Innovating the Innovation Process

390 Int. J. Technology Management, Vol. 34, Nos. 3/4, 2006

Copyright 2006 Inderscience Enterprises Ltd.

Innovating the innovation process

A.J. Berkhout*, Dap Hartmann, Patrick van der Duin and Roland Ortt Delft University of Technology Faculty of Technology, Policy and Management Jaffalaan 5, 2628 BX Delft, The Netherlands E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: In the innovation literature, the development of innovation models is subdivided into generations. Until now, we distinguish three generations. Because current models provide a poor representation of what happens in todays open innovation networks, there is a growing need for a fourth-generation concept. So far, requirements for next-generation concepts have been discussed but well-defined models have not reached the open literature yet. This paper describes a fourth-generation innovation model, which describes the innovation regime by a circle of change. It links changes in scientific insights, technological capabilities, product design and manufacturing, and markets. The model replaces the traditional chain concept by a circle with four nodes of change, connected by four interacting cycles of change. Collectively, they may be seen as the arena of opportunity with processes crossing traditional boundaries. These processes have a cyclic nature and are representative of todays open innovation.

Keywords: innovation; change; creativity; entrepreneurship; knowledge management; knowledge economy innovation management; innovation economy; sociotechnical; socioeconomic; Lisbon strategy.

Reference to this paper should be made as follows: Berkhout, A.J., Hartmann, D., van der Duin, P. and Ortt, R. (2006) Innovating the innovation process, Int. J. Technology Management, Vol. 34, Nos. 3/4, pp.390404.

Biographical notes: A.J. (Guus) Berkhout started his career with Shell in 1964, where he held several international positions in R&D and technology transfer. In 1976, he accepted a Chair at Delft University of Technology in the field of geophysical and acoustical imaging. During 19982001, he was a member of the Board, responsible for scientific research, knowledge management and intellectual property. In 2001, he also accepted a chair in the field of innovation management. Professor Berkhout is a member of the Royal Netherlands Academy of Arts and Sciences (KNAW) and the Netherlands Academy of Engineering (NFTW).

Dap Hartmann received his PhD in astronomy from Leiden University in 1994. Subsequently, he was Visiting Scientist at the Harvard-Smithsonian Center for Astrophysics (Cambridge, MA), at the University of Bonn, and at the Max-Planck-Institut fr Radioastronomie. After returning to Holland, he

Innovating the innovation process 391

founded Cygnus Consulting, providing expert advice in Science and Technology. Since 2003, he is Assistant Professor in Intellectual Property Valorisation and Innovation Management at Delft University of Technology.

Patrick van der Duin is a Research Fellow at Delft University of Technology, Faculty of Technology, Policy and Management, Section Technology, strategy and entrepreneurship. He receives his master in economics from the University of Amsterdam. He was a Researcher and Senior Advisor at KPN Research where he participated in future studies research on use of telecommunication services and products. Currently, he is finalising his PhD on the use of qualitative methods of futures research in innovation processes in large corporations.

Roland Ortt studied economics and specialised in the analysis of high-tech markets. He was an Assistant Professor and received a PhD from the faculty of Industrial Design Engineering of Delft University of Technology. His thesis was devoted to developing market analysis methods for a breakthrough communication technology, the video telephone. He has worked as an R&D Manager at the research institute of KPN (the incumbent Dutch telecom operator). Currently, he is Associate Professor in technology management at the faculty of Technology, Policy and Management of Delft University of Technology.

1 Introduction

In former times, economies were largely based on the production of goods for local markets, using the two factors of production: capital and labour. Mass globalisation has brought about growing competition, forcing companies to produce goods with higher performance and at lower cost. This started the knowledge economy, in which smarter tools and machines expanded possibilities; knowledge became the third factor of production. Workflows were designed more intelligently, and the training and education of employees assumed ever greater importance. Today, technical know-how and improved skills, particularly in the field of Information and Communication Technology (ICT), have made existing work processes far more efficient.

Yet we have not mentioned the most important development in the economy. The real changes are now taking place in the so-called innovation economy, in which besides capital, labour and knowledge creativity is the fourth principal factor of production. The emphasis on creativity makes the difference. In a knowledge economy logic is predominant, while in an innovation economy everything revolves around imagination.1 In an innovation economy, processes are not only designed more efficiently (with knowledge-intensive solutions), above all they are made more effective (with creative solutions). Innovation starts therefore with management of ideas. Florida (2003) correctly argues that creativity becomes the principal driving force behind economic growth, and Brown (2003) concludes that innovation itself needs to be innovated. We can best describe the innovation economy as a creative knowledge economy. An apt description of the activities in an innovation economy is creative enterprise with knowledge (Berkhout, 2000). It is not just a question of creativity or knowledge or enterprise. It is the combination that counts: creativity and knowledge and entrepreneurship that is what makes the innovation economy so powerful.

392 A.J. Berkhout et al.

In the introduction to this Special Issue, the first author argues that in large companies strategic planning plays a dominant role in innovation management. To minimise risks, innovation projects are managed according to a top-down strategic plan, representing a linear stage-gate process along the innovation path. It is important to realise that in this model, organisation of the multi-disciplinary workflows within and between the innovation teams is not addressed. In the following, this aspect will obtain ample attention.

2 Departure from linear thinking

The history of the development of innovation models is often divided into three generations (Liyanage et al., 1999; Miller, 2001; Rothwell, 1994; Roussel et al., 1991; Chiesa, 2001). In the traditional linear model (first generation), innovation is represented by a pipeline of sequential processes, which starts at pure scientific research and ends with commercial applications. This 1G model, however, incorporates market information very late in the process, so that commercial applications are often merely technical inventions and therefore often not adopted by the market. Next, second-generation models emerged, which focus on the flow of information originating from the market, essentially reversing the linear pipeline of the first generation. Here, science is replaced by the market as the source of innovation, and processes are still largely seen as sequential steps. A major disadvantage of the 2G models is that there is too much emphasis on market-driven improvements of existing products (optimisation), resulting in a large variety of short-term projects.

In the last decade, third-generation models have been introduced that show less linearity owing to feedback paths in the chain. In these models, investments in innovation are closely linked to company strategy. Third-generation models can be seen as open R&D models, emphasising product and process innovation (technical), and neglecting organisational and market innovations (non-technical). This means that 3G innovation models tend to focus on the companys new technological capabilities rather than including solutions for institutional barriers and societal needs. Table 1 summarises the characteristics of the current generations of innovation models.

Table 1 Characteristics of current innovation models

Generation Characteristics

First Technology push, linear process with markets at the end of the pipeline, scientific freedom is very important, no strategic goals, no chain management

Second Market pull, linear process with science at the end of the pipeline, contract research is very important, weak ties with corporate strategy, little emphasis on chain management

Third Combination of technology push and market pull, innovation projects are linked to R&D and company goals (open R&D), strong emphasis on chain management

Note: For more detail, see, Ortt and Smits in this special issue


Fundamental changes in industry have generated a new commercial environment in which business processes cross traditional company boundaries and combine across industrial sectors. This means that innovation develops in a new direction, requiring rethinking of the current concepts underlying innovation. This rethinking is at the basis of fourth-generation innovation models (Niosi, 1999; Chesbrough, 2003; Christensen and Raynor, 2003). Fourth-generation innovation models can be characterised by the following properties:

1 Innovation is embedded in partnerships: open innovation.

2 Attention is given to an early interaction between science and business.

3 Hard knowledge of emerging technologies is complemented by soft knowledge of emerging markets.

4 The need for new organisational concepts is acknowledged by emphasising skills for managing networks with specialised suppliers as well as early users.

5 Entrepreneurship plays a central role.

The Cyclic Innovation Model (CIM) is proposed as a fourth-generation innovation model. It was developed in the 1990s as an instrument for the continuous reform of science and industry (Berkhout, 2000). Since processes in todays innovation economy are characterised by interaction and change, the CIM complies with the principles of system dynamics (Forrester, 1961; Roberts, 1978). Among other things, this means that equilibrium processes have been replaced by processes of change.2 This is why CIM focuses on the interaction between changes of the involved subsystems without the need for a full scientific description of the system at one specific moment in time. Using modern system dynamics in innovation has one other major advantage. It also shows that models must include feedback paths so that adaptive steering and learning processes can be made more explicit. Senge (1994) used this cyclical concept to draw up a clear framework for learning organisations.

3 Principle of cyclical interaction

With the addition of feedback paths, models of dynamic systems often referred to as regimes are represented by interconnected cycles and (work) processes become cyclical.3 It presents the basis for modern control and is the precondition for operating in a flexible manner; it also presents the inspiration for human creativity and is a necessary condition for sustainability. Thanks to feedback, (human) actors are constantly confronted with the consequences of their actions, preferably through in-built early signals. In that way quick adjustments can be made in the event of surprises. The cyclical architecture also ensures that mistakes can be learned from, a very important property of innovation.

In summary, cyclic interaction is a prerequisite for the model of dynamic systems as well as for the network structure of competitive organisations: start quickly, adjust quickly and learn quickly.


Figure 1 illustrates the basic principle. A represents a subsystem that maintains a cyclical interaction with subsystem B. Examples are interactions between technical products and their users, commercial organisations and their customers, hospitals and their patients.

Figure 1 Cyclical interaction is the basis for modern control and a precondition for operational flexibility. It is also the inspiration for creativity and a necessary condition for sustainability

The cycle in Figure 1 is proposed as an elementary building block for designing non-linear models for innovation systems, similar to those we find everywhere in physics and biology. In particular, non-linear models for sociotechnical and socioeconomic change can be constructed from the basic unit in Figure 1.

4 Dynamics around technological development

Figure 2 shows two linked basic units the double loop in which technological research plays a central role. The cyclical interaction processes for the development of new technology take place in the so-called technical-oriented sciences cycle (the left-hand side of Figure 2) with the help of a wide range of disciplines from the hard sciences.4 Technological research in this cycle is a multi-disciplinary activity: a package of different disciplines from the hard sciences is needed to develop a new technology (many-to-one relationship). Similarly, the cyclical interaction processes for the development of new products take place in the integrated engineering cycle (the right-hand side of Figure 2). Modern product development is a multi-technology activity: a package of different often patented technologies is needed to develop a new product (many-to-one relationship). Like multi-disciplinary science, here too we see that many different skills are needed to be successful. Experience shows that the skills of specialised suppliers play an important role in making the engineering process successful.

A

IInnssiiddee--oouutt

OOuuttssiiddee--iinn

BB


Figure 2 The dynamics surrounding technological research changes in demand and supply of new methods and tools are driven by the cyclical interaction between new scientific insights into technical processes (left-hand side) and new functional requirements for process-product combinations (right-hand side).

Figure 2 visualises that in the sciences cycle technological research is driven by new scientific insights: science push. Figure 2 also shows that in the engineering cycle technological research is driven by new functional requirements in product development: function pull. The dynamics in technological research are therefore driven by both new scientific insights and new functional requirements. In technological research, scientists and engineers must constantly inspire one another (Stokes, 1997). To achieve this, research must be organised in a different manner. The Technological Top Institutes (TTIs) in The Netherlands are a good example of how this can be addressed: scientists from the hard sciences work together with engineers from industry to create new technical functions (products). The European Commission of the EU has recently announced plans to start a TTI at European level, based on the push and pull in Figure 2.

It is important to realise that the concept products is used here in the widest sense: everything we design and make. Hence, it includes immaterial products such as databases, computer software, financial instruments, governmental regulations and governance models. This means that the concept technology is also used in the widest sense: knowledge both implicit and explicit on how to design and make products in the widest sense. Broadening the concept of technology and product is characteristic of the fourth-generation innovation concept.

5 Dynamics around market transitions

Figure 3 again shows two linked cycles, but in this case it is the world of human needs rather than the world of technology that plays the central role. The cyclical interaction processes for the development of new insights into emerging and receding socioeconomic trends (market transitions) take place in the so-called social-oriented sciences cycle (left-hand side of Figure 3) with the help of a wide range of different disciplines from the soft sciences.5 With these insights new sociotechnical solutions can be developed

IInntteeggrraatteedd eennggiinneeeerriinngg ccyyccllee

TTeecchhnniiccaall--oorriieenntteedd sscciieenncceess ccyyccllee

SScciieennttiiffiicc eexxpplloorraattiioonn

TTeecchhnnoollooggiiccaall rreesseeaarrcchh

PPrroodduucctt ddeevveellooppmmeenntt


faster and with less economic risk. Anticipating successful market transitions is very much a multi-disciplinary activity: a package of different disciplines from the soft sciences is needed to explain and predict transitions in markets in a scientific manner and to establish new solutions while knowing what the underlying socioeconomic forces are (many-to-one relationship). This is often called futures research.

Figure 3 The dynamics around market transitions (changes in demand for and supply of sociotechnical solutions) are driven by the cyclical interaction between new scientific insights in socioeconomic processes (left hand side) and new investments in product-service combinations (right-hand side).

Likewise, the cyclical interaction processes required to serve the changing society with new product-service combinations take place in the differentiated service cycle (right-hand side of Figure 3). Experience shows that in this cycle, early users play an important role in making the innovation process successful (Von Hippel, 2005); this means using the creativity of customers. It is interesting to note that in recent years the services sector has expanded considerably, not only because of the greater demand for services from the consumer but also because industry has outsourced many of its support processes. This trend is still going on. If a branch of industry disappears, it is important to realise that the accompanying services would disappear with it.

In the sciences cycle, market transitions are seen as a dynamic socioeconomic process in which the changing demand for product-service combinations is determined by the dynamics of the needs and concerns of society. On the other hand, in the service cycle market transitions are seen as a dynamic commercial process in which the change in the supply of product-service combinations is determined by the innovative capacity of the business community. In an innovation economy both components, scientific insight into changing demand (left-hand side of Figure 3) and commercial investment in changing supply (right-hand side of Figure 3), should be constantly inspiring and reinforcing one another.

In industrial innovation programmes a lot of implicit knowledge concerning technological research is created in the engineering cycle, and the task of the hard sciences cycle is then to make this knowledge explicit. Likewise, a lot of implicit

DDiiffffeerreennttiiaatteedd sseerrvviiccee ccyyccllee

SSoocciiaall--oorriieenntteedd sscciieenncceess ccyyccllee

SScciieennttiiffiicc eexxpplloorraattiioonn

MMaarrkkeett ttrraannssiittiioonnss

PPrroodduucctt ddeevveellooppmmeenntt


knowledge about market research is created in the service cycle and the soft sciences cycle then has the task of making this knowledge explicit. The explication of implicit knowledge is a crucial role for science in innovation.

No equivalent of the aforementioned TTIs has yet been established with the aim of trying to understand, influence and exploit the regime of socioeconomic changes. This confirms the current imbalance between investment in knowledge of emerging technology and in knowledge of emerging markets, where markets are seen as an integral part of society. The recent Franco-German initiative to create a European top economic institute as a counterpart to the American Institute for International Economics presents a too-limited view on this issue. Insight into societal changes cannot be created from an economic point of view only.

Model of the innovation arena

If we compare Figures 2 and 3, the dual nature of scientific exploration and product development becomes clear: science has both hard and soft aspects and product development has both technical and social aspects. Figure 4 combines Figures 2 and 3. The result is a systems view of the cyclical change processes and their interactions as they take place in a successful innovation arena: hard and soft sciences as well as engineering and commercialisation are brought together in a cohesive system of creative processes. Entrepreneurship plays a central role: without entrepreneurship there is no innovation.

Figure 4 System model showing the fundaments of the innovation economy, a circle of mutually influencing dynamic processes: the Circle of Change. In the model, changes in science (left) and industry (right) and changes in technology (top) and markets (bottom) are cyclically interconnected. Here, entrepreneurship is given a central role.

Scientificexploration

Markettransitions

Entrepreneurship Productdevelopment

Technical-orientedsciences cycle

Integratedengineering cycle

Differentiatedservice cycle

Socal-orientedsciences cycle

Technologicalresearch

HHaarrdd kknnoowwlleeddggeeiinnffrraassttrruuccttuurree

MMaannuuffaaccttuurriinngg aannddpprroocceessssiinngg iinndduussttrryy

PPuubblliicc aanndd pprriivvaatteesseerrvviiccee sseeccttoorr

SSoofftt kknnoowwlleeddggeeiinnffrraassttrruuccttuurree


The first striking feature of Figure 4 is that the architecture is not one of a chain but of a circle: innovations build on innovations. Ideas create new developments, successes create new challenges and failures create new insights, and so the creation of value is constantly accumulating. New macroeconomic instruments are needed to preserve the strength of the dynamics in the circle. Large-scale failures like the recent dotcom debacle shake confidence in the innovation economy and cause investment capital to become scarce.6 In terms of CIM: the processes of change are decoupled. The economy enters a phase of stagnation in which companies focus on more of the same at lower cost (life-cycle management) until confidence returns and investment capital becomes available again to spur innovation. The dynamics in the circle then accelerate again.

Figure 4 also shows that the proposed model portrays a system of dynamic processes Circle of Change with four creative nodes of change: scientific exploration, technological research, product development and market transitions. More importantly, though, between these nodes there are cycles of change which ensure that the dynamic processes in the nodes influence each other; in other words, they inspire, correct and supplement each other (first-order dependency).

This produces a system of linked cycles, which in turn also influence each other (higher-order dependencies). The result is a more or less synchronised regime of interconnected dynamic processes that spark a creative interaction between changes in science (left-hand side) and industry (right-hand side), and between changes in technology (top) and market (bottom). In a successful innovation economy, there will be few barriers between nodes and cycles: institutions and organisational structures facilitate the change processes (Volberda, 1998). Throughout the circle there is a continuous exchange of ideas and concepts, of knowledge and information, of capital and labour, of products and services, and of technical and socio-economic values.

Note that autonomous social transitions manifest themselves in markets as changes in the need for products and services (the demand). Such societal changes stimulate innovation. On the other hand, autonomous technological developments generate new products and services (the supply). Such technological changes change society. It is the cyclic interaction of both innovation drivers that will create maximum economic and social value.

At a lower conceptual level of the model, it can be said that every node in Figure 4 represents a collection of different actors that form cyclical networks with other actors in neighbouring nodes (Berkhout, 2000). So at this level it becomes apparent that innovation processes take place along a circle with synchronised networks the so-called CIM matrices in which knowledge suppliers, design firms, supply companies, production companies, marketing organisations and early users reinforce each others activities (see Figure 5). These open networks are increasingly empowered by new capabilities of the information and communication sector. Institutional factors, in particular governmental regulations, have a dramatic effect on the dynamics within and between the networks of projects. Ultimately, institutional factors determine the maximum rate of circulation that can be realised along the circle. Therefore, governments can exercise enormous influence over this, in both a positive and a negative sense.

In the future, many innovations will be the result of combining technical capabilities and customer needs from different sectors (transsectoral innovations). Fourth-generation innovation models should be able to show that clearly (Berkout and Van der Duin, 2006). In terms of CIM, take for example innovations in the healthcare sector that will be made


possible by new developments in the IC sector: the top half of Figure 6 (developments in information and communication technology) focuses cyclically on the bottom half of Figure 6 (needs and concerns in the healthcare market).

Figure 5 CIM in terms of a multi-disciplinary project organisation; representing Science-Technology (ST) teams and Technology-Product (TP) teams, Science-Market (SM) teams and Market-Product (MP) teams. Together the project teams form an open innovation network, being driven by the opportunities in the Circle of Change (Figure 4).

Scientific laboratories

Product divisions

Technology platforms

ST TP Technical- oriented

Market groups

SM MP Customer- oriented

Science-oriented

Business-oriented

Figure 6 Example of sector-crossing innovation. The upper part of CIM shows the innovation activities of the Information and Communication sector (IC sector); the lower part shows the innovation activities of the Healthcare sector (HC sector). The left-hand part of CIM shows the science-based foresight activities directed towards new opportunities; the right-hand part shows the business-driven development activities directed towards new socioeconomic value. CIM reveals the necessity of cyclic interaction between what is technically possible (the leading edge of IC) and socially desirable (value creation by HC).

Soft-knowledge infrastructure

EntrepreneurshipScientific

exploration New HC products/services

ChangingHC markets

-HC-orientedIC projects

IC-basedHC projects

HC foresight cycle

IC foresight cycle

NewIC technology

Public and private health care sector

Information and communication industry

Hard-knowledge infrastructure


6 Classification of innovations

The arena in Figures 4 and 5 shows that new innovations arise from previous generations. New innovations are therefore a mixture of the old and the new and the ensuing changes can therefore be large or small; the terms generally used are incremental and radical innovations. This is not a very clear distinction. The CIM shows that innovations can be divided into different classes. This classification may be more informative.

Class 1 innovations are the result of new developments in a single node. These might involve existing product-service combinations where only the market concept is radically changed. Examples are switching to internet shops, focusing on a different market segment and responding to a new lifestyle.

Class 2 innovations are the result of new developments in two nodes. This might involve the development of new product-service combinations together with a unique market concept. Examples would include technical installations with intelligent sensors connected directly to the internet (connected products). This new combination would create significant added value since information about access, use/abuse and the physical condition of installations becomes available wherever and whenever it is needed (early warning signals). The result is that entirely new services become possible, which in turn means that the traditional product-oriented market concepts will have to be replaced. A wide variety of innovations can be expected in this area.7

Class 3 innovations follow from new developments in three nodes. They might involve the development of new technologies, which in turn make new product-service combinations possible, which for their part call for new market concepts. Many emerging information and communication technologies will make spectacular Class 3 innovations possible. Take the development of broadband communication technology, which will bring about a revolution in healthcare, or developments in mobile identification technology (RFID), which will bring about a major change in the battle against crime and terrorism.

Class 4 innovations require new developments in four nodes. Science plays a crucial role here. All important developments in the life sciences generate new knowledge for new biotechnologies, which in turn set off a revolution in the development of new product-service combinations in the pharmaceutical and food industries. But spectacular progress is also being made in the nanosciences, where technical building blocks are on a molecular scale. Nanosciences is yielding radical new knowledge for nanotechnologies, which will in turn cause a revolution in the development of product-service combinations in all technical-industrial sectors. An example would be nanotechnology for the new energy era, characterised by internet-like decentralised networks.

Innovations based on life sciences and nanosciences will change society so radically that the overwhelming increase in technological possibilities (top half of Figure 4) will have to be accompanied by a major effort to increase our understanding of societys needs and, above all, societys concerns (bottom half of Figure 4). This will require a great deal of cohesion in the development of knowledge in the bta and gamma sciences. Innovations that are from the outset active throughout the entire Circle of Change creative enterprise with bta and gamma knowledge are ranked in the very highest category: Class 5. In this top league all cycles of change are linked to each other in the innovation process, continuously driven by creative entrepreneurship, like playing chess at all levels. That is an enormous challenge, but it is where the real opportunities lie.


Economic superpowers such as the USA and Japan, but increasingly also China and India, are investing heavily in bio and nanosciences with the aim of creating five-star innovations. That is why the aim of the Lisbon strategy of the European Union should not only be to achieve more competition in the internal market but rather to seek more cooperation. After all, innovations on this scale increasingly require megainvestments. Moreover, the real competitors in these fields are outside Europe and attention should therefore be focused entirely on cooperation in the internal market, and competition with the external market (Berkout, 2006).

7 System faults in the innovation arena

In a static society traditional institutions are principal barriers in creating value in the circle of change, usually owing to outdated internal culture and structure. Figures 7 and 8 illustrate two notorious obstacles that are referred to here as scientific isolation and technical arrogance.

Figure 7 A society can be excellent in science, but still underperforms economically (vertical valorisation barrier)

PPuubblliicc aanndd pprriivvaattee sseerrvviiccee sseeccttoorr

Scientific exploration

Mar ket tran sitions

Product development

Technical-oriented sciences cycle

Concurrent engineering cycle

Customised service cycle

Social-oriented sciences cycle

techno logical re search

HHaarrdd kknnoowwlleeddggee iinnffrraassttrruuccttuurree

MMaannuuffaaccttuurriinngg aanndd pprroocceessssiinngg iinndduussttrryy

SSoofftt kknnoowwlleeddggee iinnffrraassttrruuccttuurree


Figure 8 A society can be excellent in technology, but still underperforms economically (horizontal valorisation barrier)

Scientific isolation refers to a society that may be excellent in scientific research, but still underperforms economically because of a valorisation barrier between the science and industry community (Figure 7). The two worlds make their own choices and plans, and throw their wishes and results to the other side.

With technical arrogance we mean a society that can be excellent in designing and developing technical functions but still underperforms socially because of a valorisation barrier between engineering and utilisation (Figure 8). Both worlds make their own choices and plans; here too they throw their wishes and results to the other side.

The failure of the Lisbon strategy of the EU has to be seen above all as a consequence of the existence of these obstacles in the European socioeconomic system. The huge emphasis on more research and more technology is therefore a far too one-sided and simplistic approach. The cyclic process model shows that the real problem lies elsewhere.

8 Conclusion

A fundamental characteristic of the proposed 4G innovation model (CIM) is that it describes a circle and not a chain. Science is not at the beginning of a chain and the market is not at the end of a chain. Both are part of a perpetual creative process along a dynamic path that has no fixed starting or ending point: innovations build on innovations. Innovation may start anywhere and anytime. The result is an endless build-up of economic and social value that is realised by the reinforcing cycles along the entire circle. In CIM, new technologies (for example, originating from new scientific discoveries) and changes in the market (for example, originating from new human needs) continually influence each other in a cyclic manner. This dual nature of innovation technical capabilities and human needs will shape the future of sociotechnical and socioeconomic regimes. Together with the central role of entrepreneurship, it is considered to be the key characteristic of fourth-generation innovation models (see Figure 4).

Scientific

exploration

Market transitions

Product

development

Technical-orientedsciences cycle

Concurrent engineering cycle

Customised service cycle

Social-oriented sciences cycle

Technological research

HHaarrdd kknnoowwlleeddggee iinnffrraassttrruuccttuurree

MMaannuuffaaccttuurriinngg aanndd pprroocceessssiinngg iinndduussttrryy

PPuubblliicc aanndd pprriivvaattee sseerrvviiccee sseeccttoorr

SSoofftt kknnoowwlleeddggee iinnffrraassttrruuccttuurree


An important consequence of the proposed model is that in all industrial sectors innovation requires early interaction between scientific discoveries and new business ideas, as well as early interaction between technological inventions and new market opportunities: turning scientific knowledge into socioeconomic value. These cyclic interactions will not only cross the boundaries of traditional sectors, they will also cross the boundaries of the different stages of innovation. This means that all nodes of the proposed innovation model are active in all phases of the innovation path: away with the pipeline concept in innovation models. An interesting example is the pharmaceutical industry, where the slow and costly in-house pipeline model is gradually replaced by a much more open concept with continuous interactions between actors along the entire circle at all stages of innovation: playing chess at four levels at the same time. In this way knowledge from other industrial sectors can be utilised, the long distance between science and markets is significantly decreased short connections between new discoveries and early users and new innovations build on old innovations.

An important consequence of the proposed model is also that the cyclic networks between the nodes of change require multi-partnerships that can start fast, adapt fast and learn fast. This means that in todays innovation arena the question should not be who is available but who is needed, meaning amongst others that the organisation of labour should be revisited: the social aspect of innovation. In fact, the proposed model makes policy makers aware that institutional factors such as governmental rules about the flow of capital, the flow of labour and the flow of knowledge along the innovation circle should be redesigned to facilitate the innovation processes in a much better way. This requires a rethinking of current governmental organisations.

References

Berkhout, A.J. (2000) The Dynamic Role of Knowledge in Innovation, Delft University Press, ISBN 9040720770.

Berkhout, A.J. (2005) Towards a radial reformulation of the Lisbon-strategy, Holland Management Review, Vol. 99, pp.5363

Berkhout, A.J. and van der Duin, P.A. (2006) New ways of innovation: an application of the cyclic innovation model to the mobile telecom industry, International Journal of Technology Management.

Brown, J.S. (2003) Innovating innovation, foreword to: Chesbrough, H.W., Open Innovation, Harvard Business School Press.

Chesbrough, H. (2003) Open Innovation, Boston: Harvard Business School Press.

Chiesa, V. (2001) R&D Strategy and Organisation. Managing Technical Change in Dynamic Contexts, London: Imperial College Press.

Christensen, C.M. and Raynor, M.E. (2003) The Innovators Solution, Boston: Harvard Business School Press.

Florida, R. (2003) The Rise of the Creative Class, New York: Basic Books.

Forrester, J. (1961) Industrial Dynamics, Cambridge: Productivity Press.

Von Hippel, E. (2005) Democratizing Innovation, Cambridge: The MIT Press.

Liyanage, S., Greenfield, P.F. and Don, R. (1999) Towards a fourth-generation R&D management model-research networks in knowledge management, International Journal of Technology Management, Vol. 18, Nos. 34, pp.372394.

Miller, W.L. (2001) Innovation for business growth, Research-Technology Management, SeptemberOctober, pp.2641.


Niosi, J. (1999) Fourth-generation R&D: from linear models to flexible innovation, Journal of Business Research, Vol. 45, pp.111117.

Roberts, E. (Ed.) (1978) Managerial applications of system dynamics, Pegasus Communication, Waltham.

Rothwell, R. (1994) Towards the fifth-generation innovation process, International Marketing Review, Vol. 11, No. 1, pp.731.

Roussel, P.A., Saad, K.N. and Erickson, T.J. (1991) Third Generation R&D. Managing the Link to Corporate Strategy, Boston: Harvard Business School Press.

Senge, P.M. (1994) The Fifth Discipline, the Art and Practice of the Learning Organization, New York: Doubleday.

Stokes, D.E. (1997) Pasteurs Quadrant: Basic Science and Technological Innovation, Brooking Institution Press.

Volberda, H.W. (1998) Building the Flexible Firm, Oxford University Press.

Notes

1 It is appropriate to quote Albert Einstein here: Logic brings us from A to B, but imagination brings us everywhere.

2 Equilibrium processes play a key role in the old economy; in the new economy it is change that counts.

3 A cycle consists of a succession of connected processes occurring repeatedly, each time with new starting conditions and a shifting context. The dynamics in a cycle are determined by cycle time and change per cycle.

4 Disciplines from the hard sciences include the areas of specialist knowledge in the natural and life sciences.

5 Disciplines in the soft sciences include the areas of specialist knowledge in behavioural and social sciences.

6 In terms of the process model, a financial black hole appeared in the service cycle at the end of the last century, which means that the circle of the innovation economy was more or less broken.

7 NEDAP N.V., a Dutch supplier of product-service combinations in areas such as security, recently launched the virtual service assistant Bob, which monitors shops fitted with Nedap anti-theft equipment 24 hours a day without losing its temper and without making a mistake. Bob checks whether the systems are working, whether the personnel are following the agreed security procedures, how many customers enter the shop and when they visit, etc.

Documents

Innovating the Innovation Process