Applying Logarithmic Fuzzy Preference Programming and VIKOR for Ranking the Solutions of Knowledge Management Based on Critical Success Factors

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1 World appl. programming, Vol(4), No (7), July, pp TI Journals World Applied Programming Applying Logarithmic Fuzzy Preference Programming and VIKOR for Ranking the Solutions of Knowledge Management Based on Critical Success Factors ISSN: Copyright All rights reserved for TI Journals. Elham Ebrahimi *1, Reza Avazpour 2, Mohammad Reza Fathi 3 1 PhD Candidate of Human Resource Management, University of Tehran, Tehran, Iran 2 M.S of Management, Faculty of Human Sciences, Shahed University, Tehran, Iran 3 PhD Candidate of Industrial Management, University of Tehran, Tehran, Iran *Corresponding author: elhebrahimi@ut.ac.ir A R T I C L E I N F O Keywords: Knowledge Management Critical Success Factors LFPP VIKOR A B S T R A C T The purpose of this paper is to propose a framework based on the Fuzzy Multiple Criteria Decision Making (FMCDM) approach for ranking Knowledge Management (KM) solutions to gain Critical Success Factors (CSFs) of a KM project implementation. For achieving this goal, first the CSFs in the KM field are recognized through the comprehensive survey of the relevant literature. The LFPP method is then applied to determine weights of the CSFs as criteria. Finally VIKOR method is used in order to rank the solutions of KM adoption as alternatives. Implementing this framework is demonstrated in a real case involving an Iranian company in the field of electric power and energy industry. The proposed framework helps the company to focus on high rank solutions and develop strategies to implement them on priority. 1. Introduction In today's globally competitive environment, organizations need to manage their resources in order to gain competitive advantage. An important role of knowledge management is creating sustainable competitive advantage for organizations (Tseng, 2011). After appearance of knowledge economy, knowledge has become not only a strategic asset but also the main source of organizational competitive advantage (Wang & Chang, 2007). In fact knowledge is the intangible resource which enables organizations to learn things, reserve their valuable heritage, and solve their problems and the most important, to create core competitive advantage (Liao, 2002). KM considers knowledge as the main asset and manages it in a systematic way in order to achieve the goal of increasing company's performance and competitiveness (Patil & Kant, 2013). Defining knowledge management is not easy because includes several different activities, such as collecting, analyzing, storing, disseminating and utilizing data in the organization (Lancioni & Chandran, 2009). Quintas, Lefrere and Jones (1997) expressed that KM is to discover, develop, utilize, deliver, and absorb knowledge from inside and outside the organization by an effective management process to meet organization's current and future needs. Kazemi and Zafar Allahyari (2010) defined KM as a process through which organizations extract value from their intellectual assets. In order to successfully KM implementation, a wide range of studies have provided several critical factors involving top management and executive management support, continues improvement, technology, culture, human resource management, time, measurement, cost and so on. However these critical success factors are all significant, a same CSF may be differently important to different firms due to their varied priorities; purposes, strategies resources, and capabilities in KM implementation. Therefore, firms should determine the relative importance of these CSFs. Since specifying the weights of CSFs is a qualitative decision making problem, it involves the vagueness of human judgments. Thus it is better to apply an effective method which can deal with the vague judgments of individuals. The fuzzy set theory is a mathematical way which can handle the vagueness in decision-making (Wu, 2012). After determining the relative weights of CSFs, organizations can prioritize the solutions to achieve effective KM implementing. It is important to prioritize these solutions so that organizations could develop appropriate strategies to implement these solutions in a stepwise manner. Ranking KM solutions based on CSFs is also an MCDM problem with human judgments. Thus in this case we apply a fuzzy MCDM method too. This study utilizes LFPP method to determine relative weights of CSFs as criteria and VIKOR method to rank the KM feasible solutions. The paper is organized as follows. Section 2 reviews the literature on CSFs and solutions of KM adoption. In Section 3 the proposed framework for prioritize the solutions of KM adoption is

2 Elham Ebrahimi *, Reza Avazpour, Mohammad Reza Fathi 162 described. The LFPP and VIKOR methods are also presented. The empirical case study is described in Section 4 and finally, the conclusion is presented in Section Literature Review 2.1. CSFs of KM Adoption A wide range of studies have identified critical success factors which play an important role in successful KM implementing in organizations. CSFs are necessary and sufficient factors for success. Each factor is necessary and the set of factors are sufficient. (Williams and Ramaprasad, 1996). Although a lot of researchers have attempted to develop a comprehensive list of CSFs for KM implementation, these factors differ because of the multidisciplinary nature of KM. the main CSFs of KM implementation which are used as criteria in this study, are shown in Table 1. These CFSs are also described briefly as follows. Table 1. Proposed Critical Success Factors for KM Implementing CSFs for KM implementation Top management support (C1) Culture (C2) References Davenport, De long and Beers (1998); Kazemi and Zafar Allahyari (2010); Wu (2012); Patil and Kant (2013); Wang and Chang (2007) Davenport et al. (1998); Kazemi and Zafar Allahyari (2010); Wu (2012); Patil and Kant (2013); Wang and Chang (2007); Chang and Wang (2009); Tseng (2011); Fan, Feng, Sun and Ou (2009) Organizational infrastructure (C3) Human resource management (C4) Kazemi and Zafar Allahyari (2010); Tseng (2011); Fan et al. (2009) Davenport et al. (1998); Kazemi and Zafar Allahyari (2010); Liang, Ding and Wang (2012) Time (C5) Patil and Kant (2013); Wu (2012) Cost (C6) Wu (2012) Information technology (C7) Patil and Kant (2013);Davenport et al. (1998); Kazemi and Zafar Allahyari (2010); Wu (2012); Fan et al. (2009); Wang and Chang (2007); Chang and Wang (2009); Communication (C8) Employees (C9) Kazemi and Zafar Allahyari (2010); Patil and Kant (2013); Wu (2012) Wang and Chang (2007); Chang and Wang (2009); Patil and Kant (2013); Wu (2012) Security (C10) Patil and Kant (2013); Wu (2012); Fan et al. (2009); Tseng (2011) Strategy (C11) Chang and Wang (2009); Patil and Kant (2013) 1. Top management support (C1): Like almost every types of change program, KM projects benefited from senior management support (Davenport et al., 1998). The CEO is completely aware of the goals and visions of KM implementation and can encourage the staff to effectively create knowledge via KM (Wang & Chang, 2007).

3 163 Applying Logarithmic Fuzzy Preference Programming and VIKOR for Ranking the Solutions of Knowledge Management Based on Critical Success Factors 2. Culture (C2): Culture is the most difficult constraint that knowledge managers must deal with (Davenport et al., 1998). As Kazemi and Zafar Allahyari (2010) pointed out, existing a culture of confidence and trust is required to encourage the application and development of knowledge in an organization. This aspect contains factors such as direct participation and trust relationships among staff, learning atmosphere of an organization and invigorate employee to share knowledge with others (Chang & Wang, 2009). 3. Organizational infrastructure (C3): Building an organizational infrastructure for KM includes establishing a set of roles and groups to serve as resources for individual projects (Davenport et al., 1998). It integrates fragmented flows of environmental information/knowledge in organization. The Infrastructure capability of an organization is Important in leveraging environmental technological architecture (Tseng, 2011). 4. Human resource management (HRM) (C4): as People are the main driver of KM projects, all of the functions of HRM including Employee empowerment, employee involvement, employee learning and development, employee recruitment and selection and reward system is crucial for effectively implementing KM (Kazemi & Zafar Allahyari, 2010). 5. Time (C5): according Wu (2012) and Patil and Kant (2013) lack of time is one of the main obstacles to KM implementation. Therefor considering this criterion is vital for to successfully create and implement a knowledge management strategy. 6. Cost (C6): according to Wu (2012), since KM implementation is extremely costly in terms of money and time, cost is an important criterion in deciding to choose among different KM solutions. 7. Information technology (C7): The explicit knowledge is easier to be digitalized and transferred, so that it can be captured and shared with others by the use of information technology (Wu, 2012). According to the related literature, this factor includes sub-criteria such as IT personnel ability, the budget available for establishing the IT infrastructure, the ability to apply IT management, the use of Internet and Intranet, and human sources of information technology (Wang & Chang, 2007). 8. Communication (C8): It has been recognized by organizations that communication would play a significant role in implementing KM as a strategic area. Some communication channels used in an organization to convey achievements of KM projects are internal magazines, journals and meetings (Kazemi & Zafar Allahyari, 2010). 9. Employees (C9): This factor refers to criteria such as staff specialty, experience, ability to create knowledge, recognition of knowledge management, widespread practice of personnel, training, participation, learning aspiration, learning opportunities and acceptance of information technology (Wang & Chang, 2007). 10. Security (C10): This factor includes protecting the knowledge from inappropriate or illegal use or theft (Tseng, 2011). If one organization spends a vast investment on IT but represents badly in knowledge security, namely, it has week process capability, then the utilization efficiency of IT equipment may be not high and the KM of organizations may be not strong Fan et al. (2009). 11. Strategy (C11): This factor includes sub-criteria such as strategic planning regarding KM adoption, and integrating with business process (Patil & Kant, 2013). Chang and Wang (2009) have also implies subfactors such as clear objective for initiating KM project, and integrating organizational development and KM Solutions of KM Adoption In this section some solutions to overcome the barriers of KM adoption and successfully implementing it are presented. These solutions are suggested by Patil and Kant (2013) and Liang et al (2012) and verified by the expert team of the case study (Iranian company). Table shows these KM solutions. Table 2. Proposed solutions for KM implementing KM Solutions Using IT system for knowledge dissemination (A1) Description Application of computers and telecommunications equipment to store, retrieve, transmit and manipulate data. Using customer relation management (CRM) system to faster exploitation of knowledge learning (A2) CRM is a process designed to collect data associated with customers to enhance the relationship between the company and its customers.

4 Elham Ebrahimi *, Reza Avazpour, Mohammad Reza Fathi 164 Establishing adequate incentives and reward systems to promote employees to share knowledge (A3) Strengthening the cultural cohesions and cooperation (A4) Adopt Supplier Development (SD) program (A5) Establishment of Knowledge based Decision support system (KB-DSS) (A6) Establish a transparent work flow or open door policy (A7) Use of groupware and other software (A8) Promotion of employees awareness of KM (A9) Establishment of a clear future vision and common values (A10) Incentives and reward systems is a formal scheme used to promote or encourage specific actions or behavior by a specific group of people during a defined period of time Cultural cohesion means having employees at every level support the core values and understands what the company needs from them as individuals and from the wider team of which they are part. SD programs are long-term cooperative efforts between a buying firm and its suppliers to upgrade the suppliers technical, quality, delivery, and cost capabilities to foster ongoing improvements. A KB-DSS can undertake intelligent tasks in a specific domain that is normally performed by a highly skilled employee. It helps decision makers use data to solve semistructured and unstructured decision-making problems Workflow concepts are closely related to other concepts used to describe organizational structure. Transparent work flow helps to eliminate difficulty of information flows from level to level and ensures agility, adaptability, and alignment. An open knowledge-sharing environment can facilitate cooperation between groups in an organization; e.g. the use of software like online bulletin boards, project management systems, and distance video conferencing. on-the-job training is used to increase awareness of KM among personnel and reduce cognitive bias and psychological resistance The promotion of KM requires the setting of specific goals, clear future vision, common values, and team spirit. 3. Research Methodology The main purpose of this study is to propose a framework based on the Fuzzy Multiple Criteria Decision Making (FMCDM) approach for ranking KM solutions in order to achieve CSFs of a KM project implementation. According to this goal, first by studying the literature related to KM the CSFs were recognized. Semi-structural interviews with scholars and managers of the case study company which was going to implement KM project, validated the framework of the study, the CSFs chosen as our criteria and the solutions which are our alternatives. Then the weight of each criterion was analyzed by the LFPP method. Finally, according to these weights, the VIKOR method was applied for the purpose of ranking KM solutions. The overall framework of the study is shown in Figure 1. Litreture reveiw and Semistructural interviews step 1: Recognition of KM CSFs and solutions LFPP Method step 2: Calculating the weights of KM CSFs as criteria VIKOR Method step 3: Ranking KM solutions

5 165 Applying Logarithmic Fuzzy Preference Programming and VIKOR for Ranking the Solutions of Knowledge Management Based on Critical Success Factors Figure 1. Research framework Decision hierarchy for prioritizing solutions of KM is illustrated in Figure 2. Goal Criteria Alternatives Prioritizing the solutions of KM implementation Top management support (C1) Culture (C2) Organizational infrastructure (C3) Human resource management (C4) Time (C5) Cost (C6) Information technology (C7) Communication (C8) Employees (C9) Security (C10) Strategy (C11) Using IT system for knowledge dissemination (A1) Using CRM system to faster exploitation of knowledge learning (A2) Establishing incentives and reward systems (A3) Strengthening the cultural cohesions and co-operation (A4) Adopt SD program (A5) Establishment of KB- DSS (A6) Establish a transparent work flow or open door policy (A7) Use of groupware and other software (A8) Promotion of employees awareness of KM (A9) Establishment of a clear future vision and common values (A10) Figure 2. Decision hierarchy for prioritizing solutions of KM

6 Elham Ebrahimi *, Reza Avazpour, Mohammad Reza Fathi The Fuzzy Logic and Linguistic Variables Fuzzy set theory was first developed in 1965 by Zadeh; he was attempting to solve fuzzy phenomenon problems, including problems with uncertain, incomplete, unspecific, or fuzzy situations. Fuzzy set theory is more advantageous than traditional set theory when describing set concepts in human language. It allows us to address unspecific and fuzzy characteristics by using a membership function that partitions a fuzzy set into subsets of members that incompletely belong to or incompletely do not belong to a given subset Fuzzy Numbers We order the Universe of Discourse such that U is a collection of targets, where each target in the Universe of Discourse is called an element. Fuzzy number is mapped onto U such that a random is appointed a real number, [ ]. If another element in U is greater than x, we call that element under A. The universe of real numbers R is a triangular fuzzy number (TFN), which means that for [ ], and { Note that, where L and U represent fuzzy probability between the lower and upper boundaries, respectively, as in Figure 3. Assume two fuzzy numbers, and ; then, μ A x 1 0 L M U Figure 3. Triangular fuzzy number Fuzzy Linguistic Variables The fuzzy linguistic variable is a variable that reflects different aspects of human language. Its value represents the range from natural to artificial language. When the values or meanings of a linguistic factor are being reflected, the resulting variable must also reflect appropriate modes of change for that linguistic factor. Moreover, variables describing a human word or sentence can be divided into numerous linguistic criteria, such as equally important, moderately important, strongly important, very strongly important, and extremely important. For the purposes of the present study, the 5-point scale (equally important, moderately important, strongly important, very strongly important and extremely important) is used.

7 167 Applying Logarithmic Fuzzy Preference Programming and VIKOR for Ranking the Solutions of Knowledge Management Based on Critical Success Factors 3.2. The LFPP-based nonlinear Priority Method In this method for the fuzzy pairwise comparison matrix, Wang et al (2011) took its logarithm by the following approximate equation: = (,, ), i,j = 1.,n (1) That is, the logarithm of a triangular fuzzy judgment a ij can still be seen as an approximate triangular fuzzy number, whose membership function can accordingly be defined as ( ) = { } (2) Where ( ) is the membership degree of belonging to the approximate triangular fuzzy judgment = (,, ). It is very natural that we hope to find a crisp priority vector to maximize the minimum membership degree λ= min { ( ) i=1,,n-1 ; j=i+1,, n}. The resultant model can be constructed (Wang et al, 2011) as Maximize λ Subject to { Or as ( ) } (3) Subject to { Maximize 1- λ } (4) It is seen that the normalization constraint = 1 is not included in the above two equivalent models. This is because the models will become computationally complicated if the normalization constraint is included. Before normalization, without loss of generality, we can assume for all such that for. Note that the nonnegative assumption for (i = 1,...,n) is not essential. The reason for producing a negative value for λ is that there are no weights that can meet all the fuzzy judgments in within their support intervals. That is to say, not all the inequalities or can hold at the same time. To avoid k from taking a negative value, Wang et al (2011) introduced nonnegative deviation variables and for such that they meet the following inequalities: (5) It is the most desirable that the values of the deviation variables are the smaller the better. Wang et al (2011) thus proposed the following LFPP-based nonlinear priority model for fuzzy AHP weight derivation: Minimize J= (1-λ) 2 + M.

8 Elham Ebrahimi *, Reza Avazpour, Mohammad Reza Fathi 168 Subject to (6) { } Where = for i = 1,, n and M is a specified sufficiently large constant such as M = The main purpose of introducing a big constant M into the above model is to find the weights within the support intervals of fuzzy judgments without violations or with as little violations as possible The VIKOR Introduction to VIKOR The VIKOR method is a compromise MADM method, developed by Opricovic.S and Tzeng (Opricovic, 1998; Opricovic, S. and Tzeng, G. H., 2002) started from the form of Lp-metric: { [ ] } The VIKOR method can provide a maximum group utility for the majority and a minimum of an individual regret for the opponent (Opricovic, 1998; Opricovic, S; Tzeng, G. H., 2002) Working Steps of VIKOR Method 1) Calculate the normalized value Assuming that there are m alternatives, and n attributes. The various i alternatives are denoted as x i. For alternative x j, the rating of the jth aspect is denoted as x ij, i.e. x ij is the value of jth attribute. For the process of normalized value, when x ij is the original value of the ith option and the jth dimension, the formula is as follows: (7) 2) Determine the best and worst values For all the attribute functions the best value was and the worst value was, that is, for attribute J=1-n, we get formulas (8) and (9) (8) Where the positive ideal solution for the jth criteria is, is the negative ideal solution for the jth criteria. If one associates all, one will have the optimal combination, which gets the highest scores, the same as. 3) Determine the weights of attributes The weights of attribute should be calculated to express their relative importance. 4) Compute the distance of alternatives to ideal solution (9)

9 169 Applying Logarithmic Fuzzy Preference Programming and VIKOR for Ranking the Solutions of Knowledge Management Based on Critical Success Factors This step is to calculate the distance from each alternative to the positive ideal solution and then get the sum to obtain the final value according to formula (10) and (11). (10) [ ] (11) Where S i represents the distance rate of the ith alternative to the positive ideal solution (best combination), represents the distance rate of the ith alternative to the negative ideal solution (worst combination). The excellence ranking will be based on values and the worst rankings will be based on values. In other words,, indicate and of -metric respectively. 5) Calculate the VIKOR values for i=1, 2,,m, which are defined as [ ] [ ] (12) Where,, and v is the weight of the strategy of the majority of criteria (or the maximum group utility ). [ ] represents the distance rate from the positive ideal solution of the ith alternative s achievements In other words, the majority agrees to use the rate of the ith.[ ] represents the distance rate from the negative ideal solution of the ith alternative; this means the majority disagree with the rate of the ith alternative. Thus, when the v is larger (> 0.5), the index of will tend to majority agreement; when v is less (< 0.5), the index will indicate majority negative attitude; in general, v = 0.5, i.e. compromise attitude of evaluation experts. 6) Rank the alternatives by values According to the values calculated by step (4), we can rank the alternatives and to make-decision. 4. Empirical Analysis The case of this study is an Iranian company which is active in the field of electric power and energy. Its mission is managing the assets of the company in the electric power industry, leading activities for the purpose of supplying reliable and economical electricity for all sectors of consumption, management and supervision on installation and operation of facilities and entering into transactions of electricity. In this paper, the weights of criteria are calculated using LFPP, and these calculated weight values are used as VIKOR inputs. Then, after VIKOR calculations, evaluation of the alternatives and selection of best Knowledge Management solution is realized. Logarithmic Fuzzy Preference Programming: In LFPP, firstly, we should determine the weights of each criterion by utilizing pair-wise comparison matrices. We compare each criterion with respect to other criteria. You can see the pair-wise comparison matrix for selection best Knowledge Management solution criteria in Table 3. Table 3.Inter-criteria comparison matrix P C 1 C 2 C 9 C 10 C 11 L m u L m u L m u L m u L m u C C C C

10 Elham Ebrahimi *, Reza Avazpour, Mohammad Reza Fathi 170 C After forming the model (6) for the comparison matrix and solving this model using Genetic algorithms, the weight vector is obtained as follow: = (0.0443, , , , 0.110, , , , , , 0.179) T VIKOR: The weights of criteria are calculated by LFPP up to now, and then these values can be used in VIKOR. So, the VIKOR methodology must be started at the second step. Thus, weighted normalized decision matrix can be prepared. This matrix can be seen from Table 4. Table 4. Weighted normalized decision matrix C 1 C 2 C 9 C 10 C 11 A A A A By following VIKOR procedure steps and calculations, the ranking of Knowledge Management solutions are gained. The results and final ranking are shown in Table 5. Table 5. Final evaluation of the alternatives i E i =Ʃe i F i =Max(e i ) P i A A A A According to Table 5, A 7 is the best Knowledge Management solution among other solutions. Other solutions are ranked as follow: 1- Establish a transparent work flow or open door policy 2- Promotion of employees awareness of KM 3- Establishment of a clear future vision and common values 4- Establishment of Knowledge based Decision support system (KB-DSS) 5- Strengthening the cultural cohesions and co-operation 6- Adopt Supplier Development (SD) program 7- Use of groupware and other software 8- Using IT system for knowledge dissemination

11 171 Applying Logarithmic Fuzzy Preference Programming and VIKOR for Ranking the Solutions of Knowledge Management Based on Critical Success Factors 9- Establishing adequate incentives and reward systems to promote employees to share knowledge 10- Using customer relation management (CRM) system to faster exploitation of knowledge learning 5. Conclusions In today's globally competitive environment, organizations need to manage their resources in order to gain competitive advantage. An important role of knowledge management is creating sustainable competitive advantage for organizations. The purpose of this paper is to propose a framework based on the Fuzzy Multiple Criteria Decision Making approach for ranking Knowledge Management solutions to gain Critical Success Factors (CSFs) of a KM project implementation. First the CSFs in the KM field are recognized. The LFPP method is then applied to determine weights of the CSFs as criteria. Finally VIKOR method is used in order to rank the solutions of KM adoption as alternatives. According to result, A 7 (Establish a transparent work flow or open door policy) is the best Knowledge Management solution among other solutions. Acknowledgement The authors wish to thank an anonymous referee for the valuable suggestions which considerably improve the quality of the paper. References [1] Chang, T., & Wang, T. (2009). Using the fuzzy multi-criteria decision making approach for measuring the possibility of successful knowledge management. Information Sciences, 179, [2] Davenport, T., De long, D., & Beers, M. (1998). Successful knowledge management projects. Sloan Management Review, 39(2), [3] Fan, Z., Feng, B., Sun, Y., & Ou, W. (2009). Evaluating knowledge management capability of organizations: a fuzzy linguistic method. Expert Systems with Applications, 36, [4] Kazemi, M., & Zafar Allahyari, M. (2010). Defining a knowledge management conceptual model by using MADM. Journal of Knowledge Management, 14(6), [5] Lancioni, R., & Chandran, R. (2009). Professional guilds, tension and knowledge management. Research Policy, 38(5), [6] Liang, G., Ding, J., & Wang, C. (2012). Applying fuzzy quality function deployment to prioritize solutions of knowledge management for an international port in Taiwan. Knowledge-Based Systems, article in press, [7] Liao, S. (2002). Knowledge management technologies and applications: literature review from 1995 to Expert Systems with Applications, 25, [8] Opricovic. (1998). Multi-criteria optimization of civil engineering systems, Faculty of Civil Engineering, Belgrade. [9] Opricovic, S., & Tzeng, G. H. (2002). Multi criteria planning of post earthquake sustainable reconstruction, Computer-Aided Civil and Infrastructure Engineering, no.17, pp [10] Patil, S., & Kant, R. (2013). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Systems with Applications, Article in press, [11] Quintas, P., Lefrere, P., & Jones, G. (1997). Knowledge management: a strategic agenda. Long Range Planning, 30(3), [12] Tseng, M. (2011). Using a hybrid MCDM model to evaluate firm environmental knowledge management in uncertainty. Applied Soft Computing, 11, [13] Wang, T., & Chang, T. (2007). Forecasting the probability of successful knowledge management by consistent fuzzy preference relations. Expert Systems with Applications, 32, [14] Wang, Y., Chin.,K. (2011). Fuzzy analytic hierarchy process: A logarithmic fuzzy preference Programming methodology, International Journal of Approximate Reasoning 52, [15] Williams, J., & Ramaprasad, A. (1996). A taxonomy of critical success factors. European Journal of Information Systems, 5(5), [16] Wu, W. (2012). Segmenting critical factors for successful knowledge management implementation using the fuzzy DEMATEL method. Applied Soft Computing, 12,