Knowledge Networking in Extended Enterprises Dimitris Apostolou1, Kostas Kafentzis2, Gregory Mentzas2, Wolfgang Maas3 1
(Corresponding author) Planet Ernst & Young, Apollon Tower, 64 Louise Riencourt Str., GR-11523 Athens, Greece. Tel: +30-210-6905000, Fax: +30-210-6981885, e-mail:
[email protected]
2
Department of Electrical and Computer Engineering, National Technical University of Athens, GR-10682 Athens, Greece.
3
Institute for Media and Communications Management, University of St.Gallen, Blumenbergplatz 9, CH-9000 St.Gallen, Switzerland.
Abstract Organisations are part of a complex network of connections with their partners and customers. This network is not merely a supply chain or financial connection – it is based on an increasingly intimate sharing of information and knowledge. This paper aims to evaluate the increase in inter-organisational knowledge sharing capabilities brought about by the Internet-driven “new-economy” technologies and the resulting managerial implications. It presents a typology of knowledge sharing networks and it discusses the benefits and challenges associated with inter-organisational knowledge sharing.
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Existing Theories & Work
A common thread running through many knowledge management initiatives is the challenge of developing and supporting new network-based communities, through which companies can improve internal collaboration and work more closely with partners and customers. Networks of people and networked organizations are emerging because the classic hierarchy of the bureaucratic model is slow to respond to the recent changes in the business environment. In the network, activities still need to be co-ordinated and integrated, but this integration relies on knowledge and relationships and a clear common sense of purpose. This has led to ideas about “work as a network of conversations” and the “hypertext organization”; see Nonaka and Takeuchi (1995). Networks may take various organisational forms, ranging from communities of practice between individuals with similar experiences and or purposes to supply chains of companies that exchange knowledge within their industry. Knowledge networks are relationships among entities (individuals, teams, organisations) working on a common concern and they embed dynamism for collective and systematic knowledge asset creation and sharing. Knowledge networks have five critical characteristics that differentiate them from other similar organisational structures and mainly from communities of practice; see e.g. Wenger (1999) and Wenger and Snyder (2000). These characteristics are the following: knowledge networks are responsible for creating, sharing, protecting and cultivating common knowledge assets; knowledge networks are working
networks and they are purpose-driven; knowledge networks require organisational commitment beyond the commitment of their participating members; knowledge networks are built on expertise, not just interest – or common practice – alone; and knowledge networks aim at the development and strengthening of the learning capacity of all members; see also Seufert et al (1999).
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Research Approach
Our classification for knowledge networks includes a qualitative mapping of five types of knowledge networks along two dimensions. The first dimension is the openness of the community model, that is whether the approach is essentially a variant of existing knowledge based intranets or whether it has more open market character. The second dimension is the extent of commercialisation. It is along this dimension that we find how much the “productizing” of knowledge within the electronic medium has succeeded. We distinguish between the following types of knowledge networks (see also Figure 1): Open Knowledge Source. The nature of the WWW allowed for the mass provision of free content available to the public. Apart from the free resources of knowledge, communities and networks of individuals and experts have arisen and gathered around topics of common interest. Participation in these communities does not require any fee for the members. The model that supports these non-commercial (in the sense of paying for knowledge products) and open knowledge-sharing communities is called open knowledge source. Information objects shared in this model can be readily available or be created by its member by answering to a knowledge need. Examples for open knowledge sources include news media (CNN, NY Times, Economist), technology consortia (W3C), experts communities (Expert.com). The free nature of this model implies that the demand may not be fulfilled since there not exist any obligations for the provision of solutions to the customers. Intra-Organizational Knowledge Networks. Within organizations, the need for continuous access to knowledge has spurred the development of various knowledge initiatives. People search for knowledge because they expect it to help them succeed in their work. The elaboration on this type of knowledge marketplaces has been based largely on the work of Davenport and Prusak (1998). They suggest that knowledge movement within the organizations is powered by market forces similar to those that animate markets for other, more tangible good. Like markets for good and services, the knowledge market has buyers and sellers and brokers and market pricing and exchange mechanisms, even though money is rarely the form of payment. Membership-based Knowledge Networks, which are closed communities with a varying degree of commercialisation (examples include Research and Technology Organisations like The Welding Institute, or business and market research organisations such as Nielsen). Knowledge Supply. Most of the modern organizations are in need of expert knowledge provided in the form of professional services. Ad hoc specialized knowledge provision or constant knowledge flow by experts into organizations calls for close interaction and deep collaboration between the trading parties. The knowledge supply model comes to support this closed knowledge communities and provide the means for relationship and trust building as well as for the frictionless transfer of expert knowledge whose codification level is relatively low. Examples of knowledge suppliers include initiatives by IT research firms
(Forrester Research or Gartner). Here, the total number of participants and the potential relations between them are much less than in the open market business model but closer mutual relations are much easier achieved, which is fundamental to the success of this model. Learning Networks. There is a great amount of knowledge and expertise that is accumulated by the interaction among different organisations with similar needs. Arguably the experience of regional clusters of small firms provides one important piece of evidence in support of this. To explore the learning potential of this novel approach, an innovative knowledge-sharing model has been introduced recently in several countries in Europe, referred to as Learning Network (Bessant and Francis, 1999). The term learning network refers to inter-organisational networks where structures and systems have been formally established to increase the participants’ knowledge and innovative capability. Examples of learning networks include initiatives by professional associations (Institute of Mechanical Engineers, UK), and by sector based associations of firms with common interests in the development of the sector (Automotive Cluster of Styria, Austria). The Learning Network model offers members the opportunity to interact and exchange knowledge with likeminded professionals and to both create and consume knowledge relevant to a topic of professional interest. General Knowledge Trading. The general knowledge trading community is an open and commercial marketplace. Increased fragmentation is necessary at least on the demand side in order for this business model to be feasible. The idea of an open marketplace with many different buyers and suppliers implies that price and volume are the driving factors of the market (Wijnhoven 2001) and that information objects traded are not too complex and sophisticated but easily codified and similar to commodity goods. This type of knowledge market, when it follows a many-to-many model, offers significant opportunities for on-line collaboration between knowledge providers in order to fulfil specific information needs otherwise impossible to be met by a single provider. Examples of the general knowledge trading model include IT expert communities (HotDispatch, Experts Exchange) and public knowledge marketplaces (Knexa). Fundamental to the success of this model is that members are provided with advanced search and navigation capabilities enabling them to locate the knowledge they need in short time among the plethora of knowledge assets available. Another possibility is that the general knowledge trader runs the marketplace and also plays the role of the only knowledge provider.
commercial Knowledge supply
General knowledge trading
Nature of business
MembershipBased KT
Learning Networks
IntraOrganizational KT
Open knowledge source
non-commercial Nature of community closed
open
Figure 1: Classification of Knowledge Networks
We have examined three real-life knowledge networks through the development of dedicated information technology tools and management principles. The analyses of these cases are presented in detail in the referenced papers: (a) a general knowledge trading network at the supply chain level; this case is based on the findings of the WIT Esprit project which focused on the wood-furniture supply chain (Apostolou, 1999); (b) a learning network; this case examines a sector based network, the AUTOMOTIVE CLUSTER of STYRIA developed in the KNOWLABORATION IST project (Apostolou, 2003); and (c) a knowledge supply network; this case describes a knowledge asset marketplace for delivering professional services currently being developed in the INKASS IST project (Apostolou, 2002).
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Findings
Independently of which knowledge network type is adopted, our practical experience shows that there are a number of elements that should be taken into consideration for efficiently conducting knowledge networks. First of all, the development and delivery of true value-added services that are offered in both digital and physical delivery systems through the evolution of trusted trading communities. We should not expect that selling offerings in the digital domain include only explicit knowledge. With careful planning the selling of tacit knowledge could be accommodated, in terms of offering expert advice through physical (e.g. selling consulting time) or virtual (e.g. through on-line collaboration) channels. Secondly, as the critical dependency of highly customer-specific knowledge product and services increases, the need for trust and established relationships increases proportionally. Trust is a critical component to true partnering to create long-term, knowledge-intensive solutions to industry pain points and to create new forms of value. It is imperative that a
trust relationship be forged either through the knowledge network or that established trust relationships be given a safe pathway to expand through “knowledge hubs”. We should keep in mind that price is not the only driving factor in knowledge transactions. Factors such as quality, expertise proven in previous cases, consistency or timely delivery weigh heavily in the decision for a knowledge purchase. Negotiated commerce with multi parameter bidding allows this type of purchases. Negotiated pricing models may include auctions, requests for quotation and exchange/matching. Furthermore, a direct benefit of negotiated commerce is the simplification of the process to screen and select buyers.
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References
Apostolou, D., Mentzas, G.N. A. Abecker, W-C. Eickhoff, P. Georgolios, K. Kafentzis, S. Kyriakopoulou (2002) Challenges and Directions in Knowledge Asset Trading, in Karagiannis and Reimer (Eds) Proceedings of the Practical Aspects of Knowledge Management 2002, Lecture Notes in Artificial Intelligence, Springer, pp. 549-564. Apostolou, D., Baraboutis, K., Mentzas, G.N. (2003) Exploiting Knowledge and Learning across Organisations: The role of the "broker" and Information Technology in Learning Networks, paper accepted for presentation in the Fourth European Conference on Organisational Knowledge. Learning and Capabilities, OKLC 2003. Apostolou, D., N. Sakkas and G. N. Mentzas (1999) Knowledge Networking in Supply Chains: A case study in the wood / furniture sector, Information, Knowledge and Systems Engineering, October / November issue, 1999, pp.267-281 Bessant, J. and D. Francis (1999) Using learning networks to help improve manufacturing competitiveness, Technovation 19 (1999) 373–381 Davenport, T. and L. Prusak (1998) Working Knowledge: How Organisations Manage What They Know, Harvard Business School Press, 1998. Mentzas, G. N., D. Apostolou, A. Abecker, R. Young (2002) Knowledge Asset Management: Beyond the Product-centred and Process-centred Approaches, Springer, 2002. Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford Univ Press. Seufert, A., G. von Krogh and A. Bach (1999) Towards knowledge networking, Journal of Knowledge Management, Volume 3, Number 3, 1999, pp. 180-190. Wenger, E. (1999) Communities of Practice: Learning, Meaning and Identity, Cambridge University Press, Cambridge, UK(1999). Wenger, E. and B. Snyder (2000) “Communities of Practice: The Organizational Frontier,” Harvard Business Review, 78, No. 1, 139–145 (2000). Wijnhoven, F. (2001) Information Markets to Improve Information Value and Utilisation, in: International Journal on Media Management, 3(3), 2001, pages 173-180
