Topics in theManagement of Technology and Innovation:

A Synopsis of Major Findings

Bedrijfseconomische Verhandeling

March 1997

Koenraad Debackere

Department of Applied Economics, K.U.LeuvenNaamsestraat 69B-3000 Leuven

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Topics in the Management of Technology and Innovation:A Synopsis of Major Findings

Koenraad Debackere, Department of Applied Economics, K.U.Leuven

0. Abstract

In this review paper, major findings on ‘best practices’ in technology and innovation management are summarised and discussed. These ‘best practices’ are situated at the strategic level as well as at the operational level in the organisation. They highlight the strategic (portfolio-level) and operational (project-level) determinants of innovation performance. The economic origins of innovation management theory are also briefly introduced and discussed.

1. Introduction

Technology is a major stimulus for change in society. We have come to look to technological

innovation to rescue us from the consequences of exhausting natural resources; to abate inflation

through productivity increases; to eliminate famine; to cure cancer; and to maintain the competitive

position of our nations’ industrial bases. Indeed, technological change has become a major driver of

competition: it propels new firms to the forefront of the competitive arena while it destroys the

competitive advantage of even well-entrenched firms. Achievements such as electronic computers,

test tube babies, supersonic aircraft, and manned space flights have bolstered our faith in technical

advance. We no longer ask if something is possible, but how soon it can be done and at what price.

There is little doubt that the rapid technological progress we have witnessed during the last

decades will come to an end soon. Today, researchers around the globe are working intently on

developing ideas that may create new branches of technological practice and could ultimately

transform industry in ways which are hard for most of us to imagine. As a consequence, the ability of

managers and policy makers to comprehend the pace and the direction of technological advancement

will largely determine a firm’s or nation’s competitive performance in world markets into the next

century. This is no small task, however. Historical accounts of industrial evolution and innovation,

such as with the development of semiconductors (Braun and Macdonald, 1978), videocassette

recorders (Rosenbloom and Cusumano, 1987), and personal computers (Smith and Alexander, 1988),

show the immense difficulties some firms encounter when confronted by new technologies. The

inertia, introduced by a firm’s existing technological base, often is a powerful barrier to internalise

new technological trajectories (Utterback, 1994). Hence, there is an obvious need to harness the

process of technological innovation effectively. To do this, technological innovations cannot be

isolated from the complex economic, social and political systems within which they operate.

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As a consequence, an extensive research agenda into the nature of the technological innovation

process started in the 1950s. This brought a recognition that innovation is an activity which needs

careful ‘managerial’ attention and actions. But, before there can be effective management, there

must be a detailed understanding of the process of innovation, its characteristics and its specific

problems.

Therefore, the first part of this paper will focus on the major characteristics of the innovation

process as they have emerged over the last three decades. The models to be discussed are chosen for

the complementary insights they offer into the complex nature of the innovation process. We start

with the theories on technical change developed by Schumpeter. Although the study of technical

advance as an economic phenomenon is a relatively recent event, it was Schumpeter who in three

books, The Theory of Economic Development (1934), Business Cycles (1939), and Capitalism,

Socialism and Democracy (1942), portrayed most fully the active role played by economic agents in

technical advance. From these studies, technological innovation emerges as a non-linear, dynamic,

interactive and complex process.

In addition, models of the innovation process were developed to support managerial actions.

Whereas the theories by Schumpeter attempt to gain a fundamental insight into the nature and the

causes of technological evolution, the three models discussed subsequently focus on the innovation

process within the firm. The first model, by Roberts and Frohman (1978), depicts technological

innovation as a process of uncertainty reduction. This process necessitates three important activities

within the firm. Ideas have to be generated. Once generated, these ideas have to be turned into good

currency. And, finally, appropriate organisational structures have to be implemented to manage the

transition from what first seems to be an abstract ‘idea’ into a ‘product’ desired by customers. As a

consequence, an important focus of this model is on managing people and their innovative ideas.

The second model explicitly makes the link between technological innovation on the one hand

and organisational strategy and structure on the other hand. The Abernathy-Utterback model (1975 &

1978) describes how product and process innovations evolve during the technological life cycle of a

‘productive unit’ or ‘business unit’ and, still more important, how competitive strategy, production

facilities, and organisation structure ‘co-adapt’ during this evolution.

Finally, the technological S-curve model (Roussel, 1984; Foster, 1986) enables managers to

better estimate the strategic importance of the different technologies in a firm’s technology portfolio.

To this end, the S-curve model is used to develop a technological typology. A distinction is made

between emerging, pacing, key, and base technologies. The competitive implications of this typology

are highlighted. A link is made with Wheelwright and Clark’s definitions of breakthrough, platform

and derivative projects (1992).

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Once we have obtained a basic understanding on the nature of the innovation process, we will

turn our attention to the management of technological innovation. This will be the theme of the

second part of this paper. From the previous discussions, we know that ‘managing’ the innovative

capabilities of the organisation involves different levels of attention: (1) attention to the relationship

between technology and strategy; (2) attention to an appropriate organisation structure in which

innovative activity can flourish; and (3) attention to the management of innovative professionals. It is

hence necessary to discuss the management implications associated with each level of attention. The

integration of these three levels of attention leads to the development of a partnership model on

organising technology and innovation, as discussed by Roussel and his colleagues in their influential

book Third Generation R&D (1991).

Finally, the third part of the paper brings together the major issues raised in the previous sections

and ends with a ‘checklist’ of focal activities that are to be considered when managing a firm’s

innovation efforts.

Before embarking upon a detailed discussion of these topics, though, there is an obvious need to

clarify two major concepts used throughout this paper, i.e. what is meant by ‘technology’ and how do

we define ‘technological innovation’?

1.1. Technology: what’s in a name?

Throughout the decades of research on the management of technology and innovation, a host of

definitions has surfaced, attempting to describe what is meant by a ‘technology.’ According to the

Oxford Dictionary, technology is “the science of industrial arts.” This definition, despite its brevity,

combines two concepts that are essential to fully grasp the meaning of ‘technology’: science and

arts. Of course, we do not imply that technology is the same as science, not even that it always has to

be based on scientific principles or developments. Indeed, examples exist where the technology was

in place before the underlying scientific principles were known or clarified. One of the most notable

examples undoubtedly is the steam-engine. It was developed before the science of thermodynamics

had originated. However, the Oxford definition does imply that technology has an important

‘knowledge’ component. Thus, a major input into technological activities is knowledge about why

things work the way they do. This is the know-how versus the know-why question. This knowledge

can be derived from scientific developments, but also, from previous technological experience.

The Oxford definition also points to the fact that technology has to do with arts. The products of

human art are artefacts. Artefacts are tangible products and processes created by human skill. Thus,

contrary to scientific activity, the major output of technological activity is embodied in hardware, i.e.

products and processes. Technological output is tangible. It is not mere knowledge. Figure 1 (adapted

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from Allen, 1977) highlights this important contrast between technological activity and scientific

activity. The major inputs into any scientific activity are information and knowledge. The major

outputs of scientific activity are, once again, information and knowledge. Oversimplified, scientists

read papers (knowledge input), they think and experiment, and they write papers (knowledge output).

The major inputs into technological activity are also information- and knowledge-related. However,

the major outputs of technological activities are embodied in hardware, i.e. products (which are more

and more frequently integrated with services, or vice versa) and processes.

— Insert Figure 1 about here —

Thus, technological activity can be defined as the processes by which knowledge (scientific and

experiential) is transformed into artefacts, i.e. products and processes. As a consequence,

technological activity is characterised by both a less-tangible knowledge component (i.e. the input-

side of the equation) and a tangible product or process component (i.e. the output-side of the

equation). Having defined ‘technology’, we still have to clarify the concept of ‘technological

innovation.’

1.2. Technological innovation

Technological innovation is the successful commercial exploitation of inventions as they become

embodied into new products and processes. The emphasis thus is on exploiting the results of

technological activity. There are, of course, different opinions of what constitutes a new product or

process. In the most general and pure sense, the product or process developed is new to the world.

This need not be the case, however. Certain experts go as far as considering any product or process

an ‘innovation’ as long as it is perceived as new to the organisations involved, even though it may

appear to others to be an ‘imitation’ of something that exists elsewhere (Van de Ven, 1986).

Whereas the invention process may be hard to manage, the management of the innovation process

(as a systematic approach to exploit inventions and reduce them to practice in a successful manner)

has been well-embedded both in theory and practice over the last decades.

Before turning to these managerial issues, though, it is necessary to address the following

question: What are the characteristics and the complexities involved along the innovation trajectory?

2. The process of technological innovation

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In this section three different approaches to unravel the characteristics of the innovation process

are discussed (the models by Roberts (1978)and by Abernathy & Utterback (1975), and the S-curve

by Roussel and Foster (1984 & 1986)). The ‘economic’ origins of innovation theory are highlighted

first. This economic debate has focused on the relationship between market structure and innovative

activity.

2.1. The economic debate: market structure, technology-push and market-pull

Schumpeter was the first to fully portray the active role played by economic agents in technical

advance. Schumpeter’s different books, though, reveal the many subtleties involved in explaining the

origins of technological innovations. It is important to grasp those subtleties since they are essential

to understand the more managerial oriented models of the innovation process to be discussed in the

next sections. In his first two books (The Theory of Economic Development, 1934 & Business Cycles,

1939), the entrepreneur plays a central role. The entrepreneur is defined as the person who creates

new combinations. He sees how to fulfil currently unsatisfied needs or he perceives more efficient

ways of doing what is already done. These acts may, though need not, involve the presence of

inventions. In some cases, it may only involve a new application of an existing technology. As a

consequence, the act of invention and the act of entrepreneurship are separate: the inventor need not

necessarily be the entrepreneur and vice versa. However, the entrepreneur plays a central role since

he is the one who turns the invention into exploitation.

Given the importance attributed to the ‘entrepreneur,’ this theory has often been called

Schumpeter’s theory of ‘heroic entrepreneurship’ or ‘creative destruction.’ Indeed, the logic of the

theory is as follows (see Figure 2). There exists a pool of inventions related in an unspecified way to

the state-of-the-art developments in scientific and technological knowledge. The important

observation now is that this pool of inventions is largely exogenous to existing firms and market

structures. Thus, they are unrelated to any specific and quantifiable type of market demand. Of

course, this does not mean that they may not be influenced by an anticipated demand or shortage. We

all know that, in an abstract manner, human needs are infinite. However, in the realm of

Schumpeter’s theory of heroic entrepreneurship, there is no direct coupling between a measured

market need (as we would detect from extensive market research, for example) on the one hand, and

the efforts invested and the directions chosen in the pool of inventions, on the other hand.

The essential link between the ‘pool’ of inventions and the ‘market’ is made through the person

of the entrepreneur. He is aware of the potential of certain inventions, and as a consequence,

becomes prepared to take the risk and the commitment necessary to turn these inventions into

innovations. Thus, innovating is more than inventing. As defined previously, it is the (successful)

commercial exploitation of inventions. Schumpeter then remarks that such a hazardous activity

would not be undertaken by an ordinary capitalist economic agent (such as an existing firm). Only

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the entrepreneur has the vision, the drive and the commitment to survive the turbulence and the

uncertainty involved. If he succeeds, though, the rewards are enormous. The entrepreneur will realise

exceptional (be it temporary) monopoly profits and he may be able to fundamentally alter existing

market structures.

— Insert Figure 2 about here —

Examples of the successes of ‘heroic entrepreneurs’ abound. For instance, the advent of Texas

Instruments as a major electronics firm can be seen as the result of heroic acts of technological

entrepreneurship. The company did not actually invent the transistor, though it made judicious use of

the new technology to create products that would meet hitherto unfulfilled customer needs. Other

examples of heroic technical entrepreneurship are Edwin Land and the development of the Polaroid

camera and Joe Wilson who turned the Haloid Company, a small photographic paper and supply

firm, into today’s giant Rank Xerox through his vision and ideas about a revolutionary copying

process. More recent examples of ‘heroic entrepreneurship’ can be found in the formation of new

biotechnology firms such as Plant Genetic Systems, Genentech, Amgen, etc. They all symbolise the

entrepreneurial vision that attempts to turn ‘knowledge’ into ‘commercial exploitation.’ In doing so,

those firms are at the origin of what Schumpeter called “the eternal gale of creative destruction.”

In his third book, Capitalism, Socialism and Democracy (1942), Schumpeter’s focus on technical

progress took on new directions (see Figure 3). Instead of focusing solely on the ‘heroic

entrepreneur,’ Schumpeter now incorporates the importance of scientific and technological activities

conducted by (mostly large) firms. In this additional model of the innovation process, the coupling

between science, technology, innovative investment and the market, which was tenuous at best in the

first model (see Figure 2), is much more intimate and continuous. Successful innovations generate

profits leading to increased in-house innovative activity and R&D investments.

— Insert Figure 3 about here —

As a consequence, the heroic entrepreneur is not the only central agent linking invention to its

subsequent exploitation. Whereas science and technology are largely exogenous in the first model

depicted in Figure 2, they are at least partly endogenised in the model described in Figure 3. Thus,

the link between invention and exploitation is internalised within existing economic agents, i.e. the

firm (and preferably the large firm, as Schumpeter hypothesised). As a consequence, the role of the

heroic entrepreneur who couples invention and exploitation, is complemented by intrapreneurial

modes of invention exploitation. Still more important, Schumpeter’s paradigm on the economics of

technical advance inspired the hypotheses that innovative activity would be proficient in (1) large

firms and (2) in monopolistic industries.

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Large firms were deemed more innovative than small firms because they can finance a larger

research and development staff, leading to economies of scale in R&D; because large firms are better

able to exploit unforeseen innovations given their more diversified product lines; and, because

indivisibility in cost-reducing innovations makes them more profitable for large firms.

In the same vein, it was hypothesised that innovation would be greater in monopolistic industries

than in competitive ones because a firm with monopoly power can prevent imitation and thereby can

capture more profit from an innovation; and, because a firm with monopoly profits is better able to

finance research and development (Kamien and Schwartz, 1982).

Although the hypotheses on the relationship (1) between firm size and innovative activity as well

as (2) between monopoly power and innovative activity have only received limited support, the

models described in Figures 2 and 3 further lead to the origins of a debate that has engaged students

of the innovation process for quite some time, namely: what is the causal direction of the relationship

between technological research and the market? In other words, is technological research the initiator

of innovations that lead to the creation of new markets (i.e. the ‘technology-push’ hypothesis)? Or,

on the contrary, is it the market that initiates innovations (i.e. the ‘market-pull’ hypothesis)?

Although the question on causality may seem superfluous, it has nevertheless important

consequences, not in the least at the macro-level of economic policy-making. Indeed, if one adheres

to the technology-push hypothesis, then one will recur to a supply-side oriented (neo-classic) macro-

economic policy with respect to technological innovation. Enough money has to be invested in

research facilities and R&D programs, and markets will ultimately follow suit. Oversimplified, a

technology-push oriented policy will allocate considerable sums of money to R&D, hoping that

heroic entrepreneurs will tap the pool of knowledge thus generated and create new products and

processes that ultimately serve markets.

On the other hand, a market-pull policy will stimulate innovation through creating a demand for

new products or processes. This demand will trigger innovative behaviour. For instance, in order to

stimulate innovations in telecommunication technology, a market-pull oriented policy might operate

through the creation of a national demand for a new telecommunication network. This demand would

then spur the innovative behaviour of the organisations participating in the national program. A

technology-push oriented policy, on the other hand, might operate through formulating R&D

programs in telecommunications technology.

A scrutiny of government policy frameworks aimed at stimulating technical innovation shows

that, in fact, both technology-push and market-pull orientations are relevant. Innovations have their

origins both in the market and in the creation of new technological capabilities. For sure, research

has shown that market-induced innovations tend to have a higher probability of commercial success

than innovations that originate from a technological capability and that are isolated from a market-

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selection environment. However, this relationship between market-relatedness and commercial

success is moderated by the fact that market-induced innovations tend to be more incremental and

thus less radical than innovations having their origins in the research laboratory (Rothwell et al.,

1977).

Moreover, it appears that technological innovation certainly is not a linear, sequential process as

might be (incorrectly) deduced from Figures 2 and 3. Instead, it is a complex, multi-stage, cross-

functional, and multi-disciplinary process in which both supply-side and demand-side arguments are

relevant and should be carefully considered. This is all the more true when studying technological

innovation at the firm-level. Here the managerial models of technological innovation become

relevant.

2.2. Managerial models of technological innovation

2.2.1. The process of technological innovation: a general model

The first managerially relevant model to be discussed is the one proposed by Roberts and

Frohman (1978). This model (see Figure 4) emphasises three key generalisations. First of all, ideas

and opportunities for innovation originate both from the supply-side (‘technology’) and the market-

side (‘market’). Thus, both the ‘technology-push’ and the ‘market-pull’ dimensions are highly

relevant. Second, the process of technological innovation is a multi-stage or -phase process.

Significant variations in the primary task as well as in the managerial issues and effective

management practices occur across these different stages. Third, in the model, six stages are

presented. The exact number of stages or phases is, of course, somewhat arbitrary. What is key,

though, is that each phase of activity is dominated by the search for answers to different managerial

imperatives. Finally, each phase requires clear go/no go decision points and phase-reviews.

— Insert Figure 4 about here —

At the outset (stages 1 and 2), emphasis is on finding a motivating idea, a notion of possible

direction for technical endeavour. Thus, ideas have to be generated and, still more important, once

generated, attention has to be paid to those same ideas. Good managerial practice at these early

stages frequently involves loose control, the pursuit of parallel and diverse approaches, fostering

conflict or at least contentiousness, and stimulating a variety of inputs. Small amounts of money

should be rather freely available to enable the assessment and evaluation of the ideas generated. A

major mistake is to set up rigid formal processes for approval of the small sums needed to try out an

idea. But, most of all, an organisational environment and culture has to be developed that tolerates

new ideas and allows proper attention to be paid to them. 3M’s statement “Thou shall not kill an

idea” clearly reflects what is meant by this attitude. In addition, a tolerance for new ideas also

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implies a tolerance for failure. As has been often documented in the innovation management

literature, false foundations can prove to be challenging new starts. This, though, is only possible

when ‘failures’ are tolerated.

As we move further along the different stages, the managerial issues and the actions required

change dramatically. During stages 4 and 5, for example, the task involves in-depth specification and

manufacturing engineering of ideas that are being reduced to an acceptable working prototype. The

managerial issues evolve towards co-ordinating a number of scientists and engineers of different

disciplinary backgrounds to achieve, within previously estimated development budgets and

schedules, a predefined technical output ready for manufacture in large volumes; reliable and at

competitive production costs. Effective managerial practice will thus involve tight control,

elimination of duplication, strong financial criteria and formal evaluations of resource use, even

somewhat rigid adherence to planning. Thus, during the later stages of the innovation process, the

managerial actions required are rather opposite to the ones advocated during the first stages of the

process. Whereas the first stages focus on ‘managing ideas,’ the later stages focus on the

management of the ‘part-whole relationships’ required to turn these ideas into a tangible product or

process. Part-whole management indeed requires the co-ordination of the efforts of people with

different disciplinary backgrounds, working together toward achieving the new product or process

goals.

As is obvious from Figure 4, innovation occurs through efforts carried out primarily within an

organisational context, but involving heavy interaction with the external technological as well as

market environments. Proactive search for technical and market inputs, as well as receptivity to

information sensed from external sources, are critical aspects of technology-based innovation. Many

studies of effective innovations have indeed shown the presence of significant contributions of

external technology (e.g. via contacts with universities) and have found success heavily dependent

upon awareness of customer needs and competitor activity.

Another remark is warranted with respect to the model in Figure 4. For ease of presentation, all

stages are shown at equidistant intervals inappropriately suggesting perhaps the similarity of these

phases from a time duration and/or resource consumption perspective. Stage 5, commercial

development, for instance, usually takes as long as the several earlier stages combined and requires

more resources than most of the other stages together. This is why tight financial control becomes

necessary at this stage. A typical cash flow diagram for new product or process development is

shown in Figure 5 (source: Twiss, 1992). In addition, current practices in the area of concurrent

engineering have emphasised the possibility to engage in overlapping problem-solving activities,

pointing to the different phases just discussed running (partly) in parallel and simultaneous.

— Insert Figure 5 about here —

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From Figure 5 it is obvious that the upstream ‘research-oriented’ activities (idea formulation and

problem solving) only consume minor expenditures compared to prototyping, manufacturing start-up,

and market launch. Of course, the relative importance of these items further depends upon the nature

of the industry. In aerospace, for example, the construction and testing of prototypes usually requires

major expenditures, while investments in manufacturing or marketing are small or non-existent. In

chemical industries, on the contrary, investments in efficient production facilities may be the major

item. Consumer industries, on the other hand, may incur extremely important marketing costs in

launching the new product.

Finally, although only a limited amount of feedback loops is shown in Figure 4, they inevitably

exist causing reiteration among the different stages in the model. For example, the problem-solving

processes in stage 3 often generate useful insights for alternative idea formulations (stage 2). Also,

during stage 6 (transfer to manufacturing) new problems may surface requiring a reiteration to the

problem-solving stage (stage 3). Thus, the real process of technical innovation involves flows back

and forth over time among differing primary activities, internal and external to the innovating

organisation, with major variations arising throughout the process with regard to specific tasks,

managerial issues and managerial answers. Still more important, as mentioned above, the practice of

concurrent engineering and parallel development has convinced managers of the possibilities of

having several innovation tasks running in parallel as well as of the potential of engaging in

overlapping problem-solving activities (Deschamps and Nayak, 1995).

2.2.2. A dynamic model of the innovation process

The above model gives a general description of the innovation process and the different stages

involved. Another extremely influential model was developed by Abernathy and Utterback (1975 &

1978). It takes us one step further. It describes how the nature of a company’s innovation activity

(and its response to innovative ideas) changes as it grows and matures. It investigates how the types

of innovations attempted by productive units change as these units evolve. Thus, the model takes a

dynamic stance towards the innovation process. It relates patterns of innovation within a productive

unit to that unit’s competitive strategy, production capabilities, and organisational characteristics.

The model implies that a productive unit’s capacity for and methods of innovation depend critically

on its stage of evolution from a small technology-based enterprise to a major high-volume producer

(see Figure 6; source: Abernathy, 1978).

— Insert Figure 6 about here —

The model thus focuses on innovation patterns within a productive unit. For a simple firm or a

firm devoted to a single product, the productive unit and the firm would be one and the same. In the

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case of a diversified firm, a productive unit would usually report to a single operating manager and

normally be a single operating division. Thus, the productive unit consists of both a manufacturing

unit and the product line produced targeted toward specific markets or market segments. For

example, an engine plant and the line of engines it produces is one productive unit. An assembly

plant and the particular car it produces is another. As a consequence, a productive unit can be

compared to the business unit form of organisation as it has been well-documented in the literature

on organisational design and development.

In the model, two major stages along the unit’s innovation trajectory are discerned. During the

first phase, the product design is subject to major changes, product characteristics are

underdetermined, product innovations are numerous. However, their emphasis is on improving the

functional performance of the product rather than reducing product costs. This phase is therefore

called the fluid stage. As the product is still in flux, production systems have to be highly flexible but

at the same time, they necessarily are inefficient. In sum, during the fluid phase, the competitive

emphasis of the productive unit is on functional product design performance. Recent insights from

the management control and accounting literature, focusing on the notion of ‘target costing,’ further

show that during these early product definition phases, about 85 percent of the product’s final and

total cost structure is being determined.

Many of the product innovations introduced during this first phase are to a large extent stimulated

by information on users’ needs and users’ technical inputs about how they want the product to

perform. von Hippel (1977) has identified these users as ‘lead users.’ They are the users who are ‘at

the front of the trend.’ Not only do they face the need for the new product or process well in advance

of the bulk of the marketplace. But, they also are willing to experiment with the new product or

process to obtain maximal satisfaction and solutions to their needs. As a consequence, they represent

an important input to the productive unit which is still experimenting with the product in order to

establish a ‘stable’ product design. This stable design or standard, once achieved, is called a

dominant design in the Abernathy-Utterback model (cf. infra).

Thus, during the fluid stage, the predominant mode of innovation are frequent and major changes

to product designs. The product line then necessarily is highly diverse, often including custom

designs. The production process is flexible and inefficient since major product changes have to be

accommodated easily. Production equipment is general-purpose and requires highly skilled labour.

Materials requirements are limited to generally available materials, as long as the product

characteristics have not stabilised (i.e. as long as a dominant design is absent). The production

facilities will be small-scale. Organisational control is informal and entrepreneurial.

The second major phase to be discerned in the dynamic model is characterised by a stabilised

product concept (a dominant design has emerged). The product is standardised, changes are highly

incremental and production systems are rigid but efficient. Therefore, this phase has been called the

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specific stage. During the specific stage, competitive emphasis is on cost reduction. Innovations level

off (both from a product and from a production process perspective) and are stimulated by the

pressures to reduce costs and to improve quality. As well product as process innovations thus become

highly incremental, with cumulative improvements in productivity and quality. The product line has

stabilised significantly. Product variety and flexibility occur within determined boundaries. Whereas

during the fluid stage, products often contain custom-designed features, the specific stage is

characterised by mostly undifferentiated standard products.

To achieve these cost and quality imperatives, the production process is streamlined. It is

increasingly efficient, capital-intensive, and rigid. The cost of change to the process is large. Major

changes in product characteristics would almost certainly involve major investments to turn around

the existing production facilities. Specific-stage production processes are characterised by special-

purpose machines, mostly automatic with labour tasks mainly monitoring and control. Specialised

materials will be demanded. If these materials are not readily available from reliable suppliers,

vertical integration will be extensive. Production plants are large-scale, often dedicated to specific

products (or product families). Economies of scale are important. Organisational control is tight.

Emphasis is on structure, goals, and rules.

As a consequence, the fluid and specific stages are quite opposite with respect to the managerial

issues and responses they raise. The stage linking the fluid to the specific patterns in the model is

called the transition stage. During the transition stage, a dominant design emerges. The dominant

design marks a reduction in product innovations. It marks the advent of a ‘product standard.’ For

example, the DC-3 aircraft is an example of a dominant design. The DC-3 was a cumulating of prior

innovations. It was not the largest, or fastest, or longest-range aircraft. It was, however, the most

economical large, fast plane able to fly long distances. All the design features introduced in the DC-3

had been proven in prior aircraft. It was the combination of those prior innovations into one stable,

‘standard’ product that made the DC-3 a unique design. No major innovations were introduced into

commercial aircraft design from 1936 onward until the development of the turbojet enabled a new

generation of aircraft in the 1950s. Instead, many incremental refinements were made to the DC-3

concept. During the period of these incremental changes, airline operating costs per passenger-mile

dropped an additional 50 percent. Similar design milestones have been identified in numerous

product lines, for example: the internal combustion engine, the standardised diesel locomotive,

sealed refrigeration units for home refrigerators and freezers (see also Abernathy & Utterback, 1975

and Abernathy, 1978), and more recently the personal computer or the Internet protocol TCP/IP.

Thus, during the transition stage, competitive emphasis is on product variation rather than on

functional performance (fluid stage) or cost reduction (specific pattern). Innovation is stimulated by

expanding the company’s internal technical capabilities. The rate of product innovations slows down

and takes on another character. The advent of a stable, dominant design enables significant

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production volumes. It has the effect of enforcing standardisation so that production economies can

be sought. This implies that subsequent product innovations, whenever they occur, will be rather

incremental.

As far as the production process is concerned, major innovations are realised during the transition

period, though (see also the changes in both curves in Figure 6). The rising volume, enabled by the

stabilised product design, calls for a more rigid production process with this rigidity being added in

major steps. Sub-processes become increasingly automated, ‘islands of automation’ are created.

Specialised materials may be demanded from some suppliers, the plant is a mixture of general-

purpose machinery combined with specialised (automated) sections. Organisational control begins to

tighten through the creation of liaison functions, the formation of project groups and task forces.

Thus, the dynamic pattern underlying innovation processes within a productive unit has important

managerial implications. As a unit moves towards large-scale production, the goals of its innovation

process change from ill-defined and uncertain targets to well-articulated design objectives. In the

early stages, there is a proliferation of product performance requirements and design criteria which

are difficult to quantify. ‘Lead users’ provide important inputs to reduce this performance

uncertainty. During this initial stage, market needs are ill-defined and market uncertainty is high.

The relevant technologies are only marginally understood. As the enterprise develops, though, both

technological and market uncertainties are reduced. Larger investments in innovative research,

process engineering, and production facilities can be justified.

As the innovation patterns evolve over time, the organisation as such also has to adapt. The

organisation’s methods of co-ordination and control change with the increasing standardisation of its

products and production processes. The enormous amounts of uncertainty during the first stage of its

development put a premium on the capacity of the organisation to process the information necessary

for uncertainty reduction. This can be done by establishing vertical and lateral information systems

as well as project management structures. Later, these may be complemented with more formal

mechanisms such as task force groups and management control systems such as job procedures and

job descriptions.

As the organisation moves into the specific stage, though, products are standardised and change is

at best incremental. This is reflected in the control and co-ordination systems in place in the

organisation: formalisation and routinisation become prevalent. They both tend to reduce the need

for information processing.

The dynamic model by Abernathy and Utterback has provided many useful insights into the

characteristics and the complexities of the innovation process. Whereas the model by Roberts and

Frohman presents a way to look at the different stages of a particular innovation effort (mostly using

a project structure as a vehicle for execution), including the relevant managerial issues and solutions,

15

the model by Abernathy and Utterback explicitly brings a multitude of organisational characteristics

and their dynamics (described at the level of a productive unit) into play. As a consequence, it yields

insights complementary to the ones derived from the Roberts and Frohman model.

At the same time, though, we have to conclude this discussion of the Abernathy-Utterback model

with some cautionary comments. First of all, although the model seems rather generalisable across

industries, it need not necessarily be applicable to all possible types of productive units that exist.

The model is easily applicable to industries such as the automotive industry, the electronics industry,

and the mechanical industry. It becomes different, though, when studying bulk chemicals, for

instance. In this latter industry, product innovations are minimal or non-existent. What matters is

building an extremely efficient and reliable process able to manufacture the standard ‘commodity’

product. This remark also holds for other ‘process industries.’ Thus, the Abernathy-Utterback model

is less appropriate to describe the dynamic innovation patterns as they occur in process industries.

The same is true for certain service industries. If we take as example a hamburger restaurant chain,

such as Burger King or MacDonald’s, we will have obvious difficulties to observe the

complementary product and process innovation patterns and their evolution over time, as they are

depicted in Figure 6. Once again, the product innovation curve is not present.

The second remark on the model is still more fundamental. Oversimplified, it boils down to the

fact that the Abernathy-Utterback model, as it has just been described, implies a fundamental

dilemma: innovativeness and productivity cannot be achieved simultaneously. During the fluid stage,

the enterprise displays a high degree of innovative behaviour. However, because of the extreme

degrees of flexibility required to be innovative, the organisation just cannot be efficient and highly

productive. The opposite is true during the specific stage. Here, the organisation puts a premium on

productivity. As a corollary, innovative activity is at best incremental, if it has not totally

disappeared.

The changing environment of the firm, with its emphasis on lead time reductions, efficient

product development, and time-based competition, necessitates the firm to achieve both objectives

(productivity and innovativeness) more or less simultaneously. This is no small task and the

Abernathy-Utterback model, through its description of the internal dynamics of innovation patterns

within productive units, points to the many hurdles and to the organisational inertia to be overcome

to develop this simultaneity. Fortunately, technological evolution in and off itself can support the

trend towards greater simultaneity. More specifically, the emergence of new technologies (such as

computer-aided-design, computer-aided-manufacturing, flexible manufacturing systems, and

computer-integrated-manufacturing), if used judiciously, precisely aim at bridging this difficult gap

between ‘innovativeness’ and ‘productivity.’ Hence, the dominant logic as presented by Abernathy

and Utterback need not be as irreversible anymore as it was suggested in the early 1980s. However,

the natural tendency for the firm’s innovative activity to become more focused, less original and

16

more formalised as the business matures of course remains a relevant threat to any innovation

management programme.

The two models of the innovation process we have discussed sofar, have yielded many significant

insights to better understand the managerial issues involved. Each model has proven to be extremely

useful at a particular level of action. The Roberts-Frohman model is appropriate to understand the

innovation process at the project level of action. In other words, it is valuable to understand the many

issues involved in managing innovation projects. The Abernathy-Utterback model is more useful to

understand the life cycle of a productive unit and to suggest appropriate managerial actions at the

organisational (and even strategic) level. However, we still lack an important perspective in order to

achieve a truly holistic view of the innovation process.

Indeed, most organisations are not committed to one specific technology. Instead, they manage a

portfolio of different technologies, some of which are more important to the company’s competitive

position than others. Thus, we need a model that allows us to prioritise different technologies in

function of their impact on the company’s competitive position. This can be achieved through the

concept of the technological S-curve as described by Roussel (1984) and Foster (1986).

2.2.3. The technological S-curve

Just like living organisms, technologies have life cycles, from birth to old age. Indeed, analysis of

historical data from a considerable number of phenomena shows that technological progress is not

random and discontinuous. Instead, it follows a regular pattern when a selected attribute, such as

functional performance (e.g. number of MIPS for computer CPUs, aircraft speed, efficiency of

internal combustion engines), a technical parameter (e.g. tensile strength to density ratio for a new

material), or economic performance (e.g. operating cost per passenger-mile for aircraft) is plotted

against time. Typically, an S-shaped pattern emerges (see Figure 7). These S-curve patterns can then

be applied to a more qualitative analysis of the different technologies represented in the company’s

technology portfolio.

At birth, a new technology is called embryonic or emerging. Emerging technologies are defined

as new technologies with a potential impact on industry structure. The exact boundaries of this

impact are unknown, though. Both technical and market uncertainty are extremely high. It may still

take one-to-two decades before tangible commercial products emerge. In the beginning of the 1990s,

technologies such as micro-machinery, optical computing or molecular farming (a biotechnology

application where plants and animals are conditioned to produce selected molecules) could be

considered ‘emerging.’ The technological problems that remain to be solved are huge, market

uncertainty is very high. The route from vision to industrial reality is still cloudy. The emerging state

is one of substantial techno-scientific tumult and controversy. However, if it can capture the interest

17

and commitment of enough bright minds all over the world, it will become the favourite pastime of

many research laboratories.

— Insert Figure 7 about here —

Technologies in the next stage of the life cycle are called pacing technologies. Pacing

technologies are technologies in an early development stage with a demonstrated potential for

changing the basis of competition. By the end of the 1980s, prominent examples of pacing

technologies were: neural network technology (a computing technology different from traditional

algorithm-based von Neumann computing) and computer integrated manufacturing technology. It

may still take several years before the competitive impact of a pacing technology is fully realised.

Important leaps in the technology’s performance still have to be realised before it reaches its full-

blown commercial potential as embodied in a product or process.

Next, key technologies are those technologies that have the greatest impact on competitive

performance at a particular time. Finally, base technologies are the very rocks on which the company

rests. They are essential, but they are no longer critical to the basis of competition typically because

they are widely available to competitors throughout the industry. An often used example are CRT-

displays. Every computer company has to have them. However, a company like IBM is rather

unlikely to realise a competitive advantage through its CRT-technology. Instead, such an advantage

may be better realised through the company’s competencies in the field of micro-processor

technology or system architecture. This technology would then be a key technology to the computer

firm. Another example of a key technology is genetic engineering applied to plant protection. Using

this technique, companies have succeeded in making hybrid seeds that are bacteria resistant. These

seeds can only be used once, though, since they loose their bacteria-resistant properties after one

harvest period. Thus, a company who has mastered plant protection technology through genetic

manipulation can acquire a substantial competitive advantage on the basis of its technological

competencies.

The S-curve pattern of technological evolution further implies that there are performance limits to

any technological system. In other words, performance increases cannot continue indefinitely. The

model points to the presence of diminishing marginal returns on R&D investments as the technology

matures. As the technology nears its natural performance limit (e.g. the efficiency of electric power

plants), the increase in performance for every man-year of effort invested to improve the technology

diminishes. This process is often described as maturation and ageing of the technology. As the

maturation process continues, the technology becomes increasingly vulnerable and exposed to

substitutes or technological discontinuities that ‘destroy and replace old practice.’ In this context,

Abernathy and Clark (1985) refer to the competence-destroying or disruptive character (as well with

respect to technological competencies as with respect to market competencies) of revolutionary and

18

architectural innovations. What is the impact of this model of technological evolution for the

practice of innovation management?

First of all, it implies that the strategic mission of R&D within any company is to exploit the

potential for improvement in competitive position in technologies that are important to the business.

This means that the technology portfolio has to be connected to the business portfolio of the firm.

This can be achieved through the development of technology roadmaps that link technology

development to product line development (see Figure 8). Among the technologies in the portfolio,

the ones that deserve foremost attention are, of course, the key technologies, then pacing

technologies, and, always, competence in base technologies.

Insert Figure 8 about here

Second, the firm has to acquire a detailed insight into the nature of its technology portfolio so as

to categorise its different technologies and to attach the appropriate priorities to each of them.

Management has to assess its competitive position with respect to the different technologies in the

portfolio. In order to structure the management of the corporate technology portfolio, many

innovation-intensive companies have appointed CTOs or Chief Technological Officers who are

responsible for portfolio management and monitoring. An important aspect of managing the

technology portfolio consists of managing the make-or-by (preferably the make-and-buy) decision.

Third, the maturity of technologies in the portfolio provide insights into the potential for future

advances. And, this maturity distribution does not only point to diminishing returns on investments in

performance increases as the technology evolves along its S-shaped life cycle curve. Still more

important, indeed, it points to the occurrence of discontinuities (see also Figure 7). A technological

discontinuity is a technology which substitutes for the more mature technology, quickly surpassing

the ‘old’ technology’s performance potential. A treacherous aspect of those technical discontinuities,

though, is that, at the outset, their performance almost always appears to be inferior to the

performance of the more ‘mature’ technology they will ultimately replace. For instance, decades ago,

mechanical typewriters were a key technology, immensely superior to the pencil they replaced. As

we all know, mechanical typewriters aged and they were superseded by electric typewriters. Today,

the electric typewriter is facing the same fate with the advent of the word processor.

To conclude, the S-curve pattern of technological growth shows companies that they need to

manage their technology portfolio carefully. Wheelwright and Clark (1992) further extend portfolio

management by introducing, besides the concept of a technology portfolio, the notion of a project

portfolio. In doing so, they explicitly link technical change to product and process output. In other

words, whereas the technology portfolio is rather input-oriented (pointing to the diverse base of the

firm’s technological capabilities), we find that the project portfolio approach, as advocated by

Wheelwright and Clark (1992), is output-oriented (pointing to the varying impact the various

19

innovation efforts may have on the firm’s technology-product-market combinations). In Figure 9, the

portfolio map proposed by Wheelwright and Clark is shown.

Insert Figure 9 about here

Research and advanced development projects or activities are taken apart in this portfolio model.

This is because their uncertain and unpredictable nature causes extreme difficulties to foresee

specific outputs and results within a pre-defined time-frame and budget constraint. Hence the plea to

consider them as a separate, long-term investment whose progress cannot be measured against well-

defined and pre-determined criteria and standards. This does, of course, not mean that the quality of

the effort cannot be measured and monitored. Only, in terms of future business performance,

outcome predictability is low.

From a strategic perspective, though, the distribution of the firm’s innovation efforts into

breakthrough, platform and derivative projects is crucial. As is obvious from Figure 9, breakthrough

projects imply fundamental changes both from a product and from a process perspective. New core

products and new core processes are created. They support the long-term competitive position of the

company. Platform projects are at the origins of the creation of new product families. They

symbolise the degree of product-market differentiation and diversification the company aims at. As a

consequence, platform projects are mostly medium-term oriented. Derivative projects, finally, point

to incremental changes (both from a product perspective and a process perspective) that further

enhance the performance (in terms of cost and/or functionality) of the firm’s existing platforms. By

their very nature, they are short-term oriented.

It is obvious that the bulk of the firm’s innovation efforts should go into the execution of platform

projects as they stand for the medium-term survival of the company. Typically, experience suggests

that 50-to-60 percent of the firm’s innovation efforts should be devoted to the creation of new

platforms. Derivative projects are important since they sustain existing market relationships.

However, portfolio management should be aware of the dangers entailed by placing too much

emphasis on derivative project activities since they quickly degenerate into imitative behaviour (as

they are often driven by short-term customer requests).

Technology and innovation thus become strategic issues that cannot be left into the hands of a

few corporate scientists and technologists. As a consequence, the three models just discussed all

point to the necessity to ‘manage’ the innovation process carefully. This then is the subject of the

next part of this paper.

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3. The management of technological innovation

In this section, the major issues related to the management of technological innovation will be

discussed. Three dimensions are used to guide this discussion. These can be succinctly summarised

as strategy, structure, and staff. We already touched upon them during the description of the different

models of the innovation process. The managerial implications of each dimension are now discussed

in greater detail.

3.1. Technology and innovation strategy

During the discussion of the technological S-curve, we already suggested the concept of a

technology portfolio, consisting of a mixture of technologies with differing competitive impact.

From the previous discussions, technological innovation appears to be a highly uncertain and

complex process. A firm cannot predict accurately the outcome of its own innovative efforts or those

of its competitors, so that the hazards and risks which is faces if it attempts any major technical

change are very great. Yet not to innovate ultimately means to die. Still more, in times of turbulent

market environments, innovation provides a powerful instrument to prevent a status-quo or steady-

state from settling in. A well-articulated innovation strategy is a starting-point to continuously

question one’s assumptions about products, markets and technologies.

3.1.1. A typology of innovation strategies

One way to approach the strategic management of innovation is to look at the various strategies

available to a firm when confronted with technical change. Any classification of strategies is

necessarily somewhat arbitrary and, to a certain extent, violates the infinite variety of strategic

solutions in the real world. However, the ‘ideal types’ to be discussed offer useful starting points to

any strategic planning and strategic implementation effort. Freeman (1982) proposes a typology

consisting of six generic ‘ideal types.’

An ‘offensive’ innovation strategy is one designed to achieve technical and market leadership by

being ahead of competitors in the introduction of new products. Since scientific and technological

developments nowadays are quickly accessible to other firms, such a strategy necessitates strong

relationships with leading parts of the world’s science-technology complex, or strong independent

R&D, or the ability to react very quick on new possibilities, or some combination of these strengths.

Strong relationships with the scientific-technological environment involves the recruitment of key

individuals, good personal linkages with leading scientists and technologists (for instance, through

the presence of ‘technological gatekeepers,’ cf. infra), consulting arrangements, contract research,

21

and good information systems. Unfortunately, the information and knowledge necessary for any

innovation is unlikely to come from a single source, and even if it does so, is unlikely to be in a

format readily applicable to products or processes. Therefore, ‘offensive’ innovators will attach

crucial importance to their internal R&D department. The R&D department must generate that

scientific and technical information which is not available from outside and it must take the

innovation to the point at which normal production operations can be launched.

Consequently, ‘offensive’ innovators will have to be highly ‘research-intensive’ firms. They will

usually have important in-house fundamental research capabilities, as well as strong development

competencies. They will attach extreme importance to patents, to the acquisition of scientific and

technical information, and to the education and training of their technologists. They will also develop

the necessary long-range planning techniques and tools to keep abreast of their competitors.

Examples of offensive innovators are: DuPont during the development of nylon and Corfam,

RCA during the development of television and colour television, and ICI during the development of

Terylene. It took ten to twenty years from the inception of the research before most of these

innovations showed any profits. Many never did so.

Only a small minority of companies in the world are willing or able to follow ‘offensive’

innovation strategies. Even companies that follow an offensive innovation strategy are only able to

do so consistently over limited time periods. In any case, they will often have a portfolio of

technologies at different stages of their S-curve, some of them emerging, others occupying a key

position and still others that are maturing or even ageing.

According to Freeman (1982), the vast majority of firms, including some of those that were once

‘offensive’ innovators, will follow a different strategy: ‘defensive,’ ‘imitative,’ ‘dependent,’

‘traditional,’ or ‘opportunist.’ A summary of the requirements for each of the six technology

strategies in terms of scientific and technical efforts by the firm is shown in Figure 10 (source:

Freeman, 1982).

Insert Figure 10 about here

A ‘defensive’ strategy does not imply absence of R&D. On the contrary, it may be equally

research-intensive as an offensive strategy. The difference lies in the nature and the timing of the

innovations. The ‘defensive’ innovators do not wish to be the first in the world, but neither do they

wish to be left behind. They may prefer to avoid the risks of being the first to innovate and they may

attempt to learn from the mistakes of first movers and from their opening up of the market. For

example, in the case of the videocassette recorder (Rosenbloom and Cusumano, 1987), Sony learned

a lot from the deficiencies of the Ampex machines (Ampex being the first mover). As is obvious

from Figure 10, the ‘defensive’ innovator attaches as much importance to development and design

22

work as the offensive innovator does. He puts less emphasis on research activities, though; while

patents, education, and the acquisition of scientific and technical information remain of utmost

importance. The emphasis on the different capabilities as shown in Figure 10 is obvious: the

‘defensive’ innovator must be capable at least to catch-up with the game, if not to ‘leap-frog.’ A

‘defensive’ strategy is more characteristic of firms in smaller industrialised countries.

‘Defensive’ innovators do not usually aim to produce an exact copy or imitation of the products

or processes introduced by early innovators. Instead, they tend to take advantage of early mistakes to

improve upon the design and to differentiate their products by minor technical improvements. To do

so, they must have the necessary technical strength. They will prefer to build an independent patent

position rather than taking licenses.

‘Imitative firms,’ on the contrary, will not aspire to ‘leap-frog’ or even to ‘keep up with the

game.’ The ‘imitative’ firm is happy to follow way behind the leaders in established technologies,

often a long way behind. As a consequence, the technical competencies of the imitator will be much

less advanced than the ones of offensive and defensive firms (see Figure 10). A ‘dependent’ strategy

involves the acceptance of a subordinate role in relation to other stronger firms. The ‘dependent’ firm

does not attempt to initiate or even imitate technical changes in its product, except as a result of

specific requests from its customers or its parent. Typically, it has lost all initiative in product design

and has no R&D facilities (see Figure 10). The pure ‘dependent’ firm can be considered a department

of a larger firm.

The ‘traditional’ firm differs from the dependent firm in the nature of its product. The product

supplied by the ‘traditional’ firm changes little, if at all. The product supplied by a dependent firm,

on the other hand, may change a lot. Though, these changes are responses to initiatives and

specifications from outside. The ‘traditional’ firm sees no reason to change its product because the

market does not demand a change, and the competition does not compel the firm to do so.

Consequently, this type of firm’s technical capabilities are absolutely minimal (see Figure 10).

Freeman calls them ‘the peasants of industry.’

Finally, a number of firms follow a strategy that can be qualified as ‘opportunist’ or ‘niche.’

Entrepreneurs identify some new opportunity in a rapidly changing market environment, which may

not require any in-house R&D, or complex design, but will enable them to prosper by finding an

important ‘niche,’ and providing a product or service which consumers value, but nobody else has

thought to provide. Consequently, ‘opportunist’ firms do not need strong internal R&D capabilities.

However, contrary to the ‘traditional’ firms, they need a strong awareness of their scientific and

technological environment (see Figure 10). Thus, the acquisition of scientific and technical

information is a central focus of any opportunist strategy.

23

It is obvious that the above typology consists of six ideal types. They may be rather difficult to

observe in their pure form in reality. However, the major managerial imperatives of each generic

strategy are clear.

3.1.2. Formulation and implementation of a technology and innovation strategy

The strategic management of technology includes both a planning and an implementation

component. Strategic planning involves the formulation of the organisation’s goals and objectives,

and the policies needed to achieve those objectives, including identification of the organisation’s

primary resources and priorities.

The technological S-curve implies that a company should carefully evaluate the position of the

different technologies in its portfolio along their growth curve or life cycle. As already described

previously, key technologies are of utmost importance in supporting the competitive advantage of the

firm. Base technologies, on the contrary, are a necessary prerequisite to the survival of the firm.

However, in and off themselves they will not procure the firm a competitive advantage. Thus, in

evaluating its firm’s technology portfolio, management should consider the following questions:

• What are the different technologies in the portfolio?

• Which of these are emerging, pacing, key, and base technologies?

• What is our position with respect to each technology in the portfolio viz. our major

competitors? Are we leading or are we lagging behind? And, if we are lagging behind, is their

a chance to bridge the gap?

• What technologies might be included in the portfolio? In which technologies should we

increase/decrease our investments given their (potential) attractiveness to our business?

• How does this technology portfolio translate into a project portfolio, distinguishing among

breakthrough, platform and derivative activities?

• Once the above questions have been answered, the firm should focus on the ‘make’ or ‘buy’

decision. In other words, what technologies in the portfolio are going to be developed

internally and what technologies will be acquired from external sources?

As further suggested by the Abernathy-Utterback model, different strategic imperatives are

associated with each stage of technological development. The earliest stage tends to feature frequent

major product innovations, heavily populated and characterised by small entrepreneurial

organisations, often closely tied to user needs. This is clearly illustrated in the case of new

biotechnology firms, most of which directly originated from university laboratories or from other

young small firms. This has lead inevitably to the explosion of alliances between large companies

24

and the new start-ups. Similar patterns have emerged in artificial intelligence, and they are today

emerging in neural network technology.

The transition stage includes major process innovations, coupled to a continuous but less

intensive emphasis on creating product variation. The number of competitors, both large and small,

is still increasing. Finally, during the late stage, less frequent minor product and process innovations

are undertaken. Large companies make the major contributions to this ‘specific’ stage of technical

development, motivated mostly by cost reduction and quality improvement. During this stage,

learning curve effects, i.e. decreasing unit manufacturing cost as cumulative production volumes

increase, may occur as the result of numerous minor innovations. This learning curve effect results

not from the volume itself but primarily from the usual ongoing allocation of engineering efforts to

incremental cost reduction.

Any application of technology planning techniques should thus reflect at least a general

awareness of the current developmental stages of the firm’s principal technologies. Also, the more

mature stages of a technology usually are the hallmark of the advent of threatening technological

discontinuities. Examples of companies that have failed to anticipate or even to respond to those

threats are numerous. Therefore special techniques and methodologies have been developed that can

support this planning exercise.

‘Competitive product profiling’ and ‘functional mapping,’ for instance, are benchmark techniques

(Deschamps and Nayak, 1995; Fusfeld, 1978; Wheelwright and Clark, 1992) that can be used to

support this planning effort. Based on such techniques, an organisation’s product line is compared to

its key competitors in terms of seven technology-based measures: (1) functional performance; (2)

acquisition cost; (3) ease-of-use; (4) operating cost; (5) reliability; (6) serviceability; and (7) system

compatibility.

But, beyond strategic planning must come strategic implementation. Tactics and operations are

ways to implement a strategy. The most important evolution in the area of strategic implementation

undoubtedly have been the ‘venture strategies.’ Collaborative partnerships among firms are growing

dramatically, involving new linkages with universities and ties with young high-technology

companies. The spectrum of possible organisational and strategic alternatives includes (Roberts,

1980 and Roberts and Berry, 1985):

• venture capital investments in young ‘emerging technology’ companies;

• sponsored spin-offs of new product development/commercialisation groups;

• joint ventures between large and small companies;

• internal ventures which emulate entrepreneurial freedom within the large company;

• mergers and acquisitions;

• licensing agreements;

25

• collaborative research/development agreements including academic or other business partners.

These venture approaches are increasingly used to broaden and to extend the company’s

technological base. These venture strategies require long-term persistence for effective

implementation and dramatic differences in management style and management policies. More

specifically, the different partners impose different beliefs and evaluation procedures to implement

the new technological development. These beliefs and procedures are based on their prior

technological as well as business expertise. To the extent that these beliefs and procedures are

consistent and aligned with one another, the venture functions smoothly. However, to the extent that

these beliefs and procedures diverge, there is a potential for disharmony and even disruption of the

alliance or venture. As a consequence, strategic ventures and alliances necessitate careful managerial

attention and monitoring in order to avoid the potential pitfalls of ambiguity and divergence.

Hence the emphasis on ‘familiarity’ with the market as well as with the technological aspects of

the new business (Roberts and Berry, 1985). The framework by Roberts and Berry suggests that a

decrease in the firm’s ‘familiarity’ with the product-market-technology content of the new business

should result in a less resource-intensive approach to the new venture. This study and a host of others

(see Meyer and Roberts, 1986) suggest that ‘familiarity’ or ‘relatedness’ indeed is a powerful

decision parameter to monitor business development strategies.

Sofar some key issues regarding the strategic planning and implementation processes to support

technological innovation. However, as many scholars noticed, strategy and structure go hand in hand.

Thus, anyone studying the process of technological innovation needs to pay close attention to

appropriate organisational structures in which the process can abide.

3.2. Technology and structure

In the previous sections, we have highlighted different technology strategies. We also pointed to

the importance of ‘venturing’ to support the implementation of the firm’s innovation strategy.

Furthermore, the model by Abernathy and Utterback points to organisational changes that occur

during the life cycle of a productive unit. The design of organisation structures that foster a climate

in which technological innovations flourish, requires an input-focus (the development of the

necessary competencies) as well as an output-focus (the development of an effective approach to

define, solve and successfully reduce to practice the technical/market challenges faced during the

innovation effort). Effective innovative organisations hence need to manage attention to the

appropriate technical and market inputs. Moreover, the outputs of the innovation process should be

linked to the organisation’s strategic objectives and should be smoothly transferred downstream, to

their ultimate users. During this process, interfaces between different functional groups in the

26

innovative organisation (R&D, marketing, production and engineering) require careful managerial

attention. This organisational issue is often called the management of ‘part-whole’ relationships.

To this end, Roussel and his colleagues, in their influential book Third Generation R&D (1991),

advocate a partnership model between the different functions involved along the innovation

trajectory. They emphasise the need for clarity, transparency, urgency, open communication

channels, information exchange, and joint processes for priority setting and decision making.

The model by Roberts and Frohman has pointed to the importance of both market and

technological inputs to successful technological innovation. This requires a culture of ‘harmony’ and

co-operation between the marketing side and the technological side of the business, which is best

achieved through partnerships among equals, rather than by commanding compliance from

subordinates. In addition, managers should be aware that in any non-trivial technical field, the vast

majority of know-how is to be found outside the organisation. Strong innovation teams thus need to

draw upon the technical knowledge that is available in the outside world, whether in the technical

literature, already developed products and processes, or especially, in the minds of other technical

professionals. Hence the tremendous need for a technological gatekeeping activity which is

conducted via special agents, namely the technological gatekeepers. The work by Allen (1977) has

demonstrated the tremendous importance of direct personal contacts, gatekeeping, prior experience

and training in acquiring and processing state-of-the-art technical information. The importance of

cross-functional communication is in sharp contrast with the minor contribution of the published

literature as a means to acquire knowledge during innovation activities.

Allen’s research (1977) further demonstrated the influence of the architectural layout of the

technologists’ work space on their propensity to engage in technical communication. The physical

distance between potential communicators heavily impedes technology flows and transfers within the

organisation. Allen found that beyond 30 meters, the chances that people get involved in direct, face-

to-face communication is asymptotically low. A similar conclusion holds for organisational

boundaries. The mere fact of belonging to different organisational entities (e.g. marketing,

engineering, R&D) negatively influences the probability of personal contacts between their

members, and thus their members’ propensity to access relevant technical and market expertise and

experience. Any effective design of organisation structures to support the innovation process should

take these inhibitors into consideration.

The dilemma of reconciling technical inputs and product or process outputs is most apparent in

the choice between functional versus project-based organisation structures. Allen (1977) proposes the

following trade-off (see Figure 11; adapted from Allen, 1977).

Insert Figure 11 about here

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Any innovative organisation should integrate two conflicting goals. On the one hand, the

activities of the various disciplines and specialities must be co-ordinated in order to accomplish the

goals of multidisciplinary projects. On the other hand, projects must be provided with state-of-the-art

information and knowledge in the technologies they rely upon. Typically, the functional form of

organisation preceded all others. In this form of organisation, disciplines or specialities are grouped

together, facilitating close contact with the state-of-the-art information in the technologies involved.

This form of organisation works well as long as projects or tasks are primarily contained within

single disciplines or specialty areas. When systems projects require the collaborative efforts of a

number of disciplines, this form of organisation can result in serious co-ordination problems. In

response to this co-ordination problem, project management came into being. In this form of

organisation, a single individual has almost sole responsibility for the management of a project. All

members of the project team report to him for work assignments, and in many cases, he has the

authority to hire, fire, or transfer project members.

Several decades ago, research by Marquis and Straight (1965) showed that cost and schedule

performance were better when administrative personnel (contract lawyers, cost accountants,

procurement officers, etc.) was organised on a project basis, while technical performance was better

when technical personnel was functionally organised. Allen (1977) explains this difference as

follows. Administrative personnel is usually working in areas that are not changing rapidly. The

state-of-the-art in cost accounting or contract law are certainly not as dynamic as are most physical

technologies. For this reason, it is not as critical that these people remain in close contacts with their

disciplinary colleagues. They can afford to work full-time on a project for three or four years, largely

out of touch with disciplinary colleagues and still not become out of touch with the field. In the case

of technical specialities, this is not necessarily true, though. To be removed from developments in a

dynamic technology for three or four years can have very drastic consequences. And, since personal

contacts are the most effective way for most people to keep up with their field, the organisation

should be structured to foster this form of interaction. Project management does not promote this

form of interaction. It tends to impede it. Functional organisation does aid disciplinary interaction.

Therefore, better technical performance results under functional organisation in the case of long-term

projects (see Figure 11).

Project duration is not the only criterion to decide between functional or project organisation,

though. Technologies can indeed be classified according to the estimates of the rate of change of

their respective knowledge bases. The stronger the rate of change in the technology’s knowledge

base, the more one has to keep in touch with the state-of-the-art in the technology; hence, the more

appropriate a functional structure becomes. This second criterion, of course, requires an (albeit

subjective) ranking of the firm’s technologies according to estimates of the rate of change of their

respective knowledge bases. Thus, the choice between functional and project structures can be based

on (1) an assessment of the duration of the project, and (2) an assessment of the rate of change of the

28

technology’s knowledge base; leading to the decision space depicted in Figure 10. In the area left

and below the line in this Figure, the benefits of better internal co-ordination that are available under

project management outweigh the benefits of improved disciplinary contact that are the hallmark of

the functional organisation. In the area above the line, disciplinary support is needed to such an

extent that it becomes reasonable to sacrifice some internal co-ordination for it. Hence, the functional

form is preferred. The decision criteria shown in Figure 9 thus provide rules to locate people in a

matrix organisation.

In addition, the recent work by Wheelwright and Clark (1992) points to the different management

styles that occur in a project environment. They make a major distinction between lightweight,

heavyweight and autonomous team structures, pointing to the increases in responsibilities and power

yielded to the project team and its management along this structural spectrum. In addition, the

project management process can be facilitated by the use of appropriate techniques such as Quality

Function Deployment (which can support and facilitate the project definition phase) or more

traditional project planning (e.g. PERT/CPM) and follow-up techniques (e.g. the Earned Value

approach).

Now that we have obtained an insight into the major relationships between technology, strategy

and structure, one final issue remains to be addressed. Indeed, plans and structures have to be staffed

with people. Staffing considerations in innovative organisations have mainly focused on the question

of what roles need to be accomplished for effective technical development. This is the subject of the

last topic of this section.

3.3. Staffing the organisation for innovation

Research into the management of innovation has focused on the critical roles by people during

the innovation process in order to achieve success. First, we discern the idea generators. They

provide the organisation with new, creative insights that help it to initiate projects and to solve the

problems encountered during the projects. Idea generators may be scientists or technologists, but

they can also originate from the marketing or sales side of the firm, or even among the company’s

management.

Unfortunately, idea originators are seldom efficient idea exploiters. Therefore, a second important

role has emerged during the innovation process, namely the entrepreneur or the product champion.

They advocate for change and innovation. They take up the ideas and attempt to turn them into good

currency by commanding attention to them. Many studies have found the presence of a product

champion to be a necessary prerequisite for innovative success.

29

A third key role is the one of the sponsor. He usually is a more senior person who is neither doing

the research nor championing the project. However, given his position in the organisation, he is able

to command resources to support the effort, to provide encouragement, and to create a ‘safe harbour’

for the innovators. His presence is therefore often necessary to support the actions of idea generators

and product champions.

A fourth key role is the new business or venture manager. The increasing importance of venture

strategies has enabled the rise of the so-called internal ventures or intrapreneurship initiatives.

Intrapreneurship attempts to stimulate entrepreneurial behaviour within the company by providing

innovative professionals with the necessary freedom and means to ‘start a business within their firm.’

In order to co-ordinate these entrepreneurial efforts, the presence of a new venture manager becomes

necessary. He supports the planning, scheduling, control and co-ordination of the new venture

programs. This role, if present within the firm, is usually an assigned job in the organisation, contrary

to the other roles that are incidental to an individual’s specific tasks.

Finally, a fifth role which needs close attention is the gatekeeper. The gatekeeper is a critical

information link-pin who brings outside information into the project group. Gatekeepers join

technical, market and manufacturing information to the potential technical users of that information.

They may be in close contact with the external scientific-technological world of the organisation,

e.g. through contacts with university researchers, or they may act as communication bridges between

different technical groups within the firm. Their role thus essentially is one of a ‘communication

bridge’ both within the organisation and between the organisation and its external environment.

These five critical roles are all needed within the innovative organisation. They have to be in

close contact with each working group in order for it to attain the objectives of the project. In

addition, the effective technology-based organisation will not only recognise the importance of those

roles, but it will also formulate and implement the appropriate human resource management

processes including recruiting, job assignments, personnel development and training, and

performance measurement and rewards (e.g. via the development of dual ladder career systems).

Furthermore, managerial actions are needed that affect staff creativity, inventiveness and

productivity. A host of techniques has emerged that stimulate and facilitate creative idea-generation

such as brainstorming, synectics, and morphological analysis. Their usefulness, however, has often

been questioned. What seems most important, though, in determining the productivity of a scientist

or technologist are the stages of his or her career and the composition of his or her immediate work

group.

Katz (1982, 1988) has demonstrated that technical professionals evolve through three career

stages: (1) socialisation; (2) innovation, and (3) stabilisation. During each career phase, different

managerial actions are required. The socialisation stage (which marks the entry of the new employee

30

into the organisation) calls for setting unambiguous work norms, providing guidance and direction,

and inserting the new employee into the technical communication network of the organisation.

During the stabilisation phase, a major managerial challenge is to prevent the employee from losing

his earlier motivation and to renew his technical skills. In other words, management has to prevent

that job maturity leads to routinisation. This can be done, for instance, by assigning challenging

projects to ‘mature’ employees which necessitate them to keep up with technological evolution.

Finally, research has demonstrated that productivity (or for that matter inventiveness) is also

heavily influenced by the individual’s immediate work and task environment. The composition and

the nature of this environment, along with its supervisory structure, are important. Variations in age,

technical background, and even personal values, correlate with enhanced group productivity. Hence,

diversity is a necessary characteristic of environments demonstrating a high propensity for

innovation. ‘Creative tensions,’ a mix between stability and challenge, are desirable. This need for

internal challenge is further raised by the existence of the NIH (not-invented-here) syndrome: the

longer a group has worked together, the less its performance. In general, once a group has been

together for two-to-three years, a marked decline in its performance can be noted (Katz and Allen,

1982).

The long-term stable technical group faces a dilemma. On the one hand, the cumulative

experience that has been developed among the group’s members induces a feeling of security and

déjà-vu. On the other hand, as the group has been investing longer and longer in particular problem-

solutions and technical choices, external information increasingly becomes threatening to the group.

Indeed, it may radically question the choices made and relegate them to the status of ‘sunk costs.’ As

a consequence, the group is isolating itself from the external technical and organisational

environment (a notable and significant breakdown in communication occurs), and as research has

shown, performance decreases result. This phenomenon, once again, calls for diversity, mobility

(rotation) and sufficient challenges in the job assignments of technical professionals in order to

stimulate them to break the deadlock of closure and closedness that destroys and inhibits their

boundary-spanning activity.

The nature of supervision also affects performance. For instance, research has shown that the

technical skills of a first-level supervisor, and not his or her human relations skills, enhance a work

group’s effectiveness (Farris, 1973). Thamhain and Wilemon (1977) further emphasise the

importance of the project manager’s technical expertise and reliance upon work challenge as a major

impetus for technical performance.

Finally, recent research (Debackere et al., 1996 & 1997) has demonstrated that the management

of technology and innovation should take into account the knowledge economy of the organisation.

This knowledge economy represents the distribution of speciality types and levels across the different

members of the organisation (see Figure 12). Research shows that speciality maps should be

31

managed carefully. Technical professionals should be encouraged not to become mono-specialists,

but they should be stimulated to embrace two-to-three different (albeit adjacent) speciality areas. The

seminal research by Pelz and Andrews (1967) already pointed to the positive influences of task

diversity on the productivity of technical professionals. This diversity provides the very foundation

of the creative tension or paradox that makes an innovative environment performant.

Insert Figure 12 about here

4. Conclusion

Sofar, we have touched upon a number of issues in the management of technological innovation.

Major managerial topics have evolved around the relationship between strategy, structure, staff on

the one hand and innovative performance on the other hand. We want to conclude this review paper

with the following summary table or check-list of the most salient decisions and actions to be taken

to support the innovation process as they have emerged from the previous discussion:

decisions to be taken at the strategic level

- What does the technology portfolio look like? What should it look like?

- What is the degree of maturity of the different technologies in the portfolio?

- What are the objectives in terms of technological prowess?

- Do we want to be offensive, defensive, imitative, dependent, ... ?

- How to support the strategic planning effort (benchmark techniques)?

- Do we advocate venture approaches to implement our strategic plan?

decisions at the organisational level

- What type of organisation structure (functional, project, matrix)?

- What about intrapreneurship?

- How to maintain innovativeness and efficiency simultaneously?

- How to develop interface/liaison mechanisms linking the different participants?

decisions about human resources

- What roles are necessary for successful innovation? Do we have them?

- How to maintain a climate of productivity (individual, work group, supervision)?

- How to maintain and stimulate effective technical communications?

- How to prevent technical professionals from ‘stabilising’?

- Decisions about rewards, job assignments, challenges, job diversity, career systems etc.?

32

decisions involving marketing

- How much market research do we intend to carry out?

- How familiar is the new product market to us?

- What should the marketing plan look like?

decisions involving R&D

- What should the level of investment be?

- What technological choices are made (see also technology portfolio)?

- What type of organisation structure (functional or project) is most appropriate?

- A milestone and evaluation plan for the different R&D projects.

- What contacts with the external scientific/technological world should be maintained?

- In-house developments (‘make’) versus external technological acquisitions (‘buy’)?

decisions involving production and manufacturing

- What part of the production process do we develop internally (‘make’) versus buy?

- Should we invest in new buildings and plants?

- Should we invest in ‘new’ production technologies (CAD, CAM, FMS, etc.)?

- Mass-production or job-shop oriented?

- The trade-off between flexibility (innovativeness) and efficiency (productivity).

decisions involving finance

- Expected profits?

- Level of investment in R&D, manufacturing, and marketing?

- What will be the different sources to finance the efforts proposed?

- Should we share the financial burden with other ‘venture’ partners?

33

5. References

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Abernathy, W.J. (1978) The Productivity Dilemma. Baltimore: The Johns Hopkins University Press.

Abernathy, W.J. and K.B. Clark. (1985) “Innovation: mapping the winds of creative destruction,” Research Policy, Vol. 14: 3-22.

Allen, T.J. (1977) Managing the Flow of Technology. Cambridge, Mass.: The MIT Press.

Braun, E. and S. Macdonald. (1978) Revolution in Miniature: The History and Impact of Semiconductor Electronics. Cambridge, UK: Cambridge University Press.

Debackere, K., Clarysse, B. and M.A. Rappa. (1996) “Autonomy in the industrial laboratory: the dilemma revisited,” Journal of High Technology Management Research, Vol. 7, No. 1: 61-78.

Debackere, K., Buyens, D. and T. Vandenbossche. (1997) “Strategic career development for R&D professionals: lessons from field research,” Technovation, Vol. 17, No. 2: 53-62.

Deschamps, J.P. and P.R. Nayak. (1995) Product Juggernauts: How Companies Mobilize to Generate a Stream of Market Winners. Boston, Mass.: Harvard Business School Press.

Farris, G.F. (1973) “The Technical Supervisor: Beyond the Peter Principle,” Technology Review.

Foster, R.N. (1986) Innovation: The Attacker’s Advantage. New York: Summit Books.

Freeman, C. (1982) The Economics of Industrial Innovation. Cambridge, Mass.: The MIT Press.

Fusfeld, A.R. (1978) “How to Put Technology into Corporate Planning,” Technology Review, Vol. 80, May.

Kamien, M.I. and N.L. Schwartz. (1982) Market Structure and Innovation, Cambridge, UK: Cambridge University Press.

Katz, R. (1982) “Managing Careers: the Influence of Job and Group Longevity,” in R. Katz (ed.) Human Resource Management. Englewood Cliffs, N.J.: Prentice-Hall.

Katz, R. and T.J. Allen (1982) “Investigating the Not Invented Here (NIH) Syndrome: A Look at the Performance, Tenure and Communication Patterns of 50 R&D Project Groups,” R&D Management, Vol. 12, No. 1.

Katz, R. (1988) Managing Professionals in Innovative Organizations. Cambridge, Mass.: Ballinger Publishing Company.

Marquis, D.G. and D.L. Straight. (1965) Organizational Factors in Project Performance. MIT Sloan School of Management, Working Paper 1331, Cambridge, Mass.

Meyer, M.H. and E.B. Roberts. (1986) “New Product Strategy in Small Technology-Based Firms: A Pilot Study,” Management Science, Vol. 32, No. 7.

Pelz, D.C. and F.M. Andrews. (1967) Scientists in Organizations: Productive Climates for R&D. New York: John Wiley and Sons.

Roberts, E.B. and A. Frohman. (1978) “Strategies for Improving Research Utilization,” Technology Review, Vol. 80, No. 5.

Roberts, E.B. (1980) “New Ventures for Corporate Growth,” Harvard Business Review, Vol. 59, No. 4.

34

Roberts, E.B. and C.A. Berry. (1985) “Entering New Businesses: Selecting Strategies for Success,” Sloan Management Review, Vol. 26, No. 3.

Rosenbloom, R.S. and M.A. Cusumano. (1987) “Technological Pioneering and Competitive Advantage: the Birth of the VCR Industry,” California Management Review, Vol. 29, No. 1.

Rothwell, R. et al. (1977) “The Characteristics of Successful Innovators and Technically Progressive Firms,” R&D Management, Vol. 7, No. 3.

Roussel, P.A. (1984) “Technological Maturity Proves a Valid and Important Concept,” Research Management, January-February.

Roussel, P.A., Saad, K.N. and T.J. Erickson. (1991) Third Generation R&D: Managing the Link to Corporate Strategy. Boston, Mass.: Harvard Business School Press.

Schumpeter, J.A. (1934) The Theory of Economic Development. Cambridge, Mass.: Harvard University Press.

Schumpeter, J.A. (1939) Business Cycles: A Theoretical, Historical and Statistical Analysis of the Capitalist Process. New York: McGraw-Hill (2 Volumes).

Schumpeter, J.A. (1942) Capitalism, Socialism and Democracy. New York: Harper and Row.

Smith, D.K. and R.C. Alexander. (1988) Fumbling the Future: How Xerox Invented, then Ignored, the First Personal Computer. New York: William Morrow & Company.

Thamhain, H.J. and D.L. Wilemon. (1977) “Leadership, Conflict, and Program Management Effectiveness,” Sloan Management Review, Vol. 19, No. 1.

Twiss, B.C. (1992) Managing Technological Innovation. London: Pitman Publishers.

Utterback, J.M. (1994) Mastering the Dynamics of Innovation. Boston, Mass.: Harvard Business School Press.

Van de Ven, A.H. (1986) “Central problems in the management of innovation,” Management Science, Vol. 32, No. 5.

von Hippel, E. (1977) “The Dominant Role of the User in Semi-Conductor and Electronic Subassembly Process Innovation,” IEEE Transactions on Engineering Management, Vol. EM-24, No. 2.

Wheelwright, S.C. and K.B. Clark. (1992) Revolutionizing Product Development: Quantum Leaps in Speed, Efficiency and Quality. New York: The Free Press.

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FIGURE 1:Modelling the differences between scientific and technological activity (adapted from Allen, 1977)

SCIENCE

TECHNOLOGY

System

Input

Input

Output

Output

By-product

Information&

Knowledge

Information&

Knowledge

Information&

Knowledge

Information&

Knowledge

Products & Processes

36

FIGURE 2:Schumpeter’s theory of heroic entrepreneurship

(based on: The Theory of Economic Development, 1934 and Business Cycles, 1939)

exogeneousscience and technology

entrepreneurialactivities

investments ininnovation

production patterns&

market structure

profits or lossesfrom innovation

37

FIGURE 3:Schumpeter’s theory of endogenised R&D

(based on: Capitalism, Socialism and Democarcy, 1942)

exogeneousscience and technology

endogenous science &technology

(mainly inhouse R&D)

investments ininnovation

production patterns&

market structure

profits or lossesfrom innovation

38

FIGURE 4:A general model of the technological innovation process

(based on Roberts and Frohman, 1978)

MARKET

TECHNOLOGY

Recognitionof technicalfeasibility

and/or

Recognitionof potentialdemand

Fusion intodesign conceptandevaluation

Search Use

Search Use Test Response

Use

Technicalinformationobtainedby search,experimentandcalculation

and/or

Technicalinformationavailable

Marketinformationobtained

Solution throughadoption oradaptationof existingtechnology

and/or

Solution throughinvention

Work out bugsandscale up

Transfer tomanufacturing

1

Recognition ofopportunity

2

Ideaformulation

3

Problemsolving

4

Prototypesolution

5

Commercialdevelopment

6

Utilisationand/ordiffusion

39

FIGURE 5:Cumulative cash flow during innovation projects

(source: Twiss, 1992)

Time

Cum

ulat

ive

Cas

h Fl

ow

+

-

break-even

end-of-lifecycle

technical feasibilityestablished

production technologyestablished

market launch

research prototype plant earnings from sales

40

FIGURE 6:A dynamic model of product and process innovation in a productive unit

(source: Abernathy, 1978)

Product Innovations

Process Innovations

Rate ofInnovation

TimeFLUID TRANSITION SPECIFIC

Dominant Design

41

FIGURE 7:The technological S-curve

Time / Cumulative R&D Investment

Perf

orm

ance

emergingpacing

key

mature

discontinuity

“inferior” technology

“superior” technology

42

FIGURE 8:Example of a technology roadmap

Time

internaltechnology

externaltechnology

productlines

product 1product 1 product 2product 2 product 3product 3

product 4product 4

technologytechnology11

technologytechnology22

technologytechnology55

technologytechnology33

technologytechnology44

MARKETSMARKETS

COMPETENCIESCOMPETENCIES

43

FIGURE 9:The innovation project portfolio as proposed by Wheelwright and Clark (1992)

Research &Advanced Development

Degree ofDegree ofProduct ChangeProduct Change

NewCore Product

Minor/IncrementalProduct Change

Degree ofDegree ofProcess ChangeProcess Change

NewCore Process

Minor/IncrementalProcess Change

BreakthroughProjects

PlatformProjects

DerivativeProjects

44

FIGURE 10:In-house scientific and technological functions of the firm according to

innovation strategy followed(source: Freeman, 1982)

Range 1-5 indicates weak (or non-existent) to very strong

Type of strategy Offensive Defensive Imitative Dependent Traditional Opportunist

Fundamental research 4 2 1 1 1 1

Applied research 5 3 2 1 1 1

Experimental development 5 5 3 2 1 1

Design engineering 5 5 4 3 1 1

Production engineering/quality

control4 4 5 5 5 1

Technical services 5 3 2 1 1 1

Patents 5 4 2 1 1 1

Scientific and technical information 4 5 5 3 1 5

Education and training 5 4 3 3 1 1

Long-range forecasting and

product planning5 4 3 2 1 5

45

FIGURE 11:Choice between functional and project organisation forms

(based on Allen, 1977)

Project duration

Rate of change of knowledge base

short

long

stable rapidly changing

ProjectOrganisation

FunctionalOrganisation

46

FIGURE 12:The knowledge economy of the innovative organisation,

hypothetical example for R&D 26 staff members

Knowledge areasor specialties

Level of competence

0 1 2 3

Hydrometallurgy

Pyrometallurgy

Physical metallurgy

Modelling

...

14 5 6 1

5 14 3 4

0 18 4 4

20 5 1 0

Note: This knowledge map shows the hypothetical distribution of specialities among 26 scientific staff and engineers of a metallurgical R&D centre.Four different competence levels are considered:(1) Level 0 indicates that a person has no competence at all in the designated area;(2) Level 1 indicates that the person has an understanding of basic principles in the

specific knowledge area considered;(3) Level 2 indicates that a person is able to collaborate on a task of activity in that

area;(4) Level 3 indicates that a person is able to conduct an independent problem-

definition and problem-solving activity in the particular area.The example shows that the group is particularly vulnerable in the “Modelling” field.