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Citation: Baden-Fuller, C. and Haefliger, S. (2013). Business Models and Technological
Innovation. Long Range Planning, 46(6), pp. 419-426. doi: 10.1016/j.lrp.2013.08.023
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Business Models and Technological Innovation
Charles Baden-Fuller, Stefan Haefliger
Business models are fundamentally linked with technological innovation, yet the business model construct is essentially separable from
technology. We define the business model as a system that solves the problem of identifying who is (or are) the customer(s), engaging
with their needs, delivering satisfaction, and monetizing the value. The framework depicts the business model system as a model
containing cause and effect relationships, and it provides a basis for classification. We formulate the business model relationship with
technology in a two-way manner. First, business models mediate the link between technology and firm performance. Secondly, developing the right technology is a matter of a business model decision regarding openness and user engagement. We suggest research
questions both for technology management and innovation, as well as strategy.
2013 Elsevier Ltd.
Introduction
The business model construct has become attractive to many academics, taking on its own momentum as is evidenced by
the fact that, in the three years since publication, the Long Range Planning (2010) special issue on business models attracted
more than 150,000 downloads and more than 3,500 Google Scholar and more than 500 ISI citations. Yet, the construct has
also attracted criticism. Zott et al., (2011) complain that business models “have yet to develop a common and widely accepted
language that would allow researchers who examine the business model construct through different lenses to draw effectively on the work of others”, because there appears to be a diverse set of business model definitions and a diverse set of
approaches to classification. These views reflect confusion that has taken energy away from proper dialogue on key questions
d What are the components of a business model, and how does business model innovation occur?
In this piece, we explore one clear emerging view of the business model construct, and examine where this view takes us
in terms of understanding an issue referred to by authors such as Chesbrough (2003, 2010) concerning the relationship
between business model innovation and technical innovation. While researchers such as Osterwalder et al. (2010) and Demil
and Lecocq (2010) have proposed that the business model concept lies within the traditional strategy lexicon of competitive
advantage, we argue that the business model is a stand-alone concept in its own right because a business model is a model
(Baden-Fuller and Morgan, 2010). Similarly, Markides and Sosa (2013) distinguish between market entry decisions and
business model choice. And we also explain how this view accords with the widespread recognition in the literature that a
business model should be able to link two dimensions of firm activity d value creation and value capture (Amit and Zott,
2001; Zott and Amit, 2010; Casadesus-Masanell and Ricart, 2010; Teece, 2010).
We have not mentioned technology in this conception of the business model. How do technology and business models
interact? Technology development can facilitate new business models d the most obvious historical example is the way the
invention and development of steam power facilitated the mass production business model. But, business model innovation
can also occur without technology development, as occurred in 1980s when the Japanese pioneered the “just in time”
production system. In fact, business models and technologies regularly interact. For example, when Amazon was founded in
1995, they applied new technology to make the traditional mail-order business model pioneered by Sears Roebuck work well
for books. Amazon did not invent a new business model, nor did Easy-Jet (one of Europe’s most successful low-cost airlines)
when it copied the business model pioneered by Southwest Airlines. Both Amazon and Easy Jet applied well-known business
model constructs and developed them in new contexts.
In contrast, Google’s two-sided dynamic search engine developed in 2003 was not just a technological leap d it was also a
business model leap. Google used Adwords to provide an interface with advertisers (one side of the two sided platform)
whose choices directly influenced the search experience of users on the other side of the platform. Google was probably the
first company in the world to create the type of scalable dynamic two-sided platform modeled by Rochet and Tirole (2006).
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http://dx.doi.org/10.1016/j.lrp.2013.08.023
Long Range Planning 46 (2013) 419–426
Open access under CC BY-NC-ND license.
Open access under CC BY-NC-ND license.
The novelty of their approach lay in linking the two sides in a constantly changing manner that allowed greater consumer
satisfaction and greater revenues for any given set of users on each side of the platform.
Discussions of the effects of business models on performance do not always separate the effects of business model
innovation from technological innovations (c.f. Zott and Amit, 2007; Casadesus-Masanell and Ricart, 2010). We know that
technological innovation influences performance (cf. Bierly and Chakrabarti, 1996; Christensen and Bower, 1996; Zaheer and
Bell, 2005; and reviews by Evanschitzky et al., 2012; Hauser et al., 2006). But, to improve our understanding, we need a more
precise appreciation of how innovation links to performance through the business model, and how changes in the business
model influence technological innovation. The fact that positive effects of technological innovation on business performance
are easily observed has diverted attention from questions about how business models change in the wake of innovation. At
the same time, management theory requires more precision concerning the means by which business model changes enable
and foster innovation.
This piece will first explore key relationships that are embedded in the business model construct, and then briefly review
what we know about these relationships with a view to building a research agenda for the future. We begin by exploring what
a novel business model is and how new business models are related to new technologies. We note that this approach of seeing
the business model as a model is similar to the logic of reasoning and understanding that exists in economics, biology and
physics. In each of these fields, as explained by philosophers of science, models are “manipulable instruments with which to
reason and into which to enquire” and tools that “allow the user of the model to explore ideas” (Morrison and Morgan, 1999;
Morgan, 2012). This allows us to assert that it is intellectually robust to ask: “Is business model innovation potentially separate
from technological innovation?” d even though business model possibilities often rely on technology.
Building the framework
As noted, work on classifying business models has proceeded along two lines. First, there are researchers and commentators who see the business model concept as part of the strategy lexicon and intertwined with technology. They talk of
“novel” and “efficient” business models if a new technology is incorporated into a business to produce a superior effect (e.g.,
Zott and Amit, 2007; Osterwalder and Pigneur, 2010). Secondly, there are researchers, such as Teece (2010) or Baden-Fuller
and Morgan (2010), who see the concept of the business model as potentially separable from technology and strategy and
examine how understanding business models and business model innovation might shed light on core strategy and technology questions. This latter approach has the potential to answer the long-standing challenges posed by Chesbrough (2010),
who asks when a novel technology requires a novel business model, and when the combination of a novel technology and a
novel business model lead to competitive advantage.
Classification is necessary in order to understand innovation because only then can we appreciate what is meant by
new. Two major approaches to classification can be found in the literature. First, classification has been approached
taxonomically by trying to build a picture from looking backwards at empirical work d taking the outputs or location of
the use of the model as central. Thus, we have the classification of Wirtz et al., 2010, which stressed the difference between
content, commerce, context and connecting business models, and from Zott and Amit (2007), who emphasize the efficiency and novelty business models. But an alternative line of argument has emphasized the dimensions of the model
rather than its consequences d a classification that could be described as typological (Hempel, 1965). This type of classification is more forward looking and has been at the heart of discussions that recognize two vital dimensions: Value
creation and Value Capture. Value reaction identifies the customer or customers and how are they engaged (cf. McGrath
and MacMillan, 2000), and value capture identifies how value is delivered and monetized (Teece, 2010). This classic twopart division of the business model (cf. Amit and Zott, 2001), leads to the possibility of a typological, theoretically-driven
categorization that can assist in identifying basic types. In line with this argument, we develop a typology with four dimensions: customer identification, customer engagement, value delivery, and monetization; and we explore what this
typology does for our understanding of business model innovation and its relationship to technical innovation. Table 1
gives examples of the classification of well-known firm-business model configurations. We explain the dimensions
more fully below.
Customers
First, we address the customer identification dimensions of the business model. We stress that with modern technological
possibilities, it is essential that the business model identifies the users and the customers and indicate whether users pay for
what they use or another group of customers actually pays (Teece, 2010). Historically, users almost always have paid, albeit in
many different ways usually at time of purchase or subsequently through a “razor-blade” model of usage charges. But with
newspapers, television, and finally the internet, technology has created the possibility that users may not pay for the services
they receive d payment instead being made by others such as advertisers, as indicated in Table 1. Two-sided platforms of this
type (Rochet and Tirole, 2006) are hybrid business models because they incorporate two value delivery systems d one for the
user (such as a consumer that wants to search) and another for the customer who pays, such as a small firm that wants to
place an advertisement where it can be seen by a particular kind of consumer. The internet did not “invent” two-sided
platforms d they have existed since before the 18th century d but it did facilitate their expansion (see Table 1 for the
example of a dynamic, advertising-supported search engine).
420 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426
Customer engagement
Secondly, we address the question of customer engagement. This requires sensing what the customer-user or groups of
customer-users need, and establishing the value proposition for each of these groups perhaps using the process explained by
McGrath and MacMillan (2000), McGrath (2010), Day and Moorman (2010), and Day (2013). A long-standing distinction
between “project based offerings” and “pre-designed (scale) based offerings” d often described as the “taxi’ and “bus”
systems d is useful in this context. Organizations such as consulting firms and movie makers use the taxi system to create
value by interacting with specific clients to solve specific problems, while organizations such as automobile assemblers and
providers of fast food utilize the bus system and add value by producing “one-size-fits-all” goods or services in a repetitive
manner from a standardized, mass-production format. We suggest this distinction fits with the argument of Thompson (1967)
and Drucker (1986), who proposed a distinction between firms organized in teams and mass production. The categories we
refer to as “project” or “taxi” and “scale” or “bus” are more than just slogans; they are well established, economically-robust,
model-based configurations that have been widely examined by scholars from communities both within and beyond management. These models exhibit clear features; they typically have recognized processes and mechanisms and commonality in
how they utilize “knowledge” and “routines”.
The project-based approach is characterized by bespoke projects responding to customer needs. Its routines are designed to
be particularly effective at three things: dealing with non-routine complex tasks that require the repeated reconfiguration of
organizational structures; responding flexibly to changing client needs; and integrating diverse bodies of knowledge (Davies
and Brady, 2000; Hobday, 2000; Söderlund and Tell, 2010). Table 1 illustrates this with the example of a defense contractor.
In contrast, the “pre-designed” or “scale-based” approach is characterized by products made through scale-based systems
using machines and routines that have a limited capacity to respond “flexibly” to unexpected client needs (e.g., Hounshell,
1984; Chandler, 1990). The fast food hamburger chain discussed in Table 1 is an example of a scale-based business. Business systems using this approach also “integrate diverse bodies of knowledge”, but typically via different processes. There is a
clear theoretical boundary between how project-based and scale-based organizations utilize knowledge in creating value
(e.g., Chandler, 1990; Nightingale, 2000; Nightingale et al., 2011; Hobday, 2000).
Value delivery and linkages
The third component is the set of linkages between identifying the customer groups, and sensing their needs on the one
hand, and monetization on the other. These linkages sometimes are described as value delivery, but they may go further than
the traditional value chain, because a two-sided business model that has two sets of customers typically also involves two
value chains d one for each side of the market. These linkages can be described by the architecture of information flows and
system governance (Amit and Zott, 2001; Casadesus-Masanell and Ricart, 2010). We do not discuss these dimensions in any
detail as they are generally very well understood. Important contributions to this idea come from the literature on vertical
integration (Williamson, 1985) and on hierarchy vs. network (Lorenzoni and Baden-Fuller, 1995), and research on other arrangements that can extend beyond the firm to upstream supplier networks, and downstream linkages with and among
customers.
Table 1
Examples of the Business Models
Fast food chain – franchised
BM
Boutique strategy
consultant BM
Defense contractor
BM
Newspaper (1990s)
BM
Search Engine BM
CUSTOMER
IDENTIFICATION
Are users paying and
if not who are the
other customers?
SIMPLE BM
User pays with
franchisee as an
intermediary
SIMPLE BM
User pays
SIMPLE BM
User is typically
the government who
pays
HYBRID BM
Readers pay per copy
Advertisers contribute
bulk of revenues
HYBRID BM
Free for users,
but advertisers pay
CUSTOMER
ENGAGEMENT
“Taxi” or “Bus”
BUS
Scale based
TAXI
Bespoke projects
TAXI
Usually project based
BUS Readers and
advertisers
are given bus service
BUS for users
TAXI for advertisers
VALUE CHAIN LINKAGES
Integrated,
hierarchy
or networked
Highly tiered system
of suppliers and
franchisees,
who are linked
hierarchically
Almost all value is
delivered
by the firm, little
outsourcing
Complex system of
arrangements among
many partners
Content and production
are typically hierarchical
but sometimes network
Complex tightly
controlled
linkages
orchestrated
by firm
MONETIZATION
When, What and How
is money raised
COMPLEMENTARY ASSETS
Franchisee collects
money from
consumer and
passes on fee
VALUE
Often priced on the
basis of fee plus share
of the value created
COST
Staged payments and
often cost plus
contract
TWO-SIDED
Everyone pays close to
point of use
TWO-SIDED
Advertisers pay
after service is
delivered
Table reserved to Charles Baden-Fuller, 2013, reproduced by permission.
C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 421
Monetization
The last component of the business model is monetization, often labeled value capture. Discussions of monetization have
often stopped with pricing ignoring important issues of timing and effectiveness which are paramount additional value
capture dimensions for organizations. Concerning pricing, there are many other possibilities, including negotiated prices, and
price based on value delivered. One of the most important contributions to our understanding of pricing comes from Teece
(1986), who stresses the role of complementary assets. Complementary assets can leverage monetizing opportunities,
particularly in the case of the razor-blade model. In cases such as the fast-food franchise-system noted in Table 1, the user pays
the complementary asset provider who in turn pays the provider.
In addition, there are important questions about when the money is collected d before the sale; at the point of sale; or
after the sale. A very important choice in the business model for durables is whether to rent a machine or sell it outright. The
rental system implies a different form of customer engagement and different timing for collecting income that can be tailored
more closely to value-based pricing.
Refining the innovation performance link
Strategy scholars have underplayed the role of business model choice in their search for establishing a link between
technology innovation and competitive advantage. The typical assumption that a radically improved product or service offering will over time automatically lead to increased profits for the innovating firm(s), ignores the enormous problems that
firms face in working out the interdependencies between business model choice and technology effectiveness.
A given technology seldom operates in isolation from other technologies; interoperability is required in order to create the
intended value. This is a well-recognized relationship, but it recently has become more intense, dynamic and uncertain, due to
the arrival of sophisticated information technology and greater availability of platform technologies. Those who assume a
simple relationships between technology development and the performance outcomes for a firm or firms ignore the
moderating influence of business model choice. Business model choice determines the nature of complementarity between
business models and technology and the paths to monetization. A poor choice can lead to low profits, a good choice to superior profits.
Many examples exist of these interdependencies. Business models for navigation systems, for example, present solutions
as standalone units or as mobile applications that take into account information shared by other drivers d such as Waze, a
mobile map application acquired by Google in 2013. In this example, the map and the satellite navigation systems of Waze
link users to each other via the Internet, and they can share local information. The technology’s main platform is the mobile
phone operating system. The application also links to Facebook, which allows drivers to discover friends from their social
network while on the road. Localized advertisement and news monetize the value created through what Waze vice president
Elish describes as an “improved driving experience” (Elish, 2013). In the example of Waze, the business model choice determines the profitability of the technology. By choosing a two-sided business model that links customers with each other
and with advertisers d as opposed to a simple single sided business model d Waze appears to have increased its value many
times to nearly $1 billion.
Competitive dynamics not only influence product margins but also the viability of the business model. Recent work by
Eisenmann et al., 2011 analyzes competition between platform firms showing that survival of the entire platform depends on
complementarity of the offering, which in turn depends on technology, features and interoperability. The interactions between technology and business model are substantially more complex and more dynamic with two-sided business models
(see for instance Casadesus-Masanell and Yoffie, 2007 on Wintel; Casadesus-Masanell and Zhu, 2010 on two sided platforms).
We can explore this issue further with a simple example: the video game industry. Video games originally were played on
either a home computer or a specialist console. Technological innovations, in the form of better animation, better controls, or
better visual experience, typically yielded profits for the innovator. Recently, the industry has become more complex.
Although many games are still available via traditional routes, some of the most successful firms are offering games through
other channels, including the web. Firms often adopt a two-sided business model where most users pay nothing and
monetizing occurs through advertising and “freemium” pricing models. This development highlights the sensing dimension
of the business model d firms are offering the same product or service to customers in different ways with different methods
of engagement and different monetization routes. These engagement and monetization structures have to adapt to the
changes taking place on the platform, including those initiated by providers of complementary assets in other sectors that
influence the other side of the platform.
Zynga, a leading web-based game publisher in 2013, offers the popular game FarmVille in the freemium mode. Users can
play for free and acquire in-game assets, if they wish. Game play happens via a social network, such as Facebook. Zynga earns
money from a small fraction of its users, depending largely on social networks to do business, earning money from crosssellers and advertisers. Zynga chose Facebook for its main platform, and took advantage of a number of critical features
the platform offers, such as comparability of game scores among friends and advertisements. But, Zynga also has to consider
whether and when it makes sense to switch between platforms and which new features of Facebook are appropriate for the
context of gaming. Product extensions that take advantage of user content shared on the social network touch upon the
element of customer engagement for the service provider, and critically depend on available technology that may or may not
be appropriated. The negotiation processes between the involved parties about innovation in gaming and access to platform
422 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426
technology also impact monetization through decisions about questions such as whether financial transactions should run via
Facebook, or bypass the platform.
Therefore, an important research agenda for technology strategy scholars is to unpick the interdependencies between
business model choice, technology development, and success. Theory building needs to be matched by skillful empirical
work. Making business model choice a moderator, and including the factors that influence business model change in a dynamic manner, will lead to a better understanding of the fundamentals of the relationship. And it will also allow strategists to
comment more succinctly and usefully on key contingencies – such as, why so many innovative products fail; how successful
firms conceive the relationship between technology and business model; and how they conceive the dynamics of the process
of business model adjustment.
Developing the right technology
A number of actors d including systems integrators (Prencipe et al., 2003), entrepreneurs (Garud and Karnøe, 2003), or
users (Von Hippel, 1988) d play a key role in trying to answer the long-standing question: What determines the direction of
technology evolution? These actors will be driven by the cognitive frames they hold that connect perceived customer desires
to the innovation agenda. Business models are not just statements of economic linkages but also cognitive devices; business
models held in the minds of these actors influence technological outcomes.
These cognitive business models exist even before the technology is designed and the products are built. At one extreme,
the developer’s business model could be something very simple and formed by the developer’s own preferences concerning
who the customer is and the method of customer engagement (Denyer et al., 2011; Haefliger et al., 2011). Or, it may be driven
by the current belief system of the company (e.g., the discussion of Kodak by Tripsas and Gavetti, 2000; or Xerox by
Chesbrough, 2010). At the other extreme, the actor may have a very rich and free-flowing view of the world, influenced by
deep knowledge and understanding of social and technical possibilities and unencumbered by immediate external biases. It is
not the purpose of this paper to explore how the cognitive frames come about; this is a separate concern (e.g., Baden-Fuller
and Mangematin, 2013 forthcoming). Our purpose is to explain why differences in these business model frames produce
widely different outcomes.
We highlight two important factors in the business model that influence development: the role of openness, and the role
of users. Openness refers to the permeability of the company boundaries. Chesbrough (2003) initially coined the term “open
innovation” to acknowledge the potential value for firms in buying and selling intellectual property that has not yet reached
the product stage (Arora et al., 2001). Openness now has come to have a broader meaning with recognition that process
technology may not be patented and is difficult to protect. It may nonetheless be valuable for users or even competitors to
share technology without asking for compensation (Henkel, 2006), because sharing may lead to learning and to the establishment of communities with similar, professional interests.
We argue that it is not just openness that matters in determining technological trajectories, but the connectivity between
openness and user engagement d again a business model choice. The paternalistic view that management knows what is
best for the customers is being challenged by the growth and success of mass customization business models (where mass
customization is taken to mean organizational responsiveness to customer requests for differentiation – see Ogawa and Piller,
2006). This responsiveness can be fostered by crowd sourcing and by open and user innovation (Jeppesen and Lakhani, 2010;
Hienerth et al., 2011), as well as by the advent of information technology that makes these new business models scalable.
This responsiveness can allow involvement by customers deep into the fabrication process, and offer toolkits to customers
that allow them access and express choice in technology development and design (Franke and Piller, 2004). These involvements have been associated with new discoveries and innovations across many industries, from extreme sports
(Baldwin et al., 2006) to software (Bonaccorsi et al., 2006).
For example, we know that software development flourishes in online communities (Roberts et al., 2006) and that
companies have increasingly taken advantage of linking and liaising with these communities to share technology and
knowledge (Colombo et al., 2013; Dahlander and Magnusson, 2008). By definition, choice of engagement with online
communities and the integration of customers into the development process is a business model choice. Customer
engagement may occur through a tool kit of standard choices, or through contributed innovative ideas and technical solutions
(Franke and Piller, 2004; Füller et al., 2008). Greater customer engagement leads to increased value creation for both sides
(Franke et al., 2010). Firms contributing to online communities as clients and developers of software products share their
adaptations of the product with vendors and competitors, for improved next release and to increase system good will
(Henkel, 2009). Thus, we can see that there is an interaction between business model choice and the direction of technology
development.
And in another example, that of the T-shirt, we see how business model choice about openness and scalability has
influenced development. The textile industry was at the core of the industrial revolution in the 19th century. Throughout
most of the subsequent history of the industry, producers followed a traditional business model of customer sensing and
engagement that involved professionals designing, large scale manufacturers assembling and the results sold in shops and by
mail order. In the late 20th century, more advanced printing and stitching machines brought about the option of customization at the final stage of production. Customers could bring their own designs and have T-shirts made to order. Firms that
adopted this novel business model became highly successful and changed the industry. More recently, the T-shirt maker
C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 423
Threadless has followed an even newer business model d inviting both designers and consumers to directly interact, making
T-shirts for themselves offering the same designs to others for cash prize rewards.
Firms such as Threadless exploit novel two sided business models that match designers to consumers. In the new
Threadless system, freelance professional designers work with far greater freedom than was possible in the old system, and a
large group of consumers experience what it is like to be a designer giving them new experiences and pleasure. This has
opened up a new innovation path d the possibility of novel designs that become popular faster than traditional designer-led
or user-led methods. Threadless can also minimize production risks by not producing shirts unless enough customers have
pre-ordered the designs they endorse.
Thus far we have talked of the way that business model choice influences technological and firm development, but it is
pertinent to ask if technology also acts on business model possibilities. Looking at the T-shirt example, we would argue that it
might have been possible to set up a similar business model before the advent of the Internet. Designs could have been posted
in a public space and pre-orders could be made known to the manufacturer. In fact, this business model did exist. Fashion
design has always involved elite circles of customers who influence design. However, the scale at which this model works
today is unprecedented. Scale influences both the reach of openness d so that anyone with access to the Internet can submit
designs and vote and buy d and, it influences the possibilities for collaboration. Published designs receive comments from
registered customers and designers can react, adapt and resubmit their designs.
Management agenda
Managers need to be creative in the face of this complex interplay between innovation and business model elements. They
can approach these problems experimentally (McGrath, 2010) or by following recipes (e.g. Sabatier et al., 2010). Models also
may help them expand their reasoning (Baden-Fuller and Morgan, 2010), and recognize the value of involving others such as
the developer of technology in the design of the business model. This is what Baldwin and Clark (2006) call the “architecture
of participation”.
Managers need to decide who should be involved, where control should be exercised in these domains and where selforganization should lead the way (von Krogh et al., 2012). Different stakeholders perceive different domains as more central or dominant. Technology developers understand the agenda and possibilities for a technology to be used but may miss the
implications for monetization or market demand. On the other hand, marketing experts may hold deep insights into
customer behavior but may not understand what a given technology could be expected to deliver.
Taking an ecosystem perspective may also help because it focuses attention on how the systems integrators need to form
expectations about the scientific and technological fields underlying the components and sub-systems (Dosi et al., 2003).
Even where innovation is not “open” beyond the specification of interfaces, the supplier needs to understand and link to the
technological and organizational environment within which their components are sold in order to understand the market.
Systems integrators, platforms, and multi-sided markets share what is sometimes referred to as a business ecosystem. For
managers, the ecosystems perspective holds the promise of opening up the wider entrepreneurial and collaborative space
that a new technology affords d and provides room for novel business models to succeed.
Discussion
In summary, we first noted that choice of business model influences the way in which technology is monetized and the
profitability for the relevant firms. We then noted that the business model frames managers, entrepreneurs, and developers
hold in their heads also determine the way in which technology gets developed d and that these connections are capable of
being very powerful. This means that the connection between business model choice and technology is two-way and
complex d something that has received little attention. But this relationship is capable of being unpicked and understood
(see Jacobides and Billinger, 2006, on make-or-buy decisions). And we also recognize that technology will itself influence
business model possibilities.
This means that technology from other sectors such as information technology influences the way in which a business
model can be created and adapted. The mobile phone application is one such technology that serves as a process innovation
for gaming or navigation. For example, Waze and Zynga (described above), use app technology extensively. Waze relies
entirely on mobile phone applications, and Zynga needs application technology to work reliably on tablet computers such as
the Apple iPad series of hardware. If performance improvements rely on both process innovation and business model changes
the traditional S-curve in technology management needs to be re-visited.
Whether we are trying to understand the past or influence the future, we need to model the link between technology
development and firm performance, taking into account competitive dynamics, the influence of technology on business
model innovation, and the organization of technology development. In many markets d especially those influenced by new
digital technology d it is not obvious how to develop and appropriate technology because customer demands are shifting and
technology possesses agency of its own (Orlikowski, 1992; Leonardi, 2011). The business model may have to change in order
to appropriate features of a technology that create customer value. Also, elements of the model may change in order to allow
technology to be developed that fits customer needs or that emerges from the customer directly (Hienerth et al., 2011).
We note that a larger theoretical issue behind this observation is to what extent the organization of technology can and
should mirror the structure of the underlying technology. The question is known as the “mirroring hypothesis” (Colfer and
424 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426
Baldwin, 2010). Sosa et al. (2004, 2007) demonstrated for large engineering projects that an alignment of team structures
with technical modules enhances development effectiveness and saves costs. This points to an important open question, to
what extent business model elements can and should map the modularity of the technology applied and under development,
and vice versa. Modularity has a long history in strategy and innovation (Alexander, 1964; Garud and Kumaraswamy, 1995;
Baldwin and Clark, 2000), and, both cognitively and practically, it offers insights that link the stage of technology development with the organization of innovation and customer engagement (Brusoni and Prencipe, 2006; Baldwin & Clark, 2006,
Argyres, 1999). Modularity is a model of technology development that could help explain technological development and the
joint implications of changing customer demands and technological evolution for the business model.
Lastly, business models are recipes and represent tools for management. They can be blindly replicated or applied to
settings that deserve attention to difference and creativity. Business models contain theory and assumptions about customer
behavior and agency that may not hold in a specific situation. Some of the key assumptions deal with rationality in decision
making. Recent work on performativity shows the existence of purposeful efforts to uphold rationality in organizations
(Cabantous and Gond, 2011). Rationality may be served by modeling but may not necessarily help business and managerial
decision-making in practice. Creativity and innovation emerge from passion and non-rational pursuits (Rindova et al., 2009),
or from unusual sensitivity to discover and disclose (Spinosa et al., 1997) in business, as much as in technology or the arts.
Thus, we call for drawing this distinction more clearly in business model research to recognize where rational decisionmaking deserves its rightful place, and where other, possibly finer forms of deliberation and perception should guide
managerial action.
The authors can be reached at: [email protected], and haefl[email protected]. We would like to acknowledge helpful
comments from Paul Nightingale, Andre Spicer, Mary Morgan, Vincent Mangematin and the financial support of the UK
Research Council (EP/K039695/1 Building Better Business Models). We thank Jim Robins for encouragement and the
opportunity.
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Biographies of authors
Charles Baden-Fuller is Centenary Professor of Strategy, Cass Business School, City U. London, and Senior Fellow, Wharton School, U. Penn. He is also a Fellow
of the Strategic Management Society, and winner of the Lord Mayor of London’s Chancellor’s Prize at City University. He is very widely published and
recognised as a thought leader on the topics of rejuvenating mature businesses, cognition and competition, high technology entrepreneurship and business
models. He advises senior executives and boards on topics of strategy in private international businesses and not-for profit organisations. He was editor-inchief of Long Range Planning from 1999 to 2010. Email: [email protected]
Stefan Haefliger is Reader in Innovation at Cass Business School. He has published in Management Science, Research Policy, and MIS Quarterly, on knowledge
reuse and private-collective innovation contributing to a deeper understanding of the development strategies and practices of open source software
developers as well as the entrepreneurial consequences of user innovation. He is an associate editor for Long Range Planning. Email: haefl[email protected]
426 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 City, University of London Institutional Repository
Citation: Baden-Fuller, C. and Haefliger, S. (2013). Business Models and Technological
Innovation. Long Range Planning, 46(6), pp. 419-426. doi: 10.1016/j.lrp.2013.08.023
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City Research Online
Business Models and Technological Innovation
Charles Baden-Fuller, Stefan Haefliger
Business models are fundamentally linked with technological innovation, yet the business model construct is essentially separable from
technology. We define the business model as a system that solves the problem of identifying who is (or are) the customer(s), engaging
with their needs, delivering satisfaction, and monetizing the value. The framework depicts the business model system as a model
containing cause and effect relationships, and it provides a basis for classification. We formulate the business model relationship with
technology in a two-way manner. First, business models mediate the link between technology and firm performance. Secondly, developing the right technology is a matter of a business model decision regarding openness and user engagement. We suggest research
questions both for technology management and innovation, as well as strategy.
2013 Elsevier Ltd.
Introduction
The business model construct has become attractive to many academics, taking on its own momentum as is evidenced by
the fact that, in the three years since publication, the Long Range Planning (2010) special issue on business models attracted
more than 150,000 downloads and more than 3,500 Google Scholar and more than 500 ISI citations. Yet, the construct has
also attracted criticism. Zott et al., (2011) complain that business models “have yet to develop a common and widely accepted
language that would allow researchers who examine the business model construct through different lenses to draw effectively on the work of others”, because there appears to be a diverse set of business model definitions and a diverse set of
approaches to classification. These views reflect confusion that has taken energy away from proper dialogue on key questions
d What are the components of a business model, and how does business model innovation occur?
In this piece, we explore one clear emerging view of the business model construct, and examine where this view takes us
in terms of understanding an issue referred to by authors such as Chesbrough (2003, 2010) concerning the relationship
between business model innovation and technical innovation. While researchers such as Osterwalder et al. (2010) and Demil
and Lecocq (2010) have proposed that the business model concept lies within the traditional strategy lexicon of competitive
advantage, we argue that the business model is a stand-alone concept in its own right because a business model is a model
(Baden-Fuller and Morgan, 2010). Similarly, Markides and Sosa (2013) distinguish between market entry decisions and
business model choice. And we also explain how this view accords with the widespread recognition in the literature that a
business model should be able to link two dimensions of firm activity d value creation and value capture (Amit and Zott,
2001; Zott and Amit, 2010; Casadesus-Masanell and Ricart, 2010; Teece, 2010).
We have not mentioned technology in this conception of the business model. How do technology and business models
interact? Technology development can facilitate new business models d the most obvious historical example is the way the
invention and development of steam power facilitated the mass production business model. But, business model innovation
can also occur without technology development, as occurred in 1980s when the Japanese pioneered the “just in time”
production system. In fact, business models and technologies regularly interact. For example, when Amazon was founded in
1995, they applied new technology to make the traditional mail-order business model pioneered by Sears Roebuck work well
for books. Amazon did not invent a new business model, nor did Easy-Jet (one of Europe’s most successful low-cost airlines)
when it copied the business model pioneered by Southwest Airlines. Both Amazon and Easy Jet applied well-known business
model constructs and developed them in new contexts.
In contrast, Google’s two-sided dynamic search engine developed in 2003 was not just a technological leap d it was also a
business model leap. Google used Adwords to provide an interface with advertisers (one side of the two sided platform)
whose choices directly influenced the search experience of users on the other side of the platform. Google was probably the
first company in the world to create the type of scalable dynamic two-sided platform modeled by Rochet and Tirole (2006).
Contents lists available at ScienceDirect
Long Range Planning
journal homepage: http://www.elsevier.com/locate/lrp
0024-6301 2013 Elsevier Ltd.
http://dx.doi.org/10.1016/j.lrp.2013.08.023
Long Range Planning 46 (2013) 419–426
Open access under CC BY-NC-ND license.
Open access under CC BY-NC-ND license.
The novelty of their approach lay in linking the two sides in a constantly changing manner that allowed greater consumer
satisfaction and greater revenues for any given set of users on each side of the platform.
Discussions of the effects of business models on performance do not always separate the effects of business model
innovation from technological innovations (c.f. Zott and Amit, 2007; Casadesus-Masanell and Ricart, 2010). We know that
technological innovation influences performance (cf. Bierly and Chakrabarti, 1996; Christensen and Bower, 1996; Zaheer and
Bell, 2005; and reviews by Evanschitzky et al., 2012; Hauser et al., 2006). But, to improve our understanding, we need a more
precise appreciation of how innovation links to performance through the business model, and how changes in the business
model influence technological innovation. The fact that positive effects of technological innovation on business performance
are easily observed has diverted attention from questions about how business models change in the wake of innovation. At
the same time, management theory requires more precision concerning the means by which business model changes enable
and foster innovation.
This piece will first explore key relationships that are embedded in the business model construct, and then briefly review
what we know about these relationships with a view to building a research agenda for the future. We begin by exploring what
a novel business model is and how new business models are related to new technologies. We note that this approach of seeing
the business model as a model is similar to the logic of reasoning and understanding that exists in economics, biology and
physics. In each of these fields, as explained by philosophers of science, models are “manipulable instruments with which to
reason and into which to enquire” and tools that “allow the user of the model to explore ideas” (Morrison and Morgan, 1999;
Morgan, 2012). This allows us to assert that it is intellectually robust to ask: “Is business model innovation potentially separate
from technological innovation?” d even though business model possibilities often rely on technology.
Building the framework
As noted, work on classifying business models has proceeded along two lines. First, there are researchers and commentators who see the business model concept as part of the strategy lexicon and intertwined with technology. They talk of
“novel” and “efficient” business models if a new technology is incorporated into a business to produce a superior effect (e.g.,
Zott and Amit, 2007; Osterwalder and Pigneur, 2010). Secondly, there are researchers, such as Teece (2010) or Baden-Fuller
and Morgan (2010), who see the concept of the business model as potentially separable from technology and strategy and
examine how understanding business models and business model innovation might shed light on core strategy and technology questions. This latter approach has the potential to answer the long-standing challenges posed by Chesbrough (2010),
who asks when a novel technology requires a novel business model, and when the combination of a novel technology and a
novel business model lead to competitive advantage.
Classification is necessary in order to understand innovation because only then can we appreciate what is meant by
new. Two major approaches to classification can be found in the literature. First, classification has been approached
taxonomically by trying to build a picture from looking backwards at empirical work d taking the outputs or location of
the use of the model as central. Thus, we have the classification of Wirtz et al., 2010, which stressed the difference between
content, commerce, context and connecting business models, and from Zott and Amit (2007), who emphasize the efficiency and novelty business models. But an alternative line of argument has emphasized the dimensions of the model
rather than its consequences d a classification that could be described as typological (Hempel, 1965). This type of classification is more forward looking and has been at the heart of discussions that recognize two vital dimensions: Value
creation and Value Capture. Value reaction identifies the customer or customers and how are they engaged (cf. McGrath
and MacMillan, 2000), and value capture identifies how value is delivered and monetized (Teece, 2010). This classic twopart division of the business model (cf. Amit and Zott, 2001), leads to the possibility of a typological, theoretically-driven
categorization that can assist in identifying basic types. In line with this argument, we develop a typology with four dimensions: customer identification, customer engagement, value delivery, and monetization; and we explore what this
typology does for our understanding of business model innovation and its relationship to technical innovation. Table 1
gives examples of the classification of well-known firm-business model configurations. We explain the dimensions
more fully below.
Customers
First, we address the customer identification dimensions of the business model. We stress that with modern technological
possibilities, it is essential that the business model identifies the users and the customers and indicate whether users pay for
what they use or another group of customers actually pays (Teece, 2010). Historically, users almost always have paid, albeit in
many different ways usually at time of purchase or subsequently through a “razor-blade” model of usage charges. But with
newspapers, television, and finally the internet, technology has created the possibility that users may not pay for the services
they receive d payment instead being made by others such as advertisers, as indicated in Table 1. Two-sided platforms of this
type (Rochet and Tirole, 2006) are hybrid business models because they incorporate two value delivery systems d one for the
user (such as a consumer that wants to search) and another for the customer who pays, such as a small firm that wants to
place an advertisement where it can be seen by a particular kind of consumer. The internet did not “invent” two-sided
platforms d they have existed since before the 18th century d but it did facilitate their expansion (see Table 1 for the
example of a dynamic, advertising-supported search engine).
420 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426
Customer engagement
Secondly, we address the question of customer engagement. This requires sensing what the customer-user or groups of
customer-users need, and establishing the value proposition for each of these groups perhaps using the process explained by
McGrath and MacMillan (2000), McGrath (2010), Day and Moorman (2010), and Day (2013). A long-standing distinction
between “project based offerings” and “pre-designed (scale) based offerings” d often described as the “taxi’ and “bus”
systems d is useful in this context. Organizations such as consulting firms and movie makers use the taxi system to create
value by interacting with specific clients to solve specific problems, while organizations such as automobile assemblers and
providers of fast food utilize the bus system and add value by producing “one-size-fits-all” goods or services in a repetitive
manner from a standardized, mass-production format. We suggest this distinction fits with the argument of Thompson (1967)
and Drucker (1986), who proposed a distinction between firms organized in teams and mass production. The categories we
refer to as “project” or “taxi” and “scale” or “bus” are more than just slogans; they are well established, economically-robust,
model-based configurations that have been widely examined by scholars from communities both within and beyond management. These models exhibit clear features; they typically have recognized processes and mechanisms and commonality in
how they utilize “knowledge” and “routines”.
The project-based approach is characterized by bespoke projects responding to customer needs. Its routines are designed to
be particularly effective at three things: dealing with non-routine complex tasks that require the repeated reconfiguration of
organizational structures; responding flexibly to changing client needs; and integrating diverse bodies of knowledge (Davies
and Brady, 2000; Hobday, 2000; Söderlund and Tell, 2010). Table 1 illustrates this with the example of a defense contractor.
In contrast, the “pre-designed” or “scale-based” approach is characterized by products made through scale-based systems
using machines and routines that have a limited capacity to respond “flexibly” to unexpected client needs (e.g., Hounshell,
1984; Chandler, 1990). The fast food hamburger chain discussed in Table 1 is an example of a scale-based business. Business systems using this approach also “integrate diverse bodies of knowledge”, but typically via different processes. There is a
clear theoretical boundary between how project-based and scale-based organizations utilize knowledge in creating value
(e.g., Chandler, 1990; Nightingale, 2000; Nightingale et al., 2011; Hobday, 2000).
Value delivery and linkages
The third component is the set of linkages between identifying the customer groups, and sensing their needs on the one
hand, and monetization on the other. These linkages sometimes are described as value delivery, but they may go further than
the traditional value chain, because a two-sided business model that has two sets of customers typically also involves two
value chains d one for each side of the market. These linkages can be described by the architecture of information flows and
system governance (Amit and Zott, 2001; Casadesus-Masanell and Ricart, 2010). We do not discuss these dimensions in any
detail as they are generally very well understood. Important contributions to this idea come from the literature on vertical
integration (Williamson, 1985) and on hierarchy vs. network (Lorenzoni and Baden-Fuller, 1995), and research on other arrangements that can extend beyond the firm to upstream supplier networks, and downstream linkages with and among
customers.
Table 1
Examples of the Business Models
Fast food chain – franchised
BM
Boutique strategy
consultant BM
Defense contractor
BM
Newspaper (1990s)
BM
Search Engine BM
CUSTOMER
IDENTIFICATION
Are users paying and
if not who are the
other customers?
SIMPLE BM
User pays with
franchisee as an
intermediary
SIMPLE BM
User pays
SIMPLE BM
User is typically
the government who
pays
HYBRID BM
Readers pay per copy
Advertisers contribute
bulk of revenues
HYBRID BM
Free for users,
but advertisers pay
CUSTOMER
ENGAGEMENT
“Taxi” or “Bus”
BUS
Scale based
TAXI
Bespoke projects
TAXI
Usually project based
BUS Readers and
advertisers
are given bus service
BUS for users
TAXI for advertisers
VALUE CHAIN LINKAGES
Integrated,
hierarchy
or networked
Highly tiered system
of suppliers and
franchisees,
who are linked
hierarchically
Almost all value is
delivered
by the firm, little
outsourcing
Complex system of
arrangements among
many partners
Content and production
are typically hierarchical
but sometimes network
Complex tightly
controlled
linkages
orchestrated
by firm
MONETIZATION
When, What and How
is money raised
COMPLEMENTARY ASSETS
Franchisee collects
money from
consumer and
passes on fee
VALUE
Often priced on the
basis of fee plus share
of the value created
COST
Staged payments and
often cost plus
contract
TWO-SIDED
Everyone pays close to
point of use
TWO-SIDED
Advertisers pay
after service is
delivered
Table reserved to Charles Baden-Fuller, 2013, reproduced by permission.
C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 421
Monetization
The last component of the business model is monetization, often labeled value capture. Discussions of monetization have
often stopped with pricing ignoring important issues of timing and effectiveness which are paramount additional value
capture dimensions for organizations. Concerning pricing, there are many other possibilities, including negotiated prices, and
price based on value delivered. One of the most important contributions to our understanding of pricing comes from Teece
(1986), who stresses the role of complementary assets. Complementary assets can leverage monetizing opportunities,
particularly in the case of the razor-blade model. In cases such as the fast-food franchise-system noted in Table 1, the user pays
the complementary asset provider who in turn pays the provider.
In addition, there are important questions about when the money is collected d before the sale; at the point of sale; or
after the sale. A very important choice in the business model for durables is whether to rent a machine or sell it outright. The
rental system implies a different form of customer engagement and different timing for collecting income that can be tailored
more closely to value-based pricing.
Refining the innovation performance link
Strategy scholars have underplayed the role of business model choice in their search for establishing a link between
technology innovation and competitive advantage. The typical assumption that a radically improved product or service offering will over time automatically lead to increased profits for the innovating firm(s), ignores the enormous problems that
firms face in working out the interdependencies between business model choice and technology effectiveness.
A given technology seldom operates in isolation from other technologies; interoperability is required in order to create the
intended value. This is a well-recognized relationship, but it recently has become more intense, dynamic and uncertain, due to
the arrival of sophisticated information technology and greater availability of platform technologies. Those who assume a
simple relationships between technology development and the performance outcomes for a firm or firms ignore the
moderating influence of business model choice. Business model choice determines the nature of complementarity between
business models and technology and the paths to monetization. A poor choice can lead to low profits, a good choice to superior profits.
Many examples exist of these interdependencies. Business models for navigation systems, for example, present solutions
as standalone units or as mobile applications that take into account information shared by other drivers d such as Waze, a
mobile map application acquired by Google in 2013. In this example, the map and the satellite navigation systems of Waze
link users to each other via the Internet, and they can share local information. The technology’s main platform is the mobile
phone operating system. The application also links to Facebook, which allows drivers to discover friends from their social
network while on the road. Localized advertisement and news monetize the value created through what Waze vice president
Elish describes as an “improved driving experience” (Elish, 2013). In the example of Waze, the business model choice determines the profitability of the technology. By choosing a two-sided business model that links customers with each other
and with advertisers d as opposed to a simple single sided business model d Waze appears to have increased its value many
times to nearly $1 billion.
Competitive dynamics not only influence product margins but also the viability of the business model. Recent work by
Eisenmann et al., 2011 analyzes competition between platform firms showing that survival of the entire platform depends on
complementarity of the offering, which in turn depends on technology, features and interoperability. The interactions between technology and business model are substantially more complex and more dynamic with two-sided business models
(see for instance Casadesus-Masanell and Yoffie, 2007 on Wintel; Casadesus-Masanell and Zhu, 2010 on two sided platforms).
We can explore this issue further with a simple example: the video game industry. Video games originally were played on
either a home computer or a specialist console. Technological innovations, in the form of better animation, better controls, or
better visual experience, typically yielded profits for the innovator. Recently, the industry has become more complex.
Although many games are still available via traditional routes, some of the most successful firms are offering games through
other channels, including the web. Firms often adopt a two-sided business model where most users pay nothing and
monetizing occurs through advertising and “freemium” pricing models. This development highlights the sensing dimension
of the business model d firms are offering the same product or service to customers in different ways with different methods
of engagement and different monetization routes. These engagement and monetization structures have to adapt to the
changes taking place on the platform, including those initiated by providers of complementary assets in other sectors that
influence the other side of the platform.
Zynga, a leading web-based game publisher in 2013, offers the popular game FarmVille in the freemium mode. Users can
play for free and acquire in-game assets, if they wish. Game play happens via a social network, such as Facebook. Zynga earns
money from a small fraction of its users, depending largely on social networks to do business, earning money from crosssellers and advertisers. Zynga chose Facebook for its main platform, and took advantage of a number of critical features
the platform offers, such as comparability of game scores among friends and advertisements. But, Zynga also has to consider
whether and when it makes sense to switch between platforms and which new features of Facebook are appropriate for the
context of gaming. Product extensions that take advantage of user content shared on the social network touch upon the
element of customer engagement for the service provider, and critically depend on available technology that may or may not
be appropriated. The negotiation processes between the involved parties about innovation in gaming and access to platform
422 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426
technology also impact monetization through decisions about questions such as whether financial transactions should run via
Facebook, or bypass the platform.
Therefore, an important research agenda for technology strategy scholars is to unpick the interdependencies between
business model choice, technology development, and success. Theory building needs to be matched by skillful empirical
work. Making business model choice a moderator, and including the factors that influence business model change in a dynamic manner, will lead to a better understanding of the fundamentals of the relationship. And it will also allow strategists to
comment more succinctly and usefully on key contingencies – such as, why so many innovative products fail; how successful
firms conceive the relationship between technology and business model; and how they conceive the dynamics of the process
of business model adjustment.
Developing the right technology
A number of actors d including systems integrators (Prencipe et al., 2003), entrepreneurs (Garud and Karnøe, 2003), or
users (Von Hippel, 1988) d play a key role in trying to answer the long-standing question: What determines the direction of
technology evolution? These actors will be driven by the cognitive frames they hold that connect perceived customer desires
to the innovation agenda. Business models are not just statements of economic linkages but also cognitive devices; business
models held in the minds of these actors influence technological outcomes.
These cognitive business models exist even before the technology is designed and the products are built. At one extreme,
the developer’s business model could be something very simple and formed by the developer’s own preferences concerning
who the customer is and the method of customer engagement (Denyer et al., 2011; Haefliger et al., 2011). Or, it may be driven
by the current belief system of the company (e.g., the discussion of Kodak by Tripsas and Gavetti, 2000; or Xerox by
Chesbrough, 2010). At the other extreme, the actor may have a very rich and free-flowing view of the world, influenced by
deep knowledge and understanding of social and technical possibilities and unencumbered by immediate external biases. It is
not the purpose of this paper to explore how the cognitive frames come about; this is a separate concern (e.g., Baden-Fuller
and Mangematin, 2013 forthcoming). Our purpose is to explain why differences in these business model frames produce
widely different outcomes.
We highlight two important factors in the business model that influence development: the role of openness, and the role
of users. Openness refers to the permeability of the company boundaries. Chesbrough (2003) initially coined the term “open
innovation” to acknowledge the potential value for firms in buying and selling intellectual property that has not yet reached
the product stage (Arora et al., 2001). Openness now has come to have a broader meaning with recognition that process
technology may not be patented and is difficult to protect. It may nonetheless be valuable for users or even competitors to
share technology without asking for compensation (Henkel, 2006), because sharing may lead to learning and to the establishment of communities with similar, professional interests.
We argue that it is not just openness that matters in determining technological trajectories, but the connectivity between
openness and user engagement d again a business model choice. The paternalistic view that management knows what is
best for the customers is being challenged by the growth and success of mass customization business models (where mass
customization is taken to mean organizational responsiveness to customer requests for differentiation – see Ogawa and Piller,
2006). This responsiveness can be fostered by crowd sourcing and by open and user innovation (Jeppesen and Lakhani, 2010;
Hienerth et al., 2011), as well as by the advent of information technology that makes these new business models scalable.
This responsiveness can allow involvement by customers deep into the fabrication process, and offer toolkits to customers
that allow them access and express choice in technology development and design (Franke and Piller, 2004). These involvements have been associated with new discoveries and innovations across many industries, from extreme sports
(Baldwin et al., 2006) to software (Bonaccorsi et al., 2006).
For example, we know that software development flourishes in online communities (Roberts et al., 2006) and that
companies have increasingly taken advantage of linking and liaising with these communities to share technology and
knowledge (Colombo et al., 2013; Dahlander and Magnusson, 2008). By definition, choice of engagement with online
communities and the integration of customers into the development process is a business model choice. Customer
engagement may occur through a tool kit of standard choices, or through contributed innovative ideas and technical solutions
(Franke and Piller, 2004; Füller et al., 2008). Greater customer engagement leads to increased value creation for both sides
(Franke et al., 2010). Firms contributing to online communities as clients and developers of software products share their
adaptations of the product with vendors and competitors, for improved next release and to increase system good will
(Henkel, 2009). Thus, we can see that there is an interaction between business model choice and the direction of technology
development.
And in another example, that of the T-shirt, we see how business model choice about openness and scalability has
influenced development. The textile industry was at the core of the industrial revolution in the 19th century. Throughout
most of the subsequent history of the industry, producers followed a traditional business model of customer sensing and
engagement that involved professionals designing, large scale manufacturers assembling and the results sold in shops and by
mail order. In the late 20th century, more advanced printing and stitching machines brought about the option of customization at the final stage of production. Customers could bring their own designs and have T-shirts made to order. Firms that
adopted this novel business model became highly successful and changed the industry. More recently, the T-shirt maker
C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 423
Threadless has followed an even newer business model d inviting both designers and consumers to directly interact, making
T-shirts for themselves offering the same designs to others for cash prize rewards.
Firms such as Threadless exploit novel two sided business models that match designers to consumers. In the new
Threadless system, freelance professional designers work with far greater freedom than was possible in the old system, and a
large group of consumers experience what it is like to be a designer giving them new experiences and pleasure. This has
opened up a new innovation path d the possibility of novel designs that become popular faster than traditional designer-led
or user-led methods. Threadless can also minimize production risks by not producing shirts unless enough customers have
pre-ordered the designs they endorse.
Thus far we have talked of the way that business model choice influences technological and firm development, but it is
pertinent to ask if technology also acts on business model possibilities. Looking at the T-shirt example, we would argue that it
might have been possible to set up a similar business model before the advent of the Internet. Designs could have been posted
in a public space and pre-orders could be made known to the manufacturer. In fact, this business model did exist. Fashion
design has always involved elite circles of customers who influence design. However, the scale at which this model works
today is unprecedented. Scale influences both the reach of openness d so that anyone with access to the Internet can submit
designs and vote and buy d and, it influences the possibilities for collaboration. Published designs receive comments from
registered customers and designers can react, adapt and resubmit their designs.
Management agenda
Managers need to be creative in the face of this complex interplay between innovation and business model elements. They
can approach these problems experimentally (McGrath, 2010) or by following recipes (e.g. Sabatier et al., 2010). Models also
may help them expand their reasoning (Baden-Fuller and Morgan, 2010), and recognize the value of involving others such as
the developer of technology in the design of the business model. This is what Baldwin and Clark (2006) call the “architecture
of participation”.
Managers need to decide who should be involved, where control should be exercised in these domains and where selforganization should lead the way (von Krogh et al., 2012). Different stakeholders perceive different domains as more central or dominant. Technology developers understand the agenda and possibilities for a technology to be used but may miss the
implications for monetization or market demand. On the other hand, marketing experts may hold deep insights into
customer behavior but may not understand what a given technology could be expected to deliver.
Taking an ecosystem perspective may also help because it focuses attention on how the systems integrators need to form
expectations about the scientific and technological fields underlying the components and sub-systems (Dosi et al., 2003).
Even where innovation is not “open” beyond the specification of interfaces, the supplier needs to understand and link to the
technological and organizational environment within which their components are sold in order to understand the market.
Systems integrators, platforms, and multi-sided markets share what is sometimes referred to as a business ecosystem. For
managers, the ecosystems perspective holds the promise of opening up the wider entrepreneurial and collaborative space
that a new technology affords d and provides room for novel business models to succeed.
Discussion
In summary, we first noted that choice of business model influences the way in which technology is monetized and the
profitability for the relevant firms. We then noted that the business model frames managers, entrepreneurs, and developers
hold in their heads also determine the way in which technology gets developed d and that these connections are capable of
being very powerful. This means that the connection between business model choice and technology is two-way and
complex d something that has received little attention. But this relationship is capable of being unpicked and understood
(see Jacobides and Billinger, 2006, on make-or-buy decisions). And we also recognize that technology will itself influence
business model possibilities.
This means that technology from other sectors such as information technology influences the way in which a business
model can be created and adapted. The mobile phone application is one such technology that serves as a process innovation
for gaming or navigation. For example, Waze and Zynga (described above), use app technology extensively. Waze relies
entirely on mobile phone applications, and Zynga needs application technology to work reliably on tablet computers such as
the Apple iPad series of hardware. If performance improvements rely on both process innovation and business model changes
the traditional S-curve in technology management needs to be re-visited.
Whether we are trying to understand the past or influence the future, we need to model the link between technology
development and firm performance, taking into account competitive dynamics, the influence of technology on business
model innovation, and the organization of technology development. In many markets d especially those influenced by new
digital technology d it is not obvious how to develop and appropriate technology because customer demands are shifting and
technology possesses agency of its own (Orlikowski, 1992; Leonardi, 2011). The business model may have to change in order
to appropriate features of a technology that create customer value. Also, elements of the model may change in order to allow
technology to be developed that fits customer needs or that emerges from the customer directly (Hienerth et al., 2011).
We note that a larger theoretical issue behind this observation is to what extent the organization of technology can and
should mirror the structure of the underlying technology. The question is known as the “mirroring hypothesis” (Colfer and
424 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426
Baldwin, 2010). Sosa et al. (2004, 2007) demonstrated for large engineering projects that an alignment of team structures
with technical modules enhances development effectiveness and saves costs. This points to an important open question, to
what extent business model elements can and should map the modularity of the technology applied and under development,
and vice versa. Modularity has a long history in strategy and innovation (Alexander, 1964; Garud and Kumaraswamy, 1995;
Baldwin and Clark, 2000), and, both cognitively and practically, it offers insights that link the stage of technology development with the organization of innovation and customer engagement (Brusoni and Prencipe, 2006; Baldwin & Clark, 2006,
Argyres, 1999). Modularity is a model of technology development that could help explain technological development and the
joint implications of changing customer demands and technological evolution for the business model.
Lastly, business models are recipes and represent tools for management. They can be blindly replicated or applied to
settings that deserve attention to difference and creativity. Business models contain theory and assumptions about customer
behavior and agency that may not hold in a specific situation. Some of the key assumptions deal with rationality in decision
making. Recent work on performativity shows the existence of purposeful efforts to uphold rationality in organizations
(Cabantous and Gond, 2011). Rationality may be served by modeling but may not necessarily help business and managerial
decision-making in practice. Creativity and innovation emerge from passion and non-rational pursuits (Rindova et al., 2009),
or from unusual sensitivity to discover and disclose (Spinosa et al., 1997) in business, as much as in technology or the arts.
Thus, we call for drawing this distinction more clearly in business model research to recognize where rational decisionmaking deserves its rightful place, and where other, possibly finer forms of deliberation and perception should guide
managerial action.
The authors can be reached at: [email protected], and haefl[email protected]. We would like to acknowledge helpful
comments from Paul Nightingale, Andre Spicer, Mary Morgan, Vincent Mangematin and the financial support of the UK
Research Council (EP/K039695/1 Building Better Business Models). We thank Jim Robins for encouragement and the
opportunity.
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Biographies of authors
Charles Baden-Fuller is Centenary Professor of Strategy, Cass Business School, City U. London, and Senior Fellow, Wharton School, U. Penn. He is also a Fellow
of the Strategic Management Society, and winner of the Lord Mayor of London’s Chancellor’s Prize at City University. He is very widely published and
recognised as a thought leader on the topics of rejuvenating mature businesses, cognition and competition, high technology entrepreneurship and business
models. He advises senior executives and boards on topics of strategy in private international businesses and not-for profit organisations. He was editor-inchief of Long Range Planning from 1999 to 2010. Email: [email protected]
Stefan Haefliger is Reader in Innovation at Cass Business School. He has published in Management Science, Research Policy, and MIS Quarterly, on knowledge
reuse and private-collective innovation contributing to a deeper understanding of the development strategies and practices of open source software
developers as well as the entrepreneurial consequences of user innovation. He is an associate editor for Long Range Planning. Email: haefl[email protected]
426 C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426