Segmenting critical factors for successful knowledge management implementation using the fuzzy DEMATEL method

Applied Soft Computing 12 (2012) 527–535

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Applied Soft Computing journal homepage: www.elsevier.com/locate/asoc

Segmenting critical factors for successful knowledge management implementation using the fuzzy DEMATEL method Wei-Wen Wu ∗ Department of International Trade, Ta Hwa Institute of Technology, 1.Ta Hwa Road, Chiung-Lin, Hsin-Chu 307, Taiwan

a r t i c l e

i n f o

Article history: Received 18 May 2008 Received in revised form 5 June 2011 Accepted 14 August 2011 Available online 22 August 2011 Keywords: Knowledge management (KM) Critical factors Fuzzy set theory Decision Making Trial and Evaluation Laboratory (DEMATEL)

a b s t r a c t Knowledge is a key source of sustainable competitive advantage. In response to increasingly drastic and competitive environments, many organizations wish to better utilize and manage knowledge for business success. For the purpose to execute formal knowledge management (KM) effectively, some works have suggested several critical factors of KM implementations. However, in a strategic view, such a list of critical factors must be further honed to increase practical usefulness, as not all critical factors necessarily share the same importance. Moreover, assessing the importance of critical factors inevitably involves the vagueness of human judgment. Hence, this study presents a favorable method combining fuzzy set theory and the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to segment the critical factors for successful KM implementations. Also, an empirical study is presented to illustrate the proposed method and to demonstrate its usefulness. © 2011 Elsevier B.V. All rights reserved.

1. Introduction In Taiwan, many firms recognize that utilizing and managing corporate knowledge provides the competitive advantage and improved performance, and try to employ a variety of ways to enhance their rate of knowledge creation and utilization. Some firms manage knowledge with formal knowledge management (KM) initiatives and structures, while other organizations do indeed manage knowledge informally as part of their normal activities without the use of the terminology and concepts of formal KM structures [20]. Knowledge has the ability to utilize information and influence decisions, as well as the capability to act effectively [2]. The power of knowledge is a very important resource for preserving valuable heritage, learning new things, solving problems, creating core competences, and initiating new situations for both individual and organizations [32]. Therefore, numerous firms desire to better activate and leverage the knowledge for achieving value creation and business success. In order to implement the KM effectively, some creditable works have provided several critical factors of KM implementation [38,53], involving business needs, KM purposes, top management support, technology, communication, culture and people, sharing knowledge, incentives, time, measurement, cost, and so on. However, in a strategic view, those critical factors are all significant but not necessarily to implement at the same time. Even

∗ Tel.: +886 3 5927700x2902; fax: +886 3 5925715. E-mail address: [email protected] 1568-4946/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.asoc.2011.08.008

a same critical factor may be differently important to individual firm with the varied priorities; due to each organization has its own purposes, strategies, conditions of resources, and capabilities in KM implementation. Especially, it is hard to obviate the possibility of the causal relationship within those critical factors. If the kind of causal relationship can be profoundly disclosed, the critical factors are able to be well prioritized and segmented into some meaningful groups. Hence firms can properly adjust the importance of critical factors according to the strategic needs of different KM phases. A list of critical factors is required to be further decomposed for higher practical usefulness. To determine the importance of critical factors is a qualitative decision-making problem and inevitably involves the vagueness of human judgments [33]. Thus, in terms of the critical factor segment, it is better to employ an effective method which can deal with the vague judgments of human and model the causal relationship within critical factors. The fuzzy set theory is a mathematical way which can handle vagueness in decision-making [1,68]. The Decision Making Trial and Evaluation Laboratory (DEMATEL) is a potent method which helps for generating a structural model and visualizing the causal relationship by offering a causal diagram [11–13,18]. Hence, this study proposes a favorable method combining the fuzzy set theory and the DEMATEL to segment the critical factors for successful KM initiatives. An empirical study is presented to illustrate the proposed method and to demonstrate its usefulness and validity. The rest of this paper is organized as follows. In Section 2, some of the prior literature related to the critical factors of KM implementation is reviewed. In Section 3, the proposed method is developed. In Section 4, an empirical study is illustrated. Finally, according to the

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findings of this research, concluding remarks and suggestions are presented. 2. KM implementation Reacting to an increasingly rival business environment, numerous organizations are emphasizing the importance of KM to create competitive advantage, and basing the KM strategy on their unique resources and capabilities. For implementing the KM successfully, it is a wise way to starts with a well understanding in terms of critical factors of KM implementation. The concept of knowledge and the related critical factors are discussed below. 2.1. The concept of knowledge As [26] emphasize, competitive advantage depends on how efficient the firm is in building, sharing and utilizing the knowledge. There are some peculiar characteristics of knowledge, such as: it is intangible and difficult to measure, is volatile, is embodied in agents with wills, sometimes increases through use, has wide ranging impacts, often has long lead times, and can be used by different processes at the same time [63]. Especially, [31] argues that knowledge inertia may enable or inhibit one’s ability on problem solving, which is stemming from the use of routine problem solving procedures, stagnant knowledge sources, and following past experience or knowledge; to conquer the problem of knowledge inertia, it is necessary to update and share knowledge. Additionally, for knowledge to make contribution, it needs to be converted into competencies, and competence is only important as a strategic resource when it is relatively distinctive to its competitors [25]. Concerning the distinction between data, information, and knowledge, as [50] states, if data becomes information when they add value, then information becomes knowledge when it adds insight, abstraction, and better understanding. In fact, data is mainly considered as raw numbers that once processed becomes information and when put in specific context, this information becomes knowledge; the knowledge as a state of mind posits that individuals expand their personal knowledge through the inputs received from their environment [2]. According to [38], in the transformation process, data is organized and structured to produce general information, and then the information is arranged and filtered to produce contextual information for specific users, next individuals assimilate the contextual information and transform it into knowledge. Ref. [24] raise many types of knowledge, such as: systemic knowledge, explicit knowledge, tacit knowledge, hidden knowledge, and relationship knowledge. Although many categories have been suggested, the most frequently used distinction is tacit versus explicit knowledge [47]. Explicit knowledge is provided by the conventional classroom instruction, which bases in data and is converted into information; by contrast, tacit knowledge bases in practice and experience, which leads to mastery provided the awareness related to the task at hand [25]. According to [40], explicit knowledge can be expressed in words and numbers and shared in the form of data, scientific formulae, specifications, and manuals, it can therefore be readily transmitted between individuals formally and systematically; whereas tacit knowledge includes subjective insights, intuitions, and hunches, is highly personal and hard to formalize, as well as is difficult to communicate or share with others. As [39] indicates, organizational knowledge is created by a continuous dialogue between tacit and explicit knowledge, and there are four patterns of interaction including socialization, internalization, externalization, and combination within a “spiral” model.

2.2. Issues of knowledge management Organizations need to discover how to motivate their people to share the tacit knowledge which is the most valuable form of knowledge and is recognized as a strategic asset, though the tacit knowledge is usually very subjective and resides inside one’s head so that is difficult to communicate, comprehend and quantify [15]. 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 [24]. Additionally, overemphasizing on explicit knowledge, especially by IT investments, may lead to a situation that companies lose their valuable tacit knowledge, whereas overemphasizing tacit knowledge may lead to a result that tacit knowledge on its own does not enhance innovation [24]. Indeed, organization’s work with KM should focus on transposing tacit knowledge into explicit knowledge and converting individual knowledge into organizational knowledge [38]. Especially, it is important to make tacit knowledge explicit at the organizational level through thrust and relationship building processes [24]. Further, in order to achieve sustainable competitive advantage, companies need to emphasize the total knowledge base of the company, i.e. the explicit-and tacit knowledge, both internally and externally [24,26]. KM is the organizational optimization of knowledge to achieve enhanced performance, increased value, competitive advantage, and return on investment, through the use of various tools, processes, methods and techniques [28]. Also, KM is a systemic way to manage knowledge in the organizationally specified process of acquiring, organizing and communicating knowledge, in order to enable employees to perform more effective and productive works [2]. KM and related strategy concepts are promoted as important components for organizations to survive, because KM is regarded as a prerequisite for higher productivity and flexibility in both the private and the public sectors [38]. There are numbers of frameworks have developed to promote the KM implementation. Most frameworks of the KM can be classified as prescriptive, descriptive, and a combination of the two; the prescriptive frameworks direct the ways to engage in KM activities, whereas the descriptive frameworks identify significant attributes for the success of KM initiatives [48]. According to [2], those different frameworks have many similarities: most of the life cycles are articulated in four phases where the first one is a “create” phase; and the last phase concerns the ability to share and use knowledge. The issues of KM can be studied into several aspects with different views. Some studies deal with the topics covering entire KM activities, such as: the successful KM process requires understanding the operations of the four stages [8]; KM can be split into four separate activities, each dealing with a particular aspect [62]; a model of knowledge creation consists of three elements, namely, the SECI process, workplace, and the knowledge assets [41]; the knowledge manipulation activities need to be properly altered and deployed by timely knowledge valuation [17]; and the knowledge development cycle as the process of knowledge generation, knowledge storage, knowledge distribution and knowledge application [2]. 2.3. Successful KM implementation In the knowledge economy, a key source of sustainable competitive advantage and consequent profitability relies on the way to create, share, and utilize knowledge as a strategic resource [9,22,37,51,52]. For a solid implementation of KM, organizations need to emphasize the knowledge base on not only explicit and tacit [24], but also internal and external [26], even individual and organizational [38]. Moreover, the frameworks of KM should consider purpose/objective, knowledge, technology, learning, and

W.-W. Wu / Applied Soft Computing 12 (2012) 527–535

people/culture of the organization, which is a holistic and peopledriven approach that considers both the knowledge cycle and the cultural environment [48]. Successful implementation of KM requires (1) aligning the contributions of key organizational actors, (2) promoting the development of knowledge networks, and (3) providing support by delivering a purposeful message [46]. For the purpose of implementing the KM successfully, there are many critical factors required to be considered. For example, it is important to well evaluate and select a favorable KM strategy, because the effective management starts with a proper strategy [14]. Moreover, it is not easy to success in implementing any business activity without top management support and trust relation in an organization, no matter how the business activity is well planned. The KM planning is only the beginning; the successful KM implementation is the real challenge. According to [45], the main obstacles to KM implementation were: lack of ownership of the problem, lack of time, organizational structure, senior management commitment, rewards and recognition, and an emphasis on individuals rather than on teamwork. As important as awareness of those main obstacles is, it is also important to recognize certain key success factors in KM implementation. In order to improve these KM initiatives and link them to business strategy, [35] suggest a process-oriented knowledge management approach to bridge the gap between human- and technology-oriented KM. Understanding critical success factors will provide a huge advantage in successful KM planning and subsequent deployment. There are several critical factors of KM implementation suggested by some scholars and experts. For example, in order to be successful in KM activities, [53] emphasizes that firms and their managements must be entrepreneurial. Moreover, [38] suggests some critical elements to successfully create and implement a knowledge management strategy, including: purposes, support from top management, communication, creativity, culture and people, sharing knowledge, incentives, time, evaluation, and cost. Further, [3] raises a list of KM success factors, involving strong unified leadership, align KM with mission and business needs, cohesive and engaged team, understand current problems and issues, collaboration and communication, innovation, best practices and lessons learned, understanding and appropriate use of current technology, IT infrastructure, workflow and change cycles, security, establish metrics, reliability and integrity, accessibility and portability, costeffective, and interoperability.

was aimed at the fragmented and antagonistic phenomena of world societies and searched for integrated solutions. In recent years, the DEMATEL has become very popular in Japan [18,27,66,67], because it is especially pragmatic to visualize the structure of complicated causal relationships with digraphs. The digraph portrays a basic concept of contextual relation among the elements of the system, in which the numeral represents the strength of influence. A digraph may typically represent a communication network, or some domination relation between individuals. Suppose a system contains a set of elements S = {s1 , s2 , . . ., sn }, and particular pair-wise relations are determined for modeling with respect to a mathematical relation R. Further, to portray the relation R as a direct-relation matrix that is indexed equally on both dimensions by elements from the set S. Then, except the case that the number is 0 appearing in the cell (i, j), if the entry is a positive integral that has the meaning of: (1) the ordered pair (si , sj ) is in the relation R, and (2) there has the sort of relation regarding that the element si causes element sj . The DEMATEL can map out complex relationships among factors and to identify key factors [34,56–60], which is based on digraphs that portrays a contextual relation among the elements of the system and can be converted into a visible structural model of the system with respect to that relation [42]. The tangible product of the DEMATEL exercise is a structural model appearing as a “causal diagram” which may divide sub-systems into cause group and effect group. In particular, DEMATEL has the ability not only to demonstrate directed relationships of sub-systems, but also to clarify the degree of interactions between sub-systems. Thus, toward analyzing a complex system, if we wish to capture the causal–effect relationship among sub-systems, apparently the DEMATEL is helpful. In order to apply the DEMATEL smoothly, this study refined the version of [11]. Essential definitions are described as below. Definition 1. The initial direct-relation matrix Z is a n × n matrix obtained by pair-wise comparisons in terms of influences and directions between criteria, in which Zij is denoted as the degree to which the criterion i affects the criterion j, i.e., Z = [Zij ]n×n . Definition 2. The normalized direct-relation matrix X, i.e., X = [Xij ]n×n and 0 ≤ xij ≤ 1 can be obtained through formulas (1) and (2), in which all principal diagonal elements are equal to zero. X =s·Z s=

(1) 1

max

n

z j=1 ij

1≤i≤n

3. Methodology For building and analyzing a model involving causal relationships between complex factors, the DEMATEL is a potent and comprehensive method. In order to extend the DEMATEL for decision-making in fuzzy environments, the essentials of the DEMATEL and the fuzzy set theory are discussed below.

,

i, j = 1, 2, ..., n

(2)

Definition 3. The total-relation matrix T can be acquired by using the formula (3), in which the I is denoted as the identity matrix. T = X(I − X)−1

(3)

Definition 4. The sum of rows and the sum of columns are separately denoted as D and R through the formulas (4)–(6). T = tij ,

3.1. The DEMATEL method Graph theory has grown tremendously in recent years, largely due to the usefulness of graphs as models for computation and optimization. Applying the graph theory, we can easily visually discover things inside the complex problem, because the graph displays mathematical results with visualization clearly and unambiguously. The DEMATEL is based on digraphs, which can separate involved factors into cause group and effect group. Directed graphs, known as digraphs, are more useful than directionless graphs, because digraphs can demonstrate the directed relationships of sub-systems. The Battelle Memorial Institute conducted a DEMATEL project through its Geneva Research Centre [12,13]. The original DEMATEL

529

D=

i, j = 1, 2, ..., n

n 

(4)

tij

(5)

tij

(6)

j=1

R=

n  i=1

where D and R denote the sum of rows and the sum of columns, respectively. Definition 5. A causal diagram can be acquired by mapping the dataset of (D + R, D − R), where the horizontal axis (D + R) is made by adding D to R, and the vertical axis (D − R) is made by subtracting D from R.

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3.2. Fuzzy set theory In the real world, many decisions involve imprecision due to goals, constraints, and possible actions are not known precisely [1], judgments for decision-making are often given by crisp values, though crisp values are an inadequate reflection of situational vagueness. To solve this kind of imprecision problem, fuzzy set theory was first introduced by [68] as a mathematical way to represent and handle vagueness in uncertainty. In fuzzy set theory, each number between 0 and 1 indicates a partial truth, whereas crisp sets correspond to binary logic: 0 or 1. Hence, fuzzy set theory can express and handle vague or imprecise judgments mathematically. Generally, decision-makers usually tend to give assessments based on their past experiences and knowledge, and also their estimations are often expressed in equivocal linguistic terms. Based on the definition of fuzzy sets, the concept of linguistic variables is introduced to represent a language typically adopted by a human expert. A linguistic variable is a variable whose values, namely linguistic values, have the form of phrases or sentences in a natural language [4,19,61]. The linguistic variable is very useful in dealing with situations which are described in quantitative expressions. Especially, linguistic variables are used as variables whose values are not numbers but linguistic terms [69]. The linguistic term approach is a convenient way for decision makers to express their assessments. In order to efficiently resolve the ambiguity arising in incomplete information and the fuzziness in human judgments, the use of linguistic scales is necessary and important. In practice, the linguistic values can be represented by fuzzy numbers, and the triangular fuzzy number is commonly used. This study builds on some important definitions and notations of fuzzy set theory from [69] and [5]. The related definitions are as follows. ˜ is a subset of a universe of discourse Definition 6. A fuzzy set A X, which is a set of ordered pairs and is characterized by a membership function A˜ (x) representing a mapping A˜ : X → [0, 1]. The ˜ is called the membership function value of A˜ (x) for the fuzzy set A ˜ value of x in A, which represents the degree of truth that x is an ˜ It is assumed that  ˜ : X ∈ [0, 1], where element of the fuzzy set A. A ˜ while  ˜ (x) = 0 A˜ (x) = 1 reveals that x completely belongs to A, A ˜ indicates that x does not belong to the fuzzy set A. ˜ = {(x,  ˜ (x))}, x ∈ X, where  ˜ (x) is the membership function A A A and X = {x} represents a collection of elements x. ˜ of the universe of discourse X is convex Definition 7. A fuzzy set A if A˜ (x1 + (1 − )x2 ) ≥ min(A˜ (x1 ), A˜ (x2 )),

∀x ∈ [x1, x2], where  ∈ [0, 1]

(7)

˜ of the universe of discourse X is normal Definition 8. A fuzzy set A if max A˜ (x) = 1 ˜ is a fuzzy subset in the universe Definition 9. A fuzzy number N of discourse X, which is both convex and normal. ˜ of the universe of Definition 10. The ␣-cut of the fuzzy set A ˜ ˛ = {x ∈ X| ˜ (x) ≥ ˛}, where ␣ ∈ [0,1]. discourse X is defined as A A ˜ can be defined as a Definition 11. A triangular fuzzy number N triplet (l, m, r), and the membership function N˜ (x) is defined as:

N˜ (x) =

⎧ x < l, ⎪ ⎪ 0, ⎪ ⎪ ⎨ (x − l) , l ≤ x ≤ m,

For achieving an effective solution of problem-solving, the group decision-making is important to any organization, because it usually impacts upon those decisions that affect organizational performance. Specifically, the group decision-making is the process of arriving at a consensus based upon the reaction of multiple individuals, and it can facilitate the exchange of ideas and information whereby an acceptable judgment may be obtained [6,30]. There are several useful defuzzification methods which can be divided into two classes by considering either the vertical or the horizontal representation of possibility distribution [44]. In achieving a favorable solution, the group decision-making is usually important to any organizations. To deal with the problems in uncertainty, an effective fuzzy aggregation method is required. Any fuzzy aggregation method always needs to contain a defuzzification method due to the results of human judgments with fuzzy linguistic variables are based on TFNs. The defuzzification refers to the selection of a specific crisp element based on the output fuzzy set, which convert fuzzy numbers into crisp may score. This study is applying the converting fuzzy data into crisp scores developed by [44], the main procedure of determining the left and right scores by fuzzy minimum and maximum, the total score is determined as a weighted average according to the membership functions. This study here adopts the CFCS (Converting Fuzzy data into Crisp Scores) defuzzification method for the fuzzy aggregation procedure, because the CFCS method can give a better crisp value than the Centroid method. The CFCS method is based on the procedure of determining the left and right scores by fuzzy min and fuzzy max, respectively, and the total score is determined as a weighted average according to the membership functions [42]. Let z˜ijk = (lijk , mkij , rijk ) indicate the fuzzy assessments of evaluator k (k = 1, 2,. . .,p) about the degree to which the criterion i affects the criterion j. To aggregate the result of these fuzzy assessments, this study uses the CFCS method which includes five-step algorithms. Assume X˜ to be an arbitrary convex and bounded fuzzy number. The assessed values of qualitative criteria metrics for NBSC, X˜ = (L xij , m xij , R xij ), i = 1,2,3,4 and j = 1,2,3. . .,7 in this study. X˜ = (L xij , m xij , R xij ) is TFNs, and xij presents at the left, middle and right positions, L xijk , m xijk , R xijk , represent overall average ratings of aspect p

ith, criteria jth over kth evaluators, and xij , p = 1, 2,. . .. . .k, is fuzzy numbers for each evaluator. The normalization of TFNs as follows: (1) Normalization:

0,

m ≤ x ≤ r, x > r,

where l, m, and r are real numbers and l ≤ m ≤ r.

max min

xmkij =

(mkij − minlijk ) max min

(rijk − minlijk )

xrijk =

max min

(8)

(9)

(10)

where max = maxrijk − minlijk . min (2) Compute left (ls) and right (rs) normalized value: xlsijk =

xrsijk =

(m − l)

(r − x) ⎪ ⎪ ⎪ ⎪ ⎩ (r − m)

(lijk − min lijk )

xlijk =

xmkij (1 + xmkij − xlijk ) xrijk (1 + xrijk − xmkij )

(11)

(12)

(3) Compute total normalized crisp value: xijk =

[xlsijk (1 − xlsijk ) + xrsijk xrsijk ] [1 − xlsijk + xrsijk ]

(13)

W.-W. Wu / Applied Soft Computing 12 (2012) 527–535 Table 1 The fuzzy linguistic scale. Linguistic terms

Triangular fuzzy numbers

Very high influence (VH) High influence (H) Low influence (L) Very low influence (VL) No influence (No)

(0.75,1.0,1.0) (0.5,0.75,1.0) (0.25,0.5,0.75) (0,0.25,0.5) (0,0,0.25)

Being in need of enhanced competitive advantage, most organizations wish to enrich and utilize knowledge effectively. In this section, an empirical study shows how a high-tech company applied the proposed method to segment a list of critical factors for a successful KM initiative. (14) 4.1. Problem descriptions

(5) Integrate crisp values: zij =

1 1 p (z + zij2 + · · · + zij ) p ij

valuable insight for problem-solving. Further, with the help of a causal diagram, we can make better decisions by recognizing the difference between cause and effect factors. 4. Empirical study and discussions

(4) Compute crisp values: zijk = minlijk + xijk max min

531

(15)

3.3. The proposed method The DEMATEL method is a highly pragmatic way to form a structural model of evaluation for better decision making. To further the practicality of the DEMATEL method for group decision making in a fuzzy environment, the analytical procedure of the proposed method is explained as follows: Step1: identifying the decision goal and forming a committee. Decision making is the process of defining the decision goals, gathering relevant information, generating the broadest possible range of alternatives, evaluating the alternatives for advantages and disadvantages, selecting the optimal alternative, and monitoring the results to ensure that the decision goals are achieved [16,43]. Thus, the first step is to identify the decision goal. Also, it is necessary to form a committee for gathering group knowledge for problemsolving. Step2: developing evaluation factors and designing the fuzzy linguistic scale. In this step, it is necessary to establish sets of significant factors for evaluation. However, evaluation factors have the nature of causal relationships and are usually comprised of many complicated aspects. To gain a structural model dividing involved factors into cause group and effect group, the DEMATEL method must be used here. For dealing with the ambiguities of human assessments, the linguistic variable “influence” is used with five linguistic terms [29] as {Very high, High, Low, Very low, No} that are expressed in positive triangular fuzzy numbers (lij , mij , rij ) as shown in Table 1. Step3: acquiring and aggregating the assessments of decision makers. To measure the relationship between evaluation factors C = {Ci |i = 1, 2, ..., n}, it is usually necessary to ask a group of experts to make assessments in terms of influences and directions between factors. Then, using the CFCS method, those fuzzy assessments are defuzzified and aggregated as a crisp value which is the zij . Hence, the initial direct-relation matrix Z = [zij ]n × n can be obtained by Eqs. (7)–(15). Step4: establishing and analyzing the structural model. On the base of the initial direct-relation matrix Z, the normalized directrelation matrix X can be obtained through Eqs. (1) and (2). Then, the total-relation matrix T can be acquired by using Eq. (3). According to Definitions 5 and 6, the causal diagram can be acquired through Eqs. (4)–(6). The causal diagram is constructed with the horizontal axis (D + R) named “Prominence” and the vertical axis (D − R) named “Relation”. The horizontal axis “Prominence” shows how much importance the factor has, whereas the vertical axis “Relation” may divide factors into cause group and effect group. Generally, if the (D − R) axis is plus, the factor belongs to the cause group; otherwise, the factor belongs to the effect group if the (D − R) axis is minus. Hence, causal diagrams can visualize the complicated causal relationships of factors into a visible structural model, providing

Case Company G is a Taiwan firm with more than USD 250 million turnover and over 1250 employees worldwide. The company is one of the world’s leading manufacturers in the Broadband Wireless Networking business, offering various solutions and products ranging from Wireless ADSL technology, Access Points, Wireless Routers, Client Adapters, and Built-in Modules. In order to succeed in a dynamic business environment, it is now a leading company strategy to apply KM to create, share, and utilize knowledge to increase competitive advantages. Also, Company G wanted to transform and leverage their knowledge into competitive advantages through formal KM implementation. However, Company G ran into trouble when making KM initiatives, because any KM initiative needs to take into account several complex factors systematically, such as: purpose; the condition of resources and their capabilities; even the preferences of a company. Although they recognized many critical factors in successful KM implementation, there arose the problem (since those critical factors were not equally important) of how to segment them into meaningful groups. In order to acquire sensible segments, Company G therefore set up a KM development committee consisting of the General Manager and several managers representing the marketing, financial, production, human resource, and information technology departments. The following shows how Company G utilized the proposed fuzzy DEMATEL method to evaluate and segment a list of critical factors for its KM initiative. 4.2. Applications of proposed method The committee followed the proposed method with the four-step procedure. First, they defined the decision goals for segmenting critical factors into significant groups in order to launch the KM initiative successfully. In step 2, the committee built and inspected a list of critical factors which was mainly based on the works of [38] and [3]. Those factors were: top management support (C1 ), communication (C2 ), culture and people (C3 ), sharing knowledge (C4 ), incentives (C5 ), time (C6 ), trust (C7 ), cost (C8 ), performance measurements (C9 ), information technology (C10 ), and security (C11 ). Also, they decided to use the fuzzy linguistic scale (Table 1) for making assessments. In step 3, once the relationships between those factors were measured by the committee through the use of the fuzzy linguistic scale, the data from each individual assessment could be obtained. For example, the assessment data of the General Manager are shown in Table 2. Then, using the CFCS method to aggregate these assessment data, the initial direct-relation matrix (Table 3) was produced. In step 4, based on the initial direct-relation matrix, the normalized direct-relation matrix (Table 4) was obtained by formulas (1) and (2). Next, the total-relation matrix (Table 5) was acquired using formula (3). Then, using formulas (4)–(6), the causal diagram (Fig. 1) could be acquired by mapping a dataset (see Table 5) of (D + R, D − R). Looking at this causal diagram, it is clear that evaluation factors were visually divided into the cause group including:

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W.-W. Wu / Applied Soft Computing 12 (2012) 527–535

Table 2 The assessment data of the general manager.

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

C1

C2

C3

C4

C5

C6

C7

CS

C9

C10

C11

No H H L H VL VL L No No No

VH No VH L H L L H VL No VL

H L No L H L L L VL VL VL

VH H VH No H L L L VL VL VL

H VL VH VL No VL VL VL No No No

VH L VH VL L No No VL No No No

VH L VH VL L No No VL No No No

VH H VH VL L VL VL No No No No

VH H VH L L H H H No VL VL

H L VH L L H H H VL No VL

H L H L H H H L VL VL No

Table 3 The initial direct-relation matrix.

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

C1

C2

C3

C4

C5

C6

C7

CS

C9

C10

C11

0.000 0.802 0.869 0.641 0.839 0.600 0.500 0.567 0.230 0.367 0.263

0.800 0.000 0.834 0.533 0.869 0.567 0.609 0.633 0.400 0.333 0.359

0.869 0.673 0.000 0.500 0.770 0.567 0.577 0.533 0.400 0.533 0.400

0.700 0.738 0.834 0.000 0.600 0.467 0.533 0.500 0.500 0.391 0.533

0.839 0.600 0.929 0.400 0.000 0.359 0.500 0.359 0.333 0.333 0.220

0.738 0.641 0.867 0.467 0.567 0.000 0.200 0.467 0.263 0.367 0.220

0.770 0.705 0.802 0.467 0.577 0.131 0.000 0.433 0.263 0.263 0.327

0.633 0.705 0.899 0.359 0.533 0.367 0.391 0.000 0.400 0.467 0.300

0.834 0.667 0.700 0.567 0.467 0.633 0.667 0.567 0.000 0.400 0.433

0.839 0.705 0.929 0.545 0.600 0.600 0.667 0.733 0.400 0.000 0.327

0.600 0.500 0.667 0.673 0.633 0.567 0.633 0.533 0.359 0.433 0.000

Table 4 The normalized direct-relation matrix.

C1 C2 C3 C4 C5 C6 C1 C8 C9 C10 C11

C1

C2

C3

C4

C5

C6

C7

CS

C9

C10

C11

0.000 0.096 0.104 0.077 0.101 0.072 0.060 0.068 0.028 0.044 0.032

0.096 0.000 0.100 0.064 0.104 0.068 0.073 0.076 0.048 0.040 0.043

0.104 0.081 0.000 0.060 0.092 0.068 0.069 0.064 0.048 0.064 0.048

0.084 0.089 0.100 0.000 0.072 0.056 0.064 0.060 0.060 0.047 0.064

0.101 0.072 0.112 0.048 0.000 0.043 0.060 0.043 0.040 0.040 0.026

0.089 0.077 0.104 0.056 0.068 0.000 0.024 0.056 0.032 0.044 0.026

0.092 0.085 0.096 0.056 0.069 0.016 0.000 0.052 0.032 0.032 0.039

0.076 0.085 0.108 0.043 0.064 0.044 0.047 0.000 0.048 0.056 0.036

0.100 0.080 0.084 0.068 0.056 0.076 0.080 0.068 0.000 0.048 0.052

0.101 0.085 0.112 0.065 0.072 0.072 0.080 0.088 0.048 0.000 0.039

0.072 0.060 0.080 0.081 0.076 0.068 0.076 0.064 0.043 0.052 0.000

C1 , C2 , C3 , C4 , C5 , C6 and C7 while the effect group was composed of such factors as C4 , C9 , C10 and C11 . 4.3. Discussions In this empirical study, the case Company wanted to implement formal KM in a stepwise manner, and needed to segment a list of critical factors into meaningful groups for making decision in

successful KM initiatives. According to the result from this proposed method, several implications about business management can be derived as follows. It is important to distinguish whether a critical factor belongs to the cause group factors or the effect group. The cause group implies the meaning of the influencing factors, whereas the effect group denotes the meaning of the influenced factors. If we want to reach a high level of performance in terms of the effect group factors,

Table 5 The total-relation matrix. C1

C2

C3

C4

C5

C6

C7

CS

C9

C10

C11

D

(D + R)

(D − R)

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

0.169 0.239 0.279 0.186 0.241 0.176 0.174 0.182 0.109 0.131 0.108

0.264 0.157 0.282 0.179 0.250 0.177 0.190 0.193 0.130 0.131 0.121

0.268 0.230 0.188 0.175 0.237 0.176 0.185 0.182 0.128 0.151 0.124

0.251 0.236 0.278 0.119 0.220 0.166 0.182 0.179 0.140 0.137 0.139

0.240 0.199 0.261 0.147 0.131 0.138 0.160 0.145 0.109 0.117 0.093

0.227 0.201 0.253 0.152 0.193 0.095 0.125 0.156 0.101 0.120 0.092

0.229 0.207 0.243 0.151 0.193 0.110 0.101 0.151 0.100 0.107 0.104

0.224 0.215 0.264 0.146 0.195 0.142 0.152 0.108 0.119 0.134 0.105

0.268 0.232 0.268 0.184 0.208 0.185 0.197 0.188 0.084 0.139 0.130

0.280 0.247 0.305 0.189 0.233 0.189 0.205 0.214 0.136 0.100 0.123

0.233 0.205 0.255 0.189 0.217 0.171 0.187 0.177 0.121 0.137 0.076

2.653 2.368 2.876 1.817 2.317 1.725 1.857 1.875 1.276 1.405 1.215

4.646 4.441 4.921 3.862 4.057 3.440 3.551 3.680 3.360 3.625 3.183

0.660 0.294 0.831 −0.229 0.578 0.010 0.163 0.070 −0.809 −0.816 −0.753

R

1.993

2.073

2.045

2.046

1.739

1.715

1.694

1.805

2.085

2.221

1.968

W.-W. Wu / Applied Soft Computing 12 (2012) 527–535 0.80

1.00 0.80

C5

0.60

C3 C1

0.60

0.40

0.40

0.00

1.00

2.00

3.00

4.00

C4

C7

0.20

C8

C6 5.00

6.00

-0.40

D-R

C6

D-R0.00

C5

C2

C7

0.20

-0.20

533

C11

-0.80

C2

0.00 0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

-1.00

4.50

5.00

C4

-0.40

C11

C9 C10

C3

C8

-0.20

-0.60

C1

C9

-0.60

D+R

C10 -0.80

Fig. 1. The causal diagram (a).

D+R

it is necessary to control and pay a great deal of attention to the cause group factors beforehand. From the result of segmenting the list of critical factors, it means that successful KM implementation requires a high level of focus on the cause group (C1 , C2 , C3 , C5 , C6 , C7 and C8 ) rather than the effect group (C4 , C9 ,C10 and C11 ); though the cause group factors are difficult to move, while the effect group factors are easily moved [18]. Further, through this causal diagram (Fig. 1.) several valuable cues can be obviously obtained for making profound decisions. For example, among these eleven critical factors, culture and people (C3 ) is the most important factor by the highest (D + R) priority of 4.921. Also, it is the most influencing factor by the highest (D − R) priority of 0.831, but it is quite difficult to be changed. As to the information technology (C10 ), it is the most easily influenced and moved factor because it has the lowest (D − R) priority of minus 0.816. Moreover, we can directly look those factors scattered in the causal diagram and perceive that three key critical factors for successful KM initiative are: culture and people (C3 ), top management support (C1 ), and incentives (C5 ). With the proposed fuzzy DEMATEL method, the case Company successfully segmented a list of critical factors into expressive groups for making decision in the KM initiative. According the results of segmentation, it was revealed that the most crucial factors are culture and people, not information technology. Although culture and people are not easily changed, they are the core part of promoting a successful KM initiative and the root of creating sustainable competitive advantage. Knowledge does not exist independent of human experience [49]. Several studies have indicated that culture and people issues are the most decisive factors [7,10,15,23,45,48]. Hence, if the case Company wishes to succeed in its KM initiative, it must emphasize the importance of people and to nurture a favorable culture such as an innovative and

Fig. 2. The causal diagram (b).

Table 7 ˜i + R ˜ i )def and (D ˜i − R ˜ i )def . The values of (D ˜i + R ˜i) (D C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

def

˜i − R ˜i) (D

4.366 4.241 4.551 3.811 3.939 3.434 3.557 3.632 3.379 3.598 3.258

def

0.479 0.262 0.552 −0.163 0.396 0.119 0.152 0.080 −0.555 −0.675 −0.563

entrepreneurial culture. Finally, all KM initiatives are unique so that a segmented result may not be completely suitable for other companies. However, the proposed fuzzy DEMATEL method is quite useful in segmenting several critical factors into profound groups for making better decisions in a fuzzy environment. Additionally, [36] suggest a fuzzy DEMATEL solution which is better than other studies that aggregating all the data of the experts right after obtaining the initial direct-relation fuzzy matrix. Hence, it is interesting to conduct comparison with the fuzzy DEMATEL solution suggested by [36]. As a result, we can obtain the causal diagram (Fig. 2) based on Tables 6 and 7. The main dissimilarity between Figs. 1 and 2 is that the location of C9 . Although these two fuzzy DEMATEL methods produce almost similar results, this does not mean that the fuzzy DEMATEL developed by [36] is not useful.

Table 6 ˜i + R ˜ i and D ˜i − R ˜i. The values of D

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

˜i R

˜i D

˜i ˜i + R D

˜i ˜i − R D

(0.520, 1.413, 3.897) (0.544, 1.466, 3.958) (0.519, 1.444, 4.036) (0.515, 1.441, 4.004) (0.403, 1.239, 3.557) (0.394, 1.196, 3.512) (0.387, 1.184, 3.505) (0.419, 1.265, 3.653) (0.532, 1.465, 4.025) (0.605, 1.579, 4.190) (0.478, 1.382, 3.922)

(0.803, 1.887, 4.578) (0.671, 1.683, 4.402) (0.911, 2.058, 4.686) (0.412, 1.279, 3.782) (0.641, 1.637, 4.338) (0.385, 1.205, 3.610) (0.449, 1.307, 3.839) (0.433, 1.311, 3.817) (0.190, 0.881, 3.043) (0.260, 0.983, 3.176) (0.160, 0.844, 2.989)

(1.322, 3.300, 8.475) (1.215, 3.149, 8.361) (1.430, 3.501, 8.722) (0.927, 2.719, 7.786) (1.044, 2.876, 7.895) (0.779, 2.401, 7.122) (0.836, 2.491, 7.345) (0.852, 2.576, 7.470) (0.722, 2.346, 7.068) (0.865, 2.561, 7.366) (0.638, 2.226, 6.911)

(−3.094, 0.474, 4.058) (−3.288, 0.216, 3.858) (−3.125, 0.614, 4.167) (−3.592, −0.162, 3.266) (−3.145, 0.398, 3.935) (−2.870, 0.009, 3.216) (−3.120, 0.122, 3.453) (−3.203, 0.045, 3.398) (−3.592, −0.584, 2.510) (−4.000, −0.596, 2.571) (−3.661, −0.537, 2.511)

534

W.-W. Wu / Applied Soft Computing 12 (2012) 527–535

These two fuzzy DEMATEL methods can complement rather than replace one another in order to conduct informed analyses. 5. Concluding remarks Knowledge is the fundamental basis of competition, so that organizations must endeavor to enrich their knowledge resources and need to design a knowledge strategy to enhance a sustainable competitive advantage. A successful KM initiative requires identifying of critical factors which guide the success of KM implementation. However, all critical factors are significant, but do not necessarily share the same importance, even having causal relationships between them. With a strategic view, such a list of critical factors must be further honed for higher practical usefulness. Rather than just simply ranking the critical factors, the DEMATEL method provides a favorable solution. The DEMATEL method is based on graph theory that enables us to project and solve problems visually, and it can divide multiple factors into cause group and effect group in order to better capture causal relationships visibly, as well as convert the relationship between critical factors into an intelligible structural model of the system. However, in many cases, the judgments of decision-making are often given as crisp values, but crisp values are an inadequate reflection of the vagueness in the real world. The fact that human judgment about preferences are often unclear and hard to estimate by exact numerical values has created the need for fuzzy set theory when handling problems characterized by vagueness and imprecision. A more sensible approach is to use, instead of numerical values, linguistic assessments in which all assessments of criteria in the problem are evaluated by means of linguistic variables. Hence, there is a need to extend the DEMATEL method with fuzzy set theory and linguistic variables for decision-making in fuzzy environments. However, in order to handle this kind of fuzzy MCDM problem in terms of the critical factor segment, this study developed the fuzzy DEMATEL method. This proposed method extends the DEMATEL method by applying both linguistic variables and a fuzzy aggregation method, so that it can effectively deal with vague and imprecise judgments in group decision-making. In particular, this method can also successfully divide a set of complex factors into a cause group and an effect group, as well as giving a visible causal diagram. Through the causal diagram, the complexity of a problem is easier to capture, whereby profound decisions can be made. The DEMATEL has been successfully applied in a variety of fields such as: finding critical services [29], importance-performance analysis [21], selecting management systems [53]; a value-created system of science park [35], choosing knowledge management strategies [64], corporate social responsibility programs choice and costs assessment [54], group decision-making [36], safety management system [34], innovation policy portfolios [20], global managers’ competencies [65], the system failure mode and effects analysis [50], performance evaluation [65], municipal solid waste management [10,15], and so on. Yet, apart from [10,15,55], it is rarely to use the DEMATEL for dealing with the issue of KM. Thus, this paper segments critical factors for successful KM implementation using the fuzzy DEMATEL method, and successfully extends the practical applications of fuzzy set theory and EDEMATEL into the field of KM. The proposed fuzzy DEMATEL method is comprehensive and applicable to all organizations facing difficult problems that require group decision-making in the fuzzy environments to segment complex factors. As concerns this empirical study, the proposed fuzzy DEMATEL method worked smoothly in tackling the problem of segmenting the critical factors into meaningful groups in order to facilitate the KM initiative. The result of this study indicates that a successful KM initiative needs to highlight critical factors such as:

culture and people, top management support, incentives, communication, and so on. Especially, the root causes is the culture and people that may influence other factors when implementing KM activities. The finding not only offers a meaningful base to deepen the understanding with regard to the KM initiative, but also provides a clue to develop effective interventions to promote the KM implementation with a stepwise manner. However, the study has some limitations. First, the study only conducted a case study; the finding should not be generalized to other enterprises. Second, it is believed that different enterprises may have different concerns about criteria for KM implementation. In this sense, it is worthwhile to perform more cases study in order to unearth new criteria for use. Additionally, it calls for periodical diagnoses in order to grasp the dynamic KM activities with different interventions and promotion strategies.

References [1] R.E. Bellman, L.A. Zadeh, Decision-making in a fuzzy environment, Management Science 17 (4) (1970) 141–164. [2] H. Benbya, G. Passiante, N.A. Belbaly, Corporate portal: a tool for knowledge management synchronization, International Journal of Information Management 24 (3) (2004) 201–220. [3] C. Bixler, Knowledge management and the learning organization converge, KM World 11 (4) (2002) 21–22. [4] M.F. Chen, G.H. Tzeng, C.G. Ding, Combining fuzzy AHP with MDS in identifying the preference similarity of alternatives, Applied Soft Computing Journal 8 (1) (2008) 110–117. [5] S.J. Chen, C.L. Hwang, F.P. Hwang, Fuzzy Multiple Attribute Decision making Methods and Applications, Springer, New York, 1992. [6] C.H. Cheng, Y. Lin, Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation, European Journal of Operational Research 142 (1) (2002) 174–186. [7] T.H. Davenport, S.L. Jarvenpaa, M.C. Beers, Improving knowledge work processes, Sloan Management Review 37 (4) (1996) 53–65. [8] M. Demarest, Understanding knowledge management, Long Range Planning 30 (3) (1997) 374–384. [9] K.C. Desouza, Strategic contributions of game rooms to knowledge management: some preliminary insights, Information & Management 41 (1) (2003) 63–74. [10] A.C. Edmondson, A.B. Winslow, R.M.J. Bohmer, G.P. Pisano, Learning how and learning what: effects of tacit and codified knowledge on performance improvement following technology adoption, Decision Sciences 34 (2) (2003) 197–224. [11] E. Fontela, A. Gabus, The DEMATEL Observer, DEMATEL 1976 Report. BATTELLE Institute, Geneva Research Center, Geneva, Switzerland, (1976). [12] A. Gabus, E. Fontela, World Problems, An Invitation to Further Thought Within the Framework of DEMATEL. BATTELLE Institute, Geneva Research Centre, Geneva, Switzerland, (1972). [13] A. Gabus, E. Fontela, Perceptions of the World Problematique: Communication Procedure, Communicating With Those Bearing Collective Responsibility (DEMATEL Report No. 1). BATTELLE Institute, Geneva Research Centre, Geneva, Switzerland, (1973). [14] C. Gopal, J. Gagnon, Knowledge, information, learning and the IS manager, Computerworld 29 (25) (1995) SS1–SS7. [15] K. Hafeez, H. Abdelmeguid, Dynamics of human resource and knowledge management, Journal of the Operational Research Society 54 (2) (2003) 153–164. [16] P. Hess, J. Siciliano, Management: Responsibility for Performance, McGraw-Hill, New York, 1996. [17] C.W. Holsapple, K.D. Joshi, Knowledge manipulation activities: results of a Delphi study, Information & Management 39 (6) (2002) 477–490. [18] S. Hori, Y. Shimizu, Designing methods of human interface for supervisory control systems, Control Engineering Practice 7 (11) (1999) 1413–1419. [19] Y.C. Hu, R.S. Chen, G.H. Tzeng, Discovering fuzzy association rules using fuzzy partition methods, Knowledge-Based Systems 16 (3) (2003) 137–147. [20] C.Y. Huang, J.Z. Shyu, G.H. Tzeng, Reconfiguring the innovation policy portfolios for Taiwan’s SIP Mall industry, Technovation 27 (12) (2007) 744–765. [21] H.Y. Hu, Y.C. Lee, T.M. Yen, C.H. Tsai, Using BPNN and DEMATEL to modify importance-performance analysis model - A study of the computer industry, Expert Systems with Applications 36 (6) (2009) 9969–9979. [22] V. Hutchinson, P. Quintas, Do SMEs do knowledge management? Or simply manage what they know? International Small Business Journal 26 (2) (2008) 131–154. [23] B.D. Janz, P. Prasarnphanich, Understanding the antecedents of effective knowledge management: the importance of a knowledge-centered culture, Decision Sciences 34 (2) (2003) 351–384. [24] J.A. Johannessen, J. Olaisen, B. Olsen, Mismanagement of tacit knowledge: the importance of tacit knowledge, the danger of information technology, and what to do about it, International Journal of Information Management 21 (1) (2001) 3–20.

W.-W. Wu / Applied Soft Computing 12 (2012) 527–535 [25] J.A Johannessen, B. Olsen, Knowledge management and sustainable competitive advantages: the impact of dynamic contextual training, International Journal of Information Management 23 (4) (2003) 277–289. [26] J. Jordan, P. Jones, Assessing your company’s knowledge management style, Long Range Planning 30 (3) (1997) 392–398. [27] M. Kamaike, Design elements in the passenger car development: the classification and the influence analysis in case of recreational vehicle, Bulletin of JSSD 48 (1) (2001) 29–38. [28] J.M. Kamara, C.J. Anumba, P.M. Carrillo, A CLEVER approach to selecting a knowledge management strategy, International Journal of Project Management 20 (3) (2002) 205–211. [29] C.W. Li, G.H. Tzeng, Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall, Expert Systems with Applications 36 (6) (2009) 9891–9898. [30] R.J. Li, Fuzzy Method in group decision making, Computers and Mathematics with Applications 38 (1) (1999) 91–101. [31] S.H. Liao, Problem solving and knowledge inertia, Expert Systems with Applications 22 (1) (2002) 21–31. [32] S.H. Liao, Knowledge management technologies and applications-literature review from 1995 to 2002, Expert Systems with Applications 25 (2) (2003) 155–164. [33] C. Lin, B. Tan, P.J. Hsieh, Application of the fuzzy weighted average in strategic portfolio management, Decision Sciences 36 (3) (2005) 489–511. [34] J.J.H. Liou, L. Yen, G.H. Tzeng, Building an effective safety management system for airlines, Journal of Air Transport Management 14 (1) (2008) 20–26. [35] C.L. Lin, G.H. Tzeng, A value-created system of science (technology) park by using DEMATEL, Expert Systems with Applications 36 (6) (2008) 9683–9697. [36] C.J. Lin, W.W. Wu, A causal analytical method for group decision-making under fuzzy environment, Expert Systems with Applications 34 (1) (2008) 205–213. [37] R. Maier, U. Remus, Implementing process-oriented knowledge management strategies, Journal of Knowledge Management 7 (4) (2003) 62–74. [38] M. Martensson, A critical review of knowledge management as a management tool, Journal of Knowledge Management 4 (3) (2000) 204–216. [39] I. Nonaka, A dynamic theory of organizational knowledge creation, Organization Science 5 (1) (1994) 14–37. [40] I. Nonaka, N. Konno, The concept of “ba”: building a foundation for knowledge creation, California Management Review 40 (3) (1998) 40–54. [41] I. Nonaka, R. Toyama, N. Konno, SECI, Ba and leadership: a unified model of dynamic knowledge creation, Long Range Planning 33 (1) (2000) 5–34. [42] S. Opricovic, G.H. Tzeng, Defuzzification within a multicriteria decision model, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 11 (5) (2003) 635–652. [43] S. Opricovic, G.H. Tzeng, Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research 156 (2) (2004) 445–455. [44] M. Oussalah, On the compatibility between defuzzification and fuzzy arithmetic operations, Fuzzy Sets and Systems 128 (2) (2002) 247–260. [45] People Management, The people factor, People Management 4 (2) (1998) 38. [46] S. Raub, D. Von Wittich, Implementing knowledge management: three strategies for effective CKOs, European Management Journal 22 (6) (2004) 714–724. [47] R. Roos, J. Roos, Measuring your company’s intellectual performance, Longe Range Planning 30 (3) (1997) 413–426. [48] B. Rubenstein-Montano, J. Liebowitz, J. Buchwalter, D. McCaw, B. Newman, K. Rebeck, The Knowledge Management Methodology Team, A systems thinking framework for knowledge management, Decision Support Systems 31 (1) (2001) 5–16.

535

[49] R. Sabherwal, I. Becerra-Fernandez, An empirical study of the effect of knowledge management processes at individual, group, and organizational levels, Decision Sciences 34 (2) (2003) 225–260. [50] S.M Seyed-Hosseini, N. Safaei, M.J. Asgharpour, Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique, Reliability Engineering & System Safety 91 (8) (2006) 872–881. [51] I. Spiegler, Technology and knowledge: bridging a “generating” gap, Information & Management 40 (6) (2003) 533–539. [52] D.J. Teece, Strategies for managing knowledge assets: the role of firm structure and industrial context, Long Range Planning 33 (1) (2000) 35–54. [53] W.H. Tsai, W.C. Chou, Selecting management systems for sustainable development in SMEs: a novel hybrid model based on DEMATEL, ANP, and ZOGP, Expert Systems with Applications 36 (2) (2009) 1444–1458. [54] W.H Tsai, J.L. Hsu, Corporate social responsibility programs choice and costs assessment in the airline industry-A hybrid model, Journal of Air Transport Management 14 (4) (2008) 188–196. [55] M.L. Tseng, Using a hybrid MCDM model to evaluate firm environmental knowledge management in uncertainty, Applied Soft Computing 11 (1) (2011) 1340–1352. [56] M.L. Tseng, Implementation and performance evaluation using fuzzy network balanced scorecard, Computers & Education 55 (1) (2010) 188–201. [57] M.L. Tseng, Using linguistic preferences and grey relational analysis to evaluate the environmental knowledge management capacities, Expert Systems with Applications 37 (1) (2010) 70–81. [58] M.L. Tseng, An assessment of cause and effect decision making model for firm environmental knowledge management capacities in uncertainty, Environmental Monitoring and Assessment 161 (1–4) (2010) 549–564. [59] M.L. Tseng, Application of ANP and DEMATEL to evaluate the decision-making of municipal solid waste management in Metro Manila, Environmental Monitoring and Assessment 156 (1–4) (2009) 181–197. [60] M.L. Tseng, Y.H. Lin, Application of Fuzzy DEMATEL to develop a cause and effect model of municipal solid waste management in Metro Manila, Environmental Monitoring and Assessment 158 (1–4) (2009) 519–533. [61] C.V. Von Altrock, Fuzzy Set Theory & Neurofuzzy Applications in Business and Finance, Prentice Hall, New Jersey, 1996. [62] K.M. Wiig, Integrating intellectual capital and knowledge management, Long Range Planning 30 (3) (1997) 399–405. [63] K.M. Wiig, R.D. Hoog, R.V.D. Spek, Supporting knowledge management: a selection of methods and techniques, Expert Systems With Applications 13 (1) (1997) 15–27. [64] W.W. Wu, Choosing knowledge management strategies by using a combined ANP and DEMATEL approach, Expert Systems with Applications 35 (3) (2008) 828–835. [65] W.W. Wu, Y.T. Lee, Developing global managers’ competencies using the fuzzy DEMATEL method, Expert Systems with Applications 32 (2) (2007) 499–507. [66] M. Yamazaki, K. Ishibe, S. Yamashita, I. Miyamoto, M. Kurihara, H. Shindo, An analysis of obstructive factors to welfare service using DEMATEL method reports of the faculty of engineering, Yamanashi University 48 (1997) 25–30. [67] A. Yuzawa, A state and subjects of TMO conception for city core vitalization countermeasure: a case study of Maebashi TMO conception, Bulletin of Maebashi Institute of Technology 5 (2002) 61–67. [68] L.A. Zadeh, Fuzzy Sets, Information and Control 8 (2) (1965) 338–353. [69] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Information Science 8 (1975) 199–249, 301–357 (II).