A Hierarchical Modelling Approach to Intellectual Capital Development Eckhard Ammann Reutlingen University, Germany [email protected] Abstract: An approach for intellectual capital development in an organisation is given. It is based on a new conception of knowledge and knowledge dynamics and raises the notion of knowledge conversions to the level of intellectual capital domains. Intellectual capital development can be modelled with this approach by means of general transformations between domains and between appropriate parts of these domains, which themselves are refined and modelled with general knowledge conversions. To attain this approach, a new conception of knowledge and knowledge dynamics is introduced. The knowledge conception is represented by a knowledge cube, a three-dimensional model of knowledge with types, kinds and qualities. The type dimension addresses the internal-external aspect of knowledge, seen from the perspective of the human being. The kind dimension distinguishes various knowledge kinds like propositional or procedural knowledge. Finally, in the quality dimension, several quality measures of knowledge are given. Built on this conception, knowledge dynamics is modelled with the help of general knowledge conversions between knowledge assets. A set of basic knowledge conversions is given in a way, such that more complex general conversions may be easily gained by building on this set. Through this conception, we gain a sound basis for knowledge management and development in an enterprise. Raising this knowledge development approach to the more strategic and resource-oriented intellectual capital level in an organisation, general transformations between the three main intellectual capital domains (individual competence, internal and external structure) and between parts of them can be described. With their help a model for intellectual capital development is gained: In a top-down approach, general transformations of intellectual capital are broken down to the notion of general knowledge conversions. This leads to development of the intellectual capital, i.e. to value creation in a company. To indicate the applicability of our approach, an example for the development of customer relations capital is given. Keywords: intellectual capital development, transformations of intellectual capital, intangible resources, value creation, conception of knowledge, knowledge conversions

1. Introduction The intellectual capital of a company is defined as all non-monetary and non-physical resources that are fully or partly controlled by the organisation and that contribute to the value creation of the organisation (Roos 2005). Three domains of intellectual capital can be distinguished: External structure is a family of intangible relationships with customers and suppliers, which partly may be converted into legal properties such as trademarks and brand names. The internal structure includes patents, concepts, models, IT systems and processes, which are created by employees and owned by the company. The third domain is the individual competence of the employees (see Sveiby 2001). This concept of intellectual capital helps to let intangible resources of a company be measured, communicated and interpreted. See (Andriessen 2004, Sveiby 2001 and Roos 2005) for more detail. Note, that also 5-domain models of intellectual capital exist. In these models the internal structure domain is renamed to structural capital and divided into organizational and technological capital. External structure is called relational capital and divided into business and social capital (Intellectus Model, IADE-CIC 2003 and Bueno 2006). As further and central domain, organizational culture is added in the proposed model in (Sánchez-Canizares et al. 2007). Economic value creation of a company is based on intangible resources to a high and increasing degree. Development of intellectual capital of a company therefore is a key activity for value creation. Especially small and medium sized enterprises (SME) rely on intellectual capital commitments in clusters and networks for sustainable competitiveness (Mertins et al. 2010). Assessing and making transparent of intellectual capital is fostered by the German ―Wissensbilanz‖ (Alwert et al. 2008) and the intellectual capital statement supported by the European commission (European Commission 2008). In generalizing the transformation approach between intellectual capital domains given by Sveiby (Sveiby 2001), we introduce general transformations between whole domains and between intangible resources, which make up the intellectual capital domains. These transformations are drivers for intellectual capital development in a company on a strategic level. Using general n-to-m transformations instead of simple 1-to-1 transformations also recognizes the frontiers of linearity of the ISSN 1479-4411 181 ©Academic Conferences Ltd Reference this paper as Ammann, E. ―A Hierarchical Modelling Approach to Intellectual Capital Development‖ Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp181 - 191), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (181 - 192)

intellectual capital metaphor, where pieces do not necessarily sum up when brought together in a transformation (Bratianu 2009). In a top-down approach, they can be refined to be modelled as general knowledge conversions. We introduce these general knowledge conversions based on a new conception of knowledge and knowledge dynamics. In a way our approach augments and complements the complex system approach to intellectual capital development, where in a hierarchical structure intellectual capital components, elements and variables together with their interconnections are identified. See (Bueno 2006) for an introduction into this kind of approach. It represents a static system view with only 1-to-1 interconnections, while our approach targets on dynamic n-to-m transformations. It is important to note however, and this a common core between the approaches, that our general transformations are taking course along interconnections, which can be identified with the complex system approach. Our new conception of knowledge and knowledge dynamics establishes a sound basis for knowledge management in a company. A number of knowledge management approaches exists, including the classic asset-oriented, the process-oriented approach, the knowledge-intensive process-oriented and the community-oriented approach, see (Ammann 2008, Gronau/Fröming 2006, and Lehner 2008). While the management aspect of knowledge management seems to be understood to some extent, there is no common concept and understanding of knowledge and of knowledge development as basis for it. Existing approaches include the knowledge development model by Nonaka and Takeuchi (Nonaka/Takeuchi 1995), which is built on the distinction between tacit and explicit knowledge and on four fundamental knowledge conversions between those knowledge types (SECI-model), and the introduction of the type/quality dimensions of knowledge in (De Jong/Fergusson-Hessler 1996). Important distinctions of implicit knowledge (namely conscious, latent and tacit knowledge) are given in (Hasler Rumois 2007). Finally, Gorman describes types of explicit and tacit knowledge and their roles in technology transfer (Gorman 2002). In this paper, we introduce a new conception of knowledge, which combines and resembles parts of existing approaches and extends them substantially. It is represented by a knowledge cube, a threedimensional model of knowledge with types, kinds and qualities. The type dimension addresses the internal-external aspect of knowledge, seen from the perspective of the human being. Here explicit knowledge is a kind of interface between those two types, which drives human interaction and knowledge externalisation. This type dimension is crucial for knowledge management, because knowledge conversions in the explicit direction make the knowledge of employees more available. As second knowledge dimension, the kind dimension distinguishes various knowledge kinds, namely propositional, procedural and strategic knowledge, and familiarity. Finally, in the quality dimension, several quality measures of knowledge are given. Using this conception we introduce general knowledge conversions between the various knowledge (and information) assets. First a basic set of such conversions is defined, which extends the set of the four conversions of the SECI-model. Building on this set, general knowledge conversions can be defined, which reflect knowledge transfers and development more realistically and do not suffer from the restrictions of the SECI-model. These conversions are the building blocks to model knowledge dynamics, i.e. all of acquisition, conversion, transfer, development and usage of knowledge. General transformations of intellectual capital can be refined to general knowledge conversions. Following this path, we end up in an approach to model those general transformations on the level of knowledge dynamics, with a model at hand which has been introduced in this paper. In total, we present an approach for intellectual capital development based on refinement to the deeper level of knowledge conversions. These general knowledge conversions have been built up in a bottom-up approach, based on a new conception of knowledge and on basic knowledge conversions. As an indication of the applicability of this approach, an example of the development of intellectual capital in the external structure domain in a company is given. This example aims at the development of the customer relations capital, more specifically at the introduction of an inquiry contact scheme for customers, developed under involvement of individual and organisational resources.

2. Intellectual capital domains and their transformations Intellectual capital of a company is defined as all non-monetary and non-physical resources that are fully or partly controlled by the organisation and that contribute to the value creation of the

www.ejkm.com

182

©Academic Conferences Ltd

Eckhard Ammann

organisation (Roos 2005). Intangible assets, which are main contributors to intellectual capital, include legal assets like trade secrets, copyrights, patents and goodwill as well as competitive assets like knowledge, collaboration, leverage and structural activities. Legal intangible assets partly generate legal property rights, which are defensible in court of law, while competitive assets do not. The concept of intellectual capital helps to let intangible resources of a company be measured, communicated and interpreted. See (Andriessen 2004, Sveiby 2001 and (Roos 2005) for introductions into the discipline. Three domains of intellectual capital can be distinguished: External structure is a family of intangible relationships with customers, suppliers and other external stakeholders, which partly may be converted into legal properties such as trademarks and brand names. The internal structure includes patents, concepts, models, IT systems and processes, which are created by employees and owned by the company. The third domain is the individual competence of the employees (see Sveiby 2001). As already explained in the introduction, five domains of intellectual capital may be distinquished instead of only three. Our approach to intellectual capital development as described in this paper can be analogously applied to these 5-domains models. Transformations between intellectual capital domains and between their parts are the means of intellectual capital development. The following two subsections introduce the concepts of domain transformations and of general intellectual capital transformations, respectively. As an ongoing example the development of an inquiry contact scheme for customers as part of the external structure of a company is presented. While in this section this development undertaking is modelled on the intellectual capital layer (see Figures 2 and 3), it will then be refined to the layer of general knowledge conversion in section 3.

2.1 Transformations between domains Intellectual capital development succeeds by means of the deployment and management of intellectual capital resources and their transformations (into other intellectual capital resources or into traditional economic resources) to enable and foster value creation in the organisation as seen by its stakeholders (changed from Roos 2005). Here transformation is understood as both conversion and transfer as will be clear in the following. The development of intellectual capital in a company is therefore modelled on an overall level by transformations between the three intellectual capital domains: external structure, internal structure and individual competence. Basic intellectual capital domain transformations are 1-to-1 transformations between the three domains. There exist nine basic domain transformation, which are depicted in Figure 1. For example, the third transformation from external structure to individual competence applies to learning effects of employees of a company from customer, supplier and community feedback such as new ideas, experiences or new technology (see Sveiby 2001). In reality intellectual capital development often succeeds not in this 1-to-1 manner, but relates to several domains on both source and destination sides of transformations. Therefore we generalise this notion of basic domain transformations to general intellectual capital domain transformations, which are n-to-m transformation between the three domains. Figure 2 gives an example of a general 2-to-1 domain transformation. This is the high-level representation for our ongoing example; it models the development of the external structure under involvement of the individual competence and the internal structure domains. Here resources from the individual competence and internal structure domains are utilized to enhance the external structure. In total there exist 43 general intellectual capital domain transformations, among the 9 basic transformations described before. From an overall perspective, the notion of intellectual capital domains and of general domain transformations between them constitutes a meta-model for intellectual capital development. In the next sub-section, we instantiate the entities and relations of this meta-model and gain the notion of general transformations between the parts, i.e. the single resources, of the three domains.

www.ejkm.com

183

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (181 - 192)

Figure 1: Basic domain transformations (reworked after (Sveiby 2001))

Figure 2: Example of a general domain transformation

2.2 General intellectual capital transformations Going down one layer of abstraction we now treat single intellectual capital resources instead of the whole domains. In analogy to the domain transformations in the previous sub-section, we are able to introduce general intellectual capital transformations, which are n-to-m transformations between single resources. Figure 3 gives an example, where in a general intellectual capital transformation the

www.ejkm.com

184

©Academic Conferences Ltd

Eckhard Ammann

individual competences of several employees and contents of the internal structure are utilized in order to further develop the external structure of the company. Here the single resources are symbolically named together with the domain they are belonging to. An inquiry contact scheme and an information system for customers are to be developed. As a method to identify the single arrow connections between source and destination assets in a general intellectual capital transformation of a company, the complex system approach (e.g. see Bueno 2006) can be used. Our approach augments and complements this approach to intellectual capital development, where in a hierarchical structure intellectual capital components, elements and variables together with their interconnections are identified in a more or less static way. Our dynamic n-to-m transformations would therefore utilize a set of possible interconnections identified with the complex system approach.

Figure 3: General intellectual capital transformation In the next section, a new conception of knowledge and knowledge dynamics is introduced. The general knowledge conversions, which constitute knowledge dynamics, will be the means for refinement of general intellectual capital transformations. That means, that a general intellectual capital transformation can be broken down and modelled as a group of interrelated general knowledge conversions.

3. A Conception of knowledge and knowledge dynamics In this section, a new conception of knowledge and knowledge dynamics in a company is described. More details of this conception are given in (Ammann 2009c).

3.1 Knowledge conception We provide a conception of knowledge, and of knowledge types, kinds and qualities. As our base notion knowledge is understood as justified true belief (at least in the propositional kind), which is (normally) bound to the human being, with a dimension of purpose and intent, identifying patterns in its validity scope, brought to bear in action and with a generative capability of new information, see (Hasler Rumois 2007, Lehner 2008). It is a perspective of ―knowledge-in-use‖ (De Jong/FergussonHessler 1996) because of the importance for its utilisation in companies and for knowledge management. In contrast, information is understood as data in relation with a semantic dimension, but without the pragmatic and pattern-oriented dimension, which characterises knowledge.

www.ejkm.com

185

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (181 - 192)

We distinguish three main dimensions of knowledge, namely types, kinds and qualities, and describe those in the following three sub-sections. The whole picture leads to the three-dimensional knowledge cube, which is introduced at the end of this section. 3.1.1 The type dimension of knowledge The type dimension is the most important for knowledge management in a company. It categorizes knowledge according to its presence and availability. Is it only available for the owning human being, or can it be communicated, applied or transferred to the outside, or is it externally available in the company’s organisational memory, detached from the individual human being? It is crucial for the purposes of the company, and hence a main goal of knowledge management activities, to make as much as possible knowledge available, i.e. let it be converted from internal to more external types. Our conception for the type dimension of knowledge follows a distinction between the internal and external knowledge types, seen from the perspective of the human being. As third and intermediary type, explicit knowledge is seen as an interface for human interaction and for the purpose of knowledge externalisation, the latter one ending up in external knowledge. Internal (or implicit) knowledge is bound to the human being. It is all that, what a person has ―in its brain‖ due to experience, history, activities and learning. Explicit knowledge is ―made explicit‖ to the outside world e.g. through spoken language, but is still bound to the human being. External knowledge finally is detached from the human being and may be kept in appropriate storage media as part of the organisational memory. Figure 4 depicts the different knowledge types.

Figure 4: Conception of knowledge types Internal knowledge can be further divided into tacit, latent and conscious knowledge, where those subtypes do partly overlap with each other, see (Hasler Rumois 2007). Conscious knowledge is conscious and intentional, is cognitively available and may be made explicit easily. Latent knowledge has been typically learning as a by-product and is not available consciously. It may be made explicit, for example in situations, which are similar to the original learning situation, however. Tacit knowledge is built up through experiences and (cultural) socialisation situations, is specific in its context and based on intuition and perception. Statements like ―I don’t know, that I know it‖ and ―I know more, than I am able to tell‖ (adapted from Polanyi 1966) characterise it.

www.ejkm.com

186

©Academic Conferences Ltd

Eckhard Ammann

3.1.2 Kind dimension of knowledge In the second dimension of knowledge, four kinds of knowledge are distinguished: propositional, procedural and strategic knowledge, and familiarity. It resembles to a certain degree the type dimension as described in (De Jong/Fergusson-Hessler 1996). Propositional knowledge is knowledge about content, facts in a domain, semantic interrelationship and theories. Experience, practical knowledge and the knowledge on ―how-to-do‖ constitutes procedural knowledge. Strategic knowledge is meta-cognitive knowledge on optimal strategies for structuring a problem-solving approach. Finally, familiarity is acquaintance with certain situations and environments, it also resembles aspects of situational knowledge, i.e. knowledge about situations, which typically appear in particular domains. 3.1.3 Quality dimension of knowledge The quality dimension introduces five characteristics of knowledge with an appropriate qualifying and is independent of the kind dimension, see (De Jong/Fergusson-Hessler 1996). The level characteristics aims at overview vs. deep knowledge, structure distinguishes isolated from structured knowledge. The automation characteristic of knowledge can be step-by-step-doing by a beginner in a domain of work or automated fast acting by an expert. All these qualities of knowledge measure along an axis and can be subject to knowledge conversions, see section 3. Modality as the fourth quality of knowledge asks for the representation of it, be it words versus pictures in situational knowledge kinds, or propositions versus pictures in procedural knowledge kinds. Finally, generality differentiates general versus domain-specific knowledge. Knowledge qualities apply to each knowledge asset. 3.1.4 The knowledge cube Bringing all three dimension of knowledge together, we gain an overall picture of our knowledge conception. It can be represented by the knowledge cube, as shown in Figure 5.

Figure 5: The knowledge cube Note, that the dimensions in the knowledge cube behave different. In the type and kind dimensions, the categories are mostly distinctive (with the mentioned exception in the sub-types), while in the quality dimension each of the given five characteristics are always present for each knowledge asset.

www.ejkm.com

187

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (181 - 192)

3.2 Knowledge dynamics In this section we give a conception of knowledge conversions. The transitions between the different knowledge types, kind and qualities are responsible to a high degree for knowledge development in an organisation. These general knowledge conversions are the building blocks to model knowledge dynamics, i.e. all of acquisition, conversion, transfer, development and usage of knowledge, in an enterprise. Most important for knowledge management purposes are conversions between the knowledge types and they will be the focus in the following. Among those especially those conversions, making individual and internal knowledge of employees usable for a company, are crucial for knowledge management. The explicitation and externalisation conversion described in this section achieve this. Implicitly, socialisations between tacit knowledge of different people also may contribute to this goal. 3.2.1 Basic knowledge conversions Five basic knowledge conversions in the type dimension are distinguished here: Socialisation, explicitation, externalisation, internalisation and combination. Basic conversion means, that exactly one source knowledge asset is converted into exactly one destination knowledge asset. Furthermore exactly one knowledge dimension is changed, i.e. the type dimension in this case. More complex conversions may be easily gained by building on this set as described later in the sub-section 3.2.2. They will consist of n-to-m-conversions and include information assets in addition. Socialisation converts tacit knowledge of a person into tacit knowledge of another person. For example, this succeeds by exchange of experience or in a learning-by-doing situation. Explicitation is the internal process of a person, to make internal knowledge of the latent or conscious type explicit, e.g. by articulation and formulation (in the conscious knowledge type case) or by using metaphors, analogies and models (in the latent type case). Externalisation is a conversion from explicit knowledge to external knowledge or information and leads to detached knowledge as seen from the perspective of the human being, which can be kept in organisational memory systems. Internalisation converts either external or explicit knowledge into internal knowledge of the conscious or latent types. It leads to an integration of experiences and competences in your own mental model. Finally, combination combines existing explicit or external knowledge in new forms. These five basic knowledge conversions are shown in Figure 6.

Figure 6: Basic knowledge conversions in the type dimension The Nonaka/Takeuchi-model (Nonaka/Takeuchi 1995) uses four basic knowledge conversions in the sense defined above and interacts in a spiral of knowledge creation, which becomes larger in scale as

www.ejkm.com

188

©Academic Conferences Ltd

Eckhard Ammann

it moves up the ontological dimension from the individual to groups and the whole organisation. This limiting linearity of its knowledge development spiral concept and the restriction to basic conversions in their approach have been criticised, besides the discussions on the meaning of explicit knowledge. Basic knowledge conversions in the kind dimension of knowledge are very seldom, normally the kind dimension of knowledge remains unchanged in a knowledge conversion changing the type dimension. Those in the quality dimension are mostly knowledge developments aiming at quality improvement and will not change the type and kind dimensions of the involved knowledge assets. In three out of the five quality measures, basic conversions can be identified, which are working gradually. Those are, firstly, a deepening conversion, which converts overview knowledge into a deeper form of this knowledge. Secondly, a structuring conversion leading to a change in the singularversus-structure scale of the structure measure. And finally, conscious and step-by-step-applicable knowledge may convert into automated knowledge in an automation conversion, which describes a process from beginner to expert in a certain domain. The remaining two quality measures of knowledge, namely modality and generality, do not lend themselves to knowledge conversions. They just describe unchangeable knowledge qualities. 3.2.2 General knowledge conversions Our conception allows the generalisation of the basic five knowledge conversions described above. General knowledge conversions are modelled converting several source assets (possibly of different types, kinds and quality) to several destination assets (also possibly different in their knowledge dimensions). In addition, information assets are considered as possible contributing or generated parts of general knowledge conversions. Note, that a general knowledge conversion may change any knowledge dimension of the involved knowledge assets. For example, in a supervised learning-by-doing situation seen as a complex knowledge conversion, a new employee may extend his tacit and conscious knowledge by working on and extending an external knowledge asset in a general conversion, using and being assisted by the tacit and conscious knowledge of an experienced colleague. A piece of relevant information on the topic may also be available on the source side of the conversion. See Figure 7 for a visual representation of this general knowledge conversion in the BPMN-KEC2 notation for knowledge-intensive business processes (Ammann 2009a). Here the grey-shading of knowledge objects is decreasing as the corresponding knowledge asset is becoming more externalized. The additional tagging of knowledge assets with their specific knowledge type and their knowledge kind has been omitted in Figure 7 for the sake of clarity.

Figure 7: Supervised learning-by doing situation

www.ejkm.com

189

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (181 - 192)

4. Implementation of intellectual capital transformations Section 2 described the path down from the three intellectual capital domains, to basic and general domain transformations and finally to general transformations between parts of the three domains. Section 3 laid the foundation in form of a knowledge conception and of basic and general knowledge conversions. Implementation of intellectual capital development activities now succeeds by sticking the two parts together. The important thing to this end is the refinement of general intellectual capital transformations as composition of general knowledge conversions. This means to refine a more strategic view of intangible resources and their transformations to a more operative view of knowledge and knowledge conversions. Important to note is, that human-to-human interactions, which are a substantial part in intellectual capital development activities, can also be modelled with the help of our knowledge dynamics approach (see Ammann 2009b). From an overall perspective, the top-down approach from intellectual capital domains and their transformations meets the bottom-up approach from knowledge and knowledge conversions, see Figure 8. Hence our approach to intellectual capital development can be seen as appropriate combination of the two single approaches.

Figure 8: Layers of modelling In the ongoing example, the development of a inquiry contact scheme for customers is described, which would allow customers to selectively contact appropriate employees of the company and/or use a customer information system. To this end Figure 2 in section 2 gave the general 2-to-1 domain

www.ejkm.com

190

©Academic Conferences Ltd

Eckhard Ammann

transformation, involving individual competence and internal structure domains on the source side and external structure on the destination side. Figure 3 broke this down into a general (n+1)-to-2 intellectual capital transformation, having several employees with their individual competences and organisational regulations on the source side and two external structure resources (customer information system, customer inquiry scheme) on the destination side. Here we concentrate on the inquiry scheme introduction and restrict n to 4. Figure 9 shows a sequence of general knowledge conversions, which together refine the general intellectual capital transformation (at least the first part of this activity until a documented proposal of a customer inquiry scheme). Two sales persons, a sales manger and a developer bring in their competences, a security person seen as part of the internal structure guarantees the compliance of the inquiry scheme to security regulations in the organisation. Note, that in Figure 9 the knowledge objects are tagged. The internal knowledge objects of the sales person 1 and the developer are of subtype conscious as defined in section 3. The three explicit and external knowledge objects all have the propositional knowledge kind.

Figure 9: Propose customer inquiry scheme (refinement of Figure 3)

5. Summary and conclusion An approach for intellectual capital development has been given, which is based on general transformations between whole domains and between parts of the three intellectual capital domains. These transformations can be refined to general knowledge conversions. In order to attain these knowledge conversions, a new conception of knowledge has been described. Built on it, knowledge dynamics in a company can be described with the help of general knowledge conversions. In the end, a comprehensive and overall approach for intellectual capital development has been gained. From the strategic domain level it refines overall development undertakings to more operative knowledge conversions, while from the knowledge management perspective it builds up from a new knowledge conception. Unlike existing approaches, it not only identifies and describes one-to-one interrelationships between intellectual capital domains and between parts of them, but general manyto-many transformations, which are further refined to the knowledge development level. To indicate the applicability of this approach, an example of the development of intellectual capital in the external structure domain in a company has been given. Specifically the given example targets at the development of the customer relations capital of a company by introducing an inquiry scheme for the company’s customers.

References Alwert, K., Bornemann, M., Will, M. (2008) Wissensbilanz – Made in Germany. Leitfaden 2.0 zur Erstellung einer Wissensbilanz, Guideline Published by the Federal Ministry for Economics and Technology, Berlin.

www.ejkm.com

191

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (181 - 192) Ammann, E. (2008) ―A Meta-Model for Knowledge Management‖, in: Proc. of the 5th International Conference on Intellectual Capital and Knowledge Management (ICICKM), New York, USA, pp. 37-44. Ammann, E. (2009a) ―BPMN–KEC2 – An Extension of BPMN for Knowledge-Related Business Process Modeling‖, Internal Scientific Report, Reutlingen University, Germany. Ammann, E. (2009b) ―Modeling of Knowledge-Intensive Business Processes with Human Interactions‖, in: Proc. th of the 4 Int. Conf. on Internet, Web Applications and Services, Venice, Italy, pp. 608-613. Ammann, E. (2009c) ―The Knowledge Cube and Knowledge Conversions‖, in: Proceedings of the World Congress of Engineering, International Conference on Data Mining and Knowledge Engineering (ICDMKE), London, UK, pp. 319-324. Andriessen, D. (2004) Making Sense of Intellectual Capital, Elsevier. Bratianu, C. (2009) ―The Frontier of Linearity in the Intellectal Capital Metaphor‖, Electronic Journal of Knowledge Management, Vol.7, Issue 4, pp. 415-424. Bueno, E., Salmador, M., Rodriguez, O., De Castro, G.M. (2006) ―Internal Logic of Intellectual Capital: A Biological Approach‖, Journal of Intellectual Capital, Vol.7, No.3, pp. 394-405. De Jong, T., Fergusson-Hessler, M.G.M. (1996) ―Types and Qualities of Knowledge‖, Educational Psychologist, 31(2), pp. 105-113. European Commission (2008) InCaS – Intellectual Capital Statement – Made in Europe, European ICS Guideline, [online] www.incas-europe.org. Gorman, M.E. (2002) „Types of Knowledge and Their Roles in Technology Transfer―, Journal of Technology Transfer, Vol.7, pp. 219-231. Gronau, N.,Fröming, J. (2006) ―KMDL® - Eine semiformale Beschreibungssprache zur Modellierung von Wissenskonversionen― (in German), Wirtschaftsinformatik, Vol. 48, No. 5, pp. 349-360. Hasler Rumois, U. (2007) Studienbuch Wissensmanagement (in German), UTB orell fuessli, Zürich. IADE-CIC (2003) ―Model for the measurement and management of intellectual capital: Intellectus Model‖, Documentos Intellectus 5, Universidad Autónoma de Madrid, Madrid. nd Lehner, F. (2008) Wissensmanagement (in German), 2 ed., Hanser, München. Mertins, K., Will, M., Meyer, C. (2010) ―Analysing and Enhancing IC in Business Networks: Results From a nd Recent Study‖, Proceedings of the 2 European Conference on Intellectual Capital (ECIC 2010), Lisbon Portugal, pp. 450-456. Nonaka, I., Takeuchi, H. (1995) The Knowledge-Creating Company – How Japanese Companies Foster Creativity and Innovation for Competitive Advantage , Oxford University Press, London. Polanyi, M. (1966) The Tacit Dimension, Routledge and Keegan, London. Roos, G., Pike, St., Fernström, L. (2005) Managing Intellectual Capital in Practice, Elsevier. Sánchez-Canizares, S.M., Ayuso Munoz, M.Á., López-Guzmán, T. (2007) ―Organizational culture and intellectual capital: a new model‖, Journal of Intellectual Capital, Vol.8, No.3, pp. 409-430. Sveiby, K.-E. (2001) ―A Knowledge-Based Theory of the Firm to guide Strategy Formulation‖, Journal of Intellectual Capital, Vol.2, No.4, pp. 344-358.

www.ejkm.com

192

©Academic Conferences Ltd

A Critical Analysis of Nonaka’s Model of Knowledge Dynamics Constantin Bratianu Academy of Economic Studies, Bucharest, Romania [email protected] Abstract: The purpose of this paper is to present a critical analysis of the well known knowledge dynamics model elaborated by Ikujiro Nonaka and his co-workers. The essence of this model consists of three layers of the knowledge-creation process: (a) the process of knowledge creation through socialization-externalizationcombination-internalization (SECI), the knowledge conversion process between tacit and explicit knowledge, (b) Ba the platform for knowledge creation, (c) knowledge assets. The success and popularity of this model created premises and temptations for using it beyond the conceptual limits initially defined, generating this way a superficial interpretation of the complex organizational knowledge dynamics. Our critical analysis aims at the investigation of the operational power of Nonakas model of knowledge dynamics within the framework of organizational knowledge. In the same time, we would like to apply the entropy law to SECI model and to see how the conversion processes conceived by Nonaka satisfy this law. Actually, although Nonaka considers socialization, externalization, combination and internalization as being conversion processes, only externalization and internalization are truly conversions. They consist in transforming tacit knowledge into explicit knowledge, and explicit knowledge into tacit knowledge, respectively. Socialization and combination are only processes of knowledge transfer, i.e. tacit knowledge to tacit knowledge, and explicit knowledge to explicit knowledge. Also, the evolving spiral is possible with inputs from the Ba platforms for knowledge creation and not with knowledge generation from within. The same evolving spiral of knowledge creation passes sequentially through individual processes and organizational processes in a deterministic way, although knowledge dynamics is not a physical process based on deterministic laws. Keywords: explicit knowledge, knowledge conversion, knowledge creation, knowledge dynamics, tacit knowledge

1. Introduction Ikujiro Nonaka and his co-workers created a consistent body of theory concerning knowledge creation in organizations based on four main ideas: a) knowledge creation at individual level is a direct result of the continuous dialogue between tacit and explicit knowledge; b) there are four basic knowledge conversion processes: socialization, externalization, combination and internalization; c) knowledge creation at the organizational level is based on these four conversion processes and a spiral driving force; d) there is a shared space Ba for knowledge creation (Nonaka, 1991, 1994; Nonaka et al., 1994; Nonaka & Takeuchi, 1995; Nonaka & Konno, 1998; Nonaka, Toyoma & Byosiere, 2001; Nonaka & Toyoma, 2007). The novelty of these ideas, and the correlation between them and Japanese companies success on the global market made of Nonaka one of the most prominent thinkers in knowledge management, and his model of knowledge creation became a new paradigm for organizational knowledge dynamics. Although we are going for simplicity of expression to refer to the Nonaka‟s model of organizational knowledge dynamics we recognize implicitly all the other contributions coming from his co-workers, in different stages of model development. Powerful concepts and paradigms have been always extended beyond their initial semantic boundaries until new ideas will integrated them into a new knowledge creating paradigm. Although such a new comprehensive paradigm has not been yet conceived, there are some new contributions showing the limits of the Nonaka‟s model, and there are some new ideas trying to build up a new perspective on knowledge creation and organizational knowledge dynamics (Agourram, 2009; Bereiter, 2002; Bratianu, 2008, 2009; Bratianu & Andriessen, 2008; Gourlay, 2006; Harsh, 2009; Hill, 2008; Styhre, 2006). The purpose of this paper is to critically analyze the conceptual and operational limits of the Nonaka‟s model of organizational knowledge dynamics, and to show the new perspective of this complex process. The next section of this paper will present briefly the fundamental elements of the Nonaka‟s model, and then we shall show its limitations and possible new directions of development.

2. The Nonaka’s model of knowledge dynamics In one of his seminal papers on the dynamic theory of organizational knowledge creation, Nonaka showed that previously the theory of organization has been dominated for a long time by the paradigm that conceptualizes a generic organization as a system designed for information processing and problem solving. Centrally to this paradigm is the efficiency of information processing in a static and deterministic environment. However, in his view “Any organization that deals with a changing ISSN 1479-4411 193 ©Academic Conferences Ltd Reference this paper as Bratianu, C. “A Critical Analysis of Nonaka‟s Model of Knowledge Dynamics” Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp193 --200), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (193 - 200)

environment ought not only to process information efficiently, but also create information and knowledge” (Nonaka, 1994, p.14). Thinking of the Japanese companies interested in innovation, he considers that a paradigm based solely on information processing is not able to explain the innovation phenomenon. Having this shortcoming in mind he develops a new perspective based on a two-phase knowledge field, four basic conversion processes of knowledge, and a spiral driving force. Nonaka defines knowledge as being “justified true belief”, and consider knowledge as “a dynamic human process of justifying personal beliefs as part of an aspiration for the truth” (Nonaka, 1994, p.15). Thus, knowledge becomes a relative concept as personal belief, a view which limits very much its status of objectivity and its role in science. Based on the seminal work of Polanyi (1983), Nonaka considers knowledge composed of tacit knowledge and explicit knowledge. In his view, “Tacit knowledge is highly personal and hard to formalize, making it difficult to communicate or to share with others. Subjective insights, intuitions, and hunches fall into this category of knowledge. Furthermore, tacit knowledge is deeply rooted in an individual‟s action and experience, as well as in the ideals, values, or emotions he or she embraces” (Nonaka & Takeuchi, 1995, p.8). Tacit knowledge contains two types of ingredients. One type refers to the skills and fingertips experience in mastering a certain domain of practical activity. The other one refers to the mental models, beliefs and perceptions so ingrained that we take them for granted. This second dimension is cognitive in its nature and generates our image of surrounding reality. The most important characteristic of the tacit knowledge is that it is hard to articulate it in words and communicate it using language. It is there in our brain and body but we do not know how to explain it. In a very suggestive expression Polanyi (1983, p.4) underlined this aspect: “I shall reconsider human knowledge by starting from the fact that we can know more than we can tell”. In contrast to this tacit knowledge which is very subjective and hard to express in words and numbers, explicit knowledge represents the rational part of our knowledge which can be express and explained easily in words and numbers. It can be communicated to other individuals and it can be processed. One of the most important ideas about these two forms of knowledge comes from their dynamics, as explained by Nonaka and Takeuchi (1995, p.9):”For tacit knowledge to be communicated and shared within the organization, it has to be converted into words or numbers that anyone can understand. It is precisely during this time this conversion takes place – from tacit to explicit, and, as we shall see, back again into tacit – that organizational knowledge is created”. Nonaka considers that developing and valuing explicit knowledge is characteristic mainly for the Western culture, while developing and using successfully tacit knowledge is a characteristic of the Eastern culture (Nonaka, 1994; Nonaka & Takeuchi, 1995). This kind of arguments may be found as well in the works of Andriessen (2006, 2008), Andriessen and Boom (2007). Nonaka (1994) considers two dimensions for knowledge creation: epistemological dimension and ontological dimension. The first dimension is related to the conversion of knowledge from tacit level to explicit level, and from explicit level to the tacit level. The second dimension is related to the conversion of knowledge from individuals to groups and further to organization. Combining these two motions Nonaka gets a spiral model for knowledge creation and processing. Also, he makes a fundamental assumption which is the core of the SECI model: ”The assumption that knowledge is created through conversion between tacit and explicit knowledge allows us to postulate four different „modes‟ of knowledge conversion: (1) from tacit knowledge to tacit knowledge, (2) from explicit knowledge to explicit knowledge, (3) from tacit knowledge to explicit knowledge, and (4) from explicit knowledge to tacit knowledge” (Nonaka, 1994, p.19). The first process, of creating tacit knowledge through shared experience has been called socialization. Tacit knowledge is hard to formalize and to express using language. It is context related. It is the way apprentices learn their craft through observation and imitation from their masters. The second process is a result of social interaction through language. This process of creating explicit knowledge from explicit knowledge has been called combination. The third and forth processes are different from the previous ones since they involve both types of knowledge. These transformation processes are based on idea that tacit and explicit knowledge are two complementary forms of knowledge in a continuous interaction. The third process of transforming tacit knowledge into explicit knowledge has been called externalization. The success of this process depends on sequential use of metaphors, analogies and models (Nonaka, Toyama & Byosiere, 2001). The fourth process is dealing with transformation of explicit knowledge into tacit knowledge, and it has been called internalization. This is a process of embodying explicit knowledge as tacit knowledge. It is closely to learning by doing. The first three processes are related in Nonaka‟s view to organizational learning, while the last one is related to individual learning. Based on these above ideas, Nonaka concludes that organizations create knowledge continuously by restructuring the existing knowledge basis through the synergy of the four fundamental processes of www.ejkm.com

194

©Academic Conferences Ltd

Constantin Bratianu knowledge transformation: “That is to say, knowledge creation centers on the building of both tacit and explicit knowledge and, more importantly, on the interchange between these two aspects of knowledge through internalization and externalization” (Nonaka, 1994, p. 20). The foundation of these four basic processes is Ba, a rather fuzzy concept proposed by the Japanese philosopher Kitaro Nishida, and further developed by Shimizu. Ba is defined “as a context in which knowledge is shared, created, and utilized, in recognition of the fact that knowledge needs a context in order to exist” (Nonaka, Toyama & Byosiere, 2001, p.499). This context can be tangible, intangible or any combination of tangible and intangible elements. In this perspective, the concept of knowledge is strongly related to a given material and cultural context, beyond the fact that it is has been considered a personal belief. Knowledge belonging to given person may be shared, recreated or amplified when that person is an active actor in Ba. To make things even more confused, Nonaka, Toyama and Byosiere (2001, p.499) consider that “Ba as an interaction means that Ba itself is knowledge rather than a physical space containing knowledge or individuals who have knowledge”.

3. Functionality of the Nonaka’s model and its limits The main assumptions of this model constitute in the same time the degree of freedom and the limits of its functionality. One such assumption is the relative consistency of knowledge as a justified true belief. That means that knowledge creation can be described with respect to a given cultural framework, which is at a microscale the cultural horizon of individual, and at macroscale the cultural horizon of a country. The Nonaka‟s model of knowledge dynamics in organizations can be very well understood and used in the context of Japanese culture, but it is unlikely to produce successful results in other cultures. The basic cornerstone is the concept of Ba which hardly can be understood in a culture where the Cartesian dualism produced such a gap between rational and non-rational worlds. Also, this concept is related to the Japanese specific interpretation of no-thing-ness: “Nothing-ness is not to be understood as a „thing‟ because it then would be based on a conception of something, which would be no-thing… If you understand what exists then you can understand that which does not exists. This means that although it is impossible to know that which does not exists, it is possible to know that if “anything is anything, then everything is everything‟… The spirit of no-thingness means that there is no such thing as relying upon anything at all outside of your individual mind” (Kaufman, 1994, pp.104-105). Postulating the four basic processes of knowledge dynamics, i.e. socialization, externalization, combination and internalization, and integrating them into a pattern of knowledge conversion Nonaka is blurring the lines between individuals and groups. Knowledge conversion from tacit to explicit and from explicit to tacit, according to the epistemological dimension (Nonaka, 1994; Nonaka & Takeuchi, 1995), is clearly a process developed at the individual level. There is no meaning for such a process to be developed between the tacit knowledge of a given person and the explicit knowledge of another person. However, the knowledge conversion from tacit to tacit, and from explicit to explicit develops between different individuals. If the whole spiral of knowledge creation would be considered for only two individuals, at the limit, it could be understood. But, if we would consider a group of people, it is hardly difficult to explain and demonstrate how the knowledge conversion works because of the sequential interplay between strictly individual processes and group processes. As a metaphor, the spiral of knowledge creation (Nonaka &Takeuchi, 1995, Fig.3-3) is an excellent solution. However, for any attempt of practical analysis and evaluation this spiral knowledge creation represents an almost impossible task. Although Nonaka and his co-workers consider all four basic processes to be designed for knowledge conversion, actually only two of them satisfy the condition of transforming one form of knowledge into another form of knowledge. They are: externalization and internalization. Externalization means to get some explicit knowledge out of the own experience, in a form that can be transferred through the process of combination. Internalization is the reverse process by which some valuable knowledge got through combination can be stored in a specific way as experience, and used accordingly in the decision making. However, there is a difference between the capacity of a given individual to perform externalization and internalization, and his or her motivation. Also, it is important to note the fact that these two processes are not done in an automatic way, but with some cognitive efforts. Socialization and combination are processes designed for exchange of knowledge from one person to another, and not for knowledge transformation. Thus, Nonaka‟s model is not actually a cycle of knowledge conversion processes, as claimed by authors. The epistemological dimension of the Nonaka‟s model is based on transforming tacit knowledge into explicit knowledge and vice versa. However, these transformations raise some questions concerning www.ejkm.com

195

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (193 - 200)

knowledge dimensions. Explicit knowledge has only one dimension, which the extensive dimension. Knowledge obtained, for instance, in mathematics like 2+2=4 cannot have intensity. It has only the extensive dimension, which is a quantitative one. However, the tacit knowledge contains emotions. Any emotion is characterized by extensive and intensive dimensions. The level of intensity is similar to temperature in characterizing the heat. Thus, an emotion may have a higher temperature than another emotion for the same person, or an emotion may have a higher temperature than the same emotion generated in a another person. Now, the question is: how can we consider transforming emotions as tacit knowledge (i.e. knowledge with two dimensions) into explicit knowledge (i.e. knowledge with only one dimension). The spiral of organizational knowledge creation considered with respect to the ontological dimension (Nonaka & Takeuchi, 1995, Fig. 3-5) originates in the middle management and evolves upward and downward. This might be the specific of Japanese management, but it is hardly efficiently in the Western management, where the decision making process is always a top-down process. The Nonaka‟s model for organizational dynamics is based on creation and flow of knowledge. The analogy is made with the flow of water, but we know from fluid dynamics that any flow is generated by a pressure difference. Looking into this knowledge dynamics model we see no such thing as a pressure field and no pressure difference able to generate the flow of knowledge. Once again, the metaphor is beautiful but the practical application is rather difficult. Socialization is the first knowledge transfer process considered by Nonaka, which reflects the tacit knowledge-tacit knowledge exchange. It is the process of bringing together tacit knowledge through shared experiences. However, since tacit knowledge is context-specific, it is important to note that people can share same experience through joint activities. However, tacit knowledge transfer meets several individual and organizational barriers, among them stickiness being the most important (Szulanski, 1996, 2000; Szulanski and Jensen, 2004). According to Szulanski, the notion of internal stickiness connotes the difficulty of transferring knowledge within the organization. Actually, von Hippel (1994) coined the expression “sticky information” to describe information that is difficult to transfer. “Contrary to the conventional wisdom that places primary blame on motivational factors, the major barriers to internal knowledge transfer are shown to be knowledge-related factors such as the recipient‟s lack of absorptive capacity, causal ambiguity, and an arduous relationship between the source and the recipient” (Szulanski, 1996, p.28). The effectiveness of the socialization process depends also on the organizational culture and the balance between individual competition and group cooperation (Bratianu and Orzea, 2010; Holste and Fields, 2010). Nissen (2006) developed the knowledge flows model for the organizational knowledge dynamics. This new concept represents more than just a metaphor; it explains the phenomenon of how knowledge moves through an organization. The Nissen‟s model is based on the Nonaka‟s model, but it is extended to a three dimensional framework with time as a fourth dimension. Thus, Nissen extends Nonaka‟s two dimensional model to integrate two complementary dimensions: life cycle and flow time. According to Nissen, “Life cycle refers to the kind of activity (e.g., creation, sharing, application) associated with knowledge flows. Flow time pertains to the length of time (e.g., minutes, days, years) required for knowledge to move from one person, organization, place, or time to another” (Nissen, 2006, p.35). Nissen is using a metaphorical approach, introducing the concepts of “light mass” and “heavy mass”. In his view, tacit knowledge would correspond comparatively to “heavy mass” in the context of knowledge dynamic, which means a slow flow and a long flow time. On the contrary, the explicit knowledge would correspond to the “light mass”, which means rapid flows and short flow time. Thus, socialization is a rather slow process because it involves the transfer of the tacit knowledge, while combination is a rapid process because it involves the transfer of explicit knowledge. The extended model developed by Nissen brings in new dimensions and better possibilities of knowledge dynamics understanding and mapping. Including time explicitly, the extended model increases its dynamic capacity of representing knowledge flows at the organizational level. However, in fluid dynamics the flow is generated by a pressure field, and the flow is characterized by a velocity field. In the Nissen,s model there is no pressure field analog which makes it difficult to understand the direction of knowledge flow and the gradient of the knowledge field. Harsh (2009) reiterates that Nonaka does not consider the fact that a significant part of the initial knowledge is flowing through the cycle many times, which actually means that there is a kind of reusable knowledge. “It is a surprise that in spite of great attention to knowledge creation and sharing theories and issues, the reusable knowledge has not been discussed explicitly during knowledge transformation in the Nonaka model” (Harsh, 2009, p.2). Also, Harsh reminds us that any conversion or transfer of knowledge consumes time, which does not appear as a variable in the Nonaka‟s www.ejkm.com

196

©Academic Conferences Ltd

Constantin Bratianu

knowledge dynamics model. The organizational knowledge changes with time, and the effective knowledge of a generic organization can be increased through the reuse of knowledge. Thus, reusable knowledge is a fact of organizational life and it must be included in the modelling of knowledge dynamics. Since the SECI model is basically a two dimensional construct, Harsh introduces a third dimension, proposing a three dimensional knowledge management and explicit knowledge reuse (Harsh, 2009).

4. Knowledge dynamics and thermodynamics Bratianu and Andriessen (2008) analyzing the metaphor knowledge as energy showed new opportunities for understanding knowledge dynamics. Knowledge can be considered as a field, a continuous nonuniform and nonhomogeneous distribution of meanings and feelings in a certain organizational design and physical space. Time variations and space nonuniformities generate forces trying to decrease field nonuniformity. This new perspective may help in explaining the generic forces able to determine the flow of knowledge in organization. If there is a concentration of the knowledge field in the middle level management with respect to the top management or the executive line management, then and only then the flow of knowledge will have the direction and motion described by the Nonaka‟s spiral knowledge dynamics. Bratianu and Andriessen (2008) made an analogy between potential energy and tacit knowledge on one hand, and kinetic energy and explicit knowledge on another hand. Having in mind the transformation process of the potential energy into kinetic energy and mechanical work, the authors postulate the same possible process for transforming tacit knowledge into explicit knowledge. That means that externalization should be used actually for generating cognitive work through explicit knowledge. Cognitive work means any rational process done in decision making. In the reverse way, kinetic energy can be transformed into potential energy by consuming mechanical work, which means that explicit knowledge cannot transform itself into tacit knowledge without some work to be done. It is necessary to consume cognitive work in order to realize the internalization process. Thus, knowledge conversion processes postulated by Nonaka and his co-workers cannot be realized by themselves without any production or consumption cognitive work. In conclusion, with all their limitations, Nonaka and his co-workers developed the dyad of tacit knowledge – explicit knowledge, and all their effort is to describe the dynamics between these two forms of knowledge. However, considering knowledge as a field of meanings and feelings already we may promote a new dyad: cognitive knowledge – emotional knowledge. Emotional knowledge is generated by emotions, which may be considered as states of our body and mind. Emotions are characterized by the following generic constituents (Hill, 2008, p.78): 

A feeling component – physical sensations, including chemical changes in the brain.



A thinking component – conscious or intuitive „thought‟ appraisal.



An action component – expressive reactions (like smiles), as well as coping behaviours (think fight or flight).



A sensory component – sights, sounds, etc., which intrude and serve to trigger the emotional response.

According to Hill (2008, p.79): “Emotionality is distinguished from rationality because the latter only involves one of these four components: thinking. Unlike an emotion, thinking may, but is less likely to, have a sensory component”. However, emotionality does not contain rationality. Rational thought involves conscious, deliberate, evaluative assessments. Emotions, on the other hand, are existential states of body and mind generated by feelings. Due to their direct short-cuts to the mind, emotions are always faster than thoughts in the decision making process, and thus they are able to mobilize the body in case of emergency. Emotions work very well with the adaptive unconscious, and they are able to yield a snap judgement based on so called “thin-slicing”. This mechanism refers to the power of our slices of experience (Gladwell, 2005). Emotional knowledge has two dimensions: time of existence, and intensity of manifestation. The first dimension is a quantitative one and it can be measured easily in a psychology laboratory. The second dimension is qualitative in nature and it can be measured more difficult. By contrast, cognitive knowledge has only one dimension which is closely related to a metrics. Thus, the quantity of cognitive knowledge should be evaluated in a different way than the quantity of emotional knowledge. However, at this moment knowledge evaluation is in its early trial and error phases, without workable method and metrics.

www.ejkm.com

197

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (193 - 200)

Knowledge as energy is a challenging metaphor since we may use the fundamental concepts of thermodynamics. As a science, thermodynamics is concerned with the generation, transport, and dissipation of heat as a form of energy. That means also the transformation process of mechanical work into heat, and of variation of heat into mechanical work in complex systems. In a similar way we can postulate that the variation of total knowledge at a certain level is a result of cognitive work and emotional heat involved in the transformational process. By cognitive work we may refer to any knowledge processing event which is capable of generating action at individual or organizational level. In the field theory, any non-uniform distribution in time or space generates forces, and any variation of these forces generates fluxes which tend to produce uniformity. This is true for the knowledge field as well, and we may coin the concept of cognitive work as a result of variation of cognitive fluxes at the individual level or organizational level. A cognitive work is actually any flux which may generate, or which can be generated by a knowledge field variation. By emotional heat we may consider the emotional flux which has been induced or produced as a result of a knowledge field variation. Considering all of these new aspects of knowledge creation and transformation, we should be re-thinking the Nonaka‟s model of knowledge dynamics. The second law of thermodynamics has many formulations and interpretations. However, the kernel of this law is that heat can flow by its nature from a body with a higher temperature, toward a body with a lower temperature. These two bodies can be in direct contact, or not. The reverse process can be done only by performing mechanical work. Using our metaphor, we may say that in the target domain knowledge can be transferred only from a person having a higher knowing level toward a person with a lower knowing level. The reverse process can be done only by performing some intellectual work. This idea can be further developed by using similarities between the Carnot cycle used in thermodynamics and the SECI cycle used in knowledge management.

5. Conclusions The purpose of this paper is to present a critical analysis of the knowledge dynamics model elaborated by Ikujiro Nonaka and his co-workers. The essence of this model consists of three layers of the knowledge-creation process, including Ba platforms for knowledge creation, and SECI (socialization-externalization-combination-internalization) evolving spiral for knowledge conversion. Our critical analysis aims at the investigation of the operational power of Nonaka‟s model of knowledge dynamics within the framework of organizational knowledge. One of our first conclusions is that the whole knowledge dynamics model is embedded in the Japanese culture and the Japanese companies‟ organizational behaviour. Thus, limitations come from the working assumptions made by these above authors. Then, considering the whole cycle we may postulate the fact that o good part of the flowing knowledge passes several times through the spiral channels, which raises the question of reusable knowledge. Introducing this reusable knowledge into the model means to expand the two dimensional knowledge dynamic model into a three dimensional one. The emergence of a new knowledge dyad composed of cognitive and emotional knowledge suggests a new dynamics: transforming cognitive knowledge into emotional knowledge, and of emotional knowledge into cognitive knowledge. However, there are some new aspects related to the dimensionality of each form of knowledge. Cognitive knowledge has only an extensive dimension, while the emotional knowledge has an extensive dimension, and an intensive dimension. By similarity to the thermal energy we may use the concept of temperature for this intensive dimension of emotional knowledge. Finally, the metaphorical analysis of knowledge as energy shows that we may consider the entropy law to suggest that knowledge can be transferred only from a higher level of knowing toward the lower level of knowing.

References Andriessen, D. (2006) “On the metaphorical nature of intellectual capital: a textual analysis”, Journal of Intellectual Capital, Vol.7, No.1, pp.93-110. Andriessen, D. (2008) “Knowledge as love. How metaphors direct our efforts to manage knowledge in organisations”, Knowledge Management Research & Practice, No.6, pp.5-12. Andriessen, D. & Van den Boom, M. (2007) East is East and West is West and (n)ever its intellectual capital shall meet. Journal of Intellectual Capital, Vol.8, No.4. Agourram, H. (2009) “The quest for the effectiveness of knowledge creation”, Journal of Knowledge Management Practice, Vol.10, No.2, June, pp.1-7. Bereiter, C. (2002) Education and mind in the knowledge age, Lawrence Erlbaum Associates, Mahwah, NJ and London.

www.ejkm.com

198

©Academic Conferences Ltd

Constantin Bratianu Bratianu, C. (2008) “Knowledge dynamics”, Review of Management and Economical Engineering, Vol.7, Special Issue, pp.103-107. Bratianu, C. (2009) “Challenges for knowledge management research”, in: Bratianu, C., Lixandroiu, D., Pop, N. (eds.) Business excellence, Vol.1, pp.52-56, Infomarket, Brasov. th Bratianu, C., Andriessen, D. (2008) “Knowledge as energy: a metaphorical analysis”, Proceedings of the 9 European Conference on Knowledge Management, Southampton Solent University, 4-5 September 2008, pp.75-82, Academic Publishing, Reading. Bratianu, C., Orzea, I. (2010), “Tacit knowledge sharing in organizational knowledge dynamics”, Proceedings of nd the 2 European Conference on Intellectual Capital, ISCTE Lisbon University Institute, Lisbon, Portugal, 29-30 March 2010, pp.107-1114, Academic Publishing, Reading. Gladwell, M. (2005) Blink. The power of thinking without thinking. New York: Back Bay Books. Gourlay, S. (2006) “Conceptualizing knowledge creation: a critique of Nonaka‟s theory”, Journal of Management Studies, Vol. 43, No.7, November, pp.1415-1436. Harsh, O.K. (2009) “Three dimensional knowledge management and explicit knowledge reuse”, Journal of Knowledge Management Practice, Vol.10, No.2, June, pp.1-10. Hill, D. (2008) Emotionomics. Leveraging emotions for business success, Revised Edition, kogan Page, London. Holste, J.S., Fields, D. (2010), “Trust and tacit knowledge sharing and use”, Journal of Knowledge Management, Vol.14, No.1, pp.128-140. Kaufman, S.F. (1994) The martial artist‟s book of five rings. The definitive interpretation of Miyamoto Musashi‟s classic book of strategy, Tuttle Publishing, Boston. Nissen, M.E. (2006), Harnessing knowledge dynamics. Principled organizational knowing & learning, IRM Press, Hershey. Nonaka, I. (1991) “The knowledge-creating company”, Harvard Business Review, Vol.69, No.6, pp.96-104. Nonaka, I. (1994) “A dynamic theory of organizational knowledge creation”, Organization Science, Vol.5, No.1, February, p. 14. Nonaka, I., Byosiere, P., Borucki, P.C., Konno, N. (1994) “ Organizational knowledge creation theory: a first comprehensive test”, International Business Review, Vol.3, No.4, pp.337-351. Nonaka, I., Takeuchi, H. (1995) The knowledge-creating company. How Japanese companies create the dynamics of innovation, Oxford University Press, Oxford. Nonaka, I., Konno, N. (1998) “The concept of „Ba‟: building a foundation for knowledge creation”, California Management Review, Vol.40, No.3, Spring, pp.40-54. Nonaka, I., Toyama, R., Byosiere, Ph. (2001) “A theory of organizational knowledge creation: understanding the dynamic process of creating knowledge”, in: Dierkes, M., Antal, A.B., Child, J., Nonaka, I. (eds.) Handbook of organizational learning and knowledge, pp.487-491, Oxford University Press, Oxford. Nonaka, I., Toyoma, R. (2007) “Why do firms differ? The theory of knowledge-creating firm”, in: Ichijo, K., Nonaka, I. (eds.) Knowledge creation and management. New challenges for managers, pp.13-32, Oxford University Press, Oxford. Polanyi, M. (1983) The tacit dimension, Peter Smith, Gloucester, Massachusetts. Styhre, A. (2004) “Rethinking knowledge: a Bergsonian critique of the notion of tacit knowledge”, British Journal of Management, Vol.15, pp.177-188. Szulanski, G. (1996), “Exploring internal stickiness: impediments to the transfer of best practice within the firm”, Strategic Management Journal, Vol.17, Winter special issue, pp.27-43. Szulanski, G. (2000), “The process of knowledge transfer: a diachronic analysis of stickiness”, Organizational Behavior and Human Decision Processes, Vol.82, No.1, May, pp.9-27. Szulanski, G, Jensen, R.J. (2004), “Overcoming stickiness: an empirical investigation of the role of the template in the replication of organizational routines”, Managerial and Decision Economics, Vol.25, pp.347-363. Von Hippel, E. (1994), “Sticky information and the locus of problem solving: implications for innovation”, Management Science, Vol.40, No.4, pp.429-439.

www.ejkm.com

199

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (193 - 200)

www.ejkm.com

200

©Academic Conferences Ltd

Knowledge as Open Space Tiit Elenurm Estonian Business School, Tallinn, Estonia [email protected] Abstract: The paper addresses the role that knowledge metaphors play in reflecting ambitions to manage knowledge and intellectual capital development as clearly regulated processes. This is contrasted with the focus on knowledge formation in intra-organizational and inter-organizational open spaces. Transfer of knowledge and open space as knowledge metaphors are emotionally attractive, but assume organizational and technological prerequisites in order to be embedded into daily management practices. The paper reflects the experience gained discussing knowledge management metaphors with MBA students and management practitioners: different ways of understanding the prerequisites of knowledge management are revealed through metaphors. Metaphors disclose conflicts between basic IT-centred and people-centred assumptions that guide efforts to accumulate intellectual capital. The desire to clarify knowledge spaces for employees as experts, and to align face-to-face knowledge sharing with virtual knowledge sharing tools in an open virtual space, are challenges in today’s rapidly changing societies and organizations that face intensifying competition and staff mobility. Open space can be treated as a specific technology for blending different knowledge sources and as a broader knowledge metaphor that focuses on self-regulating knowledge processes. The paper briefly outlines the experience of applying the open space technology in a large-scale civic society initiative, My Estonia, that on 1 May 2009 involved more than 11,000 participants in 527 think tanks. The experience demonstrated that diversity and virtual networking are critical success factors of the open space, but are not easy to sustain in more closed communities where participants have a pre-determined status and shared experiences of non-productive conversations. The paper goes on to specify the limitations of the open space metaphor from the point of view of society and organizations. Knowledge metaphors can be applied to link intra-organizational knowledge management to the global village vision. The paper discusses some related metaphors and development practices that support the concept of knowledge as open space. It is a metaphor that reveals the contradictions between traditional knowledge management initiatives inside organizations and open space practices that can be found in civic society initiatives. Keywords: knowledge metaphor, open space, knowledge sharing, self-regulation, knowledge management prerequisites, intellectual capital

1. Introduction Metaphors have been used to express new management ideas and to differentiate them from earlier management concepts for many decades. The comparison of mechanistic and organic organisations was introduced half a century ago (Burns and Stalker, 1961). The metaphor of an organic organisation became an important vision for organisational behaviour experts and researchers looking for self-regulative forms of organisation that enable adaptive evolution in an unstable environment. Nonaka (1994), in his dynamic theory of organizational knowledge creation, has stressed that although ideas are formed in the minds of individuals, the interaction of these individuals in communities of interaction contributes to the amplification and development of new knowledge. He was critical of bureaucratic hierarchies and ―top-down‖ management as knowledge management tools and suggested a ―middle-up-down‖ model that facilitates simultaneous knowledge creation processes at the top, middle and lower levels of an organization. Spatial metaphors have been widely used in classical knowledge management concepts. Nonaka and Takeuchi (1995), in their seminal book, have developed the model of four modes of knowledge conversion that explains the spiral of knowledge through conversions between tacit and explicit knowledge domains. The knowledge spiral itself is an essential metaphor that has influenced discourse between knowledge management developers. Bratianu (2010) has presented a critical analysis of the spiral of knowledge creation and stressed that socialization and combination are processes for exchange of knowledge from one person to another. His view is that only externalization and internalization transform one form of knowledge into another form of knowledge and the spiral of knowledge in this framework is a metaphor that does not match attempts of its practical analysis. The use of metaphors is, however, also effective as a method for the management of knowledge conversion processes (Nonaka 1994). Nonaka and Konno (1998) disseminated the space metaphor in the knowledge management research community by introducing the Japanese concept “ba” that unifies physical, virtual and mental space. They explained how four types of ba (for originating, ISSN 1479-4411 201 ©Academic Conferences Ltd Reference this paper as Elenurm, T. ―Knowledge as Open Space‖ Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp201 - 208), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (201 - 208)

dialoguing, systemizing and exercising) are available to support knowledge creation and conversion processes. Accorsi and Costa (2008) have proposed that an additional connecting ba can stimulate the inclusion of geographically dispersed collaborators to virtual interaction. Rice and Rice (2005) have reviewed applications of the SECI model in multi-organizational projects. The need to facilitate knowledge conversions in small groups, organizations, inter-organizational value chains and broader communities is now an even more crucial knowledge management development challenge, when the internet and virtual communities have changed knowledge sharing practices. The present paper studies how the logic of knowledge management can be aligned with the metaphor of open space. In section 2, knowledge metaphors are discussed in the framework of closed versus open approaches to knowledge management and self-regulation in knowledge creation and application activities. Section 3 focuses on synergies and contradictions between knowledge management prerequisites and related metaphors. Section 4 reflects upon the experience of applying the open space technology in a large-scale civic society initiative, ―My Estonia‖, in 2009. Contradictions between open space as a metaphor and open space as a knowledge sharing and knowledge combination technology and implications of these contradictions for capitalizing knowledge are discussed.

2. Knowledge metaphors and closed versus open approaches to knowledge management Andriessen (2010) has proposed three types of metaphor research in the knowledge management and intellectual capital arena: 

Studying the role of metaphors in theorizing and practice;



Finding new alternative metaphors;



Using metaphors in knowledge management interventions.

He has also stressed that the aptness of knowledge related metaphors depends on the richness of the semantic field of the source domain, the validity of mapping, and the ideological implications of the mapping (Andriessen 2010). Using metaphors in knowledge management interventions is a way to apply their aptness potential but also a way to check the validity of the mapping of the semantic field of the metaphor compared to its practical application. Metaphors whose source domains refer to rich semantic fields can be often turned to slogans if they are applied in interventions that are supposed to support some ideology. If the intervention practice does not fully match the promise that is offered by the metaphor used for labelling this intervention, it may in long term discredit both the knowledge management intervention and the related metaphor. The concept of a ―global village‖ (McLuhan 1962) provides global socialization and knowledge sharing opportunities but also new global business synergies. Marcel Castells (2000) has pointed out three fundamental features of the new economy: 

Informational – capacity to generate knowledge and managing determines the productivity;



Global – core business activities (finances, technology development) become global, although most jobs are not in fact global;



Networked – network enterprises are at the heart of connectivity in the global economy.

Networking has become an especially important business model under the influence of internet-based learning communities and collaborative innovation networks (Gloor 2006) that help to overcome distance barriers in business co-operation. The network is an interesting metaphor that raises at least five crucial questions. Is the main source of intellectual capital really a formal network of enterprises as legal persons or an informal network of individuals from different organizations? How easily can a network be extended? Is it extended by the fabricator of the network or through self-regulation by a large number of people creating new nodes? Can it be easily broken? Can people sometimes envelop themselves in a network? In the social networking literature, an essential feature of social networking software is said to be its ability to provide the individual with control of his self-generated content (Avram 2005). The assumption is that networkers as individuals themselves choose the network they would like to join and control their knowledge-sharing activities in the network. Individuals typically strengthen their network ties through peripheral participation in the network initially and then gradually

www.ejkm.com

202

©Academic Conferences Ltd

Tiit Elenurm

build up their personal trust using personal knowledge, informal contacts, shared norms and mutually recognized co-operation principles. These developmental stages assist the communities of practice to deepen their knowledge and expertise (Wenger et al. 2002). Other researchers however stress that company blogging policy should give directions on how employees should participate in informal networks, and thus accumulate intellectual capital and generate profit at the firm level (Castiaux 2006). It is evident that supporters of these two views in fact have different answers to the question concerning self-regulation in creating and extending networks that make use of social software tools. They should use more specific metaphors than a network composed of nodes and connecting lines in order to express their answers to the question of ―Who is the network fabricator?‖ In the discourse about those knowledge-intensive organizations that should have more developed knowledge management practices and more intellectual capital than other organizations, the metaphor ―knowledge rich‖ has been differentiated from the term knowledge-intensive. Scholars point out that knowledge-rich universities are not necessarily knowledge-intensive (Greenwood 2009). ―Knowledge rich‖ is a good metaphor for organizations in which different structural units or expert groups might accumulate and preserve deep professional knowledge in their specific field, but which lack processes to combine the knowledge that is kept in the isolated ―treasuries‖ in order to increase the value of the intellectual capital for the whole organization. The English noun ―knowledge‖ does not permit a distinction between knowledge as a single object and knowledge as a combination of different knowledge domains. In the Estonian language, which belongs to the group of Finno-Ugric languages, one can use four different forms and derivatives of the same core word to explain the differences between some important meanings of the term ―knowledge‖. In the knowledge management context, these meaning may have conflicting roles: – knowledge about some subject, concept or fact ("I know this



Teadmine (singular form) definition")



Teadmised (plural form) – knowledge of some broader field "he has good knowledge in the field of knowledge management and in related training activities"



Teave – useful knowledge provided by some source that is not yet linked to a specific action of the receiver. "BBC World is a good source of new constantly updated knowledge"



Teadmus - integrated action-focused knowledge derived from different sources, including experience and experts.

The focus of knowledge management is on managing the process of creating and applying actionoriented combined knowledge, so the most suitable Estonian term for knowledge management is ―teadmusjuhtimine”. Although "teadmine", "teadmised" and "teave" also reflect important aspects of the knowledge management process. It is also possible in Estonian and the other Finno-Ugric languages to differentiate the self-regulative and manageable aspects of ―development‖, which is such a key term for organisation and management developers. The term in Estonian for development as the organic self-regulative process is “arenemine” whereas the term for externally-directed development is “arendamine”. The outcome or the function of the interplay between externally managed development and self-regulative development is “areng” if management efforts are aligned with self-regulative development processes. A similar linguistic logic applies to the term ―change‖. As has been shown, in the Estonian language there are several derivational paradigms. This can be explained by the fact that English is an analytic language and Finno-Ugric languages are syntheticagglutinative (Piits et al. 2007). Metaphors may be especially useful in analytic languages, where, if discussing knowledge and knowledge management, the use of derivatives of the word ―knowledge‖ to explain the role of self-regulation and combining diverse knowledge sources does not reveal the different, although interrelated, meanings of the term knowledge that should be understood in the knowledge management development discourse. Knowledge creation is often described using the metaphor of the learner as a builder and knowledge as a building. Kövecses (2002) considers construction and building an important cognitive domain for knowledge that reveals the role of structure. Knowledge as a building describes well the role of fundamental theories and axioms as the foundations of the knowledge house. The building is however also a shelter and protection against external disturbances and in the rapidly changing environment one has to think about the implications of emotional earthquakes and winds of change. A knowledge house may have conceptual or ideological walls that isolate some school of thought from interdisciplinary discourse or followers of a guru from external realities. www.ejkm.com

203

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (201 - 208)

Information-centred and knowledge-centred trends have been competing for many decades in the field of developing knowledge-based assets (Sveiby 1997). In the ―global village‖ knowledge environment that has been shaped by new applications of social software in recent years, it is, however, also important to distinguish closed and open approaches to developing knowledge management practices. Closed approaches to knowledge management focus on creating a relatively stable and integrated structure of knowledge representation, but see uncontrolled knowledge exchange with external sources as a danger to knowledge constructs inside the organization. Open approaches accept diversified external and internal sources of knowledge that cannot be fully controlled. Open approaches focus on combining different knowledge sources. Crucial choices between closed and open approaches can be revealed by discussing the prerequisites of knowledge management and its related metaphors.

3. Metaphors that reflect knowledge management prerequisites In the course of running knowledge management training courses since 2001, we have asked participants to rank knowledge management prerequisites by their importance to the companies they work for. A checklist was introduced to compare rankings, but students were also encouraged to add their own prerequisites to the original checklist. Respondents were asked to provide supporting arguments for their evaluation of the importance of the knowledge management prerequisites. In 2006-2009, the highest placed prerequisite on average was: employees have recognized fields, where their expert knowledge can support others (table 1). Table 1: High priority knowledge management prerequisites Knowledge management prerequisites

Employees have recognized fields, where their expert knowledge can support others Virtual information processing and knowledge sharing tools are used actively Trust between employees Free circulation of information Promoting information sharing between colleagues

Average priority rank 2001-2004 (N=115)

Average priority rank 2006-2009 (N=116)

VIII

I

VII I II-III II-III

II III IV-V IV-V

It was followed by: virtual information processing and knowledge sharing tools are used actively. A difference was found in that this prerequisite ranked higher in larger companies and lower in smaller enterprises. In 2001–2003 the same prerequisites had received much lower ratings. Promoting information sharing between colleagues shared with free circulation of information fourth and fifth places in 2006-2009. During the economic crises free circulation of information has again gained higher rank among other knowledge management prerequisites. We have found that the rankings do vary depending on the size of the MBA students’ companies and on the different business sectors they operate in, but there is evidence that respondents are rating the knowledge management prerequisites that can be linked to virtual space more highly recently. In recent years, MBA students have also tended to give higher priority to receiving a clear answer to the question: ―Where is the space in my organization for my expert knowledge?‖ In 2009, we added an explicit metaphor task — to describe one metaphor for the organization that has created knowledge management prerequisites that our MBA students considered important. Students were encouraged to draw a picture that visually explains the metaphor. 80% of drawings depicted some kind of network. There were however, network metaphors presenting face-to-face communication and drawings that focused on networks linking functional units or interconnected people, intranet and quality systems. Some pictures had closed networks whereas others stressed external knowledge inputs to the organization. A few visual metaphors reflected the influence of the hierarchy on an employee possessing some knowledge and manager-subordinate relations in the knowledge capture process. At the beginning of the course in 2009, MBA students were also asked to identify the five most important knowledge metaphors from the metaphor analysis scoring form by Andriessen (2006). Students were permitted to add their own metaphors. This scoring form was introduced before explaining any definitions or basic concepts of knowledge management. Share knowledge was identified among the five most important knowledge metaphors by 90% of respondents. Among other

www.ejkm.com

204

©Academic Conferences Ltd

Tiit Elenurm

popular resource-related metaphors were: use knowledge, need knowledge, acquire and store knowledge, invest in knowledge, exploit and manage knowledge. Equally popular were such knowledge-as-object metaphors as seek, link and exchange knowledge but also hold knowledge. The metaphor hide knowledge was added to the knowledge-as-object metaphor list. Among knowledgeas-product metaphors the metaphor develops knowledge was the most popular and among the knowledge-as-capital metaphors measure knowledge and value knowledge were preferred. Verbal metaphors favoured by MBA students and visual metaphors presented in their drawings reveal two challenges in the knowledge management and intellectual capital context. First, how to match face-to-face communications supported by personal relations and focused on tacit knowledge with the understanding of an open network that is focused on combining diversified internal and external knowledge sources? Second, how to match the desire to share knowledge with such metaphors as hide and hold knowledge as a resource and with the desire to value knowledge as the intellectual capital of a specific organization or community?

4. Open space as a metaphor and as a knowledge creation and knowledge sharing technology Knowledge transfer has been an important metaphor for reflecting processes, where knowledge acquired through experience in one situation is taken on by users in a different location. In the Soviet totalitarian system, ―exchange of experience‖ was promoted as a knowledge transfer tool that was considered a substitute for research activity. Such campaigns were supported by massive propaganda, where slogans promoting exchange of experience usually ignored different situations, the context and the needs of organizations that should have participated in a knowledge transfer process. Carraher and Schliemann (2002) consider the transportation metaphor of ―carrying over‖ fundamentally flawed for learning. In response to criticism, several alternative metaphors have emerged, including transfer as preparation for future learning (Schwartz and Martin 2004). Transfer can be interpreted as the generalization of learning and as the influence of the learner’s earlier activities on their activity in novel situations. It is suggested that the transportation metaphor should be replaced with production or transformation metaphors (Lobato 2006). The path metaphor is seen in expressions such as ―achieving distant goals‖, ―heading in the right direction‖ or ―getting back on track‖ (Moser 2007). ―Open space‖ is a spatial metaphor that has gained popularity as a result of Harrison Owen introducing in 1985, the open space technology to run self-organizing knowledge sharing and knowledge creation meetings for groups of any size. Owen (2008) employs several metaphoric expressions in his user guide, such as ―creating and holding time and space‖ and ―opening the village marketplace‖. Open space technology empowers all participants to raise issues and questions they would like to discuss with any interested participants in order to find new ideas. Participants may freely move from one discussion group to another. Open space technology follows ―the law of two feet‖: if participants find themselves in a situation where they are neither learning nor contributing in a group, they can move somewhere where they can learn and contribute. There are also four key principles: ―whoever comes is the right people‖; ―whatever happens is the only thing that could have happened‖; ―whenever it starts is the right time‖, ―when it’s over it's over‖ (Open Space World 2009). Open space has inspired green organization developers (Yeganeh and Glavas 2008) and organizers of participant-driven ―unconferences‖ offering a more flexible knowledge sharing space for professionals than conventional research conferences (Crossett et al. 2009). Open space technology has been used in 124 countries over the last two decades (Owen 2008). On 1 May 2009 the large-scale civic society initiative ―My Estonia‖, applied open space principles simultaneously in idea generation sessions conducted by 527 think tanks across Estonia. 11 000 people gained open space experience on that day (My Estonia homepage 2009). A year earlier, on 1 May 2008, more than 50,000 volunteers had joined forces and cleaned the Estonian forests of garbage. Encouraged by this experience, the target was to involve 100 000 people in a My Estonia Brainstorming Day that was compared to a mental garbage clean up. Its primary goal was to bring people together to raise and find solutions to urgent problems in order to improve life in Estonia (My Estonia homepage, Facilitator’s manual (2009)). The actual number of participants did not meet the optimistic aspiration, but was still more than 0.8% of the Estonian population. Two questions can however be raised when following the metaphoric expressions. Why it is easier to get people to collect physical garbage in a real open space than to involve them in a mental garbage clean up?

www.ejkm.com

205

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (201 - 208)

Does the open space technology fully meet the aspirations that are created by the open space metaphor? Our answers to these questions are based on personal experience as think tank facilitators, on analyzing the experience of other think tank facilitators and participants, and also on reflecting the discourse in the traditional media and in virtual forums and weblogs before and after the 1 May 2009. Open space technology focuses on processes during the open space event — the ―here and now‖— but large-scale civic society initiative organizers had to arrange the process of informing and registering potential participants before the event. At the first stage, the procedure assumed that electronic ID cards would be used by participants for authentication in the self-registration procedure. Although a large part of the population in Estonia is used to identifying themselves electronically in ebanking and in many electronic services provided by the state, there were also people that were concerned that a ―big brother‖ would someday check the identity of all people involved in a civic society initiative. Later the registration procedure was simplified. There were also views that the civic society initiative was trying to take over the political space that in a representative democracy should belong to local municipalities and their elected councils. Especially in rural areas, the idea of using a nice spring day for some kind of ―village marketplace of ideas‖ was considered less natural than being in the physical open space either working in the garden or going to a nearby forest to collect garbage together with other people that care about nature. Some students that had studied knowledge management justified staying away from the My Estonia open space meeting by pointing out that their priorities were to increase the intellectual capital of their own business organizations, and did not believe that in the open space mode anybody would share ideas that may be valuable for their organization. This paper refers to the think tanks of My Estonia, but the terms brainstorming session and brainstorming bee in English and mõttekoda (meaning thought hall in Estonian) were also used. Some of the think tanks consisted mainly of participants that belonged to the same social community or network and had already discussed similar or identical issues to those raised. This situation posed a challenge to facilitators. Some followed the open space technology guidelines too rigidly and did not offer discussion teams more structured creative thinking tools or new ideas from outside sources during the open space meeting. Such meetings then lacked the broader mental space that would have been useful for participants to explore potential new solutions. In other think tanks, the degree of diversity of the participants was greater, for instance, permanent local residents met people that lived in their area only during the summer months. The diversity of participants broadened the space available for searching out new ideas and allowed the facilitator to follow the original open space technology to attain good results. The „My Estonia’ civic initiative made extensive use of social software, including weblogs and twitter in the preparation of the event and in the follow-up activities. There were also 16 virtual think tanks. Many younger participants feel that virtual space is the real open space for idea generation and dissemination, whereas older participants tend to feel more secure in the open space that utilized face-to-face communication. The website of My Estonia lists 4832 ideas as of 20.10.09. (My Estonia homepage 2009). Many of these ideas have been fine-tuned in follow-up activities and have served as departure points of new projects. The follow-up process demonstrated that some participants were more eager to broaden the time frame of the open space than others. They also initiated online discussions, where they compared the open space technology of Harrison Owen and idea generation and knowledge sharing methods that had already been used in Estonia in the 1980s under another metaphoric label the ―thought bee” (in Estonian ―mõttetalgud”). Ruttas (2009) compares one version of the thought bee, called a thought sieve, with the open space technology. He points out that the thought sieve is effective for consolidating ideas of interconnected stakeholders that have to agree on common ground and priorities for further joint activities, bearing in mind a certain deadline. In contrast, open space tends to produce more diffused ideas. Some veterans of the thought bee approach see a potential problem of the open space technology being that the ideas and initiatives resulting from the exercise have been scattered in directions that are too different. When modifying the open space metaphor, their goal is to create a focused space.

5. Discussion and conclusions Open space has appeared as a powerful but controversial metaphor in the knowledge sharing and intellectual capital context. In terms of safeguarding their intellectual capital in a competitive environment, organizations tend to draw borders between the knowledge space they are eager to control and the knowledge space they agree to share with outsiders. It is difficult to share and hide knowledge simultaneously.

www.ejkm.com

206

©Academic Conferences Ltd

Tiit Elenurm

Comparing the rankings of knowledge management prerequisites from 2001-2004 to 2006-2009 demonstrates the increasing role of the virtual space and related knowledge sharing tools, but also the desire to clarify knowledge spaces for employees as experts. Visual metaphors of knowledge management prerequisites depict networks that have different degrees of openness. The contradiction between increasing the intellectual capital through knowledge sharing and at the same time holding and hiding knowledge was demonstrated in the metaphor analysis scoring process. The scoring tool developed by Andriessen (2006) can be used for revealing controversial intellectual capital development efforts and knowledge management practices that are reflected in preferred verbal metaphors. In the context of the SECI model and ba concept, where Nonaka and Takeuchi have embedded spatial metaphors, the open space technology has to compete with other knowledge sharing and idea generation methods that either focus more on group dynamics or support the more explicit prioritization of ideas for further action. Bowman (2001) has distinguished deeply ingrained tacit skills from tacit skills that can be accessed but cannot be expressed through the normal use of words. He suggests that metaphors and storytelling can serve as tools for articulating such tacit skills. It cannot, however, be taken for granted that people are ready to articulate deeply ingrained tacit skills in the open space event, although they may appreciate the diversity of ideas that are shared in the open space. The ―law of two feet‖ may support diversity and free choice but can simultaneously inhibit the consolidation of teams. ―My Estonia‖ experience demonstrates that the open space technology can be aligned with the open space metaphor in some situations, but in other situations the technology, at least in its classic format, does not fully match the open space metaphor. Amendments to the open space technology may be needed if the composition of participants does not result in a diverse range of questions and the facilitator has to bring in additional knowledge sources. Open space is a metaphor that reveals the contradictions between traditional knowledge management initiatives within organizations and open space practices as found in civic society initiatives. Constructive use of this metaphor allows discussion in the inter-organizational context of the IT-centred and people-centred basic assumptions that guide knowledge management and the accumulation of intellectual capital.

References Accorsi, F. L., Costa, J. P. (2008) ―Peer-to-Peer Systems Consubstantiating the Ba Concept‖, The Electronic Journal of Knowledge Management, Vol 6, Issue 1, pp 1-12. Andriessen, D (2006) „On the metaphorical nature of intellectual capital: a textual analysis―, Journal of Intellectual Capital, Vol 7 No. 1, pp 93-110. Andriessen, D. (2010) ―The Aptness of Knowledge Related Metaphors: a Research Agenda‖ in Proceedings of nd the 2 European Conference on Intellectual Capital, Susana Rodrigues (Ed), Academic Publishing Limited, Reading, pp 59-66. nd Bratianu, C. (2010) ―A Critical Analysis of Nonaka’s Model of Knowlege Dynamics‖ in Proceedings of the 2 European Conference on Intellectual Capital, Susana Rodrigues (Ed), Academic Publishing Limited, Reading, pp 115-120. th Avram, G. (2005) ―At the Crossroads of Knowledge Management with Social Software‖ in Proceedings of the 6 European Conference on Knowledge Management, Dan Remenyi (Ed), Academic Conferences, Reading, pp 49-58. Bowman, C. (2001) ―Tacit knowledge: some suggestions for operationalization‖, Journal of Management Studies, Vol 38, No. 6, pp 811-829. Burns, T., Stalker. G.M. (1961) The Management of Innovation, Tavistock Publications, London. Carraher, D., Schliemann, A. (2002) ―The transfer dilemma‖, The Journal of the Learning Science, No. 11, pp 124. Castells, M. (2000) The Rise of the Network Society, 2nd ed, Blackwell Publishing, Oxford. Castiaux, A. (2006) ―Knowledge Building in Innovation Networks: The Impact of Collaborative Tools‖, in th Proceedings of the 7 European Conference on Knowledge Management, Péter Feher (Ed), Academic Conferences, Reading, pp 99-107. Crossett, L., Kraus, J., Lawson, S. (2009), ―Collaborative Tools Used to Organize a Library Camp Unconference‖, Collaborative Librarianship, Vol 1, No 2, pp 66-69. Gloor, P. (2006) Swarm creativity. Competitive advantage through collaborative innovative networks, Oxford University Press, Oxford. Greenwood, D. J. (2009) ―Are research universities knowledge-intensive learning organizations?‖ In Handbook of research on knowledge-intensive organizations?‖ in Handbook of research on knowledge-intensive organizations, Jemielniak, D., Kociatkiewicz, J. (Eds), Information Science Reference, Hersey. Kövecses, Z. (2002) Metaphor: a practical instruction, Oxford Universtity Press, Oxford. www.ejkm.com

207

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (201 - 208) Lobato, J. (2006) ―Alternative Perspectives of the Transfer of Learning: History, Issues, and Challenges for Future Research‖, The Journal of the Learning Sciences, Vol 15, No. 4, pp 431-449. McLuhan, M. (1962) The Gutenberg Galaxy, Routledge & Kegan Paul, London. Moser, K. (2007) ―Metaphors as symbolic environment of the self: how self-knowledge is expressed verbally‖, Current Research in Social Psychology, Vol 12, No. 11, pp 151-178. My Estonia homepage [online], http://www.minueesti.ee/?lng=en, accessed 20.10.09. My Estonia homepage. Facilitator’s manual [online], http://www.minueesti.ee/?lng=&leht=394,455 accessed 20.10.09 Nonaka, I. (1994) ―The Dynamic Theory of Organizational Knowledge Creation‖, Organization Science, Vol 5, No. 1, pp 14-37. Nonaka, I, Takeuchi, H. (1995) The Knowledge-Creating Company, Oxford University Press, Oxford. Nonaka, I, Konno, N. (1998) ―The Concept of ―BA‖: Building a Foundation for Knowledge Creation‖, California Management Review, Vol 40, No. 3, pp 673-684. Open Space World (2009) [online], http://www.openspaceworld.org/cgi/wiki.cgi?OpenSpaceExplanations, accessed 18.10.09. rd Owen, H. (2008) Open Space Technology: A user‟s Guide. 3 edition, Berrett-Koehler, San Francisco. Piits, L., Mihkla, M., Nurk, T., Kiissel, I. (2007) ―Designing a Speech Corpus for Estonian Unit Selection Synthesis― in Nodalida, Conference Proceedings, J. Nivre, H.-J. Kaalep, K. Muischnek and Mare Koit (Eds.), pp 367–371 [online], http://www.keeletehnoloogia.ee/projektid/konesyntees/Nodalida2007syntees.pdf accessed 24.10.09. Rice J., Rice, B. (2005 ) ―The applicability of the SECI model to multi-organisational endeavours: an integrative review‖, International Journal of Organizational Behaviour, Vol 9, No. 8, pp 671-682. Ruttas, V. (2009) ‖Mõttesõela ja avatud ruumi meetodi võrdlus‖, [online], Talgujad http://talgujad.forum.co.ee/mottetalgute-ning-teiste-meetodite-vordlus-f6/mottesoela-ja-avatud-ruumimeetodi-vordlus-t34.htm, accessed 20.10.09. Schwartz, D., Martin, T. (2004) ―Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction‖, Cognition and Instruction, Vol. 22, No. 2, pp 129-184. Sveiby, K.E. (1997) The New Organizational Wealth – Managing & Measuring Knowledge-Based Assets, BerrettKoehler, San Francisco. Wenger E., McDermott, R., Snyder, W. (2002) Cultivating communities of practice, Harvard Business School Press, Boston. Yeganeh, B., Glavas, A. (2008) ―Green Organization Development‖, OD Practitioner, Vol 40, No. 2, pp 6-11.

www.ejkm.com

208

©Academic Conferences Ltd

An IC-based Conceptual Framework for Developing Organizational Decision Making Capability Christine van Winkelen and Jane McKenzie University of Reading, UK [email protected] [email protected] Abstract: Organizations need to be able to access, coordinate and integrate knowledge more efficiently than ever before to make sense of the complex and unpredictable forces shaping business conditions. Often knowledge is sourced from diverse yet interconnected networks of individual experts and organisations. In this environment, it is hard to ensure that decisions are based on the best available knowledge and do not work against one another. More alliances and inter-organizational partnerships and various contractual relationships with individuals broaden the range of perspectives and values to be considered, which makes it even harder to determine what a ―good‖ decision looks like. The proposition underpinning this research is that intellectual capital investments can help turn organizational decision making into the dynamic capability required to handle a changing world. The aim is to present a conceptual framework that can be used to target these investments more effectively. A focused literature review of current decision making research to identify the relevant knowledge and knowledge management perspectives identified five factors that map to the IC framework: human capital factors involve accessing and developing experts, as well as supporting reflective practice; structural capital factors involve using technology to structure, integrate and provide access to explicit knowledge resources, as well as designing an effective decision review process; and finally a relational capital factor based on adopting an integrated approach to internal and external collaboration. IC investments in these five areas could enhance decision making in different contexts. Most importantly, the organizational capacity to recognise and respond to different situations with the most appropriate approach to decision making would improve over time. We are not suggesting this is a complete set of factors, but it is a coherent approach to investments in five areas that can contribute to better organizational decision making capability. Further research is recommended to confirm this. Keywords: decision making, knowledge management, intellectual capital, dynamic capabilities

1. Introduction Many organizations, both private and public sector, find they need to depend on a wider variety of knowledge resources to better serve the needs of increasingly demanding and sophisticated customers. There is also a trend is towards more varied and complicated inter-organisational relationships and a variety of contractual relationships with individuals.(McKenzie and van Winkelen 2008). This creates challenges for organisational decision making given the definition of a decision adopted here: “a ―decision” is a commitment to a course of action that is intended to yield results that are satisfying for specified individuals‖ (Yates and Tschirhart 2006, p422). As Yates and Tschirhart point out, ―the specification of beneficiaries is critical, implicating what is arguably the single feature of decision problems that distinguish them most sharply from more general problems – differences among people in the values they attach to decision results‖. More alliances and inter-organisational partnerships and different contractual relationships with individuals increase the problem of determining what a ―good‖ decision looks like, particularly in complex and rapidly changing environments. Yet, the speed of strategic decision making in particular, has been shown to be directly related to firm performance (Baum and Wally 2003) making the time taken to explore and benefit from different knowledge bases even more problematic. Consistent with Simon’s (1960) classical view of decision making involving three stages of intelligence, design of alternatives and choice between options, we take an integrative perspective on the knowledge management implications of the full process. The purpose of this research is to identify those factors that will enable knowledge managers to help build decision making capability in their organizations as the internal and external environment evolves. This capability building approach is consistent with a view of strategy that sustainable success comes from constructing and consolidating distinctive resources and capabilities over the long term (Barney 1991, Prahalad and Hamel 1990, Stalk et al. 1992). Investments in intellectual capital are viewed as the basis for developing this capability. The components of intellectual capital have been defined in subtly different ways in the literature, though the three core components are consistently human capital, structural and relational capital. The ISSN 1479-4411 209 ©Academic Conferences Ltd Reference this paper as van Winkelen, C and McKenzie, J. ―An IC-based Conceptual Framework for Developing Organizational Decision making Capability‖ Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp209 - 216), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (209 - 216)

definitions adopted in this paper are slight modifications of those used by Sveiby (2002). The term human capital encompasses all the individuals available to work for the organization. Relational capital encompasses all the external players in the industry (customers, suppliers, strategic partners, key members of the industry, regulators etc.). This is in line with thinking about the extent of an organization’s ―value net‖ (Allee 2000). The term structural capital describes the systems, processes, culture and other mechanisms for capturing and coordinating the knowledge available within the formal boundaries of the organization.

2. Literature review Although a single definition of a decision is possible, there is very little else that remains consistent across all decision making situations. The following sections provide an overview of current research into human decision making processes and the characteristics and implications of different decision making contexts. For an overview of the historical development of decision making, see for example Buchanan and O’Connell (2006). Knowledge and knowledge management perspectives are introduced throughout the review and synthesised into an IC-based conceptual framework at the end.

2.1 Human decision making The influential book ―A Behavioral Theory of the Firm‖ (Cyert and March 1963) has shaped understanding of many aspects of organizational behaviour for 30 years (see Argote and Greve 2007 for a review). Amongst these is a view of how people make decisions in organizations; basically flawed humans with incomplete information seek to make good enough decisions through negotiation with others. This triggered detailed exploration of key influences on human decision making. Various perspectives have been adopted, amongst them the psychological perspective which starts from the cognitive mechanisms people have developed to cope with their environment and identifies heuristics which speed up decision making, but have potential traps associated with them (Tetlock 1991). Studies of the risks associated with decision-making when viewed from the psychological perspective have identified a number of biases, summarised in Table 1. Table 1: Cognitive biases that affect decision-making Bias Escalation Anchoring Status-quo Sunk-cost Confirming-evidence Framing Over-confidence Prudence Recallability Preference for outsiders

Description Commitment to a losing course of action: stems from holding to initial positive beliefs in the face of negative new information (1) Giving disproportionate weight to the first information received (2) Preferring alternatives that preserve the status quo (2) Making choices that justify past choices (2) Seeking information that supports own point of view and avoiding information that does not (2) The choice made about how to position the question, for example as a gain or a loss, or in relation to particular reference points (2) Tendency of most people to be over confident in their accuracy with which they make estimates or forecasts (2) Tendency to be over cautious, adjusting estimates or forecasts ―to be on the safe side‖ (2) Being over influenced by past dramatic events or those that have left a strong impression (2) Valuing knowledge from external sources more than from internal ones (3)

Key to references: (1) (Biyalogorsky et al. 2006) (2) (Hammond et al. 2006) (3) (Menon and Pfeffer 2003) All the recommendations to manage cognitive and emotional biases involve improving access to knowledge or increasing individual and organisational reflection. They include exposing decision makers to additional experience and analysis, stimulating more debate and providing opportunities for challenge and oversight (Campbell et al. 2009). Paying attention to the emotions of decision makers and other stakeholders is known to be important to prevent ―toxic decision processes‖ escalating within organizations, which shape current behaviours unproductively as well as creating future emotional biases (Maitlis and Ozcelik 2004). Finding ways to introduce multiple stakeholder perspectives in decision making is a way to access the range of pertinent value systems and emotional issues.

www.ejkm.com

210

©Academic Conferences Ltd

Christine van Winkelen and Jane McKenzie

There is a growing acknowledgement that decision makers need to think in new ways if they are to be effective in more complex organizational environments. For example, one model of the way strategic decision makers need to behave in conditions of uncertainty, ambiguity, and contradiction identifies three ―non-conventional thinking capacities‖ (McKenzie et al. 2009). These involve delaying crystallising an interpretative frame of reference to define the problem, exploring multiple interpretations and perspectives of the situation and, having identified potential contradictory elements, acknowledging the tensions and seeking creative solutions that address them by encompassing the value judgements of more stakeholders. Such thinking complements conventional thinking capacities normally learned in response to expectations to frame a problem space, simplify contradictions and make a choice. The emphasis that this model places on personal reflection, engaging with other stakeholders in a collaborative process and integrating multiple perspectives through a deliberate process is similar to Snowden and Boone’s (2007) recommendations for the style of leadership that is needed in complex situations. Such views are somewhat contradictory to Buchanan and O’Connell’s (2006) explanation of the current fascination with ―gut‖ decision-making. This values the courage for making the decision, as much, if not more than, the quality of the decisions themselves. Their review highlights the extent to which this has gained popular attention as demonstrated by Gladwell’s book ―Blink‖ (2006) where he argued that instantaneous decisions drawing on intuition and creativity are sometimes better than those based on lengthy analysis. Research studying ―intuitive‖ decisions has attempted to explain this by considering the mental simulation and pattern recognition processes associated with the Naturalistic Decision Making processes of experts (Akinci and Sadler-Smith 2009) (see the next section for more on NDM). Technology developments that allow neuroscientists to watch the brain in action as it deliberates and decides are being used to understand how some of these apparently instantaneous decision processes work. The part played by the emotion-driven primitive structures of our brains in decision making is the subject of extensive current research (Morse 2006). The organisational environment provides conditions which shape emotional responses and affect how managers make decisions. Turbulent, dynamic, rapidly changing environments place particular pressures on managers and mean that the psychological traps that everyone experiences in every decision situation potentially have an even greater effect. Structural capital investments in processes that support collective reflection and human capital investments in the development activities that encourage individuals to become reflective practitioners can seek to mitigate this, but the greater the complexity and pressure, the more difficult this will be. In the next section, the implications of various specific organizational contexts for decision making will be considered in more detail.

2.2 The context for decision making Snowden and Boone (2007) provide a useful framework (see Table 2) that categorises decision making contexts according to the extent of the link between cause and effect. Table 2: Categorising decision making contexts Decision making context Simple Complicated

Complex

Chaotic

Characteristic Clear cause and effect relationships are evident to all and right answers exist. Cause and effect relationships can be discovered, though they are not immediately apparent. Expert diagnosis is required and more than one right answer is possible. There are no right answers, but emergent and instructive patterns can be seen in retrospect. Efforts need to be made to probe the situation and sense what is happening to find the patterns of relationships. The relationships between cause and effect are impossible to determine because they shift constantly and no manageable patterns exist. Acting to establish order is needed through directive leadership.

Decision making approach in this domain Best practice Expertise

Emergence

Rapid response

Considerable bodies of research examine decision making in situations that fall broadly within these contexts. Some of the trends will be identified in relation to the first three. The chaotic context will not be considered here because of Snowden and Boone’s recommend directive action ―to transform the situation from chaos to complexity, where the identification of emerging patterns can both help

www.ejkm.com

211

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (209 - 216) prevent future crises and discern new opportunities‖ (Snowden and Boone 2007, p74). Insight into what such action involves will be provided through consideration of Naturalistic Decision Making (NDM) and then through looking at strategic decision making in highly turbulent business environments. 2.2.1 Simple decision making contexts Simple decisions are not necessarily easy decisions, it is just that, with effort, a direct relationship can be found between taking an action and a particular effect; it is here that structural capital to support decision making can be most readily formulated. However, ensuring that the systems and processes are used effectively is not necessarily straightforward as cognitive and emotional biases come into play so appropriate human capital investments need to be made alongside the structural capital development. As an example of a process based approach to decision making, new product development programmes typically include key review points to systematise the investment decisions involved in developing new products and services. However, research has shown that there is often a relatively low level of proficiency in applying the decision criteria effectively. Risk aversion often prevents radical projects from progressing, while reviews of incremental developments are often ―too liberal, allowing weak projects to continue for too long and resulting in wasted resources and missed opportunities.” (Schmidt et al. 2009, p533). Technology advance in the 1960s and 1970s led to decision support systems that aim to improve consistency in this simple context (Buchanan and O'Connell 2006). For example, decision tree type methods which systematically explore the implications of alternatives can be converted into readily accessible software applications. Artificial intelligence systems focus on well-defined problems and have been particularly effective in operational environments (Yim et al. 2004). Within many organizations, different kinds of knowledge maps are used to structure systematically explicit knowledge (Mansingh et al. 2009) to better inform those making decisions. This kind of knowledge is particularly useful for situations where people rely on explicit knowledge for operational and task based decisions (Yim et al. 2004). Clearly, as with any technology solution to a knowledge-related problem, the way it is used determines the benefit it offers. Empirical research into the human motivations to use KM decision-support systems (He and Wei 2009) showed that people contribute knowledge to the systems due to social relationships, enjoyment of helping others, management support and the cost of doing so. They seek information from the systems based on perceived utility, social relationships and the effort involved. Again, the need to match human capital development with structural capital technology investments is apparent. In simple decision contexts, the outcome of actions following a decision choice can be foreseen. However, whether or not that choice is made also depends on whether the outcome is valued by the decision makers and their key stakeholders. Where there is ambiguity about the value of the outcome, political negotiation needs to be incorporated into the decision making process (see for example Choo and Johnston 2004). Improvement in decision making capability in the organization needs to include structural capital investments in the learning processes that openly consider whether the appropriate perspectives were properly considered. 2.2.2 Complicated decision making contexts In Snowden and Boone’s ―domain of experts‖ where complicated decisions are being made and more than one right answer is possible, research has tended to focus on the individual as decision maker. One body of research, ―Naturalistic Decision Making‖ (NDM), revolves round the extremes of expertbased decision-making: ―The focus of NDM research is on expert practitioners trying to figure out what to do under difficult circumstances. The need to understand decision making in the context of time pressure, uncertainty, ill-defined goals and high personal stakes was a major impetus for the emergence of NDM‖ (Ross et al. 2006, p403). NDM research has provided insights into how individuals and groups use pattern matching, story telling and argumentation for sensemaking, situation awareness and decision making (Lipshitz et al. 2006), all tools that KM practitioners will be familiar with. The NDM field is developing to consider some of the changes to the organizational context mentioned earlier. For example, as ―organizations are evolving to become “smaller-sized communities of practice where people work primarily as collaborators rather than as experts‖ NDM approaches need to explore distributed cognition, rather than the cognitive processes of a single expert acting under pressure (Gore et al. 2006, p936).

www.ejkm.com

212

©Academic Conferences Ltd

Christine van Winkelen and Jane McKenzie

The judgement of experts has a particular role to play when the value of the outcome of a decision is widely recognised, but the path to achieve it is not clear (Choo and Johnston 2004). In general, the value placed on expertise depends on that organization’s view of what constitutes knowledge and ―truth‖ (Mitroff 2008). Experts aren’t always right (Drior 2005, Drior and Charlton 2006), but when working with familiar situations have a higher success rate and are much faster than novices or merely competent colleagues (Ericsson 2006). However, there is a suggestion that placing too high a value on the voice of experts in the sensemaking stage of decision making may risk the decision being framed incorrectly (Gore et al. 2006, p931). It is important to recognise the situations when a wider range of perspectives need to be incorporated in order to make sure that the ―right problem‖ is being solved (Mitroff 2008). As expertise is dynamic, experts need to keep their knowledge base up to date and continue to refine their thinking about how to apply knowledge for impact. Being an effective reflective practitioner is an important characteristic of becoming an expert. This enables the expert to seek out opportunities for deliberate practice to improve their level of expertise, benefit from exposure to new experiences, and build mental models that incorporate new knowledge (Klein 1997). Situating this reflection within the context of interactions with another expert guide in a collaborative environment can be particularly effective (van Winkelen et al. 2009). The risks created by over-confident experts who do not adopt such an open-minded and reflective approach are becoming increasingly evident, producing calls for decision makers to remain sceptical and challenging when they use expert judgments to support decisions (Cassidy and Buede 2009). Human capital investments therefore include both providing the means to identify experts and ways of developing their thinking processes. Experts can use technology systems that codify and structure explicit knowledge as a ―scaffold‖ to support their decision making (Pech and Durden 2004). Some companies even try to replace human experts with technology based structural capital investments to extend the reach of their knowledge. Knowledge based expert systems can be effective for operational and tactical decisions, capturing the structure of a knowledge domain by codifying human expertise and integrating it with other computer systems such as forecasting and reporting systems (Yim et al. 2004). 2.2.3 Complex decision making contexts Complexity theory is generating insights into new approaches to management practice. In this ―domain of emergence,‖ Snowden and Boone’s (2007) emphasis on stepping back and looking for emergent patterns is similar to Stacey’s (2001) call for increased attention to reflection in action. Mitroff (2008, p19) argues that we have to ―assume that complex problems are managed, not ever fully solved‖. Organizations also need ways to ensure that the ―right problem‖ is explored. This means considering multiple perspectives, areas of disagreement and drawing on ―soft‖ concepts such as ethics, values and aesthetics as well as ―hard‖ factual information, rather than simply seeking expert consensus or a single theoretical ―truth‖. The public sector provides some of the most complex environments for decision making because social policy problems, often labelled ―wicked‖, are unbounded in time, scope and resources. They are inherently complex because they involve unpredictable interdependencies. Essentially, they are insoluble. Stakeholders profoundly disagree about what the problems are, as well as the improvements that can be made (Rittel and Webber 1973). Structural capital investments in new collaboration technologies can help support decision making by facilitating multi-participant issue articulation, simultaneous evaluation of the pros and cons of each perspective, access to relevant codified knowledge, and preference assessment (Karacapilidis et al. 2005). Relationship capital investments create the collaborative links with diverse external stakeholders. In a business context, strategic decisions fall predominantly within this complex domain. Often they involve many changing variables (Harrison 1996) and are made at higher management levels (Cooke and Slack 1984, Yim et al. 2004). In the specific case of rapidly changing, highly competitive markets, certain approaches to strategic decision making need to be adopted. In these situations, it has been proposed that the organisational context needs to be shaped through approaches that build the capacity for collective sensemaking, challenge cognitive biases, manage political differences regarding what is valued and experiment to find patterns in the situation (Eisenhardt 1999). Human capital investments to develop the capacity of individual decision makers need to be supported by structural capital investments in processes that support collective learning about how to make decisions in these contexts. www.ejkm.com

213

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (209 - 216)

An extensive empirical study (Nicolas 2004) has been carried out into of how knowledge management strategies support strategic decision making. This related the pattern of transformations between tacit and explicit knowledge at each stage of decision making to different knowledge management strategies. It was found that a KM strategy based on the codification of explicit knowledge (often through technology) helps in the intelligence phase and then again at the end of the process where ―codified knowledge helps to legitimise the strategic choice‖ (Nicolas 2004, p27) produced by negotiating different valued outcomes. A KM strategy based on knowledge being developed and held by individuals (personalisation) and shared through dialogue, personal contact and shared experience, most effectively supports the intelligence phase when dialogue is needed to share experiences and emotional intelligence required to develop collective understanding of the issue. A KM strategy based on fostering knowledge communities that exchange and pool knowledge (socialisation) contributes most to the conception phase by enabling the rapid location of knowledge across the organization and creatively generating alternative solutions. This study demonstrates the synergistic interplay of human, structural and relational capital in strategic decision making.

3. Proposing an IC-based conceptual framework Decision making is a knowledge intensive activity. Knowledge is both raw materials, work in process and deliverable (Holsapple 2001). The effective use of technology, the use of experts and an integrated approach to internal and external collaboration are present in different ways in the three contexts that we have explored. Organizational decision making becomes a dynamic capability when individual decision makers have the capacity to learn from their decisions and the organization has the collective capacity adaptively to improve its decision making processes. Effectiveness in these five areas would support decision making in simple, complicated and complex contexts, through the recognised phases of decision making. This leads to five target areas for intellectual capital investments, summarised in Table 3, that when implemented in an integrated and coherent way, might be expected to support organizational decision making capability. Table 3: KM Factors that support organizational decision making as a dynamic capability Intellectual Capital Component

Human Capital

Structural Capital

IC investment area

Most significant contributions

Identifying experts and developing expertise. Supporting reflective practice.

Decision making in complicated situations. Sensemaking and identifying options. Managing cognitive bias, increasing range and depth of experience, increasing debate, challenge and openness. Developing expertise. Reflection on practice and self awareness to develop strategic decision making skills. Access to current and well structured explicit knowledge to provide input for simple decision making. Support expert decision making. Support data collection and selection phases of complex decision making. Recognising different kinds of decision making situations. Developing an appropriate repertoire of decision making modes. Gathering intelligence. Accessing multiple perspectives to formulate the decision to be made in complex contexts. Making connections to create knowledge to generate new options.

Using technology to structure, integrate and provide access to explicit knowledge resources. Decision review process.

Relational Capital

Adopting an integrated approach to internal and external collaboration.

This is not an exhaustive list of factors, but they connect intellectual capital investments into a coherent approach which address the major requirements of effective organizational decision making. This literature review suggests that these five factors make important contributions to developing organizational decision making capability. In so doing, they provide a framework to help KM practitioners orient their thinking to supporting an activity that plays a central role in organisational performance. Current empirical research is being carried out to understand the application of this framework in practice.

Acknowledgements This work was carried out in conjunction with members of the Henley Knowledge Management Forum based at the Henley Business School of the University of Reading.

www.ejkm.com

214

©Academic Conferences Ltd

Christine van Winkelen and Jane McKenzie

References Akinci, C. and Sadler-Smith, E. (2009) ""Hit or miss?" The role of intuition in decision making", British Academy of Management. University of Brighton, Allee, V. (2000) "Reconfiguring the value network", Journal of Business Strategy, Vol. 21, No. 4, pp. 36- 40. Argote, L. and Greve, H. R. (2007) "A behavioral theory of the firm - 40 years and counting: introduction and impact", Organization Science, Vol. 18, No. 3, pp. 337-349. Barney, J. (1991) "Firm resources and sustained competitive advantage", Journal of Management, Vol. 17, No. 1, pp. 99-120. Baum, J. R. and Wally, S. (2003) "Strategic decision speed and firm performance", Strategic Management Journal, Vol. 24, No. 11, pp. 1107-1129. Biyalogorsky, E., Boulding, W. and Staelin, R. (2006) "Stuck in the Past: Why Managers Persist with New Product Failures." Journal of Marketing, Vol. 70, No. 2, pp. 108-121. Buchanan, L. and O'Connell, A. (2006) "A brief history of decision making", Harvard Business Review, Vol. 84, No. 1, pp. 32-41. Campbell, A., Whitehead, J. and Finkelstein, S. (2009) "Why good leaders make bad decisions", Harvard Business Review, Vol. 87, No. 2, pp. 60-66. Cassidy, M., F. and Buede, D. (2009) "Does the accuracy of expert judgement comply with common sense: caveat emptor", Management Decision, Vol. 47, No. 3, pp. 454-469. Choo, C. W. and Johnston, R. (2004) "Innovation in the knowing organization: a case study of an e-commerce initiative", Journal of Knowledge Management, Vol. 8, No. 6, pp. 77-92. Cooke, S. and Slack, N. (1984) Making management decisions, Prentice-Hall, Englewood cliffs, NJ. Cyert, R. M. and March, J. G. (1963) A Behavioral Theory of the Firm, Prentice Hall, Englewood Cliffs, NJ. Drior, I. (2005) "Perception is far from perfection: the role of the brain and mind in constructing realities", Behavioral and Brain Science, Vol. 28, No. 6, pp. Drior, I. and Charlton, D. (2006) "Why experts make errors", Journal of Forensic Identification, Vol. 56, No. 4, pp. Eisenhardt, K. M. (1999) "Strategy as Strategic Decision Making." MIT Sloan Management Review, Vol. 40, No. 3, pp. 65-72. Ericsson, K. A. (2006) "The influence of experience and deliberate practice on the development of superior expert performance", Ericsson, K. A., Charness, N., Feltovich, P. And Hoffman, R. (Eds.) The Cambridge Handbook of Expertise and Expert Performance. Cambridge, Cambridge University Press Gladwell, M. (2006) The Power of Thinking Without Thinking, Penguin Books Ltd, Gore, J., Banks, A., Millward, L. and Kyriakidou, O. (2006) "Naturalistic decision making and organizations: reviewing pragmatic science", Organization Studies, Vol. 27, No. 7, pp. 925-942. Hammond, J. S., Keeney, R. L. and Raiffa, H. (2006) "The hidden traps in decision making", Harvard Business Review, Vol. 84, No. 1, pp. 118-126. Harrison, E. F. (1996) "A process perspective on strategic decision making", Management Decision, Vol. 34, No. 1, pp. 46-53. He, W. and Wei, K.-K. (2009) "What drives continued knowledge sharing? An investigation of knowledgecontribution and -seeking beliefs", Decision Support Systems, Vol. 46, No. 826-838. Holsapple, C. W. (2001) "Knowledge management support of decision making", Decision Support Systems, Vol. 31, No. 1-3. Karacapilidis, N., Loukis, E. and Dimopoulos, S. (2005) "Computer-supported G2G collaboration for public policy and decision-making", Journal of Enterprise Information Management, Vol. 18, No. 5, pp. 602-624. Klein, G. (1997) "Developing expertise in decision making", Thinking and Reasoning, Vol. 3, No. 4, pp. 337-352. Lipshitz, R., Klein, G. and Carroll, J. S. (2006) "Introduction to the special issue. Naturalistic decision making and organizational decision making: exploring the intersections", Organization Studies, Vol. 27, No. 7, pp. 917923. Maitlis, S. and Ozcelik, H. (2004) "Toxic decision processes: a study of emotion and organizational decision making", Organization Science, Vol. 15, No. 4, pp. 375-393. Mansingh, G., Osei-Bryson, K.-M. and Riechgelt, H. (2009) "Building ontology-based knowledge maps to assist knowledge process outsourcing decisions", Knowledge Management Research and Practice, Vol. 7, No. 3751. McKenzie, J. and van Winkelen, C. (2008) "Knowledge management (KM) for a changing world: callenges for third generation knowledge practice", International Journal of Knowledge, Culture and Change Management Vol. 8, No. 8. McKenzie, J., Woolf, N., van Winkelen, C. and Morgan, C. (2009) "Cognition in strategic decision making: a model of non-conventional thinking capacities for complex situations", Management Decision, Vol. 47, No. 2, pp. 209-232. Menon, T. and Pfeffer, J. (2003) "Valuing internal vs external knowledge: explaining the preference for outsiders", Management Science, Vol. 49, No. 4, pp. 497-513. Mitroff, I. I. (2008) "Knowing: how we know is as important as what we know", Journal of Business Strategy, Vol. 29, No. 3, pp. 13-22. Morse, G. (2006) "Decisions and desire", Harvard Business Review, Vol. 84, No. 1, pp. 42-51. Nicolas, R. (2004) "Knowledge management impacts on decision making process", Journal of Knowledge Management, Vol. 8, No. 1, pp. 20-31.

www.ejkm.com

215

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (209 - 216)

Pech, R. J. and Durden, G. (2004) "Where decision-makers went wrong: from capitalism to cannibalism", Corporate Governance Vol. 4, No. 1, pp. 65-75. Prahalad, C. and Hamel, G. (1990) "The core competence of the corporation", Harvard Business Review, Vol. 3, No. 79-91. Rittel, H. W. J. and Webber, M. M. (1973) "Dilemmas in a general theory of planning", Policy Sciences, Vol. 4, No. 2, pp. 155-169. Ross, K. G., Shafer, J. L. and Klein, G. (2006) "Professional judgements and "naturalistic decision making"", Ericsson, K. A., Charness, N., Feltovich, P. J. and Hoffman, R. R. (Eds.) The Cambridge Handbook of Expertise and Expert Performance. New York, Cambridge University Press Schmidt, J. B., Sarangee, K. R. and Montoya, M. M. (2009) "Exploring new product development project review practices", Journal of Product Innovation Management, Vol. 26, No. 520-535. Simon, H. A. (1960) The New Science of Management Decision, Harper Brothers, New York. Snowden, D. J. and Boone, M. E. (2007) "A leader's framework for decision making", Harvard Business Review, Vol. 85, No. 11, pp. 69-76. Stacey, R. (2001) Complex Responsive Processes in Organizations: Learning and Knowledge Creation, Routledge, London. Stalk, G., Evans, P. and Shulman, L. (1992) "Competing on capabilities", Harvard Business Review, Vol., No. 5769. Sveiby, K.-E. (2002) "Creating Knowledge Focused Strategies: Good and Bad Practices", Henley KM Forum 2nd Annual Conference. Henley Management College, UK, Tetlock, P. E. (1991) "An alternative metaphor in the study of judgement and choice: people as politicians", Theory and Psychology, Vol. 1, No. 4, pp. 451-475. van Winkelen, C., McDermott, R. and Grimwood, S. (2009) "Knowledge management's role in making visible the th thinking processes of experts", KM Forum Meeting, 4 June, Henley Business School, UK, Yates, J. F. and Tschirhart, M. D. (2006) "Decision-making expertise", Ericsson, K. A., Charness, N., Feltovich, P. J. and Hoffman, R. R. (Eds.) The Cambridge Handbook of Expertise and Expert Performance. New York, Cambridge University Press Yim, N.-H., Kim, S.-H., Kim, H.-W. and Kwahk, K.-Y. (2004) "Knowledge based decision making on higher level strategic concerns: system dynamics approach", Expert Systems with Applications, Vol. 27, No. 143-158.

www.ejkm.com

216

©Academic Conferences Ltd

On the Importance of Managing Intangible Assets as Part of Corporate Strategy Annie Brooking Anglia Ruskin University, Cambridge, UK [email protected] Abstract: Given that a high number of companies return value to investors via acquisition rather than a public offering the development of intangible assets is the bait that sets up the acquisition. This paper discusses how companies can fast track to high valuation by strategic growth of certain intangible assets such as customer tribes, brands, and intellectual property, comparing those strategies to larger companies. It further describes a strategic planning methodology using four asset categories (Market, Infrastructure, Human Centred and Intellectual property) to describe the enterprise as it would be if it had achieved its strategic goals. This state is referred to as the ―Dream‖. The Dream is characterised by a set of affirmations describing the ―health‖ of the enterprise’s assets. This is called a ―Dream Ticket‖. Keywords: intellectual capital, SMEs, IC methodology, strategic business planning

1. Introduction Strategic planning can be described as the process of determining what a business should become and how it should go about achieving its goals whilst capitalising on its opportunities and addressing its challenges. For management teams this can be a real challenge, especially if the strategic goal is couched solely in terms of revenue. For SMEs this is likely to be the case as the number one priority may well be just to stay afloat in very difficult times. For small companies defining corporate strategy can be a challenge, especially where they have limited access to business mentors or consultants that can assist in facilitating the process, so the CEO typically sets the scene by stating where the company needs to be in one or more years time by way of a mission statement that encapsulates values, vision and purpose. As such it’s a ―what we will do‖ type statement that may mean a lot to the management team that brainstormed it but hard for employees who did not participate in the process to grasp or believe in. In the same way as a book title may not convey the story to the reader until after the story has been read, a mission statement may not convey the strategy until the story can been shared. The Dream Ticket method enables management to share the story with employees. Entrepreneurs start companies by bootstrapping or raising angel or venture capital. The high technology sector typically takes both angel and venture capital as there is usually a time to market issue when the company will be in research and development mode and will not be generating revenue. Given most VC funds seek to realise a return on investment that requires an exit in less than ten years it doesn’t leave the entrepreneur much time to create an enterprise of value. This situation is exacerbated when there are several years of development ahead before product is ready for market. Ironically the more revolutionary or disruptive the technology, the longer the time to mass market share shortening the time to grab it. Further, given exit is a requirement for investors, the challenge facing the management team and Board is how to get the highest valuation at exit in the time available. As high technology companies tend to manifest value by way of intangible assets it makes sense to develop a corporate strategy that grows and develops them. The challenge is to figure out what assets to develop and how to do it for maximum value. The Dream Ticket method acts as the mechanism to do this.

2. The Dream Ticket methodology There are four sets of assets that are used to build a Dream Ticket: 

1. Market Assets, these are assets which belong to the company and give it power in the marketplace. They include brands, positioning, customer base, company name, backlog, distribution channels, collaborations, franchise agreements, licensing agreements, favorable contracts and so on.

ISSN 1479-4411 217 ©Academic Conferences Ltd Reference this paper as Brooking, A. ―On the Importance of Managing Intangible Assets as Part of Corporate Strategy‖ Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp217 - 224), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (217 - 224)



2. Infrastructure Assets, these are assets which include management philosophy, corporate culture, management and business processes, compliance to standards such as FDA, financial relations, methodologies and IT systems which enable the organization to function and communicate with its customers. Examples include methodologies for assessing risk, methods of managing a sales force, financial structure, databases of information on the market or customers, communication systems such as e-mail and teleconferencing systems. Also included is the financial status of the business, whether it is stable or at risk, wealthy or constantly seeking funding. Basically infrastructure assets are the elements which make up the way the organization works. These assets belong to the company, and make the company operationally strong.



3. Intellectual Property Assets are assets resulting from the mind that belong to the company and are protectable in law. These include patents, copyright, design rights, trade secrets, and trademarks.



4. Human Centred Assets include the collective expertise, creative, problem solving capability, leadership, entrepreneurial and managerial skills embodied by the employees in the organization. Key is knowledge of aspects of the business to include market knowledge and management expertise. Human Centered Assets also include psychometric indicators on how individuals may perform in given situations, such as in a team or under stress. These assets do not belong to the company but are contracted to the company by way of employee contracts, unless they can be made explicit, thus becoming Infrastructure assets (Brooking, A. 1998)

Thus market, infrastructure and intellectual property assets can be bought and sold, human centred assets can’t. These four categories of assets are used to develop a Dream Ticket that describes the business as it would be if it had already achieved its strategic goal. For example if the goal of the business was to double its revenue to Euros 10 million this may require a more favourable relationship with its target customers than it had today.

3. Case study: Company X Take as an example a 30 person company (X) that manufactured facial scanning systems that assessed the photo damage in a woman’s skin and estimated her skin age. If she was a sun worshipper or had excessively used sun beds and tanning salons a 30 year old woman’s skin age might be calculated as 35 and a woman of the same age who had always used sun block might be assessed as having the skin age of 25. The target market is spas and salons who charge the client £50 for a skin age consultation. For company X a desirable set of market assets that may enable them to double revenues could be: 

M1. Every customer who buys from us recommends us to three of their peers.



M2. Every salon or spa who buys from us buys a second system in less than two years.



M3. Every prospect we have in the UK recognises our brand.



M4. Every woman over the age of 30 has a skin consultation twice a year.



M4. All salons and spas retain 100% of their customers.



M5. Every UK health magazine has an article on skin scanning four times a year.



M6. There is a stable of celebrity key opinion leaders who are followed by women aged 30.



M7. Doctors and medical practitioners recommend tracking photo damage as part of a woman’s health regimen.



M8. Company X takes 20 phone queries a day from prospects concerning its facial scanner.



M9. The salon and spa business is growing by 20% per annum

Each of the affirmations above translates or contributes towards an intangible market asset. 

M1. Refers to a set of evangelists



M2. Is repeat business



M3 and M5. Refer to brand recognition

www.ejkm.com

218

©Academic Conferences Ltd

Annie Brooking



M4. Refers to product ROI



M6 and M7 Will contribute to market pull



M8 Is market pull



M9 Shows the market is expanding not retracting, reflecting a business opportunity.

For company X if the above affirmations were all true it could be said that the business was in a favourable position to double its revenue. Below are outlined intellectual property assets for Company X. 

IP1. The facial scanner is protected by a thicket of 5 patents.



IP2. The brand name of the facial scanner is trademarked in all target markets.



IP3. The patent portfolio is managed and measured for ROI every year.



IP4.’All software is patented

This set of assets would mean that the scanner would be have a strong competitive position from a technology perspective. With respect to human centred assets the following set would be desirable given the company wanted to double revenues. 

H1. Every employee knows how to take a customer query and follow through



H2. Every employee understands the scanner technology and can explain its USPs



H3. All sales executives have deep product knowledge



H4. The marketing team are all intimate with the target market sector



H5. The company has a high staff retention rate



H6. All customer training staff have previously worked in salons or spas



H7. All knowledge relating to the manufacturing process is documented.

Human centred assets H1, H2, H3, H4 and H6 all demonstrate that staff is fit for purpose. H5 means the company probably has content employees and H7 means that tacit and explicit knowledge are documented for this task, this is desirable for a potential acquirer.. Finally with respect to infrastructure assets the following would be desirable: 

I1. All products have FDA approval



I2. All products are CE marked



I3. All customers are correctly recorded in a database for customer support



I4. Customer supports sells support contracts to all customers



I5. Quality manuals are updated and reviewed monthly



I6. All prospects are tracked in a sales tracking system

The above affirmations relate to the strength of the infrastructure of a company intent on increasing sales by use of internal systems. FDA approval is a major asset for any company wanting to branch out in the USA and having clean databases is an important asset for any company wanting to both increase sales and harvest incremental revenues from the sale of support contracts to customers. The four sets of affirmations make up the Dream Ticket for company X. It’s easy to share this with employees who are then able to visualise where the business needs to go to achieve its goal. They understand the story. The next step is to measure the gap between where the business is now and the Dream Ticket. An index is assigned to each affirmation reflecting its relative strength, 5 being strong and 0 weak. The business creates methods for measuring each asset’s index. Customer surveys, percentage through a process such as CE marketing, staff questionnaires for competence, auditing databases or getting staff such as telesales and customer support to validate and clean databases are a few measures that are typically used. In using this method for over ten years in numerous high technology businesses estimates by the management team have proven to be just as accurate as in-depth analysis, survey and measurement (which can take a team months to complete) especially in small technology companies where the staff www.ejkm.com

219

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (217 - 224)

tends to be both smart and insightful. The benefit of this is that the Dream Ticket can be brainstormed by the management team and indexed in the same session. Once the assets are indexed they can be plotted on a target. If the asset is strong it will have an index of five and be at the centre. If it is weak it will have a zero value and be at the outer edge of the target. Dream Ticket for Company X with Index (5 is strong) index/5 M1. Every customer who buys from us recommends to us three of their peers. 1+ M2. Every customer who buys from us buys a second system in under two years. 1+ M3. Every prospect we have in the UK recognises our brand. M4. Every woman over the age of 30 has a skin consultation twice a year. M4. All spas retain 100% of their customers. M5. Every health magazine has an article on skin scanning four times a year. 2+ M6. There is a stable of celebrity key opinion leaders who are followed by women aged 30 M7. Doctors and medical practitioners recommend tracking photo damage as part of a woman’s health regimen. M8. Company X takes 20 phone queries a day from prospects concerning its facial scanner. M9. The salon and spa business is growing by 20% per annum IP1. The facial scanner is protected by a thicket of 5 patents. IP2. The brand name of the facial scanner is trademarked in all target markets. IP3. The patent portfolio is managed and measured for ROI every year. IP4. All software is patented H1. Every employee knows how to take a customer query and follow through 2+ H2. Every employee understands the scanner technology and can explain its USPs 3+ H3. All sales executives have deep product knowledge H4. The marketing team are all intimate with the target market sector H5. The company has a high staff retention rate H6. All customer training staff have worked in salons or spas H7. All knowledge relating to the manufacturing process is documented. I1. All products have FDA approval 0+ I2. All products are CE marked I3. All customers are correctly recorded in a database for customer support 2+ I4. Customer supports sells support contracts to all customers I5. Quality manuals are updated and reviewed monthly I6. All prospects are tracked in a sales tracking system

2 2 3

0 0 1 5 5 1 2 5

4 4 5 4 2

5

2 3 2

At this stage it may be useful to note the assets direction of travel. If the asset is getting stronger, that is measures are underway to strengthen it, its arrow points toward the centre of the target, if it’s getting weaker the arrow points to the outside of the target as shown in Figure 1. Assets moving away from the target are not always a bad thing. In young companies they typically show something wrong with the business. In more mature companies they may show a change in strategy where perhaps a particular asset is no longer considered to be of value, or a patent has been in place for 18 years and has only two years of its life left. However the position of all assets on the target will be relative to the goal and also to the context of the business at the time. This pictorial representation tells us a lot about the Company X. We can see that it has a number of assets in the middle of the target: 

I2. All products are CE marked (5)



H5. The company has a high staff retention rate (5)



IP1. The facial scanner is protected by a thicket of 5 patents (5)



IP4. All software is patented (5)

www.ejkm.com

220

©Academic Conferences Ltd

Annie Brooking



M9. The salon and spa business is growing by 20% per annum (5)

Figure 1: Direction of travel of assets From a strategic planning perspective this is good, and especially good as this company is small and early stage. Its target market is growing, it has taken care to file intellectual property and high staff retention tells us the company is stable from a critical knowledge function perspective. This is what we would expect to find in a high technology start-up. The issue for Company X is to build its strength in the market and fix its infrastructure so that it was able to both target and support its customers generating both loyalty and revenue. The asset M1 tells us that the company seeks to grow through referral, creating a tribe from its customer base and this is a good strategic move for a company that is resource constrained. M6 and M7 tell us that the business thinks that it understands who the key influencers in its target market are; the issue now is how to make these assets strong so the Dream Ticket becomes a reality. Once the target has been populated the next step is to design measures that will close the gap and move the assets toward the centre of the target as quickly as possible. In this particular Dream Ticket there are twenty six assets. This is quite a low number, as this business scenario is only looking at one product and one distribution channel in one country. When this business went global and included a full product range it had close to sixty assets it was managing. Designing sixty measures to grow sixty assets is too complex so assets need to be grouped and prioritised so that ―super‖ measures can be designed to strengthen the Dream Ticket as a whole. In this particular scenario measures might include a KOL generation and management programme, or partnering with a medical organisation that would verify that increased photo aging is linked with other diseases like skin cancer. An example of grouping assets is shown in Figure 2.

4. Asset grouping This company is a biotechnology company that was seeking to generate revenue by licensing intellectual property. The patent portfolio is quite strong with the exception of one weak patent that can be viewed (IP7). However the key weakness here is the lack of staff with appropriate expertise www.ejkm.com

221

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (217 - 224)

and this can be seen from the number of human centred assets that remain on the outside of the target. Early stage high technology companies cannot afford to have either weak intellectual property or weak human centred assets. The cross target linking of human centred assets and infrastructure assets tells us the wrong people are in place to set up the infrastructures that are required. So people will need to be trained or replaced. The mix of market assets here: four strong the other seven weak is in fact because whilst there is a market for the service this company wishes to offer it has neither the market presence, infrastructure nor people to grab it.

Figure 2: Asset coupling Once the measures have been designed to strengthen the assets their cost can be estimated and a budget drawn up. The measures become part of the tactical and timelines planned. If the business does not have the finance to fund the plan then either less expensive measures can be designed or a less ambitious goal agreed. Should that be the case the Dream Ticket may need to be redesigned completely or in part, and re-indexed. It is likely that some assets will change their relative strength if a less ambitious goal is chosen, for example the business may be in a stronger position to increase revenues by 50% rather than doubling them. Intangible assets have been shown to be the major driver for acquisition of high technology businesses. Sometimes this manifests as an intellectual property portfolio of patents, trademarks and design rights. Sometimes it’s access to market via a distribution channel or a network of KOLs that an acquiring organisation can leverage for their own product range. One thing is certainly clear to this author, if exit is to be by acquisition the value of assets changes depending upon the goals of the acquirer. Different context, different value, and for that reason research to put a monetary value on individual assets has ceased as it is impossible to predict who will acquire the business. The alternative strategy is to build a strong business composed of strong intangible assets in the hope that there is an acquirer who will consider the entire package valuable rather than just the intellectual property portfolio or the customer base. That said, building a Dream Ticket that describes the business as it will be in two or three years time gives the business the opportunity to build a profile of potential acquirers for the business as a whole

www.ejkm.com

222

©Academic Conferences Ltd

Annie Brooking or in part. This also opens up the opportunity to sell the business ―many times over‖ by transferring ownership of intangibles to a holding company and licensing them all or in part to various subsidiaries then selling them off one at a time, in parallel building a valuable holding company that has a royalty stream from licensing, and consulting revenues using critical knowledge skills only found in the holding company, all of which are also intangible assets. This is a much more pro-active strategy with intangible assets that sets out from day one to plan for value and exit.

5. Conclusion In a knowledge based economy, where the growth of high technology companies is the business trend that delivers highest returns to investors, building valuable intangible assets is the corporate strategy. The Dream Ticket method allows the management team to describe and build a Dream scenario that is easy to understand and communicate to all employees and stakeholders in the business to both achieve its corporate goals and in parallel build and position a company of high value for potential acquisition.

References Boekstein, B (2009) ―Acquisitions reveal the hidden intellectual capital of pharmaceutical companies‖, Journal of Intellectual Capital, Vol 10 Issue 3, pp 389-400. Brooking, A. (2009) ―Dream Ticket: Using Intellectual Capital Management as a Strategic Planning Paradigm‖, th ―Dream Ticket, Using Intellectual Capital Management as a Strategic Planning Paradigm‖, 5 Workshop on Visualising, Measuring and Managing Intangibles and Intellectual Capital, Dresden, October 2009 Brooking, A. (1996) ―Intellectual Capital: Core Asset for the Third Millennium Enterprise‖, Thompson International Business Press, London Brooking, A (1999) ―Corporate Memory: Strategies for Knowledge Management‖, Thompson International Business Press, London. Brooking, A. Board, P, Jones, S. (1998) ―The Predictive Potential of Intellectual Capital‖, International Journal of Technology Management, Volume 16, Nos 1/2/3 pp 115-125. Durst, S (2008) ―The Relevance of Intangible Assets in German SMEs, Journal of Intellectual Capital, Vol 9 Issue 3, pp 410-432.

www.ejkm.com

223

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (217 - 224)

www.ejkm.com

224

©Academic Conferences Ltd

Valuing Knowledge Assets in Renewable Energy SMEs: Some Early Evidence Maria Weir1, Robert Huggins2, Giovanni Schiuma3, Antonio Lerro3 and Daniel Prokop2 1 Intellectual Assets Centre, Glasgow, UK 2 University of Wales Institute, Cardiff, UK 3 University of Basilicata, Potenza, Italy [email protected] [email protected] [email protected] [email protected] [email protected] Abstract: It is widely recognized that knowledge-based resources and assets are fundamental to performance improvement and organizational value creation. Limited progress has been made, however, in valuing and managing organizational knowledge in Small and Medium Enterprises (SMEs) operating in the renewable energy sector. This paper provides first insights of an ongoing research project managed by the Intellectual Assets Centre in Scotland, in order to support the adoption of knowledge asset management initiatives for driving the improvement of value creation mechanisms of SMEs operating in the renewable energy sector. We employ research related to resource-based and knowledge-based theory and performance measurement and management, to gain a better understanding of how Scottish SMEs operating in the renewable energy industry acquire and manage knowledge assets in their bid to improve performance and value creation. Using a sample of organizations from the Scottish renewable energy sector we draw first insights about their perception and orientation to identifying, measuring and managing knowledge, and to qualitatively identify a feasible set of knowledge resources and assets potentially driving performance improvement. The first evidence indicates an awareness of the relevance of the knowledge-based factors, and engagement in knowledge acquisition and creation processes. These firms possess a good endowment of knowledge assets, with significant internal knowledge management processes, but also external inflows from agents such as firms and education or research institutions. However, the visible lag in the exploitation of knowledge assets and processes, together with observed under-financing of the sector and difficulty in accessing skilled labor, indicates the need for efforts to better address specific needs of renewable energy sector SMEs. Keywords: renewable energy, renewables, intellectual capital, knowledge asset management, Scotland, SMEs

1. Introduction Great attention has increasingly been placed on the role and relevance of the Knowledge Asset Management (KAM) to support and drive the improvement of organizational performance in the energy industry (Edwards 2007; Foxon et al. 2005). However most of the interest has been developed by big oil companies searching for improvements in their efficiency (Etkind et al. 2003; Nelson 1997; Smith and Farquhar 2000). On the other hand, SMEs operating in the energy industry, and particularly in the renewables sector, are increasingly recognizing the relevance of KAM, even if they still do not have formal KAM initiatives in place within the organization. Indeed, SMEs’ knowledge and intangible assets are fundamentally managed implicitly, i.e. without the use of formal approaches and tools (Weir et al. 2009). Moreover, although knowledge–based resources and assets are recognized as being fundamental to organizational success (Carlucci et al. 2004; Schiuma and Lerro 2008; Schiuma et al. 2008), the tools for valuing organizational knowledge in renewable energy industry are still crude and often inadequate. In this paper, we suggest a process for valuing organizational knowledge that can be effectively applied in the energy industry and specifically in renewable sector. On the basis of a literature review (Wilcox King and Zeithaml 2003), Using a sample of organizations from the Scottish Small and Medium Enterprises (SMEs) in the renewable energy sector, we draw first insights about their perceptions and orientations to identifying, measuring and managing knowledge. Following this discussion, the paper defines the next steps of the research, which, moving from the recognition of an awareness of the relevance of the knowledge assets as key-source of organizational value creation dynamics, advocates efforts to better address specific needs of renewable energy sector SMEs. ISSN 1479-4411 225 ©Academic Conferences Ltd Reference this paper as Weir, M, Huggins, R, Schiuma, G, Lerro, A and Prokop, D. ―Valuing Knowledge Assets in Renewable Energy SMEs: Some Early Evidence‖ Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp225 - 234), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (225 - 234)

2. A methodology for valuing organizational knowledge assets To achieve a more fine-grained insight into knowledge resources and assets, we present a four-step methodology for valuing organizational knowledge in renewable energy industry constructed from practicing owners and managers’ perspectives, and we test it with 8 Scottish organizations.

2.1 Step 1 - defining scope: Industry and organization selection Industry and organization selection was driven by the aims of an ongoing research project managed by the Intellectual Assets Center in Scotland, in order to support the adoption of knowledge asset management initiatives for driving the improvement of value creation mechanisms of SMEs operating in the renewable energy sector. In particular, we selected established SME companies having welldefined boundaries and operating in: solar energy, i.e. the energy of the sun’s rays that can be converted into other forms of energy, such as heat or electricity; wind power (onshore and offshore), i.e. the harnessing of wind by turbines that convert the energy into electricity; hydrogen, as potential use in the operation of hydrogen-powered automobiles; biomass, which includes plants grown for the production of fuels; and hydro-power (wave and tidal), harnessing of the sea or river water by turbines that convert the water energy into electricity. Similarities in the competitive environment and value chains suggest consistency in industry context across competing organizations (Porac et al. 1995). Established industry boundaries reduce potential delineation issues that often arise when industry boundaries are fuzzier, or when an industry’s competitive dynamics are influenced by different configurations of strategic groups (Porac et al. 1989; Mehra 1996). This control increases the likelihood that we can identify a relatively comprehensive inventory of knowledge resources and assets, and that owners and managers in the industry could evaluate their perceptions of the knowledge factors’ importance to their companies’ strategic success. In this case, we restricted the renewable energy by geography, domain, ownership and size (Graeff 1980). We solicited more than one hundred renewable energy industry companies and drew together a complementary cohort. We sent letters to these companies describing the project, requesting a participation in the research project and an interview with the owners or top-managers as well as inviting them to participate in a series of Workshops and Masterclasses about the project. In this paper we focus on our interactions with eight organizations. The involved companies had different core-business but by comparing key-variables for the sample of organizations we recognized no significant differences. These organizations include: 

Organization A – a major regional market player in the civil engineering and construction sector, also recycling materials and supplies through its subsidiary business – Highland Recycling Limited.



Organization B – an environmental consultancy specializing in renewable energy, environmental and planning services for organisations investing in renewable energy and environmental projects.



Organization C – a firm promoting better use of energy and energy resources available locally through provision of education for local schools and communities on energy and energy efficiency, and acting as a promoter of developing renewable energy sources dominating in the locality of Argyll.



Organization D – a firm that concentrates its operations on servicing and operation of wind turbines and towers.



Organization E – a firm founded with an aim to help reduce the carbon footprint of buildings through improvements to the fabric and internal equipment.



Organization F – a specialist in the provision of equipment and services for renewable energy projects.



Organization G – a specialist in IP development and consultancy.



Organization H - a private membership body for the companies and organisations working and/or interested in energy sector.

2.2 Step 2 - research and protocol design: Building scholars knowledge The next step began with in-depth research. On the basis of insights from a literature review (Weir et al. 2009) we improved our familiarity with industry-specific issues and terminology. This www.ejkm.com

226

©Academic Conferences Ltd

Maria Weir et al.

understanding enhanced our credibility and enabled us to investigate richer and more probing questions when conducting the interviews. To structure the interviews with the owners or top managers, we developed a protocol that included an overview letter and a broad outline of questions. The protocol was pre-tested on a panel of academics and experts of knowledge management, performance management and energy industry, soliciting feedback regarding clarity, overall impressions and terminology to be used to improve organizations’ participation. Based on their feedback, we decided to use the label ―knowledge” as broadly consisting of research and development, ideas, skills, expertise and other information that is, or potentially can be, used to make the operation of a company more effective, ―knowledge management” (KM) as the group of approaches, methodologies and tools used in an organisation to identify, create, represent and exploit knowledge to improve organisational performance and value creation, and ―knowledge assets” as any resource made of, or incorporating, knowledge which provides an ability to carry out a process or an activity aimed to create and/or deliver value.

2.3 Step 3 - data collection: Survey of owners and top management to identify organizational knowledge resources and assets To identify an inventory of organizational knowledge resources and assets we interviewed a range of renewable energy companies’ owners and top-managers. Prior to each interview, we faxed and emailed the overview to the owners and top-managers to establish expectations for the meeting and provide initial guidance in scoping the knowledge resources and assets. During each interview, we asked respondents to identify the ―knowledge or skills that may provide competitiveness‖ at their organization and in the industry. The interview involved an iterative process as the respondents talked through the sources of competitive advantage at the company and worked to articulate organizational knowledge on an appropriate scale, specific enough to clearly relate to ways organizational knowledge can add value in the industry, and general enough to allow for reapplication to support value in future endeavours. Interviewers used a line-by-line open coding method to generate a list of the knowledge resources and assets discussed during that interview (Strauss and Corbin 1990). For each organization, we then compared our lists and our notes for content, tone and accuracy and, following a discussion, determined a final company-specific list. Based on these interviews, we identified 15 knowledge assets, 5 dimensions of knowledge assets, and 7 knowledge management processes in the renewable energy industry. In particular, about the dimensions of knowledge assets, it emerged the relevance of Human Capital (HC), i.e. competences, skills, abilities, know-how and leadership of their employees, Relational Capital (RC), i.e. relationships with customers, suppliers, community, regulators and other stakeholders, Organizational Capital (OC), i.e. all those assets tangible and intangible in nature characterizing companies’ business model and relevant for creation, acquisition, use and diffusion of knowledge across organizational structure, Intellectual Properties (IP), i.e all codified knowledge protected by law such as copyrights, patents, ecc., and Technological Capital (TC), i.e. all those assets characterized by a technology-intensive knowledge. We made every effort to describe knowledge-based factors that were simple, not ambiguous and not ―double-barreled‖ (DeVellis 1991). External readers familiar with the issues reviewed the final list of knowledge assets, dimensions and processes to confirm their clarity and comprehensiveness as well as the capacity of these assets to adequately define the business. These insights were incorporated into an explorative survey.

2.4 Step 4 - data collection: Explorative survey of owners and top managers to value organizational knowledge resources and assets In order to value organizational knowledge resources and assets, a questionnaire was administered with owners and managers of twenty-four companies. These teams completed surveys appraising the knowledge factors generated through the interviews. The questionnaire was divided in four main sections for a total of 13 questions, 9 of which were using a 5-point Likert scale (Q1, Q3, Q5-10, Q13). In the Likert scale we used, point 1 means ―unimportant‖ or ―never‖ and point 5 means ―very important‖ or ―very frequently‖. Other questions (Q2, Q11) offered multiple choice answers with tick boxes, and included two variants: a) ―Yes‖, ―No‖, ―Don’t know‖ answers (Q4); b) ―Yes‖, ―No‖ answers (first parts of: Q9 and Q11). Finally, Q12 asked an open-end question where expected input data format was a number. Data obtained were not manipulated and were reported as collected. The aim of the first section (Q1Q3) was to investigate the level of awareness of the importance to effectively manage knowledge www.ejkm.com

227

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (225 - 234)

within the organizations, identifying the ways by which knowledge is important to the renewables SME companies as well as the most important knowledge dimensions for maintaining or improving the competitiveness of the organization. The second section (Q4-Q7) was finalized to better understand the state-of-the art about the possession and the exploitation of knowledge assets by companies and the knowledge management processes organizations consider most important for their competitiveness. Specific attention was paid to investigate the knowledge sources that organizations use to undertake their innovation dynamics. The third section (Q8-Q11) aimed to explore the knowledge flows and the relationships system in the renewable energy industry, in terms of collaboration engaged with universities or research centres around specific activities, relationships with the big energy companies, emergence of supply-chains among renewable energy industry companies, nature and characteristics of these relationships. Finally, the fourth section (Q12-Q13) was aimed to investigate the issue of the new products or services or adaptations to existing products and services made by the companies in the last three years as well as the most important constraints that the companies face in acquiring or creating the knowledge required to maintain or improve innovation dynamics and competitiveness. The questionnaires were treated confidentially. A total of eight usable surveys were returned, reflecting a response rate of 33%. The low level of the sample and the response rate determined that the survey was considered as a pre-test survey mainly based on qualitative data supported by the first empirical investigation to be further developed in the next steps of the ongoing research. Additionally, the data on companies was supported by the qualitative information.

3. First evidence and implications of organizational knowledge in Scottish renewable energy industry SMEs Energy industry companies, and in particular SMEs in the renewable energy sector, are increasingly asked to create and support competitive advantages, especially in situations of rapid and unpredictable market and normative change, pressures about environmental issues, and increase of energy demand from developing nations. The knowledge asset management represents a fundamental way to improve and develop organizational value creation capacity. This methodology for valuing owners and managers’ perceptions of knowledge assets makes a novel and important contributions to the Knowledge Management research streams. The level of awareness of the importance to effectively manage knowledge within the organizations is relatively high, scoring 4.37 on a 5-point Likert scale as indicated by companies. However, the identification of the ways by which knowledge is important to the renewable energy SMEs shows that companies still do not address some specific mechanisms enhancing their performance dimensions. In particular, SMEs do not recognize specific stronger impact of knowledge assets in terms of innovation, organizational value creation, competitors differentiation, strategic planning, finance, but attribute the same importance to all of these. Regarding the most important knowledge dimensions for maintaining or improving the competitiveness of the organization, as shown in Figure 1, it emerges that SMEs pay particular attention to the Relational Capital (RC), i.e. relationships with customers, suppliers, community, regulators and other stakeholders (4.37), as, for instance, one interviewed does with its involvement in educating the local schools and communities on energy and energy efficiency. Moreover, the SMEs consider the Human Capital (HC), i.e. competences, skills, abilities, know-how and leadership of their employees, also very significant (4.25), what could be best shown by the example of a consulting firm employing well experienced staff, many of whom belong to specialist chartered bodies, such as Royal Town Planning Institute. However, competitive factors linked to Intellectual Properties (IP) and Organizational Capital (OC) have good relevance (respectively 3.12 and 3.00), while, surprisingly, the importance assigned to the Technological Capital (TC) has a lower value (2.50). This is a first aspect of difference in respect to the big energy companies that traditionally pay particular attention to the technological dimension of knowledge management (Weir et al. 2009). Moreover, at an aggregate level, renewable energy SMEs consider to possess a good endowment of knowledge assets, particularly in terms of reputation of the organization, organization’s customers databases, organization name and logo, processes and technological know-how, relationships and quality of the human capital. As firm outlined: „[Our] Major skills base is in the planning, in the technological bits and also in project management... It‟s a competitive market but not many companies actually specialize in the same way.‟ www.ejkm.com

228

©Academic Conferences Ltd

Maria Weir et al. „Currently, at this stage I suppose we‟ve got a bit of a mix of consultancy with contractor... Depending on our client... we fulfil a consultancy role, supply, installation...essentially some contractor, but we‟re a manufacturer as well. And some of that we outsource so that‟s a combination we‟re using just now.‟ „Well, we‟re first and foremost a consultancy, but about 30% of our sales come through subcontracted work... so it‟s quite a bit of outsourcing. They‟re all suppliers that we know.‟

IP

OC

TC

RC

HC 0.00

1.00

2.00

3.00

4.00

5.00

Figure 1: Importance of the organizational factors for competitiveness However, most of the organizations from the sample are aware that for majority of these assets they have not taken steps to fully exploit or manage them and recognize the wide space for improvement in these areas in order to gain new sustainable competitive advantages in their industry: „There‟s an awful lot of noise in the market about being the first wave business] or the first person to build a wave farm, the first person to build a prototype grid connected for. None of that matters if the devices actually don‟t deliver what the client‟s expected.‟ „We are brand led but I think our model has to change to adapt to the market…if you‟re totally brand led then the brand should be strong.‟ Figure 2a and Figure 2b depicts the levels of possession and exploitation of knowledge assets from Scottish renewable energy companies, taking into account that the respondents had the opportunity to select multiple choices in the questionnaire. For this reason, the scale of the graphs in the figures represents the cumulative responses for each item. In terms of the importance of knowledge management processes for organizational competitiveness, as shown in Figure 3, the sampled renewable energy SMEs pay particular attention to the knowledge sharing within the organization (4.12), - i.e. the creation of organizational capital - are best pictured by the example of NSIG where all members (companies and organizations) have access to a wide range of skills and services. In addition to that knowledge transfer to the organization (3.87) - through the formation of relational capital - and the codification of know-how (3.75) - the transfer of human capital to organizational capital - are also significant knowledge management processes, as has emerged from the focus group for Scottish renewable energy SMEs: „Certainly, the wind industry is still evolving. [But] the feedback [about specific installations] from the performance in the field back to the manufacturers is huge.‟

www.ejkm.com

229

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (225 - 234)

12 10 8 6 4 2

Organisation processes and know how

Organisation brand(s)

Organisation logo(s)

Organisation name

Quality of products/services

Organisation’s customer database

Reputation of the organisation

0

Figure 2a: Possession (in blue) and exploitation (in red) of knowledge assets 12 10 8 6 4 2 0 Technological Goodw ill in the Supplier know how organisation relationships

Trade secrets

Relationship capital netw orks

Human resources

Intellectual Property (IP)

Figure 2b: Possession (in blue) and exploitation (in red) of the knowledge assets Regarding the process of knowledge creation through learning mechanisms (3.12) facilitated by organizational capital, most of the respondents underline again the importance of the relationships with other firms, universities and research centers and institutions, as in the case of a firm associated with the University of the Highlands and Islands, while limited attention is paid to communities of practice. Decision Support Systems are not considered particularly relevant for competitiveness and this aspect can be a second element of differentiation in respect to the traditional energy industry companies. However, it is important to underline that companies do not still have explicit awareness about how knowledge assets are affected by each process and in what way. Regarding the sources of knowledge for undertaking innovation, as shown in Figure 4, at the aggregate level, SMEs effectively use a wide spectrum of modalities. According to a Likert scale 1-5, strong relevance is assigned to the external dimension of sourcing knowledge, in terms of analysis of the competitors (3.75), conference, trade fairs and exhibitions (3.62), scientific journals and trade and technical publications (3.50), suppliers and customers (3.12). However, internal sources (3.25) are still important at the same level as the external ones, while potentialities still not fully exploited stand in building better collaborations with commercial laboratories, institutions, research centers and universities as well as with consultants highly-skilled in KM in renewables. Moreover, it emerges that SMEs source knowledge mainly from organizations and companies located in Scotland, and then in UK, while contacts overseas are very limited. This insight highlights a possible need for a stronger internationalization of the SMEs and alliances to search new inspiring sources of knowledge driving their value creation dynamics:

www.ejkm.com

230

©Academic Conferences Ltd

Maria Weir et al.

Decision Support System

Technologies for storing knowledge

Mapping knowledge Knowledge creation through learning mechanisms Knowledge sharing within the organization Knowledge transfer to the organization

Codification of know-how 0

1

2

3

4

5

Figure 3: Importance of the knowledge management processes for competitiveness „Certainly the wind industry is still evolving….we‟re heading towards a sort of combination of licensing and joint venture.‟ „We‟ve got a series of VC investors, and we‟re developing technology which will eventually allow us to become an infrastructure development company.‟ „We may want to enter emerging markets through license agreements or franchising, although there must be a fair package in place.‟ However, the routes to knowledge and new markets are not without their own problems: „The franchise model didn‟t work for us because the market in which our technology provider was operating wasn‟t quite the same.‟ „Scottish Power should bring us their competence as an organisation, but the problem is that they are operating in slightly different markets and looking at different models.‟ The latter evidence is supported by the evidence emerging from the engagement of the companies with universities and research centres in the past three years. These are presented in Figure 5 according to a Likert scale 1-5. The main collaboration activities are mostly concentrated on the recruitment of highly-skilled people, although with a relative low level of importance (2.75), while other activities, such as patents development, contract research, and knowledge transfer partnerships are still in exploration stage (Figure 5). One example of such weak performance in collaborations is a firm which as a major regional market player owns a reasonable amount of Intellectual Capital, however it does not protect it or exploit well: „We have accumulated quite a lot of Intellectual Capital, but none of that [is] really captured or registered…at the moment.‟ Nevertheless, this kind of relationship is not considered strategically relevant for the SMEs.

www.ejkm.com

231

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (225 - 234)

Institutions Market studies Scientific journals and publication Conference, trade fairs, exhibitions Commercial labs Research centres Universities Consultants Competitors Customers Suppliers Internal sources 0

1

2

3

4

Figure 4: Sources of knowledge for undertaking innovation

Knowledge transfer partnership Recruitment of high-skilled people Establishment of a new company Licensing of technology or knowledge Patent development Bespoke training or executive education Contract research undertaken by a university Collaborative research

0

1

2

3

4

5

Figure 5: Activities of engagement of SMEs with universities and research centers In contrast with the importance of the relationships with universities and research centers, SMEs consider the relationships with big and/or multinational energy companies strategically important for their current and future competitiveness. In particular, the big energy companies are a means for SMEs to access market or competitor intelligence, professional information, skills and expertise, and results of research and development activities. „We have major intellectual assets in the form of the collection of other people‟s information.‟ Although this is not always the case in such collaborations: „Our friends E.On said: “no, we‟re not going to tell you that [marketing strategy]. We‟re just not going to tell you that.”‟ www.ejkm.com

232

©Academic Conferences Ltd

Maria Weir et al. „I think sometimes they [large energy company] see us as being a pain in the backside.‟ Nevertheless, this evidence could strongly drive a better definition and implementation of the policies aimed to support the growth of the renewable energy industry in Scotland, in terms of enhancing collaborative dynamics and investments attractions from big energy companies. Figure 6 depicts these aspects according to a Likert scale 1-5. Access to market or competitor intelligence Access to skills and expertise Licensing of technology or knowledge Access to professional information and intelligence Access to research and development Access to scientific information Access to new technology

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Figure 6: Types of knowledge frequently accessed from big energy companies Moreover, it emerges that the supply chains of the renewable energy industry companies are still evolving, and that the nature of the relationships is mainly based on competitive dynamics, despite considering the limited number of the companies operating until today in the renewable energy industry. Finally, as shown in Figure 7, according to a Likert scale 1-5, it emerges that the most important barriers that the SMEs face in acquiring or creating the knowledge they require to maintain or improve competitiveness are mainly linked to the inability to access suitable finance to plan investments in knowledge assets, and to access to skilled labour (3.12 and 2.75 respectively), to the quality or applicability of available business support or advice (3.12), as well as to issues related to training and education. An illustration to these results is a firm highly dependent on grants, but plans to switch its financing model to for-profit business to achieve a higher degree of commercialization: „So the main aims outlined are that the company needs to be less grant dependent, more commercial…‟ Unable to access suitable equipment or plant Unable to access suitable training Unable to access skilled labour Unable to access suitable finance Quality or applicability of available business support Inapplicabiliy of knowledge created by others Unable to access relevant collaborators Unable to access relevant networks 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Figure 7: Barriers for acquiring or creating knowledge in renewables

4. Closing remarks In this paper we have presented a research protocol to identify a domain of organizational knowledge resources and assets within SMEs operating in the renewable energy industry. Using a sample of

www.ejkm.com

233

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (225 - 234)

organizations from the Scottish renewable energy sector SMEs, we draw first insights about their perceptions and orientations to identifying, measuring and managing knowledge, and to qualitatively identify a feasible set of knowledge resources and assets potentially driving performance improvement. This first evidence shows an awareness of the relevance of the knowledge-based factors. Since energy industry has been traditionally focused on tangible assets and renewable energy industry is still in its infancy regarding the business model definition and knowledge assets management, the emergence of an awareness of these issues is a relevant insight in respect to the traditional approaches applied until recent years. Generally, firms possess a good endowment of knowledge assets and perceive their organizational competitiveness to be attributed typically to the knowledge management of relational and human dimensions. The highly significant knowledge management processes are considered to be internal, i.e. knowledge sharing within the organization, but also a knowledge flow from external agents has emerged as important, and is mainly attributed to relationships with other firms and education or research institutions. Furthermore, this exploratory study has shown that firms access a wide range of knowledge sources for undertaking innovation activities. Nevertheless, there are apparent deficiencies in the exploitation of knowledge assets and knowledge management processes, as well as a limited access to suitable finance and skilled labor, which suggests the need for efforts to better address the development requirements of renewable energy sector SMEs. In this paper we have defined a first empirical base for further developing an ongoing investigation, led by the Intellectual Assets Centre, aimed at targeting a larger sample, firms in the Highlands and Islands renewable energy sector. In particular, on the basis of the first insights, we have outlined some key research and policy issues which will drive the design and implementation of a more extended survey for exploring at both micro and macro level the main knowledge asset management issues grounding organizational performance improvement and value creation dynamics.

References Carlucci, D., Marr, B. and Schiuma, G. (2004) ―The Knowledge Value Chain – How Intellectual Capital Impacts Business Performance‖, International Journal of Technology Management, Vol 27, Nos. 6-7, pp 575-590. DeVellis, R.F. (1991) ―Scale development: theory and application‖, Applied Social Research Methods Series, Vol 26, Sage, Newbury Park, CA. Edwards, J. (2007) ―Knowledge Management in the energy sector: review and future directions‖, International Journal of Energy Sector Management, Vol 2, No. 2, pp 197-217. Etkind, J., Bennaceur, K., Dmec, M. and Luppens, C. (2003) ―Knowledge portals support widely distributed oil fields projects―, Proceedings of IEEE International Professional Communication Conference IEEE, Orlando, FL, pp 189-200. Foxon, T.J., Gross, R., Chase, A., Howes, J., Arnall, A. and Anderson, D. (2005) ―UK innovation systems for new and renewable energy technologies: drivers, barriers and systems failures‖, Energy Policy, Vol 33, pp 21232137. Graeff, C.L. (1980) ―Some methodological issues concerning comparative hospital studies‖, Academy of Management Review, No. 5, pp 539-548. Mehra, A. (1996) ―Resource and market based determinants of performance in the U.S. banking industry‖, Strategic Management Journal, Summer Special Issue, No. 3, pp 307-322. Nelson, H.R. (1997) ―Virtual seminars‖, Computers and Geosciences, Vol 23, No. 5, pp 601-606. Porac, J.F., Thomas, H. and Baden Fuller, C. (1989) ―Competitive groups as cognitive communities: the case of Scottish knitwear manufacturers‖, Journal of Management Studies, Vol 26, No. 4, pp 397-416. Porac, J.F., Thomas, H., Wilson, F., Paton, D. and Kanfer, A. (1995) ―Rivalry and the industry model of Scottish knitwear producers‖, Administrative Science Quarterly, Vol 40, No. 2, pp 203-227. Schiuma, G. and Lerro, A. (2008) ―Foreword: Intellectual capital and company’s performance improvement‖, Measuring Business Excellence, Vol 12, Issue 2, pp 3-9. Schiuma, G., Lerro, A. and Sanitate, D. (2008) ―Intellectual Capital Dimensions of Ducati’s Turnaround – Exploring Knowledge Assets Grounding a Change Management Program‖, International Journal of Innovation Management, Vol 12, No. 2, pp 161-193. Smith, R.G. and Farquhar, A. (2000) ―The road ahead for knowledge management - an AI perspective‖, Ai Magazine, Vol 21, No. 4, pp 17-40. Strauss, A. and Corbin, J. (1990) Basics of Qualitative Research: Grounded Theory Procedures and Techniques, Sage, Newbury Park, CA. Weir, M., Huggins, R., Schiuma, G., Lerro, A. and Prokop, D. (2009) ―Managing Knowledge Assets in Renewable th Energy Industry: Defining a Research Agenda for Scotland’s SMEs‖, Proceedings of the 5 EIASM Workshop on Visualing, Measuring and Managing and Intangibles and Intellectual Capital, 7-8 October, Dresden, Germany. Wilcox King, A. and Zeithaml, C.P (2003) ―Measuring Organizational Knowledge: A Conceptual and Methodological Framework‖, Strategic Management Journal, Vol 24, pp 763-772.

www.ejkm.com

234

©Academic Conferences Ltd

Why Intellectual Capital Management Accreditation is a Tool for Organizational Development? Florinda Matos1, 2, Albino Lopes1, Susana Rodrigues2 and Nuno Matos3 1 ISCTE - Lisbon University Institute, Portugal 2 ESTG - Polytechnic Institute of Leiria, Portugal 3 PMEConsult, Portugal [email protected] [email protected] [email protected] [email protected] Abstract: In March 2000, the European Council held an extraordinary meeting to agree a new strategic goal for the European Union in order to strengthen a knowledge-based economy. The Council has a strategy - the Lisbon Strategy - aiming in the next 10 years to make the EU the most competitive and dynamic knowledge-based economy in the world. Intellectual capital has become a key element of the knowledge economy. Its management is therefore a factor influencing the competitive advantage of companies, regions and even countries. The purpose of this paper is to discuss the importance of intellectual capital management accreditation as a factor in the organizational development of companies, especially small and medium-sized enterprises (SMEs). The methodology ICMA - Intellectual Capital Management Accreditation (Matos and Lopes, 2009) will be discussed here, as well as the effect of this methodology on SMEs' innovation processes. It is considered that intellectual capital management accreditation may be a relevant process in the consolidation of an innovative dynamic, which will contribute to the continuous creation of competitive advantages. There are various intellectual capital valuation methodologies, but the research about the effect of the certification and accreditation is still very limited so it is necessary to get more results. However, the methodological research that supported the ICMA system points to the fact of accreditation procedures favouring better management of intellectual capital, thus contributing significantly to improving the organizational performance of accredited companies. This paper also aims to contribute to the international recognition of the importance of the audit of intellectual capital. Keywords: intellectual capital management, ICMA, accreditation

1. Introduction The environment in which businesses operate has changed substantially. The most valuable and productive assets do not appear on the Balance sheet and the traditional tools do not allow us to know what influence they have on business performance. The financial indicators appear not to be sufficient because they do not tell us whether we are increasing our competitive advantages. Empirical studies, conducted by Matos and Lopes (Matos and Lopes, 2008) showed that the real competitive advantage results in, increasingly, the management of intangible assets. ICMA Methodology - Intellectual Capital Management Accreditation (Matos and Lopes, 2009) is designed precisely to fill this gap in assessing the management of intangible assets. The various investigations carried out in Portuguese SMEs demonstrate that the high innovative potential of some SMEs may be recognized and enhanced through ICMA, which is thus a tool capable of enhancing the SMEs' competitiveness. In fact, business innovation is essentially incremental and routines are very important in supporting this type of innovation. The accreditation function is to monitor and to guide these routines. Intellectual capital management accreditation can, therefore, be very important in reducing the variance and in consolidating the innovation process. The purpose of this paper is to make a critical exploration of intellectual capital management accreditation as a factor inducing dynamic innovation in SMEs. Over the next few sections of this paper we will discuss accreditation and demonstrate its importance for the consolidation of intellectual capital management as an organizational driver.

2. Literature review: Intellectual capital Since we will analyze the process of accreditation of intellectual capital management, it is important to understand the concepts of intellectual capital, through the interpretation of some academics who ISSN 1479-4411 Reference this paper as

235

©Academic Conferences Ltd

Matos, F, Lopes, A, Rodrigues, S and Matos, N. ―Why Intellectual Capital Management Accreditation is a Tool for Organizational Development?‖ Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp235 244), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (235 - 244)

have studied the issue. There are various definitions of intellectual capital and the concept continues to have a degree of subjectivity. Different words have been used to describe the concept of intellectual capital: intangibles, knowledgebased, and non-financial assets are some examples. Sveiby, (1997), developed a measurement methodology, "The Intangible Asset Monitor", by dividing the intangible assets into three groups: individual skills, internal structure and external structure. This author considers that the skills of the employees of a company are an intangible asset which, together with the other intangible assets, is added to the tangible assets, becoming the full assets of the organization. Edvinson and Malone (1997), proposed a model, "Skandia Navigator" which divides intellectual capital into two categories: human capital and structural capital. Human capital is, according to these authors, the capital of the human resources in the company, consisting of its skills, the accumulated value of its practices, its creativity, its relationship capacity, its values, etc.. Part of this capital is also the culture and the organizational values of the company. In the opinion of the authors, it is this capital which is the source of innovation and renovation. Structural capital, on the other hand, is understood as the value left in the company by the human resources when they go home, for instance, the database, the manuals, the list of clients, etc.. This capital can still be divided into organizational capital and client capital. And, in turn, organizational capital is divided into process capital, innovation capital and client capital. Thus, according to this vision, intellectual capital is the sum of structural capital and human capital, this being the basic capacity for the creation of high quality value. To Brooking (1996), the concept of intellectual capital arises from the association of different intangible assets, split into four categories: market assets, human assets, ownership of intellectual assets and assets of substructures. Roos (1997) has a similar concept of intellectual capital to that of Edvisson and Malone (1997), but he considers intellectual capital as a result of the interaction between human capital, infrastructure capital and relationship capital. Andriessen (2005) defines intellectual capital as "all intangible resources that are available to an organization, that give a relative advantage, and which in combination are able to produce future benefits." We define intellectual capital as "an intangible element, resulting from the sum of knowledge of each individual in an organization arising from the wealth of people in the organization, their level of education, their experience, their information and their willingness to develop the acquisition of knowledge - i.e. individual talent‖ Intellectual capital is divided into individual capital, team capital, processes capital and clients capital (See Intellectual Capital Model – ICM, Matos and Lopes, 2009) Despite these differences in the classification of intellectual capital, it appears that these authors present unanimously the following points: 

Intellectual capital is an intangible asset that needs to be managed.



Management of intellectual capital can create value in the organization.



Management of intellectual capital can generate competitive advantages.



Human capital, Clients capital and Processes capital are the main components of intellectual capital.

It is assumed as unquestionable the importance of intellectual capital as a factor inducing business development. By creating systems of certification and accreditation, we are looking for tools to help entrepreneurs in managing this valuable resource.

www.ejkm.com

236

©Academic Conferences Ltd

Florinda Matos et al.

3. What is accreditation? The concept of accreditation is not unique and often we find some confusion between certification and accreditation. In Portugal, Law No. 125/2004 of 2004-05-31, defines accreditation as "the procedure by which the national accreditation body recognizes formally that an entity is technically competent to perform a specific function specifically, in accordance with international standards, European or national based, in addition, the guidelines issued by international accreditation bodies to which Portugal is party.‖ However, we find differences between the accreditation of intellectual capital management and accreditation systems of higher education (see Matos, 2008). In Portugal, Law No. 1 / 2003-01-6, defines the concept of academic accreditation as a ―verification of the fulfillment of the requirements for the establishment and registration of courses" Academic accreditation corresponds therefore to an official recognition of an institution or course, assuming an assessment based on pre-established standards, which serve as reference levels and for determining if the institution falls within those parameters, facilitating the recognition of diplomas, or degrees, by the legislator. The significance of accreditation is therefore usually associated with official recognition and quality assurance, that is, to general acceptance. We can thus say that the purpose of accreditation is to ensure certain standards of quality. Based on previous concepts, the accreditation of intellectual capital management is a public statement that the company meets a set of established criteria for accreditation by the Accrediting Body. The significance of accreditation is, therefore, usually associated with an operation of technical validation and recognition of the overall capacity of the entity, making it a member of a sort of "club" where they create the conditions for the dominance of best practices that make the accredited entity continuously seek alignment with the best performance.

4. What is ICMA methodology? ICMA Methodology - Intellectual Capital Management Accreditation (Matos and Lopes, 2009) presents itself as a tool for the development of the accreditation process. This methodology has been developed progressively from a variety of research studies and aims to become the highest standard of recognition of intellectual capital management. Companies with this accreditation have a commitment to quality and continuous improvement of the management of their intellectual capital. ICMA is a process that looks at the overall performance of the company and is designed to promote the skills of intellectual capital management with a view to innovation and sustainable competitiveness. The methodology that supports ICMA is the result of theoretical research and several empirical research studies conducted over the past four years (see Lopes and Matos, 2005; Lopes and Matos, 2006; Matos and Lopes, 2008; Matos, 2008; Matos and Lopes, 2009). The accreditation is based on the evaluation of a set of parameters - ICMA indicators. These indicators, allow us to evaluate the management of intellectual capital of companies, checking that there is evidence of the presence of indicators related to the dimensions of intellectual capital, and whether they are valued and managed. The ICMA criteria are based on the ICM - Intellectual Capital Model which consists of 4 Quadrants specified by twenty five parameters (Matos and Lopes, 2009). To achieve ICMA, companies have to demonstrate that they meet the ICMA parameters in 4 areas: Individual Capital, Team Capital; Processes Capital and Clients Capital. The Quadrant Individual Capital, Team Capital and Processes Capital are related to the company's internal environment, the Quadrant Clients Capital is related to the external environment. In ICM, Individual Capital is called the Tacit Knowledge / Human Capital Quadrant. It is the knowledge inherent to the individual himself, and containing the real source of value, talents and the skills to www.ejkm.com

237

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (235 - 244)

generate innovation. Here, one has included the theoretical and practical knowledge of the individuals and the capacities of different types, such as artistic, sporting or technical.

Figure 1: ICM - intellectual capital model Team Capital is the Human Capital / Explicit Knowledge Quadrant. The team shares the explicit knowledge. In this area, knowledge applies to the individual in the form of facts, concepts or tools. When Explicit Knowledge is associated with Structural Capital, we are in the presence of applied experience, as the whole organization is the holder of formalized knowledge, able to be passed on, this is the Processes Capital. This Quadrant represents the ensemble of shared knowledge, summed up by experts (scientific community), recognized as the most advanced form of knowledge. This type of knowledge covers, among other dimensions, the organizational routines or the organizational memory. Organizational memory represents the register of an organization, represented by a set of documents and artefacts. Its goal is to expand and amplify knowledge through its acquisition, organization, dissemination, usage and refinement. Organizational memory can be a way of registering tacit knowledge, making it explicit, so that through business processes it becomes part of the patrimony of the company, to be shared and recreated. Clients Capital is the result of the interaction Structural Capital / Tacit Knowledge. This typology represents the organizational knowledge in its practical form and is already incorporated into the tacit experiences formalized in the team. This knowledge, although hidden, becomes accessible through interaction, and it is the principal characteristic of the performance of highly specialized teams. In the Model presented, the Network and NTIC are essential in the relationship between the 4 Quadrants.

www.ejkm.com

238

©Academic Conferences Ltd

Florinda Matos et al.

Thus, the companies that put the NTIC at the service of human resources have a great advantage, because they can reduce the administrative difficulties in solving simple problems, increase the quality of services and promote continuous improvement and personal growth. The approach to the concept of Network is not a new concept. The network, as a social concept, is the genesis of the social constructs of individuals. More recent is the approach to the concept of network system as a factor in the acquisition of knowledge and innovative action. In conclusion, the NTIC are crucial to be effective Networks. It should be noted that the ICM is a dynamic model and therefore is not a completely stabilized model. Thus, as it is applied to more companies, there may be further adjustments. Indeed, this is one of the advantages of the model: its interactive dynamism, which has proved very good in turbulent business contexts. ICM parameters are: Individual Capital Quadrant 

1. Use of NTIC: New technologies are an essential tool for company’s organizational development. The purpose of this parameter is to demonstrate your domain for all employees.



2. Networks: The networks, supported by new technology, are essential for the development of a networking culture. The purpose of this parameter is to prove the existence of an internal network with knowledge and talents that the company can use.



3. Training / Qualification: Training / qualification are seen as the empowerment of individual employees. The purpose of this parameter is to examine how the company encourages the acquisition of knowledge and develop the talents of each of its employees.



4. Valuation of Know – How: All employees of an organization have an inexhaustible stock of knowledge. However, often companies do not value and do not encourage these skills. Thus, the purpose of this parameter is to see how the company rewards and encourages the development and availability of knowledge and individual skills of their employees.



5. Investment in Innovation and Development (ID): Innovation is a source of competitive advantage of companies. The purpose of this parameter is to check whether the investment in ID, conducted by the company, aims to simplify processes or innovation.

Team Capital Quadrant 

1. Use of NTIC: New technologies should be used as a management tool, integrated in a networking culture. The objective of this parameter is to see, how the new technologies are used in building a team culture.



2. Networks: he networks are forums for sharing knowledge and enable the dissemination of good practices. The purpose of this parameter is to demonstrate that the company promotes the existence of a network culture, where the teams interactive control, discuss and improve the procedures quality in order to satisfy the clients.



3. Training / Qualification; Training / qualification should be understood as an instrument that enables the exchange of synergies between the organization employees.The company must have a policy of training and qualification perfectly synchronized with the team culture. The aim of this policy is to transform the group into cohesive, highly motivated and productive teams. The purpose of this parameter is verifying the existence of this policy of training and qualification.



4. Team Work: The work must be organized into teams whose size will be most appropriate to the needs of the company. This parameter must show a teamwork culture.

Processes Capital Quadrant 

1. Use of NTIC: The company should use the new technologies as an administration tool, maximizing the use of these technologies in their organizational performance. New technologies are very important in the register of organizational knowledge and the operationalization of the whole process. The purpose of this parameter is to demonstrate how new technologies promote the improvement of procedures.

www.ejkm.com

239

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (235 - 244)



2. Networks:This parameter enables us to evaluate how the company uses the "networks", articulated with the NTIC, to improve the processes and create interactivity between different stakeholders.



3. Processes Systematization:The purpose of this parameter is to confirm process systematization and if it allows the formalization and transfer of knowledge among stakeholders.



4. Existence of Certification: Companies should be granted certification, including the ISO 9001 certification. This parameter should confirm the existence of certification.



5. Registration of Organizational Knowledge: Organizational knowledge must be registered. These records should be computerized in order to be protected and easily be shared. This parameter must verify the operability of the record of organizational knowledge.



6. Partnerships: his parameter must verify the existence of a network of partnerships with various stakeholders.



7. Investment in Innovation and Development (ID): The company must demonstrate how innovation and development enable connection and simplification of procedures. The parameter should demonstrate such evidence.



8. The Brands Creation and Management: The purpose of this parameter is to demonstrate how the company's strategy relies on a process of creating and managing brands, which enables improved reliability of products or services and organizational differentiation.



9. Complaints System: The company should have a formal system for registering complaints that serves its relationship with customers. The purpose of this parameter is to demonstrate the proper functioning of this complaints system.



10. The existence of Awards: The awards are understood as the recognition of the process / customer relationship, resulting from the interaction of explicit knowledge with the structural capital. The purpose of this parameter is to check whether the company was awarded as a result of this recognition.

Clients Capital Quadrant 

1. Use of NTIC: This parameter must verify the functionality of the use of NTIC in improving the quality of service and interaction with customers.



2. Networks:The networks should be part of an "act of collective intelligence" in which the expertise of each employee of the company is put at the service of customer satisfaction. The parameter must verify the existence of these networks, as part of the company's culture.



3. Market Audits: Systematic market audits should enable the company to view the market where it will identify opportunities and threats. The purpose of the parameter is to check if the company performs these audits as part of their strategy.



4. Management of the Clients' Satisfaction: The analysis of clients’ satisfaction should be part of the company's organizational routines. Reports should be obtained, allowing the management of the company's relationship with clients. This parameter should check how the company manages its relationship with clients.



5. Complaints System: This parameter must demonstrate that the complaints system, in addition to being part of a process, is an intrinsic element in the company culture.



6. New Markets: The purpose of this parameter is to check if the company has a market strategy, in which the internationalization is one of the goals. The strategies of the market must be accompanied by strategies for innovation of products and services for new markets.

The various studies that have been made allow us to conclude that the ICMA process empowers intellectual capital, converting it into an innovation mechanism.

5. Is accreditation a tool for organizational development? We live in a knowledge society which has seen the transition from a product economy to a service economy. Not only are we concerned with the process of creating valued products but also how the customer uses those products. This requires a broad collection of data across the value chain. This data is valuable because it can become information, which is transformed into knowledge that can produce innovation.

www.ejkm.com

240

©Academic Conferences Ltd

Florinda Matos et al.

This innovation can be created from two types of resources: talented individuals that create disruptive innovation at the level of process, product or market, and incremental innovation that is supported by all workers, in the value chain, that are knowledge workers. However, these knowledge workers are able to introduce micro-innovations that could continuously improve the value chain. So, if business innovation is essentially incremental, depending on system management, accreditation based on the audit of the management of intellectual capital gains importance. Accreditation has the ability to put in the value chain of organizations, a surveillance system for each of its elements. Accreditation facilitates the monitoring of organizational routines. These routines, when based on the intellectual capital management of the whole team, generate creativity. Accreditation, by imposing rules on intellectual capital management, requires organization and discipline that can generate dynamic innovation. The university is the support of this new paradigm of the knowledge economy since this institution has the task of transforming knowledge into innovative and marketable products. Thus, we find the model of the university the inspiration for a model for the company that is complemented by the principles of quality assurance. In a context of economic globalization, the institutions of higher education are required to review their development strategies and integrate their activities on an international plane. The policies for developing and even funding higher education are currently based on models of development that are based on accreditation systems. It is believed that these models are a source of innovation, quality and competitiveness. There is evidence that, in some higher education institutions, the academic quality of products is currently limited by several aspects, including: the growing number of institutions of higher education, internationalization of higher education systems, competition between educational institutions at the national and international level, the Bologna Process and the attempt to harmonize programs. The adoption of accreditation systems has become an essential instrument to promote and guarantee quality. At a time, when the labor market has become global and companies recruit their employees in various countries, institutions of higher education live in a dynamic environment, accompanied by increased mobility of teachers, and students who require a quick adjustment and anticipation of emerging trends. Particularly affected by this phenomenon, management schools and universities are placed in competition at both the national and international level, where they have to compete with the best foreign institutions. In the field of management sciences, several international tables are used to measure the performance of education institutions, among the most representative include the following: AACSB (Association to Advance Collegiate Schools of Business), AMBA (Association for Masters of Business Administration) and EQUIS (European Quality Improvement System). Among these accreditation systems, EQUIS is the most international with more than 100 accredited schools, throughout the world. EQUIS accreditation is an international system of quality evaluation, implementation and accreditation for Higher Education Institutions that have courses in the area of management and business administration. EQUIS is based on a group of principles which facilitate benchmarking, mutual learning and the dissemination of good practices. It is a multicultural and global system but of European inspiration. Considering the analogy that we have been making between the accreditation systems of higher education and accreditation of intellectual capital management, we wanted to know the effect of accreditation in the innovative performance of higher education institutions. Thus, we analyzed the effect of this accreditation system in the performance of two Portuguese universities.

www.ejkm.com

241

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (235 - 244)

6. EQUIS empirical research 6.1 Methodology This empirical research consists of 2 interviews with the managers responsible for the accreditation system EQUIS in two Portuguese Universities - Catholic University and New University of Lisbon, both of which have the accreditation system. The first is a private higher education institution and the second a public higher education institution. The semi-structured interviews are each about 1.5 hours and were recorded. The interviews were transcribed and their contents were analysed. The processing and analyses of the information had the following stages: 

Creation of a content analysis matrix;



Processing of statistical information;



Application of the Support Model;



Summary of the results obtained;



Conclusions

6.2 Interview script The script for the interview had the following questions: 

What are the reasons why the University decided to join EQUIS?



Who were the actors involved in the EQUIS process? (directors, teachers, students, staff, alumni, business recruiters, other clients).



Did the actors participate readily? Where was there most resistance?



What is the role of each of these actors in the implementation of EQUIS?



With EQUIS, was there improvement in terms of teams?



With EQUIS, were there changes to the University management?



What are the areas in which the Faculty has most difficulty in implementing the EQUIS criteria?



In terms of EQUIS, can one say that there is a set of best practices which are transmitted by people?



With EQUIS, have there been changes in the sharing of knowledge in the team?



Is there an Intranet where the processes are managed?



What kind of innovations have emerged with EQUIS?



Does the entire University staff have training?



How is the know-how of employees transmitted? Orally? Written? Have there been changes with EQUIS?



Does each process (e.g. the creation of a new course) follow a methodology which is described within EQUIS?



Is the University well aware of its customers? How evident is it? How does the University try to meet the needs of its customers?



Does the University deal with complaints? When there is some kind of complaint, how is it treated? Is it written down?



Are there partnerships with various organizations? Are they the same as those that existed before EQUIS or improvements were made? What kind of partnerships are these?



Does the University share knowledge in the network of EQUIS organizations? What are the advantages?



Do you consider that EQUIS has made processes simpler or more bureaucratic?



Do students recognize the added value of EQUIS Accreditation?

www.ejkm.com

242

©Academic Conferences Ltd

Florinda Matos et al.



Is the leadership of EQUIS Accreditation process important? Should this leadership be collective or individual?

6.3 Summary of conclusions In this paper we only summarized the findings from empirical research. Detailed results of this research were presented in a previous paper (see Matos, 2008). Using the Intellectual Capital Model (Matos and Lopes, 2009) as a starting point, we analyzed the content of the interviews carried out in the two universities. The findings are presented below. From this analysis, we can conclude that EQUIS accreditation, as a brand, has become the base for the creation of value for the education institutions analysed. It strengthened innovation capacity. This cannot be disassociated from the improvement of processes and giving greater value to the relationship with the client. It was clear that, in each of the cases, EQUIS accreditation did not require significant additional financial efforts besides those inherent to adherence to the accreditation system itself. We conclude that by following this route to accreditation, there was innovation. We also know the intellectual capital of the education institutions analyzed was energized. This shows that managing and energizing intellectual capital allows for the stimulation of sustainable innovation. In reality, the analysis of EQUIS accreditation, based on bibliographical research and on the interviews that have taken place, has shown evidence that to reach an accreditation process it is necessary to have very well systematized processes, which will be enhanced through a permanent search for excellence. One other piece of evidence is the partnerships, together with a culture of the network, where new information and communication technologies have been introduced. Another notion reflected in the accredited institutions is the notion of ―good practices‖ also applied to the processes of continuous improvement. It is also verifiable that there is a prestige and attractiveness effect, in which the benefits of the ―brand‖ give the Institution indirect publicity, exempting it from an equivalent financial effort. We can further state that, even if one does not bring into practice any other modification, the analysis of the practices that have taken place in similar contexts produces previously unexploited potential. This innovation is a result of this analysis, and one can label it as ―innovation arising from the process‖, supported by a ―network culture‖ and by new technologies. On the other hand, it was the implementation of the accreditation process that led to the increase in performance, which concurs also with our theory about the effect of these processes on the capacity of organizational innovation, meaning that the creation of the accreditation process empowers intellectual capital converting it into an innovation mechanism. The findings of this research indicate the need to promote and enhance the intellectual capital of organizations and particularly the institutions of higher education, which is one of the ways to generate innovation and competitiveness. The accreditation processes seem to favour this task because they compel a better fixation on the management of intellectual capital.

7. Final conclusion In various surveys conducted in SMEs (see Matos and Lopes 2008, Matos e Lopes 2009) it is concluded that companies need systems to standardize routines, instill discipline and reduce dispersion. Thus, the accreditation of intellectual capital management, developed through a simple nonbureaucratic process can be very important, allowing us to create an environment of teamwork and networking, encouraging the sharing of knowledge which is essential to creating a dynamic innovative. Like the accreditation of higher education, which currently is a benchmark of quality for the courses and universities, accreditation can be an important competitive advantage for SMEs because it www.ejkm.com

243

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (235 - 244)

guarantees to their partners that they have the capacity to generate relevant, shared knowledge and induce incremental innovation. Just as the EQUIS system was designed to help prospective students and recruiting companies from one country to identify those institutions in other countries that deliver high quality education for international management, the ICMA system can also be the best guarantee of SMEs, which can be used in their promotion to key partners. In research we conducted in Portuguese SMEs, we have found that the innovative capacity of some SMEs can be recognized. The use of an accreditation system could be seen as a guarantee of innovation capacity and therefore an important reference for their partners, so accreditation can be considered as a tool for organizational development.

References AACSB (Association to Advance Collegiate Schools of Business), [Online], Available: http://www.aacsb.edu/accreditation/ [05 December 2009]. AMBA (Association for Masters of Business Administration) [Online], Available: http://www.mbaworld.com/ [05 December 2009]. Andriessen D. (2005) ―Implementing the KPMG Value Explorer: Critical success factors for applying IC measurement tools‖ Journal of Intellectual Capital, Vol. 6, Issue 4, pp. 474 – 488. Brooking, A. (1996) Intellectual Capital: core asset for the third millennium enterprise. Thompson, Boston. Edvinson, L. and Malone, M.S. (1997) ―Intellectual Capital‖, New York ,Harper Collins Publishers Inc. EQUIS – The European Quality Improvement System [Online], Available: http://www.efmd.org/html/home.asp [05 December 2009]. Law No. 1 / 2003-01-6 [Online], Available: http://snesup.terradasideias.net/htmls/_dlds/lei_1_2003.pdf [07 December 2009]. Law No. 125/2004 of 2004-05-31 [Online], Available: http://www.ipq.pt/customPage.aspx?modid=1076&pagID=1290 [07 December 2009].. Lopes, A., Matos, F. (2005) ―Técnicas de Gestão do Conhecimento – Métodos de Aplicação e Desenvolvimento Empresarial‖, Associação Empresarial da Região de Viseu - AIRV, Viseu. Lopes, A., Matos, F. (2006) – ―Avaliação do Programa REDE‖, Gest-in, ISCTE, Lisboa. Matos F.; Lopes A. (2008) ―Intellectual Capital Management - Certification Model‖ Paper read at 9th European Conference on Knowledge Management, Southampton Solent University, Southampton, 4-5 September. Matos, F. (2008) ―Searching for an Accreditation Model of Intellectual Capital Management‖, Paper read at 5th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning, New York Institute of Technology, New York, 9-10 October. Matos F.; Lopes A. (2009) ―Intellectual Capital Management – SMEs Accreditation Methodology‖ Paper read at European Conference on Intellectual Capital 09, INHolland University of Applied Sciences, Haarlem, The Netherlands, 28-29 April. Roos, Göran and Johan Roos. (1997) ―Measuring your Company’s Intellectual Performance―.Long Range Planning 30 p. 3. Sveiby, K. E. (1997) ―The New Organizational Wealth. Managing & Measuring Knowledge-Based Assets‖. Berrett-Koehler Publishers, San Francisco.

www.ejkm.com

244

©Academic Conferences Ltd

Analysing and Enhancing IC in Business Networks: Results from a Recent Study Kai Mertins, Markus Will and Cornelia Meyer Fraunhofer IPK, Berlin, Germany [email protected] [email protected] [email protected] Abstract: Since the acknowledgement of intellectual capital (IC) as the major driver of a company’s competitive and innovative performance numerous scientific models and practical approaches were developed to capture and display the IC elements of businesses in order to make them visible and accessible for management. But since the economy has gone global and businesses are acting within a global business landscape the view on IC needs to be expanded to these new structures. Especially for small and medium sized enterprises (SME) commitment in networks and clusters is crucial for sustainable competitiveness on international markets. Alike single companies, also business networks highly depend on IC in order to perform successfully and effectively. The way businesses cooperate, exchange and acquire knowledge, find suitable partners, solve problems, develop and profit from the network has an impact on the company itself as well as on the network. Aiming at an IC-based assessment and support of SME networks, the methodology presented in this paper follows a bottomup approach starting with the assessment of IC in the single company. Within the research projects ―InCaS: Intellectual Capital Statements – Made in Europe‖ and ―Wissensbilanz – Made in Germany‖ a methodology has been developed which has proved to be capable of collecting comparable qualitative IC data. Based on these results, a consolidated approach has been designed recently, collecting IC data from more than 600 companies in Germany. The results are comparable and individual at the same time, allowing to display IC settings of single companies as well as to aggregate IC information within an IC portfolio for a whole group of companies. The paper will present the methodology as well as some first results from the study taking a look at the German IC landscape based on more than 600 individual IC assessments. The paper will also discuss the possibilities of using the results for IC enhancement in networks and clusters from the view of the single company. Keywords: intellectual capital assessment, clusters, networks, SME, IC benchmarking

1. Introduction In recent years, the importance of clusters and networks for successful and innovative company performance has been widely acknowledged. Several projects were initiated by the European Commission addressing the question ―How can clusters and networks really offer a favourable framework for enhancing the productivity, the innovation and the competitiveness of SMEs across the 1 European Union, as one way to achieve the Lisbon Summit’s Goal?" Today, successful cooperation between SMEs mainly takes place on a horizontal supply chain basis, i.e. companies delivering parts to the next step of a linear value chain. Apart from that, the synergy potential of SME cooperation in Europe, e.g. vertical clusters or other forms of collaboration, is not utilized effectively yet. Moreover, promising networks and clusters are seldom transparent at all (even to their members) and suitable partners and contact persons mostly unknown. Entering or starting high performing clusters therefore often is a huge and resource consuming challenge and to some extend a matter of luck. An additional problem arises from the fact that a key element to successful cooperation lies within the intangible resource base of companies. This so called Intellectual Capital (IC) which is understood as the stocks and flows of knowledge inside and outside the company that are critical to its business success, is the key capital of the knowledge economy. To enhance the value-adding flow of Intellectual Capital between SME’s provides a huge chance of synergies for them to face the challenges of the global knowledge economy. But this presupposes that suitable partners by means of complementary IC-configurations find each other and start IC transfer on a barrier free basis. In order to make value-added use of network relationships between enterprises, IC flows and IC flowmanagement within and between the member network member companies have to be build up and supported efficiently. To enrich the opportunities and the quality of business cooperation in networks 1 EU MAP project on Enterprise Clusters and networks: http://europa.eu.int/comm/enetrprise/entrepreneurship/support_measures/cluster/index.htm

ISSN 1479-4411 245 ©Academic Conferences Ltd Reference this paper as Mertins, K, Will, M and Meyer, C. ―Analysing and Enhancing IC in Business Networks: Results From a Recent Study‖ Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp245 - 252), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (245 - 252) is a major challenge and ways to stimulate the ―relational capital‖ in networks and clusters through collaboration and bonding among local firms and agencies across all sectors need to be identified (Migliarese 2008). The approach presented in this paper aims to outline the single steps which are necessary to support IC flows and collaboration in networks. Based on individual assessment of IC, strengths and weaknesses of a company’s Intellectual Capital need to be displayed in a standardised manner in order to compare results between companies. After giving a short overview on the theoretical background of the methodology, the paper presents the approach and some first results, showing how IC comparisons between different groups are possible: starting from the analysis and descriptive comparison between groups like business sector as a first step, it is possible to compare IC assessments of a single company with a peer group (e.g. same size, branch/ sector, region, strategy etc.) in a second step. In a third step, these comparisons can be used to match potential partners for collaborative knowledge- and best-practice-transfers or other forms of collaboration.

2. Background The new paradigm on innovation supports the idea that the enhanced competitiveness and superior performance of a SME depends greatly on its ability and capacity to enter into collaborative and dynamic networks in open business environments. This has led to a myriad of new concepts (e.g. open innovation, virtual clusters, ―Internet Work Enterprises‖, business ecology) as well as a significant body of recent research aimed at conceptualising and understanding the impact of such strategies on the firm’s overall performance. This research has mainly focused on reasons, enablers and barriers for networking at organisational and business environment levels; the effectiveness of networks (Mazzanti, Mancinelli 2007) and the needs and practices of SMEs with respect to networking (Chen et al. 2005). In particular, the EFFORT project (www.effortproject.eu), albeit addressed to SMEs, mainly concerned policy on SME networks and clusters. Most of this research analyses the opinion of network participants, some employ complex networking analysis techniques and models. So far, none of it has focused on IC-flows that, if properly understood and managed, could convert such networks into truly value-adding ones. In order to actively manage IC and enhance networking between cluster members, the value-driving elements of IC need to be understood and identified. The generation of data and information on intangible/ knowledge resources has been one of the major research issues during the last decades addressing the question of how to find a suitable way to describe the highly individual information on a companies’ Intellectual Capital. Different national approaches on IC measurement and reporting have been developed and tested in the recent years leading to the fact, that there is no European wide standard regarding the measurement and disclosure of IC. A decisive step towards the harmonisation of international approaches in the field of IC measurement and reporting has been taken by the European Research Project ―InCaS: Intellectual Capital Statement – Made in Europe‖ (2006 – 2008) (European Commission 2008). It aimed at bridging the gap between individuality and comparability of IC-related information. The InCaS project has developed a common methodology for the measurement of intangibles and set up a common structure for the display the intangible resources of a company within an Intellectual Capital Statement (ICS). Moreover, the project also laid down a concept on how to harmonise the huge variety of intangible elements which are the basis for superior business performance (Mertins et al. 2009). Following the most frequently used structure to describe intangible assets (Alwert et al. 2008), the InCaS approach divides Intellectual Capital into three dimensions: Human, Structural and Relational capital. Human Capital (HC) includes the staff’s competencies, skills, attitudes and the employee’s motivation. Human Capital is owned by the employee and can be taken home or onto the next employer. Structural Capital (SC) comprises all structures and processes needed by the employee in order to be productive and innovative. According to a sloppy but useful definition, it ―consists of those intangible structures which remain with the organisation when the employee leaves‖ (Edvinsson, Malone 1997). Relational Capital (RC) sums up the organisation’s relations to customers, suppliers, partners and the public. The EU project has started with the harmonisation of ICS content based on the empirical results collected in 50 pilot implementations: The results from practice proved, that approx. 80-90% of individual IC elements could be harmonised on an aggregated level, while remaining 10-20% are completely individual (Mertins, Will 2008). The table below shows those factors

www.ejkm.com

246

©Academic Conferences Ltd

Kai Mertins et al.

of Intellectual Capital, which could be harmonised across all participating companies and have been empirically identified as a standard set of so called ―IC factors‖ relevant for all enterprises. Table 1: Harmonised IC factors (European Commission 2008) Type of IC Human Capital

IC Factor Professional Competence

Social Competence

Employee Motivation

Leadership Ability

Structural Capital

Internal Co-operation and Knowledge Transfer Management Instruments

IT and Explicit Knowledge

Product Innovation

Process Optimisation and Innovation

Corporate Culture

Relational Capital

Customer Relationships Supplier Relationships

Public Relationships

Investor Relationships Relationships to Cooperation Partners

www.ejkm.com

Definition The expertise gained within the organisation or in the employee’s career: professional training, higher education, training courses and seminars, as well as practical work experiences gained on-the-job. The ability to get on well with people, communicate and discuss in a constructive manner, nurturing trust-enhancing behaviour in order to enable a comfortable co-operation. Furthermore the learning ability, the self-conscious handling of critique and risks as well as the creativity and flexibility of individual employees. The motivation to play a part within the organisation, to take on responsibility, committed to the fulfilment of tasks and the willingness for an open knowledge exchange. Typical sub areas are for example satisfaction with the labour situation, identification with the organisation, sense and participation of achievement. The ability to administrate and motivate people and to develop and communicate strategies and visions and their empathic implementation. Negotiation skills, assertiveness, consequence and credibility as well as the ability to create a scope of self dependant development belong to this IC factor. The manner how employees, organisational units and different hierarchy levels exchange information and co-operate together (e.g. conjoint projects). The focused knowledge transfer among employees and between generations. Tools and instruments supporting the efforts of leadership and therefore have an impact on the way how decisions are made and what information paths are incorporated in the decision-making process. The computer assisted working environment including all elements of explicit knowledge. Among these are for example specific technical operating principles, networks, fileserver, intra- and extranet, databases, internet and software applications including the content. Innovations of great importance for the future of the organisation. Characterised by the fact, that they will bring new products into being or fundamentally change existing products and eventually result in a patent application Optimisation and improvement of internal procedures and processes, e.g. continuous improvement of all business processes as well as idea management in order to gather suggestions of improvement The corporate culture comprises all values and norms, influencing joint interaction, knowledge transfer and the working manner. Compliance to rules, good manners, "Do's and Don'ts" and the handling of failures are important aspects of this factor. Relationships to former, current and potential customers. The management of these relations comprises activities like sales and marketing, CRM and face-to-face customer cultivation by employees. Relationships to former, current and potential suppliers. The management of these relations comprises activities concerning purchases and the cultivation of suppliers. Relationships to the public. Including the relationships to former and potential employees and the public in general, all activities of public relationship management as well as corporate citizenship, e.g. supporting regional activities. All relations to investors - external and internal investors - i.e. banks, owners, stockholders. The management of these relations comprises all activities providing specific information to the faction, e.g. accountability. All relations to professional associations, bodies, and societies. The management of these relationships comprises activities like joint acquisition of customers, suppliers, investors as well as an active knowledge transfer on R&D partnerships, best-practice transfer and networking activities.

247

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (245 - 252)

Within the European Guideline for Intellectual Capital Statements (European Commission 2008), these harmonised IC factors was agreed upon as a basic standard set of factors which are relevant to the major part of companies when assessing and analysing IC. As already stated above, a certain percentage of factors will remain individual. Standardised elements to display intangible elements can serve as a basis for the comparison of IC factors and configurations between companies. Several approaches to compare companies on the basis of common indicators have been developed in the recent years. These approaches can be located in the field of benchmarking or rating methodologies. While most benchmarking or rating approaches are quite common and focus on a comparison of business process indicators to display the company performance in relation to others, IC-based approaches are at the very beginning and aim to compare special IC configurations in order to identify knowledge strategies. On the basis of a firms’ assessment of a set of standardised IC factors, strengths and weaknesses of IC can be compared between different companies e.g. the respective industrial sector, different branches, sizes, regions etc and reveal the very specific IC strategy of a sector, branch, region or any other group of companies.

3. The study approach While the European research project aimed at the assessment, analysis and comparison of IC in individual companies on a micro level, the German research project ―Wissensbilanz – Made in Germany‖ took the results from the European harmonisation efforts to design an approach which is able to analyse IC on a macro level. The German approach aims at displaying the German business landscape based on its Intellectual Capital. Here, the aim is not to make the IC assessments of individual companies comparable, but to show the IC configuration of a whole group of companies (e.g. sectors, regions or strategy). In order to generate valid empirical data for such large-scale comparisons, a large number of IC data sets had to be collected. Therefore, an online questionnaire was designed, based on the European standard set of IC factors shown in the table above. The questionnaire captures each IC factor with two questions: 

How important is the IC factor for your business success (importance)?



How good is that IC factor today (quality)?

Apart from the three IC categories human, structural and relational capital, a fourth category on traditional tangible resources (like financial resources, machinery and equipment and raw material) was added and could as well be rated by the participating companies according to its importance and quality. Participants could answer on a scale from zero to ten points. Zero meaning ―no importance‖ or ―very bad‖, and ten meaning ―highly important‖ or ―excellent‖. Additionally, general data on size, 2 sector, age, market strategies etc. was asked. Meanwhile, 615 companies from Germany, Switzerland and Austria created 615 valid data sets. For analysis, only data sets of companies located in Germany were taken into account (N=532). The individual company assessments of the single IC factors were aggregated in order to generate a summarised picture of the importance and quality of IC in the German business landscape. Mean values were calculated from the company assessments of IC factors in order to show the relative differences between different IC factors.

4. First results In order to describe the study sample, the basic general data about company size, sector age etc. was analysed first. 452 companies indicated their sectoral focus: about 29% of the participating 3 companies are working in industry and 71% are service companies . Of 502 companies which indicated the number of their employees, 74,7 per cent are small and medium sized companies. Thereof, nearly 30% are micro enterprises (up to 10 employees) and more than 25% are small enterprises (between 11 and 50 employees). Medium-sized companies employing 51 to 250 persons 4 make up 19,7% of this group. The remaining 25,2 per cent are big enterprises which employ more 2

The questionnaire is available at www.wissensbilanz-schnelltest.de in German This indicates a slight overrepresentation of industry in this study. Official statistics operate with a ratio of 20/80 between industry and services sector in Germany. 4 Categorisation based on EU SME definition: Article 2 of the Annex of Recommendation 2003/361/EC

3

www.ejkm.com

248

©Academic Conferences Ltd

Kai Mertins et al. 5

than 251 persons. The next paragraph shows the results for importance and quality of IC factors for all enterprises (N=532). In the subsequent paragraph displays the results for two subgroups and distinguishes between IC in industry and services sector.

4.1 Ranking of intellectual capital factors Having a look at an overview ranking of the most important IC factors in the German business landscape clearly reveals, that the most decisive intangible factors are „human capital factors’. Only one factor from the category „structural capital’ ranks among the top 5 IC factors: professional competence heading the ranking, followed by customer relationships and another three factors from the category human capital, employee motivation, social competence and leadership ability. Least important are tangible factors like raw material or machinery and equipment, which confirms the developments and changes, businesses have undergone in the past years on their way to the knowledge economy.

Importance of all Factors 8,47

Professional Competence

8,37

Customer Relationships

8,19

Employee Motivation

8,07

Social Competence

7,78

Leadership Ability Internal Co-operation and Know ledge Transfer

7,63

IT and Explicit Know ledge

7,54

Corporate Culture

7,23

Management Instruments

7,03

Financial Resources

6,80

Process Optimisation and Innovation

6,79

Relationships to Cooperation Partners

6,45

Product Innovation

6,41 5,91

Public Relationships Supplier Relationships

5,63

Investor Relationships

5,32

Machinery, technical equipment and buildings

4,18

Raw Material

4,05 0

1

2

3

4

5

6

7

8

9

10

Figure 1: Ranking of the most important IC factors 5 The ratio of SMEs in the study sample does not reflect the real SME ratio of the German economy. The German Institut für Mittelstandsforschung calculates an SME proportion of 99,7% of all German enterprises which are registered for VAT. See http://www.ifm-bonn.org/index.php?id=9

www.ejkm.com

249

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (245 - 252)

It is rather interesting that in term of structural capital, only customer relations are of great importance to companies. Relations to investors, suppliers and cooperation partners seem to be a less decisive element of business success. The same analysis has been done for the second question of the online questionnaire similarly (―How good is that IC factor today?‖): comparing the graph below with the results for the question about the importance of the single IC factors (see graph above) shows first, that generally the assessments for quality are lower than for assessments of importance. Moreover, the graph below comprises the same set of IC factors than the ranking of importance but displays a slightly different order of IC factors. While the first two factors (professional competence and customer relationships are still heading the ranking, employee motivation drops from the third to fifth position in the ranking concerning the factors quality. A similar change can be noted for the factor ―IT and explicit knowledge‖ but the other way around: here the importance was ranked lower than its quality, moving the factor from rank 7 to rank 4 in the ranking of IC quality.

Assessment of all Factors 7,13

Professional Competence

6,80

Customer Relationships Social Competence

6,65

IT and Explicit Know ledge

6,65

Employee Motivation

6,56 6,33

Leadership Ability Management Instruments

6,19

Internal Cooperation and Know ledge Transfer

6,14

Corporate Culture

6,09

Supplier Relationships

5,94

Financial Resources

5,87

Relationships to Cooperation Partners

5,83

Process Optimisation and Innovation

5,68

Investor Relations

5,65

Product Innovation

5,64

Public Relationships

5,47

Raw Material

4,87

Machinery, technical equipment and buildings

4,83 0

1

2

3

4

5

6

7

8

9

10

Figure 2: Ranking of the best IC factors This very brief example already reveals differences between importance and status/quality of single IC factors. They give a first impression of strengths and weaknesses of how companies assess and deal with their Intellectual Capital. Apparently, importance and quality differ between IC factors, www.ejkm.com

250

©Academic Conferences Ltd

Kai Mertins et al.

meaning that some factors are important, but not developed accordingly (e.g. employee motivation). Others are less important but have been attaining more attention or have been invested in more than necessary (e.g. IT and explicit knowledge). Taking into account, that these results are aggregated for a all German companies which took part in the study, a closer look at different types of companies allows us to analyse strengths and weaknesses in more detail.

4.2 IC comparisons between different groups Due to the additional information which was asked about size, sector, region or market strategies, it is possible to zoom in the picture of the business IC landscape and have a detailed look at different groups of companies. As the most important differences in terms of IC were expected between sectors, i.e. service sector and industry, the paper presents a comparison between those two groups. 130 enterprises could be categorised as service companies, 322 as industry companies. The picture below shows the answers to the question ―how important is the factor?‖ displayed in mean values. Generally, the valuation of importance differs heavily between the sectors when it comes to the traditional, tangible factors like material and machinery. As expected, tangible factors are still much more important to industry, while being rated rather low by the services sector. Nevertheless, the results also show, that in terms of intangibles, both sectors do have more IC issues in common than expected: IC factors like human capital, relational capital and structural capital factors are most important for both sectors and are rated very high in average. Taking a closer look at the valuations of IC factors reveals important differences which indicate different IC settings and IC strategies in sectors: As already shown in the figure above, the human capital factors are ranked the highest compared to almost all other IC factors. More interesting is that the difference between companies working in the services or the industrial sector are only marginal: although generally evaluated lower by industrial companies, it can be stated that human capital factors are almost equally important to the different sectors, although industry is sometimes said to be less dependent on highly qualified personnel. Only the factor ―social competence‖ is rated significantly higher by companies working in the services sector. Some interesting differences also appear related to the importance of some structural capital factors: although the importance of most factors has been rated higher by service companies, the factors ―product innovation‖ and ―process optimisation‖ are more important to industrial enterprises. Also, the factors ―supplier relationships‖ and ―investor relations‖ in the category relational capital have been rated more important by industry. Industry (N=130) 10

Human Capital

Services Sector (N=322)

Structural Capital

Relational Capital

Tangible factors

9 8 7 6 5 4 3 2 1

Financial Resources

Raw Material

Machinery, technical equipment and buildings

Relationships to Cooperation Partners

Investor Relations

Public Relationships

Supplier Relationships

Customer Relationships

Corporate Culture

Process Optimisation and Innovation

Product Innovation

IT and Explicit Knowledge

Management Instruments

Internal Cooperation and Knowledge Transfer

Leadership Ability

Employee Motivation

Social Competence

Professional Competence

0

Figure 3: Importance of IC factors shown for industry and services sector

www.ejkm.com

251

ISSN 1479-4411

Electronic Journal of Knowledge Management Volume 8 Issue 2, (245 - 252)

5. Summary and outlook The paper has shown up the possibility of IC comparisons between company groups based on a standard set of harmonised IC factors. As an example, the differences of IC configurations between the services sector and industry have been displayed and analysed. Apart from some general results, the detailed look at the differences between sectors concerning IC factors’ importance and valuation indicates the different IC strategies within the two groups. Due to the data available, many other groups are comparable, e.g. region, size, market strategy etc. Thus, special IC settings or IC strategies can be analysed and identified in other groups as well. Starting from this first step, future research will now focus on a second step: an IC-based comparison of a single company with a peer group (e.g. same size, branch/ sector, region, strategy etc.). Based on the individual IC data, IC settings of single companies can be compared and the strengths and weaknesses of one company can be displayed in relation to the peer group. This so called ―IC Benchmarking‖ can provide a new method to compare individual IC configurations across SMEs with the aim to strengthen their competitive capabilities based on a structured and focused knowledge transfer. Therefore, the third step will need to focus on how to bring single SMEs together on an IC basis to find suitable cooperation partners, initiate best-practice transfers in order to exchange relevant knowledge, find partners with similar IC problems, find partners with similar IC solutions, etc. Finally, the IC benchmarking shall support enterprises to actively act in and profit from different types of networks and clusters. Moreover, IC Benchmarking may take place on different levels adding value for different scopes and purposes: individual IC data of different companies in an existing cluster can be aggregated on the cluster level to find out about potential gaps concerning the intellectual resource base of the cluster (this may also be supported by a new IC assessment on the emergent cluster level). Based on that, well-aimed activities to grow and strengthen the cluster by actively inviting new partners to close those specific IC-gaps may be initiated from inside the cluster. Also, cluster-to-cluster comparisons become possible for evaluating the whole network and to assess the cluster performance compared to the performance of other clusters.

References Alwert, K.; Bornemann, M.; Will, M. (2008) Wissensbilanz – Made in Germany. Leitfaden 2.0 zur Erstellung einer Wissensbilanz, Guideline published by the Federal Ministry for Economics and Technology, Berlin, [online], http://www.bmwi.de/BMWi/Redaktion/PDF/W/wissensbilanz-made-in-germanyleitfaden,property=pdf,bereich=bmwi,sprache=de,rwb=true.pdf. Chen et al., (2005) Towards understanding inter-organizational knowledge-transfer needs in SMEs. Journal of Knowledge Management, 10, 6-23 Edvinsson, L.; Malone, M. (1997) Intellectual Capital; New York, Harper Business. European Commission (2008) InCaS: Intellectual Capital Statement – Made in Europe. European ICS Guideline, [online] www.incas-europe.org Mazzanti; Mancinelli (2007) www.ssrn.com; OECD (2008) Open innovation in global networks; EFFORT EU Project. Mertins, K.; Alwert, K.; Will, M. (2006) ―Measuring Intellectual Capital in European SME‖, Proceedings of I-KNOW '06, 6th International Conference on Knowledge Management, published by Tochtermann, K.; Maurer, H., Graz, Austria, pp 21-25. Mertins, K.; Will, M. (2008) ―Strategic Relevance of Intellectual Capital in European SMEs and Sectoral Differences. InCaS: Intellectual Capital Statement – Made in Europe‖, Proceedings of the 8th European Conference on Knowledge Management, Barcelona, Spain. Mertins, K.; Will, M.; Meyer, C. (2009) InCaS: Intellectual Capital Statement. Measuring Intellectual Capital in European small and medium sized enterprises. ECKM 2009, Conference Proceedings Migliarese, P. (2008) Knowledge Management, Relational Knowledge and Competitiveness: Results from a research study on SMEs in the South of Italy. Proc. CDM08, Toulouse.

www.ejkm.com

252

©Academic Conferences Ltd

What National Intellectual Capital Indices Can Tell About the Global Economic Crisis of 2007-2009? Carol Yeh-Yun Lin1 and Leif Edvinsson2 1 National Chengchi University, Taiwan 2 Lund University, Sweden [email protected] [email protected] Abstract: In the past few years, the concept of intellectual capital has been expanded from organizational level to national and regional level. A model of 29 national intellectual capital indices (NICI40) has been constructed and validated by utilizing data of the IMD Competitiveness Yearbook. Research on the 14 years data, spanning from 1995 to 2008, indicates the pattern and progression of national intellectual capital in 40 countries. Trend analyses of this set of panel data disclose very interesting phenomena, such as Iceland shows the most rapid progress in national intellectual capital and is a rising star in economic development with an outstanding 94% GDP per capita (ppp) growth over 14 years. Moreover, it was number one of the OECD countries according to The Global Benchmark Report 2006 and received a consistently high ranking in world standard-of-living surveys. Unfortunately, the recent financial crisis almost crippled its national financial system and wrote off its past economic performance. On the contrary, Norway lags behind its Nordic peers in national intellectual capital development; yet its resilience to the 2007-2009 financial crisis impact is noticeable. Furthermore, Norway generously offers financial support for IMF to assist some ailing neighboring countries. Why the intellectual capital rising star crumbled down and the seemingly static country sustained the crisis? What is the implication of this controversy? The research results of this study have some implications for relevant policy makers. Keywords: national intellectual capital, financial crisis, national wealth

1. Introduction Over the past few decades, intangible assets, such as knowledge, patents, and innovation, have been identified as fundamental sources of wealth and progress. These assets represent a major concern for business firms and their stakeholders (Garcia-Ayuso, 2003). Drucker predicts the emergence of a society dominated by knowledge-based resources and a competitive landscape in the allocation of intellectual capital (IC) (Bontis, 2004). In addition to firm level studies, a number of intellectual capital assessments have also taken place at the national level (e.g., Sweden, Denmark, The Nordic Project, and Israel) and at the regional level (e.g., the Arab nations and the Pacific Islands studies) (Bontis, 2004; Bounfour, 2003). As knowledge assets fuel a country’s growth and have significant implications for future national value – innovation, learning, and gross domestic product (GDP), they represent the source of the competencies and capabilities deemed essential for national economic growth, human development, and quality of life (Malhotra, 2003). Consequently, countries rich in intangible assets fare better in terms of national wealth than those whose assets are limited to land, tools, and labor (Malhotra, 2003; World Bank, 1998). Although the assessment of national intellectual capital can not explain in full or predict an abrupt occurrence such as a sudden stock market plunge, currency depreciation, regional political strife, or global financial crisis, it did reflect a nation’s past efforts in terms of human resource development, national economic relationships, infrastructural investment, renewal capability building, and national financial management. This paper briefly introduces the concept of ―national intellectual capital‖, presents its measurement model, national intellectual capital rankings, describes the early signs of 2007-2009 financial crises, and then discusses the lessons learned for regaining competitiveness.

2. National intellectual capital The recognition that intangibles are one of the most important sources of prosperity and progress has prompted an increasing need for developing more understanding of intellectual capital; as a result, studies of intellectual capital and related topics have proliferated over the years. However, the intellectual capital of a nation requires the articulation of a comprehensive system of variables that helps uncover and manage that nation’s invisible wealth. Past studies either propose ISSN 1479-4411 253 ©Academic Conferences Ltd Reference this paper as Lin, C, Y. and Edvinsson, L. ―What National Intellectual Capital Indices Can Tell About the Global Economic Crisis of 2007-2009? Electronic Journal of Knowledge Management Volume 8 Issue 2 (pp253 - 266), available online at www.ejkm com

Electronic Journal of Knowledge Management Volume 8 Issue 2, (253 - 266)

models from a limited perspective (e.g., inputs or intellectual property rights) or models containing too many variables to be easily replicated for trend analysis. This paper presents a moderate set of national intellectual capital indices that are valid and can be easily replicated for follow-up studies. Intellectual capital is defined as ―intellectual material – knowledge, information, intellectual property, experience – that can be put to use to create wealth‖ (Stewart, 1997) and is the roots for future earning capabilities (Edvinsson & Malone 1997). In the global competition, these features, such as educational system, international trade, infrastructure, and renewal capability affect national competitiveness and constitute the major components of national intellectual capital, namely human capital, market capital, process capital, and renewal capital. The first type of national intellectual capital, human capital, is defined as the competencies of individuals in realizing national goals (Bontis, 2004). According to OECD (2000), human capital consists of knowledge about facts, laws, and principles in addition to knowledge relating to teamwork, and other specialized and communication skills. Education is the foundation of human capital. The variables used in this study include the amount of skilled labor, the degree of employee training, the rate of literacy, the level of enrollment in institutions of higher education, the pupil-teacher ratio, the number of Internet subscribers, and public expenditure on education. The second type of national capital, market capital, is similar to external relational networking and social capital in a micro setting in that it represents a country’s capabilities and successes in providing an attractive and competitive incentive in order to meet the needs of its international clients, while also sharing knowledge with the rest of world (Bontis, 2004). The present study takes into consideration investment in foreign countries and achievements in foreign relations, as well as exports of goods and services. In this study, the authors focus primarily on each country’s corporate tax encouragement, cross-border ventures, openness to foreign cultures, degree of globalization, and transparency of economic information, as well as the image that the country projects abroad, and the country's export and import of commercial services. The third type of national capital, process capital, comprises the non-human sources of knowledge in a nation. Embedded in a country’s infrastructure, these sources facilitate the creation, accessibility, and dissemination of information. This type of capital is measured through fair business competition environment, government efficiency, intellectual property rights protection, the availability of capital, the number of computers per capita, the ease with which new firms can be established, and the number of mobile phone subscribers. The fourth type of national capital, renewal capital, is defined as a nation’s future intellectual wealth and the capability for innovation that sustains a nation’s competitive advantage. Business R&D spending, basic research, R&D spending as a percentage of GDP, the number of R&D researchers, the level of cooperation between universities and enterprises, scientific articles, and USPTO & EPO (patent number recorded in both United States Patent and Trademark Office & European Patent Office) per capita are included in this type of capital. The fifth type of national capital, financial capital, is represented by a single indicator: the logarithm of GDP per capita adjusted by purchasing power parity. This is the most common measurement of the financial wealth of a nation.

3. National intellectual capital measurement model During the past decade, knowledge assets and intellectual capital have been attracting an increasing amount of attention, not only from academics and CEOs, but also from national policy makers. A World Bank report (1998) points out that the adoption of policies to increase a nation’s intellectual wealth can improve people’s lives, besides giving them higher incomes. Yet, a majority of intellectual capital studies have been analyzed particularly from the interest of the business firms to explain the differences between the accounting value and the market value as possible basic source of competitive advantages in companies (Bontis, 2001; Edvinsson, 2002). Nowadays, there are approaches trying to adopt these methodologies to a broader scope with the objective of comparing the intellectual capital indices at national or regional level, that mainly are applications of business models translated to the nations or regions.

www.ejkm.com

254

©Academic Conferences Ltd

Carol Yeh-Yun Lin and Leif Edvinsson

For instance, in Europe there is a quest for promoting the competitive investment into intangibles, also called the Lisbon agenda, from the EU summit in Lisbon, Portugal in 2002. The aspiration is to lift the investment of R&D to the level of 3% of GDP for EU-12 countries (http://www.fm-kp.si/zalozba/ ISBN/961-6486-71-3/009 -012.pdf). This highlights the magnitude and the shift of investments into intangibles as well as the need for a systematic intellectual capital report and the outcome of the value creation based on these expenditures. This growing space of intangible investments is among others demanding more strategic intelligence, or knowledge navigation capability on the society. In the past, researchers from different backgrounds have proposed different models to evaluate national intellectual capital. Since this field of study is still developing, a consensus regarding the set of determinants that should be employed has yet to be reached. Building on past relevant research, we propose a framework and model of IC measurement and then test this model by using the widely accepted International Institute for Management Development (IMD) databases, which contain both quantitative and qualitative indicators. Indicators we used were selected in two rounds. In the first round, variables that were used at least two times in relevant studies were matched with the IMD World Competitiveness Yearbook. Market capital turned out to have the fewest number of variables supported by at least two studies. In the second round, a focus group was formed to obtain feedback regarding the appropriateness of the selected variables. With input from ten Taiwanese professors who also engage in intellectual capital–related research, focal variables were finalized, as shown in Table 1. Financial capital is also included as it is a key indicator of national wealth and represents the output dimension of the Input-Process-Output theory. Balance in the number of variables for each of the four types of capital (7 variables each, excluding financial capital) as well as in the number of quantitative and qualitative variables (14 vs. 15) was achieved through the literature review, focus group discussion, and IMD database matching. Consequently, a total of 29 variables were selected—seven each for human capital, market capital, process capital, and renewal capital, and a single variable (GDP per capita adjusted by purchasing power parity) representing financial capital. Herein after, this set of 29 variables is referred as NICI40 (National Intellectual Capital Indices for 40 countries) Table 1: Variables in each type of capital proposed by this study Human Capital index

Market capital index

Skilled labor*

1. Corporate tax*

Employee training*

2. Cross-border venture*

Literacy rate

3. Culture openness*

Higher education enrollment

4. Globalization*

Pupil-teacher ratio

5. Transparency*

Internet subscribers

6. Image of country*

Public expenditure on education

7. Exports of goods

Process capital index

Renewal capital index

Business competition environment*

Business R&D spending

Government efficiency*

Basic research*

Intellectual property rights protection*

R&D spending/GDP

Capital availability*

R&D researchers

Computers in use per capita

Cooperation between universities and enterprises*

Convenience of establishing new firms*

Scientific articles

Mobile phone subscribers

Patents per capita (USPTO + EPO)

Remark: Financial capital is the logarithm of GDP per capita adjusted by purchasing power parity. Variables marked with an asterisk are rated qualitatively using a scale of 1–10.

www.ejkm.com

255

©Academic Conferences Ltd

Electronic Journal of Knowledge Management Volume 8 Issue 2, (253 - 266)

Using the variables listed in Table 1, we collected data for 40 countries from the IMD World Competitiveness Yearbook. The data analyzed in this book, therefore, describes 40 countries over a period of 14 years, from 1995 to 2008. To differentiate from other National IC Models and for easier reference, ―NICI40‖ is named to represent the 29-indicator national intellectual capital model for 40 countries developed by the authors. In this study, there are two different types of data: data with an absolute value, such as ―patents per capita‖; and data with a qualitative rating based on a scale of 1 to 10, such as ―image of country.‖ Although subjective, qualitative rating on the degree or magnitude of certain variables is unavoidable, as evaluating intangible assets cannot be fully represented by merely adding up absolute numbers. For a meaningful integration of the quantitative score and qualitative rating, the ratio of the absolute value relative to the highest value of each quantitative variable was calculated and multiplied by 10 to transform the number into a 1-to-10 score. The data transformation procedures have been repeated for all numerical indicators of human capital, market capital, process capital, and renewal capital. Financial capital is represented by the logarithm of GDP per capita adjusted by the purchasing power parity of each country, calculated its ratio to the highest value and then transformed it into a 1-to-10 score. The Overall National Intellectual Capital Ranking as shown in Table 2 includes the mean scores of the five types of capital and the total score of national intellectual capital of each country.

4. National intellectual capital rankings for 40 countries Based on the data analysis described in the preceding section, Table 2 displays the score and ranking of the five types of national intellectual capital. The overall and individual indices provide valuable information for policy makers. With 14 years of data, the top 10 countries in the overall ranking list are, in order, Finland, Sweden, Switzerland, Denmark, the USA, Singapore, Iceland (2007 and 2008 data are missing, very likely due to the 2008 financial crisis), the Netherlands, Norway, and Canada. The overall results of NICI40 confirm the general perception that the Nordic countries have the highest degree of national intellectual capital; as a result, all five Nordic countries are in the top ten NICI40. For an easier reference of the NICI40 ranking, Table 2 is prepared according to the country sequence. Table 2: NICI40 - national intellectual capital score and ranking based on country sequence 1995~200 Human 8 capital

Market capital

Process capital

Renewal capital

Financial capital

Overall NICI40

Mean

6.13

5.64

5.32

3.76

9.14

29.99

SD

1.16

0.94

1.51

2.01

0.73

5.67

Country

Score

Ranki Score Ranking Score ng

Ranking Score

Ranking Score

Ran Score king

Ra nki ng

Argentina 5.12

31

4.01

40

2.78

40

1.43

38

8.65

32

22.00

38

Australia

7.01

10

6.16

14

7.03

7

4.49

18

9.65

11

34.34

11

Austria

6.89

12

6.25

13

6.52

13

4.60

16

9.70

8

33.96

12

Belgium

7.01

9

5.86

18

5.79

18

4.66

15

9.64

12

32.97

16

Brazil

4.43

37

4.81

33

3.11

38

1.63

35

8.43

35

22.41

37

Canada

7.84

3

6.31

9

6.57

12

4.84

12

9.71

6

35.26

10

Chile

4.96

33

6.60

6

4.91

25

1.80

33

8.70

30

26.97

28

China

4.18

39

5.27

25

3.49

33

2.12

27

7.49

38

22.55

36

Czech Republic

5.34

29

5.56

22

4.51

28

2.57

24

9.16

26

27.15

27

Denmark

8.15

1

6.62

4

7.42

2

5.81

7

9.69

9

37.69

4

Finland

7.79

4

6.61

5

7.71

1

7.36

3

9.56

18

39.03

1

France

6.50

15

4.67

35

5.51

20

4.96

10

9.60

17

31.24

20

www.ejkm.com

256

©Academic Conferences Ltd

Carol Yeh-Yun Lin and Leif Edvinsson

1995~200 Human 8 capital

Market capital

Process capital

Renewal capital

Financial capital

Overall NICI40

Mean

6.13

5.64

5.32

3.76

9.14

29.99

SD

1.16

0.94

1.51

2.01

0.73

5.67

Country

Score

Ranki Score Ranking Score ng

Ranking Score

Ranking Score

Ran Score king

Ra nki ng

Germany

6.31

20

5.55

23

6.12

16

5.88

6

9.62

14

33.48

15

Greece

5.43

28

4.82

32

4.32

29

1.96

29

9.36

23

25.87

29

Hungary

6.33

19

5.68

19

4.71

27

2.34

26

8.94

27

27.99

23

Iceland

7.58

7

6.55

7

6.86

9

5.14

9

9.67

10

35.80

7

India

3.79

40

4.91

30

3.22

36

1.78

34

6.96

40

20.67

40

Ireland

6.42

17

7.21

2

6.58

11

3.87

20

9.70

7

33.77

13

Italy

6.07

23

4.60

36

5.07

23

2.62

23

9.54

19

27.90

24

Japan

6.82

13

4.60

37

5.68

19

7.03

5

9.60

16

33.73

14

Korea

6.15

22

4.79

34

4.87

26

4.23

19

9.24

24

29.28

21

Malaysia

5.53

27

6.50

8

5.11

22

2.01

28

8.66

31

27.80

25

Mexico

4.75

35

4.95

28

3.19

37

1.24

40

8.75

29

22.88

35

11

6.95

3

6.79

10

5.21

8

9.73

5

35.56

8

14

6.26

11

6.27

14

3.50

21

9.36

22

32.18

18

5

5.96

15

7.06

5

4.71

14

10.00

1

35.45

9

Philippine 4.85 s

34

4.93

29

3.25

34

1.47

37

7.38

39

21.88

39

Poland

5.61

26

4.04

39

3.23

35

1.95

30

8.77

28

23.59

33

Portugal

5.94

25

5.26

26

4.95

24

1.91

31

9.22

25

27.28

26

Russia

5.30

30

4.07

38

2.85

39

2.87

22

8.57

34

23.65

32

Singapore 6.39

18

8.39

1

7.25

3

4.77

13

9.85

3

36.65

6

South Africa

4.71

36

4.84

31

4.02

30

1.84

32

8.37

36

23.79

31

Spain

5.98

24

5.39

24

5.16

21

2.50

25

9.47

20

28.51

22

Sweden

7.98

2

6.31

10

7.18

4

7.78

2

9.63

13

38.89

2

Switzerlan 7.17 d

8

6.26

12

7.04

6

7.99

1

9.78

4

38.24

3

Taiwan

6.47

16

5.94

16

5.92

17

4.84

11

9.38

21

32.55

17

Thailand

5.02

32

5.68

20

3.96

31

1.36

39

8.15

37

24.16

30

Turkey

4.33

38

4.98

27

3.61

32

1.51

36

8.57

33

22.99

34

UK

6.18

21

5.62

21

6.13

15

4.57

17

9.61

15

32.11

19

USA

7.60

6

5.91

17

6.97

8

7.16

4

9.88

2

37.53

5

Netherlan 6.89 ds New 6.80 Zealand Norway

7.72

www.ejkm.com

257

©Academic Conferences Ltd

Electronic Journal of Knowledge Management Volume 8 Issue 2, (253 - 266)

A quick review of Table 2 shows that although the ranking is for the intangible national intellectual capital, the result agrees with the general perception that the top ten countries are well developed countries with relatively better economic and social status, and the bottom ten countries are developing countries somewhat ailed by either economic, social, or political problems in the past decade. To further explore the relationship between national intellectual capital and financial performance, we correlated the sum of the human capital, market capital, process capital, and renewal capital scores with GDP per capita (ppp) in real dollars for the 40 countries, and a strong correlation of .88 was found. This result indicates that the national intellectual capital model proposed in this book carries very valuable information in explaining national economic development and competitiveness. That is, the results presented in this paper support to a significant degree the issues policy makers are concerned about: bottom-line financial performance. Therefore, NICI40 can serve as an extension of GDP or other commonly used economic indicators, particularly for the intangibles. While GDP reflects the present economic situation, NICI40 is not only an indicator of future wealth creation capabilities but also has good explaining power for current financial performance. However, a reminder is that this set of data reflects the past national intellectual capital performance of the 40 countries, spanning 1995 to 2008 rather than their current status. For Iceland particularly, the reported statistics is its past performance before the 2008 global financial crisis (from 1995 to 2006, with 2007 and 2008 IMD data missing Iceland only). Irrespective to the recent financial crisis, the 14 years panel data provides valuable clues for what they are today and what they may become in the future.

5. Early signs of financial crisis from this set of data The above 40-country national intellectual capital study has led us to look into the intangible assets development of the eleven country clusters, namely Nordic European, larger European, smaller European, Southern European, Eastern European, Canada and USA, Latin American, Australia and New Zealand, East Asian, Southeast Asian, and BRIC (Brazil, Russia, India, and China) countries (please refer to the authors’ coming book published by Springer). When interpreting this set of data, we have detected the early signs of financial crisis in some countries. Our observation sequence and findings are presented hereunder.

5.1 Step #1 When checking the background information of the 40 countries, we have been able to obtain the GDP growth rate of some countries for the last three years, namely 2006, 2007, and 2008 as shown in Table 3. Judging from the magnitude of the growth rate decline between 2007 and 2008, we identified Iceland, Ireland and Singapore as the three countries that have been strongly impacted by the financial crisis. Iceland’s GDP growth rate dropped from +4.9% in 2007 to -3.5% in 2008 with a difference of 8.4%; Ireland dropped from +6.0% in 2007 to -1.7% in 2008 with a difference of 7.7%; and Singapore dropped from +7.7% in 2007 to +1.2% in 2008 with a difference of 6.5%. Although most of the other countries listed in Table 3 are also influenced by the financial crisis, the magnitude is not as large as that of the above three countries. Brazil has only -0.2% GDP drop between 2007 and 2008, which shows almost no impact by the financial crisis. Why Brazil was not influenced by the global financial crisis poses an interesting topic for further pursuit.

5.2 Step #2 Our second step is to investigate the problems of the identified three countries – Iceland, Ireland and Singapore. The thread of thinking comes from our 40 countries research findings. That is, human capital and renewal capital are better predictors for national long term intellectual capital development; whereas market capital and process capital are better predictors for national short term intellectual capital development. In other words, short term or more efficient national intellectual capital development may lie in the increase of market capital and process capital. On the other hand, a rapid decline in national intellectual capital may be caused by the decrease of market capital or process capital as well. Based on this clue, indeed we found that Iceland, Ireland and Singapore have a down-ward trend in market capital and process capital. For Iceland, an early sign might be detected in the first decline of market capital/process capital in 2001, and then a second one in 2006 as shown in Figure 1 (IMD 2007 and 2008 data of Iceland are missing). For Ireland, with respect to the value-creating capability of market capital and process capital, a peak is reached in 2000, then a decline sets in until 2003, with a short upturn to 2005, and then a decline to a low level in 2008 as shown in Figure 2. However, the negative correlation between market capital and www.ejkm.com

258

©Academic Conferences Ltd

Carol Yeh-Yun Lin and Leif Edvinsson

process capital from year 2000 onward up to 2008 is a strong watershed signal. Most notably, the big slide from 2007 to 2008 reflects the 2008 financial crisis and shows that economic activity in Ireland dropped sharply in 2008-09 as it entered into a recession for the first time in more than a decade (http://www.theodora.com/wfbcurrent/ireland/ireland_economy.html). Table 3: GDP growth rate of the last three years and the impact of financial crisis GDP growth

2006

2007

2008

2008- 2007#

Affected by 2008 Financial Crisis

Australia

2.9%

4.0%

2.2%

-1.8%

Some Impact

Austria

3.4%

3.1%

1.6%

-1.5%

Some Impact

Belgium

3.0%

2.6%

1.3%

-1.3%

Some Impact

Brazil

4.0%

5.4%

5.2%

-0.2%

Almost No Impact

Canada

3.1%

2.7%

0.6%

-1.9%

Some Impact

Denmark

3.3%

1.6%

0.6%

-1.0%

Some Impact

Finland

4.9%

4.2%

1.5%

-2.7%

Significant Impact

France

2.4%

2.1%

0.7%

-1.4%

Some Impact

Germany

3.0%

2.5%

1.3%

-1.2%

Some Impact

Greece

4.2%

4.0%

2.8%

-1.2%

Some Impact

Iceland

4.4%

4.9%

-3.5%

-8.4%

Very Strong Impact

India

9.6%

9.0%

6.6%

-2.4%

Significant Impact

Ireland

5.7%

6.0%

-1.7%

-7.7%

Very Strong Impact

Italy

1.9%

1.4%

-0.7%

-2.1%

Significant Impact

Japan

2.0%

2.4%

-0.4%

-2.8%

Significant Impact

Korea

5.1%

5.0%

2.5%

-2.5%

Significant Impact

Netherlands

3.4%

3.5%

1.8%

-1.7%

Some Impact

New Zealand

2.0%

3.1%

0.2%

-2.9%

Significant Impact

Norway

2.5%

3.7%

1.8%

-1.9%

Some Impact

Portugal

1.4%

1.9%

0.2%

-1.7%

Some Impact

Russia

7.7%

8.1%

6.0%

-2.1%

Significant Impact

Singapore

8.2%

7.7%

1.2%

-6.5%

Very Strong Impact

Spain

3.9%

3.7%

1.1%

-2.6%

Significant Impact

Sweden

4.6%

2.7%

0.7%

-2.0%

Some Impact

Switzerland

3.4%

3.3%

1.9%

-1.4%

Some Impact

Taiwan

4.8%

5.7%

1.9%

-3.8%

Significant Impact

United Kingdom

2.8%

3.0%

0.7%

-2.3%

Significant Impact

USA

2.8%

2.0%

1.3%

-0.7%

A little Impact

www.ejkm.com

259

©Academic Conferences Ltd

Electronic Journal of Knowledge Management Volume 8 Issue 2, (253 - 266)

#Remark: between -1.0% and -2.0%: some impact; between - 2.1% and -5.0%: significant impact; and between -5.1% and -9.0%: very strong impact For Singapore, Figure 3 indicates that the first sign of market capital decline appears in 2002 with a short upturn in 2003, 2004 marks another drop in both market capital and process capital and then follows a relatively strong upsurge. Unfortunately, the sharp market capital/process capital plunge from 2007 to 2008 discloses the strong impact of the 2008 financial crisis, which agrees with the finding of Table 3.

Scatterpl ot of Market Capi tal vs. Process Capi tal of I cel and 2005

7.25 2006 2003 2004

7.00 2002

Market Capital

6.75

2001

1999

6.50

2000

1998

6.25 1997

6.00 1996

5.75 1995

5.50 6.0

6.2

6.4

6.6 6.8 Process Capital

7.0

7.2

7.4

7.6

Figure 1: Scatter plot of market capital vs. process capital of Iceland

Scatterpl ot of Market Capi tal vs. Process Capi tal of I rel and 8.00 1999

7.75 2000

Market Capital

7.50

1997 1998

2001

1996

7.25

2005

2006 2002

1995

2003 2004 2007

7.00 6.75 6.50

2008

5.0

5.5

6.0 6.5 Process Capital

7.0

7.5

Figure 2: Scatter plot of market capital vs. process capital of Ireland www.ejkm.com

260

©Academic Conferences Ltd

Carol Yeh-Yun Lin and Leif Edvinsson

Scatterpl ot of Market Capi tal vs. Process Capi tal of Si ngapore 8.8 2007

8.7

Market Capital

8.6

2003

1995

8.5

1999

2006

1998

1996

8.4 2005 2001

2004 1997

8.3

2000

8.2 2002

8.1

2008

8.0 6.50

6.75

7.00

7.25 Process Capital

7.50

7.75

8.00

Figure 3: Scatter plot of market capital vs. process capital of Singapore

5.3 Step #3 Since there is initial evidence that market capital and process capital decline may partially explain the financial problem of Iceland, Ireland and Singapore, a raw data check was conducted to identify the problem variable. The investigation has indeed pointed to the variation of ―capital availability‖ in the process capital dimension, which agrees with the common perception that financial crisis is directly impacted by money related indicator. Our further check covering all human capital, market capital, and renewal capital indicators did not show a clear connection with the financial crisis. As a result, we have decided to settle on the in-depth examination of the capital availability changes of some countries from 1999 to 2008 as indicated in Table 4. Table 4: Capital availability ratings of the countries strongly or significantly impacted by the financial crisis Capital availability Finland Iceland India Ireland Italy Japan Korea New Zealand Russia Singapore Spain Taiwan United Kingdom

1999 8.67 5.60 2.76 8.34 6.09 6.58 4.34 5.79 2.70 7.80 7.57 6.43 5.92

2000 8.00 3.00 2.93 7.85 5.46 6.50 4.31 4.72 2.81 6.93 6.32 5.95 5.24

2001 7.79 2.09 3.69 7.09 5.37 5.36 5.42 5.41 3.36 7.42 6.67 6.05 6.40

2002 8.27 3.95 4.61 7.52 5.25 5.51 4.86 5.10 3.63 7.08 7.00 6.31 5.69

2003 8.03 3.68 5.67 7.17 5.56 6.36 4.67 5.56 4.00 7.24 7.60 6.63 6.02

2004 8.07 4.93 5.33 7.25 5.34 6.43 5.51 4.18 4.29 7.37 7.33 6.49 5.33

2005 7.66 4.86 5.24 7.68 5.35 6.60 4.86 3.93 5.25 6.81 6.40 5.58 5.42

2006 6.19 3.33 5.53 6.71 5.63 6.45 5.18 4.25 4.39 7.19 5.92 6.00 5.28

2007 5.55 4.29 5.12 4.44 6.29 5.11 3.38 4.22 7.13 4.43 5.61 4.48

2008 5.43 3.93 5.15 4.87 6.30 4.84 4.24 2.70 5.29 4.40 4.95 4.15

Based on Table 4, Iceland’s lacking of capital has begun to show starting from 2000 even though its 2007 and 2008 data are missing. In its better year of 1999, Iceland’s 5.60 capital availability rating is still much lower than its Nordic peer – Finland’s rating of 8.67 in the same year. Iceland’s lasting low capital availability rating should have conveyed a very strong signal for the interested parties. If someone picked up the message early enough and adopted proper coping measures, Iceland’s financial system meltdown might have been prevented. As for Ireland, Table 4 also indicates a relatively large scale

www.ejkm.com

261

©Academic Conferences Ltd

Electronic Journal of Knowledge Management Volume 8 Issue 2, (253 - 266) decline, from 7.68 in 2005 to 5.15 in 2008. The drastic drop of Singapore’s capital availability from 7.13 in 2007 to 5.29 in 2008 is another evidence of the connection between GDP decline and capital availability. For comparison purpose, Table 4 also includes the countries with the 2008-2007 GDP gap of more than -2.1%, based on Table 3 results. In regard to the 2008 global financial crisis, Iceland has been the hardest hit of all Nordic countries. This Icelandic financial crisis involves the collapse of all three of the country's major banks. The full cost of the crisis exceeds 75% of the country's 2007 GDP. It is not surprising that past excellent performer, such as Iceland with inherent system weakness, banking in the case of Iceland, tumbled down within a short time. This research provides valuable data for a deep reflection in contemplating the future national competition.

6. Early signs of PIGS-country problems from this set of data In early 2010, people began to talk about the PIGS countries. ―PIGS‖ is an acronym for a group of four countries: Portugal, Ireland, Greece and Spain – all in deep economic trouble in the eurozone (http://news.kyero.com/2009/1/26/pigs- portugal-ireland-greece-spain). Our dataset also reflects the troubles of these countries. The following presentation proceeds according to the alphabetical order of country names. The recent discovery of the weak situation of Greece might have been possible to detect in 2000. Figure 4 indicates that market capital/process capital peaked in 2000, after which it continued to fall until it halted at a very low level in 2008 – a clear negative correlation line from 2000 to 2008. Consequently, the financial capability of Greece eroded into its current situation that requires external loan support. Somehow, our data predicted what has been reported by EU governments, namely, that Greece shocked bond markets and its deficit in 2009 was a staggering 12.5 percent of GDP, far above its estimate of 3.7 percent made in the spring (http://www.taipeitimes.com/News/worldbiz/archives/ 2010/01/14/2003463439). This downturn somewhat reflects Greece’s troubling 9.7% unemployment rate for 2009, and the forecast of gross debt in 2010 of 125% GDP and stock performance of -10.5% as of Feb. 11, 2010 (http://news.bbc.co.uk/2/hi/8510603.stm).

Scatterpl ot of Market Capi tal vs. Process Capi tal of Greece 1996

1999

5.5 1997

1998

1995

2000

Market Capital

5.0

2006 2005 2004

2003

2001 2002

4.5 2007

4.0 2008

3.5 3.0

3.5

4.0 4.5 Process Capital

5.0

5.5

Figure 4: Scatter plot of market capital vs. process capital of Greece Ireland’s situation has been reported in the last section and Figure 2. For Portugal, as shown in Figure 5, its short-term value creating capability began to slide in 1998, with market capital declining year after year although a slight improvement in process capital was recorded. This downturn reflects Portugal’s troubling 10.4% unemployment rate in 2009, a projected gross debt in 2010 of 84.6% GDP and a stock market decline of -9.7% as of Feb. 11, 2010 (http://news.bbc.co.uk/2/hi/8510603.stm). www.ejkm.com

262

©Academic Conferences Ltd

Carol Yeh-Yun Lin and Leif Edvinsson

Scatterpl ot of Market Capi tal vs. Process Capi tal of Portugal 6.2

1996

1997

6.0 1995

5.8

Market Capital

1998

5.6

1999

5.4 2003

5.2 2002

2006 2001

5.0 2004

4.8

2005 2000 2007

2008

4.6 3.0

3.5

4.0

4.5 Process Capital

5.0

5.5

6.0

Figure 5: Scatter plot of market capital vs. process capital of Portugal For Spain, in regard to the development of market capital and process capital, as shown in Figure 6, a generally negative correlation appears starting from 2001 with 2008 hitting rock bottom. The declining short-term value creating capability partially explains Spain’s disturbing 19.5% unemployment rate in 2009, a projected gross debt in 2010 of 66.3% GDP and a stock market decline of -13% as of Feb. 11, 2010 (http://news.bbc.co.uk/2/hi/8510603.stm). Spain has been very hard-hit by huge declines in its property markets, and so it remains the only major economy in Europe still in recession; as a result, Spain government announced a 50bn-euro austerity package, including a civil-service hiring freeze, at the end of January 2010 (http:// news.bbc.co.uk/2/hi/8510603.stm).

Scatterpl ot of Market Capi tal vs. Process Capi tal of Spai n 1998

1996 1997

6.0

1999 2001 2000 2003

1995

Market Capital

5.5

2002

2006 2004

5.0 2007

2005

4.5 2008

3.0

3.5

4.0

4.5 Process Capital

5.0

5.5

6.0

Figure 6: Scatter plot of market capital vs. process capital of Spain

www.ejkm.com

263

©Academic Conferences Ltd

Electronic Journal of Knowledge Management Volume 8 Issue 2, (253 - 266)

7. Lessons learned for regaining competitiveness On the one hand, market capital and process capital are the two key aspects that are more easily attainable if a nation is willing to engage a heavier investment for a short term development. On the other hand, they are susceptible to decline when the situation becomes unfavorable. To remedy the problem of Iceland, restructuring Iceland’s banking system to increase capital availability, rather than heavily on loan is a ―must‖. In addition, the countries in trouble can invest more in the long term national development determinants - human capital and renewal capital. Although these two capitals take time to create and accumulate, they are the key elements of national sustainable competitiveness. It seems that national intellectual capital development also undergoes a life cycle. Those currently at the peak may decline some day, especially as more and more countries are becoming aware of the value of intellectual capital and are aggressively pursuing it. Yet, while competitors are catching up, the key success factor in the future may lie in aspiring to a national vision, leveraging human capital, consolidating the roots of innovation, and enhancing social skills (Stahle, 2007) for the renewal and effective transfer from intellectual capital to financial performance, or, in other words, to enhance human capital with components of process capital, which leads to financial performance. This process may require more social innovation and societal entrepreneurship. Although intellectual capital does not necessarily translate to financial performance and current financial performance may not guarantee future national development, yet the ebb and flow of Iceland in the current financial crisis may provide insights for policy makers. Iceland has been a rising star in economic development in the past few years and has received a consistently high ranking in world standard-of-living surveys; moreover, it was number one of the OECD countries according to The Global Benchmark Report 2006. But its 2008 national financial meltdown hit the country very hard. On the contrary, the relatively intellectual capital static Norway thrives by going its own way. In 2006, Norway’s economy grew by just under three percent (GDP growth 2.5%). Instead of spending its riches lavishly, it passed legislation ensuring that oil revenue would go straight into its sovereign wealth state fund, which is used to make investments around the world. Now its sovereign wealth fund is close to being the largest in the world and its banks represent just two percent of the economy and tight public oversight over their lending practices have kept Norwegian banks from taking on the risk that brought down their Icelandic counterparts (http://www.nytimes.com/2009/05/14/business/global/14frugal.html? _r=2&hp). However, countries recently in trouble as reported earlier should still have great potential to regain competitiveness as shown in Table 5. For instance, human capital of Iceland has been advanced from #7 to #3 and renewal capital advanced from #18 to #7, comparing the earlier 1995-2000 and the most recent 2005-2008 period ranking. In the same rein, Greece, Ireland, Portugal and Singapore all have human capital and renewal capital advancement comparing the first and the most recent third period ranking. The only exception is that Spain declines three ranks in human capital, yet increases two ranks in renewal capital. Although gloomy at the moment, those countries which encountered financial problems recently should have the potential to regain their national strength, provided that their long-term determinant – human capital and renewal capital continue to steadily increase over the coming decade, for in today’s knowledge economy the intangible intellectual capital is surely the key foundation for future national development. Table 5: Human capital and renewal capital changes of the six countries recently in trouble st

st

( 1 Period st ( 1 Period st 1 Period 1 Period Renewal rd 3 Period 1995-1999 1995-1999 Capital rd rd 3 Period ) 2005-2008 3 Period ) Difference Ranking Difference Ranking Ranking Greece Greece 1 30 29 6 34 Iceland Iceland 4 7 3 11 18 Ireland Ireland 4 20 16 0 20 Portugal Portugal 6 25 19 5 32 Singapore Singapore 8 23 15 8 17 Spain Spain -3 22 25 2 25 Human Capital

www.ejkm.com

264

rd

3 Period 2005-2008 Ranking

28 7 20 27 9 23

©Academic Conferences Ltd

Carol Yeh-Yun Lin and Leif Edvinsson

References Bontis, N. (2004), ―National intellectual capital index: A United Nations initiative for the Arab region‖, Journal of Intellectual Capital, Vol.5 No.1, pp.13-39. Bontis, N., Chua, W. and Richardson, S. (2000), ―Intellectual capital and the nature of business in Malaysia‖, Journal of Intellectual Capital, Vol.1 No.1, pp.58-100 Bounfour, A. (2003), ―The IC-dVal approach‖, Journal of Intellectual Capital, Vo.4 No.3, pp.396-412. Bounfour, A. and Edvinsson, L. (2004), IC For Communities, Nations, Regions, Cities, and other Communities. Butterworth-Heinemann, Boston, MA Drucker, P. F. (1993), Post-capitalist society (1st ed.). New York, NY.: HarperCollins. Edvinsson, L. and Malone, M. (1997), Intellectual capital. Harper Business, New York, NY. Garcia-Ayuso, M. (2003), ―Intangibles: Lessons from the past and a look into the future‖, Journal of Intellectual Capital, Vol. 4 No.4, pp.597-604. Malhotra, Y. (2003), ―Managing and Measuring Knowledge Assets in the Public Sector‖, Working Paper, Syracuse University. OECD. (2000), ―International Science and Technology Co-Operation: Towards Sustainable Development‖, Proceedings of the OECD Seoul Conference. Paris: OECD. Pasher, E. (1999), The Intellectual Capital of the State of Israel, Kal Press, Herzlia Pituach. Stewart, T. A. (1997), Intellectual Capital: The New Wealth of Organizations, Nicholas Brealey Publishing, New York, NY. United Nations Economic Commission for Europe (2003), Status of and Trends in the Development of E-Government, New York, NY. World Bank. (1998), World development report: Knowledge for development, Oxford University Press. World Bank (2002), The knowledge assessment Methodology and Scoreboards, Available from http:// www1.worldbank.org. World Bank (2007), Global Economic Prospects 2007, Available from http://web.worldbank.org GDP per capita (ppp) from the IMF (International Monetary Fund April 2009 data), http://en.wikipedia.org/wiki/List_of_countries_by_GDP_%28PPP%29_per_capita http://en.wikipedia.org/wiki/Broadband_Internet_access_worldwide#Sweden (Number of Broadband Internet subscribers) http://en.wikipedia.org/wiki/List_of_countries_by_broadband_usershttp://www.oecd.org/dataoecd/15/13/3972522 4.pdf http://en.wikipedia.org/wiki/List_of_countries_by_number_of_Internet_users http://en.wikipedia.org/wiki/Iceland http://en.wikipedia.org/wiki/Norway http://en.wikipedia.org/wiki/2008%E2%80%932009_Icelandic_financial_crisis http://news.bbc.co.uk/2/hi/8510603.stm http://news.kyero.com/2009/1/26/pigs- portugal-ireland-greece-spain http://www.foreignpolicy.com/story/cms.php?story_id=4030 http://www.imd.ch/ research/publications/wcy/upload/Overall_ranking_5_years.pdf http://www.norway-un.org/News/Latest+news/300309_IMF.htm http://www.oecd.org/document/32/0,3343,en_2649_34569_37216992_1_1_1_1,00.html http://www.taipeitimes.com/News/worldbiz/archives/ 2010/01/14/2003463439 http://www.thelocal.se/10000/20080215/ http://www.theodora.com/wfbcurrent /ireland/ireland_economy.html

www.ejkm.com

265

©Academic Conferences Ltd

Electronic Journal of Knowledge Management Volume 8 Issue 2, (253 - 266)

www.ejkm.com

266

©Academic Conferences Ltd