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Submitted: Annual Information Technology Congress, CATI 2005, June 29th –July 1st, São Paulo, Brazil Authors: Andre Saito & Katsuhiro Umemoto, Graduate School of Knowledge Science, Japan Advanced Institute of Science and Technology

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LINKING KNOWLEDGE MANAGEMENT TECHNOLOGIES TO STRATEGY Abstract: Although social and cultural issues are of critical importance to the success of knowledge management (KM), information and communication technologies are still regarded as a significant enabler of knowledge processes in organizations. Due to the extremely dynamic nature of the domain, however, it is very difficult for non-technologists to make sense of it. This paper provides a mapping of knowledge management technologies according to their applicability and level of integration. In addition, it establishes a link between technologies and knowledge strategy, as a way to guide their adoption and facilitate decision making. We used conceptual maps as tools to identify key concepts, to eliminate ambiguity in terminology, and to reveal their inter-relations. Also, by using maps to represent three KM sub-domains, we were able to connect them and uncover some unapparent links. The resulting map can help practitioners to deploy a KM strategy and advise on the construction of a technological infrastructure for KM initiatives. It can also provide guidance for research and development on novel KM technologies.

Keywords: knowledge management technologies, knowledge management practices, knowledge strategies, knowledge processes.

Palavras-chave: Tecnologias para gestão do conhecimento, práticas em gestão do conhecimento, estratégias de conhecimento, processos de conhecimento.

1 INTRODUCTION

Knowledge management (KM) has attracted substantial attention in the last decade, having accumulated a large volume of academic research contributed from varied fields, and having been extensively experimented in many public and private organizations seeking to leverage their intellectual resources. After an initial period of heavy investments in information systems for building knowledge repositories and managing content, often without much success, organizations have reached the conclusion that, in order to effectively manage knowledge, attention to social and cultural issues are of critical importance. Nevertheless, information and communication technology is still regarded, if not the most relevant, as a significant enabler for knowledge processes in organizations.

Information and communication technologies supporting the management of knowledge, or knowledge management technologies, constitute an extremely dynamic domain. Research and development has been prolific and new products and applications burgeon in the market. There have been several attempts to give sense to this apparently chaotic arena, and some schemes for categorizing technologies have been suggested (e.g. Binney, 2001; Luan & Serban, 2002; Tsui, 2003). Those schemes, however, assume particular stances and lack generality and comprehensiveness. Some focus on verifying how supportive to the management of knowledge existing technologies can be, others focus on identifying commercially available technology, without much concern with their effectiveness to organizational objectives.

The objective of this paper is to create a comprehensive map of existing technologies being used in the management of knowledge, to clarify their support to KM by relating them to knowledge processes, and to establish a link to business strategy as way to guide adoption and facilitate decision making. We use conceptual maps as tools for synthesizing previous studies. Along the study, we seek to: 1) clarify terminology and eliminate ambiguity, since different terms are used


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to refer to the same issue, and same terms are used to refer to different issues; 2) identify major concepts and their inter-relations, and recognize areas where there is general agreement; and 3) connect three existing sub-domains which have traditionally been studied by different research communities.

We describe in the next section our analytical framework, comprised of selected contributions to the knowledge management field according to three critical aspects of knowledge: product, process and power. Then, we review previous studies classifying KM technologies, identify two important criteria for categorization, and present our map of technologies. In the following sections, we review contributions focusing on the process and power aspects of knowledge in order to clarify their support to KM and to build a link to strategy. We close the paper with a discussion on our findings and present a summary in the conclusion.

2 ANALYTICAL FRAMEWORK

As a young discipline, knowledge management still presents a wide variety of approaches and perspectives. One of the reasons for this might be the broad range of meanings usually associated with the word knowledge. We focus here on three specific attributes of knowledge, and use them to select a few contributions to knowledge management research that are of special relevance for this study.

We argue that knowledge comprises three different aspects: it can be, at one time or another, a product, a process, or power. Knowledge is perceived as product when it is seen as something relatively tangible, manifest. It is the most usual notion of knowledge, evident in the form of concepts, models, theories. Knowledge is perceived as process when it is seen as something that unfolds while in motion, a progression, a development. It is the process of knowing, of understanding, and it is mostly tacit, since the knowledge it represents is constantly moving and changing. It can be made explicit, though, as explanations, or descriptions of methods and techniques. The notion of knowledge as power is the most unusual of all three: knowledge is perceived as power for its capacity to cause change or action. Although not commonly taken for knowledge in Western societies, it is what is usually referred to as skills and abilities. It may also represent causes, reasons, or goals.

In organizations, knowledge as product exists in the form of documents and archives, in information systems, and as intellectual assets like design, brands, patents. Knowledge as process exists in organizational practices and routines, and the world-view and values they represent. Also, it is manifest in procedures and process around the organization. Knowledge as power exists as employees’ skills and organizational capabilities, it is represented in the organization’s strategy, and can be perceived in existing motives and intentions (Table 1).

One stream of research on knowledge management can be classified as focusing on the product aspect of knowledge, and is represented by research in the fields of computer and information science, management information systems, and library and information studies. The computer and information sciences are contributing, with research on artificial intelligence and methods for knowledge representation and discovery. Management information systems have focused on applications for knowledge repositories, collaboration and communication systems, and the use of technology for supporting decisions. And library and information studies have been investigating ways to organize knowledge, storage and retrieval processes, and information user behavior.


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Table 1: The three aspects of knowledge.

Product Process Power

Knowledge, manifest Understanding, unfolding Capability, transforming

Object, artifact Progression, development Strength, force

Result, outcome Method, means Capacity, competency

Stock Flow Energy

Know-what, declarative knowledge Know-how, procedural knowledge Know-why, causal knowledge

Concepts, models, theories Methods, procedures, techniques Causes, reasons, purposes

Documents, archives, information systems, intellectual assets

Procedures, routines, practices, world views, norms, values

Skills, expertise, capabilities motives, intentions, objectives

Assets, structure Processes, culture Strategies, strategy

Another stream of research focuses on the process aspect of knowledge, and inherited contributions from previous research on organizational learning and organization science. Some authors emphasized the importance of social contexts for organizational learning, stressed the role of social capital, and proposed the concept of communities of practice (Brown & Duguid, 1991; Lave & Wenger, 1991; Tsai & Ghoshal, 1998). Others have described organizational knowledge processes, highlighted the importance of context and underscored the significance of enabling factors like trust and care (Huber, 1991; Nonaka & Takeuchi, 1995; von Krogh, Ichijo & Nonaka, 2000).

A different set of contributions focusing on the power aspect of knowledge came mainly from the field of strategy. Building on the resource-based view of the firm and the concepts of competencies and capabilities (Wernerfelt, 1984; Prahalad & Hamel, 1990; Teece, Pisano & Shuen, 1990), some authors have articulated a knowledge-based view of the firm, arguing that knowledge is the most strategically relevant resource, and that knowledge processes are an important capability for a firm to develop and maintain (Kogut & Zander, 1992; Nonaka, 1994; Grant, 1996). Other authors have suggested that, in order to achieve those goals, a firm should formulate a knowledge strategy, or a competitive strategy built around a firm’s intellectual resources and capabilities (Zack, 1999a, von Krogh, 2001; van der Spek, Hofer-Alfeis & Kingma, 2003).

This illustrate the variety of the field, and show how the study of distinct research communities add to the knowledge management puzzle. We use this distinction among product, process, and power aspects of knowledge and the respective contributions to the field to map the range of KM technologies and to link them to knowledge processes and business strategy.

3 DEFINING AND CATEGORIZING KM TECHNOLOGIES

There has been many attempts to shed light on the supporting role of information and communication technology to knowledge management and knowledge processes. Previous research classifying knowledge management technologies tended to fall into one of three categories. The first and most numerous is composed of studies which adopt a knowledge management framework, identify a set of major knowledge processes, and describe KM technologies according to the processes supported (Alavi & Leidner, 2001; Marwick, 2001;


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Nonaka, Reinmoller & Toyama, 2001; Awad & Ghaziri, 2004; Jashapara, 2004). The set of chosen processes vary according to the authors’ stance on knowledge management; that is, whether they emphasize the product or the process aspect of knowledge, and whether they focus on personal and group processes, or on processes in a departmental and organizational level.

A second set of studies take a market perspective and look for commercially available solutions that allegedly support knowledge management, or for applications implemented in KM initiatives in organizational settings (Wenger, 2001; Luan & Serban, 2002; Lindvall, Rus & Sinha, 2003; Rao, 2005). These classification schemes seem to be more useful from a practitioner’s point of view, in the sense that they present KM technologies in a manner that is closer to the implementation context. The categories, though, are relatively numerous, vary widely according to functionality and scope, and do not show clearly the link between KM technologies and knowledge processes.

A third set of studies focuses on state-of-the-art technologies supporting knowledge management, and is mainly intended to map hot topics for research and development (Zdrahal, 2002; Kicin, 2002). KM technologies presented in this way are usually not currently being used in organizational settings or, if they are, it is either in a specific way, or embedded in other commercial products. These studies highlight, for instance, ontologies and the semantic web, business-process-driven KM technologies, and technologies for knowledge life-cycle maintenance.

Some studies do not fit in either of the above schemes. Binner (2001) takes a technology investment decision standpoint and classifies KM technologies in order to facilitate executives’ and strategist’ decision making. The author proposes six categories of knowledge management applications: transactional, analytical, asset management, process, developmental, and innovation and creation. It is an idea that fits the present study’s aim by linking KM technologies to business, but the author mix different levels of functionality scope. For instance, semantic networks, intelligent agents and push technologies are listed in the same level as document management, data analysis and reporting, and computer-based training. We argue below that a categorization according to the scope of the technology’s functionality is important to better understand KM technologies.

Maier (2004) presents a very comprehensive and even exhaustive mapping of existing KM technologies, and highlights some of the classification schemes above. The author’s focus, though, is the development of knowledge management systems, or integrated KM technologies solutions that bundle a wide set of technologies in order to cover the whole range of knowledge processes. Maier stresses concepts like knowledge management systems’ architecture, required functionalities and their implementation according to architecture layer, and centralized and decentralized architectures.

Different levels of aggregation and different purposes

The first thing that calls attention when analyzing KM technologies is the high level of modularity and integration, and the inter-relatedness among them. This is a usual feature of systems in general, and is sometimes referred to as systemic characteristics. All the previous works, though, provide clues and shed some light on how to better understand and organize KM technologies.

Two sets of criteria emerged from the analysis: first, there seems to be difference in the level of


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integration and range of applicability in the listed technologies. For instance, expert systems are focused on solving specific problems, and business intelligence systems, which are designed to provide for a wide range of problems, can integrate expert systems and also data warehousing and data mining, multi-dimensional analysis, reporting tools and enterprise portals. Second, it is necessary to distinguish between technologies as methods and technologies as purpose. For instance, case-based reasoning is often cited as a promising KM technology. Some author include in the list help-desk and customer support systems that, most probably, make use of case-based reasoning technology. In the same way, search tools can be part of a solution, or the solution per se.

Based on these two criteria, we first differentiate KM technologies between component technologies and KM applications. The first, component technologies, are basic building blocks that are usually integrated into or required by the second, KM applications. For instance, Internet and communication technologies and artificial intelligence in general are examples of component technologies. They can be used for a broad range of purposes and are either embedded in other KM applications like document management, groupware and e-learning systems, or are required by them as infrastructure.

Component technologies can be further distinguished between infrastructure and knowledge technologies. Infrastructure technologies are those common, general-purpose and widely available technologies already in use in most organizations. Examples are Internet and intranet technologies; communication technologies like e-mail, mailing lists, discussion forums, chat and instant messaging, and audio and video conferencing; database management systems and data warehousing; authoring tools like word processors, spreadsheets, presentation and graphic tools; and workflow systems. Knowledge technologies are a new breed of tools that build on developments from artificial intelligence and its related fields. Examples are expert systems and case-based reasoning; ontologies, taxonomies and knowledge mapping tools; and profiling, clustering, collaborative filtering and datamining tools, to cite a few.

KM applications can also be further distinguished between KM systems (KMS) and business applications. KM systems address knowledge management and knowledge processes in general, regardless their specific business purpose. They are, for example, content and document management systems; groupware and community support systems; and learning management systems. Business applications usually focus on specific business processes and purposes. They are, for instance, business intelligence for decision making; customer relationship management for marketing, sales and customer service; computer integrated manufacturing for engineering, design and manufacturing; and supply chain management for planning and forecasting, and transportation management.

By distinguishing KM technologies among infrastructure and knowledge technologies, and KM systems and business applications, it is easier to understand to what extent each technology supports generic knowledge processes and specific business processes. It is also easier, given a set of KM objectives, to identify which technologies are most adequate for deployment. Figure 1 shows a conceptual map of KM technologies, according to these criteria.

4 KNOWLEDGE PROCESSES, KM PRACTICES AND KM APPROACHES

Since the most frequent classification scheme is around knowledge processes, there seems to be an agreement that they are the basis of knowledge management. There is no consensus, though,


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Figure 1: Knowledge management technologies.

about which knowledge processes should be considered to support KM.

The most frequently cited set of knowledge processes is that proposed by Nonaka and Takeuchi (1995): socialization, externalization, combination, and internalization, the SECI spiral of knowledge creation. By putting emphasis on the social nature of knowledge processes, since all of them occur mainly between and among people, the authors focused on the process aspect of knowledge. A different perspective is offered by, for instance, Alavi and Leidner (2001). They adopt a knowledge processing perspective, which put emphasis on the product aspect of knowledge. For them, knowledge is something that is created, stored and retrieved, transferred, and applied.

What makes the analysis difficult is that those processes are usually meshed and intertwined. For instance, Alavi and Leidner’s create process include the whole SECI spiral from Nonaka and Takeuchi. On the other hand, Nonaka and Takeuchi’s combination process can be interpreted to contain all four processes from Alavi and Leidner’s model. In fact, knowledge processes in organizations are complex, treat both product and process aspects of knowledge, and apply to different levels of analysis: individual, group, departmental, organizational and even inter-organizational level. In spite of this complexity, there seems to be a degree of similarity among the terminology used by different authors. By comparing the meanings of the terms employed in different models, it is possible to recognize some semantic proximity among them (Table 2).

It is important to notice that authors sometimes refer to processes occurring at different levels when classifying KM technologies. For instance datamining and collaborative filtering create knowledge at the machine level. E-learning can be used to create knowledge at the individual level. Group-decision support tools facilitate the creation of knowledge at the group-level. And community support tools can help to create knowledge at the organizational level. In this manner, it is not feasible to directly relate a given KM technology to a specific knowledge process, due to the systemic characteristics of both of them


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Table 2: Knowledge processes in the literature.

Nonaka & Takeuchi, 1995

Alavi & Leidner, 2001

Jashapara, 2004 Maier, 2004 CEN,

2004

Identification Identify

Externalization Creation Capturing Acquisition Creation Create

Combination Storage and retrieval Organizing Storing

Organization Publication

Search and retrieval Deletion and archiving

Store

Socialization Transfer Sharing Distribution Collaboration Share

Internalization Application Evaluating Application Evolution

Selling Use

KM practices: the basis of KM in organizations

Another important clarification to be made is that related to the KM instruments that enact knowledge processes. KM tools, applications, instruments, and solutions are used in the literature to refer indistinctively to either KM technologies or what some authors refer to as KM practices or activities.

KM practices refer to instruments or methods that pioneering organizations have developed along the time in their KM initiatives. KM practices enact knowledge processes, providing a practitioners’ perspective to knowledge management and, in a certain sense, embodying some somewhat abstract concepts presented in theory. Examples of KM practices are: collecting lessons learned, selecting best practices, developing expertise directories or employee yellow pages, cultivating communities of practice, conducting knowledge audits or elaborating knowledge maps, implementing balanced scorecards, managing intellectual assets, developing new roles or adapting compensation systems, and improving processes, to name a few. KM practices can also be based on KM technologies, like developing knowledge repositories, implementing groupware, or building enterprise portals, for instance. Since many of the pioneering efforts in knowledge management were related to technology, those terms were being used without regard to this difference in focus.

A given practice usually enact several knowledge processes, at several levels. A collection of best practices, for instance, may involve an internal communication campaign to encourage employees to contribute, the conduction of workshops to disseminate methods and techniques for describing best practices, the creation of committees responsible for evaluating and selecting contributions, and the collection of best practices in knowledge repositories.

It is important to emphasize this term because, as already mentioned, KM is made visible and manifest in organizations through KM practices, instead of knowledge processes. It is KM practices that organizations consider and decide upon when implementing knowledge management.


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KM approaches: collaboration and codification

There is considerable agreement in the literature regarding two main approaches to knowledge management. One focus on the product aspect of knowledge, is based mainly on explicit knowledge that can be stored in and retrieved from information systems, and relies heavily in information and communication technology. This approach is usually referred to as the codification or technology-oriented approach, or the product or stock view of knowledge. The other focus on the process aspect of knowledge, is based on personal exchanges of mainly tacit knowledge between and among people, and relies strongly on human communication and collaboration. This other approach is usually referred to as the personalization or human-oriented approach, or the process or flow view of knowledge (Hansen, Nohria & Tierney, 1999; Alavi & Leidner, 2001; Mentzas, Apostolou, Young & Abecker, 2001; Maier, 2004). Table 3 summarizes characteristics of each approach.

Table 3: Collaboration and codification approaches to KM.

Codification Collaboration

Mainly explicit knowledge Mainly tacit knowledge

Codify, store Collaborate, communicate

Knowledge repositories, search and retrieval Expertise directories, communication systems

Information systems Communities

Here we use the terms collaboration and codification, instead of the more familiar personalization and codification proposed by Hansen et al. to make explicit the distinction between collaboration/ codification, and knowledge creation/ reuse. Hansen et al. asserted that a personalization strategy is usually associated to knowledge creation, and a codification strategy to knowledge reuse, which we argue is not necessarily true. It is possible, for instance, to reuse knowledge through collaboration, by building a directory of employee’s expertise and putting people in contact for personal exchange. It is also common to create knowledge through codification, by collecting and aggregating customer information, for instance, and mining it for clusters and patterns.

These two main approaches to KM can be used to categorize both KM technologies (Zack, 1999b; Mentzas et al., 2001; Tsui, 2003; Maier, 2004) and KM practices. The relationship among KM technologies, knowledge processes, KM practices and KM approaches is depicted in Figure 2. In this concept map, we argue that organizations improve knowledge processes by implementing KM initiatives, which adopt a collaboration or codification approach to select a set of appropriate practices and technologies.

5 KNOWLEDGE MANAGEMENT AND STRATEGY

There is also strong agreement in the literature that knowledge management should be linked to strategy, if it is to be of value to the organization. The way to establish this link is through what is called a knowledge strategy


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Figure 2: KM technologies and their relationship to KM practices and KM approaches.

Zack (1999a) combines a knowledge-based view of the firm to a market-based approach to strategy, proposing a framework that shows how to balance a firm’s capabilities against environmental requirements. First, he identifies three types of knowledge: core, advanced and innovative. Core knowledge is that minimum required to participate in an industry, and provides no competitive advantage. Advanced knowledge provides some differentiation, either in degree or in scope, and provides some competitiveness. Innovative knowledge often change the industry’s rules and enables a firm to be its leader. By comparing its knowledge against its competitors (Figure3), a firm can identify knowledge gaps, which must then be filled by either creating new knowledge or reusing existing knowledge.

Figure 3: A firm’s positioning according to its knowledge (Source: Zack, 1999)

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In a similar vein, von Krogh (2001) proposes that knowledge strategies are based on either the creation or reuse of knowledge, but he adds the concept of knowledge domains and argue that knowledge can be created or reused in existing or new knowledge domains. For him, knowledge domain is an area of expertise, and includes both the explicit knowledge in the form of books, manuals, reports, designs, systems, etc., and the key people that hold the tacit knowledge in the form of experiences, stories, practices, etc.

A knowledge strategy, then, can be derived by choosing to either create or reuse knowledge in either existing or new domains. The leveraging strategy is to reuse knowledge in an existing domain. This means to transfer internal knowledge through best practices, lessons learned and benchmarking, for instance. The expanding strategy is to create knowledge in an existing domain. This means to assemble groups to improve processes or develop new products in existing areas of expertise, or conduct research to broaden or deepen existing knowledge, for instance. The appropriation strategy is to reuse knowledge in a domain new for the company. This means to capture and transfer external knowledge through acquisitions, and partnerships, for instance. And last, the probing strategy is to create a new knowledge domain from scratch. This means to gather a task force, or project team, or a business unit around a loose idea or vision of a future knowledge domain.

By combining the ideas of knowledge gaps, creation and reuse of internal and external knowledge in existing or new domains, it is possible to design a knowledge strategy that should guide the management of knowledge inside an organization. It is worth noting here the difference between a knowledge strategy and a KM strategy. Zack (2002) helps to distinguish both by defining knowledge strategy as a competitive strategy built around intellectual resources and capabilities. A knowledge strategy tries to understand which knowledge is strategic to a firm and why, and to identify where the knowledge gaps and surpluses are. A KM strategy, in contrast, coordinates the actions to manage those gaps and surpluses once they are identified, by choosing suitable KM approaches and defining required practices and technologies. This is illustrated in Figure 4.

Figure 4: Knowledge strategy and KM strategy


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6 DISCUSSION

In the previous sections, we have identified two criteria that are relevant for categorizing KM technologies and classified them in infrastructure and knowledge technologies, KM systems, and business applications. We have also identified a link between KM technologies and strategy; that is, KM technologies can be aligned to business strategy via the design of adequate KM initiatives that support knowledge strategies. This can be summarized in the Figure 5.

Figure 5: The link among KM technologies, KM initiatives and knowledge strategies

Let us illustrate how this conceptual map could be used. Suppose that a company had identified the need to develop a better knowledge on its customers. The knowledge strategy could be to create knowledge (e.g. customers’ categorization, purchase patterns, identification of expansion opportunities) in an existing domain (e.g. marketing, sales and customer service personnel, current customers). KM initiatives could be: a) to build a data warehouse integrating different customer databases and mining the information for clusters of clients and patterns of interaction; and b) to establish a project team responsible for conducting in-depth interactions with selected customers, combining findings with results from the datamining effort, and devising campaigns to be extended to the whole customer base. This team would be supported by a group-support system with communication tools, document management and workflow functionalities.

As we can see, the starting point is a knowledge gap to be filled, and the link is established not directly between technology and strategy, but via well designed KM initiatives. KM initiatives usually favor either a collaboration or a codification approach, but the combination of KM initiatives seems to seek a balance among them. KM technologies can support both approaches, and decision should be made only after the KM initiatives are chosen. A good understanding of KM technologies and its capabilities, though, can broaden the range of KM initiatives available for choice..


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Difficulties during the study and limitations of the conceptual map

Although we tried to be comprehensive and to cover the broadest range of technologies possible, we are aware that the mapping is far from exhaustive. The quantity of different technologies is astonishing, and, to make things worse, a given technology can be used in sundry different ways. Take ontologies, for instance: it can be used for categorizing content in a repository, to enhance search results in a search engine, to create customer profiles in a datamining effort, and to map expertise among employees, among many other applications. As knowledge technologies continue to evolve, we will be witnessing an avalanche of new KM applications. We tried here to identify broad categories among existing technologies, and the inter-relations among them, so its easier to identify possible applications and to foresee future developments.

Another difficulty is the already mentioned systemic characteristics of technology. Component technologies are integrated in applications, which in turn are combined again in even larger systems. We are currently witnessing the appearance of full-featured, robust and scalable KM systems that integrate a broad range of KM functionalities, from document and content management, to group and community support, to e-learning and competency management. But this same systemic characteristic means that it is possible to obtain equivalent functionalities with separate modules. In other words, a given functionality can be implemented with an expensive, full-featured system, or with cheaper, smaller-scale component technologies. Group support, for instance, can be implemented with a scalable groupware platform, or with cheap and simple to use internet technologies. Each option has pros and cons, so decision should be made case by case.

A direct link between KM technologies and strategy could not be established, in the sense that specific KM technologies would support specific strategies – like, for instance, e-commerce support mass customization strategies. We concluded that this link must be made via KM initiatives, which in turn can be linked to KM technologies. This study, though, has only sketched how this link is established: KM initiatives are defined by practices and approaches, and the combination of practices and approaches suggest the technology to be adopted. Therefore, further research must be made in order to clarify this link.

7 CONCLUSION

In this study, we mapped existing technologies being used for KM, identified four categories that facilitate the understanding of their characteristics and applications, and established a link between KM technologies and knowledge strategies. We have concluded that appropriate KM initiatives should be designed in support to well defined knowledge strategies, and KM technologies should be chosen according to the practices and approaches of those initiatives, if KM technologies are to be of strategic value.

This conclusions can be helpful to KM practitioners that are leading KM efforts in business settings. They can facilitate decision making on how to build a technological infrastructure and how to take advantage of that infrastructure in ways that contribute to business strategy. We also believe that the mapping of KM technologies, and the mapping connecting domains that represent three different research communities in KM will facilitate interchange and interdisciplinary research. Finally, we also consider this mapping a contribution to the development of a generally agreed body of knowledge of the KM field, which is a requisite for its wider acceptance as an established discipline.


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We suggest that further research should be conducted in order to establish the KM technology-strategy link in setting other than business, that is, non-profit and governmental organizations. The mapping also needs a broader and deeper coverage of the field, which we will be seeking in further studies.

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