, and to address the issue of whether the
approaches could be considered as complementary rather than as alternatives.
Second, related to the first, the survey attempts to link the theoretical
methodologies to the variety of empirical and historical evidence and evaluate
how the theories best fit the evidence on the dynamics of the technological
diffusion process.
I have organised this paper as follows. The concept of diffusion is defined in
Section 2. A brief overview of the epidemic models of diffusion is provided in
Section 3. The NE approach to modeling the process of diffusion is discussed in
Section 4. A critique of this approach is presented in Section 5. The ED approach
and its characteristics are discussed in Section 6. Section 7 summarises the relative
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efficacy of the NE and ED approaches. Section 8 discusses alternative theories vs.
evidence. Concluding comments are made in Section 9.
2. Defining the concept of diffusion
Following Stoneman (1983), the concept of diffusion can be represented as
follows. Let S* be the post-diffusion level of the stock of a new product, that is a
consumer durable or a new process embodied in a new capital good owned by the
population of potential users in the aggregate, and let St , be the current stock. The
diffusion problem concerns the process or mechanism by which, S t tends to S*
over time. In the case of instantaneous diffusion, St = S* for all t. In any other
case, S t may differ from S* for any t.
Alternative to measuring the concept of diffusion in terms of the stock of goods
held at different points of time, one may define diffusion in terms of the extension
of ownership across the population of potential users. That is, if N* equals the
number of owners of the new technology when diffusion is complete and Nt the
number of owners of the technology in time t, then the process of diffusion entails
how Nt approaches N* over time.
A distinction is made in the literature between intra-firm diffusion and interfirm diffusion. Intra-firm diffusion concerns the level of use of technology by a
firm, that is, the proportion of firm output or the proportion of its capital stock
that is under the new technology. Thus, consistent with the preceding notations,
intra-firm diffusion for the ith firm can be defined as the ratio, S it /Si*. Inter-firm
diffusion, on the other hand, is defined as the proportion of firms in the industry
using the new technology, i.e., N t /N*. By considering both the rates of intra and
inter-firm diffusion, one can get the rate of growth of the share of total industry
output produced by the new technology. This share would be a more aggregated
measure of the spread of a new technology across an industry. Finally, as
Stoneman (1976) has shown, an economy-wide measure of the diffusion of a new
technology can be obtained by aggregating over the different industries that have
adopted the technology.
The debate in the literature concerning the mechanics of the diffusion process
deals with the question of whether the process by which St (Nt ) approaches S*(N*)
over time can be characterized as an equilibrium process or a disequilibrium
process; as an exogenously or an endogenously generated process; and as a
continuous quantitative or a discontinuous qualitative process.
3. Epidemic models of diffusion
The predominant objective of the growing body of diffusion research since the
fifties has been to identify the presence of empirical regularities in the diffusion
process and to theoretically explain existing regularities as functions of different
socio-economic factors at the micro and macro levels. Important among the
empirical regularities that different theories have sought to explain are, first, the
fact that the adoption of a new technology within and across firms takes time;
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second, the fact that the rate of diffusion varies across firms, technologies and
industries; and third, the fact that the diffusion of an innovation across firms in an
industry often follows a sigmoid (S-shaped) time path with the pace of adoption
being slow initially, stepping up later on, and finally tapering off. The foremost
contributions in the literature that shed valuable insights into these regularities are
the epidemic models of diffusion.
The process of technological diffusion in epidemic models is likened to the
spread of disease by infection. The number of adopters of an innovation is
assumed to increase over time as nonadopters come in contact with the
adopters and gather information on the innovation. Diffusion in epidemic models
is thus determined by the ‘epidemic’ spread of information among potential
adopters.
Formally, the epidemic model assumes that the rate of increased adoption is a
function of the product of the number of uninfected members of a fixed
population and the share of that population that is already infected (For a review
of mathematical specifications of epidemic models, see for example, Stoneman,
1980; Karshenas and Stoneman, 1995).
The theoretical specification of epidemic models leads to the standard logistic
S-shaped curve, which has formed the starting point of a large volume of
empirical research on diffusion in economics. The pathbreaking works by
Grilliches (1957) of the diffusion of hybrid corn in US agriculture, and of
Mansfield (1961, 1968) of the diffusion of a number of industrial innovations,
were based on the dynamics underlying the epidemic theories of diffusion.
Grilliches fitted data to a logistic curve and showed that regional differences in the
time of innovation and the rate of adoption could be explained in terms of such
economic variables as profitability of entry into the production of hybrids by seed
producers and the profitability of adoption by farmers. Mansfield tested more
elaborate models of diffusion by incorporating additional variables such as
uncertainty surrounding the performance of the innovation. In these models too,
the diffusion path followed the logistic curve.
Epidemic models of diffusion have been criticised by many economists for
having weak theoretical foundations and restrictive assumptions. The crux of
different strands of criticism is that while the models give a description of
aggregate industry behaviour in the adoption of innovations, they do not shed
light on an individual firm’s adoption decision and hence fail to provide a
‘behavioural explanation’ as to why some firms adopt faster than the others
(Jensen, 1982). In other words, these models do not seek to establish theoretical
links between a ‘decision-theoretic’ model of individual firms’ behaviour and the
diffusion of innovations (Bhattacharya et al., 1986). Davies (1979) criticises
these models on the grounds that in a mass media society it is somewhat
‘unrealistic’ to rely on information being spread by personal contact. Karshenas
and Stoneman (1995) criticise these models for their ‘primitive’ treatment of
information acquisition and provision where potential adopters are assumed to be
‘passive recipients’ rather than being ‘active seekers” of information. Further
Antonelli (1995) highlights the fact that epidemic models do not take into account
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the effects of a large variety of dynamic effects which influence both the supply of
an innovation and the characteristics of potential adopter. 2
It is in response to the consistent wave of criticism of epidemic models that a
new avenue of diffusion research opened up that attempted to model in a more
meaningful way the behavioral phenomena underlying diffusion processes. The
central concern of these theories has been to explain why individual firms
(households) adopt an innovation at different points of time (theories of inter-firm
diffusion), or why an individual firm takes time in switching its production from
an old to a new technology (theories of intra-firm diffusion), or at a more macrolevel, how diffusion patterns at the firm and industry levels translate into overall
economic growth and impact employment. As mentioned earlier, two strands can
be identified in this decision-theoretic line of diffusion research, the neoclassical
equilibrium (NE) and evolutionary disequilibrium (ED) approaches, a comparative evaluation of which we turn to in the forthcoming sections.
The evaluation below will focus on theories of intra and inter-firm diffusion
and leave out of its purview models that link diffusion patterns to economic
growth. This is dictated by the fact that the focus of this survey paper is to review
the literature on the microfoundations of the diffusion process rather than the
macro-economic effects of diffusion. Besides, the literature on the latter subject is
quite distinct from the literature on the link between diffusion and economic
growth and employment and is worthy of a separate survey in its own right.
Nevertheless, the interested reader is encouraged to refer to the seminal work by
Romer (1990) and other endogenous growth theorists who analyse how
technological innovation and diffusion can generate economy wide productivity
gains and economic growth (See, for example, Barro and Salai Martin, 1995;
Breshnahan and Trajtenberg, 1995; Helpman and Trajtenberg, 1994, 1996;
Jovanovic and Lach, 1991, 1993; for models within the neoclassical framework;
and the collection of papers edited by Silverberg and Soete, 1994, for analysis
within the evolutionary framework). For analysis on the impact of diffusion on
employment, see for example, Englmann (1992), Freeman and Soete (1994), and
Caballero and Hammour (1996).
4. Neoclassical equilibrium approaches to modeling diffusion
The NE approach to modeling diffusion has its roots in the neoclassical school of
thought which, as conventionally expounded, bears the imprint of Alfred
Marshall (1959; first edition, 1890). Existing NE models of technological
diffusion are based on at least the first two of the following three basic tenets of
mainstream neoclassical theory, namely, equilibrium, infinite rationality, and full
information. Similar to dynamic neoclassical analysis, the diffusion process in NE
models is characterised by a sequence of shifting static equilibria in which agents
are perfectly adjusted at each point of time. Moreover, these models assume
decision making procedures similar to that postulated by neoclassical theory;
decision makers are infinitely rational. The third tenet of ‘hard core’ neoclassical
models, namely full information, has, however, been less of a regular feature in
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the NE models of diffusion. While in some, agents are assumed to possess perfect
information on the existence, nature and returns of new innovations in the
economy, in some others, adoption decisions are modeled under conditions of
uncertainty regarding the true worth of the innovation.
NE models characterising the processes of intra and inter-firm diffusion which I
review below, may be classified into two types, namely the probit models and the
game-theoretic models. 3 In the former, a diffusion path is realised because
potential users of an innovation differ from each other in some important
dimension which in turn causes some to adopt earlier than others. In the gametheoretic models, on the other hand, strategic interaction among potential
adopters, rather than differences in adopter characteristics, plays a critical role in
determining the pattern of diffusion.
4.1. The probit approach
Underlying the probit model is the theoretical principle that whenever or wherever
some ‘stimulus variate’ affecting the profitability of an innovation takes on a value
exceeding a critical level (or threshold value), the potential adopter (the subject of
stimulation), responds instantly by adopting the innovation. The reason all
potential adopters do not simultaneously decide to adopt is because at any
moment, the critical level to elicit adoption is not a unique value appropriate to all
members of the population. Instead, the critical value is distributed heterogenously across the population according to some density function and adopters can
be ranked in terms of the benefits to be obtained from the new technology. It is
because of the ‘ranking’ dimension that probit models are also termed as ‘rank
effects’ models (Karshenas and Stoneman, 1993).
Given heterogenously distributed net benefits, at any point of time t, one can
divide the population of potential adopters into two categories; adopters for whom
benefits from adoption, b i is positive, and non-adopters for whom bi are negative.
The former group will adopt the innovation in t, and will constitute the equilibrium level of adopters in that period. The change in this equilibrium level of
diffusion between periods is postulated in the probit models to occur only through
exogenous changes over time in either the economic environment (e.g., change in
relative prices in favour of the innovation, in income or in population) or
technological environment (technical improvements in the innovation, developments in complementary and competing technologies). For instance, as the
acquisition costs of the innovation exogenously fall over time, the threshold value
of adoption decreases, and more and more firms adopt the innovation. Thus, one
gets a diffusion path.
A number of authors have analysed the mechanics of diffusion using the probit
approach (David, 1969, 1975; Davies, 1979). Both David and Davies build
diffusion models around the concept of adopter heterogeneity. However, unlike
David, Davies assumes uncertainty in returns from the innovation so that firms
make decisions based on expected pay-offs. Expected pay-offs are assumed to
vary across firms so that not all firms adopt at the same time. Stoneman (1980),
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Stoneman and Ireland (1983), and David and Olsen (1984) extend Davies’ model
of diffusion under uncertainty by explicitly linking equilibrium patterns of
diffusion of a durable capital good to learning economies. In these models,
diffusion occurs from learning by experience. An adopter decides to switch to an
innovation by observing the actual adoption returns of existing adopters, and then
updating his prior information about the true returns and risks associated with the
innovation. Some other NE models incorporating learning under uncertainty, but
not derived directly from Davies’ model, are by Jensen (1982), Bhattacharya et
al. (1986), Balcer and Lippman (1984), and Jovanovic and MacDonald (1993).
These models essentially focus on determining the optimal decision rule regarding
the adoption of an innovation with an uncertain distribution of pay-offs, and
specifying for each ‘partition of its information structure’ whether at a particular
point of time, a firm should adopt the innovation or reject it.
While all of the above-mentioned models incorporate only the demand for
innovations, there are other models which consider the equilibrium level of
diffusion as the outcome of supply-demand interaction (David and Olsen, 1984;
Ireland and Stoneman, 1985, 1986; Stoneman and Ireland, 1983). Such
interaction determines the price of adoption in these group of models. However,
as is characteristic of the demand-based probit models, changes in the equilibrium
price and diffusion are generated through an exogenous change in unit cost of
production over time, besides being modeled to depend on the nature of the costfunction of suppliers, the market structure of the innovation-supplying industry
and the expectations formation process of buyers (Karshenas and Stoneman,
1995).
4.2. The Game Theoretic Approach
Under the game theoretic approach, the process of diffusion is modeled as
resulting from the strategic behaviour among potential adopters — the strategy
involved being to decide on the optimal time to adopt an innovation so as to be
ahead in the competition.
Reinganum (1981a, b) examines the diffusion process in which she considers a
capital-embodied process innovation whose adoption cost decreases over time and
the profit to be gained from adoption decreases with an increase in the number of
users. The latter assumption of interdependence between adopters is in contrast to
the probit models where the benefits from adoption are independent of the number
of other users of the innovation. Moreover, unlike in the case of probit models,
Reinganum assumes firms to be identical in terms of their costs. Under the
assumptions that (i) information on technology is perfect, (ii) firms maximise the
present discounted value of profits, and, (iii) firms undertake strategic behaviour
in an oligopolistic market setting, Reinganum shows that even when firms are
identical, the equilibrium of the game will generate different adoption dates for
firms, and hence, a staggered pattern of diffusion.
This kind of endogenous asymmetry result, i.e., firms that are identical ex-ante
end up behaving differently in equilibrium, stands in contrast to the NE models
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discussed earlier. In those models, the assumption of full information combined
with the assumption of identical firms would have necessarily implied identical
adoption dates and hence rule out the existence of any diffusion curve. The
driving force behind Reinganum’s result is the presence of strategic interaction
and pre-commitment by firms to adoption dates. Given that the pay-off of each
firm’s action depends on what actions its rivals have chosen, it turns out to be
optimal, in equilibrium, to adopt sequentially than to adopt at identical dates.
Some extensions and refinements of Reinganum’s work have been undertaken by
Fudenberg and Tirole (1985), Quirmbach (1986), and Mariotti (1989). For
instance, Fudenberg and Tirole (1985) relaxes the ‘precommitment’ assumption
in Reinganum’s model (which is equivalent to infinite information lags) and
allows pre-emption by rival adopters. They show that even when firms can
respond immediately, staggered adoption is an equilibrium outcome. (For a more
detailed discussion of specific models, see Tirole, 1988, and surveys by
Reinganum, 1989, and Beath et al., 1994).
While in the above-mentioned game-theoretic models, increased adoption
confers ‘negative externalities’ on existing non-adopters, there is another class of
game-theoretic models based on the assumption of positive externalities from
increased adoption. Positive externalities may be in the form of an informational
externality in the adoption process, and/or can arise from benefits that are created
from a growing network of complementary products and services and the cost
savings that follow from mass production and standardisation.
Models incorporating informational externalities within a game-theoretic
structure (Mariotti, 1992; Besley and Case, 1992; Kapur, 1995) build upon the
learning models of adoption discussed in the preceding sub-section, but deal with
strategic interaction between firms which was not explicitly incorporated in the
earlier models (Kapur, 1995). The diffusion process in the game-theoretic models
with learning results from a waiting contest where every firm would prefer to wait
for other firms to adopt prior to it and learn from their experience. While Mariotti
(1992) models such a learning process among ex-ante identical firms within the
framework of a single waiting contest where the first move is made by one player
and all others follow immediately, Kapur (1995) models the process as a
sequence of waiting contests, the outcome of which is staggered adoptions.
Game-theoretic models incorporating positive externalities in a more general
way, rather than focusing only on informational externalities and learning, are the
network models of Farell and Saloner (1986), and Katz and Shapiro (1986). In
these models, users are heterogeneous, with different preferences for the
innovation, and simultaneously decide whether to switch to the innovation or stick
with the status quo, given that the benefits from adoption are positively related to
the number of existing adopters. The optimal strategy of any firm is arrived at in a
non-cooperative game-theoretic setting by considering all possible strategies of
rival firms. Farell and Saloner, for instance show that a firm’s decision to switch
would depend on its preference parameter, θ. For low values of θ, a firm will not
switch regardless of the other firm’s behaviour in the first period; for intermediate
values of θ, a firm decides to switch in the second period if the other firm has
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shifted in the first period; and, for high levels of θ, to switch in the first period. The
diffusion outcome would depend on the individual θ values. For instance, if both
firms have θ that lie in the low to intermediate range, then the equilibrium will be
characterised by excess inertia where none of the firms adopt the innovation.
The preceding review of some of the NE models of diffusion reveals that there
is considerable heterogeneity across these models with respect to the factors that
determine the diffusion of innovations. While in the early probit models of David
and Davies, the diffusion process is exogenously driven by changes in model
parameters, extensions of such models, as well as the game-theoretic models view
the process as an endogenously driven one where the benefits from adoption
depend on the expected number of users of the innovation. Also, while some of
the models assume that adopters have perfect information about the existence and
returns of an innovation, some of the more recent models explicitly incorporate
uncertainty in the returns. Despite several differences in specifications, what is
common across all the NE models is that adopters are assumed to be infinitely
rational in their decision making in the sense that they are able to explore their
strategies, and determine the optimal strategy (of whether to adopt or not) before
any diffusion actually takes place. Moreover, the underlying adjustment
mechanism through which diffusion progresses from one period to another is an
equilibrium one where agents instantaneously adjust to changing circumstances.
Such commonality in features has led to the development of models that seek to
integrate both the probit and game-theoretic approaches within a single framework (Karshenas and Stoneman, 1993; 1995).
5. Evolutionary critique of the neoclassical equilibrium approach
The existing literature on the adoption and diffusion of innovations abounds with
various criticisms of the equilibrium approach, criticisms that are couched in a
general criticism of neoclassical economics on which the equilibrium approach is
based. The sources of criticism against orthodox neoclassical economics are
diverse. Grouped under the rubric of evolutionary economics, they range from the
Schumpeterian tradition to the institutionalists, to self-organisation theorists
(Witt, 1992; Hodgson, 1996). While some among this ‘new heterodoxy’ do
recognise the fact that the NE approach has ‘undoubtedly’ provided important
insights into the diffusion process by showing the importance of (i) differences
between potential adopters (ii) the interactions between supply and demand for
innovations and the pace of adoption (iii) the technological expectations of
suppliers and potential adopters (iv) different forms of strategic interactions
amongst adopters, and (v) the market structure in adoption decisions, they
contend that such results have been achieved at a ‘high theoretical price’
(Silverberg et al., 1988). Criticisms are directed mainly at the three fundamental
assumptions of the NE models, namely, (i) the assumptions of perfect to
relatively complete information, and infinite rationality, (ii) the notion that the
diffusion process is an equilibrium process, and (iii) the conception of diffusion as
a continuous, quantitative process.
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The general evolutionary critique is that, in its ‘anxiety’ to be the theoretical
physics of social sciences and to achieve logical elegance and mathematical
formalisation, the neoclassical approach, although useful as a modeling exercise
on highly restrictive assumptions, has abstracted from the complexity of the
economic environment (Freeman, 1988). It has abstracted ‘in such a ruthless
fashion that only a few variables and relationships survive (Enos, 1982, p. 69)’.
According to Allen (1988), the creation, acceptance, rejection, diffusion or
suppression of innovations and technical changes has been considered in
neoclassical economics, abstracted from history, culture, social structure, the
ecological system and so on. Although such abstraction may have rendered
equilibrium models simpler, critics argue that these models come at the price of
not only having very low economic plausibility of its assumptions, thereby
making it difficult to test the models rigourously for falsifiability of its predictions, but also at the price of being ‘historically irrelevant’.
Regarding the NE assumption of infinite rationality, which implies unlimited
cognitive capacities of adopters by which adopters are able to interpret and
process any information on the innovation accurately, and explore completely the
pay-off structure from adopting the innovation, Silverberg (1988) notes that it is a
rather superhuman assumption. This is because it places extraordinary informational and computational burdens on individual agents.
Silverberg’s observation is in line with a growing school of thought that
maintains that individuals, instead of being infinitely rational, are boundedly
rational because of their biological limitations to receive, store, retrieve, and
process information (Simon, 1972). Thus, the decision of an agent to adopt or not
to adopt, may in reality be based on local ‘routines of behaviour’, rather than on
any global optimisation exercise — routines which Nelson (1995) defines as
‘behaviour conducted without much explicit thinking about it, as habits and
customs’ and which can be regarded as the best an agent ‘knows and can do’.
Such behaviour is boundedly rational rather than being infinitely rational in the
sense that the decision making agent would not necessarily go through any
attempt to compare all possible contingencies to arrive at an adoption decison. It is
again, because of such cognitive limitations, as also the costs of acquiring and
interpreting additional information that it is unlikely that potential adopters would
have perfect information about the availability and nature of new technologies —
‘knowing what is best may be impossible to pin down in terms of objective
circumstances’ (Metcalfe, 1994, italics mine).
Infinite rationality is also brought into question by institutionalists who question
the relevance of rational choice in adoption decisions in contexts where existing
cultural values, moral attitudes, folkways, traditionally oriented behaviour, fear
of ostracisation, power relationships and vested interests may impinge on rational
decision making by adopters, causing them in many cases to stick to existing
routines of behaviour rather than switching to new ones (James, 1987; Pacey,
1983).
Evolutionary economists have also directed their criticism at the NE characterisation of diffusion as an equilibrium process under which decision making agents
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are in equilibrium, fully adjusted at each point of time and that diffusion patterns
over time are reflected by a sequence of shifting static equilibria. Disequilibrium
within such a framework is created in the course of transition from one
equilibrium to the other, but such disequilibrium is postulated to be instantaneously dissipated. Such mechanics of adjustment to dynamic equilibrium
postulated under the neoclassical paradigm have their roots in classical Newtonian
mechanics. Under the Newtonian paradigm, any disequilibrium created within a
mechanical system is eventually damped until the system reaches a thermodynamic equilibrium and all its initially high-grade energy has been dissipated
into random thermal motion. The image is of a system winding down as it uses its
potential for creativity (Allen, 1988).
The critics of this Newtonian/neoclassical paradigm argue that the processes of
innovation and diffusion are about creative forces — forces which, instead of
dissipating are continuously evolving over time; which instead of equilibrating are
disequilibrating; and which instead of being generated exogenously are endogenously generated within the system ‘without reference to adjustment to some
equilibrium state’. Such a characterisation of the diffusion process, as inspired by
evolutionary theories in biological sciences, is argued to have more empirical and
historical validity than the neoclassical characterisation. For instance, Metcalfe
(1994) argues that ‘it is not in the least surprising …that scholars with a concern
to understand historical patterns of technical change have begun to develop
evolutionary theory’. Also, Arthur and Lane (1993) observe that such scholars
seem to have ‘abandoned optimization as the route to explaining individual
behaviour’.
Finally, evolutionary economists have questioned the NE conception of
diffusion as a continuous, quantitative process. Since the ‘marginalist revolution’,
the neoclassical paradigm of change has been characterised as being so incremental as to constitute an ‘eventless’ continuum (David, 1991). The continuous
dynamics of the neoclassical models aptly finds expression in the Marshallian
dictum natura non facit saltum (nature does not take a leap), and in the use of
differential calculus in formalising the process of change. However, some
evolutionary economists, notable of whom is Schumpeter (1912), have
highlighted the discontinuity in the process of technological change, characterising it as ‘that kind of change arising from within the system which so displaces
the equilibrium point that the new one cannot be reached from the old by
infinitesimal steps (p. 64)’. Accordingly, evolutionary economists point to the
need to give more attention to discontinuities and abrupt change, the mathematical
formalisation of which can be done in terms of catastrophe theory (Zeeman,
1976).
6. The evolutionary disequilibrium approach to modeling diffusion
The preceding section points to four fundamental features of evolutionary
disequilibrium models of diffusion, namely that (i) adopters are boundedly
rational (ii) decision making may not necessarily be based on profit maximisation
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(ii) the diffusion process is disequilibrating, as also necessarily endogenously
driven and (iii) the diffusion process may not necessarily be continuous.
6.1. Technological diffusion and biological evolution
The evolutionary paradigm of technological change has its roots in Schumpeter’s
theory of technological innovation and diffusion. Schumpeter’s (1934, 1947)
theory, in turn, was inspired by the evolutionary theories of Charles Darwin
(1859), as expounded in the Origin of Species. Schumpeterian theory attempted
to analyze technological change as a process of industrial mutation that incessantly destroys the old system and creates a new one, a process that is in a
constant state of flux or disequilibrium.
The discussion in the existing literature of the evolutionary paradigm is mostly
conducted in terms of the phenomenon of technological change in general without
making clear-cut distinctions between the processes that constitute technological
change, namely invention, innovation and diffusion. The evolutionary paradigm
can be applied to any of these processes by suitably choosing the unit of
comparison. Thus, species could be compared to technologies or firms, and while
the innovation process could be interpreted as mutation of technologies, the
diffusion process could be likened to a mutation of firms adopting an innovation.
As in the case of species, technological diffusion under the evolutionary
paradigm is conceived as a selection process under which the competitive
advantages of different technologies, in conjunction with certain behavioral
attributes of agents (like the strive for efficiency, creativity) and the economic and
institutional environments, determine the ‘spread’ of rival technologies over time
(Metcalfe, 1988). The process by which rival technologies spread in the economic
system is, as with respect to species, open-ended, and is driven endogenously. 4
Moreover, the process of competitive selection in evolutionary analysis is so
characterised that economic agents, at least some of them, are unable to discern
ex ante, the relative merits of alternative technologies that they might adopt
(because of cognitive limitations and limited information). This is in contrast with
neoclassical perfect information and infinite rationality where agents correctly
perceive the returns from alternative technologies even before any diffusion takes
place. Further, under the evolutionary approach, different agents have different
valuations of the alternatives. Some choose one (say, A), some another (say, B),
and this choice is modeled as being random. If those who choose A outperform
those who choose B, then, more and more agents will choose A in future, and
resultantly, B users will be eliminated. Thus, while neoclassical optimisation
entails that, everybody chooses A given the information that A is the superior
technology, evolutionary analysis implies that such choice is random to start with
and it is the diffusion process itself that endogenously unravels the relative
superiority of A over B (Mathews, 1984).
Notwithstanding the aforementioned general characteristics of evolutionary
models of diffusion, not all evolutionary theorists agree on what is the most
meaningful characterization of the selection process: should it be characterized as
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incremental, continuous, cumulative and optimal, or should it be characterized as
being subject to ‘feverish bursts’, discontinuity and abruptness, and inefficiency.
The former characterisation is representative of the ‘gradualist’ school of
technological change, and the latter characterisation is representative of the
‘saltationalist’ school of technical change, and the latter represents the neoDarwinian theory of evolution as expounded in the works of Eldredge and Gould
(1972), Eldredge (1985), Gould (1989), and other paleontologists and
paleobiologists.
Under Darwinian theory, displacement of existing species by new ones
proceeds by competition under natural selection and the better adapted species win
(survival of the fittest), with environmental pressure compelling selection.
Moreover, according to the theory, displacement of each major group of species
by its superior competitors takes place slowly through infinitely small steps too
insignificant to be noticed. The features of efficiency, incrementalism, cumulativeness, continuity, and ordered yet unpredictable change are thus implicit in the
Darwinian/gradualist perspective of evolutionary change.
The neo-Darwinian perspective in evolutionary theory that has emerged in the
last twenty-five years, stresses that evolution does not advance as much through
selection processes as is commonly believed; perfectly fit mutations may
disappear without any clear-cut selective mechanism. One of the reasons for this
is that, contrary to the Darwinian perspective, nature is not at all times smoothly
and continuously ordered. Long periods of stagnation and very slow gradual
change are ‘punctuated’ by feverishly rapid and random changes in the form of
catastrophes like the genuine disruptions in geological flows; ‘natura facit saltum’
sometimes. During such catastrophes, organisms cannot adjust by the usual
processes of natural selection, and survival may be a matter of luck rather than of
fitness.
This new view of evolutionary progression is rooted in contingency or pathdependency under which order is not guaranteed by basic laws, such as natural
selection through mechanical superiority of anatomical design, but under which
the final outcome is dependent (contingent) on a sequence of antecedent states
which are not derived from laws of nature but from randomness. If, any of the
antecedent states is altered by chance by even an apparently ‘insignificant jot or
tittle’, the outcome will be different but equally sensible and equally explicable as
a function of its antecedent states. Under such a scenario, nothing is inevitable
about a particular outcome, least of all because the outcome is somehow efficient
or ‘optimal’. Implicit in the characterization of the evolutionary process under the
neo-Darwinian perspective, thus, are the properties that the process has a strong,
perhaps controlling component of randomness rather than of order, and that the
process is not necessarily optimal, incremental and cumulative-properties that are
in contrast to those under the Darwinian perspective.
6.2. Evolutionary models: two examples
Compared to NE models of technological diffusion, there exists a much more
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diverse array of models under the ED approach, primarily because of the more
open-ended modeling of decision making procedures under the latter approach.
ED models are more ‘observation-based’ so that a wide range of ‘rational’
behaviour and selection dynamics are accounted for. As Tisdell (1996) notes,
individuals may differ in their decision making with respect to (i) motivation (ii)
perceptions of the decision possibility set (iii) differences in search behaviour and
exploration paths (iv) degrees of enthusiasm to engage in search and maximising,
and (v) inferences drawn from observations.
Two examples of evolutionary models, not mutually exclusive in terms of its
features, are presented below. The first example, an ED selection model,5 is
representative of the Darwinian perspective of a diffusion process — incremental
changes in adoption shares and the survival of the fittest (most efficient)
technology. The second example is that of a density dependent model, representative of the neo-Darwinian perspective, under which the diffusion process could be
discontinuous, and where the diffusion outcome could be inefficient.
6.1.1. Evolutionary disequilibrium selection models
Evolutionary disequilibrium selection models in their purest form deal with the
adjustment of a disequilibrium industry to a fixed best practice technique, with
such adjustment, in contrast to most neoclassical models, taking place in historical
time. The models also employ variants of the same mathematical structure known
as replicator dynamics, the basic equation of which was first introduced by R. A.
Fisher (1930) in his mathematical formulation of natural selection.
In Fisher’s model, the frequency of a species grows differentially according to
whether it is characterized by above or below average ‘fitness’, while average
fitness itself varies in response to changes in species frequencies. Fisher shows
that the system monotonically converges to a pure population consisting of the
species with the highest fitness.This result is known in population genetics as
Fisher’s ‘fundamental theorem of natural selection’ (Fisher, 1930).
Fisher’s model along with its several variations when applied to the selection
among alternative technologies, implies that the rate of diffusion is directly linked
to that technology’s distance from the average-practice technology. Provided the
technology has unit costs below the average level, its level of diffusion increases,
otherwise its level of diffusion decreases. The diffusion levels in these models
usually change through endogenously generated feedback mechanisms like the
reinvestments of profits in either technologies or firm capacities, or through
stochastic changes in the industry environment as through exogenous changes in
factor prices.
Some of the models of technological change based on Fisher’s model of natural
selection are by Steindl (1952), Downie (1955), Nelson and Winter (1982), Iwai
(1984a, b), Silverberg (1987), Gibbons and Metcalfe (1988), Silverberg, Dosi,
and Orsenigo (1988), and Metcalfe (1988). While in some selection models, the
best-practice unit cost level is assumed to be fixed (Nelson and Winter, 1982;
Gibbons and Metcalfe, 1988), in some others, changes and expectations of
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changes of the best-practice technology frontier are assumed to influence
investment decisions in new technologies (Nelson and Winter, 1974, 1982,
Chapter 9; Nelson, Winter, and Schuette 1976; Iwai, 1984b). Such dynamics, it
can be shown, brings to economic dominance the lowest cost best-practice
technology and the diffusion of all other technologies in relative terms drops to
zero (For a more formalised account of Fisherian dynamics in diffusion models,
see Metcalfe, 1988). 6 Apart from analysing the selection among technologies, ED
selection models have been set up to explore the competitive process by which
new technological standards are created and adopted from among several rival
standards (Metcalfe and Miles, 1994).
ED selection models of technological diffusion exhibit several characteristics of
the evolutionary process. The system is non-linear. Non-linearity of the system
stems from out-of-equilibrium interactions, both in the growth dynamics and in
the market-share dynamics. Indeed, it is disequilibrium which drives the system
forward, via the firm specific dynamics of costs, and related adjustments in
market shares of the technologies. Another characteristic of this class of models is
that they easily fit in with various sorts of institutions and ‘routines’ of behavior.
For example, the propensity to adopt may be shaped by some routinized decision
rules (rules of thumb such as adopt an innovation if profits fall below a certain
level), as well as by behavioral attributes such as strive for expansion.
The dynamics of the diffusion process implied by selection models of the
Fisherian genre appear to be gradual and continuous, given that the mathematical
structure underlying these models is based on continuously differentiable selection
functions. Elster (1983), in his discussion of Nelson and Winter’s (1982) model
of technical change, corroborates this observation by stating that the Schumpeterian theme of discontinuity of technical change is not apparent in the model.
Nelson and Winter themselves suggest that the process of technological change is
incremental when they argue that generally a new technique will not be ‘too far’
from the old one and furthermore, when all such behavior is aggregated, the result
is a relatively smooth macroeconomic result. However, there is some difference in
opinion regarding such a characterisation. Rosser (1991) identifies streaks of
saltationalism in the way Nelson and Winter model firm behavior-sequence of
periods of little change, broken by occasional discontinuous transitions to
radically new techniques.
Finally, the ‘Fisherian’ selection model of diffusion characterises evolutionary
dynamics such that the diffusion outcome converges to the lowest cost bestpractice technology with the relative diffusion of all other competing technologies
dropping to zero. This indicates that the fittest technology survives the process of
selection. Such an outcome is therefore consistent with the Darwinian view that
selection is necessarily an optimizing process.
6.2.2. Density-dependent multiple-equilibria models
Arthur (1988, 1989) analyzes the relative diffusion of competing technologies in
the presence of interdependencies in decision making among adopters. Such
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interdependencies arise because of ‘increasing returns to adoption’ — the more a
technology is adopted, the more developed and useful it becomes. Increasing
returns might stem from learning by using, network externalities, scale economies, informational increasing returns, and technological interrelatedness,
among other factors.
Given two technologies A and B that are near-equally competitive, Arthur
shows that the presence of increasing returns can cause the diffusion process to be
swayed in favor of one technology by the cumulation of ‘small historical events’,
small heterogeneities among adopters, or small differences in timing. For instance,
if A initially gets ahead of B by some fortuitous circumstance, however small,
this advantage can amplify over time through positive feedbacks and cause A to
gain a monopoly.
Thus, through small events, the diffusion process can be driven into the
‘gravitational orbit’ of one of the two possible outcomes or multiple equilibria: A
gaining a monopoly, or B gaining a monopoly. However, since the sequence of
small events is assumed to arrive randomly and cannot be foreseen in advance (as
characteristic of an evolutionary process), which of the two technologies would
ultimately diffuse cannot be predicted a priori. The diffusion process is also path
dependent in the sense that the outcome depends on the way in which adoptions
build up, that is, on the path the process takes. Therefore, history is ‘not
forgotten’ and it matters. Finally, the diffusion process is characterized by
‘potential inefficiency’ in the sense that the process may not converge to a
technology with the highest long-run pay-off.
Arthur’s analysis is a prime example of a collective phenomenon, in which the
decision of the individuals is constrained by the collective in such a way that
several possibly exclusive alternatives contend for dominance. It also underscores
the crucial role of small historical events which can be decisive in triggering the
eventual choice between these alternatives. Selection models as that of Arthur’s
fall in the class of models known in the literature as ‘density-dependent’
evolutionary models. In such models, an individual adopter’s payoff from a given
technological option is assumed to depend positively on the number choosing the
option (as the assumption of increasing returns to adoption in Arthur’s model).
Besides various economic and technological factors, like informational increasing
returns, network externalities and technological interrelatedness, and social factors
such as individual’s fear of isolation and group pressure, have been invoked in
these models to rationalize such interdependencies in technological choice.
Some other density dependent models of technological diffusion in the spirit of
Arthur’s are by Arthur and Lane (1993), and Lane and Vescovini (1996). These
models highlight the effects of informational feedback from existing adopters
about a new technology, the basic mechanism being that an agent makes his
choice of a technology on the basis of private information obtained from
sampling some previous adopters. While Arthur and Lane (1993) postulate a
Bayesian updating mechanism by which agents assimilate the information they
obtain from their samples, Lane and Vescovini (1996) specify some ad hoc rules
of thumb. An interesting result in the former model is that informational feedback
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can lead to market domination by one technology even when two technologies are
identical in performance. A counter-intuitive result in the latter extension is that
giving individual agents access to more information can lead to smaller market
share for the superior technology.
Other density-dependent models that have found ready applicability to
modeling the process of technological diffusion, and which exhibit similar
properties of multiple-equilibria, path-dependency, hysteresis and sub-optimality
of selection processes, are by Granovetter (1978), David (1985, 1987), Kuran
(1987, 1995), Witt (1989), and Bikchandani et al. (1992). 7
The diffusion process in density-dependent critical mass models, like Arthur’s,
exhibits the characteristics of path-dependency, hysteresis and sensitivity to initial
conditions, characteristics similar to that under the neo-Darwinian/saltationalist
process of evolutionary change. These characteristics, as in the neo-Darwinian
process of biological evolution, highlight the role of instabilities in the evolutionary process. 8 For instance, as Arthur’s model demonstrated, the diffusion process
is inherently unstable, being critically influenced by certain initial configurations
and small changes in parameter values. Granovetter (1978), and Granovetter and
Soong (1986) have also demonstrated with constructed examples that slight
perturbation of parameters, such as distribution of preferences, can have a wholly
discontinuous and ‘catastrophic’ effect on the evolutionary process. 9 Such
examples imply that two technological trajectories starting out very close together
can lead to widely divergent system states over time.
The neo-Darwinian view, that order in the selection process emerges largely
from random and chance events and sub-optimality of outcome cannot be ruled
out, is also brought out in multi-equilibria density dependent models of diffusion.
For instance, in Arthur’s model, in the competition between alternative technologies, there is no a priori guarantee that the most efficient technology will be
the long-run diffusion outcome.
Apart from the two ‘prototypes’ of evolutionary models of diffusion presented
above, there are several other types of such models. Among these are the selforganisation models by Coricelli, et al. (1991), Silverberg (1988, 1990),
Silverberg, et al. (1988), in which the diffusion outcome at the ‘macroscopic’
level is modeled as the largely unintentional outcome of complex thread of
interactions between adopters at the ‘microscopic level;’ where adopters
endogenously adjust both their objectives and expectations; and where adopters
produce positive and negative externalities that they might not be able to govern
or individually forecast.
There is another important class of ED models of diffusion based on evolutionary game theory. The problem that is posed in these models (Silverberg et al.,
1988; Witt, 1989) is this: Given that individuals strategically decide on whether or
not to adopt an innovation in view of the possible choices of others in the
population, how does a particular strategy (to adopt or not to adopt) propagate
over time? The basic mechanism of an evolutionary game is that success
differentially breeds more of the same strategy, implying positive feedbacks. A
distribution of strategies in the population that survives and is propagated in the
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course of evolution is called an evolutionary stable strategy (ESS). Unlike
neoclassical game theory, evolutionary games are distinct in the sense that while
in the former, agents are assumed to maximise their respective pay-offs, in the
latter, such maximising behaviour is not necessarily required. Thus, theoretically,
a larger variety of strategies can emerge in evolutionary game-theoretic models.
7. The evolutionary approach to modeling diffusion: a better
alternative?
The overview of the ED approach to modeling technological diffusion points to
certain apparent differences in the ‘norms’ of characterisation of the dynamics of
diffusion, as compared to the NE approach. A stylized view of these norms as
essentially perceived from the point of view of evolutionary theorists, are
summarised in Table 1.
As already outlined in the critique of NE models in Section 5 above, the
reasons behind the claim that ED models are a better alternative than NE models
in analyzing the process of change in general, and technological change in
particular, is that ED models explicitly or implicitly take into consideration the
influence of a wide range of behaviour, contexts, environments, initial conditions,
learning processes and the influence of both market and non-market institutions in
characterising the diffusion process. From these underlying microfoundations, the
important results derived in the context of ED models, highlighted in the Chart,
are that the diffusion process may be characterised by multiple equilibria, pathdependency, unpredictability, and potential inefficiency. Moreover, the process
Table 1. Decision-theoretic approaches to modeling diffusion
Neoclassical equilibrium
Evolutionary disequilibrium
1. Scientific analogy
„ Newtonian mechanics
1. Scientific analogy
„ Evolutionary biology
2. Assumptions
„ Full-information/limited information
„ Infinite rationality
„ Equilibrium mechanism
„ Exogenous/endogenous
„ Continuous and quantitative
2. Assumptions
„ Necessarily limited-information
„ Bounded rationality
„ Disequilibrium mechanism
„ Necessarily endogenous
„ – Continuous and quantitative
(Darwinian);
– Discontinuous and qualitative
(neo-Darwinian)
3. Characteristics of the diffusion process
„ Predictable
„ Ahistorical
„ Efficient
3. Characteristics of the diffusion process
„ Unpredictable
„ Path-dependent (historicity)
„ – Efficient (Darwinian);
– Possible inefficiency
(neo-Darwinian)
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may be discontinuous. However, notwithstanding some apparent differences in the
microfoundations underlying the NE and ED modeling approaches, and
substantial differences emerging in the properties of the diffusion path derived
therefrom, such differences seem to get blurred in the light of more recent
literature under the NE approach. It is becoming increasingly clear from a reading
of such literature that modern neoclassical analysis has taken up some of the
criticisms and challenges raised by evolutionary economists and incorporated
specific elements of the evolutionary paradigm into NE analysis. I make an
attempt below at identifying some of the areas of divergence and convergence
between the two approaches, and address the issue of whether the ED approach is
indeed a better alternative in modeling the diffusion process.
In the neoclassical equilibrium models, the diffusion process is one of
adjustment to given conditions. This, along with the assumption of full information and infinite rationality predicate that the diffusion outcome in NE models is a
priori predictable in the ‘temporal sense’ of inference from the past to the future,
and can be continuously and almost timelessly tracked by the system. Although,
the learning models of diffusion under the neoclassical approach do incorporate
certain aspects of imperfect information, like risk and uncertainty, the outcome in
each time period can be predicted, given that the learning procedure is exogenously specified and is common knowledge, and that prior information to carry on
the first updating is perfect. 10 Also, in the neoclassical models, the assumption of
infinite rationality implies that agents have perfect knowledge of their possibility
sets even under uncertainty, can correctly evaluate the benefits from the
innovation corresponding to their available strategies, and choose their most
preferred option from their possibility set in an ex ante sense, before any
diffusion takes place. Thus, in these models, contingent on the values of the
parameters (and the strategies chosen by the rivals), the diffusion outcome can be
perfectly predicted.
In contrast to the NE models, the assumptions of bounded rationality, limited
information and randomness in the choice process in ED models predicate that the
outcome is, a priori unpredictable. The worth of the innovation at any point of
time unravels only with the realisation of the state so that the prediction of a
future state would be ‘highly tentative’. While each course of action (i.e., adopt or
not adopt) has a distribution of rewards, the prototype adopter under the ED
paradigm, unlike its counterpart within the NE framework, do not know the
distributions in advance (Arthur, I989).
The unpredictability of diffusion outcomes in the ED models is related to
another distinguishing feature of these models, that of path-dependency or
historicity of the diffusion process. ED theorists contend that the diffusion process
in the NE models is characteristically ahistorical, i.e., history does not matter.
The Newtonian dynamics of adjustment in NE models imply that any disequilibrium that is created in the course of adjustment by a change in given conditions
is self-correcting and the process converges to a unique equilibrium. History is
subsumed in the equilibrium realizations, but neither initial conditions, nor the
‘history’ of the adjustment process matters once the system eventually gets to the
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equilibrium state. 11 In evolutionary dynamics, in contrast, the outcome in any
state is contingent on all previous states including the initial state, and the entire
‘path’ of the process will determine which equilibrium will be attained. That is,
evolutionary models characteristically have multiple equilibria or ‘absorbing
states. 12 Any departure from prevailing behaviour in these models, however,
small and apparently insignificant, instead of being dissipated, can become selfamplifying due to the presence of positive feedbacks (non-convexities), and the
outcome will gravitate to one of the absorbing states. It is in the sense of small
random events swaying a diffusion outcome that history is considered to matter in
ED models, thereby making it necessary to trace the entire ‘history’ of the
diffusion process in order to understand why particular technologies have diffused
over time. History matters in even a more ‘profound’ way in ED models when
small historical events may trigger off a process in the direction of an inefficient
equilibrium as the first inferior steps actually taken may set off a self-reinforcing
irreversible process in motion so that after a while existing superior lines of action
cannot outcompete the inferior ones anymore. Such dynamics lead to lock-in
effects that are an intrinsic feature of many ED models.
The notion that NE models are characteristically ahistorical has however been
recently questioned by some analysts. They claim that history does also matter in
neoclassical analysis but in a sense different from that under the evolutionary
paradigm. Liebowitz and Margolis (1995) argue that the NE conception of the
role of economic history lies in the ‘search for purpose in past actions’, so that the
technology outcome is based on purposeful, rational behaviour. This, they
observe, is different from the ED conception that the role of history lies in
understanding what rationality and efficiency cannot explain, that is, the ‘random
sequence of events not addressable by economic theory’. In this context,
Leibowitz and Margolis strongly argue that most ‘common’ forms of path
dependency or historicity, in the sense of dynamic processes being sensitive to
initial conditions, can be ‘best handled’ within the traditional NE approach. They
argue that, contrary to the perception of many evolutionary economists, processes
of change in NE models are indeed sensitive to initial conditions and actions, to
the extent that the present outcome is the result of the initial choice of the decision
maker. 13 However, it is only path dependence of a very strong form, dubbed as
‘third-degree path dependence’ where the decision-maker may be aware of better
alternatives initially, but intertemporal effects (e.g., small events) propagate error
to the extent that an inefficient option gains ascendance over time and the system
gets ‘locked in’, that is clearly inconsistent with the neoclassical axiom of
‘relentless’ rational behaviour. However, this form of path-dependence and
historicity, the authors argue, is based on ‘highly restrictive and implausible
assumptions’ that require important restrictions on prices, institutions or foresight.
It is only with respect to this strong form of path dependence that the diffusion
outcome would not be predictable as a function of initial conditions.
A scanning of the most recent literature on path-dependency reveals that claims
and counterclaims on this issue continue to be made between the neoclassicals and
the evolutionary economists. 14 However, objectively speaking, the possibility of
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path-dependency, albeit in a weak form, the existence of multiple equilibria in the
presence of increasing returns, and the possibility that optimizing agents may end
up bringing about non-optimal outcomes have been increasingly recognized in
recent neoclassical literature analyzing the adoption and diffusion of innovations.
As surveyed earlier, the former has been recognized in the partial equilibrium
neoclassical models in industrial organization and game theory literature, where
positive feedbacks under network externalities have been explicitly modeled and
the existence of multiple equilibria, some stable, some unstable, have been
demonstrated (e.g., excess inertia, excess momentum and bandwagon in Farrell
and Saloner, 1986). Elements of first and second degree path dependence can be
found in the network game-theoretic models of diffusion, and the NE learning
models. For instance, the diffusion outcome in Farell and Saloner’s (1986) model,
does depend on the initial values of the preference parameter, θ, and there are
multiple equilibria. Game-theoretic models as those by Reinganum have as their
roots the dynamics of imitation and diffusion as postulated by Schumpeter.
Selection and density-dependent models in the evolutionary literature which
analyze the diffusion of multiple process technologies under increasing returns,
too have parallels in more recent neoclassical literature that build on single
technology NE diffusion models (see, for e.g. Stoneman and Kwon, 1994). There
are also hybrid models of technological diffusion which combine elements of both
ED and NE models (Amable, 1992). Finally, as Coricelli and Dosi (1988) argue,
extensive form game models in this literature hint at the importance of institutions
governing repeated behaviors and explicit accounts of market signaling highlight
the importance of initial conditions.
It is also of interest to note that Marshall (1923) was the first economist to
recognize explicitly the possibility of multiple equilibria, although he considered
such possibility highly unlikely. He also understood that in such a situation there
would be a tendency for stable and unstable equilibria to alternate. This, in turn,
opened up the possibility that a small shift in parameters could cause a large
change in the system as it shifted from one stable equilibrium to another one quite
far way. Similar dynamics were also discussed by Debreu (1970) in the context of
his analysis of critical economies containing equilibria that were structurally
unstable. In short, as discussed in Rosser (1991), equilibrium models are
susceptible to catastrophes in the general sense.
Other points of convergence between the NE and the Darwinian school of
evolutionary modeling of the diffusion process are with respect to the continuity
and efficiency of the diffusion outcome. This, in spite of the fact that the
respective mechanisms underlying the gradualist approach and the neoclassicalequilibrium approach have quite different roots-the former in the Darwinian
disequilibrium theory of biological evolution and the latter in the Newtonian
equilibrium theory of mechanics.
The reason why both approaches conceive the diffusion process as being
continuous and incremental can be traced back to the origins of neoclassical
theory. As a neoclassical economist, Marshall wholeheartedly adopted Darwin’s
gradualism (Clark and Juma, 1988; Rosser, 1991). Marshall (1959, p. xi)
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observed: ‘Economic evolution is gradual. Its progress is sometimes arrested or
reversed by political catastrophes: but its forward movements are never sudden’.
Marshall also declared in the preface to the first edition of his Principles that the
application to economics of the ‘Principle of Continuity’ represents the ‘special
character’ of his whole book (Rosser, 1991, p. 224). His work was also highly
influenced by Cartesian-Newtonian mechanics under which reality was viewed as
being fundamentally continuous rather than discontinuous.
The convergence of NE approach and the Darwinian ED approach on the issue
of efficiency of diffusion outcome can be traced back to the materialist ideas of
the mechanical paradigm of Newtonian physics. Carried deep within the
materialist ideas ‘is the idea of ‘progress’, of the rightful ‘survival of the fittest’,
and of a natural ‘justice’ which should characterize the long-term evolution of a
complex system’ (Allen, 1988, p. 98).
Finally, it is important to draw attention to the fact that both the NE and ED
approaches, although ‘somewhat divorced’ from the epidemic models of diffusion
by being embedded in individual decision-making, have some identifiable and
common roots in these models. This is especially true of the limited information
NE models and the density-dependent ED models. The dynamics of diffusion
postulated in these models in some way links the decision to adopt to the existing
(or the expected number of adopters). In the limited information NE models,
existing non-adopters learn from the adoption experiences of existing adopters
and diffusion progresses as more information (via increasing number of adopters)
is spread and uncertainty about the returns from the innovation are reduced. In the
density-dependent ED models, informational externality and information
contagion play a critical role in reducing adoption cost for boundedly rational and
imperfectly informed non-adopters. Such dynamics have obvious parallels with
the fundamental assumption of epidemic models that all agents do not have equal
information about an available technological innovation, and diffusion rates are
determined by the ‘epidemic contagion’ that early adopters spread among
potential ones. One can in fact find in recent diffusion literature the development
of a ‘neo-epidemic’ approach which weaves in elements of bounded rationality,
limited knowledge, and strategic interaction into epidemic models (see for e.g.,
Antonelli, 1995, chap. 5; Ziesemer, 1995).
Given that there is a growing overlap of assumptions and results between the
neoclassical and evolutionary approaches, one might ask the following questions.
Should the evolutionary approach to modeling technological diffusion be indeed
considered a better alternative to the neoclassical approach as often claimed by the
former’s proponents? What is the difference in the analytical power between the
equilibrium and the evolutionary models?
One could argue in defence of the NE approach that it can derive many of the
important results under the ED approach without the ‘theoretical detours’ and a
diverse array of, often ad hoc, behavioral assumptions that characterise the latter.
Moreover, it can argued that the factors which the NE approach abstracts from
and which the ED approach takes as its ‘building blocks’-such as increasing
returns, non-stationarity, path-dependency, complex strategic and collective
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interactions, bounded rationality and fundamental uncertainty-could be treated
either as extensions or exceptions to the equilibrium approach or as some
empirical imperfections whose effects tend to cancel out in the aggregate. For
instance, as discussed earlier, some NE theorists claim by citing empirical
evidence that, contrary to the perception that path dependency is a ‘revolutionary
reformulation of the neoclassical paradigm’, most instances of path dependence
can be accommodated within the neoclassical framework of analysis, and the
ones that cannot be accommodated are only ‘rare’ occurrences (Leibowitz and
Margolis, 1995). As Silverberg (1988, p. 540) comments, ‘simply to use
evolutionary modeling to reproduce the common currency of orthodox theory
strikes one as too modest a program to justify the theoretical detours involved,
even if the evolutionary approach may claim to be more realistic or plausible’.
Such an argument would thereby imply that evolutionary approaches, at best,
contribute only marginally to enhancing an understanding of technical change.
This argument is essentially countered by the fact that since the evolutionary
approach allows for more system diversity in its modeling, it is more robust, i.e.,
lower sensitivity to mis-specification of the optimization problem and to
fluctuations in the environment, in its characterization of diffusion mechanisms
and outcomes. The neoclassical approach can be treated as a special case of the
evolutionary approach. Moreover, the evolutionary approach being intrinsically
non-linear can consistently account for a more structured and differentiated
pattern of such outcomes. As a result, under the evolutionary approach, diverse
experiences across regions and societies with respect to the adoption and diffusion
of innovations can be treated as being ‘normal’ rather than as empirical
imperfections as is the case under the equilibrium approach.
As Coricelli and Dosi (1988) remark, one of the few robust results that can be
obtained by relaxing some of the most demanding assumptions of the
‘unrestricted’ equilibrium/maximization model is precisely the lack of robustness
of its results (in terms of existence, determinacy, stability and Pareto-optimality).
This stands in contrast to simpler and robust solutions that can be obtained under
the evolutionary approach by taking as its ‘building blocks’ precisely what the
neoclassical theorists consider as exceptions. For instance, while the gametheoretic models under the neoclassical approach assume in large measure that the
agents maximize their respective pay-offs, those under the evolutionary approach
do not require such maximizing behavior at all. Thus, theoretically, a larger
variety of strategies can emerge under the latter class of models, which can
account for a wider variety of technological outcomes. Similarly, the densitydependent models under the evolutionary approach, while similar to the gametheoretic network models in considering adoption under increasing returns,
conduct the analysis independent of the assumptions of profit maximisation, full
information and infinite rationality that are fundamental to the analysis of NE
models.
That there is some merit to the ‘robustness’ argument, is apparent from the
following point. One of the crucial assumptions of the equilibrium approach, to
which solutions are very sensitive, is the infinite rationality postulate. This
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postulate in its strong form assumes not only that agents prefer higher payoffs to
lower ones, but that they are able to explore completely the payoff matrix prior to
any interaction taking place. In contrast, the solution under the disequilibrium
approach is based on no such ‘extraordinary informational assumptions’ but on
the assumption that agents are boundedly rational, an assumption which in the
limit does not preclude the possibility that agents could be infinitely rational.
Finally, an assessment of the comparative advantages and disadvantages of the
neoclassical-equilibrium and the evolutionary-disequilibrium approaches should
take into account their predictive power, i.e., how well these approaches can
account for observed empirical phenomenon. Clearly, in most cases, there will be
differences between the ‘empirical stories’ and the ‘theoretical stories’. The
predictive power of any theoretical approach lies in the fact that the core
hypotheses made by theory should not openly conflict with empirical phenomenon
and that they should ‘show enough persistence over time and/or across economic
environments’. This is an exercise that we turn to in the next section.
8. Theories vs evidence
The evidence that I present in my assessment of the predictive power of
alternative theories of diffusion is selective rather than comprehensive, primarily
to avoid unnecessary duplication of some recent empirical surveys on diffusion
research, notably the ones by Karshenas and Stoneman (1995), and Freeman
(1994). Nonetheless, the present exercise is meaningful to the extent that some
fresh empirical and historical evidence not covered in earlier surveys are
presented here, and some existing evidence re-analyzed within a unified structure,
in order to highlight the conflicts and contradictions that existing evidence may
have with the alternative theoretical approaches.
8.1. Alternative modes of assessment
The assessment of predictive power of any approach requires operationalizing the
underlying propositions for empirical testing. The degree to which the propositions derived under the neoclassical and evolutionary approaches can be
operationalized, are qualitatively different. This is because the nature of
‘explanation and testing’ is different between these two approaches.
The neoclassical approach to explaining is by setting up empirical diffusion
models which can capture the essential features of the NE theories of diffusion.
Following Karshenas and Stoneman (1995), these models can be classified into
two broad categories, namely (i) aggregate inter- and intra-industry diffusion
models that seek to explain the S-shaped diffusion path in terms of exogenous and
endogenous factors, and (ii) disaggregated duration models that explain the time
of technology adoption by a firm as a function of several factors, and which are
also applied to test for the validity of the alternative NE theories of diffusion.
In the aggregate empirical models of diffusion, the general approach has been
to preselect a logistic or some other S-shaped function, and test for closeness of
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fit with respect to available time-series data on the number or proportion of
adopters of an innovation. Different modified or generalised logistic curves
(lognormal, Gompertz) have been devised in order to achieve better fits to
empirical data. 15 The exercise also sometimes involves using linear regression to
explain the speed of diffusion (i.e., the slope coefficient of the fitted curves) in
terms of several exogenous factors. The epidemic models of Griliches (1957),
Mansfield (1959; 1961; 1968), Romeo (1977), and the probit model of Davies
(1979) fall within this class of models.
Notwithstanding the widespread use of aggregate diffusion models in
explaining an S-shaped diffusion path, the choice of functional form of the
growth curve has been to a large extent ad hoc (Karshenas and Stoneman, 1995).
Large biases in the estimates of diffusion speed have also been reported in spite of
very good fits with the data (Trajtenberg and Yitzhaki, 1989). Several other
authors have also questioned the general validity of sigmoid diffusion curves in
explaining diffusion phenomenon. Gold (1981), for example, suggests a need to
reexamine ‘the validity of sigmoid curves in generalising diffusion patterns; their
interpretation; and some of the further uses to which they have been put’ (p. 251).
Gold cites an international study by Ray (1969) which show that diffusion curves
may be linear, and also a 1970 publication covering the first fifteen years after
commercialisation of the diffusion of 14 major innovations in the United States
which do not find any support for the ‘general applicability’ of sigmoid diffusion
curves. Further, Nabseth and Ray (1974) have questioned whether sigmoid
diffusion curves do indeed give a good statistical fit to observed data.
The disaggregated duration models have attempted to improve on the
aggregated models by building in a more micro-based modeling framework.
These models seek to model the central concern of the NE theories of diffusion as
to why it takes time for firms to adopt a particular innovation. The methodology
underlying the disaggregated duration models is based on ‘hazard models’ in the
econometrics literature where the conditional probability of a firm to adopt a new
technology in any period (not having adopted in the previous period), defined as
the hazard rate, is estimated as a function of a vector of explanatory variables (for
a detailed description of the methodologies of duration models, see Karshenas
and Stoneman, 1995). Disaggregated duration models have also been set-up to test
which of the alternative diffusion models, namely the epidemic, probit and gametheoretic models, most adequately explain (and best fit) existing data on diffusion
(notable of which is by Karshenas and Stoneman, 1993, and Stoneman and
Kwon, 1994). The methodology adopted for such ‘model selection’ marks a
significant departure from existing empirical studies which ‘almost without
exception’ have adopted the methodology of estimating single, preselected
diffusion models. The applicability of duration models have, however, been more
limited compared to the aggregate diffusion models mainly because of limitation
of data; to estimate duration models, one ideally needs a data set on complete life
histories of the population of potential adopters, as well as the characteristics of a
well-defined new technology over a sufficiently long period of time since its
inception. Such ideal data sets have been ‘relatively rare’, and in particular,
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disaggregated data on the adoption of new technologies have been scarce
(Karshenas and Stoneman, 1995).
Unlike the NE models, evolutionary theories of diffusion are seldom tested
econometrically. The main objective of quantitative research under the evolutionary approach has been to explain inter-firm, inter-industry and inter-country
differences in rates of diffusion in terms of differences in individual behaviour,
skill intensity, learning and training, and several institutional and organisational
factors such as labour relations, incentives, hierarchical and managerial
structures, communiction systems, and so forth. The emphasis has been on an
‘historical-descriptive method of approach that can shed more light upon the knot
of elements affecting the emergence of a technology than a formal model’
(Amendola, 1990). Given that many of these factors cannot be satisfactorily
quantified or have data limitations, it is therefore not surprising that the
propositions derived from evolutionary models are tested either through
simulation exercises (e.g., Nelson and Winter, 1982; Dosi et al., 1992a; Mokyr,
1994) in which case the outcomes of such exercises are compared with observed
data, or are validated through detailed historical case studies of technological
diffusion at the firm, industry or country level (see for e.g., Arcangeli, et al.
(1991); Foray and Grubler (1990); Amendola (1990); Cainarca et al.(1989);
Ehrnberg and Jacobsson (1995); Kindleberger (1995); Dosi, et al., 1992b;
Higonnet et al., 1991). Case studies have also been the norm in empirically
analysing the selection of technologies, where there is reportedly much more
scope for ‘full-blown’ empirical work (Karshenas and Stoneman, 1995).
All of the case studies conducted within an ED framework lend support to the
basic contention of the ED analysts that the factors affecting the diffusion of
technologies are quite diverse and are specific to the technology, firm, industry or
country in question. Many of these studies, while highlighting the importance of
many demand and supply side factors conventionally taken into account in formal
econometric models, emphasise a host of non-market, qualitative factors that
influence the incentives for and capabilities of adopting a new technology. Some
examples of the latter are country-specific user-producer links (Arcangeli, et al.,
1991), technological and organisational profile of firms (Cainarca et al., 1989),
attitude of management towards innovation (Ray, 1989), and cultural and
habitual aspects of technology-practice (Pacey, 1983).
To give a structure to the assessment of empirical evidence with respect to the
alternative theoretical approaches, I have chosen the main elements of differences
between the NE and ED approaches with respect to the assumptions and
characterisation of the diffusion process, as indicated in the Chart, and have tried
to relate them to the existing evidence which span both historical case studies and
empirical work. In doing so, I have attempted to highlight the conflicts and
differences in opinion that are prevalent in the interpretation of critical empirical
and historical data. Most of such work has been confined to the United States and
Europe.
The evidence presented below is not always confined to the dynamics of
technological diffusion, but spills over at times to the associated literature on the
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related phenomena of invention and innovation. Although, the process of
diffusion is quite distinct from that of invention and innovation, it is important to
recognise that unlike the linear model of Schumpeter, recent theoretical and
historical literature suggests complicated feedbacks between the three processes
with technologies tending to get reinvented and innovated in the course of its
diffusion. This makes it difficult at times to disentangle evidence on diffusion
from that on invention and innovation. In this context, Freeman (1994) surmises
that the widespread evidence of feedbacks between invention, innovation and
diffusion, may have led Fleck (1988) to coin the expressions ‘Innofusion and
Diffusation’ in his analysis of the diffusion of industrial robotics.
8.2. Rationality and adoption behaviour
Neoclassical rational behaviour, perfect information, and the pursuit of profit
maximisation necessarily imply that a more efficient technology will be adopted,
although, potential adopters of a new technology may have different adoption
dates either due to population heterogeneity (as in the probit models) or due to
strategic considerations (as in the game-theoretic models). Such a proposition is
found to have empirical validity with respect to the diffusion of a number of
important innovations, for instance, of hybrid corn (Ryan and Gross, 1943;
Griliches, 1957) in US; of agricultural reapers and mowers in US and Canada
(David,1966, 1971; Pomfret, 1976; Jones, 1977; Atack and Bateman, 1987;
Headlee, 1991); major innovations in heavy equipment in US (Mansfield, 1968).
Conversely, one of the empirical regularities derived from empirical research on
diffusion, that the diffusion path often follows a Sigmoid path, has been
consistently explained by the probit models of diffusion (Davies, 1979; Karshenas
and Stoneman, 1993).
While empirical support for NE models of diffusion exists in the literature, the
basic micro assumptions underlying the diffusion path itself remain shielded from
being refuted as these assumptions are seldom testable. For example, the
assumption of infinite rationality which is at the heart of most neoclassical models
does not lend itself to rigorous falsifiabilty. Thus the validation of the propositions derived under these models does not, and cannot, imply the validation of the
fundamental assumptions on which these propositions are based. 16 In this respect,
the case-study approach to testing under the evolutionary approach enjoys a
distinct advantage. As case studies have seldom gone for formal mathematisation
and have instead attempted to record in ‘fine grain’ the behaviour of economic
agents, the complex processes involved in decision making, and the institutions
that support and mould these processes, a validation of behavioral assumptions
can easily be searched for in these studies. Here, as Freeman (1994) notes in his
survey, the evidence seem to confirm the assumption of bounded rationality of
decision makers, as commonly postulated under the ED approach.
Another weakness with respect to existing empirical analysis based on NE
assumptions is that there seems to exist an implicit but obvious ‘selection bias’ or
‘pro-innovation bias’ in existing empirical research — testable data exists only on
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‘successful’ diffusions that could be retrospectively investigated by diffusion
researchers while an unsuccessful diffusion does not leave visible traces that can
be easily analysed (Rogers, 1983). Thus, the NE empirical framework, with its
underlying assumptions of rationality and the notions of technology selection
based on economic criterion as profit maximisation, does not examine or provide
explanation for why particular innovations may not diffuse at all, and may be
largely rejected, possibly leading to inefficient outcomes. As Landes (1969)
comments in the context of his analysis of the diffusion of British technology to
other European countries during the Industrial Revolution, the choice of
production functions are not always governed by the rational calculations of
theory — habit, social prejudice, and entrepreneurial caution may lead to a
conservative attitude on the part of an individual and prevent adoption of ‘even
the most advanced techniques and equipment available’. That such factors are
empirically relevant is evident from a host of historical case studies on technological adoption and diffusion, a select few among which I review below.
Kuran (1989), in his case study of Tunisian guilds, highlights the point that,
institutions as traditional attitudes and norms can impinge on rational behaviour.
He notes that the ‘guilds formed a stagnant system … (with) an anti-competitive
conduct and their rules precluded organizational, technological and financial
innovations … (and) imparted to their members neither the motivation nor the
skills necessary to succeed in a dynamic economy’ (Kuran, 1989, p. 253).
Instances of adhering to ‘technological norms’ can be found in cases where, due
to vested interests, social pressures and religious and patriotic sentiments, the
majority of guildsmen went along with the amin’s conservative policies of
continuing with traditional production practices even when more efficient
practices were available. Evidence, similar in spirit to that provided by Kuran is
presented by Mokyr (1990, 1992). In analyzing technological inertia in economic
history, Mokyr highlights the fact that institutional rigidities, manifested in
resistance from interest groups and in political and social attitudes, have
historically been a central element in governing technological change. To support
his hypothesis, Mokyr (1990) provides, among other evidence, several instances
of how interest groups resisted the introduction and advancement of printing
technology in France in the fifteenth and sixteenth centuries.
The fact that a profit enhancing innovation need not necessarily be adopted is
also highlighted by Henning and Trace (1975) in their case study on the delayed
adoption of the motorship over the steamship in twentieth century Britain. On the
basis of ‘tests of performance’, the authors conclude that the British shipowners
would have increased their profits had they switched from steamships to
motorships in the 1920s. 17 Besides several supply side constraints, Henning and
Trace point out to the strength of the coal lobby and pro-coal sentiment in Britain
in preventing the adoption of the potentially more profitable motorship.
The preceding examples question the basic postulates of rationality and profit
maximisation of NE models, but have the underpinnings of the ED models of
diffusion — that adopters are boundedly rational, that the decision processes are
often routine-based rather than guided only by the quest for maximising profits,
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and that decision making is interdependent (as in the critical mass models) and
rooted in both formal and informal institutions. Historical examples as the above
do seem to suggest that often new and old techniques have been found to co-exist
for long periods, with sometimes the new one becoming the dominant one, and
sometimes just surviving in a small niche in spite of higher technological
superiority, with the old one remaining the dominant design. 18
8.3. Equilibrium vs. disequilibrium
The NE conception of technological diffusion as a sequence of shifting static
equilibria, where any disequilibrium created during the process of adjustment is
instantaneously dissipated is inbuilt in all the NE-based empirical models of
diffusion. Empirical work on ‘model selection’ using duration models, noted
above, do find some support for the equilibrium models of diffusion, but the
evidence is far from conclusive. Karshenas and Stoneman (1993), in comparing
alternative models of diffusion using data on the diffusion of CNC machine tools
in UK engineering industry find support for probit type of models. However, they
find ‘very little’ support for the game-theoretic models. Stoneman and Kwon
(1994), on the other hand, considering the joint diffusion of CNC machine tools
and coated carbide tools in the UK, find considerably more support for gametheoretic models, but much less support for probit models. It is of interest to note
that both of these exercises find significant support for epidemic effects as
manifested in endogenous learning in the course of diffusion.
Among historical case studies, the characterisation of diffusion as an equilibrium process finds the most convincing support in the case study by Harley
(1973) on the diffusion of metal in place of wood as the structural material in
North American shipbuilding. The author questions the ‘common tendency in the
economic and historical literature to treat … (the) persistence of old techniques as
a case of market disequilibrium’ (p. 372). His theory differs from such Schumpeterian notions, and is based on the NE notion of diffusion as a sequence of
shifting equilibria. He argues that the continued existence of wooden shipbuilding
industries long after the new industry of metal building was firmly established
‘appears better explained by a hypothesis that assumes (that) the period was
characterised by a series of competitive equilibria rather than by a Schumpeterian
view that diffusion was delayed by prejudice, ignorance and inertia’ (p. 373).
Harley shows that the series of equilibria generated during the transition from
wooden ships to metal ships were functions of differential supply curve shifts and
differing supply elasticities.
A similar characterisation of the diffusion process as that by Harley is found in
David’s (1966) study of the slow initial diffusion of the McCormick reaper in the
nineteenth century in the American mid-west. In line with the probit models, his
basic thesis is that the extent of adoption of mechanical reapers in any period was
a function of the factor price ratio and farm size. With given relative price of
capital to labour prevailing at a particular point of time, there were some farm
sizes, for which switching to the reaper was profitable and for some it was not.
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Diffusion of the reaper, David argues, only proceeded (i) as the price of reapers
fell relative to wage rates, and (ii) as farm sizes increased, both of which over
time effectively shifted downwards the critical (threshold) level of farm size
required for adoption, thereby bringing in more and more adopters.
As Harley (1973) himself notes, his view of the diffusion process, constituting
of a series of equilibria, is rather uncommon. It especially runs into conflict with
case studies of technology diffusion in recent years that highlight the role of
positive feedbacks and self-amplifying processes. For example, Arthur (1988,
1989) and David (1991), demonstrate that the diffusion process is disequilibrating. Both authors argue in the course of analyzing competition between alternative
technologies (VHS vs. Beta in Arthur and direct vs. alternating current systems in
David) characterised by network externalities, that benefits from these technologies continuously evolved as the diffusion process progressed (for a number
of other case studies on competing technologies, see Utterback, 1994). Unlike the
neoclassical conception that any disequilibrium arising in the course of adjustments is self-correcting, what Arthur and David show is that the disequilibrium
created in the course of diffusion could become self-amplifying. Apparently
insignificant historical accidents and unforseeable small events that arose over
time early on in the diffusion process of these technologies in the form of
‘idiosyncratic personal perceptions’ and ‘predilections’ of agents as well as in the
form of extraneous and transient circumstances, instead of being self-correcting,
had caused increasing departures from one juncture to the next.
The notion that diffusion is a disequilibrium process also seems to be supported
strongly by the growing body of evidence that an innovation undergoes
continuous transformation during the course of its diffusion, so that the processes
of innovation and diffusion reinforce each other in a cumulative process of
selection and evolution, rather than being characterised by shifts from one
equilibrium state to another. Cainarca et al. (1989), for instance, in their case
study of the diffusion of flexible automation in Italy, provide evidence in support
of an evolutionary pattern of innovation diffusion under which persistent
disequilibria exist along the diffusion paths as multiplicity of innovations are
‘continuously generated, compete intensively with each other and evolve together
with the structure of supply and demand’. Similar evidence has also been
forwarded by Olmstead (1975) and Olmstead and Rhode (1995), who question
the dynamics postulated in David’s (1966) analysis of the diffusion of McCormick reaper reported above, that it was the changing factor costs that led to the
rapid diffusion of the reaper. Instead, they provide evidence to show that diffusion
of the reaper was essentially an evolutionary disequilibrium process under which
the technology of the reaper continuously improved with its increased usage so as
to transform the reaper from an ‘experimentally crude, heavy, unwieldy and
unreliable prototype of the 1830s into the relatively finely engineered machinery
of the 1860s’ (p. 329). Other examples of continuing and open-ended adjustments
during diffusion, span a wide range of technologies — the automobile, airplane,
electronic computers, and so forth (see, for e.g., Rosenberg, 1976, 1982; Hughes,
1992).
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8.4. Historicity, efficiency and predictability
As the overview in the theoretical section suggested, it is with respect to the
features of historicity, efficiency and predictability of the diffusion process that
the NE and ED approaches remain the most divided and non-conciliatory. Also
noted was the fact that this theoretical divide is far from resolved, and as we will
see in this sub-section, evidence and counter-evidence abound in the empirical
literature that question one or the other viewpoint.
Evidence on path dependency or the lack of it can be best identified in historical
case studies. Such a feature tends to get ‘lost’ in empirical analysis, especially in
the aggregate diffusion models where neither individual characteristics, nor
historical processes are treated as central to the analysis. The most common source
of evidence on path dependency has been histories of competing technologies.
These, as told by subscribers of the path dependency school, bring out the
inefficiency and unpredictability of diffusion outcomes. The choice of technology
that seemed at a time to be a rational choice may turn out to be an inefficient
choice ex post due to some unforseen consequences and has the underpinning of
the neo Darwinian view of diffusion, that the specie that survives may not
necessarily be the fittest.
Some of the most commonly cited examples in support of the notion of pathdependence and the possible inefficiency of the diffusion outcome are found in the
literature on the diffusion of competing ‘network’ technologies (QWERTY vs.
Dvorak typewriters (David, 1985); VHS vs. Beta video tapes (Arthur, 1989);
alternating current vs. direct current (David, 1991; 1992)) characterised by
increasing returns to adoption. In all of these examples, the argument has been
made that the technology that ultimately diffused and competed out the other one
was the inferior one, and that such inefficiency was the cumulative outcome of
‘small events’ early on in the diffusion of the technologies that tipped the long run
outcome in favour of the inferior technology. For example, David argues that
between the two typewriter keyboards, QWERTY and Dvorak, the QWERTY
keyboard became the technological standard, despite there being evidence that
Dvorak was technologically superior. Similarly, it has been argued that the VHS
videotaping format effectively monopolised the market despite Beta being
technologically superior.
The apparently ‘strong’ evidence forwarded by the path-dependency theorists
have come under considerable fire in recent years. Leibowitz and Margolis in a
series of papers (1990, 1994, 1995) seek to establish that the evidence on the
QWERTY/Dvorak keyboards and VHS/Beta videotaping formats forwarded by
David and Arthur is ‘both scant and suspect’. For example, they claim that the
superiority of the Dvorak keyboard is a ‘myth’ and studies in the ergonomics
literature do not find significant advantage for Dvorak that can be deemed
scientifically reliable.
In the case of VHS vs. Beta, they question the common belief about Beta’s
superiority with evidence from some technical experts who claim that the Beta
format appeared to hold no advantages over the VHS format, and in fact there
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were some features of the latter (like longer playing time) that led to its
popularity.
The evidence on path-dependence and inefficiency also does not find universal
acceptance among all evolutionary economists; some analysts interpret the
dominance of particular technologies in the Darwinian spirit of the survival of the
fittest. Gasoline engines overtook steam and battery powered car engines, or the
AC system dominated the DC system because these technologies had major
advantages relative to their counterparts (Nelson, 1995). Also, there are
technology historians who discount the role of historical accidents and stochastic
elements in determining technological outcomes. Landes (1991; 1994; 1995), for
example is one such historian subscribing to the view that evolution is an
optimising process and there is very little room for accidents in history, a view
that runs counter to that of Crafts (1977; 1985; 1995), who by Landes’ own
admission, is a ‘brilliant and ingenious cliometrician’. Landes (1995) questions
the ‘faith’ of Crafts in the possibility of ‘major accidents in history’ whereby
stochastic elements in technological progress; could have catapulted Britain over
France in pioneering the first Industrial Revolution. Instead, Landes claims that ex
ante the probability of Britain being the pioneer was rather high, given among
several factors, the vastly superior British pool of mechanics compared to those
residing outside Britain. 19 Like Leibowitz and Margolis, Landes (1994) subscribes
to the view that empirical cases of path dependency are rather ‘rare’:
“ It is not hard to devise mathematical models of intrinsic inevitability — of
small differences that are reinforced over time to produce an ever-widening
gulf, of lines of development locked into ‘path dependency’. But any
resemblance between such lucubrations and the real world is purely
coincidental and highly occasional — fortunately ”. (p. 653, italics mine)
As with respect to the theoretical notions of path dependency, the empirical
importance of path dependency remains to this day an inexhaustible and open
issue. 20 The very latest in this is the response by Magnusson and Ottosson (1997)
to Leibowitz and Margolis’ contention that occurrences of path dependencies are
‘rare’. The former duo has dubbed the latters’ claim as a ‘wishful suggestion’, and
observe that many economists and social scientists dealing with applied matters
could ‘most certainly list an endless number of instances’ where the ED
conception of path dependency might be evidenced.
8.5. Continuity vs. discontinuity
As the Chart reveals, both the NE approach and the ‘gradualist’ evolutionary
school of technological change conceive the diffusion process as an incremental
and continuous one. In contrast, the neo-Darwinian (saltationist) perspective
claims that the diffusion process may not be necessarily continuous, but may be
‘punctuated’ with discontinuities or gaps. That is, the adoption pattern of an
innovation is sporadic rather than continuous. Both perspectives claim to have
their roots in historical evidence on the process of technological evolution. For
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instance, on the one hand, we have Rosenberg (1972) observing:
“Although we find it a convenient verbal shorthand to speak of the
“displacement” of one technique by another, the historical process is often
one of a series of smaller and highly tentative steps… . Their introduction
into the texture of the economy is more accurately — if less dramaticallyviewed as occurring along a gradual downward slope of real costs rather than
as a Schumpeterian gale of creative destruction” (p. 8; 33).
On the other hand, we have Mokyr (1990) commenting in the context of
analyzing the history of technological change:
“We cannot hope, however, to understand the historical changes that really
mattered without realizing that nature makes leaps, from time to time
(p. 354)”.
The division between the gradualist and saltationist perspectives is most evident in
the way the Industrial Revolution in Britain, and European technological
dynamism in general, has been assessed by technological historians. During the
years of the Industrial Revolution beginning around the 1760s, several major
innovations developed and diffused. In textiles, the self-actor and power loom
replaced the mule and handloom; the iron industry shifted from vegetable to
mineral fuels; the steam engine replaced the water wheel; the heavy chemical
industry was firmly established; and machines replaced manpower in practically
every other activity. Disagreements between the gradualist and saltationist
perspectives are primarily based on the speed and characterisation of the process
of such technological transformation during the years of the hundred years or so
of the Industrial Revolution.
The gradualist perspective on the Industrial Revolution, implied in several
historical accounts (notably, Hughes, 1970; Lee, 1986; Jones, 1981, 1988;
Kindleberger, 1995) is that the Industrial Revolution in Britain, and its spread into
other European countries, was the culmination of a long drawn out process of
development. That is, economic growth was already taking place before the
Industrial Revolution, and growth during the revolution proceeded at a moderate
pace rather than exhibiting a discontinuous jump relative to the preceeding years.
For example, Hughes (1970) stresses the ‘long historical background’ of several
major inventions as Arkwright’s water driven spinning factory and Watt’s steam
engine. In the case of the former, for instance, Hughes argues that there were
large scale agglomerations of workmen resembling factories for at least three
hundred years preceding the setting up of Arkwright’s factory in 1771, and that
the evolution of the factory system was a continuous process. The Darwinian
notions of order and continuity are clearly found in such interpretations of
Western technological evolution; the role of randomness and discontinuities in
such evolution being clearly discounted.
The saltationist perspective of the Industrial Revolution, on the other hand,
emphasising the theme of discontinuity and randomness in characterising the
Revolution is evident from accounts of Crafts (1977; 1995), Pacey (1983), and
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Mokyr (1991). This perspective presents evidence to reject the idea that
everything that happened during 1760 – 1830 had precedents and that there was
‘nothing new under the sun’. Instead, it argues that the Revolution represented a
‘sudden movement, a historical jolt away from the past’. The saltationists regard
the years 1760 – 1830 as a period in which the clustering of technological changes
in a wide array of industries were ‘sudden and violent’ occurring and spreading
with ‘feverish bursts’ and in which the British economy reached a ‘revolutionary
epoch’.
According to one account (Mokyr, 1991), while it is true that during the two
hundred years preceding the British Revolution, there was steady and intentional
technological progress on a variety of fronts in the form of ‘micro-inventions’
such progress was running into diminishing returns by 1700. The progress would,
have all but ceased and technological stasis would have set in, if not for the
sudden burst after 1700 of ‘macro-inventions’ that were successfully diffused —
inventions that were governed by ‘chance discoveries, luck and inspiration’.
Another account (Pacey, 1983) also seeks to highlight the fact that technological
progress was anything but smooth — ‘occasional brilliant achievements were not
sustained, and phases of stagnation occurred’. Such fluctuations seem to have
resulted not only from technological bottlenecks, but from variations in the
capacity of an ‘average individual’ to master and use innovations ‘effectively’
(Cardwell, 1974, as quoted in Pacey, 1983).
Apart from conflicting evidence on the continuity of technological evolution
with regard to the Industrial Revolution, a variety of evidence exists on the
subject at the firm, industry and country level in more recent times. For instance,
on the one hand, we come across a large pool of empirical evidence, derived
mostly from econometric studies within the neoclassical framework, which have
obtained good fits of time-series diffusion data with characteristically continuous
functions such as logistic and Gompertz. This is consistent with the NE
conception of the diffusion process being incremental and gradual. Some case
studies conducted within the evolutionary framework also highlight the continuity
of the technology diffusion process. Arcangeli et al., for example, in their 1991
analysis of the diffusion of electronics technology across Europe, USA and Japan,
identify continuity and gradualism in the microeconomic processes of automation
at the level of individual firms. Evidence on discontinuities in the diffusion
process are on the other hand found in Cainarca et al. (1989) in the adoption of
flexible automation systems, Foray and Grubler (1990) in the adoption of ferrous
casting technologies, and Ehrnberg and Jacobsson (1995) in machine tool
technology. Cainarca et al., for instance, in their case study of flexible automation
technology, identify a ‘step-by-step’ approach to adoption by which successive
adoptions of the technology are preceded by substantial accumulation of technical
expertise and of organizational capital, combined with the firms’ desire to
minimise sunk costs associated with investment in the technology.
The contrasting positions taken by the gradualists and saltationists regarding the
dynamics of technological innovation and diffusion can be somewhat reconciled
by resolving two definitional issues regarding technology adoption. One issue
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relates to the difficulties in defining in concrete terms the technology that is being
diffused, and the second relates to differences in dating procedures in historical
accounts. With respect to the first, empirical and historical accounts abound in the
literature documenting the fact that products and processes get continuously
modified and innovated during the process of diffusion, sometimes to the extent
that the modified product in the course of diffusion may be transformed to a
distinctly different product (Rosenberg, 1982; Sahal, 198l; Freeman, 1994). Thus,
it is a moot question as to whether, for example, one should treat the advent of the
steam engine along with all its subsequent innovations, as a single technology, or
demarcate between the various forms of the steam engines (Newcomen engines,
the Watt engines, the Cornish engines, etc.) that have been in use over the last two
centuries, and treat each as a separate innovation. While improvements and
accompanying diffusion of a particular type of engine may indeed be continuous,
the pattern of advance from one type to the next may be in a step-wise discontinuous fashion rather than in a smooth continuous manner. This is akin to shifts
in ‘technological trajectories’ within a particular ‘technological paradigm’. A
graphical illustration of such a pattern is found in Pacey (1983) who identifies
two upward steps in the performance associated with the Watts and Cornish
engines. Similarly, Foray and Grubler (1990) in their case study of the diffusion
of ferrous casting technology in France and Germany identifies three distinct and
discontinuous stages of the diffusion of the gasifiable pattern (GP) process
technology which coincided with two distinct clusters of innovations with respect
to the technology. However, their ‘morphological’ analysis of technological
diffusion, by which the diffusion process is periodized to coincide with principal
transformations of the technology in question, permitted them to avoid
‘misinterpretation concerning the asymmetrical character and discontinuities of
the diffusion trajectory’. As Foray and Grubler (1990) comment in the context of
a technology which evolves in the course of its diffusion,
“ In a sense, it is no longer the same technology at the end of the process.
However in another sense it is still the same technology because it is indeed
the knowledge accumulated during the first period that is mobilized for
competition in the second period (p. 550) ”.
With regard to the differences in interpretation of continuity arising from dating
procedures, the rate as well as the pattern of diffusion of an innovation would
obviously depend on the period chosen by an observer to study the diffusion
process. As Kindleberger (1995) succinctly puts it, ‘industrial technology moves
rapidly or slowly in the eyes of different beholders depending on the implicit
counterfactual they have in mind’. While Mokyr (1990) expresses surprise at how
rapidly the Continent picked up British technology and new technologies moved
within Europe, Landes (1965) emphasizes how long it took for Europe to catch
up. Rosenberg (1972) highlights the extent to which differences in dating
procedures may lead to differences in the characterisation of the diffusion process.
According to him, if one dates the diffusion process of the steam engine from the
achievements of Newcomen around the first decade of the eighteenth century
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rather than from the work of Watt in the last thirty years of the eighteenth
century, as is commonly done, one would get a much more gradual rate of
diffusion. As Rosenberg argues, there could be compelling reasons for dating the
diffusion process from Newcomen’s invention.
9. Concluding comments
The foregoing survey of the neoclassical equilibrium and evolutionary disequilibrium approaches to modeling the dynamics of diffusion, as also of selected
empirical and historical evidence gives us an insight into the detailed and
extensive work — some overlapping, some complementary, and some contradictory — that has been undertaken to provide a better understanding of the process
of diffusion. What the survey essentially indicates is that the alternative
approaches are not mutually exclusive as is often considered, and it is a
formidable task to entangle historical evidence in support of one approach to the
exclusion of the other. Elements of both approaches are found in existing case
studies tracing the process by which technologies are diffused over time. The
survey also brings out differences that often exist between different technological
historians’ interpretation of any particular event — be it the Industrial Revolution, or the diffusion of the QWERTY keyboard. All of these naturally justify the
‘theoretical heterogeneity’ that exists in diffusion research at present, the
important contributions to which I have reviewed in this survey.
While several conclusions of empirical studies seem to contradict the historical
accounts of diffusion, the value of the former lies in determining the statistical
significance of factors determining diffusion. Such significance cannot, for
obvious reasons be isolated in case studies which characteristically provide us
with the ‘whole picture’, although the latter could act as good pointers in such
exercises. The significance of case studies also lie in the fact that there is a certain
element of asymmetry in the empirical tests of significance, however precise they
might be — because of the pro-innovation bias in empirical research, the tests
would clearly bring out the determinants of diffusion, but not necessarily the set
of factors that limit diffusion.
In 1972, Rosenberg, one of the stalwarts in diffusion research, noted that, it is
a ‘striking historiographical fact’ that the serious study of diffusion of new
techniques was no more than fifteen years old and that our ‘ignorance’ of the
rate at which technologies are adopted and the factors determining such adoption
is ‘if not total, certainly no cause for professional self-congratulation’ (p. 3).
More than twenty years have passed since Rosenberg’s comments. Given the
substantial body, and several hues, of theoretical and applied diffusion research
since then as revealed to some extent in the present survey, he would certainly be
more encouraged today to be less critical of the professional achievements in his
field. What needs to be achieved in the field of diffusion research now is a
balance between the two archetypal modeling mechanisms of diffusion, their
underlying assumptions, and the postulated modes of interaction. Such a
balance, if achieved, is more likely to explain better the major distinguishing
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features of the diffusion process over historical periods, and of different
industries and countries.
Acknowledgements
This paper is an expanded version of a chapter of my Ph.D. thesis on technological diffusion written at the University of Southern California. I would like to
thank my thesis advisor Timur Kuran, as well as Vai-Lam Mui, Subrata Sarkar,
two anonymous referees, and Stuart Sayer (editor) for helpful comments on
earlier drafts of the paper. The responsibility of any error rests with me.
Notes
1. Other detailed accounts of NE models of diffusion are in Stoneman (1983, 1986,
1987). For other detailed account of ED models, see Freeman (1994), and Metcalfe
(1994).
2. Also see Gold (1981), Stoneman (1983) and Mahajan and Peterson (1985) for
detailed discussion and critiques of epidemic models.
3. This classification is somewhat broader than that made by Karshenas and Stoneman
(1993, 1995) where the authors classify probit models as rank models, and divide
game-theoretic models into stock effects and order effects models.
4. Notwithstanding these general analogies, many economists have however, recognised
the limits to drawing a one to one correspondence between biological evolution and
economic/technological evolution. For more detailed discussion of the analogies
between biological evolution and technological change and their limits, see Gowdy
(1986), DeBresson (1987), Mokyr (1996), and Witt (1996).
5. Strictly speaking, most models of technological diffusion, whether under the NE
approach or the ED approach, can be generically classified as selection models where
diffusion of the technology depends on the selection of technologies (old vs. new, or
different types of new) by potential adopters. What distinguishes models under the
different approaches are the assumptions and adjustment mechanisms underlying the
selection process.
6. Often, the unit of selection, instead of being the technology, is the firm; in that case
the system converges to a state under which only the most efficient firms survive.
7. For an overview of this literature, see Kuran (1988, 1995).
8. The role of instabilities in evolution was recognized quite early on by the biologist
Lotka (1925, p. 407 – 408), who argued that human beings regularly encounter
singular points where they are on ‘unstable orbits, such as that of a ball rolling along
the ridge of a straight watershed … where an imperceptible deviation is sufficient to
determine into which two valleys we shall descend’. In such cases, ‘infinitesimal
interference will produce finite, and it may be, very fundamental changes’.
9. For instance, Granovetter and Soong demonstrate the result in the context of
bandwagon and snob effects in the demand for a good: bandwagon effects occur when
other people buying more stimulates one to buy more and snob effects occur when one
is less inclined to buy more as others buy more.
10. One may quote Witt (1985) in this context: ‘… even in the attempt to realistically take
account of uncertainty and ignorance, the neoclassical approach is forced to let in an
embryonic perfect information assumption through the back-door of prior information
in order to secure the idea of perfect coordination’ (p. 575).
11. Dosi et al. (1992, p. 3) alludes to the natural science experiment of throwing different
objects from the Leaning Tower of Pisa and from other towers. While details of the
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12.
13.
14.
15.
16.
17.
18.
19.
20.
169
various objects and various towers would differ from one experiment to the other, all
experiments would have the same conclusion that every object anywhere on earth
would fall with an acceleration of approximately 9.8 m/s 2.
Here the allusion is that of a ball rolling along the ridge of a straight watershed where
even an imperceptible deviation is sufficient to determine into which two valleys it
shall descend.
Leibowitz and Margolis define these more common forms of path dependence as first
degree and second degree path dependence, and explain why an economy may be
locked into an inefficient outcome based on initial conditions. In the case of firstdegree path dependency, while an outcome today may appear inefficient in retrospect,
the initial choice was the most efficient given all available options. In the case of
second-degree path dependence, the initial choice was made under uncertainty, and
the possible inferiority of a chosen path is ‘unknowable’ at the time the choice was
made. However, the choice is not inefficient in any meaningful sense given the
assumed limitations on knowledge regarding available alternatives.
See for example the edited volume by Magnusson and Ottosson (1997) where several
authors counter the Leibowitz-Margolis position.
For an overview of different functional forms used, see Mahajan, Muller and Bass
(1990).
It is instructive in this regard to quote from Greasley’s (1982) study of the diffusion of
machine cutting in the British coal industry, 1902–1938. In estimating the factors
affecting the choice of the technology, he discounts the possibility that a
heterogeneous population of potential adopters of machine coal-cutting might arise
from ‘uncommon behavioral goals’, he decides to ‘proceed by postulating economic
rationality as a common behavioral goal … . The procedure necessarily leaves
economically irrational behaviour as an unexplained residual, resulting either from
failure to achieve or improper specification of behavioral goals’ (p. 251).
For instance, according to Net Present Value (NPV) calculations, the NPV of
motorship turns out to be positive at each rate of discount, ranging between 148,000
pounds to 171,000 pounds. In contrast, NPV for oil and coal fired steamships were
found to be negative for the entire range of discount factors considered. On the basis
of such estimates, the authors conclude that ‘British owners interested in profit
maximisation should have ordered motorships’.
Apart from the examples presented, the history of engines subscribes to such patterns,
an excellent account of which is found in Mokyr (1996).
Landes (1991) also forcefully argues that technological development in the West did
not start randomly in just any branch, but rather in critical branches such as
navigation and armament. Western dominance in the world, he argues, was not the
product of ‘a small, better yet accidental, moment of superiority, which was then
successfully nourished and fortified’, not the product of a small gap becoming a gulf.
Being such a product ‘is not history … history abhors leaps … it also abhors stunted
chains of causality, big consequences derived from small accidents (Landes, 1991,
p. 18)’.
It is, however, not always evident from the literature that there exists disagreements
surrounding the concept of path-dependency. Freeman’s 1994 survey for example
overlooks existing criticisms on the empirical work on path dependency, and instead
observes that empirical evidence on lock-in is ‘strong’.
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