ارزیابی قابلیت مدیریت دانش در سازمانها: یک روش زبان شناسی فازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|12581||2009||9 صفحه PDF||سفارش دهید||5600 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 2, Part 2, March 2009, Pages 3346–3354
nowledge management capability (KMC) is the source for organizations to gain the sustainable competitive advantage. KMC evaluation is a required work with strategic significance. However it still has not been addressed in the existing literatures. So the objective of this study is to investigate a fuzzy multiple attributes decision-making method (FMADM) for evaluating KMC. In this paper, a framework for evaluating KMC is presented, which includes two parts, one is an evaluation hierarchy with attributes, the other a judgment matrix model with two dimensions to identify the evaluation results of KMC. Then, a fuzzy linguistic approach is proposed to evaluate the KMC of organizations. The evaluation results of KMC obtained through the proposed approach are objective and unbiased due to two reasons. Firstly, the results are generated by a group of experts in the presence of motile attributes. Secondly, the fuzzy linguistic approach employed in this paper has more advantage to reduce distortion and losing of information than other fuzzy linguistic approaches. Through evaluation result of KMC, managers could judge the necessity to improve the KMC and determine which dimension of KMC is the most needed direction to improve. Additionally, an example is used to illustrate the availability of the proposed method.
Knowledge management (KM) has been described for its possible role in creating sustained competitive advantages for organizations (Chuang, 2004, Grant, 1996, Johannessen and Olsen, 2003 and Nonaka and Takeuchi, 1995). The contributions of KM to competitive advantage may include: improved ability of innovation, improved coordination of efforts and rapid commercialization of new products. Other contributions may include: the ability to anticipate surprise, responsiveness to market change, and reduced redundancy of information/knowledge. So, many organizations are making extensive KM efforts. Unfortunately, many KM projects are, in reality, information management ones. When these projects yield some consolidation of data but little innovation in products and services, the concept of KM is cast in doubt (Gold, Malhotra, & Segars, 2001). The main reason for this problem is that organizations may not identify and assess the preconditions that the efforts to KM are necessary. Therefore, organizations cannot understand the success and failure of KM within organizations. These preconditions are described broadly as ‘capability’ or ‘resources’ within the organizational behavior literature (Kelly and Amburgey, 1991, Law et al., 1998 and Leonard, 1995). There has been much research dealing with KM capability (KMC). Desouza (2003) argued that the ideal organization with well-matured KMC can ensure the identification, distribution, protection, application and destruction of knowledge. Therefore, KMC is the key to preempting an organizational crisis. Lubit (2001) argued that tacit knowledge and superior KMC are now the keys to sustainable competitive advantage in many industries. Liu, Wen, and Tsai (2004) examined the association between KMC and competitiveness by empirical study. The result reveals that KMC has a tremendous effect on organizational competitiveness. KMC is considered more than a catch-all for information and knowledge. It is a tool for maintaining information and knowledge that will help employees to work more efficiently (Liu et al., 2004). Collinson (2001) emphasized the significance of contextual factors for transferring some KM practices by case study. Bresnen, Edelman, Newell, Scarbrough, and Swan (2003) examined the significance of social factors in enhancing KMC in project environments by case study. Gold et al., 2001 and Chuang, 2004 presented and validated the framework for analysis of KMC using different attributes. Thus, many efforts have been made to emphasize the significance of KMC, and analyze and explore the attributes of KMC. However, the evaluation of KMC with the qualitative multi-attributes has seldom been addressed. Indeed, there are many approaches that can be used to evaluate the KMC. For example, scoring tool may be the simplest approach to evaluate the KMC. However, usually, most experts can not give exact numerical values to express their opinions based on human perception. More realistic measurement is to use linguistic assessments instead of numerical values (Beach et al., 2000, Gerwin, 1993, Herrera and Herrera-Viedma, 2000, Kacprzyk, 1986 and Vokurka and O’Leary-Kelly, 2000). Attributes can be measured as linguistic labels (or terms) such as ‘very high’, ‘high’, ‘middle’, ‘low’, and ‘very low’ (Wang & Chuu, 2004). After Zadeh (1965) introduced fuzzy set theory to deal with vague problems, linguistic labels have been used within the framework of fuzzy set theory (Zadeh, 1975a, Zadeh, 1975b and Zadeh, 1976) to handle the ambiguity in evaluation data and the vagueness of linguistic expression (Wang & Chuu, 2004). Therefore, the purpose of this study is to establish an evaluation framework of KMC for organizations and to investigate a fuzzy linguistic approach to evaluate the KMC in a fuzzy environment. Section 2 presents an evaluation framework of KMC for organizations, in which, the dimensions and attributes of KMC are introduced and a judgment matrix model is presented. Based on the characteristics of dimensions and attributes discussed in Section 2, a fuzzy linguistic approach is then proposed to evaluate the KMC of organizations in Section 3. Section 4 illustrates the proposed method with an example.
نتیجه گیری انگلیسی
The proposed fuzzy linguistic method based on 2-tuple linguistic representation model has the advantages that include avoiding loss and distortion of experts’ assessment information, obtaining the computation results as linguistic labels and simplifying the calculation process. It is appropriate for the situations in which assessment information is qualitative, or the precise quantitative information is unavailable or the cost of its computation is too high. The approach seems to be complex, but the calculation process and principle are actually very easy. The comparative analyses between this fuzzy linguistic approach and others are illustrated in detail in the research of Herrera and Martinez (2000). However, the approach is limited in that experts must perfectly distinguish the set of labels under a similar conception, and must use linguistic labels to express their opinions. The above method with the group evaluation structure in the presence of multiple attributes, used to evaluate the KMC of organizations, is very useful in KM initiation. If the KMC is too low according to the evaluation results, it should be improved until acceptable. The dimensions of KMC needed improvements can be determined by the judgment matrix model. Some distinguished contributions of this study are as follows: 1. Based on the evaluation hierarchy, a judgment matrix model for KMC is presented. The evaluation results of the KMC can be visualized in the matrix model. Thus, the manager could easily judge the result by it. Only when the result locates at the quadrant IV, the KMC of an organization is high. According to the evaluation result, managers could know the necessity to promote the KMC and determine which dimension should be improved. 2. A multiple criteria fuzzy linguistic approach with 2-tuple linguistic representation model is proposed to evaluate the KMC of organizations. The 2-tuple linguistic representation model is newly developed and has been applied successfully to several areas such as engineering evaluation, SCM performance evaluation and competences evaluation, etc.