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Chapter Three
Types and Patterns
of Innovation
Innovating in India: The chotuKool Project
Godrej & Boyce, founded in India in 1897, sold a range of products to the Indian
market including household appliances, office furniture, and industrial process
equipment. In recent years, international competitors such as Haier and Samsung
were cutting deep into Godrej’s market share for household appliances such as
refrigerators, washing machines, and air conditioners, and management knew
that to preserve the company would require innovative solutions.
One such solution was the chotuKool, a small, portable refrigerator. Though
around the world refrigeration was considered a mature technology, in rural India
as many as 90 percent of families could not afford household appliances, did not
have reliable access to electricity, and had no means of refrigeration. This significantly limited the kinds of foods they could eat and how they could be prepared.
Finding a way to provide refrigeration to this segment of the population offered
the promise of both a huge market and making a meaningful difference in people’s quality of life. As noted by Navroze Godrej, Directed of Special Projects at
Godrej, “We imagined we would be making a shrunken down version of a refrigerator. Make it smaller, make it cheaper. And we had preconceived notions of
how to build a brand that resonated with these users through big promotions
and fancy ad campaigns.”
These assumptions would turn out to be wrong. First, as Godrej’s team
looked at the options of how to reduce the cost of a conventional compressorbased refrigerator, they quickly realized that they could not reduce its cost by
enough to make a meaningful difference.a Second, they discovered that having
the refrigerator be lightweight was more important than they had previously
thought because many rural Indians lived migratory lives, moving to follow the
availability of work. Third, because of the lack of refrigeration, most people
were in the habit of cooking just enough for the day, and thus had relatively
low refrigeration capacity needs. Fourth, of those few rural Indians that did
have refrigerators, many did not plug them in for most of the day for fear of
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44 Part One Industry Dynamics of Technological Innovation

them being damaged by power surges. As Godrej notes, “We were surprised
by many things, we were shocked by many things . . . we realized our original
hypothesis was quite wrong.”b
Based on these insights, the company designed a small and portable refrigerator based on thermoelectric cooling (rather than compressor technology).
Thermoelectric cooling was the cooling method used in laptops; it involved
running a current between two semiconductors. It was far more expensive
on a per-unit-of-cooling basis, but it had much lower power requirements and
could be used on a much smaller scale than compressor cooling. This enabled
Godrej to make a very small, lightweight refrigerator with a relatively low price
(35–40 percent cheaper than traditional refrigerators). It also lowered the power
costs of operating a refrigerator, and made the refrigerator able to operate for
several hours on a 12-volt battery, making it much more adaptable to situations
where power was unreliable.
In Godrej’s initial plan for the chotuKool, the refrigerators would be cherry
red and look like coolers. Soon, however, managers at chotuKool realized that
if the refrigerators were just perceived as inexpensive alternatives to refrigerators, they had the potential to be stigmatizing for consumers who, in turn, would
not talk about them to their friends. This was a serious problem because the
company had counted on word of mouth to spread information about the refrigerators deep into rural communities. To get people to talk about the coolers they
needed to be aspirational—they needed to be cool.
Godrej decided to revamp the design of the coolers, giving them a more
sophisticated shape and making them customizable (buyers could choose
from over 100 decorative skin colors for the chotuKool).c They also decided
to market the refrigerators to the urban affluent market in addition to the
rural market, as adoption by the urban affluent market would remove any
stigma associated with buying them. To attract this market they positioned the
refrigerators as perfect for picnics, parties, offices, dorm rooms, use in cars,
and so on.
To get the chotuKool to rural customers would require a dramatically different distribution system than Godrej had traditionally used. However, building
out a distribution system into rural communities would prohibitively raise the
cost of chotuKool, potentially rendering the product nonviable. The development team was initially stumped. Then one day G. Sunderraman, vice president of Godrej and leader of the chotuKool project, happened to inquire with
a university official about obtaining college application forms for his youngest
son and the official pointed out that Sunderraman could get the forms at any
post office. At that moment, Sunderraman realized that the post office, which
had offices in every rural area of India, could be an ideal distribution channel
for the chotuKool.d It was a very novel proposition, but India Post agreed to
the collaboration and soon chotuKools were available in all post offices in the
central region of India.e As Sunderraman noted, “The India Post network is
very well spread in India and is about three or four times larger than the best
logistic suppliers.”f

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Chapter 3 Types and Patterns of Innovation 45

The chotuKool won several design awards in its first years, and after selling
100,000 units in its second year Fast Company gave Godrej its “Most Innovative
Company” award. Godrej and Sunderraman were disappointed to discover that
it was not as rapidly adopted by rural poor households as they had hoped; the
roughly $50 price was still too expensive for most poor rural families in India.
However, the chotuKool turned out to be much more popular than anticipated
among hotels, food stalls, flower shops, and other small stores because it
enabled these small stores to offer higher valued products (such as cold drinks)
or to keep products fresh longer, thereby increasing their profits. The chotuKool
also became a popular lifestyle product among the urban affluent population
who began to widely use them in their cars.
Godrej’s experience developing and launching the chotuKool had provided
many lessons. They had learned that to radically reduce the cost of a product might require completely rethinking the technology—sometimes even in
ways that initially seemed more expensive. They learned that customers who
had adapted their way of life to the lack of a technology (like refrigeration)
might not adopt that technology even if it was made markedly less expensive. Finally, they learned not to underestimate the value of making a product
work for multiple market segments, including those that might not be initially
obvious as customers. Though some people considered chotuKool a failure
because it had not achieved its original objective of wide adoption by the rural
poor, Godrej (and many others) considered it a success: the product expanded
Godrej’s market share, penetrated new market segments in which Godrej had
not formerly competed, and demonstrated Godrej’s innovative capabilities to
the world.

Discussion Questions
1. What were the pros and cons of attempting to develop a refrigerator for
India’s rural poor?
2. What product and process innovations did the chotuKool entail? Would
you consider these incremental or radical? Architectural or component?
Competence enhancing or competence destroying?
3. Did the chotuKool pose a threat of disrupting the traditional refrigerator
market? Why or why not?
4. Is there anything you think Godrej should have done differently to penetrate the market of rural poor families in India?
5. What other products might the lessons Godrej learned with chotuKool
apply to?
a

McDonald, R., D. van Bever, and E. Ojomo, “chotuKool: ‘Little Cool,’ Big Opportunity,” Harvard Business
School Case 616–020 (June 2016), revised September 2016.
b
Furr, N., and J. Dyer, “How Godrej Became an Innovation Star,” Forbes (May 13, 2015).
c
www.chotukool.com, accessed June 26, 2018.
d
Furr, N., and J. Dyer, “How Godrej Became an Innovation Star,” Forbes (May 13, 2015).
e
Nadu, T., “chotuKool Offer in Post Offices,” The Hindu (June 9, 2013).
f
“chotuKool: Keeping Things Cool with Frugal Innovation,” WIPO Magazine, (December 2013).

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46 Part One Industry Dynamics of Technological Innovation

OVERVIEW

technology
trajectory

The path a
technology
takes through
its lifetime.
This path may
refer to its rate
of performance
improvement, its
rate of diffusion,
or other change
of interest.

The previous chapters pointed out that technological innovation can come from many
sources and take many forms. Different types of technological innovations offer different opportunities for organizations and society, and they pose different demands
upon producers, users, and regulators. While there is no single agreed-upon taxonomy
to describe different kinds of technological innovations, in this chapter we will review
several dimensions that are often used to categorize technologies. These dimensions
are useful for understanding some key ways that one innovation may differ from
another.
The path a technology follows through time is termed its technology trajectory.
Technology trajectories are most often used to represent the technology’s rate of performance improvement or its rate of adoption in the marketplace. Though many factors
can influence these technology trajectories (as discussed in both this chapter and the
following chapters), some patterns have been consistently identified in technology trajectories across many industry contexts and over many periods. Understanding these
patterns of technological innovation provides a useful foundation that we will build
upon in the later chapters on formulating technology strategy.
The chapter begins by reviewing the dimensions used to distinguish types of innovations. It then describes the s-curve patterns so often observed in both the rate of
technology improvement and the rate of technology diffusion to the market. In the last
section, the chapter describes research suggesting that technological innovation follows a cyclical pattern composed of distinct and reliably occurring phases.

TYPES OF INNOVATION
Technological innovations are often described using dimensions such as radical versus
incremental. Different types of innovation require different kinds of underlying knowledge and have different impacts on the industry’s competitors and customers. Four of
the dimensions most commonly used to categorize innovations are described here:
product versus process innovation, radical versus incremental, competence enhancing
versus competence destroying, and architectural versus component.

Product Innovation versus Process Innovation
Product innovations are embodied in the outputs of an organization—its goods or services, even if those products are services. For example, Snapchat’s filters and special
effects that enable users to augment their photos are product innovations. Process innovations are innovations in the way an organization conducts its business, such as in the
techniques of producing or marketing goods or services. For example, Elon Musk’s
use of automation for most of the production process for the Model 3 with giant robots
is a process innovation. Process innovations are often oriented toward improving the
effectiveness or efficiency of production by, for example, reducing defect rates or
increasing the quantity that may be produced in a given time. For example, a process
innovation at a biotechnology firm might entail developing a genetic algorithm that
can quickly search a set of disease-related genes to identify a target for therapeutic

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Chapter 3 Types and Patterns of Innovation 47

intervention. In this instance, the process innovation (the genetic algorithm) can speed
up the firm’s ability to develop a product innovation (a new therapeutic drug).
New product innovations and process innovations often occur in tandem. First, new
processes may enable the production of new products. For example, as discussed later
in the chapter, the development of new metallurgical techniques enabled the development of the bicycle chain, which in turn enabled the development of multiple-gear
bicycles. Second, new products may enable the development of new processes. For
example, the development of advanced workstations has enabled firms to implement
computer-aided manufacturing processes that increase the speed and efficiency of
production. Finally, a product innovation for one firm may simultaneously be a process innovation for another. For example, when United Parcel Service (UPS) helps a
customer develop a more efficient distribution system, the new distribution system is
simultaneously a product innovation for UPS and a process innovation for its customer.
Though product innovations are often more visible than process innovations, both
are extremely important to an organization’s ability to compete. Throughout the
remainder of the book, the term innovation will be used to refer to both product and
process innovations.

Radical Innovation versus Incremental Innovation
radical
innovation

An innovation
that is very new
and different
from prior
solutions.

incremental
innovation

An innovation
that makes a
relatively minor
change from
(or adjustment
to) existing
practices.

One of the primary dimensions used to distinguish types of innovation is the continuum between radical versus incremental innovation. A number of definitions have
been posed for radical innovation and incremental innovation, but most hinge
on the degree to which an innovation represents a departure from existing practices.1
Thus, radicalness might be conceived as the combination of newness and the degree
of differentness. A technology could be new to the world, new to an industry, new to
a firm, or new merely to an adopting business unit. A technology could be significantly different from existing products and processes or only marginally different.
The most radical innovations would be new to the world and exceptionally different
from existing products and processes. The introduction of wireless telecommunication products aptly illustrates this—it embodied significantly new technologies that
required new manufacturing and service processes. Incremental innovation is at the
other end of the spectrum. An incremental innovation might not be particularly new
or exceptional; it might have been previously known to the firm or industry, and
involve only a minor change from (or adjustment to) existing practices. For example,
changing the screen of a cell phone to make it more crack resistant or offering a
new service plan with better international texting rates would represent incremental
innovation.
The radicalness of innovation is also sometimes defined in terms of risk. Since radical innovations often embody new knowledge, producers and customers will vary in
their experience and familiarity with the innovation, and in their judgment of its usefulness or reliability.2 The development of third generation (3G) telephony is illustrative.
3G wireless communication technology utilizes broadband channels. This increased
bandwidth gave mobile phones far greater data transmission capabilities that enabled
activities such as videoconferencing and accessing the most advanced Internet sites.
For companies to develop and offer 3G wireless telecommunications service required
a significant investment in new networking equipment and an infrastructure capable

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48 Part One Industry Dynamics of Technological Innovation

competenceenhancing and
competence
destroying
innovation
A competenceenhancing
innovation builds
on existing
knowledge and
skills whereas a
competencedestroying
innovation
renders existing
knowledge and
skills obsolete.
Whether an innovation is competence enhancing
or competence
destroying
depends on
whose perspective is being
taken. An
innovation can
be competence
enhancing to
one firm, while
competence
destroying for
another.

of carrying a much larger bandwidth of signals. It also required developing phones
with greater display and memory capabilities, and either increasing the phone’s battery power or increasing the efficiency of the phone’s power utilization. Any of these
technologies could potentially pose serious obstacles. It was also unknown to what
degree customers would ultimately value broadband capability in a wireless device.
Thus, the move to 3G required managers to assess several different risks simultaneously, including technical feasibility, reliability, costs, and demand.
Finally, the radicalness of an innovation is relative, and may change over time or
with respect to different observers. An innovation that was once considered radical may eventually be considered incremental as the knowledge base underlying the
innovation becomes more common. For example, while the first steam engine was
a monumental innovation, today its construction seems relatively simple. Furthermore, an innovation that is radical to one firm may seem incremental to another.
Although both Kodak and Sony introduced digital cameras for the consumer market within a year of each other (Kodak’s DC40 was introduced in 1995, and Sony’s
Cyber-Shot Digital Still Camera was introduced in 1996), the two companies’ paths
to the introduction were quite different. Kodak’s historical competencies and reputation were based on its expertise in chemical photography, and thus the transition to
digital photography and video required a significant redirection for the firm. Sony,
on the other hand, had been an electronics company since its inception, and had a
substantial level of expertise in digital recording and graphics before producing a
digital camera. Thus, for Sony, a digital camera was a straightforward extension of its
existing competencies.

Competence-Enhancing Innovation versus
Competence-Destroying Innovation
Innovations can also be classified as competence enhancing versus competence
destroying. An innovation is considered to be competence enhancing from the perspective of a particular firm if it builds on the firm’s existing knowledge base. For
example, each generation of Intel’s microprocessors (e.g., 286, 386, 486, Pentium,
Pentium II, Pentium III, Pentium 4) builds on the technology underlying the previous
generation. Thus, while each generation embodies innovation, these innovations leverage Intel’s existing competencies, making them more valuable.
An innovation is considered to be competence destroying from the perspective of
a particular firm if the technology does not build on the firm’s existing competencies or renders them obsolete. For example, from the 1600s to the early 1970s, no
self-respecting mathematician or engineer would have been caught without a slide
rule. Slide rules are lightweight devices, often constructed of wood, that use logarithm
scales to solve complex mathematical functions. They were used to calculate everything from the structural properties of a bridge to the range and fuel use of an aircraft.
Specially designed slide rules for businesses had, for example, scales for doing loan
calculations or determining optimal purchase quantities. During the 1950s and 1960s,
Keuffel & Esser was the preeminent slide-rule maker in the United States, producing
5000 slide rules a month. However, in the early 1970s, a new innovation relegated
the slide rule to collectors and museum displays within just a few years: the inexpensive handheld calculator. Keuffel & Esser had no background in the electronic

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Chapter 3 Types and Patterns of Innovation 49

components that made electronic calculators possible and was unable to transition to
the new technology. By 1976, Keuffel & Esser withdrew from the market.3 Whereas
the inexpensive handheld calculator built on the existing competencies of companies
such as Hewlett-Packard and Texas Instruments (and thus for them would be competence enhancing), for Keuffel & Esser, the calculator was a competence-destroying
innovation.

Architectural Innovation versus Component Innovation

component
(or modular)
innovation

An innovation
to one or more
components
that does not
significantly
affect the overall
configuration of
the system.

architectural
innovation

An innovation
that changes the
overall design
of a system or
the way its components interact
with each other.

Most products and processes are hierarchically nested systems, meaning that at any
unit of analysis, the entity is a system of components, and each of those components is,
in turn, a system of finer components, until we reach some point at which the components are elementary particles.4 For example, a bicycle is a system of components such
as a frame, wheels, tires, seat, brakes, and so on. Each of those components is also
a system of components: The seat might be a system of components that includes a
metal and plastic frame, padding, a nylon cover, and so on.
An innovation may entail a change to individual components, to the overall architecture within which those components operate, or both. An innovation is considered
a component innovation (or modular innovation) if it entails changes to one or
more components, but does not significantly affect the overall configuration of the
system.5 In the example above, an innovation in bicycle seat technology (such as
the incorporation of gel-filled material for additional cushioning) does not require any
changes in the rest of the bicycle architecture.
In contrast, an architectural innovation entails changing the overall design of
the system or the way that components interact with each other. An innovation that
is strictly architectural may reconfigure the way that components link together in the
system, without changing the components themselves.6 Most architectural innovations, however, create changes in the system that reverberate throughout its design,
requiring changes in the underlying components in addition to changes in the ways
those components interact. Architectural innovations often have far-reaching and complex influences on industry competitors and technology users.
For example, the transition from the high-wheel bicycle to the safety bicycle was
an architectural innovation that required (and enabled) the change of many components of the bicycle and the way in which riders propelled themselves. In the 1800s,
bicycles had extremely large front wheels. Because there were no gears, the size of
the front wheel directly determined the speed of the bicycle since the circumference
of the wheel was the distance that could be traveled in a single rotation of the pedals. However, by the start of the twentieth century, improvements in metallurgy had
enabled the production of a fine chain and a sprocket that was small enough and light
enough for a human to power. This enabled bicycles to be built with two equally sized
wheels, while using gears to accomplish the speeds that the large front wheel had
enabled. Because smaller wheels meant shorter shock-absorbing spokes, the move to
smaller wheels also prompted the development of suspension systems and pneumatic
(air-filled) tires. The new bicycles were lighter, cheaper, and more flexible. This architectural innovation led to the rise of companies such as Dunlop (which invented the
pneumatic tire) and Raleigh (which pioneered the three-speed, all-steel bicycle), and
transformed the bicycle from a curiosity into a practical transportation device.

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50 Part One Industry Dynamics of Technological Innovation

For a firm to initiate or adopt a component innovation may require that the
firm have knowledge only about that component. However, for a firm to initiate or
adopt an architectural innovation typically requires that the firm have architectural
knowledge about the way components link and integrate to form the whole system.
Firms must be able to understand how the attributes of components interact, and
how changes in some system features might trigger the need for changes in many
other design features of the overall system or the individual components. Modularity, and its role in the creation of platform ecosystems, is discussed in greater detail
in Chapter Four.

Using the Dimensions
Though the dimensions described above are useful for exploring key ways that one
innovation may differ from another, these dimensions are not independent, nor do
they offer a straightforward system for categorizing innovations in a precise and consistent manner. Each of the above dimensions shares relationships with others—for
example, architectural innovations are often considered more radical and more competence destroying than component innovations. Furthermore, how an innovation is
described on a dimension often depends on who is doing the describing and with
what it is being compared. An all-electric vehicle, for example, might seem like a
radical and competence destroying innovation to a manufacturer of internal combustion engines, but to a customer who only has to change how they fuel/charge the
vehicle, it might seem like an incremental and competence-enhancing innovation.
Thus, while the dimensions above are valuable for understanding innovation, they
should be considered relative dimensions whose meaning is dependent on the context
in which they are used.
We now will turn to exploring patterns in technological innovation. Numerous studies of innovation have revealed recurring patterns in how new technologies emerge,
evolve, are adopted, and are displaced by other technologies. We begin by examining
technology s-curves.

TECHNOLOGY S-CURVES
Both the rate of a technology’s performance improvement and the rate at which the
technology is adopted in the marketplace repeatedly have been shown to conform to an
s-shape curve. Though s-curves in technology performance and s-curves in technology
diffusion are related (improvements in performance may foster faster adoption, and
greater adoption may motivate further investment in improving performance), they are
fundamentally different processes. S-curves in technology improvement are described
first, followed by s-curves in technology diffusion. This section also explains that
despite the allure of using s-curves to predict when new phases of a technology’s life
cycle will begin, doing so can be misleading.

S-Curves in Technological Improvement
Many technologies exhibit an s-curve in their performance improvement over their lifetimes.7 When a technology’s performance is plotted against the amount of effort and
money invested in the technology, it typically shows slow initial improvement, then

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Chapter 3 Types and Patterns of Innovation 51

FIGURE 3.1

Limit of Technology

S-Curve of
Technology
Performance

Performance

Effort

accelerated improvement, then diminishing improvement (see Figure 3.1). Performance
improvement in the early stages of a technology is slow because the fundamentals
of the technology are poorly understood. Great effort may be spent exploring different paths of improvement or different drivers of the technology’s improvement. If the
technology is very different from previous technologies, there may be no evaluation
routines that enable researchers to assess its progress or its potential. Furthermore,
until the technology has established a degree of legitimacy, it may be difficult to attract
other researchers to participate in its development.8 However, as scientists or firms
gain a deeper understanding of the technology, improvement begins to accelerate. The
technology begins to gain legitimacy as a worthwhile endeavor, attracting other developers. Furthermore, measures for assessing the technology are developed, permitting
researchers to target their attention toward those activities that reap the greatest
improvement per unit of effort, enabling performance to increase rapidly. However,
at some point, diminishing returns to effort begin to set in. As the technology begins
to reach its inherent limits, the cost of each marginal improvement increases, and the
s-curve flattens.
Often a technology’s s-curve is plotted with performance (e.g., speed, capacity, or
power) against time, but this must be approached with care. If the effort invested is not
constant over time, the resulting s-curve can obscure the true relationship. If effort is
relatively constant over time, plotting performance against time will result in the same
characteristic curve as plotting performance against effort. However, if the amount of
effort invested in a technology decreases or increases over time, the resulting curve
could appear to flatten much more quickly, or not flatten at all. For instance, one of the
more well-known technology trajectories is described by an axiom that became known
as Moore’s law. In 1965, Gordon Moore, cofounder of Intel, noted that the density of
transistors on integrated circuits had doubled every year since the integrated circuit
was invented. Figure 3.2 shows Intel’s microprocessor transistor density from 1971 to
2007 and reveals a sharply increasing performance curve.

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52 Part One Industry Dynamics of Technological Innovation

FIGURE 3.2

Improvements in Intel’s Microprocessor Transistor Density over Time
Transistors

Intel CPU

800,000,000

1971
1972
1974
1978
1982
1985
1989
1993
1997
1999
2000
2002
2003
2005
2006
2007

2250
2500
5000
29,000
120,000
275,000
1,180,000
3,100,000
7,500,000
24,000,000
42,000,000
55,000,000
220,000,000
291,000,000
582,000,000
731,000,000

4004
8008
8080
8086
286
386™
486™ DX
Pentium®
Pentium II
Pentium III
Pentium 4
Pentium M
Itanium 2
Pentium D
Core 2 Quad
Core i7 (Quad)

700,000,000

FIGURE 3.3

Transistor Density

Year

600,000,000
500,000,000
400,000,000
300,000,000
200,000,000
100,000,000
–

1970 1975 1980 1985 1990 1995 2000 2005 2010
Year

800,000,000

Graph of
Transistor
Density versus
Cumulative
R&D Expense,
1972–2007

700,000,000

Transistor Density

600,000,000
500,000,000
400,000,000
300,000,000
200,000,000
100,000,000
0
0

10,000

20,000

30,000

40,000

50,000

60,000

Cumulative R&D Expense ($millions)

However, Intel’s rate of investment (research and development dollars per year) also
increased rapidly over that time frame, as shown in Figure 3.3. Not all of Intel’s R&D
expense goes directly to improving microprocessor power, but it is reasonable to
assume that Intel’s investment specifically in microprocessors would exhibit a similar pattern of increase. Figure 3.3 shows that the big gains in transistor density have
come at a big cost in terms of effort invested. Though the curve does not yet resemble

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Chapter 3 Types and Patterns of Innovation 53

discontinuous
technology
A technology
that fulfills a
similar market
need by building
on an entirely
new knowledge
base.

FIGURE 3.4

Technology
S-Curves—
Introduction of
Discontinuous
Technology

the traditional s-curve, its rate of increase is not as sharp as when the curve is plotted
against years.
Technologies do not always get the opportunity to reach their limits; they may be
rendered obsolete by new, discontinuous technologies. A new innovation is discontinuous when it fulfills a similar market need, but does so by building on an entirely
new knowledge base.9 For example, the switches from propeller-based planes to jets,
from silver halide (chemical) photography to digital photography, from carbon copying to photocopying, and from audio on compact discs to MP3 were all technological
discontinuities.
Initially, the technological discontinuity may have lower performance than the
incumbent technology. For instance, one of the earliest automobiles, introduced in 1771
by Nicolas Joseph Cugnot, was never put into commercial production because it was
much slower and harder to operate than a horse-drawn carriage. It was three-wheeled,
steam-powered, and could travel at 2.3 miles per hour. A number of steam- and gaspowered vehicles were introduced in the 1800s, but it was not until the early 1900s that
automobiles began to be produced in quantity.
In early stages, effort invested in a new technology may reap lower returns than
effort invested in the current technology, and firms are often reluctant to switch. However, if the disruptive technology has a steeper s-curve (see Figure 3.4a) or an s-curve
that increases to a higher performance limit (see Figure 3.4b), there may come a time
when the returns to effort invested in the new technology are much higher than effort
invested in the incumbent technology. New firms entering the industry are likely to
choose the disruptive technology, and incumbent firms face the difficult choice of trying to extend the life of their current technology or investing in switching to the
new technology. If the disruptive technology has much greater performance
Second
technology
potential for a given amount of effort, in
the long run it is likely to displace the
Performance
incumbent technology, but the rate at
First
technology
which it does so can vary significantly.

S-Curves in Technology Diffusion
Effort
(a)

technology
diffusion

The spread of
a technology
through a
population.

First
Performance technology
Second
technology

Effort
(b)

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53

S-curves are also often used to describe the
diffusion of a technology. Unlike s-curves
in technology performance, s-curves in
technology diffusion are obtained by
plotting the cumulative number of adopters of the technology against time. This
yields an s-shape curve because adoption is initially slow when an unfamiliar technology is introduced to the
market; it accelerates as the technology
becomes better understood and utilized
by the mass market, and eventually the
market is saturated so the rate of new

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54 Part One Industry Dynamics of Technological Innovation

adoptions declines. For instance, when electronic calculators were introduced to the
market, they were first adopted by the relatively small pool of scientists and engineers.
This group had previously used slide rules. Then the calculator began to penetrate the
larger markets of accountants and commercial users, followed by the still larger market
that included students and the general public. After these markets had become saturated, fewer opportunities remained for new adoptions.10
One rather curious feature of technology diffusion is that it typically takes far more
time than information diffusion.11 For example, Mansfield found that it took 12 years
for half the population of potential users to adopt industrial robots, even though these
potential users were aware of the significant efficiency advantages the robots offered.12
If a new technology is a significant improvement over existing solutions, why do some
firms shift to it more slowly than others? The answer may lie in the complexity of
the knowledge underlying new technologies, and in the development of complementary resources that make those technologies useful. Although some of the knowledge
necessary to utilize a new technology might be transmitted through manuals or other
documentation, other aspects of knowledge necessary to fully realize the potential of a
technology might be built up only through experience. Some of the knowledge about
the technology might be tacit and require transmission from person to person through
extensive contact. Many potential adopters of a new technology will not adopt it until
such knowledge is available to them, despite their awareness of the technology and its
potential advantages.13
Furthermore, many technologies become valuable to a wide range of potential users
only after a set of complementary resources are developed for them. For example,
while the first electric light was invented in 1809 by Humphry Davy, an English chemist, it did not become practical until the development of bulbs within which the arc of
light would be encased (first demonstrated by James Bowman Lindsay in 1835) and
vacuum pumps to create a vacuum inside the bulb (the mercury vacuum pump was
invented by Herman Sprengel in 1875). These early lightbulbs burned for only a few
hours. Thomas Alva Edison built on the work of these earlier inventors when, in 1880,
he invented filaments that would enable the light to burn for 1200 hours. The role of
complementary resources and other factors influencing the diffusion of technological
innovations are discussed further in Chapters four, five, and thirteen.
Finally, it should be clear that the s-curves of diffusion are in part a function
of the s-curves in technology improvement: As technologies are better developed,
they become more certain and useful to users, facilitating their adoption. Furthermore, as learning-curve and scale advantages accrue to the technology, the price
of finished goods often drops, further accelerating adoption by users. For example,
as shown in Figures 3.5 and 3.6, drops in average sales prices for video recorders, compact disc players, and cell phones roughly correspond to their increases in
household penetration.

S-Curves as a Prescriptive Tool
Several authors have argued that managers can use the s-curve model as a tool for predicting when a technology will reach its limits and as a prescriptive guide for whether
and when the firm should move to a new, more radical technology.14 Firms can use
data on the investment and performance of their own technologies, or data on the

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Chapter 3 Types and Patterns of Innovation 55

FIGURE 3.5

$1000

Average
Sales Prices
of Consumer
Electronics

$800
$600

Source: Consumer
Electronics
Association.

$400
$200

VCR

FIGURE 3.6

CD Player

04

02

20

00

20

20

98
19

96
19

94
19

92
19

90
19

88
19

86
19

84
19

82
19

19

80

$0

Cell Phone

100%
Percent of U.S. Households

Penetration
of Consumer
Electronics
Source: Consumer
Electronics
Association.

90%
80%
70%
60%
50%
40%
30%
20%
10%

92

94

96

98

19

19

19

19

4

90
19

20
0

88
19

2

86
19

20
0

84
19

CD Player

0

82
19

VCR

20
0

80
19

0%

Cell Phone

overall industry investment in a technology and the average performance achieved by
multiple producers. Managers could then use these curves to assess whether a technology appears to be approaching its limits or to identify new technologies that might
be emerging on s-curves that will intersect the firm’s technology s-curve. Managers
could then switch s-curves by acquiring or developing the new technology. However,
as a prescriptive tool, the s-curve model has several serious limitations.

Limitations of S-Curve Model as a Prescriptive Tool
First, it is rare that the true limits of a technology are known in advance, and there is
often considerable disagreement among firms about what a technology’s limits will
be. Second, the shape of a technology’s s-curve is not set in stone. Unexpected changes

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56 Part One Industry Dynamics of Technological Innovation

in the market, component technologies, or complementary technologies can shorten or
extend the life cycle of a technology. Furthermore, firms can influence the shape of
the s-curve through their development activities. For example, firms can sometimes
stretch the s-curve through implementing new development approaches or revamping
the architecture design of the technology.15
Christensen provides an example of this from the disk-drive industry. A disk
drive’s capacity is determined by its size multiplied by its areal recording density;
thus, density has become the most pervasive measure of disk-drive performance.
In 1979, IBM had reached what it perceived as a density limit of ferrite-oxide–
based disk drives. It abandoned its ferrite-oxide–based disk drives and moved to
developing thin-film technology, which had greater potential for increasing density. Hitachi and Fujitsu continued to ride the ferrite-oxide s-curve, ultimately
achieving densities that were eight times greater than the density that IBM had
perceived to be a limit.
Finally, whether switching to a new technology will benefit a firm depends on a
number of factors, including (a) the advantages offered by the new technology, (b) the
new technology’s fit with the firm’s current abilities (and thus the amount of effort
that would be required to switch, and the time it would take to develop new competencies), (c) the new technology’s fit with the firm’s position in complementary resources
(e.g., a firm may lack key complementary resources, or may earn a significant portion
of its revenues from selling products compatible with the incumbent technology), and
(d) the expected rate of diffusion of the new technology. Thus, a firm that follows an
s-curve model too closely could end up switching technologies earlier or later than
it should.

TECHNOLOGY CYCLES
The s-curve model above suggests that technological change is cyclical: Each new
s-curve ushers in an initial period of turbulence, followed by rapid improvement, then
diminishing returns, and ultimately is displaced by a new technological discontinuity.16 The emergence of a new technological discontinuity can overturn the existing
competitive structure of an industry, creating new leaders and new losers. Schumpeter
called this process creative destruction, and argued that it was the key driver of progress in a capitalist society.17
Several studies have tried to identify and characterize the stages of the technology cycle in order to better understand why some technologies succeed and others
fail, and whether established firms or new firms are more likely to be successful in
introducing or adopting a new technology.18 One technology evolution model that rose
to prominence was proposed by Utterback and Abernathy. They observed that a technology passed through distinct phases. In the first phase (what they termed the fluid
phase), there was considerable uncertainty about both the technology and its market.
Products or services based on the technology might be crude, unreliable, or expensive,
but might suit the needs of some market niches. In this phase, firms experiment with
different form factors or product features to assess the market response. Eventually,
however, producers and customers begin to arrive at some consensus about the desired

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Chapter 3 Types and Patterns of Innovation 57

Research Brief

The Diffusion of Innovation and Adopter
Categories

S-curves in technology diffusion are often explained
as a process of different categories of people adopting the technology at different times. One typology
of adopter categories that gained prominence was
proposed by Everett M. Rogers.a Figure 3.7 shows
each of Rogers’s adopter categories on a technology diffusion s-curve. The figure also shows that if
the non cumulative share of each of these adopter
groups is plotted on the vertical axis with time on the
horizontal axis, the resulting curve is typically bell
shaped (though in practice it may be skewed right
or left).

make excellent missionaries for new products or processes. Rogers estimated that the next 13.5 percent
of individuals to adopt an innovation (after innovators) are in this category.

EARLY MAJORITY
Rogers identifies the next 34 percent of individuals
in a social system to adopt a new innovation as the
early majority. The early majority adopts innovations
slightly before the average member of a social system. They are typically not opinion leaders, but they
interact frequently with their peers.

LATE MAJORITY

INNOVATORS
Innovators are the first individuals to adopt an innovation. Extremely adventurous in their purchasing
behavior, they are comfortable with a high degree
of complexity and uncertainty. Innovators typically
have access to substantial financial resources (and
thus can afford the losses incurred in unsuccessful
adoption decisions). Though they are not always well
integrated into a particular social system, innovators play an extremely important role in the diffusion
of an innovation because they are the individuals
who bring new ideas into the social system. Rogers
estimated that the first 2.5 percent of individuals to
adopt a new technology are in this category.

EARLY ADOPTERS
The second category of adopters is the early adopters. Early adopters are well integrated into their
social system and have the greatest potential for
opinion leadership. Early adopters are respected
by their peers and know that to retain that respect
they must make sound innovation adoption decisions. Other potential adopters look to early adopters for information and advice; thus early adopters

The next 34 percent of the individuals in a social
system to adopt an innovation are the late majority,
according to Rogers. Like the early majority, the late
majority constitutes one-third of the individuals in a
social system. Those in the late majority approach
innovation with a skeptical air and may not adopt the
innovation until they feel pressure from their peers.
The late majority may have scarce resources, thus
making them reluctant to invest in adoption until
most of the uncertainty about the innovation has
been resolved.

LAGGARDS
The last 16 percent of the individuals in a social system to adopt an innovation are termed laggards.
They may base their decisions primarily upon past
experience rather than influence from the social
network, and they possess almost no opinion leadership. They are highly skeptical of innovations and
innovators, and they must feel certain that a new
innovation will not fail before adopting it.
a

E. M. Rogers, Diffusion of Innovations, 5th ed. (New York:
Free Press, 2003).

continued

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58 Part One Industry Dynamics of Technological Innovation

concluded

FIGURE 3.7

Technology Diffusion S-Curve with Adopter Categories
S-Curve of Cumulative Adopters
Cumulative
Adopters

100%
Laggards
84%
Late Majority
50%
Early Majority
16%
Early Adopters
2.5%

Innovators
Time
(a)

Normal (Bell-Shaped) Curve of Market Share
Innovators

Early
Adopters

Early
Majority

Late
Majority

Laggards

34%

Share

13.5%

2.5%
Time
(b)

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Theory in Action

“Segment Zero”—A Serious Threat to Microsoft?

From 1980 to 2012, Microsoft was entrenched as the
dominant personal computer operating system, giving it
enormous influence over many aspects of the computer
hardware and software industries. Though competing
operating systems had been introduced during that time
(e.g., Unix, Geoworks, NeXTSTEP, Linux, and the Mac OS),
Microsoft’s share of the personal computer operating system market held stable at roughly 85 percent throughout
most of that period. In 2013, however, Microsoft’s dominance in computer operating systems was under greater
threat than it had ever been. A high-stakes race for dominance over the next generation of computing was well
underway, and Microsoft was not even in the front pack.

As Andy Grove, former CEO of Intel, noted in 1998,
in many industries—including microprocessors, software, motorcycles, and electric vehicles—technologies
improve faster than customer demands of those technologies increase. Firms often add features (speed,
power, etc.) to products faster than customers’ capacity to absorb them. Why would firms provide higher
performance than that required by the bulk of their
customers? The answer appears to lie in the market
segmentation and pricing objectives of a technology’s providers. As competition in an industry drives
prices and margins lower, firms often try to shift sales
into progressively higher tiers of the market. In these
tiers, high performance and feature-rich products can
command higher margins. Though customers may also

expect to have better-performing products over time,
their ability to fully utilize such performance improvements is slowed by the need to learn how to use new
features and adapt their work and lifestyles. Thus,
while both the trajectory of technology improvement
and the trajectory of customer demands are upward
sloping, the trajectory for technology improvement
is steeper (for simplicity, the technology trajectories
are drawn in Figure 3.8 as straight lines and plotted
against time in order to compare them against customer requirements).
In Figure 3.8, the technology trajectory begins at
a point where it provides performance close to that
demanded by the mass market, but over time it
increases faster than the expectations of the mass market as the firm targets the high-end market. As the price
of the technology rises, the mass market may feel it is
overpaying for technological features it does not value.
In Figure 3.9, the low-end market is not being served; it
either pays far more for technology that it does not need
or goes without. It is this market that Andy Grove, former
CEO of Intel, refers to as segment zero.
For Intel, segment zero was the market for low-end
personal computers (those less than $1000). While segment zero may seem unattractive in terms of margins, if
it is neglected, it can become the breeding ground for
companies that provide lower-end versions of the technology. As Grove notes, “The overlooked, underserved,
and seemingly unprofitable end of the market can
provide fertile ground for massive competitive change.”a

FIGURE 3.8

FIGURE 3.9

“SEGMENT ZERO”

Trajectories of Technology Improvement and
Customer Requirements

Low-End Technology’s Trajectory Intersects Mass
Market Trajectory

Technology
trajectory

High-end
technology

High-end market
Mass market

Performance

Low-end market

High-end market
Mass market

Performance

Low-end market
Low-end
technology

Time

Time

continued
59

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concluded
As the firms serving low-end markets with simpler
technologies ride up their own trajectories (which are
also steeper than the slope of the trajectories of customer expectations), they can eventually reach a performance level that meets the demands of the mass market,
while offering a much lower price than the premium technology (see Figure 3.9). At this point, the firms offering
the premium technology may suddenly find they are losing the bulk of their sales revenue to industry contenders
that do not look so low end anymore. For example, by
1998, the combination of rising microprocessor power
and decreasing prices enabled personal computers
priced under $1000 to capture 20 percent of the market.

THE THREAT TO MICROSOFT
So where was the “segment zero” that could threaten
Microsoft? Look in your pocket. In 2018, Apple’s iPhone
operating system (iOS) and Google’s Android collectively
controlled almost 100 percent of the worldwide market for smartphones (with Android at 86.1 percent and
iOS at 13.7 percent), followed by Research in Motion’s
Blackberry.b Gartner estimates put Microsoft’s share at
3 percent. The iOS and Android interfaces offered a

dominant
design

A product design
that is adopted
by the majority
of producers,
typically creating
a stable architecture on which
the industry can
focus its efforts.

double whammy of beautiful aesthetics and remarkable
ease of use. The applications business model used for
the phones was also extremely attractive to both developers and customers, and quickly resulted in enormous
libraries of applications that ranged from the ridiculous
to the indispensible.
From a traditional economics perspective, the phone
operating system market should not be that attractive
to Microsoft—people do not spend as much on the
applications, and the carriers have too much bargaining
power, among other reasons. However, those smartphone operating systems soon became tablet operating systems, and tablets were rapidly becoming fully
functional computers. Suddenly, all of that mindshare
that Apple and Google had achieved in smartphone
operating systems was transforming into mindshare in
personal computer operating systems. Despite years of
masterminding the computing industry, Microsoft’s dominant position was at risk of evaporating.
a

A. S. Grove, “Managing Segment Zero,” Leader to Leader,
1999, p. 11.
b
www.Gartner.com, 2018.

product attributes, and a dominant design emerges.19 The dominant design establishes a stable architecture for the technology and enables firms to focus their efforts
on process innovations that make production of the design more effective and efficient or on incremental innovations to improve components within the architecture.
Utterback and Abernathy termed this phase the specific phase because innovations
in products, materials, and manufacturing processes are all specific to the dominant
design. For example, in the United States, the vast majority of energy production is
based on the use of fossil fuels (e.g., oil, coal), and the methods of producing energy
based on these fuels are well established. On the other hand, technologies that produce
energy based on renewable resources (e.g., solar, wind, hydrogen) are still in the fluid
phase. Organizations such as Royal Dutch/Shell, General Electric, and Ballard Power
are experimenting with various forms of solar photocell technologies, wind-turbine
technologies, and hydrogen fuel cells in efforts to find methods of using renewable
resources that meet the capacity and cost requirements of serving large populations.
Building on the Utterback and Abernathy model, Anderson and Tushman studied the history of the U.S. minicomputer, cement, and glass industries through several cycles of technological change. Like Utterback and Abernathy, Anderson and
Tushman found that each technological discontinuity inaugurated a period of turbulence and uncertainty (which they termed the era of ferment) (see Figure 3.10). The
new technology might offer breakthrough capabilities, but there is little agreement
about what the major subsystems of the technology should be or how they should be

60

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Chapter 3 Types and Patterns of Innovation 61

FIGURE 3.10
The Technology Cycle

Era of Ferment
Design Competition
Substitution

Technological
Discontinuity

Dominant Design
Selected

Era of Incremental Change
Elaboration of Dominant Design

configured together. Furthermore, as later researchers noted, during the era of ferment
different stakeholders might have different concepts of what purpose the technology
should serve, or how a business model might be built around it.20 Thus, while the new
technology displaces the old (Anderson and Tushman refer to this as substitution),
there is considerable design competition as firms experiment with different forms of
the technology. Just as in the Utterback and Abernathy model, Anderson and Tushman
found that a dominant design always arose to command the majority of the market
share unless the next discontinuity arrived too soon and disrupted the cycle, or several producers patented their own proprietary technologies and refused to license to
each other. Anderson and Tushman also found that the dominant design was never
in the same form as the original discontinuity, but it was also never on the leading
edge of the technology. Instead of maximizing performance on any individual dimension of the technology, the dominant design tended to bundle together a combination
of features that best fulfilled the demands of the majority of the market.
In the words of Anderson and Tushman, the rise of a dominant design signals the
transition from the era of ferment to the era of incremental change.21 In this era, firms
focus on efficiency and market penetration. Firms may attempt to achieve greater market segmentation by offering different models and price points. They may also attempt
to lower production costs by simplifying the design or improving the production process. This period of accumulating small improvements may account for the bulk of
the technological progress in an industry, and it continues until the next technological
discontinuity.
Understanding the knowledge that firms develop during different eras lends insight
into why successful firms often resist the transition to a new technology, even if it
provides significant advantages. During the era of incremental change, many firms
cease to invest in learning about alternative design architectures and instead invest in
refining their competencies related to the dominant architecture. Most competition
revolves around improving components rather than altering the architecture; thus,
companies focus their efforts on developing component knowledge and knowledge

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62 Part One Industry Dynamics of Technological Innovation

related to the dominant architecture. As firms’ routines and capabilities become more
and more wedded to the dominant architecture, the firms become less able to identify
and respond to a major architectural innovation. For example, the firm might establish
divisions based on the primary components of the architecture and structure the communication channels between divisions on the basis of how those components interact.
In the firm’s effort to absorb and process the vast amount of information available to
it, it is likely to establish filters that enable it to identify the information most crucial
to its understanding of the existing technology design.22 As the firm’s expertise, structure, communication channels, and filters all become oriented around maximizing its
ability to compete in the existing dominant design, they become barriers to the firm’s
recognizing and reacting to a new technology architecture.
While many industries appear to conform to this model in which a dominant design
emerges, there are exceptions. In some industries, heterogeneity of products and production processes are a primary determinant of value, and thus a dominant design is
undesirable.23 For example, art and cuisine may be examples of industries in which
there is more pressure to do things differently than to settle upon a standard.

Summary
of
Chapter

1. Different dimensions have been used to distinguish types of innovation. Some of
the most widely used dimensions include product versus process innovation, radical versus incremental innovation, competence-enhancing versus competencedestroying innovation, and architectural versus component innovation.
2. A graph of technology performance over cumulative effort invested often exhibits
an s-shape curve. This suggests that performance improvement in a new technology
is initially difficult and costly, but, as the fundamental principles of the technology are worked out, it then begins to accelerate as the technology becomes better
understood, and finally diminishing returns set in as the technology approaches its
inherent limits.
3. A graph of a technology’s market adoption over time also typically exhibits an
s-shape curve. Initially the technology may seem uncertain and there may be great
costs or risks for potential adopters. Gradually, the technology becomes more certain (and its costs may be driven down), enabling the technology to be adopted by
larger market segments. Eventually the technology’s diffusion slows as it reaches
market saturation or is displaced by a newer technology.
4. The rate at which a technology improves over time is often faster than the rate at
which customer requirements increase over time. This means technologies that
initially met the demands of the mass market may eventually exceed the needs of
the market. Furthermore, technologies that initially served only low-end customers (segment zero) may eventually meet the needs of the mass market and capture
the market share that originally went to the higher-performing technology.
5. Technological change often follows a cyclical pattern. First, a technological
discontinuity causes a period of turbulence and uncertainty, and producers and
consumers explore the different possibilities enabled by the new technology. As
producers and customers begin to converge on a consensus of the desired technological configuration, a dominant design emerges. The dominant design provides

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Chapter 3 Types and Patterns of Innovation 63

a stable benchmark for the industry, enabling producers to turn their attention
to increasing production efficiency and incremental product improvements. This
cycle begins again with the next technological discontinuity.
6. The first design based on the initial technological discontinuity rarely becomes
the dominant design. There is usually a period in which firms produce a variety of
competing designs of the technology before one design emerges as dominant.
7. The dominant design rarely embodies the most advanced technological features
available at the time of its emergence. It is instead the bundle of features that best
meets the requirements of the majority of producers and customers.

Discussion
Questions

1. What are some reasons that established firms might resist adopting a new
technology?
2. Are well-established firms or new entrants more likely to (a) develop and/or
(b) adopt new technologies? Why?
3. Think of an example of an innovation you have studied at work or school. How
would you characterize it on the dimensions described at the beginning of the
chapter?
4. What are some reasons that both technology improvement and technology diffusion exhibit s-shape curves?
5. Why do technologies often improve faster than customer requirements? What are
the advantages and disadvantages to a firm of developing a technology beyond the
current state of market needs?
6. In what industries would you expect to see particularly short technology cycles? In
what industries would you expect to see particularly long technology cycles? What
factors might influence the length of technology cycles in an industry?

Suggested
Further
Reading

Classics
Anderson, P., and M. L. Tushman, “Technological discontinuities and dominant
designs,” Administrative Science Quarterly 35 (1990), pp. 604–33.
Bijker, W. E., T. P. Hughes, and T. J. Pinch, The Social Construction of Technological
Systems (Cambridge, MA: MIT Press, 1987).
Christensen, C. M., The Innovator’s Dilemma: When New Technologies Cause Great
Firms to Fail (Boston: Harvard Business School Publishing, 1997).
Dosi, G., “Technological paradigms and technological trajectories,” Research Policy
11 (1982), pp. 147–60.
Rogers, E., Diffusion of Innovations, 5th ed. (New York: Simon & Schuster Publishing, 2003).
Schilling, M. A., and M. Esmundo, “Technology S-Curves in Renewable Energy
Alternatives: Analysis and Implications for Industry and Government,” Energy Policy,
37 (2009), pp. 1767–81.

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64 Part One Industry Dynamics of Technological Innovation

Recent Work
Ander, R., and R. Kapoor, “Innovation Ecosystems and the Pace of Substitution:
Re-examining Technology S-curves,” Strategic Management Journal (2015), 37:625–648.
Ethiraj, S., D. Levinthal, and R. R. Roy, “The dual role of modularity: Innovation and
imitation,” Management Science, 54 (2008), pp. 93–955.
Park, W., Y. K. Ro, and N. Kim, 2018. “Architectural Innovation and the Emergence
of a Dominant Design: The Effects of Strategic Sourcing on Performance,” 47 (2018),
pp. 326–341.
Schilling, M. A., “Technology Shocks, Technological Collaboration, and Innovation
Outcomes,” Organization Science, 26 (2015), pp. 668–686.
Slater, S. F., J. J. Mohr, and S. Sengupta, “Radical Product Innovation Capability: Literature Review, Synthesis, and Illustrative Research Propositions,” Journal of Product
Innovation Management, 31 (2014), pp. 552–566.
Young, H. P., “Innovation diffusion in heterogeneous populations: Contagion, social
influence, and social learning,” American Economic Review 99 (2009), pp. 1899–1924.

Endnotes

1. R. L. Daft and S. W. Becker, Innovation in Organizations (New York: Elsevier, 1978);
T. D. Duchesneau, S. Cohn, and J. Dutton, A Study of Innovation in Manufacturing: Determination, Processes and Methodological Issues, vol. 1 (Social Science Research Institute, University
of Maine, 1979); and J. Hage, Theories of Organization (New York: Wiley Interscience, 1980).
2. R. D. Dewar and J. E. Dutton, “The Adoption of Radical and Incremental Innovations: An
Empirical Analysis,” Management Science 32 (1986), pp. 1422–33; and J. Dutton and
A. Thomas, “Relating Technological Change and Learning by Doing,” in Research on Technological Innovation, Management and Policy, ed. R. Rosenbloom (Greenwich, CT: JAI Press,
1985), pp. 187–224.
3. C. Scuria-Fontana, “The Slide Rule Today: Respect for the Past; History of the Slide Rule,”
Mechanical Engineering-CIME, July 1990, pp. 122–24.
4. H. Simon, “The Architecture of Complexity,” Proceedings of the American Philosophical Society 106 (1962), pp. 467–82.
5. L. Fleming and O. Sorenson, “Navigating the Technology Landscape of Innovation,” Sloan
Management Review 44, no. 2 (2003), p. 15; and M. A. Schilling, “Towards a General Modular
Systems Theory and Its Application to Interfirm Product Modularity,” Academy of Management Review 25 (2000), pp. 312–34.
6. R. Henderson and K. Clark, “Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms,” Administrative Science Quarterly
35 (1990), pp. 9–30.
7. R. Foster, Innovation: The Attacker’s Advantage (New York: Summit Books, 1986).
8. R. Garud and M. A. Rappa, “A Socio-Cognitive Model of Technology Evolution: The Case
of Cochlear Implants,” Organization Science 5 (1994), pp. 344–62; and W. E. Bijker,
T. P. Hughes, and T. J. Pinch, The Social Construction of Technological Systems (Cambridge,
MA: MIT Press, 1987).
9. Foster, Innovation.
10. R. Brown, “Managing the ‘s’ Curves of Innovation,” Journal of Consumer Marketing 9 (1992),
pp. 61–72.

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Chapter 3 Types and Patterns of Innovation 65

11.
12.
13.
14.
15.
16.
17.
18.

19.

20.
21.
22.
23.

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E. Rogers, Diffusion of Innovations, 4th ed. (New York: Free Press, 1995).
E. Mansfield, “Industrial Robots in Japan and the USA,” Research Policy 18 (1989), pp. 183–92.
P. A. Geroski, “Models of Technology Diffusion,” Research Policy 29 (2000), pp. 603–25.
Foster, Innovation; and E. H. Becker and L. M. Speltz, “Putting the S-curve Concept to Work,”
Research Management 26 (1983), pp. 31–33.
C. Christensen, Innovation and the General Manager (New York: Irwin/McGraw-Hill, 1999).
P. Anderson and M. Tushman, “Technological Discontinuities and Dominant Designs: A Cyclical Model of Technological Change,” Administrative Science Quarterly 35 (1990), pp. 604–34.
J. Schumpeter, Capitalism, Socialism and Democracy (New York: Harper Brothers, 1942).
See, for example, J. M. Utterback and W. J. Abernathy, “A Dynamic Model of Process and
Product Innovation,” Omega, the International Journal of Management Science 3 (1975),
pp. 639–56; and D. Sahal, Patterns of Technological Innovation (Reading, MA: AddisonWesley Publishing Co., 1981).
Utterback and Abernathy, “A Dynamic Model of Process and Product Innovation”; F. F. Suarez
and J. M. Utterback, “Dominant Designs and the Survival of Firms,” Strategic Management
Journal 16 (1995), pp. 415–30; and J. M. Utterback and F. F. Suarez, “Innovation, Competition
and Industry Structure,” Research Policy 22 (1993), pp. 1–21.
Kaplan, S. and Tripsas, M. “Thinking about Technology: Applying a Cognitive Lens to Technical Change,” Research Policy, 37 (2008):790–805.
P. Anderson and M. Tushman, “Technological Discontinuities and Dominant Designs: A Cyclical Model of Technological Change,” Administrative Science Quarterly 35 (1990), pp. 604–34.
R. Henderson and K. Clark, “Architectural Innovation: The Reconfiguration of Existing
Product Technologies and the Failure of Established Firms,” Administrative Science Quarterly
35 (1990), pp. 9–30.
M. E. Porter, “The Technological Dimension of Competitive Strategy,” in Research on Technological Innovation, Management and Policy, ed. R. S. Rosenbloom (Greenwich, CT: JAI Press,
1983); and S. Klepper, “Entry, Exit, Growth, and Innovation over the Product Life Cycle,”
American Economic Review 86 (1996), pp. 562–83.

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Chapter Two
Sources of Innovation
The Rise of “Clean Meat”a
In late 2017, Microsoft founder Bill Gates and a group of other high-powered
investors—who comprise Breakthrough Energy Ventures, such as Amazon’s
Jeff Bezos, Alibaba’s Jack Ma, and Virgin’s Richard Branson—announced their
intention to fund a San Francisco–based start-up called Memphis Meats with
an unusual business plan: it grew “clean” meat using stem cells, eliminating the
need to breed or slaughter animals. The company had already produced beef,
chicken, and duck, all grown from cells.b
There were many potential advantages of growing meat without animals. First,
growth in the demand for meat was skyrocketing due to both population growth
and development. When developing countries become wealthier, they increase
their meat consumption. While humanity’s population had doubled since 1960,
consumption of animal products had risen fivefold and was still increasing. Many
scientists and economists had begun to warn of an impending “meat crisis.” Even
though plant protein substitutes like soy and pea protein had gained enthusiastic followings, the rate of animal protein consumption had continued to rise. This
suggested that meat shortages were inevitable unless radically more efficient
methods of production were developed.
Large-scale production of animals also had a massively negative effect on
the environment. The worldwide production of cattle, for example, resulted
in a larger emissions of greenhouse gases than the collective effect of the
world’s automobiles. Animal production is also extremely water intensive: To
produce each chicken sold in a supermarket, for example, requires more than
1000 gallons of water, and each egg requires 50 gallons. Each gallon of cow’s
milk required 900 gallons of water. A study by Oxford University indicated that
meat grown from cells would produce up to 96 percent lower greenhouse gas
emissions, use 45 percent less energy, 99 percent less land, and 96 percent
less water.c
Scientists also agreed that producing animals for consumption was simply
inefficient. Estimates suggested, for example, that it required roughly 23 calories worth of inputs to produce one calorie of beef. “Clean” meat promised to
bring that ratio down to three calories of inputs to produce a calorie of beef—
more than seven times greater efficiency. “Clean” meat also would not contain
15

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16 Part One Industry Dynamics of Technological Innovation

antibiotics, steroids, or bacteria such as E. coli—it was literally “cleaner,” and that
translated into both greater human health and lower perishability.

The Development of Clean Meat
In 2004, Jason Matheny, a 29-year-old recent graduate from the John Hopkins
Public Health program decided to try to tackle the problems with production of
animals for food. Though Matheny was a vegetarian himself, he realized that
convincing enough people to adopt a plant-based diet to slow down the meat
crisis was unlikely. As he noted, “You can spend your time trying to get people
to turn their lights out more often, or you can invent a more efficient light bulb
that uses far less energy even if you leave it on. What we need is an enormously
more efficient way to get meat.”d
Matheny founded a nonprofit organization called New Harvest that would be
dedicated to promoting research into growing real meat without animals. He
soon discovered that a Dutch scientist, Willem van Eelen was exploring how to
culture meat from animal cells. Van Eelen had been awarded the first patent on
a cultured meat production method in 1999. However, the eccentric scientist
had not had much luck in attracting funding to his project, nor in scaling up his
production. Matheny decided that with a little prodding, the Dutch government
might be persuaded to make a serious investment in the development of meatculturing methods. He managed to get a meeting with the Netherland’s minister
of agriculture where he made his case. Matheny’s efforts paid off: The Dutch
government agreed to invest two million euros in exploring methods of creating
cultured meat at three different universities.
By 2005, clean meat was starting to gather attention. The journal Tissue Engineering published an article entitled “In Vitro-Cultured Meat Production,” and
in the same year, the New York Times profiled clean meat in its annual “Ideas
of the Year.” However, while governments and universities were willing to invest
in the basic science of creating methods of producing clean meat, they did not
have the capabilities and assets needed to bring it to commercial scale. Matheny
knew that to make clean meat a mainstream reality, he would need to attract the
interest of large agribusiness firms.
Matheny’s initial talks with agribusiness firms did not go well. Though meat
producers were open to the idea conceptually, they worried that consumers
would balk at clean meat and perceive it as unnatural. Matheny found this criticism frustrating; after all, flying in airplanes, using air conditioning, or eating meat
pumped full of steroids to accelerate its growth were also unnatural.
Progress was slow. Matheny took a job at the Intelligence Advanced Research
Projects Activity (IARPA) of the U.S. Federal Government while continuing to run
New Harvest on the side. Fortunately, others were also starting to realize the
urgency of developing alternative meat production methods.

Enter Sergey Brin of Google
In 2009, the foundation of Sergey Brin, cofounder of Google, contacted Matheny
to learn more about cultured meat technologies. Matheny referred Brin’s

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Chapter 2 Sources of Innovation 17

foundation to Dr. Mark Post at Maastricht University, one of the leading scientists
funded by the Dutch government’s clean meat investment. Post had succeeded
in growing mouse muscles in vitro and was certain his process could be replicated with the muscles of cows, poultry, and more. As he stated, “It was so clear
to me that we could do this. The science was there. All we needed was funding to actually prove it, and now here was a chance to get what was needed.”e
It took more than a year to work out the details, but in 2011, Brin offered Post
roughly three quarters of a million dollars to prove his process by making two
cultured beef burgers, and Post’s team set about meeting the challenge.
In early 2013, the moment of truth arrived: Post and his team had enough cultured beef to do a taste test. They fried up a small burger and split it into thirds
to taste. It tasted like meat. Their burger was 100 percent skeletal muscle and
they knew that for commercial production they would need to add fat and connective tissue to more closely replicate the texture of beef, but those would
be easy problems to solve after passing this milestone. The press responded
enthusiastically, and the Washington Post ran an article headlined, “Could a TestTube Burger Save the Planet?”f

Going Commercial
In 2015, Uma Valeti, a cardiologist at the Mayo Clinic founded his own culturedmeat research lab at the University of Minnesota. “I’d read about the inefficiency of meat-eating compared to a vegetarian diet, but what bothered me
more than the wastefulness was the sheer scale of suffering of the animals.”g
As a heart doctor, Valeti also believed that getting people to eat less meat
could improve human health: “I knew that poor diets and the unhealthy fats
and refined carbs that my patients were eating were killing them, but so many
seemed totally unwilling to eat less or no meat. Some actually told me they’d
rather live a shorter life than stop eating the meats they loved.” Valeti began
fantasizing about a best-of-both-worlds alternative—a healthier and kinder
meat. As he noted, “The main difference I thought I’d want for this meat I was
envisioning was that it’d have to be leaner and more protein-packed than a
cut of supermarket meat, since there’s a large amount of saturated fat in that
meat. . . . Why not have fats that are proven to be better for health and longevity, like omega-3s? We want to be not just like conventional meat but healthier
than conventional meat.”h
Valeti was nervous about leaving his successful position as a cardiologist—
after all, he had a wife and two children to help support. However, when he sat
down to discuss it with his wife (a pediatric eye surgeon), she said, “Look, Uma.
We’ve been wanting to do this forever. I don’t ever want us to look back on why
we didn’t have the courage to work on an idea that could make this world kinder
and better for our children and their generation.”i And thus Valeti’s company,
which would later be named Memphis Meats, was born.
Building on Dr. Post’s achievement, Valeti’s team began experimenting with
ways to get just the right texture and taste. After much trial and error, and a growing number of patents, they hosted their first tasting event in December 2015.
On the menu: a meatball. This time the giant agribusiness firms took notice.

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18 Part One Industry Dynamics of Technological Innovation

At the end of 2016, Tyson Foods, the world’s largest meat producer, announced
that it would invest $150 million in a venture capital fund that would develop
alternative proteins, including meat grown from self-reproducing cells. In
August of 2017, agribusiness giant Cargill announced it was investing in Memphis Meats, and a few months later in early 2018, Tyson Foods also pledged
investment.
That first meatball cost $1200; to make cultured meat a commercial reality
required bringing costs down substantially. But analysts were quick to point out
that the first iPhone had cost $2.6 billion in R&D—much more than the first cultured meats. Scale and learning curve efficiencies would drive that cost down.
Valeti had faith that the company would soon make cultured meat not only
competitive with traditional meat, but more affordable. Growing meat rather than
whole animals had, after all, inherent efficiency advantages.
Some skeptics believed the bigger problem was not production economies,
but consumer acceptance: would people be willing to eat meat grown without animals? Sergey Brin, Bill Gates, Jeff Bezos, Jack Ma, and Richard Branson
were willing to bet that they would. As Branson stated in 2017, “I believe that in
30 years or so we will no longer need to kill any animals and that all meat will
either be clean or plant-based, taste the same and also be much healthier for
everyone.”j

Discussion Questions
1. What were the potential advantages of developing clean meat? What were
the challenges of developing it and bringing it to market?
2. What kinds of organizations were involved in developing clean meat? What
were the different resources that each kind of organization brought to the
innovation?
3. Do you think people will be willing to eat clean meat? Can you think of
other products or services that faced similar adoption challenges?

a

Adapted from a NYU teaching case by Paul Shapiro and Melissa Schilling.
Friedman, Z., “Why Bill Gates and Richard Branson Invested in ‘Clean’ Meat,” Forbes (August 2017).
c
Tuomisto, H. L., and M. J. de Mattos, “Environmental Impacts of Cultured Meat Production,” Environmental
Science and Technology 14(2011): 6117–2123.
d
Shapiro, P. Clean Meat: How Growing Meat without Animals Will Revolutionize Dinner and the World
(New York: Gallery Books, 2018), 35.
e
Shapiro, P. Clean Meat: How Growing Meat without Animals Will Revolutionize Dinner and the World
(New York: Gallery Books, 2018), 60.
f
“Could a Test-Tube Burger Save the Planet?” Washington Post, August 5, 2013.
g
Shapiro, P. Clean Meat: How Growing Meat without Animals Will Revolutionize Dinner and the World
(New York: Gallery Books, 2018), 113.
h
Shapiro, P. Clean Meat: How Growing Meat without Animals Will Revolutionize Dinner and the World
(New York: Gallery Books, 2018), 115.
i
Shapiro, P. Clean Meat: How Growing Meat without Animals Will Revolutionize Dinner and the World
(New York: Gallery Books, 2018), 118.
j
Friedman, Z., “Why Bill Gates and Richard Branson Invested in ‘Clean’ Meat,” Forbes (August 2017).
b

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Chapter 2 Sources of Innovation 19

OVERVIEW
innovation

The practical
implementation
of an idea into
a new device or
process.

Innovation can arise from many different sources. It can originate with individuals, as in the familiar image of the lone inventor or users who design solutions for
their own needs. Innovation can also come from the research efforts of universities, government laboratories and incubators, or private nonprofit organizations.
One primary engine of innovation is firms. Firms are well suited to innovation
activities because they typically have greater resources than individuals and a
management system to marshal those resources toward a collective purpose.
Firms also face strong incentives to develop differentiating new products and services, which may give them an advantage over nonprofit or government-funded
entities.
An even more important source of innovation, however, does not arise from any
one of these sources, but rather the linkages between them. Networks of innovators
that leverage knowledge and other resources from multiple sources are one of the most
powerful agents of technological advance.1 We can thus think of sources of innovation as composing a complex system wherein any particular innovation may emerge
primarily from one or more components of the system or the linkages between them
(see Figure 2.1).
In the sections that follow, we will first consider the role of creativity as the underlying process for the generation of novel and useful ideas. We will then consider how
creativity is transformed into innovative outcomes by the separate components of the
innovation system (individuals, firms, etc.), and through the linkages between different components (firms’ relationships with their customers, technology transfer from
universities to firms, etc.).

FIGURE 2.1

Sources of
Innovation as a
System

Firms

Individuals

Private
Nonprofits

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Universities

GovernmentFunded Research

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20 Part One Industry Dynamics of Technological Innovation

CREATIVITY
idea

Something imagined or pictured
in the mind.

creativity

The ability to
produce novel
and useful work.

Innovation begins with the generation of new ideas. The ability to generate new and
useful ideas is termed creativity. Creativity is defined as the ability to produce work
that is useful and novel. Novel work must be different from work that has been previously produced and surprising in that it is not simply the next logical step in a series
of known solutions.2 The degree to which a product is novel is a function both of how
different it is from prior work (e.g., a minor deviation versus a major leap) and of the
audience’s prior experiences.3 A product could be novel to the person who made it,
but known to most everyone else. In this case, we would call it reinvention. A product
could be novel to its immediate audience, yet be well known somewhere else in the
world. The most creative works are novel at the individual producer level, the local
audience level, and the broader societal level.4

Individual Creativity
An individual’s creative ability is a function of his or her intellectual abilities,
knowledge, personality, motivation, and environment.
The most important intellectual abilities for creative thinking include intelligence, memory, the ability to look at problems in unconventional ways, the ability
to analyze which ideas are worth pursuing and which are not, and the ability to
articulate those ideas to others and convince others that the ideas are worthwhile.
One important intellectual ability for creativity is a person’s ability to let their mind
engage in a visual mental activity termed primary process thinking.5 Because of its
unstructured nature, primary process thinking can result in combining ideas that are
not typically related, leading to what has been termed remote associations or divergent thinking. Sigmund Freud noted that primary process thinking was most likely
to occur just before sleep or while dozing or daydreaming; others have observed
that it might also be common when distracted by physical exercise, music, or other
activities. Creative people may make their minds more open to remote associations
and then mentally sort through these associations, selecting the best for further
consideration. Having excellent working memory is useful here too—individuals
with excellent working memory may be more likely or more able to search longer
paths through the network of associations in their mind, enabling them to arrive at a
connection between two ideas or facts that seem unexpected or strange to others.6 A
connection that appears to be random may not be random at all—it is just difficult
for other people to see the association because they are not following as long of a
chain of associations.
Consistent with this, studies by professors Mathias Benedek and Aljoscha Neubauer found that highly creative people usually follow the same association paths as
less creative people—but they do so with such greater speed that they exhaust the
common associations sooner, permitting them to get to less common associations earlier than others would.7 Benedek and Neubauer’s research argues that highly creative
people’s speed of association is due to exceptional working memory and executive
control. In other words, the ability to hold many things in one’s mind simultaneously

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Chapter 2 Sources of Innovation 21

and maneuver them with great facileness enables a person to rapidly explore many
possible associations.8
The impact of knowledge on creativity is somewhat double-edged. If an individual
has too little knowledge of a field, he or she is unlikely to understand it well enough
to contribute meaningfully to it. On the other hand, if an individual knows a field
too well, that person can become trapped in the existing logic and paradigms, preventing him or her from coming up with solutions that require an alternative perspective. Thus, an individual with only a moderate degree of knowledge of a field
might be able to produce more creative solutions than an individual with extensive
knowledge of the field, and breakthrough innovations are often developed by outsiders to a field.9
Consider, for example, Elon Musk. Elon Musk developed a city search Web portal called Zip2 in college, then founded an Internet financial payments company that
merged with a rival and developed the PayPal financial payment system. Then after
selling PayPal, Musk decided to found SpaceX to develop reusable rockets, and also
became part of the founding team of Tesla Motors, an electric vehicle company.
Tesla subsequently acquired Solar City (a solar panel company that Elon Musk had
helped his cousins create) and diversified into energy storage and more. Musk crosses
boundaries because he enjoys tackling new, difficult problems. He has been able to be
successful in a wide range of industries in part because he challenges the traditional
models in those industries.10 For example, SpaceX was able to dramatically decrease
the price of rocket components by building them in-house, and Solar City was able to
dramatically increase solar panel adoption by offering a business model based on leasing that gave customers the option of putting no money down and paying for the panels
with part of their energy savings.
Another great example is provided by Gavriel Iddan, a guided missile designer
for the Israeli military who invented a revolutionary way to allow doctors to see
inside a patient’s gastrointestinal system. The traditional approach for obtaining
images inside the gut is a camera on the end of a long flexible rod. This method is
quite uncomfortable, and cannot reach large portions of the small intestine, but it
was the industry standard for many decades. Most gastroenterologists have invested
in significant training to use endoscopic tools, and many have also purchased
endoscopic equipment for their clinics. Not surprisingly then, most innovation in
this domain has focused on incremental improvements in the rod, cameras, and
imaging software. Iddan, however, approached the problem of viewing the inside
of the gut like a guided missile designer—not a gastroenterologist. He did not have
the same assumptions about the need to control the camera with a rod, nor to transmit images with a wire. Instead, he invented a capsule (called the PillCam) with
a power source, a light source, and two tiny cameras that the patient can swallow.
The patient then goes about her day while the camera pill broadcasts images to a
video pack worn by the patient. Roughly eight hours later, the patient returns to the
doctor’s office to have the images read by a software algorithm that can identify
any locations of bleeding (the camera pill exits naturally). The PillCam has proven
to be safer and less expensive than traditional endoscopy (the PillCam costs less
than $500), and it is dramatically more comfortable. For patients, the camera pill

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22 Part One Industry Dynamics of Technological Innovation

was a no brainer; getting doctors to adopt it has been slower because of their existing investment and familiarity with endoscopy. The PillCam is now sold in more
than 60 countries, and several companies now offer competing products. The camera pill is a remarkable solution to a difficult problem, and it is easy to see why it
came from an outsider, rather than an endoscope producer.11
Outsiders often face resistance and skepticism. People tend to discount generalists
and are suspicious of people who engage in activities that seem inconsistent with their
identity. Outsiders like Musk, however, bring an advantage that insiders and industry
veterans often lack. They aren’t trapped by the paradigms and assumptions that have
long become calcified in industry veterans, nor do they have the existing investments
in tools, expertise, or supplier and customer relationships that make change difficult
and unappealing.
The personality trait most often associated with creativity is “openness to
experience.”12 Openness to experience reflects an individual’s use of active imagination, aesthetic sensitivity (e.g., the appreciation for art and literature), attentiveness
to emotion, a preference for variety, and intellectual curiosity. It is assessed by asking
individuals to rate their degree of agreement or disagreement with statements such
as “I have a vivid imagination,” “I enjoy hearing new ideas,” “I have a rich vocabulary,” “I rarely look for deeper meaning in things” (reversed), “I enjoy going to art
museums,” “I avoid philosophical discussions” (reversed), “I enjoy wild flights of
fantasy,” and more. Individuals who score high on the openness to experience dimension tend to have great intellectual curiosity, are interested in unusual ideas, and are
willing to try new things.
Intrinsic motivation has also been shown to be very important for creativity.13
That is, individuals are more likely to be creative if they work on things they are
genuinely interested in and enjoy. In fact, several studies have shown that creativity
can be undermined by providing extrinsic motivation such as money or awards.14
This raises serious questions about the role played by idea collection systems in
organizations that offer monetary rewards for ideas. On the one hand, such extrinsic rewards could derail intrinsic motivation. On the other hand, if the monetary
rewards are small, such systems may be primarily serving to invite people to offer
ideas, which is a valuable signal about the culture of the firm. More research is
needed in this area to know exactly what kind of solicitation for ideas, if any, is
most effective.
Finally, to fully unleash an individual’s creative potential usually requires a supportive environment with time for the individual to explore their ideas independently,
tolerance for unorthodox ideas, a structure that is not overly rigid or hierarchical, and
decision norms that do not require consensus.15

Organizational Creativity
The creativity of the organization is a function of creativity of the individuals within the
organization and a variety of social processes and contextual factors that shape
the way those individuals interact and behave.16 An organization’s overall creativity
level is thus not a simple aggregate of the creativity of the individuals it employs. The
organization’s structure, routines, and incentives could thwart individual creativity or
amplify it.

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Chapter 2 Sources of Innovation 23

intranet

A private
network, accessible only to
authorized
individuals. It is
like the Internet
but operates only
within (“intra”)
the organization.

The most familiar method of a company tapping the creativity of its individual
employees is the suggestion box. In 1895, John Patterson, founder of National Cash
Register (NCR), created the first sanctioned suggestion box program to tap the ideas of
the hourly worker.17 The program was considered revolutionary in its time. The originators of adopted ideas were awarded $1. In 1904, employees submitted 7000 ideas, of
which one-third were adopted. Other firms have created more elaborate systems that
not only capture employee ideas, but incorporate mechanisms for selecting and implementing those ideas. Google, for example, utilizes an idea management system whereby
employees e-mail their ideas for new products and processes to a company-wide database where every employee can view the idea, comment on it, and rate it (for more
on how Google encourages innovation, see the Theory in Action on Inspiring Innovation at Google). Honda of America utilizes an employee-driven idea system (EDIS)
whereby employees submit their ideas, and if approved, the employee who submits
the idea is responsible for following through on the suggestion, overseeing its progress
from concept to implementation. Honda of America reports that more than 75 percent of all ideas are implemented.18 Bank One, one of the largest holding banks in the
United States, has created an employee idea program called “One Great Idea.” Employees access the company’s idea repository through the company’s intranet. There they
can submit their ideas and actively interact and collaborate on the ideas of others.19
Through active exchange, the employees can evaluate and refine the ideas, improving
their fit with the diverse needs of the organization’s stakeholders.
At Bank of New York Mellon they go a step further—the company holds enterprisewide innovation competitions where employees form their own teams and compete in
coming up with innovative ideas. These ideas are first screened by judges at both the
regional and business-line level. Then, the best ideas are pitched to senior management in a “Shark Tank” style competition that is webcast around the world. If a senior
executive sees an idea they like, they step forward and say they will fund it and run
with it. The competition both helps the company come up with great ideas and sends a
strong signal to employees about the importance of innovation.20
Idea collection systems (such as suggestion boxes) are relatively easy and inexpensive to implement, but are only a first step in unleashing employee creativity.
Today companies such as Intel, Motorola, 3M, and Hewlett-Packard go to much
greater lengths to tap the creative potential embedded in employees, including
investing in creativity training programs. Such programs encourage managers to
develop verbal and nonverbal cues that signal employees that their thinking and
autonomy are respected. These cues shape the culture of the firm and are often
more effective than monetary rewards—in fact, as noted previously, sometimes
monetary rewards undermine creativity by encouraging employees to focus on
extrinsic rather than intrinsic motivation.21 The programs also often incorporate
exercises that encourage employees to use creative mechanisms such as developing alternative scenarios, using analogies to compare the problem with another
problem that shares similar features or structure, and restating the problem in a
new way. One product design firm, IDEO, even encourages employees to develop
mock prototypes of potential new products out of inexpensive materials such as
cardboard or styrofoam and pretend to use the product, exploring potential design
features in a tangible and playful manner.

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Theory in Action

Inspiring Innovation at Google

Google is always working on a surprising array of projects, ranging from the completely unexpected (such as
autonomous self-driving cars and solar energy) to the
more mundane (such as e-mail and cloud services).a
In pursuit of continuous innovation at every level of
the company, Google uses a range of formal and
informal mechanisms to encourage its employees to
innovate:b
20 Percent Time: All Google engineers are encouraged
to spend 20 percent of their time working on their own
projects. This was the source of some of Google’s most
famous products (e.g., Google Mail, Google News).
Recognition Awards: Managers were given discretion
to award employees with “recognition awards” to celebrate their innovative ideas.
Google Founders’ Awards: Teams doing outstanding work could be awarded substantial s…
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