Value chain

A Knowledge Value Chain for Knowledge Management Jean-Louis Ermine

Journal of Knowledge & Communication Management, Volume 3 Number 2 pp. 85-101, October 2013

A Knowledge Value Chain for Knowledge Management , Jean-Louis Ermine Telecom Business School, Department of Social Science, 9 Rue Charles Fourier, 91011 Evry, France ABSTRACT : The usual value chain is a powerful tool to identify strategic actions to ensure competitive advantages of the firms. A value chain is a chain of production activities in a firm, starting from the inputs up to the final customer delivery. Products or services pass through all activities of the chain in order and, at each activity, the product or service gains some value. In a knowledge-based economy, the main strategic resource of a company is its knowledge capital. Knowledge management (KM) is then critical to bring competitive advantages. There is a strong need to define what is the value chain for corporate knowledge, in order to manage the knowledge transformation process in the organisation, up to the best knowledge performance. This article proposes a knowledge value chain (KVC), based on the famous but fuzzy DIKW hierarchy (Data, Information, Knowledge and Wisdom), as a chain of fundamental intellectual tasks (cognitive activities). The added value of each task is explained and discussed. It defines the different concepts, based on the current literature, with some definitions adapted for the KM point of view. Then, the KVC is integrated in a management perspective. Finally, the KVC is interpreted as a continuum of knowledge processes adding new value at each step. Keywords: Knowledge Management; Performance management; Value creation; Knowledge assets;

Where is the life we have lost in living?

Information is not knowledge,

Where is the wisdom we have lost in knowledge?

Knowledge is not wisdom,

Where is the knowledge we have lost in

Wisdom is not truth,

information?

Truth is not beauty,

T.S. Eliot, ‘Choruses from the Rock’

Beauty is not love, Love is not music, And music is the best. Frank Zappa, ‘Packard Goose’

INTRODUCTION

The value chain is a concept from business management that was coined and popularised by Porter (1985). A value chain is a chain of production activities in a firm, starting from the inputs up to the final customer delivery. Products or services pass through all activities of the chain in order and, at each activity; the product or service gains some value. A value chain is a decomposition of the activity of the firm into value-processing activities. These processing components and activities are the building blocks by which a corporation creates a product or provides service valuable to its customers. The chain of activities gives the products or services more added value than the sum of added values of all activities. Capturing the value generated along the chain is now the approach taken by top management to ensure competitiveness. Differences among competitor value chains are a key source of competitive advantage. In competitive terms, value is the amount customers are willing to pay for what a firm provides them. Value is measured by total revenue, a reflection of the price a firm’s product commands and the units it can sell. A firm is profitable if the value it commands exceeds the costs involved in creating the product. Creating value for customers that exceeds the cost of doing so is the goal of any competitive strategy. Value, instead of cost, must be used in analysing competitive position. The value chain categorises the generic value-adding activities of a firm: the ‘primary activities’ include inbound logistics, operations (production), outbound logistics, marketing and sales, and services, the ‘support activities’ include administrative infrastructure management, human resource management, R&D and procurement. The costs and value drivers are identified for each value activity. 2

It is now recognised that we have entered a ‘Knowledge-Based Economy” (Foray, 2004), where knowledge is seen as the main key factor of business success and as the foundation of competitive advantage. Knowledge is seen as the most important strategic resource (Davenport and Prusak, 1998; Drucker, 1993; Hall, 1993; Stalk et al. 1992). The value incorporated in the products and services is mainly due to the development of organisational knowledge resources (Quinn, 1992). In fact, the capability of a firm to produce outputs can be considered as integration and application of the specialised knowledge held by individuals in the organisation (Grant, 1991). Knowledge management (KM) is the set of strategies, methods and tools to manage the intangible knowledge asset of a firm, in order to improve its global performance. To support KM success, there is a need to analyse the chain of knowledge creation in the firm, in order to assess the added value that leads to performance. If there exists a good knowledge creation process in the organisation without linking this process to upper capability, it may be inefficacy. The process must be seen as overall capability. Knowledge performance can be measured in two categories. One is financial performance. However, despite the wide acknowledgements of knowledge as a strategic resource, it is still not well understood how KM impacts business performance, and firms are unable to evaluate the return on investment in knowledge, though it is a mean of resource optimisation, which impacts on costs (Chong et al., 2000). The other way of measurement can be based on the competence-based view theory of the firm. This theory considers the firm as a portfolio of competencies and its competitiveness is based on the creation and development of competencies and on the realisation of a strategy able to create a link between aims, resources and competencies (Prahalad and Hamel, 1990). Those competencies have a cognitive nature, and it allows the identification of processes to manage capabilities. Knowledge creation or organisational learning is the main processes for the development of competencies (Leonard-Barton, 1995; Nelson, 1991; Prahalad and Hamel, 1990). Carlucci et al. (2004) state ‘the cognitive competence perspective can be summarised in the interpretation, which defines a company’s competence as a combination of all ‘knowledge assets’ and ‘knowledge processes’ that allow an organisation to carry out its business processes’. Thus, there are two ways of considering a knowledge value chain (KVC), the first one is a chain of knowledge activities acting on the knowledge assets of the firm, and the other one is a chain of cognitive activities acting on the knowledge processes in the firm. Following the tremendous development of KM during those last years, the concept of KVC appeared and has been discussed recently (Carlucci et al., 2004; Lee and Yang, 2000; Wang and Ahmed, 2005; Eustace, 2003; Powell, 2001; Holsapple and Singh, 2001). In most of the cases, the proposed KVC is a set of KM processes. KVC is then a KM framework organising fundamental KM activities as the ‘Knowledge Process Wheel’ given in Carlucci et al. (2004). The main KM activities included in the different KVCs are mainly: Knowledge Creation. It is of course the most important, as it accumulates the knowledge capital, which is the ‘raison de vivre’ of any knowledge-based organisation. Knowledge Codification. It is about capturing tacit knowledge, which is a very complex problem, because such knowledge lays in the brain of the knowledge holders without their conscious awareness about it! Knowledge Sharing. Once a knowledge corpus is identified and a knowledge repository is built, sharing that knowledge within a community is not really a standard task. It requires a lot of efforts from building the community to implement access infrastructures.

3

Knowledge Dissemination. Access to knowledge for majority of people (at least the concerned ones: the right information to the right people) has been coined as ‘the last mile’ problem, which implies Information Technology IT infrastructures and design processes. Knowledge Identification (analysis and structuring). It is a of course a basic question: what is the relevant knowledge to create a knowledge firm? The question of what are the knowledge needs is quite often raised. Knowledge Evaluation. In order to perform good knowledge processes, it is now necessary to have different grids of evaluation. The KVC gives a KM framework to analyse the added value brought by each KM process. Figure 1 shows an example of KVC (Wang and Ahmed, 2005), as a Porter-like model.

Figure 1: An example of knowledge value chain based on knowledge management (KM) processes.

This kind of KVC is then a chain of knowledge activities acting on the knowledge assets of the firm. Concerning the second kind of KVC, a chain of cognitive activities acting on the knowledge processes in the firm, Wikipedia, the free encyclopaedia, defines a KVC as ‘a sequence of intellectual tasks by which knowledge workers build their employer’s unique competitive advantage and/or social and environmental benefit’. This definition can be illustrated by the KVC proposed by Powell (2001). There are very few references for those kinds of KVC; doubtless, there is a difficulty to chain intellectual tasks in Porter-like model. In this paper, we propose a KVC that chains fundamental intellectual tasks (cognitive activities) for knowledge workers, and we justify the value chain by the added value of each activity, in an overall knowledge creation chain. This work is inspired by the work published by the Moradi (2009) Moradi et al. (2008), and Brunel et al (2010). The KVC is built on the DIKW (Data, Information, Knowledge and Wisdom) model, and interprets that model as a succession of cognitive activities to transform data up to the most adding value for the firm, which are strategic capabilities. Those transformation activities are described precisely. THE DIKW MODEL

The DIKW model is one of the most famous and ‘taken-for-granted’ models in the information and knowledge literatures. It is widely used in information and knowledge management (KM), 4

but this model still remains rather ‘loose’, and has not been deeply discussed or validated. For the history of this model, and a critical study on it, we refer to the Wikipedia, the free encyclopaedia entry and to Rowley (2007). The most popular graphical representation for DIKW is a pyramid, as in the celebrated Maslow’s pyramid, with data at its base and wisdom at its apex. This representation implicitly explains that the higher elements in the pyramid need the lower elements to be defined, and that they can be reached after a transformation process of these lower elements. The DIKW is then a chain where information is the result of processing data, knowledge is the result of processing information and wisdom is the result of processing knowledge. The elements of the pyramid can be seen as having increasing values corresponding to their level. It may then appear as a value chain (Figure 2), according to Chaffey and Wood (2005) quoted in Rowley (2007). High

High

Wisdom

Knowledge Meaning

Value

Information

Data Low

Low

Figure 2: The DIKW pyramid.

Another graphical representation of the DIKW model is a flow diagram where the relationships between the components are less hierarchical, with feedback loops and control relationships. We will use that kind of graphical representation, to visualise the value chain (Figure 3).

Data

Information

Knowledge

Wisdom

Increasing Added Value

Figure 3: The DIKW value chain.

There seems to be only a loose consensus in the abundant literature on the DIKW model for the definition of the different levels, as well as for the transformation processes. We will give our own definitions of the different levels in order to give a refutable framework for DIKW, and study the different possible transformations. Data Data are defined as raw facts, and learning about data as the process of accumulating facts (Bierly et al., 2000). Data are raw materials that were accumulated by person- or machinebased observation. According to Rowley (2007), some authors (Jashapara, 2005; Choo, 2006) introduce a new element in the DIKW chain, which is ‘signal’, that represents the 5

reality perceived, selected and process through our senses to get data. In fact, in the theory of semiotics (Eco, 1976), founded by Peirce (1934), it is assumed that reality is always perceived as a ‘system of signs. We define data as the perception of reality by senses (that can be extended by machine-based observation). Data are then the result of a communication process, through sign systems. Information The only unambiguous definition of information is a mathematical definition given by Shannon and Weaver (1949). This information theory is a probabilistic point of view on information produced by a system. During the communication process, the receptor is waiting for a certain message. Let’s take the case of a traffic light. When a person looks at this light (the signs system observed), he already has an idea of the set of messages transmitted by this light. A priori, he is unaware of what message is precisely going to be transmitted. However, thanks to his experience, he expects to receive some messages with different probabilities. The information received through a set of messages (the signs system observed) is calculated as a mean of occurrence probabilities of that set of messages, called entropy. In information theory, the introduction of the entropy function was a considerable innovation that was incredibly fruitful, even as a metaphorical tool to understand what information is. When information is considered as a concept, this information theory is not often invoked. According to Nonaka (1994), information can be viewed from two perspectives: syntactic (or volume of) and semantic (or meaning of) information. The syntactic perspective is ruled by the Shannon’s theory, but the semantic aspect of information is more important for knowledge creation, as it focuses on conveying meaning. In the analysis of Floridi (2010), over the past decades, a standard general definition of information (GDI) as emerged in terms of data+meaning. A straightforward way of formulating GDI is as a tripartite definition: information is made of data, the data are well formed (remember that ‘information’ comes from Latin ‘in-formare’, i.e., ‘put in form’), the well-formed data are meaningful (the data must comply with the meanings -semantics- of the chosen system, code or language in question). Knowledge The most common definition for knowledge is a justified true belief (Chisholm, 1982): ‘I know something, if I believe it, if I have evidence that it is true, and if it is true’. But in the KM perspective, definitions of knowledge are much more diverse and complex than those for data or information. Summarising all the definitions in the DIKW literature, Rowley (2007) states that knowledge might be viewed as a mix of information, understanding, capability, experience, skills and values. In Ermine and Leblanc (2007), an attempt is made to have a formal theory of knowledge that is an extension of Shannon’s theory of information. In that theory, knowledge has three tangled components: information, meaning and context. The information is rules by the Shannon’s theory, meaning is ruled by semiotics theory, and context by connected graphs theory. It is possible to define formal entropy that represents knowledge, based on these three components. Meaning is strongly depending on context that can be social, professional or operational. Hence, we will define knowledge as information (a set of messages produced by a system), which have a specific meaning in a specific context. This theory has been fully developed in Ermine (2000). In the scope of KM, there is an important distinction between explicit and tacit knowledge. In general, tacit knowledge is defined as embedded in the individual and explicit knowledge as residing in documents, databases and other recorded formats. Knowledge is a resource for an entity’s capacity for effective action, for instance Spender (1996) considers knowledge as data, meaning and practice. Wisdom If the definition of knowledge is complex and not really consensual, the definition of wisdom is nearly inexistent, and there are very limited discussions of that concept in the DIKW literature (Rowley, 2007). Wisdom is usually defined as in Wikipedia: ‘a deep understanding and realising of people, things, events or situations, resulting in the ability to choose or act to 6

consistently produce the optimum results with a minimum of time and energy’. Therefore, we define wisdom as the ability to best use of knowledge for establishing and achieving desired goals and learning about wisdom as the process of discerning judgments and action based on knowledge. Considering that we develop our work as a practical and to a certain extent in the organisational context, for it to be practical, we must to avoid the terminology that is indistinguishable and ambiguous. We believe that in the engineering and modelling context the notion of wisdom is not very clear and so it is necessary to clarify that to be understandable and usable. For this reason, we divided this indistinct and abstract concept as individual wisdom, which is competence and expertise, and collective wisdom as capability.  Individual Wisdom (Competence/Expertise) Competence is a standardised requirement for an individual to properly perform a specific job. It encompasses a combination of knowledge, skills and behaviour utilised to improve performance. More generally, competence is the state or quality of being adequately or well qualified, having the ability to perform a specific role. For instance, management competency includes the traits of systems thinking and emotional intelligence, and skills in influence and negotiation. A person possesses a competence as long as the skills, abilities and knowledge, which constitute that competence, are a part of him, enabling the person to perform effective action within a certain workplace environment. Therefore, one might not lose knowledge, a skill or an ability, but still lose a competence if what is needed to do a job well changes. Expertise is a characteristic of individuals and is a consequence of the human capacity for extensive adaptation to physical and social environments. Prahalad and Hamel (1990) in their seminal work defined competence as the roots of competitiveness. Then, competence can be defined as individual mobility, integration and transfer of knowledge and capacity in order to obtain the results.  Organisational Wisdom (Capability) Capability is the ability to perform actions. In human terms, capability is the sum of expertise and capacity. We consider capability as high level of competence in organisation level. Grant (1996) views organisational capability as the outcome of knowledge integration, complex, team-based productive activities and dependent upon firm’s ability to harness and integrate the knowledge of many individual specialists. So make practicable and usability of competence in organisational wide range will generate some core competency and dynamic capability for organisation. In this context, organisation wide wisdom is a specific capability for that organisation. Capability does not represent a single resource in the concert of other resources such as financial assets, technology or manpower but rather a distinctive and superior way of allocating resource, complex process as product development, customer relationship and supply chain management. Furthermore, organisational capability could be defined as: absorptive capacity (Cohen and Levinthal, 1990) (organisational ability to assimilate new exterior knowledge,), combinative capability (Kogut and Zander, 1992; organisational ability to aggregate actual internal knowledge), dynamic capability (Teece et al., 1997), core competency (Prahalad and Hamel, 1990), organisational learning (Huber, 1991) and agility (Roth, 1996). KVC AND MANAGEMENT

In terms of management activity, data management has for role to control, protect, deliver and enhance the value of data assets. It ensures the continued existence and the quality of the organisational memory. In ‘cognitive’ terms, data management ensures the memorisation function of the organisation. Usually, information management includes the same types of functionalities of organisation of and control over the structure, processing and delivery of information. Considering the definition we have given of information (data+meaning), information management has for role to give sense to data, to help workers and managers to take decision in their tasks at various levels (operational, tactical and strategic). Information processing is crucial 7

for decision making, as it is well known for a long time (Simon and March, 1958). Information management allows conceptualisation and brings understanding as added value to the organisation. In Averson (2010), KM is suggested as a strategic management activity in the learning and growth perspective, in the framework of the Intellectual Capital given by the Balanced Scorecard (Kaplan and Norton, 1996): ‘A learning and growing organisation is one in which KM activities are deployed and expanding in order to leverage the creativity of all the people in the organisation’. The KM needs for competencies development is stressed by many authors. An internal learning process is necessary for development and maintenance of competencies (Nelson, 1991; Prahalad and Hamel, 1990). One of the conclusions in the survey by Carlucci et al. (2004) is that KM sustains the dynamics of organisational learning and improvements of performances in organisational processes and then enables an organisation to grow and develop organisational competencies. KM supports different learning capacities: synthesising different types of information acquiring new knowledge, behaviours and skills. In an organisation, KM facilitates the learning of its members who are continually learning to learn together and then provide a continuous transformation of the organisation itself. This is what is called a ‘learning organisation’ (Pedler et al., 1997; Argyris, 1999). In the KVC, the added value brought by KM is learning. Competence is knowledge in action. In the DIKW chain, Rowley (2007) quotes different definition of ‘wisdom’ that may fit to the concept of competence as efficient knowledge in action: accumulation of knowledge, which allows you to understand how to apply concepts from one domain to new situations or problems; ability to act critically or practically in any given situation; use of knowledge and information; and ‘right judgement’; manner in which knowledge is held, and how that knowledge is put to use; capacity to put into action the most appropriate behaviour, taking into account what is known (knowledge) and what does the most good Competence reflects a broad and deep ability for comprehending the environment and adapting to it by taking proper decisions and actions. It is the appropriate use of knowledge to improve performance (usually, it is considered essentially a personal issue but can also have some collective issue aspects). That ability is generally called ‘intelligence’. In that sense, within the KVC, the added value brought by competence management is intelligence. The difference between implementation in enterprise competence and capability management is in the collective, overall and organisational wide nature of capability. Finally, capability management leads to better success for organisation and so obtain the upper performance and overall wealth. The competence-based view, and the knowledge-based view theories (Grant, 1991; Sveiby, 2001), consider knowledge as drivers for the formulation and development of strategy. Knowledge capacity is then fully integrated in the company’s objectives. The benefit for the company is an overall capacity of innovation, as a global change (incremental or radical) in thinking, products, processes or organisations. Aligning the strategy with the competence portfolio leads the company to global wisdom, although this concept is not yet defined in the literature. If the individual wisdom is a superior cognitive process involving knowledge, judgement and awareness, leading to an appropriate behaviour (Rowley, 2007), then we can say that the capability management corresponds to a high level maturity of the organisation, which acts properly, with respect to its commitments and its environment. We can summarise the KVC and its management in (Figure 4). 8

Maturity

Capability Management

Intelligence

Understanding Memorisation Tacit

Competence

Knowledge

ma nc e

Explicit

Pe rf or

Data Management Information

ge ed

Learning

Information Management

Data

wl

Knowledge Management

o Kn

Competence Management

Cognitive Value Chain

Capability

Wisdom

Knowledge Value Chain

Figure 4: Knowledge value chain and management. TRANSFORMATION PROCESSES IN KVC

According Rowley (2007), if it is difficult to find some consensus on the different definitions of the concepts in the DIKW chain, there is less agreement as to the nature of the processes that convert one concept into another concept. The discussion about wisdom is one more time inexistent. If the DIKW chain is seen as a value chain, in the context of KM in a firm, then we can be more explicit and clear. According Moradi (2009, Chap. 4, p. 10), transformation process as supportive activities in KVC may be divided into two main categories: the first category is more tangible and objective and could be done by human being and machine-based reasoning. This category concerns adding value from reality to explicit knowledge. For that category, the role of information technology as processor is mainly accepted. The second processing category is going from information and explicit knowledge up to capability. In this category, human being is a key point and it is intangible, subjective, about beliefs and commitments and action. In this category, the role of information technology is enabler and not the main element. To describe the transformation processes, in an effective and understandable way, we will decompose them in three points of view, related to the definition of knowledge given in the description of KVC: - Syntactic point of view, which gives the final forms of the outcomes of the transformation process. This is the visible part of the results in the process. - Semantic point of view, which gives the enablers to build sense in the process. These enablers are cognitive filtres allowing interpretation activities in the process to deliver the outcomes. - Context point of view, which gives the (cognitive) situation where the process takes place. Learning

Experience

Semantics (Interpretation)

Perceptive filters

Conceptual filters

Theories

Action

Syntax (Form)

Signs

Codes

Models

Practices

Data

Information

Vision Strategic filters

e

g ed

Structuring

wl

Observation

o Kn

Context (Situation)

Capability

an ce

Knowledge

Competence

Pe rf or m

Explicit / Tacit

Knowledge Strategy

Wisdom

Figure 5: Transformation processes in knowledge value chain

This decomposition is in the style of the one called « triple instrumentation », also used to describe the KVC in Brunel (2008) and Moradi (2009). We will not discuss deeply the different 9

concepts, but give some standards definitions, that are shared by common sense, essentially extracted from dictionaries. Hence, the framework for transformation processes in the KVC we propose is just a starting point for future research, and is oriented towards efficient application (Figure 5). The starting point of the transformation chain is reality, as a set of things possessing actuality, existence or essence, which exists independent of human awareness. 1) Transforming reality into data is acquiring signs (signals) through perceptive filtres via observation A sign is something that suggests the presence or existence of a fact, condition or quality. More precisely, a signal is an indicator that serves as a means of communication. This is the ‘semiotic hypothesis’ that assumes that reality is communicated to us as a ‘sign system’. The transformation process is a perception process that is an organisation (in a sign system) of an unprocessed result of the stimulation of sensory receptors (it may be artificial sensors or sensory receptors like the eyes, the ears…). Observation is a detailed examination of phenomena prior to analysis, diagnosis or interpretation. It implies usually the act of recording something, eventually with instruments. 2) Transforming data into information is coding data trough conceptual filtres via a structuring activity A code is a system of symbols, given certain arbitrary meanings, used for transmitting messages. The transformation process consists in building concepts that are something formed in the mind; a thought or notion that corresponds to some class of entities and that consists of the characteristic or essential features of the class. Building concepts need a structuring posture, which is a frame of mind favourable to make interrelation or arrangement of parts in a complex entity. 3) Transforming information into knowledge is building models through theories via learning A model is a schematic description of a system, theory or phenomenon that accounts for its known or inferred properties and may be used for further study or action. A model is supported by a theory, which is, in the common sense usage, a well-substantiated explanation of some aspect of the natural world; an organised system of accepted knowledge that applies in a variety of circumstances to explain a specific set of phenomena. It is a conceptual model (explanation) of how the world works. Using models and theories in KM can be made in a context of learning that is by definition, the cognitive process of acquiring knowledge (and more generally skills). 4) Transforming knowledge into competences is implementing a set of practices through action via experience Practice is a repeated performance of an activity in order to learn or perfect a skill, a habitual or customary action or act (often used in the plural). Economists speak about routines (Nelson and Winter, 1982; Lazaric, 2000) as collective competences in the form of prescribed, detailed course of action to be followed regularly, although they are essentially personal and tacit. They have a global formulation for achieving a collective task, but they are only collective in their result. This codified knowledge needs individual experience for appropriation and use by the actors. These practices are built step by step through action, which denotes as usual an organised activity to accomplish an objective. Action is seen like a cognitive filtre, enabling the pertinence of the learned practices. The adequate posture is then experience : experience is the situation by which a person acquires knowledge about the world, as contrasted with reason. Experience is an active participation in events or activities, leading to the accumulation of knowledge or skill. 10

5) Transforming competences into capabilities is building a strategy (knowledge strategy) through strategic filtres (alignment) via a vision A strategy is a particular long-term plan for success. Alignment, which is a proper or desirable coordination or relation of components, is the adequate tool as integration or harmonisation of aims, practices, etc. within an organisation. The capacity to build a strategy involving the corporate knowledge, aligned with the corporate strategy requires a vision, as an unusual competence in discernment or perception; an intelligent foresight. That term of vision, especially for future developments, has a religious connation, but here stops the KM context! CONCLUSION

The deliberate and consciously management of KVC will lead to multiple benefits for organisation. It will result on rapid and more innovation, effectiveness, efficiency and performance of firms. By improving learning and agility of organisation, it will produce competitive advantage, expanded economic intelligence and so generating wealth for organisational stakeholders. We have studied a KVC that takes in account the nature of individual and organisational knowledge in a firm. It is a continuous transformation chain from reality perception through data, to organisational wisdom reflecting the maturity of the firm. If, in that chain, data, information and, in some aspects, knowledge are concepts that begin to make consensual sense, wisdom, despite its position at the ultimate end of the chain, is very fuzzy, especially in an organisational context. In the KM context, it may be interpreted as individual wisdom (competence) and organisational wisdom (capability). Management of KVC brings gradually the organisation to superior cognitive capacities, from memorisation, understanding, learning and intelligence up to maturity. Processing KVC requires the transformation of data into codified information, knowledge models, portfolio of practices up to a knowledge strategy. It requires also different postures from observation, structuring, learning, experience and vision. This article gives a first simplified framework and seeks to provoke debate about the basic concepts of information management and KM. It is a first brick to help knowledge strategists in organisations, and to understand where is the added value in the knowledge capital, that new wealth of organisations. REFERENCES Argyris, C. (1999). On Organizational Learning. 2nd Edn. Blackwell Publishing: Oxford. Averson, P. (2010). The Balanced Scorecard and Knowledge Management. Balance Scorecard Institute (http://balancedscorecard.org/) Bierly, P.E., Kessler, E. H. and Christensen, E. W. (2000). Organizational Learning, Knowledge and Wisdom. Journal of Organizational Change Management 13(6): 595-518. Brunel, S. (2008). Ingenition. , Edition Universitaires Européennes (Sarrebrück, Germany), Isbn: 978613-1-50850-9 (In French). Brunel, S., Zolghadri, M. and Moradi, M. (2010). Global approach for Knowledge management in design. In GECSO 2010, 3th Conference Gestion des Connaissances, Société et Organisations, Strasbourg, France, 27-28 mai 2010, to appear in « Information Sciences for Decision Making » Carlucci, D., Marr, B. and Schiuma, G. (2004). The knowledge value chain: how intellectual capital impacts on business performance. Int. J. Technology Management 27 (6/7): 575–590. Chaffey, D. and Wood, S. (2005). Business Information Management: Improving Performance Using Information Systems. FT Prentice Hall: Harlow. Chisholm, R. (1982). Knowledge as Justified True Belief. The Foundations of Knowing. University of Minnesota Press: Minneapolis. 11

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Powell, T. (2001). The Knowledge Value Chain (KVC): how to fix it when it breaks. In KnowledgeNets 2001, New York City, May 2001. Published in M.E. Williams (ed), 22nd National Online Meeting. Information Today, Inc: Medford, NJ. Prahalad, C.K. and Hamel, G. (1990). The core competence of the corporation. Harvard Business Review 68 (3): 79–91. Quinn, J.B. (1992). Intelligent Enterprise: A Knowledge and Service Based Paradigm for Industry. Free Press: New York. Roth, A.V. (1996). Achieving Strategic Agility through Economies of Knowledge. Strategy and Leadership 24: 30-37. Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science 33 (2): p.163-180 (http://jis.sagepub.com/cgi/content/abstract/33/2/163) Shannon, C.E., & Weaver, W. (1949). The Mathematical Theory of Information. University of Illinois Press: Urbana, USA. Simon H. and March J.G. (1958). Organizations. Wiley: New York. Spender, J.C. (1996). Making Knowledge the Basis of a Dynamic Theory of the Firm. Strategic Management Journal 17(Winter Special Issue): 45-62. Stalk, G., Evans, P. and Shulman, L.E. (1992). Competing on capabilities: the new rule of corporate strategy, Harvard Business Review, March–April: 57–69. Sveiby, K.E. (2001). ‘A knowledge based theory of the firm to guide strategy formulation’. Journal of Intellectual Capital 2(4): 344–358. Teece, D.J., Pisano, G. and Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal 18(7): 509-533. Wang, C. L. and Ahmed, P. K. (2005). The knowledge value chain: a pragmatic knowledge implementation network. Handbook of Business Strategy 6 (1): 321-326 About The author : Jean-Louis Ermine has a PhD in fundamental mathematics at the University Denis Diderot of Paris, and a National Research Director Title in computer science at the University of Bordeaux. He worked in the French Atomic Energy Commission (CEA) as a KM expert for more than 10 years. Since 2003, he is professor at the national public organisation “Institut TELECOM” where he is now Director for Innovation at TELECOM Business School. He has written 4 books and more than 100 articles in peer-reviewed journals and conferences. He is president of the French Knowledge Management Club since 1999, an association rallying a lot of French speaking companies. He is Expert for UN (UIT, AIEA) since 2003. He is also the chairman of the French Speaking Congress in Knowledge Management (GeCSO) since 2008, and the president of the French speaking Knowledge Management Society. He has been project leader or advisor in numerous research or industrial KM projects in public or private companies in France (Industry, Energy, Transport, Defence, Bank, SMEs …) and abroad (Sonatrach (Algeria), Hydro-Québec, Public Administration (Canada), IPEN (Brazil), National Nuclear Safety Authorities (Asia)…)

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