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OCR for page 14
Technological Trajectories and the Human Environment. 1997.
Pp. 14-32. Washington, DC: National Academy Press.
Time for a Change:
On the Patterns of Diffusion of Innovation
ARNULF GRUBLER
A MEDIEVAL PRELUDE
The subject of this essay is the temporal patterns of the diffusion of techno-
logical innovations and what these patterns may imply for the future of the human
environment.) But first let us set the clock back nearly one thousand years: return
for a moment to monastic life in eleventh-century Burgundy.
Movement for the reform of the Benedictine rule led St. Robert to found the
abbey of Chateaux (Cistercium) in 1098. Chateaux would become the mother house
of some 740 Cistercian monasteries. About 80 percent of these were founded in
the first one hundred years of the Cistercian movement; nearly half of the
foundings occurred in the years between 1125 and 1155 (see Figure 11. Many
traced their roots to the Clairvaux abbey founded as an offshoot of Chateaux in
1115 by the tireless St. Bernard, known as the Mellifluous Doctor. The nonlinear,
S-shaped time path of the initial spread of Cistercian rule resembles the diffusion
patterns we will observe for technologies. The patterns of temporal diffusion do
not vary across centuries, cultures, and artifacts: slow growth at the beginning,
followed by accelerating and then decelerating growth, culminating in saturation
or a full niche. Sometimes a symmetrical decline follows or a new growth pulse.
Over time the Cistercians also diffused in space. Their pattern of settlements
shows significant differences in spatial density. The innovation origin, Burgundy,
was home to the four major mother houses and hosted the highest spatial concen-
tration of settlements. From there, daughter houses were founded ("regional
subinnovation centers," in the terminology of spatial diffusion), from which
14
OCR for page 15
400
300
a,
In
o
to 200
Q
A
100
o
-
.
.
.
.
...~-~-e
, 1 , ·-- ·- 1 , 1 1 1
.
.
.
.
.
.
.
.
.
.
.
1100 1120
Year
1140 1160
FIGURE 1 The initial diffusion of Cistercian monasteries in Europe. DATA SOURCE:
Janauschek (1877~.
Cistercians spread further into their respective hinterlands ("the neighborhood
effect") and to other subregional centers, originating yet further settlements. The
density of settlements decreased at the periphery, away from innovation centers,
implying persistent regional diversity and disparities. The Cistercians also differ-
entiated into "subfamilies," named after their respective parental houses. In fact,
each subfamily followed its own pattern of settlements, regional specialization,
and implementation of the Cistercian rule.
Some of the additions to the Cistercian rule were not genuine new settle-
ments but "takeovers." For example, the existing Benedictine monastery of
Savigny, with all its daughter houses, submitted to the rule of the Clairvaux
Cistercians in l 147 and in turn became the mother house of all Cistercian settle-
ments in the British Isles.
Despite distance and differentiation, all the monasteries communicated
closely. The industrious Cistercians thus introduced and channeled influential
innovations, including new agricultural practices and the water mill, throughout
Europe in the thirteenth and fourteenth centuries. The British monks excelled in
wool production. In fact, according to the Cistercian rule, settlements were to be
located in remote, undeveloped areas. Thus, Cistercian monasteries became im
OCR for page 16
6
ARNULF GRUBLER
portent local nodes for the colonization of land within Europe and, hence, for
deforestation.
The Cistercian topology reveals a hierarchy of centers of creation and struc-
tured lines of spread. The patterns bear witness to the existence of networks. As
we shall see, social and spatial networks, and their interactions, support and
shape the diffusion process.2
INVENTION, INNOVATION, THEN DIFFUSION
In discussing the time for a change associated with a technology, it is neces-
sary to consider invention and innovation as well as diffusion. Discourse now
customarily distinguishes among these three concepts following the classic analy-
ses made in the 1930s by the Austrian economist Joseph Schumpeter (1939~.
Invention is the first demonstration of the principal feasibility of a proposed new
artifact or solution. Fermi's Chicago reactor demonstrated the feasibility of a
controlled nuclear fission reaction (invention). In 1958, sixteen years after the
inauguration of Fermi's pile, the Shippingport, Pennsylvania, reactor went into
operation to generate commercial electric power (innovation). Some forty years
later more than one hundred nuclear reactors now generate some 20 percent of the
electricity in the United States (diffusion). Analogously, we might say St. Robert
invented the Cistercian rule, St. Bernard innovated, and diffusion followed.
In fact, considering the Cistercian rule as a technology makes an important
point. In the narrowest definition, technology is represented by the objects people
make, axes and arrowheads and their updated equivalents. Anthropologists call
them "artifacts"; engineers call them "hardware." But technology does not end
here. Artifacts must be produced, that is, invented, designed, and manufactured.
This process requires a larger system of hardware (machinery, a manufacturing
plant), factor inputs (labor, energy, raw materials), and finally "software" (human
knowledge and skills).
The third of these elements, which French scholars call technique, represents
the disembodied aspect of technology, its knowledge base. Technique is required
not only for the production of given artifacts but ultimately also for their use, both
at the level of the individual and at the level of society. An individual must know,
for example, how to drive a car; a society must know how to conduct an election.
Organizational and institutional forms (including markets), social norms, and
attitudes all shape how particular systems of production and use of artifacts
emerge and function. They are the originating and selection mechanisms of par-
ticular artifacts (or combinations thereof) and set the rate at which they become
incorporated into a given socioeconomic setting. This process of filtering, tailor-
ing, and acceptance is technology diffusion.
Before discussing diffusion further, let us return to the prior processes, in-
vention and innovation. In truth, a realistic history of social and technological
innovations would consist mostly of nonstarters. The overwhelming share of
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TIME FOR A CHANGE: ON THE PATTERNS OF DIFFUSION OF INNOVATION 17
. .
Inventions are ignored. And an analysis of several hundred major innovations
over the past two centuries shows a typical span of about fifteen to forty years
between invention and innovation (Mensch, 1975~. Moreover, the existence of
one or more possible innovations in itself hardly guarantees subsequent diffusion.
To appreciate the uncertainty in the early phases of technology development,
let us look at a historical problem of technological hazard and environmental
pollution from steam railways. In the early days of railroad expansion in the
United States, sparks in the smoke from wood-burning steam locomotives caused
a considerable fire hazard to both human settlements and forests (Basalla, 1988~.
Inventors and entrepreneurs registered more than one thousand patents on
"smoke-spark arresters" during the nineteenth century in a futile search for a
solution, which arrived finally not by an add-on technology but by the replace-
ment of steam by diesel and electric locomotives. This large number of alterna-
tives illustrates that diversity and experimentation are precursors to diffusion.
Many are called, but few are chosen.
Moreover, what is chosen for diffusion is not necessarily the best. The selec-
tion of a particular technological alternative may not conform to ex ante or ex
post judgments about optimality. Sometimes selection of a particular alternative
stems from an accumulation of small, even random events, eventually "locking
in" a particular configuration. Thereafter, positive feedback mechanisms yield
increasing returns to adoption of the standardized alternative. We suspect that the
standard gauge of railroads or the disk operating systems in use now in personal
computers are not the "best" but simply prevailed at a certain time in history and
therefore can only be dislodged with great difficulty (see Arthur, 19889.
What are the factors in setting the diffusion clock? One is simply opposition
to change. Opposition to proposed and diffusing technologies always recurs. The
most cited case is the Luddites, who destroyed knitting and other textile machin-
ery between 1811 and 1816. A similar movement, led by Captain Swing, resisted
the introduction of mechanical threshing in rural England in the 1 830s. As shown
in Figure 2, the opposition to the machines was itself an orderly diffusive process.
The time it took for the craze to smash machines to spread two weeks shows
that social interaction and communication were highly effective far in advance of
modern transport and telephony. Although opposition causes uncertainty about
the eventual fate of an innovation, it fulfills two important evolutionary roles.
First, it can operate as a selection mechanism for rejecting socially unsustainable
solutions or technologies. Second, it helps qualify technologies to respond to
societal concerns, improving their performance and thus enabling further, even
. , ... .
pervasive, c .lituslon.
In a classic article, Earl Pemberton (1936) provided many illuminating ex-
amples of curves of gradual cultural diffusion. The first country to introduce
postage stamps was England in 1840. Such a good idea; yet it took close to fifty
years for a sampling of thirty-seven independent states in Europe, North America,
and South America to imitate. A more delicate idea, touching on the nature and
OCR for page 18
18
ARNULF GRUBLER
300
u,
a)
._
c' 240
o
En
c`, 1 80
o
Q
~ 120
of
. _
60
o
.
K=250
·/
.,
/
·/
/
l. tm = November 23
,/ At=13days
November ~ November 18 November 28 December 8
Date (starting October 29, 1830)
FIGURE 2 Resistance to technology as a diffusion process: number of threshing ma-
chines attacked during the Captain Swing Movement in England in 1830. NOTE: Actual
data and a fitted three-parameter logistic curve. See endnote 5. DATA SOURCE: Hob-
sbawm and Rude (19681.
control of the family, is the first compulsory school attendance law, enacted at the
state level in the United States in 1847. It took fully eighty years, until 1927, for
the last state then belonging to the United States to adopt similar legislation.
These examples already emphasize that changes in technologies and social tech-
niques are not one-time, discrete events but rather a process characterized by time
lags and often lengthy periods of diffusion.
They also suggest that when diffusion succeeds, the forces and factors deter-
mining its speed and extent may change over time.3 Performance, cost, fashion,
and familiarity are among the considerations. Nevertheless, the diversity and
complex interactions at the micro level appear often to lead to smooth, orderly
behavior at the macro level, whether of Cistercians and Luddites, or, as we shall
see, canals and passenger cars. Some theorists argue that orderly macroeconomic
evolution requires such microeconomic diversity, which at first glance might
instead seem likely to dissipate order (see Dosi et al., 1986; Silverberg, 1991; and
Silverberg et al., 1988~.
In addition to sociological and economic factors, straightforward, generic
OCR for page 19
TIME FOR A CHANGE: ON THE PATTERNS OF DIFFUSION OF INNOVATION 19
considerations appear to influence the speed of diffusion. The scope of technical
change itself is a powerful one. We might distinguish four levels: 1) incremental
improvements; 2) radical changes in individual technologies and artifacts; 3)
changes in technology systems, that is, combinations of radical changes in tech-
nologies combined with organizational and managerial changes; and 4) changes
in clusters and families of technologies and in associated organizational and
institutional settings.4 The latter levels of change, as well as larger system sizes,
will likely entail longer times for diffusion (Grubler, 1991~.
In sum, inventive and innovative activities provide the potentials for change.
However, diffusion translates these potentials into changes in social practice. One
abbey could not transform European agriculture; 740 did. Diffusive, largely imi-
tative or repetitive phenomena are at the heart of the changes in society and its
material structures, infrastructures, and artifacts. Thus, in the subsequent discus-
sion, the analysis of time required for diffusion provides the central metric to
analyze processes of social and technological change. Let us now try to grasp the
main patterns.
THE DURATION OF DIFFUSION
We will consider an increasingly complex series of cases of technology
diffusion, characterized by the environment in which diffusion processes operate.
In the simplest case, an idea, practice, or artifact represents so radical a departure
from existing solutions that it largely creates its own market niche. In practice,
preexisting means for meeting basic social functions, such as transport and com-
munication, are always present; nothing is truly new or free of competitors.
Physicist Elliott Montroll (1978) called evolution a sequence of replacements.
But clearly, some technologies enter much more accommodating environments
than others.
The development of canals in the early nineteenth century offers a reason-
able case of simple diffusion. In fact, the actual data on the growth of the canal
network in the United States are approximated very well by a symmetrical growth
curve, a three-parameter logistic equation in this case (Figure 3~.5 The estimated
upper limit of the diffusion process, some 4,000 miles of canals, matches the
historical maximum of 4,053 miles of canal in operation in 1851. The character-
istic duration of diffusion (or fit), defined as the time required for the process to
unfold from 10 percent to 90 percent of its extent, is thirty-one years. The canals
spread through the United States at about the same rate as the Cistercians initially
spread through Europe. The entire canal diffusion cycle from 1 percent to 99
percent spans some sixty years. The year of maximum growth, or midpoint (tm)'
occurred in 1835.
Subsequent major transport infrastructures, rails and roads, evolved along
a dynamic pattern similar to canals, as Figure 4 illustrates (Grubler and
Nakicenovic,l9911. In the figure the sizes of individual networks have been
OCR for page 20
20
4,000
a, 3,000
._
-
u'
~ 2,000
o
s
~ 1,000
ARNULF GRUBLER
Deb
1780 1790 1800 1810 1820 1830
Year
1840 1850 1860 1870 1880
FIGURE 3 Growth of the canal network in operation in the United States. SOURCE:
Grubler (l990).
100
90
80
70
c 60
c, 50
40
30
20
10
. ~
1/Canals Railways // //
l
t 1835 1891// /
~ D
1 1~ 1
So=
Ri/ telegraphs /Roads
~ `1946
1 800 1 820
Airways
-
1840 1860 1880 1900 1920 1940 1960 1980 2000
Year
FIGURE 4 Growth of infrastructures in the United States as a percentage of their
maximum network size. SOURCE: Grubler and Nakicenovic (l99l).
normalized for better comparability; in absolute extension, railways and surfaced
road networks were one and two orders of magnitude larger, respectively, than
canals at their maximum network length. Not surprisingly, the duration of the
growth of railway and surfaced road networks is somewhat slower, /\t's of fifty-
five and sixty-four years, respectively. Interestingly, we see the three major his-
toric transport infrastructures spaced rhythmically apart in their development by
a half century or so.
Transport infrastructures strongly influence nearly every aspect of daily life.6
OCR for page 21
TIME FOR A CHANGE: ON THE PATTERNS OF DIFFUSION OF INNOVATION 21
Here we will comment only on their close relationship with other infrastructures.
As Figure 4 suggests, the railway and the telegraph evolved together, as did the
road network and the oil pipelines delivering the fuel for the cars on the roads.
This synchronization illustrates technological interdependence and cross enhance-
ment. Particular technologies and techniques do not diffuse in isolation but in a
larger context, as we shall discuss below.
In fact, a new solution does not evolve in a vacuum but interacts with exist-
ing practices and technologies. One technology replaces or substitutes for an-
other, with varying degrees of direct one-to-one competition. For example, after
reaching its maximum size, the canal network declined rapidly because of vicious
competition from railways. Looking at relative "market shares" of competing
alternatives rather than at absolute volumes makes the interaction visible.
Probably the most famous case of technological substitution is motor cars for
horses. In this case, the diffusion of one technological artifact, the passenger car,
began simply by replacing another, the riding horse and the carriage. Looking at
the absolute numbers of draft animals and cars in the United States (Figure S), we
see that the millions of horses and mules used for transport practically disap-
peared from the roads within fewer than three decades. Measured by a curve fit to
a model of logistic substitution (for the model see Marchetti and Nakicenovic,
1979), the duration of the replacement process (At) was only twelve years, fast
enough to traumatize the oat growers and the blacksmiths (see Nakicenovic,
1o6
105
o
cn
~ 104
cn
3
103
1o2
1 850
-
Horses _ _ _
Cars
-
~\ 1 1 1 1 1
1 950
1 900
Year
2000
FIGURE 5 Number of nonfarm draft animals and automobiles. SOURCE: Nakicenovic
(1986).
OCR for page 22
22
1o2
1o1
ILL
lo 1 10°
10-1 L
10-2
1960 1965 1970 1975
No Con
Year
First Emission Controls
,~
Catalytic Converter/
\
1980 1 985
1 r I \1; 01
1 990
\
ARNULF GRUBLER
0.99
-
o.so LL
o
._
C'
0.70 ,
0.50
a)
co
0.30 cn
a)
0.10 ~
FIGURE 6 Diffusion of cars with first emission controls and catalytic converters in the
United States, in fractional shares of total car fleet. SOURCE: Nal~icenovic (19861.
1986~. Interestingly, the diffusion of a modern anti-pollution device, the catalytic
converter, also occurred with a /\t of twelve years in the United States (Figure 6~.
The reason is probably that the lifetime of the road vehicle has not changed since
the horse-and-carriage era; the working lives of horses and cars both last about
ten to twelve years.
The continuing growth of the car population in Figure 5 illustrates another
dynamic feature of technological evolution: growth beyond the initial substitu-
tion or field of application. Use of the car grew initially by replacing horses. After
completion of that process in the 1930s, new markets were created. Higher aver-
age speeds, greater reliability in all weather conditions, and other features opened
chances both for competition with trains for long-distance travel and for short-
distance commuting that created suburbs, which in turn created more demand for
cars. Currently some 150 million passenger cars are registered in the United
States, about 0.6 cars per capita.
Mention of the sequence of horses, trains, and cars brings us to consider the
most realistic process of technological change: multiple competing technologies.
In steel manufacturing as many as four technologies have competed simulta-
neously with decreasing and increasing market shares (Figure 7~. The diffusion
trajectories of the processes are diverse, with /\t's ranging from less than two
decades (replacement of the crucible process) to nearly seven decades (diffusion
of electric arc steel). These changes in process technology not only enabled
significant expansion of production but mattered greatly from an environmental
perspective. They coincided with changes in energy supplies toward higher qual
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TIME FOR A CHANGE: ON THE PANELS OF DIFFUSION OF INNOVATION 23
~ .0
0.e
0.6
s
0.4
0.2
o
\ | Crucible
\1 ~ ~7
it/
/:1 1~1 4,~
A/Open Hearth ~
BE
~ Selectric
it'
1850 1900 1950 2000
Year
FIGURE 7 Process technology change in US steel manufacturing, in fractional shares
of raw steel tonnage produced. SOURCE: Nalcicenovic (1987J.
ity and cleaner energy carriers, consistent with the overall evolution of energy
supply (see Nakicenovic, this volume). Between 1800 and 1930 in the United
States, one hundred million cords of hardwood are estimated to have been cut for
charcoal for smelting iron (Reynolds and Pierson, 1942~.
Let us now bring space back into our time picture. We have drawn examples
so far from the United States. We commented at the outset about the patterns in
space as well as the time of the diffusion of the Cistercian rule. Does the same
hold true for a modern technology such as the motor car? Like Burgundy and its
Cistercians, the United States was the earliest adopter of the car and has achieved
the highest density of cars. Having started to adopt cars rapidly about the year
1910, America now has almost six hundred cars per thousand people. Having
started in 1930, the United Kingdom now parks about four hundred cars per
thousand people, while Japan parks about three hundred per thousand, having
started the adoption process only in the l950s. As Figure 8 suggests, empirical
data from numerous countries show that later adopters manifest both an acceler-
ated diffusion rate (shorter diffusion time) and a declining density of adoption as
a function of the introductory date. The case of cars is corroborated by analysis of
the declining adoption densities of "late-starters" in the railway development of
the nineteenth century (Grubler, 19901.
OCR for page 24
24
ARNULF GRUBLER
U.S Total New Zealand
cn
a)
^100
~ 50
CC
o
. _
= 10
, .
Cant
France
\ Denmark
/ Italy Czechoslovakia
Australia ,1~,,T Japan
Great Britain / -
E. Germany
Mexico
U.S. Total Canada
' \ New Zealand
/ France/ Mexico
,~ / / /~ E. Germany
1\~
Poland
-
Denmark
Australia /~ Poland
I ~-Czechoslovakia
Great Britain/ ~
World / Japan
Italy
~1 1 1 1 1 1 1 1 1 1 1 1 1 1
-
1880 1920 1960 2000
Year
.~
to
to
1 000
500
u,
100 ~
sat
.o
cl)
50
FIGURE 8 Passenger car diffusion at the global level: Catch-up, but at lower adoption
levels. NOTE: Estimated saturation density and diffusion rates expressed as a function
of the introduction date of the automobile. SOURCE: Grubler (1990).
The spread of railway networks in fact clearly shows how both spatial densi-
ties and the temporal rates of the adoption of technologies remain diverse. In the
United States, the early innovation centers for railways on the East Coast and
around the Great Lakes achieved by far the greatest spatial density of networks.
Railway construction reached the West Coast some fifty years after the East
Coast, and network densities remained significantly lower. In Europe, rails spread
from the north of England in the 1 820s to the rest of England and also to Belgium.
By 1836 independent innovation centers had arisen in the Lyons region of France
and Austria-Bohemia. The railway innovation wave spread from the early conti-
nental centers to cover most of Western and Central Europe by the 1 850s. By the
mid 1870s all of Eastern Europe, as well as most of European Russia, southern
Scandinavia, and part of the Balkans, were networked. The final European
subinnovation center was Greece, toward 1900. Rails penetrated the Albanian
region almost a century after England. Starting first, England built a network
(with attendant costs and benefits) one-third denser than Germany, almost twice
OCR for page 25
TIME FOR A CHANGE: ON THE PATTERNS OF DIFFUSION OF INNOVATION 25
the density of France, and ten or more times denser than other countries that
might have appeared comparable at the outset of the railroad era.
In this light, we can ask, is the United States a likely guide for future mass-
motorization globally? According to our understanding, no. Instead, the high
density of cars in the United States results from specific initial conditions, includ-
ing high individual mobility before the advent of the automobile and a long
period of diffusion, which created precisely the conditions in life-style, spatial
division of labor, and settlement patterns of an "automobile society." As Figure 8
indicates, heterogeneity in rates of diffusion and thus levels of adoption follows
orders and thus is likely to persist, not only for railways and autos but in general
for systems that diffuse globally. This perspective leads to lower-than-usual esti-
mates of future demand for transport energy for China, for example (Grubler,
1992).
SEASONS OF SATURATION
We have noted that clusters of radical innovations and technology systems,
interdependent and mutually cross-enhancing, give rise to families of technologi-
cal innovations with associated new institutional and organizational settings. For
example, the development of the automotive industry was contingent on develop-
ments in materials (high-quality steel sheets), the chemical industries (oil refin-
ing, in particular catalytic cracking), production and supply infrastructures (ex-
ploration and oil production, pipelines and gasoline stationsJ, development of
public infrastructures (roads), and a host of other technological innovations. The
growth of the industry was based on a new production organization (Fordist mass
production combined with Taylorist scientific management principles), yielding
significant real-term cost reductions that made the car affordable to more social
strata, thus changing settlement patterns, consumption habits of the population,
and leisure activities. In turn, the automobile is just one artifact among many
consumer durables now standard in every household in industrialized countries.
These linkages multiply the effects of such techno-institutional clusters on the
economy and society and account for their pervasive impact.
To quantify the emergence of technology clusters, I analyzed the history of a
large sample of technologies for the United States (Grubler, 1990, 1991) Consis-
tent with the definition of technology adopted here, the sample used in the analy-
sis was not taken from the hard technology field alone. The cases included diffu-
sion of energy, transport, manufacturing, agriculture, consumer durables,
communication, and military technologies, as well as diffusion of economic and
social processes, such as literacy, reduction of infant mortality, and changes in
job classes. Two samples were analyzed. The first consisted of 117 diffusion
cases that my colleagues at the International Institute for Applied Systems Analy-
sis and I had studied ourselves (see Grubler, 1990; Marchetti, 1980; Marchetti
and Nakicenovic, 1979; and Nakicenovic, 1986~. The second sample was aug
OCR for page 26
26
ARNULF GRUBLER
20
10
Sample A (117 cases)
mean = 57.5
st. dev. = 52.5
---Sample B (265 cases)
mean = 41.0
st. dev. = 42.0
R ~ ,
o ~ ~ ~ Q ~ o ~ o ~ o ~ Q ~ o ~ o ~ o ~ o At
~ ~ ~ ~ ~ o ~ ~ Us ~ Go ~ ~ ~ ~ ~ ~Do at
~ ~ ~ ~ ~ ~ ~ ~ CM Cal ~ Cal ~ ~
FIGURE 9 Histogram of diffusion rates of samples of 117 and 265 processes of tech-
nological, economic, and social change in the United States. NOTE: The At equals the
time in years for a process to extend from 10 to 90 percent of its duration. St. dev. =
standard deviation. SOURCE: Grubler (1990, 19911.
mented by additional, well-documented cases with a quantification of diffusion
parameters that we found in the literature. This sample totaled 265 cases of
.
innovation.
The profile of the diffusion rates, or /\t's, was quite similar for the two
samples. The rates ranged from very short-term processes of only a few years to
processes that extended over two to three centuries. The mean value ranged
between forty and sixty years, with a standard deviation of about equal size
(Figure 9~. The largest number of diffusion processes in our samples have char-
acteristic durations, /\t's, of between fifteen and thirty years.7 If our diffusion
studies had documented more of the seemingly numerous short-term phenomena
such as clothing fashions, the profile of the histogram in Figure 9 would likely
approach a "rank-size" or Zipf distribution in which the frequency of diffusion
rates would be highest for fast processes and decline as the rates became slower.8
The good news for the human environment from our analysis is that the
majority of artifacts and practices can be replaced within a few decades. How-
ever, some key processes have demonstrably long durations. For example, the
global quests for improvements in the thermodynamic efficiency of prime mov-
ers and for the decarbonization of the energy system both clock in at about three
hundred years (see Ausubel and Marchetti, this volume; Nakicenovic, this vol
OCR for page 27
TIME FOR A CHANGE: ON THE PATTERNS OF DIFFUSION OF INNOVATION 27
ume). In general, pervasive transformations take time. The transformation of the
US population from a society of farmers to manufacturers to service workers took
some two hundred years (Herman and Montroll, 1972~. Societies starting the
move from brown to blue and to white collars later may accordingly move faster,
but such all-embracing processes will never collapse to weeks and months.
We might summarize by saying that at any time, change in a society can be
decomposed into a large number of diffusion (or substitution) processes with
great variety in their rates. We can then ask whether aggregate measures exist for
the average diffusion rate over time for the whole socioeconomic system and
whether it changes. For such a measure, I calculated the average diffusion rates of
the innovation samples, that is, the sum of the first derivatives of the diffusion (or
substitution) trajectories at each point in time divided by the number of diffusion
processes then occurring. This indicator is the diffusion equivalent of the annual
GNP growth rate. The resulting measure rates the average annual technical (and
economic and social) change at the country level (Grubler, 1990, 1991~.
For the United States since 1800, the calculated average diffusion rate por-
trays clear peaks and troughs, which vary by a factor of two or more. The process
of change is not gradual and linear but is instead characterized by long swings
and discontinuities. In addition, rates of change tend to increase over time. This
rise may reflect that the closer we approach the present, the more processes are
included in the sample. However, the rising average rate of change could also
result from the cumulative nature of technological change. Even though no indi-
vidual diffusion process may proceed faster when compared to the past, the
number and variety of artifacts (particularly those with faster turnover rates) are
in fact much larger today than earlier. This could increase the average rate of
change. In other words, while no individual technology or artifact diffuses faster
than it did in the past (other things being equal), many more technologies and
objects are in use, and thus more change. In any case, the analyses show pro-
nounced discontinuities and also a decline in the diffusion rate in the decades
after 1970, indicating an increase in saturation phenomena in the United States
since then.
The fluctuations and discontinuities in the long-term rate of sociotechnical
change result from the complex dynamics of the discontinuous rates at which
individual innovations appear and from the different rates of absorption of these
innovations in the socioeconomic system. Periods of accelerating rates appear to
indicate the emergence of a technology cluster in which a large number of inter-
related innovations diffuse into the economic and social environment. These in
turn contribute, by means of backward and forward linkages, to prolonged peri-
ods of economic growth.
Periods in which progressively more and more innovations enter their satura-
tion phase of diffusion follow the growth periods. Thus, each major peak in the
average rate of change characterizes the start of saturation of a corresponding
cluster or family of diffusion processes. This "season of saturations" results in a
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ARNULF GRUBLER
significant decline in the average rate of technical and social change and, through
market saturation and a decrease in investments, also contributes to a slowdown
. .
In economic growth.
Presumably many inventions of the past few decades now await their chance
to become successful innovations. Were they included, these could reverse the
recent downward trend in the rate-of-change curve by the late l990s. Then the
successful innovations, after a slow initial diffusion, would enter into the rapid,
indeed exponentially growing part of their life cycle.
The turning points in the rates of diffusion of technological and social inno-
vations coincide with the turning points of so-called long-waves of economic
growth as identified by several researchers (Marchetti, 1980; van Duijn, 1983;
Vasko, 1987~. In the analysis of US data, the peaks the maxima in the rate of
sociotechnical change and the onset of leveling off and saturation phenomena
occurred in 1840, 1912, and 1970, respectively. Troughs, maxima of saturation
periods and the slow beginning of a new phase of accelerated sociotechnical
change, occurred in 1820, 1875, and 1930. Appropriately, these troughs corre-
spond to periods of pronounced recession, even depression, in the economic
development of the United States.
From a historical perspective we can associate four technology clusters with
this statistical pattern and speculate on the emergence of a fifth. The clusters may
be identified by their most important economic branches, infrastructures, or func-
tioning principles. Extending to the 1820s, we find textiles, turnpikes, and water
mills; extending until about 1870 we find steam, canals, and iron; extending until
about 1940 we find coal, railways, steel, and industrial electrification; extending
to the present we find oil, roads, plastics, and consumer electrification (Grubler,
1994~. Currently we appear to be in transition to a new era of industrial and
economic development. We can speculate that it will be characterized by natural
gas, aviation, "total quality control" of both the internal and external (or environ-
mental) quality of industrial production, and the massive expansion of informa-
tion handling.
These observations add up to an essentially Schumpeterian view of long-
term development. Major economic expansion periods appear driven by the wide-
spread diffusion of a host of interrelated innovations a technology cluster
leading to new products, markets, industries, and infrastructures. These diffusion
processes are sustained by, in fact are contingent on, mediating social and organi-
zational diffusion processes. The growth or diffusion of a dominant cluster can-
not be sustained indefinitely, however.
Market saturation, the dwindling improvement of possibilities for existing
process technologies, managerial and organizational settings, and an increasing
awareness of the negative (specifically, environmental) externalities involved in
the further extension of the dominant growth regime pave the way to a season of
saturations. During such periods, opportunities arise for the introduction of new
technological, organizational, and social solutions, some of which may have been
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TIME FOR A CHANGE: ON THE PATTERNS OF DIFFUSION OF INNOVATION 29
latent but were barred from market entry by the dominance of the previous
growth paradigm. Even when such innovations are introduced successfully, their
penetration rates in the initial phase of their diffusion life cycle are rather slow,
and a matching new social and economic mediating context has still to emerge. In
the phase-transition period, the old is saturating, and the new is still embryonic.
Only after such a period of transition, crisis, and mismatch does a prolonged
period of widespread diffusion of a new sociotechnical "bandwagon" and thus of
growth become possible.
CONCLUSIONS
Empirical examination of diffusion processes, as illustrated in this essay,
highlight the following observations:
(1) No innovation spreads instantaneously. Instead, a typical S-shaped tem-
poral pattern seems to be the rule. This basic pattern appears invariant, although
the regularity and timing of diffusion processes vary greatly.
(2) Diffusion is a spatial as well as temporal phenomenon. Originating from
innovation centers, a particular idea, practice, or artifact spreads out to its hinter-
land by means of a hierarchy of subinnovation centers and into the periphery,
defined spatially, functionally, or socially.
(3) The periphery, while starting adoption later, profits from learning and
the experience gained in the core area and generally has faster adoption rates. As
the development time is shorter, however, the absolute adoption intensity is lower
than in innovation centers or in core areas (spatial or functional) proximate to
them.
(4) Although diffusion is essentially a process of imitation and homogeniza-
tion, it clusters and lumps. The densities of application remain discontinuous in
time and heterogeneous in space among the population of potential adopters and
across different social strata. In fact, overall development trajectories appear
necessarily punctuated by crises that emerge in transitional periods. As such,
diffusion and its discontinuities may be among the inherent features of the evolu-
tionary process that governs social behavior.
Nevertheless, appropriate incentives and policies may nurture the develop-
ment of more benign technologies and their diffusion, and many changes can be
implemented over a time frame of two to three decades. However, sectors and
areas will also remain in which changes will occur much more slowly, particu-
larly those related to the long-lived structures of our built environment: for ex-
ample, infrastructures for transport and energy as well as housing stock. Here
rates of change and diffusion constants ranging from several decades to a century
are typical and will be costly to accelerate. Therefore, the efficiency with which
existing systems are used merits attention.
In essence we have two strategies in light of diffusion. One focuses on
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ARNULF GRUBLER
incremental changes, for example, environmental add-on or "end-of-pipe" tech-
nologies. Such policies can bring quick changes but tend to reinforce the domi-
nant trajectory, blocking more systemic and radical changes. A second strategy
opts for more radical departures from existing technologies and practices. How-
ever, these strategies, such as the development of fuel cells and hydrogen for
energy, although more effective in the long run, require much more time to
implement because of the multiplicity of forward and backward linkages between
technologies, infrastructures, and forms of organization for their production and
use.
The interdependence between individual artifacts and long-lived infrastruc-
tures creates our dilemma. Within two to three decades the United States could in
principle change its entire fleet to zero-emission vehicles. In fact, 99 percent of
vehicles now on the road will be scrapped in this interval. Yet, this interval is too
short for the diffusion of the required associated energy supply, transport, and
delivery infrastructures, which will inevitably distend the rate of diffusion of end-
use devices. Thus, key technologies that we can already envision to raise the
quality of the environment probably must await the second half of the twenty-
first century to become widespread and influential.
Historically, technology clusters have been instrumental in raising produc-
tivity and also in alleviating many adverse environmental effects. The emergence
of a new cluster could hold the promise of an environmentally more compatible
technological trajectory. But it will take time. There are times of change and
times for change, and unless our individual and collective behavior is modified,
these times will remain to frustrate and excite us.
NOTES
1. For an extended version of this essay, see Grubler (1995).
2. On the spatial diffusion of Cistercians, see Donkin (1978). For a general overview of diffu-
sion theory, see Hagerstrand (1967), Morill (1968, 1970), and Rogers (1983). For a more recent
overview of diffusion theory, see Grubler and Nakicenovic (1991). On the role of networks, see
Kamann and Nijkamp (1991).
3. For an overview from sociology and anthropology, see Rogers (1983); for an overview from
economics, see Mansfield (1961, 1968). For industrial innovations, see Nasbeth and Ray (1974) and
Ray (1989).
4. For a more detailed discussion, see Freeman and Perez (1988) and Grubler (1992).
5. The equation to which the data are fitted has the form Y = k/(1 + e(-b(~-tm))), where Y(t)
represents the sigmoidal growth through time of a population or process, Y. This is often referred to
as the logistic model. Three parameters control the shape of the sigmoidal growth trajectory: b
controls the steepness (or diffusion rate) of the model; k denotes the asymptotic limit (or saturation
level); and tm denotes the middle or inflection point. The inflection point occurs at k/2, where the
growth rate (dY/dt) is at a maximum. Note that k is sometimes also referred to as the "carrying
capacity." A convenient notation for the diffusion rate (b) is l\t, where At is the time it takes for the
process to grow from 10 to 90 percent of the saturation level, k. Approximately the same length of
time is required for the process to grow from 1 to 50 percent. Through simple algebra, it can be
shown that At = ln(81)/b.
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TIME FOR A CHANGE: ON THE PATTERNS OF DIFFUSION OF INNOVATION 31
6. For an account of the dynamic interactions in US transport infrastructure development, see
Nakicenovic (1988). For a discussion of the impacts of transport infrastructure development on
economic growth and discontinuities in economic development, see Berry et al. (1993), Grubler
(1990), and Isard (1942). Berry (1990) also provides a good account of their impact on urbanization.
7. Starr and Rudman (1973) suggested a doubling time of twenty to thirty years for the techno-
logical component of economic growth, an estimate that our data sample corroborates.
8. For discussion of such distributions, see Montroll and Badger (1974).
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Representative terms from entire chapter:
diffusion processes
