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Introduction In recent years, knowledge management has become a critical subject of discussion in the business literature. Both business and academic communities believe that by leveraging knowledge, an organization can sustain its long-term competitive advantages. The resource based view (RBV) of organizations and competencies perspectives highlight the reflection of this changing trend in the business strategy arena (Nelson and Winter, 1982). Although management is aware of the potential that can be realized from knowledge resources, there is not a consensus about the characteristics of knowledge and the ways these knowledge resources should be used. Researchers and academics have taken different perspectives on knowledge management, ranging from technological solutions to the communities of practices, and the use of the best practices. For example, a majority of business managers believe in the power of computers and communication technologies in knowledge management, as they argue that information technology (IT) can provide an edge in harvesting knowledge from piles of old buried data repositories, consisting of point of sales (POS), customer credit cards, promotional sales, and seasonal discount data. Some others, however, contend that knowledge resides in human minds and, therefore, employee training and motivation are the key factors to knowledge management. This paper takes a comprehensive view on knowledge and argues that defining knowledge management through technological or social systems alone engenders the bias in overemphasizing one aspect at the expense of the other. As we will show later, technologies and social systems are equally important in knowledge management. The conversion between data and information is efficiently handled through information technologies, but IT is a poor substitute for converting information into knowledge. The conversion between information and knowledge is best accomplished through social actors, but social actors are slow in converting data to information. That is one of the reasons we believe that knowledge management is best carried out through the optimization of technological and social subsystems. The roots of this view can be found in the sociotechnological perspective of the organization (Emery, 1959, 1967; Trist, 1981; Trist and Bamforth, 1951). Despite the fact that a number of researchers highlight the competitive advantages of 3M, Hewlett-Packard, Buckman Laboratories, Scandia AFS, and Xerox as a result of knowledge management projects, they do not clearly describe the principles and procedures of knowledge management. This paper clarifies the concept of knowledge management and shows why technological as well as social systems become critical in knowledge management. This paper makes important contributions to academic and business circles. The academic community is beginning to consider organizations as repositories of knowledge. The competitiveness of organizations is determined by organizational capabilities and core-competencies. By focusing on knowledge management, we hope to strengthen the knowledge-based view of the firms. To managers, this research is important for two reasons. First, while they have heard a lot of discussion on knowledge management, they are baffled with divergent perspectives carried on knowledge management. Seeing that, in the present time, most jobs are becoming ever more information intensive, and a majority of employees are moving to these industries, this paper provides a theoretical framework on knowledge management. Second, by emphasizing the capabilities of information technologies such as Internet, intranet, and telecommunications, and social systems such as employee training and motivation, this paper explains why an understanding of knowledge management has become much more important. The outline of the paper follows. The paper begins by describing data, information, and knowledge. Next, we explain the concept of knowledge management. Later, we describe the importance of technological and social systems in knowledge management. The paper ends by describing the major implications and the conclusion of the study. Data, information, and knowledge Defining data, information, and knowledge is difficult. Only through external means or from a user's perspectives, can one distinguish between data, information, and knowledge. In general, data are considered as raw facts, information is regarded as an organized set of data, and knowledge is perceived as meaningful information. This paper posits the idea that the relationship between data, information, and knowledge is recursive and depends on the degree of the "organization" and the "interpretation" as shown in Figure 1. Data and information are distinguished based on their "organization", and information and knowledge are differentiated based on the "interpretation". To understand this difference, let us take an example of a patient's visit to a doctor's office. The doctor elicits a lot of "information" from the patient. Some of this information becomes relevant as the doctor considers it important for the medical diagnosis of the patient. Some of the information elicited by the patient, however, is irrelevant for the doctor and becomes "data". The doctor quickly assimilates the acquired information in his (her) "knowledge base", and after finding a useful pattern in the information prescribes medication to the patient. If the doctor is unable to find a relevant pattern in the information, the doctor may recommend further lab-tests, and/or refer the patient to a specialist, who may be in a better position to find a useful pattern in the information. Let us take the following possibilities now. If the doctor recommends the patient for some lab-tests, he (she) may try to elicit more information from the patient and may find some other pieces of information through the lab-tests. The information acquired through the lab-tests may confirm or disconfirm the doctor's initial hypotheses about the diagnosis. It may also happen that the preliminary analysis of the "data" (which was insufficient and incomplete without lab-tests) could be quite relevant to the doctor for medical diagnosis of the patient. The point is that the doctor moves back and forth, recursively, between data, information, and knowledge. If the doctor recommends the patient to a specialist, the specialist might elicit quite a different sort of information. It could also happen that the specialist may find some pieces of information quite relevant, which were earlier discarded by the doctor in making his (her) preliminary diagnosis of the patient.
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