Proceedings of the 34th Hawaii International Conference on System Sciences - 2001
Creating Feedback Loops to Support Organizational Learning and Knowledge Management in Inquiring Organizations Dianne J. Hall Texas A&M University College Station, Texas 77843-4217
[email protected]
David B. Paradice Florida State University Tallahassee, Florida 32306-1110
[email protected]
Abstract Information technology both facilitates and exacerbates the need for organizations to manage growing information and knowledge bases. Soon, the only successful enterprises may be the ones who have successfully evolved into learning organizations. The philosophical bases underlying traditional decision support systems (DSS) are ill equipped to handle this explosion of information, today’s rapidly changing business environment, or support a learning organization. A comprehensive knowledge management system (KMS) based on the philosophies of inquiring systems (IS) allows organizational learning to occur by providing both decision support and management of existing and created knowledge. By combining the DSS paradigm with IS, an organization will be capable of designing a comprehensive KMS that will fully support learning within the organization. Development of such a system will allow executives to rely more fully on the support system, freeing up valuable time to establish goals and guide the organization to its ultimate success.
1. Introduction As the information system (IS) field matures, more attention is being given to identifying the fundamental philosophical concepts underlying the field. There are advantages to identifying and understanding these fundamental issues. Fundamental philosophical concepts form the foundation of assumptions taken for granted in a field. As such, they can help academics and practitioners alike to understand why systems are built the way they are and why they perform the way they do. One commonly accepted system used in organizations is a decision support system (DSS). DSS are designed to complement a decision maker’s ability and expertise by providing information in an efficient manner. DSS must be able to organize, store, retrieve, and use data and
James F. Courtney University of Central Florida Orlando, Florida 32816-1400
[email protected]
models in a manner that allows decision makers to perform effectively. The DSS concept is well developed. Gorry and Scott Morton [18] suggested that an effective DSS should combine Simon’s [42] decision continuum with Anthony’s [1] managerial tasks to support decisionmaking. Gorry and Scott Morton also moved away from traditional programming-based problem definitions toward more general terms emphasizing problem structure [8]. Organizations often rely on DSS as a means to formulate efficient and effective decision-making that will guide it toward its goals. In organizational settings, individual or group decision-making activities can affect organizational outcomes. When individuals or groups make decisions without benefit of full consideration of organizational parameters, the decisions made may be sub-optimal from the organizational perspective. Thus, organizations need information technology support structures that have many of the same characteristics of DSS and are also capable of considering broader organizational parameters during the decision-making process. These support structures must be both efficient and flexible, and must provide the organization's decision makers with timely, accurate information on which to base decisions. Most importantly, organizational systems must also allow the organization to learn and effectively manage the knowledge they create. DSS do not generally provide such capabilities; inquiring systems [7] do. In this paper, we argue that DSS is on the verge of a paradigm shift. Though arguably more evolutionary than revolutionary [29], this shift is needed to adequately support organizational learning and knowledge management. Simon [40, p. 223] has noted a growing body of evidence that the activity called human problem solving is a form of means-ends analysis that aims to discover a process description of the path that leads to a desired goal. We argue that organizational learning and knowledge management are activities executed to take organizations toward desired goals. The organizational perspective is imperative -- and generally lacking in DSS
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philosophies. This paper examines the implicit philosophical bases of typical DSS. We argue that the philosophical bases underlying DSS explain how typical DSS function and why they have certain shortcomings. These issues explain why knowledge management systems (KMS) are receiving greater attention. Finally, we address how the integration of DSS into the concept of inquiring systems (IS) can move systems technology to a new level of decision support and knowledge management.
2. Inquiring Systems and Organizational Learning Complex, rapidly changing business environments affect decision makers in several ways. Such environments spawn problems and opportunities requiring prompt attention. The increasingly large number of interactions between factors in the decision maker's environment necessitate that any actions taken be not only rapid but also appropriate. Support systems must be capable of physically managing data, and adapting existing organizational knowledge to the changing environment. This type of system is an inquiring system. An inquiring system is a system that has the ability to gather evidence, model that evidence in a way that represents that system's reality, and present the result as knowledge. Churchman [7] (see also [33] and [9]) describes five archetypal inquiring systems, each of which has a philosophical basis. Named after Western philosophers, the systems are, in order of relative complexity, Leibnizian, Lockean, Kantian, Hegelian, and Singerian. These systems each contain the concept of a guarantor, which is a component of the system that “guarantees” the knowledge created by the system is not false. Because the outcome of these systems is knowledge, organizations that use such systems are learning, or inquiring, organizations [9]. Organizational learning has been a topic of discussion during the last decade. Senge [40] and Argyris and Schön [3] suggest that a learning organization is one in which the members recognize that they can both create and change their reality. Huber [26] believes that a learning organization is not only one whose members are skilled at creating and changing their reality, but also one that is capable of considering multiple interpretations of that reality. Garvin [17] stresses the need for the organization to change its behavior based on the new knowledge it creates. Hine and Goul [22] stress the need for an organization to utilize interpretive learning, which is a process during which members develop a perception of their reality and share those perceptions with other members. The goal of the organization is not to achieve consensus, but to achieve a general understanding of the multitude of perspectives. Organizational learning,
therefore, is a combination of interpretation of environmental variables, application of the impact of those variables to the organization's current and desired states, and action taken on the new knowledge that has been created. A comprehensive KMS based on the philosophies of IS allows organizational learning to occur by providing both decision support and management of existing and created knowledge. Among the types of knowledge that are critical to organizational learning are tacit, procedural, and deep knowledge. Tacit knowledge is contained within an individual’s mind and is difficult to articulate. (Explicit knowledge, on the other hand, is communicable knowledge, often already expressed in documents or databases.) Procedural knowledge involves “how to” do things, such as how to catch a trout or invert a matrix. Deep knowledge is knowledge used by experts, who are able to draw on their tacit knowledge of "first principles" in problem domains. (Shallow knowledge is that used by novices, who normally require guidance.) Deep knowledge is not to be confused with deep problem domains [11]. A deep problem domain is often characterized by well-formulated, relatively static underlying principles. All explicit or procedural knowledge types can be captured and stored in any DSS with little problem. Tacit or deep knowledge can be supported in an IS by providing an individual with the means to articulate and communicate the knowledge. Once articulation has taken place, the knowledge has become explicit and can be stored. These activities are supported by an inquiring organization's ability to consider multiple perspectives and because an inquiring organization encourages communication among its members [9]. Inquiring systems easily encompass the three paradigms of knowledge management discussed by Schultze [39], which are functional, interpretive, and critical. The functional perspective supports the idea that organizations use knowledge management to achieve organizational objectives. The interpretive perspective applies a social theory to information, stressing communication and interpretation in the system. The critical perspective examines the organization for conflict.
3. DSS, Organizational Learning, and Knowledge Management There are many similarities between IS and DSS. For instance, DSS have been designed to focus on analytical support for decision-making. This reflects a rational, Leibnizian perspective. Group support systems provide a much more Lockean approach, focusing on facilitating interpersonal and organizational communication. DSS tend to focus on the well-structured part of illstructured organizational problems, and as a result,
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operate from the functional or interpretive knowledge management perspective. The Leibnizian, Lockean, and Kantian IS serve these perspectives [8, 34]. Leibnizian and Lockean IS are the primary underlying inquiring philosophies for most DSS. Leibnizian thought is reflected in analytical, model-oriented DSS, and Lockean thinking underlies the communications-focus of group DSS. The Kantian philosophy is observable in modern data-warehousing – data-mining systems in which many different analytical techniques are applied to very large databases in search of patterns that can lead to “actionable” information (that is, functional knowledge). An overview of Churchman's [7] Leibnizian, Lockean, and Kantian IS is presented in Table 1. Table 1. Churchman’s Leibnizian, Lockean, and Kantian inquirers Guarantor Knowledge Management Perspective and Primary Managed or Supported Knowledge Types Leibnizian Consistency Functional perspective; Lockean
Consensus
Kantian
Fit between data and model
explicit and shallow knowledge types Functional and interpretive perspectives; explicit and shallow knowledge types Functional perspective; explicit, tacit, deep, and shallow knowledge types
Problem Type/Example
Structured (has a solution, analytical); Transportation Structured, strongly consensual; Five year plan Moderately unstructured (may not have clear solution); Budgeting
The use of these philosophical bases to support DSS has been effective in evolving decision support systems for the structured part of unstructured problems, in wellestablished problem domains. Unfortunately, many (perhaps most) problems that organizations face, especially during times when the business environment is rapidly changing, are at best moderately structured, and are often highly ill-structured. The nature of the commonly used underlying philosophies of DSS is such that these problems are not easily handled.
3.1. DSS Problem Formulation and Modeling Another crucial aspect of decision-making that is rather neglected by DSS research involves problem formulation. Work in DSS support for problem formulation exists but has not been as widely pursued as might be expected. Perhaps the most extensive examination of the subject occurred in a series of studies in the mid-1980s (see [10] for a summary). Kasper and Cerveny [27] identified the value of model building. They found that subjects who constructed a model of a complex business simulation game performed better at the game than those who did not create a model.
Also, subjects who used the model they constructed performed better than subjects who only constructed a model. This would clearly seem to imply that the subjects learned from constructing the models, but the DSS was not specifically designed to support and nurture the learning process. Long before the widespread existence of graphical user interfaces, Pracht [38] examined the benefits of graphical representations of problems. Pracht’s study showed a benefit to individual decision makers with good spatial skills in construction of these graphical models. Loy [31] extended Pracht’s work to small groups of decision makers, and found more striking results for groups. The graphical models did seem to reinforce and facilitate group learning and communication. Courtney et al. [11] investigated the use of simple structural models in the support of business problem formulation. They showed the feasibility of using such models for simple problem structuring and explanation. Paradice [36, 37] extended this work by developing a more robust taxonomy of structural model relationships and developed a system capable of formulating problem structure from pair-wise relationships and of using that structure to provide rudimentary advice on problem solutions. Notably, Paradice’s system was explicitly constructed with a Kantian philosophical basis. Hodges [23] subsequently expanded (in a sense) Paradice’s work using a Hegelian philosophical basis, producing a system capable of representing diametrically opposed models of the same problem domain. Unfortunately, this system was not tested with human subjects, so its impact on learning is unknown. Smith [44] suggested that problem formulation is a three-phase process consisting of problem identification, definition, and structuring. Problem identification is the point at which a need or an opportunity is recognized. Problem definition is the conceptualization of the problem through observation of the relevant information concerning the perceived problem. Problem structuring is the analysis of the relevant information and formation of that information into a structure that allows the problem to be analyzed in a structured fashion, and alternative plans of action to be formulated. Simon [43] set forth problem structure and solution criteria. Included are testing the current state against the desired state, progression from one state to the next (toward the desired state), and micro-actions to allow the system to eliminate the differences between the current and next state. The criteria stress continual testing of the current state against the desired state, and formulation of action as necessary to close the gap. Traditional DSS do not contain the architecture necessary to accomplish testing during problem structure and solution, and therefore do not have the ability to generate feedback that is critical to the organization's ability to learn.
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3.2. Feedback Loops and Learning Learning in the organizational environment is considered by many authors to be facilitated by transferring knowledge among members, ultimately affecting organizational behavior ([21], [3], [35], [16], [14] and others). Implicit is a constant communication loop among the members of the organization that is separate from any one decision-making process; instead, this loop functions continuously within the organization, providing the organization with updated information as members identify needs, opportunities, or results from prior decision-making activity. Traditional DSS has focused on problem solution; that is, the process is considered complete upon the selection of a course of action deemed appropriate for the current problem situation. This approach has certainly been successful in helping deal with individual decisions and individual learning. Our argument, simply stated, is that DSS can contribute much more effectively to learning at the organizational level by focusing on feedback loops among decisions and decision makers. The advanced inquirers (Hegelian and Singerian), by incorporating critical and interpretive perspectives, provide a framework for designing DSS to support learning and knowledge management. Much of the DSS literature has focused on Simon's [42] Intelligence-Design-Choice (IDC) model as a noninquiring philosophical basis for the development of DSS. "Choice" is interpreted as the selection of a particular DSS alternative, implying that a decision is made. However, Simon [41] has also noted that non-artificial, self-adapting systems (i.e., living systems capable of learning) exhibit feedback characteristics, and states that without the ability to continuously define the current state in its relation to the desired state and the actions necessary to close the gap, growth cannot occur. Unfortunately, humans have not effectively built this characteristic into the otherwise sophisticated artificial systems they have constructed. Feedback has certainly been an aspect of decision support noticeably absent from systems research. (See, however, [5].) An integral part of the feedback process is constant scanning of both the internal and external environment, as well as existing knowledge, for the opportunity to create new knowledge or the remove irrelevant information from the knowledge base. Understandably, DSS have focused on decision support and the job at hand has typically been considered finished when a decision has been made. While model-based systems may have provided means for modeling prior similar decision contexts or situations, rarely have DSS methodologies included an explicit phase focused on updating existing decision models as a routine event. Models were updated when new decisions needed
to be made and so the opportunities for learning from the decision-making situation were minimized as attention became focused on solving the decision problem at hand. Churchman's [7] more complex IS, however, address this shortcoming and others by providing a platform from which multiple perspectives can be viewed, multiple models constructed, and during which the process can be interrupted for the testing that is critical to the feedback mechanism. An overview of these IS is presented in Table 2. Note that, unlike the more simple inquirers, these inquirers allow for unstructured problem types. The Hegelian and Singerian systems also support the critical knowledge management perspective, which is dependent on communication and therefore, feedback. Table 2. Churchman’s Hegelian and Singerian inquirers
Hegelian Singerian
Guarantor
Knowledge Management Perspective and Primary Managed or Supported Knowledge Types
Problem Type/Example
Conflict, overobserver Replication
Critical perspective; explicit, tacit, deep, and shallow knowledge types Functional, interpretive, and critical perspectives; explicit, tacit, deep, and shallow knowledge types
Unstructured, divisive; Worker vs. management Structured, moderately unstructured, or unstructured; Some tenure decisions
4. Similarities between Decision Support Systems and Inquiring Systems Churchman [7] and Gorry and Scott Morton [18] began work on their respective approaches at the same time. Both Gorry and Scott Morton’s DSS concept and Churchman’s IS use computer-based support for problems generally categorized as moderately or fully unstructured, and provide for structured problems to be solved primarily at the machine level. Since the introduction of both concepts, there have been developments in DSS research that demonstrate similarities between DSS and IS and suggest that they can be successfully integrated [8]. Both Churchman [7] and Keen and Scott Morton [28] emphasize that while structured problems can often be handled by machines, an inquiring system or DSS is not intended to replace the decision maker. Rather, it enhances the decision maker’s efficiency and effectiveness. Additionally, Keen and Scott Morton [28] describe four levels of support that can be provided by a decision support system: information retrieving, filtering, computing/comparing, and modeling. All of these are supported by inquiring systems. Models and model management, and their importance to a decision support system, has been a theme in the literature since the mid-1980s (for example, [2], [15],
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[30], [46], [47] and others). Bonczek, Holsapple, and Whinston [4, 25] also discuss the importance of modeling in support systems. The authors note that integration between modeling and data handling is important when designing a DSS. They discuss the importance of specialized DSS in organizational units, and examine the trend within organizations toward integrated systems that can handle a wider range of problems. Such integration is the nature of Churchman’s [7] inquiring organizations.
5. Why Knowledge Management is Receiving Greater Attention The DSS characteristics mentioned previously are some of the reasons why traditional DSS are not sufficient for supporting a modern organization’s needs, which extend beyond the conventional technically-oriented nature of traditional decision-making support. Organizations of the 21st century will require systems that are able to interpret organizational culture, environmental issues, and the impact of people in decision-making situations as well as support traditional decision-making processes. Knowledge management systems (KMS), especially those based on Churchman's [7] more complex inquiring systems (Hegelian and Singerian) compensate for shortcomings in traditional DSS and provide support for organizational learning. Knowledge management (KM) is a combination of effective storage and retrieval of an organization's knowledge. It includes management of a process that allows for continually creating, capturing, organizing, and using knowledge to reach goals and make decisions. It also includes establishing an environment in which knowledge can evolve [12], and in which different knowledge management perspectives can be utilized. This is the type of environment provided by IS, enabling decision makers to make viable, timely decisions, regardless of whether the problem is structured or not. Very often, KM is considered memorization of successful, verifiable results from previous ventures [32]. This is frequently the case in organizations using traditional DSS. This process works for structured problems, where the decision maker can scan the knowledge base for a similar situation and choose an approach that worked previously. Many KMS support transferring knowledge but a truly efficient system must support integrating knowledge [19]. Knowledge integration is the process by which a firm supports acquiring and using knowledge by individuals based on their area of expertise; each employee complements the organization with his or her knowledge, rather than attempting to transfer all knowledge between employees. Grant states that management should encourage workers to challenge assumptions and "truths" in the workplace to allow the organization's knowledge base to evolve. When
knowledge is continuously challenged, the organization is exhibiting characteristics of the system that Churchman [7] describes as a Singerian inquirer. Using more advanced inquiring systems to evolve DSS toward a system with knowledge management potential has been discussed in the literature. For example, Chuang and Yadav [6] discuss adaptive decision support systems (ADSS). Using Holsapple et al.'s [24] definition of adaptive as being self-teaching, the authors further define ADSS as a type of DSS that can adapt itself to its users' needs. An important feature of ADSS discussed in this paper is the ability of the system to adapt to its changing environment and to new information. The potential to accomplish these changes is present in Churchman's [7] inquiring systems. Chuang and Yadav [6] also list five functions that an ADSS should be able to handle - multiple scenarios, multiple views, multiple modes, multiple problem situations, and automated learning for presentation. Inquiring system components, discussed later in this paper, can handle those functions as necessary, and can handle simplistic scenarios as well. In their paper on the use of critique and argumentation in DSS, Vahidov and Elrod [45] identify several elements (for example, proactive nature, creative discontent) that should be included in an effective DSS. Several of the desired elements are inherent in inquiring systems. Combining inquiring organizations with Mitroff and Linstone's [34] Unbounded Systems Thinking, Courtney [8] develops a new paradigm for decision support systems. In this paper, Courtney discusses how organizations that use Churchman's Singerian inquiring system go beyond the traditional concept of DSS support (analytical and inductive) by encompassing multiple perspectives and embracing the social aspect of problem solving and unbounded systems thinking. These changes in thinking about systems suggest that the direction of systems support is changing. Among other things, organizational systems must begin to differentiate between the management of information and the management of knowledge. Information management is generally achieved using an information base, usually in the form of a database. In these structures, information can be stored, sorted, and retrieved. Knowledge, on the other hand, requires a system that cannot only store existing knowledge as information, but also can retrieve and use that information as knowledge when needed, especially during problem formulation. In this manner, new knowledge can be created from existing knowledge in combination with new information. Additionally, such a comprehensive KMS can support an organization's utilization of its members' tacit knowledge by maintaining information on each member's area of expertise and by providing for communication between members of the organization. This begins a process of de-centralizing the decision-making process, which is attractive because it
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disperses the decision-making burden from a single, central organizational system to the individuals or groups most likely to (a) understand the problem at hand, (b) have the requisite knowledge to solve a particular problem and (c) recognize a beneficial course of action. Inquiring systems contain several components that enable them to act as comprehensive KMS [20]. Eleven components have been identified by Hall, Paradice, and Courtney, of which seven are applicable here. These components act together to form a comprehensive KMS, facilitating organizational learning by providing an environment in which new information and existing knowledge can be combined in a timely manner to create new knowledge. These seven components and a brief description of their tasks are listed in Table 3. Note that the executor, best measures guarantor, and system guarantor each provide for system integrity, and are therefore grouped together. Each of these components, however, performs the task differently depending on which inquiring system is active at the time the component is evoked. Table 3. Components of inquiring systems Component Environmental Verifier Self-adaptation Verifier Time/space Assessor Best Fit Analyzer Executor, Best Measures Guarantor, System Guarantor
Description of Component's Task Ensures the knowledge base is current and not outdated Monitors knowledge base changes to identify new relationships or new knowledge and prompts action if required Provides the ability to follow time-critical missions of the organization Ensures best data-to-model fit Provide for system integrity by performing checks of system performance
These components allow systems based on Simon's [42] IDC model to be enhanced by placing greater emphasis on the intelligence phase, during which the critical problem formulation takes place, and by enhancing the choice phase with a component that provides evidence that the choice will be the best one developed with information currently available. The “intelligence” stage is an important, and often overlooked, stage of Simon's model. This is the stage during which Simon says the environment is scanned for "conditions calling for decision." The environmental verifier and self-adaptation verifier support this type of activity. The environmental verifier will scan and verify knowledge base information to identify knowledge store components of the system (basis or new knowledge) that have become outdated to prevent errors from perpetuating throughout the system. The environmental verifier will ensure the knowledge base is not outdated. The selfadaptation verifier will support management by preparing reports of recommended action in the face of new knowledge (or a changing environment). This component
monitors knowledge base changes to identify new relationships or new knowledge. The time/space assessor provides support here as well, as it prevents the organization from getting off course in terms of its goals. The time/space assessor also monitors and controls the order in which steps must progress for the organization's goals to be realized. There is not a specific component listed in Table 3 that supports Simon's “design” phase. This is because the “design” phase consists of "inventing, developing, and analyzing possible courses of action," which in effect summarizes the actions of the systems themselves. Although one might argue that there is a blurred line between the analyzing part of design and the selection part of “choice”, we believe best fit analyzer enhances the “choice” phase because it performs the critical function of analyzing all possible choices that have been designed and selecting from those the one best choice for the current problem state.
6. Discussion For many years, organizations have been faced with increasing amounts of information but have not been able to adequately use it in a way that allows for growth. DSS, among others, are often seen as an effective way to store and manipulate isolated information that is available to the organization for the specific problems for which they have been designed. However, DSS have been less successful at managing and distributing knowledge and lessons learned in building models and solving problems. For example, they seldom implicitly allow feedback to occur during the decision-making process. DSS have traditionally focused primarily on the "choice" phase of Simon's [42] intelligence-design-choice decision-making model, with less emphasis on "design" and almost no attention to "intelligence." "Design" has often been a process of identifying variables that fit into a predefined model structure (e.g., a spreadsheet in the simplest case; an optimization model in more complex cases). Implicit in this situation is an assumption that the existing DSS model structure is appropriate and that the assumptions inherent in the model structure are applicable. Integration of DSS into IS concepts explicitly recognizes that multiple model perspectives may be needed, that problems may, in fact, be quite ill-structured, and that problem solutions may need to be constructed or synthesized from new combinations of existing knowledge with new information, rather than simply derived from existing data sets. Inappropriate problem formulation leads to an inability to apply existing relevant organizational knowledge to a problem. Thus, decision-making effectiveness suffers and the organization fails to learn as efficiently as it could. These consequences may be critical in today's business
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environment. It is likely that the only way for an organization to maintain competitive advantage in the future will be to evolve into a learning organization that excels in its decision-making processes. Integrating aspects of DSS and IS will produce a learning organization that is capable of more efficient and effective complex problem formulation and solution. By shifting some of the human decision-making process of problem formulation into the DSS arena, executives will have greater opportunities to focus more on the "intelligence" aspects of decision-making, that is, problem identification and opportunity recognition. The guarantors inherent in the IS and their enhanced problem formulation and resolution capabilities will give executives greater confidence in the decision-making support provided. Support for feedback must also be integrated into the DSS design. Without feedback, it is difficult for an organization to gauge whether the courses of actions on which it embarks will ultimately lead to the goal or desired state. Because of the flexibilities inherent in IS, feedback is a prominent element in a comprehensive KMS based on inquirers. Feedback is a necessary element in any problem-solving situation, and it is critical in those scenarios where the problem is unstructured. In Simon's article on ill-structured problems [43], he demonstrates the power of feedback over the problem formulation process. He defines a proposed ill-structured problem and introduces a module whose job it is to scan the external environment and long-term memory to find information applicable to the problem at hand, and add it to or substitute it for information already in the process. This retrieval system has the ability to interrupt the ongoing process to add or modify information. This is not a one-time event; rather, this feedback process can take several iterations to completely formulate the problem, generate alternatives, and choose an alternative. Feedback is a necessary element in the decisionmaking process, and timing of the testing and feedback loops is critical. This timing must be such that it maximizes the efficiency of the process. Ill-timed tests are arguably as ineffective as no tests. If the feedback assessment is done at the optimum time, the subsequent choice of action is likely to be correct. If the feedback assessment is not done at the optimum time, the organization may choose to continue for the wrong reason, may choose to continue when it should not, or may choose to discontinue an otherwise beneficial action. An important part of a feedback system's ability to properly time feedback loops is its ability to recognize both new potentially relevant knowledge or information and the temporal considerations of the organization. The self-adaptation verifier and the time/space assessor, in conjunction with the environmental verifier, ensure that this aspect of the feedback system functions properly.
A comprehensive KMS, such as that conceptualized by Hall, Paradice, and Courtney [20], has this capability. In addition, that conceptual model includes components that consider whether the proposed solution is still valid, and will remove information in the problem space that has become invalid. Figure 1 shows where the feedback loops and associated components appear in the system proposed by Hall, Paradice, and Courtney. In Figure 1, fact nets and measures represent the knowledge base of the organization. These elements, in combination with environmental variables, are used by the verifiers and the assessor as needed to complement information in the immediate problem space. The immediate problem space can request additional information from these three components or can pass new information or newly created knowledge resulting from the process to the three components. The components can pass information to the immediate problem space as it becomes relevant. Hence, there is a continuous process of transferring information and knowledge during the design stage. Notice that, in this schematic, if the desired state is achieved, the process ends. Of course, at that point, the desired state has become the current state and the process will repeat as a new desired state is defined. Yes Yes
No
Is it too late? (Time/Space Assessor)
No
Current State
Are we there yet? Immediate Problem Space (Design)
Problem Resolution (Choice) (Best Fit) Yes
Fact Nets, Measures
Self Adaptation Verifier Environmental Verifier Time/Space Assessor
Is it too late? (Time/Space Assessor)
No External Environment
Desired State
Figure 1. Feedback loops within a comprehensive knowledge management system The model is not designed to be a fully automated system. It is highly dependent on the decision maker(s), who in turn must be cognizant of the availability of the knowledge base and experiential knowledge available within the organization. The knowledge base itself is dependent on frequent communication between organizational members, during which some element of tacit knowledge is often articulated and becomes storable. While supporting the decision maker(s) by providing some element of analysis, the system is not independent
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of human interaction. Individuals ultimately will determine the desired state, interpret environmental variables, make temporal considerations, select a solution, and determine checkpoints at which to check for progress toward the desired state. The individual will have benefited from the system’s support and can react more effectively and quickly than otherwise might be possible. Ultimately, this is a system of individuals and advanced technology that might include transactional systems, decision support structures, expert systems, data warehousing, data mining, and collaborative software.
7. Implications for Further Research It is clear that by enhancing problem formulation/solution routines and increasing an organization's propensity to learn through efficiently timed feedback mechanisms, a comprehensive KMS can be designed that will allow an organization to reach its full potential. There are, however, many unanswered questions involving the implementation of such elements, along with the integration of DSS and IS into a comprehensive system. Two areas of concern have emerged in problem formulation. The perceived size of the knowledge base is important if the problem-solving machine is to recognize a problem as being solvable. This implies, therefore, that a concise method of codifying and storing information and knowledge must be in place so that a solver can scan only the relevant areas for information, eliminating the perception of too much information. Secondly, the ability of the management system itself to call on the inquirer that most naturally supports the problem type requires that the management system have the ability to quickly review and process the potential problem situation in order to pass the problem to the correct inquirer for action. In addition, then, the inquirer must be able to recognize when a problem is adequately solvable by itself, or when the problem should be passed on to another inquirer. A particularly interesting research question will be how to time the feedback loop tests in this system. Timing is critical, and the system must be able to initiate the testing at the right time to ensure that the path being taken is the correct one. Along those same lines, there is the issue of the assessment of the current state. Not only is timing of the assessment critical, but the assessment component itself must be validated to ensure the quality of its analysis. Therefore, research into the appropriate assessment tool or tools is important. Problem representation issues must be resolved. For an inquirer to work with a problem, the problem must be structured in a fashion appropriate for that inquirer. Little is known regarding which problem representation schemes are appropriate for specific inquiring system types. Perhaps new problem representation schemes are
needed. Research must be done into codification schemes that allow problems to be structured such that the problem can be manipulated by the system. Also, representing knowledge as "erroneous" or "correct" may be ill advised. Information taken as knowledge, especially in a business environment, may have varying degrees of "correctness." For example, "knowledge" that decreasing prices will increase sales volume may be "correct" under certain economic conditions, but "less correct" under others. A better approach may be to follow Mason's [33] example and classify knowledge on a continuum from internally valid to externally valid. Internally valid knowledge may be created under very controlled conditions but have relatively narrow applicability. Externally valid knowledge may be more widely applicable but also less rigorous in its internal consistency. Knowledge creation and organizational learning feedback processes rely on the social perspective. This perspective has been largely ignored in information systems research. With socially oriented theories such as Adaptive Structuration [13] being examined more recently, the inter-relationships of knowledge creation, organizational learning, and the organization as a social entity should be carefully considered. A comprehensive KMS is likely to affect changes within the organization, and inter-organizational communication will begin to play a larger role as we move toward a more global economy. With executives being less tied to decision-making processes, more time may be spent defining the organization's goals. The impact of this system on the organization’s structure, processes, and communication modes should be examined.
8. Summary Current systems such as DSS are not flexible enough to sustain knowledge creation and management in a way that will allow an organization to attain full competitive advantage. Organizations fail to learn because the characteristics inherent in traditional DSS do not allow for problem formulation/resolution at a variety of levels, the necessary feedback loops, nor do they allow for infusion of environmental variables into the problem-solving equation. DSS enhanced with inquiring systems characteristics overcome these constraints by providing for, among other things, problem formulation/resolution at a variety of levels, consideration of environmental variables, and continuous feedback loops. An important part of any new system, especially one so complex, is gaining organizational acceptance. DSS are a known and accepted system in the business environment. Demonstrating the similarities between DSS and IS is the first step in allowing a complex support system to gain acceptance within the organizational
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community. Learning organizations equipped with a complex support structure designed using both DSS and IS will be better equipped to withstand the pressures inherent in a rapidly changing environment and as the new global economy moves from a production orientation to a service orientation. This paper suggests that decision support will be enhanced by integrating the theory of decision support in a comprehensive inquiring system that is capable of adapting to changes in the business environment. The philosophical bases on which this system is built provide decision makers with decision-making support under all problem definitions. The philosophical bases provide flexibility through varied conceptualizations of knowledge, and validity by using comprehensive guarantors. Systems such as these can form the foundation upon which learning organizations can be built. A learning organization can utilize support systems designed in this manner to create, accumulate, and manage knowledge critical to its core competencies. Development of such a system will ensure that decision makers and managers can focus on the task of guiding an organization to its ultimate success rather than expending energy sorting through information to make accurate and timely decisions. This support system can offer expedient and accurate problem solving assistance in most organizational environments. Development of learning capability will be critical to the ultimate success of organizations, both today and into the future.
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