the impacts of knowledge management on business

In: Advances in Business and Management. Volume 5 ISBN: 978-1-62100-510-0 Editor: William D. Nelson ©2011 Nova Science Publishers, Inc.

Chapter 7

THE IMPACTS OF KNOWLEDGE MANAGEMENT ON BUSINESS DECISION MAKING Narges Farzaneh Kondori*, Feryal Aslani, Kosar Salimi Khorshidi, Iman Raeesi Vanani‡ and Babak Sohrabi≠ Management School, University of Tehran, Iran

ABSTRACT In the order in disorder of a competitive world, the upward trend of technology progress forces the organizations to be more and more competitive to survive and thrive. They must attempt to select the most optimized and applicable decisions by utilizing the immediate and accurate information. They also need to apply new tools and techniques so as to achieve the best results through capturing, acquiring, refining, storing, retrieving and reusing the problem-oriented knowledge which is generated in the business processes of a learning organization. The success or even the survival of any business depends on how top managers effectively manage the internally present and externally acquirable knowledge. This article explores the broad literature of knowledge management and investigates the important issues and impacts of knowledge efforts on business decision making. It also explains and analyzes the knowledge management approaches used for supporting the business decision-making processes. Finally, it discusses the process of innovative and knowledge-oriented decision making so as to provide the managers with practical solutions to solve the less structured organizational problems.

INTRODUCTION Knowledge refers to what one knows and understands. It is ‗‗meaningful links people make in their minds between information and its application in action in a specific setting‘‘. In the industry, it refers to the sum of information relevant to a certain job and usually, we get * [email protected] ‡ [email protected] ≠ [email protected]

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Narges Farzaneh Kondori, Feryal Aslani, Kosar Salimi Khorshidi, et al.

things done successfully by knowing an answer or how to find an answer, or knowing someone who can. [12, 13] Pearlson and Saunders highlight that one way of thinking about knowledge is to consider the different types of knowing (knowing what - knowing how - knowing why). Knowing what often is based upon assembling information and eventually applying it. It requires the ability to recognize, describe and classify concepts and things. Knowing how, to know how to do something, it requires an understanding of an appropriate sequence of events or the ability to perform a particular set of actions. Finally knowing how and knowing what can be synthesized through a reasoning process that can result in knowing why. Knowing why is the causal knowledge of why something occurs.[8,11] According to Stenmark, knowledge is considered tacit while information and data are explicit and tangible. [17, 30] Davenport and Prusak elaborate on how information becomes knowledge by activities of making comparisons, thinking of consequences, making connections and sharing opinions in conversations. [15, 30] Knowledge practices involve reasoning about information and data while leveraging performance, problem-solving, decision-making, learning and teaching capabilities. [14, 30] Against this background, managing knowledge has become an important strategy for improving organizational competitiveness and performance. [16, 18, and 30] Checkland and Holwell propose a continuum by which data are first turned into information, and information then into knowledge. [3, 4] A simple summary of the main components of knowledge management as:  Data is a set of facts or observations that can be computationally processed  Information is a human interpretation of the data, which will vary depending on viewpoint and manipulation of the data  Knowledge is an abstraction of a learning process which can be viewed as value added information‖.

DEFINITION OF KNOWLEDGE MANAGEMENT There are so many definitions of knowledge management, some are similar some are different. It really depends on how individuals perceive knowledge management; some may perceive KM in terms of the human perspective some in terms of business terms. I believe it should be both, the combination of the two, enables KM focused organizations.[3] Thus knowledge management is a combination of the following: ―Capturing, organizing, and storing knowledge and experiences of individual workers and groups within an organization and making this information available to others in the organization‖ Other definition of knowledge management is as ―a process of creating value added information (i.e. knowledge) so that the information is made available to all its users to help them perform their work more effectively‖ [1,3]. Based on this definition, KM in its most abstract computational sense can be thought of as the organization required making knowledge available to those that need it, where they need it, when they need it, and in the form in which they need it in order to increase the organizational performance and competitive advantage [2, 3].

The Impacts of Knowledge Management on Business Decision Making

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Consequently KM involves, business processes, systems, both software and hardware, sharing of data and information, the transformation of these into value added assets to the organizations competitive advantage. [3]

BENEFITES OF KM TO ORGANIZATION Having identified what KM it is important to prove why KM is so vital to today‘s digital economy. As a summary KM can offer the following:       

KM can offer competitive advantage May help to reduce cost and increase organizational efficiency Enable‘s organization‘s to be flexible enough to react and adapt to changing business and customer requirements Enhancement of innovation and creativity Ensuring of sustainable excellence Improves ―working on the go‖ by implementing knowledge repositories. Increases employee productivity, and makes management decisions more efficient.

The list can grow into an enormous size. Certainly in the information driven society of today, organization cannot ignore KM. Of course not organizations have the resources and capabilities to build KM systems, but that does not necessarily mean that corporations should close the eyes to the benefits of KM. [37]

ALIGNED TO BUSINESS STRATEGY In order for KM to be measurable and be aligned to the business strategy the following need to be applied and taken under consideration: 1. How would the KM initiative change the organization in terms of how it conducts daily business function, how will it change the productivity and the organization‘s employees and how will it affect the current business processes. 2. What technologies are available for implementing KM what are their advantages and their drawbacks? 3. What is the likely ROI? Is KM really feasible? It is however clear that KM is not a concept that can be applied instantly, and there are no measuring agents that will determine whether it is successful or not. Organizations must adhere to the following:  

Develop deep sharing relationships internally between departments, and externally between clients, suppliers and other business partnerships. Improve information flow, between suppliers, clients, shareholders and the community

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Narges Farzaneh Kondori, Feryal Aslani, Kosar Salimi Khorshidi, et al.    

Keep up to date with technological, social, political economic and cultural trends Embed knowledge into business processes and management decision making. Embed knowledge in products e.g. in user guides, and enhanced knowledge intensive services. Knowledge sharing - learning networks, Communities of knowledge, online documents, procedures and discussion forums, Intranets.

The above can be the first step which must be fulfilled in order for knowledge management to work. Although, knowledge management is all about information, data and how these are shared between individuals in an organization with the use of technology were it is needed, it is also important that senior management is ―on board‖ with the concept, and be able to set examples of best practice for employees to follow, thus automatically setting up a roadmap for a successful implementation. [37]

DECISION MAKING Decision making can be broadly thought of as "the process of selecting from a set of options the alternative(s) that are most likely to lead to desired outcomes" [4], The decision making process can be viewed as a know I edge-intensive activity [19], In order to select a set of options, the decision maker must first obtain information regarding each possible alternative. Once this information is gathered and culminated in an alternative, this is considered a new piece of knowledge. In turn, once a choice is made from all presented alternatives, this choice, or decision, can also be considered a new piece of knowledge. From this, decision making can be viewed as the process of manufacturing new pieces of knowledge. The knowledge surrounding the alternatives not chosen can be considered as byproducts that were manufactured during the decision making process [19], A recent survey conducted by Kepner-Tregoe (KT) in Princeton, N.J. showed that employees are having to make more decisions and in less time than in the past [15]. By making these decisions quickly, many decision makers are sacrificing quality, productivity and customer service, Kevin Rollins, CEO of Dell, stated, "Get the best data you can, because making a decision with no data is a sin" [48], The state of business today indicates that better quality decisions are crucial [32], The better the decisions of decision makers, the more of a chance the organization has to succeed [41]. Almost every aspect of the business can be a reflection of how well the employees perform the decision making process. However, most decision making is carried out by decision makers who are unconscious of what they are doing. Or they are so focused on the particulars (such as the time constraints, the complexity of the decision, etc) that they neglect the dynamics of the decision making process itself [32]. By understanding the dynamics of the process, decision makers can make decisions more effectively and efficiently. Therefore, it is important for decision makers to review all aspects involved in the decision making process.


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DECISION SUPPORT SYSTEM Problem-solving is an essential task for corporations that seek to improve their profitability. Consequently, process control needs much attention as it aims at preventing or reducing the effects of these malfunctions. Most control methods have, however, something in common; they manipulate data and parameters in order to take decisions based upon the estimated behavior of the system. Moreover, problem-solving is a complex process based upon the subjective and knowledge intensive evaluation of the situation and leads to actions with uncertain effects. Without relevant information about the situation workers are unable to choose an appropriate course of action to solve a problem. [13] These difficulties might be overcome by taking knowledge about the environment, the task, and the user into consideration during the data analysis. [24, 27] A DSS can be defined as ‗‗an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker‘s own insights‘‘ [9, 24]. This definition is interesting as it emphasizes the idea that a DSS is mainly a framework in which problemsolving takes place and data analysis can be implemented efficiently along with other methods. Moreover, the development of knowledge-based DSS is justified by the inability of decision makers to efficiently diagnose based upon measurement data many malfunctions, which arise at machine, cell, and entire system levels during manufacturing operations. [24, 82] In this context, knowledge- based approach takes the advantage of the fact that the people operating the process most likely have the best ideas for its improvement. The integration of these ideas into the problem-solving approach leads to the solution for the long term process improvement. [24,81] Additionally, as the use of knowledge and more generally qualitative information better explains the relationships between input process settings and output response, knowledge integration well indicates the improvement in the understanding and usability of DSS. For a DSS to be efficient, it should therefore not rely only on one single type of input, nor should it rely on one single source. [24, 83]

A BUSINESS PROCESS CONTEXT FOR KNOWLEDGE MANAGEMENT Using the business process as a knowledge context provides a richer and more complex phenomenon to study. The business process context also provides an assessment framework that makes it easier to evaluate the impact of KM efforts in improving business process performance. KM itself is embedded in an iterative process that fluctuates between storage and retrieval, and knowledge sharing; with the ultimate aim of knowledge reuse and knowledge synthesis. The iterative view of KM enables the tailoring of KM efforts to the process needs. It is increasingly clear that KM is not just about technology, and cannot be realized simply through information systems. Knowledge and management of it emphasize and expect collaboration between a wide spectrum of contributors that ranges from people and processes to supportive technologies in an organization. [68]. It is generally accepted that performance improvements from KM and associated technologies result when knowledge is actually

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applied [10, 68]. In this section will focus to a business decision and process setting as we describe a knowledge context. Business knowledge is defined in the extant literature as a complex conglomeration of information, workflow, decision and collaborations and all the associated interactions. Business processes are a collection of interdependent activities or tasks organized to achieve specific business goals. Researchers have attempted to gain a better understanding of business processes through the concepts of coordination framework [44.68 .69.71]. Coordination is defined as arranging or organizing to achieve a desired or effective combination [68]. Coordination is achieved through mechanisms that are created to bind or organize the various aspects of a business process to meet the process objectives [68,70]. Decision-making components in a business process that aid in the achievement of process objectives and solutions include business procedures and business rules. Procedures govern the sequence of actions or heuristics that interpret and implement business rules. It is these procedures and rules that are intended to govern the actions of the members (or agents) in the day to day execution of the business process. While, workflow patterns can often embody business procedures, individual tasks also constitute enactment of business procedures, thus meriting separate consideration under decision-making structure. A decision-making structure that has formally defined business rules and where decision procedures follow the rules without room for alternative interpretations is termed rigid. A decision-making structure which is evolving in procedure and formal in the application of rules is considered as being oriented towards meaning and order. While the formal rules are intended to ensure equity in the process, the procedures that implement the rules tend to evolve over time to allow some amount of flexibility in the interpretation of the rules. A decision-making structure comprising of static procedures, but informal rules are considered contextual and interactive. A decision-making structure which is evolving in procedure and informal in business rules is considered autonomous. This is the most flexible among the possible decision-making structure designs. Different business processes of an organization employ different decisionmaking structures to suit the needs of the objectives of the process being performed. Rigid decision-making structures tend to benefit the least from KM efforts. Any innovation or knowledge synthesis in the decision-making structure will have to be externally influenced through a change initiative. Decision-making structures emphasizing ―meaning and order‖ would benefit from KM efforts that drive innovations geared towards increasing the efficiency of procedural aspects of decision making. As with rigid decision-making structures, innovations and synthesis in business rules can only come through external influence. In essence, processes that emphasize formal business rules tend to depend on external forces for driving knowledge innovations in the process. We assert that technological solutions would have limited impact in such scenarios. Contextual and interactive decision-making structures depend on effective interpretation of informal business rules. KM efforts designed to better store and retrieve lessons learnt from past decision-making episodes would be beneficial in this regard. However, due to a static procedural orientation, knowledge within the context of the decision-making problem would be useful. There is very limited scope for learning from decisions made in contexts outside of the problem domain at hand. On the other hand, autonomous decision-making structures are the most amenable to knowledge sharing and storage and retrieval solutions. Such decision-making structures benefit from interactivity among decision makers both


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within and outside the process domain; they also benefit from retrieving knowledge related to solutions and procedures applied to similar decision problems from within and outside the problem domain.

FACETS OF KM While the core of knowledge is defined through business process, the management of knowledge can be defined as a cyclical set of phases. We define the phases as the following: Storage and Retrieval, Knowledge Sharing and Knowledge Synthesis. [68] 

Knowledge storage and retrieval

The relationship between storage and retrieval aspects of KM and the business process is symbiotic. Effective knowledge storage practices enable storing of observations, consequences, and exceptions that occur in workflow execution and decision-making activities. When knowledge storage activities are external (and artificial) to the business process context, effective control and motivational structures are necessary to ensure that the observations, consequences and exceptions are stored in the KM systems as per expectations. Furthermore, notions and concepts of storage developed from traditional data management practices are sometimes at odds with effective knowledge storage and retrieval practices. Traditional data management practices focus on redundancy reduction as an enhancement to storage practices. However, from a KM perspective, redundancy in storage often is a facilitator to better usage and synthesis of knowledge. When these perspectives differ and reflect on the different aspects of the problem scenario and the interpretations thereof, it actually enhances the inquiry and problem solving process rather than diminishing it. Effective storage does not necessarily ensure knowledge synthesis or reuse during process execution through retrieval activities. To a great extent, this challenge in KM system usage stems from the nature of knowledge itself. [68] 

Knowledge sharing

Knowledge must be shared to be useful and applicable. When codified knowledge is stored, sharing is facilitated through defined access and security mechanisms and shared semantics of the stored knowledge. In cases where knowledge, however, is not systematically stored, it becomes necessary to create communication and collaborative mechanisms to enable knowledge sharing. Codification of the knowledge domain through at least simple taxonomical structures enables the search for expertise within the social network. Knowledge sharing in this context would depend to a great extent on the motivational aspects within the process context and the cultural setting within which the process operates. Sharing or integrating data in traditional data management systems increases complexity (as in distributed database systems) through requirements such as simultaneous updates and locking. In knowledge sharing systems, increased size usually increases the utility of the knowledge shared through externalities.

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With increased size of the repository, structuring of data becomes a necessity for data sharing in data management systems through relational, network or hierarchical approaches. Structured data and informational focus in data sharing facilitates queries on the relations that exist within data and summarization. Informational focus in knowledge sharing contexts emphasizes inquiry where search is opportunistic. Querying within a data sharing context has the notion of an accurate end-state (e.g., factual information). Inquiry in a knowledge sharing context may not necessarily involve an end-state. When an end-state exists, it is usually in the form of a solution to an unstructured problem with no verifiable true end-state. [68] 

Knowledge synthesis

The intent of storage and retrieval systems, and knowledge sharing systems is to enable and enhance knowledge synthesis capabilities of the organization. While workflow and information processing aspects of business processes may create environments for effective storage and sharing practices, the decision making and motivation structure would have a critical impact on knowledge reuse and knowledge synthesis. Rigid decision-making structures clearly impede any innovative practices in business processes, thereby effectively nullifying the benefits of well-designed KM systems. Such processes often have to depend on innovations from outside the process boundaries. We, however, contend that KM systems have minimal impact on knowledge synthesis in processes characterized by rigid decision-making structures. Processes that emphasize contextual and interactive decision making clearly are conducive to knowledge reuse. Such processes can use the existing knowledge stores and knowledge sharing facilities to enhance the applicability of codified knowledge and increase decision-making efficiencies. Innovations in such contexts come through novel modifications to the rules of engagement and execution to maximize process objectives. Procedural adherence can however severely limit the innovation capabilities of the process. In a similar vein, decision-making structures emphasizing meaning and order to the process encourage knowledge synthesis through evolutions in decision procedures, however, are hampered by adherence to formally defined business rules. Processes emphasizing autonomous decision- making structures present the most conducive process environment for knowledge synthesis as they are inherently designed to allow innovations in procedures and business rules. KM efforts in such processes would have the most impact in enabling knowledge synthesis through effective sharing, and storage and retrieval practices. [68]

INTERACTION BETWEEN KM AND DECISION MAKING PROCESSES While the role of knowledge management (KM) for decision support is well acknowledged, there is a gap between existing KM theory and actual KM practice in real-life decision-making. [ 40] The interrelation and interaction between KM processes and decision-making processes is well recognized. In fact, effective KM is considered a must for decision-making in general, and within the space industry in particular. [35, 40, 59] Moreover, Leidner and Kayworth [39,40] argue that Knowledge Management Systems (KMS) can support such processes as staff reducing, business dynamics, decision-making and problem identification, as did


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Management Information Systems (MIS), Executive Information Systems (EIS) and Decision Support Systems (DSS) in the past. KM can also affect strategic decision-making in the sense that decision makers can learn from decentralized strategic decisions made by autonomous managers, allowing the organization to be more responsive to a volatile environment [40, 75]. Moreover, KM can contribute to decision-making not only by sharing past experiences, but also by providing knowledge resources [36,40] and decision-making structures based on knowledge inside and outside the problem domain [40,68]. KM may also empower decision makers who face time pressures, risks, contradictions and information overflow, under mission-critical decision scenarios involving prevention, event recognition, early and sustained response, recovery and the like. [40, 68] In addition, KM is valuable for determining which information is needed for decision-making and for overseeing the acquisition and dissemination of information. [40,93] Courtney incorporates KM components into a new DSS paradigm to allow organizations address complex decision-making situations in global, volatile and dynamic environments, encompassing technological, organizational, social, individual and ethical perspectives, thus requiring unbounded system thinking that KM can provide. [15,40] Several theoretical KM frameworks, which interact with organizational decision processes, have been developed as well [40,53, 68,75,91,93]. Yet, according to Garrett there exists a gap between these theoretical KM models and their practical implementation in real-life decision-making. [28,40]

KM FRAMWORKS FOR SUPPORTING KNOWLEDGE Management efforts can be classified as prescriptive, descriptive or a combination of both. A prescriptive KM framework provides a general idea on how to manage knowledge, while a descriptive one aims at specifying knowledge and procedures for a successful KM initiative. The KM frameworks that are discussed in this paper can be classified as combination of prescriptive and descriptive. Adding a systems-thinking perspective to the frameworks addresses the need for a cohesive KM framework which accommodates business strategies and goals. Some KM frameworks are general and some are oriented toward decision-making processes. [22, 40, 73] The framework ‗Distributed Knowledge Model‘ (DKM) enhances DSS with a network of repositories, each of which is specialized in specific knowledge and knowledge contributors, facilitating knowledge exchange between decision makers [40, 64]. DKM was examined in a health-care environment showing increases in efficiency, patient satisfaction and service quality. Another KM framework deals with project definition, an activity that occurs prior the design phase, which should be aligned with client and organizational knowledge [40, 91]. This project-definition framework fosters a collaborative decision-making process which enables eliciting tacit knowledge towards establishing transparency of decision networks among various multi-discipline groups and stakeholders. A more specific KM framework deals with such KM strategies as personalization and socialization, as well as with tacit and explicit knowledge transformation, during the three intelligence, conception and selection phases of decision-making [75]. Also worth mentioning is a KM framework which emphasizes the importance of the business-process context for realizing KM efforts, where autonomous decision-making structures exist. Under this framework, decision processes are

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part of the operational core of knowledge, which includes also information processing, motivation structure and workflow execution, while KM efforts include knowledge storage and retrieval, knowledge sharing and knowledge synthesis [40, 68]. We focus in this chapter on three decision-oriented KM frameworks that are most comprehensive for decision support purposes. [33, 4]

KM Framework Development The theoretical basis of the developed research tool is composed of two existing decisionoriented KM frameworks: Framework A deals with crisis situations where knowledge is distributed internally and externally [93,40] while Framework B embeds KM processes within decision support environments [53,40]. Development of the Framework C, to serve as the research tool in the study, involved three methodological phases (Section 4). First (Section 4.1), we visually described Framework A (Section 4.1.1) and Framework B (Section 4.1.2), with data flow diagrams, depicting every KM component of each of the two frameworks. Second (Section 4.1.3), we compared and contrasted both frameworks to identify every desirable KM attribute characteristic of each framework. Third (Section 4.2), to have the best of both frameworks, we consolidated them into Framework C while making sure that the KM components of Framework C enable the union of all desirable KM attributes featured by either Framework A and Framework B or both.

Developing a KM Framework as a Lens for Analysis The two parts of this Section describe how we developed the Framework C, which served as research tool used to yield the content analysis results:

Frameworks A and B 

Framework A

Zhang et al. [40, 93]were motivated to present their KM framework in the context of humanitarian assistance and disaster relief, since many decisions taken in emergency situations are based on little knowledge other than that in the minds of the decision makers. Their framework deals with how to effectively gather relevant information in a timely and accurate manner, as well as with how to efficiently store, organize and manage knowledge to enable access, sharing and reuse. A Knowledge base at the core of Framework A (Fig. 2) holds structured and unstructured information about disaster events, needs assessment, relief organizations, satellite images and geographic maps, along with past experiences and recommendations. The knowledge base is constructed by means of knowledge activities as knowledge acquisition, organization, creation, and sharing, to serve decision makers and various stakeholders during real-time decision-making. Designing the knowledge base includes specifying: what the critical knowledge is; who the knowledge contributors are; how the knowledge is to be structured,


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categorized and linked to other knowledge assets; how the knowledge activities are to be embedded within organizational processes; and which infrastructure is the most suitable for supporting the knowledge activities. Amplifying knowledge reuse is done by including in the knowledge base an ontology, which is a description of the objects, concepts, and relationships that exist in various areas of interest. Framework A delivers the following desirable KM attributes:  Distributed — Framework A enables decision-making by distributed groups, based on shared knowledge, and facilitates decision sharing and validating among different stakeholders distributed in the organization.  Knowledge base — Framework A has a central and collaborative infrastructure, where knowledge can be organized for decision makers across the organization.  Knowledge lifecycle activities — Framework A encompasses all knowledge-related activities (e.g., create, identify, collect, organize, share, adapt and use) that leverage knowledge usage and transfer [1].  DSS (Decision Support System) — Framework A stands for an organizational system that facilitates decision-making.  CBR (Case-Based Reasoning) — Framework A features a case-based repository of past cases and their solutions.  Recommendations — Framework A has the capability to recommend a solution for a problem.  Internet infrastructure — Framework A enhances the system usability by allowing remote access.  Connection to people — Framework A defines the stakeholders, both internal and external to the organization, as inherent part of the framework.  Security and billing — Framework A acknowledges its relationships to the different stakeholders.

Figure 2.Framework A.

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Framework B

Bollogu et al. [40, 53] claim that decision-making processes and KM processes are interdependent and consist of activities that complement one another. In their KM framework, therefore, KM components, such as knowledge acquisition and distribution, are aimed at enabling and enhancing decision-making, whereas DSS components are aimed at supporting decision-making activities by providing means for acquisition and storage of decision makers' tacit and explicit knowledge. The DSS+KM core of Framework B (Fig. 3) combines DSS and KM practices. Framework B's knowledge base is composed of two components. The first component consists of model repositories, relying mainly on database management, data warehousing, data mining and OLAP (On Line Analytical Processing).

Figure 3.Framework B.

The second component incorporates Nonaka's [54,40]SECI model regarding Socialization, Externalization, Combination and Internalization human based knowledge creation activities, thus reflecting the integration of DSS and KM aspects in the knowledge base. The externalization of decision models involves elicitation of problem-solving knowledge and decision-making argumentation from the decision makers; The combination of decision models can be achieved during their integration and generalization; the internalization of decision models corresponds to DSS building using elicited decision models; and the socialization of decision models is analogous to knowledge sharing by different decision makers (e.g., through group discussions), reflecting their tacit models. The


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processes of Framework B are based on machine-learning techniques which enhance decision-making effectiveness by fostering not only validation and verification of consistency in decision-making, but also alignment of decisions with organizational goals. Framework B delivers four desirable KM attributes also featured by Framework A: knowledge base, DSS, CBR and Recommendations. Since Framework B focuses on decision models in accordance with Nonaka's model [54] of knowledge creation, it partially delivers the KM attribute fully featured by Framework A: knowledge lifecycle activities. In addition, three desirable KM attributes that Framework A does not feature are delivered by Framework B:   

Generic — Framework B is general purpose in the sense that it can serve decisionmaking processes in diverse organizations. EIS (Enterprise Information Systems) — Framework B interfaces between the KM and DSS components with an EIS. Automated decision models collaboration — Framework B refers to intelligent agents capable of analyzing the current problem and finding similar ones in the knowledge base. 4.1.3. Comparing Frameworks A and B

Framework A enables distributed usage and Framework B is more focused on local usage. Framework A explicitly describes KM lifecycle activities, using the terminology creation, linking, sharing, maintenance, acquisition, filtering, indexing and categorization, whereas Framework B focuses on decision models in accordance with Nonaka's model [40,54] of knowledge creation, thus ranked ―partial‖ on knowledge lifecycle activities. EIS is mentioned in Framework B but not in Framework A. Internet orientation is mentioned in Framework A but not in Framework B. There is no mention of automated decision-making collaboration in Framework A, other than discussing tools that help retrieving knowledge from the knowledge base, while Framework B does foster automated tools for sharing of decision models. The security and billing attribute is featured only by Framework A that deals with its relevant stakeholders. Both frameworks aim at supporting decision-making by different stakeholders (hence the positive DSS ranking for both), but only Framework A explicitly considers stakeholders as an inherent part of its model. Finally, both frameworks are ranked positively with regard to the CBR and the recommendations attributes. 

Framework C: consolidating Frameworks A and B

Framework A emphasizes global and specialized KM for distributed stakeholders who share a central knowledge base via an infrastructure that facilitates retrieving relevant knowledge during crisis situations in real time [40, 93]. Framework B addresses the need for collaboration with regard to modeling knowledge within a general purpose decision support environment [53,40]. Neither KM framework alone, however, suffices for analyzing the CAIB report due to no delivery of some desirable KM attributes. We therefore propose consolidating Frameworks A and B in Framework C, taking the generic view of Framework B, transforming its local perspective to a global one, and enhancing with a general-purpose perspective special purpose Framework A components. Furthermore, as in Framework A, the

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Internet serves as a communication enabler and access channel in Framework C. The knowledge base component at the core of Framework C (Fig. 4) holds internal and external data sources like databases and data warehouses, as well as internal and external knowledge sources like functional knowledge, organizational knowledge, and problem specific knowledge [56,40]. In addition to including domain ontology and personal contact information, the knowledge base is enhanced with data mining, OLAP and search functionalities. Framework C's web-based KM+DSS activities component facilitates creating, sharing, categorizing, indexing, filtering, acquiring, maintaining, and linking of knowledge. This component is enhanced with machine-learning techniques that can automatically organize and mobilize knowledge in the knowledge base, via a web-based platform. It also includes KM tools like: web client agents; case-based reasoning, new model construction and integration; shared space for decision makers' interactions; information retrieval; recommendations; situational awareness that uncover perceptions, comprehensions and projections; and multimedia digitizing technologies. This knowledge infrastructure enhances web-based decision-making by decision makers as well as internal and external stakeholders.

Figure 4. Framework C.


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THE INTEGRATION BETWEEN KNOWLEDGE MANAGEMENT SYSTEMS AND DECISION SUPPORT SYSTEMS Nowadays, businesses need different types of information systems to support decision making and work activities for various organizational levels and functions to respond to new competitive pressures [6,79, 85]. Early information technologies were designed to support and assist employees in their managerial and professional duties by processing and disseminating enormous amounts of information to managers. Over several decades Management Information Systems evolved to other systems, such as Decision Support Systems (DSS) which focus on providing tools for ad-hoc decision analysis to specific decision makers or intended to provide updated, often concurrent, significant and relevant information to senior and middle managers. These systems contribute to the development of both individuals and organizations along with improvements at different degrees and continue to be the most significant components of an organization‘s information technology investment [5, 6]. DSS have been employed in organizations as means to deal with an overwhelming flow of data, information and knowledge stemming from an increasing number of internal and external sources. Several tools have emerged to support complex decision making processes and facilitate effective analytical thinking. [6, 47] Nemati et al.[ 6,55] state that ―knowledge provides the perceptual and conceptual filters which the decision maker uses to firstly select and organize data into information and then to use that information to forecast, take decision or to support and inference‖. [94, 6] Shim et al. describe Simon‘s description of the most frequently used model of the DM process in a DSS environment. [84,6] The importance of this model appears in its development and problem analysis. Once the problem is recognized, defined in terms that facilitate the creation of models, alternative solutions are shaped and created, models are then developed to analyze a variety of possible alternatives and finally a choice is made to be implemented. See figure (5)

Figure 5. The decision-making process.

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KNOWLEDGE MANAGEMENT TOOLS (KMT) KMT have the main aim of simplifying access or providing direction to the knowledge and information within KM systems, in a timely manner by reducing the quantity of information available to the user. The tool will typically decide upon what is relevant for the individual user based upon a user request or profile. They act like a lens onto other KM systems, giving the user a better perspective as to what knowledge he is looking for and where he can find it. Typical examples include search tools, portal applications, and other information or knowledge filtering tools. These tools do not follow the virtuous circle of knowledge management – create, capture, store, and share – but rather act as data manipulators – taking information and knowledge that is already stored and using it to answer questions, stimulate insight, and even predict the location of as yet unavailable knowledge. These tools are frequently mislabeled as knowledge creators. A tool cannot create knowledge; it can only present data in such a way as to stimulate the insight and knowledge creation in someone who has the knowledge and ability to interpret what they see. Ruggles (1997) provides a classification of KM technologies as tools that: 1. 2. 3. 4.

Enhance and enable knowledge generation, codification, and transfer. Generate knowledge (e.g., data mining that discovers new patterns in data). Code knowledge to make knowledge available for others. Transfer knowledge to decrease problems with time and space when communicating in an organization.

Rollet (2003) classifies KM technologies according to the following scheme: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Communication Collaboration Content creation Content management Adaptation E-learning Personal tools Artificial intelligence Networking

The initial knowledge capture and creation phase does not make extensive use of technologies. A wide range of diverse KM technologies may be used to support knowledge sharing and dissemination as well as knowledge acquisition and application.


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MAJOR KM TECHNIQUES, TOOLS, AND TECHNOLOGIES In knowledge Creation and Capture Phase Content creation: Authoring tools Templates Annotations Data mining Expertise profiling Blogs

Content management: Metadata tagging Classification Archiving Personal KM

Knowledge Sharing and Dissemination Phase: Communication and collaboration technologies: Telephone Fax Videoconferencing Chat rooms Instant messaging Internet telephony E-mail Discussion forums Groupware Wikis Workflow management Networking technologies: Intranets Extranets Web servers, browsers Knowledge repository Portal

Knowledge Acquisition and Application Phase E-learning technologies: CBT WBT EPSS

Artificial intelligence technologies: Expert systems DSS Customization- personalization Push/pull technologies Recommender systems Visualization Knowledge maps Intelligent Agents

All of them need to be mixed and matched in the appropriate manner in order to address all the needs of the KM discipline, and the choice of tools to be included in the KM toolkit must be consistent with the organization‘s overall business strategy. [19]

CONCLUSION This chapter explored the broad literature of knowledge management and decision making and investigated the important issues and impacts of knowledge efforts on business decision making. The management of knowledge was defined as a cyclical set of phases: Storage and Retrieval, Knowledge Sharing and Knowledge Synthesis. KM frameworks discussed and the business process context provided an assessment framework that made it easier to evaluate the impact of KM efforts in improving business process performance. The chapter elaborated on the information systems and techniques used for creating or acquiring, storing, and distributing knowledge. It also explained and analyzed the knowledge management approaches and systems used for supporting the business decision-making processes. A DSS was defined and additionally, as the use of knowledge and more generally qualitative information better explained the relationships between input process settings and output response, knowledge integration well indicated the improvement in the understanding and usability of DSS.

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