Knowledge-Based Systems

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1 -Based Systems 22 (2009) Contents lists available at ScienceDirect -Based Systems journal homepage: An measurement model for knowledge management Yuan-Feng Wen * Department of Logistics Management, National Kaohsiung Marine University, 142 Haijhuan Rd., Nanzih District, Kaohsiung City 81157, Taiwan, ROC article info abstract Article history: Received 26 November 2005 Received in revised form 12 August 2008 Accepted 17 February 2009 Available online 26 February 2009 Keywords: management Measurement Analytic hierarchy process (AHP) The aim of this study is to develop a model for the measurement of the of knowledge management in Taiwanese high-tech enterprises. Following a survey of the relevant literature on the subject, the study describes the construction of the model including the opinions of specialists, scholars, and practitioners of knowledge management practice among Taiwan s high-tech firms, the use of focus groups, the application of analytic hierarchy process (AHP), and a questionnaire analysis of qualitative and quantitative methods. The study summarizes the experts opinions, selects the measurement indicators, and calculates the weightings of dimensions and items. An empirical study is then conducted to test the validity and reliability of the model, and its suitability for improving the measurement of knowledge management in high-tech firms. Ó 2009 Elsevier B.V. All rights reserved. 1. Introduction 2. Literature survey In today s competitive business environment, knowledge management (KM) is increasingly recognized as a significant factor in gaining a competitive advantage [8,9,14 16,18,23]. To obtain such a competitive advantage, companies must know how to manage organizational knowledge by expanding, disseminating, and exploiting it effectively [6,21]. Assessing the of KM operations is thus an important issue, but the measures that are available to evaluate the of KM are generally unsatisfactory. There is a need to develop an assessment model that can be used to make an accurate assessment of the of KM. This paper establishes such a model. The model can be used to evaluate KM performance in terms of the control and coordination of organizational knowledge. The steps adopted in the construction of the model include: (i) consulting with specialists, scholars, and practitioners in knowledge management among Taiwan s high-tech enterprises; (ii) obtaining the views of a focus group; (iii) the application of analytic hierarchy process (AHP); and (iv) a questionnaire survey of qualitative and quantitative methods used in KM assessment. The validity and reliability of the model is then tested through empirical tests in high-tech firms. An objective and comprehensive KM assessment model is thus established and presented for use by organizations engaged in knowledge management. * Tel.: x3461; fax: x address: ywen@mail.nkmu.edu.tw KM can be defined as the creation, acquisition, sharing, and utilization of knowledge for the promotion of organizational performance [12]. Arthur Anderson Business Consulting [4] proposed a schematic representation of the relationships among data, information, knowledge, and wisdom (see Fig. 1), and stated that data, information, and knowledge are necessary for dealing with regular affairs, whereas wisdom is necessary for dealing with irregular affairs and adopting appropriate actions when faced with a changing environment. According to their view, KM not only manages knowledge, but also encourages individuals to utilize knowledge effectively while working. The term performance refers to a measurement of extent to which an organization reaches a given objective; and the term operational performance refers to the measured effect of each operational variable within overall performance. To evaluate the operational performance of KM, the American Productivity and Quality Center [3] and Arthur Anderson Business Consulting [4] developed a knowledge management assessment tool (KMAT) in KMAT can be used by enterprises to select the appropriate type of KM. KMAT is composed of five fundamental elements: (i) strategy and leadership; (ii) culture; (iii) technology; (iv) measurement; and (v) knowledge management process. Four key success factors were proposed: (i) procedures of KM adopted; (ii) persons involved in KM; (iii) supporting organizational structure for KM; and (iv) information technology utilized in KM. However, at present, the structure, processes, and procedures of KM have not been defined as a concrete standard, and it is difficult to find comprehensive and explicit reference criteria. Andrew et al. [2] identified a strong relationship between organizational and KM capability (see Fig. 2) /$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi: /j.knosys

2 364 Y.-F. Wen / -Based Systems 22 (2009) Information Data Allee [1] classified organizational KM activities as: (i) knowledge creation; (ii) knowledge retention; (iii) knowledge sharing; and (iv) knowledge innovation. This classification can be further extended as organizational KM activity measures (as shown in Table 1). Allee [1] was only focused on the organizational KM activities without considering the of organizational activity. Hoy and Miskel [11] felt that measurement of the of organizational activity could be classified into four phases: (i) knowledge adaptation ; (ii) knowledge achievement ; (iii) knowledge integration ; and (iv) knowledge potential (as shown in Table 2). Therefore, the four phases shown in Table 3 are used as a basis for the measurement of in this study. Fernandez and Sabherwal [5] also proposed some measures of KM. Sveiby [20] presented an operational of KM. Some assessment tables were suggested by Storck and Hill [19], Levett and Guenov [13], and Gold et al. [7]. Wen [22] has also provided some measures of KM. Housel and Bell [10] pointed out that the most important index is the feedback received. The above-mentioned measures are taken into account in this study in developing a measurement index of KM for Taiwan high-tech enterprises. 3. Data analysis methodology Create value via action and application Direct material of value creation Arrange and transmit intention with purpose Illustrate the truth quantitatively and qualitatively Fig. 1. Schematic representation of relationships among data, information, knowledge, and wisdom (Arthur Andersen Business Consulting [4]). This study analyzes qualitative data obtained from: (i) focus group interviews and (ii) consultation with specialists, scholars, and practitioners in knowledge management practice in Taiwan s Technology Structure Infrastructure Capability Table 1 Organizational KM activity measures. Criteria creation retention sharing innovation high-tech firms. These qualitative data are collected, compared, and analyzed to reach a conclusion. Quantitative data are obtained from questionnaires and are analyzed statistically by computer using analytic hierarchy process (AHP), simple additive weight (SAW), and descriptive statistics. 4. Establishment of KM assessment model 4.1. Developing the performance hierarchy The first step of AHP consists of developing a hierarchical structure of the assessment problem. The first level in the present hierarchy is KM. This is divided in the second level into Table 2 Organizational KM measures. measures of knowledge management Criteria adaptive achievement integration Potential of knowledge generation acquiring arrangement storing collection presentation analysis classification socialization contribution distribution instruction changing improving extending deepening adaptability innovation growth and development achievement quality efficiency satisfaction climate communication loyalty inspiring identification Culture Acquisition Organizational Table 3 Judgment scores for the importance/preference of criteria using AHP. Conversion Application Protection Process Capability Fig. 2. Schematic representation of relationship between knowledge capability and organizational [2]. Verbal judgment Extremely important/preferred 9 Very strongly to extremely important/preferred 8 Very strongly important/preferred 7 Strongly to very strongly important/preferred 6 Strongly important/preferred 5 Moderately to strongly important/preferred 4 Moderately important/preferred 3 Equally to moderately important/preferred 2 Equally important/preferred 1 Numerical rating

3 Y.-F. Wen / -Based Systems 22 (2009) Goal KM H1 (0.062) H2 (0.058) H3 (0.020) H4 (0.012) H5 (0.038) Criteria Staff Data Information Staff (0.223) Alternatives Measures H6 ( ) Fig. 3. Hierarchy for KM. I1 (0.078) I2 (0.047) I3 (0.049) five main criteria: (i) human resources; (ii) information; (iii) data; (iv) knowledge; and (v) wisdom. The third level consists of 30 alternatives. (Fig. 3) shows the performance hierarchy designed for this problem. Information (0.277) I4 (0.034) I5 (0.036) 4.2. Pair-wise comparison of criteria and calculation of relative weights In the past, an equal weight for each measure was commonly used to assess the of KM; however, the present study posits that the importance of each measure is not equal in practice. According to the AHP methodology, weights can be determined using a pair-wise comparison within each pair of criteria. To determine the relative weights, experts or scholars were asked to make pair-wise comparisons using a 1 9 preference scale (see Table 3). Each comparison was then transformed to a numerical value. The pair-wise comparison data were organized in the form of a matrix and summarized on the basis of Saaty s eigenvector procedure [17]. Saaty s method computes w as the principal right eigenvector of the matrix A. The pair-wise comparison data are translated into absolute values and the normalized weight vector w ¼ðw 1 ; w 2 ;...; w n Þ is obtained by solving the following matrix equation: Aw ¼ k max w; where A is the pair-wise comparison matrix, w is the normalized weight vector, and k max is the maximum eigenvalue of the matrix A (used to calculate the consistency index) ð1þ Model of KM Measurement Data (0.103) (0.184) I6 (0.033) D1 (0.025) D2 (0.015) D3 (0.009) D4 (0.022) D5 (0.015) D6 (0.017) K1 (0.037) K2 (0.029) K3 (0.061) K4 (0.026) K5 (0.019) K6 (0.012) k max ¼ Xn j¼1 a ij w j w i : The result is a positive reciprocal matrix A ¼fa ij g with a ij ¼ 1=a ij, where a ij is the numerical equivalent of the comparison between criteria i and j. A judgment or comparison is made on the basis of a numerical representation of the relationship between two elements that share a common parent [17]. Each judgment represents the dominance (relative importance) of an element in the column over an element in the row. It is obvious that each criterion in level 2 should contribute differently to excellence in human performance. The experts were asked to compare the relative importance of the three criteria on a pair-wise scheme. From the data, a square pair-wise comparison matrix was constructed. Each judgment represents the dominance (relative importance) of an element in the column over an element in the row. To assist in the pair-wise comparisons, Saaty [17] created a nine-point scale of importance between two elements. The suggested numbers express degrees of preference between the two elements, as shown in Table 3. Intermediate values (2, 4, 6, and 8) can be used to represent compromises between the preferences. The relative priorities can be considered as the results of using the geometric mean of the pair-wise relative importance obtained from a set of participants. ð2þ (0.213) Fig. 4. Structure of hierarchy. AHP s results are obtained using the computer program entitled: Saaty the expert choice software package [17] Overall ranking of alternatives W1 (0.054) W2 (0.051) W3 (0.039) W4 (0.021) W5 (0.019) W6 ( ) After setting up the hierarchy and pair-wise comparison of the criteria and alternatives, it was necessary to calculate the global value of priority of the alternatives. Saaty [17] offered a proof to show that the optimal set of scores is the principal eigenvector of the pair-wise comparison matrix. The principal vector is the relative ranking of the evaluation criteria with respect to the goal. Applying Saaty s eigenvector method to the data, estimates of the weights were calculated for each pairwise comparison matrix for each level of hierarchy. To synthesize

4 366 Y.-F. Wen / -Based Systems 22 (2009) the results over all levels, the priorities at each level were weighted by the priority of the higher-level criterion with respect to which the comparison was made. The eigenvector scaling technique of AHP then models the relative weights for each category (priorities) and for each ratio (local weights). Global weights for each ratio were calculated as the product of its local weight and its category s priority. There were 5 criteria and 6 alternatives, and then there were 5 principal vectors, all of which had 6 elements. Once the matrices in each level were completed, the relative importance of the elements in that level was given by the principal right eigenvector of the matrix of judgments. The number of eigenvectors (that is, local priority vectors) was therefore equal to the number of criteria. The results quantified the decision-maker s preference for each alternative and provided a means for answering the type-of-management question. The procedures used to solve this hierarchy system were as follows. A pair-wise comparison was made of the criteria with respect to the goal. In the problem there were 5 criteria in level 2. The experts made three pair-wise judgments among 10 with respect to level 1. After the construction of the pair-wise comparison matrix, the next step was to retrieve the weights of each element in the matrix. The comparison results and the weights of three criteria are shown in Fig. 4. The principal vector was computed and can be interpreted as the relative importance of each of the criteria. A pair-wise comparison was made of the 30 alternatives in level 3 with respect to 5 criteria in level 2. The comparison and the relative contributions (that is, weights) among 30 alternatives with respect to the three criteria results are shown in Fig. 4. The final stage of the AHP was to compute the contribution of each alternative to the overall goal (that is, of KM). The overall priority for each alternative was obtained by totalling the product of the criteria weight and the contribution of the alternative, with respect to that criterion. The final weights were obtained, and a ranking was made of the alternatives with respect to the goal. The results are shown in Fig Measurement of pair-wise comparison consistency To control the result of the method, the consistency ratio for each of the matrices and overall inconsistency for the hierarchy were calculated. The deviations from consistency are expressed by the following equation, and the measure of consistency is called the consistency index (CI) CI ¼ k max n n 1 : ð3þ The consistency ratio (CR) is used to estimate directly the consistency of pair-wise comparisons. The consistency ratio (CR) is computed by dividing the CI by a value obtained from a table of the random consistency index (RI). CR ¼ CI RI : ð4þ If the CR is less than 0.10, the comparisons are acceptable. For this application, all CR inconsistency ratios values were less than 0.1 (CR < 0.1); therefore, all the judgments were consistent. Each alternative possesses a score on all criteria. The criteria scores were combined into an overall score. The overall score indicates the relative importance of each alternative. In the past, most of the relative weights of a KM measurement index were said to be of equal weight. However, in Table 4 The weight of each index (overall weights). practice, the importance of each KM measurement index is not exactly the same. Therefore, 10 experts were asked to answer an AHP questionnaire and the relative weights of KM were compared Results of the AHP application Following the procedure described above, the relative ranking of the five criteria was then determined to facilitate the selection of the best alternative. On the basis of the preference vectors, the five criteria of KM were ranked as follows: information (0.277), staff (0.223), wisdom (0.213), knowledge (0.184), and data (0.103) (see Fig. 4 and Table 4). To synthesize the opinion of academic and enterprise experts, information and staff were the most important criteria. Rate of staff severance was the most important alternative among those in the Staff criterion. Respect for intellectual property rights was the most important alternative among those under the criterion of wisdom Calculation of KM Weights Staff (H1 H6) 1. Rate of staff severance (H1) Work attitude of staff (H2) Average work seniority of staff (H3) Number of staff who have obtained professional a certificate (H4) Number of KM staff (H5) Cost of education & training (H6) Information (I1 I6) 1. Flow and utilization rate of network (I1) Periodical evaluation and knowledge update rate (I2) Degree of information network system construction (I3) Coordinating and integrating through interior information (I4) Information management capability (I5) Information resources application and data completeness (I6) Data (D1 D6) 1. Time validity of customer complaint response Number of improvement proposals by staff Information-based degree of knowledge document standardization Plan of KM Establishment of customer knowledge database (K1 K6) 1. Return rate of innovation Acquiring and utilizing extent of knowledge Support of KM by high-level superintendent sharing on staff Repaid by superintendent for working performance Contribution of technology innovation (W1 W6) 1. Respect for intellectual property rights Innovation capability of staff Investment on professional staff Number of staff who obtained qualified international accreditation Operating income of innovation staff Number of patent obtained After obtaining the weights of constructs and criteria of KM, 30 indices were normalized to obtain a standardized value (that is, Z value). The purpose of normalization of these indices was to deal with the problem caused by different units. The Z value is between 0 and 1. The sum of each index multiplied by the corresponding weight was the standardized value of constructs. The sum of each construct multiplied by the corresponding weight created a score of KM.

5 Y.-F. Wen / -Based Systems 22 (2009) Table 5 Rank of the KM for 76 high-tech enterprises in Taiwan. Rank Company no. KM score Rank Company no. KM score The weights of indices and constructs obtained using the AHP method were combined with the simple additive weight (SAW) to construct the KM assessment model. By using a linear combination, the score of the KM assessment model can be calculated by using the following equation: A i ¼ X W ij Z ij ; where Z ij is normalized value of the ith construct and the jth index; W ij is the relative weight of the ith construct and the jth index; and A i is the score of the ith construct E ¼ X5 i¼1 W i A i ; where A i is the standardized value of the ith construct; W ij is the relative weight of the ith construct; and E is the total score of KM of Taiwan s high-tech enterprises. A total of 350 high-tech firms in Taiwan s Hsin-Chu Industrial Park were selected in the empirical sample using cluster random sampling. The people who answered the questionnaire were senior managers in the firms KM departments or administrative departments. The ranking of the KM from the total of 76 valid questionnaires obtained from 85 companies is shown in Table Conclusions The results from the AHP method and the synthesizing of 10 experts opinions have shown the following rankings in order of importance: information (0.277); staff (0.223); wisdom (0.213); knowledge (0.184); and data (0.114). Information and ð5þ ð6þ staff were thus the most important constructs in KM practice. Flow and utilization rate of network (I1) was the most important index in information. Rate of staff severance (H1) was the most important index in staff. Such results show that the quantity and quality of staff are recognized as the most important factors in the operation of KM. In the past, few studies have undertaken a systematic investigation of the of the KM process. This subject is strongly related to a range of output variables, including productivity, organizational performance, competitive advantage, and competitiveness. In the establishment of an assessment model of KM, there are several problems to be overcome including: (i) KM has multiple objectives; (ii) KM is difficult to evaluate; (iii) KM is associated with fuzzy problems; and (iv) KM is related to individual cognitive behavior. To establish a comprehensive and integrated assessment model, the opinions of experts with different interests from various areas of expertise have to be merged. In addition to utilizing the related literature, this study has used focus groups, analytic hierarchy process (AHP), and questionnaire analysis. These qualitative and quantitative methods have been integrated to find the relative weights of different levels and indices. The final model consists of 5 major constructs and 30 indices with ensured validity. References [1] V. 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