Knowledge management adoption and assessment for SMEs by a novel MCDM approach

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Knowledge management adoption and assessment for SMEs by a novel

MCDM approach

Ying-Hsun Hung

a,b,

⁎

, Seng-Cho T. Chou

a

, Gwo-Hshiung Tzeng

c

a

Department of Information Management, National Taiwan University, Taipei 106, Taiwan b_{Department of Information Management, Hwa-Shia Institute of Technology, Taipei 235, Taiwan} c

Institute of Management of Management, National Chiao Tung University, Hsinchu 300, Taiwan

a b s t r a c t

a r t i c l e i n f o

Available online 25 November 2010 Keywords:

Knowledge Management (KM) Small and Medium Enterprises (SME) Multiple Criteria Decision Making (MCDM) Knowledge management adoption Knowledge management assessment

This paper aims to clarify the misunderstanding of high expenditure on knowledge management systems adoption, and provides a novel approach for the most emergent knowledge management components to catch up to the pace of their rivals for the late adopters of knowledge management systems. This paper adopts MCDM (Multiple Criteria Decision Making) approaches to solve this KM adoption problem, and ranks the gaps of the KM aspects in control items to achieve the aspired level of performance. Theﬁndings demonstrate that the knowledge management gaps within the service industry are higher than the gaps within the IC (Integrated Circuit) and banking industries. After normalization and computation, the knowledge manage-ment gap of the service industry is 0.4399(1), the knowledge managemanage-ment gap of the IC (Integrated Circuit) industry is 0.3651(2), and the knowledge management gap of the banking industry is 0.2820(3). Theﬁndings also show that the criteria for weighting in different industry sectors are quite different; and the adoption strategies for different industry sectors should be considered separately according to the SME industry sectors.

1. Introduction

Most SMEs (Small and Medium sized Enterprises) are suffering

because of low proﬁts caused by hyper competition and OEM

(Original Equipment Manufacturer) dead-end. Moreover, since the

middle of 2008, theﬁnancial tsunami has caused serious damage to

the global economy. Since 80% of the enterprises fall into the category of "Small and Medium Enterprises", they lack the ﬁnancial and systematic basis to introduce knowledge management practices and make innovations. Several researchers have explored the gaps in the knowledge management activities of enterprises. Their studies reveal

that corporate performance is signiﬁcantly inﬂuenced by those gaps.

The researchers have stressed the need for further investigation of knowledge management gaps. To this end, we use Grounded Theory to study the gaps in knowledge management activities in enterprises. From our pilot survey, we have discovered that gaps indeed exist between the theory and practice of Knowledge Management; thus, further development and testing of models are necessary.

Our research aims to clarify the misunderstanding of high expendi-ture on knowledge management systems adoption, and provides a novel approach for the most emergent knowledge management

components to catch up with the pace of their rivals for the late adopter of knowledge management systems. We adopt MCDM (Multiple Criteria Decision Making) approach to solve this KM (Knowledge

Management) adoption problem (Fig. 1), in which this new method

allows the decision maker to understand these gaps of the aspects and rank them to improve those large gaps in control items to achieve the aspired level.

There are certain concepts within the general domain of

Knowl-edge Management that have not been fully explored. We will beneﬁt

from a more detailed look at various risks, gaps and strengths[25].

There are ﬁve management gaps in the implementation of KM

(Knowledge Management) activities and these gaps exist in the links between KM activities and corporate performance. Corporate

perfor-mance is signiﬁcantly inﬂuenced by these knowledge management

gaps. Lin and Tseng[19]explore the gaps of knowledge management

activities for the enterprise to build a framework that analyze the corporate knowledge needs, and identify any inhibitors to the success of the implementation activities of the KM system. Their study is based on the literature review, expert interviews and questionnaires. Recently much research has studied Knowledge Management Maturity Model (KMMM) to examine the knowledge management capability and maturity for organizations recently[7,14–16]. In this paper, we survey the gaps of KMMM (Knowledge Management Maturity Model) achievements, and provide an approach for the ranking of KM aspects by the most-urgent aspects to reach the next capability stage as soon as possible. Group decision-making, the ⁎ Corresponding author. Postal address: Hwa-Shia Institute of Technology, 111 Gong

Jhuan Rd., Chung Ho, Taipei, Taiwan. Tel.: +886 919 68598; fax: +886 2 29415730. E-mail address:horninch@gmail.com(Y.-H. Hung).

Contents lists available atScienceDirect

Decision Support Systems

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essence of KM, lets us consider multi-dimensional problems for the decision-maker, sets priorities for each decision factor, and assesses rankings for all alternatives.

The remainder of this paper is organized as follows. Section 2

describes the related works to knowledge management capabilities

and Knowledge Management Maturity Model. Section 3 describes

the Multiple Criteria Decision Making approaches.Section 4describes

the research methods used in this study.Section 5proposes a novel

MCDM approach for SME (Small and Medium sized Enterprises)

knowl-edge management adoption, andSection 6presents data collected and

represented in this study. Finally, inSection 7, we present our con-clusions and suggest some directions for future research.

2. Related works

In this knowledge-based economy, knowledge has become an important asset to an organization and, consequently, Knowledge Management has emerged as an issue managers have to deal with. Numerous works on knowledge management capabilities are reported in literature[1,3,8,17,18,33]. In this section, we will discuss the related works in knowledge management capability, Knowledge Manage-ment Maturity Model, and knowledge manageManage-ment gaps.

2.1. Knowledge management capability

Knowledge management capability (KMC) is the source for organizations to gain a sustainable competitive advantage. KMC evaluation is a required work with strategic signiﬁcance[8,18]. Previous KM research has developed integrated management frameworks for building organizational capabilities of Knowledge Management. Based on these frameworks, they propose stage models of organizational

knowledge management encompassing the KM process stages[17].

Gold et al.[10]examine the issue of effective Knowledge

Man-agement from the perspective of organizational capabilities. They suggest that a knowledge infrastructure consisting of technology, structure, and culture along with knowledge processes architecture of acquisition, conversion, application, and protection is essential for the organizational capabilities of effective Knowledge Management.

2.2. Knowledge Management Maturity Model

Knowledge Management Maturity Modeling (KMMM) has been a major topic of research in recent years[7,14_–16]. In practice, a few

KMM models[16]have been proposed by consultingﬁrms as well.

However, a common KMM model that both academics and practi-tioners agree on has yet to materialize and moreover, details are often missing from models in practice.

Most KMM models inherit the spirit of the Capability Maturity

Model (CMM)[5] of SEI with its ﬁve levels of maturity — initial,

repeated, deﬁned, managed, and optimizing. Capability, another

important attribute of CMM, can be translated into the enabling factors or infrastructure of KM. While most KMM models treat KM as a holistic activity, we view it as a process and divide it into four KM sub-processes, namely knowledge creation, knowledge storage, knowledge sharing, and knowledge application. The added dimension allows us to gain better insight into how KM practices are supported

at each maturity level and reﬂects our emphasis on the need for

continuous process improvement. 2.3. Knowledge management gaps

Several researchers have explored the gaps in knowledge management activities of enterprises and identiﬁed the links between these activities and corporate performance. Their results reveal that

corporate performance is signiﬁcantly inﬂuenced by these

manage-ment gaps.

Previous research has demonstrated that making a more detailed observation of risks, gaps and strengths is beneﬁcial[25]. According

to theﬁndings of Lin and Tseng[19], there areﬁve management gaps

in implementation of KM activities and these gaps exist in the links

between KM activities and corporate performance[19]. Their study

explores the gaps of knowledge management activities for the enter-prise to build a framework that analyzes the corporate knowledge

needs, and identiﬁes any inhibitors to the success of the

implemen-tation activities of the KM system. It shows that corporate

perfor-mance is signiﬁcantly inﬂuenced by these knowledge management

gaps.

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3. Some basic concepts for MCDM (Multiple Criteria Decision Making) methods

The decision-making process involves identifying problems, constructing preferences, evaluating alternatives, and determining the best alternative[20,23,24,35,39]. However, when decision-makers evaluate the alternatives with multiple criteria, many problems, such

as the weights of the criteria, preference dependence, and conﬂicts

among criteria, seem to complicate the decision-making process and should be resolved by more sophisticated methods.

Decision-making is extremely intuitive when considering single criterion problems, since we only need to choose the alternative with the highest preference rating. However, adopting a knowledge management system is not just a single criterion problem. Decision-makers need to evaluate the alternatives based on multiple criteria. Many problems, such as the weights of criteria, preference dependence, and con_ﬂicts among criteria, seem to complicate the decision-making process and should be resolved by more sophisticated methods.

3.1. The MCDM (Multiple Criteria Decision Making) methodology processes

Dealing with Multiple Criteria Decision Making (MCDM) problems involves 5 key steps.

(1) Identi_{ﬁcation of the problem/issue: decision-makers need to}

identify the nature of the research problem. They must

deter-mine speciﬁcally which criteria should be considered, and

which decision-making strategies should be adopted. (2) Problem structuring: practitioners/decision-makers need to

identify the goals, values, constraints, external environment, key issues, uncertainties, and stakeholders of this enterprise. In this step, we need to collect the appropriate data or infor-mation so that the preferences of decision-makers can be correctly identiﬁed and considered.

(3) Model building: decision-makers then specify the alternatives, deﬁne all criteria, and elicit values for model building. This process allows them to compile a set of possible alternatives or strategies in order to guarantee that the goal will be achieved. (4) Using the model to inform and challenge established thinking: especially decision-makers collect and synthesize information, challenge people's intuition, suggest other new alternatives, and analyze the robustness and sensitivity of the model. (5) Developing an action plan: in theﬁnal step, an action plan is

constructed as a solution. In other words, we can select the appropriate method to help us to evaluate and rank the possible alternatives or strategies (i.e., determine the best alternative). 3.2. Analytic Network Process (ANP)

The Analytic Network Process (ANP) is an extension of Analytic

Hierarchy Process (AHP) by Saaty[30]to overcome the problem of

interdependence and feedback among criteria or alternatives[30–32]. Although the AHP and the ANP derive ratio scale priorities by making pair-wise comparisons of elements (such as dimensions or criteria),

there are differences between them. Theﬁrst is that the AHP is a

special version of the ANP; the ANP handles dependence within a cluster (inner dependence) and among different clusters (outer dependence). Secondly, the ANP is a nonlinear structure, while the AHP is hierarchical and linear, with the goal at the top and the alternatives in the lower levels[31]based on the dynamic concept of the Markov chain[32].

The initial step of the ANP is to compare the criteria in the entire system to form a super-matrix through pair-wise comparisons by asking "How much importance does one criterion have compared to another criterion, with respect to our interests or preferences?" The

relative importance is determined using a scale of 1–9 representing

equal importance to extreme importance[11].

3.3. The DEMATEL (Decision MAking Trial and Evaluation Laboratory) technique

The DEMATEL (Decision MAking Trial and Evaluation Laboratory) method gathers collective knowledge to capture the causal relation-ships between strategic criteria. This paper applies the DEMATEL technique in the strategic planning of Knowledge Management to help managers address the above situations and related questions.

Because evaluation of knowledge management capabilities cannot accurately estimate each considered criterion in terms of numerical values for the alternatives, fuzziness is an appropriate approach. The DEMATEL technique is an emerging method that gathers group knowledge to capture the causal relationships between criteria. It is especially practical and useful for visualizing the structure of compli-cated causal relationships with matrices or digraphs, which portray the contextual relations between the elements of a system, where a numeral

represents the strength of inﬂuence [34]. Therefore, the DEMATEL

technique can convert the relationship between the causes and effects of criteria into an intelligible structural model of the system.

The DEMATEL technique is utilized to investigate the interrelations among criteria to build a Network Relationship Map (NRM). This technique has been successfully applied in many situations, such as the development of strategies, management systems, e-learning

evaluations, and Knowledge Management [20,34,37]. The method

can be arranged as follows:

Step 1: Obtain the direct-inﬂuence matrix by scores. Respondents are

required to point out the degree of direct inﬂuence among

each criterion. We suppose that the comparison scales, 0, 1, 2, 3 and 4, stand for the levels from "no inﬂuence" to "very high inﬂuence". Then, the graph which can describe the inter-relationships between the criteria of the system is shown

in the ﬁgure below. For instance, an arrow from w to y

symbolizes that w impacts on y, and the score of inﬂuence is 1.

The direct-inﬂuence matrix, A, can be derived by indicated

one criterion i impact on another criterion j as aij.

w x z 1 2 3 3 4 y

A

=

11 1 1 1 1 j n i ij in n nj nn

a

⎡

⎤

⎢

⎥

⎢

⎥

⎢

⎥

⎢

⎥

⎢

⎥

⎢

⎥

⎣

⎦

...

Step 2: Calculate the normalized direct-inﬂuence matrix X. X can be

calculated by normalizing A through Eqs.(1) and (2).

X = m_⋅A ð1Þ m = min 1 maxi∑ n j = 1jaijj ; 1 maxj∑ n i = 1jaijj 2 6 4 3 7 5 ð2Þ

Step 3: Derive the total direct-inﬂuence matrix T. T of NRM (Network Relationship Map) can be derived by using a formula (3), where I denotes the identity matrix; i.e., a continuous decrease of the indirect effects of problems along the powers of X, e.g., X2_{, X}3_,_{…, X}q_{and lim}

q→∞X q_{= 0}_½

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0≤xij≤1, 0 b∑j = 1n xij≤1 and 0b∑i = 1n xij≤1. If at least one row or column of summation is equal to 1, but not all, then limq→ ∞Xq= [0]n × n. The total-inﬂuence matrix is listed as follows. T = X + X2+⋯ + Xq = X I + X + X 2+⋯ + Xq−1_ð_I_−X_{Þ I−X}_ð _Þ−1 = X I−Xq I−X ð Þ−1 when q→∞, Xq_{= [0]} n × n, then T = X Ið−XÞ−1 ð3Þ where T = [tij]n × n, i, j = 1,2,…,n.

Step 4: Construct the NRM based on the vectors r and s. The vectors r and s of matrix T represent the sums of rows and columns respectively, which are shown as Eqs.(4) and (5).

r = r½ in×1= ∑ n j = 1 tij " # n×1 ð4Þ s = sj h i n×1= ∑ n i = 1 tij _′ 1×n ð5Þ where ridenotes the sum of the i-th row of matrix T and displays the sum of direct and indirect effects of criterion i on another criteria. Also, sjdenotes the sum of the j-th column of matrix T and represents the sum of direct and indirect effects that criterion j has received from another criteria. Moreover, when i = j (ri+ si), it presents the index of the degree of influences given and received; i.e., (ri+ si) reveals the strength of the central role that factor i plays in the problem. If (ri−si) is positive representing that other factors are impacted by factor i. On the contrary, if (ri−si) is negative, other factors have influences on factor i and thus the NRM can be constructed[22,34]. Therefore, a causal graph can be achieved by mapping the dataset of (ri+ si, ri−si), providing a valuable approach for decision-making. The vector r and vector s express the sum of the rows and the sum of the columns from the total-influence matrix T=[tij]n × n, respectively, and the

superscript denotes the transpose [2]. Now we call the

total-inﬂuence matrix TC= [tij]n × n obtained by criteria and TD=

[tijD]m × mobtained by dimensions (clusters) from experts'

opi-nions. Then we normalize the ANP weights of dimensions (clusters) by using inﬂuence matrix TD.

3.4. VIKOR (the Serbian name, VlseKriterijumska Optimizacija I Kompromisno Resenje)

Opricovic[28]and Opricovic and Tzeng (2002) developed VIKOR

(the Serbian name, VlseKriterijumska Optimizacija I Kompromisno Resenje, means Multi-criteria Optimization and Compromise Solution) [27–29]. The basic concept of VIKOR lies in deﬁning the positive and negative ideal solutionsﬁrst. The positive ideal solution indicates the alternative with the highest value (score of 100), while the negative ideal solution indicates the alternative with the lowest value (score of 0). In our study, the highest performance value of SMEs (Small and Medium sized Enterprises) is 5, and the lowest performance value is 0. They are used to help DMs (Decision Makers) by representing the present status of KM components for KM assessment and adoption.

The VIKOR method is developed as a multi-criteria decision-making method to solve a discrete decision problem with non-commensurable and conﬂicting criteria[27,29]. The method ranks a set of alternatives,

and selects the alternative with the highest score. It then suggests compromise solutions to a problem with conﬂicting criteria in order to help practitioners reach a_{ﬁnal decision. Here, the compromise solution} is the feasible solution that is the closest to the ideal, and a compromise means an agreement reached on the basis of mutual concessions. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which is also a distance-based approach, derives a solution with the shortest distance from the positive ideal solution and the farthest distance from the negative-ideal solution, but it does not con-sider the relative importance of the distances. A detailed comparison of TOPSIS and VIKOR is presented in Opricovic and Tzeng[29].

Multi-criteria ranking and compromise solutions Criteria Weights Alternatives maxk

(or aspired value) mink

(or the worst value) a1 … ak … am c1 w1 x11 … xk1 … xm1 x1⁎ x−1 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ci wi xi1 … xik … xim x⁎i x−1 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ cn wn xn1 … xnk … xnm xn⁎ x−n

(Data matrix: larger is better)

dpk= ∑ n i = 1 wi x⁎_i−xik x⁎_i−x− i ! " #p ( )1= p when d_kp = 1= Sk=∑ n i = 1 wi x⁎ i−xik x⁎ i−x−i !

for average degree of regret (average gap) dp =∞_k = Qk= maxi

x⁎_i − xik x⁎_i− x− i ! ji = 1; 2; :::; n ( ) for maximal degree of regret (priority improvement).

Ranking (small is better for distance Skand Qk) Rk= v Sk−S⁎

= S −−S⁎

+ 1ð −vÞ Q½ð k−Q⁎Þ = Qð −−Q⁎Þ;

Let v = 0.5 be the majority criteria, where, S = minkSk(or S⁎ = 0, i.e.,

achieving aspired level, gap equals zero), S−= maxkSk (or S−= 1

denotes that the index is the worst value) and Q = minkQk(Q⁎ = 0),

Q−= maxkQk(or Q−= 1).

3.5. Simple Additive Weighting method (SAW)

Churchman and Ackoff[4]ﬁrstly utilized the SAW method to cope

with portfolio selection problem[4]. SAW method is probably the best-known and widely used method for MCDM (Multiple Criteria Decision Making). Because of the simplicity, the SAW is the most popular method in the MCDM (Multiple Criteria Decision Making) problems and the best alternative can be derived by the following equation:

A⁎= ukð Þj maxx k ukð Þx ð6Þ and ukð Þ = ∑x n i = 1 wirikð Þx ð7Þ

where uk(x) denotes the utility of the k-th alternative, widenotes the weights of the i-th criterion, and rik(x) is the normalized preferred ratings of the k-th alternative with respect to the i-th criterion. In addition, the normalized preferred ratings (rik(x)) of the i-th alter-native with respect to the j-th criterion can be deﬁned by:

For beneﬁt criteria, rikð Þ =x xik x⁎ i

, where x⁎

i = maxkxik, and it is clear

0≤rik(x)≤1; for cost criteria, rikð Þ =x 11= x= xik i =

min

k xik

xik ; or setting aspired

level (the best value) as xi* and the worst value as xi−, then rik= xik−x−i

x i−x−i .

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where x_ikis the normalized preferred ratings of the k-th alternative with respect to the i-th criterion.

4. The research architectures and methods for Knowledge Management

Knowledge management adoption is also an MCDM (Multiple Criteria Decision Making) problem. Theﬁrst step involves identifying how many attributes or criteria are involved in the adoption of a knowledge management system. Next, the appropriate data or information must be collected so that the preferences of different

stakeholders can be correctly identiﬁed and considered (i.e.,

con-structing the preferences). Our goal is to establish objective and

measurable patterns to deﬁne the anticipated achievements of

Knowledge Management by conducting group-decision analysis. Group decision-making as previously mentioned, the essence of KM, allows decision-makers to consider multi-dimensional problems, sets priorities for each decision factor, and assesses the rankings of all alternatives.

The procedures of MCDM (Multiple Criteria Decision Making) for KM adoption in this study:

1. More than sixty KM experts were invited and academic focus groups were constructed in KMAP2004 (International Conference

of Knowledge Management in Asia Paciﬁc), ECKM2005 (European

Conference of Knowledge Management 2005), workshop of NSC2006 (National Science Council in Taiwan) to address the research issues of knowledge management gaps between practical

activities and theoretical ﬁndings of enterprises to identify the

links between these activities and corporate performance. 2. In 2007, we joined Knowledge Management Project of Small and

Medium sized Enterprises (SMEKM) of the Taiwan Ministry of Economic Affairs. The Delphi method was used to clarify the guidelines and bottlenecks of Small and Medium sized Enterprises.

More than fortyﬁve KM domain experts/consultants were involved

in this KMMM (Knowledge Management Maturity Model) surveys. After the SMEKM forum and pilot survey, we discovered that a gap indeed existed between the theory and practice of Knowledge Management; thus, Grounded Theory was used for further de-velopment and testing of our model to investigate the unknown reasons behind the SMEKM report.

3. Between the years of 2008 and 2009, we clariﬁed the KM gaps

which existed in KM practices of SMEs and proposed a hybrid MCDM (Multiple Criteria Decision Making) approach combining DEMATEL, SAW, VIKOR and ANP for weighting to rank the gaps that had not been reduced or improved (the unimproved gaps) for the alternatives/projects or aspects of a project to get the most beneﬁt and reach the aspired KMMM (Knowledge Management Maturity Model) level.

4. From the years of 2006 to 2009, we collected empirical data by using the KMMM (Knowledge Management Maturity Model) capability questionnaires to investigate KM maturity performance from CEOs (Chief Executive Ofﬁcers)/practitioners of three different industries, namely the Integrated Circuits industry, banking industry, and services industry. Performance values of KM aspects of SMEs were multiplied with the weighting values used to rank the KM gaps and KM alternatives for knowledge management adoption.

4.1. Grounded Theory

From our pilot survey, we discovered that a gap indeed exists between the theory and practice of Knowledge Management; thus, further development and testing of models is necessary.

After we studied the results of interviews with senior managers from Taiwanese banking organizations, we discovered something

interesting needed to be discussed. Then we adopt Grounded Theory (GT), which has become popular for conducting management research because it can be used to identify emerging issues from interviews. This forms theﬁrst phase of this doctoral study. Our goal is to develop a knowledge management model for these organizations. Grounded Theory (GT), which is most often associated with the social sciences, such as psychology, was developed by the sociologists Barney Glaser (1930–Present) and Anselm Strauss (1916–1996). Their collabo-rative research on terminally ill hospital patients led them to write the book Awareness of Dying. As a result of their research, they developed the constant comparative method, subsequently known as Grounded Theory

[9], which was developed as a systematic methodology[9]. Its name

underscores the generation of theories from data. By following the principles of Grounded Theory, researchers can formulate a theory,

either substantive (setting speciﬁc goals) or formal, about the

phenomena they are studying and evaluating, e.g., gaps in Knowledge Management.

4.2. Delphi method

The Delphi method originated in a series of studies conducted by the RAND Corporation in the 1950s[13]. The objective was to develop a technique to obtain the most reliable consensus from a group of

experts [6]. While researchers have developed variations of the

method since its introduction, Linstone and Turoff[21]captured its

common characteristics in the following description: Delphi may be characterized as a method for structuring a group communication process; so the process is effective in allowing a group of individuals,

as a whole, to deal with a complex problem[21]. To accomplish this

‘structured communication,’ certain aspects should be provided: some feedback of individual contributions of information and knowledge; some assessment of the group judgment or viewpoint; some op-portunity for individuals to revise their views; and some degree of anonymity for individual responses[21].

The Delphi technique enables a large group of experts to be surveyed cheaply, usually by mail using a self-administered questionnaire (although computer communications also have been used), with few geographical limitations on the sample. Speciﬁc situations have included a round in which the participants meet to discuss the process and resolve any uncertainties or ambiguities in the wording of the questionnaire[13]. 5. Building a novel MCDM (Multiple Criteria Decision Making) model with ANP, DEMATEL, and VIKOR for SMEKM adoption

Because practitioners often manage several KM alternatives with conﬂicting, and wonder what are the differences of KM practices with other competitors? What is the next step? How can we assess and measure the practiced activities of knowledge management process? These questions should be answered. We wish to consider several non-commensurable criteria to reduce the gaps to achieve the aspired KMMM (Knowledge Management Maturity Model) stage by ranking the gaps that have not been reduced or improved (the unimproved gaps) for the alternatives/projects or aspects of a project to get the most

beneﬁt and reach the aspired KMMM (Knowledge Management

Maturity Model) level.

As any criterion may impact each other, this study used the DEMATEL (Decision MAking Trial and Evaluation Laboratory) technique to acquire the structure of the MCDM (Multiple Criteria Decision Making) problems. The weights of each criterion from the structure are obtained by utilizing the ANP (Analytic Network Process). The VIKOR technique will be leveraged for calculating compromise ranking and gaps of the alternatives. In short, the framework of evaluation contains three main phases: (1) constructing the Network Relationship Map (NRM) among criteria by the DEMATEL technique, (2) calculating the weights of each criterion by the ANP based on the NRM, and (3) ranking or improving the priorities of alternatives of portfolios through the VIKOR.

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5.1. The ANP (Analytic Network Process) for calculating weights of criteria based on the NRM

The AHP (Analytic Hierarchy Process) supposes independence among criteria, which is not reasonable in the real world. Saaty[30] thus extended AHP to ANP (Analytic Network Process) to resolve problems with dependence or feedback between criteria, which primarily divides problems into numerous different clusters and every cluster includes multiple criteria[30–32]. Moreover, there is outer dependence among clusters and inner dependence within the

criteria of clusters. In addition, we ﬁgured the relative weights of

criteria of respective matrices by pair-wise comparison and modifying the weights as eigenvectors. Then we integrated multiple matrices into a super matrix, because the capacity to examine the inner and outer dependence of clusters is the largest beneﬁt of a super matrix as in Eq.(8).

There are three steps for the decision process of ANP. First, the decision problem and the structure of problem were built to offer an evident depiction of the problem and separate it into a relation network structure. Second, not only is the pair-wise comparison

matrix established, but also eigenvalue and eigenvector wereﬁgured.

C1 e11e12⋯ e1n1 C2 e21e22⋯ e2n2 ⋯ ⋯ Cn en1en2⋯ ennn W = C1 C2 ⋮ Cn e11 e12 ⋮ e1n1 e21 e22 ⋮ e2n2 ⋮ en1 en2 ⋮ ennn W11 ⋯ W12 ⋯ W1n W21 ⋯ W22 ⋯ W2n ⋮ ⋮ ⋮ Wn1 ⋯ Wn2 ⋯ Wnn 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5 ð8Þ

Pair-wise comparison is composed of clusters and criteria. Fur-thermore, the pair-wise comparison of clusters was separated into comparison of criteria within and between clusters. We utilize ratio

scale (1–9) to determine the level of importance of the comparison.

In addition, the data deriving from the survey of ANP were combined and transferred into pair-wise comparison matrix by geometric

aver-age. After building the matrix, we received the eigenvector Wii

through an equation: Aw =λmaxw, where A is pair-wise comparison

matrix, w = (w1,…,wi,…,wn)′ is the eigenvector, wiis the eigenvalue, then λmax= 1 n∑ n i = 1 Aw ð Þi wi ð9Þ

where (Aw)i=∑j = 1n aijwjand n equals the number of comparative criteria. Third, the super-matrix, tagged W was formed. It was constructed by the dependence table obtained from the interrelations among criteria, and the eigenvectors received from the pair-wise comparison matrix served as the weights of it. No inner dependence among criteria or clusters was shown by a blank or zero. By Wu and

Lee [38], the usage of power matrix by Wh _{(multiplication) and}

limh→ ∞Whis aﬁxed convergence value; therefore, we can acquire

weights in every criterion[38].

5.2. The revised VIKOR for ranking and improving the alternatives

Opricovic[28]proposed the compromise ranking method (VIKOR)

as one applicable technique to implement within MCDM (Multiple

Criteria Decision Making)[28,29]. Suppose the feasible alternatives are represented by A1, A2,…, Ak,…, Am. The performance score of alternative Akand the j-th criterion is denoted by fik; wiis the weight (relative importance) of the i-th criterion, where i = 1, 2,…, n, and n is the number of criteria. Development of the VIKOR method began with the following form of Lp-metric:

Lp_k= ∑n i = 1 wi fi−fik = f i−fik− p 1= p ; ð10Þ

where 1≤p≤∞; alternative k=1,2,…,m; weight wiis derived from

the ANP. To formulate the ranking and gap measure Lkp = 1(as Sk) and Lkp =∞(as Qk) are used by VIKOR[28,29,34–37].

Sk= L p = 1 k = ∑ n j = 1 wi fi−fik = f i−fi− ð11Þ Qk= L p =∞ k = max i f i−fik = f i−fi−

j

i = 1; 2; ⋯; n : ð12Þ

The compromise solution minkLpk shows the synthesized gap to

be the minimum and will be selected for its value to be the closest to the aspired level. Besides, the group utility is emphasized when p is small

(such as p=1); on the contrary, if p tends to become inﬁnite, the

individual maximal regrets/gaps obtain more importance in prior improvement in each dimension/criterion. Consequently, minkSk

stres-ses the maximum group utility; however, minkQk accents on the

selecting the minimum from the maximum individual regrets/gaps. The compromise ranking algorithm VIKOR has four steps according to the above-mentioned ideas.

Step 1: Obtain an aspired or tolerable level. We calculate the best fi* values (aspired level) and the worst fi−values (tolerable level) of all criterion functions, i = 1,2,…,n. Suppose the i-th

function denotes beneﬁts: f

i = maxkfik (or setting the

aspired level as fi*) and fi−= minkfik(or setting the worst

value as fi−) or these values can be set by decision makers, i.e., fj* = aspired level and fj−= the worst value. Further, an original rating matrix can be converted into a normalized weight-rating matrix by using the equation:

rik= fi−fik

= f i−fi−

: ð13Þ

Step 2: Calculate mean of group utility and maximal regret. The

values can be computed respectively by Sk= ∑

n

i = 1

wirik(the

synthesized (average) gap for all criteria) and Qk= maxi

{rik|i = 1, 2,..., n} (the maximal gap for prior improvement). Step 3: Calculate the index value. The value can be counted by

Rk= v Sk−S = S −−S+ 1ð −vÞ Qk−Q = Q −−Q;ð14Þ where k = 1,2,…,m. S= min i Sior setting S⁎ = 0 and S −_{= max} i Sior setting S −₌ 1; Q= min i Qior setting Q⁎ = 0 and Q −_{= max} iQior setting

Q−= 1; and v is presented as the weight of the strategy of the maximum group utility.

Step 4: Rank or improve the alternatives for a compromise solution. Order them decreasingly by the value of Sk, Qkand Rk. Propose

as a compromise solution the alternative (A(1)_{) which is}

arranged by the measure min{Rk|k = 1, 2,..., m} when the two conditions are satisﬁed:

C1. Acceptable advantage: R(A(2)₎_−R(A(1)₎_{≥1/(m−1), where} A(2)_{is the second position in the alternatives ranked by R.} C2. Acceptable stability in decision making: Alternative A(1)

must also be the best ranked by Skor/and Qk. When one

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solutions is selected. The compromise solutions are com-posed of: (1) Alternatives A(1)_{and A}(2)_{if only condition} C2 is not satisﬁed or (2) Alternatives A(1)

, A(2),…,A(M) if condition C1 is not satisﬁed. A(M)_{is calculated by the}

relation R(A(M)₎_−R(A(1)₎_{b1/(m−1) for maximum M}

(the positions of these alternatives are close).

The compromise-ranking method (VIKOR) is applied to determine the compromise solution and the solution is adoptable for decision-makers in that it offers a maximum group utility of the majority (shown by min S), and a maximal regret of minimum individuals of the opponent (shown by min Q). This model utilizes the DEMATEL and ANP processes to get the weights of criteria with dependence and feedback and employs the VIKOR method to acquire the compromise solution.

5.3. Assessing the KM maturity of the IC (Integrated Circuit) design, banking, and services industries

In this section, we present an empirical study for applying the proposed model to assess the knowledge management gaps in the industries mentioned above. First, we use the same weighted preferences for knowledge management components to assess the

three industries, and then compile a proﬁle of the knowledge

man-agement gaps and the best adoption strategies for the industries. Second, based on the weighted preferences of knowledge manage-ment components provided by different domain experts, we discuss the results of using those preferences to assess the three industries and determine the best KM adoption strategy for each one.

The knowledge management gaps between the theoretical knowl-edge management practices and practical knowlknowl-edge management activities of enterprises have signiﬁcantly inﬂuenced corporate perfor-mance. Therefore, proper measurement and decision-making processes are critical for knowledge management adoption and success. In the context of strategic goals and transformation, using different KM

alter-natives will in_{ﬂuence resource allocation and overall achievement}

of success. Group decision-making is a process where experts make decisions and consolidate an optimal strategy.

6. Data collection and representation 6.1. Constructing the NRM by DEMATEL

To analyze the interrelationships between the twelve determi-nants summarized through literatures, the DEMATEL method intro-duced inSection 3.3will be utilized in the decision problem structure. Table 1

The initial inﬂuence matrix A for criteria (banking industry).

Criteria KCT KCS KCC KSHT KSHS KSHC KST KSS KSC KAT KAS KAC

Knowledge Creation Technology (KCT) 0 0.076923 0.038462 0.076923 0.076923 0.038462 0.076923 0.076923 0.038462 0.076923 0.076923 0.038462 Knowledge Creation Structure (KCS) 0.115385 0 0.115385 0.038462 0.115385 0 0.038462 0.076923 0.038462 0.038462 0.076923 0.038462

Knowledge Creation Culture (KCC) 0.076923 0.115385 0 0 0 0.115385 0 0 0.115385 0 0 0.115385

Knowledge SHaring Technology (KSHT) 0.115385 0.038462 0.038462 0 0.076923 0.115385 0.076923 0.038462 0.038462 0.076923 0.076923 0.038462 Knowledge SHaring Structure (KSHS) 0.115385 0.115385 0.076923 0.115385 0 0.115385 0.115385 0.076923 0.115385 0.038462 0.076923 0.038462 Knowledge SHaring Culture (KSHC) 0 0.038462 0.076923 0.115385 0.038462 0 0.038462 0.076923 0.076923 0.038462 0.038462 0.076923 Knowledge STorage Technology (KST) 0.076923 0.038462 0.038462 0.115385 0.038462 0.038462 0 0.076923 0.076923 0.038462 0.038462 0

Knowledge STorage Structure (KSS) 0 0.076923 0 0 0.076923 0.038462 0.076923 0 0.115385 0.038462 0.076923 0.038462

Knowledge STorage Culture (KSC) 0 0.038462 0.076923 0.076923 0.076923 0.115385 0.076923 0.076923 0 0.038462 0.038462 0.038462 Knowledge Application Technology (KAT) 0.076923 0 0 0.076923 0.038462 0.038462 0.076923 0.038462 0.038462 0 0.076923 0.076923

Knowledge Application Structure (KAS) 0 0.076923 0.076923 0 0.076923 0.038462 0 0.076923 0 0.076923 0 0.076923

Knowledge Application Culture (KAC) 0.038462 0.076923 0.076923 0 0.038462 0.076923 0 0.038462 0.076923 0.076923 0.115385 0

Knowledge

Creation

Knowledge

Sharing

Knowledge

Application

KC Technology

KC Structure

KC Culture

Assessment the

KM Adoption Strategy

for

IC design industries

Knowledge

Storage

KSt. Structure

KSt. Culture

KSt. Technology

KA Technology

KA Structure

KA Culture

KSh. Technology

KSh. Structure

KSh. Culture

Assessment the

for

Banking industries

Assessment the

for

Services industries

Fig. 2. The KM adoption strategy for three different industries.

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First, the direct influence matrix A for criteria was presented (see Table 1). Then, the normalized direct-influence matrix S for criteria can be calculated by Eq.(1). Third, the total direct in_{fluence matrix} T for criteria/dimensions was derived based on Eq.(3). Finally, the NRM (Network Relationship Map) was constructed by the r and s (Eqs.(4) and (5)) in the total direct in_{fluence matrix T as shown in} Fig. 2.

6.2. Calculating weights of each criterion by ANP

Many experts were recruited including SME (Small and Medium sized Enterprises) consultants, knowledge management domain scho-lars, and executive managers of SMEs in several stages. There are

twenty-ﬁve SME consultants recruited from SMEKM (Knowledge

Management Plan for Small and Medium Enterprises) project of the Small and Medium Enterprise Administration, Ministry of Economic Affairs, Taiwan. Fifteen knowledge management domain scholars in ECKM2005 (6th European Conference on Knowledge Management), seven knowledge management domain scholars in Kmap2004

(Inter-national Conference on Knowledge Management in Asia Paciﬁc), and

nine knowledge management domain scholars in Taiwan NSC (National Science Council) doctoral students research workshop in 2007 were also invited. Finally, KM performance questionnaire data from SME executive managers in SMEKM project, EMBA (Executive Master of Business Administration) program students of NTU (National Taiwan University, Taiwan), NCCU (National Chengchi University, Taiwan), NTPU (National Taipei University, Taiwan), and NCTU (National Chiao Tung University, Taiwan) was collected. According to their expertise

of industry sectors, industry-speciﬁc SME consultants and knowledge

management domain scholars were invited to complete the ANP and DEMATEL questionnaires from different industry perspectives. The executive managers of SMEs were invited to complete the matrix questionnaire for the performance value of their organizational knowl-edge management capability.

The level of importance (global weights) of 12 criteria can be

calculated by ANP shown asTables 1–4. Results showed that experts

were most concerned with Knowledge STorage Culture (rank 1) and Knowledge SHaring Culture (rank 2), and least concerned with Knowledge Application Technology (rank 12) and Knowledge STorage Technology (rank 11).

6.3. Compromise ranking by VIKOR

The VIKOR technique was applied for compromise ranking after

the weights of determinants were calculated by ANP in Table 4.

Calculation results (Table 5) demonstrated that the total gaps were highest in the services industry, followed by the IC (Integrated Circuit) industry and the banking industry. Therefore, both VIKOR and ANP came to the same conclusions that the KM adoption strategies provided by this study indicated that services industry practitioners are suggested to focus their investment in KM gaps.

When considering the KMCs (knowledge management compo-nents), it seems to a serious mistake to apply the same weighting

preferences across industries (Table 6). The KMC weighting

prefer-ence of the banking industry is quite different from both the KMC weighting preferences of the IC (Integrated Circuit) industry and the service industry. Therefore, we should assess the KMC capability of

Table 2

The sum of inﬂuences given and received on dimensions (banking industry). Dimension ri si ri+ si ri−si Knowledge Creation D1 5.4276 5.3865 10.8141 0.0411 Knowledge SHaring D2 6.5061 5.6084 12.1144 0.8977 Knowledge STorage D3 5.0086 5.4958 10.5044 -0.4872 Knowledge Application D4 4.5928 5.0444 9.6371 -0.4516 Table 3 Weighting the unweighted super-matrix based on total in ﬂ uence normalized matrix (banking industry).

Knowledge Creation Technology Knowledge Creation Structure Knowledge Creation Culture Knowledge SHaring Technology Knowledge SHaring Structure. Knowledge SHaring Culture. Knowledge STorage Technology Knowledge STorage Structure Knowledge STorage Culture Knowledge Application Technology. Knowledge Application Structure Knowledge Application Culture

Knowledge Creation Technology 0.06345620 0.10077795 0.08692885 0.10414359 0.08544691 0.05830946 0.08869009 0.05485304 0.05392349 0.11776410 0.05015476 0.06492572 Knowledge Creation Structure 0.11304172 0.05999107 0.11845530 0.07725408 0.09169038 0.08925526 0.07412048 0.11325413 0.08153067 0.07131082 0 .10497532 0.09571033 Knowledge Creation Culture 0.08495168 0.10068059 0.05606545 0.07187047 0.07613085 0.10570342 0.06815391 0.06285731 0.09551033 0.06411628 0.0 9806112 0.09255514 Knowledge SHaring Technology 0.08614570 0.07201157 0.05525401 0.05581322 0.09512415 0.11250879 0.11855249 0.06513007 0.08630336 0.09542590 0 .05901901 0.05801885 Knowledge SHaring Structure 0.09041978 0.11410316 0.06096366 0.08968982 0.05894947 0.07757949 0.08054046 0.12116961 0.08601445 0.07974976 0. 12185552 0.08772273 Knowledge SHaring Culture 0.07623523 0.06668598 0.13658304 0.11099749 0.10242691 0.06641225 0.08564541 0.09843867 0.11242055 0.07333005 0.06 763118 0.10276412 Knowledge STorage Technology 0.08382554 0.07078848 0.04900293 0.09384944 0.08595311 0.06811593 0.05489448 0.08991790 0.09475343 0.08840888 0 .05237778 0.04906488 Knowledge STorage Structure 0.08950142 0.09446741 0.06192273 0.08315777 0.08087607 0.09313759 0.10330958 0.05516700 0.10133690 0.07433969 0. 11369118 0.08186347 Knowledge STorage Culture 0.07648150 0.08455257 0.13888280 0.08636249 0.09654052 0.10211617 0.10752563 0.12064480 0.06963936 0.07578261 0.07 246222 0.10760283 Knowledge Application Technology 0.07975629 0.06653576 0.04840045 0.07667973 0.06596026 0.06382855 0.07621670 0.06175417 0.06664314 0.05110 558 0.09530539 0.08713419 Knowledge Application Structure 0.09174375 0.09766281 0.06200836 0.08705050 0.09218873 0.07555262 0.08842612 0.09329414 0.07915144 0.111082 87 0.06010617 0.11919556 Knowledge Application Culture 0.06444119 0.07174267 0.12553242 0.06313140 0.06871263 0.08748046 0.05392464 0.06351916 0.07277289 0.09758347 0.10436037 0.05344217 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000

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banking industry by the weighting preferences of domain experts in the banking industry. This is true for the assessing of KMC capability for the service industry and the IC (Integrated Circuit) industry.

7. The researchﬁndings and managerial implications

The empirical results were discussed as follows. In the_{ﬁrst place,} the most important criteria calculated by ANP when making adopting KM components decisions were Knowledge STorage Culture (weight-ing 0.0947) for bank(weight-ing industry, Knowledge SHar(weight-ing Culture (weighting 0.0956) for IC (Integrated Circuit) industry, and Knowl-edge SHaring Structure (weighting 0.0963) for service industry.

The Knowledge STorage Culture is most critical for knowledge man-agement adoption of banking industry. The more popular the joining of Knowledge STorage Culture, the better the successful Knowledge Management is. But the preferences of emphasizing on knowledge management components are different. Moreover, the performances of knowledge management components (KMC) in these three industries differ separately. The highest score of KMC in IC (Integrated Circuit) industry is in knowledge storage (3.3733), in addition, the Knowledge STorage Structure (3.5750) gets the highest score among 12 criteria. The highest score of KMC in the banking industry is in knowledge application (3.6086), and the Knowledge Application Structure (3.6719) gets the highest score among 12 criteria. The highest score of KMC in services industry is in knowledge creation (2.8476), and the Knowledge Creation Culture (3.0536) gets the highest score among 12 criteria.

7.1. Researchﬁndings

We discovered that the weighting preferences among experts and raters in different industry sectors are quite different; therefore,

we should invite the speciﬁc domain experts or SME consultants to

provide the respective industry weighting. Moreover, the perfor-mance of KMCs in industry should be rated/assessed by SME (Small and Medium sized Enterprises) executives because of their

experience and understanding of their speciﬁc industry domain

knowledge.

Theﬁndings showed the rankings of knowledge management gaps

and performance of knowledge management components in these three industries. The knowledge management gaps of service industry are higher than the gaps of IC (Integrated Circuit) industry and

banking industry (Table 7). After normalization and computation,

the knowledge management gap of service industry is 0.4399(1), the knowledge management gap of IC (Integrated Circuit) industry is 0.3651(2), and the knowledge management gap of banking industry is 0.2820(3). After the completion of rating for performance of knowledge management components, the knowledge management performance of service industry is 2.8006 (rank 3), the knowledge management performance of IC (Integrated Circuit) industry is 3.1715 (rank 2), and the knowledge management gap of banking industry is 3.5899 (rank 1).

The compromise ranking by VIKOR showed that the bottleneck components of Knowledge Management for banking industry are both Knowledge Application Technology component (0.2969) and Table 4

The stable matrix of ANP when lim

h→∞W

h_{; h→∞ (ANP) (banking industry).}

W^1000 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.07647990 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.09092040 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.08221000 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.07980600 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.08940720 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.09168080 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.07363880 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.08637770 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.09473890 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.06990690 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.08742580 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 0.07740770 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 1.00000010 Table 5

The weights of criteria for assessing 3 industries maturity and Total Performance (SAW Method) while using the same weighting preference from banking industry.

Dimensions/criteria Local weight Global weight

(by ANP) Perform. of IC industry (A1) Perform. of banking (A2) Perform. of services (A3) Knowledge Creation (D1) 0.2496 2.9673 3.5872 2.8476 K.C. Technology 0.3064 0.0765(10) 2.9136 3.5556 2.6383 K.C. Structure 0.3642 0.0909(3) 3.2639 3.5938 2.8374 K.C. Culture 0.3294 0.0822(7) 2.6892 3.6094 3.0536 Knowledge Sharing (D2) 0.2609 3.0702 3.5833 2.8157 K.Sh. Technology 0.3059 0.0798(8) 3.1852 3.6111 2.7295 K.Sh. Structure 0.3427 0.0894(4) 3.1250 3.5156 2.8889 K.Sh. Culture 0.3514 0.0917(2) 2.9167 3.6250 2.8194 Knowledge Storage (D3) 0.2548 3.4271 3.5826 2.8268 K.St. Technology 0.2891 0.0736(11) 3.5000 3.5625 2.5972 K.St. Structure 0.3391 0.0864(6) 3.5750 3.5216 2.8634 K.St. Culture 0.3719 0.0947(1) 3.2346 3.6528 2.9712 Knowledge Application (D4) 0.2347 3.2236 3.6086 2.7307 K.A. Technology 0.2978 0.0699(12) 3.1389 3.5156 2.5926 K.A. Structure 0.3724 0.0874(5) 3.3472 3.6719 2.6574 K.A. Culture 0.3298 0.0774(9) 3.1605 3.6210 2.9383 Total Performance 3.1715(2) 3.5900(1) 2.8065 (3) Example:

Calculating Total Performance by global weights: 0.0765⁎2.9136+0.0909⁎3.2639+0.0822⁎2.6892+0.0798⁎3.1852+0.0894⁎3.1250+0.0917⁎2.9167+0.0736⁎3.5000+ 0.0864⁎3.5750+0.0947⁎3.2346+0.0699⁎3.1389+0.0874⁎3.3472+0.0774⁎3.1605=3.1715.

Calculating Total Performance by local weights:

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Knowledge SHaring Structure component (0.2969). The bottleneck components of Knowledge Management for IC (Integrated Circuit) industry are Knowledge Creation Culture component (0.4622) and Knowledge Creation Technology component (0.4173). This demon-strates that the culture and technology of knowledge creation process are the critical bottleneck for IC (Integrated Circuit) industry. The bottleneck components of Knowledge Management for the service industry are Knowledge Application Technology component (0.4815) and Knowledge STorage Technology component (0.4806). The compromise ranking by VIKOR showed that the best adoption strategy for these three industries are Knowledge Application Technology (order 1) and Knowledge STorage Technology compo-nent (order 2) for service industry, Knowledge Creation Culture component (order 1) and Knowledge Creation Technology compo-nent (order 2) for IC (Integrated Circuit) industry, and Knowledge Application Technology component (order 1) and Knowledge SHaring Structure component (order 2) for banking industry.

This is why we suggest that the adoption strategy for different industry sectors should be considered separately according to which industry they belonging to SME (Small and Medium sized Enterprises) industry sectors.

Although the adoption strategy and assessment model provided by this study can be used in most of the countries of the world, there are some differences that practitioners should keep in mind when applying this model: the level of importance of the twelve criteria could be varied according to the situations of the country so that practitioners can adopt the most critical knowledge management components that they want to invest in and compare them and then make the optimal investment decision even their small enterprise scaling and lack of capital among the most of SME.

7.2. Conclusion

We have demonstrated that by using the Delphi method and Grounded Theory approach to consolidate the research issues by aggregating suggestion of experts/practitioners including SME con-sultants, knowledge management domain scholars, and executive managers of SMEs, and by implementing the DEMATEL technique to acquire the structure of Impact-Direction Map of knowledge man-agement components can indeed improve gaps in performance values (Figs. 3–6). The weights of each criterion from the structure were obtained by utilizing the ANP, and the VIKOR technique was leveraged for calculating compromise ranking gaps of the alternatives for improving the priorities of alternatives of portfolios.

We have also found that the weighting preferences among experts and raters differ between industry sectors. Therefore, speciﬁc domain experts or SME consultants should be invited to provide that industry adoption weighting. Additionally, the performance of KMCs in each industry should be rated/assessed by SME (Small and Medium sized Enterprises) executives based upon the experiences and

understand-ing of their speciﬁc industry domain knowledge.

7.3. Limitations and future works

This study was based on theﬁnding of knowledge management

gaps in SMEKM (Knowledge Management Plan for Small and Medium Enterprises) project of Small and Medium Enterprise Administration, Ministry of Economic Affairs, Taiwan. Since banking industry, services industry, and Integrated Circuit industry are three major industries in Taiwan. Most of the data, SME consultants, knowledge management domain scholars, and executive managers of SMEs are all from these Table 6

The weights of criteria for assessing 3 industries maturity and Total Performance (VIKOR method) while using the same weighting preference from banking industry. Dimensions/criteria Local weight Global weight (by ANP) banking Gaps of IC industry (A1) Gaps of banking (A2) Gaps of services (A3)

Knowledge Creation (D1) 0.2496 0.4065 0.2826 0.4305 K.C. Technology 0.3064 0.0765(10) 0.4173 0.2889 0.4723 K.C. Structure 0.3642 0.0909(3) 0.3472 0.2812 0.4325 K.C. Culture 0.3294 0.0822(7) 0.4622 0.2781 0.3893 Knowledge Sharing (D2) 0.2609 0.3738 0.2833 0.4369 K.Sh. Technology 0.3059 0.0798(8) 0.3630 0.2778 0.4541 K.Sh. Structure 0.3427 0.0894(4) 0.3750 0.2969 0.4222 K.Sh. Culture 0.3514 0.0917(2) 0.4167 0.2750 0.4361 Knowledge Storage (D3) 0.2548 0.3109 0.2836 0.4347 K.St. Technology 0.2891 0.0736(11) 0.3000 0.2875 0.4806 K.St. Structure 0.3391 0.0864(6) 0.2850 0.2957 0.4273 K.St. Culture 0.3719 0.0947(1) 0.3531 0.2694 0.4058 Knowledge Application (D4) 0.2347 0.3553 0.2783 0.4539 K.A. Technology 0.2978 0.0699(12) 0.3722 0.2969 0.4815 K.A. Structure 0.3724 0.0874(5) 0.3306 0.2656 0.4685 K.A. Culture 0.3298 0.0774(9) 0.3679 0.2758 0.4123 SA1 Total gaps 0.3657(2) 0.2820 (1) 0.4387 (3) QA1 Maximal gaps 0.4622(2) 0.2969 (1) 0.4815 (3) Example:

Calculating dimension gap by dimensions of local weights:

SD1= d p = 1 D1 =∑ 3 i = 1 wD1 i fD1 i −f D1 ik fD1 j −fi−D1 0 @ 1 A = 0:3064 ×5−2:9136₅₋₀ + 0:3642 × 5−3:2639₅₋₀ + 0:3294 × 5−2:6892₅₋₀ ¼0:4065 Calculating total gap by criteria of global weights:

SA1= d p = 1 A1 =∑ 8 i = 1 wi fi−fiA1 f i−fi− ! = 0:0765 × 5−2:9136 5−0 + 0:0909 × 5−3:2639 5−0 + 0:0822 × 5−2:6892 5−0 + 0:0798 × 5−3:1852 5−0 + 0:0894 × 5−3:1250 5−0 + 0:0917 × 5−2:9167₅₋₀ + 0:0736 × 5−3:5000₅₋₀ + 0:0864 × 5−3:5750₅₋₀ + 0:0947 × 5−3:2346₅₋₀ + 0:0699 × 5−3:1389₅₋₀ + 0:0874 × 5−3:3472₅₋₀ + 0:0774 × 5−3:1605 5−0 = 0:3657 QA1= d p =∞ A1 = max fi−fiA1 fi−fi− ji = 1; :::; n ( ) = 0:4622

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three industries because of the resources accessibility. The data were also collected from the EMBA students, and most of these EMBA students were from these three industries. Therefore, we choose these three industries as targets of this study. This is the limitation of the

study. Moreover, as previously mentioned speciﬁc countries may have

speciﬁc requirements of their knowledge management solutions and

our study was solely the lesson learned from Taiwanese managers. Our future work should focus on two issues: our knowledge management adoption and assessment strategies were based on the KM staged model, which inherit the spirit of CMMI (Capability Maturity Model Integration) staged models. However, sometimes knowledge management processes and components can be well represented and managed by knowledge management continuous representation in-stead of knowledge management staged representation. Therefore, we suggest that we should revise the KMMM (Knowledge Management Maturity Model) template and further discuss further the usability and reliability for the Multiple Criteria Decision Making on continuous knowledge management representation.

Second, we should deal with the qualitative assessment issues, such as the subjective judgment of the experts' perception. This is especially true when we need to determine the weights of decision criteria for each relative interest group, including the owners', users', and experts' subjective perceptions in any future work. We can facilitate this through Fuzzy Analytic Network Process (FANP) to determine the weights of decision criteria for each expert group. Then the Fuzzy Multiple Criteria Decision Making (FMCDM) approach can be used to synthesize the Table 7

The weights, performance, gaps of banking industry (SAW method, VIKOR).

Dimensions/criteria Local weight Global weight (by ANP) banking Perform. of banking industry Gaps of banking industry

Knowledge Creation (D1) 0.2496 3.5872 0.2826 K.C. Technology 0.3064 0.0765(10) 3.5556 0.2889 K.C. Structure 0.3642 0.0909(3) 3.5938 0.2812 K.C. Culture 0.3294 0.0822(7) 3.6094 0.2781 Knowledge Sharing (D2) 0.2609 3.5833 0.2833 K.Sh. Technology 0.3059 0.0798(8) 3.6111 0.2778 K.Sh. Structure 0.3427 0.0894(4) 3.5156 0.2969 K.Sh. Culture 0.3514 0.0917(2) 3.6250 0.2750 Knowledge Storage (D3) 0.2548 3.5826 0.2836 K.St. Technology 0.2891 0.0736(11) 3.5625 0.2875 K.St. Structure 0.3391 0.0864(6) 3.5216 0.2957 K.St. Culture 0.3719 0.0947(1) 3.6528 0.2694 Knowledge Application (D4) 0.2347 3.6086 0.2783 K.A. Technology 0.2978 0.0699(12) 3.5156 0.2969 K.A. Structure 0.3724 0.0874(5) 3.6719 0.2656 K.A. Culture 0.3298 0.0774(9) 3.6210 0.2758 Total Performance 3.5899(1) 0.2826 (1) 0.2969(3) Example:

Calculating Total Performance by global weights: 0.0765⁎3.5556+0.0909⁎3.5938+0.0822⁎3.6094+0.0798⁎3.6111+0.0894⁎3.5156+0.0917⁎3.6250+0.0736⁎3.5625+ 0.0864⁎3.5216+0.0947⁎3.6528+0.0699⁎3.5156+0.0874⁎3.6719+0.0774⁎3.6210=3.5899.

Calculating Total Performance by local weights:

0.2496⁎3.5872+0.2609⁎3.5833+0.2548⁎3.5826+0.2347⁎3.6086=3.5900. Calculating dimension gap by dimensions of local weights:

SD1= d p = 1 D1 =∑ 3 i = 1 wD1 j fD1 i −f D1 ik fD1 i −fi ! = 0:3064 × 5−3:5556₅₋₀ + 0:3642 × 5−3:5938₅₋₀ + 0:3294 × 5−3:6094₅₋₀ = 0:2826 Calculating total gap by criteria of global weights:

SA1= d p = 1 A1 =∑ 8 i = 1wi fi−fiA1 f i−fi− ! = 0:0765 × 5−3:5556 5−0 + 0:0909 × 5−3:5938 5−0 + 0:0822 × 5−3:6094 5−0 + 0:0798 × 5−3:6111 5−0 + 0:0894 × 5−3:5156 5−0 + 0:0917 × 5−3:6250 5−0 + 0:0736 × 5−3:5625 5−0 + 0:0864 × 5−3:5216 5−0 + 0:0947 × 5−3:6528 5−0 + 0:0699 × 5−3:5156 5−0 + 0:0874 × 5−3:6719 5−0 + 0:0774 × 5−3:6210₅₋₀ = 0:2820 QA1= d p =∞ A1 = max fi−fiA1 fi−fi− ji = 1; :::; n ( ) = 0:2969

IC Industry Preference by DEMATEL

KC

KSh

KSt

KA

-1.6 -1.1 -0.6 -0.1 0.4 0.9 1.4 1.9 19

r

i

-s

i

r

_i

+s

_i KA, 19.8106 , 0.4927 Gaps:0.3553 KSt,(20.1044 ,-0.7785). Gaps:0.3109 KSh,(20.1924,-1.6089), Gaps:0.3738 KC,(20.6359,1.8947) Gaps:0.4065 20 21

KC

KSh

KSt

KA

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group decision. This process might enable decision makers to formalize and effectively solve the complicated, multi-criteria, and fuzzy/vague perception problems for most of the appropriate strategies in knowledge management alternative adoption. From the criteria weights of

industry-speciﬁc domain expert groups by Fuzzy ANP and the average fuzzy

performance values of each criterion from SME (Small and Medium sized Enterprises) practitioners for each alternative, then theﬁnal fuzzy synthetic decision can then be processed.

Appendix A. VIKOR for emergent unimproved gaps

In this example, the organization fulfills all the requirements of the first stage of KMM (i.e., the initial stage), but some KM activities do not reach the minimum required threshold of the second KMMM (Knowledge Management Maturity Model) stage. Hence, to progress to the next stage, the organization should focus on these critical KM activities and refine them to meet the threshold criteria. In the figure the gaps highlighted in orange are deemed the most urgent. The breakthrough activities (ivory color) should be maintained, but some of the resources should be used to strengthen and support the urgent KM activities that do not meet the minimum thresholds.

KMC weighting preference of IC Industry

KCT, 7.1441 , 0.7958 KSHS, 6.6262 , -0.3425 KSHC, 6 . 4 3 9 1 , - 1 . 4 2 8 3 KAT , 6 . 8 1 9 1 , 1 . 0 5 6 1 KCS, 7 . 2 7 6 0 , 1 . 1 9 4 9 KCC, 6 . 2 1 5 8 , - 0 . 0 9 6 0 KSHT , 7 . 1 2 7 1 , 0 . 1 6 1 9 KST T , 6 . 7 2 4 5 , 0 . 7 6 0 7 KST S, 7 . 0 2 9 9 , - 0 . 6 1 1 8 KST C, 6 . 3 5 0 0 , - 0 . 9 2 7 4 KAS, 6 . 6 8 8 7 , - 0 . 8 4 9 1 KAC, 6 . 3 0 2 8 , 0 . 2 8 5 7 -1.5 -1.0 -0.5 0.0 0.5 1.0 6.2 6.4 6.6 6.8 7.0 7.2

r

_i

+s

_i

r

i

-s

i KCT KCS KCC KSHT KSHS KSHC KSTT KSTS KSTC KAT KAS KAC

Fig. 4. The Impact-Direction Map of KMCs for improving gaps (Integrated Circuit industry).

Banking Preference by DEMATEL

KC

KSh

KSt

KA

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 8 9 10 11 12 13

r

_i

+s

_i

KC

KSh

KSt

KA

(12.1144,0.8977) Gaps:0.2940 (10.8141,0.0411) Gaps: 0.2736 (9.6371,-0.4516) Gaps:0.2812 (10.5044,-0.4872) Gaps:0.2776

r

i

-s

i

Fig. 5. The Impact-Direction Map for improving gaps in performance values (banking industry).

KMC weighting preference of banking Industry

KCT KCS KCC KSHT KSHS KSHC KSTT KSTS KSTC KAT KAS KAC -0.6 -0.4 -0.2 0.1 0.3 0.5 0.7 0.9 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6

r

_i

+s

_i KCT KCS KCC KSHT KSHS KSHC KSTT KSTS KSTC KAT KAS KAC (4.5973,0.9044) (3.3192,-0.5016) (3.7478,-0.3225) (3.8635,-0.1964) (3.9072,0.0324) (3.0178,0.0009) (3.7694,0.3157) (3.6301,0.2507) (3.2768,-0.2420) (3.3820,-0.3102) (3.3001,0.0491) (3.2589,0.0193)

r

i

-s

i

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Appendix A1. VIKOR for emergent unimproved gaps

Appendix A2. An example of examining the current KM capability position

Fig. A1. VIKOR for emergent unimproved gaps.

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Appendix A3. Complete the weighting by consultants and KMC performance by SME CEO/Rater

Appendix B. Demonstrations of the procedures of DEMATEL in banking industry

Appendix B1. The pair-wise inﬂuence matrix for KM components was rated by focus group of KM experts

KCT KCS KCC KSHT KSHS KSHC KSTT KSTS KSTC KAT KAS KAC Row sum Original In short

KCT 0 2 1 2 2 1 2 2 1 2 2 1 18 Knowledge Creation Technology KCT

KCS 3 0 3 1 3 0 1 2 1 1 2 1 18 Knowledge Creation Structure KCS

KCC 2 3 0 0 0 3 0 0 3 0 0 3 14 Knowledge Creation Culture KCC

KSHT 3 1 1 0 2 3 2 1 1 2 2 1 19 Knowledge SHaring Technology KSHT

KSHS 3 3 2 3 0 3 3 2 3 1 2 1 26 Knowledge SHaring Structure KSHS

KSHC 0 1 2 3 1 0 1 2 2 1 1 2 16 Knowledge SHaring Culture KSHC

KSTT 2 1 1 3 1 1 0 2 2 1 1 0 15 Knowledge STorage Technology KSTT

KSTS 0 2 0 0 2 1 2 0 3 1 2 1 14 Knowledge STorage Structure KSTS

KSTC 0 1 2 2 2 3 2 2 0 1 1 1 17 Knowledge STorage Culture KSTC

KAT 2 0 0 2 1 1 2 1 1 0 2 2 14 Knowledge Application Technology KAT

KAS 0 2 2 0 2 1 0 2 0 2 0 2 13 Knowledge Application Structure KAS

KAC 1 2 2 0 1 2 0 1 2 2 3 0 16 Knowledge Application Culture KAC

Colum sum 16 18 16 16 17 19 15 17 19 14 18 15

The degree of inﬂuence from KCT to KCT, KCS, KCC, KSHT, KSHS, KSHC, KSTT, KSTS, KSTC, KAT, KAS, and KAC are 0, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, and 1, respectively.

Appendix B2. After the normalization of pair-wise inﬂuence matrix X (Divided by the maximum value of sum of rows/sum of columns.) matrix X

KCT KCS KCC KSHT KSHS KSHC KSTT KSTS KSTC KAT KAS KAC

KCT 0 0.07692 0.03846 0.07692 0.07692 0.03846 0.07692 0.07692 0.03846 0.07692 0.07692 0.03846 KCS 0.11538 0 0.11538 0.03846 0.11538 0 0.03846 0.07692 0.03846 0.03846 0.07692 0.03846 KCC 0.07692 0.11538 0 0 0 0.11538 0 0 0.11538 0 0 0.11538 KSHT 0.11538 0.03846 0.03846 0 0.07692 0.11538 0.07692 0.03846 0.03846 0.07692 0.07692 0.03846 KSHS 0.11538 0.11538 0.07692 0.11538 0 0.11538 0.11538 0.07692 0.11538 0.03846 0.07692 0.03846 KSHC 0 0.03846 0.07692 0.11538 0.03846 0 0.03846 0.07692 0.07692 0.03846 0.03846 0.07692 KSTT 0.07692 0.03846 0.03846 0.11538 0.03846 0.03846 0 0.07692 0.07692 0.03846 0.03846 0 KSTS 0 0.07692 0 0 0.07692 0.03846 0.07692 0 0.11538 0.03846 0.07692 0.03846 KSTC 0 0.03846 0.07692 0.07692 0.07692 0.11538 0.07692 0.07692 0 0.03846 0.03846 0.03846 KAT 0.07692 0 0 0.07692 0.03846 0.03846 0.07692 0.03846 0.03846 0 0.07692 0.07692 KAS 0 0.07692 0.07692 0 0.07692 0.03846 0 0.07692 0 0.07692 0 0.07692 KAC 0.03846 0.07692 0.07692 0 0.03846 0.07692 0 0.03846 0.07692 0.07692 0.11538 0

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I I I I I I I I I I I I I I 1 0 0 0 0 0 0 0 0 0 0 0 I 0 1 0 0 0 0 0 0 0 0 0 0 I 0 0 1 0 0 0 0 0 0 0 0 0 I 0 0 0 1 0 0 0 0 0 0 0 0 I 0 0 0 0 1 0 0 0 0 0 0 0 I 0 0 0 0 0 1 0 0 0 0 0 0 I 0 0 0 0 0 0 1 0 0 0 0 0 I 0 0 0 0 0 0 0 1 0 0 0 0 I 0 0 0 0 0 0 0 0 1 0 0 0 I 0 0 0 0 0 0 0 0 0 1 0 0 I 0 0 0 0 0 0 0 0 0 0 1 0 I 0 0 0 0 0 0 0 0 0 0 0 1 I–X 1 −0.077 −0.038 −0.077 −0.077 −0.038 −0.077 −0.077 −0.038 −0.077 −0.077 −0.038 −0.1154 1 −0.1154 −0.0385 −0.1154 0 −0.0385 −0.0769 −0.0385 −0.0385 −0.0769 −0.0385 −0.0769 −0.1154 1 0 0 −0.1154 0 0 −0.1154 0 0 −0.1154 −0.1154 −0.0385 −0.0385 1 −0.0769 −0.1154 −0.0769 −0.0385 −0.0385 −0.0769 −0.0769 −0.0385 −0.1154 −0.1154 −0.0769 −0.1154 1 −0.1154 −0.1154 −0.0769 −0.1154 −0.0385 −0.0769 −0.0385 0 −0.0385 −0.0769 −0.1154 −0.0385 1 −0.0385 −0.0769 −0.0769 −0.0385 −0.0385 −0.0769 −0.0769 −0.0385 −0.0385 −0.1154 −0.0385 −0.0385 1 −0.0769 −0.0769 −0.0385 −0.0385 0 0 −0.0769 0 0 −0.0769 −0.0385 −0.0769 1 −0.1154 −0.0385 −0.0769 −0.0385 0 −0.0385 −0.0769 −0.0769 −0.0769 −0.1154 −0.0769 −0.0769 1 −0.0385 −0.0385 −0.0385 −0.0769 0 0 −0.0769 −0.0385 −0.0385 −0.0769 −0.0385 −0.0385 1 −0.0769 −0.0769 0 −0.0769 −0.0769 0 −0.0769 −0.0385 0 −0.0769 0 −0.0769 1 −0.0769 −0.0385 −0.0769 −0.0769 0 −0.0385 −0.0769 0 −0.0385 −0.0769 −0.0769 −0.1154 1 Inverse(I_–X) 1.1047 0.1864 0.1401 0.1758 0.1845 0.1555 0.1716 0.1832 0.1566 0.1630 0.1874 0.1317 0.2130 1.1268 0.2128 0.1378 0.2183 0.1276 0.1368 0.1826 0.1634 0.1271 0.1865 0.1370 0.1447 0.1972 1.0933 0.0815 0.0899 0.2014 0.0709 0.0897 0.2011 0.0714 0.0914 0.1851 0.2116 0.1570 0.1460 1.1163 0.1870 0.2314 0.1761 0.1560 0.1621 0.1687 0.1915 0.1389 0.2496 0.2678 0.2224 0.2574 1.1595 0.2772 0.2454 0.2309 0.2756 0.1643 0.2296 0.1712 0.0906 0.1386 0.1642 0.1930 0.1331 1.1139 0.1209 0.1653 0.1813 0.1158 0.1371 0.1588 0.1583 0.1323 0.1217 0.1967 0.1336 0.1421 1.0881 0.1658 0.1726 0.1143 0.1326 0.0809 0.0785 0.1621 0.0900 0.0874 0.1627 0.1321 0.1506 1.0924 0.2021 0.1068 0.1613 0.1098 0.0966 0.1461 0.1711 0.1732 0.1726 0.2256 0.1637 0.1751 1.1203 0.1186 0.1409 0.1295 0.1482 0.0897 0.0807 0.1537 0.1232 0.1308 0.1480 0.1244 0.1268 1.0755 0.1641 0.1442 0.0767 0.1604 0.1499 0.0692 0.1502 0.1231 0.0686 0.1488 0.0948 0.1347 1.0850 0.1475 0.1173 0.1729 0.1672 0.0849 0.1320 0.1743 0.0790 0.1318 0.1733 0.1483 0.2028 1.0909 T = X⁎Inverse(I–X)

KCT KCS KCC KSHT KSHS KSHC KSTT KSTS KSTC KAT KAS KAC ri

KCT 0.1047 0.1864 0.1401 0.1758 0.1845 0.1555 0.1716 0.1832 0.1566 0.1630 0.1874 0.1317 1.9404 KCS 0.2130 0.1268 0.2128 0.1378 0.2183 0.1276 0.1368 0.1826 0.1634 0.1271 0.1865 0.1370 1.9698 KCC 0.1447 0.1972 0.0933 0.0815 0.0899 0.2014 0.0709 0.0897 0.2011 0.0714 0.0914 0.1851 1.5174 KSHT 0.2116 0.1570 0.1460 0.1163 0.1870 0.2314 0.1761 0.1560 0.1621 0.1687 0.1915 0.1389 2.0426 KSHS 0.2496 0.2678 0.2224 0.2574 0.1595 0.2772 0.2454 0.2309 0.2756 0.1643 0.2296 0.1712 2.7509 KSHC 0.0906 0.1386 0.1642 0.1930 0.1331 0.1139 0.1209 0.1653 0.1813 0.1158 0.1371 0.1588 1.7126 KSTT 0.1583 0.1323 0.1217 0.1967 0.1336 0.1421 0.0881 0.1658 0.1726 0.1143 0.1326 0.0809 1.6391 KSTS 0.0785 0.1621 0.0900 0.0874 0.1627 0.1321 0.1506 0.0924 0.2021 0.1068 0.1613 0.1098 1.5359 KSTC 0.0966 0.1461 0.1711 0.1732 0.1726 0.2256 0.1637 0.1751 0.1203 0.1186 0.1409 0.1295 1.8335 KAT 0.1482 0.0897 0.0807 0.1537 0.1232 0.1308 0.1480 0.1244 0.1268 0.0755 0.1641 0.1442 1.5094 KAS 0.0767 0.1604 0.1499 0.0692 0.1502 0.1231 0.0686 0.1488 0.0948 0.1347 0.0850 0.1475 1.4088 KAC 0.1173 0.1729 0.1672 0.0849 0.1320 0.1743 0.0790 0.1318 0.1733 0.1483 0.2028 0.0909 1.6746 si 1.6897 1.9374 1.7594 1.7268 1.8464 2.0351 1.6198 1.8461 2.0300 1.5084 1.9104 1.6255

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Appendix B3. The Impact-irection Map for improving gaps in performance values (banking industry) ri si ri+ si ri−si KCT 1.9404 1.6897 3.6301 0.2507 KCS 1.9698 1.9374 3.9072 0.0324 KCC 1.5174 1.7594 3.2768 −0.2420 KSHT 2.0426 1.7268 3.7694 0.3157 KSHS 2.7509 1.8464 4.5973 0.9044 KSHC 1.7126 2.0351 3.7478 −0.3225 KSTT 1.6391 1.6198 3.2589 0.0193 KSTS 1.5359 1.8461 3.3820 −0.3102 KSTC 1.8335 2.0300 3.8635 −0.1964 KAT 1.5094 1.5084 3.0178 0.0009 KAS 1.4088 1.9104 3.3192 −0.5016 KAC 1.6746 1.6255 3.3001 0.0491