Abstract
Purpose
The purpose of the paper is to develop a model for the selection of knowledge management system (KMS), in which the assessment criteria are defined and the TOPSIS method with multiple distances in fuzzy environment is proposed.
Design/methodology/approach
First, the paper establishes the evaluation criteria from functional, performance and economic aspects. Second, a new TOPSIS method is proposed to deal with the linguistic evaluation information. In the proposed method, in order to eliminate the bias of TOPSIS with single distance, six kinds of distances that are commonly used in TOPSIS including Hamming distance, Euclidean distance, Dp,q distance, Hausdorff distance, L2 distance and vertex distance are extended in fuzzy environment and employed in the TOPSIS to generate six independent pre-rankings. Afterwards these pre-rankings are combined by Condorcet method to generate the final joint ranking.
Findings
Since the final ranking is the collective result, the bias in each single pre-ranking is eliminated and the selection is more objective and accurate. The example shows the proposed model is practical.
Research limitations/implications
The linguistic preferences are given in the single granularity linguistic information.
Practical implications
The proposed model can be applied as a tool for decision makers in the evaluation and selection of KMS.
Originality/value
The paper gives an overall evaluation of KMS and proposes the new TOPSIS method with multiple distances in fuzzy environment.
Keywords
Citation
Li, M. (2013), "A multi-criteria group decision making model for knowledge management system selection based on TOPSIS with multiple distances in fuzzy environment", Kybernetes, Vol. 42 No. 8, pp. 1218-1234. https://doi.org/10.1108/K-05-2013-0095
Publisher
:Emerald Group Publishing Limited
Copyright © 2013, Emerald Group Publishing Limited
1 Introduction
The changing social and business environment are characterized by uncertainty and dynamism (Gray and Meister, 2006). The globalization of market economics has shortened the life cycles of products and has stimulated technological innovation (Löffler et al., 2011). Reacting to diversifying and fast-changing market needs is a significant challenge to a company. The nature of engineering product development within modern organizations has changed dramatically over the past few decades as products have become more complex, especially the knowledge intensive products (McMahon et al., 2004). For example, within the aircraft industry, integrated product development processes based multi-functional product teams are becoming the norm. Competing in such an environment requires companies to effectively leverage the knowledge to shorten the development cycles of products. Information technology has been used for knowledge creation, storage, maintenance, sharing and application to assist in knowledge-intensive work (Chang et al., 2011; Christine and Michael, 1996; Li et al., 2011a, b; Liu and Lin, 2012; Sher and Lee, 2004).
Knowledge management system (KMS) refers to the computer information system employed to better retain and utilize organizational knowledge, as well as support knowledge utilization within and between organizations (Li et al., 2011a, b). Organizations are devoting considerable resources to implementing KMS to improve the efficiency of engineering product development. However, many of such investments end in less than desirable outcomes possibly due to a mismatch between the KMS and the organization requirements (Nevo and Chan, 2007). The functions provided by the KMS may not be the needs of the organization. For example, some KMS focuses on the knowledge sharing but neglects the access control. This kind of KMS is not fit for the security departments since access control is more important than knowledge sharing in these departments. Therefore, the selection of suitable KMS for the organization is necessary. Since the evaluation of KMS from various aspects is the complex task, models and tools are needed to assist the decision makers in evaluating and selecting KMS. Although KM-related issues have received fairly extensive attention in previous researches (Hahn and Wang, 2009; Lee and Chen, 2010; Lin et al., 2009; Mothe et al., 2006; Nie et al., 2009; Serenko et al., 2010; Xu and Bernard, 2011; Wei and Zhi, 2009; Wen, 2009), little attention has been paid to the selection of KMS.
The multi-criteria decision making (MCDM) method is a systematic method for incorporating multiple criteria into the process by evaluating, comparing and rating different alternatives. It has been wildly used in engineering such in industrial engineering and manufacturing systems (Chiou and Tzeng, 2002; Kahraman et al., 2007; Karsak, 2002; Li, 2012; Kodali and Anand, 2010; Sari, 2013), energy engineering (Boran et al., 2012; Rouhani et al., 2012), aerospace mechanical engineering (Hsia et al., 2008), bioengineering (Nik et al., 2009), computer engineering (Jain et al., 1991; Ribeiro et al., 2011), chemical engineering (Jia et al., 2004; Pirdashti et al., 2009) and construction engineering (Hsieh et al., 2004). Many aspects in engineering cannot be assessed in a quantitative form but rather in a qualitative way, i.e. with vague or imprecise knowledge. For example, when evaluating airline safety, linguistic labels like “high”, “medium”, “low” are used (He et al., 2010). In the condition, decision makers often provide their evaluation information in a linguistic form and decisions have to be made in the fuzzy environment. The technique for order of preference by similarity to ideal solution (TOPSIS) is a popular MCDM method. The main principle is that the chosen alternative should have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. Kinds of distances are used in TOPSIS, such as Euclidean distance (Sun, 2010), Dp,q distance (Chen, 2000), Hamming distance (Iraj et al., 2008), vertex distance (Mohammad, 2009), L2 distance (Park et al., 2011) and Hausdorff distance (Li and Wang, 2007). The kind of distance has great influence on the ranking result of alternatives (Wang, 2011), that is, the different order of the alternatives may be derived from the TOPSIS with different kinds of distances. However, in the application of TOPSIS for MCDM problems, it is difficult to determine which kind of distance is fittest. In order to eliminate the bias of TOPSIS with single distance and improve the ranking performance in fuzzy linguistic environment, we propose the new TOPSIS method in fuzzy environment. In the new method, six kinds of commonly used distances in TOPSIS are extended in fuzzy environment to generate the pre-rankings and these pre-rankings are combined to derive the joint final ranking of the alternatives. Since the final ranking is the collective result, the bias in each single pre-ranking is eliminated.
The objective of this paper is to establish the model for the selection of KMS among a set of available alternatives. In the model, besides the construction of the MCDM method, the criteria for KMS selection also needs established. We need to evaluate the characteristics of KMS from the technology perspective and the costs from the economic perspective. To make the KMS been evaluated fully and directly, the comprehensively criteria for the KMS selection need to be established.
To do that, the rest of this paper is organized as follows. The next section reviews the basic concepts of TOPSIS, Condorcet method and fuzzy linguistic method. Section 3 establishes the evaluation criteria for KMS selection. Section 4 proposes the new TOPSIS method with multiple distances. In Section 5, an example is given and the results are analyzed. The final section makes conclusions.
2 Preliminaries
2.1 TOPSIS
TOPSIS is proposed by Hwang and Yoon (Zadeh, 1965). It is assumed that the best alternative should have the smallest distance from the ideal solution and the farthest distance from negative solution. The TOPSIS method could be presented as following steps (Hwang and Yoon, 1981).
Step 1. Calculate the normalized decision matrix.
The normalized value n ij is calculated as: Equation 1 Step 2. Calculate the weighted normalized decision matrix.
The weighted normalized value v ij is calculated as: Equation 2 where w j is the weight of the ith criterion, and ∑ j=1 n w j =1.
Step 3. Define the positive ideal solution (PIS) A+ and negative ideal solution (NIS) A− as: Equation 3 where, for benefit criterion: Equation 4 Equation 5 for cost criterion: Equation 6 Equation 7 Step 4. Calculate the distances d i + and d i − of each alternative from PIS and NIS using the following equations, respectively: Equation 8 where, dis(v ij −v j +) is the distance between evaluation value of alternative i and FPIS on the j criterion, dis(v ij −v j −) is the distance between evaluation value of alternative i and NPIS on the j criterion.
Step 5. Calculate the relative closeness to the ideal solution.
The relative closeness R i of the alternative A i with respect to A+ is defined as: Equation 9 According to the relative closeness degree R i , the ranking order of the alternatives can be determined. If any alternative has the highest R i value, then, it is the most desirable alternative.
2.2 Condorcet method
Condorcet method is taken from social theory of voting. It has been used in the data fusion to combine ranked lists (Dağdeviren et al., 2009). It is defined as a mapping from a set of individual rankings to a combined ranking leading to the most relevant decision. It compares every possible pair of candidates to decide the preference of them (Nuray and Can, 2006). The matrix is used to present the pairwise comparison. If there are m alternatives, then we need m2 elements in the matrix in total. Initially 0 is written to all the elements. If A i is preferred to A j , then we add 1 to the element at row i and column j. This is done over and over until all the individual rankings are processed. Afterwards, for each alternative, all the scores are added up and the alternative with the highest scores is the best alternative.
Let A={A 1,A 2, … ,A q } be a set of alternatives. Suppose that B=(b ij ) q×q is the Condorcet matrix, which represent the pairwise comparison, where b ij is the score for alternative A i over A j .
In order to get the final score of the each alternative, each row is summed as the final score. S i represents the final score of the alternative A i , which can be calculated as follows: Equation 10 Finally, the alternatives are re-ranked in descending order of final scores.
2.3 Fuzzy set theory
Fuzzy set theory is a commonly used method to deal with the linguistic information. It is an extension of ordinary set theory for dealing with uncertainty and imprecision associated within formation. It is possible to use different fuzzy numbers depending on the situation. In applications, since triangular fuzzy numbers is computational simplicity and is an effective way for formulating decision problems in linguistic environment, it is adopted in the study. In the following, the preliminary of fuzzy set theory is given (Deng et al., 2007).
Definition 1
A fuzzy set A˜ in a universe of discourse X is characterized by a membership function μ A˜ (x) which associates with each element x in X a real number in the interval [0, 1]. The function value μ A˜ (x) is termed the grade of membership of x in A˜.
Definition 2
A triangular fuzzy number a˜ can be defined by a triplet (a 1,a 2,a 3) shown in Figure 1. The membership function μ a˜ (x) is defined: Equation 11
Definition 3
Arithmetic operations on fuzzy numbers.
While there are various operations of triangular fuzzy numbers, only main operations used in this study are illustrated. If we define two positive triangular fuzzy numbers a˜=(a 1,a 2,a 3) and b˜=(b 1,b 2,b 3) then: Equation 12 Equation 13 Equation 14 Equation 15 Equation 16
3 The establishment of evaluation criteria for KMS selection
The establishment of evaluation criteria is the base for the KMS selection. We identify the evaluation criteria from functional, performance and economic aspects. From the technology perspective, the function and performance aspects should be considered. Besides the technology perspective, the economic aspect also needs to be considered in the implementation of KMS. Functional aspect indicates the functions that have to be done which include knowledge maintenance (C1), knowledge retrieval (C2), expert yellow page (C3), knowledge map (C4) and access control (C5). In the criteria, knowledge retrieval (C2) and knowledge map (C4) focus on explicit knowledge sharing. Expert yellow page (C3) is used for implicit knowledge sharing. Knowledge maintenance (C1) safeguards the quality of knowledge repository. Access control (C5) makes the knowledge be shared among authorized users. Performance aspect means the indispensable attributes and quality of the system, which include safety (C6), expansion (C7) and integration (C8). In the criteria, safety (C6) means the data in KMS are stored safely and can be restored quickly. Expansion (C7) means the new functions can be provided in need of less costs. Integration (C8) states the KMS can be integrated with other information systems. Economic aspect refers to the amount of resource to be consumed in the deployment of KMS. Besides the investment cost (C9) it includes the operating and maintenance cost (C10).
The hierarchical structure of evaluation criteria is shown in Table I. From Table I, it can be seen that the evaluation criteria prefer linguistic values.
4 The TOPSIS method based on multiple distances in fuzzy environment
Let A={A 1,A 2, … ,A m } be a discrete set of alternatives, C={C 1,C 2, … ,Cn} be the set of criteria, n is the number of criteria, in this study, n=10, D={D1,D2, … ,Dt} be the set of decision makers. The performance rating of each decision maker D k ∈D for each alternative A i ∈A with respect to criterion C j ∈C is denoted by R˜ k =x˜ ij (k) with triangular fuzzy number. The criterion weight rating of each decision maker D k ∈D for each criterion C j ∈C is denoted by W˜ k =w˜ j (k) with triangular fuzzy number. The decision makers use the fuzzy linguistic terms to express their preferences. The fuzzy linguistic terms and their corresponding values proposed by Chen and Hwang (1992) are used in the study and are shown in Table II.
The proposed method consists of nine steps in total. The detailed explanations for each step are presented as follows.
Step 1. Aggregate the fuzzy ratings for the criteria and the alternatives.
The aggregated fuzzy rating R˜=x˜ ij of alternative A i with respect to criterion C j is given by: Equation 17 where: Equation 18 The aggregated fuzzy rating W˜=w˜ j of criterion C j is given by: Equation 19 where: Equation 20 Step 2. Normalize the fuzzy decision matrix.
The raw data are normalized to eliminate anomalies with different measurement units and scales in several MCDM problems. R˜=[r˜ ij ] m×n denotes the normalized fuzzy decision matrix.
Where, for the benefit criteria: Equation 21 for cost criteria: Equation 22 Step 3. Construct the weighted normalized fuzzy decision matrix.
The weighted normalized decision matrix V˜ is defined as: Equation 23 Equation 24 where, w˜ j is fuzzy weight of the criteria C j .
Step 4. Determine the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS). The FPIS and FNIS of the alternatives are calculated as follows: Equation 25 where, for benefit criterion, v˜ j += max i v ij 3, for cost criterion, v˜ j += min i v ij 1: Equation 26 where, for benefit criterion, v˜ j += min i v ij 1, for cost criterion, v˜ j += max i v ij 3
Step 5. Calculate the distances of each alternative from FPIS and FNIS by the following kinds of distances.
(1) Hamming distance
The first kind of distance that is used in the new TOPSIS method is Hamming distance. In fuzzy environment, it can be expressed as follows: Equation 27
(2) Euclidean distance
The second kind of distance that is used in the new TOPSIS method is Euclidean distance. In fuzzy environment, it can be expressed as follows: Equation 28
(3) Dp,q distance
The third kind of distance that is used in the new TOPSIS method is Dp,q distance. In fuzzy environment, it can be expressed as follows: Equation 29
(4) Hausdorff distance
The fourth kind of distance that is used in the new TOPSIS method is Hausdorff distance. In fuzzy environment, it can be expressed as follows: Equation 30
(5) L2 distance
The fifth kind of distance that is used in the new TOPSIS method is L 2 distance. In fuzzy environment, it can be expressed as follows: Equation 31
(6) Vertex distance
The sixth kind of distance that is used in the new TOPSIS method is vertex distance. In fuzzy environment, it can be expressed as follows: Equation 32 Step 6. Calculate the relative closeness degrees (RCD) of each alternative from FPIS using the following equations: Equation 33 Equation 34 Equation 35 Equation 36 Equation 37 Equation 38 The alternatives are ranked in descending order of each kind of RCD. Then, the six pre-rankings are derived.
Step 7. Construct the Condorcet matrix B=(b ij ) m×m .
We construct the matrix B with m2 elements. Initially 0 is written to all the elements. For each pre-ranking, if A i is preferred to A j , then we add 1 to the element at row i and column j. This is done over and over until all the six pre-rankings are processed.
Step 8. Calculate the final score of each alternative.
S i represents the final score of the alternative A i , which is calculated as follows: Equation 39 Step 9. Select of the best alternative. Rank the alternatives in the descending order of final scores. The alternative that gets the maximum final score is the best.
5 Illustrative example
The aircraft company in the northwest of China is selecting the KMS. There are six KMSs to be evaluated. We invited three respondents to participate in this study. First, the objective is stated clearly. Second, the criteria are explained to respondents in great details, in order that the respondents could provide objective and precise responses. Then each respondent is requested to fill in a questionnaire using the linguistic terms in Table II. The linguistic evaluation information of KMSs given by respondents is shown in Tables III-V. The linguistic evaluation information of criteria weights is shown in Table VI.
The proposed TOPSIS method is used to deal with the evaluation information. The steps to be taken are described below:
Step 1. Aggregate the fuzzy ratings for the criteria and the alternatives. The aggregated results are obtained and they are presented in Table VII.
Step 2. Normalize the fuzzy decision matrix. The normalized fuzzy decision matrix is presented in Table VIII.
Step 3. Construct weighted normalized fuzzy decision matrix. The weighted normalized fuzzy decision matrix is constructed, as is shown in Table IX.
Step 4. Determine the FPIS and the FNIS, the results of which are shown in Table X.
Step 5. Calculate the distances of each alternative from FPIS and FNIS based on the six kinds of distances, the results of which are shown in Table XI.
Step 6. Calculate the RCD of each alternative and then construct the pre-rankings, the results of which are shown in Table XII.
Step 7. Construct the Condorcet matrix, as is shown in Table XIII.
Step 8. Calculate the final scores of the alternatives. The results are shown in Table XIV.
Step 9. Re-rank the alternatives in descending order of the final scores. The final ranking of the alternatives is: Equation 40
6 Conclusions
In this paper, we have proposed the model for KMS selection. The proposed model comprises of two parts. The first part is the criteria for KMSs selection. These criteria are identified from functional, performance and economic aspects. From the criteria, we see that the linguistic evaluation values are preferred. For example, considering the criterion expansion, it is difficult to provide precise numeric values. On the contrary, the linguistic values such as high or low is more fit. Therefore, the MCDM method in fuzzy environment is needed to deal with the linguistic evaluation information. The second part is the new TOPSIS method with multiple kinds of distances under fuzzy environment, which is used to cope with the linguistic evaluation information. In the method, the multiple per-rankings are obtained and are combined to generate the final joint ranking. The alternative with the highest final score is selected as the best KMS. The feasibility of the selection model is validated by an example.
The main contributions of the paper are as follows:
The new TOPSIS method with multiple distances is proposed for MCDM problems in fuzzy environment. In the method, six commonly used distances in TOPSIS are extended in fuzzy environment to generate corresponding six pre-rankings. Afterwards these pre-rankings are combined as the joint final ranking of the alternatives. Since the final ranking which is the collect result reduces the bias in each single pre-ranking, it is more objective and accurate.
The evaluation criteria are constructed from functional, performance and economic aspects. It gives an overall evaluation of KMS.
About the author
Ming Li is a Lecturer at the School of Business Administration of China University of Petroleum. He received his PhD degree in the School of Economics and Management, Beijing University of Aeronautics and Astronautics, China, in 2010. His research interests include knowledge management and knowledge engineering. Ming Li can be contacted at: [email protected]
References
Boran, F.E. , Boran, K. and Menlik, T. (2012), “The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS”, Energy Sources, Part B: Economics, Planning, and Policy, Vol. 7 No. 1, pp. 81-90.
Chang, H.C. , Tsai, M.T. and Tsai, C.L. (2011), “Complex organizational knowledge structures for new product development teams”, Knowledge-Based Systems, Vol. 24 No. 5, pp. 652-661.
Chen, C.T. (2000), “Extensions of the TOPSIS for group decision-making under fuzzy environment”, Fuzzy Sets and Systems, Vol. 114 No. 1, pp. 1-9.
Chen, S.J. and Hwang, C.L. (1992), Fuzzy Multiple Attribute Decision Making, Lecture Notes in Economics and Mathematical System Series, Vol. 375, Springer, New York, NY, pp. 289-486.
Chiou, H.K. and Tzeng, G.H. (2002), “Fuzzy multiple-criteria decision-making approach for industrial green engineering”, Environmental Management, Vol. 30 No. 6, pp. 816-830.
Christine, W.C. and Michael, J. (1996), “Knowledge modeling for constructing an expert system to support reforestation decisions”, Knowledge-Based Systems, Vol. 9 No. 1, pp. 41-59.
Dağdeviren, M. , Yavuz, S. and Kılınç, N. (2009), “Weapon selection using the AHP and TOPSIS methods under fuzzy environment”, Expert Systems with Applications, Vol. 36 No. 4, pp. 8143-8151.
Deng, Y. , Xiong, J. and Fu, P. (2007), Airline Safety Evaluation Based on Fuzzy TOPSIS, Lecture Notes in Computer Science, Vol. 4430, Springer, Berlin, pp. 282-287.
Gray, P.H. and Meister, D.B. (2006), “Knowledge sourcing methods”, Information & Management, Vol. 43 No. 2, pp. 142-156.
Hahn, J. and Wang, T.W. (2009), “Knowledge management systems and organizational knowledge processing challenges: a field experiment”, Decision Support Systems, Vol. 47 No. 4, pp. 332-342.
He, Y.Y. , Zhou, D.Q. and Gong, Z.W. (2010), “The fuzzy TOPSIS decision method and experimental analysis”, Systems Engineering, Vol. 28 No. 11, pp. 95-103.
Hsia, T.C. , Chen, H.T. and Chen, W.H. (2008), “Measuring the readability performance (RP) of aircraft maintenance technical orders by fuzzy MCDM method and RP index”, Quality & Quantity, Vol. 42 No. 6, pp. 795-807.
Hsieh, T.Y. , Lu, S.T. and Tzeng, G.H. (2004), “Fuzzy MCDM approach for planning and design tenders selection in public office buildings”, International Journal of Project Management, Vol. 22 No. 7, pp. 573-584.
Hwang, C.L. and Yoon, K. (1981), Multiple Attribute Decision Making: Methods and Applications – A State of the Art Survey, Springer, New York, NY.
Iraj, M. , Nezam, M.A. , Armaghan, H. and Rahele, N. (2008), “Designing a model of fuzzy TOPSIS in multiple criteria decision making”, Applied Mathematics and Computation, Vol. 206 No. 2, pp. 607-617.
Jain, H.K. , Tanniru, M.R. and Fazlollahi, B. (1991), “MCDM approach for generating and evaluating alternatives in requirement analysis”, Information Systems Research, Vol. 2 No. 3, pp. 223-239.
Jia, X. , Han, F. and Tan, X. (2004), “Integrated environmental performance evaluation of chemical processes”, Computers & Chemical Engineering, Vol. 29 No. 1, pp. 243-247.
Kahraman, C. , Çevik, S. , Ates, N.Y. and Gülbay, M. (2007), “Fuzzy multi-criteria evaluation of industrial robotic systems”, Computers & Industrial Engineering, Vol. 52 No. 4, pp. 414-433.
Karsak, E.E. (2002), “Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives”, International Journal of Production Research, Vol. 40 No. 13, pp. 3167-3181.
Kodali, R. and Anand, G. (2010), “Application of analytic network process for the design of flexible manufacturing systems”, Global Journal of Flexible Systems Management, Vol. 11 Nos 1/2, pp. 39-54.
Lee, M. and Chen, T.T. (2010), Visualizing Intellectual Structure in Ubiquitous Computing, Lecture Notes in Artificial Intelligence, Vol. 6232, Springer, Berlin, pp. 261-272.
Li, D.F. and Wang, X.D. (2007), “Compromise ratio method for fuzzy multi-attribute group decision makes”, Applied Soft Computing, Vol. 7 No. 3, pp. 807-817.
Li, M. (2012), “The extension of quality function deployment based on 2-tuple linguistic representation model for product design under multi-granularity linguistic environment”, Mathematical Problems in Engineering, Vol. 2012, ID 989284.
Li, M. , Liu, L. and Li, C.B. (2011a), “An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems”, Expert Systems with Applications, Vol. 38 No. 7, pp. 8586-8596.
Li, M. , Liu, L. , Yin, L. and Zhu, Y.Q. (2011b), “A process mining based approach to knowledge maintenance”, Information Systems Frontiers, Vol. 13 No. 3, pp. 371-380.
Lin, X. , Zhang, Q. and Han, X. (2009), “Application of Wuli-Shili-Renli system methodology in knowledge management”, Kybernetes, Vol. 38 Nos 3/4, pp. 346-353.
Liu, D.R. and Lin, C.W. (2012), “Modeling the knowledge-flow view for collaborative knowledge support”, Knowledge-Based Systems, Vol. 31 No. 7, pp. 41-54.
Löffler, C. , Westkämper, E. and Unger, K. (2011), “Method for analysis and dynamism of factory structure in automotive manufacturing”, Robotics & Computer-Integrated Manufacturing, Vol. 27 No. 4, pp. 741-745.
McMahon, C. , Lowe, A. and Culley, S. (2004), “Knowledge management in engineering design: personalization and codification”, Journal of Engineering Design, Vol. 15 No. 4, pp. 307-325.
Mohammad, I. (2009), “Using the Hamming distance to extend TOPSIS in a fuzzy environment”, Journal of Computational and Applied Mathematics, Vol. 231 No. 1, pp. 200-207.
Mothe, J. , Chrisment, C. , Dkaki, T. , Dousset, B. and Karouach, S. (2006), “Combining mining and visualization tools to discover the geographic structure of a domain”, Computers, Environment and Urban Systems, Vol. 30 No. 4, pp. 460-484.
Nevo, D. and Chan, Y.E. (2007), “A temporal approach to expectations and desires from knowledge management systems”, Decision Support Systems, Vol. 44 No. 1, pp. 298-312.
Nie, K. , Ma, T. and Nakamori, Y. (2009), “An approach to aid understanding emerging research fields – the case of knowledge management”, Systems Research and Behavioral Science, Vol. 26 No. 6, pp. 629-643.
Nik, M.A. , Khademolhosseini, N. and Abbaspour-Fard, M.H. (2009), “Optimum utilization of low-capacity combine harvesters in high-yielding wheat farms using multi-criteria decision making”, Biosystems Engineering, Vol. 103 No. 3, pp. 382-388.
Nuray, R. and Can, F. (2006), “Automatic ranking of information retrieval systems using data fusion”, Information Processing & Management, Vol. 42 No. 3, pp. 595-614.
Park, J.H. , Park, I.Y. , Kwun, Y.C. and Tan, X.G. (2011), “Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment”, Applied Mathematical Modeling, Vol. 35 No. 5, pp. 2544-2556.
Pirdashti, M. , Ghadi, A. and Mohammadi, M. (2009), “Multi-criteria decision-making selection model with application to chemical engineering management decisions”, Engineering and Technology, Vol. 49, pp. 54-59.
Ribeiro, R.A. , Moreira, A.M. , Van den Broek, P. and Pimentel, A. (2011), “Hybrid evaluation method for software engineering decisions”, Decision Support Systems, Vol. 51 No. 1, pp. 208-219.
Rouhani, S. , Ghazanfari, M. and Jafari, M. (2012), “Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS”, Expert Systems with Applications, Vol. 39 No. 3, pp. 3764-3771.
Sari, K. (2013), “Selection of RFID solution provider: a fuzzy multi-criteria decision model with Monte Carlo simulation”, Kybernetes, Vol. 42 No. 3, pp. 448-465.
Serenko, A. , Bontis, N. , Booker, L. , Sadeddin, K. and Hardie, T. (2010), “A scientometric analysis of knowledge management and intellectual capital academic literature (1994-2008)”, Journal of Knowledge Management, Vol. 14 No. 1, pp. 3-23.
Sher, P.J. and Lee, V.C. (2004), “Information technology as a facilitator for enhancing dynamic capabilities through knowledge management”, Information & Management, Vol. 41 No. 8, pp. 933-945.
Sun, C.C. (2010), “A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods”, Expert Systems with Applications, Vol. 37 No. 12, pp. 7745-7754.
Wang, X.D. (2011), “An interval multiple attribute decision-making model based on TOPSIS and it's application in smart grid evaluation”, 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, Deng Leng, pp. 4948-4951.
Wei, F. and Zhi, M.C. (2009), “Research on knowledge management visualization of deep web”, Key Engineering Materials, Vol. 439, pp. 189-194.
Wen, Y.F. (2009), “An effectiveness measurement model for knowledge management”, Knowledge-Based Systems, Vol. 22 No. 5, pp. 363-367.
Xu, Y. and Bernard, A. (2011), “Quantifying the value of knowledge within the context of product development”, Knowledge-Based Systems, Vol. 24 No. 1, pp. 166-175.
Zadeh, L.A. (1965), “Fuzzy sets”, Information and Control, Vol. 8 No. 3, pp. 338-353.
Acknowledgements
The research is supported by the National Natural Science Foundation of China under Grant No. 71101153 and the Research Funds Provided to New Recruitments of China University of Petroleum-Beijing (QD-2010-06).