Macro process of knowledge management for continuous innovation

Macro process of knowledge managementfor continuous innovation

Jing Xu, Remy Houssin, Emmanuel Caillaud and Mickael Gardoni

Abstract

Purpose – The purpose of this research is to explore the mechanisms of knowledge management (KM)

for innovation and provide an approach for enterprises to leverage KM activities into continuous

innovation.

Design/methodology/approach – By reviewing the literature from multidisciplinary fields, the concepts

of knowledge, KM and innovation are investigated. The physical, human and technological perspectives

of KM are distinguished with the identification of two core activities for innovation: knowledge creation

and knowledge usage. Then an essential requirement for continuous innovation – an internalization

phase – is defined. The systems thinking and human-centered perspectives are adopted for providing a

comprehensive understanding about the mechanisms of KM for innovation.

Findings – A networking process of continuous innovation based on KM is proposed by incorporating

the phase of internalization. Three sources of organizational knowledge assets in innovation are

identified. Then, based on the two core activities of innovation, a meta-model and a macro process of

KM are proposed to model the mechanisms of KM for continuous innovation. Then, in order to

operationalize the KM mechanisms, a hierarchical model with four layers is constructed by integrating

three sources of knowledge assets, the meta-model and the macro process into the process of

continuous innovation.

Practical implications – According to the lessons learned about KM practices in previous research, the

three perspectives of KM should collaborate with one another for successful implementation of KM

projects for innovation; the networking process of innovation provides a new way to integrate KM

process in innovation; the hierarchical model provides a suitable architecture to implement systems of

KM for innovation.

Originality/value – The meta-model and macro process of KM explain how the next generation of KM

can help the value creation and support the continuous innovation from the systems thinking

perspective. The hierarchical model illustrates the complicated knowledge dynamics in the process of

continuous innovation.

Keywords Knowledge management, Innovation, Thinking

Paper type Research paper

1. Introduction

Under the fierce competition, companies are compelled to innovate in order to be successful

even to survive in the global market. It is reported that successful companies produce 75

percent of revenues from new products or services that did not exist five years ago (Smith,

2006). The competition based on knowledge and innovation as an effective strategy is highly

valued by companies. Therefore, knowledge and innovation are considered as the crucial

sources for sustaining the competitive advantage of a company (Nonaka and Takeuchi,

1995). Recently, a survey in KPMG (1998) of 100 leading companies in UK had found that

nearly half of the investigated firms were undertaking some kind of knowledge management

(KM) initiatives for promoting their competitive performance. Meanwhile, academic

publications about KM and innovation rocket during the past decades according to the

statistics of major databases such as the ProQuest database, ScienceDirect and ISI web of

DOI 10.1108/13673271011059536 VOL. 14 NO. 4 2010, pp. 573-591, Q Emerald Group Publishing Limited, ISSN 1367-3270 j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE 573

Jing Xu, Remy Houssin,

Emmanuel Caillaud and

Mickael Gardoni are based

at the Laboratoire du Genie

de la Conception, Institut

National des Sciences

Appliquees de Strasbourg,

Strasbourg, France.

Portions of the research in thispaper are supported by theChinese Scholarship Council,and the authors’ partnercompany. The authorsappreciate sincerely the effortsand comments of theanonymous reviewers.

Received: 16 October 2009Accepted: 1 April 2010

science (Scarbrough and Swan, 2001; Fagerberg et al., 2005). KM and innovation have

come into the forefront of scientific research (Goh, 2005). Consequently, how to turn the

available knowledge into profitable innovations and bring them to the market in a continuous

manner become a major concern both of industries and academics.

Many research programs on KM have been carried out from different points of view:

economics, management, technology and engineering (Liebowitz, 1999). Since innovation

was first introduced by Schumpeter (1934), it has been studied from the various

perspectives such as from the point of view of management (Drucker, 1993), of creativity

(Amabile, 1996), of technology evolution (Althshuller, 1988) and recently of information and

engineering with a focus on computer aided innovation (Leon, 2009). It is clear that

knowledge as an important assets in a company should be managed in order to foster more

innovation.

Owing to the multidisciplinary natures of KM and innovation, only a few studies try to

investigate their complex relationships but mainly from a specific point-of-view. From the

point of view of management, Swan and his research fellows (Swan and Newell, 2000; Swan,

2007) have contrasted three perspectives of KM such as production, process and practice

for different episodes of innovation. From the technological viewpoint, Alavi and Leidner

(2001) argue that information and communication technologies can be instrumental to KM

process and catalyze innovation. In the community of creativity and innovation, human

creativity and tacit knowledge are regarded as the main sources of continuous innovation

(Nonaka and Takeuchi, 1995; Koskinen and Vanharanta, 2002). However, there is a lack of a

comprehensive integration of the mechanisms of KM for innovation from a systems thinking

viewpoint (Rubenstein-Montano et al., 2001; Jung et al., 2007).

Managing knowledge for innovation has several difficulties (Chapman and Magnusson,

2006) such as the reconcilement of perspectives on innovation and knowledge, the

heterogeneity and distribution of knowledge in companies and the balance between

knowledge exploration and exploitation. As KM and innovation are closely linked, these

difficulties should be investigated from a systemic point of view. Thus, the purpose of this

study is to explore the mechanisms of KM for innovation from the viewpoint of systems

thinking so that the comprehensive effects of KM in innovation process can be analyzed.

In sections 2 and 3, the multidisciplinary literature on knowledge and innovation has been

reviewed with the identification of their current problems and trends. By seeing knowledge

as a concept with multifaceted nature, three perspectives of KM are distinguished and

compared and knowledge creation and usage are proposed as core activities for innovation.

By studying the different innovation processes in literature, an essential requirement of

continuous innovation is identified and the absence of an integral model of KM for innovation

has been outlined. In section 4, according to our analyses, a networking process of

continuous innovation through KM is constructed and the functioning of an additional phase

of internalization is discussed. In section 5, three sources of organizational knowledge are

distinguished. Then in section 6, KM is modeled by a meta-model by focusing on the

interactions of knowledge creation and usage. A macro process of KM for innovation is built

by extending the meta-model into the knowledge lifecycle from three perspectives. Then, in

order to operationalize the mechanisms of KM for innovation, a hierarchical model with four

layers is proposed by integrating the networking process of innovation, meta-model and

macro process of KM into a cogent framework from a human-centered point-of-view. This

model visualizes the knowledge flows and the interactions between KM and innovation

through four layers. Finally, the future perspective of our research and an application of our

model are discussed.

2. Perspectives and core activities of KM for innovation

That knowledge is a key component of all forms of innovation is a widely accepted principle

of modern innovation management (Chapman and Magnusson, 2006). Knowledge is more

and more regarded as a vital asset and the main source of the competitive advantage of a

company. Because of its mysterious nature, there is no simple definition of knowledge

PAGE 574 j JOURNAL OF KNOWLEDGE MANAGEMENTj VOL. 14 NO. 4 2010

generally accepted. The epistemology has also changed from a monist view to the pluralist

one (Cook and Brown, 1999). Knowledge is different from data and information, but related

to both of them (Davenport and Prusak, 1998). Unlike data and information, knowledge

emerges from human interpretations and their complex interactions (Stacey, 2000) that

make it intangible and socially constructed and embedded in social networks.

From the systems thinking perspective, knowledge should be regarded as a multilevel

concept embedded in environment (Carayannis and Campell, 2005). Thus for innovation, a

more holistic view should see knowledge as multilayered and multifaceted, comprising

cognition, actions and resources (Swan and Newell, 2000). Both its static and dynamic

aspects should be managed in order for better reuse and innovation. In order to capture the

multifaceted nature of knowledge, we adopt the working definition of knowledge proposed

by Davenport and Prusak (1998):

Knowledge is a mix of framed experience, values, contextual information and expert insight that

provides a framework for evaluating and incorporating new experiences and information.

Although this definition is not a strict one, it can include both the static and dynamic aspects

of knowledge.

With the increasing amount of knowledge, a urgent task for companies is how to effectively

manage their knowledge and to create added values with further innovation. Since Wiig

coined the term ‘‘knowledge management’’, it has been studied with many approvals and

critiques. There are several generations of KM (Vorakulpipat and Rezgui, 2008). In the first

generation, it is about information processing and transferring, then turns to the knowledge

codification and reuse (Wiig, 1997). In the second generation, KM focuses on the

knowledge creation and sharing (Nonaka and Takeuchi, 1995). In the next generation, KM

focuses on the evolution of knowledge lifecycle (McElroy, 2003) and the value creation of

knowledge assets (Liebowitz, 1999). However, in the latest generation of KM, the

mechanisms of KM for value creation, especially for innovation are still under development

and less integrated.

Because innovation is not an one-act drama for companies (Nonaka and Takeuchi, 1995),

the process view on KM and innovation is prevalent in the domains of engineering and

management. Thus, from the process perspective, numerous models, processes and

frameworks of KM have been proposed to unveil the nature of KM (Holsapple and Joshi,

1999; Rubenstein-Montano et al., 2001; Alavi and Leidner, 2001). By studying these

processes or models, it is noticed that a lot of diverse activities exist in the processes of KM.

In order to distinguish the core activities of KM in innovation, we synthesize the different

forms of a same activity and calculate its appearing frequency in all these processes. In

Table I, the activities with high frequency are grouped and compared. In terms of the

function of these activities, they are divided into three functional groups for further research:

1. The first group concerns the creation or emergence of new knowledge: knowledge

production, creation, generation, development, etc.

2. The second group concerns the usage of knowledge: knowledge utilization, use,

application, reuse, etc.

Table I Frequency of KM activities (appearance more than three times)

Group 1 Group 2 Group 3Knowledgecreation Fre.

Knowledgeusage Fre.

Knowledgepreparation Fre.

Knowledgediffusion Fre.

Knowledgepreservation Fre.

Knowledgemaintenance Fre.

Creation 20 Use/reuse 14 Acquisition 14 Sharing 16 Storage 15 Evaluation 4Generation 6 Application 11 Identification 7 Distribution 10 Capturing 10 Refinement 3Development 4 Utilization 4 Retrieval 5 Transfer 10 Codification 3 Integration 3Production 3 Collection 3 Dissemination 8 Retention 3

VOL. 14 NO. 4 2010 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 575

3. The third group concerns the processing of knowledge: knowledge acquisition, sharing,

transferring, storage, codification, evaluation, etc.

Concerning the first group, different theories are created to explain the emergence of new

knowledge. In the pioneer research (Machlup, 1962), new knowledge is produced as a

product and there always exists a willingness to use it. Newell and Simon (1972) proposed a

general problem solver to create new knowledge for the solution of a new problem. The

structure of data-information-knowledge-wisdom by Ackoff (1989) also explains the

appearance of new knowledge from data and information by syntax, meanings and

patterns. Recently, Nonaka and Takeuchi (1995) and Davenport and Prusak (1998) have

respectively proposed the concept of knowledge creation and knowledge generation, which

have great influence on the fields of KM and innovation. Wierzbicki and Nakamori (2005)

construct a creative space and propose the Nanatsudaki model of knowledge creation. In

Table II, three main activities in this group: knowledge production, creation and generation

are compared from several aspects.

Alavi and Leidner (2001) argue that the source of competitive advantage resides in the

application of knowledge rather than the knowledge itself. Knowledge creation provides the

potential for value creation, but knowledge usage realizes it. Landry et al. (2001) studied four

models of knowledge utilization in previous research. The technological model regards the

results of research as knowledge – the only determinant for its utilization, been called the

science push model. The economic model concerns the needs and context of users, often

named as the demand pull model. The institutional model recognizes that knowledge

transfer is not automatic, and the quality of findings and their dissemination are two

determinants of the use. The social interaction model focuses on the interactions between

researchers and users in a nonlinear manner. Recently, the systems approach has been

introduced in the research of knowledge utilization (Green, 2006), which considers the

interdependency, integrity and worth of all the stakeholders and emphasizes needs of end

users. The activities in the second group have delicate differences but very often are

neglected and used identically and interchangeably. In Table III, knowledge utilization, use

and application are compared from the same aspects in the first group.

It is observed that in the first group, knowledge production focuses on the physical aspects

of knowledge that is regarded as a product and is embedded in artifacts. Knowledge

creation considers knowledge as a process and capability, which is held by human beings.

Knowledge generation focuses on the knowledge embodied by the technologies and the

methods to produce artifacts. In the second group, knowledge utilization focuses on the

Table II The comparisons among knowledge production, creation and generation

Activity

AspectsKnowledge production (KP)(Machlup, 1962)

Knowledge creation (KC) (Nonakaand Takeuchi, 1995)

Knowledge generation (KG)(Davenport and Prusak, 1998)

Key concepts onknowledge

Knowledge as business product;knowledge as productive asset;knowledge embedded in theproduct

Knowledge as a dynamic humanprocess of justifying personalbelief towards the ‘‘truth’’;Importance of tacit knowledge

Knowledge as a mix of framedexperience, values contextualinformation and expert insight;Importance of explicit knowledge

Definition KP as an achievement andcodification of meaning through thecommunication of information

KC as a spiral process through theconversion of tacit and explicitknowledge

KG as specific activities andinitiatives undertaken by acompany to increase their stock ofcorporate knowledge

Relations to innovation System of innovation as a mode forKP; KP as a sub-process ofinnovation process

KC leading to continuousinnovation; tacit knowledge is themost important source ofinnovation

KG as an activity in the process ofinnovation

Related theories Social and economic theory;System of innovation theory;

Cognition theory; Social networktheory;

Activity theory; informationscience

Classification Subjectively new KP; Socially newKP

Individual KC; Collective ornetworked KC

Five modes of KG: acquisition;dedicated resource, fusion;adaptation; knowledge networks


physical aspects of knowledge and concerns the economical field. Meanwhile, knowledge

use focuses on the conceptual and cognitive aspects of knowledge and concerns the

solution of a particular problem. Knowledge application is often used in a specific context,

which focuses more on the technological aspects of knowledge.

With reference to the above analyses, we argue that the multifaceted nature of knowledge

leads to the different perspectives about its management and innovation. By comparing the

connotations of KM activities in the first and second groups, we distinguish three different

points-of-view about knowledge and its management. They are the physical, human and

technological points of view. Knowledge production and utilization concern the physical

perspective of KM. Knowledge creation and use focus on the human perspective of KM.

Knowledge generation and application deal with the technological viewpoint of KM.

As knowledge in a company is distributed and socially constructed, the creation and usage

of knowledge often do not happen at the same place and at the same time. This results in a

gap between them, andmany KM activities are carried out to fill the gap, which belong to the

third group. They can be further divided into three subcategories according to their functions

in the lifecycle of knowledge. Some activities focus on the preparation for new knowledge

creation such as knowledge identification, acquisition, retrieval and collection. Some focus

on the preparation for knowledge usage such as storage, codification, capture, sharing,

transfer distribution, and dissemination. Others concern the maintenance of knowledge after

its usage such as refinement, integration and evaluation.

Since innovation can be seen as the application of knowledge to produce new knowledge

(Drucker, 1993), thus for our purpose, we argue that the creation and usage of knowledge

are two groups of core KM activities for innovation. Within the next generation of KM, we

contend that the comprehensive understanding on KM can be achieved by integrating the

three points of view and focusing on the two core activities of innovation. So we define KM as

the appropriate configuration of the interdependent activities by focusing on the creation

and usage of knowledge from the physical, human and technological perspectives.

3. Innovation process and its interactions with KM

Thanks to the intensive competition in the global market, innovation has been a condition of

survival for a company; as a consequence, the continuous innovation comes into being for

Table III The comparisons about knowledge utilization, use and application

Activity

AspectsKnowledge utilization (KUt)(Landry et al., 2001)

Knowledge use (KUs) (Deshpandeand Zaltman, 1982; Majchrzaket al., 2004)

Knowledge application (KAp)(Alavi and Leidner, 2001; Songet al., 2005)

Key concepts aboutknowledge

Knowledge as the results ofresearch or research findings orthe results of data or informationprocessing

Knowledge as the result ofcognitive processing triggered bythe inflow of new stimuli

Knowledge as elements generatedby technologies and stored in theknowledge base

Definition KUt defined as whether and towhat degree sources of evidenceare applied to managementdecisions or policy making

KUs as the application of wisdomto solve a particular problem or amake a particular decision

KAp as the term for the use ofknowledge in a particular contextand is the ultimate goal ofinformation dissemination

Relations to innovation Innovation diffusion as its subfield;knowledge as innovation

KUs as one of the determinants ofinnovation

KAp as a process of keyimportance in the development ofinnovation

Related theories Sociology of knowledge; systemstheory

Social network theory Information science; artificialintelligence

Classification Instrumental and behavioral KUt;conceptual and cognitive KUt;symbolic, political and strategicKUt

Knowledge use as replication;knowledge use for innovation

KAp through directions; throughroutines; through self-containedtask teams


sustaining the competitive edge in the market. The initial research on innovation

(Schumpeter, 1934) defined it as the successful introduction of the new things such as

products, methods of production, market and so on. As its research extends from

economics to management and engineering fields, innovation has became a

multi-dimensional phenomenon. It has been regarded as an object or a tool for

entrepreneurship (Drucker, 1993; Rothwell, 1994). It can be viewed as a process or as the

outcome of the process (OSLO, 2005; Trott, 2005). Among different perspectives of

innovation, the process perspective is more appropriate for our research. Here, we adopt the

process definition of innovation in Trott (2005) – which is a process of interrelated activities

from ideas to invention and to its commercialization, where new knowledge is created and

used through these activities.

From the process perspective, different innovation models and processes have been put

forward in specific literature. They can be basically categorized into two types: linear and

non-linear types. The linear type includes the technology push and market pull models, and

the non-linear type consists of the chain-linked, interactive, system integration and open

innovation models. By studying specific literature on innovation, the existing innovation

processes are analyzed and compared in Table IV.

Table IV The diverse processes of innovation

Years Models Sources Phases in innovation process

1950s Technology push model Rothwell (1994) Basic science, applied science and engineering, manufacturing,marketing, sales

1960s Market pull model Rothwell (1994) Marketplace, technology development, manufacturing, sales1980s Five-stages process Modesto (1980) Recognition, invention, development, implementation, diffusion

Coupling model Rothwell (1994) Research and development, manufacturing, marketing(interrelated triangle)

Chain-linked model Kline and Rosenberg(1986)

Potential market, invent/or produce, design and test, redesign andproduce, distribute and market (interlinked with research andknowledge)

Henry Ergas’s dynamicmodel of innovation

Branscomb and Choi(1997)

Research and development, application, adaptation, socialresponse

1990s Innovation life cycle as anS curve

Howard and Guile (1992) Emergence (development), growth (pervasion, diffusion), maturity(saturation)

Interactive model Rothwell (1994) Idea, basic research, applied research, design and development,prototype, production, marketing and sales

Linear process of threestages

Deschamps and Nayak(1995)

Fertilization, seeding, incubation

Innovation life cycle withthree phases

Amabile(1996) Individual or group creativity, implementation of creativity,diffusion

Three-phases model ofproduct innovation

Buckler (1997) Front end phase, middle phase, back end phase

2000s Innovation process offour episodes

Swan and Newell (2000) Agent formation, selection, implementation, routinization

Innovation model onNonaka’s SECI model

Schulze (2001) Impulse, idea, invention, innovation

Stage model ofinnovation

Hislop (2005) Idea conception, appraisal of needs, design/buy, implementation,institutionalization-routinization

Innovation equation Trott (2005) Theoretical conception, technical invention, commercialexploitation

Four phases from theknowledge creationperspective

Popdiuk and Choo(2006)

Idea phase, feasibility phase, capability phase, launch phase

Integrated innovationprocess

Bernstein and Singh(2006)

Idea generation, innovation support, development,implementation

Core innovation processin system thinking

Galanakis(2006) Knowledge creation, idea generation, product development andmanufacturing, product success

Open innovation withthree core processarchetypes

Enkel et al. (2009) Out-in process, coupled process, inside-out process


It is observed that the phases in innovation can not be fully separated, and even some are

overlapped. We acknowledge that the interactions and iterations in the process are

inevitable. As a result, innovation process are often executed in a parallel and concurrent

manner. A systemic process of innovation should emphasize both the generation of new

ideas and its interactions with other phases in the process (Cantisani, 2006). The

perspective of lifecycle and systems thinking need to be applied into the process of

innovation in the dynamic environment.

In the reviewed innovation models, few models pay attention to the activities after the

commercialization or implementation of innovation, where the lesson learned, experiences

and best practices will accumulate into knowledge bases and new impulses will be

generated for more innovation. Inspired by the SECI model (socialization, externalization,

combination, internalization) in Nonaka and Takeuchi (1995), we think that an additional

phase of internalization should be introduced into innovation process from the perspective of

lifecycle. So that innovation can be achieved and launched by companies in a continuous

manner. Further details on this special phase will be discussed later.

Obviously, innovation and knowledge are two deeply interlinked subjects. There is a general

consensus that both the explicit and tacit components of organizational knowledge play an

important role in innovation (Nonaka and Takeuchi, 1995; Davenport and Prusak, 1998). In

order to fulfill values of organizational knowledge, managing knowledge for innovation has

been studied from both the theoretical and the practical levels.

KM can help to promote knowledge creation and innovation. At theoretical level,

Johannessen et al. (1999) proposed that innovation presupposes the organizational

learning system including developing, integrating and using knowledge. Gopalakrishnan

and Bierly (2001) propose a new topology of two types of innovation based on the theories of

knowledge from three dimensions: tacit-explicit, systemic-autonomous and simple-

complex. Based on this topology, Abou-zeid and Cheng (2004) argue that the two types

of innovation are respectively associated with knowledge creation and utilization activities.

Popdiuk and Choo (2006) analyze the relationship between innovation and knowledge

creation and synthesize into a generic classification of innovation in a knowledge creation

perspective. Swan (2007) analyzes the links between knowledge and innovation from three

different perspectives: production, process and practice. He concludes that none of them is

universally applicable but further research should focus on how these different approaches

interact, complement and even contradict each other. The important roles of KM in the

process of innovation are recently studied in (Basadur and Gelade, 2007) by integrating KM,

creativity and innovation into a single framework and du Plessis (2007) identifies the drivers

and values of application of KM in innovation. An integral approach towards KM can help to

maximize innovation performance for the competitive advantage of a company (Gloet and

Terziovski, 2004).

At empirical level, Hall and Andriani (2002, 2003) put forward a technique for managing

knowledge associated with innovation, which can identify the knowledge gaps for radical

or incremental innovation. Jang et al. (2002) find that in reality KM is deeply linked with

process innovation; and knowledge activities facilitate the recursive relationships between

knowledge produced in innovation and organizational knowledge. Based on case studies,

Leiponen (2006) builds a framework of knowledge assets with degree of tacitness

and collectiveness to analyze the relationships between knowledge and innovation. By

conducting a survey she concludes that different forms of knowledge have different

contributions to different types of innovation.

After careful examinations of the various models for innovation, knowledge creation and

knowledge usage presented above, we notice that there are some similarities among them.

Considering the processes of innovation in Table IV, different models about knowledge

creation and usage have been proposed to explain effects of knowledge in innovation as

shown in Table V.

For the last generation of open innovation or continuous innovation model, we did not find

any corresponding models of knowledge creation and usage in literature. As we argue that


knowledge creation and usage are the two core activities in the process of innovation, we

realize that the separated research on knowledge creation and usage should be integrated

into a cogent framework to understand the mechanisms of KM for innovation.

4. Networking process of continuous innovation based on KM

Due to the multifaceted nature of knowledge and innovation, the relationships between

innovation and KM are complex and should be investigated from the view of systems

thinking. Based on our proposition about the additional phase of internalization for

innovation, a networking process of continuous innovation is presented below by integrating

the KM process into it. Then the detailed functioning of internalization phase is discussed

through the lens of KM. The networking process provides a global but simplified view of the

interactions between KM activities and innovation.

4.1 A networking process of continuous innovation

Innovating in a continuous manner is necessary for sustaining the advantage. The

capabilities of continuous innovation are closely associated with the knowledge

management systems and processes in a company (Chapman and Hyland, 2004).

Considering the intensive iterations and feedbacks during innovation process, we propose

that the continuous innovation should be a networking process for better communication and

cooperation in a dynamic environment. By summarizing the basic common phases in the

reviewed innovation processes, we include four basic phases in the networking process of

innovation such as idea generation, research and development, prototyping and

manufacturing, and marketing, sales and diffusion. Based on our previous analyses of

innovation process, the activities after the phase of commercialization are scarcely

mentioned but important for continuous innovation. Thus a phase of internalization is

introduced into the networking process from the perspective of lifecycle, as illustrated in

Figure 1.

In the networking process, the common phases and the additional phase of internalization

interact with each other and communicate with the knowledge bases through KM process.

The common phases can keep the compatibility with existing best practices of innovation,

while the internalization phase parallel to the common phases provides a powerful conduit

for integrating the KM process into innovation. Then the functioning of internalization phase

is discussed.

Table V The corresponding models of innovation, knowledge creation and usage

Models of innovationModels about knowledge creation andknowledge usage

Technology push model (Rothwell, 1994) Knowledge production model (Machlup, 1962)Technological model (Landry et al., 2001)

Demand pull model (Rothwell, 1994) Problem-solving methods (Newell and Simon,1972)Economical model (Landry et al., 2001)

Chain-linked model (Kline and Rosenberg, 1986) Data-information-knowledge model (Ackoff,1989)Institutional model (Landry et al., 2001)

Concurrent and evolutionary model (Rothwell,1994)

SECI Spiral (Nonaka and Takeuchi, 1995)

Social interaction model (Landry et al., 2001)Innovation with systemic integration (Rothwell,1994)

Nanatsudaki Model (Wierzbicki and Nakamori,2005)Systems approach of knowledge utilization(Green, 2006)

Open innovation/continuous innovation model(Enkel et al., 2009; Chapman and Magnusson,2006)

No correspondent model


4.2 The internalization phase of continuous innovation through KM

Despite that knowledge is a key component for continuous innovation, the deliberate KM in

order to support innovation has still not found its ways into all companies (Chapman and

Magnusson, 2006), that is partially because of the absence of a way to integrate KM process

into innovation. Since there are increasing requirements to integrate the KM process into

business process (Scholl et al., 2004) including the process of innovation, the phase of

internalization can provide a platform for carrying out the activities of KM. This platform will

facilitate the interactions and iterations in innovation and KM. In previous research, this

phase has been performed in an implicit way such as market research, capitalization of

experience, community of practice and so on.

Owing to the changing customer needs, extensive competition and rapid technological

change, innovation is extremely dependent on the availability of internal and external

knowledge (du Plessis, 2007). Through the additional phase of internalization, innovation

process can exchange rapidly knowledge and information with internal and external

knowledge bases. The feedbacks from customers, the responses from competitors, best

practices, errors and lessons learned about the innovation project will be capitalized so as to

be transferred to and assimilated by the stakeholders. By this way, the capabilities of the

continuous innovation can be improved.

The internalization phase makes the networking process of innovation more flat and

concurrent. The iterations and feedbacks can be rapidly transmitted to the right place and

the right person. Organizational learning in double loop can emerge and a learning network

can be built on this platform. The requirements of the latter phases in innovation can be

better considered in its early phases. This phase will increase the shared common

understanding during the innovation process and improve the knowledge level in the

knowledge bases. Furthermore, the sparks and impulses of new ideas are conceived and

matured for continuous innovation. With the phase of internalization continuously providing

seeds for more innovation, the continuous innovation can become a propeller of companies.

To summarize, we conclude that five phases exist in the continuous innovation process,

which are idea generation, research and development, implementation (prototype and

manufacturing), commercialization (marketing, sales, diffusion), and internalization

(analysis, reflection, synthesis). The intensive interactions between the networking

process of innovation and knowledge bases reflect the necessity of the integration of KM

process in innovation. The additional phase of internalization provides a suitable platform for

this requirement. In the following, the mechanisms of KM for innovation are expounded.

5. Three sources of knowledge in innovation and their relationships

On account of its uncertainty and complexity, innovation requires diverse resources such as

human resources, advanced technologies, financial resources and so on. Owing to the

multifaceted nature of knowledge, the knowledge assets are accumulated in different

Figure 1 The global networking process of continuous innovation through KM

IdeaGeneration

PrototypingManufacturing

MarketingSales Diffusion

Internalization (Analysis, reflection, synthesis)

ResearchDevelopment

Knowledge Bases

KM process


resources in a company. Different knowledge assets signify different approaches for its

management (Spender, 2005). In correspondence with the three perspectives of KM,

artifacts, human beings and technologies are identified as three main sources of

organizational knowledge assets. Each source gives a form of knowledge asset:

1. In artifacts: it is embodied in physical objects: documents, products, services and so on.

2. In human beings: it is held by human whether inside or outside the firms, for instance the

expertise, skills, and competences of the employees or customers.

3. In technologies: it is embodied in the methods and technologies used to produce artifacts

such as the methods and processes for using machines and software.

These different forms of knowledge have different implications for its management.

Knowledge asset embodied in artifacts operates as both an input to and an output of

activities of research and production in a company, which emphasizes the physical

perspective of KM. Knowledge asset held by human beings is subjective and socially

constructed, which underlines the embeddedness and heterogeneity of KM. Knowledge

asset embodied in technologies draws close attention to the methods and processes in

technological practices of knowledge intensive activities.

A company is a human-centered and socio-technical system, where humans, technologies

and artifacts interact intensively. Human beings use technologies to act on artifacts and

artifacts react on humans in reverse. Humans’ creative ideas and innovative use of existing

knowledge are crucial to KM and innovation. Among the three perspectives of KM, the

human perspective is situated in a centered position and complemented by the other two

perspectives. The knowledge assets embodied in three sources provide new implications

for managing knowledge and implementing the different strategies of KM for innovation.

6. Modeling the mechanisms of KM for continuous innovation

Because of the difficulties in managing knowledge for innovation, the KM strategies are not

extensively installed in companies. On the other side, due to the complex relationships

between knowledge and innovation, we did not find, in literature, any framework that

generally accepted for overcoming these difficulties. Here, from the systems thinking

perspective, we model the KM with a meta-model of KM based on knowledge creation and

usage – the two core activities for innovation. Then a lifecycle of knowledge is created based

on the meta-model. A macro process of KM is built on the knowledge lifecycle from the three

perspective of KM. Finally in order to operationalize the mechanisms of KM, the meta-model

and the macro process of KM are united into innovation and constitute a hierarchical model

in four layers with the help of a cross-functional team and Information and Communication

Technologies (ICTs) tools.

6.1 Meta-model of KM for continuous innovation

Knowledge creation and codification do not necessarily lead to an improvement in

performance, nor do they create values. The values of individual and organizational

knowledge are embodied primarily in the application. For innovation to occur, knowledge

must not just be shared, but also be used and recombined. Sometimes, the distinctions

between knowledge creation and usage seem to be especially obscure, when facing

complex systems such as innovation. Because people may only be able to create valid

knowledge about complex systems by testing their beliefs through application (cited in

Starbuck, 1992). Since innovation is a result of the combination of existing and new

knowledge (Kogut and Zander, 1992), the seamless integration of knowledge creation and

usage is essential for the continuous innovation.

The recombination of new and existing knowledge heavily depends on knowledge creation

and use. In the next generation of KM, the importance of knowledge creation and usage has

been substantially argued for value creation and they become crucial factors in the

innovation process. Placing knowledge creation and knowledge usage in the meta-model of

KM for innovation is based on the following considerations:


B The creation/usage are two activities so closely related that one can not present without

the other.

B In the innovation process, the two activities reciprocally function as cause and effect,

which form a spiral evolution of knowledge.

B The natures of the two activities are different for innovation. For knowledge creation, the

creativity of human beings and the heterogeneity, tacitness and specialization of

knowledge are keys for the novelty of innovation; for knowledge use, the harmony

amongst users and the shared understanding, codifiability and diversity of knowledge are

important for the success of innovation.

Based on the above arguments, we propose ameta-model of KM for innovation as illustrated

in Figure 2. In this meta-model, the interactions between knowledge creation and usage

create new knowledge for innovation. Here, we consider knowledge creation as the

exploration of new knowledge and knowledge usage as the exploitation of existing

knowledge as defined in (March, 1991). A well-defined balance between them will help to

innovate.

In the meta-model, we distinguish the two aspects of knowledge creation and usage: one for

innovation and the other for non-innovative activities (Machlup, 1962; Majchrzak et al.,

2004). Knowledge creation has two senses. one is to create subjectively new knowledge just

like individual learning as preparations for innovation. The other is to create socially new

knowledge that is potential for innovation. Knowledge usage also has two aspects, one is the

creative and recombinative integration of existing knowledge for innovation, and the other is

the replication of knowledge for routine tasks in innovation.

According to the physical, human and technological perspectives of KM, we elaborate the

meta-model of KM for innovation into three detailed explanations for the emergence of three

new knowledge assets. From the physical view of KM, the meta-model is composed of

knowledge production and utilization, and during the interactions, the new knowledge

artifacts are produced. From the human view of KM, the meta-model consists of knowledge

creation and usage, in which the new tacit knowledge, experience, and capabilities are

created and used. From the technological view of KM, it is made up of knowledge generation

and application, and new knowledge is generated and applied by technologies and

methods for innovation. The three explanations are described in Figure 3.

Figure 2 The meta-model of KM for continuous innovation

Knowledgecreation

Knowledgeusage

Subjectivelynew knowledge

Socially newknowledge

Knowledge usedfor routine tasks

Knowledge usedfor innovation

Newknowledge

in innovationprocess

Figure 3 The three views of meta-models of KM for innovation

Knowledgegeneration

Knowledgecreation

Knowledgeuse

Knowledgeutilization

Knowledgeproduction

Knowledgeapplication

Newknow-ledge

Newknow-ledge

Newknow-ledge


Because of gaps between knowledge creation and usage in reality, the meta-model should

be viewed as an ideal state of KM. In this state, the created knowledge can be used without

any impediments and new knowledge will emerge during the use of existing knowledge in

new situations. The gap between knowledge creation and usage can be seamlessly bridged

by the intensive networking and system integration. According to the analyses in Table V, we

contend the meta-model of KM can correspond to the open innovation or continuous

innovation model.

With the meta-model of KM for innovation, we do not mean that other activities in KM such as

sharing, storage etc. are not important. On the contrary we think that the two core activities:

knowledge creation and usage must be supported and complemented by other activities of

KM. As a consequence, the meta-model of KM is extended into a macro process.

6.2 Macro process of KM for continuous innovation

By considering the process of KM in (Alavi and Leidner, 2001) from the point of view of

lifecycle, we argue that the knowledge lifecycle model can be founded on the core activities

of KM for innovation: knowledge creation and usage. Thus five phases are included in the

lifecycle such as the phases of pre-creation, creation, intermediate, usage and post-usage.

From the perspective of systems thinking, a macro process of KM is constructed on the

meta-model by combining the knowledge lifecycle and the three perspectives of KM. As the

equilibrium between knowledge creation and usage is a difficulty in managing knowledge

for innovation, the macro process can help companies to innovate in a balanced manner

focusing on both knowledge creation and usage.

The macro process illustrates the lifecycle of KM in a prescriptive manner. The first phase is

to define the knowledge requirements, to search, acquire and retrieve existing knowledge

whether it is inside or outside of enterprise and then to identify the gap between the

requirements and existing knowledge. The second phase is the creation of new knowledge,

in which the creativity of human is crucial. The intermediate phase focuses on the retaining

and spread of new and old knowledge, which acts as a bridge between the phase of

creation and usage. Then, it is the phase of usage, where the new and existed knowledge is

put into practice to solve problems and to inspect their authenticities and validity. In the final

phase, knowledge is refined, integrated and evaluated for a new cycle. This macro process

of KM is depicted in the Figure 4.

Three knowledge assets from different sources are managed from the physical, human and

technological perspectives. Each perspective focuses on one aspect of the phenomenon of

KM. Although the macro process of KM is illustrated in sequence, the activities in each

phase are not necessarily carried out in sequence, and sometimes they can be performed

concurrently. For example, in order to create new knowledge, the old knowledge not only

needs to be acquired from the inside and outside of a company but also to be shared.

Figure 4 Macro process of KM for continuous innovation

Knowledge Lifecycle Phases

Creation Intermediate UsagePre-creation Post-usage

Physical View:Knowledge assets inartifacts

Knowledgeextraction

Knowledgeproduction

Knowledgestorage &transfer

Knowledgeutilization

Knowledgeevaluation

Human View:Knowledge assets inhuman beings

Knowledgeacquisition

Knowledgecreation

Knowledgepersonalization

& sharing

Knowledgeuse

Knowledgerefinement

Technological View:Knowledge assets intechnologies

Knowledgeidentification

Knowledgegeneration

Knowledgecodification &dissemination

Knowledgeapplication

Knowledgeintegration


Previously, many initiatives of KM focusing on information technology or on community of

practice have failed (Swan, 2007). This implies that it is not enough to stress one or two of the

three perspectives, but let them work together in a collaborative and complementary

manner. Different strategies for each perspective need to coordinate and collaborate with

each other in order to enable the effective and efficient management of organizational

knowledge.

The macro process of KM explains the management of organizational knowledge at a macro

level, while the meta-model describes the crucial knowledge activities for individual

knowledge at a micro level. The macro process emphasizes on the integrity and lifecycle of

KM in the total process of innovation, while the meta-model focuses on the human creativity

and heterogeneity of knowledge for new ideas in innovation. The individual knowledge

manipulated by the meta-model will transcend its own boundaries into the organizational

level in which the macro process dominated. So we conclude that KM in a company is a

dynamic process, and its operation often takes place in a concurrent and collaborative

manner. In order to operationalize the mechanisms of KM for continuous innovation, a human

centered view is adopted and a hierarchical model is built by integrating meta-model and

macro process of KM into continuous innovation from the three perspectives.

6.3 Human-centered perspective for KM activities in innovation

Innovation is about creativity and its realization. The rapid development of the information

and communication technologies (ICTs) and teambuilding technologies can facilitate the

innovation process so that better creativity and human collaboration can be achieved.

However, technologies can not substitute human creativity, due to the complexity and

uncertainty of innovation task. Therefore, we argue that amongst the three perspectives of

KM, the human perspective should be concentrated in innovation and be complemented by

two other physical and technological viewpoints. Thus the human centered viewpoint is

adopted for analyzing the nature of KM activities in innovation.

We recognize that human activities and information technologies have different effects on

KM activities in the knowledge lifecycle. In the phases of knowledge creation and usage,

human activities are of crucial importance, because the human creativity, the heterogeneity

and diversity of knowledge are keys to their success. In the other three phases such as the

pre-creation, intermediate and post-use, effects of human activities have been strongly

weakened by the extensive utilization of the ICTs and the wide building up of KM culture. So

we distinguish two categories of activities in the macro process according to different effects

of human activities in them.

The activities about knowledge creation and usage in the knowledge lifecycle are included

in the first category, which need high intensity of human interventions. The activities about

the pre-creation, intermediate and post-usage of knowledge lifecycle belong to the second

category, which are extensively supported by ICTs with low intensity of human interventions.

The activities in the two categories play different roles in the networking process of

innovation. The activities in the first one directly create values for continuous innovation.

While, those in the second one facilitate and support knowledge flows in innovation and

create a productive environment for it. Since knowledge creation and usage should be

supported by other KM activities, the comprehensive interrelationships are illustrated by a

hierarchical model for linking KM and innovation.

6.4 Hierarchical model of KM for continuous innovation

Owing to the different natures of KM activities in the two categories, knowledge flow has

different modes in them. Knowledge flow in the activities of the first category is slow and

laborious because of the high intensity of human interventions and the low efficiency of the

ICTs tools for them. Further more, human creativity, the diversity and heterogeneity of

knowledge increase the complexity of these activities. As to other activities in the second

category, due to the availableness and convenience of information technologies, knowledge

flow runs rapidly and easily. These knowledge flows among KM activities are illustrated in a

hierarchical model with four layers as shown in Figure 5.


The four layers in the hierarchical model are distinguished according to the sequence of

knowledge flow in the networking process of continuous innovation. Form bottom to up they

are the knowledge repository layer, the computer supported layer, the human centered layer

and the knowledge synthesis layer. The knowledge repository layer is aimed for the

organization of knowledge bases used in innovation. Different knowledge assets are placed

on this layer and managed by the knowledge base management system. Considering the

nature of knowledge flow in KM activities in pre-creation, intermediate and post-usage, they

are arranged on the computer supported layer. Organizational knowledge is retrieved from

the knowledge repository layer and processed on this computer supported layer for

knowledge creation and usage. Then the knowledge flows into the human centered layer, on

which activities about knowledge creation and knowledge usage are positioned. Herein,

their interactions of lead to emergence of new knowledge and the creative recombination of

existing knowledge for innovation through the phase of internalization. The process of

continuous innovation is organized on the knowledge synthesis layer, where new and

existing knowledge is implemented and the value of organizational knowledge is embodied

in the innovative products or services. Finally, the knowledge created and used in the

innovation process flows back to the knowledge repository layer for a new cycle of

innovation.

Taking account of uncertainty and complexity of innovation, any single individual or

enterprise can not have all the knowledge to create its innovation. Thus different people with

Figure 5 The human-centered hierarchical model for KM and innovation

Idea Generation

PrototypingManufacturing

Marketing Sales Diffusion

ResearchDevelopment

Internalization

Knowledge creation

Knowledge use

Human Perspective

Knowledge

acquisition

Knowledge refinement

Knowledge personal-

ization Knowledge sharing

Knowledge assetsin human beings

Knowledge utilization

Newknow-ledge

Newknow-ledge

Newknow-ledge

Knowledge assetsin artifacts

Knowledge production

Physical Perspective

Knowledge

extraction

Knowledge evaluation

Knowledge storage Knowledge

transfer

Knowledge assetsin technologies

Knowledge generation

Knowledge application

Technological

Perspective

Knowledge identifi-

cation

Knowledge integration

Knowledge codification

Knowledge dissem-

ination

External Environment

The human-centred

layer

The knowledgesynthesis

layer

The computer-supported

layer

The knowledgerepository

layer

Adva

nced

ICT

Tool

s fo

r Inn

ovat

ion

Func

tiona

l Tea

m in

Inno

vatio

n

Knowledge Base Management System


different knowledge and specialties are required in innovation and are formed into a

cross-functional team (Grant, 1996). We contend that the team consisting of diverse

stakeholders for innovation should be integrated into the hierarchical model as one pillar.

The cross-functional team not only provides a physical space to communicate and

collaborate, but also create a innovative climate and a culture to encourage the continuous

innovation.

As the availability of advanced information technologies has been instrumental in catalyzing

KM and innovation (Davenport and Prusak, 1998). Using advanced ICTs tools to aid KM and

innovation is another important issue. These tools provide great convenience for the

communication and collaboration between individuals and groups, and also ease the human

efforts in KM and innovation. So, they should be viewed as another pillar in the hierarchical

model of KM for innovation.

Innovation is an open activity in the dynamic and competitive environment. The exchange of

information of innovation process with the environment is necessary for its quick response to

the circumferential changes of customers’ needs and competitors’ reactions. The

exchanges can be improved with the support of cross-functional team and ICTs tools.

With the enhanced exchanges, an innovation can be more robust to the changes in

environment, and has more chances to be successful.

Through the four layers in the hierarchical model, the meta-model and macro process of KM

have been integrated into the networking process of innovation. The mechanisms of KM for

innovation are expounded in four levels from a human centered perspective. New and

existing knowledge can be leveraged into the process of innovation whenever they are

needed with the help of two pillars: cross-functional team and ICTs tools. The two pillars can

shorten the gaps between the phases of knowledge creation and usage, and then improve

the global efficiency and effectiveness of KM. It makes possible for the individuals to

enhance their creativity and to concentrate on the value creation for innovation. As a result,

the innovativeness of outcomes can be achieved and improved. The influences of ICT tools

on the different layers through different manners should be further analyzed. And the

working mechanisms of cross-function team for the immediate interactions and

collaborations among different layers also need to be further studied.

7. Conclusion

Although extensive research on KM and innovation has been conducted during the past

decades, there still exist difficulties in managing knowledge for innovation both theoretically

and in practice. In this study, based on the multidisciplinary literature on KM and innovation,

the physical, human and technological perspectives of KM are distinguished and compared

with the identification of two core KM activities for innovation: knowledge creation and

knowledge usage. By studying the existent process of innovation, an essential requirement

for continuous innovation is proposed. Through comparing the similarities among the

different generation of innovation model, knowledge creation and usage models, we further

argue that the diverse research on KM should be integrated for leveraging KM activities into

innovation.

In this study, a networking process of continuous innovation is proposed consisting of four

common phases in traditional paradigms and an additional phase of internalization. This

additional phase provides a suitable platform for integrating KM process into innovation and

its specific functions are explained. Then from the physical, human and technological

perspectives, three distinct sources of organizational knowledge in innovation are identified

and their relationships are discussed. By focusing on knowledge creation and usage, a

meta-model of KM and a macro process are built up to manage the multifaceted

organizational knowledge. They can comprehensively interpret the mechanisms of KM for

the value creation especially for innovation at the macro and micro levels. From the

human-centered perspective, a hierarchical model is set up for integrating KM with the

process of continuous innovation. Through this model, knowledge flows between KM


activities and innovation are visualized in four layers. And more work about the effects of

cross-functional team and ICTs tools in innovation are suggested.

In future step, based on our theoretical propositions, several investigations in some

companies will be carried out for identifying the detailed specifications for KM and

innovation. And a prototype of knowledge management system for promoting innovation is

being constructed based on our propositions. Certain existing information technologies will

be integrated into the system to facilitate the creation and usage of knowledge. An

application is previewed at one of our partner companies who is interested in the

capitalization of professional knowledge for innovation. The application will inspect our

theoretical propositions, test their validity, and then provide feedbacks to improve the

prototype system. The impact of the internalization phase in innovation and the return of

invest for this phase should be subject of further research.

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About the authors

Jing Xu is a PhD candidate in the Genie de la Conception (LGeCo) Laboratory at the InstitutNational des Sciences Appliquees at Strasbourg in France. He is also a lecturer onmechanical engineering and vehicle engineering in Yangzhou University in China and aResearch Fellow in the Engineering Centre of Digital Design and Manufacturing ofAutomotive Parts and Components in Yangzhoy City. He received his Master’s degree inmechanical engineering from Yangzhou University in 2003. He has published severalarticles in several core journals in China. His research focuses on knowledge management,product innovation, and knowledge-based design. Jing Xu is the corresponding author andcan be contacted at: [email protected]

Remy Houssin is an Associate Professor at Universite de Strasbourg and at Graduate Schoolof Science and Technology of Strasbourg (INSA) in the Engineering Design Laboratory. He isa Mechanical Engineer from the mechanical and electrical Faculty of the University ofTechreen (Syrie) and Doctor in Industrial Engineering from Henri Poincare University ofNancy (France). He works on performance evaluation from the design stage of productmodeling. He works on two axes: first, on behavioral design approach and focused onavailability-based safety assessment; and second, on knowledge management forinnovation.

Emmanuel Caillaud received his Engineering degree from Ecole Nationale d’Ingenieurs deTarbes, France, in 1990 and his PhD from the University of Bordeaux, France, in 1995. Hehas been Professor in Mechanical and Industrial Engineering at University of Strasbourgsince 2002. He was involved in several European and French projects on engineeringdesign and he is a reviewer for international journals and expert in international researchfunds. He has published in, among others, International Journal of Production Research,Concurrent Engineering: Research and Applications, International Journal of AdvancedManufacturing Technology, Computers and Industrial Engineering. His research focuses onknowledge and performance in engineering design, concurrent engineering andcollaborative design.

Mickael Gardoni is a French Professor in Industrial Engineering at the Institut National desSciences Appliquees de Strasbourg (INSA), France and works at Ecole de TechnologieSuperieure, a leading Engineering School for Industry at Montreal (Quebec – Canada). Heholds a mechanical engineering certificate from the Ecole Nationale d’Ingenieurs de Metz(ENIM). He conducts his research at the Genie de la Conception (LGeCo) laboratory at INSAde Strasbourg in partnership with the Products, Processes, and Systems EngineeringLaboratory (P2SEL) at ETS. His research interests include CSCW, Information Management,Knowledge Management, data exchange, concurrent engineering and more recently theManagement of Design, Research and Development and Research activities.


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Macro process of knowledge management for continuous innovation

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