ARTWORK Damián Ortega Controller of the Universe, 2007foun.docx

ARTWORK Damián Ortega
Controller of the Universe, 2007
found tools and wire, 285 x 405 x 455 cm
Spotlight
100 Harvard Business Review January–February 2011
SPOTLIGHT ON BUSINESS MODEL INNOVATION
1568 JanFeb11 Casadesus-Masanell.indd 1001568 JanFeb11
Casadesus-Masanell.indd 100 12/3/10 3:52:19 PM12/3/10
3:52:19 PM
Joan E. Ricart ([email protected]
edu) is the Carl Schroder
Professor of Strategic Man-
agement and Economics
at IESE Business School in
Barcelona.
Ramon Casadesus-
Masanell ([email protected]
gmail.com) is an associate
professor at Harvard Busi-
ness School in Boston.
How to Design
A Winning
Business Model

Smart companies’ business models generate
cycles that, over time, make them operate
more eff ectively. by Ramon Casadesus-Masanell
and Joan E. Ricart
STRATEGY HAS been the primary building block of
competitiveness over the past three decades, but
in the future, the quest for sustainable advantage
may well begin with the business model. While the
convergence of information and communication
technologies in the 1990s resulted in a short-lived
fascination with business models, forces such as de-
regulation, technological change, globalization, and
sustainability have rekindled interest in the concept
today. Since 2006, the IBM Institute for Business
Value’s biannual Global CEO Study has reported that
senior executives across industries regard develop-
ing innovative business models as a major priority.
A 2009 follow-up study reveals that seven out of 10
companies are engaging in business-model innova-
tion, and an incredible 98% are modifying their busi-
ness models to some extent. Business model innova-
tion is undoubtedly here to stay.
That isn’t surprising. The pressure to crack open
markets in developing countries, particularly those
at the middle and bottom of the pyramid, is driving
a surge in business-model innovation. The economic
slowdown in the developed world is forcing compa-
nies to modify their business models or create new
ones. In addition, the rise of new technology-based
and low-cost rivals is threatening incumbents, re-
shaping industries, and redistributing profi ts. Indeed,
S
STRATEGY HAS been the primen the p

competitiveness over the pacompetitiveness over the
in the future, the quest for sin the future, the quest fo
may well begin with the businmay well begin with the b
convergence of informationconvergence of inform
technologies in the 1990s restechnologies in the 1990s
fascination with business mofascination with business mo
regulation, technological chanregulation, technological chan
sustainability have rekindled sustainability have rekindled
today. Since 2006, the IBM Itoday. Since 2006, the IBM I
Value’s biannual Global CEO Siannual Global CEO S
senior executives across induves across ind
ing innovative business modbusiness m
A 2009 follow-up study revea-up study
companies are engaging in bucompanies are eng
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HBR.ORG
January–February 2011 Harvard Business Review 101
3:52:32 PM
1122111 33222
the ways by which companies create and capture

value through their business models is undergoing
a radical transformation worldwide.
Yet most enterprises haven’t fully come to grips
with how to compete through business models. Our
studies over the past seven years show that much of
the problem lies in companies’ unwavering focus on
creating innovative models and evaluating their ef-
fi cacy in isolation—just as engineers test new tech-
nologies or products. However, the success or fail-
ure of a company’s business model depends largely
on how it interacts with models of other players in
the industry. (Almost any business model will per-
form brilliantly if a company is lucky enough to be
the only one in a market.) Because companies build
them without thinking about the competition, they
routinely deploy doomed business models.
Our research also shows that when enterprises
compete using business models that diff er from one
another, the outcomes are diffi cult to predict. One
business model may appear superior to others when
analyzed in isolation but create less value than the
others when interactions are considered. Or rivals
may end up becoming partners in value creation.
Appraising models in a stand-alone fashion leads
to faulty assessments of their strengths and weak-
nesses and bad decision making. This is a big reason
why so many new business models fail.
Three Characteristics of a Good Business Model
How can you tell if a business model will be eff ective? A good
one will meet three criteria.
Is it aligned with
company goals?

The choices made while designing a busi-
ness model should deliver consequences
that enable an organization to achieve its
goals. This may seem obvious until you
consider a counterexample. In the 1970s,
Xerox set up Xerox PARC, which spawned
technological innovations such as laser
printing, Ethernet, the graphical user
interface, and very large scale integration
for semiconductors. However, Xerox PARC
was notoriously unable to spawn new
businesses or capture value from its inno-
vations for the parent due to a distressing
lack of alignment with Xerox’s goals.
Is it self-reinforcing?
The choices that executives make while
creating a business model should comple-
ment one another; there must be internal
consistency. If, ceteris paribus, a low-cost
airline were to decide to provide a level
of comfort comparable to that off ered by
a full-fare carrier such as British Airways,
the change would require reducing the
number of seats on each plane and off er-
ing food and coff ee. These choices would
undermine the airline’s low-cost structure
and wreck its profi ts. When there’s a lack
of reinforcement, it’s possible to refi ne
the business model by abandoning some
choices and making new ones.
Is it robust?
A good business model should be able to
sustain its eff ectiveness over time by fend-
ing off four threats, identifi ed by Pankaj

Ghemawat. They are imitation (can com-
petitors replicate your business model?);
holdup (can customers, suppliers, or
other players capture the value you create
by fl exing their bargaining power?); slack
(organizational complacency); and sub-
stitution (can new products decrease the
value customers perceive in your products
or services?). Although the period of eff ec-
tiveness may be shorter nowadays than
it once was, robustness is still a critical
parameter.
Moreover, the propensity to ignore the dynamic
elements of business models results in many compa-
nies failing to use them to their full potential. Few ex-
ecutives realize that they can design business mod-
els to generate winner-take-all eff ects that resemble
the network externalities that high-tech companies
such as Microsoft, eBay, and Facebook have created.
Whereas network eff ects are an exogenous feature
of technologies, winner-take-all eff ects can be trig-
gered by companies if they make the right choices
in developing their business models. Good business
models create virtuous cycles that, over time, result
in competitive advantage. Smart companies know
how to strengthen their virtuous cycles, weaken
those of rivals, and even use their virtuous cycles to
turn competitors’ strengths into weaknesses.
“Isn’t that strategy?” we’re often asked. It isn’t—
and unless managers learn to understand the dis-
tinct realms of business models, strategy, and tactics,
while taking into account how they interact, they
will never fi nd the most eff ective ways to compete.

What Is a Business Model, Really?
Everyone agrees that executives must know how
business models work if their organizations are to
thrive, yet there continues to be little agreement on
an operating definition. Management writer Joan
Magretta defi ned a business model as “the story that
3:52:40 PM
explains how an enterprise works,” harking back
to Peter Drucker, who described it as the answer to
the questions: Who is your customer, what does the
customer value, and how do you deliver value at an
appropriate cost?
Other experts defi ne a business model by speci-
fying the main characteristics of a good one. For ex-
ample, Harvard Business School’s Clay Christensen
suggests that a business model should consist of
four elements: a customer value proposition, a profi t
formula, key resources, and key processes. Such de-
scriptions undoubtedly help executives evaluate
business models, but they impose preconceptions
about what they should look like and may constrain
the development of radically diff erent ones.
Our studies suggest that one component of a busi-
ness model must be the choices that executives make

about how the organization should operate—choices
such as compensation practices, procurement con-
tracts, location of facilities, extent of vertical inte-
gration, sales and marketing initiatives, and so on.
Managerial choices, of course, have consequences.
For instance, pricing (a choice) aff ects sales volume,
which, in turn, shapes the company’s scale econo-
mies and bargaining power (both consequences).
These consequences infl uence the company’s logic
of value creation and value capture, so they too must
have a place in the defi nition. In its simplest concep-
tualization, therefore, a business model consists of a
set of managerial choices and the consequences of
those choices.
Companies make three types of choices when cre-
ating business models. Policy choices determine the
actions an organization takes across all its operations
(such as using nonunion workers, locating plants in
rural areas, or encouraging employees to fl y coach
class). Asset choices pertain to the tangible resources
a company deploys (manufacturing facilities or sat-
ellite communication systems, for instance). And
governance choices refer to how a company arranges
decision-making rights over the other two (should
we own or lease machinery?). Seemingly innocuous
diff erences in the governance of policies and assets
infl uence their eff ectiveness a great deal.
Consequences can be either flexible or rigid. A
fl exible consequence is one that responds quickly
when the underlying choice changes. For example,
choosing to increase prices will immediately result
in lower volumes. By contrast, a company’s culture
of frugality—built over time through policies that

oblige employees to fl y economy class, share hotel
rooms, and work out of Spartan offi ces—is unlikely
to disappear immediately even when those choices
change, making it a rigid consequence. These dis-
tinctions are important because they aff ect competi-
tiveness. Unlike fl exible consequences, rigid ones
are diffi cult to imitate because companies need time
to build them.
Take, for instance, Ryanair, which switched in
the early 1990s from a traditional business model to
a low-cost one. The Irish airline eliminated all frills,
cut costs, and slashed prices to unheard-of levels.
The choices the company made included offering
low fares, fl ying out of only secondary airports, ca-
tering to only one class of passenger, charging for all
additional services, serving no meals, making only
short-haul fl ights, and utilizing a standardized fl eet
of Boeing 737s. It also chose to use a nonunionized
workforce, offer high-powered incentives to em-
ployees, operate out of a lean headquarters, and so
on. The consequences of those choices were high
volumes, low variable and fi xed costs, a reputation
for reasonable fares, and an aggressive management
team, to name a few. (See “Ryanair’s Business Model
Then and Now.”) The result is a business model that
enables Ryanair to off er a decent level of service at a
low cost without radically lowering customers’ will-
ingness to pay for its tickets.
Idea in Brief
There has never been as much
interest in business models
as there is today; seven out
of 10 companies are trying to
create innovative business

models, and 98% are modify-
ing existing ones, according
to a recent survey.
However, most companies
still create and evaluate
business models in isola-
tion, without considering the
implications of how they will
interact with rivals’ business
models. This narrow view
dooms many to failure.
Moreover, companies
often don’t realize that busi-
ness models can be designed
so that they generate virtu-
ous cycles—similar to the
powerful eff ects high-tech
fi rms such as Facebook, eBay,
and Microsoft enjoy. These
cycles, when aligned with
company goals, reinforce
competitive advantage.
By making the right
choices, companies can
strengthen their business
models’ virtuous cycles,
weaken those of rivals, and
even use the cycles to turn
competitors into comple-
mentary players.

This is neither strategy nor
tactics; it’s using business
models to gain competitive
advantage. Indeed, com-
panies fare poorly partly
because they don’t recognize
the diff erences between
strategy, tactics, and busi-
ness models.
Business
Model
Choices
POLICIES
FLEXIBLE
Consequences
ASSETS
GOVERNANCE
RIGID
A business model comprises
choices and consequences.
HOW TO DESIGN A WINNING BUSINESS MODEL
HBR.ORG

3:52:46 PM
How Business Models Generate
Virtuous Cycles
Not all business models work equally well, of course.
Good ones share certain characteristics: They align
with the company’s goals, are self-reinforcing, and
are robust. (See the sidebar “Three Characteristics
of a Good Business Model.”) Above all, successful
business models generate virtuous cycles, or feed-
back loops, that are self-reinforcing. This is the most
powerful and neglected aspect of business models.
Our studies show that the competitive advan-
tage of high-tech companies such as Apple, Micro-
soft, and Intel stems largely from their accumulated
assets—an installed base of iPods, Xboxes, or PCs,
for instance. The leaders gathered those assets not
by buying them but by making smart choices about
pricing, royalties, product range, and so on. In other
words, they’re consequences of business model
choices. Any enterprise can make choices that allow
it to build assets or resources—be they project man-
agement skills, production experience, reputation,
asset utilization, trust, or bargaining power—that
make a diff erence in its sector.
The consequences enable further choices, and so
on. This process generates virtuous cycles that con-
tinuously strengthen the business model, creating
a dynamic that’s similar to that of network eff ects.
As the cycles spin, stocks of the company’s key as-
sets (or resources) grow, enhancing the enterprise’s

competitive advantage. Smart companies design
business models to trigger virtuous cycles that, over
time, expand both value creation and capture.
For example, Ryanair’s business model creates
several virtuous cycles that maximize its profits
through increasingly low costs and prices. (See
the exhibit “Ryanair’s Key Virtuous Cycles.”) All of
the cycles result in reduced costs, which allow for
lower prices that grow sales and ultimately lead to
increased profi ts. Its competitive advantage keeps
growing as long as the virtuous cycles generated by
its business model spin. Just as a fast-moving body
is hard to stop because of kinetic energy, it’s tough to
halt well-functioning virtuous cycles.
However, they don’t go on forever. They usually
reach a limit and trigger counterbalancing cycles, or
they slow down because of their interactions with
other business models. In fact, when interrupted,
the synergies work in the opposite direction and
erode competitive advantage. For example, one of
Ryanair’s cycles could become vicious if its employ-
ees unionized and demanded higher wages, and the
airline could no longer off er the lowest fares. It would
then lose volume, and aircraft utilization would fall.
Since Ryanair’s investment in its fleet assumes a
very high rate of utilization, this change would have
a magnifi ed eff ect on profi tability.
It’s easy to see that virtuous cycles can be cre-
ated by a low-cost, no-frills player, but a diff erentia-
tor may also create virtuous cycles. Take the case of
Irizar, a Spanish manufacturer of bodies for luxury
motor coaches, which posted large losses after a

series of ill-conceived moves in the 1980s. Irizar’s
leadership changed twice in 1990 and morale hit an
all-time low, prompting the new head of the compa-
ny’s steering team, Koldo Saratxaga, to make major
changes. He transformed the organization’s busi-
ness model by making choices that yielded three
rigid consequences: employees’ tremendous sense
of ownership, feelings of accomplishment, and trust.
The choices included eliminating hierarchy, decen-
tralizing decision making, focusing on teams to get
work done, and having workers own the assets. (See
the exhibit “Irizar’s Novel Business Model.”)
Ryanair’s Business Model Then
This depiction of Ryanair’s business model in the 1980s
highlights the airline’s
major choices at the time: off ering excellent service and
operating with a stan-
dardized fl eet. The airline was forced to redesign its business
model in the face
of stiff competition.
FEW TICKET
RESTRICTIONS
FIRST-RATE
CUSTOMER
SERVICE
44-SEAT
TURBOPROPS
LEAN STAFF
Large
volume

Low fares
Low
cost
Economies of scale
REPUTATION FOR
FAIR FARES
RYANAIR’S KEY VIRTUOUS CYCLES
CYCLE 1 Low fares >> High volumes >> Greater bargaining
power with suppliers >> Lower fi xed costs >> Even lower
fares
CYCLE 2 Low fares >> High volumes >> High aircraft
utilization >> Low fi xed cost per passenger >> Even lower
fares
CYCLE 3 Low fares >> Expectations of low-quality service
>> No meals off ered >> Low variable costs >> Even lower
fares
3:52:53 PM
Irizar’s main objective, as a cooperative, is to in-
crease the number of well-paying jobs in the Basque
Country, so the company developed a business
model that generates a great deal of customer value.

Its key virtuous cycle connects customers’ willing-
ness to pay with relatively low cost, generating high
profi ts that feed innovation, service, and high quality.
In fact, quality is the cornerstone of Irizar’s culture.
Focusing on customer loyalty and an empowered
workforce, the company enjoyed a 23.9% compound
annual growth rate over the 14 years that Saratxaga
was CEO. Producing 4,000 coaches in 2010 and gen-
erating revenues of about €400 million, Irizar is an
example of a radically diff erent business model that
generates virtuous cycles.
Competing with Business Models
It’s easy to infuse virtuousness in cycles when there
are no competitors, but few business models operate
in vacuums—at least, not for long. To compete with
rivals that have similar business models, companies
must quickly build rigid consequences so that they
can create and capture more value than rivals do. It’s
a diff erent story when enterprises compete against
dissimilar business models; the results are often un-
predictable, and it’s tough to know which business
model will perform well.
Take, for instance, the battle between two of
Finland’s dominant retailers: S Group, a consumers’
cooperative, and Kesko, which uses entrepreneur-
retailers to own and operate its stores. We’ve tracked
the firms for over a decade, and Kesko’s business
model appears to be superior: The incentives it off ers
franchisees should result in rapid growth and high
profi ts. However, it turns out that the S Group’s busi-
ness model hurts Kesko more than Kesko’s aff ects
the S Group. Since customers own the S Group, the
retailer often reduces prices and increases customer

bonuses, which allows it to gain market share from
Kesko. That forces Kesko to lower its prices and its
profi ts fall, demotivating its entrepreneur- retailers.
As a result, Kesko underperforms the S Group. Over
REPUTATION
FOR FAIR
FARES LOW FIXED
COST
TOUGH
NEGOTIATORS
ANCILLARY
BUSINESS
(BUS SERVICE)
RIGID
CONSEQUENCE
STANDARDIZED
FLEET OF 737s
SHORT-HAUL
FLIGHTS
SECONDARY
AIRPORTS
HIGH-POWERED
INCENTIVES
CHOICE
NON-
UNIONIZED

WORKFORCE
SPARTAN
HEADQUARTERS
REINVEST
NOTHING
IS FREE
NO MEALS
LOW COMMISSIONS
TO TRAVEL AGENCIES
ALL PASSENGERS
TREATED EQUALLY
LOW FARES
Bargaining power
with suppliers
Flexible
consequence
High volume
Attracts
combative team
Low-quality
service
expected
Additional

revenue
Low
variable
cost
High profi t
YOUNG AND
LEISURE
TRAVELERS
WORD-OF-
MOUTH
ADVERTISING
Ryanair’s Business Model Now
Ryanair’s current business model rests on the key choices of off
ering customers low fares and
providing nothing free. The rigid consequences include a
reputation for fair fares and low fi xed
costs. Ryanair’s choices are aligned with its goals, generate
cycles that reinforce the business
model, and are robust given that it has been operating as a low-
cost airline for 20 years.
High aircraft
utilization
HOW TO DESIGN A WINNING BUSINESS MODEL
HBR.ORG

3:52:59 PM
time, the S Group’s opaque corporate governance
system allows slack to creep into the system, and
it is forced to hike prices. This allows Kesko to also
increase prices and improve profi tability, drive its
entrepreneur-retailers, and win back more custom-
ers through its superior shopping experience. That
sparks another cycle of rivalry.
Companies can compete through business mod-
els in three ways: They can strengthen their own vir-
tuous cycles, block or destroy the cycles of rivals, or
build complementarities with rivals’ cycles, which
results in substitutes mutating into complements.
Strengthen your virtuous cycle. Companies
can modify their business models to generate new
virtuous cycles that enable them to compete more
eff ectively with rivals. These cycles often have con-
sequences that strengthen cycles elsewhere in the
business model. Until recently, Boeing and Airbus
competed using essentially the same virtuous cycles.
Airbus matched Boeing’s off erings in every segment,
the exception being the very large commercial trans-
port segment where Boeing had launched the 747 in
1969. Given the lumpiness of demand for aircraft,
their big-ticket nature, and cyclicality, price compe-
tition has been intense.
Historically, Boeing held the upper hand because
its 747 enjoyed a monopoly, and it could reinvest
those profi ts to strengthen its position in other seg-
ments. Analysts estimate that the 747 contributed 70

cents to every dollar of Boeing’s profi ts by the early
1990s. Since R&D investment is the most important
driver of customers’ willingness to pay, Airbus was
at a disadvantage. It stayed afl oat by obtaining low-
interest loans from European governments. Without
the subsidies, Airbus’s cycle would have become
vicious.
With the subsidies likely to dry up, Airbus modi-
fi ed its business model by developing a very large
commercial transport, the 380. To dissuade Airbus,
Boeing announced a stretch version of the 747. How-
ever, that aircraft would cut into the 747’s profi ts, so
it seems unlikely that Boeing will ever launch it. Not
only does the 380 help maintain the virtuousness of
Airbus’s cycle in small and midsize planes, but also
it helps decelerate the virtuousness of Boeing’s cy-
cle. The increase in rivalry suggests that the 747 will
become less of a money-spinner for Boeing. That’s
why it is trying to strengthen its position in midsize
aircraft, where competition is likely to become even
tougher when sales of the 380 take off , by develop-
ing the 787.
Weaken competitors’ cycles. Some compa-
nies get ahead by using the rigid consequences of
their choices to weaken new entrants’ virtuous cy-
cles. Whether a new technology disrupts an industry
or not depends not only on the intrinsic benefi ts of
that technology but also on interactions with other
players. Consider, for instance, the battle between
Microsoft and Linux, which feeds its virtuous cycle
by being free of charge and allowing users to contrib-
ute code improvements. Unlike Airbus, Microsoft
has focused on weakening its competitor’s virtu-
ous cycle. It uses its relationship with OEMs to have

Windows preinstalled on PCs and laptops so that it
can prevent Linux from growing its customer base. It
discourages people from taking advantage of Linux’s
free operating system and applications by spreading
fear, uncertainty, and doubt about the products.
In the future, Microsoft could raise Windows’
value by learning more from users and off ering spe-
cial prices to increase sales in the education sector, or
decrease Linux’s value by undercutting purchases by
strategic buyers and preventing Windows applica-
tions from running on Linux. Linux’s value creation
potential may theoretically be greater than that of
Windows, but its installed base will never eclipse
that of Microsoft as long as the software giant suc-
ceeds in disrupting its key virtuous cycles.
Turn competitors into complements. Rivals
with different business models can also become
partners in value creation. In 1999, Betfair, an online
betting exchange, took on British bookmakers such
as Ladbrokes and William Hill by enabling people to
anonymously place bets against one another. Un-
like traditional bookmakers who only offer odds,
Betfair is a two-sided internet-based platform that
allows customers to both place bets and off er odds
to others. One-sided and two-sided businesses have
diff erent virtuous cycles: While bookmakers create
value by managing risk and capture it through the
odds they off er, betting exchanges themselves bear
no risk. They create value by matching the two sides
of the market and capture it by taking a cut of the net
winnings.
Over the past decade, Ladbrokes’ and William
Hill’s gross winnings have declined, so Betfair has

hurt them, but not as much as expected. Because
Betfair has improved odds in general, gamblers lose
less money. They then place more wagers, and when
bookies pay out, bettors gamble again, feeding a vir-
tuous cycle. This has expanded the British gambling
market by a larger proportion than just the improve-
When Irizar—a Spanish
cooperative that manu-
factures luxury motor
coach bodies—created
a radically diff erent busi-
ness model, it made sev-
eral innovative choices.
SHARED
OWNERSHIP
> Workers own assets
and contribute fi nan-
cially to join Irizar
> Teams set their own
goals and choose
leaders
> No bosses, only
coordinators
> Flat hierarchy, with only
three levels
> No overtime pay
TRUST
> Decentralized decision

making
> Shared information and
transparency about
performance
> No walls inside plants
or offi ces; no assigned
parking spaces
> Tenure after three years
of probation; no evalua-
tion or fi rings thereafter
> No clocking in and out
QUALITY
> Only one product for all
markets
> Most repetitive tasks
outsourced
ACCOMPLISHMENT
> Relatively high product
prices
> Pay scale ratio of just
3:1
> Some profi t (or loss)
sharing every year

These choices have led to
innovation, high quality,
and excellent service,
generating high sales vol-
ume as well as customer
loyalty.
Irizar’s Novel
Business Model
3:53:05 PM
ment of odds might suggest. The better odds Betfair
off ers also help traditional bookmakers gauge mar-
ket sentiment more accurately and hedge their ex-
posures at a lower cost. When a new business model
creates complementarities between competitors, it is
less likely that incumbents will respond aggressively.
The initial …
Journal of Management
Vol. 43 No. 1, January 2017 200 –227
DOI: 10.1177/0149206316675927
© The Author(s) 2016

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200
Fifteen Years of Research on Business
Model Innovation: How Far Have We Come,
and Where Should We Go?
Nicolai J. Foss
Bocconi University
Tina Saebi
Norwegian School of Economics
Over the last 15 years, business model innovation (BMI) has
gained an increasing amount of
attention in management research and among practitioners. The
emerging BMI literature
addresses an important phenomenon but lacks theoretical
underpinning, and empirical inquiry
is not cumulative. Thus, a concerted research effort seems
warranted. Accordingly, we take
stock of the extant literature on BMI. We identify and analyze
150 peer-reviewed scholarly
articles on BMI published between 2000 and 2015. We provide
the first comprehensive system-
atic review of the BMI literature, include a critical assessment
of these research efforts, and
offer suggestions for future research. We argue that the
literature faces problems with respect to
construct clarity and has gaps with respect to the identification
of antecedent conditions, con-
tingencies, and outcomes. We identify important avenues for
future research and show how the

complexity theory, innovation, and other streams of literature
can help overcome many of the
gaps in the BMI literature.
Keywords: business model; business model innovation;
literature review; design and boundaries
The concepts of business models (BMs) and, more recently, BM
innovation (BMI) have
become influential in macromanagement research in recent
years (Spieth, Schneckenberg, &
Supplemental material for this article is available online.
Corresponding author: Nicolai J. Foss, Bocconi University, Via
G. Roentgen, 1, 20136 Milano, Italy.
E-mail: [email protected]
XXX10.1177/0149206316675927Journal of ManagementFoss,
Saebi / Fifteen Years of Research on Business Model Innovation
review-article2016
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1177%2F01492063
16675927&domain=pdf&date_stamp=2016-11-23
Foss, Saebi / Fifteen Years of Research on Business Model
Innovation 201
Ricart, 2014; Zott, Amit, & Massa, 2011). Recent reviews of the
BM literature have high-
lighted the usefulness of the BM construct in research on e-
commerce, strategy, and technol-
ogy management (cf. Zott et al., 2011); its use in different
theories (cf. George & Bock,

2011); and the evolution of the BM term itself (cf. Wirtz,
Pistoia, Ullrich, & Gottel, 2016).
Such reviews also point to definitional convergence so that
many contributions to the litera-
ture now proffer a notion of BM as the “design or architecture
of the value creation, delivery,
and capture mechanisms” of a firm (Teece, 2010: 172).
In contrast, the innovation of BMs—ostensibly, a new source of
innovation that “comple-
ments the traditional subjects of process, product, and
organizational innovation” (Zott et al.,
2011: 1032)—is less well understood, perhaps reflecting that
the BMI literature is more
recent than the BM literature. However, it is also rapidly
growing, suggesting that BMI is an
important phenomenon that needs to be conceptualized and
theorized on its own. Thus, while
BM and BMI are no doubt related, research on BMI introduces
the additional dimension of
innovation and hereby raises a number of crucial theoretical and
empirical questions: What
are the drivers, facilitators, and hindrances of the innovation of
a BM? Under which circum-
stances can BMI give rise to sustained competitive advantage?
Does BMI exclusively origi-
nate in the upper echelons, or may it also originate in lower
levels of the organization?
However, such fundamental questions are not currently being
systematically posed,
addressed, and answered, reflecting the emergent nature of BMI
research. Furthermore,
reviews on BMI are limited (e.g., Schneider & Spieth, 2013;
Spieth et al., 2014) and do not
provide systematic discussions of the phenomenon or the
challenges that it represents for

research. Therefore, a more comprehensive review and
assessment of the BMI literature are
warranted.
Accordingly, the purpose of this article is to take stock of the
literature on BMI, evaluate it,
and outline avenues for future research. We first conduct a
comprehensive literature review of
150 scholarly publications on BMI, finding that the BMI
literature is mainly focused on either
examining the facilitators of BMI as an organizational process
or identifying new and “innova-
tive” types of ventures. Thus, the BMI construct is to a large
extent used as mainly a classifica-
tory device, and a part of the literature does not seem to have
aspirations of developing a
distinct theory of BMI.1 In contrast, systematic research on the
antecedents, moderators, and
implications of BMI remains limited, leading us to question
whether a true theory of BMI
exists. To address this question, we assess the extent to which
the BMI field is characterized by,
for example, clear-constructed, well-delineated boundary
conditions, identification of explana-
tory mechanisms, and other traditional hallmarks of good
theory. We find that the literature is
lacking (to varying degrees) in all dimensions. We provide a
simple organizing framework that
outlines the causal web of potential antecedent, moderating, and
mediating influences on BMI,
as well as the consequences of such innovation. As part of the
framework, we define BMI as
“designed, novel, nontrivial changes to the key elements of a
firm’s business model and/or the
architecture linking these elements.” Finally, we argue that BMI
research may be advanced by

drawing on theorizing in the innovation, entrepreneurship,
complexity, and other streams of
literature, all of which can help meet the gaps in the BMI
literature.
BMI represents a novel and more holistic form of organizational
innovation that warrants
theory building, operationalization, and testing. Early CEO-
level surveys indicated that BMI
is a key source of sustained value creation (IBM Global
Business Services, 2006), even
trumping new products and services as a source of future
competitive advantage (Economist
202 Journal of Management / January 2017
Intelligence Unit, 2005). In fact, innovative BMs are found to
positively influence the perfor-
mance of entrepreneurial firms, “even under varying
environmental regimes” (Zott & Amit,
2007: 181). Similarly, established firms that innovate their BMs
experience positive perfor-
mance effects (Cucculelli & Bettinelli, 2015). While this adds
legitimacy to the fast-growing
academic and corporate interest in the field, it highlights the
need for additional conceptual
and empirical research on BMI aimed at deriving a better
understanding of the phenomenon.
Consequently, undertaking some ground clearing in this
emerging field is vital to its develop-
ment, and such ground clearing is what we seek to offer in this
article.
Research on BMI: Emergence of the Field and Prior Reviews

From BMs to BMI
The notion of BMs is several decades old (e.g., Bellman, Clark,
Malcolm, Craft, &
Ricciardi, 1957). The original definitions associated it with an
operative activity for system
modeling in the context of information technology (Wirtz et al.,
2016). It was only in the mid-
1990s that entrepreneurship and strategy scholars applied the
construct as a holistic descrip-
tion of a firm’s key business processes and how they are linked
(Zott et al., 2011). Although
definitions differ across studies (see Table 1 in the online
supplemental material), most current
definitions are close to or consistent with Teece’s (2010: 172)
definition of a BM as the “design
or architecture of the value creation, delivery, and capture
mechanisms” of a firm. Furthermore,
as Saebi, Lien, and Foss (2016) show, despite using different
terminology, the literature con-
verges on the components that constitute a BM—namely, “the
firm’s value proposition and
market segments, the structure of the value chain required for
realizing the value proposition,
the mechanisms of value capture that the firm deploys, and how
these elements are linked
together in an architecture.”
The evolution of the BM literature has been broadly categorized
into three streams of
research (cf. Lambert & Davidson, 2013; Zott et al., 2011).
First, the BM is used as a basis
for enterprise classification: By the early 21st century, as new
e-business ventures emerged,
the BM construct was increasingly employed to understand and

classify value drivers of
(e-commerce) BMs (see Amit & Zott, 2001; Magretta, 2002).
Second, the BM is seen as an
antecedent of heterogeneity in firm performance; specifically,
BMs are argued to be an
important factor contributing to firm performance. As some
types of BMs are found to out-
perform others (cf. Weill, Malone, D’Urso, Herman, &
Woerner, 2005; Zott & Amit, 2007,
2010), successful BMs are seen as examples to be imitated (cf.
Chesbrough, 2010; Teece,
2010) or replicated (Doz & Kosonen, 2010; Winter & Szulanski,
2001). Third, the BM is
seen as a potential unit of innovation (Zott et al., 2011). The
idea that managers can purpose-
fully innovate their BM was first explicitly discussed in 2003
by Mitchell and Coles. Since
then, an increasing number of studies have focused on the
innovation dimension of the BM
and examine BMI from a variety of angles (which we discuss
here). Thus, while BMI is an
extension of BM, it incorporates a number of important research
questions that reach beyond
the boundaries of traditional BM literature.
Notably, in comparison with the huge volume of research on
BMs, the number of pub-
lished papers that address BMI per se is still comparatively low
at 349 (peer reviewed and
otherwise; see Figure 1).
Innovation 203

Figure 1 shows that over the last two decades, the BM literature
has expanded massively—
even faster than the related dynamic capabilities literature,
which emerged at about the same
time. The Scopus database lists 7,391 publications on the topic
of “business model” for the
period 1980–2015 (see Figure 1),2 with the number of special
issues and edited volumes dedi-
cated to exploring aspects of BMs increasing over time.3 While
the BMI literature has
expanded, it is a much smaller literature, considerably smaller
than the somewhat-related lit-
erature on open innovation. Two closely related factors may
account for the relatively low
number of publications on BMI—namely, that BMI research is
relatively recent and noncu-
mulative. The BMI literature is a recent outgrowth of the BM
literature. Although the notion
that BMs can be innovated dates back to at least Mitchell and
Coles (2003), it is only relatively
recently that this insight has become more than an afterthought
(Zott et al., 2011). Second,
despite much practitioner and scholarly interest in BMI, the
literature exhibits many of the
characteristics of an emerging research stream—notably, a lack
of construct clarity (Suddaby,
2010). As Casadesus-Masanell and Zhu (2013: 480) observe,
BMI is “a slippery construct to
study.” In turn, a lack of construct clarity makes
operationalization and measurement difficult.
Additionally, the BMI literature does not possess clearly
articulated research models that lay
out the basic causal web-connecting antecedent, moderating,
and mediating variables with the
key construct and consequences. All these characteristics hinder
cumulativeness of research

efforts. In fact, similar observations have been made concerning
the BM literature (Foss &
Saebi, 2015; Zott et al., 2011), so it is perhaps not surprising
that characteristics of BM research
carries over to BMI research. However, cumulativeness in
science is usually taken to be
dependent on constructs, models, and heuristics being clear and
agreed on (Singer, 1975).
Figure 1
Use in the Scholarly Literature of Some Key, Related
Macroconstructs
Source: Scopus, 1972–2015. “Business model” (BM), 7,391
hits; “business model innovation” (BMI), 349 hits;
“open innovation” (OI), 1,700 hits; “dynamic capability” (DC),
1,562 hits (peer reviewed and otherwise). Scopus
searched for the terms “business model,” “business model
innovation,” “open innovation,” and “dynamic capability”
in the search field “abstract, title, keyword” within the field of
“social sciences and humanities,” thereby excluding
physical, health, and life sciences (January 2016).
Prior Reviews of BMI Research
There are now a number of systematic literature reviews of the
BM literature (e.g.,
George & Bock, 2011; Lambert & Davidson, 2013; Wirtz et al.,
2016; Zott et al., 2011;
see Table 1).
Table 1

Articles Reviewing Business Models and Business Model
Innovation
Focus: Authors Findings Data source and sample
Business models (BMs)
George and Bock
(2011)
Use of business models
• Organizational design
• Resource-based view
• Narrative and sense making
• Nature of innovation
• Transactive structure
• Opportunity facilitator
EBSCO Business Source Premier and
ISI Web of Science, n = 108 articles
Zott et al. (2011) Three themes of BM literature
• E-business
• Business models and strategy
• Innovation and technology
management
EBSCO Business Source Premier,
n = 103 articles

Lambert and
Davidson (2013)
Three themes of BM literature
• Business model as basis for
enterprise classification
• Business models and enterprise
performance
• Business model innovation
ProQuest database, n = 69 articles
Wirtz et al. (2016) Four research foci
• Innovation
• Change and evolution
• Performance and controlling
• Design
EBSCO Business Source Complete,
n = 681 articles
Business model innovation (BMI)
Schneider and
Spieth (2013)
Three streams of BMI research:

• Prerequisites of conducting BMI
• Process and elements of BMI
• Effects achieved through BMI
ISI Web of Knowledge and SSRN,
n = 35 articles
Spieth et al. (2014) Three motivations for engaging in BMI
research:
• Explaining the business
• Running the business
• Developing the business
Not provided
Current study Theory assessment and research agenda:
• Construct clarity
• Congruence
• Contingency hypotheses
• Boundary conditions
EBSCO Business Source Complete
and Science Direct, n = 150 articles
Innovation 205
However, only one article specifically reviews the BMI

literature—namely, that by
Schneider and Spieth (2013: 134), which reviews 35 papers on
BMI. The authors identify
the “prerequisites,” “process,” and “effects” of BMI as the three
leading themes in the BMI
literature, and they call for further research on “the process and
elements of business model
innovation as well as its enablers and effects in anticipation and
response to increasing
environmental volatility.” A few other papers are (at best)
borderline review papers. For
example, Spieth et al. (2014) do not undertake a literature
survey but discuss three ways in
which the BMI construct may be relevant for academics and
practitioners. Zott and col-
leagues’ (2011) review of the BM literature analyzes 103
articles and identifies three
streams of BM research, one of which focuses on BMs as a
source of innovation or an
instance of organizational innovation. However, although Zott
et al. highlight the BM as a
new subject in relation to innovation, they do not review the
BMI literature per se. Similarly,
Lambert and Davidson (2013) discuss a sample of 69 articles on
BMs and identify a num-
ber of research streams, one of which addresses BMI. In sum,
while the emerging BMI
field is expanding quickly, scholars in it do not yet have access
to a review that is compre-
hensive, analytical, and forward looking.
Method
We searched the EBSCO Business Source Premier database for
academic articles contain-
ing the term “business model innovation” in the title, abstract,

or keywords (Boolean phrase,
English, limited to peer-reviewed work in academic journals).
We used quotation marks to
exclude irrelevant mentions based on grammatical coincidence.
Furthermore, it became clear
that other concepts are often used to describe what is effectively
BMI, such as BM “reinven-
tion” (Voelpel, Leibold, & Tekie, 2004), “renewal” (Doz &
Kosonen, 2010), “dynamics”
(e.g., Achtenhagen, Melin, & Naldi, 2013; Cavalcante, Kesting,
& Ulhøi, 2011), “transfor-
mation” (e.g., Aspara, Lamberg, Laukia, & Tikkanen, 2013),
and “evolution” (e.g., Bohnsack,
Pinkse, & Kolk, 2014; Demil & Lecocq, 2010). Additionally,
terms such as BM “innovation”
or “dynamics” are often used interchangeably to refer to a
similar phenomenon. Therefore,
we searched the EBSCO database for additional articles
including these search terms or a
combination thereof (in titles, abstracts, or keywords). This led
to the following breakdown:
“business model innovation” (234 hits), “innov* business
model” (36 hits), “business model
transformation” (10 hits), “business model renewal” (3 hits),
“business model reinvention”
(4 hits), “business model evolution” (12 hits), and “business
model dynamics” (5 hits).
Omitting repetitions, these search criteria yielded 276 unique
citations.4 The first publication
dated from the year 2000 (i.e., Malhotra, 2000). A second
search for “business model innova-
tion” (and related terms) was conducted via the Science Direct
search engine through the
“title, abstract, keywords” feature. This second search generated
61 citations. The combina-
tion of the search results yielded a total of 313 unique citations

in the review set (24 citations
occurred in both search outputs).
To identify relevant articles, we required that the topic of BMI
be dealt with in an essential
way (see George & Bock, 2011; Lambert & Davidson, 2013;
Zott et al., 2011). Specifically, we
eliminated articles that mentioned the term “business model
innovation” (often in the abstract
or keywords) but failed to explain or use the concept (n = 135).
We also excluded book reviews,
interviews, case studies, and summaries of articles published
elsewhere (n = 46). This left us
with 132 relevant articles. In addition, we included work found
in leading practitioner-oriented
journals, such as MIT Sloan Management Review, California
Management Review, and
Harvard Business Review. Articles in these and other
publications that we deemed relevant
(based on the above criteria) were included in our review.5,6
The final 150 publications were reviewed in terms of their
conceptual, theoretical, and
empirical development and contributions. Furthermore, to
identify the main streams of
research within the BMI literature, all papers in our sample
were coded according to the main
research focus (e.g., BMI as a process, BMI as an outcome) and
research method (qualitative,
quantitative). We ensured the integrity of coding by having the
authors assess each paper

individually before comparing results and reaching a consensus.
Overall, we were able to
distinguish four streams of research.
BMI Field: Four Streams of Research
On the basis of our literature review and coding procedure, we
distinguish four partly
overlapping streams of BMI research (see Table 2). These four
streams represent important
strides forward; however, they also have limitations that are
scrutinized as follows.
Table 2
Streams of Business Model Innovation Research
Research Focus Method Examples
1. Conceptualization
and classification
of BMI
Conceptual,
case examples
Amit and Zott (2012), Johnson et al. (2008), Koen et al. (2011),
Markides (2006), Santos et al. (2009), Sorescu et al. (2011)
Survey data Giesen et al. (2007)
2. BMI as a process
(e.g., importance
of capabilities,
leadership, learning
mechanisms)

Conceptual,
case examples
Berglund and Sandström (2013), Cavalcante (2014), de Reuver
et al. (2009), Deshler and Smith (2011), Evans and Johnson
(2013), Girotra and Netessine (2013, 2014)
Single/multiple
case studies
Achtenhagen et al. (2013), Aspara et al. (2013), Demil and
Lecocq (2010), Deshler and Smith (2011), Dmitriev et al.
(2014), Doz and Kosonen (2010), Dunford et al. (2010), Enkel
and Mezger (2013), Frankenberger et al. (2013), Günzel and
Holm (2013), Khanaga et al. (2014), Moingeon and Lehmann-
Ortega (2010), Mezger (2014), Pynnonen et al. (2012), Sosna
et al. (2010)
Content
analysis
Bohnsack et al. (2014)
Experimental Eppler and Hoffmann (2012), Eppler et al. (2011)
3. BMI as an outcome
(e.g., identifying/
describing
innovative business
models)
Single/multiple
case studies
Abdelkafi et al. (2013), Anderson and Kupp (2008),

Gambardella and McGahan (2010), Sánchez and Ricart
(2010), Yunus et al. (2010), Wirtz et al. (2010), Berman
(2012), Holm et al. (2013), Richter (2013), Visnjic Kastalli
and Van Looy (2013)
4. BMI and
organizational
implications/
performance
Survey data Zott and Amit (2007), Giesen et al. (2007), Aspara
et al. (2010),
Bock et al. (2012), Denicolai et al. (2014), Huang et al. (2012,
2013), Pohle and Chapman (2006), Cucculelli and Bettinelli
(2015), Wei et al. (2014), Velu and Jacob (2014), Kim and
Min (2015)
Innovation 207
Research Stream 1: Conceptualizing BMI
The first stream highlights the phenomenon itself, offering
definitions and conceptualiza-
tions of BMI (e.g., Amit & Zott, 2012; Santos, Spector, & Van
der Heyden, 2009; Teece,
2010). Thus, it focuses on such issues as the minimum
meaningful definition of “business
model innovation” and the dimensions along which companies
can innovate the BM (e.g.,
Amit & Zott, 2012; Santos et al., 2009; Sorescua, Frambach,
Singh, Rangaswamy, & Bridges,
2011). The aim seems to be the development of classificatory
schemes. However, as we

show, definitions abound, differ markedly, and are often
ambiguous.
Research Stream 2: BMI as an Organizational Change Process
Innovation is known to often strongly challenge organizational
processes (e.g., Damanpour,
1996). It is not surprising, therefore, that a stream of research
relates BMI to organizational
change processes. This stream emphasizes the capabilities,
leadership, and learning mecha-
nisms that are needed for successful BMI. Studies within this
stream describe BMI as a
dynamic process by
• highlighting the different stages of the BMI process (e.g., de
Reuver, Bouwman, & Haaker,
2013; Frankenberger, Weiblen, Csik, & Gassmann, 2013;
Girotra & Netessine, 2013, 2014;
Pynnonen, Hallikas, & Ritala, 2012),
• identifying the different organizational capabilities and
processes required to support this
change process (e.g., Achtenhagen et al., 2013; Demil &
Lecocq, 2010; Doz & Kosonen, 2010;
Dunford, Palmer, & Benviste, 2010),
• citing the importance of experimentation and learning (e.g.,
Andries & Debackere, 2013;
Cavalcante, 2014; Eppler, Hoffmann, & Bresciani, 2011; Günzel
& Holm, 2013; Moingeon &
Lehmann-Ortega, 2010; Sosna, Trevinyo-Rodriguez &
Velamuri, 2010), and
• proposing practitioner-oriented tools for managing the process
(e.g., Deshler & Smith, 2011;

Evans & Johnson, 2013).
Research Stream 3: BMI as an Outcome
A third stream of BMI literature focuses on the outcome of the
organizational change
process—new and innovative BMs, which are typically
contextualized in some way. This
stream often addresses the emergence of new BMs in a
particular industry, such as electric
mobility (Abdelkafi, Makhotin, & Posselt, 2013), newspapers
(Holm, Günzel, & Ulhøi,
2013; Karimi & Zhiping, 2016), tourism (Souto, 2015), and
aviation (Schneider & Spieth,
2013). Other research in this stream examines one particular
type of new BM, such as that for
low-income markets (Anderson & Kupp, 2008; Sánchez &
Ricart; 2010; Yunus, Moingeon,
& Lehmann-Ortega, 2010), sustainable energy (Richter, 2013),
manufacturing firms (Witell
& Löfgren, 2013), or service (Visnjic Kastalli & Van Looy,
2013). Other articles describe a
particular company’s “innovative” BM, such as Nestlé’s
Nespresso (Matzler, Bailom, den
Eichen, & Kohler, 2013). Perhaps surprising, contributions to
this research stream do not
usually build on the first research stream. Instead, the focus is
on describing one particular
type of change of BM, often claimed to be a new kind.
However, this descriptive stream does
not offer a discussion of the criteria under which the relevant
BM change can be regarded as
being novel.

Research Stream 4: Consequences of BMI
The fourth stream addresses the organizational performance
implications of BMI. In this
stream, we can differentiate between studies that link the “act,”
or process, of BMI to out-
come implications (e.g., Aspara, Hietanen, & Tikkanen, 2010;
Bock et al., 2012; Cucculelli
& Bettinelli, 2015; Giesen, Berman, Bell, & Blitz, 2007) and
those that examine the effects
of different types of BMs on firm performance (e.g., Huang,
Lai, Lin, & Chen, 2013; Wei,
Yang, Sun, & Gu, 2014; Zott & Amit, 2007, 2008). In the first
case, studies assume a pro-
cess view and investigate whether an innovative change in the
existing BM leads to superior
performance outcomes. For example, Aspara et al. (2010)
compare the financial perfor-
mance implications of BMI to those of replication, while Giesen
et al. (2007) find that BMI
targeted at disrupting the industry chain, revenue model, or
organizational boundaries yields
no significant variation in financial performance across the
different types of BMI. In the
context of entrepreneurial firms, Cucculelli and Bettinelli
(2015) find that firms who modi-
fied their BMs over time and, in an innovative way, experienced
a positive effect on venture
performance.
In the second case, studies do not directly link BMI to
performance outcomes. Instead,
they empirically test the effects of different BM designs on
innovation performance. For

example, after differentiating between novelty- and efficiency-
centered BM designs, Zott
and Amit (2007) found a positive relation between novelty-
centered BMs and firm perfor-
mance in entrepreneurial firms. In a later study (2008), these
same authors show the impor-
tance of fit between product market strategy and BM design for
enhancing firm performance.
After adopting the same differentiation of novelty- and
efficiency-centered BM designs, Wei
et al. (2014) examined how exploitative and exploratory
innovation fit with different BM
designs to promote growth in Chinese firms.
Strengths and Weaknesses of the Four Streams
The BMI literature has yielded several key insights. A
fundamental contribution to mac-
romanagement research in its own right is that firms can
introduce changes into the design
and architecture of their BMs that are novel to a context and
potentially the basis of substan-
tial appropriable value creation and competitive advantages.
Several advances have been
made in our understanding of the nature of such innovation, its
process dimension, and its
consequences. In particular, two main lines of argumentation
are apparent. Studies either
adopt a dynamic view of BMI and conceptualize it as an
organizational change process
requiring appropriate capabilities, leadership, and learning
mechanisms (Research Stream 2)
or view BMI statically as new types of innovative ventures
(Research Stream 3) that may
affect firm performance (Research Stream 4).

Nevertheless, as the above brief characterization of the
literature suggests, BMI research
does not exhibit the characteristics of a well-defined cumulative
research stream. Many con-
tributions are conceptual rather than theoretical or are
fundamentally descriptive rather than
explanatory. For example, our review did not result in the
identification of articles that clearly
deal with the antecedents of BMI. Furthermore, the four
research streams have largely
evolved in relatively isolated silos (as indicated by little cross-
citation among the streams)
and do not seem to build off one another.
Innovation 209
Gaps and Challenges in BMI Research
In management research, theory is conventionally understood as
requiring the specifica-
tion of (a) the constructs or variables of interest; (b)
congruence, which is the set of laws of
relationship …
Journal of Intelligent & Fuzzy Systems 32 (2017) 4357–4365
DOI:10.3233/JIFS-16731
IOS Press
4357
An approach to evaluating the knowledge

innovation ability of new ventures based
on knowledge management with fuzzy
number intuitionistic fuzzy information
Wei Fan∗
Graduate School, Chang Chun University of Science and
Technology, Chang Chun, China
Abstract. Currently, most new ventures are knowledge-based
enterprises; moreover, knowledge innovation constitutes the
major activities of the enterprises’ production and knowledge
management. Therefore, it is of great significance to be able
to properly analyze and evaluate a new venture’s knowledge
innovation ability. In this paper, we investigate the multiple
attribute decision making problems with fuzzy number
intuitionistic fuzzy information. Then, we develop the fuzzy
number
intuitionistic fuzzy Hamacher power weighted geometric
(FNIFHPWG) operator. Then, we apply the FNIFHPWG
operator
to deal with multiple attribute decision making for evaluating
the knowledge innovation ability of new ventures based
on knowledge management under the fuzzy number
intuitionistic fuzzy environments. Finally, an illustrative
example for
evaluating the knowledge innovation ability of new ventures
based on knowledge management is given to verify the
developed
approach.
Keywords: Multiple attribute decision making, fuzzy number
intuitionistic fuzzy sets, Hamacher aggregation operators, fuzzy
number intuitionistic fuzzy Hamacher power weighted
geometric (FNIFHPWG) operator, knowledge innovation ability,
knowledge management

1. Introduction
Knowledge innovation refers to the processes of
creating and possessing new knowledge, which occur
during the creation, dissemination and application of
knowledge in enterprises. Through knowledge inno-
vation, an enterprise can develop its core competence
by integrating explicit knowledge and tacit knowl-
edge acquired in and out of the enterprise [1, 2].
Corporate knowledge innovation is an indispensable
link between knowledge area and economic area,
which combines the two areas into an organic whole
∗ Corresponding author. Wei Fan, Graduate School, Chang
Chun University of Science and Technology, Chang Chun,
China.
Tel./Fax: +86 13843176394; E-mail: [email protected]
through knowledge creation and application [3, 4].
Thus, knowledge innovation is a significant compo-
nent of business management and essential to the sur-
vival and sustainable development of an enterprise.
Besides, scientific and effective innovation manage-
ment is a prerequisite for knowledge innovation to be
systemic and comprehensive so that it can turn into a
corporate advantage and increase corporate value.
New ventures are major contributors to eco-
nomic progress [5]. At present, most of them are
knowledge-based enterprises; in this way, knowledge
innovation and knowledge management figure promi-
nently therein. Organizational knowledge already
possessed, that is, knowledge assets or intellectual
capital, forms the basis of a new venture’s knowledge
1064-1246/17/$35.00 © 2017 – IOS Press and the authors. All
rights reserved

mailto:[email protected]
4358 W. Fan / An approach to evaluating the knowledge
innovation ability
innovation, which refers to the knowledge an enter-
prise has possessed or controlled that is not physical
or tangible yet contributes greatly to corporate pro-
duction and services and continuously generates
economic gains. Knowledge assets are a special type
of resources indispensible to a new venture [6], which
are mainly composed of human capital, technolog-
ical assets, market assets and infrastructure assets.
Knowledge innovation is a systemic project involving
the whole texture of a new venture, requiring the par-
ticipation and cooperation of each department, such
as HR, technical, marketing and management depart-
ments. The writer of this paper deems that either
knowledge creation or knowledge acquisition should
base themselves on the new venture’s ability to dis-
cover, to accumulate, to absorb, to understand, to
grasp and to apply knowledge, which constitutes the
new venture’s knowledge innovation ability [7].
Although the new ventures’ knowledge innovation
is very important, but few studies have been done
and the existing evaluation methods are incomplete
[8]. In this paper, we investigate the multiple attribute
decision making problems with fuzzy number intu-
itionistic fuzzy information. Then, we develop the
fuzzy number intuitionistic fuzzy Hamacher power
weighted geometric (FNIFHPWG) operator. Then,
we apply the FNIFHPWG operator to deal with mul-
tiple attribute decision making for evaluating the

knowledge innovation ability of new ventures based
on knowledge management under the fuzzy number
intuitionistic fuzzy environments. Finally, an illustra-
tive example for evaluating the knowledge innovation
ability of new ventures based on knowledge manage-
ment is given to verify the developed approach.
2. Preliminaries
Liu and Yuan [9] introduced the concept of fuzzy
number intuitionistic fuzzy set(FNIFS) which funda-
mental characteristic of the FNIFS is that the values of
its membership function and non-membership func-
tion are triangular fuzzy numbers rather than exact
numbers.
Definition 1. [9] Given a fixed set X = {x1, x2,
· · · , xn}, An FNIFS Ã over X is an object having
the form:
Ã =
{⟨
xi, t ̃A (xi) , f ̃A (xi)
⟩
|xi ∈ X
}
(1)
where t ̃A(xi) ⊂ [0, 1] and f ̃A(xi) ⊂ [0, 1] are tri-
angular fuzzy numbers, and t ̃A(xi) = (a(xi), b(xi),
c(xi)), X → [0, 1], f ̃A(x) = (l(xi), m(xi), p(xi)),
X → [0, 1], 0 ≤ c(xi) + p(xi) ≤ 1, ∀ x ∈ X.

For convenience, let t ̃A(xi) = (a(xi), b(xi),
c(xi)), f ̃A(x) = (l(xi), m(xi), p(xi)), so ã(xi) =
〈(a (xi) , b (xi) , c (xi)) , (l (xi) , m (xi) , p (xi))〉 and
we call ã(xi) an fuzzy number intuitionistic fuzzy
value (FNIFV).
Definition 2. [10] Let ã (xi) = 〈(a (xi) , b (xi) ,
c (xi)), (l (xi) , m (xi) , p (xi))〉 be a collection of
FNIFEs, a score function S of a FNIFV ã(xi) can
be represented as follows:
S (ã (xi)) =
a (xi) + 2b (xi) + c (xi)
4
− l (xi) + 2m (xi) + p (xi)
4
(2)
Definition 3. [10] Let ã (xi) = 〈(a (xi) , b (xi) ,
c (xi)), (l (xi) , m (xi) , p (xi))〉 be a collection of
FNIFEs, an accuracy function H of a FNIFV ã (xi)
can be represented as follows:
H (ã (xi))
= (a (xi) + 2b (xi) + c (xi)) + (l (xi) + 2m (xi) + p (xi))
4
,
H (ã (xi)) ∈ [0, 1] . (3)
to evaluate the degree of accuracy of the

FNIFV ã (xi) = 〈(a (xi) , b (xi) , c (xi)) , (l (xi) ,
m (xi) , p (xi))⊂, where H (ã (xi)) ∈ [0, 1]. The larger
the value of H (ã (xi)), the more the degree of
accuracy of the FNIFV ã (xi) is.
In recent years, more and more scholars inves-
tigated the multiple attribute decision making
problems with fuzzy number intuitionistic fuzzy
information [11–18].
3. FNIFHWG operator
Bsed on the Hamacher operation [19–22], Wang
et al. [18] proposed the fuzzy number intuitionistic
fuzzy Hamacher weighted geometric (FNIFHWG)
operator as follows:
Definition 4. Let ã
(
xj
)
= 〈(a
(
xj
)
, b
(
xj
)
, c

(
xj
)
),(
l
(
xj
)
, m
(
xj
)
, p
(
xj
))⟩
be a collection of FNIFEs,
then we define the fuzzy number intuitionistic fuzzy
Hamacher weighted geometric (FNIFHWG) operator
as follows:
W. Fan / An approach to evaluating the knowledge innovation
ability 4359
FNIFHWG (ã (x1) , ã (x2) , · · · , ã (xn)) =
n⊗

j=1
(
ã
(
xj
))ωj
=
⟨ ⎛⎜⎜⎜⎝
γ
n∏
j=1
a
(
xj
)ωj
n∏
j=1
(
1 + (γ − 1)
(
1 − a
(
xj

)))ωj + (γ − 1) n∏
j=1
a
(
xj
)ωj ,
γ
n∏
j=1
b
(
xj
)ωj
n∏
j=1
(
1 + (γ − 1)
(
1 − b
(
xj
)))ωj + (γ − 1) n∏
j=1
b

(
xj
)ωj ,
γ
n∏
j=1
c
(
xj
)ωj
n∏
j=1
(
1 + (γ − 1)
(
1 − c
(
xj
)))ωj + (γ − 1) n∏
j=1
c
(
xj
)ωj
⎞

⎟⎟⎟⎠ ,
⎛
⎜⎜⎜⎝
n∏
j=1
(
1 + (γ − 1) l
(
xj
))ωj − n∏
j=1
(
1 − l
(
xj
))ωj
n∏
j=1
(
1 + (γ − 1) l
(
xj
))ωj + (γ − 1) n∏
j=1

(
1 − l
(
xj
))ωj ,
n∏
j=1
(
1 + (γ − 1) m
(
xj
))ωj − n∏
j=1
(
1 − m
(
xj
))ωj
n∏
j=1
(
1 + (γ − 1) m
(

xj
))ωj + (γ − 1) n∏
j=1
(
1 − m
(
xj
))ωj ,
n∏
j=1
(
1 + (γ − 1) p
(
xj
))ωj − n∏
j=1
(
1 − p
(
xj
))ωj
n∏
j=1

(
1 + (γ − 1) p
(
xj
))ωj + (γ − 1) n∏
j=1
(
1 − p
(
xj
))ωj
⎞
⎟⎟⎟⎠
⟩
(4)
where ω = (ω1, ω2, · · · , ωn)T be the weight
vector of ã(xj ) (j = 1, 2, · · · , n), and ωj > 0,∑n
j=1 ωj = 1.
Yager [23] developed a nonlinear weighted aver-
age aggregation operator called power geometric
(PG) operator based on the PA operator [24–31] and
geometric mean [32–42], which can be defined as
follows:
PG (a1, a2, · · · , an) =
n∏

i=1
(ai)
(1+T (ai))
/
n∑
i=1
(1+T (ai))
(5)
where T (ai) =
n∑
j=1
j /= i
Sup
(
ai, aj
)
, and Sup (a, b) is
the support for a from b, which satisfies the following
three properties:
(1) Sup (a, b) ∈ [0, 1];
(2) Sup (a, b) = Sup (b, a);
(3) Sup (a, b) ≥ Sup (x, y), if |a − b| < |x − y|.
In this Section, we shall investigate the PG operator
under fuzzy number intuitionistic fuzzy environ-
ments and propose the fuzzy number intuitionistic

fuzzy Hamacher power weighted geometric (FNIFH-
PWG) operator as follows.
Definition 5. Let ã
(
xj
)
= 〈(a
(
xj
)
, b
(
xj
)
, c
(
xj
)
),(
l
(
xj
)
, m
(

xj
)
, p
(
xj
))⟩
be a collection of FNIFEs,
ω = (ω1, ω2, · · · , ωn)T be the weighting vector
of FNIFEs α̃j (j = 1, 2, · · · , n) and ωj ∈ [0, 1],∑n
j=1 ωj = 1, then we define the fuzzy number intu-
itionistic fuzzy Hamacher power weighted geometric
(FNIFHPWG) as follows:
innovation ability
FNIFHPWG (ã (x1) , ã (x2) , · · · , ã (xn))
=
n
⊕
j=1
(
ãj
)ωj (1+T (ãj ))
/

n∑
j=1
ωj (1+T (ãj ))
=
⟨
⎛
⎜⎜⎜⎜⎜⎜⎝
γ
n∏
j=1
a
(
xj
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏
j=1
(
1 + (γ − 1)
(
1 − a

(
xj
)))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
a
(
xj
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,
γ
n∏
j=1
b
(

xj
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏
j=1
(
1 + (γ − 1)
(
1 − b
(
xj
)))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
b
(

xj
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,
γ
n∏
j=1
c
(
xj
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏
j=1
(
1 + (γ − 1)
(
1 − c

(
xj
)))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
c
(
xj
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
⎞
⎟⎟⎟⎟⎟⎟⎠
,
⎛
⎜⎜⎜⎜⎜⎜⎝

n∏
j=1
(
1 + (γ − 1) l
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
−
n∏
j=1
(
1 − l
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏

j=1
(
1 + (γ − 1) l
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
1 − l
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,

n∏
j=1
(
1 + (γ − 1) m
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
−
n∏
j=1
(
1 − m
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏

j=1
(
1 + (γ − 1) m
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
1 − m
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,

n∏
j=1
(
1 + (γ − 1) p
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
−
n∏
j=1
(
1 − p
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏

j=1
(
1 + (γ − 1) p
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
1 − p
(
xj
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
⎞
⎟⎟⎟⎟⎟⎟⎠

⟩
(6)
ability 4361
where
T
(
α̃j
)
=
n∑
i=1
i /= j
ωiSup
(
α̃j, α̃i
)
(7)
and Sup
(
α̃j, α̃i
)
is the support for α̃j from α̃i, with

the conditions:
1) Sup
(
α̃i, α̃j
)
∈ [0, 1];
2) Sup
(
α̃i, α̃j
)
= Sup
(
α̃i, α̃j
)
;
3) Sup
(
α̃i, α̃j
)
≥ Sup (α̃s, α̃t ), if d
(
αi, αj
)
≥

d (αs, αt ), where d is a distance measure.
It can be easily proved that the FNIFHPWG oper-
ator has the following properties.
Theorem 1. (Monotonicity) Let α̃j (j = 1, 2, · · · , n)
and α̃′j (j = 1, 2, · · · , n) be two collection of
FNIFEs, ω = (ω1, ω2, · · · , ωn)T be the weighting
vector of picture fuzzy numbers α̃j (j = 1, 2, · · · , n)
and ωj ∈ [0, 1],
∑n
j=1 ωj = 1, if α̃j ≤ α̃′j , for all j,
then
FNIFHPWG (α̃1, α̃2, · · · , α̃n)
≤ FNIFHPWG
(
α̃
′
1, α̃
′
2, · · · , α̃′n
)
(8)
Theorem 2. (Boundedness) Let α̃j (j = 1, 2, · · · , n)
be a collection of FNIFEs, ω = (ω1, ω2, · · · , ωn)T
be the weighting vector of FNIFEs α̃j (j = 1, 2,
· · · , n), and let

α̃
− = min
j
α̃j, α̃
+ = max
j
α̃j
Then
α̃
− ≤ FNIFHPWG (α̃1, α̃2, · · · , α̃n) ≤ α̃+ (9)
Theorem 3. (Idempotency) Let α̃j (j = 1, 2, · · · , n)
be a collection of FNIFEs, ω = (ω1, ω2, · · · , ωn)T
be the weighting vector of FNIFEs
α̃j (j = 1, 2, · · · , n) and ωj ∈ [0, 1],
∑n
j=1 ωj = 1.
If all α̃j (j = 1, 2, · · · , n) are equal, i.e. α̃j = α̃ for
all j, then
FNIFHPWG (α̃1, α̃2, · · · , α̃n) = α̃ (10)
4. An approach to evaluating the knowledge
innovation ability of new ventures based
on knowledge management with fuzzy
number intuitionistic fuzzy information
Let A = {A1, A2, · · · , Am} be a discrete set of
alternatives, and G = {G1, G2, · · · , Gn} be the set

of attributes, ω = (ω1, ω2, · · · , ωn) is the weight-
ing vector of the attribute Gj (j = 1, 2, · · · , n),
where ωj ∈ [0, 1],
∑n
j=1 ωj = 1. Suppose that R̃ =(
r ̃ij
)
m×n =
⟨ (
aij, bij, cij
)
,
(
lij, mij, pij
)⟩
m×n is the
fuzzy number intuitionistic fuzzy decision matrix,
i = 1, 2, · · · , m, j = 1, 2, · · · , n.
Then, we apply the FNIFHPWG operator to
MADM problems for evaluating the knowledge inno-
vation ability of new ventures based on knowledge
management with fuzzy number intuitionistic fuzzy
information.
Step 1. Utilize the information given in matrix R̃ , and
the FNIFHPWG operator.
r ̃i = FNIFHPWGω (r ̃i1, r ̃i2, · · · , r ̃in) =
n⊕

j=1
(
r ̃ij
)ωj (1+T (ãj ))
/
n∑
j=1
ωj (1+T (ãj ))
=
⟨
⎛
⎜⎜⎜⎜⎜⎜⎜⎝
γ
n∏
j=1
(
aij
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))

n∏
j=1
(
1 + (γ − 1)
(
1 − aij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
aij
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,
γ
n∏

j=1
(
bij
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏
j=1
(
1 + (γ − 1)
(
1 −
(
bij
)))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1

(
bij
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,
innovation ability
γ
n∏
j=1
(
cij
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏
j=1

(
1 + (γ − 1)
(
1 − cij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
cij
)ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
⎞
⎟⎟⎟⎟⎟⎟⎟⎠
,
⎛

⎜⎜⎜⎜⎜⎜⎜⎝
n∏
j=1
(
1 + (γ − 1)
(
lij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
−
n∏
j=1
(
1 −
(
lij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))

n∏
j=1
(
1 + (γ − 1)
(
lij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
1 −
(
lij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,

n∏
j=1
(
1 + (γ − 1)
(
mij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
−
n∏
j=1
(
1 −
(
mij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))

n∏
j=1
(
1 + (γ − 1)
(
mij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
1 −
(
mij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
,

n∏
j=1
(
1 + (γ − 1)
(
pij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
−
n∏
j=1
(
1 −
(
pij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
n∏

j=1
(
1 + (γ − 1)
(
pij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
+ (γ − 1)
n∏
j=1
(
1 −
(
pij
))ωj (1+T (h̃j ))
/
n∑
j=1
ωj (1+T (h̃j ))
⎞
⎟⎟⎟⎟⎟⎟⎟⎠

⟩
(11)
to derive the overall preference values
r ̃i (i = 1, 2, · · · , m) of the alternative Ai.
Step 2. Calculate the scores S (r ̃i) (i = 1, 2, · · · , m)
of the overall preference values r ̃i (i = 1, 2, · · · , m)
to rank all the alternatives Ai (i = 1, 2, · · · , m) and
then to select the best one(s)
Step 3. Rank all the alternatives Ai (i = 1, 2, · · · , m)
and select the best one(s) in accordance with S (r ̃i)
and H (r ̃i) (i = 1, 2, · · · , m).
5. Numerical example
As a source of competitive advantage, new ven-
tures’ knowledge innovation ability plays a key role
in the business operating process based on knowledge
management. Since china’ new ventures are low in
knowledge innovation control, it is of great signifi-
cance to improve their knowledge innovation ability,
therefore how to correctly evaluate the knowledge
innovation ability are new topics. Suppose a group
of experts plan to evaluate the knowledge innova-
tion ability based on knowledge management. There
are five possible enterprises Ai (i = 1, 2, 3, 4, 5) to
select. The expert group selects four attributes to
evaluate the five enterprises: ①G1 is knowledge
capturing ability; ②G2 is knowledge internal-
izing ability; ③G3 is knowledge externalizing
ability; ④G4 is the ability of knowledge competi-

tion and cooperation. The five possible enterprises
Ai (i = 1, 2, 3, 4, 5) are to be evaluated by the deci-
sion maker by using the fuzzy number intuitionistic
fuzzy information under the four attributes men-
tioned above, as listed in the following matrix.The
five possible enterprises Ai (i = 1, 2, · · · , 5) are to
be evaluated using the fuzzy number intuitionistic
fuzzy values by the decision maker under the above
four attributes, and construct, respectively, the deci-
sion matrices as listed in the following matrices
R̃ =
(
r ̃ij
)
5×4 as follows:
ability 4363
R̃ =
⎡
⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣
〈(0.5, 0.6, 0.7) , (0.1, 0.2, 0.3)〉〈(0.4, 0.5, 0.6) , (0.2, 0.3,
0.4)〉
〈(0.3, 0.4, 0.5) , (0.1, 0.2, 0.3)〉〈(0.2, 0.3, 0.4) , (0.3, 0.4,
0.5)〉
〈(0.5, 0.6, 0.7) , (0.1, 0.2, 0.3)〉〈(0.2, 0.3, 0.4) , (0.4, 0.5,
0.6)〉
〈(0.4, 0.5, 0.6) , (0.2, 0.3, 0.4)〉〈(0.3, 0.4, 0.5) , (0.2, 0.3,
0.4)〉

〈(0.4, 0.5, 0.6) , (0.1, 0.2, 0.3)〉〈(0.2, 0.3, 0.4) , (0.3, 0.4,
0.5)〉
〈(0.2, 0.3, 0.4) , (0.3, 0.4, 0.5)〉〈(0.5, 0.6, 0.7) , (0.1, 0.2,
0.3)〉
〈(0.4, 0.5, 0.6) , (0.1, 0.2, 0.3)〉〈(0.4, 0.5, 0.6) , (0.1, 0.2,
0.3)〉
〈(0.1, 0.2, 0.3) , (0.3, 0.4, 0.5)〉〈(0.3, 0.4, 0.5) , (0.3, 0.4,
0.5)〉
〈(0.4, 0.5, 0.6) , (0.1, 0.2, 0.3)〉〈(0.5, 0.6, 0.7) , (0.1, 0.2,
0.3)〉
〈(0.5, 0.6, 0.7) , (0.1, 0.2, 0.3)〉〈(0.4, 0.5, 0.6) , (0.1, 0.2,
0.3)〉
⎤
⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦
In the following, we apply the FNIFHPWG opera-
tor to MADM problems for evaluating the knowledge
innovation ability of new ventures based on knowl-
edge management with fuzzy number intuitionistic
fuzzy information.
Step 1. We utilize the information given in matrix R̃ ,
and the FNIFHPWG operator to obtain the values r ̃i
of the enterprises Ai (i = 1, 2, · · · , 5).
r ̃1 = 〈(0.345, 0.446, 0.521) , (0.265, 0.323, 0.378)〉
r ̃2 = 〈(0.238, 0.343, 0.479) , (0.356, 0.488, 0.590)〉
r ̃3 = 〈(0.232, 0.298, 0.377) , (0.489, 0.521, 0.645)〉
r ̃4 = 〈(0.398, 0.478, 0.592) , (0.289, 0.383, 0.408)〉
r ̃5 = 〈(0.376, 0.492, 0.563) , (0.323, 0.383, 0.476)〉
Step 2. Calculate the scores S (r ̃i) (i = 1, 2, · · · , 5)
of the values r ̃i (i = 1, 2, · · · , 5).
S (r ̃1) = 0.178, S (r ̃2) = −0.101, S (r ̃3) = −0.237
S (r ̃4) = 0.254, S (r ̃5) = 0.107

Step 3. Rank all the enterprises Ai (i = 1, 2, 3,
4, 5) in accordance with the scores S (r ̃i) (i = 1, 2,
· · · , 5): A4 � A1 � A5 � A3 � A2, and thus the
most desirable enterprises is A4.
6. Conclusion
Knowledge innovation is the essential part of
knowledge-based new ventures’ management opera-
tion. Only when the knowledge innovation ability of a
new venture can be properly evaluated can the enter-
prise establish a rational incentive system so as to
motivate employees to get actively involved in knowl-
edge innovation and help raise the overall innovation
level of the enterprise. In this paper, we investi-
gate the multiple attribute decision making problems
with fuzzy number intuitionistic fuzzy information.
Then, we develop the fuzzy number intuitionistic
fuzzy Hamacher power weighted geometric (FNIFH-
PWG) operator. Then, we apply the FNIFHPWG
operator to deal with multiple attribute decision mak-
ing for evaluating the knowledge innovation ability
of new ventures based on knowledge management
under the fuzzy number intuitionistic fuzzy environ-
ments. Finally, an illustrative example for evaluating
the knowledge innovation ability of new ventures
based on knowledge management is given to ver-
ify the developed approach. In our future studies, we
shall extend our proposed operators and approaches
to other application domains [43–54].
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Awareness of the fi ve forces can help a company understand
the structure of its
industry and stake out a position that is more profi table and
less vulnerable to attack.
78 Harvard Business Review | January 2008 | hbr.org
1808 Porter.indd 781808 Porter.indd 78 12/5/07 5:33:57
PM12/5/07 5:33:57 PM
P
e
te
r
C
ro

w
th
e
r
Editor’s Note: In 1979, Harvard Business Review
published “How Competitive Forces Shape Strat-
egy” by a young economist and associate professor,
Michael E. Porter. It was his fi rst HBR article, and it
started a revolution in the strategy fi eld. In subsequent
decades, Porter has brought his signature economic
rigor to the study of competitive strategy for corpora-
tions, regions, nations, and, more recently, health care
and philanthropy. “Porter’s fi ve forces” have shaped a
generation of academic research and business practice.
With prodding and assistance from Harvard Business
School Professor Jan Rivkin and longtime colleague
Joan Magretta, Porter here reaffi rms, updates, and
extends the classic work. He also addresses common
misunderstandings, provides practical guidance for

users of the framework, and offers a deeper view of
its implications for strategy today.
THE FIVE
COMPETITIVE
FORCES THAT
by Michael E. Porter
hbr.org | January 2008 | Harvard Business Review 79
SHAPE
IN ESSENCE, the job of the strategist is to under-
STRATEGYSTRATEGY
stand and cope with competition. Often, however,
managers defi ne competition too narrowly, as if
it occurred only among today’s direct competi-
tors. Yet competition for profi ts goes beyond es-
tablished industry rivals to include four other
competitive forces as well: customers, suppliers,
potential entrants, and substitute products. The
extended rivalry that results from all fi ve forces
defi nes an industry’s structure and shapes the
nature of competitive interaction within an

industry.
As different from one another as industries
might appear on the surface, the underlying driv-
ers of profi tability are the same. The global auto
industry, for instance, appears to have nothing
in common with the worldwide market for art
masterpieces or the heavily regulated health-care
PM12/5/07 5:34:06 PM
LEADERSHIP AND STRATEGY | The Five Competitive
Forces That Shape Strategy
delivery industry in Europe. But to under-
stand industry competition and profi tabil-
ity in each of those three cases, one must
analyze the industry’s underlying struc-
ture in terms of the fi ve forces. (See the ex-
hibit “The Five Forces That Shape Industry

Competition.”)
If the forces are intense, as they are in
such industries as airlines, textiles, and ho-
tels, almost no company earns attractive re-
turns on investment. If the forces are benign,
as they are in industries such as software,
soft drinks, and toiletries, many companies
are profi table. Industry structure drives
competition and profi tability, not whether
an industry produces a product or service, is
emerging or mature, high tech or low tech,
regulated or unregulated. While a myriad
of factors can affect industry profi tability
in the short run – including the weather
and the business cycle – industry structure,
manifested in the competitive forces, sets
industry profi tability in the medium and
long run. (See the exhibit “Differences in

Industry Profi tability.”)
Understanding the competitive forces, and their under-
lying causes, reveals the roots of an industry’s current profi t-
ability while providing a framework for anticipating and
infl uencing competition (and profi tability) over time. A
healthy industry structure should be as much a competitive
concern to strategists as their company’s own position. Un-
derstanding industry structure is also essential to effective
strategic positioning. As we will see, defending against the
competitive forces and shaping them in a company’s favor
are crucial to strategy.
Forces That Shape Competition
The confi guration of the fi ve forces differs by industry. In
the market for commercial aircraft, fi erce rivalry between
dominant producers Airbus and Boeing and the bargain-
ing power of the airlines that place huge orders for aircraft
are strong, while the threat of entry, the threat of substi-
tutes, and the power of suppliers are more benign. In the

movie theater industry, the proliferation of substitute forms
of entertainment and the power of the movie producers
and distributors who supply movies, the critical input, are
important.
The strongest competitive force or forces determine the
profi tability of an industry and become the most important
to strategy formulation. The most salient force, however, is
not always obvious.
For example, even though rivalry is often fi erce in com-
modity industries, it may not be the factor limiting profi t-
ability. Low returns in the photographic fi lm industry, for
instance, are the result of a superior substitute product – as
Kodak and Fuji, the world’s leading producers of photo-
graphic fi lm, learned with the advent of digital photography.
In such a situation, coping with the substitute product be-
comes the number one strategic priority.
Industry structure grows out of a set of economic and
technical characteristics that determine the strength of

each competitive force. We will examine these drivers in the
pages that follow, taking the perspective of an incumbent,
or a company already present in the industry. The analysis
can be readily extended to understand the challenges facing
a potential entrant.
THREAT OF ENTRY. New entrants to an industry bring
new capacity and a desire to gain market share that puts
pressure on prices, costs, and the rate of investment nec-
essary to compete. Particularly when new entrants are
diversifying from other markets, they can leverage exist-
ing capabilities and cash fl ows to shake up competition, as
Pepsi did when it entered the bottled water industry, Micro-
soft did when it began to offer internet browsers, and Apple
did when it entered the music distribution business.
Michael E. Porter is the Bishop William Lawrence University
Pro-
fessor at Harvard University, based at Harvard Business School
in
Boston. He is a six-time McKinsey Award winner, including for
his

most recent HBR article, “Strategy and Society,” coauthored
with
Mark R. Kramer (December 2006).
The Five Forces That Shape Industry Competition
Bargaining
Power of
Suppliers
Threat
of New
Entrants
Bargaining
Power of
Buyers
Threat of
Substitute
Products or
Services
Rivalry
Among
Existing
Competitors
PM12/5/07 5:34:13 PM

hbr.org | January 2008 | Harvard Business Review 81
The threat of entry, therefore, puts a cap on the profi t po-
tential of an industry. When the threat is high, incumbents
must hold down their prices or boost investment to deter
new competitors. In specialty coffee retailing, for example,
relatively low entry barriers mean that Starbucks must in-
vest aggressively in modernizing stores and menus.
The threat of entry in an industry depends on the height
of entry barriers that are present and on the reaction en-
trants can expect from incumbents. If entry barriers are low
and newcomers expect little retaliation from the entrenched
competitors, the threat of entry is high and industry profi t-
ability is moderated. It is the threat of entry, not whether
entry actually occurs, that holds down profi tability.
Barriers to entry. Entry barriers are advantages that incum-
bents have relative to new entrants. There are seven major
sources:
1. Supply-side economies of scale. These economies arise

when fi rms that produce at larger volumes enjoy lower costs
per unit because they can spread fi xed costs over more units,
employ more effi cient technology, or command better terms
from suppliers. Supply-side scale economies deter entry by
forcing the aspiring entrant either to come into the industry
on a large scale, which requires dislodging entrenched com-
petitors, or to accept a cost disadvantage.
Scale economies can be found in virtually every activity
in the value chain; which ones are most important varies
by industry.
1
In microprocessors, incumbents such as Intel
are protected by scale economies in research, chip fabrica-
tion, and consumer marketing. For lawn care companies like
Scotts Miracle-Gro, the most important scale economies are
found in the supply chain and media advertising. In small-
package delivery, economies of scale arise in national logisti-
cal systems and information technology.
2. Demand-side benefi ts of scale. These benefi ts, also known

as network effects, arise in industries where a buyer’s willing-
ness to pay for a company’s product increases with the num-
ber of other buyers who also patronize the company. Buyers
may trust larger companies more for a crucial product: Re-
call the old adage that no one ever got fi red for buying from
IBM (when it was the dominant computer maker). Buyers
may also value being in a “network” with a larger number of
fellow customers. For instance, online auction participants
are attracted to eBay because it offers the most potential
trading partners. Demand-side benefi ts of scale discourage
entry by limiting the willingness of customers to buy from a
newcomer and by reducing the price the newcomer can com-
mand until it builds up a large base of customers.
3. Customer switching costs. Switching costs are fi xed costs
that buyers face when they change suppliers. Such costs may
arise because a buyer who switches vendors must, for ex-
ample, alter product specifi cations, retrain employees to use
a new product, or modify processes or information systems.

The larger the switching costs, the harder it will be for an en-
trant to gain customers. Enterprise resource planning (ERP)
software is an example of a product with very high switching
costs. Once a company has installed SAP’s ERP system, for ex-
ample, the costs of moving to a new vendor are astronomical
because of embedded data, the fact that internal processes
have been adapted to SAP, major retraining needs, and the
mission-critical nature of the applications.
4. Capital requirements. The need to invest large fi nan-
cial resources in order to compete can deter new entrants.
Capital may be necessary not only for fi xed facilities but also
to extend customer credit, build inventories, and fund start-
up losses. The barrier is particularly great if the capital is
required for unrecoverable and therefore harder-to-fi nance
expenditures, such as up-front advertising or research and
development. While major corporations have the fi nancial
resources to invade almost any industry, the huge capital
requirements in certain fi elds limit the pool of likely en-

trants. Conversely, in such fi elds as tax preparation services
or short-haul trucking, capital requirements are minimal
and potential entrants plentiful.
It is important not to overstate the degree to which capital
requirements alone deter entry. If industry returns are at-
tractive and are expected to remain so, and if capital markets
are effi cient, investors will provide entrants with the funds
they need. For aspiring air carriers, for instance, fi nancing
is available to purchase expensive aircraft because of their
high resale value, one reason why there have been numer-
ous new airlines in almost every region.
5. Incumbency advantages independent of size. No matter
what their size, incumbents may have cost or quality advan-
tages not available to potential rivals. These advantages can
stem from such sources as proprietary technology, preferen-
tial access to the best raw material sources, preemption of
the most favorable geographic locations, established brand
identities, or cumulative experience that has allowed incum-

Industry structure drives competition and profi tability,
not whether an industry is emerging or mature, high tech or
low tech, regulated or unregulated.
PM12/5/07 5:34:18 PM
LEADERSHIP AND STRATEGY | The Five Competitive
Forces That Shape Strategy
bents to learn how to produce more effi ciently. Entrants try
to bypass such advantages. Upstart discounters such as Tar-
get and Wal-Mart, for example, have located stores in free-
standing sites rather than regional shopping centers where
established department stores were well entrenched.
6. Unequal access to distribution channels. The new en-
trant must, of course, secure distribution of its product or
service. A new food item, for example, must displace others
from the supermarket shelf via price breaks, promotions,
intense selling efforts, or some other means. The more lim-
ited the wholesale or retail channels are and the more that

existing competitors have tied them up, the tougher entry
into an industry will be. Sometimes access to distribution
is so high a barrier that new entrants must bypass distribu-
tion channels altogether or create their own. Thus, upstart
low-cost airlines have avoided distribution through travel
agents (who tend to favor established higher-fare carriers)
and have encouraged passengers to book their own fl ights
on the internet.
7. Restrictive government policy. Government policy can
hinder or aid new entry directly, as well as amplify (or nul-
lify) the other entry barriers. Government directly limits or
even forecloses entry into industries through, for instance,
licensing requirements and restrictions on foreign invest-
ment. Regulated industries like liquor retailing, taxi services,
and airlines are visible examples. Government policy can
heighten other entry barriers through such means as ex-
pansive patenting rules that protect proprietary technol-
ogy from imitation or environmental or safety regulations

that raise scale economies facing newcomers. Of course,
government policies may also make entry easier – directly
through subsidies, for instance, or indirectly by funding ba-
sic research and making it available to all fi rms, new and old,
reducing scale economies.
Entry barriers should be assessed relative to the capa-
bilities of potential entrants, which may be start-ups, foreign
fi rms, or companies in related industries. And, as some of
our examples illustrate, the strategist must be mindful of the
creative ways newcomers might fi nd to circumvent appar-
ent barriers.
Expected retaliation. How potential entrants believe in-
cumbents may react will also infl uence their decision to
enter or stay out of an industry. If reaction is vigorous and
protracted enough, the profi t potential of participating in
the industry can fall below the cost of capital. Incumbents
often use public statements and responses to one entrant
to send a message to other prospective entrants about their
commitment to defending market share.

Newcomers are likely to fear expected retaliation if:
Incumbents have previously responded vigorously to
new entrants.
Incumbents possess substantial resources to fi ght back,
including excess cash and unused borrowing power, avail-
•
•
able productive capacity, or clout with distribution channels
and customers.
Incumbents seem likely to cut prices because they are
committed to retaining market share at all costs or because
the industry has high fi xed costs, which create a strong mo-
tivation to drop prices to fi ll excess capacity.
Industry growth is slow so newcomers can gain volume
only by taking it from incumbents.
An analysis of barriers to entry and expected retaliation is
obviously crucial for any company contemplating entry into
a new industry. The challenge is to fi nd ways to surmount

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