Knowledge and continuous innovation

The current issue and full text archive of this journal is available at
http://www.emerald-library.com/ft
IJOPM
21,4
490
Knowledge and continuous
innovation
The CIMA methodology
Harry Boer
Aalborg University, Denmark
Sarah Caffyn
CENTRIM University of Brighton, UK
Mariano Corso
University of Pisa, Italy
Paul Coughlan
Trinity College, Dublin, Ireland
Jose Gieskes
Twente University, Enschede, The Netherlands
Mats Magnusson
Chalmers University, GoÈteborg, Sweden
Sara Pavesi and Stefano Ronchi
Politecnico di Milano, Italy
Keywords New product development, Innovation, Kaizen, Learning
Abstract Competition today is forcing companies to increase their effectiveness through
exploiting synergy and learning in product innovation. Literature, however, is still mainly focused
on how product development projects, seen largely as isolated efforts, should be organised and
managed. This article proposes a model to describe and explain how companies can gain a
substantive competitive advantage by extending their innovation efforts to other phases of the
product life cycle and by facilitating knowledge transfer and learning both within the company and
with other partner organisations. The model is based on collaborative research by the authors,
based on their involvement in the Euro-Australian co-operation project CIMA (Euro-Australian
co-operation centre for Continuous Improvement and innovation MAnagement).
Introduction
To survive in a demanding and turbulent competitive environment companies
are investing a growing amount of resources and managerial attention in
product innovation. With pressures to reduce product development intervals
and to increase the frequency of new product introductions, this attention is
more and more continuous and the efforts involve partners outside the
International Journal of Operations &
Production Management,
Vol. 21 No. 4, 2001, pp. 490-503.
# MCB University Press, 0144-3577
Many people contributed to this article through their involvement in the CIMA-project, by
triggering and challenging the authors' thinking in previous discussions, or in the form of
useful comments on draft versions of the article. All these contributions are gratefully
acknowledged and in particular those from Roberto Verganti and Emilio Bartezzaghi
(Politecnico di Milano); Niklas Sundgren (CORE-Chalmers University, GoÈteborg), Ross
Chapman and Paul Hyland (University of Western Sydney at Macarthur).
organisational boundaries, often on a global basis. Yet, with few exceptions, for
example, Meyer and Utterback (1993) and Sundgren (1998), the literature
remains focused on the management and organisation of new product
development (NPD) projects as isolated efforts. An alternative perspective
developed by Bartezzaghi et al. (1997a, b) proposes NPD projects as discrete
steps within a more comprehensive process of continuous product innovation
(CPI). As well as focusing on products in a family context, CPI includes all the
phases of the product life cycle that follow the launch in the market place.
Evidence from best practice companies, for instance, shows how
manufacturing, maintenance and service, though not integral parts of the
development phase, can still provide valuable feedback and additional
opportunities to innovate future related products. From this perspective then,
product innovation is a continuous and cross-functional process involving and
integrating a broad range of different competencies inside and outside the
organisational boundaries. Mastering the sharing and transfer of knowledge
within this process requires new managerial skills, but can become a powerful
competitive weapon. This article proposes a methodology, based on a
behavioural model, to help companies facilitate knowledge transfer and foster
learning in the process of CPI.
In the next section, the research setting will be defined, introducing the
concept of continuous product innovation and focusing on how knowledge can
be generated and transferred within the process of CPI. Subsequently, the
CIMA methodology will be introduced, and in particular the underlying
behavioural model which explains how management can foster continuous
improvement and learning in CPI. Then, three cases will be reported to show
how the methodology was used in companies to prompt managerial actions to
improve continuous improvement and learning in their product innovation
activities. The paper will conclude with the main managerial implications
arising from the work so far, as well as suggestions for further development.
The research reported here started as part of the Euro-Australian cooperation project CIMA (Euro-Australian co-operation centre for Continuous
Improvement and innovation Management ESPRIT 26056). The objective of
CIMA was to facilitate co-operation and knowledge/technology transfer
between European and Australian organisations, through:
(1) The establishment of a Co-operation Centre, with sites in Europe and
Australia, which were to promote and support bilateral activities
between the two continents involving mutual learning and technology
and knowledge transfer.
(2) The development of a first Euro-Australian ``trial'' project, involving
consortium of five European and three Australian research centres,
aimed at developing a methodology to support companies in managing
learning and continuous improvement in product innovation.
This article describes some of the results of the CIMA trial project, focusing in
particular on the development of the methodology of learning in product
innovation (PI), and reporting on its first applications in companies in Italy,
Sweden and The Netherlands.
Knowledge and
continuous
innovation
491
IJOPM
21,4
492
Knowledge management in PI
A model of CPI
Since the early 1970s, managerial and scientific interest in (product) innovation
has increased rapidly, with publications focusing, initially, on aspects such as
factors of success and failure, the diffusion of innovations, innovation roles,
and organisational characteristics beneficial (or detrimental) to different stages
of the innovation process. During the 1980s, various researchers started to call
for more process-oriented research, believing that the successful management
of innovation also depends on a thorough understanding of what is really
happening during innovation processes. As a consequence, a plethora of
process models of product innovation was proposed which, however, mostly
focussed on the management of the NPD process (see e.g. Saren, 1984).
Towards the end of the 1980s, quality and speed, in addition to price, emerged
as order winners or even qualifiers in many markets (e.g. Smith and Reinertsen,
1991). In response, concurrent engineering was thought to represent a long
lasting paradigm for product innovation management. Integration among
different phases of a project, heavy-weight project management and project
team autonomy were considered as synonymous with best practices. By the
mid 1990s, a new stream of studies emerged which enlarged this perspective,
proposing that a focus on single projects is not enough to stay competitive.
Rather, success depends even more on exploiting synergy among projects, for
example by fostering commonality and reusing design solutions over time
(Wheelwright and Sasser, 1989; Wheelwright and Clark, 1992; De Maio et al.,
1994; Cusumano and Nobeoka, 1992). Correspondingly, attention progressively
shifted from single projects to families of projects (Meyer and Utterback, 1993;
Sanderson and Uzumeri, 1995) and to the process of learning and knowledge
transfer and reuse (Imai et al., 1988; Bartezzagi et al., 1997a; Sundgren, 1998).
These streams of literature, however, consider PI as occurring only within
the boundaries of the product development process. Downstream phases in the
product life cycle are still important for innovation but only as long as they
represent valuable sources of information or constraints that should be
anticipated and considered during development (Clark and Fujimoto, 1991).
However, evidence is emerging that other phases in the product life cycle (such
as for instance manufacturing, consumption and maintenance) are not only
sources of information. They may actually present additional opportunities to
innovate products. This is a direct consequence of rapid product development
and time to market reduction where companies, especially in rapidly shifting
environments, deliberately release products that are not fully optimised. For
example, in the software industry, platform package releases are followed
frequently by a rapid, almost continuous, stream of enhanced releases, which
have major bugs fixed and features optimised.
The concept and the boundaries of PI are therefore changing dramatically.
Feedbacks and opportunities coming in from the later field phases are stored
not only for feeding next generation product development projects, but also as
valuable opportunities for PI within a product life cycle. These two dimensions
are combined in the model of CPI proposed by Bartezzaghi et al. (1997b) (see
Figure 1). In their view, first, CPI embraces not only NPD (concept, product and
Knowledge and
continuous
innovation
493
Figure 1.
Main directions for
knowledge transfer in
the CPI process
process design, and product launch), but also subsequent phases in product life
cycle (improvement in manufacturing, customisation in sales and installation,
and enhancements and upgrading during product use). Second, they suggest a
move from the traditional perspective of single products to that of product
families. Consequently, at the level of the product family, the CPI process
includes all the interactions among products in the family. Hence, innovation
may concern a product that is in its development phase, a product that has been
already released to market, or a transfer of solutions between products. In the
next section, we will focus on the last of these concerns, knowledge transfer.
Knowledge transfer in CPI
The emerging literature on knowledge management includes five dimensions
that should be taken into account when analysing knowledge transfer in CPI:
(1) The setting of knowledge transfer.
(2) Level of dissemination.
(3) The scope of knowledge.
(4) The degree of abstraction and generalisation.
(5) The degree of articulation or embodiment.
These five dimensions can help in interpreting the process of acquiring,
transferring, consolidating and applying knowledge in order to design appropriate
enablers to foster and sustain it. We will discuss each dimension in turn.
The first dimension concerns the setting (routes, directions) of knowledge
transfer. In the CPI model, nine main directions or routes of improvement and
IJOPM
21,4
494
learning are distinguished (arrows 1 to 9 in Figure 1). Categorically, the
first three routes concern knowledge transfer within the same product life
cycle. The remaining six routes concern transfer between different products in
the same family. See Corso (forthcoming) for further details on the nine routes.
In brief:
(1) Intra-product transfer in development: knowledge is transferred from
one phase of the development project to another.
(2) Intra-product transfer from development to field: knowledge is
transferred from the development project to the operations of the
organisation.
(3) Intra-product transfer in field: knowledge is transferred between
different in-field activities, e.g. from maintenance to production
(improvement).
(4) Intra-phase transfer in development: knowledge is transferred from the
development phase of one PI project to that of another.
(5) Inter-phase transfer in development: knowledge is transferred from one
phase of a PI project to another phase of another, usually subsequent,
project, e.g. early experience with one product to the development phase
of the next product.
(6) Intra-phase transfer in field: transfer of knowledge on the same kind of
in-field activities related to different products.
(7) Inter-phase transfer in field: knowledge on different in-field phases is
exchanged between products/projects.
(8) Inter-product transfer from field to development: knowledge acquired
from field activities is transferred to the development of new products.
(9) Inter-product transfer from development to field: transfer of knowledge
generated during the development of new products to improve products
already launched.
All these routes present a potential for learning and innovation which,
however, can be exploited only by actively designing, implementing and
managing adequate mechanisms to enable this transfer of knowledge. Each
knowledge transfer route can be fostered by particular enablers whose
successful implementation strongly depends on the actors involved, the way
they influence the process, and on the type of knowledge involved.
The second dimension of knowledge is its level of dissemination. Depending
on the specific culture of the organisation, emphasis can be placed on sharing
knowledge and fostering learning at different levels: from individuals, to
groups, to the organisation as a whole or even the inter-organisational system.
The third dimension, is the scope of knowledge. This scope can range from
component knowledge, which refers to the mastering of specialist skills and
technologies and their embodiment into components, to architectural
knowledge, which refers to how components and skills are integrated and
linked together into a coherent whole (Henderson and Clark, 1990).
A fourth dimension of knowledge concerns the degree of abstraction and
generalisation (scope of applicability to different situations) (Arora and
Gambardella, 1994).
The fifth and final dimension is the degree of articulation or embodiment. In
order to facilitate knowledge transfer and to prevent its drain, organisations
can embody knowledge in vehicles such as design solutions (e.g. components
and architectures), standard methodologies and procedures, or organisational
structure and routines (Nelson and Winter, 1982). Such embodied knowledge is
more easily transferable (Barney, 1991; Itami, 1987). In contrast, tacit
knowledge is more effective but difficult to imitate (Collis and Montgomery,
1995). Companies, therefore, need to be able to effectively manage both the
processes of embodiment of tacit knowledge into articulated forms as well as
internalisation of articulated knowledge into tacit forms (Hedlund, 1994;
Nonaka, 1991). Awareness and explicitation, moreover, are fundamental in
order to enable knowledge to be questioned and, if necessary, changed (Bohn,
1994).
In the next section, the CIMA methodology to support the management and
improvement of learning in CPI will be introduced.
Supporting knowledge management in PI: the CIMA methodology
The CIMA methodology is designed to be used by researchers acting as
facilitators to help companies in fostering and sustaining the process of
learning and knowledge management in CPI. The methodology comprises four
closely related elements:
(1) The CIMA process, aimed at mapping the current level of learning and
knowledge management within product innovation, identifying
strengths and weaknesses and then suggesting enabling mechanisms
which can be implemented by the company to stimulate continuous
improvement and learning, depending on specific contingencies. The
process has been applied in three different modes (and several variants):
action research, single-company or multi-company workshops (with
either a paper-based or electronic questionnaire), and remote setting
(also either with paper-based or electronic questionnaire). See Coughlan
et al. (2000) for further details.
(2) The CIMA model, a behavioural model of learning in CPI, the main
conceptual underpinning of the methodology.
(3) The self-administered CIMA questionnaire, which is essentially an
operationalisation of the CIMA model, to collect data on user companies.
The questionnaire is available in two formats, on paper and on diskette.
(4) The CIMA knowledge base in which all the data are stored. The
knowledge base provides the basis for intra-firm and inter-firm
comparison leading to company-specific suggestions for improvement.
The next subsection will focus on the CIMA model as an explanatory model for
learning in CPI.
Knowledge and
continuous
innovation
495
IJOPM
21,4
496
The CIMA behavioural model of learning in CPI
The first version of the CIMA model was developed to reflect a wide range of
theoretical perspectives on innovation, NPD, learning, continuous
improvement, organisation theory and performance measurement. The model
was then piloted in a couple of exploratory in-depth case studies. Subsequently,
the model was refined and then applied again in over 80 companies in Europe
and Australia using the CIMA process and questionnaire referred to above.
Along the way, the CIMA knowledge base was gradually filled, which allowed
for ever-better feedback and suggestions to the user companies. The refined
CIMA model is presented briefly in the remainder of this section.
The CIMA behavioural model helps to describe and analyse the learning and
knowledge generation processes within PI in terms of a number of interrelated
variables. From the analysis, company-specific guidelines for improving
learning and knowledge generation processes can be developed to assist
managers in sharing and learning from experiences of improvement practices
with a view, ultimately, to improving PI performance. The variables
distinguished are:
.
continuous Innovation performance;
.
behaviours underpinning continuous innovation and learning within PI;
.
levers that can foster these behaviours;
.
continuous learning/innovation capabilities;
.
company-specific contingencies.
The relationships between these variables are illustrated in Figure 2.
Operationalisation of the CIMA model
Based on the literature and the first set of case studies, the variables in the model
were operationalised as follows. See Corso and Pavesi (2000) for further details.
Performance is the result of improvement activities carried out in the PI
process. This variable was operationalised and measured in terms of:
P1 Improvement generation.
P2 Improvement coherence with corporate goals.
Figure 2.
Elements in the CIMA
behavioural model of
learning in CPI
P3 Improvement diffusion within the same PI process, both within and
across organisational boundaries.
P4 Improvement diffusion between different PI processes, both within and
across organisational boundaries.
P5 Improvement consolidation.
Knowledge and
continuous
innovation
Improvement performance is achieved through a set of eight discrete
behaviours enacted by individuals:
B1 Individuals and groups use the organisation's strategic goals and
objectives to focus and prioritise their improvement and learning
activities.
B2 Individuals and groups use innovation processes as opportunities to
develop knowledge.
B3 Individuals use part of available time/resources to experiment new
solutions.
B4 Individuals integrate knowledge between all different phases of product
innovation.
B5 Individuals transfer knowledge between different PI processes.
B6 Individuals abstract knowledge from experience and generalise it for
application to new processes.
B7 Individuals embed knowledge into vehicles.
B8 Individuals assimilate and internalise knowledge from external sources.
497
These behaviours can be influenced by the implementation and application of
levers, or mechanisms that managers can use when managing the PI process.
These levers may be in evidence even though managers may not be trying
consciously to stimulate learning. If appropriately oriented, however, these
levers can have a substantial influence on the attitudes and practices of
individuals in creating, storing and transferring knowledge. Eight categories of
levers have been identified:
L1 Product family strategies.
L2 Innovation process definition.
L3 Organisational integration mechanisms.
L4 Human resource management policies.
L5 Project planning and control.
L6 Performance measurement.
L7 Design tools and methods.
L8 Computer-based technologies.
The capabilities can be described as integrated stocks of resources that are
accumulated over time through learning, or established through deliberate
decisions. These stocks of resources include internalised behaviours, technical
IJOPM
21,4
498
skills, organisational routines, and corporate assets such as information
systems, databases, libraries, tools, and handbooks. The level of a company's
CI capabilities determines the efforts that are needed to stimulate the
corresponding behaviours. In the model the following classes were
distinguished:
C1 Knowledge generation capability.
C2 Learning alignment capability.
C3 Capability to integrate knowledge within PI process.
C4 Capability to transfer and diffuse knowledge between PI processes.
C5 Knowledge consolidation capability.
Finally, contingencies are factors that influence the choice of levers to foster
behaviours. Among the contingent factors distinguished in the model are
exogenous variables such as the market situation of the company, and
endogenous factors such as company size, and product and process complexity.
In addition to the operationalised variables, the model proposes a theory in
the form of hypothesised or tested relationships between the variables. The
first version of the model was based entirely on the literature and on the
experience of the researchers. The pilot studies added variables, removed
others, and confirmed, rejected or changed the originally-hypothesised
relationships between them. The subsequent series of more than 80 case
studies has improved and enriched the model. In the next section, three
examples of the application of the CIMA model are reported.
Interpreting cases through the CIMA model
To date, the CIMA methodology (including the process, model, questionnaire
and knowledge base) described in the previous section has been applied in
more than 80 companies in order to suggest specific mechanisms that can be
implemented to foster behaviours and, through that, achieve higher levels of
CPI performance. In this section, results from three applications of the CIMA
methodology are reported: company X in Italy, company Y in Sweden, and
company Z in The Netherlands. In each case, data were gathered during semistructured interviews with the main actors in each company's CPI process,
using a common questionnaire. For the Italian case, company X, a PC-diskette
version of the questionnaire was used allowing for immediate feedback to the
company. The case summary below will include one of the diagrams used in
the methodology. We will outline the observations in each case in turn.
Company X (Italy)
Company X competed in the tractor industry which was characterised by
increasing concentration and globalisation. A strong emphasis on the renewal
and enlargement of the product range, and a recognised excellence in product
design allowed company X to survive and grow in an industry dominated by
larger competitors with correspondingly greater technical and financial resources.
Once a country-based company with strong roots in its local environment,
company X had recently taken over a number of its traditional competitors
becoming one of the world leaders in its segment, with a broad portfolio of famous
tractor marks. The resulting increased size and complexity and the competitive
pressure both for reduced time-to-market and for improved use of critical
resources, required a quantum leap in the effectiveness of knowledge sharing and
capitalisation over time across different sites and product families.
The CIMA methodology highlighted integration and improvement diffusion
within the PI process (P3) as the key performance area to be addressed and,
correspondingly, behaviour B4 (Individuals integrate knowledge between all
different phases of PI) as the key behaviour to enhance. This finding is
illustrated in Figure 3. A joint diagnosis with the company's managers outlined
how weaknesses in this behaviour were responsible for rework, design changes
during the production phase and other critical effects that were hardly
considered by the R&D department.
Drawing examples of management practices in similar cases from the CIMA
knowledge base, alternative interventions were discussed and finally an
integrated plan of actions on four different levers was decided. Through L3,
organisational integration mechanisms, the project manager's role and that of
the project work team were redesigned. Through L1, a product platform
strategy was introduced. Furthermore, a structured database to store and
retrieve project knowledge throughout the company was implemented, which
was a combination of levers L7 and L8.
Knowledge and
continuous
innovation
499
Figure 3.
Company X synoptic
diagram
IJOPM
21,4
500
Company Y (Sweden)
At company Y, a leading global company in the telecommunications industry,
an R&D centre located in Sweden was studied. This centre, which specialised
in a particular technological field, organisationally was part of a larger unit, but
was geographically isolated from the rest of the company. The R&D centre had
been established a few years previously in an attempt to retain skilled
engineers by providing interesting jobs in an attractive environment. The
centre performed R&D tasks on projects spanning several organisational units.
Time was the main performance parameter in this extremely dynamic industry
and, correspondingly, the requirements for effective and timely co-ordination
were substantial.
Using the CIMA methodology to map the CPI process, it was seen that, in
general, the R&D centre performed well with respect to knowledge transfer and
learning. The area that emerged as the most important to attend to was
coherence of improvements with corporate goals (P2). This shortcoming
pointed to the importance of enhancing the frequency and diffusion of
behaviour B1, individuals and groups use the organisation's strategic goals
and objectives to focus and prioritise their improvement and learning activities
in the innovation process. The relatively limited evidence of this behaviour was
discussed with the site manager, who thought that it was most likely a result of
their way of working. Performing rather clearly demarcated tasks within large
projects made it difficult for the employees involved to have a clear picture of
strategies and goals at a company level. Two factors were seen to contribute to
this problem. First, a clearly communicated product family strategy (L1) was
lacking. To some extent this was explained by the unit's short history, a
constant lack of time, and an outspoken opportunistic orientation. Furthermore,
there was a limited focus on process improvements (C4). The latter was further
complicated by integration problems between the units involved in R&D,
which were due to geographical distances and different interpretations of
company-wide models and processes.
The mapping and the subsequent analysis revealed clearly a need for an
increased use of a product platform strategy (L1). In addition, the introduction
of new performance measurements (L6) that could take knowledge transfer and
learning into account in a more explicit way was recommended.
Company Z (The Netherlands)
Company Z was a large Dutch company, specialised in designing and
producing integrated defence systems for command and control, sensor and
communications purposes. Some of the most important contingencies were:
(1) Product and market complexity, articulated in:
.
high internal complexity due to size, dimension, lead-time and life;
expectancy of the systems, which were developed by different
partners and (sub) contractors, and then integrated;
.
high external complexity, due to different goals of partners, which
were related, amongst others, to political constraints.
(2) High technology innovation: defence-industry was highly innovative,
combining IT, radar-technology with naval systems expertise.
(3) High political complexity, due to decision-making process and national
government and industrial interests.
The CIMA methodology was used for mapping and analysing three related
projects. This exercise led to the conclusion that process improvement was
heavily emphasised at the expense of B3 (individuals use part of available time/
resources to experiment with new solutions) and B4 (individuals integrate
knowledge among all different phases of product innovation). It was also
concluded that there was neither an explicitly articulated product family
strategy (L4) nor, more generally, a PI strategy. However, when looking at daily
practice, the conclusion was that there was a more or less clear emergent
strategy with implicit goals.
Drawing examples of levers from the other CIMA cases of companies with
similar contingencies, possible interventions based on various levers were
identified. Human resource management activities (L4) such as the
development of specialist skills, departmental assessment and development
plans, could contribute to intensifying B3 (individuals use part of available
time/resources to experiment with new solutions). Furthermore, it was felt that
L5 (project planning and control systems directed at improvement projects) and
L6, performance measurement (feedback systems, benchmarking activities,
measures for creativity e.g. number of improvements proposed) could impact
also on B3.
In order to improve B4, (individuals integrate knowledge among all different
phases of PI), three levers were discussed. L2 (innovation process definition)
could diffuse the activities involved in developing and implementing a new
innovation process quicker and to a wider part of the company. Through L3,
organisational integration mechanisms, temporary teams and/or liaison roles
and cross-disciplinary meetings could be established. Finally, through L4,
human resource management concepts, job rotation could spread the results of
the new PI process and, through that, stimulate, diffuse and increase the
frequency of favourable behaviours.
The conclusion was that explicit use of levers that related to the transfer of
knowledge within the PI process and between PI projects/processes could
support improvement of the PI process.
Conclusions: managerial implications and future development of the
CIMA methodology
This article has presented some interim results of the application of the CIMA
methodology. In its current form, the methodology provides a structured, stepby-step approach to mapping the user company's current level of learning
within PI, identifying strengths and weaknesses and then suggesting enabling
mechanisms which can be implemented by the company to stimulate
continuous improvement and learning, depending on specific contingencies.
This process is supported by a behavioural model, explaining relationships
between learning behaviours and outcomes, capacities enabling these
Knowledge and
continuous
innovation
501
IJOPM
21,4
502
behaviours, levers that managers can use to change existing or promote new
behaviours, and contingencies affecting this whole set of relationships.
Furthermore, this model is operationalised and available as a questionnaire in
various different modes. Finally there is a knowledge base comprising the data
of over 80 companies.
Experience with its application thus far shows that the methodology is not
only usable by companies, even in a remote setting, but also yields useful
results for them in the form of an increased understanding of the strengths and
weaknesses of their PI ``system'' and suggestions for improvement. In this
article three case studies were used to demonstrate the usefulness of the
methodology. As the number of cases analysed grows over time, the richness
and robustness of the analysis of and recommendations to subsequent
companies will improve.
In the future, application of the tool will be complemented with an
automated data collection and report generation system, linked to the
knowledge base in which also the actions undertaken by every company will be
included. Using feedback from companies and facilitators assisting the
implementation, the methodology will be refined and consolidated. Moreover
the enlargement of the user network and of the CIMA knowledge base will
provide the opportunity to analyse and study the application of levers in very
different (contingent) situations. Exchange of knowledge and experiences is
thus facilitated, as well as allowing for essential benchmarking.
References
Arora, A. and Gambardella, A. (1994), ``The changing technology of technological change:
general and abstract knowledge and the division of innovative labour'', Research Policy,
Vol. 23, pp. 523-32.
Barney, J.B. (1991), ``Organization resources and sustained competitive advantage'', Journal of
Management, Vol. 1, pp. 99-120.
Bartezzaghi, E., Corso, M. and Verganti, R. (1997a), ``Continuous improvement and inter-project
learning in new product development'', International Journal of Technology Management,
Vol. 14 No. 1, pp. 116-38.
Bartezzaghi, E., Corso, M. and Verganti, R. (1997b), ``Managing knowledge in continuous product
innovation'', Proceedings 5th International Product Development Management Conference,
EIASM, Como.
Bohn, R.E. (1994), ``Measuring and managing technological knowledge'', Sloan Management
Review, Fall.
Clark, K.B. and Fujimoto, T. (1991), Product Development Performance. Strategy, Organization,
and Management in the World Auto Industry, Harvard Business School Press, Boston,
MA.
Collis, D.J. and Montgomery, C.A. (1995), ``Competing on resources: strategy in the 1990s'',
Harvard Business Review, July-August, pp. 118-28.
Corso, M. (forthcoming), ``From product development to continuous product innovation: mapping
the routes of corporate knowledge'', International Journal of Technology Management.
Corso, M. and Pavesi, S. (2000), ``How management can foster continuous product innovation'',
Integrated Manufacturing Systems, Vol. 11 No. 3, pp. 199-211.
Coughlan, P., Harbison, A., Corso, M., Pavesi, S., Ronchi, S., Caffyn, S., Magnusson, M., Sundgren,
N., Boer, H., Gieskes, J., Chapman, R. and Hyland, P. (2000), ``Developing continuous
innovation as an organisation-wide process'', Proceedings 7th International EurOMA
Conference, 5-6 July, Ghent.
Cusumano, M.A. and Nobeoka, K. (1992), ``Strategy, structure and performance in product
development: observations from the auto industry'', Research Policy, Vol. 21.
De Maio, A., Verganti, R. and Corso, M. (1994), ``A multi-project management framework for new
product development'', European Journal of Operational Research, Vol. 78, pp. 178-91.
Hedlund, G. (1994), ``A model of knowledge management and the N-form corporation'', Strategic
Management Journal, Vol. 15, pp. 73-90.
Henderson, R.M. and Clark, K.B. (1990), ``Architectural innovation: the reconfiguration of existing
product technologies and the failure of established firms'', Administrative Science
Quarterly, Vol. 35, pp. 9-30.
Imai, K., Nonaka, I. and Takeuchi, H. (1988), ``Managing the new product development
process: how Japanese companies learn and unlearn'', in Clark, K.B., Hayes, R.H. and
Lorenz, C. (Eds), The Uneasy Alliance: Managing the Productivity-Technology Dilemma,
Harvard Business School Press, Boston, MA, pp. 337-75.
Itami, H. (1987), Mobilizing Invisible Assets, Harvard University Press, Cambridge, MA.
Meyer, M.H. and Utterback, J.M. (1993), ``The product family and the dynamics of core
capability'', Sloan Management Review, Spring, pp. 29-47.
Nelson, R.R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Harvard
University Press, New York, NY.
Nonaka, I. (1991), ``The knowledge-creating company'', Harvard Business Review, NovemberDecember, pp. 96-104.
Sanderson, S. and Uzumeri, M. (1995), ``Managing product families: the case of the Sony
walkman'', Research Policy, Vol. 24, pp. 761-82.
Saren, M.A. (1984), ``A classification and review of models of the intra-firm innovation process'',
R&D Management, Vol. 14 No. 1, pp. 11-24.
Smith, P.G. and Reinertsen, D.G. (1991), Developing Products in Half the Time, Van Nostrand
Reinhold, New York, NY.
Sundgren, N. (1998), Product Platform Development ± Managerial Issues in Manufacturing
Firms, Chalmers University of Technology, GoÈteborg.
Wheelwright, S.C. and Clark, K.B. (1992), ``Creating plan to focus product development'', Harvard
Business Review, March-April, pp. 70-92.
Wheelwright, S.C. and Sasser, W.E. (1989), ``The new product development map'', Harvard
Business Review, May-June, pp. 112-23.
Knowledge and
continuous
innovation
503