From Disruptive Technology to Disruptive Business Model Innovation: In
Need for an Integrated Conceptual Framework
Solomon R. Habtay
Faculty of Commerce, Law and Management
University of Witwatersrand, Private Bag 3, Wits, 2050, South Africa
Magnus Holmén
Department of Technology Management and Economics
Chalmers University of Technology, 412 96 Göteborg, Sweden
Address correspondence to:
Dr. Solomon R. Habtay
School of Economic and Business Sciences,
Faculty of Commerce, Law and Management,
University of Witwatersrand
Private Bag 3
Wits 2050
South Africa
Email: Solomon.Habtay@wits.ac.za
Phone (27) 117178085
Fax: (27) 117178081
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Abstract
Recently, scholars have conceptualized disruptive innovation as a function of conflict
between the entrant’s and incumbent’s business models, not only an outcome of technological
change. This conceptualization requires examination of disruptive phenomenon from both the
innovator and incumbent’s perspectives. However, extant research mainly examines this
problem from incumbent’s perspective. Drawing on the literature of disruptive innovation,
business model innovation and entrepreneurship, this study proposes a hierarchically linked
two phased conceptual models to analyse disruption from both the entrant’s and incumbent’
perspectives.
Taking the entrant’s perspective, the results of the first model show that a potentially
disruptive niche market is an outcome of (a) an initially substandard value propositions
viewed from incumbents managers’ customer orientation, (b) asymmetric strategic
orientations between the entrant and the incumbent, (c) the entrant’s differential value chain
configurations, (b) the entrant’s emergent innovation capabilities, (d) enter-entrants
competition and (e) mainstream entry barriers. Considering the incumbent’s standpoint, the
second phase model tests the effects of (a) capabilities mismatch between the entrant and the
incumbent, (b) asymmetric incentive systems, and (c) incumbents’ managerial dilemma on
disruptive innovation. The results reveal that a potentially disruptive niche market only
becomes disruptive if conditions of (a) asymmetric incentive systems and (b) incumbents’
managerial dilemma are present. (c) Capabilities misfit does not seem to affect disruptive
innovation. Theoretical and managerial implications are discussed.
Key Words: Disruptive technology, Disruptive Business Model Innovation, Niche Market,
Asymmetric Cognitive Orientations, Asymmetric Incentives.
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Paper
Introduction
Evolutionary theories generally depict a continuously changing business environment at times
punctuated by discontinuous shifts (Tushman and Anderson, 1986; Dosi, 1982; Nelson and
Winter, 1982). While established firms are generally capable to adapt their organizations to
continuously changing environments, they often encounter difficulties dealing with
discontinuous changes. Incumbents’ success or failure to adapt their organizations to
discontinuous innovation was attributed to timing of entry (Mitchell, 1991), firm size and
scale (Suarez and Utterback, 1995), engineering and technical capabilities misfit (Hill and
Rotharmel, 2003; Henderson and Clark, 1990; Tushman and Anderson, 1986; Dewar and
Dutton, 1986), path dependency (Nelson and Winter 1982), co-evolutionary ‘fit’ or lock-in
(Siggelkow, 2002; Burgelman, 2002), embedded managerial strategic orientation (Prahalad
and Bettis, 1995) and structural inertia (Hannan and Freeman, 1984, Leanoard-Barton, 1992).
These studies offered rich typologies for distinguishing between technological innovations
that could enhance existing performances and innovations and that could cause problems to
incumbents.1
By drawing on the resource-base dependency theory (Pfeffer and Salancik, 1978),
Christensen’s (1997) brought in a different perspective. By distinguished between sustaining
and disruptive innovations, he argued that established firms can manage radical or
competence destroying technological innovation despite the difficulties and risks involved.
Sustaining innovations may contain incremental and radical innovations that evolve along the
incumbent’s established trajectory, which existing customers will value. In contrast,
disruptive innovation typically underperforms in mainstream markets compared to the
established product, but it has other features that new and low-end customers value. After
1
Technology based categorizations include, among others, incremental vs. radical innovations (Dewar and
Dutton, 1986), competence-enhancing discontinuities vs. competence-destroying discontinuities (Tushman and
Anderson, 1986) and modular vs. architectural innovation (Henderson and Clark, 1990).
3
growing a niche market over time, a disruptive innovation displaces the mainstream market of
incumbents by moving up-market through a fundamentally different performance trajectory
(Christensen and Raynor, 2003).
Recent empirical studies supported Christensen’s (1997) argument that incumbents can
manage radical technological innovation successfully but stumble with disruptive innovation
(Govindarajan, Kopalle and Danneels, 2011; Zhou, Yim, and Tse, 2005). The reasons are that
the disruptive impact of radical innovation on existing capabilities tends to be obvious when it
emerges and trigger the need for transformation (Henderson and Clarck, 1990), and most
importantly, because it appeals to incumbent’s existing customers (Govindarajan et. al.,
2011). Taking this view, scholars have further advanced disruptive innovation theory by
showing that disruptive innovation is a business model problem, not only a technology
problem (Chesbrough, 2010; Christensen, 2006; Markides, 2006). This definition has
extended the power of the theory to explain different types of disruptive innovations across a
wide range of industries (Schmidt and Druehl, 2008; Slater and Mohr, 2006; Walsh, 2002).2
However, although disruptive innovation is viewed as a business model problem
(Chesbrough, 2010; Christensen, 2006; Markides, 2006), the lack of widely agreed upon
definition of the concept of a business model (Markides, 2008) makes the concept of
disruptive business model innovation a topic that warrants further research. By definition,
there are at least three types of actors involved in disruption, the entrants, the incumbents and
the customers. A business model approach to innovation considers all aspects of innovation
processes and business activities for developing or responding to disruptive innovation, as
opposed to a technology solution alone. However, extant research mainly focuses on
problems facing incumbents (Markides and Charitou, 2004; Christensen and Raynor, 2003).
2
Consequently, disruptive innovation theory has made a profound effect on academics and practitioners in
understanding incumbents’ upheaval in face of discontinuous changes (Schmidt and Druehl, 2008; Danneels,
2004). While Christensen’s (1997) work has generated extensive research, the recent shift of research focus from
a technology to a business model change raises one major issue that needs more attention.
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Thus, conceptualizing disruptive innovation as a function of conflict between the entrant’s
and incumbent’s business models requires examination of disruptive phenomenon from both
the innovator and incumbents’ perspectives.
Therefore, the purpose of this study is to analyze disruptive innovation from a business
model perspective in terms of the relation between the entrants and the incumbents. This
study aims to make three contributions. First, business model innovation research mostly
takes an entrepreneurial perspective to study firm’s innovation processes in developing new
business models (McGrath, 2010; Osterwalder and Pigneur, 2009; Magretta, 2002). But
disruptive innovation research begins where these studies “end” and takes a normative
perspective to understand and address incumbents misfortune in face of disruptive innovation
(Christensen and Raynor, 2003; Gilbert and Bower, 2002). By drawing on business model
and disruptive innovation literature and systematically linking the two concepts, this paper
models the evolution and development of disruptive business model innovation by bringing
the two perspectives together.
Second, disruptive innovation theory views a market change as an antecedent of the
disparity between the disruptive and sustaining markets’ trajectories (Gilbert, 2003;
Christensen and Raynor, 2003). Beyond this explanation, this study shows how asymmetric
managerial cognitive orientations between the entrant and the incumbents’ managers can
inform this disparity during the early stage of the innovation. Third, by systematically
unpacking the differential effects of capabilities, incentive systems and managerial cognition
as underlying mechanisms of disruptive innovation, this study exposes the degree and
magnitude of each of this effect on disruptive innovation.
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What is a business model?
Although there seems no widely agreed upon definition of a business model and its distinct
components (Markides, 2008), at a basic level, scholars do agree that the concept of a
business model concerns with how a firm creates value for customers and how it captures
some of the value (Teece, 2010; Morris, Schindehutte and Allen, 2005; Magretta, 2002).
Chesbrough and Rosenbloom (2002) identified ten interrelated components of a business
model whereas Osterwalder and Pigneur (2009) proposed nine business elements. Integrating
earlier works, Al-Debei and Avison (2010) offered a theoretical framework defining value
proposition, value architecture, value finance and value network as four higher layer
dimensions with sixteen lower layer operational elements. As a theoretical framework, it
consists of complex, granular, dynamic and reciprocally reinforcing components or business
model elements (Al-Debei and Avison, 2010).
Building on these definitions and integrating entrepreneurship and disruptive innovation
literatures, Figure 1 depicts a business model concept with five major components and
subsequent operational elements. These will be discussed in turn.
Figure 1: A Business Model Concept
Cognitive Dimension
Customer
Revenue Model
Market Segmentation
Economic Dimension
Cost Structure
Targeting
Pricing Mechanism
Positioning
Value
Propositions
Quality
Technology
Cognitive Modeling
Price
Brand
Strategy
Value Network
Configuration
Planning
Hard-to-imitate core
capabilities
Cognitive Modeling
Customer Interface
Margin level
Capabilities Dimension
Emergent capabilities
Value Chain
Core Capabilities
Relationships
Cognitive Modeling
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Actors and Roles
A business model innovation often begins with the discovery of (first element) viable
customer value propositions (McGrath, 2010; Afuah and Tucci, 2001) for a (second element)
specific customer segment (Osterwalder and Pigneur, 2009; Shafer, Smith, and Linder, 2005)
by configuring a (third element) value network for creating and delivering the customer value
(Zott and Amit, 2010; Chesbrough and Rosenbloom, 2002). Research shows how a new
business model can result by reinventing systematically across these three dimensions of a
business model, specifically by radically changing the established value propositions,
redefining the existing customer base, deconstructing traditional value network and altering
the firm’s role in the existing value chain (Magretta, 2002; Govindarajan and Gupta, 2001).
Zott and Amit (2010: 216) define the business model “as a system of interdependent
activities that transcends the focal firm and spans its boundaries”. The view of the business
model concept as an interdependent activity system is rooted in the theories of value chain
(Porter, 1985), the resources based view (RBV) (Wernerfelt, 1984), transaction cost
economics (TCE) (Williamson, 1985) and industrial networks (Gulati, Nohria and Zaheer,
2000). The value chain concept describes actors and their activities sequentially linked from
procurement, R&D, production, marketing and sales (Porter, 1985). The RBV theory
identifies a focal firm’s core capabilities and predicts its likely role and position within a
value chain (Barney, 1991). For example, a new firm with engineering competencies may
adopt the R&D model and access complementary capabilities through alliances (Chesbrough,
2003). Using TCE theory, we can examine the innovator’s opportunism, i.e. its relative value
capture potential by assessing whether a firm would integrate vertically to avoid being ‘taken
hostage’ or specialize on few competencies and decide to access other capabilities through
alliances (Williamson, 1985). Industrial network theory identifies actors and roles involved in
a focal firm’s linear and non-linear linkages (Gulati, Nohria and Zaheer, 2000).
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The fourth aspect is strategy. With regard to the relationship between a business model
and strategy concepts, some scholars treat the two concepts as identical (Hedman and Kalling,
2003; Casadesus-Masanell & Ricart, 2010), while others view the two as different (Timmers,
1998; Magretta, 2002; Chesbrough, 2010). This study takes the latter’s view. While a
business model defines the key components and the relationships that hold among them as a
system to create and capture value, a strategy concerns with how organizations should sustain
competitive advantage over rivals (Osterwalder and Pigneur, 2009; Teece, 2010).
The fifth component of a business model is a revenue model, which deals with the
mechanisms used to determine the focal firm’s cost structure, pricing strategy and margin
level, i.e. how much to charge its customers (Zott and Amit, 2010).
The five components seem to be connected theoretically in mind or by managerial
cognition. Teece (2010) argues that a business model reflects managerial hypotheses and a
business logic (economic dimension) by which the relationships of major components
(capabilities dimension) are formed, which means it is a cognitive model that helps managers
to simplify and model their complex business environments using the economic and
capabilities logics. Managerial cognition informs the hypotheses. In other words, managers
create and shape the relationships upon which the components are linked that may set a firm’s
business model’s evolution (Kaplan, and Tripsas, 2008) within the firm’s unique context.
Therefore, a business model has an economic, capabilities and managerial cognition systems
dimensions.
The evolution of disruptive business model innovation along these three dimensions rests
on two key assumptions. First, although managers may have their initial hypotheses, customer
value innovation and the direction of the business model may not be fully anticipated in
advance (McGrath, 2010). So it involves constant market experimentations and learning
(Thomke, 2002; McGrath, 2010; Chesbrough, 2010). Second, one the direction emerges,
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disruptive business model innovation is path dependent (Nelson and Winter 1982). A
potentially disruptive innovation originates and grows in a remote or periphery of the
incumbent’s cognitive radar (Gilbert, 2003). Once its market unfolds, a disruptive firm grows
through the process of adapting to exogenous and endogenous forces, retaining profitable
markets, successful products, investments, processes, routines and values and discouraging
those that do not confirm with its current business model. Siggelkow (2002) characterizes this
adaptation in terms of augmentation, reinforcement, and deletion. A path dependent
explanation of disruptive innovation theory (Nelson and Winter 1982) depicts two trajectories
that progresses in parallel without any intersection (Christensen, 2006).
Once an entrant firm grows through a disparate trajectory, the interwoven capabilities,
cognition and incentive model (Kaplan, 2008) within the niche market may disrupt the
mainstream market, if conditions amplify asymmetric revenue models between the entrant
and incumbent (Reinganum, 1983) and entrants can gain first mover advantage (Cooper and
Schendel, 1976). On the other hand, established firms, particularly successful ones, have
developed efficiently functioning and closely interwoven strategy, technological capabilities,
processes, values and profit models. Although useful in continuously changing business
environments, once an incumbent firm confronts a disruptive change, the co-evolutionary fit
among these components (Sigglekow, 2002; Burgelman, 2002) may impede change if prior
capabilities become unfit (Leonard-Barton, 1992) and inertial forces surface (Terrien and
Mills, 1955). Alternatively, incumbents may isolate a potentially disruptive innovation in a
niche market (Adner and Zemsky, 2005), if industry conditions constrain differential
incentives or if they can leverage prior capabilities (Cohen and Levinthal, 1990).
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A Framework and Hypotheses
Drawing on disruptive innovation theory (Christensen and Ranynor, 2003), capabilities
(Teece, Pisano and Shuen, 1997), business model innovation (Chesbrough, 2010; Zott and
Amit, 2010; Magretta, 2002) and entrepreneurship literature (Carlsson, 1999; Acs and
Audretsch, 1993), this study proposes a conceptual model for disruptive business model
innovation development (Figure 2). Based on the literature review, the conceptual model
depicts two market phases and ten hypotheses.
Figure 2: A conceptual model for disruptive business model innovation
Dilemma
H9
H8
BMs Conflict
Capabilities Misfit
H7
Mainstream Market
Innovation’s
Relative
Advantage (RA)
H1
Incumbents
Customer
Orientation
Differential value
chain
configuration
Competition
Continuous
Innovation (CI)
H6
Potentially
Disruptive
Niche Market
H3
Disruptive
Innovation (DI)
Entrant’s Business Model
Innovation Dynamics for
Creating Potentially
Disruptive Niche Market
Constraints
H5
H2
H4
The first phase posits that a latent disruptive business model innovation creates a niche
market outside of the incumbent’s mainstream market long before it becomes disruptive
(Gilbert, 2003; Rafii and Kampas, 2002). This niche market emerges through evolutionary
and dynamic interplay between the innovator’s emergent capabilities, incumbents’
competitive customer orientation, enter-entrants competition and mainstream market entry
barriers. The second phase portrays that a business model innovation only becomes disruptive
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when it encroaches the mainstream market (Schmidt and Druehl, 2008), while there are
conditions of capabilities misfit (Henderson, 2006; Leanard-Barton, 1992), differential
incentives (Cockburn and Henderson, 1994) and incumbents’ dilemma (Christensen, 1997).
Each of the nine hypotheses is discussed below.
Incumbents Customer Orientation and Emergence of Disruptive Niche Market
The starting point for business model innovation is the formulation of a viable value
proposition (Amit and Zott, 2001) and customer segment (Magretta, 2002). New entrants may
introduce variant customer value attributes with different levels of performances in a
continuum, ranging from ‘inferior’ innovation characterized by higher levels of technological
complexities and market uncertainties at one extreme and radical innovation with better
performance at the high-end extreme. Roger’s (1995) concept of an innovation’s relative
advantage in terms of price, ease of use, technology, quality and brand recognition compared
to established products is a key to examine firms competitive reactions (Kim and
Maubourgne, 1999; Agarwal and Prasad, 1998; Davis, 1989).
Typically, a potentially disruptive innovation emerges in low-end or remote markets in
part because they initially cannot attract the mainstream market because of an inferior value
proposition (Christensen and Raynor, 2003). Research documents that disruptive innovation is
negatively associated with incumbent’s customer orientation during its introduction stage
(Govindarajan et. al., 2011; Zhou et. al., 2005). Firms’ market orientation (Frosch, 1996;
Bennet and Cooper, 1976) is shaped by established industry recipes (Spender, 1989) or
‘macrocultural homogeneity’, a shared belief or common frame about product/market scope
and how firms compete, i.e. who their customers and competitors are (Abrahamson and
Fombrun, 1994). Seen from the incumbents’ managers’ mainstream customers’ value metrics,
initial disruptive innovation’s value propositions are likely to be considered inferior by
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incumbents (Govindarajan et. al., 2011). Incumbents’ customer orientation refers to
managers’ tendency to focus on existing markets and to reject information coming from
emerging markets (low-end or un-served markets) that does not conform to their past and
current successful ways of doing business (Govindarajan et. al., 2011; Frosch, 1996).
Therefore, our first hypothesis is:
H1: A latent disruptive innovation’s value proposition is negatively related to incumbent’s
customer orientation.
Market orientation is informed by managerial cognition (Prahalad and Bettis, 1995). The
role of managerial cognition can then be central to understanding the disparate trajectories
between the disruptive and established markets (Kaplan and Tripsas, 2008). Cognitive frames
are knowledge structures that help executives to reduce uncertainty and ambiguity, make
sense when faced with complex choices, and filter out information that may not conform the
previously successful frames or ways of doing business (Weick, 1995) or ‘dominant logic’
(Prahalad and Bettis, 1995). Early disruptive innovation life cycle is characterized by higher
levels of technological and market uncertainties (Tushman and Anderson, 1986). Kaplan and
Tripsas (2009) argue that given the uncertainty of a technology, cognitive processes can
inform trajectory. We extend this view by arguing that cognition can as well explain the
disparate trajectories between disruptive and sustaining innovations.
From the entrant’s side, in absence of prior established dominant logic, frames, or market
data (van Putten and MacMillan, 2004), a disruptive entrant is likely to be driven by
‘effectuation’ (Sarasvathy, 2008), rather than by analysis and planning of existing market.
This new cognition involves scanning the periphery (Day and Schoemaker. 2005) and search
for ill-defined market that often triggers off a different evolution (Chrsitensen, 1997). In
contrast, the incumbents’ established cognitive frames focus on searching for information or
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scanning technology (Srinivasan, Lilien, and Rangaswamy, 2002) to better serve current
markets (Slater and Narver, 1998). For example, although radical innovation may cause
technological discontinuities, it is arguably presumed to be less disruptive to industry
incumbents because its imminent threat of obsolescence to existing dominant design tends to
be obvious to incumbents’ managers from the outset (Henderson and Clark, 1990) and draws
attention for resource allocation because it often appeals to most profitable mainstream
customers (Goivndarajan et. al., 2011). But in absence of signals about a latent disruptive
innovation, the incumbent’s senior managers may borrow frames from their dominant logic to
evaluate the uncertain innovation (Kaplan and Tripsas, 2008) which often results identifying
it as a risk or less profitable venture that may even encourage incumbents to flee disruptive
markets in order to concentrate their resources on more profitable customers (Christensen and
Raynor, 2003), consequently providing potential disruptive entrants competition-free space
and momentum to experiment and grow.
H2. In the long run, incumbents’ customer orientation is positively related to emergence
of disruptive niche market.
An Entrant’s Different Value Chain Configuration and Emergent Innovation Capabilities
Disruptive business model innovation often results from fundamental changes in the
established value propositions or altering the firm’s role in the existing value chain or both
(Moore, 2004). An activity system (Zott and Amit, 2010) or value chain analysis (Porter,
1985) can help us to understand the new value chain configuration that drives disruptive
innovation (Chesbrough and Rosenbloom, 2002). Looking into the value chain, one can also
evaluate the focal firm’s ability to develop dynamic capabilities (Teece et. al. 1997).
Disruptive innovation may emerge initially inferior, but the innovator’s ability to develop
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emergent capabilities to adapt to endogenous and exogenous innovation drivers is critical
(McGrath, 2010; Thomke, 2002).
Emergent disruptive business model innovation capabilities often result from developing
unique core capabilities that guarantee the innovating firm a unique place in a value chain
(Barney, 1991). This place often results from several advantages including creating novel
value propositions, locking-in customers through network effects (Amit and Zott, 2001),
efficiency or lower cost and high volume advantage (Porter, 1985) and downstream
capabilities that bring innovation to market faster than rivals (Chesbrough, 2003).
In developing a disruptive business model, the most critical source of advantage is the new
value chain’s lower cost structure that can provide the opportunity to experiment with small
investments (Carlsson, 1999). In some circumstances, there is a greater scope for continuous
technological innovation with relatively smaller incremental investments (Acs and Audretsch,
1993). When a disruptive entrant takes upward its low-cost business model to compete against
established firms, much of the incremental costs are likely to fall into the bottom line
(Christensen and Raynor, 2003). For example, Dell’s “built-to-order” direct business model
was synonymous with emergent disruptive business model innovation capabilities in terms of
lower cost advantage, swift learning and speed to market (Govindarajan and Gupta, 2001).
H3: An entrant’s different value chain configuration from the incumbent is positively
related to emergent innovation capabilities.
H4: Emergent innovation capabilities are positively related to emergence of disruptive
niche market.
Enter-Entrants Competition and Emergence of Disruptive Niche Market
There are two general views with regards to how firms compete. In the ‘planning oriented’
perspective, a firm first recognizes a strategic position within the value network by analyzing
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the industry forces (Porter, 1985) or internal hard-to-imitate strategic capabilities (Barney,
1991), then it proceeds to architect a business model to realize its strategy (Hedman and
Kalling, 2003).
In the ‘emergent’ (Mintzberg, 1994) or ‘discovery-driven’ perspective
(McGrath & MacMillan, 1995), firm do not initially commit to a specific strategy. Rather,
they begin life by searching for market opportunities and continue to innovate with less regard
to strategy at least in the initial stage (Magretta, 2002). Technology evolution theory depicts a
ferment era characterized by competitive technological innovations that precedes selection of
a dominant design (Utterback and Suarez, 1993; Anderson and Tushman, 1990; Abernathy,
1978). A disruptive entrant may gain the first mover advantage (Cooper and Schendel, 1976)
which in return might enable to exploit demand-side network externalities in some
technologies (Katz and Shapiro, 1985). However, the disruptiveness capacity of a niche
market as a whole can only increase if the innovation can be replicated and attracts new
competition. Therefore, the emergence of enter-entrants competition can be seen as a signal of
the emergence of a disruptive trajectory (Adner and Zemsky, 2005).
H5: Enter-entrants competition is positively related to emergence of disruptive niche
market.
Mainstream Market Entry Barriers and Emergence of Disruptive Niche Market
The positive effects of a firm’s emergent innovation capabilities on creating and growing a
potentially disruptive niche market can be moderated significantly by lack of latent disruptive
innovation’s economic feasibility, mainstream customers’ switching barriers (Rogers, 2003)
and institutional factors. Population ecologists and evolutionary theories suggest that an
emergent dominant design and market selection of a new and equivocal technology are
defined not only by organizational capabilities but also by complex economic, market,
institutional and competitive factors (Suarez and Utterback, 1995; Barnett and Carol, 1995;
15
Tushman and Rosenkopf, 1992; Anderson and Tushman, 1990). From economic perspective,
established firms generally do not see incentives to invest in latent disruptive innovation
(Reinganum, 1983). Likewise, low incentives may impact negatively a potentially disruptive
firm’s ability to mobilize capital, resources and actors who can invest in a new value network
(Cockburn and Henderson, 1994; Zheng, Liu and George, 2010). A resource scarce entrant
may not scale up its disruptive niche market. It may create a niche market and remain isolated
there. Another factor that may constrain or enable disruptive innovation is complementary
assets. Teece (1986) argues that an entrant’s innovation may be appropriated by established
firms that possess complementary assets. Therefore, a niche market business model can be
isolated in niche market by mainstream entry barriers if necessary conditions are not in place
(Barnett and Carol, 1995).
H6: Mainstream entry barriers are negatively related to emergence of disruptive
niche market.
Mainstream Market Disruption
The six hypotheses presented above model the process of business model innovation in
creating a potentially disruptive niche market. While any business model innovation may
create a niche market, disruption occurs only when the innovation reaches a good enough
point to attract mainstream customers and that incumbents confront business model conflict to
respond effectively (Gilbert, 2003). The second section models disruption as a function of
business models conflict that arises due to multiple effects including (H7) capabilities
mismatch (Henderson, 2006; Leanard-Barton, 1992; Henderson and Clarck, 1990), (H8)
asymmetric incentive systems (Christensen and Raynor, 2003; Cockburn and Henderson,
1994) and (H9) incumbents’ managerial dilemma (Christensen, 1997).
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Capabilities Misfit
Capabilities can be defined as the company’s ability to identify, integrate, develop and
configure its own and others’ competences and resources to innovate (Teece et. al., 1997).
Key capabilities that determine what a firm can and cannot do are defined as processes,
resources and value systems (Christensen and Overdorf, 2000). A firm’s capabilities are
interwoven with its incentive system and cognitive frames (Kaplan, 2008) and evolve
embedded within its business model (Christensen and Overdorf, 2000). Capabilities can
enable radical change when prior competences can be leveraged (Shane, 2000; Cohen and
Levinthal, 1990) or obstruct change when conditions amplify capabilities misfit (Barnet and
Carrol, 1995; Tushman and Anderson, 1986).
Capabilities misfit is attributed to the extent an innovation departs from established R&D,
scientific and engineering principles (Dewar and Dutton, 1986; Rothwell, 1986; Ettlie,
Bridges and O’Kefe, 1984) and accumulated technical competencies (Henderson and Clark,
1990; Tushman and Anderson, 1986). Henderson (2006) argues that embedded organizational
technical competences are sources of incumbent’s’ managerial dilemma in face of disruptive
innovation.
H7. Capabilities misfit is positively related to disruptive innovation.
Asymmetric Incentive Systems
Christensen and Raynor’s (2003) concept of asymmetric motivation explains that incumbents
that have already committed substantial resources to profitable established markets
(Reinganum, 1983) are normally less motivated to cannibalize the flow of income (Mason and
George, 1994) when confronted with disruptive innovation. Because of this conflicting
business models, integrating disruptive innovation in an existing organizational structure may
negatively affect established value propositions including prices, product qualities and
17
company image (Markides and Charitou, 2004). It is argued that these factors can force an
incumbent to create an autonomous structure to isolate disruptive innovation (Christensen and
Raynor, 2003). Therefore, asymmetric incentive systems between the entrant and incumbent
are a necessary condition for disruptive innovation.
H8. Asymmetric incentive models are positively related to disruptive innovation.
The incumbent’s dilemma
Capabilities mismatch and business model conflicts create a serious challenge to senior
management in how to deal with disruptive innovation. This problem referred to as “the
innovator’s dilemma” is one of the most distinguishing precursors of disruptive innovation
(Christensen, 1997). Managerial cognitive dilemma arises when, on the one hand, established
firms must continue to allocate resources to the most profitable markets in order to satisfy
their profitable customers as well as investors by earning strong margins. On the other hand,
failing to allocate resources to disruptive innovation, while managing their traditional
business models, can pave the way for their own disruption (Christensen and Raynor, 2003).
Responding to disruptive innovation entails risks of competing against their own established
products and damaging relationships with traditional value network partners (Chandy and
Tellis, 1998).
H9. Incumbents’ managerial dilemma is positively related to disruptive innovation.
Research Methods
Sample and Data Collection
The empirical setting of this study focused on disruptive entrants and incumbents that
embraced
disruptive
innovations
in
five
industries
namely,
fixed-line,
mobile
communications, insurance, banking and airlines industries in South Africa. While our level
18
of analysis treated small to medium firms as single respondents, large diversified corporations
were represented by strategic business units (SBU) (Govindarajan et al., 2011; Chandy and
Tellis, 1998). The sample selection was carried out over three course of screening process; (1)
indentifying disruptive innovations in South Africa, (2) entrants and incumbents that
embraced disruptive innovation and (3) informants.
Disruptive innovations in South Africa:
To identify disruptive innovations in South Africa, we conducted 36 in-depth interviews with
top level managers with an average length of 1.30hrs. 27 interviews were tape-recorded and
transcribed. In addition, given disruptive innovation often attracts high attention, mostly from
the media, because it is intriguing and different (Moore, 2004: 88), we used secondary
sources to identify these samples. The interviews and secondary data yielded a pool of 19
candidates.
As does previous research on this topic, we rather focused on few innovations that
introduced fundamentally different business models with significant impact on established
markets (Gilbert, 2003; Chesbrough and Rosenbloom, 2002). From the original sample of 19
innovations, the authors selected five innovations (see Table 1). These innovations were
screened out based on the theoretically established criteria of disruptive innovation and
innovations. Specifically, one of its important characteristics is the introduction of a
fundamentally different business model relative to the archetypal industry business model
(Chesbrough and Rosenbloom, 2002). In addition, we identified innovations that prior
researchers have already categorized as disruptive innovations.
19
Table 1: Samples of Disruptive Innovations
Disruptive innovation
New business model
Prior research
Social networking via
membership
subscription
Comparable existing
product/business model
Voice via GPRS/3G
network - airtime per
minute based business
model
Short Message System
(SMS) – charging per
SMS business model
Voice over Internet Protocol
(VoIP); voice over wireless
local area network (VoWLAN)
via GPRS/3G
Instant mobile messaging
(IMS), social networking on
Java software application using
the GPRS/3G packet data
Internet- mobile banking (Mbanking) Internet, USSD and
WAP technologies platforms
that processes virtual
transaction on any cell-phone
via any mobile network
operator
Direct low-cost short-term
insurance model
Direct low-cost airlines model
A flat rate subscription
business model via IT
terminators
Mobile low-cost high
volume banking
Full-service branch
based banking model
Markides and Charitou
(2004). Christensen et.
al., (2004).
Direct lo-cost shortterm insurance model
Direct low-cost airlines
model
Full-service broker’s
based business model
Full-service agent based
business model
Markides and Charitou
(2004).
Raynor, (2011);
Christensen et.
al.,(2004).
Habtay (2012); Madjdi
and Huesig (2011);
Huesig, Hipp and
Dowling (2005);
Christensen, Anthony
and Roth (2004).
The five selected disruptive innovations were: (a) Voice over Internet Protocol (VoIP)
technology, alternatively known as voice over wireless local area network (VoWLAN), b)
mobile social networking technology that runs on java software application. These two
technologies transmit Internet voice and data communications via the Internet over GPRS/3G
mobile networks and Public Switched Telephone Networks (PSTN). Research has already
identified these innovations as disruptive technologies to the mobile cellular and fixed-line
communications (Habtay, 2012; Madjdi and Huesig, 2011; Huesig, Hipp and Dowling ,
2005). (c) Low-cost no-frills airlines’ business model, (d) online direct insurance model, and
(e) Internet and mobile banking. Previous research have identified these innovations as
disruptive business models to the full-service mainstream business models in the airlines,
insurance and banking industries (Raynor, 2011; Markides and Charitou, 2004; Christensen
et. al., 2004).
20
Firms and Informants Selection
The key criterion of selection was to identify firms that embraced disruptive innovations
under varying organizational arrangements (Mardkies and Charitou, 2004). To identify these
companies and informants, we used open interviews with managers and industry experts, desk
research, referrals from business schools and industry institutions, the media and Internet. We
focused on five industries that were likely to be affected by a priori chosen five innovations.
We used the guidelines advocated by Huber and Power (1985) to identify key informants.
To ensure that the right informant participated in the survey, a respondent selection was based
on several criteria: (a) founders of a potentially disruptive spin-off in corporations (b)
managers with responsibility for strategic business units (SBUs), (c) active involvement in
managing innovation or major company transformation projects and (d) seniority in
management position.
Based on the literature review, open interviews and the conceptual model, a structured
questionnaire was developed. Most of the constructs were adapted from previously used
scales and all question items used a seven-point Likert type scale. The introduction of the
questionnaire was adapted for each of the five innovations and pilot tested in two stages. First,
three innovation scholars and MBA students tested the questionnaire based on instructions to
identify any inappropriately or ambiguously worded questions and to provide comments on
the content. Based on their feedback, the scale items were modified and reworded. Next the
questionnaire was pilot tested with 20 middle managers across the five industries. The
returned questionnaires were examined for completeness of responses, reliability and
construct validity. After necessary changes were made, 500 questionnaires were sent with a
letter of support from the university explaining the academic purpose of the study, with brief
illustrations of disruptive innovation examples of the mobile-phone relative to fixed-line
(Govindarajan and Koppale, 2006) and Southwest LCC model relative to the traditional full-
21
services legacy airlines model (Christensen et. al., 2004). Respondents were informed of the
confidentiality of their responses and promised a summary report of the research.
As noted by Hoskisson, Eden, Lau and Wright (2000), in emerging countries collaboration
with researchers who are well informed about the specific phenomenon under investigation
and who can effectively contact informants to conduct face-to-face interviews is a major
source of obtaining reliable and valid data. Using the funds granted by the university and
national institutions, three South African research associates who were highly familiar with
the literature of disruptive innovation and this project were employed under the principal
researchers to administer the questionnaire, set up appointments and conduct face-to-face
interviews to fill the structured questionnaire. This resulted n = 128 or 26% response rate. 14
were removed for miscellaneous reasons including incomplete information, inconsistencies
and outliers. 114 complete responses were used to test the entrant’s model (H1 – H6). This
data comprises 18% banking, 16% insurance, 16% airlines companies, and 50% mobile and
fixed-lines communications industries.
From the total 114, only 88 responses were used to test the second model (H7 – H9). 26
responses were excluded for a number of reasons from testing the second mode. 21 were
entrants that introduced disruptive innovation but did not manage traditional businesses
before launching the innovation. 5 were large diversified entrants that entered into the
disrupted industries by setting up independent companies but managed unrelated businesses
in other industries that were not affected by disruptive innovations. The n = 88 data consisted
only firms that embraced disruptive innovations under varying organizational arrangements
while simultaneously continued to manage traditional business units that existed prior to
embracing the innovations in the same industries. The sample covered mobile and IT
communications 26%, fixed-line 20%, insurance 24%, banking 19% and airlines 11%. The
non-response bias was tested using t-test between early and late respondents firms, and
22
between respondents and non-respondents in terms of the two firm size indicators,
specifically annual sales revenues and number of employees. The test revealed no significant
differences (p > .10).
Measures
Dependent Variables
Emergence of disruptive niche market: Respondents were asked to rate the growth of the
niche market (1 = “very small”, 7 = “very big”): a) the market share of the pioneering entrant
that launched this innovation/new business model, b) the market share of all new competitors
(with similar new business models), c) the growth potential of the innovation in future
(Markides and Charitou, 2004).
Disruption: Adopting Mardkies and Charitou (2004) scales, we developed four scales to
measure disruption (1 = “very small”, 7 = “very big”): a) the market share of all new
competitors (with the new business model), b) the market share your company lost as the
consequence of this innovation's growth, c) the market share other incumbent’s from the
industry lost as the consequence of this innovation's growth, d) the threat this innovation
poses to existing traditional business models in the industry in future (see also Dess and
Robinson, 1984),
Independent Variables
Disruptive innovation’s relative advantage:
To determine from established firms’
managers’ perspective whether the innovation had a relative advantage compared to existing
products (Agarwal and Prasad, 1997), we asked managers to rate the innovation in terms of:
(a) price, (b) ease-of-use, (c) technology “newness”, (d) quality, (c) brand recognition (1 =
“very least advantage” to 7= “very great”). Typically disruptive innovation is perceived on
23
average low on these attributes, but radical innovation is perceived high on average
(Govindarajan and Kopalle, 2006).
Incumbents’ customer orientation: We asked incumbents managers to rate the innovation
on a 7-point Likert scale (1 = “strongly disagree”, 7 = “strongly agree”) that (a) innovation is
attractive to mainstream customers, (b) attractive to established incumbents revenue model,
(c) does not appeal to low-end or previously un-served markets, and (d) creates a niche
market size that is big enough to attract established competitors (Govindarajan and Koppale
2006). Radical innovation is positively related to incumbents’ manager’s mainstream
customer orientation. But disruptive innovation is negatively related to incumbents’
managers’ customer orientation (Govindarahan et. al., 2011; Zhou et. al., 2005).
Differential value chain configuration is measured by rating how different (1 = “very
similar”, 7 = “very different”) the components of entrant’s value chain were compared to the
industry’s archetype traditional value chain in the following four items: a) production process,
b) supply channels, c) distribution channels, d) marketing and sales (Adner, 2002;
Chesbrough, 2006; Gilbert, Newberyand Reinganum, 1984; Reinganum, 1983).
Emergent innovation capabilities is measured by three observed variables: a) cower cost
advantage, b) consumer-insight, c) speed-to-market factors (1= “very ineffective”, 7 ‘ “very
effective”) (Govindarajan and Gupta, 2001; Holmstrom, Hoover Jr,., Louhiluoto and Vasara,
2000; Evans and Wurster, 1997):
Enter-entrants competition is assessed by asking how big was the size of new competitors
with disruptive business models competing in the disruptive market (1 = “very small”, 7 =
“very big”) (Burns and Stalker, 1966; Porter, 1985).
Mainstream entry barriers is evaluated by asking respondents to rate the barriers (1 =
“strongly disagree”, 7 = “strongly disagree”) on five scales a) lack of access to capital or
resources (finance, skilled labor, technology, equipment), b) mainstream customer lack of
24
accessibility (shortage of business infrastructure), c) mainstream customers lack of
affordability (large population with
low income and/or low education), d) constraining
regulations, and e) incompatibility of the innovation with existing complementary devices
(Chesbrough, 1999; Porter and Stern, 2001; Teece, 1986).
Capabilities misfit: This construct measured the innovation’s relative departure from the
established firm’s competencies and skills (1 = “very similar”, 7 ‘ “very dissimilar”) on two
aspects: a) innovation’s departure from the company's previously existing core product or
service design (Dewar and Dutton, 1986; Henderson and Clark, 1990), and b) innovation’s
departure from previously existing core competencies (knowledge, technology, processes)
(Tushman and Anderson, 1986).
Asymmetric economic models: Disruptive innovation is a function of business models
conflicts. To establish that firms indeed confronted conflicts and risks between the innovation
and traditional businesses, we development a 7-point Likert scale (1 = “strongly disagree”, 7=
“strongly agree”) from Markides and Charitou’s (2004) scales: a) risk of cannibalization of
existing sources of revenue flow (Mason and George, 1994), b) lowering profit margin
(Abernathy and Clark, 1985), c) degrading existing product’s quality, d) damaging company’s
image, e) damaging relationship with existing distribution channels and f) defocusing from
main strategic market (Porter, 1985).
Incumbents’ managerial dilemma: Based on existing literature, we developed five scales to
measure managerial dilemma using the following questions (1 = “strongly disagree”, 7 –
“strongly agree”): a) difficult to anticipate the impact of the innovation on our main market
(Paap and Katz 2004), b) difficult to believe the innovation was the right way to do business
in the industry (Markides and Charitou, 2004), c) responding to the innovation involved a
risk of cannibalizing existing market share, e) responding to that innovation involved risk of
losing our main partners (distributors) (Chandy and Tellis, 2000), and d) our management
25
was not willing to embrace the innovation when it emerged (Chandy and Tellis, 2000, Hannan
and Freeman, 1984).
Potential Limitation of the Research Method
The questions were designed to ask respondents to reflect retrospectively on the specific
disruptive innovation process phenomenon, starting from inception to the point of disruption.
Respondents answered questions not only about their companies’ experiences but also about
the other side’s experiences during disruptive business model innovation process. Such ex
post design may raise the issue of retrospective bias (Huber and Power, 1985). We tried to
minimize the potential bias by taking two actions. First, we selected the most senior managers
with average seven years seniority in the company.
The second action taken to minimize retrospective bias was to increase respondents recall
accurateness. As mentioned earlier, five case studies were developed for each disruptive
innovation. This increased the familiarity of respondents with specific innovations. The
questionnaire was adapted to each industry for each specific innovation. Following
Govindarajan and Kopalle (2006), example the first page of the questionnaire provided two
brief case examples of disruptive innovations; the introductions of cellular phone relative to
fixed-line phone and Southwest’s no-frills low-cost business model relative to network
carriers’ full-service business models.
While giving clues to help respondent answer the right question, the recall time was also
carefully considered. The disruptive innovation sample items selected in this study had on
average five years age at the time of data collection. This means that our questionnaire was
designed on a maximum five year recall time. Kuczmarski (2000) suggested a three year
development period before an innovation unfolds in a market. Govindarajan and Kopalle
(2006) used a five year recall time to investigate retrospectively disruptive innovation. The
26
five selected disruptive innovations in South Africa were introduced between 2001 and 2006.
Our data collection took place in 2008. Thus, this time span can be considered as adequate
recall time.
Reliability and Validity Tests
Cronbach coefficient alphas range for all items range from 0.61 to 0.90 (Table 1). This
demonstrates sufficient internal consistency and reliability (Nunnally, 1978). Table 2 shows
the Pearson Correlation Coefficients for all hypothesized variables. The significant
correlations for most variables may be considered as evidence of construct validity (Hull and
Covin, 2010). Multicolinearity is modest as most of the predictor variables have correlations
less than r = .50.
We tested convergent and discriminant validities using exploratory factor analysis
(Principal Component Analysis). While convergent validity tests whether respective manifest
variables (MVs) or scales represent or load onto a respective single latent variable (LV), the
discriminant validity asses whether the MVs that load on a single LV discriminate adequately
from other LVs (Hair, Babin, and Samouel, 2003). All factors of the major variables are with
Eigenvalues greater than 1; the percentages of variance explained for the major variables are
above 0.5, and all relevant factor loadings are greater than 0.5. These results demonstrate that
the manifest variable (MV) items cleanly load onto their intended latent variables (LVs),
showing significant convergent and discriminant validity (Nunnally, 1978). All hypotheses
were tested using SAS 9.3 regression analysis method on aggregated 114 and 88 data for the
first and second models respectively.
27
Table 1: Descriptive Statistics and Correlations (n=114)
Alpha
Mean
SD
Pearson Correlation Coefficients, N = 114 Prob > |r| under H0: Rho=0
1
2
3
4
5
6
1. DI Inferior VP
0.821
3.354
1.007
1
2. Customer Orientation
0.760
2.695
0.963
-0.190***
1
3. Value Chain
0.769
5.149
1.096
0.465****
-0.407****
1
4. Innovation Capabilities
0.774
5.018
1.219
0.558****
-0.327****
0.705****
1
5. Barriers
0.614
4.614
0.903
-0.008
-0.213***
0.297****
0.096
1
6. Competition
0.674
4.050
1.357
0.406****
0.117
0.094
0.145
-0.115
1
7. Capabilities Misfit
8. Asymmetric Incentives
0.750
0.890
4.794
4.347
1.059
1.213
0.382****
0.242****
-0.227***
0.0859
0.534****
0.127
0.453****
0.246****
0.0627
-0.1651*
9. Dilemma
10. Disruptive Innovation
0.900
0.800
3.882
3.667
1.237
1.211
0.428****
0.446****
0.296****
0.236***
0.069
0.193**
0.1965**
0.328****
-0.404****
-0.20448**
****p<.001, ***p < .01, **p < .05, *p < .1
28
7
8
9
0.148
0.158*
0.147
0.122
1
0.083
1
0.406****
0.23727***
0.426****
0.365****
0.223**
0.169*
0.411****
0.443****
10
1
0.777****
Results
First, H1 and H3 were tested independently using single regression analysis, as basic tests
to define disruptive innovation. As expected in H1, a potentially disruptive innovation’s initial
value proposition is negatively related to incumbent’s customer orientation (B = -0.145,
p<.001 see Table 2). Consistent with H3, the entrant’s differential value chain configuration is
strongly positively related to emergent innovation capabilities (B = 0.584, p<.0001 see Table
3).
Table 2: H1: Regression Model 1: Dependent Variable - Customer Orientation
Model Pr > F 0.0427, Adjusted r2= 0.0276, Dependent Variable ICB
β
Variable
B
SE
Intercept
13.223***
1.243
0
0
-0.145**
0.071
-0.190
1.000
H1: Disruptive Innovation’s Initial Value Propositions
VIF
**** p<.0001, ***p<.001, **p<.01, *p<.05
Table 3: Regression Model 2: H3: Dependent Variable - Emergent Capability
Model Pr > F <.0001, Adjusted r2= 0.4927, Dependent Variable EC
Variable
B
SE
β
VIF
Intercept
3.013***
1.170
0
0
H3: Disruptive Value Chain
0.584****
0.056
0.705
1.000
**** p<.0001, ***p<.001, **p<.01, *p<.05
The dependent variables of H1 (incumbents customer orientation) and H3 (emergent
innovation capabilities) were fed into the multiple regression model 3 (Table 4) as
independent variables (H2 and H4). In support of H2, Table 4 shows strong positive effect of
incumbents’ customer orientation on potentially disruptive business model niche market
innovation (B = 0.323, p<.001). Consistent with H4, the result shows a strong significant
positive relationship between emergent innovation capabilities and potentially disruptive
29
business model niche market innovation (B = 0.396, p<.0001). Similarly, and H5 competition
has a strong positive effect on potentially disruptive niche market business model innovation
(B = 0.372p<.0001). H6 mainstream entry barriers is negatively related to niche market
business model innovation (B = -0.661, p<.0001)
Table 4: Regression Model 3: Dependent Variable – Potentially Disruptive – Niche
Market
Pr > F <.0001, Root MSE 1.289, Adjusted r2= 0.372, Coeff Var 31.942, Dependent Mean 4.036, n = 144
Variable
B
SE
Β
VIF
Intercept
2.724***
1.048
0
0
H2: Incumbents orientation
0.323***
0.137
0.191
1.192
H4: Emergent innovation capabilities
0.396****
0.108
0.295
1.169
H5: Competition
0.372****
0.092
0.310
1.067
H6: Mainstream entry barriers
-0.661****
0.138
-0.367
1.059
****p<.0001,***p<.001, **p<.01, *p<.05
Next, regression model 4 (Table 5) tests the multiple effects of capabilities misfit (H7),
incentive systems conflict (H8) and incumbent’s managers’ dilemma (H9) on disruptive
innovation (using n = 88). Contrary to expectation in H7, capabilities misfit do not have
significant effect on disruptive innovation (B = -0.006, p>0.1). H8 asymmetric incentive
models (B = 0.153, p>.0.001) and managerial dilemma (B = 0.679, p>.0.0001) have strong
effects on disruptive innovation.
Table 5: Regression Model 4: Dependent Variable –Disruptive Innovation
Pr > F <.0001, Root MSE 0.756, Adjusted r2= 0.610, Coeff Var 20.620, Dependent
Mean 3.67, n = 88
Variable
B
SE
Β
VIF
Intercept
0.209
0.427
0
0
H7: Capabilities misfit
-0.006
0.069
-0.006
1.056
H8: Asymmetric incentive systems
0.153***
0.065
0.153
1.208
H7: Dilemma
0.679****
0.070
0.694
1.495
****p<.001,***p<.01, **p<.05, *p<.1
30
Discussions and Conclusions
This study modelled the evolutionary development of disruptive business model innovation
by proposing two hierarchically linked phases. Consistent with previous research, the result of
this paper suggests that latent disruptive innovation is negatively related to incumbent’s
managers’ mainstream customer orientation at initial stage (Govindarajan et. al., 2011; Adner,
2002; Christensen, 1997). This negative relationship should define a potentially disruptive
innovation, and should distinguish at the most basic level between disruptive and sustaining
innovations. In addition, it should also distinguish disruptive kinds of innovations from other
types of discontinuous innovations, e.g. radical innovations that are positively related to
incumbent’s managers’ mainstream market orientation (Govindarajan et al., 2011).
In the long run, however, incumbents’ mainstream customer orientation can be positively
associated with the emergence of a potentially disruptive niche market. This suggests that
beyond the disruptive innovation’s endogenous characteristics, exogenous asymmetric
cognition orientations between the innovator and incumbent can inform the disparate
trajectories.
This insight provides theoretical reasoning to consider ex ante incumbents’ customer
orientation as one of the precursors of a potentially disruptive niche market. This negative
(incumbents) customer orientation towards disruptive innovation arises because neither the
new business model’s disruptiveness potential nor its trajectory is perceptible ex ante
(Danneels, 2004; Gilbert, 2003; Nightingale, 2004) in niche markets that emerge outside of
incumbents radar (Christensen and Raynor, 2003). While the flood of new firms in a niche
market can increase the threat of disruption, incumbents’ absence or possibly misguided
responses to disruptive firms during the niche market phase may also increase the
disruptiveness potential of the niche market. We find support in Christensen’s (1997)
argument that established firms are likely to flee the niche market in order to concentrate on
31
profitable markets, consequently providing new firms a competitive free space and
momentum to grow the niche market.
The incumbent’s part of the disruptive innovation model hypothesized that a potentially
disruptive niche market business model innovation can be transformed into disruptive
innovation, if conditions amplify asymmetric capabilities and incentives and associated
incumbents’ managerial dilemma. Studies on technological change have argued that technical
competencies mismatch can cause incumbents misfortune in face of discontinuous innovation
(Henderson, 2006; Henderson and Clark, 1990; Tushman and Anderson, 1986). But our
findings suggest the notion of capabilities misfit may not be present in disruptive innovation
phenomena.
Confirming to Christensen’s (1997) argument, our result suggests that incumbents may be
disrupted, despite mastering radical, competence-destroying or architectural innovation
capabilities. There are two possible explanations. First, some disruptive business model
innovations, such as no-frills low-cost airlines, insurance and banking models may create
misfits in downstream capabilities such as in distribution and sales, but they do not
significantly depart from established upstream technical activities. When conditions allow,
incumbents can leverage accumulated capabilities and thus respond successfully. Second,
although some technologically sophisticated disruptive innovations entail downstream and
upstream capabilities misfit, resource endowed firms may either hire skills and technologies
or acquire disruptive firms to develop disruptive capabilities.
This study confirms that disruptive innovation is the function of asymmetric incentive
systems and managerial dilemma. The economic asymmetric explanation depicts disruptive
innovation as a process outcome when entrants pursue competition through low-cost high
volume business models against differentiated business models in the mainstream market.
This creates a dilemma for incumbents which in turn may trigger disruption.
32
Theoretical and managerial implications
Our study makes several key contributions to theory and practice. First, most business model
innovation studies adopt entrants’ entrepreneurial perspectives in examining new business
model development. Conversely, extant disruptive innovation research begins when these
studies “end” and adopt incumbents perspectives in exploring firms impediments to adaptive
efforts. This study systematically links both perspectives together and explores disruptive
business model innovation. Second, disruptive innovation theory attributes to technological
change or asymmetric economic motivations for the departure of disruptive paradigm.
Beyond these explanations, this study shows how asymmetric cognitive orientations can
inform the disparities between the disruptive and sustaining business models trajectories.
Third, this study systematically unpacks the differential effects of capabilities, incentive
systems and managerial cognition as underlying mechanisms of disruptive innovation. While
asymmetric incentives and cognition orientations can explain disruptive innovation,
asymmetric technical capabilities have no effect on disruptive innovation. The study further
breaks down the cognitive explanation into incumbents’ customer orientation and incumbents
managerial dilemma. Incumbents’ customer orientation refers to ex ante difficulties in
identification of latent or emerging disruptive niche market. On the other hand, incumbents
dilemma is an ex post construct that explains the difficulties incumbents’ managers encounter
in responding to disruptive innovation when the incumbent confronts disruptive innovation.
Limitations and Future Research
First, although the business model literature has offered a number of important theoretical
frameworks of business model concept, strategic management research has yet to offer
psychometrically reliable, valid and widely agreed upon definition with operational constructs
of the concept that can assist for innovation research on the topic of disruptive business model
33
innovation (Markides, 2008). Since the business model concept is evolutionary, complex,
dynamic and multidimensional that considers all aspects of business activities over
development period, this study focused only on few aspects. Other important explanatory
constructs could have been excluded from our analysis.
Second, we tested the conceptual model on aggregated data from five industries. However
this attempt to generalize the findings across industries makes data interpretation problematic.
Disruptive innovation is relative, “not an absolute phenomenon” (Christensen, 2006: 42). In
other words, disruptiveness can only be defined in relation to a certain incumbent’s business
model in a specific context. If each disruptive innovation is investigated separately across the
five industries, each could have drawn alternative explanations. Study on disruptive business
model innovation heterogeneity across industries could thus shed further knowledge.
Third, our results show that a potentially disruptive niche market business model does not
have inherent disruptive capacity on its own. Although a latent disruptive innovation can
initially create a niche market, some business models functioning as “tickets to entry” may
fail (Brink and Holmén, 2009), while others may remain isolated in niche markets. Still in
other circumstances some may progress over time through a disparate performance trajectory
to spark an era of disruption. But this situation appears unpredictable and difficult to make
theoretical connections ex post.
The challenge of identifying early signals of latent disruptive innovation remains
unsolved. Further research could investigate the inherent features of latent disruptive business
model innovation in order to distinguish from other types of low-cost business models that do
not materialize disruptive threats. Furthermore, little is known of incumbents competitive
behaviour before disruption unfolds. Our conclusion that incumbents’ customer orientation
leads to absence is a necessary generalization and considers incumbents as homogenous. A
34
future study on the heterogeneity of incumbents’ reactions during a potentially disruptive
niche market evolution period could be an interesting topic to explore.
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