Inter-organisational Knowledge Sharing and Trading 1
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Gregoris MENTZAS , Dimitris APOSTOLOU , Kostas KAFENTZIS 1 National Technical University of Athens 9, Iroon Politexniou Str., 15780 Zografou, Greece Tel: +30-210-772-3895, Fax: +30-210-772-3550, Email:
[email protected] 2 Planet Ernst & Young, Apollon Tower, 64 Louise Riencourt Str., 11523 Athens, Greece Tel: +30-210-6905-000, Fax: + 30-210-6981-885, Email:
[email protected] 1 National Technical University of Athens 9, Iroon Politexniou Str., 15780 Zografou, Greece Tel: +30-210-772-3895, Fax: +30-210-772-3550, Email:
[email protected] Abstract: Organisations are in the midst of two significant transformations: The first is the positioning of knowledge centre stage as a valuable resource and a dr iver of wealth creation. The second is the impact of the Internet, leading to the evolution of business into ebusiness. Until recently these developments were considered in isolation; but there are connections, and these are becoming increasingly apparent. This paper defines and develops the concept of a knowledge marketplace in which knowledge assets are shared and traded. It describes the business challenges associated with the design of electronic knowledge marketplaces and provides a typology of various types of knowledge sharing and trading models. Through three case studies, it illustrates how a knowledge sharing and trading model can be implemented for real-life organizations. 1. 1. Introduction Organisations are in the midst of two significant transfor mations: The first is the positioning of knowledge centre stage as a valuable resource and a driver of wealth creation. The second is the impact of the Internet, leading to the evolution of business into e-business. Until recently these developments were considered in isolation [7]. But there are connections, and these are becoming increasingly apparent. Knowledge is an asset that can be re-packaged into knowledge-based products and services. The Internet provides an effective vehicle for marketing and delivering knowledge. The new term knowledge trading captures the convergence of these two strands. Increasingly more companies are expanding the knowledge management concept externally: they explore new ways to put enterprise knowledge in the hands of customers, suppliers, and partners and share with them their intellectual capital. In the new global economy, where value and differentiation are the two essential ingredients for business success [4], organisation’s knowledge is often the company’s primary value proposition. Already the idea of knowledge trading is an exciting topic with manifold implications in both technological areas and business engineering [8]. This paper defines and develops the concept of a knowledge marketplace in which knowledge assets are shared and traded. It describes the business challenges associated with the design of electronic knowledge marketplaces and provides a typology of various types of knowledge sharing and trading models. Through three case studies, it illustrates how a knowledge sharing and trading model can be implemented for real-life organizations.
2. Knowledge Marketplaces A knowledge marketplace is a (virtual) place where knowledge is shared and traded. Within organizations, the knowledge market, very much like markets for goods and services, has buyers, sellers, and brokers, as well as market pricing and exchange mechanisms, even though money is rarely the form of payment; see e.g. [6]. Outside the organization, similar knowledge exchange mechanisms exist in knowledge networks, whether these are professional societies or special interest groups in informal networks. The growing importance of knowledge indicates that the time is right for the creation of mechanisms to improve the flow of knowledge and to increase the efficiency of knowledge exchange and trading. The pervasiveness of the Internet has already started to shift existing knowledge markets into the Web; see e.g. [10]. Other developments are also influencing the creation of online knowledge markets. One is the growth of the Internet as a vehicle for e-commerce and knowledge exchange. Many of the commerce models of e-marketplaces, such as auctions, can be adapted to the marketing of knowledge. Business–to–business exchanges, in particular, offer significant potential for increasing the efficiency of buying and selling. The popularity of online communities demonstrates the high interest in seeking and sharing knowledge with likeminded people. Here the same factors that apply in internal knowledge markets-reciprocity, repute and altruism [6] -are also important. But many knowledgeable people who are not active in these communities may be encouraged to do so, too, if they were compensated financially for their time and expertise. Apart from buyers and sellers, markets need a market-making mechanism to work. As a minimum an online website will need facilities to capture and process details of needs and offers. It may add intelligence that includes matchmaking capabilities and a set of business rules. These rules may filter out specific matches, based on personal preferences of buyer and seller, or they may include rules for dynamic pricing to maximize revenues. If the market is a full trading hub, order processing and account management facilities will be needed. They may even host various delivery mechanisms, including online knowledge repositories and communities. In return for providing these facilities, the market maker will seek revenues from one or more sources, such as commissions from buyers and/or sellers, advertisers, sponsors, or from affiliate fees for successful referrals to complementary websites. 3. A Typology of Knowledge Marketplaces This section introduces a terminology and classification for knowledge marketplaces. It includes a qualitative mapping of five types of knowledge networks along two dimensions. commercial Nature of business
Knowledge supply
General knowledge trading
Learning network
Intraorganizational KT
Open knowledge source
non-commercial
closed
Nature of community
open
Figure 1: Typology of knowledge marketplaces
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. In the following we outline the basic characteristics of each knowledge marketplace type. 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). In this knowledge trading model the firm of interest – the open knowledge source provider- is positioned on the one side of the market while on the other side a number of individuals or organizations act as knowledge buyers. 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 [6]. They suggests 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 knowle dge market has buyers and sellers and brokers and market pricing and exchange mechanisms, even though money is rarely the form of payment. 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 [5]. The term learning network does not refer to networks where learning simply happens but 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). In this business model the firm of interest – the learning network broker- sits in the centre, positioned between members of the community (at the right of the diagram) and suppliers (at the left). The Learning Network model offers members the opportunity to interact and exchange knowledge with like -minded professionals and to both create and
consume knowledge relevant to a topic of professional interest. The network broker can gain revenue from: (a) membership fees; (b) sales of value -added services (such as premium learning packages compiled by broker); and (c) commission of side products and services sold to members. These products and services are usually related to the topics of interest of the learning network members. For example in an automotive industry learning network, tooling machinery may be sold to the members through the network. 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) and industry research and technology networks (The Welding Institute, UK). In this Knowledge trading model the firm of interest – the knowledge supplier- is positioned on the one side of the market while on the other side a number of independent organizations or supply chains act as knowledge buyers. The total number of participants and the potential relations between them are much less than in the open market business model but closer mutua l relations are much easier achieved, which is fundamental to the success of this model. 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 [12] 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-tomany 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), public knowledge marketplaces (Knexa) and IT research firms (Gartner and IDC). In this knowledge trading model the firm of interest – the general knowledge trader- sits in the centre, positioned between members of the community (at the right of the diagram) and suppliers (at the left). 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. 4. Cases We have examined three real-life knowledge marketplaces 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 [1]. WIT: Knowledge Sharing at the Supply Chain Although firms work hard to invent and improve processes themselves, they also at times want to share process or product knowledge among firms. For example, knowledge sharing within a supply chain has become a common practice because it promises to enhance the competitive advantage of the supply chain as a whole (Bell et al., 2002); that is the benefit of the cooperation is mutual (Holland, 1995). It is sometimes the case that companies even require that their suppliers implement inter-organizational information systems to improve organizational coordination and product quality (Holland, 1995). In other cases it the introduction of such systems that is triggering the formation of new organizational entities to resume the role of the information broker (Sakkas et al., 1999). The WIT toolset provides an infrastructure based on the Internet to support the collaborative enterprise paradigm in the wood / furniture supply chain, focusing primarily in the field of design, sales, and marketing. This toolset, addressing the main functions required within the targeted supply chain, operates partly locally on the end-user’s computer and partly in collaboration with remote entities (WIT-servers). The architecture of the client as well as the server applications allows for the possible modification of the existing tools or the extension of the basic tool set by integrating other functions. This approach, along with the careful design of the infrastructure allows WIT to be adopted and used in the domain of other supply chains, outsides the wood industry. automated query processing product data
automated query processing
push generic space settings
deliver offer and receive order
product data
directoryassisted search
WIT SERVER 1
direct
initiate collaborative session with involved users
access visualise create design
Internet
WIT SERVER N deliver offer and receive order
push software and updates
push generic space settings
push software and updates
direct
search
composition tool
WIT CLIENT
query
set-up space data request offer
Internet and send order
Figure 2: The WIT functional architecture KNOWLABORATION: Knowledge Applications for collaborative Learning Networks The potential of knowledge networks for the distribution of explicit knowledge, i.e. knowledge which is pinned down verbally in writing or electronically and can therefore be
communicated and distributed, is undisputed. However, what us required is an integrated approach which includes both explicit and tacit knowledge. Tacit knowledge can be conceptualized as processing a technical and a cognitive dimension (Suefert et al., 1999). Whereas the technical dimension contains informal, personal abilities and skills, the cognitive dimension includes mental models, influenced by beliefs, values and convictions (Nonaka and Takeuchi, 1995). For this reason and in order to make effective use of knowledge, a network must be built up in which the knowledge and experience of employees are available (Suefert et al., 1999). The KNOWLABORATION toolset was developed specifically with these objectives in mind: to provide an infrastructure based on the Internet to support the advancement of knowledge as well as the learning of the participating employees. It exploits the widely used approach termed “action learning”: the active participation, challenge and support of groups of employees facing similar problems (Pedler et al., 1991). LN Members’ employees participating in learning sessions
Broker Managers
e-learning Policy Makers
Decisionmaking KnowledgeSharing
Members’ Managers participating in the Network Board Legend Strong involvement
Associated Members
LN Members’ employees not participating in learning sessions
Access but weaker involvement
Figure 3: Main functions and roles of a Learning Network The KNOWLABORATION toolset is designed to support six main categories of users (figure 3): (a) The managers of the broker - organization, which co-ordinates or wish to coordinate the knowledge network. (b) The managers of the collaborating organizations who have decision making responsibilities within the network; the number of managers with such responsibilities vary from a few people representing all members to one representative from each collaborating organization. (c) The employees who participate in actual learning and knowledge sharing sessions of the network. (d) The employees who do not participate in specific learning sessions of the network; usually members appoint specific persons to follow the learning sessions of the network who however find it difficult to convey the learning content of the sessions to the rest of the organization. (e) The employees of associated members (if existing) who can also reap the fruits of learning that is taking place within the network, if the network decides to allow access to the shared knowledge base; this is the case of members which pay reduced subscription and have limited participation and access to the network.
INKASS: Developing a Knowledge Asset Marketplace In the INKASS IST project, we are developing knowledge marketplaces for three end-users that come from different business sectors; TWI - a UK-based institute that offers expertise on welding engineering, PLEY - a Greek management and IT consultancy - and ACCI, the Athens chamber of commerce and industry. Next, we describe the PLEY Knowledge Trading approach that is based on the Knowledge Supply model. The PLEY marketplace has been designed in order to support a situation called “Option Planning”, which occurs when a client of PLEY has a general problem, for which he/she wants to get different options about how to solve it. The general question asked here is: “Here’s my problem, bring me back an answer and tell me how much it will cost.” The fundamental assumption that led the marketplace design was that the marketplace should provide direct or indirect means to its clients for solving their specific problems. The solution is constituted by a number of information objects, a collection, exchanged via the marketplace. In a first step of the design the different roles and their intentions, needs, rights and duties have to be identified and described for this specific case. For the Option Planning we have identified 3 different roles; the Knowledge User, who needs different options for the planning and implementation of a solution to a specific problem, the Knowledge Broker, who is responsible for routing client queries to appropriate Experts or knowledge objects, and the Knowledge Expert, who answers a specific question of a Knowledge User at best knowledge and helps her to develop a solution for her problem. After the definition of the different roles a number of possible interactions between them was designed based on the basic consulting process. Two sub processes support these interactions and realize the Option Planning situation. The first sub process supports the interaction of the customer with the electronic medium and the second the interaction between the customer and the PLEY consultant. A number of services have been developed to realize the whole process. The fundamental service is the intelligent search, which allows the user to select different core areas and industries he wants to search in and to phrase a concrete question. The search takes into context the profile of the customer and previous relevant cases, thus leading to more precise search results and increased customer’s benefit in using the electronic medium. In addition to this, the platform facilitates the creation of all the compound documents (document collections that constitute solutions to a problem) in a semiautomatic fashion. The customer is also provided with a private individualised web space where he can save his search results and where the consultant can provide further information to him/her. This area is visible to the consultant who gets the history of the customer’s choices (downloaded information objects) and thus can communicate with the client in a more efficient and knowledgeable way. The main challenge in the realization of PLEY’ s marketplace was the creation of a selfdescriptive, therefore easily migrating to other systems, ontology that would serve as the backbone of the system and would copy with all the issues arising in the supporting of a transaction in a consistent way. These issues include the understanding of the client’s problem space and the matching with the proper knowledge objects from the solution space that would constitute a solution to his specific problem. The fusion of automatic systems and human-expert involvement is INKASS proposal to the inefficiency of existing systems for the fully automatic delivery of solutions in a succeeding and legitimate way. Furthermore, appropriate mechanisms are under implementation supporting the pricing of these solutions in terms of delivered value to the client, whose willingness to pay depends on various factors arising from the nature of the market and knowledge itself.
5. 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 valueadded 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. 6. References [1] 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. [2] Apostolou, D., N. Sa kkas 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. [3] 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. [4] APQC - American Productivity and Quality Center (1998) Expanding Knowledge Management Externally: Putting Your Knowledge to Work for Customers. (1 Jan 2003) [5] Bessant, J. and D. Francis (1999) Using learning networks to help improve manufacturing competitiveness, Technovation 19 (1999) 373–381 [6] Davenport, T. and L. Prusak (1998) Working Knowledge: How Organisations Manage What They Know, Harvard Business School Press, 1998. [7] Gartner Group (2000) Strategic Planning, SPA-09-4188, Research Note, 8 October 2000. [8] Kafentzis, K., Apostolou, D., Mentzas, G., and Georgolios, P. (2002) A Framework for Analysis and a Review of Knowledge Asset Marketplaces. In: D. Karagiannis, U. Reimer (eds.): PAKM2002, Springer LNAI 2569. [9] Schmid, B. and Lindemann, M. A. (1998) ‘Elements of a Reference Model for Electronic Markets’ In Proceedings of the 31st Hawaii International Conference on Systems Science (HICCS'98), pages 193-201, Hawaii, January 1998. [10] Skyrme, D. J. (2001) Capitalizing on Knowledge: From e-business to k-business”, Butterworth-Heinemann, London.
