bessant et al 2005(1)
TRANSCRIPT
Managing innovation beyond the steady state
John Bessanta,*, Richard Lammingb,1, Hannah Nokec,2, Wendy Phillipsd,3
aCranfield University School of Management, Cranfield, Bedford MK43 0AL, UKbSchool of Management, University of Southampton, Highfield, Southampton SO17 1BJ, UK
cCranfield University School of Management, Cranfield, Bedford MK43 0AL, UKdCRiSPS, University of Bath School of Management, Bath BA2 7AY, UK
Abstract
Research on the innovation process and its effective management has consistently highlighted a set of themes constituting ‘good practice’.
The limitation of such ‘good practice’ is that it relates to what might be termed ‘steady state’ innovation - essentially innovative activity in
product and process terms which is about ‘doing what we do, but better’. The prescription works well under these conditions of (relative)
stability in terms of products and markets but is not a good guide when elements of discontinuity come into the equation. Discontinuity arises
from shifts along technological, market, political and other frontiers and requires new or at least significantly adapted approaches to their
effective management. This paper explores relevant routines which organisations can implement to enable discontinuous innovation.
q 2005 Elsevier Ltd. All rights reserved.
Keywords: Discontinuous innovation; Inter-firm learning; Managing innovation
1. Introduction
Innovation represents the core renewal process in any
organisation. Unless it changes what it offers the world
(product/service innovation) and the ways in which it
creates and delivers those offerings (process innovation) it
risks its survival and growth prospects. But innovation is not
an automatic attribute of organisations; the process has to be
enabled through sophisticated and active management.
There are no guaranteed formulae for success in what is
inevitably a risk-based activity, but extensive research
dating back over a century suggests a series of convergent
themes from which guidelines for effective innovation
management can be extracted (Tidd et al., 2001).
Organisations across a wide range of sizes, sectors,
geographical locations, etc have evolved a series of
behaviours which help them deal with the challenge of
0166-4972/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.technovation.2005.04.007
* Corresponding author. Tel.: C123 475 4850; fax: C123 475 1806.
E-mail addresses: [email protected] (J. Bessant), r.c.
[email protected] (R. Lamming), [email protected]
(H. Noke), [email protected] (W. Phillips).1 Tel.: C44 238 059 2559; fax: C44 238 059 3844.2 Tel.: C44 123 475 4850.3 Tel: C44 122 538 6650; fax: C44 122 538 3223.
innovation. Trial and error learning eventually gives rise to
the accumulation of knowledge about successful behaviours
which then become organisational ‘routines’ which build
into ‘the way we do things around here’—the innovation
culture and its attendant organisational structures, policies
and procedures.(Nelson and Winter, 1982; Cohen et al.,
1996; Pavitt, 2002) Significantly some bundles of routines
are demonstrably more effective than others under particular
conditions and give rise to a degree of competitive
advantage which is often difficult to imitate because of its
firm specific nature and the lengthy learning processes
required to absorb and embed them.
Of course such practices are not always straightforward
to implement and do not guarantee success, but they can
increase the likelihood that new products and processes will
emerge. There will always be a serendipitous element to
innovation, but with careful planning firms can make their
own luck. Or, as Pasteur noted, ‘Fortune favours the
prepared mind’.
2. Managing innovation under steady state conditions
This convergence of experience around successful
innovation management routines has given rise to a ‘good
practice’ model which embeds some key guidelines or
design principles for effective innovation management
Technovation 25 (2005) 1366–1376
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J. Bessant et al. / Technovation 25 (2005) 1366–1376 1367
(Ettlie, 1999; Dodgson, 2000; Shavinina, 2003). Their
adoption still requires extensive configuration to suit
particular circumstances but the emergent model provides
a starting point for such organisational development. In
particular it can be used as a structured framework against
which organisations can begin a process of audit and
consequent organisational development activity (Johne and
Snelson, 1988; DTI, 1994; Chiesa et al., 1996).
It is important here to recognise that effective innovation
management is less about doing one thing particularly
well—for example R&D investment or stage gate risk
management—than about being able to manage an internal
system of innovation with a number of dimensions.
(Rothwell, 1992) Examples of organisations acknowledged
to be ‘good’ innovators such as 3M, Philips, Siemens, GE,
Nokia, etc—underline the systemic nature of their approach,
sustained and developing over a period of time, evolving
and developing along the way (Kanter, 1997; Gundling,
2000; Graham and Shuldiner, 2001).
4 The example of video recorders is a demonstration of this in which the
technically superior Betamax system was unable to achieve the dominant
position of the VHS approach. For more details see Tushman and Moore
(1988). Readings in the management of innovation. New York, Harper
Collins.5 A good example of this can be seen in the case of bicycles which went
through an extended period of fluidity in design options before the dominant
diamond frame emerged which has characterized the industry for the past
century. Walsh et al. (1992). Winning by design: Technology, product
design and international competitiveness. Oxford, Basil Blackwell.6 For example, Microsoft was able to manage the shift towards web-
based services and towards PDA/mobile phones by extending its operating
system and leveraging its marketing strength.
3. Beyond the steady state
Most of the time the environment within which
organisations operate is (relatively) stable—there may be
rough water but the overall outline of the sea is clear and
they can navigate some kind of course. Some do better than
others but the ‘rules of the game’ are accepted and do not
change. This is not to say that innovation doesn’t take place;
on the contrary there is plenty of room for experimentation
and exploration but within a particular space (Francis and
Bessant, 2004).
Occasionally something happens which dislocates this
framework and changes the rules of the game. By definition
these are not everyday events but they have the capacity to
reframe the space and the boundary conditions—they open
up new opportunities but also challenge existing players to
reframe what they are doing in the light of new conditions
(Evans and Wurster, 2000; Hamel, 2000; Foster and Kaplan,
2002). This is a central theme in Schumpeter’s original
theory of innovation which he saw as involving a process of
‘creative destruction’ (Schumpeter, 1950; Abernathy and
Clark, 1985; Boisot, 1995).
A good example of this is when dislocation happens
because of the emergence of a completely new technology
which offers new or significantly different/improved
functionality. Studies of such ‘revolutions’ suggest a
common pattern to the innovation process associated with
this kind of discontinuous shift. (Abernathy and Utterback,
1975; Utterback, 1994) There is a phase in which the new
options become explored by many players, all learning fast
but all trying to elaborate the technology into a form which
can become widely adopted. The ‘fluid’ or ‘ferment’ phase
is characterised by co-existence of old and new technologies
and by rapid improvements of both. (Foster, 1986; Tushman
and Anderson, 1987) (It is here that the so-called ‘sailing
ship’ effect can often be observed, in which a mature
technology accelerates in its rate of improvement as a
response to a competing new alternative—as was the case
with the development of sailing ships in competition with
newly-emerging steamship technology. (Gilfillan, 1935;
Cooper and Schendel, 1988) Eventually there is a crystal-
lisation of a ‘dominant design’—not always the best in
purely technological terms4 but one which becomes the
innovation standard and sets up a technological trajectory
(Dosi, 1982). The establishment of the dominant design then
gives way to a phase of consolidation innovation, first
around stabilising the product concept and later around the
processes which create and deliver it. Eventually it moves
from the mature phase into a new period of fluidity and the
cycle repeats itself associated with a new technology.5
The pattern can be seen in many studies and its
implications for innovation management are important.
Existing players who may be strong in the mature phase of
an existing trajectory often find it hard to move into the new
one. This is partly a consequence of sunk costs and
commitments to existing technologies and markets and
partly because of psychological and institutional barriers.
The famous ‘not invented here’ effect is a good example of
the latter. They may respond but in slow fashion—and they
may make the mistake of giving responsibility for the new
development to those whose current activities would be
threatened by a shift (Foster, 1986).
But we need to be careful here. Not all existing players
do badly—many of them are able to build on the new
trajectory and deploy/leverage their accumulated knowl-
edge, networks, skills and financial assets to enhance their
competence through building on the new opportunity
(Tushman and Anderson, 1987).6 Equally whilst it is true
that new entrants—often small entrepreneurial firms—play
a strong role in this early phase we should not forget that we
see only the successful players. We need to remember that
there is a strong ecological pressure on new entrants which
means only the fittest or luckiest survive.
It is more helpful to suggest that there is something about
the ways in which innovation is managed under these
conditions which poses problems. Good practice of the
‘steady state’ kind described above is helpful in the mature
phase but can actively militate against the entry and success
J. Bessant et al. / Technovation 25 (2005) 1366–13761368
in the fluid phase of a new technology (Christensen, 1997).
How do you pick up signals about changes if they take place
in areas where you don’t normally do research? How do you
understand the needs of a market which doesn’t exist yet but
which will shape the eventual package which becomes the
dominant design? If you talk to your existing customers they
will tend to ask for more of the same, so which new users
should you talk to—and how do you find them?
The challenge seems to be to develop ways of managing
innovation under highly uncertain and rapidly evolving and
changing conditions which result from a dislocation or
discontinuity. The kinds of organisational behaviour needed
here will include things like agility, flexibility, the ability to
learn fast, the lack of preconceptions about the ways in
which things might evolve, etc.—and these are often
associated with new small firms. There are ways in which
large and established players can also exhibit this kind of
behaviour but it does often conflict with their normal ways
of thinking and working. We will return to this key question
shortly but first it will be useful to consider the multiple
sources of discontinuity.
4. Sources of discontinuity
As Utterback and others note, discontinuities can arise
with significant shifts along the technological frontier which
lead to a dislocation and create new conditions within which
innovation opportunities emerge (Utterback, 1994). Seizing
those opportunities requires a degree of organisational
agility which is often lacking in established incumbents but
a strength of new entrants. Equally new entrants may lack
knowledge, capital or other kinds of assets which enable
them to capitalise on the opportunities available. In either
case a precondition is having the organisational capability to
detect and respond to discontinuous changes.
The danger here is that we may assume that discontinuity
only takes one form and develop capability in that direction.
Technological shifts are an obvious example and many
organisations invest in R&D and technological intelligence
systems partly to counter the potential challenge or to
position themselves to pick up and respond more quickly.
But investment in R&D will not help if the fundamental
shift is in the market-place—for example, emergence of a
completely new market with different needs. (As Christen-
sen points out, in the case of disruptive innovation in
industries like disk drives the technology involved was often
developed in the existing incumbents but taken up and used
by new entrants working with different market configur-
ations)(Christensen, 1997). Equally having good market
antennae may not help if the issue is triggered by a
fundamental political shift or a sea change in public opinion.
Table 1 lists some typical sources of discontinuity and
highlights the difficulties facing otherwise ‘smart’ organis-
ations in detecting and dealing with them.
5. Why is it hard to manage discontinuous innovation?
The problem lies less in the absolute scale of novelty or
dislocation but rather in the firm’s experience of these
conditions as something which takes it beyond its normal
operating envelope. Since, such conditions do not emerge
every day—they are essentially discontinuous—established
firms are often unable to deal with them effectively even
though they may have very sophisticated routines for
managing the steady state innovation process.
Firms that manage steady state innovation well work
closely with customers and suppliers, they make use of
sophisticated resource allocation mechanisms to select a
strategically relevant portfolio of projects, they use
advanced project and risk management approaches in
developing new products and processes and so on. These
routines are the product of well-developed adaptive learning
processes which give the firm a strong position in managing
innovation under steady-state conditions—but they also act
as a set of barriers to picking up signals about, and
effectively responding to, innovation threats and opportu-
nities associated with discontinuous shifts. Christensen’s
work on ‘the innovator’s dilemma’ highlights this problem
of a virtuous circle which operates in a successful firm and
its surrounding value network, and describes in detail the
ways in which their markets become disrupted by new
entrants (Christensen, 1997).
This is not simply a matter of being surprised by a single
unexpected event such as being caught out by a new
technology which a new entrant has brought to market.
Anyone can get unlucky once just as they can get lucky once
in the innovation game. Nor is it the case that each new
discontinuity brings with it a wave of new players with the
old falling away. As Tushman and Anderson (1987) point
out, radical technological shifts do not necessarily disrupt
the existing order and in many cases can be competence
enhancing rather than competence destroying. Similarly not
all existing incumbents failed in picking up on newly
emerging markets in Christensen’s studies.
The real challenge is in building the capability within the
firm so that it is prepared for, able to pick up on and
proactively deal with innovation opportunities and threats
created by emerging discontinuous conditions. In other
words, to develop alternative routines for discontinuous
innovation (‘do different’ routines) which can sit alongside
those for steady state ‘do better’ innovation (Francis et al.,
2003).
Working ‘out of the box’ in this way requires a new set of
approaches to organising and managing innovation—for
example how the firm searches for weak signals about
potential discontinuities, how it makes strategic choices in
the face of high uncertainty, how it resources projects which
lie far outside the mainstream of its innovation operations,
etc. Established and well-proven routines for ‘steady state’
conditions may break down here—for example, an effective
‘stage gate’ system would find it difficult to deal with high
Table 1
Sources of discontinuity
Triggers/
sources of dis-
continuity
Explanation Problems posed Examples (of good and bad experiences)
New market
emerges
Most markets evolve through a process of
growth, segmentation, etc. But at certain
times completely new markets emerge
which can not be analysed or predicted in
advance or explored through using conven-
tional market research/ analytical techniques
Established players don’t see it because they
are focused on their existing markets
Disk drives, excavators, mini-mills (Chris-
tensen, 1997)
May discount it as being too small or not
representing their preferred target market—
fringe/cranks dismissal
Mobile phone/SMS where market which
actually emerged was not the one expected
or predicted by originators
Originators of new product may not see
potential in new markets and may ignore
them,—e.g. text messaging
New technol-
ogy emerges
Step change takes place in product or
process technology—may result from con-
vergence and maturing of several streams (e.
g. industrial automation, mobile phones) or
as a result of a single breakthrough (e.g.
LED as white light source)
Don’t see it because beyond the periphery of
technology search environment
Ice harvesting to cold storage (Utterback,
1994)
Not an extension of current areas but
completely new field or approach
Valves to solid state electronics (Braun and
Macdonald, 1980)
Tipping point may not be a single break-
through but convergence and maturing of
established technological streams, whose
combined effect is underestimated
Photos to digital images
Not invented here effect—new technology
represents a different basis for delivering
value—e.g. telephone vs. telegraphy
New political
rules emerge
Political conditions which shape the econ-
omic and social rules may shift dramati-
cally—for example, the collapse of
communism meant an alternative model—
capitalist, competition—as opposed to cen-
tral planning—and many ex-state firms
couldn’t adapt their ways of thinking
Old mindset about how business is done,
rules of the game, etc. are challenged and
established firms fail to understand or learn
new rules
Centrally planned to market economy e.g.
former Soviet Union
Apartheid to post-apartheid South Africa—
inward and insular to externally linked
(Barnes et al., 2001)
Free trade/globalisation results in disman-
tling protective tariff and other barriers and
new competition basis emerges (Kaplinsky
et al., 2003)
Running out
of road
Firms in mature industries may need to
escape the constraints of diminishing space
for product and process innovation and the
increasing competition of industry structures
by either exit or by radical reorientation of
their business
Current system is built around a particular
trajectory and embedded in a steady-state set
of innovation routines which militate against
widespread search or risk taking exper-
iments
Medproducts (Bessant, 2005)
Encyclopaedia Britannica (Evans and Wur-
ster, 2000)
Preussag (Francis et al., 2003)
Sea change in
market senti-
ment or beha-
viour
Public opinion or behaviour shifts slowly
and then tips over into a new model—for
example, the music industry is in the midst
of a (technology-enabled) revolution in
delivery systems from buying records, tapes
and CDs to direct download of tracks in
MP3 and related formats
Don’t pick up on it or persist in alternative
explanations—cognitive dissonance—until
it may be too late
Apple, Napster, Dell, Microsoft vs. tra-
ditional music industry (Prahalad, 2004)
(continued on next page)
J. Bessant et al. / Technovation 25 (2005) 1366–1376 1369
Table 1 (continued)
Triggers/
sources of dis-
continuity
Explanation Problems posed Examples (of good and bad experiences)
Deregulation/
shifts in regu-
latory regime
Political and market pressures lead to shifts
in the regulatory framework and enable the
emergence of a new set of rules—e.g.
liberalization, privatization or deregulation,
environmental legislation
New rules of the game but old mindsets
persist and existing player unable to move
fast enough or see new opportunities opened
up
Old monopoly positions in fields like
telecommunications and energy were dis-
mantled and new players/combinations of
enterprises emerged. In particular, energy
and bandwidth become increasingly viewed
as commodities. Innovations include skills
in trading and distribution—a factor behind
the considerable success of Enron in the late
1990s as it emerged from a small gas
pipeline business to becoming a major
energy trade (Hamel, 2000). The EU’s
Waste Electrical and Electronic Equipment
Directive is an example of discontinuous
environmental legislation
Fractures
along ‘fault
lines’
Long-standing issues of concern to a
minority accumulate momentum (some-
times through the action of pressure groups)
and suddenly the system switches/ tips
over—for example, social attitudes to
smoking or health concerns about obesity
levels and fast-foods
Rules of the game suddenly shift and then
new pattern gathers rapid momentum
wrong-footing existing players working
with old assumptions
McDonalds and obesity concerns
Tobacco companies and smoking bans
Oil/energy and others and climate change
Opportunity for new energy sources like
wind-power—c.f. Danish dominance
(Douthwaite, 2002)
Unthinkable
events
Unimagined and therefore not prepared for
events which—sometimes literally—change
the world and set up new rules of the game
New rules may disempower existing players
or render competencies unnecessary
9/11
Business
model inno-
vation
Established business models are challenged
by a reframing, usually by a new entrant
who redefines/reframes the problem and the
consequent ‘rules of the game’
New entrants see opportunity to deliver
product/service via new business model and
rewrite rules—existing players have at best
to be fast followers
www.Aamzon.com
Charles Schwab
Southwest, Easyjet, Ryanair and other low
cost airlines
(Hamel, 2000; Day and Schoemaker, 2004;
Prahalad, 2004)
Architectural
innovation
Changes at the level of the system archi-
tecture rewrite the rules of the game for
those involved at component level
Established players develop particular ways
of seeing and frame their interactions—for
example who they talk to in acquiring and
using knowledge to drive innovation—
according to this set of views. Architectural
shifts may involve reframing but at the
component level it is difficult to pick up the
need for doing so—and thus new entrants
better able to work with new architecture
can emerge
Photo-lithography in chip manufacture
(Henderson and Clark, 1990; Henderson,
1994)
Shifts in
‘techno-econ-
omic para-
digm’—sys-
temic changes
which impact
whole sectors
or even whole
societies
Change takes place at system level, invol-
ving technology and market shifts. This
involves the convergence of a number of
trends which result in a ‘paradigm shift’
where the old order is replaced
Hard to see where new paradigm begins
until rules become established. Existing
players tend to reinforce their commitment
to old model, reinforced by ‘sailing ship’
effects
Industrial Revolution
(Dosi, 1982; Freeman and Perez, 1989;
Perez, 2002)
Mass production
J. Bessant et al. / Technovation 25 (2005) 1366–13761370
J. Bessant et al. / Technovation 25 (2005) 1366–1376 1371
risk project proposals which lie at the fringes of the firm’s
envelope of experience. Developing new behaviours more
appropriate to these conditions—and then embedding them
into routines—requires a different kind of learning—
‘generative learning’ (Senge, 1990)or ‘double loop’
(Argyris and Schon, 1970).
In part this explains our observation that new entrants do
better under discontinuous conditions than exiting incum-
bents in an industry; put simply the new players do not face
the problem of having to ‘unlearn’ well-established
behavioural routines but can put in place a new set from a
zero base. Equally it also explains why those same new
entrants are themselves often upstaged by subsequent
generations of change when they have become the existing
incumbents (Foster and Kaplan, 2002).
The problem is further compounded by the networks of
relationships the firm has with other firms. Typically, much
of the basis of innovation lies at a system level involving
networks of suppliers and partners configuring knowledge
and other resources to create a new offering. Discontinuous
innovation is often problematic because it may involve
Table 2
Different innovation management archetypes
Type 1 - Steady state- archetype
Interpretive schema—
how the organisation
sees and makes sense of
the world
There is an established set of ‘rules of the game’ by
competitors also play
Particular pathways in terms of search and selection
technological trajectories exist and define the ‘innov
to all players in the game
Strategic direction is highly path dependent
Strategic decision
making
Makes us of decision-making processes which alloc
basis of risk management linked to the above ‘rules of
proposal fit the business strategic directions? Does i
competence base?)
Controlled risks are taken within the bounds of the ‘
Political coalitions are significant influences maintai
trajectory
Operating routines Operates with a set of routines and structures/proced
them which are linked to these ‘risk rules’—for exa
monitoring and review for project management
Search behaviour is along defined trajectories and u
techniques for R&D, market research, etc. which assu
be explored—search and selection environment
Network building to support innovation—e.g. user i
partnership, etc.—is on basis of developing close an
building and working with a significantly different set of
partners than those the firm is accustomed to working with.
Whereas ‘strong ties’—close and consistent relationships
with regular partners in a network—may be important in
enabling a steady stream of continuous improvement
innovations, evidence suggests that where firms are seeking
to do something different they need to exploit much weaker
ties across a very different population in order to gain access
to new ideas and different sources of knowledge and
expertise (Philips et al., 2004).
6. Two archetypes for managing innovation
So far we have suggested that there is a set of conditions
under which innovation can be ‘managed’. It is by no means
easy but there is a ‘good practice’ prescription or recipe
which can be used to help organisations design and operate
ways of enabling innovation to happen under what we have
termed ‘steady state’ conditions. These correspond to
Type 2 - Discontinuous innovation - archetype
which other No clear ‘rules of the game’—these emerge over
time but cannot be predicted in advance
environments and
ation space’ available
Need high tolerance for ambiguity—seeing
multiple parallel possible trajectories
‘Innovation space’ defined by open and fuzzy
selection environment
Probe and learn experiments needed to build
information about emerging patterns and allow
dominant design to emerge
Highly path independent
ate resources on the
the game’. (Does the
t build on existing
High levels of risk taking since no clear
trajectories—emphasis on fast and lightweight
decisions rather than heavy commitment in initial
stages
innovation space’ Multiple parallel bets, fast failure and learning as
dominant themes
ning the current High tolerance of failure but risk is managed by
limited commitment
Influence flows to those prepared to ‘stick their
neck out’—entrepreneurial behaviour
ures which embed
mple, stage gate
Operating routines are open ended, based around
managing emergence
ses tools and
me a known space to
Project implementation is about ‘fuzzy front end’,
light touch strategic review and parallel exper-
imentation. Probe and learn, fast failure and learn
rather than managed risk
nvolvement, supplier
d strong ties
Search behaviour is about peripheral vision,
picking up early warning through weak signals of
emerging trends
Linkages are with heterogeneous population and
emphasis less on established relationships than on
weak ties
Steady state vs. discontinuous innovation strategies
Degree of instability
Degree of uncertainty
(1) Exploit
(2) Uncover
(3) Flex
(4) Co-evolve
Fig. 1.
J. Bessant et al. / Technovation 25 (2005) 1366–13761372
the mature phase in Utterback and Abernathy’s model
(Abernathy and Utterback, 1975).
To successfully manage innovation organisations have
to adapt and configure and learn their own version of
this—and building such routines is a hard process (Pavitt,
2002). But equally once in place such routines and
accompanying structures provide a degree of hard to
imitate capability which can confer strategic advantage.
For example, a company like 3M has sufficient confidence
in its product innovation routines to set stretching
strategic goals for the business—for example, that it
will derive a significant (20–30%) proportion of sales
form products introduced during the preceding 3 years.
The scale of their challenge can be seen if we consider
that their product range extends to some 50,000 items—in
other words it is betting on its capability to renew this on
a continuous basis.
One way of thinking about the bundle of ‘good practice’
routines for steady state innovation is in terms of an
‘organisational archetype’. That is a representation of a
particular model of an ‘ideal type’ organisation against
which organisations can compare themselves and through
which they can identify areas for development. (Greenwood
and Hinings, 1993) discuss this concept and suggest that
such archetypes can be considered in terms of three core
elements:
† How they see the world—‘interpretative schema’
† How they take decisions—their strategy and resulting
resource allocation
† How they operate on a day to day basis—their routines
and consequent structures and procedures
We can apply this approach to defining an archetype to
manage steady state innovation—and we can also attempt
the same thing for a second, different archetype associated
with discontinuous innovation. It is important to remember
that these are ideal types but they do help us see more
clearly the need for very different approaches to looking at
the world, deciding strategically what to do and actually
operating the search and implementation processes around
innovation. Table 2 gives an indication of these two
archetypes.
Essentially the problem facing ‘steady state’ archetype
organisations is one of systematic search within known or
‘knowable’ selection environments. By contrast discon-
tinuous innovation requires a much more open ended and
agile approach to managing and emergent field where
search strategies are difficult to predict in advance.
(McKelvey, 2004) Fig. 1 offers a simple representation
of the issue.7
7 This model and the accompanying discussion is based on the work of
Jean Boulton, Complex Systems Group, Cranfield University. For more
details see Boulton and Allen (2004). Strategic management in a complex
world. BAM annual conference, St Andrews, Scotland, BAM.
In this the bottom axis is one of stability and as we move
to the right so we reach an area of unpredictable, unstable
conditions. Here the existing rules of the game break down
and new ones emerge only slowly. The vertical axis is about
the extent to which knowledge can be acquired to help deal
with the environment—for example, understanding users,
exploring technologies, other kinds of search behaviour.
Again we move from the knowable—we can build certainty
because we know what to find out and how to find out about
it. But as we move up so we reach the point where we don’t
know how to find out and need to experiment and search in
much more extensive ways.
If we map innovation management strategies on to this
we can see that in zone 1 there is a steady state environment
with stable rules of the game and we can use tried and tested
approaches to fill gaps in our knowledge and develop
certainty. This is classically ‘exploit’ innovation strategy
where mature markets lead to incremental product and
process improvement. Zone 2 is still stable in terms of the
rules of the game but the state of knowledge is less clear and
we need to find new things out—essentially research aimed
at exploring around a technological or market trajectory. We
still have an established market or product technology, the
challenge is to uncover via strategic and targeted research.
Zone 3 is where there is instability—rules of the game
change—but we have a high degree of knowledge about
these shifts or how to find out and respond. For example,
fashion markets can flip suddenly and a degree of local
instability within that market emerges. But we can respond
through building in a degree of flexibility where existing
innovation management routines can be used to create new
responses to such instabilities. Mass customisation, flexible
customer response, late configuration systems, etc.—are all
elements of this (Pine et al., 1993; Gann, 2004; Squire et al.,
2004).
Strategic choice
ImplementationTriggering the process Learn?
Innovation Strategy
Innovative organization
Pro-active linkages
Emerging routines for managing DI
Fig. 2.
J. Bessant et al. / Technovation 25 (2005) 1366–1376 1373
It is zone 4 that poses significant problems because none
of our existing repertoire of innovation management
routines may help. Not only is there a shift in the rules of
the game but it occurs in ways which we don’t have
knowledge about or even knowledge about how to know.
How do we research a market that doesn’t yet exist? How
can we explore and develop a technology whose trajectory
Table 3
Emergent ‘good practice’ model outline for discontinuous innovation
Element in innovation model Type 2 characteristics
Triggering the process Search at the periphery—pick up and
Use multiple and alternative perspecti
Manage the idea generation process—
Develop an external scanning capabili
Use technological antennae to seek ou
Tune in to weak market signals—e.g.
Internet) (Moore, 1999)
Develop future exploring capability—
Explore at periphery of firm—subsidia
Bring in outside perspectives
Strategic choice and portfolio man-
agement
Build pluralism into decision-making
Create ‘markets for judgment’ (Hame
Decentralize seed funding for new ide
Build dual structures for innovation d
Develop ‘fuzzy front end’ approaches
Implementation Build flexible project development org
planning
Work actively with users on co-evolu
Build parallel resource networks (Leif
Innovation strategy Explore alternative future scenarios an
Identify strategic domains within whic
Build capacity for ambiguity/ multiple
Actively explore ‘how to destroy the
Innovative organization Build a culture which supports and en
Set up appropriate incentive structures
Enable complex knowledge flows
Pro-active linkages Develop non-committal exploratory su
relationships in addition to longer-term
Explore and develop parallel ‘weak ti
Learning and capability development Enhance absorptive capacity (Cohen a
Encourage heterogeneity in learning g
has yet to coalesce? This high instability, high uncertainty
zone is rightly called fluid (Utterback, 1994) or ferment
(Tushman and Anderson, 1987)—but what it means is that
our innovation management approaches need to be much
more flexible and emergent, based on principles such as co-
evolution of emergent market and technological trajectories,
rapid learning, experimentation, tolerance of failure, etc.
7. Building ambidextrous organisation
It is important to recognise that these archetypes define
very different organisational arrangements. Neither is
good or bad in an absolute sense but rather the question
is one of appropriateness to external conditions for
innovation. In simple terms one reason why existing
players do badly when discontinuous conditions emerge is
that there is a mismatch between their dominant steady
state archetype and the very different requirements for
discontinuous innovation. New entrants exhibit a fluid and
flexible archetype better suited to these conditions—but
they, in turn, will need to develop a ‘steady state’
archetype to help them manage under more stable
amplify weak signals (Day and Schoemaker, 2004)
ves (Allen, 2001)
enable systematic and high involvement in innovation (Bessant, 2003)
ty
t potential new technologies
working with fringe users, early trend locations (such as chat rooms on
scenario and alternatives (de Geus, 1996)
ries, joint ventures, distributors as sources of innovation
processes
l, 2000)
as- for example via internal venture funds or development budgets
evelopment and decision making (Buckland et al., 2003)
(Koen et al., 2001)
anisations—emphasise probe and learn rather than predictive project
tion of innovation (Von Hippel, 1988; Prahalad, 2004)
er et al., 2000)
d consider parallel possibilities (de Geus, 1996)
h targeted hunting can take place
parallel strategies
business’ to enable reframing (Welch, 2001)
courages diversity and curiosity-driven behaviour
(McKelvey, 2004)
pply
strategic alliances—‘strategic dalliances’(Philips et al., 2004)
es’ (Mariotti and Delbridge, 2005)
nd Levinthal, 1990)
roup (Allen, 2001)
J. Bessant et al. / Technovation 25 (2005) 1366–13761374
conditions as the dominant design emerges and we move
into the mature phase. In doing so they become the
existing players and, when the next discontinuity appears,
they too find themselves at a disadvantage.
This raises an important question. Is it possible to
operate both archetypes under the same organisational
roof and develop the ability to switch between them—an
approach called ‘ambidextrous’ by some commentators?
(Tushman and O’Reilly, 1996) Or does successful
management under different sets of conditions require
setting up completely new organisations—for example,
spinning off a completely new company to exploit new
opportunities under discontinuous conditions? (Chris-
tensen and Raynor, 2003).
The ambidextrous approach poses formidable challenges
but a number of mechanisms have been tried—for example,
various forms of corporate entrepreneurship structures,
‘skunk works’ and ‘intrapreneurship’ schemes (Pinchot,
1985; Rich and Janos, 1994; Gundling, 2000; Buckland et
al., 2003). Although difficult these carry the advantage that
the existing firm can leverage its financial, experience and
knowledge assets to help exploit new opportunities.
8. Building type 2 organisational capability
What are the dimensions of, and how can organisations
develop type 2 capability? Clearly they need to exper-
iment, imitate, adapt and in other ways learn new routines
which can become structured and embedded as a
capability, just as type 1 ‘good practice’ does. In this
section we list some of the emerging principles around
which such learning can take place, based on the
experience of organisations experimenting in this way.
We have structured this according to a simple process
model of innovation which involves five key dimensions
shown in Fig. 2.
Table 3 distils some elements of the emerging ‘good
practice’ model for type 2 discontinuous innovation
management.
8 An example of such a ‘co-laboratory’ for exploring with users the
emergence of alternative models for managing discontinuous innovation is
the Discontinuous Innovation Forum. Set up as a pilot cross-industry
learning network with pump-priming support from the UK Department of
Trade and Industry and the Advanced Institute of Management Research,
this involves an extended shared learning process with around 30 firms and
3 university research groups. More details can be found at the website,
www.dif.org.uk.
9. Conclusions
Discontinuous innovation poses significant challenges
both for research and for practice. It is clear that managing
innovation under such conditions requires a new approach
and we have tried in this paper to explore some of the
dimensions of the alternative archetype required. It is also
clear from a wide range of studies that even otherwise
successful firms can run into difficulties in trying to deal
with this challenge underlining the need for active learning
around a new model.
This learning involves not only developing new capa-
bilities within the enterprise but increasingly it raises
questions about system level innovation. Interactions with
other firms, with courses of knowledge and specialist
expertise, with users and those who influence users and with
many other players are becoming a key focus in the
emerging discontinuous innovation picture. A key element
of the emerging innovation management challenge is about
developing capabilities to work at this network or systemic
level.
It also raises an important challenge for the approach
taken to research on innovation management. Central to the
discontinuous innovation problem is the fact that it is
difficult at the outset to predict the emergence of the
dominant design or trajectory—and therefore pre-planned
models for organising and managing to deal with it are of
limited value. The strategy instead needs to be one of ‘co-
evolution’ amongst players in the process, emphasising fast
learning from what will necessarily be a series of
experiments with a high level of failure associated with
them. This -co-evolution-model can also be extended to the
ways in which we might learn about how to manage
discontinuous innovation—and it suggests the need to build
close links between researchers and innovating organis-
ations with the common goal of shared learning about
emergent ‘good practice’.8
Whilst we can specify the general direction and frame-
work for such an archetype (as in Table 3) there is
significant work to be done to elaborate and populate it with
specific tools, techniques and enabling mechanisms. At a
time when the potential sources and frequency of triggers
for discontinuity is on the increase the need to develop such
responses becomes an urgent priority.
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John Bessant is Professor of Innovation Man-
agement at the School of Management, Cranfield
University. He also holds a Fellowship of the
Advanced Institute for Management Research,
which he was awarded in 2003. He served on the
Business and Management panel of the 2001
Research Assessment Exercise. In 2003 he was
elected a Fellow of the British Academy of
Management.
Richard Lamming is Professor of Manage-
ment and Director of the School of Manage-
ment at the University of Southampton in the
UK. Before taking up this executive role he
was for 13 years CIPS Professor of Purchasing
and Supply Management at the University of
n 25 (2005) 1366–1376
Bath School of Management. Before this,
while working at MIT and the University of
Sussex, Science Policy Research Unit, he was
part of a team that wrote The Machine That
Changed The World (1990), the best selling
manufacturing management book which introduced the concept of lean
production. Prof Lamming developed and published the principles of lean
supply in 1993, as an application of creative destruction in process
innovation in supply chain relationships. Since, that time his research has
been based upon developing theories within the lean supply paradigm, and
pursuing their implementation in a variety of industries.