2

MRR33,5

452

Management Research ReviewVol. 33 No. 5, 2010pp. 452-471# Emerald Group Publishing Limited2040-8269DOI 10.1108/01409171011041893

Use of analytic network processin selecting knowledgemanagement strategies

Selcuk PercinThe Faculty of Economics and Administrative Sciences, Department of

Business Administration, Karadeniz Technical University, Trabzon, Turkey

Abstract

Purpose – In order to maintain competitive advantage, companies need to have and managesuccessful knowledge management (KM) strategies which are difficult to imitate. However, variouscritical factors, such as determinants, dimensions, and enablers, which affect the evaluation of asuccessful KM strategy, have not been systematically investigated. The purpose of this paper is tobridge this gap by providing a good insight into the use of analytic network process (ANP) that is amulti-criteria, decision-making methodology in selecting an appropriate KM strategy.Design/methodology/approach – In this study, after the decision criteria of KM strategy areintroduced, an ANP model is developed and applied to KM strategy selection problem as aframework to guide KM managers.Findings – First, the comprehensive ANP framework presents a roadmap for successfully selectingan appropriate KM strategy for Turkish manufacturing organizations. Second, as compared to thehuman-oriented KM strategy (HKM) and system-oriented KM strategy (SKM), dynamic KM strategy(DKM) can lead to a more targeted improvement in terms of knowledge transparency, knowledgesharing and communication. Finally, findings demonstrate that the ANP model, with minormodifications, can be useful to all firms in their KM strategy selection decisions.Research limitations/implications – The developed ANP model provides firms with a simple,flexible, and easy to use approach to evaluate KM strategies efficiently. However, ANP is a highlycomplex methodology and requires more numerical calculations in assessing composite prioritiesthan the traditional analytic hierarchy process (AHP) and hence it increases the effort.Originality/value – While the selection of a suitable KM strategy is an important component oforganization’s success, very little research has devoted explicit attention to this issue. ANP has theability to be used as a decision-making analysis tool since it incorporates feedback andinterdependent relationships among decision criteria and alternatives. In addition, it gives valuableinformation and guidelines which hopefully will help the KM managers to evaluate KM strategiesthrough their organizations in an effective way.

Keywords Knowledge management, Knowledge management systems, Decision making, Decisionsupport systems, Manufacturing industries, Turkey

Paper type Research paper

1. IntroductionIt is widely recognized that knowledge is a valuable strategic resource for firms toremain competitive, and adequately respond to the needs of their customers (Zack,1999). As knowledge is created and disseminated throughout the firm, it has thepotential to contribute to the firm’s value by leveraging its capability to respond to newsituations (Choi et al., 2008). In this context, one of the most important items for theeffective sharing of knowledge is a clear and conscious knowledge management (KM)strategy. Therefore, there is growing realization that firms are increasingly relying onKM strategies in their pursuit of this unique resource.

KM strategies require the organizational optimization of knowledge resources, suchas human power, capital, and managerial efforts, to achieve enhanced performancethrough the use of various methods and techniques (Davenport et al., 1998;

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Use of analyticnetworkprocess

453

Kamara et al., 2002). Also, appropriate KM strategies can be accomplished byvarious approaches such as building an information technology (IT) infrastructure,structuring a learning organization, fostering a knowledge-oriented culture, establishingknowledge-based systems, leveraging intellectual capital, and executing KM projectsand programs (O’Dell et al., 1999; Lee and Kim, 2001; Kim et al., 2003). Therefore, KMand related strategy concepts are promoted as important components for organizationsto achieve superior competitive advantage (Martensson, 2000).

Much of the existing research on KM has concentrated on various critical factors thatinfluence the success of a KM strategy, such as people, organizational structure andprocesses, strategy, culture, resources, training and education, measurement, andtechnology (Davenport et al., 1998; O’Dell et al., 1999; Holsapple and Joshi, 2000; Goldet al., 2001; Grover and Davenport, 2001; Liebowitz, 2001; Forcadell and Guadamillas,2002; Lee and Kim, 2001; Lee and Choi, 2003; Wong, 2005). These critical factors enablethe organization to apply maximum effort and commitment to creating, sharing,applying, and improving its knowledge (Zack, 1999). Although these factors are essentialfor a firm’s capability to manage knowledge effectively, it is still unclear how toincorporate them in a complex decision environment. By managing and integrating allthe various factors in a comprehensive decision framework, managers take the first stepnot only to increase competitiveness, but also to improve organizational success. Hence,the objective of this paper is to introduce a comprehensive decision methodology for theselection of an appropriate KM strategy that managers can apply to their organization.

Multi-criteria decision-making (MCDM) methods that involve multiple, and usuallyconflicting criteria allow decision makers to deal with complex evaluation problems toachieve a certain goal. Among these MCDM models, the analytic hierarchy process(AHP) and the analytic network process (ANP) are very widely used methods to solvesuch problems. In AHP, a multi-level hierarchy considers the distribution of a goalamongst the decision criteria and alternatives being compared, and judges whichelement has a greater influence on that goal (Korpela et al., 2002; Korpela andTuominen, 1996; Kengpol and Tuominen, 2006). Several decision-making problems, onthe other hand, cannot be solved by examining the interactions among goals, criteria,and alternatives since they may involve dependencies in higher/lower level elements.Thus, AHP is a limited approach since it assumes independence among the elements ofa hierarchy. In contrast to AHP, ANP considers a network system in which all criteriaand alternatives involved are connected that accepts various dependencies. Therefore,ANP has the ability to consider feedback and to connect clusters of elements (Kengpoland Tuominen, 2006). It can also measure all relevant criteria, such as thedeterminants, dimensions, and enablers of KM strategies, in the model in arriving atthe best decision. Even more important, ANP is relatively new and there are fewapplications due to its complexity and time consuming nature. Some examples of itsapplications include balanced scorecard, business process improvement, supplierselection, project selection, quality function deployment, energy policy planning, andtotal quality management decisions (Leung et al., 2006; Sarkis and Talluri, 2002a, b;Meade and Sarkis, 1999; Partovi and Corredoira, 2002; Hamalainen and Seppalainen,1986; Bayazit and Karpak, 2007). While the selection of a suitable KM strategy isimportant component of an organization’s success, very little research has devotedexplicit attention to this issue. Thus, there is need for research for the appropriate KMstrategy selection, highlighting the criteria for both selection and evaluation of a KMstrategy. The proposed ANP model in this paper structures the problem related to theselection of KM strategies in a hierarchical form and integrates the determinants,

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dimensions, and enablers of KM strategies with different KM strategy alternatives.It also explicitly captures interdependencies among these various factors and allowsa more systematic analysis. Furthermore, the use of proposed model is illustratedin Turkish manufacturing firms.

The paper is then organized as follows. In section 2, a review of KM strategiesis explained. In section 3, ANP framework and selection criteria for Turkishmanufacturing companies are presented. In section 4, the proposed methodology forevaluating the KM strategy is presented and followed by the ANP model. The finalsection of this paper concludes the paper with a discussion and conclusions.

2. Knowledge management strategiesA growing body of KM research has examined the range of KM strategies, andattempted to classify them. One strategy emphasizes the capability to help create,store, share, and use an organization’s explicitly documented knowledge (Choi and Lee,2002). In this strategy, explicit knowledge is carefully classified and stored in databasesready to be accessed and used by anyone in the company (Hansen et al., 1999; Ewingand West, 2000). In this paper, this strategy is referred to as system-oriented strategy.System-oriented strategy attempts to increase organizational efficiencies by codifyingand reusing knowledge mainly through advanced ITs (Davenport et al., 1998; Lee andKim, 2001). By contrast, another strategy concentrates on the belief that the mostvaluable knowledge is tacit knowledge existing in peoples’ heads, and communicatedthrough direct person-to-person contacts and through social relationships (Hansenet al., 1999; Zack, 1999; Ewing and West, 2000; Keskin, 2005; Choi et al., 2008). Thisstrategy can be referred to as human-oriented strategy. In this strategy, the process ofacquiring knowledge through the peoples’ beliefs and experiences is time consuming,expensive and slow. Thus, efficient transmission of tacit knowledge requires itscodification into explicit formats (Schulz and Jobe, 2001).

It is important for an organization to understand which KM strategies it shouldfocus on under various circumstances. Some studies suggest a complementaryrelationship among KM strategies while others insist that KM strategies are betterfollowed in isolation (Choi et al., 2008). Hansen et al. (1999) suggested that companiesshould mainly focus on a single strategy while using another to support it. Swan et al.(2000) proposed that a human-oriented strategy is superior to system-oriented strategy.Schulz and Jobe (2001) suggested that a focused strategy is superior to the otherstrategies. Keskin (2005) found that the impact on organizational performance is higherwith system-oriented strategy than the human-oriented one. However, Jordan andJones (1997), Liebowitz (2001), and Choi et al. (2008) argued that organizations shouldpursue a balanced approach to KM which calls for the combining of KM strategiesappropriately. Bierly and Chakrabarti (1996) found that firms, which acquire and shareknowledge by combining system and human-oriented strategies, tend to be moreprofitable. Choi and Lee (2003) showed that complementary set of human and system-oriented strategies resulted in higher performance. From this point of view, Choi andLee (2003) and Wu and Lee (2007) suggested a new KM strategy on the classification,which is dynamic KM strategy. The dynamic KM strategy integrates the conceptualscope of system and human-oriented KM (HKM) strategies. It emphasizes both explicitand tacit knowledge (Choi and Lee, 2003; Wu and Lee, 2007). Combining tacit andexplicit knowledge also involves sharing knowledge. Sharing of knowledge makesexisting knowledge more productive and helps create new knowledge ( Johannessen


455

and Olsen, 2003). It is necessary to take account of different criteria in the practice ofselecting the suitable KM strategy.

3. Research methodology3.1 Selection criteria for evaluation of KM strategyEvaluating the KM strategy is not a well defined or structured problem in literature.This kind of problem has some special characteristics that make it different from otherMCDM problems. First, a rational decision to KM strategy is to take into accountcareful examination of company’s unique needs and expectations. Thus, strategic,technological, cultural, and financial aspects of KM strategy must be carefullyconsidered in the decision process. In other words, finding the most suitable KMstrategy requires careful screening of unique resources and capabilities of companyand can be time consuming process. Second, selection criteria for evaluation of KMstrategy may be tangible or intangible, objective or subjective etc. So, decision criteriamay not be independent of each other, and moreover, there may even be relationshipamong some criteria. Thus, organizations should consider these dependent relationsbetween their decision criteria when selecting KM strategies. Therefore, an appropriateevaluation methodology and evaluation criteria have to be identified.

In this paper, ANP methodology is used to identify decision criteria for KM strategyselection problem. For this purpose, we interviewed 12 functional managers in variousmanufacturing areas responsible for identifying decision criteria of KM strategy. Inaddition to the interviews, we used the KM literature to corroborate participants’statements (mainly based on the studies of O’Dell et al., 1999; Zack, 1999; Ewing and West,2000; Lee and Kim, 2001; Sunassee and Sewry, 2002; Liebowitz, 2001, 2003; Lee and Choi,2003). After piloting, we formulated the ANP model and determined the criteria, sub-criteria, alternatives and their connections through the related references and managers’statements. The decision criteria for the selection of a KM strategy, which have beenwidely discussed in the literature, are compiled and presented in Table I. Then, aquestionnaire for ANP was prepared and mailed to a total number of 350 managers,working in different manufacturing organizations in Istanbul and Kocaeli, in Turkey, toreceive the individual weights. This sample was selected randomly from the database ofIstanbul Chamber of Commerce. The response rate was 42 per cent, that is, 147 of 350.Then, the influences of various criteria on the goal criteria have been evaluated. The goalof our framework is to select an appropriate KM strategy for Turkish manufacturingfirms.

In using ANP to model a decision problem, the first step is to structure the hierarchy ateach level and a definition of relationships between the criteria (Agarwal and Shankar,2003; Ravi et al., 2005): The top-level criteria in this model are cost (CST), time (TME),quality (QLT), and flexibility (FLX). These four criteria are termed as the determinants.The determinants of KM strategy are integrated into the model to have dominance overthe identified dimensions in the ANP model. In the second level of the hierarchy, four sub-criteria termed as dimensions of the model is placed which supports all the fourdeterminants at the top level of hierarchy. These are strategic perspective (STR),technological perspective (TCH), cultural perspective (CLT), and financial perspective(FNC). In addition, each of the four dimensions has some enablers, which help achievethat particular dimension. Therefore, these are dependent on the dimensions, but there areinterdependencies among them, hence the looped arc is used in the ANP model to showsuch interdependencies within the same level of analysis. The three KM strategies are

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Table I.Decision criteria for theselection of a KMstrategy

Cri

teri

aR

elev

ance

inK

MR

efer

ence

s

Cos

tIt

focu

ses

onk

eep

ing

the

kn

owle

dg

etr

ansa

ctio

nco

sts

aslo

was

pos

sib

lean

d/o

ru

nd

erco

ntr

olM

arte

nss

on(2

000)

;L

ieb

owit

z(2

003)

;K

esk

in(2

005)

Qu

alit

yIt

refe

rsto

the

imp

rov

ing

the

qu

alit

yan

dco

nsi

sten

cyof

kn

owle

dg

eL

ieb

owit

z(2

003)

Tim

eIt

refe

rsto

the

shor

ten

ing

the

amou

nt

ofti

me

req

uir

edto

inp

ut

and

acce

ssin

form

atio

nM

arte

nss

on(2

000)

;L

ieb

owit

z(2

003)

Fle

xib

ilit

yIt

refe

rsto

the

app

lyin

gth

ek

now

led

ge

ton

ewco

nte

xt

and

circ

um

stan

ces

by

focu

sin

gto

dif

fere

nt

inn

ovat

ive

area

sZ

ack

(199

9)

Str

ateg

icp

ersp

ecti

ve

KM

stra

teg

yn

eed

sto

be

alig

ned

wit

hth

eb

usi

nes

sst

rate

gy.

Th

us,

KM

stra

teg

yis

pu

rsu

edei

ther

by

inte

gra

tin

git

wit

hth

eov

eral

lb

usi

nes

sst

rate

gy

orb

ytr

eati

ng

itin

par

alle

lw

ith

oth

erst

rate

gie

s

Zac

k(1

999)

;L

ieb

owit

z(1

999)

;O

’Del

let

al.

(199

9);

Ru

ben

stei

n-M

onta

no

etal.

(200

1);

Su

nas

see

and

Sew

ry(2

002)

;M

aier

and

Rem

us

(200

2)

Top

man

agem

ent

sup

por

tT

opm

anag

emen

tp

rom

otes

the

init

ial

pro

cess

ofK

M,

sup

por

tsid

eas

for

imp

rov

emen

t,an

dg

ives

sup

por

tan

dad

vic

eto

the

emp

loye

es.

Insu

ffic

ien

tto

pm

anag

emen

tsu

pp

ort

and

com

mit

men

tca

nle

adto

pot

enti

also

urc

esof

fail

ure

for

the

KM

stra

teg

y

Hol

sap

ple

and

Josh

i(2

000)

;L

ieb

owit

z(2

001)

;M

arte

nss

on(2

000)

;F

orca

del

lan

dG

uad

amil

las

(200

2)

Str

ateg

ical

ign

men

tK

Mst

rate

gy

nee

ds

tob

eal

ign

edw

ith

the

org

aniz

atio

nst

rate

gy.

An

org

aniz

atio

nal

stra

teg

ysh

ould

be

able

tosu

pp

ort

the

test

ing

ofn

ewid

eas/

app

roac

hes

by

con

sid

erin

gth

eor

gan

izat

ion

’sco

reco

mp

eten

cies

Zac

k(1

999)

;S

un

asse

ean

dS

ewry

(200

2);

Lie

bow

itz

(200

1);

Mai

eran

dR

emu

s(2

002)

Em

plo

yees

’re

spon

sib

ilit

yfo

rk

now

led

ge

Em

plo

yees

are

per

son

ally

resp

onsi

ble

for

iden

tify

ing

,m

ain

tain

ing

,an

dex

pan

din

gth

eir

own

kn

owle

dg

eas

wel

las

un

der

stan

din

g,

ren

ewin

g,

and

shar

ing

thei

rk

now

led

ge

asse

ts.

Th

us,

the

focu

ssh

ould

be

onth

eir

con

trib

uti

onto

war

ds

asu

cces

sfu

lK

Mst

rate

gy

O’D

ell

etal.

(199

9);

Su

nas

see

and

Sew

ry(2

002)

;M

arte

nss

on(2

000)

Org

aniz

atio

nal

cap

abil

ity

An

org

aniz

atio

n’s

cap

abil

ity

tocr

eate

new

kn

owle

dg

eb

yco

mb

inin

gn

ewan

dex

isti

ng

kn

owle

dg

eis

ak

eysu

cces

sfa

ctor

.T

hu

s,an

org

aniz

atio

nsh

ould

enco

ura

ge

the

effe

ctiv

eK

Mst

rate

gy

Non

aka

and

Tak

euch

i(1

995)

;R

ub

enst

ein

-Mon

tan

oet

al.

(200

1);

Su

nas

see

and

Sew

ry(2

002)

;G

old

etal.

(200

1);

Lee

and

Ch

oi(2

003)

;V

alk

okar

ian

dH

elan

der

(200

7)

(con

tinued

)


457

Table I.

Cri

teri

aR

elev

ance

inK

MR

efer

ence

s

En

vir

onm

enta

lsc

ann

ing

Org

aniz

atio

ns

hav

eli

ttle

con

trol

over

env

iron

men

tal

infl

uen

ces.

Itis

imp

orta

nt

toan

aly

zek

now

led

ge

abou

tg

over

nm

enta

lre

gu

lati

ons,

mar

ket

com

pet

itio

nan

dtu

rbu

len

ce,

soci

alan

dp

olit

ical

tren

ds,

tech

nol

ogic

altr

end

s,an

dfi

nal

lyco

mm

un

ity

dem

and

s

Hol

sap

ple

and

Josh

i(2

000)

;S

un

asse

ean

dS

ewry

(200

2);

Kes

kin

(200

5);

Wal

ker

(200

6)

Tec

hn

olog

ical

per

spec

tiv

eT

ech

nol

ogy

isim

por

tan

td

riv

erfo

rim

ple

men

tin

gK

Mst

rate

gie

s.R

ecen

tad

van

ces

inte

chn

olog

yan

dco

mm

un

icat

ion

hav

een

able

dm

anag

ers

tota

pin

to,

man

age

and

exp

loit

thei

rK

Mst

rate

gie

sto

ag

reat

erex

ten

tth

anev

enb

efor

e

Ew

ing

and

Wes

t(2

000)

;G

old

etal.

(200

1);

Wal

ker

(200

6);

Tia

go

etal.

(200

7)

Info

rmat

ion

tech

nol

ogy

tosu

pp

ort

KM

KM

stra

teg

yre

qu

ires

afa

irly

adv

ance

dIT

infr

astr

uct

ure

ofd

atab

ases

,co

mm

un

icat

ion

and

inte

llig

ent

syst

emte

chn

olog

ies,

com

pu

ter

net

wor

ks

and

soft

war

e.A

non

-in

teg

rate

dIT

infr

astr

uct

ure

and

bu

sin

ess

pro

cess

sev

erel

yre

stri

cts

the

firm

’sk

now

led

ge

shar

ing

,an

dn

ewcr

eati

on

Ew

ing

and

Wes

t(2

000)

;L

eean

dK

im(2

001)

;A

lav

ian

dL

eid

ner

(200

1);

Lee

and

Ch

oi(2

003)

E-c

omm

erce

oper

abil

ity

Th

eq

ual

ity

ofK

Mm

ayd

eter

min

ea

succ

ess

tem

pla

tefo

re-

com

mer

ce.

Web

-bas

ed,

inte

rnet

,an

din

tran

ette

chn

olog

ies

can

pro

vid

eth

eco

nn

ecti

vit

yb

etw

een

the

var

iou

sk

now

led

ge

bas

esto

form

the

nec

essa

ryb

rid

ges

and

faci

lita

teth

esh

arin

gof

kn

owle

dg

e

Lie

bow

itz

(199

9);

Ew

ing

and

Wes

t(2

000)

;M

aier

and

Rem

us

(200

2);

Lee

and

Ch

oi(2

003)

;T

iag

oet

al.

(200

7)

Kn

owle

dg

etr

ansf

erca

pab

ilit

yK

now

led

ge

tran

sfer

inv

olv

esse

nd

ing

kn

owle

dg

ein

tern

ally

and

exte

rnal

lyto

thos

ew

ho

cou

ldb

enef

itfr

omth

eu

sean

dap

pli

cati

onof

the

kn

owle

dg

e

O’D

ell

etal.

(199

9);

Lie

bow

itz

(200

1);

Ala

vi

and

Lei

dn

er(2

001)

Kn

owle

dg

ecr

eati

onT

he

tran

sfer

ofex

isti

ng

kn

owle

dg

ean

dth

ecr

eati

onof

new

kn

owle

dg

ep

lay

anim

por

tan

tro

leon

imp

lem

enti

ng

and

exec

uti

ng

KM

O’D

ell

etal.

(199

9);

Ala

vi

and

Lei

dn

er(2

001)

;W

alk

er(2

006)

Cu

ltu

ral

per

spec

tiv

eK

Mst

rate

gy

nee

ds

tob

eco

mp

atib

lew

ith

its

org

aniz

atio

nal

cult

ure

.A

sup

por

tiv

ecu

ltu

reen

cou

rag

esfi

rm’s

emp

loy

ees

tocr

eate

and

shar

ek

now

led

ge

wit

hin

anor

gan

izat

ion

Bar

ney

(198

6);

Hol

sap

ple

and

Josh

i(2

000)

;S

un

asse

ean

dS

ewry

(200

2);

Mar

ten

sson

(200

0);

Lie

bow

itz

(199

9);

Ru

ben

stei

n-M

onta

no

etal.

(200

1)

(con

tinued

)

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458

Table I.

Cri

teri

aR

elev

ance

inK

MR

efer

ence

s

Ash

ared

vis

ion

by

emp

loye

esan

dm

anag

emen

t

Th

ev

isio

nen

com

pas

ses

the

core

bel

iefs

and

val

ues

ofth

eor

gan

izat

ion

.S

har

edv

isio

ns

and

goa

lsb

yem

plo

yees

and

man

agem

ent

ult

imat

ely

det

erm

ine

the

succ

ess

orfa

ilu

reof

firm

’sK

Mst

rate

gie

s

Bar

ney

(198

6);

Lie

bow

itz

(200

3);

For

cad

ell

and

Gu

adam

illa

s(2

002)

;S

un

asse

ean

dS

ewry

(200

2)

Com

mu

nic

atio

nan

dre

war

din

cen

tiv

esA

kn

owle

dg

esh

arin

gcu

ltu

ren

eed

sto

be

crea

ted

toin

clu

de

com

mu

nic

atio

nan

din

cen

tiv

e/re

war

dsy

stem

tom

otiv

ate

emp

loye

esto

shar

eth

eir

kn

owle

dg

e

Joh

ann

esse

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459

shown at the bottom level of the model. These are HKM, SKM, and DKM. A graphicalsummary of the proposed ANP model is shown in Figure 1.

3.2 The analytic network processANP is a special case of ANP. Both AHP and ANP derive relative priority scales ofabsolute numbers from individual judgments by making paired comparisons ofelements on a common property or a control criterion. In AHP, these judgmentsrepresent independence assumptions of higher-level elements from lower-levelelements in a multi-level hierarchical structure. On the other hand, ANP is a moregeneral form of AHP, incorporating feedback and interdependent relationships amongdecision elements and alternatives (Saaty, 1996). Therefore, AHP is a weak method indetermining interrelationships among factors. However, ANP uses a network withoutthe need to specify levels (Sarkis, 1998; Saaty, 2003). This provides a more accurateapproach when modelling complex decision-making problems (Meade and Sarkis,1999; Agarwal and Shankar, 2003; Agarwal et al., 2006).

In ANP, there is a network of influences among the elements and clusters. ANPallows both interaction and feedback, within clusters of elements (inner dependence)and between clusters (outer dependence), with respect to an underlying controlcriterion (Saaty, 1996, 2003). Inner and outer dependencies can capture and representthe concepts of influencing or being influenced relationships, within and between

Figure 1.ANP model for the

selection of anappropriate KM strategy

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460

clusters of elements. Then pairwise comparisons of the elements in each cluster areconducted with respect to their relative importance towards their control criterion.These pairwise comparisons are based on Saaty’s nine-point scale (1-9), and representhow many times an element dominates another, where a score of 1 indicates equalimportance between the two elements and nine represents the extreme importance ofone element compared to the other one. The reciprocal of these values are automaticallyassigned to the reverse comparison within the matrix. A two-way arrow or arcs amongdifferent levels of criteria may graphically represent the interdependencies in an ANPmodel. If interdependencies are present within the same level of analysis, a looped arcmay be used to represent such interdependencies (Meade and Sarkis, 1999). ANP isable to handle interdependencies among elements through the calculation of compositeweights for each control criterion as developed in a super-matrix. Finally, each of thesesuper-matrices is weighted by the priority of its control criterion and then the resultsare synthesized through addition for the entire control criterion (Saaty, 1996, 2003;Agarwal and Shankar, 2003; Agarwal et al., 2006). Now we will focus on ANP model forselecting an appropriate KM strategy.

3.3 Application of ANP frameworkANP has been applied to a large variety of decisions such as marketing, medical,political, military, social, forecasting and prediction, and many others (Saaty, 1996, 2003;Bayazit, 2006). The ANP methodology and its applicability in various areas are welldocumented in operations research literature (Saaty, 1996, 2003; Sarkis, 1998; Meade andSarkis, 1999; Partovi and Corredoira, 2002; Bayazit, 2006). In the following, the steps ofthe ANP methodology from Sarkis (1998), Meade and Sarkis (1999), Agarwal andShankar (2003), Agarwal et al. (2006), Ravi et al. (2005), and Jharkharia and Shankar(2007) are reviewed and summarized for Turkish manufacturing companies.

Step 1. Pairwise comparisons and relative-importance weight vectors. In this step,the decision maker is asked to respond to a series of pairwise comparisons with respectto an upper level control criterion. These are conducted with respect to their relativeimportance towards their control criterion (Agarwal and Shankar, 2003). In suchcomparisons, ANP uses the same fundamental comparison scale (1-9) as the AHP. Inthe case of interdependencies, components within the same level may be viewed ascontrolling components for each other, or levels may be interdependent on each other(Meade and Sarkis, 1999).

Then, the geometric means of individual weights from the survey results arecomputed to develop a required pairwise comparison matrix. An example of thepairwise comparison matrix for the cost determinant along with the derived localpriority vectors (e-vectors) is presented in Table II. The e-vectors are the weightedpriorities of the determinants and given in the last column of the matrix. For thecomputation of the e-vector, we first add the values in each column of the matrix. Then,dividing each value in each column by the total value of that column, the normalized

Table II.Pairwise comparison ofdeterminants

Determinants CST TME QLT FLX E-vectors

Cost (CST) 1 3 2 1/2 0.283Time (TME) 1/3 1 1/2 1/3 0.106Quality (QLT) 1/2 2 1 1/3 0.164Flexibility (FLX) 2 3 3 1 0.447


461

matrix is obtained. Finally, averaging over the row is made to determine the e-vectors(Ravi et al., 2005). These e-vectors will be evaluated in the first row of the final table forthe calculation of KM strategy weighted index (KMWI) for alternatives. In thisexample, flexibility was given the highest rating with a score of 0.447. Similarly,pairwise comparison matrix is required for the relative importance of each dimension(STR, TCH, CLT, and FNC) on the cost determinant. There will be four such matrices,one for each of the determinants. The matrix for the cost determinant is given inTable III. The results of this comparison (e-vectors) are presented as Pja in the thirdcolumn of Table IV. In Table IV, Pja presents the relative importance weight ofdimension j on the determinant a.

Additional pairwise comparisons between the applicable enablers of a given KMdimension cluster are performed to calculate the weighted priorities of these enablers.For example, Table V presents the pairwise comparison matrix for STR dimensionunder the cost determinant. For the pairwise comparison, the question asked to the

Table III.Pairwise comparison of

dimensions

Cost (CST) STR TCH CLT FNC E-vectors

Strategic perspective (STR) 1 2 3 1/2 0.272Technological perspective (TCH) 1/2 1 2 1/3 0.157Cultural perspective (CLT) 1/3 1/2 1 1/5 0.088Financial perspective (FNC) 2 3 5 1 0.483

Table IV.Cost desirability indices

Dimensions Enablers Pja AkjaD Akja

I S1kja S 2kja S3kja HKM SKM DKM

Strategicperspective (STR) TM 0.272 0.468 0.211 0.793 0.076 0.131 0.02130 0.00204 0.00352

SA 0.272 0.140 0.057 0.102 0.682 0.216 0.00022 0.00148 0.00047ER 0.272 0.071 0.098 0.615 0.117 0.268 0.00116 0.00022 0.00051OC 0.272 0.264 0.324 0.185 0.156 0.659 0.00430 0.00363 0.01533ES 0.272 0.057 0.310 0.160 0.691 0.149 0.00077 0.00332 0.00072

Technologicalperspective (TCH) IT 0.157 0.509 0.409 0.114 0.405 0.481 0.00373 0.01324 0.01572

EO 0.157 0.308 0.305 0.109 0.630 0.261 0.00161 0.00929 0.00385KT 0.157 0.119 0.180 0.286 0.143 0.571 0.00096 0.00048 0.00192KC 0.157 0.064 0.106 0.073 0.727 0.200 0.00008 0.00077 0.00021

Culturalperspective (CLT) SV 0.088 0.496 0.332 0.172 0.102 0.726 0.00249 0.00148 0.01052

CI 0.088 0.267 0.278 0.315 0.603 0.082 0.00206 0.00394 0.00054TR 0.088 0.083 0.179 0.751 0.178 0.071 0.00098 0.00023 0.00009EX 0.088 0.154 0.211 0.211 0.084 0.705 0.00060 0.00024 0.00202

Financialperspective (FNC) MS 0.483 0.271 0.327 0.230 0.122 0.648 0.00984 0.00522 0.02774

PR 0.483 0.418 0.340 0.493 0.311 0.196 0.03384 0.02135 0.01345GR 0.483 0.191 0.139 0.276 0.128 0.596 0.00354 0.00164 0.00764IV 0.483 0.120 0.194 0.582 0.109 0.309 0.00654 0.00123 0.00347

Desirabilityindices Dia 0.09402 0.0698 0.10772

Normalizeddesirabilityindices DiaNV 0.3462 0.2571 0.3967

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462

decision maker is, ‘‘What is the relative impact on STR by enabler ‘‘a’’, when comparedto enabler ‘‘b’’, under cost determinant?’’ The e-vectors for this matrix are presented asthe last column in Table V. In this example, top management support was given thehighest rating (0.468). Similarly, pairwise comparison matrices for other enablers areprepared and the e-vectors obtained from these matrices are presented as AD

kja in thefourth column of Table IV. AD

kja present the relative importance weight for enabler k,dimension j, and determinant a for the dependency (D) relationships between enablers’component levels.

The final standard pairwise comparison evaluations are required for the relativeimpact of each of the alternatives (HKM, SKM, and DKM) on the enablers ininfluencing the determinants. One such pairwise comparison matrix is given inTable VI. As shown in Table VI, the impacts of three alternatives are evaluated on theenabler SA in influencing the cost determinant. The e-vectors from this matrix will beused in the 6-8 columns of the cost desirability indices matrix in Table IV. The columns6-8 in Table IV correspond to S1kja, S2kja, and S3kja, respectively. Also, Sikja is therelative impact of alternative i on enabler k of dimension j for determinant a.

Step 2. Pairwise comparison matrices of interdependencies. To reflect theinterdependencies in network, pairwise comparisons need to be conducted among allthe enablers (Agarwal and Shankar, 2003). Table VII represents the CST-STR clusterwith ES as the control attribute over other enablers. As shown in Table VII, OC (0.524)has the maximum impact on STR-CST cluster with ES as the control enabler overothers. The e-vectors and remaining matrices will be used in Table VIII.

Step 3. Super-matrix formation and analysis. In this model, there are four super-matrices, one for each of the determinants of KM strategy hierarchy network, which

Table V.Pairwise comparisonmatrix for STR underthe cost determinant

Strategic perspective (STR) TM SA ER OC ES E-vectors

Top management support (TM) 1 4 7 2 6 0.468Strategic alignment (SA) 1/4 1 3 1/2 2 0.140Employees’ responsibility (ER) 1/7 1/3 1 1/4 2 0.071Organizational capability (OC) 1/2 2 4 1 5 0.264Environmental scanning (ES) 1/6 1/2 1/2 1/5 1 0.057

Table VI.Matrix for alternatives’impact on enabler SA ininfluencing the costdeterminant

Alternatives HKM SKM DKM E-vectors

Human-oriented KM (HKM) 1 1/7 1/2 0.102System-oriented KM (SKM) 7 1 3 0.682Dynamic-oriented KM (DKM) 2 1/3 1 0.216

Table VII.Pairwise comparisonmatrix for enablersunder cost, STR andenvironmental scanning

Environmental scanning (ES) TM SA ER OC E-vectors

Top management support (TM) 1 4 2 1/2 0.271Strategic alignment (SA) 1/4 1 1/2 1/7 0.070Employees’ responsibility (ER) 1/2 2 1 1/4 0.135Organizational capability (OC) 2 7 4 1 0.524


463

Table VIII.Super-matrix M for cost

before convergence

TM

SA

ER

OC

ES

ITE

OK

TK

CS

VC

IT

RE

XM

SP

RG

RIV

TM

00.

214

0.10

30.

322

0.27

1S

A0.

063

00.

058

0.05

00.

070

ER

0.11

00.

072

00.

089

0.13

5O

C0.

300

0.64

30.

625

00.

524

ES

0.52

70.

071

0.21

40.

539

0IT

00.

728

0.58

20.

778

EO

0.57

10

0.30

90.

143

KT

0.28

60.

181

00.

079

KC

0.14

30.

091

0.10

90

SV

00.

309

0.61

50.

648

CI

0.60

00

0.29

20.

122

TR

0.30

00.

109

00.

230

EX

0.10

00.

582

0.09

30

MS

00.

625

0.66

70.

117

PR

0.54

00

0.22

20.

683

GR

0.16

30.

137

00.

200

IV0.

297

0.23

80.

111

0

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need to be calculated. Table VIII shows the super-matrix M, detailing the results of therelative importance measures for each of the enablers for the cost determinant. Thevalues of the super-matrix M, have been taken from the pairwise comparison matricesof interdependencies (Table VII). Since there are 17 pairwise comparison matrices, onefor each of the interdependent enablers in the cost determinant, there will be 17 non-zero columns in this super-matrix (Agarwal and Shankar, 2003). Each of the non-zerovalues in the column in the super-matrix M is the relative importance weightassociated with the interdependent pairwise comparison matrices (Ravi et al., 2005;Jharkharia and Shankar, 2007).

The super-matrix M (Table VIII) is converged for getting a long-term stable set ofweights. For convergence to occur, power of super-matrix needs to be raised to anarbitrarily large number. In other words, the sum of each column of the super-matrixneeds to be one (Meade and Sarkis, 1999; Ravi et al., 2005; Agarwal et al., 2006). In thisexample, convergence is reached at 31st power. Table IX presents the values afterconvergence.

Step 4. Selection of the best alternative. The selection of the best alternativedepends on the calculation of various desirability indices ( Jharkharia and Shankar,2007). In this case, for each determinant, there are three desirability indices, one eachfor the three alternatives HKM, SKM, and DKM. The desirability index, Dia, for thealternative i and the determinant a, is defined as (Meade and Sarkis, 1999):

Dia ¼Xj

j¼1

XKja

k¼1

PjaADkjaAI

kjaSikja: ð1Þ

In equation (1), AIkja is the stabilized relative importance weight for enabler k of the

dimension j in the determinant a cluster for interdependency (I ) relationships withinthe enablers’ component levels. These values are taken from the converged super-matrix (Table IX). Kja is the index set of enablers for dimension j of determinant a, andJ is the index set for dimension j.

Table IV presents the calculations for the desirability indices (Dia) and theirnormalized values (DiaNV) for the cost determinant. These values are based on the costhierarchy by evaluating the relative weights obtained from the pairwise comparisonsof the alternatives, dimensions, and weights of enablers from the converged super-matrix (Agarwal and Shankar, 2003; Jharkharia and Shankar, 2007).

The values in the third column of Table IV present the relative importance weightsof the dimensions on the cost determinant. These values have been taken from TableIII. The values in the fourth column of Table IV present the relative importanceweights of the enablers on the cost determinant through their respective dimensions.The relative importance weights of the enablers (in column four) corresponding to thedimension STR have been taken from Table V. The values in the fifth column of TableIV present the stable independent weights of enablers obtained through a convergedsuper-matrix (Table VII). The values in the 6-8 columns of the cost desirability indicesmatrix in Table IV, which correspond to S1, S2, and S3, respectively, present therelative importance weights of the three alternatives on the enablers. These valueshave been obtained by comparing three alternatives for every enabler of KM strategies.For example, the values corresponding to SA in the 6-8 columns of the cost desirabilityindices matrix in Table IV have been taken from Table VII. The final three columnsrepresent the weighted values of the alternatives (Pja � AD

kja � AIkja � Sikja) for each of


465

Table IX.Super-matrix M for cost

after convergence

TM

SA

ER

OC

ES

ITE

OK

TK

CS

VC

IT

RE

XM

SP

RG

RIV

TM

0.21

10.

211

0.21

10.

211

0.21

1S

A0.

057

0.05

70.

057

0.05

70.

057

ER

0.09

80.

098

0.09

80.

098

0.09

8O

C0.

324

0.32

40.

324

0.32

40.

324

ES

0.31

00.

310

0.31

00.

310

0.31

0IT

0.40

90.

409

0.40

90.

409

EO

0.30

50.

305

0.30

50.

305

KT

0.18

00.

180

0.18

00.

180

KC

0.10

60.

106

0.10

60.

106

SV

0.33

20.

332

0.33

20.

332

CI

0.27

80.

278

0.27

80.

278

TR

0.17

90.

179

0.17

90.

179

EX

0.21

10.

211

0.21

10.

211

MS

0.32

70.

327

0.32

70.

327

PR

0.34

00.

340

0.34

00.

340

GR

0.13

90.

139

0.13

90.

139

IV0.

194

0.19

40.

194

0.19

4

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466

the enablers. For example, the value corresponding to alternative HKM for SA is0.00022 (0.272 � 0.140 � 0.057 � 0.102). The summations of these results for each ofthe alternatives under cost determinant provide the values of desirability indices, (Dia)( Jharkharia and Shankar, 2007). These desirability indices (Dia) and their normalizedvalues (DiaNV) appear as the last two rows in Table IV. The results show that the impacton cost is most influenced by DKM (0.3967) followed by HKM (0.3462) and SKM(0.2571). The analysis has been conducted only for the cost determinant. To completethe analysis, similar steps must be applied for the other three determinants. TheKMWI would then be determined to incorporate the results of all the four determinants( Jharkharia and Shankar, 2007).

Step 5. Calculation of KMWI. The KMWI for an alternative i (KMWIi) is thesummation of the products of the normalized desirability indices (DiaNV) and therelative importance weights of the determinants (Ca). KMWI is represented as (Meadeand Sarkis, 1999; Jharkharia and Shankar, 2007):

KMWIi ¼X

DiaNV Ca: ð2Þ

For example, the KMWI for HKM is calculated as:

KMWIHKM ¼ ½ð0:283� 0:3462Þ þ ð0:106� 0:2412Þ þ ð0:164� 0:2216Þþ ð0:447� 0:2244Þ� ¼ 0:2602:

The final results of KMWI are shown in Table X. The Table X indicates that forTurkish manufacturing companies DKM is the most significant strategy (0.4208)followed by SKM (0.3190) and HKM (0.2602).

4. Results and discussionsThis paper has addressed the need for a MCDM model to assist managers in thecomplex decision environment for evaluating and selecting their appropriate KMstrategies. This model integrates and relies on the various determinants, dimensions,and enablers of KM strategies and their relationships. In the selection process,strategic, technological, cultural, and financial measures have been considered toweight KM alternatives. During the assessment process, consistency checks have alsobeen conducted to increase decision quality. We have checked the consistency ofpairwise comparisons using method of consistency ratio as suggested by Saaty (1996).In our example, it is shown that all the pairwise comparisons are consistent. Inaddition, from Table X, it has been observed that DKM is the most significant strategy(0.4208), which is followed by SKM (0.3190) and HKM (0.2602). Thus, DKM strategy isthe first choice of Turkish manufacturing companies. The higher value of KMWI forDKM strategy supports the policy for integrating the HKM and SKM approaches. Thismeans that neither the HKM strategy nor the SKM strategy alone is sufficient to

Table X.KM strategies weightedindex for alternatives(KMWI)

Alternatives e-vectors Cost (0.283) Time (0.106) Quality (0.164) Flexibility (0.447) KMWI

HKM 0.3462 0.2412 0.2216 0.2244 0.2602SKM 0.2571 0.3481 0.3073 0.3555 0.3190DKM 0.3967 0.4107 0.4711 0.4201 0.4208


467

manage knowledge. SKM strategies seek to document and store knowledge indatabases, and HKM strategies seek to develop networks of people for communicatingideas. However, DKM strategies provide an integrated approach that considers bothexplicit and tacit knowledge and the cultural environment within which people shareand communicate the knowledge they possess. This means that DKM strategy canlead to a more targeted improvement in terms of knowledge transparency, knowledgesharing and communication.

The model also provides the priority values of the determinants for selecting KMstrategies. From Table II, it has been observed that the flexibility (0.447) is the mostimportant determinant in the selection of the KM strategies. This is followed by cost(0.283), quality (0.164), and time (0.106). For Turkish manufacturing companies, theresult supports improvement in flexibility and reduction in cost through implementingthe appropriate KM strategy. Therefore, the increased flexibility in an organizationalstructure may result in increased creation of new knowledge. In addition, reducingcosts by obtaining information from customers may result in increased customersatisfaction. On the other hand, the lower values for other two determinants may beattributed to their interdependency on flexibility and cost. However, the ANP model iscapable of handling such interdependencies.

5. ConclusionsANP is a robust decision tool for decision making across multiple criteria. It has beenused in many applications across many fields. The major contribution of this researchlies in the development of a comprehensive ANP model, which incorporates variousfactors for selecting and evaluating an appropriate KM strategy for Turkishmanufacturing companies. In addition, the proposed methodology serves as guidelineto the KM managers for making a strategic decision.

Results show that the DKM strategy is the best choice for Turkish manufacturingcompanies. This finding also suggests that DKM strategy is more appropriate forimproving knowledge transparency, knowledge flows and access, and communicationwithin the organization. This finding also confirms that the manufacturing companiesin Turkey can achieve strategic benefits through focusing on effective DKM strategy.Thus, the facilitation of knowledge sharing through informal networking, and theestablishment of common language for knowledge codification would be realized byusing DKM strategy. As compared to the HKM and SKM, DKM strategy is alsosuperior on criteria like cost, time, quality, and flexibility.

Although the proposed model provides a comprehensive framework for selectingthe best KM strategy, there are some limitations in this study. First, the model did notconsider all possible clusters, elements, and their interactions. Depending on thedecision environment, additional factors and interactions, within and between decisionelements and alternatives, could be added. For example, several factors that have beensupported in the selection of KM strategies like organizational learning, innovativecapabilities, and strategic goals of the organizations were not explicitly included in thismodel, but could be easily considered to improve the selection of the best KM strategy.However, the additional factors and their interactions require the additional time andeffort necessary for completion of such a model. Second, the model is very dependenton the weightings provided by the decision makers. While this model effectivelyincorporates tangible and intangible measures into the evaluation process, its efficacydepends on the accuracy and the value of judgment provided by the decision makers.The questionnaire survey helped to utilize decision makers’ experience and eliminated

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the biases in the weights for the alternatives. Third, an analysis of the robustness of thedecision model using sensitivity analysis may also be carried out to observe the impactof variation in the preferences of decision makers in assigning the weights. Finally,managing knowledge is a dynamic and multidimensional process. KM relies onbusiness strategies, technology, culture, and financial measures to meet its goals.Therefore, there is very heavy dependency on the specific type of organization, wherethe KM strategy is being evaluated. Thus, the generalizability of the model findings todifferent sectors may be limited; however, the criteria and dimensions identified in theproposed model are quite generic and with minor adjustments can be applied to a widerange of complex real world problems. Future work includes applying the ANP-basedmodels to different companies operating in various industries. Also, comparing thismodel with other tools and different ANP-based models may be investigated.

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About the authorSelcuk Percin holds a PhD from Ankara University, Ankara, Turkey. He is presently working asan Assistant Professor in the Faculty of Economics and Administrative Sciences, KaradenizTechnical University, Trabzon, Turkey. His research interests focus on business performance,supply chain management, structural equation modeling (SEM) and operations researchestechniques (MCDM, DEA, linear and mixed-integer programming, goal programming, fuzzysets, etc.). His works have been published in Measuring Business Excellence, Journal of EnterpriseInformation Management, Information Management & Computer Security, and Benchmarking:An International Journal, and various conference proceedings. Selcuk Percin can be contacted at:[email protected]

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