Long Range Planning 43 (2010) 354e363 httpwww.elsevier.com.docx

Long Range Planning 43 (2010) 354e363
http://www.elsevier.com/locate/lrp
Business Model Innovation:
Opportunities and Barriers
Henry Chesbrough
Companies commercialize new ideas and technologies through
their business models.
While companies may have extensive investments and processes
for exploring new ideas
and technologies, they often have little if any ability to innovate
the business models
through which these inputs will pass. This matters - the same
idea or technology taken to
market through two different business models will yield two
different economic outcomes.
So it makes good business sense for companies to develop the
capability to innovate their
business models.
This paper explores the barriers to business model innovation,
which previous academic
research has identified as including conflicts with existing
assets and business models, as
well as cognition in understanding these barriers. Processes of
experimentation and ef-
fectuation, and the successful leadership of organizational
change must be brought to
bear in order to overcome these barriers. Some examples of
business model innovation are
provided to underline its importance, in hopes of inspiring
managers and academics to
take these challenges on.

� 2009 Elsevier Ltd. All rights reserved.
a mediocre technology pursued within a great business model
may be
more valuable that a great technology exploited via a mediocre
business model
Introduction
Technology by itself has no single objective value. The
economic value of a technology remains
latent until it is commercialized in some way via a business
model. The same technology commer-
cialized in two different ways will yield two different returns.
In some instances, an innovation can
successfully employ a business model already familiar to the
firm, while, other times, a company
will have a business model that can make use of the technology
via licensing. In still other cases,
0024-6301/$ - see front matter � 2009 Elsevier Ltd. All rights
reserved.
doi:10.1016/j.lrp.2009.07.010
http://www.elsevier.com/locate/lrp
though, a potential new technology may have no obvious
business model, and in such cases tech-
nology managers must expand their perspectives to find an
appropriate business model in order to
be able to capture value from that technology. [In fact, it is
probably true that a mediocre technol-
ogy pursued within a great business model may be more
valuable that a great technology exploited
via a mediocre business model.] Unless a suitable model can be
found, these technologies will yield
less value to the firm than they otherwise might e and if others,

outside the firm, uncover a business
model more suited for a given technology, they may realize far
more value from it than the firm that
originally discovered the technology.
To begin at the beginning e what is a business model? In
previous work with my colleague
Richard Rosenbloom we have suggested that a business model
fulfils the following functions:1
� Articulates the value proposition (i.e., the value created for
users by an offering based on
technology);
� Identifies a market segment and specify the revenue
generation mechanism (i.e., users to whom
technology is useful and for what purpose);
� Defines the structure of the value chain required to create and
distribute the offering and com-
plementary assets needed to support position in the chain;
� Details the revenue mechanism(s) by which the firm will be
paid for the offering;
� Estimates the cost structure and profit potential (given value
proposition and value chain
structure);
� Describes the position of the firm within the value network
linking suppliers and customers (incl.
identifying potential complementors and competitors); and
� Formulates the competitive strategy by which the innovating
firm will gain and hold advantage
over rivals.

I came to understand the importance of business models through
a research program conducted
with the cooperation of the Xerox Corporation, particularly
their (now retired) CTO, Mark Myers.
This research examined in detail the activity history
surrounding more than 35 technology projects
throughout Xerox’s five research laboratories around the world.
By design, I selected projects that
were judged not worth pursuing internally within Xerox, and
were either pushed outside the com-
pany, or allowed to leave if a researcher wanted to continue the
project after Xerox terminated its
support internally. I then followed the subsequent experience of
each of these projects after their
departure from Xerox. It eventually became clear that the many
research projects that remained
within Xerox’s R&D system (and proved to be quite valuable
economically) differed from those
that left Xerox in one important respect: the former fitted well
with Xerox’s business model, while
those that ‘went outside’ did not. Thus, to understand Xerox’s
technology innovation successes and
failures, one has to grapple with Xerox’s business model.
In the 1980s, Xerox was known as ‘the copier company’ e it
made industry leading copiers and
also printers. While these products were profitable in their own
right, the really big money was in
the consumables (especially toner and paper) they required: and,
therefore, the higher the copy or
print volumes of each machine sold, the greater the returns for
Xerox. So Xerox’s business model
searched widely (and effectively) for technologies that would
enable more copies, faster. Xerox’s
business model motivated them to develop ever-faster machines

that could handle very high
copy volumes, and had maximum machine uptime and
availability. This resulted in a strong cog-
nitive bias within Xerox whose business model discouraged the
development of low-speed personal
copiers. As Xerox’s CEO at the time observed later: ‘.our
profits came from how many copies were
made on those machines. If a copier was slow in generating
copies, that was money plucked out of our
pocket’.2
At that same time, however, Xerox was funding significant
industrial research activity - most
prominently developed at its Palo Alto Research Center (PARC)
- in the domains of man-machine
interfaces and other key building blocks of what would go on to
become the personal computer
industry. Some of this work, such as semiconductor diode
lasers, and the technologies that assisted
Long Range Planning, vol 43 2010 355
users in identifying the source of a copier malfunction so the
user could fix the copier without call-
ing in an outside service technician, did assist the copier and
printer business. But much of the
work developed at this time e which later gave rise to the point-
and-click user interface as well
as Ethernet, Postscript, and many other technologies - lacked
any obvious way to increase the vol-
ume or quality of copies made by a Xerox copier.
Xerox literally did not know what to do with these technologies
..[they
were] ‘orphans’ in the company.

In fact, Xerox literally did not know what to do with these
technologies, which became ‘orphans’
within the company. While the research was solid, and was
publicized quite effectively, the sales and
marketing executives at Xerox could see no clear way to profit
from them. 35 of these projects were
either shown the door, or the scientists working on the projects
got fed up with the internal delays,
and took the project to the outside world on their own. Although
my research found that most of
them were ultimately not successful outside Xerox, a few
subsequently became very valuable. Signif-
icantly, none of the valuable projects employed a business
model similar that of the Xerox copier or
printer - their journey to success involved each of them
identifying very different business models.
Based on this research, I would argue that a company has at
least as much value to gain from
developing an innovative new business model as from
developing an innovative new technology.
Like Xerox, however, companies have many more processes,
and a much stronger shared sense
of how to innovate technology, than they do about how to
innovate business models. And that
is the point of this article: companies need to develop the
capability to innovate their business
models, as well as their ideas and technologies.
An example of business model innovation among Xerox
spinoffs: 3Com
To illustrate business model innovation, I will briefly recap the
story of 3Com - one of the technol-
ogy spinoffs examined in my earlier study. 3Com’s business
model did not emerge fully formed - in
fact, it was the product of extensive experimentation. This

example shows how business model in-
novation is not a matter of superior foresight ex ante e rather, it
requires significant trial and error,
and quite a bit of adaptation ex post.
3Com commercialized the Ethernet networking protocol created
at PARC, which, while it proved
quite valuable later for computers, offered real and immediate
benefits to copiers as well, by en-
abling Xerox to use a single wiring harness to support a variety
of equipment configurations in
its copiers and printers and connect its proprietary devices and
options. Xerox sought to reduce
its cost, and leased the Ethernet technology in 1979 to a former
PARC employee, Robert Metcalfe,
who had invented it while on its staff, for a one-time payment
of $1,000, Metcalfe, in turn, worked
with DEC and Intel to create a standard around the Ethernet
protocol.
Although this approach benefited Xerox, the technology proved
in time to hold a much greater
opportunity for creating value: in developing and controlling an
important industry standard for
networking computers, printers, and file servers. This
opportunity was not lost on Metcalfe. Armed
with his license from Xerox, and with the Ethernet standard that
was supported by DEC and Intel,3
he raised venture capital and started 3Com. He initially targeted
the Unix workstation market, with
the intention of utilizing his own direct sales force, using the
business model of a systems company
with its own distribution organization: not too dissimilar from
that of Xerox itself. But that is not
how matters ended up. His work on the Ethernet standard made

Metcalfe known to a small but ar-
dent group of people in the emerging Local Area Networking
(LAN) market and among his activities
he compiled (with his wife) a directory of LAN dealers and
resellers, which sold for $125 a copy.
As a result of these and other experiments, Metcalfe changed
his business model. As he was estab-
lishing 3Com, the IBM PC was launched, and opened up a new
market area which quickly eclipsed
the originally targeted Unix market. So he went after the IBM
PC market, initially intending to
356 Business Model Innovation: Opportunities and Barriers
develop his own direct sales force, but soon shifting to
distributing his products through retailers
and value-added resellers e many of whom were entries in his
directory of LAN dealers.
Ethernet turned out to be far more valuable as an independent
product
and standard than as an internal wiring harness component.
Ethernet turned out to be far more valuable as an independent
product and standard for local area
networking than as an internal Xerox component for copier
wiring harnesses. Instead of designing,
manufacturing, and marketing entire computer systems (as
Xerox did) 3Com limited its business to
designing add-in boards to provide networking capabilities to
IBM compatible personal computers
and shared laser printers. 3Com went public in 1984 and has
continued to operate for many years as
a public company. Neither the many experiments Metcalfe
conducted on his business model, nor the

resulting model he deployed, would likely have happened inside
Xerox’s business model.
More recent examples of business model experimentation
We also can see the importance of business model
experimentation in more recent examples. One
concerns the October 2007 launch of ‘In Rainbows’,
Radiohead’s most recent CD. For various rea-
sons, the band’s managers decided not to follow the
conventional release process with its record
company, EMI but, as an experiment, to release the CD on the
band’s website. Fans were invited
to pay whatever they wished for the tracks, which also offered a
collector’s box set and other
merchandise.
The problems with the music recording industry’s business
model are well known: its traditional
business model was failing and revenues and profits were
falling rapidly. CD unit sales are down
substantially from just a few years ago, while alternative
formats for music distribution like iTunes
have grown more important. It is in times like these - when it is
clear that the ‘old’ business model
is no longer working - that business model experimentation
becomes so important:, but it is not at
all clear what the eventual ‘new’ business model will turn out to
be. Only experimentation can help
identify it and create the data needed to justify it. In
Radiohead’s case, the experiment is widely
considered to have been a success. The band’s website
registered over 3 million visits during the
first 60 days after the release e while about 1/3 choose to pay
nothing, the remaining 2/3 paid
an average of £4. The net revenue to the band thus came in at
around £2.67 pounds on average e

far more than the band’s share would have been under their
normal business agreement.4
But here is where it gets interesting. ‘In Rainbows’ was then
taken off the website, licensed to a pub-
lisher for sale in the US, UK and elsewhere and released
through the regular commercial distribution
channels. Even though it had been available for downloaded for
over 60 days at low prices (even for
free), the CD debuted at #1 in both the US and the UK, and sold
over 1.7 million CDs through com-
mercial channels in the subsequent 21 months - 5e6 times more
than Radiohead’s earlier CDs. More
than 100,000 collector box sets also were sold e a new revenue
source for the band. Whatever revenue
Radiohead might have lost through its initial download
experiment was more than compensated for
by the far greater publicity the band received, which seems to
have accounted for the surge in
commercial sales, and no doubt also benefited ticket sales for
its subsequent world tour.
[Any] revenue the band lost in the download experiment was
more
than compensated by greater publicity and sales of the
commercial
[release] and tickets for its world tour.
A different business model experiment is under way in the
pharmaceutical industry where e as in
the music recording industry e the traditional business model is

in real crisis. Fewer new chemical
entities are being approved for sale, the FDA regulatory
requirements remain challenging and R&D
spending on new drug development is at an all time high. While
it is clear that the ‘blockbuster’
business model era is over, what will replace it remains highly
uncertain - again, this is exactly
the time for business model experimentation. Johnson and
Johnson’s experiment with Velcade -
a drug for multiple myeloma, a form of bone cancer - involves
J&J offering the drug to European
health ministries with a novel proviso. If the drug is not
efficacious in 90% of their patients, the
ministries need not pay for it. (An alternative framing in the UK
involves payment only where Vel-
cade has proven efficacious.).
Barriers to business model innovation from previous academic
research
It is too soon to know if J&J’s particular experiment will
succeed or not. But it is quite timely to be trying
such things in these industries where the extant business models
are demonstrably broken. The
question is: why don’t more organizations conduct such
experiments, to probe for potential new busi-
ness models before the time comes when external innovations
render their traditional ones redundant?
The immediate answer is that businesses face significant
barriers to business model experimen-
tation, which previous academic research has helped to identify.
One of the best such studies is by
Amit and Zott.5 Choosing the business model as their unit of
analysis, they identify novelty, lock-in
complementarities and efficiency as key aspects of business
model innovation. However, these may
often conflict with the more traditional configurations of firm

assets, whose managers are likely to
resist experiments that might threaten their ongoing value to the
company. A vice president of
a field sales organization, for example, might take strong
exception to experiments with online sales
of the same products, whether they are successful or not.
While it was not as clear in his early work, Clayton
Christensen’s concepts of ‘disruptive technology’
- and especially the later notion of ‘disruptive innovation’ - call
attention to similar barriers to business
model experimentation.6 What disrupts incumbent firms in
Christensen’s story is not their inability
to conceive of the disruptive technology: like Amit and Zott, he
identifies the root of the tension in
disruptive innovation as the conflict between the business
model already established for the existing
technology, and that which may be required to exploit the
emerging, disruptive technology. Typically,
the gross margins for the emerging one are initially far below
those of the established technology.
The end customers may differ, as may the necessary distribution
channels. As the firm allocates its
capital to the most profitable uses, the established technology
will be disproportionately favored
and the disruptive technology starved of resources. Christensen
quotes Andy Grove, former CEO
of Intel, ‘Disruptive technologies is a misnomer. What it is, is
trivial technology that screws up your
business model’.7
the root of tension [is] the conflict between the business model
established for the existing technology, and that required to
exploit
the emerging, disruptive technology.

My work with Richard Rosenbloom has noted a different,
cognitive barrier to business model
experimentation not found in either Amit and Zott or
Christensen, arguing that the success of es-
tablished business models strongly influence the information
that subsequently gets routed into or
filtered out of corporate decision processes. This approach
builds upon Prahalad and Bettis’ earlier
notion of a ‘dominant logic’ of how the firm creates value and
then captures value.8 Amid all the
noise of daily business life, this logic aids the firm in assessing
what information is important, and it
will seek information that fits with this logic and eschew that
which conflicts with it. This stance
helps organizations operate in otherwise chaotic environments,
which are quite typical in early stage
R&D, where both the technological potential and the market
potential are highly uncertain. But
that same dominant logic can act as is a double-edged sword
with regard to business model exper-
imentation - following it too slavishly can lead firms to risk
missing potentially valuable uses of
their technology when they do not fit obviously with their
current business model.
So these accounts highlight different barriers: in Christensen,
and in Amit and Zott, managers
readily recognize the right business model, but its development
is resisted due to its conflicts
with the prevailing business model, or with the underlying
configuration of assets that support
that prevailing model Our work, by contrast, has seemed to

show that, in fact, it is far from clear
to them even what the right business model ought to be. In
either case - whether the barrier is con-
fusion or obstruction - the way forward is via a commitment to
experimentation. Undertaking ac-
tive tests to probe nascent markets with new potential
configurations of the elements of a business
model can allow a firm to learn ahead of the rest of the market,
and to begin to generate the new
data that can power its change process. However, as we will
see, experiments alone are not enough.
following ‘dominant logic’ can lead firms to miss potentially
valuable
uses of technology [which] do not fit their current business
model.
Experimenting with and adopting new business models
If managers want to strive to overcome these barriers and
experiment with alternative business
models, how can they construct these experiments? One
promising approach is to construct maps
of business models, to clarify the processes underlying them,
which then allows them to become
a source of experiments considering alternate combinations of
the processes. One example of this
mapping approach has comes from Alex Osterwalder who,
following his dissertation at Lausanne,
has consulted and spoken widely on business models and
business model innovation.9 His empirical
focus utilizes a 9 point decomposition that characterizes a
business model, illustrated in Figure 1.
Another mapping approach comes from the concept of
‘component business modelling’. IBM has
been an early leader in this area, and has published white papers
on the approach, and is even filing

patents on the method. Figure 2 shows a visual depiction of
IBM’s view of a component business mode.
This modelling approach provides a pro-active way to actually
experiment with alternative busi-
ness models, by enabling firms to simulate various possibilities
before committing to specific invest-
ments in reality. It also has the great virtue of explicitly
visualizing the processes underlying
a business model. Thus, theoretical considerations of
configuring elements of a business model
here can become far more concrete.
KEY
ACTIVITIES
PARTNER
NETWORK
KEY
RESOURCES
COST
STRUCTURE
REVENUE
FLOWS
DISTRIBUTION
CHANNELS

VALUE
PROPOSITION
CLIENT
SEGMENTS
CLIENT
RELATIONSHIPS
Source:http://bussiness-model-design.blog-
spot.com/2005/11/what-is-bussiness-model.html
Figure 1. Osterwalder’s 9 point decomposition of a Business
Model
Business
Administration
New
Business
Development
Relationship
Management

Servicing
and Sales
Product
Fulfillment
Financial
Control and
Accounting
Business
Planning
Sector
Planning
Account
Planning
Sales
Planning
Fulfillment
Planning
Portfolio
Planning
Business Unit
Tracking

Sector
Management
Relationship
Management Sales
Management FulfillmentPlanning
Compliance
Reconciliation
Staff
Appraisals
Product
Management
Credit
Assessment
Staff
Administration
Product
Delivery
Credit
Administration
Sales ProductFulfillment
Customer
Accounts
Product
Administration

Marketing
Campaigns
Customer
Dialogue Document
Management General LedgerContact
Routing
D
ire
ct
Ex
ec
ut
e
C
on
tro
l
Source:http//www.935.ibm.com/services/us/igs/cbm/html/cbmpa
ge.html
Figure 2. IBM’S Component Business Model
From maps to alternative business models: experimentation,
effectuation, and
organizational leadership
Tools such as mapping are useful to explicate business models,
but cannot by themselves promote
experimentation and innovation with those models. For that

managers need organizational pro-
cesses and enough authority to undertake the experiments, and
then the ability to take actions
based on results from those tests.
One set of processes relate to experimentation. Thomke
provides a useful summary of principles
and parameters for effective experimentation.10 While his
concepts are focused on new product and
process innovation, they apply equally to business models. An
important principle concerns the fi-
delity of the experiment: the extent to which the experimental
conditions are representative of the
larger market. Trying out an alternative business model on real
customers paying real money in real
economic transactions provides the highest fidelity.11 Important
parameters include the cost of con-
ducting the test, both in terms of the direct cost, and in the cost
of failure if the experiment does not
yield the hoped-for learning, the time required to obtain
feedback from the experiment and the
amount of information learned from the test. Here Thomke is
clear about distinguishing ‘failures’
from ‘mistakes’: the former are a natural outcome of the
experimentation process and can be quite
useful; the latter are experiments that are too poorly designed to
yield any new learning. So com-
panies should strive to develop processes that provide high
fidelity as quickly and cheaply as pos-
sible, aiming to gain cumulative learning from (perhaps) a
series of ‘failures’ before discovering
a viable alternative business models.
McGrath and Macmillan’s concept of discovery-driven planning
(the methodology is described
elsewhere in this issue) figures in here as well.12 The method

has the important ability to model
unknown assumptions so that they can be directly tested, and to
clarify the required results of ex-
periments to arrive at an economically attractive business
model. Most economic justifications for
new innovative spending presume answers to as yet unknown
questions. Discovery-driven planning
enables the company to evaluate the key economic assumptions
explicitly, which can then be up-
dated as the results of further experiments become known.
A second set of processes relate to what Sarasvathy calls
effectuation, a term that is the opposite of
causation and derives from Simon’s work on the science of the
artificial.13 In effectuation processes,
actors (such as firms or entrepreneurs that create new
businesses - and associated business models)
do not analyze their environment so much as take actions that
create new information that reveals
latent possibilities in that environment. In other words, they do
not study the market so much as
enact it. There is a strong bias in effectuation for action over
analysis, because there may be insuf-
ficient data available to analyze one’s way towards a new
business model: without action, no new
data will be forthcoming.
There is a strong bias in effectuation for action over analysis .
[where]
there is insufficient data .. firms do not study the market - they
enact it.
Such action is particularly critical for the cognitive act of

reframing the dominant logic of one’s
business model. Emergent opportunities typically lack the deep
wealth of data that are used to jus-
tify corporate actions in the mainstream business. Indeed, it is
only through taking experimental
actions that new data will be generated. Mapping tools can
again be quite helpful here - by provid-
ing depictions of both current and prospective business models,
managers can quickly surmise
many of the likely implications of making such a change. Such
tools can also assist in characterizing
and communicating new cognitive models effectively to others.
A third process that is vital for changing the business models of
already existing organizations is
that of leading change in the organization. This can be a puzzle
e who is responsible for business
model experimentation? Functional heads will lack authority
over the whole organization: but busi-
ness models will require testing aspects of and interactions
between operations, engineering, mar-
keting, sales and finance and (as we have already seen) business
model experimentation may well
involve conflicts with some or all of these functions.
CEOs of small companies may be ideally suited to the task,
especially if they are also owners of
the business. However, a real problem with relying upon the
CEO to lead change is that they
likely rose to their position via the current business model,
which is now deeply familiar -
even comforting - while potential alternative models will be
unfamiliar and may even seem
threatening. Thus e although in the best position to lead it - the
CEO may actually act in
ways that retard the experimentation process. Another possible

locus of business model innova-
tion could be the general managers of specific businesses in
larger firms. But while these man-
agers may have the authority, they are typically rotated from
one position to another every
2e3 years, which may be too little time to formulate the
experiments, conduct them, collect
the date, analyze the data, develop inferences and
interpretations of that data, and then reframe
the analysis in ways that are sufficiently persuasive to guide the
transformation to a new business
model.14
Doz and Kosenen discuss in this issue the need for firms to have
strategic agility if they are to be
able to transform their business models in the pursuit of
strategic innovation. This demands leader-
ship meta-skills in perceptions of the environment, in
maintaining unity among the leadership
team, and in the ability to reallocate resources to support new
models.15 This bears a close resem-
blance to the idea of organizational ambidexterity also
advocated by Tushman and O’Reilly,16 al-
though neither conception sufficiently considers the ability of
middle managers to shape the
strategic agenda, as shown in Burgelman’s research.
Both conceptions also note the problems involved in
organizations needing to continue to per-
form well in their current business (and business model), while
at the same time undertaking the
experiments necessary to nurture a new model. Indeed, this is
part of the organizational problem,
as the search for a new business model often requires an
extended period of co-existence between
the current and new models. Knowing when to shift resources

from the former to the latter is
a delicate balancing act, and rife with possible career
consequences for the managers involved.
It takes a strong organizational culture to navigate through these
treacherous shoals, so that
the local objectives of individual middle managers give way to
the imperatives of the larger
whole.
the search for a new business model [may mean] extended
co-existence between current and new models. Knowing when to
shift resources [towards] the latter is a delicate balancing act
Organizations must address these leadership issues to ensure
effective governance of business
model experimentation, and that the results of their experiments
lead on to action within the or-
ganization. As we have seen from the academic literature, there
are powerful barriers to business
model innovation, but the way forward is for leaders to adopt,
explicitly, an experimental stance
toward business model innovation. Leaders can authorize the
launch of high fidelity, low cost, quick
performing and usefully informative experiments. These new
data will be reflected in new discovery
driven models, and leaders must be empowered to take action
based on these findings, and over-
come the barriers that surround and protect the extant business
model. The leadership process
must address the many affected constituencies within the
organization without becoming mired
in the infighting between them. To quote an old aphorism, ‘In

God we trust, all others bring
data’ and: it is the experimental process that can bring that data
to bear.
Conclusion
In sum, business model innovation is vitally important, and yet
very difficult to achieve. The bar-
riers to changing the business model are real, and tools such as
maps are helpful, but not enough.
Organizational processes must also change (and these are not
mapped by those tools). Companies
must adopt an effectual attitude toward business model
experimentation. Some experiments will
fail, but so long as failure informs new approaches and
understanding within the constraints of
affordable loss, this is to be expected - even encouraged. With
discovery driven planning, companies
can model the uncertainties, and update their financial
projections as their experiments create new
data. Effectuation creates actions based on the initial results of
experiments, generating new data
which may point towards previously latent opportunity.
And organizations will need to identify internal leaders for
business model change, in order to
manage the results of these processes and deliver a new, better
business model for the company.
The discretion and judgment of middle managers must be
subjected to empirical data if local objec-
tives are to be subordinated to those of the overall organization.
At the same time, the organization’s
culture must find ways to embrace the new model, while
maintaining the effectiveness of the current
business model until the new one is ready to take over
completely. Only in this way can business
model innovation help companies escape the ‘trap’ of their
earlier business models, and renew growth

and profits.
business model innovation is vital, yet very difficult .. the
barriers to
change are real. [Model] experiments will fail, but [if] they
inform new
approaches and understanding, this is to be expected - even
encouraged.
Acknowledgements
I am indebted to Simon Wakeman, for able research assistance,
as well as to David Teece, and Pat-
rick Sullivan of ICMG for thoughtful comments and discussion.
I have also received thoughtful
comments from the special issue editors, Charles Baden-Fuller
and Ian Macmillan e and I thank
them for the invitation to contribute - and patient editing
guidance from Jon Morgan.
References
1. H. Chesbrough and R. S. Rosenbloom, The role of the
business model in capturing value from innovation:
evidence from xerox corporation’s technology spin-off
companies, Industrial and Corporate Change 11(3),
529 (2002); This definition is utilized in a number of contexts
in H. Chesbrough, Open Innovation: The
New Imperative for Creating and Profiting from Technology,
Harvard Business School Press, Cambridge,
MA (2003); and also in H. Chesbrough Open Business Models:
How to Thrive in the New Innovation

Landscape, Harvard Business School Press, Cambridge, MA
(2006).
2. D. Kearns and D. Nadler, Prophets in the Dark: How Xerox
Reinvented Itself and Beat Back the Japanese,
Harper Business, New York, NY (1992) p. 88. See also Y. Doz
and M. Kosenen, Embedding strategic
agility: A leadership agenda for accelerating business model
renewal, Long Range Planning 43(2e3),
370e382 (2010), who observe that what help Xerox did obtain in
the low end of the copier market largely
came through Fuji-Xerox, its joint venture with Fuji in Japan.
3. This protocol soon became industry standard - IEEE 802
which published the protocols so that numerous
companies could incorporate them into their products and
services.
4. The commercial sales data are made more impressive by the
reported additional 2.3 million illegal down-
loads taken of the tracks while they were on the site: see
http://www.independent.co.uk/arts-entertain-
ment/music/news/radiohead-sales-show-fans-loyalty-to-illegal-
sites-884239.html.
5. R. Amit and C. Zott, Value creation in e-business, Strategic
Management Journal 22, 493e520 (2001).
6. C. Christensen, The Innovator’s Dilemma, Harvard Business
School Press, Cambridge, MA (1997); C. Christensen
and M. Raynor, The Innovator’s
Solution

, Harvard Business School Press, Cambridge, MA (2003).
7. This quote is taken from Christensen’s course review slides
for his Harvard Business School class, Building
Sustainably Successful Enterprises, at HBS, December 2002.
8. C. K. Prahalad and R. Bettis, The dominant logic:
retrospective and extension, Strategic Management
Journal 16(1), 5e14 (1995).
9. A. Osterwalder, The Business Model Ontology: A Proposition
in the Design Science Approach, unpublished
dissertation, University of Lausanne (2004).
10. S. Thomke, Experimentation Matters, Harvard Business
School Press, Cambridge, MA (2002).
11. This is one reason why startup companies may yield
important insights into new business models - they
are, in effect, ‘experiments’ with real companies making real
products selling to real customers.
12. R. G. McGrath and I. C. Macmillan, Discovery driven
planning, Harvard Business Review 73(4), 44e54
(1995).
13. S. Sarasvathy, Effectuation, Edward Elgar, London, UK

(2008); H. Simon, The Sciences of the Artificial (3rd
edn), MIT Press, Cambridge, MA (1996).
14. R. Burgelman, A process model of internal corporate
venturing in the diversified major firm, Administra-
tive Science Quarterly 28(2), 223e244 (1983).
15. Y. L. Doz and M. Kosonen (2009) op. cit at Ref 2.
16. M. Tushman and C. O’Reilly, Ambidextrous organizations:
managing evolutionary and revolutionary
change, California Management Review 38(4), 8e30 (1996).
Biography
Henry Chesbrough is Executive Director of the Center for Open
Innovation and Adjunct Professor at the Haas
School of Business at UC Berkeley. Previously he taught for six
years at Harvard Business School and, before ac-
ademia, spent ten years with Silicon Valley companies. His
research focuses on managing technology and inno-
vation. His academic work has been published widely in major
journals, and he has authored more than 20 case

studies on companies in the IT and life sciences sectors. His
2003 book, Open Innovation was a Strategyþ Business
magazine ‘Best Business Book’, while his most recent book,
Open Business Models (2006) extends his analysis of
innovation to business models, intellectual property
management, and markets for innovation. He is a member of
the Editorial Boards of Research Policy and the California
Management Review. Center for Open Innovation, F402
Haas School of Business, University of California-Berkeley, CA
94720-1930. Tel +1-510-643-2067; E-mail:
[email protected]
http://www.independent.co.uk/arts-
entertainment/music/news/radiohead-sales-show-fans-loyalty-
to-illegal-sites-884239.html
http://www.independent.co.uk/arts-
entertainment/music/news/radiohead-sales-show-fans-loyalty-
to-illegal-sites-884239.html
mailto:[email protected]Business Model Innovation:
Opportunities and BarriersIntroductionAn example of business

model innovation among Xerox spinoffs: 3ComMore recent
examples of business model experimentationBarriers to business
model innovation from previous academic
researchExperimenting with and adopting new business
modelsFrom maps to alternative business models:
experimentation, effectuation, and organizational
leadershipConclusionAcknowledgementsReferences
Journal of Strategic Information Systems 18 (2009) 46–55
Contents lists available at ScienceDirect
Journal of Strategic Information Systems
journal homepage: www.elsevier .com/ locate / js is
Disruptive technology: How Kodak missed the digital
photography revolution
Henry C. Lucas Jr. *, Jie Mein Goh
Decisions, Operations and Information Technologies, Robert H.
Smith School of Business, University of Maryland, College
Park, MD 20740, United States
a r t i c l e i n f o

Article history:
Available online 25 February 2009
Keywords:
Innovation
Information and communications
technologies
Disruptive technology
Core rigidities
Case study
Qualitative research
0963-8687/$ - see front matter � 2009 Elsevier B.V
doi:10.1016/j.jsis.2009.01.002
* Corresponding author. Tel.: +1 301 314 1968.
E-mail addresses: [email protected] (H.C.
a b s t r a c t
The purpose of this paper is to analyze how a firm responds to a
challenge from a transfor-
mational technology that poses a threat to its historical business
model. We extend
Christensen’s theory of disruptive technologies to undertake
this analysis. The paper makes
two contributions: the first is to extend theory and the second is

to learn from the example
of Kodak’s response to digital photography. Our extensions to
existing theory include con-
siderations of organizational change, and the culture of the
organization. Information tech-
nology has the potential to transform industries through the
creation of new digital
products and services. Kodak’s middle managers, culture and
rigid, bureaucratic structure
hindered a fast response to new technology which dramatically
changed the process of
capturing and sharing images. Film is a physical, chemical
product, and despite a succes-
sion of new CEOs, Kodak’s middle managers were unable to
make a transition to think
digitally. Kodak has experienced a nearly 80% decline in its
workforce, loss of market share,
a tumbling stock price, and significant internal turmoil as a
result of its failure to take
advantage of this new technology.
� 2009 Elsevier B.V. All rights reserved.
1. Introduction
The purpose of this paper is to explore how firms respond to

challenges from rare transformational technology that
threatens a traditional, successful business model. We propose
an extension of Christensen’s theory of disruptive technolo-
gies and illustrate the extensions with a longitudinal case study
of Kodak. Kodak is unique in that it developed and patented
many of the components of digital photography, yet this new
form of photography has had a serious, negative impact on the
firm. The two main contributions of the paper are the extension
to Christensen’s theory and the lessons from Kodak’s unsuc-
cessful response to a major technological discontinuity.
The digital camera combined with information and
communications technologies (ICT), specifically the
capabilities of the
computer to store and display photographs, and the Internet to
transmit them, transformed the major customer processes asso-
ciated with photography. The consumer could take many photos
at virtually no cost, and delete unwanted ones by pushing a
button. Rather than waiting to develop a photo and then sending
it by mail to another person, the customer uploads the pic-
ture to a PC and sends it as an email attachment to multiple
recipients. If the customer wants a hard copy, she can print a
picture locally on an inexpensive color printer on a PC, send it
to an Internet photo service, or go to a store that had a devel-
oping kiosk.

. All rights reserved.
Lucas Jr.), [email protected] (J.M. Goh).
mailto:[email protected]
mailto:[email protected]
http://www.sciencedirect.com/science/journal/09638687
http://www.elsevier.com/locate/jsis
H.C. Lucas, J.M. Goh / Journal of Strategic Information
Systems 18 (2009) 46–55 47
1.1. Past research: Christensen’s theory of disruptive
technologies
Christensen’s theory of disruptive technologies is one of the
most popular for explaining the plight of the incumbent firm
facing a significant new technology. He proposes a theory of
response to disruptive technologies in two books about inno-
vation (Christensen, 1997; Christensen and Raynor, 2003). He
argues that investing in disruptive technologies is not a ra-
tional financial decision for senior managers to make because,
for the most part, disruptive technologies are initially of
interest to the least profitable customers in a market
(Christensen, 1997). The highest-performing companies have

systems
for eliminating ideas that customers do not ask for, making it
difficult for them to invest resources in disruptive technologies.
By the time lead customers request innovative products, it is too
late to compete in the new market. The root cause of the
failure to adapt to disruptive technologies is that the company
practiced good management. The decision-making and re-
source-allocation processes that make established companies
successful cause them to reject disruptive technologies.
Christensen and Overdorf (2000) present a framework for
dealing with disruptive change that focuses on resources, pro-
cesses and values. Resources include people, equipment,
technologies, cash, product designs and relationships. Processes
are
the procedures and operational patterns of the firm, and values
are the standards employees use to set priorities for making
decisions. Managers design processes so that employees
perform tasks in a consistent way every time; they are not meant
to
change. The most important processes when coping with a
disruptive technology are those in the background such as how
the company does market research and translate it into financial
projections, and how the company negotiates plans and
budgets. Employees exhibit their values every day as they

decide which orders are more important, what customers have
priority and whether an idea for a new product is attractive. The
exercise of these values constitutes the culture of the orga-
nization. Culture defines what the organization does, but it also
defines what it cannot do, and in this respect can be a dis-
ability when confronting a new innovation.
1.2. Extending Christensen’s theory
When a firm is confronted with a discontinuous, highly
disruptive technology, senior management has to bring about
sig-
nificant changes in the organization at all levels. Our first
extension to Christensen is to emphasize the change process re-
quired to adopt a disruptive technology. Senior management has
to convince others of the need to move in a new direction.
Specifically we are interested in how middle managers change
themselves and also bring about change in the organization
(see Rouleau, 2005; Balogun, 2006).
Christensen argues that the firm is not ready to adapt a
disruptive technology because it does not see a demand from its
customers for the new innovation. He maintains that high-
performing companies have systems in place that tend to kill
ideas that customers are not asking for. We propose to extend

this part of his theory to encompass the culture of the orga-
nization, by which we mean the beliefs of employees, the way
the firm organizes itself and the nature of the interactions
among employees (Schein, 1983).
1.3. A first extension: the struggle for change
In confronting a technological disruption, a firm faces a
struggle between employees who seek to use dynamic
capabilities
to bring about change, and employees for whom core
capabilities have become core rigidities. Management
propensities for
change drive the process (see Fig. 1). We describe this ongoing
struggle using concepts from dynamic capabilities, core rigid-
ities and management propensities.
Dynamic
Capabilities
Core
Rigidities
Management
Propensities

Response to
Disruptive
Technology
Reduce capacity to changeIncrease capacity to change
Attack Rigidities Organize and marshal
capabilities for change
Fig. 1. A framework for responding to disruptive change.
48 H.C. Lucas, J.M. Goh / Journal of Strategic Information
Systems 18 (2009) 46–55
1.3.1. Dynamic capabilities
The theory of dynamic capabilities is an extension of the
resource-based view of the firm (Barney, 1991; Peteraf, 1993;
Mata et al., 1995; Eisenhardt and Martin, 2000; Barney and
Arikan, 2001). Dynamic capabilities is defined as ‘‘the firm’s
abil-
ity to integrate, build external competences to address rapidly
changing environments” (Teece et al., 1997). They ‘‘consist of
specific strategic and organizational processes like product

development, alliancing and strategic decision-making that cre-
ate value for firms within dynamic markets by manipulating
resources into new value-creating strategies” (Eisenhardt and
Martin, 2000; Helfat et al., 2007; Teece, 2007).
The theory suggests that a firm has three classes of assets to use
in seeking new forms of competitive advantage when
confronted with a novel situation including:
Processes: assemblies of firm-specific assets that span
individuals and groups. These processes have three roles
including
coordination, learning and reconfiguration.
Positions: specific assets including plant and equipment,
knowledge and reputational assets, that determine competitive
advantage at a given point in time.
Paths: the sequence of events that have led to a firm’s current
position i.e. ‘‘. . .a firm’s previous investments and its rep-
ertoire of routines constrain its future behavior” (Teece et al.,
1997).
1.3.2. Core rigidities
Dynamic capabilities may not, however, always enable a firm to
reconfigure its business in response to an external threat.
Leonard-Barton (1992) introduces the idea that the core

activities of the firm can become so rigid that it cannot respond
to
new innovations. Her four dimensions of a core capability
include: (a) employee knowledge and skills; (b) technical
systems
which embed knowledge and support innovation; (c) managerial
systems which guide knowledge creation and control and
(d) values and norms associated with various types of
knowledge.
Leonard-Barton suggests that core capabilities that are
appropriate in one situation may turn out to be inappropriate in
another, for example, the challenges for an incumbent firm from
a new entrant. These core capabilities, rather than being
dynamic and helpful in coping with change, become core
rigidities that inhibit a response. There are a number of paths
to rigidity. Because corporate resources are limited, firms often
emphasize one discipline, which makes the company less
attractive to people from non-dominant disciplines. It is easy
for technical systems to become outdated, especially when
they involve expensive plant and equipment or complex
software. Management systems also become rigid over time as
peo-
ple respond to incentive and reward systems; there is little
interest in performing tasks that appear to be undervalued by

senior management. It is easy for the organization to fall into
the competency trap; employees convince themselves that
their current processes and technology are superior to a new,
disruptive technology, and they fail to respond appropriately.
Rigidities in these core capabilities inhibit individual and
organizational learning when confronted with a rare, techno-
logical disruption. Employees may be comfortable with their
existing knowledge and skills and resist learning the new tech-
nology. There may be little incentive to build new technical and
managerial systems, or to learn new knowledge to create the
systems.
1.3.3. Management propensities
Management propensities determine the outcome of the battle
between dynamic capabilities and core rigidities in
responding to a transformational technology. This implication is
an extension to research demonstrating the importance
of managers in determining firm performance outcomes
(Holcomb et al., 2008; Castanias and Helfat, 2001; Bantel and
Jack-
son, 1989; Hambrick and Mason, 1984). Managers have to
develop a strategy that emphasizes the response to a disruptive
technology, and they must communicate this strategy throughout

the firm (O’Reilly, 1989). Senior managers have to learn a
new technology and develop cognitions that change is
necessary; they must lead the change effort (Sherif and Menon,
2004).
Managers must also help subordinates develop cognitions that
respond to a new direction for the firm. They must teach oth-
ers in the organization about their vision for the firm and see
that employees learn this new business model and all that it
entails. We refer to these managerial activities as propensities
or managers’ inclinations to act in a certain way.
During the course of responding to the disruptive technological
change, complications result and cause different manage-
ment levels to have different managerial cognitions (Gavetti,
2005a). If it is desirable to change the overall direction of a
firm,
senior managers are likely to be faced with one group of long-
term employees who exhibit core rigidities, and newer
employees who are trying to innovate and take advantage of the
firm’s dynamic capabilities.
It is interesting to note that the discussion above has a parallel
in the IS strategy literature. For example, Galliers (2004,
2006) has proposed a framework for information systems
strategizing which focuses on exploitation, exploration and

change
management. A firm confronted with a technological
discontinuity needs to explore, utilize its dynamic capabilities
and
learn a new, agile response to a threat. It needs to create
knowledge, which is a key component of Gallier’s IS strategy
frame-
work as well.
1.4. A second extension: organization culture
Organizational culture shapes organizational cognition and has
a very important role in its response to technology-en-
abled transformations. We have adopted Schein’s (1983)
definition of culture for the purposes of this paper. Culture is
‘‘a
Systems 18 (2009) 46–55 49
pattern of basic assumptions that a given group has invented,
discovered, or developed in learning to cope with its problems
of external adaptation and internal integration – a pattern of
assumptions that has worked well enough to be considered

valid, and therefore, to be taught to new members as the correct
way you perceive, think, and feel in relation to these prob-
lems” (Schein, 1983, p. 14). Founders teach organizational
members through their actions and through this process, culture
is
developed, learned and embedded (Schein, 1985).
Culture operates at both the macro and micro levels within an
organization. As defined by Schein, culture is a multilevel
concept that is fragmented across domains such as different
types of management. Literature (Burke, 2002) often focuses on
the role of senior management in creating a firm’s culture; we
see a need to consider the role of middle management which
has been less emphasized in prior research (Balogun and
Johnson, 2004; Danneels, 2004). Middle managers are typically
the
largest managerial group and they play a key role in
implementing firm strategy. Given their position in the
organizational
hierarchy, middle managements’ propensities may be different
from those of senior management.
Previous literature on organizational change acknowledges the
role of culture in facilitating, managing, or impeding
change (e.g., Burke, 2002; Tripsas and Gavetti, 2000). A

bureaucracy is associated with slow response and employees
who
value security over risk-taking. Bureaucratic structure leads to
organizational inertia (Merton, 1957). Thus, an organization
culture that promotes hierarchy and maintaining the status quo
will be resistant to disruptive technologies.
2. The case of Kodak
2.1. Data collection
The research reported here comes from primary and secondary
sources. We obtained annual reports from Kodak and
searched the literature to build an historical time line of the key
events in digital photography and Kodak’s response to this
new technology. We looked at past Kodak web sites on
www.archive.org to get a sense of changes in marketing and
strategy.
As a part of a larger project on IT-enabled transformations,
members of a research team visited Kodak and interviewed two
employees the company was willing to make available to us.
Also as a part of the larger study we asked Carly Fiorina, ex-
CEO
of HP, to expand on her early analysis and comments on
Kodak’s history with digital photography. We consulted a

teaching
case study, books and a videotaped interview with one of the
Kodak CEOs during this time period.
2.2. The rise and fall of Kodak
George Eastman founded the Eastman Kodak Company in 1880
and developed the first snapshot camera in 1888. It be-
came clear early on that consumables provided the revenue;
cameras did not need to be expensive because their owners
used large amounts of film. Kodak invested heavily in film and
when color photography was introduced, it was one of the
few companies that had the knowledge and processes to
succeed. The company achieved $1 billion in sales in 1962. By
1976,
Kodak captured the majority of the US film and camera market
(90% and 85%, respectively). Kodak’s photofinishing process
quickly became the industry standard for quality. As a result,
most of the power of the corporation centered on its massive
film-making plant, and historically CEOs came from
manufacturing jobs at the factory (Gavetti et al., 2004).
Kodak’s sales hit $10 billion in 1981, but then competitive
pressures, especially from Fuji, hindered future increases
(Gavetti et al., 2004). In 1986, Kodak invented the fist

megapixel sensor capturing 1.4 million pixels to produce a
high-quality
5 � 7 print. Kodak had introduced more than 50 products that
were tied to the capture or conversion of digital images. In
1990 Kodak began to sell its Photo CD system in which a
consumer took a roll of film to a photofinisher who placed
images
on a CDROM rather than paper. The consumer needed a Photo
CD player to see the images on a TV screen. However, costs
were too high and the product never achieved the success Kodak
had forecasted.
Kodak went through a total of seven restructurings during the
period between 1983 and 1993. In 1993 Kay Whitmore, a
Kodak insider, stepped down as chairman to be replaced by
George Fisher, the CEO who had turned around Motorola. The
board saw Fisher as a ‘‘digital man”. One of Fisher’s first
strategic moves was to refocus Kodak on photography; he sold
the
companies in its health segment, collecting $7.9 billion he used
to repay debt (Gavetti et al., 2004). He also went after Fuji
and the Japanese government for restraining the sales of Kodak
products. Fisher did not give up on film; he believed that
China was an emerging market with great potential for
photography and invested heavily there in a joint venture with

the Chinese government.
By 1996, Kodak had cut $50 million from the cost of film and
paper production and had reduced cycle times; what used to
take months could be done in less than a day (Swasy, 1997). By
1997, digital camera sales were increasing by 75% a year
while film camera sales increased by only 3%. By this time
there were many new entrants in digital photography, mostly
Japanese electronics firms. In 2000, the value of digital cameras
sold passed the value of film cameras. That year Fisher left
as CEO and was replaced by Daniel Carp. In 2001 sales of
analog cameras dropped for the first time.
In 2002, Kodak bought Ofoto, an online picture service,
signaling a greater commitment to digital photography. Kodak’s
2003 annual report’s chairman’s letter stated that Kodak
‘‘implemented a digitally oriented strategy to support revenue
and
sustainable earnings”. In the same year, Kodak closed its film
camera factory in the US. The 2004 chairman’s letter reported
on progress: ‘‘In the first full year of its digital transformation
strategy, Kodak came out of the gate at a full gallop-and we
continue to build momentum”. In 2005, Carp stepped down
early as chairman and was replaced by Antonio Perez.

http://www.archive.org
Systems 18 (2009) 46–55
Since 1993, Kodak has reduced its labor force by close to 80%
through retirements and layoffs, over 100,000 employees, a
strong indication of the difficulties the company has
encountered (see Fig. 2). Kodak net sales reached $20 billion in
1992,
and dropped to below $15 billion in the ensuing 5 years, though
some of the decline was due to divestitures. This change is
particularly dramatic when compared with Fuji’s net sales,
which have been growing since 2001. Fuji and other brands
began
to compete heavily with Kodak, offering high quality film at
20% below Kodak’s price. By 1993 Fuji had a 21% market share
of
worldwide film sales (Gavetti et al., 2004). In addition to
pressure from competitors, investors have been highly critical
of the
company and its management. Share prices in Fig. 3 rose during
Fisher’s first 4 years of leadership (1993–1997), and then
began a precipitous decline during Carp’s chairmanship starting
in 2000.

2.3. The movement to digital photography
The transformation from conventional photography to digital
photography took about two decades. Information and
communications technologies play as important a role in digital
photography as the camera, itself. The computer is a vehicle
for editing, saving, storing and ultimately sharing photographs
with others. The Internet is the vehicle for the distribution of
multiple copies of an image to different recipients.
Steven Sasson, The inventor of the digital camera at Kodak,
remarks on the history of digital research at the company.
Well, you’d be surprised at some of the breakthroughs and
innovations that Kodak was doing. We were sort of in an odd
position where we were certainly supporting Silver Hallide
photography for all our customers, but we were also doing
Fig. 2. Kodak’s net sales and number of employees.
Fig. 3. Kodak’s monthly share price.

Systems 18 (2009) 46–55 51
advanced research into digital imaging. You know, Kodak made
the first megapixel imager in the mid-1980s. We were
doing image compression research and even making products
using, what we call, DCT compression back in the mid
1980s. And we made some of the first cameras. You might be
surprised that a Kodak digital camera went aboard the
1991 space shuttle mission.
Paul Porter, Kodak’s Director of Design and Usability,
commented:
We were way ahead of the curve in digital even though we were
pretty much a film and chemical company. We did a lot
of research in digital because we knew at some point in time the
world would change. We invented the digital camera. So,
being the first ones there we continuously worked in the labs so
to make sure when that change was made we were pre-
pared for it. So we have the expertise in the research labs to
generate these innovations that make our experience either,
more gratifying, more intuitive or better connected than what
other people do.
As prices fell and performance of digital cameras improved in
the 1998 time frame, there was a dramatic increase in the
sales of digital products (see Fig. 4). The movement toward
digital photography has a huge adverse impact in firms that had

historically been in the photography business such as Kodak,
Fuji and Konica Minolta. When photography moved from film
to digital, it invited a whole new group of competitors into the
marketplace. Companies like HP, Lexmark, Epson and Canon
suddenly became photofinishers with their color printers, some
of which were designed to work easily with digital cameras
to produce prints. A number of online services like Ofoto
sprung up. Fig. 5 shows a timeline of the key events in Kodak’s
his-
tory related to digital photography.
Fig. 4. Sales of digital camera.
Time Line 14Stopped selling film based
camera 7 Sold Sterling
19901985 1995 2000
1 Lost
Polaroid
patent
infringement
suit
2Invented first
megapixel
sensor

3,4 165 6 7,8 9 10 11 12 13 14,15
2005
3 Acquired
Sterling Drug
4Acquired
IBM copiers
5Produced
Photo CD
system
6 George
Fisher became
CEO
8 Divested Clinical
Diagnostics Division
15 First full year in digital
transformation
16 Antonio

Perez became
CEO
9 Kodak
charged Fuji
& Japan
13 Implemented
digital strategy
10WTO rules
against Kodak
11Daniel Carp
became CEO
12 Acquired
Ofoto
17 Outsourced
digital camera
production
18 Produced
low cost

cartridge
printers
1,2 17 18
2007
Fig. 5. Kodak’s time line.
Systems 18 (2009) 46–55
3. An analysis of Kodak’s response to digital photography
For Kodak, the invention and growth of digital photography was
clearly a disruptive technology that had a dramatic im-
pact on film sales. It was a once-in-a-hundred-years change for
the company. Unlike the disk drive industry that is promi-
nent in Christensen’s work, the move to ICT and digital
changed the process by which the consumer captured, displayed
and
shared images. Table 1 describes how Christensen’s theory
applies to Kodak, and how Kodak’s history deviates from this
theory.

Christensen comments that disruptive technologies produce
products that are typically cheaper, smaller and often more
convenient to use than traditional products. Digital cameras
were an expensive curiosity at first, but soon producers im-
proved their performance and they constantly reduced prices.
Digital photography, as noted earlier, was not just a product,
but a change in the entire process of capturing, displaying and
transmitting images. Kodak seriously underestimated how
quickly the demand for this new technology would grow.
Christensen’s theory predicts that firm resource-allocation
processes discourage investment in potentially disruptive
technologies. However, contrary to his disk drive industry
examples, Kodak did invest massive amounts in digital photog-
raphy. It just never had much to show for it. Fisher arrived after
Kodak had spent $5 billion on digital imaging R&D with little
coming from the labs. Product development and sales were
scattered over more than a dozen divisions, at one point the
com-
pany had 23 different digital scanner projects under
development (Gavetti et al., 2004).
Table 1
Christensen’s theory of disruptive technology and Kodak.

Christensen’s theory How it applies at Kodak Differences with
Kodak
Products based on disruptive technologies are
typically cheaper, simpler, smaller and,
frequently, more convenient to use ID p. xv
At first digital cameras were more expensive and
large; gradually they became cheaper, simpler
and smaller
Digital cameras changed more than the physical
artifact; they changed the process of photography
– one now captured an image and a photo was
only one way of displaying the image. Digital
photography also changed the distribution,
sharing and copying of images via the Internet
Technologies can progress faster than market
demand. ID p. xvi
It appeared that digital cameras created their
own market demand
‘‘. . .investing aggressively in disruptive

technologies is not a rational financial
decision for them to make. . .By and large, a
disruptive technology is initially embraced by
the least profitable customers in a market. ID
p. xvii
As digital cameras became smaller and easier to
use, consumers adopted them. Not clear if they
spent less on photography or not, but the
suppliers of imaging services changed
Kodak thought at first that the main market for
digital photography would be the professional
photographer, not the amateur consumer. It did
invest heavily in digital products, but did not
manage that investment well ($5 billion by the
time Fisher arrived).
The highest-performing companies. . .have well-
developed systems for killing ideas their
customers don’t want. As a result, these
companies find it very difficult to invest
adequate resources in disruptive
technologies-lower-margin opportunities that
their customers don’t want-until their

customers want them. And by then it is too
late. ID xix
Kodak underestimated the speed with which the
consumer segment would adopt digital
photography
Kodak seemed to be ignoring customers; it
focused on film because it was comfortable and
so profitable. The company had an analog,
chemistry mindset and could not think digitally
The reason is that good management itself was
the root cause. Managers played the game the
way it was supposed to be played. The very
decision-making and resource-allocation
processes that are the key to the success of
established companies are the very processes
that reject disruptive technologies. ID p. 98
Kodak appears to be more in a state of denial.
Possibly at first this reason could have kept them
from investing, but Kodak began to develop a
digital strategy long after it was obvious to
everyone else that it needed one

Senior management allocated resources to digital
products, but middle managers rejected the
disruptive technology
Give responsibility for disruptive technologies to
organizations whose customers need them.
Chap. 5 title, p. 100 ID
Kodak did try a separate organizational unit Kodak kept
organizing and re-organizing. Data
suggest that when a separate organization was
created, the digital subunit and traditional
photography had serious conflict over resources
It is the middle managers who must decide
which of the ideas that come bubbling in or
up to them they will support and carry to
upper management for approval, and which
ideas they will simply allow to languish. Their
job is to sift the good ideas from the bad and
to make good ideas so much better that they
regularly secure funding from senior
management. IS p. 10

Kodak’s middle managers impeded the
conversion to digital
Senior management was trying to change middle
management, and had little success. In this case
it was not middle management bringing ideas to
senior management; the direction was the
opposite
ID is from The Innovator’s Dilemma, IS is from The
Innovator’s

Long Range Planning 43 (2010) 354e363 httpwww.elsevier.com.docx

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Long Range Planning 43 (2010) 354e363 httpwww.elsevier.com.docx