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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: [email protected]

Phone (27) 117178085

Fax: (27) 117178081

mailto:[email protected]

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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).

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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

Revenue Model

Cost Structure

Pricing Mechanism

Margin level

Value Network

Configuration

Value Chain

Core Capabilities

Relationships

Actors and Roles

Strategy

Planning

Hard-to-imitate core

capabilities

Emergent capabilities

Value

Propositions

Price

Tech

nolo

gy

Quality Brand

Customer

Market Segmentation

Targeting

Positioning

Customer Interface

Economic Dimension

Capabilities Dimension

Cognitive Dimension

Cog

nitiv

e M

odel

ing

Cog

nitiv

e M

odel

ing

Cognitive Modeling

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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

Innovation’s

Relative

Advantage (RA)

Continuous

Innovation (CI)

Entrant’s Business Model

Innovation Dynamics for

Creating Potentially

Disruptive Niche Market

Differential value chain

configuration Competition

ConstraintsIncumbents

Customer

Orientation

Potentially Disruptive

Niche MarketH1 H3

H5

H6

H4

H2

Capabilities Misfit

BMs Conflict

Dilemma

Disruptive Innovation (DI)

H9

H8

H7Mainstream Market

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;

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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

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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

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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.

20

Table 1: Samples of Disruptive Innovations

Disruptive innovation New business model Comparable existing

product/business model

Prior research

Voice over Internet Protocol

(VoIP); voice over wireless

local area network (VoWLAN)

via GPRS/3G

A flat rate subscription

business model via IT

terminators

Voice via GPRS/3G

network - airtime per

minute based business

model

Habtay (2012); Madjdi

and Huesig (2011);

Huesig, Hipp and

Dowling (2005);

Christensen, Anthony

and Roth (2004). Instant mobile messaging

(IMS), social networking on

Java software application using

the GPRS/3G packet data

Social networking via

membership

subscription

Short Message System

(SMS) – charging per

SMS business model

Internet- mobile banking (M-

banking) Internet, USSD and

WAP technologies platforms

that processes virtual

transaction on any cell-phone

via any mobile network

operator

Mobile low-cost high

volume banking

Full-service branch

based banking model

Markides and Charitou

(2004). Christensen et.

al., (2004).

Direct low-cost short-term

insurance model

Direct lo-cost short-

term insurance model

Full-service broker’s

based business model

Markides and Charitou

(2004).

Direct low-cost airlines model Direct low-cost airlines

model

Full-service agent based

business model

Raynor, (2011);

Christensen et.

al.,(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).

21

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-

22

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

23

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

24

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

25

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

26

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

27

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.

28

Table 1: Descriptive Statistics and Correlations (n=114)

****p<.001, ***p < .01, **p < .05, *p < .1

Pearson Correlation Coefficients, N = 114 Prob > |r| under H0: Rho=0

Alpha Mean SD 1 2 3 4 5 6 7 8 9 10

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 0.750 4.794 1.059 0.382**** -0.227*** 0.534**** 0.453**** 0.0627 0.148 0.147 1

8. Asymmetric Incentives 0.890 4.347 1.213 0.242**** 0.0859 0.127 0.246**** -0.1651* 0.158* 0.122 0.083 1

9. Dilemma 0.900 3.882 1.237 0.428**** 0.296**** 0.069 0.1965** -0.404**** 0.406**** 0.426**** 0.223** 0.411**** 1

10. Disruptive Innovation 0.800 3.667 1.211 0.446**** 0.236*** 0.193** 0.328**** -0.20448** 0.23727*** 0.365**** 0.169* 0.443**** 0.777****

29

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

Variable B SE β VIF

Intercept 13.223*** 1.243 0 0

H1: Disruptive Innovation’s Initial Value Propositions -0.145** 0.071 -0.190 1.000

**** p<.0001, ***p<.001, **p<.01, *p<.05

Table 3: Regression Model 2: H3: Dependent Variable - Emergent Capability Model Pr > F <.0001, Adjusted r

2= 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

30

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 r

2= 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 r

2= 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

31

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

32

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.

33

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

34

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

35

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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