Knowledge Management Research Paper

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Knowledge management (KM) is a hot topic in many business communities. Although, the title knowledge management might suggest a rather simple definition, there are plenty of opinions as to what it is and how it should be used, if used at all. However, because of the ever increasing pace of business development, the task of effective and competitive management of organizations becomes essential and KM, if understood and implemented properly, may be a useful tool for business transformation as well as the key to competitive advantage. This research-paper presents an overview of KM including knowledge, knowledge management systems (KMS) communities of practice, knowledge transfer, and KM technologies and of how KM is utilized in organizational initiatives.


Davenport and Prusak (1998) viewed knowledge as an evolving mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. They found that in organizations, knowledge often becomes embedded in documents or repositories and in organizational routines, processes, practices, and norms. They also say that for knowledge to have value it must include the human additions of context, culture, experience, and interpretation. Nonaka (1994) expanded this view by stating that knowledge is about meaning in the sense that it is context specific. This implies that users of knowledge must understand and have experience with the context, or surrounding conditions and influences, in which the knowledge is generated and used for it to have meaning to them. This also implies that for a knowledge repository to be useful it must also store the context in which the knowledge was generated. That knowledge is context specific argues against the idea that knowledge can be applied universally; however, it does not argue against the concept of organizational knowledge. Organizational knowledge is considered an integral component of what organizational members remember and use meaning that knowledge is actionable.

Polanyi (1967) and Nonaka and Takeuchi (1995) described two types of knowledge, tacit, and explicit. Tacit knowledge is that which is understood within a knower’s mind and which cannot be directly expressed by data or knowledge representations and is commonly understood as unstructured knowledge. Explicit knowledge on the other hand is that knowledge which can be directly expressed by knowledge representations and is commonly known as structured knowledge. Knowledge transfer in an organization occurs when members of an organization pass tacit and explicit knowledge to each other. Nonaka and Takeuchi (1995) proposed four modes of knowledge creation and transfer.

  • Socialization is the process of sharing experiences and, thereby, of creating tacit knowledge such as mental models and technical skills. Tacit knowledge can be obtained without using language through observation, imitation, and practice.
  • Externalization is the process of articulating tacit knowledge in the form of explicit concepts, taking the shapes of metaphors, analogies, concepts, hypotheses, or models.
  • Combination is the process of systemizing concepts into a knowledge system by combining different bodies of explicit knowledge. Explicit knowledge is transferred through media such as documents, meetings, e-mail, and/or phone conversations. Categorization of this knowledge can lead to the generation of new knowledge.
  • Internalization is the process of converting explicit knowledge to tacit knowledge and is closely related to learning by doing.

These four modes or processes show that the transfer of knowledge is dependent upon the transfer of a common understanding from the knower to the user of the knowledge. Common understanding consists of the context (the story behind the knowledge, conditions, and situations that make the knowledge understandable) and the experience (those activities that produce mental models of how the knowledge should be used) expressed in a culturally understood framework.

What is culture and context? The United Nations Educational, Scientific, and Cultural Organization (UNESCO, 2002) stated that culture is the “set of distinctive spiritual, material, intellectual and emotional features of society or a social group and that it encompasses, in addition to art and literature, lifestyles, ways of living together, value systems, traditions and beliefs.” The American Heritage Dictionary of the English Language (2000, p. 316) defined context as the part of a text or statement that surrounds a particular word or passage and determines its meaning and/or the circumstances in which an event occurs. Culture forms the basis for how we process and use knowledge by providing belief frameworks for understanding and using the knowledge; context provides the framing for the knowledge explaining how it is created and meant to be used. Both are critical to the transfer and reuse of knowledge. We normally expect explicit knowledge to be easily transferred while we expect issues with transferring tacit knowledge. However, we are finding that transfer of either dimension of knowledge in a multicultural environment is not easy.

Why consider culture? Hofstede (1980) refined the definition of culture as the following:

Culture consists in patterned ways of thinking, feeling and reacting, acquired and transmitted mainly by symbols, constituting the distinctive achievements of human groups, including their embodiments in artifacts; the essential core of culture consists of traditions (i.e., historically derived and selected) ideas and especially their attached values. (p. 25)

His work focuses on identifying cultural differences between nations and illustrates that value systems are not the same the world over. The key to the impact of culture on knowledge transfer is how values impact how different social groups will externalize metaphors, analogies, hypotheses, and models; how groups will systemize concepts; how groups internalize concepts; and how groups understand experiences. Differences in culture and, as Hofstede (1980, 2001) showed, significant differences between nations can lead to differences between national groups within the same organization, which can cause those groups either to understand knowledge differently or to have significant barriers to participating in the sharing of knowledge. We must understand that culture is a unique component, so deeply embedded into peoples’ lives that our ignorance of it usually leads to failures. KMS as well as other systems created to improve organization’s performance should use all possible information about culture to escape system’s mistakes due to lack of cultural awareness and understanding. Probably no theory ever will be capable to capture all or even full knowledge about a specific culture, but there are enough theories (as previously discussed) to establish a process and methodology for including cultural parameters in the design of KM initiatives and the system analysis and design activities.

Why consider context? Davenport and Prusak (1998) found that for knowledge to have value it must include the elements of human context, experience, and interpretation (p. 5). Nonaka (1994) expanded this view by stating that knowledge is about meaning in the sense that it is context specific. This implies that users of knowledge must understand and have experience with the context (surrounding conditions and influences) in which the knowledge is generated and used for it to be meaningful. This suggests that for a knowledge repository to be useful it must also store the context in which the knowledge was generated. The suggestion that knowledge is context specific argues against the idea that knowledge can be applied universally.

Context is the collection of relevant conditions and surrounding influences that make a situation unique and comprehensible to the users of the knowledge (Degler & Battle, 2000). Context can be stored with knowledge and/or can be possessed by knowledge users. When a system’s knowledge users are known, the knowledge that is captured is used to support specific activities. KMS users are readily known when the KMS is built to support a specific team, project, or process and the users are those involved with that team, project, and/or process. These users tend to possess a high degree of shared understanding where understanding incorporates context and experience. Experience is what knowledge users use to generate mental models of how to use or apply the knowledge (Degler & Battle, 2000). Experience comes from the individual’s own experience with the knowledge domain, others’ shared experience with the knowledge domain, and/or a collective experience with the knowledge domain (Degler & Battle, 2000). Combined, this means that knowledge users in teams, projects, or even processes understand the organizational culture, the structure of organizational documents, the organizational terminology and jargon, and how the organization works, and they are able to use posted knowledge, even if it does not include context, as they implicitly understand the context in which the knowledge was created and have experience in using this knowledge. On the other hand, when KMS users are not known, it is not possible to assume that these users possess a common understanding or experience associated with the generation of the knowledge. This means the KMS will have to capture this context and experience for users to be able to utilize the captured knowledge effectively.

Knowledge Management

KM is better understood when the concepts of organizational memory (OM) and organizational learning (OL) are incorporated. Jennex and Olfman (2002) found that the three areas are related and have an impact on organizational effectiveness. Organizational effectiveness is how well the organization does those activities critical to making the organization competitive. OL is the process the organization uses to learn how to do these activities better. OL results when users utilize knowledge. That OL may not always have a positive effect is examined by the monitoring of organizational effectiveness. Effectiveness can improve, get worse, or remain the same. How effectiveness changes influences the feedback provided to the organization using the knowledge. KM and OM are the processes used to identify and capture critical knowledge. Knowledge workers and their organizations “do” KM; they identify key knowledge artifacts for retention and establish processes for capturing it. OM is what IT support organizations “do”; they provide the infrastructure and support for storing, searching, and retrieving knowledge artifacts. Figure 83.1 illustrates these relationships and the following sections expand on these concepts.

knowledge-management-research-paper-f1Figure 83.1     The KM/OM/OL Model

Jennex (2005b, page iv) defined KM as the practice of selectively applying knowledge from previous experiences of decision making to current and future decision-making activities with the express purpose of improving the organization’s effectiveness. Also, Jennex viewed a KMS as that system created to facilitate the capture, storage, retrieval, transfer, and reuse of knowledge. The perception of KM and KMS is that they holistically combine organizational and technical solutions to achieve the goals of knowledge retention and reuse to ultimately improve organizational and individual decision making. This is a Churchman (1979) view of KM that allows KMS to take whatever form necessary to accomplish these goals. Another key definition of KM includes Holsapple and Joshi (2004), who considered KM as an entity’s systematic and deliberate efforts to expand, cultivate, and apply available knowledge in ways that add value to the entity, in the sense of positive results in accomplishing its objectives or fulfilling its purpose. Finally, Alavi and Leidner (2001) concluded that KM involves distinct but interdependent processes of knowledge creation, knowledge storage and retrieval, knowledge transfer, and knowledge application.

Organizational Learning

OL is defined as a quantifiable improvement in activities, increased available knowledge for decision making, or sustainable competitive advantage (Cavaleri, 1994; Dodgson, 1993; Easterby-Smith, 1997; Miller, 1996). In this view, organizations learn through individuals acting as agents for them. Individual learning activities are seen as being facilitated or inhibited by an ecological system of factors that may be called an OL system. Learning in this perspective is based on Kolb’s (1984) model of experiential learning where individuals learn by doing. Huber, Davenport, and King (1998) believed an organization learns if, through its processing of information, its potential behaviors are changed. Huysman, Fischer, and Heng (1994) as well as Walsh and Ungson (1991) believed organizational learning has OM as a component. In this view, OL is the process by which experience is used to modify current and future actions. Huber (1991) considered four constructs as integrally linked to OL: knowledge acquisition, information distribution, information interpretation, and organizational memory. In this case, OM is the repository of knowledge and information acquired by the organization. OL uses OM as its knowledge base.

A different perspective on OL from Sandoe et al. (1998) is that organizations do not learn; rather, only individuals learn. During work, people gain experience, observe, and reflect in making sense of what they are doing. As they analyze these experiences into general abstractions, their perceptions on how work should be done changes. As these individuals influence their coworkers, the “organization” learns and the process is gradually changed. Learning in this perspective is also based on Kolb’s (1984) model of experiential learning.

Organizational Memory

Stein and Zwass (1995) defined OM as the means by which knowledge from the past is brought to bear on present activities resulting in higher or lower levels of organizational effectiveness. Walsh and Ungson (1991) defined OM as stored information from an organization’s history that can be brought to bear on present decisions. OM, like knowledge, can be viewed as abstract or concrete. It is comprised of unstructured concepts and information that exist in the organization’s culture and the minds of its members, and it can be partially represented by concrete/physical memory aids such as databases. It is also comprised of structured concepts and information that can be exactly represented by computerized records and files. Sandoe and Olfman (1992) and Morrison (1997) described these two forms of OM as having two functions: representation and interpretation. Representation presents just the facts (or knowledge or expertise) for a given context or situation. Interpretation promotes adaptation and learning by providing frames of reference, procedures, guidelines, or a means to synthesize past information for application to new situations. Comparing to the definition of knowledge, it is obvious that knowledge and OM are related through experience and learning. We consider knowledge to be a subset of OM and the processes of KM a subset of OM processes.

Knowledge Management Systems

Alavi and Leidner (2001) defined KMS as “IT (Information Technology)-based systems developed to support and enhance the organizational processes of knowledge creation, storage/retrieval, transfer, and application” (p. 114). They observed that not all KM initiatives will implement an IT solution, but they supported IT as an enabler of KM. Maier (2002) expanded on the IT concept for the KMS by calling it an ICT (information and communication technology) system that supported the functions of knowledge creation, construction, identification, capturing, acquisition, selection, valuation, organization, linking, structuring, formal-ization, visualization, distribution, retention, maintenance, refinement, evolution, accessing, search, and application. Stein and Zwass (1995) defined an organizational memory information system (OMS) as the processes and IT components necessary to capture, store, and apply knowledge created in the past on decisions currently being made. Jennex and Olfman (2006) expanded this definition by incorporating the OMS into the KMS and adding strategy and service components to the KMS.

KMS Classifications

There are different ways of classifying a KMS and/or KMS technologies where KMS technologies are the specific IT/ ICT tools being implemented in the KMS. Alavi and Leidner (2001) classified the KMS based on the knowledge life cycle stage being predominantly supported. Marwick (2001) classified the KMS by the mode of Nonaka’s (1994) SECI model being implemented. Borghoff and Pareschi (1998) classified the KMS based on the class of the KM architecture being supported. This architecture has four classes of components: repositories and libraries, knowledge worker communities, knowledge cartography/mapping, and knowledge flows. Hahn and Subramani (2001) classified the KMS by the source of the knowledge being supported: structured artifact, structured individual, unstructured artifact, or unstructured individual. Binney (2001) classified the KMS using the knowledge spectrum. The knowledge spectrum represents the ranges of purposes a KMS can have. Zack (1999) classified a KMS as either integrative or interactive. Integrative KMS support the transfer of explicit knowledge using some form of repository and support. Interactive KMS support the transfer of tacit knowledge by facilitating communication between the knowledge source and the knowledge user. Jennex and Olfman classified the KMS by the type of users being supported. Users are separated into two groups based on the amount of common understanding they have with each other resulting in classifications of process-/task-based KMS for groups that have a common understanding and a generic/infrastructure KMS for groups that do not share a common understanding. An example of a group with a common understanding is a community of practice (CoP), which is a set of people who share a concern, a set of problems, or a passion about a topic (Wenger, McDermott, & Snyder, 2002).

Knowledge Repositories

Key to the KMS is knowledge repositories. Three types of knowledge repositories are paper documents, computer-based documents/databases, and self-memories:

  • Paper documents incorporate all hard-copy documents, and they are organizationwide and groupwide references that

reside in central repositories such as a corporate library. Examples include reports, procedures, pictures, videotapes, audiocassettes, and technical standards.

  • Computer-based documents/databases include all computer-based information that is maintained at the work group level or beyond. These may be made available through downloads to individual workstations or may reside in central databases or file systems. Additionally, computer documents include the processes and protocols built into the information systems. These are reflected in the interface between the system and the user, by who has access to the data, and by the formats of structured system inputs and outputs. New aspects of this type of repository are digital images and audio recordings. These forms of knowledge provide rich detail but require expanded storage and transmission capacities.
  • Self-memory includes all paper and computer documents that are maintained by an individual as well as the individual’s memories and experiences. Typical artifacts include files, notebooks, written and unwritten recollections, and other archives. These typically do not have an official basis or format. Self-memory is determined by what is important to each person and reflects his or her experience with the organization.

Repositories have overlapping information and knowledge. Paper documents are indexed or copied into computer databases or files, self-memory uses paper and computer-based documents/databases, and computer databases or files are printed and filed. Spheres for self-memory and others’ memory reflect that organizations consist of many individuals, and that the knowledge base contains multiple self-memories. Finally, the relative size of each sphere depends on the nature of the organization. Organizations that are highly automated and/or computerized would be expected to have a greater dependence on computer-based repositories while other organizations may rely more on paper or self-memory based repositories.

Use of Knowledge Repositories

Should organizations focus more on computerized repositories or on self-memory repositories? Computerized repositories provide a measure of permanence coupled with better tools for searching and retrieving knowledge. However, organizations should consider the transience and experience level of their workers when selecting reposito-lies. Sandoe and Olfman (1992) found that the increasing transience of organizational workers requires a shift in the location of knowledge. Organizations that have large num-bers of transient workers are at risk of losing knowledge if it is allowed to remain in self-memories. These organizations need to capture and store knowledge in more concrete forms such as paper or computer-based repositories. They also suggested that stronger attempts should be made to capture the unstructured, abstract information and knowledge in concrete forms. Additionally, Jennex and Olfman (2002) found that new organizational workers have trouble using the document and computer-based repositories and rely on the self-memories of longer term members. This continues until the new member gains sufficient context and culture to understand and use the information and knowledge in the paper and computer-based repositories. While these guidelines are contradictory because transient organizations will tend to have more new members, they emphasize that organizations should minimize reliance on self-memories for the retention of concrete, structured knowledge while using self-memories as the mechanism for teaching organizational culture and passing on unstructured, abstract knowledge.

KM/KMS Success

Jennex, Smolnik, and Croasdell (2007) found KM success to be a multidimensional concept defined by capturing the right knowledge, getting the right knowledge to the right user, and using this knowledge to improve organizational and/or individual performance. KM success is measured using the dimensions of impact on business processes, strategy, leadership, efficiency and effectiveness of KM processes, efficiency and effectiveness of the KM system, organizational culture, and knowledge content. KM success is considered the same as KM effectiveness and KM success is equated to KMS success when the Churchman (1979) view of a KMS is used.

While these measures are useful for measuring success, it is also important to understand what is needed to have successful KM/KMS. Jennex and Olfman (2005) summarized and synthesized the literature from 17 studies on over 200 KM projects about KM/KMS critical success factors (CSFs) into an ordered set of 12 KM CSFs. The following 12 CSFs were ordered based on the number of studies identifying the CSF:

  • A knowledge strategy that identifies users, sources, processes, storage strategy, knowledge, and links to knowledge
  • Motivation and commitment of users including incentives and training
  • Integrated technical infrastructure including networks, databases/repositories, computers, software, and experts
  • An organizational culture and structure that supports learning and the sharing and use of knowledge
  • A common enterprisewide knowledge structure that is clearly articulated and easily understood
  • Senior management support including allocation of resources, leadership, and providing training
  • Learning organization
  • A clear goal and purpose for the KMS
  • Measures established to assess the impacts of the KMS and the use of knowledge as well as verifying that the right knowledge is being captured
  • The search, retrieval, and visualization functions of the KMS support easy knowledge use
  • Work processes are designed that incorporate knowledge capture and use
  • Security/protection of knowledge

knowledge-management-research-paper-t2Figure 83.2 Jennex and Olfman’s (2006) KM Success Model SOURCE: Copyrighted by IGIGlobal. Reprinted by permission of the publisher.

While it is useful to identify the KM CSFs, it is more useful to express the CSFs in a model that relates them to KM success. Jennex and Olfman’s (2006) KM success model (Figure 83.2) is a causal model that relates KM CSFs to KM success. It has three basic dimensions as antecedents to KM success: system quality, which deals with the technical infrastructure; knowledge/information quality, which deals with KM strategy for identifying critical knowledge and then how that knowledge is stored; and service quality, which deals with management support and allocation of resources. The model also has the dimensions of perceived benefit, user satisfaction, and net benefits. These dimensions deal with ensuring that the KM initiative meets the needs of the users and the organization.

Examples Of KMS

Internet-Based KMs

One of the most commonly cited KMS success factors (Jennex & Olfman, 2005) is having an integrated technical infrastructure including networks, databases/repositories, computers, software, and KMS experts. KM designers are using the Internet to obtain this integrated network and are using browsers as common software. Various approaches are being utilized by KMS designers to achieve common databases and repositories. Common taxonomies and ontologies are being used to organize storage of unstructured knowledge files and to facilitate knowledge retrieval while other Internet-based KMS serve as interfaces to large enterprise databases or data warehouses. Some Internet KMS are being used to facilitate communication and knowledge transfer between groups. Knowledge portals are being used by organizations to push knowledge to workers and by CoP to facilitate communication and to share knowledge between community members. The following section describes some examples of Internet-based KMS.

Internet networks can be scaled to fit any size KMS. Browsers can be tailored to fit processes as desired. Taxonomies can be created that support unstructured knowledge sharing for any size KMS. The following examples illustrate this flexibility as the examples include a project KMS, an industrywide project KMS, and enterprise KMS. Knowledge portals can be scaled to fit either form of KMS but are more commonly used for enterprise KMS. CoP KMS is a variation of process/task KMS.

Examples of Internet-Based KMS

Project-Based KMS for a Single Organization

Jennex (2000) discussed an intranet-based KMS used to manage knowledge for a virtual Y2K project team. This KMS used two different site designs over the life of the project. The purpose of the initial site was to facilitate project formation by generating awareness and providing basic information on issues the project was designed to solve. The design of this site was based on Jennex and Olfman (2002), which suggested a structure providing linkages to expertise and lessons learned were the knowledge needed by knowledge workers. This was accomplished by providing hot links to sites that contained Y2K knowledge, a project team roster that indicated the areas of expertise for each of the project team members, additional entries for individuals with expertise important to the project, and some basic answers to frequently asked questions (FAQs). The site did not contain guidelines and accumulated knowledge as reflected in test plans, test results, inventories of assets referenced to the division who owned them, and general project knowledge such as project performance data, meeting minutes and decisions, presentations, and other project documentation. This information had not been generated at the time the site was implemented. Once generated, this information was stored on network servers with shared access to acknowledged project team members.

As the project team formed and began to perform its tasks, the requirements for the intranet site changed from generating awareness to supporting knowledge sharing. The site was redesigned and expanded to include detailed FAQs, example documents, templates, meeting minutes, an asset database, guidelines for specific functions that included lessons learned, and so forth. The knowledge content of the site was distributed into the other components of the site and persons were identified as being responsible for the information and knowledge content of their responsible areas. Additionally, access to the site was enhanced by the addition of a hot link to the Y2K site placed and prominently displayed on the corporate intranet home page. The basic layout of the site provided for access to seven specific subsites: major initiatives, contacts, documents, what’s new, hot links, issues and questions, and Y2K MIS.

The effectiveness of the two sites was considered good. The first site was successful in generating interest and starting the project. The second site succeeded in taking a project that was performing in the bottom one third of projects to being a leading project within 6 months after its release. Effectiveness of the sites was established using the model in Figure 83.2 and by ensuring that the information quality was high and that the system quality, especially the search, retrieval, and infrastructure, was good.

KMS as a Knowledge Portal

This example from Cross and Baird (2000) is an intranet site built by Andersen Consulting. Consulting firms have had a long tradition of brokering their knowledge into business. In the early 1990s, Andersen Consulting began to produce global best practices CDs for distribution to project personnel. This evolved into the development of an intranet site called KnowledgeSpace that provided consultants with various forms of knowledge including methodologies, tools, best practices, reports from previous like engagements, and marketing presentations. Support was also provided for online communications for online communities of practice and virtual project teams. The site was effective for personnel with access to the Internet and adequate bandwidth. It should be noted that current modem technology and improved dial-in access, as well as the proliferation of broadband connections, have made sites such as this much more effective for field or remote personnel.

The second example, from Bartczak and England (2005), describes the system used by the United States Air Force to support the Material Command, called the AFKM Hub. The AFKM (Air Force knowledge management) Hub is the primary Web site for the Air Force Lessons Learned utility. Although the Web site has evolved, Lessons Learned is still the centerpiece of the Hub. Lessons Learned have been captured and categorized by subject area and provide valuable knowledge about past processes and events. The AFKM Hub also acts as a portal for all other AFKM components and serves as the default AFKM home page. The AFKM Hub provides a conduit to select relevant information and knowledge resources and provides an avenue for creating a knowledge-sharing organization. The Air Force Material Command (AFMC) Help Center of the AFKM Hub allows AFMC customers to perform a natural language or keyword search of over 130 AFMC Web sites and selected databases. It connects AFMC customers throughout the Air Force and Department of Defense with the appropriate AFMC information source or point of contact. The CoP work space supports the growing number of Air Force CoPs. CoP work spaces are virtual environments where members can exchange information to complete work tasks and solve problems. Each CoP serves a specific customer set. The AFKM Hub provides work spaces for a variety of CoPs

and supports over 1,300 active CoPs. The effectiveness of the AFKM Hub has also been mixed. Air Force leadership sees the value in KM and many examples of successful uses of knowledge have been recorded. However, articulating a knowledge and KM strategy has been difficult and has allowed for wasted effort in supporting AFKM needs.

KMS as a Topic Map

The last examples come from Eppler (2001). There are five types of knowledge maps: source, asset, structure, application, and development. A multimedia company intranet site is used to illustrate a knowledge source map. This site provides graphical buttons representing individuals with specific expertise color coded to indicate the expert’s office location. The knowledge asset map provides a visual balance sheet of an organization’s capabilities of a skills directory or core competency tree. Colors are used to indicate knowledge domains while the size of symbols indicates level of expertise. The knowledge structure map divides knowledge domains into logical blocks that are then broken into specific knowledge areas. The knowledge application map breaks an organization’s value chain into its components parts and then indicates what knowledge, tools, or techniques are needed to implement the component part. The last example is a knowledge development map. This map is used to plot the activities needed to acquire the indicated knowledge competence. Clicking on the displayed competence displays the steps needed to develop the competence. Effectiveness of these maps has only been determined for the knowledge asset map. This map, developed for a telecommunications consultant firm, was found to be very useful for the planning of training activities and for identifying experts quickly when required during an emergency. IT should be noted that knowledge maps enhance the linkage aspects of information quality.

Enterprise System Support for KMS

As organizations strive to improve their competitive position/advantage, they are implementing enterprise wide systems. These systems integrate processes and data/information/knowledge across the enterprise and in many cases with suppliers and customers to improve efficiency and effectiveness (Koch, 2002). This usually results in lowered operating costs and improved response times, economies of scale, and user satisfaction. Typical of these systems are enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM), and data warehouse implementations. As these systems are refined and improved, organizations are finding that incorporating knowledge and KM improves system performance. Unfortunately, several issues are also involved in successfully using enterprise systems to support KM; chief among these are organizational culture issues. Many enterprises suffer from fragmentation, meaning that many organizations within the enterprise own and use their own data and systems. Enterprise systems seek to integrate these systems but to be successful they must overcome issues of ownership and a reluctance to share data, information, and knowledge. This issue is usually characterized by the presence of “silos” in the enterprise. Corral, Griffen, and Jennex (2005) discussed this issue with respect to integrating data warehouses and KM. The following examples describe how enterprise systems and KMS are being fused together.


Li, Yezhuang, and Ping (2005) described an ERP implementation in a Chinese paper manufacturing company. The ERP was implemented to help the company respond to market and customer changes more rapidly by integrating enterprise data information and knowledge and by centralizing process control. Unfortunately, China lacked experience with ERP implementation and was not overly familiar with Western concepts of centralized data, information, and knowledge management. This was an issue in getting the ERP implemented and utilized. Once this was accomplished, decision making was greatly enhanced through improved knowledge transfer provided by the ERP’s integration of organizational data, information, and knowledge into a single accessible location. Other issues faced in implementing the ERP was management support for the various suborganizations being integrated into the ERP and creating a culture that used data, information, and knowledge in the expected way.

White and Croasdell (2005) described ERP implementations in Nestle, Colgate-Palmolive, and Chevron-Texaco. Each of these implementations was performed to improve data, information, and knowledge integration with an expectation of improved decision making and transfer of key knowledge such as lessons learned and process improvements. All three implementations were ultimately successful after initial difficulties including cost overruns due to unrealistic project estimates of schedule and cost and overcoming employee resistance to changing to new processes and merging data, information, and knowledge ownership. These examples also incorporated the KMS success factor of metrics for measuring success, and they illustrate the importance of measuring KMS performance.


Al-Shammari (2005) described a knowledge-enabled customer relationship management (KCRM) system in a large Middle Eastern telecommunications company. The KCRM was composed of three major parts: enterprise data warehouse (EDW), operational CRM, and analytical CRM. The KCRM initiative was designed to automate and streamline business processes across sales, service, and fulfillment channels. The KCRM initiative was targeted at achieving an integrated view of customers, maintaining long-term customer relationships, and enabling a more customer-centric and efficient go-to-market strategy. The driver for the initiative was that the company faced deregulation after many years of monopoly. The company initiated a customer-centric KM program and pursued understanding customers’ needs and forming relationships with customers, instead of only pushing products and services to the market. Unfortunately, the KCRM program ended as an ICT project. The company did not succeed in implementing KCRM as a business strategy, but it did succeed in implementing the KCRM as a transactional processing system. Several challenges and problems were faced during and after the implementation phase. Notable among these is that the CRM project complexity and responsibilities were underestimated, and as a result, the operational CRM solution was not mature enough to automate CRM processes effectively and efficiently. Changing organizational culture was also a tremendous effort in terms of moving toward customer-centric strategy, policy, and procedures, as well as sharing of knowledge in a big organization with lots of business silos. Employee resistance to change posed a great challenge to the project. Ultimately, this project failed to achieve to expectations.

Data Warehouse, Enterprise Databases, and KMS

White and Croasdell (2005) described Xerox’s use of an enterprise database to facilitate the sharing of experience knowledge across the company. Xerox’s had difficulty in fostering best practice among its group of printer maintenance employees. The problem centered on an inability to circulate employee expertise using existing organizational infrastructure. To help the maintenance technicians share their experience and expertise, Xerox created a database to hold top repair ideas in order to share those ideas with other technicians in all areas. This strategy called for only the most favored ideas to be kept within the database as it often occurred that what one person thought useful others found absurd or redundant. Xerox also realized that many databases had been created by managers who filled the databases with information they thought would be useful for their employees. However, most of those databases were rarely used by the employees. When Xerox created the Eureka database, it also formed a process for entering and updating the ideas within the database. The process is based on a peer-review system. Within this practice the representatives, not the organization, supply and evaluate tips. In this way, a local expert would work with the representative to refine the tip. Representatives and engineers evaluate the tips, calling in experts where appropriate. As of July 2000, the Eureka database held nearly 30,000 ideas and it was being utilized by 15,000 Xerox technicians who answered one quarter of one million repair calls per year. The shared knowledge in Eureka saved Xerox about $11 million in 2000, and customers also saved money in terms of the reduction in downtime.

Eureka later extended the role of the Eureka Database to collect, share, and reuse solutions to software and network problems as well as those involving hardware.

Advanced Technologies

Although there is strong support for using the Internet as a knowledge infrastructure, there are concerns. Chief among these concerns is the difficulty in organizing, searching, and retrieving unstructured knowledge artifacts. Ezingeard, Leigh, and Chandler-Wilde (2000) pointed out that Ernst & Young U.K., in the beginning of 2000, had in excess of one million documents in its KMS. Another concern is the tendency to not to use the system. Cross and Baird (2000) discussed this tendency but came to the conclusion that repositories are essential. Jennex (2005a) found that use and importance for knowledge do not correlate, suggesting that use is not a true measure of the value of a KMS. Jennex and Olfman (2002) found that voluntary use is enhanced if the system provides near and long-term job benefits, voluntary use is not too complex, and the organization’s culture supports sharing and using knowledge and the system. Stenmark (2002) found that if the Internet is visualized as a system for increasing awareness of knowledge and the KMS, a system for retaining and sharing knowledge, and as a system for enhancing communication and collaboration between teams and knowledge experts and users; then, it should be successful as a KMS. In all cases, researchers are experimenting with technologies that improve the handling of unstructured knowledge. These are discussed in the following paragraphs.

Newman and Conrad (2000) proposed a framework for characterizing KM methods, practices, and technologies. This framework looks at how tools can impact the flow of knowledge within an organization, IT’s role in manipulating knowledge artifacts, and the organizational behavior most likely to be affected. The framework also looks at the part of the KM process the tool works in. The activity phase looks at the utilization, transfer, retention, and creation of knowledge. This framework can be used to show that Internet- and browser-based KMS tools are effective.

Gandon, Dieng, Corby, and Giboin (2000) proposed using XML to encode memory and knowledge and suggested using a multiagent system that can exploit this technology. The proposed system would have improved search capabilities and would improve the disorganization and poor search capability normally associated with Internet systems. Chamberlin et al. (2001) and Robie, Lapp, and Schach (1998) discussed using XML query language to search and retrieve XML encoded documents.

Dunlop (2000) proposed using clustering techniques to group people around critical knowledge links. As individual links go dead due to people leaving the organization, the clustered links will provide a linkage to people who are familiar with the knowledge of the departed employee. This technique would improve the reliability of the links to knowledge called for in Figure 83.2. Lindgren (2002) proposed the use of Competence Visualizer to track skills and competencies of teams and organizations.

Te’eni and Feldman (2001) proposed using task-adapted Web sites to facilitate searches. This approach requires the site be used specifically for a KMS. Research has shown that some tailored sites such as ones dedicated to products or communities have been highly effective. This approach is incorporated in the examples in this paper with the exception of the use of dynamic adaptation.

Eppler (2001), Smolnik and Nastansky (2002), and Abramowicz, Kowalkiewicz, and Zawadzki (2002) discussed the use of knowledge maps to graphically display knowledge architecture. This technique uses an intranet hypertext clickable map to display visually the architecture of a knowledge domain. Knowledge maps are also known as “topic maps” and “skill maps.” Knowledge maps are useful as they create an easy to use standard graphical interface for the intranet users and an easily understandable directory to the knowledge.

The use of ontologies and taxonomies to classify and organize knowledge domains is growing. Zhou, Booker, and Zhang (2002) proposed the use of Rapid Ontology Development (ROD) as a means of developing an ontology for an undeveloped knowledge domain.

Making sense of seemingly unrelated structured data, information, and knowledge can also be difficult. Data mining is being used as a method for identifying patterns in this data, information, and knowledge that can then be assessed for meaning. Zaima and Kashner (2003) described data mining as an iterative process that uses algorithms to find statistically significant patterns in structured data, information, and knowledge. These patterns are then analyzed by business process experts to determine if they actually have meaning in the business process context. CRM tends to use this technology the most as illustrated by the example from Al-Shammari (2005).

Organizing and visualizing data and information into usable knowledge is a challenge that digital dashboard technologies are seeking to solve. Few (2005) described dashboards as providing single screen summaries of critical data and information. Key to developing effective dashboards is the use of KM to identify critical knowledge for key decision making and then linking it to the appropriate context data and information that indicates the status of the key knowledge. Dashboards can be used with an Internet browser or any other KMS infrastructure.


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