Modern Accounting and Auditing - iris.unive.it Volume... · Modern Accounting and Auditing David...

Volume 8, Number 5, May 2012 (Serial Number 84)

Journal ofModern Accounting and Auditing

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Publication Information: Journal of Modern Accounting and Auditing is published monthly in hard copy (ISSN1548-6583) and online (ISSN1935-9683) by David Publishing Company located at 9460 Telstar Ave Suite 5, EL Monte, CA 91731, USA. Aims and Scope: Journal of Modern Accounting and Auditing, a monthly professional academic journal, covers all sorts of researches on accounting research, financial theory, capital market, audit theory and practice from experts and scholars all over the world. Editorial Board Members: Benedetta Siboni, assistant professor, Italy. Haihong He, associate professor, USA. João Paulo Torre Vieito, Ph.D., dean, Portugal. Lindita Rova, Ph.D., Albania. Mohammad Talha, Ph.D., associate professor, Saudi Arabia. Narumon Saardchom, Ph.D., associate dean, Thailand. Peter Harris, Ph.D., USA. Philip Yim Kwong Cheng, senior lecturer, Australia. Thomas Gstraunthaler, associate professor, South Africa. Tumellano Sebehela, Ph.D., United Kingdom. Vintilescu Belciug Adrian, Ph.D., Romanian. Wan Mansor W. Mahmood, professor, Malaysia. Zeynep Özsoy, Ph.D., Turkey. Manuscripts and correspondence are invited for publication. You can submit your papers via Web Submission, or E-mail to [email protected]. Submission guidelines and Web Submission system are available at http://www.davidpublishing.com. Editorial Office: 9460 Telstar Ave Suite 5, EL Monte, CA 91731 Tel: 1-323-9847526; Fax: 1-323-9847374 E-mail: [email protected], [email protected] Copyright©2012 by David Publishing Company and individual contributors. All rights reserved. David Publishing Company holds the exclusive copyright of all the contents of this journal. In accordance with the international convention, no part of this journal may be reproduced or transmitted by any media or publishing organs (including various websites) without the written permission of the copyright holder. Otherwise, any conduct would be considered as the violation of the copyright. The contents of this journal are available for any citation, however, all the citations should be clearly indicated with the title of this journal, serial number, and the name of the author. Abstracted/Indexed in: Database of EBSCO, Massachusetts, USA ProQuest Cambridge Scientific Abstracts (CSA)-Natural Sciences Chinese Database of CEPS, Airiti Inc. & OCLC Chinese Scientific Journals Database, VIP Corporation, Chongqing, P.R.China Ulrich’s Periodicals Directory Database of Summon Serials Solutions, USA Subscription Information: Price (per year): Print $600; Online $480; Print and Online $800 David Publishing Company 9460 Telstar Ave Suite 5, EL Monte, CA 91731, USA Tel: 1-323-9847526; Fax: 1-323-9847374 E-mail: [email protected]

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Journal of Modern Accounting and Auditing

Volume 8, Number 5, May 2012 (Serial Number 84)

Contents Finance Analysis & Management Accounting

An Empirical Analysis on the Prediction of Chinese Financially Distressed Listed Companies 593 Robert G. Biscontri, Chen Gongmeng, Oliver M. Rui

Performance and Stock Return in Australian Banking 616 Amir Moradi-Motlagh, Ali Salman Saleh, Amir Abdekhodaee, Mehran Ektesabi

An Exploratory Study Into the Implementation of Management Accounting Changes in Australia 628

Davood Askarany

Fair Value Disclosure, External Appraisers, and the Reliability of Fair Value Measurements 641 Nicola Moscariello

Corporate Governance & Cost Control

Using SEM to Verify the Impacts of Accumulated Intellectual Capital on Organizational Performance of Taiwan Listed Biotechnology Companies With the Moderator of Brand Equity 657

Lee Yu-Je, Wang Gao-Liang, Wu Mei-Fen

Cost of Production at Business Unit in Aquaculture Industry: Study at Aquafarm Nusantara Company 671

Ilham Hidayah Napitupulu, Widyo Nugroho

Estimating the Three-Stage Cost Malmquist Productivity Index in the Taiwan Biotech and Biopharmaceutical Industry 679

Sheng Tzu-Chun, Liu Kai-Ping, Yang Yung-Lieh

Board Characteristics and Firm Performance 688 Masood Fooladi

Study on Transnational Business Operation Strategy of China Xinhua News Network 695 Xiong De

Macro Economy & International Marketing

The Information Risk in the Latest Crisis: A Driver or a Driven Factor for Global Financial Markets Equilibrium? 702

Giorgio Bertinetti, Guido Max Mantovani

Impact of Fiscal Policy on Economy After Accession to the Euro Area: The Case of Poland 733 Piotr Krajewski

Clusters as a Way of Penetration of International Market 740 Martina Fronkova

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-)/,$(/G,(F&DE',F(")()0%&%.0&'(+0F/$,FF(&$,(+,/,$D0%,+2(W,(#F,(/G,()"''"W0%1(E$0%.0E',F(/"(F,',./(/G,(."%/$"'(F&DE',FA(I"DE&%0,F(0%(/G,(."%/$"'(F&DE',(&$,(.G"F,%(N&F,+(E$0D&$0'=("%(/G,0$(0%+#F/$=(D,DN,$FG0EA(3%+#F/$=(."+,F(D#F/(N,(,e#0R&',%/(&%+()0$F/(FG"#'+(%,0/G,$(FG"W(&%=(4S(.'&FF0)0.&/0"%(%"$(%,1&/0R,(E,$)"$D&%.,A(J0$D(F0`,(0F(&'F"(D&/.G,+(N=(#F0%1(/"/&'(&FF,/F(&/(=,&$ T:8;.(

Observation Window *&%=( F/#+0,F( YP,&R,$2( 6B;;i( -'/D&%2( 6B;9i( g&R1$,%2( 6B97i( 4G,EE&$+2( 6BB8i( K"#DE"F(p( g"E"#%0+0F2(

6BBBZ( #F,+( )0%&%.0&'( +&/&( 0%( /G,( )0R,( =,&$F( N,)"$,( &( ."DE&%=( ,bE,$0,%.,F( )0%&%.0&'( +0F/$,FF2( WG0',( "/G,$F(YC+D0F/,$2(6Bc>i(I"&/F(p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`,(0/F()0%&%.0&'(F/&/,D,%/F(0%(>??62(>???2(&%+(6BBBA(

Sample Size and Proportion V#$()0%&'(F&DE',(."DE$0F,F(")(6??()0%&%.0&'(+0F/$,FF,+(."DE&%0,F(&%+(6??(G,&'/G=(."DE&%0,F(&F(&(."%/$"'(

1$"#EA(X,($&%+"D'=(F,',./(9?(+0F/$,FF,+()0$DF(&%+(9?(G,&'/G=()0$DF(/"(&EE'=(/G,(R&$0"#F(D"+,'FA(SG,($,D&0%0%1(8?()0$DF(&$,(G,'+()"$("#/(")(F&DE',(/,F/FA(SG,$,(0F(%"(."%F,%F#F($,1&$+0%1(/G,(E$"E"$/0"%(")(+0F/$,FF,+(R,$F#F(G,&'/G=( )0$DF( FG"#'+( N,( #F,+( W0/G0%( /G,( /,F/( F,/A( SG,$,( &$,( /G$,,( ."DD"%( .G"0.,F( )"$( F,',./0%1( &( F&DE',(E$"E"$/0"%A( SG,( )0$F/2( E&0$,+( F&DE',F( #F,+( N=( P,&R,$( Y6B;;Z2( -'/D&%( Y6B;9Z2( -'/D&%( ,/( &'A( Y6BccZ2( I"''0%F(Y6B9?Z2(U0D(&%+(*.L,"+(Y6BBBZ2(K"#DE"F(&%+(g"E"#%0+0F(Y6BBBZA(SG,(F,."%+(&EE$"&.G(#F,F(&(E$"E"$/0"%(0%(WG0.G( /G,( %#DN,$( ")( )0%&%.0&'( +0F/$,FF,+( )0$DF( 0F( ',FF( /G&%( /G&/( ")( G,&'/G=( )0$DF( /"( &R"0+( +,R0&/0%1( /G,(E$"E"$/0"%(")(+0F/$,FF,+()0$DF(0%(,F/0D&/,+(F&DE',()$"D(/G,(E"E#'&/0"%A(SG0F(&EE$"&.G(&'F"(G&F(."%.,$%F(W0/G(,F/&N'0FG0%1(&%(&..#$&/,(E$"E"$/0"%(")(+0F/$,FF,+()0$DF(,b0F/0%1(0%( /G,(E"E#'&/0"%A(J$=+D&%2(-'/D&%2(&%+(U&"(Y6B97Z2(I"&/F(&%+(J&%/(Y6BB<Z2(H'&//(,/(&'A(Y6BB8Z2(&%+("/G,$F(#F,+(&(E$"E"$/0"%(N,/W,,%(6(h(>(/"(6(h(8Ac( VG'F"%(Y6B9?Z2(T#&%12(K"$F,=2(&%+(P"FF,(Y6BB8Z2(T0''2(H,$$=2(&%+(-%+,F(Y6BB;Z2(S0$&E&/(&%+(50//&=&1&F,/W&/(Y6BBBZ2(&%+(*.[,,(&%+(O$,,%F/,0%(Y>???Z(,DE'"=,+(/G,(/"/&'(E"E#'&/0"%(&F(/G,0$(F&DE',FA(XG,%(#F0%1(%,#$&'(%,/W"$[F2(/G,(#F,(")(,%/0$,(F&DE',F( 0F(%"/( $,."DD,%+,+(&F( $,F#'/F(D&=(N,(+$0R,%(N=(D"+,'("R,$:)0//0%1(&%+(%"/(N=( /G,(E$,+0./0R,( E"W,$( ")( /G,( D"+,'( 0/F,')A(X,( ."DE&$,( /G,( E$,+0./0R,( E"W,$( ")( D"+,'F( ."%F/$#./,+( #%+,$( /G$,,(D"F/'=(#/0'0`,+(E$"E"$/0"%Fh(6(h(62(6(h(>2(&%+(6(h(<A(

Specifications of Initial Independent Variables and the Model Variables

H$,R0"#F(F/#+0,F(")(."$E"$&/,()0%&%.0&'(+0F/$,FF(&%+(N&%[$#E/.=(G&R,(1,%,$&''=(F,',./,+()0%&%.0&'(R&$0&N',F(0%()0R,(.&/,1"$0,Fh(&FF,/('0e#0+0/=2(F"'R,%.=2("E,$&/0%1(.&E&.0/=2(E$")0/&N0'0/=2(&%+(1$"W/G(E"/,%/0&'A9( T"W,R,$2((((((((((((((((((((((((((((((((((((((((( (((((((((((((((((((((((((;( SG0F(E&E,$(+,)0%,F(/G,("NF,$R&/0"%(E,$0"+(&F(/G$,,(=,&$F(N,)"$,()0%&%.0&'(+0F/$,FF(YT:62(T:>2(&%+(T:<ZA(3/(0F(1,%,$&''=(."%F0+,$,+(/G&/( /G,( F0`,( ")( &FF,/( W0''( FG$0%[( WG,%( &( ."DE&%=( $#%F( 0%/"( )0%&%.0&'( +0F/$,FFA(X,( F,',./( /G,( /"/&'( &FF,/F( 0%( =,&$( T:8( &F( /G,(N,%.GD&$[(/"(D,&F#$,(&(F&DE',\F(F0`,A(c( SG,=(+"(%"/(E$"R0+,(/G,($&/0"%&',()"$(.G""F0%1(&(E$"E"$/0"%(")(N,/W,,%(6(h(>(&%+(6(h(8A(9( -F(/"(.&FG()'"W(F/&/,D,%/(+&/&2(&.."$+0%1(/"(I&F,=(&%+(P&$/.`&[(Y6B97Z(&%+(O"DN"'&(,/(&'A(Y6B9cZ2(/G,F,(0%+0.&/"$F(."%/$0N#/,('0//',(/"(/G,(0DE$"R,D,%/(")(/G,(E$,+0./0R,(E"W,$(")(D"+,'FA(-/(/G,(F&D,(/0D,2(%"(F/&%+&$+0`,+(.&FG()'"W(F/&/,D,%/(+&/&(")('0F/,+(IG0%,F,(."DE&%0,F(&$,(&R&0'&N',(N,)"$,(6BB9A(SG,$,)"$,2(W,(+"(%"/(/&[,(.&FG()'"W(+&/&(0%/"(."%F0+,$&/0"%(0%("#$(F/#+=A(


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Specification of Models X,(,b&D0%,(68(D"+,'F(.'&FF0)0,+(0%/"(/W"(N$"&+(.&/,1"$0,Fh(F/&/0.(&%+(+=%&D0.(D"+,'FA(X0/G0%(/G,(1$"#E(

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S"(&R"0+(E"FF0N',(0%)'#,%.,(")(/G,(&N%"$D&'(R&'#,F(")()0%&%.0&'(0%+0.&/"$F(0%(0%+0R0+#&'(=,&$F2(W,(#/0'0`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+=%&D0.()0%&%.0&'(0%+0.&/"$F(G&R,(D"$,(0%)"$D&/0"%(/"($,)',./()0$DF\()#/#$,()0%&%.0&'(E,$)"$D&%.,A(3%(*"+,'(;2(/G,( F/&/0.( )0%&%.0&'( $&/0"F( 0%( =,&$( T:6( &%+( /G,( /G$,,:=,&$( &R,$&1,( .G&%1,( ")( )0%&%.0&'( $&/0"F( &$,( #F,+( &F(0%+,E,%+,%/(R&$0&N',FA( 3%(*"+,'(c2( /G,(D,&%F(")( /G,( F/&/0.( )0%&%.0&'( $&/0"F( 0%( /G,( /G$,,(=,&$F(N,)"$,( )0%&%.0&'(+0F/$,FF(&%+(/G,(/G$,,:=,&$(&R,$&1,(.G&%1,(")()0%&%.0&'($&/0"F(&$,(#F,+(&F(0%+,E,%+,%/(R&$0&N',FA(

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Comparison of Discrimination Results SG0F(E&E,$(&+"E/F(/W"([0%+F(")(.'&FF0)0.&/0"%(&..#$&.=(/"(,R&'#&/,(&%+(."DE&$,(/G,(E$,+0./0R,(E"W,$(")(/G,(

R&$0"#F(D"+,'F(#%+,$(/G,(+0)),$,%/(F&DE',(E$"E"$/0"%FA(K0F.$0D0%&%/(&..#$&.=($&/,(0F(/G,(.'&FF0)0.&/0"%(&..#$&.=($&/,(0%(,F/0D&/0"%(F&DE',(1$"#EF2(WG0',(E$,+0./0"%(&..#$&.=($&/,(0F(/G,(.'&FF0)0.&/0"%(&..#$&.=($&/,(0%(E$,+0./0"%(F&DE',(1$"#EFA(

40%.,(/G,(E$,+0./0"%(&..#$&.=($&/,(+",F(%"/(."%/&0%(&%=(0%)"$D&/0"%(&'$,&+=(#F,+(/G,(N#0'+0%1(")(D"+,'F2(0/(0F(&(N,//,$($,)',./0"%(")(/G,(1,%,$&'0`&/0"%(&N0'0/=(&%+(F/&N0'0/=(")(/G,(D"+,'($&/G,$(/G&%(/G,(+0F.$0D0%&%/(&..#$&.=($&/,A(X,(/&[,(/G,(E$,+0./0"%(&..#$&.=($&/,(&F(/G,(N,%.GD&$[(/"(+,/,$D0%,(/G,(E$,+0./0R,(E"W,$(")(D"+,'FA(

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

Analysis of Empirical Results Descriptive Statistics

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X79( ;A>>( 6Ac7( 7A??( BA??( 8A;;( 7A>7(X8?( ;A68( ;A??( cA7?( <A7?( 7A<<( >A??(X97( 7Ac6( >Ac7( cA??( cA??( 7A9<( 6A7?(X9>( 7A8<( 7A??( cA??( >A7?( 8A7?( >A7?(X6c( 8Ac6( 6A>7( ( <A7?( ;A7?( 7A<<( >A>7(XB8( 8A7?( 8A??( ?A7?( >A??( <A6c( 7Ac7(X7>( 8A8<( <Ac7( ;A??( ?A??( 8A7?( >A??(Xc9( 8A>6( 7A7?( >A7?( ?A??( >A7?( 8A>7(XB7( 8A?c( <Ac7( >A7?( 8A??( 8A7?( ?A7?(X69( <A;8( 8A7?( 6A7?( 8A??( <A??( 6A??(X9c( <A7c( >A>7( ?A??( ;A7?( 8A6c( ?Ac7(X98( <A8<( 6A??( >A??( 9A??( 6A;c( <A7?(X<7( <A<;( >A7?( >A??( 7A7?( >A;c( 6A7?(Xc8( <A>B( >A>7( 6A??( 6A7?( <A??( <A7?(X89( <A>B( ?A??( 8A7?( 7A??( <A9<( 6A??(X>?( <A68( ?Ac7( 7A??( 7A??( <A??( ?Ac7(X9?( <A??( >A>7( 8A??( 6A7?( >A??( >A7?(X;7( <A??( ?Ac7( >A7?( 6A??( <A9<( >A>7(X>6( >AcB( >A>7( <A7?( <A??( >A??( 6A>7(X99( >A;8( 8A??( ?A??( >A??( >A6c( 6A??(Xc?( >A7c( ?A>7( 7A??( 6A7?( >A??( >A7?(X8c( >A7c( <A>7( >A??( <A7?( ?A;c( >A??(X96( >A7?( <A??( <A7?( >A7?( 6A??( 6A>7(X;B( >A7?( <Ac7( 6A??( >A??( 6A<<( 6A7?(X>c( >A7?( ?Ac7( cA??( ?A??( 6A7?( >A>7(X87( >A7?( ?A??( ?A7?( >A7?( 6A<<( 7A>7(X<;( >A8<( <A7?( ?A??( ?A7?( >A;c( ?Ac7(XB6( >A>B( ?A??( ;A??( >A7?( 6A7?( 6A7?(X86( >A>B( ?A??( >A7?( >A??( >A;c( 6Ac7(X;>( >A>B( 6A>7( 8A??( 6A??( >A??( 6A>7(X8?( >A>B( >A??( <A??( ?A7?( >A7?( ?A7?(X67( >A>6( >A??( 6A7?( 8A??( ?A9<( 6Ac7(

(((((((((((((((((((((((((((((((((((((((( (((((((((((((((((((((((((6>( SG,(/&N',("%'=('0F/F( /G,(R&$0&N',F(WG"F,(/"/&'(%#DN,$(")("..#$$,%.,F(0F(&N"R,(>A68("$(WG"F,(%#DN,$(")("..#$$,%.,F(0%("/G,$(0/,DF(0F(&N"R,(8A??A(


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;?7

YS&N',(8(."%/0%#,+Z(

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R&$0&N',F(IG&%1,(/$,%+(R&$0&N',F(

X8B( >A>6( 6A??( ?A??( 7A??( >A6c( 6A??(XB<( >A68( ?A??( 8A7?( <A??( 6A9<( 6A??(X6( >A68( ?Ac7( 6A7?( >A7?( >A6c( 6A7?(X;c( >A68( >A>7( 6A7?( >A7?( 6A;c( ?Ac7(X;9( >A?c( ?A??( 6A??( 8A7?( 6A<<( >A7?(X>7( >A?c( 7A??( ?A7?( 6A7?( ?A7?( ?A7?(Xc<( >A??( ?Ac7( ?A??( >A??( ?A??( 7A>7(Xcc( >A??( ?A7?( ?A??( >A7?( ?A??( 7A>7(X7?( >A??( ?A??( ?A??( ;A7?( 6A6c( >A??(X78( 6A9;( 6A7?( 8A??( 6A??( ?A9<( 6A>7(X>( 6AcB( 6A??( 8A??( 6A??( 6A<<( ?Ac7(X><( 6Ac6( 6A>7( ?A??( 7A??( 6A??( ?Ac7(X;6( 6A7c( ?A??( 8A7?( <A??( ?A7?( 6A??(X;<( 6A7c( 6A>7( 8A??( 6A??( ?A6c( 6A7?(X;8( 6A>B( 6A7?( 8A??( ?A??( ?A<<( ?A7?((

40%.,(&(R&$0&N',6<( D&=(N,(#F,+(&F(/G,0$(0%0/0&'(0%+,E,%+,%/(R&$0&N',(N=(F,R,$&'(D"+,'F2(/G,(D"$,(/0D,F(/G,(R&$0&N',(0F(0%.'#+,+2(/G,(D"$,('0[,'=(0/(0F(/"(&EE,&$(0%(/G,(#'/0D&/,()#%./0"%F(")(D"+,'FA(X,(#/0'0`,(/G,(Y%#DN,$(")("..#$$,%.,Fq%#DN,$(")(D"+,'FZ(/"(&%&'=`,(/G,(+0F/$0N#/0"%(")(R&$0&N',FA(3%(1,%,$&'2(/G,('&$1,$(/G,(%#DN,$(")(0%+0.&/"$( "..#$$,%.,( 0F2( /G,( D"$,( F/&N',( 0/F( +0F.$0D0%&/"$=( E"W,$( 0FA( -D"%1( B7( 0%+,E,%+,%/( R&$0&N',F2( 8c(0%+0.&/"$F(FG"W(F/&N',(+0F.$0D0%&/"$=(E"W,$A(*"F/(")(/G,D(G&R,(&(R&'#,(")(D"$,(/G&%(>A68(0%(/G,(."'#D%(")(j/"/&'(%#DN,$(")( "..#$$,%.,kA(V/G,$(R&$0&N',F( 0%(S&N',(8(G&R,(G01G(R&'#,F( 0%( /,$DF(")("/G,$( 0/,DFA(-D"%1(/G,D2( /G,$,( &$,( 69( 0%+0.&/"$F( 0%( /G,( E$")0/&N0'0/=( .&/,1"$=2( %0%,( 0%( /G,( 1$"W/G( E"/,%/0&'( .&/,1"$=2( ,01G/( 0%("E,$&/0%1( ,))0.0,%.=2( F,R,%( 0%( '0e#0+0/=2( &%+( F0b( 0%( /G,( F"'R,%.=( .&/,1"$=A( SG,$,)"$,2( /G,( 0%+0.&/"$F( ")(E$")0/&N0'0/=2(1$"W/G(E"/,%/0&'(&%+("E,$&/0%1(,))0.0,%.=(.&%($,)',./(."$E"$&/,()0%&%.0&'(."%+0/0"%(N,//,$(/G&%(/G,(0%+0.&/"$F(")("/G,$(.&/,1"$0,FA(

Analysis of Key Models

*"F/(")( '"&%F( 0FF#,+(N=(IG0%,F,(."DD,$.0&'(N&%[F(/"(,%/,$E$0F,F(&$,(FG"$/:/,$D(0%(%&/#$,A(SG,=($,e#0$,($,E&=D,%/(W0/G0%("%,( /"( /W"(=,&$FA(I"DD,$.0&'(N&%[F(&$,(D"$,(."%.,$%,+(&N"#/( /G,( )0%&%.0&'(W,'':N,0%1(")(0%R,F/,+(,%/,$E$0F,F(0%(&("%,:("$(/W":=,&$(/0D,()$&D,A(3%(&++0/0"%2(D"F/(F/".[(0%R,F/"$F(&$,(FG"$/:/,$D(0%R,F/"$FA(SG,$,)"$,2(W,( )".#F( "%(G"W( /"( E$,+0./(WG,/G,$( &( ."DE&%=(W0''( $#%( 0%/"( FG"$/:/,$D( )0%&%.0&'( +0F/$,FF( 0%( /G0F(F,./0"%A(SG,(FG"$/:/,$D(+0F.$0D0%&%/(D"+,'F2(0%.'#+0%1(*"+,'F(62(;2(c2(6<2(&%+(68A(

2 value for discriminant.( 3%(*K-( &%+( LVO3S( D"+,'F2( /G,( >( ")( 67( +0F.$0D0%&%/( ,e#&/0"%F( ")( )0R,(D"+,'F(#%+,$(/G$,,(E$"E"$/0"%F(")(F&DE',F(&$,(FG"W%(0%(S&N',(7A(SG,(R&'#,(0%(."'#D%(IG0:4e#&$,(0F( >(")(/G,(."$$,FE"%+0%1(+0F.$0D0%&%/(,e#&/0"%2(df 0F(/G,(+,1$,,(")()$,,+"D(")(/G,(+0F.$0D0%&%/(,e#&/0"%(Y0A,A2(/G,(%#DN,$(")( /G,( #'/0D&/,( 0%+,E,%+,%/( R&$0&N',F( ")( /G,( ,e#&/0"%( &%+( Sig.Z( 0%+0.&/,F( /G,( F01%0)0.&%.,( ',R,'( ")( /G,(+0F.$0D0%&%/(,e#&/0"%A(O0R,%(&(.,$/&0%(%#DN,$(")(R&$0&N',F2(/G,('&$1,$(/G,(R&'#,(")( > 0F2(/G,(G01G,$(/G,(+,1$,,((((((((((((((((((((((((((((((((((((((((( (((((((((((((((((((((((((6<( T,$,2(W,(01%"$,(/G,(+0)),$,%.,F(N,/W,,%(jF0%1',()0%&%.0&'(0%+0.&/"$k(&%+(jF0%1',()0%&%.0&'(0%+0.&/"$q0%+#F/$=(D,&%kA(


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")()0/%,FF(")(&%(,e#&/0"%(0FA(J0)/,,%(,e#&/0"%F(W,('0F/(&$,(&''(F01%0)0.&%/'=(,)),./0R,(&/(/G,(?A6n(',R,'A((

S&N',(7(2 of Discriminant Functions of MDA and LOGIT Models

(*K-( LVO3S(

IG0:4e#&$,( df 401A( IG0:4e#&$,( df 401A(4&DE',(6h(6??:6??(*"+,'(6( 69<A<>9( 6;( ?A???( 6cBA88B( 66( ?A???(*"+,'(;( >9<A789( 88( ?A???( >76A?c6( 6c( ?A???(*"+,'(c( >96A8B9( 89( ?A???( 6B6A>;;( 67( ?A???(*"+,'(6<( >B<A6;<( 87( ?A???( >c8A<c6( 6c( ?A???(*"+,'(68( >98A;7B( 87( ?A???( >8;A;?6( 6c( ?A???(4&DE',(>h(7?:6?(*"+,'(6( 6;?A>>B( >?( ?A???( 69cA;c;( 67( ?A???(*"+,'(;( >8<A99>( <B( ?A???( 69BA97>( 6<( ?A???(*"+,'(c( >cBAc78( 8c( ?A???( 69>A<9;( 6;( ?A???(*"+,'(6<( >B>A;B<( 7c( ?A???( 69BA>96( 6B( ?A???(*"+,'(68( >B9A<9>( ;>( ?A???( 6B?A7?>( 67( ?A???(4&DE',(<h(<?:6??(*"+,'(6( 6;?A>>B( >?( ?A???( 6<BA<>>( 68( ?A???(*"+,'(;( >7cA?;;( 8<( ?A???( 6<cA7c7( 6?( ?A???(*"+,'(c( ><<A>8( <8( ?A???( 6<7A96B( 68( ?A???(*"+,'(6<( >6cA>76( <c( ?A???( 68?A667( 6?( ?A???(*"+,'(68( >78AB;6( 78( ?A???( 6<;Ac88( 66( ?A???(

Notes. *K-h(*#'/0E',(+0F.$0D0%&%/(&%&'=F0Fi(LVO3Sh(L"10F/0.($,1$,FF0"%(D"+,'i(55*h(5,#$&'(%,/W"$[(D"+,'A((

Analysis of estimation samples.68( S"( )#$/G,$( 0%R,F/01&/,( /G,(+0F.$0D0%&/"$=(E"W,$(")(R&$0"#F(D"+,'F2(W,(,b&D0%,()&./"$F(&)),./0%1(+0F.$0D0%&%/(&..#$&.=($&/,2(0%.'#+0%1(S=E,(3(&%+(S=E,(33(,$$"$($&/,FA(S=E,(3(,$$"$($&/,($,),$F(/"(/G,(E,$.,%/&1,(")(+0F/$,FF,+()0$DF(/G&/(&$,(W$"%1'=(+0F.$0D0%&/,+(&F(G,&'/G=2(WG0',(S=E,(33(,$$"$($&/,( $,),$F( /"( /G,(E$"E"$/0"%(")(G,&'/G=( )0$DF( /G&/(&$,(W$"%1'=(+0F.$0D0%&/,+(&F(+0F/$,FF,+A(SG,("R,$&''( ,$$"$($&/,(D&=(N,(,R&'#&/,+(/G$"#1G(&(W,01G/,+(&R,$&1,(")(S=E,(3(&%+(S=E,(33(,$$"$($&/,F(N=(/G,(F&DE',(F0`,(")(,&.G(1$"#EA(

S&N',(;('0F/F(S=E,(3(,$$"$($&/,F2(S=E,(33(,$$"$($&/,F(&%+("R,$&''(,$$"$($&/,F(")(R&$0"#F(D"+,'F(#%+,$(/G$,,(+0F.$0D0%&%/(D,/G"+F(&%+(/G$,,(F&DE',(1$"#EFA(

3%(1,%,$&'2('"10F/0.($,1$,FF0"%(D"+,'F(G&R,(/G,('"W,F/(,$$"$($&/,F(Y0%(/,$DF(")(S=E,(32(S=E,(332(&%+("R,$&''(,$$"$( $&/,Z( ")( +0F.$0D0%&/0"%( &D"%1( /G,( ,F/0D&/0"%( F&DE',F2( D"F/'=( $,&.G0%1( &%( &..#$&.=( $&/,( ")( 6??n2()"''"W,+(N=(D#'/0E',(+0F.$0D0%&%/(&%&'=F0F(D"+,'FA(O0R,%(/G,(W&=(0%(WG0.G($,1$,FF0"%(D"+,'F(&$,(F/&/0F/0.&''=(.&'.#'&/,+( /G,F,( $,F#'/F( &$,( ,bE,./,+A(5,#$&'( %,/W"$[(D"+,'F( G&R,( /G,( G01G,F/( ,$$"$( $&/,FA(SG0F( 0F( ."%F0F/,%/(W0/G("#$(E$,R0"#F(&%&'=/0.&'( $,F#'/F(")( /G,(+0F.$0D0%&%/(&..#$&.=($&/,(")( /G,F,(D"+,'FA( 3%()&./( 0%( /,$DF(")( /G,(%,#$&'( %,/W"$[( D"+,'2( '"W( S=E,( 3( &%+( S=E,( 33( ,$$"$F( 0%( /G,( ,F/0D&/0"%( F&DE',( W"#'+( N,( &%( 0%+0.&/"$( ")(F/&/0F/0.&'("R,$)0//0%1A(

(((((((((((((((((((((((((((((((((((((((( (((((((((((((((((((((((((68( -%(,F/0D&/0"%(F&DE',2(&'F"(.&'',+(&%("$010%&'(F&DE',2(0F(#F,+(N=(/G,(F=F/,D(/"(."%F/$#./(/G,(+0F.$0D0%&%/()#%./0"%(")(&(D"+,'A(3%(%,#$&'(%,/W"$[(D"+,'F2(&%(,F/0D&/0"%(F&DE',(0F(+,)0%,+(&F(&(/$&0%0%1(F&DE',A(


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;?c

S&N',(;(Error Rate of Estimation Samples C$$"$(/=E,( S=E,(3(,$$"$(YnZ( S=E,(33(,$$"$(YnZ( VR,$&''(,$$"$(YnZ(( *K-( LVO3S( 55*( *K-( LVO3S( 55*( *K-( LVO3S( 55*(4&DE',(6h(6??:6??(*"+,'(6( cA?( cA?( 6>A?( 6?A?( 6?A?( 9A?( 9A7( 9A7( 6?A?(*"+,'(;( >A?( 6A?( 6>A?( >A?( >A?( 9A?( >A?( 6A7( 6?A?(*"+,'(c( ;A?( 8A?( 9A?( 6A?( cA?( 6;A?( <A7( 7A7( 6>A?(*"+,'(6<( 8A?( 6A?( 6>A?( >A?( ?A?( 6cA<( <A?( ?A7( 68Ac(*"+,'(68( >A?( 6A?( 7A<( >A?( 7A?( 9A?( >A?( <A?( ;A9(4&DE',(>h(7?:6??(*"+,'(6( 9A?( ?A?( <<A<( ;A?( ?A?( 8A6( ;Ac( ?A?( 68A>(*"+,'(;( 8A?( ?A?( 69A?( ?A?( ?A?( 7A8( 6A<( ?A?( BAc(*"+,'(c( >A?( >A?( cAc( ?A?( ?A?( >Ac( ?Ac( ?Ac( 8A8(*"+,'(6<( >A?( ?A?( 69A?( ?A?( ?A?( ;A9( ?Ac( ?A?( 6?A;(*"+,'(68( ?A?( ?A?( <<A<( ?A?( ?A?( >Ac( ?A?( ?A?( 6<A<(4&DE',(<h(<?:6??(*"+,'(6( >;Ac( ?A?( <<A<( 6A?( ?A?( 8A6( ;AB( ?A?( 66A>(*"+,'(;( ?A?( ?A?( 6>A7( ?A?( ?A?( >Ac( ?A?( ?A?( 7A6(*"+,'(c( <A<( ?A?( >?A9( 6A?( ?A?( ?A?( 6A7( ?A?( 7A6(*"+,'(6<( ;Ac( ?A?( >7A?( ?A?( ?A?( >Ac( 6A7( ?A?( 9A>(*"+,'(68( ?A?( ?A?( 87A9( ?A?( ?A?( 6A<7( ?A?( ?A?( 6>A>(

Notes.(*K-h(*#'/0E',(+0F.$0D0%&%/(&%&'=F0Fi(LVO3Sh(L"10F/0.($,1$,FF0"%(D"+,'i(55*h(5,#$&'(%,/W"$[(D"+,'A((

Analysis of prediction samples.67( H$,+0./0"%( F&DE',F( .&%( N,//,$( /G&%( /G,( ,F/0D&/0"%( F&DE',( /"( E$,+0./(."$E"$&/,()0%&%.0&'(."%+0/0"%(0%(E$&./0.&'(&EE'0.&/0"%FA(J"$(/G,(*K-(&%+(LVO3S(D"+,'F6;2(W,(#/0'0`,(E$,+0./0"%(F&DE',F( ")( /G$,,( +0)),$,%/( E$"E"$/0"%F( /"( /,F/( /G,(D"+,'F\( E$,+0./0"%( &..#$&.=( $&/,h( 8?( F&DE',F( Y>?( h( >?Z( )"$(D"+,'F(#%+,$(/G,(F&DE',(E$"E"$/0"%(")(6??(h(6??2(<?(F&DE',F(Y6?(h(>?Z()"$(D"+,'F(#%+,$(/G,(F&DE',(E$"E"$/0"%(")(7?:6??(&%+(>;(F&DE',F(Y;(h(>?Z()"$(D"+,'F(W0/G(/G,(F&DE',(E$"E"$/0"%(")(<?:6??A(J"$(%,#$&'(%,/W"$[(D"+,'F2(/G,(%#DN,$(")(/,F/0%1(F&DE',F(0F(6q8(")(/G,(%#DN,$(")("$010%&'(F&DE',F2(WG0.G(&$,($&%+"D'=(F,',./,+A(

SG,(,$$"$( $&/,F(")(R&$0"#F(D"+,'F(W0/G(E$,+0./0"%(F&DE',F(&$,(FG"W%( 0%(S&N',(cA(SG,$,(,b0F/(F01%0)0.&%/(+0)),$,%.,(N,/W,,%(/G,($,F#'/F(")(D"+,'F(0%(E$,+0./0"%(F&DE',F(&%+(/G"F,(0%(,F/0D&/0"%(F&DE',FA(3%(*K-(&%+(LVO3S(D"+,'F2( /G,( ,$$"$( $&/,F( 0%( E$,+0./0"%( F&DE',F( &$,(D#.G(G01G,$( /G&%( /G"F,( 0%( ,F/0D&/0"%( F&DE',F2( N#/("R,$&''( ,$$"$( $&/,F( &$,( N,'"W( >?nA( SG,( ,$$"$( $&/,F( ")( '"10F/0.( $,1$,FF0"%(D"+,'F( #%+,$( /G$,,( E$"E"$/0"%F( ")(E$,+0./0"%(F&DE',F(&$,($,'&/0R,'=('"W,$(/G&%(/G"F,(")(D#'/0E',(+0F.$0D0%&%/(&%&'=F0F(D"+,'FA(Q%+,$(*K-(&%+(LVO3S(D"+,'F2(*"+,'( ;(W0/G( /G,( F&DE',( $&/0"( ")( 6( h( <( G&F( /G,( '"W,F/( "R,$&''( E$,+0./0"%( ,$$"$( $&/,2( &%+( 0/F(E$,+0./0"%( ,$$"$( $&/,F( &$,( 66A7n( &%+( cAcn2( $,FE,./0R,'=A( 3%( ."%/$&F/2( %,#$&'( %,/W"$[( D"+,'F( G&R,( 1$,&/(&+R&%/&1,F("R,$(*K-(&%+(LVO3S(D,/G"+FA(C$$"$($&/,F(")(%,#$&'(%,/W"$[(D"+,'F(W0/G(E$,+0./0"%(D"+,'F(&$,(%"/("%'=(D#.G( '"W,$( /G&%( /G"F,(")(*K-(&%+(LVO3S(D"+,'F2(N#/(&'F"( '"W,$( /G&%( /G"F,(")(,F/0D&/,(F&DE',F(#%+,$(%,#$&'(%,/W"$[(D,/G"+FA(SG,("R,$&''(,$$"$($&/,F(&$,(D"F/'=(N,'"W(6?nA(*"+,'(;(W0/G(&(6(h(6($&/0"(G&F(/G,('"W,F/("R,$&''(E$,+0./0"%(,$$"$($&/,2(&/("%'=(>A?nA(

(((((((((((((((((((((((((((((((((((((((( (((((((((((((((((((((((((67( H$,+0./0"%(F&DE',2(&'F"(.&'',+(%,W(F&DE',2(0F(#F,+(N=(/G,(F=F/,D(/"(/,F/(/G,(E$,+0./0R,(E"W,$(")(/G,(+0F.$0D0%&%/()#%./0"%(")(&(D"+,'A(3%(%,#$&'(%,/W"$[(D"+,'F2(&(E$,+0./0"%(F&DE',(0F(+,)0%,+(&F(&(/,F/0%1(F&DE',A(6;( H$,+0./0"%( F&DE',F( &$,( $&%+"D'=( F,',./,+( )$"D( &''( F&DE',F( &%+( +"( %"/( %,.,FF&$0'=( ."$$,FE"%+( /"( /G,( "$010%&'( F&DE',F(W0/G($,FE,./(/"(0%+#F/$=(&%+(F0`,A(


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79B:;?BA(-'/D&%2(3A(CA2(p(C0F,%N,0F2(MA(-A(Y6Bc9ZA(J0%&%.0&'(&EE'0.&/0"%F(")(+0F.$0D0%&%/(&%&'=F0Fh(-(.'&FF0)0.&/0"%A(Journal of Finance and

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22Y<Z2(68>:677A(I"''0%F2(MA(-A(Y6B9?ZA(-%(,DE0$0.&'(."DE&$0F"%(")(N&%[$#E/.=(E$,+0./0"%(D"+,'FA(Financial Management, 9Y>Z2(7>:7cA(K"#DE"F2(*A2(p(g"E"#%0+0F2(IA(Y6BBBZA(-(D#'/0.$0/,$0&(+0F.$0D0%&/0"%(D,/G"+()"$(/G,(E$,+0./0"%(")()0%&%.0&'(+0F/$,FFh(SG,(.&F,(")(

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Quantitative Analysis, 7Y>Z2(68cc:68B<A(J$=+D&%2( TA2( -'/D&%2( CA( 3A2( p(U&"2( KA( LA( Y6B97ZA( 3%/$"+#.0%1( $,.#$F0R,( E&$/0/0"%0%1( )"$( )0%&%.0&'( .'&FF0)0.&/0"%h( SG,( .&F,( ")(

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Management, 16Y8Z2(77:;7A(T0''2(5A(SA2(H,$$=2(4A(CA2(p(-%+,F2(4A(Y6BB;ZA(CR&'#&/0%1()0$DF(0%()0%&%.0&'(+0F/$,FFh(-%(,R,%/(G0F/"$=(&%&'=F0FA(Journal of Applied

Business Research, 12Y<Z2(;?:c6A(T#&%12(IA(4A2(K"$F,=2(MA(CA2(p(P"FF,2(*A(-A(Y6BB8ZA(L0),(0%F#$,$()0%&%.0&'(+0F/$,FF(E$,+0./0"%h(-(%,#$&'(%,/W"$[(D"+,'A(Journal of

Insurance Regulation, 132(6<6:6;cA(!&0%2(PA(-A2(p(5&12(PA(5A(Y6BBcZA(H,$)"$D&%.,(,R&'#&/0"%(")(%,#$&'(%,/W"$[(+,.0F0"%(D"+,'FA(Journal of Management Information

Systems, 14Y>Z2(>?6:>6;A(U0D2( IA( 5A2( p( *.L,"+2( MA( Y6BBBZA( CbE,$/2( '0%,&$( D"+,'F2( &%+( %"%'0%,&$( D"+,'F( ")( ,bE,$/( +,.0F0"%( D&[0%1( 0%( N&%[$#E/.=(

E$,+0./0"%h(-(',%F(D"+,'(&%&'=F0FA(Journal of Management Information Systems, 16Y6Z2(69B:>?;A(*&#$0.,2( !A( VA2( p( S"'',)F"%2( !A( VA( Y6Bc7ZA( V%( /G,( )0%&%.0&'( &EE'0.&/0"%( ")( +0F.$0D0%&/0"%( &%&'=F0FA( Journal of Financial and

Quantitative Analysis, 102(c><:c<BA(*.[,,2(SA(CA2(p(O$,,%F/,0%2(*A(Y>???ZA(H$,+0./0%1(N&%[$#E/.=(#F0%1($,.#$F0R,(E&$/0/0"%0%1(&%+(&($,&'0F/0.&''=(E$"E"$/0"%,+(+&/&(F,/A(

Journal of Forecasting, 19Y<Z2(>6B:><?A(*"FFD&%2( CA( IA2( P,''2( OA( OA2( 4W&$/`2( LA( *A2( p( S#$/',2( TA( Y6BB9ZA( -%( ,DE0$0.&'( ."DE&$0F"%( ")( ,bE,$/( +,.0F0"%( D&$[0%1( 0%(

N&%[$#E/.=(E$,+0./0"%A(Financial Review, 33Y>Z2(<7:7<A(VG'F"%2(!A(-A(Y6B9?ZA(J0%&%.0&'($&/0"F(&%+(/G,(E$"N&N0'0F/0.(E$,+0./0"%(")(N&%[$#E/.=A(Journal of Accounting Research, 18Y6Z2(6?B:6<6A(H'&//2(TA(KA2(H'&//2(*A(PA2(p(H,+,$F,%2(!A(OA(Y6BB8ZA(P&%[$#E/.=(+0F.$0D0%&/0"%(W0/G($,&'(R&$0&N',FA(Journal of Business Finance

and Accounting, 21Y8Z2(8B6:766A(4G,EE&$+2(!A(HA(Y6BB8ZA(SG,(+0',DD&(")(D&/.G,+(E&0$F(&%+(+0R,$F0)0,+()0$DF(0%(N&%[$#E/.=(E$,+0./0"%(D"+,'FA(The Mid-Atlantic

Journal of Business, 30Y6Z2(B:>7A(


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g&R1$,%2(@A(IA(Y6B97ZA(-FF,FF0%1(/G,(R#'%,$&N0'0/=(/"()&0'#$,(")(-D,$0.&%(0%+#F/$0&'()0$DFh(-('"10F/0.(&%&'=F0FA(Journal of Business Finance and Accounting, 122(6B:87A(

Appendix A

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(

Appendix B

S&N',(P6(Variables of Model 1 Under Logistic Regression Method

6(h(6( 6(h(>( 6(h(<(H&$&DA( I",))A( X&'+( 401A( H&$&DA( I",))A( X&'+( 401A( H&$&DA I",))A( X&'+( 401A(X7>( :?A697( 9A?7B ?A??7( X7<( :?A<>7( >Ac9<( ?A?B7( X>( :>6AB8B( <AB9<( ?A?8;X;<( 7BA<><( 7A686 ?A?><( X67( :>>A<Bc( 8A8;8( ?A?<7( X8<( :78A8<<( 8AB87( ?A?>;X>8( :?A867( >ABB< ?A?98( X88( :668A<<9( 8A<>;( ?A?<9( X78( :c<;AB7c( 8Ac<8( ?A?<?X>7( :?A?8c( ;Ac>9 ?A??B( X78( :BB9AB?9( 8A8B6( ?A?<8( X>8( :BA<<8( 6A;6;( ?A>?8X9>( >A<8>( 6;A;9c ?A???( X9<( 6;cAc9>( 8A;6( ?A?<>( X<<( 6BAB>8( <A;99( ?A?77X99( >AB?B( 9A>>7 ?A??8( X98( B<A9>6( <A;7;( ?A?7;( X9<( 678A7c6( 8A66>( ?A?8<X69( ?A?><( <A7c6 ?A?7B( X99( 6<<AcB6( 8A8c8( ?A?<8( X69( :?A9<8( 8A<>c( ?A?<9X>6( ?A?>9( >A8cc ?A66;( X8c( :68?A>B8( 6AB8c( ?A6;<( X>?( 67<A?98( 8A>?8( ?A?8?X<7( :?A<;( 7Ac76 ?A?6;( X<7( :6cA68( 8A78( ?A?<<( X<7( :68A?c7( 8A;;;( ?A?<6X8?( :?A9c;( ;A7c9 ?A?6?( X8B( :>A6<B( 8A>6>( ?A?8?( X8?( :>8A;c>( 8A7<<( ?A?<<Xc8( >?Ac( <6A?99 ?A???( X8?( :89A<6c( 8A798( ?A?<>( Xc9( 99?AB>9( 8A;cc( ?A?<6( ( ( ( Xc9( 9c>A;9>( 8A879( ?A?<7( X96( ?A69B( 8Ac97( ?A?>B( ( ( ( Xcc( ?A<>( 8A;6;( ?A?<>( XB8( :;Ac<6( 6A>c>( ?A>7B( ( ( ( XB>( 7<A677( <AcB>( ?A?7>( XB7( 68A>>6( 6AB9B( ?A679( ( ( ( XB7( <A8( 8A>6;( ?A?8?( ( ( ( (I"%F/&%/( :8AB>6( 6>A8BB ?A???( ( 68;Ac9>( 8A887( ?A?<7( ( 6>BA8c( 8AB7B( ?A?>;

Note. Xh()0%&%.0&'($&/0"(")(&(F0%1',(=,&$A((


(

;68(

S&N',(P>(Variables of Model 6 Under Logistic Regression Method

6(h(6( 6(h(>( 6(h(<(H&$&DA( I",))A( X&'+( 401A( H&$&DA I",))A( X&'+( 401A( H&$&DA I",))A( X&'+( 401A(X6( <?AB97( 8A;6;( ?A?<>( X77( 82;76A7>>( 8A>9;( ?A?<9( X7>( :6A;B>( 6Ac6<( ?A6B6X7;( 686AB;;( ;Ac?<( ?A?6( X;;( :>2969A;B( 8A??9( ?A?87( X88( :6>A7cc( ?A;;<( ?A86;X;>( :78A;;>( 8A?B>( ?A?8<( X97( 7A;;>( >A<>c( ?A6>c( X7c( :?A78>( 6A7>B( ?A>6;X9>( 9A>;9( ;AB6B( ?A??B( X9c( 99A976( >A77;( ?A66?( X99( ;BA8><( >AB<B( ?A?9;X97( :6AB?>( 8A8c<( ?A?<8( X69( :?AB86( 8A<;( ?A?<c( X97( :>8;A8c9( >A888( ?A669X99( 6<A9<6( 8Ac?>( ?A?<( X97( :676A98( <A>?<( ?A?c8( Xc9( 7<8A66( >A9c6( ?A?B?TIX7>( :67AcB7( cA>7c( ?A??c( TIX;;( :?A?;;( ?( ?ABB>( TIX<8( :;>A>87( >A7;<( ?A6?BTIX88( :BA9c8( >A>>>( ?A6<;( TIX6c( :>cA??B( <A9B<( ?A?89( TIX9?( :;A;>9( >A7?9( ?A66<TIX>8( :>6A7>7( ;A>8>( ?A?6>( TIX99( B7A>?;( 8A86c( ?A?<;( TIX96( 6A;c;( >A9?9( ?A?B8TIX9c( :69A;7B( ;A669( ?A?6<( TIX7( >>Ac8;( >A?>c( ?A677( TIXB8( :?A;>B( ?AB9>( ?A<>>TIX87( :6cABc( ;A>cc( ?A?6>( TIX7?( 8?A<69( <A9?c( ?A?76( ( ( ( (TIX8c( :?A6c( ;A>9;( ?A?6>( TIXc>( :<A99;( ?A>;( ?A;6?( ( ( ( (TIX8?( 68A;B6( cA??c( ?A??9( TIXB8( :?Ac<6( 8A89B( ?A?<8( ( ( ( (TIXc<( ?AB8<( ?A;BB( ?A8?<( ( ( ( ( ( ( ( (TIXc7( :?Ac87( ;A>;9( ?A?6>( ( ( ( ( ( ( ( (TIXcc( :?A6B8( 8A>?6( ?A?8( ( ( ( ( ( ( ( (TIXB8( :?A6?c( cA7<>( ?A??;( ( ( ( ( ( ( ( (I"%F/&%/( cA<<8( ?A><<( ?A;>B( ( :6<cA897( >Ac<c( ?A?B9( ( :cA>>8( 6ABB8( ?A679

NotesA(Xh()0%&%.0&'($&/0"(")(&(F0%1',(=,&$i(TIXh(.G&%1,F(0%(R&$0&N',F()"$(/G$,,(=,&$FA((

S&N',(P<(Variables of Model 7 Under Logistic Regression Method

6(h(6( 6(h(>( 6(h(<(H&$&DA( I",))A( X&'+( 401A( H&$&DA( I",))A( X&'+( 401A( H&$&DA I",))A( X&'+( 401A(AX;7( :BA786( 67Ac<( ?A???( AX86( :6A6<6( >A<c6( ?A6>8( AX97( <A7<c( ?A9?6( ?A<c6AX9c( 6BAc;c( 68AB;> ?A???( AX77( 86cA;;6( ?A788( ?A8;6( AX9c( 6>7A?;>( <A?8c( ?A?96AX;( :>A>?>( 66ABc8 ?A??6( AX;7( :67A;6( >A676( ?A68>( AX69( :?A( >A<8;( ?A6>;AXc8( <9AB<>( >?A6?6 ?A???( AX;;( :;AcB( ?( ?ABB?( AXB8( <A<<8( ?A>>8( ?A;<;AXB7( 6A<6;( 9A>68 ?A??8( AX6c( ;>A;6( >A<8;( ?A6>;( TIX86( :>AB;6( >A<89( ?A6>7TIX77( :?A<<B( 8A?;< ?A?88( AX97( :;A?>>( >A;;7( ?A6?<( TIX;7( :69A<69( ?AB6( ?A<8?TIX;<( ?A?8B( ?A?97 ?Acc?( AX9c( 6c<Ac;B( >A9?c( ?A?B8( TIX<8( :>6A9<9( >A9;( ?A?B6TIX;7( :?A?c;( 6A<?8 ?A>7<( AX<7( ?ABc6( >A8>7( ?A66B( TIX9B( :6;A<c>( >AcBc( ?A?B8TIX;9( ?A668( ;AcA>c<( >A76;( ?A66<( TIXc?( :7AB8>( ?A;6c( ?A8<>TIX<8( :6A988( 6Ac9; ?A696( TIX>B( :89A<B( >A698( ?A6<B( TIXc6( 76A>8>( 6A9Bc( ?A6;9TIX9>( 6<A766( 6<A7<7 ?A???( TIX87( :>8A9>>( >A;66( ?A6?;( TIXcc( :6A<86( ?A;;( ?A86;TIX87( :<A>88( 66A?79 ?A??6( TIX76( :69A8;9( 6ABB<( ?A679( TIXc9( :>A;?<( 6Ac<>( ?A699TIXcc( :?A?c>( 7A;68 ?A?69( TIXc<( >cA7?>( >A<9( ?A6><( TIXB6( 8Ac>>( >A;8B( ?A6?8TIXc9( ?A6B6( 8A;8; ?A?<6( TIXc8( ;A796( >A;c( ?A6?>( TIXB8( :?A79;( >A<97( ?A6><TIXB8( :?A?6>( 8A66> ?A?8<( TIXc7( :6A768( <A?6>( ?A?9<( ( ( ( (( ( ( ( TIXB8( :?A689( ?Ac89( ?A<9c( ( ( ( (I"%F/&%/( :<A8<;( BA<>9 ?A??>( ( c<A;8>( >A>;<( ?A6<>( ( :9;A89<( 6A?8<( ?A<?c

NotesA(AXh(-R,$&1,($&/0"(0%(/G$,,(=,&$Fi(TIXh(.G&%1,F(0%(R&$0&N',F()"$(/G$,,(=,&$FA(


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Journal of Modern Accounting and Auditing, ISSN 1548-6583 May 2012, Vol. 8, No. 5, 616-627

Performance and Stock Return in Australian Banking

Amir Moradi-Motlagh, Ali Salman Saleh, Amir Abdekhodaee, Mehran Ektesabi Swinburne University of Technology, Melbourne, Australia

This paper examines the relationship between the total shareholder return (TSR) and performance of Australian

banks over the period 2001-2010. In particular, it investigates whether returns of banks in the stock market can be

explained by changes in their performance. First, we use a weighted financial ratio-based Data Envelopment

Analysis (DEA) model to estimate the performance of banks. We then regress changes in performance against the

total shareholder returns to investigate their relationship. The results indicate that changes in performance are

reflected in TSR. That is, well-performed banks tend to generate more return for their stockholders.

Keywords: performance, data envelopment analysis (DEA), shareholder return, banks

Introduction

A well-performed banking system is critically important for businesses development, given the role it plays in the economy of nations. In Australia, in March 2011, the financial sector was the largest industry sector by 32% of the whole capital in the market with value of $480 billion. Additionally, caused by compulsory superannuation Australia has the 4th largest pension fund pool in the world which highlights the role of banking industry in this country. Due to this key position, measuring banks and financial institutions’ performance is an issue of major interest for academics and policy makers (e.g., Avkiran, 1999, 2000, 2004; Kirkwood & Nahm, 2006; Moradi-Motlagh, Saleh, Abdekhodaee, & Ektesabi, 2011; Neal, 2004; Paul & Kourouche, 2008; Sathye, 2001, 2002; Sturm & Williams, 2004; Walker, 1998; Wu, 2008).

Recent competitive pressures have progressively driven banks to strategically focus on generating returns to shareholders. Therefore, the investigation of the determinants of bank performance and their relationship with share prices has become increasingly important (Beccalli, Casu, & Girardone, 2006). However, only very limited studies have examined the relationship between bank performance and stock performance in Australia and to our knowledge, Kirkwood and Nahm (2006) have conducted the only study which uses non-parametric techniques to measure the performance and examines this relationship for the time period between 1995-2002.

Due to the lack of any recent studies and after gathering through the literature as well as industry reports, it is clear to us that there is a need not only to measure the performance of Australian banks using non-parametric techniques but also to examine its relationship with stock performance. In this study, Data Envelopment Analysis (DEA) as a non-parametric technique is applied to aggregate four main dimensions of the performance namely profitability, growth, efficiency, and marketability to construct the performance measure. This approach can give us a wider view for better understanding of Australian banks performance in

Amir Moradi-Motlagh, Ph.D. candidate, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology. Ali Salman Saleh, senior lecturer, Faculty of Business and Enterprise, Swinburne University of Technology. Amir Abdekhodaee, senior lecturer, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology. Mehran Ektesabi, senior lecturer, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology.

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PERFORMANCE AND STOCK RETURN IN AUSTRALIAN BANKING

617

comparison with other studies in this area where they consider one or two aspects of the performance as discussed in the following sections. Additionally, to examine the relationship between the banks performance and their stock performance, banks total shareholder returns are regressed against the performance indexes measured by a weighted financial ratio-based DEA model.

This study makes four main contributions. It is the first study in the Australian banking industry which measures the performance using a financial ratio-based DEA model. Second, the study period is unique and distinct from other studies in Australia and includes recent years. Third, this selection of financial ratios has not been implemented in the previous bank performance studies in the international literature. Fourth, it is the only study that the relationship between the performance induced from a ratio-based DEA model and the total shareholder return has been examined in the banking industry across countries.

The reminder of this paper is organized as follows: Section 2 reviews the relevant literature, and the methodology is discussed in Section 3. Empirical results are detailed in Section 4, and the summary and conclusion are given in Section 5.

Literature Review

In recent years, DEA has been increasingly applied to evaluate banks performance in most counties (see Fethi & Pasiouras, 2010). However, based on the current literature, studies on the relationship between stock return and bank performance are limited. Although, there is only one study focused on such a relationship in the case of Australian banking, but there are a number of studies that use DEA based models to examine the performance of Australian banking (Avkiran, 2004; Kirkwood & Nahm, 2006; Neal, 2004). However, as Paul and Kourouche (2008) explained the studies that have focused on banking efficiency are limited and all have applied input oriented DEA to data relating primarily to the pre-Willis period.

Avkiran in a series of papers (1999, 2000, 2004) applied DEA to analysis the efficiency of Australian banks. In 1999, he measured operating efficiencies, employee productivity, profit performance, and average relative efficiency for Australian trading banks from 1986 to 1995. In his investigations on the role of the mergers and the benefit to publics, he concluded that the role of mergers in efficiency gain is not necessarily positive, a sentiment share by most other researchers in this field. Avkiran (2000) examined the change in productivity of the retail banking in the deregulated period 1986-1995. His findings indicated an overall rise in total productivity (on average 3.2% per year) driven more by technological progress than technical efficiency. Finally for the same study period, in 2004 by decomposing the technical efficiency, he discussed that pure technical inefficiency emerges as a greater source of inefficiency than scale efficiency.

Avkiran studies in Australian banking were followed by other academics after 2000. Sathye (2002) measured productivity changes in Australian banks by the Malmquist index using DEA technique during the period 1995 to 1999. He concluded that there is a decline in technical efficiency and total productivity factor during the study period and also no correlation was found between size and productivity which is important in the context of bank merger debate in Australia. Neal (2004) investigated X-efficiency and productivity changes in Australian banking between 1995 and 1999. His study differed from the earlier studies by examining efficiency by bank type and finds the regional banks are less efficient than other banks.

More recent study in Australia by Paul and Kourouch (2008) examined the technical efficiency of Australian banks during the post-Wallis period (1997-2005). The results based on DEA reveal that the extent of


618

technical efficiency varies across the banks and over the years. As on one side, the National Australia Bank, Commonwealth Bank and Macquarie Bank were found to be technically efficient, and on the other side, Adelaide Bank, the Bank of Queensland and Westpac Bank are found to be prominently inefficient. To sum up, more details about different approaches are provided in Table 1 to present a summary of studies in banking industry using DEA.

Table 1 A Summary of Bank Performance Studies Using DEA Technique in Different Countries Author(s) Country Method Inputs Outputs Indexes

Halkos and Salamouris (2004) Greek Profitability and

efficiency ratios

RDIBA, ROE, ROA, profit/loss per employee, Efficiency ratio, net interest margin

Performance

Sathye (2001) Australia Intermediation Labor, capital, loanable funds Loans, demand deposit

Technical efficiency/allocative efficiency

Luo (2003) USA Production Market

Stage 1: Employees, assets, equity Stage 2:

Revenue, profit

Revenue, profit Market value, stock

price, EPS

Profitability efficiency

Marketability efficiency

Asmild, Paradi, Aggarwall, and Schaffnit (2004)

Canada Production

Employees, book value of physical; Assets, other non-interest expense

Deposits, loans, securities, deposits with other banks, other non-interest income

Productivity

Ho and Zhu (2004) China Production Profitability

Stage 1: Capital stocks, assets, branches, employees Stage 2:

Sales, deposit

Sales, deposits Net income, interest

income, non-interest income

Efficiency Effectiveness

Angelidis and Lyroudi (2006) Italy Value added Total earning assets, loans,

deposits

Personnel expense, other operating expense, total fixed assets

Productivity

Beccalli et al. (2006) Europe Intermediation Deposits, labor, capital Total loans and securities Efficiency

Kirkwood and Nahm (2006) Australia Intermediation

Model A: Employees, property, plant and equipment, interest-bearing liabilities Model B:

Employees, property, plant and equipment, interest-bearing liabilities

Interest-bearing assets, non-interest income

Profit before tax

Efficiency Profit efficiency

Paul and Kourouche (2008) Australia Intermediation Interest expense,

non-interest expense Net interest income, non-interest income Technical efficiency

In comparison with all mentioned studies in the banking industry performance, a small number of them investigate the relationship between their results and the stock performance (e.g., Beccalli et al., 2006; Fiordelisi & Molyneux, 2010; Pasiouras, Liadaki, & Zopounidis, 2008). Kirkwood and Nahm (2006) conducted the only study in Australia which examined the relationship between changes in efficiency and stock returns. Their results indicated that changes in bank efficiency are reflected in stock return. The reported coefficient of determination (R-squared) in their study was 29.2%.

Beccalli et al. (2006) linked changes in efficiency to changes in stock performance of European banks across five countries using DEA and Stochastic Frontier Analysis (SFA). They concluded that results derived


619

from DEA are reflected in changes in stock prices while this trend is less clear for SFA. The coefficient of determination for DEA is reported 14.6% while this measure for SFA is only 0.01%.

Sufian and Majid (2007) investigated the long efficiency change of Singapore commercial banks during the period of 1993-2003. They established statistical relationship between cost efficiency and share price performance by employing regression analysis. The results suggested that cost efficiency explain the share price performance of Singapore banks with determination coefficient of 47%.

In order to advance the aforementioned literature, we made several contributions by developing a weighted financial ratio DEA model with unique combination of financial ratios. Additionally, it is the first study that the result of a financial ratio DEA model is examined and linked to the total shareholder return as a reliable performance measure which has been noted by Neslihan (2007) as a measure that cannot be manipulated by executives in the same way that earnings can.

Methodology and Model

Performance Measurement

Performance measurement is the process whereby an organization establishes the parameters within which programs, investments, and acquisitions are reaching the desired results (Thompson, Strickland, & Gamble, 2007). Although, in general a number of studies have been conducted related to the performance measurement, but Carton and Hofer (2006) believed that despite the importance of accurately measuring organizational performance in most areas of academic research, there have been very few studies that have directly addressed the question of how overall organizational performance is or should be measured.

There is no doubt that the performance is a multi-dimensional concept. However, the earlier studies have been conducted on the banking industry focus mostly on one or two aspects of the performance as demonstrated in Table 1 which is in line with the finding of Murphy, Trailer, and Hill (1996). They asserted that the frequently analysis shows an overwhelming proportion of studies are measuring only one or two dimensions of performance predominantly efficiency and profitability measures.

Traditionally, due to simplicity and ease of understanding of financial ratios, they have been applied in banks’ performance analysis. Although the use of financial ratios assists the evaluation of bank performance, but there are several limitations that must be considered. Unlimited number of ratios that can be created from financial statement data are often contradictory and confusing, thus ineffective for the assessment of overall performance (Paradi, Vela, & Yang, 2004). Additionally, failure to account for generating an overall measure, combined with the inability to distinguish the best performers makes financial ratios analysis inadequate as a sole tool for performance measurement. Moreover, one bank might be strong on one ratio and poor on another one.

Not only DEA does not have the mentioned drawbacks but also as a frontier method it has several advantages like, dealing with multi input and output processes, no need for assigning a weight for inputs or outputs and the ability of constructing an overall relative measure based on the distance to frontiers in the sample. As a result, there is a consensus between academics that frontier approaches (parametric or non-parametric) have preference than financial ratios as Berger and Humphrey (1997) emphasized that although partial performance ratios are informative, they are not as broadly-based as frontier analysis.


620

DEA Models

DEA is a non-parametric linear programming technique that estimates the relative performance of the decision making units (DMUs) based on the observations in the sample. Since DEA was initiated by Charnes, Cooper, and Rhodes (1978), it has been widely applied to measure the performance of organizations in both public and private sectors (see Emrouznejad, Parker, & Tavares, 2008). Although DEA applications in multiple-input and output environment has spread rapidly but the capability of this technique in aggregating ratios has been neglected for many years. Fernandez-Castro and Smith (1994) reformulated DEA to introduce a ratio-based model to aggregate a set of financial ratios to a single measure which is called the General Non-Parametric Corporate Performance (GNCP). The mathematical formula of their model is as follows:

max l (1)

Subject to:

ill

N

ninn RR

1

11

N

nn

0, 0 ( 1, 2, ..., )l n n N

where Rin = the ith ratio of the nth DMU, and n = the nth DMU weight value. Not only a review of the literature indicates that few studies have focused on financial ratio DEA models

across countries but also specifically, it is worth mentioning that this model has not been applied in Australian banking industry beforehand.

Halkos and Salamouris (2004) conducted the first study applying the GNCP model proposed by Fernandez-Castro and Smith (1994) in measuring the performance of the Greek commercial banks. They used six financial ratios which are profitability and efficiency ratios. Neglecting to cover the other aspects of bank performance such as growth and marketability and lack of clear explanation about the selection process of financial ratios in similar studies were the key motivations for us to introduce a multi-dimensional model based on performance studies which will be explained in details in this section.

Proposed Model

Three main gaps in the literature are as follows: (1) the selection process of financial ratios is poorly explained. For example, Fernandez-Castro and Smith (1994) remarked that the selection of ratios in their studies is not of any particular significance. (2) Ignoring the use of value judgment and prior knowledge about the importance of ratios associated in the model. This draw back exists in all similar previous studies. For instance, Halkos and Salamouris (2004) used six financial ratios to measure the performance of Greek commercial banks without considering any assumption about their importance. (3) Almost all of the studies which estimate efficiency and then regress it on sets of explanatory variables have been unable to explain more than just a small proportion of its total variation (Berger & Humphrey, 1997). For example, Kirkwood and Nahm (2006) in case of Australian banks, reported an R-squared of 0.29 that although it demonstrates a positive relationship but it is not able to explain a high proportion of changes in stock returns.

In the present paper, we are driven by all the above issues to develop an overall and comprehensive model


621

to assess the banking industry in Australia by formulating the preference of financial ratios, constructing weight restrictions to our multiplier model and examining the result to investigate the capability of the model to explain stock return.

The advantage of full flexibility in identifying inefficiency can be seen as a disadvantage of DEA where there is value judgments which can reflect known information about how the factors used by DMUs behave, and/or accepted beliefs or preference on the relative worth of inputs, outputs or even DMUs (Thanassoulis, Portela, & Allen, 2004). In GNCP model, the full flexibility of weight has been considered, therefore, to avoid the mentioned problem and to have a realistic result, we develop a weighted model which enables us to consider the importance of selected ratios as follows:

01

maxN

n in

R (2)

Subject to

11

N

ninn R

jnjini RR

0 ( 1, 2, ..., )n n N

where Rn = the ith ratio of the nth bank, and n = the nth ratio weight value. The proposed model in this paper consists of four financial ratios related to four main different dimensions

of the performance which have been selected based on their importance and findings of other studies. There is no doubt that the profitability is the primary dimension of the performance and among the profitability indexes the return on asset (ROA) commonly has been utilized. Murphy et al. (1996) demonstrated that ROA is one of the most frequently used measures in performance studies.

Murphy et al. (1996) showed that the growth is the second most common performance dimension used to measure the overall organizational performance. Typical growth measures are change in total assets and sales. We selected change in total asset which according to the finding of Carton and Hofer (2006), it provided the most relative information among other measures of growth.

The efficiency indexes have been measured in the most of bank performance studies as shown in Table 1. The total asset turnover is one of the key ratios used to determine how efficiently a firm is using its assets in generating sales, which is a major determinant of operating income (Keown, Martin, Petty, & Scott, 1994). Due to the importance of this measure, it has been selected as a proxy of the efficiency in this paper.

The marketability is the last, but not the least measure we consider. Recently, it is a dimension that has taken more attention of researchers. Luo (2003) measured marketability for the USA banks and concluded that the time is ripe for researchers and policy makers to place more emphasis on the marketability efficiency and marketability related issues. We chose price to book value as a proxy of market ratios due to the findings of other studies that mention price to book value is a useful predictor of future returns (Carton & Hofer, 2006; Fama & French, 1998; Malkiel, 2003).

Finally, the article highlights and examines the relationship of the performance and total shareholder return to compare the result of GNCP model and the proposed model which is presented in the next section.


622

Data

Observing a bank’s relative efficiency on a selection of variables over a number of years provides an insight into performance of that bank compared to its peers (Avkiran, 1999). In this study, we consider 10 years for seven Australian banks. As a result, there are 70 data observations. Due to limited sample size, we employ data pooling approach assuming there is no technical change during the study period similar to recent studies in Australia (Kirkwood & Nahm, 2006; Moradi-Motlagh et al., 2011; Paul & Kourouche, 2008).

Seven Australian banks based on the market value in the Australian Stock Exchange (ASX) are considered in this paper. Names and abbreviations used in this study are demonstrated in Table 2.

Table 2 List of Sample Banks DMU Name of bank Abbreviation used Category 1 Commonwealth CBA Large 2 Westpac WBC Large 3 Australia and New Zealand Bank ANZ Large 4 National Australia Bank NAB Large 5 Macquarie Group MQG Medium 6 Bendigo and Adelaide BEN Medium 7 Bank of Queensland BOQ Medium

As described in the previous section, four financial ratios of ROA, change in total asset, asset turnover, and change in price to book value have been collected for seven sample banks. Data are obtained from FinAnalysis database and Table 3 demonstrates a descriptive statistics of these ratios from 2001 to 2010.

Table 3 Summary of Financial Ratios 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

ROA (%)

Max 1.07 1.18 1.20 1.19 1.54 1.07 1.10 1.08 0.79 0.95 Mean 0.90 0.92 0.90 0.89 1.02 0.87 0.88 0.74 0.58 0.76 SD 0.21 0.22 0.23 0.21 0.31 0.21 0.20 0.24 0.15 0.18 Min 0.53 0.61 0.62 0.62 0.66 0.52 0.53 0.43 0.37 0.53

Change in asset

Max 1.42 1.25 1.36 1.38 1.18 2.15 1.28 2.82 1.34 1.13 Mean 1.16 1.08 1.13 1.22 1.10 1.33 1.20 1.46 1.09 1.07 SD 0.12 0.09 0.11 0.13 0.05 0.38 0.07 0.61 0.17 0.05 Min 1.06 0.99 1.05 1.03 1.02 1.12 1.12 1.15 0.89 0.98

Asset turnover

Max 8.24 8.71 10.31 7.84 13.39 15.35 8.17 8.51 7.67 7.15 Mean 7.39 6.78 6.80 6.93 8.63 8.60 7.63 7.55 6.48 6.22 SD 0.59 0.90 1.60 0.53 2.21 2.99 0.40 0.78 0.74 0.51 Min 6.82 5.93 5.63 6.16 7.15 7.17 6.92 6.06 5.47 5.66

P/B

Max 3.64 3.26 2.31 2.75 3.01 3.35 3.34 2.28 2.23 2.14 Mean 2.33 2.31 1.99 1.97 2.15 2.58 2.69 1.58 1.48 1.46 SD 0.65 0.48 0.17 0.41 0.46 0.47 0.47 0.47 0.59 0.50 Min 1.69 1.74 1.84 1.52 1.67 2.03 2.14 0.93 0.7 0.79


623

Carton and Hofer (2006) emphasized in their key findings that growth rate of total asset does not provide statistically significant incremental information beyond that provided by the corresponding static financial performance measure. Moreover, they mentioned that the change in price to book value provides the best information about the return to shareholder after Alman’s Z score. Based on their finding and other mentioned studies in the previous section, we impose the following restrictions on model where 1, 2, 3, and 4 note the weight of change in price to book value, ROA, asset turnover and change in asset growth, respectively:

44332211 RRRR

Bank Performance and Stock Performance

In this study, stock performance is represented by total shareholder return, which has been used as the most appropriate comparative measure as it focuses on the delivery of shareholder value and is a well understood and tested mechanism to measure performance. Additionally, it has been applied as one of the main long term incentive criteria to pay reward to bank mangers. For example, in the Commonwealth bank, the long term incentive that was granted under the Equity Reward Plan (ERP) in 2005 fully vested in July 2008, reflecting total shareholder returns from July 14, 2005 to July 14, 2008 that were above the 75th percentile of returns of companies within the peer group.

Following Chu and Lim (1998), we examine the relationship between performance and total shareholder return according to the following regression model:

ititit PR 10 (3)

where Rit is the total shareholder returns and it is the change in banking performance measured by our proposed DEA model. This regression model aims to establish the extent to which changes in the estimated performance scores influence total shareholder return.

Empirical Result

Derived from the proposed model the banks performances have been measured and demonstrated in Table 4. The results demonstrate that the average of the overall performance of the selected banks has decreased in the second half of the study period. Precisely, the average of the performance between 2001 and 2005 is greater than the period 2006 to 2010.

Table 4 DEA Performance Scores 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Average 0.72 0.72 0.66 0.65 0.71 0.76 0.78 0.61 0.50 0.54 Average (2001-2005) 0.69 Average (2006-2010) 0.64

The trend has been presented in Figure 1. Although the performance has an upward trend from 2003 to 2006, it has been fallen sharply after 2006. Moreover, there is an improvement in performance after recent financial crisis which is in line with Demirgüç-Kunt, Detragiache, and Gupta (2006) who found that banks enhance their operational efficiency after a crisis, on average during the following two years. Moreover, the results reveal that banks performance varies across the banks and over the years. While Macquarie bank has the best performance in 2006 and 2010, NAB has the least average score among the large banks and between the


624

smaller banks, Bank of Queensland has the least average score which are consistent with figures of total shareholder returns.

Figure 1. The trend of performance and TSR from 2001 to 2010.

Most performance studies utilize only one variable to represent organizational performance, and ROA is the most frequently used measure. However, ROA explain only 4% of the variation in market return to shareholder (Carton & Hofer, 2006). To investigate whether there is any relationship between the performance scores and the total shareholder return, we examine this relationship using regression analysis. As presented in Table 5 and Figure 2, these two indexes are positively correlated which means as we expected that the banks performance is reflected in their total shareholder returns. Specifically, Table 5 illustrates the result of regression analysis not only for changes in performance versus total shareholder return but also demonstrates the correlation of performance components in our model with total shareholder return.

Table 5 Regression Analysis Parameter Change in performance score ROA Asset turnover Asset growth P/B Constant 0.148 -0.048 -0.027 0.158 -0.24 Independent variable 1.31 20.842 0.021 -0.025 0.179 R 0.77 0.21 0.14 0.028 0.47 R2 0.60 0.04 0.02 0.0008 0.22 Adjusted R2 0.59 0.03 0.005 -0.01 0.21 Significance level > 0.001 > 0.1 > 0.3 > 0.8 > 0.001

To be precise, R-square of 0.6 at a high level of significance proves a strong correlation between two measures. It is worth mentioning that if we use the GNCP model and recalculate the R-square again, it will be reached at 0.15. In other words, the much higher explanatory power of proposed model in comparison to the GNCP model implies that the effect of value judgment is higher that can be neglected. Finally, these results seem to suggest that total shareholder return can be explained better by performance changes using the proposed model than by financial ratios.


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Figure 2. Performance and total shareholder return.

Conclusions

In this paper, we introduced a weighted financial ratio-based DEA model consist of four dimensions of performance namely, profitability, growth, efficiency, and marketability. On the contrary of previous studies that consider limited aspects of the performance, the proposed model in this paper covers four determinants of the performance which have been integrated and presented as an overall performance index. The results indicate during the 10 years study period, the average of the performance of Australian banks in the first half of the period is less than the second half which can be explained mostly due to the effect of the financial crisis. We also found that the performance changes has a positive and statistically significant relationship with the total shareholder return, and that this relationship is stronger for the proposed weighted model in this paper compare to the GNCP model which has been used in similar studies. This finding confirms Thanassoulis et al.’s (2004) arguments that DMUs have in some contexts value judgments that can be formalized, a priori, and therefore should be taken into account in the performance assessments.

This study makes several unique contributions and extends the literature by: (1) identifying the most important financial ratio from four main aspect of organizational performance which can explain the total shareholder return; (2) developing a weighed financial ratio-based DEA model to integrate mentioned ratios; and (3) investigating the ability of the model to explain the total shareholder return and comparing with the GNCP model. Moreover, this study can help banks to apply more practical non-parametric models to integrate traditional performance measures like financial ratios in a way that not only enables them to compare their positions in comparison with rivals but also gives them more knowledge about the stock return in market.

Finally, this paper suggests that using the proposed model has the capability to explain the total shareholder return better than financial ratios. Also it is worth mentioning that although the results from the proposed model are interesting, further studies are necessary to expand the result to other countries or industries.


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An Exploratory Study Into the Implementation of Management

Accounting Changes in Australia

Davood Askarany The University of Auckland, New Zealand

This paper is an exploratory study which investigates the introduction and implementation of management

accounting changes in Australia. The study first examines the introduction of relatively new management

accounting techniques in professional journals, conferences, workshops, and professional development programs

over a four-year period in Australia. These are among the main sources of transferring information on cost and

management accounting changes to potential users in Australia. The purpose of such a study is to get a clear picture

of the scope and extent of recently developed cost and management accounting techniques introduced to

practitioners in Australia. By identifying the major management accounting changes and developments faced by

organizations, the study then investigates the extent of implementations of such changes in practice. The study

further explores the hindering and facilitating factors contributing to the implementation of new management

accounting changes in organizations.

Keywords: activity-based costing (ABC), activity-based management (ABM), balanced scorecard (BSC), target

costing (TC), just-in-time (JIT), strategic management, management accounting innovations

Introduction

A number of theories and metaphors have been used to explain the implementation of management accounting changes in organizations. For instance, some studies have used metaphors like translation, imitation, and fashion to describe the processes of changes by which new ideas (changes) travel between the members of a social system (Røvik, 1996). Wang, Heng, and Chau (2010) addressed several further theories which could explain the processes of the diffusion of an innovation or implementation of changes in organizations as follows: the theories of transaction cost economics, stakeholder theory, organization learning theory, institutional theory, transaction cost theory, and social cost theory. As with the above, the diffusion innovation theory (Rogers, 2003) has been used to describe the processes of changes and the implementation of new techniques/practices in organizations. This theory suggested that a wide range of contextual factors (such as organizational strategy, organizational culture, organizational structure, characteristics of innovations, communication channels, and environmental factors, etc.) could influence the diffusion of innovations in organizations (Adam & Fred, 2008; Al-Omiri & Drury, 2007a; Askarany, 2006; Askarany & Smith, 2004, 2008; Askarany & Yazdifar, 2009; Askarany & Yazdifar, 2012; Berling, 2008; Englund & Gerdin, 2008; Qian & Ben-Arieh, 2008; Yazdifar & Askarany, 2012; Yazdifar, Askarany, Askary, & Daneshfar, 2005). However,

Davood Askarany, Ph.D., CPA, ACMA, CIMA, senior lecturer, Business School, Accounting & Finance, The University of Auckland. Correspondence concerning this article should be addressed to Dr. Davood Askarany, Department of Accounting & Finance,

Business School, The University of Auckland, Private Bag: 92019, Auckland, New Zealand. Email: [email protected].

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IMPLEMENTATION OF MANAGEMENT ACCOUNTING CHANGES IN AUSTRALIA

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while the majority of studies initiated by the above theories and metaphors and found some associations between management accounting changes and some contextual factors (addressed by adopted theories), the findings are inconclusive, inconsistent, and mixed (Al-Omiri & Drury, 2007a; Askarany & Yazdifar, 2012; Baird, 2007; Baird, Harrison, & Reeve, 2007; Cobb, Innes, & Mitchell, 1993; Innes & Mitchell, 1995; Innes, Mitchell, & Sinclair, 2000; Langfield-Smith, 1997; Pierce, 2004; Yazdifar & Askarany, 2012). Furthermore, from organizations’ point of views, understanding factors contributing to innovative behavior in organizations and influencing organizations’ decisions to proceed with the changes have remained relatively undeveloped, inconclusive, and inconsistent (Cho & Pucik, 2005). This situation encourage an exploratory study to identify the most prevalent contextual factors (from organizations’ perspectives) influencing the implementation of new managerial changes or the adoption of relatively new management accounting techniques in organizations.

Given the above, current study is aiming to investigate the introduction and the implementation of management accounting changes in Australia. It also intends to explore the hindering and facilitating factors (from organizations’ perspectives) contributing to the implementation of new management accounting changes in organizations.

Our research inquiry in the current study is guided by an exploratory theoretical paradigm (Lincoln & Guba, 1985). The aim of this study is to explore a current gap in our understanding about the contextual factors (from organizations’ perspectives) influencing the implementation of management accounting changes in organizations.

The remainder of the paper is structured as follows. Next sections present the literature review, the research methodology adopted, the findings and data analysis and finally the conclusions.

Literature Review

Recent studies suggest that the take-up of most of management accounting changes introduced during the past three decades still lag behind those of traditional systems (Al-Omiri & Drury, 2007b; Anderson & Young, 1999; Askarany, 2003; Askarany, Smith, & Yazdifar, 2007a, 2007b; Askarany & Yazdifar, 2007; Askarany, Yazdifar, & Askary, 2010; Baird, 2007; Gosselin, 1997; Langfield-Smith, 1997). For example, Chenhall and Langfield-Smith (1998) have found that the level of adoption of most of relatively new management accounting techniques in Australia were fairly lower than those of the traditional techniques. For instance, the ranking in terms of the extent of adoption of some of the new techniques were as follows: “activity-based costing ranked” (24), “activity-based management” (21), “product life cycle analysis” (20), and “target costing” (27). Given the above, they found the following rankings in terms of the extent of adoption of some of traditional management accounting techniques: “analysis for budgeting and for planning financial position” (1), “capital budgeting” (2), and “performance evaluation using return on investment” (3). Likewise, the published studies on the diffusion of management accounting innovation in the UK and New Zealand depict a similar picture (Abdel-Kader & Luther, 2006; Al-Omiri & Drury, 2007a; Askarany et al., 2010; Cotton, Jackman, & Brown, 2003; Yazdifar & Askarany, 2009).

However, except few (e.g., activity-based costing and balanced scorecard) other management accounting changes and innovations have relatively received less publicity in the literature. So, it is not very clear what the other main management accounting innovations/changes that organizations are faced with. We may refer to any relatively new management accounting technique as “innovation” or “change” in this paper. Reviewing cost and management accounting innovations of the past few decades, Björnenak and Olson (1999: 327) identified


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the major recently developed cost and management accounting techniques in the literature (which received adequate attention by practitioners) as follows:

(1) Activity-based costing (ABC); (2) Activity management (AM) and activity-based management (ABM); (3) Local information systems (LS); (4) Balanced scorecard (BSC); (5) Life cycle costing (LCC); (6) Target costing (TC); (7) Strategic management accounting (SMA). Besides above techniques, following managerial tools are also considered as relatively recent management

accounting innovations: Key performance indicators (KPIs), resource accounting and budgeting (RAB), zero-based budgeting (ZBB), functional analysis, and resource management (Lapsley & Wright, 2004).

In line with the above techniques, Chenhall and Euske (2007) referred to some of the recent management accounting innovations under management control systems as follows: activity-cost-management, target costing, LCC, quality costing, and performance management innovations such as economic value added (EVA), shareholder value analysis (SVA), value based management (VBM), and BSC. There is no universal consensus with respect to what techniques constitute recent management accounting innovations (Cadez & Guilding, 2008). It is argued that many management accounting techniques drawn from other disciplines such as engineering and economics (Miller, 1998; Miller, Kurunmäkii, & O’Leary, 2008). According to Miller et al. (2008), practices such as standard costing, discounted cash flow (DCF), the distinction between fixed and variable costs, break-even analysis, and much more have been drawn from disciplines other than accounting and then adapted, and constituted as the core of accounting. However, regardless of the origins which management accounting techniques are drawn from, according to Chenhall and Langfield-Smith (1998), the most popular recently developed techniques which have received considerable attention by Australian practitioners can be listed as follows: ABC; ABM; BSC; benchmarking; SMA; and TC.

Given the above, to gain a better picture regarding the awareness of Australian practitioners about recent management accounting innovations, the following section examines the extent of introduction of recently developed cost and management accounting techniques to potential practitioners and users in Australia.

The Extent of Introduction of Recently Developed Cost and Management Accounting Techniques to Practitioners in Australia

To examine the extent of introduction of recently developed cost and management accounting techniques to practitioners in Australia, the most widely available technical, professional, and Australian practitioner journals in the field of management accounting were reviewed. These journals included Business Review Weekly (BRW), CPA Journal (the journal of CPA Australia), and Charter (the journal of ICAA). Furthermore, Institute of Chartered Accountants in Australia [ICAA] and CPA conferences, workshops, and professional developments programs in South Australia during a four-year period (1996-1998) were investigated. These were among the main sources of transferring information on cost and management accounting innovations to potential users in Australia. The purpose of such an investigation was to get a clear picture of the scope and the extent of recently developed cost and management accounting techniques introduced to practitioners in Australia. Table 1 exhibits the frequency of the most relevant topics regarding recently developed cost and


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management accounting techniques/changes discussed in the above-mentioned professional journals, conferences, workshops, and professional development programs over the period 1996 to 1999 in Australia.

Table 1 Frequency of Discussion of Recently Developed Cost and Management Accounting Innovations/Changes

Techniques BRW Charter CPA Conferences Total No. of discussions

Percentage (%)

Performance measurement and BSC 10 12 6 32 60 19.6 ABC 4 14 7 28 53 17.2 Value chain analysis 5 9 18 32 10.4 Total quality management (TQM) 16 6 2 7 31 9.8 Strategic measurement 8 6 1 12 27 8.8 Risk measurement 1 3 18 22 7.1 Change in measurement 5 7 2 4 18 5.8 Benchmarking 7 4 6 17 5.5 Re-engineering 6 3 1 5 15 4.9 EVA, SVA, and VBM 3 2 5 10 3.2 TC 2 2 3 7 2.3 LCC 1 4 5 1.6 Learning organization 1 1 3 5 1.6 Outsourcing 4 4 1.3 Just-in-time (JIT) 1 1 1 3 1

The above examination shows that ABC is the most talked about techniques introduced to practitioners in Australia. This is in line with the suggestions made by studies which examined the implementation of ABC in practice (Al-Omiri & Drury, 2007a; Alcouffe, Berland, & Levant, 2008; Alsaeed, 2005; Baird et al., 2007; Englund & Gerdin, 2008; Kallunki & Silvola, 2008; Major & Hopper, 2005). However, as Table 1 shows, there are a variety of other management accounting practices which have been addressed in the literature and introduced to Australian practitioners through professional journals, workshops and conferences in recent years.

Given the above, a further survey was carried out to explore the extent of the implementation of above management accounting changes/innovations in practice. The survey also intended to explore the contextual factors influencing the implementation of management accounting changes (from users’/organizations’ perspectives).

Research Method and Findings

A cross-sectional mailed survey was implemented in this study to examine the extent of implementation of management accounting innovations in Australia. The survey conducted in 2003, targeting 501 CPA members employed in building and construction, energy, engineering, healthcare, metals, mining and extraction, paper and packaging, retail, distribution and transport. CPA is the largest professional accounting body in Australia with the majority of its members dealing with cost and management accounting techniques in commerce and industry, making it an appropriate sample for this study.

The targeted respondents were asked to identify the extent of implementation of six most widely cited management accounting techniques in their organizations as follows: ABC; ABM; BSC; benchmarking; SMA; and TC. In an open question, targeted respondents were also asked to list any other management accounting technique implemented, discussed, or introduced in their organizations.


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Following Booth and Giacobbe (1998), a questionnaire was designed to seeking information on the level of implementation of above management accounting innovations/changes as follows: Discussions have not taken place regarding the introduction of this practice (with anchor of 1); a decision has been taken not to introduce the innovation (with anchor of 2); some consideration is being given to the introduction of the innovation (with anchor of 3); the innovation has been introduced on a trial basis(with anchor of 4); the innovation has been implemented and accepted (with anchor of 5).

Using the above scale, the targeted respondents were asked to identify the extent of implementation of the following six management accounting techniques/changes in their organizations: ABC; ABM; BSC; benchmarking; SMA; and TC. In an open question, the targeted respondents were also asked to list any other management accounting technique implemented, discussed or introduced in their organizations.

The questionnaire includes two further open questions seeking information on the major strengths and weaknesses of their implemented management accounting techniques as potential contextual factors influencing managers’ decisions to implement (or not) new management accounting changes in their organizations.

Pilot tests of the instrument were initially undertaken with a group of university academics, managers, and management accountants. Before the survey instrument was mailed to the organizations under investigation, its content validity was addressed by asking a group of management accounting lecturers and postgraduate students with manufacturing experience to review the instrument for clarity and meaning and to refine the design and focus of the content further. Modifications were made as deemed necessary. To help motivate response, respondents were offered a final report of the results together with the resulting recommendations to facilitate the implementation of recent cost and management accounting innovations in their organizations.

Responses to the survey were gathered by 100 questionnaires, representing a response rate of 20%. Non-response bias was examined both by using the aggregate details provided by CPA members (number of employees, implemented innovations, and the activities of the firms) and through a comparison between early and late responses. The former showed responses to be representative, the latter showed that there was no perceived difference between these responses, suggesting that non-response bias would not influence the outcomes. Table 2 illustrates the extent of the implementation of relatively new management accounting changes in Australian firms.

Table 2 The Extent of the Implementation of Relatively New Management Accounting Changes/Innovations in Australian Firms Management accounting innovations

No discussion (%)

Decided not to introduce (%)

Some consideration is given (%)

Introduced on trial basis (%)

Implemented and accepted (%) Total (%)

ABC 38 13 21 9 19 100 ABM 54 9 17 9 11 100 BSC 36 4 16 21 23 100 Benchmarking 25 3 20 17 35 100 SMA 53 4 13 17 13 100 TC 53 5 13 13 16 100

According to Table 2, the implementation of six management accounting changes addressed in the current study ranges from 11% (ABM) to 35% (benchmarking). Table 3 reports the details of the analysis of scale reliability and unidimensionality for the above management accounting innovations/changes. The six-item


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measure achieved an alpha value of 0.6406 (Cronbach, 1951), and a standardized item alpha of 0.6407. This figure is marginally below the value of 0.70 recommended by Nunnally (1978, p. 245), but can be regarded as moderate (Brownell & Dunk, 1991, p. 697) and acceptable (Daft & Macintosh, 1981, p. 214). Table 3 further shows the total-item correlations for each of the scale composites, with ranges from 0.3280 to 0.4184. According to De Vaus (1991, p. 239), values above 0.30 generally indicate acceptable scale unidimensionality.

Table 3 Analysis of Scale Reliability and Unidimensionality: Management Accounting Innovations Management accounting techniques Cases Mean Std. Dev. Correlated item-

total correlation Alpha if item deleted

ABC 100 2.5800 1.5320 0.3280 0.6129 ABM 100 2.1400 1.4356 0.3609 0.6014 BSC 100 2.9600 1.6387 0.3632 0.6005 Benchmarking 100 3.3900 1.5884 0.3601 0.6014 SMA 100 2.3800 1.5941 0.4184 0.5789 TC 100 2.3900 1.6261 0.3918 0.5892

The above innovations were tested for univariate normality using the Kolmogorov-Smirnov test and the tests of the indices of skewness and kurtosis. In all cases, the assumption that the sample represents a normal population could not be rejected. The findings of current study suggest that the frequency of adopters of recently developed cost and management accounting innovations (addressed in this study) in Australia are still less than those of non-adopters. This is despite the expectation that the adoption of recent management accounting changes are growing due to their contribution to overall performance of organizations (Adam & Fred, 2008; Banker & Mashruwala, 2007; Dikolli, Kinney, & Sedatole, 2007; Kelly, 2007; Vera-Munoz, Shackell, & Buehner, 2007).

Besides above six techniques, we asked our targeted respondents (in an open question) to list any other management accounting changes/innovations which were either discussed or used by them. According to the findings, there were a number of other managerial tools listed by respondents (which were either discussed or at least used by one or more organizations) as follows:

DCF, TQM, CUSUM charts (cumulative sum control chart) and optimum transfer pricing, computer technology, opportunity cost budgeting, ZBB, decision trees, critical path scheduling, and management by objectives, information economics and agency theory, JIT scheduling, strategic business units, experience curves, portfolio management, materials resource planning, diversification, matrix organization and product repositioning, LCC, value-added management, theory of constraints, vertical integration, private labels throughput accounting, business process reengineering, quality functional deployment, outsourcing, gain-sharing, core competencies, time-based competition and learning organization, lean accounting, KPIs, RAB, ZBB, functional analysis, resource management, and time-driven activity-based costing.

Not all above techniques are pure management accountings changes/innovations. Furthermore, the development and introduction of some of above techniques dates back to more than 50 years ago (as explained in the following paragraph). However, the implementation of many of these techniques are not very prevalent in the literature. So, these findings could help future researchers in this area to expand their views and address various issues/questions regarding the implementation of above techniques in practice. The following paragraph provides some information regarding the history and introduction of above techniques in the


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literature. According to Hagerty (1997) and Smith (1999), we can categorize the development and introduction of above techniques into different decades as follows:

(1) 1950s: Discounted cash flow (DCF), total quality management (TQM), CUSUM charts, and optimum transfer pricing.

(2) 1960s: Computer technology, opportunity cost budgeting, zero-base budgeting (ZBB), decision trees, critical path scheduling, and management by objectives.

(3) 1970s: Information economics and agency theory, JIT scheduling, strategic business units, experience curves, portfolio management, materials resource planning, diversification, matrix organization, and product repositioning.

(4) 1980s: Activity-based costing (ABC), activity-based management (ABM), strategic management accounting (SMA), activity management (AM), life cycle costing (LCC), target costing (TC), value-added management, theory of constraints, vertical integration, private labels, and benchmarking.

(5) 1990s: Business process reengineering, quality functional deployment, balanced scorecard (BSC), outsourcing, gain sharing, core competencies, time-based competition, and learning organization.

According to Dugdale and Colwyn (1998), the application of TOC which is one of the developments of 1980s is usually labeled as “throughput accounting”. Completing the above list, we can also include “lean accounting” as one of the developments of 1990s (Maskell & Baggaley, 2006). The main purpose of lean accounting is to reduce steps in transaction processing, eliminate standard costs in favor of actual costs and discontinue cost allocations (Kennedy & Widener, 2008). In describing lean accounting, Kennedy and Widener (2008) considered this technique as continuous improvement and reducing time by eliminating waste and reduction of costs which are the main principles of lean accounting.

Updating the above techniques, we can expand the above list including the Second Generation BSC and the Third Generation BSC as further developments of management accounting innovations in 1990s (Lawrie & Cobbold, 2004). Expanding the above management accounting innovations, we may also add time-driven activity-based costing as another management accounting development in 2000s (Kaplan & Anderson, 2007).

The study further explored the level of organizational satisfactions with their implemented management accounting techniques (as a proxy for organizational performance) to see if the adoption of management accounting changes has contributed to organizational performance. It was expected that increasing organization satisfaction as a consequence of implementation of management accounting changes can be considered as an encouraging factor promoting the implementation of management accounting changes in organizations. Table 4 reveals the level of satisfaction of Australian firms with their implemented cost and management accounting systems as follows.

Table 4 The Level of Organizational Satisfaction With Implemented Management Accounting System Level of satisfaction Percentage (%) Cumulative percentage (%) Very dissatisfied 7 7 Dissatisfied 13 20 Needs improvement 39 59 Moderately satisfied 32 91 Very satisfied 9 100 Total 100 100


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The findings indicate that the majority (59%) of establishments were either dissatisfied with their adopted cost and management accounting systems or believed that their systems need improvement. Only 9% were very satisfied and 32% moderately satisfied with their current system.

Table 5 reveals the significance of association between organizational satisfaction and the levels of implementation of six management accounting techniques in Australia.

Table 5 The Significance of the Association Between Organizational Satisfaction and the Levels of Implementation of Six Management Accounting Techniques in Australia ABC ABM BSC Benchmarking SMA TC Satisfaction with current systems 0.851 0.294 0.617 0.767 0.734 0.333

According to Kendall’s tau-b, the level of association between the organizational satisfaction and the extent of implementation of ABC has a value of 0.016 (standard error 0.085), which is statistically significant only at the 0.851 level. Thus the findings do not support the notion that the higher the levels of adoption of ABC, the higher the levels of organizational satisfaction. Though the above findings are not in line with what advocates of ABC (e.g., Adam & Fred, 2008) believe, the results are consistent with those findings (e.g., Banker, Bardhan, & Chen, 2008) which suggested that ABC adoption by itself does not improve organizational performance.

Kendall’s tau-b for the level of association between the organizational satisfaction and the extent of implementation of ABM has a value of 0.092 (standard error 0.087), which is statistically significant only at the 0.294 level. Thus as with the ABC, the findings do not support the notion that the higher the levels of implementation of ABM, the higher the levels of organizational satisfaction.

Kendall’s tau-b for the level of association between the organizational satisfaction and the extent of implementation of BSC has a value of 0.042 (standard error 0.084), which is statistically significant only at the 0.617 level. So, similar to ABC and ABM, the findings do not support the notion that the higher the levels of adoption of BSC, the higher the levels of organizational satisfaction.

According to Kendall’s tau-b, the level of association between the organizational satisfaction and the extent of implementation of benchmarking has a value of 0.025 (standard error 0.086), which is statistically significant only at the 0.767 level. Thus these findings also do not support the notion that the higher the levels of adoption of benchmarking, the higher the levels of organizational satisfaction.

Kendall’s tau-b for the level of association between the organizational satisfaction and the extent of implementation of SMA has a value of 0.029 (standard error 0.084), which is statistically significant only at the 0.734 level. So, similar to other four techniques, the findings do not support the notion that the higher the levels of adoption of SMA, the higher the levels of organizational satisfaction.

And finally, Kendall’s tau-b for the level of satisfaction with implemented accounting systems and the diffusion of target costing has a value of -0.078 (standard error 0.080), which is statistically significant only at the 0.333 level. As with other five management accounting changes, the findings are not supporting that the implementation of target costing increases the level of organizational satisfaction in organizations.

According to the findings, the extent of organizations satisfactions with their implemented management accounting systems is not significantly related to the adoption of any of six management accounting innovations (addressed in this study). This might appear to be at odds with the literature (especially the ABC


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literature), which considers “organizational dissatisfaction with their implemented traditional accounting systems” as a major motivation for the diffusion of new management accounting techniques (Beng, Schoch, & Yap, 1994; Bork & Morgan, 1993; Gosselin, 1997).

Exploring other contextual factors (from organizations points’ of view), we asked our targeted respondents to identify the “strengths” and the “weaknesses” of their implemented management accounting techniques as potential hindering or facilitating factors influencing their decision to implement new management accounting changes and innovation in their organizations.

Tables 6 and 7 list the “strengths” and the weaknesses’ of management accounting techniques (implemented by organizations). These tables also show the levels of association between these factors (listed under the strengths and the weaknesses of management systems) and the implementation of relatively new management accounting techniques respectively. According to Table 6, the level of association between “simplicity” (as one of the of strengths of the implemented systems) and the implementation of benchmarking is statistically significant (significant at p < 0.062), while the findings provide no support for a significant association between “simplicity” and the other five management accounting techniques addressed in this study. Likewise, there are some relatively significant individual relationships between other factors (listed as strengths of implemented accounting systems) and six management accounting changes addressed in this study as follows: between “widely understood” and the diffusion of the BSC (significant at p < 0.036); between “tailored to demand” and ABM (significant at p < 0.005); between “accurate (reliable)” and SMA (significant at p < 0.017); between “flexible, reconcile and links well to other parts of business” and the BSC (significant at p < 0.037) and finally between “easy to maintain, follow, and use” and target costing (significant at p < 0.076).

Table 6 The Significance of the Association Between Management Accounting Techniques and the Strengths of Implemented Accounting Systems The major strengths of implemented accounting system ABC ABM BSC Benchmarking SMA TC Simplicity 0.904 0.562 0.220 0.062 0.819 0.784 Widely understood 0.238 0.963 0.036 0.744 0.535 0.141 Tailored to demand 0.297 0.005 0.877 0.772 0.198 0.520 Up to date information 0.611 0.786 0.795 0.642 0.128 0.216 Cost effective 0.240 0.907 0.112 0.932 0.658 0.832 Accurate (reliable) 0.195 0.193 0.430 0.135 0.017 0.794 Detailed 0.914 0.761 0.132 0.619 0.551 0.145 Relevant, meaningful, stable, and standard 0.452 0.201 0.211 0.359 0.776 0.777 Flexible, reconcile, and links well to other parts of business 0.641 0.383 0.037 0.140 0.891 0.591 Easy to maintain, follow, and use 0.132 0.315 0.461 0.175 0.186 0.076

According to Table 7, as with the preceding factors, the associations between factors constituting the weaknesses of implemented accounting systems and all six management accounting innovations/changes vary significantly. As Table 7 shows, the association between the weakness termed as “arbitrary, not consistent, open to interpretation” and the diffusion of ABC is statistically significant (significant at p < 0.010), while the finding provide no support for a significant association between this factor and any of the other five management accounting techniques addressed in this study. Likewise, there are some significant individual relationships between other weaknesses of implemented accounting systems and the remaining five cost and management accounting innovations as follows: between “difficult to trace costs” on one hand and the


637

diffusion of ABC (significant at p < 0.007) and ABM (significant at p < 0.078) respectively; between “complex, complicated, and diversified” and benchmarking (significant at p < 0.043); between “inaccurate” and ABC (significant at p < 0.067); between the BSC on one hand, and “needs regular fine tuning and resources” (significant at p < 0.096) and “lack of understanding by the business” (significant at p < 0.026) on the other hand respectively.

Table 7 The Significance of the Association Between Management Accounting Techniques and Weaknesses of Implemented Accounting Systems Major weaknesses of implemented accounting system ABC ABM BSC Benchmarking SMA TC Arbitrary, not consistent, open to interpretation 0.010 0.916 0.403 0.225 0.521 0.251 Difficult to trace costs 0.007 0.078 0.461 0.744 0.779 0.395 Complex, complicated, and diversified 0.208 0.738 0.559 0.043 0.781 0.529 Time consuming 0.650 0.401 0.270 0.847 0.160 0.346 Not easy to analyze variances and report 0.342 0.318 0.582 0.317 0.34 0.494 Inaccurate 0.067 0.853 0.683 0.923 0.530 0.778 Out of date (not detailed) 0.810 0.244 0.257 0.906 0.654 0.918 Lack of integration with other parts of the system 0.339 0.429 0.407 0.457 0.890 0.206 Not flexible 0.402 0.898 0.835 0.356 0.613 0.670 Needs regular fine tuning and resources 0.211 0.639 0.096 0.366 0.923 0.912 Lack of understanding by the business 0.795 0.445 0.026 0.220 0.526 0.370

The above results show that individual management accounting changes/innovations may have different reactions to different influencing factors. However, the above contextual factors (listed as the strengths and the weaknesses of management accounting systems) explored in this study are likely to influence the decisions to implement (or not) any new management accounting change in organizations. Further studies are recommended to examine the impact of above contextual factors (explored in this study) on the implementation of recently developed management accounting changes/innovations in larger scales.

Conclusion and Limitations

The current exploratory study confirms the exposure of Australian practitioners to a variety of management accounting changes/innovations through professional journals, conferences, workshops, and professional development programs. The findings suggest that most prevalent cost and management accounting changes/innovations introduced to Australian organizations during the past few decades include: performance measurement and BSC techniques, ABC, valued added concepts, ABM, TQM, strategic management, risk management, benchmarking, re-engineering, EVA, and target costing. However, from implementation perspective, the findings of current study suggest that recent management accounting innovations/changes (addressed in this study) are not widely implemented in Australia.

The study further explored the level of organizational satisfactions with their implemented management accounting techniques to see if the adoption of management accounting changes has contributed to organizational satisfaction. The findings show no significant association between organizational satisfaction and the implementation of any management accounting changes/innovations addressed in this study. Considering the cost and benefit of any changes (or implementation of a new technique), this might imply that perceived advantages of these relatively new management accounting changes are not adequate enough to make


638

a fundamental changes in organizational performance. Exploring contextual factors (from organizations points’ of view) influencing the implementation of

management accounting changes, we asked our targeted respondents to list the “strengths” and the “weaknesses” of their implemented management accounting techniques as potential hindering or facilitating factors influencing their decision to implement new management accounting changes or innovation in their organizations. The respondents listed the following factors as some of the strengths of management accounting systems which could influence their decisions to implement (or not) a new management accounting change: simplicity; widely understood; tailored to demand; up to date information; cost effective; accurate (reliable); detailed; relevant, meaningful, stable, and standard; flexible, reconcile and links well to other parts of business; easy to maintain, follow, and use.

The respondents further listed the following factors as some of the weaknesses of management accounting systems which could influence their decisions to implement (or not) a new management accounting change: being arbitrary, not consistent, open to interpretation; difficult to trace costs; complex, complicated and diversified; time consuming; not easy to analyze variances and report; inaccurate; out of date (not detailed); lack of integration with other parts of the system; not flexible ; needing regular fine tuning and resources; and lack of understanding by the business.

However, the findings suggest that individual management accounting changes/innovations may have different reactions to the above factors. Further studies are recommended to examine the impact of the above contextual factors (explored in this study) on the implementation of recently developed management accounting changes/innovations in larger scales.

The findings, conclusions and the implications of this study should be interpreted based on the normal limitations of mail surveys, such as lack of researcher interaction with respondents and follow up interviews. Caution should be exercised for generalizing the findings of current study (especially due to the relatively low response rate of 20%, though associated tests for non-response bias yielded satisfactory outcomes).

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Conclusions

a$,G&$,$D2(&#+0/"$D2(&%+(#D,$D(")()0%&%.0&'(0%)"$K&/0"%(H&M,(J,,%(")/,%(+,D.$0J,+(&D(/H,(."%D/0/#,%.0,D(")(

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M&'#,( Sinformative disclosureT( DH"#'+(J,( $,G'&.,+(J=(H&$+( 0%)"$K&/0"%(G,$/&0%0%1( /"( /H,( $,'&/0"%DH0G(J,/Q,,%(/H,(0%+,G,%+,%/(M&'#,$(&%+(H0D(.'0,%/(Sprotective disclosureTA(3%()&./2(D#.H(&(+0D.'"D#$,(DH"#'+(%,1&/0M,'=(&)),./(/H,( G$"J&J0'0/=( ")( &( ."''#D0M,( J,H&M0"$( J,/Q,,%(K&%&1,$D( &%+( ,F/,$%&'( M&'#,$D2(Q0/H"#/( H&M0%1( &( D01%0)0.&%/(

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Designing the Questionnaire MF,(R#,D/0"%%&0$,(0%(/F0D(D/#+=(N&D(+,D01%,+("%(/F,(Z&D0D(")(*#'/0:d0G,%D0"%(*,&D#$,G,%/(&.."$+0%1(/"(

,&.F("ZD,$W&Z',(+0G,%D0"%A(3/(#D,D(&(B:H"0%/(O0V,$/(4.&',(/"(G,&D#$,(,&.F(&%DN,$2(N0/F(B(Z,0%1(D/$"%1'=(&1$,,(&%+(6(Z,0%1( D/$"%1'=(+0D&1$,,A(-(F01F,$(H"0%/( $,H$,D,%/D( &(F01F,$(+,1$,,(")( &1$,,G,%/2( &%+(W0.,(W,$D&A(d&/&(."'',./,+( )$"G( /F,( D&GH',D( N&D( ^.,%/$&'0K,+_2( D"( /F,( D#G( ")( D."$,D( 10W,%( /"( &''( R#,D/0"%%&0$,( 0/,GD( &)/,$(+,+#./0%1(/F,(&W,$&1,(0D(K,$"A(MF&/(N&=2(/F,(G#'/0."''0%,&$0/=(Z,/N,,%(0%+,H,%+,%/(&%+(,[/$&%,"#D(W&$0&Z',D(0D(,$&D,+(/"(Z,//,$(/,D/(/F,(0%/,$&./0"%(Z,/N,,%(0%+,H,%+,%/(&%+(,[/$&%,"#D(W&$0&Z',DA(MF,()"''"N0%1(G&/F,G&/0.&'(,R#&/0"%(0''#D/$&/,D(/F,(^.,%/$&'0K&/0"%_(."%.,H/e(

X Y ?8 8L H B (

MF,(R#,D/0"%%&0$,(")(3I(N&D(+,D01%,+(0%('0%,(N0/F(/F,(3I(/F,"$0,D(H$"H"D,+(Z=(IF,%(X>??6Y2(MD,%(&%+(c#(X>?6?Y2(N0/F( /F,( ^F#G&%(.&H0/&'_2( ^D/$#./#$&'( .&H0/&'_2( &%+( ^$,'&/0"%DF0H(.&H0/&'_(Z,0%1( /F,( /F$,,(+0G,%D0"%DA(MF0D(R#,D/0"%%&0$,(."%/&0%D(6>(0/,GD(0%(/"/&'A(

MF,(R#,D/0"%%&0$,(")(Z$&%+(,R#0/=(N&D(+,D01%,+(0%('0%,(N0/F(/F,(/F,"$0,D(H$"H"D,+(Z=(-&V,$(X6bb6Y2(i,'',$(X6bb<Y2( &%+(Q,0( X>?66Y( N0/F( /F,( ^Z$&%+( '"=&'/=( &%+( Z$&%+( &DD".0&/0"%D_2( ^Z$&%+( &N&$,%,DD( &%+( H,$.,0W,+(Z$&%+_2( &%+( ^Z$&%+( 0%%"W&/0"%( &%+( "/F,$( H$"H$0,/&$=( Z$&%+( &DD,/D_( Z,0%1( /F,( /F$,,( +0G,%D0"%DA( MF0D(R#,D/0"%%&0$,(."%/&0%D(6>(0/,GD(0%(/"/&'A(

MF,( "$1&%0K&/0"%&'( H,$)"$G&%.,( 0D( G,&D#$,+( #D0%1( /N"( 0%+0.&/"$D2( %&G,'=( /F,( ^0%%"W&/0"%:$,'&/,+(H,$)"$G&%.,_(&%+(^]JE_a(/F,()"$G,$(N&D("Z/&0%,+()$"G(/F,(D/#+=(")(O0%1(&%+(c"%1(X>?6?Y(&%+(/F,('&//,$()$"G(ME!(+&/&Z&D,A(

Sampling Method MF0D(D/#+=(D#$W,=,+()0%&%.0&'(&%+(G&$V,/0%1(D,./0"%(.F0,)D("$(,GH'"=,,D(")(F01F,$(',W,'D(&/(M&0N&%('0D/,+(

Z0"/,.F(."GH&%0,D2(D,',./,+(#D0%1(/F,(."%W,%0,%.,(D&GH'0%1(G,/F"+A(J)(,[H,$/(R#,D/0"%%&0$,2(<?(."H0,D(N,$,(10W,%("#/(0%(&(H0'"/:/,D/A(-(H"D/:/,D/(N&D(."%+#./,+(&)/,$(G"+0)=0%1(/F,(R#,D/0"%%&0$,(0%(&.."$+&%.,(N0/F(,[H,$/(D#11,D/0"%DA(J)(/F,("))0.0&'(R#,D/0"%%&0$,2(<??(."H0,D(N,$,(10W,%("#/2(N0/F(><?(W&'0+(."H0,D($,/#$%,+(&/(&($,/#$%($&/,(")(B;ABlA(

Data Obtained From Questionnaire and the Measurement Model (

M&Z',(6(;1*2"&.%=.M1"(-8%44+8&".3-"*(.=%&.N3*70858-.D+&8+20"(O.+4'.N>2("&/+20".D+&8+20"(O.

3GH'0.0/(W&$0&Z',D( E[H'0.0/(W&$0&Z',D( M"/&'(%#GZ,$(")(R#,D/0"%%&0$,(0/,GD( O0/,$&/#$,(

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'&/,%/( W&$0&Z',D( X0A,A2( 3I2( Z$&%+( ,R#0/=2( &%+( "$1&%0K&/0"%&'( H,$)"$G&%.,Y2( ,&.F( ."%/&0%0%1( "ZD,$W&Z',h,[H'0.0/(W&$0&Z',D( &D( D/&/,+( Z,'"NA( MF,( D#$W,=(N&D( ."%+#./,+( #D0%1( /F,D,( "ZD,$W&Z',h,[H'0.0/( W&$0&Z',D2(N0/F( D,W,$&'(R#,D/0"%%&0$,( 0/,GD( .&/,1"$0K,+( #%+,$( /F,G( ,&.FA( -)/,$( H$".,DD0%1( +&/&( ."'',./,+( 0%( /F,( D#$W,=2( )0',D( N,$,(.$,&/,+()"$(/F,(H$0G&$=(+&/&A(-'/F"#1F(/F,(R#,D/0"%%&0$,(+,D01%(N&D(Z&D,+("%(*#'/0:d0G,%D0"%(*,&D#$,G,%/2(^d#,'(*,&D#$,G,%/_(N&D(&+"H/,+(/"(G&V,(D#$,(/F,(."GH#/,$(D")/N&$,:&0+,+(+&/&(H$".,DD0%1(1",D(&D(,[H,./,+(XIF,%2( >?6?YA(M&Z',(6( DF"ND( /F,(%#GZ,$(")(R#,D/0"%%&0$,( 0/,GD(#%+,$( 0GH'0.0/( &%+( ,[H'0.0/(W&$0&Z',D( 0%( /F0D(D/#+=2(&'"%1(N0/F(/F,0$($,),$,%.,($,D"#$.,DA(

Results and Analysis Linear Structure Model Analysis

MF,( IU-( 0D( &%( &%&'=/0.&'( &HH$"&.F( "HH"D0/,( /"( /F,( E[H'"$&/"$=( U&./"$( -%&'=D0D( XEU-YA( MF0D( D/#+=(."%+#./,+( &( IU-( ")( /F$,,( #%"ZD,$W&Z',h0GH'0.0/( W&$0&Z',D( X0A,A2( 3I2( Z$&%+( ,R#0/=2( &%+( "$1&%0K&/0"%&'(H,$)"$G&%.,YA( I"%D0D/0%1( ")( /F,( 4/$#./#$&'( *"+,'( &%+( *,&D#$,G,%/( *"+,'2( &( 4E*( H$"W0+,D( &%( ,)),./0W,(D"'#/0"%( /"( /F,( .&#D,:,)),./( $,'&/0"%( Z,/N,,%( 0GH'0.0/h'&/,%/( W&$0&Z',DA( MF,(G"+,'D( W,$0)0,+( 0%( /F0D( D/#+=( &$,(+0W0+,+( 0%/"( /F$,,( H&$/De( X6Y( W,$0)=0%1( /F,( 1""+%,DD:"):)0/( ")( *,&D#$,G,%/( *"+,'a( X>Y( W,$0)=0%1( /F,(1""+%,DD:"):)0/( ")( 4/$#./#$&'( *"+,'a( &%+( X<Y( W,$0)=0%1( /F,( "W,$&''( G"+,'\D( 1""+%,DD:"):)0/( /"( G&V,( D#$,( 0/(."%)"$GD(/"(/F,(1""+%,DD:"):)0/(0%+0.,DA(MF&/(0D2(/F,(1""+%,DD:"):)0/(")(/F,("W,$&''(4E*(N&D(f#+1,+(N0/F($,'&/,+(1""+%,DD:"):)0/(0%+0.,D(Xd0&G&%/"H"#'"D(`(401#&N2(>???YA(

Analyzing Fit of Measurement Model MF,( )&./"$( '"&+0%1D( ")( '&/,%/h0GH'0.0/( W&$0&Z',D( &%+( G&%0),D/h,[H'0.0/( W&$0&Z',D( G&0%'=( G,&D#$,( /F,(

0%/,%D0/=(")('0%,&$(."$$,'&/0"%(Z,/N,,%(,[H'0.0/(&%+(0GH'0.0/(W&$0&Z',DA(-()&./"$('"&+0%1(.'"D,(/"(6(0%+0.&/,D(/F,(,[H'0.0/(W&$0&Z',(0D($,'&/0W,'=(.&H&Z',(")(G,&D#$0%1(/F,(0GH'0.0/("%,A(3%(/F0D(D/#+=2(&''(,[H'0.0/(W&$0&Z',D\()&./"$('"&+0%1D(&$,(Z,/N,,%(?A9(&%+(?Ab2(F,%.,(/F,(D&/0D)=0%1($,'0&Z0'0/=A(I"%D,R#,%/'=2(&''(,[H'0.0/hG&%0),D/(W&$0&Z',D(0%( /F,( G"+,'\D( G,&D#$,G,%/( D=D/,G( &$,( .&H&Z',( ")( &HH$"H$0&/,'=( G,&D#$0%1( /F,( 0GH'0.0/h'&/,%/( W&$0&Z',DA(*"$,"W,$2( /F,( -W,$&1,( @&$0&%.,( E[/$&./,+( X-@EY( 0D( #D,+( /"( .&'.#'&/,( /F,( ,[H'&%&/"$=( H"N,$( ")( W&$0&%.,(Z,/N,,%( 0GH'0.0/h'&/,%/( W&$0&Z',D( W,$D#D( ,[H'0.0/hG&%0),D/( "%,Da( /F,( F01F,$( /F,( @E( W&'#,2( /F,( 1$,&/,$( /F,($,'0&Z0'0/=(&%+(."%W,$1,%/(W&'0+0/=(")(&('&/,%/h0GH'0.0/(W&$0&Z',A(CD#&''=2(/F,(@E(W&'#,(G#D/(Z,('&$1,$(/F&%(?A7(/"(0%+0.&/,(/F,(,[H'&%&/"$=(W&$0&%.,(")(&%(,[H'0.0/(W&$0&Z',(0D('&$1,$(/F&%(G,&D#$,G,%/(,$$"$(XU"$%,''(`(O&$.V,$2(6b96YA( 3%( /F0D( D/#+=2( &''( -@ED( &$,( '&$1,$( /F&%( ?A72( F,%.,( /F,( ,[H'0.0/( W&$0&Z',D\( ,[.,'',%/( $,'0&Z0'0/=( &%+(."%W,$1,%/(W&'0+0/=(X4,,(M&Z',(>(&%+(U01#$,(>YA((

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The Indices of Fit of the Overall Model MF,(H#$H"D,(")(&+"H/0%1(4E*(0%(/F,(G"+,'0%1(HF&D,(")(/F0D(D/#+=(0D(/"(,[H'"$,(/F,($,'&/0"%DF0H(Z,/N,,%(

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Analytical Testing of Path Effect for the Structural Model

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Conclusion and Suggestions

MF,()"''"N0%1(DH,.0)0.(."%.'#D0"%D(&$,(+,$0W,+()$"G(/F,(&)"$,:G,%/0"%,+(+&/&(&%&'=D,D(&%+($,D#'/De(X6Y(-D()"$(4E*(W,$0)0.&/0"%2(/F,(4E*(,D/&Z'0DF,+(0%(/F0D(D/#+=(F&D(&(D&/0D)=0%1(1""+%,DD:"):)0/(0%(/,$GD(")(

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Contributions of This Present Study X6Y(I"%/$0Z#/0"%D(/"(/F,(Z#D0%,DD(H$&./0.,D(&/(M&0N&%('0D/,+(Z0"/,.F(."GH&%0,DA(QF0',( /F,( H$,W0"#D( D/#+0,D( ")( Z0"/,.F%"'"1=( )0$GD( /,%+( /"( )".#D( "%(EU-2( /F0D( H$,D,%/( D/#+=( H,$)"$G,+(

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Limitations and Suggestions X6Y(-D( /F0D(D/#+=(&+"H/,+( /F,(^"%,(.&#D,2("%,(,)),./2(&%+("%,(,[/$&%,"#D(W&$0&Z',_(H&//,$%(N0/F( /F,(D"',(

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References -&V,$2(dA(-A(X6bb6YA(P+4+?84?.2&+4'."Z18-#).6+78-+08@84?.%4.-:"./+01".%=.+.2&+4'.4+*"A(5,N(!,$D,=2(5,N(S"$Ve(MF,(U$,,(L$,DDA(P&1"KK02(]A(LA2(`(S02(SA(X6b99YA(J%(/F,(,W&'#&/0"%(")(D/$#./#$&'(,R#&/0"%(G"+,'DA(Q%1&4+0.%=.-:".U5+'"*#.%=.P+&,"-84?.R58"45"V.

J[X6Y2(B8:b8A(IF,%2( cA( SA( X>??bYA( d,W,'"H0%1( ,W&'#&/0"%( 0%+0.&/"$D( )"$( &1$0.#'/#$&'( Z0"/,.F%"'"1=( ."GH&%0,D\( D&/,''0/,( )&$GDe( M&V0%1( /F,(

,[&GH',(")(&1$0.#'/#$&'(Z0"/,.F%"'"1=(H&$VD(X*&D/,$(/F,D0D2(d,H&$/G,%/(&%+(T$&+#&/,(3%D/0/#/,(")(P#D0%,DD(-+G0%0D/$&/0"%2(5&/0"%&'(L0%1/#%1(C%0W,$D0/=(")(4.0,%.,(&%+(M,.F%"'"1=2(M&0N&%YA(

IF,%2( *A( IA( X>??6YA( -( D/#+=( ")( /F,( 0%)'#,%.,( ")( 0%)"$G&/0"%( /,.F%"'"1=( 0%W,D/G,%/( &%+( 0%/,'',./#&'( .&H0/&'( "%( Z#D0%,DD(H,$)"$G&%.,(Xd"./"$&'(+0DD,$/&/0"%2(d,H&$/G,%/(")(3%)"$G&/0"%(*&%&1,G,%/2(5&/0"%&'(I,%/$&'(C%0W,$D0/=2(M&0N&%YA(

IF,%2( 4A(cA2( 4F&N2(*A(UA2(O&02(IA(IA2(`(IF&%12( !A( !A( X>??9YA( 3%/,$,D/( $&/,( $#',D( &%+( /$&%D0/0"%&'( +=%&G0.D( 0%( &%( ,%+"1,%"#D'=(1$"N0%1("H,%(,."%"G=A(Q%1&4+0.%=.34-"&4+-8%4+0.P%4"#.+4'.E84+45"V.Y\X6Y2(78:B7A(

IF,%2(4A(SA(X>?6?YA(RIPA(M&0N&%e(LD=.F"'"10.&'(L#Z'0DF0%1(I"A2(O/+AA(IF,%2( SA( cA2( `( O02( QA( kA( X>??6YA( -( +0D.#DD0"%( &%+( ,."%"G0.( ,W&'#&/0"%( ")( &1$0.#'/#$&'( Z0"/,.F%"'"1=. X5&/0"%&'( M&0N&%(

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Cost of Production at Business Unit in Aquaculture Industry:

Study at Aquafarm Nusantara Company

Ilham Hidayah Napitupulu Politeknik Negeri Medan, Indonesia

Widyo Nugroho Gunadarma University, Indonesia

Types of industry are manifold, patterns used in the determination of the cost of production can also vary.

Elements of the cost of production are raw materials, direct labor, and factory overhead. Value of raw materials

used in aquaculture industry in particular needs to calculate the cost of feed consumed by fish, thus greatly affect

the price of fish feed cost of production. The calculation of the value of raw materials in fish production cost

element to consider is the calculation of the value of raw material components, namely, (1) biomass of harvestable

fish as the basic multiplication cost of production per kilogram or per fish harvested fish; (2) the ratio of fish feed

intake by the amount of fish produced or often called the Feed Conversion Ratio (FCR); and (3) the average

purchase price of feed on fish harvest period is used as the basis for calculating the cost of production as well.

This research is applied research that uses data archive aquaculture companies. The results in the observed period,

the value FCR is 0.80, which means to fish have been consumed as much as 0.80 kg of fish feed. Biomass of fish

that are harvested at 47,399.95 kg or 1,869,647 individuals. The average purchase price of fish feed on the

observation period amounted to IDR 4,855.79. So the price of raw materials contained in harvested fish in the

observation period calculates the value of multiplying all the components of raw materials (i.e., IDR

184,131,362.57). Method of calculating the cost of production companies is process method and charging method

used is the full costing method.

Keywords: aquaculture industry, cost of production, feed conversion ratio (FCR), biomass, fish feed

Introduction Management of a business enterprise must have a wide variety of information for decision-making to

achieve the goal. Quite a lot must be considered as a manager in making decisions and one of them is considering the cost of production, where production costs are enough to influence survival of the company mainly in producing products with the purpose of obtaining profits.

Given the form of a company that quite a lot of variety, of patterns used in determining the cost of production at the company is different but basically the same as the sorting costs contained in the products. Likewise with aquaculture companies, management is required to produce a superior product to minimize the cost of production, so that may be obtained profits for the company.

In aquaculture companies, cost of goods is not much different when compared with other manufacturing companies. Aquaculture companies calculate the cost to produce fish with a view of the amount of fish feed

Ilham Hidayah Napitupulu, lecturer, Accounting Department, Politeknik Negeri Medan. Widyo Nugroho, Ph.D., lecturer, Faculty of Economic, Gunadarma University.

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COST OF PRODUCTION AT BUSINESS UNIT IN AQUACULTURE INDUSTRY

672

consumed by fish that are kept, where the fish feed used in aquaculture companies has two types: (1) wet feed that can be produced in the local farming area, and pellets are produced commercially, so must be purchased from feed mills (Sim et al., 2005); (2) high-quality feed that affect the growth of fish faster. Thus the quality of feed, the price of fish feed, and the fish feed ratio of the amount consumed (feed conversion ratio/FCR) greatly affect the value of the cost of production, the higher the value of production at harvest period, the higher the value of the raw materials used. Djarijah (1998) and Tjakrawijaya (2001) found that the lower the value of production of FCR, the better use of feed, and production costs can be lower. To avoid the high cost of fish feeds resulting from the purchase of feed mills, Sriharti and Sukirno (2003) found that to meet the needs of fish feed can be used local raw materials including agricultural waste using simple technology. The calculation of the cost of production is also direct labor costs and overhead costs (indirect costs) as costs of maintenance done by the company, depreciation on equipment associated with the production, indirect labor cost, and other costs.

Theoretical Review Elements of Cost of Production

Elements of the cost of production are intended to the cost. Prawironegoro and Purwanti (2008) found that the costs are cash, and cash equivalents are sacrificed to produce or obtain goods or services that are expected to obtain benefits or benefits in the future. Mulyadi (2003) found that the cost is the sacrifice of economic resources, measured in units of money, which has occurred or that may occur for a particular purpose. Book of Financial Accounting Standards applicable as basic bookkeeping in Indonesia (Ikatan Akuntan Indonesia [IAI], 2007), the cost is defined as:

All the charges cover both losses and expenses incurred in conducting activities ordinary company. Expenses incurred in the ordinary activities of a company that covers expenses such as cost of goods sold, salaries, and depreciation. These expenses are usually in form of outflow or decrease in assets such as cash immediately (cash equivalents), inventory, and fixed assets.

The elements of the cost of production are: (1) Raw Materials, namely all of the materials that form an integral part of finished products and explicitly

included in the calculation of product cost (Carter & Usry, 2004; Blocher, Chen, & Lin, 2000). In aquaculture companies, which have become so are the fish itself, and therefore in determining the value

of finished goods necessary cost calculations are contained in fish produced. In other industries, the material used is certain it will be made the value of their raw materials without any special calculation except the calculation of inventory valuation. At the company’s aquaculture industry, to determine the value of raw materials required special calculations in order to know how the value of fish produced was for sure. The calculation used is:

Fish Prices = Biomass ! FCR ! Average Purchase Price Feed Biomass production is a calculation based on the number of fish which was initially spread in a cage

(pool), the approximate level of fish that survive are often referred to as survival rate (survival ratio) and size of fish harvested and sold as (Sim et al., 2005).

FCR is the amount of feed consumed fish during the production period compared with a total weight of fish produced (Sim et al., 2005).


673

(2) Direct Labor, labor that converts raw materials into finished products directly and can be reasonably charged to a specific product (Carter & Usry, 2004; Letricia, 1999).

(3) Overhead costs, costs incurred by the business entity to produce a product outside of the cost of raw materials and direct labor costs (Garrison & Noreen, 2007; Blocher et al., 2000).

Calculation Method of Cost of Production There are two methods of collecting fees in determining the cost of production (Garrison & Noreen, 2007;

Hansen & Mowen, 1999; Carter & Usry, 2004): (1) The calculation of costs based on order. In this method, costs of production are collected for specific

orders and production cost per unit is calculated by dividing the total cost of production for those orders with the number of product units in the respective orders.

(2) The calculation of costs based on the process. Cost calculation method based on the process normally used to collect production costs for companies that produce continuously and intended to meet the production of finished goods inventory. In this method, the company’s production activities are determined by the production budget or just a certain time unit of production as well as serving as a base by the production to carry out production.

In calculating the cost of production is known there are two methods of charging fees based on the types of costs, full costing and the method of variable costs (variable costing) (Carter & Usry, 2004; Mulyadi, 2003):

(1) The full costing method is a method of determining the cost of the product by incorporating all components of production costs as the price of goods, including raw materials, direct labor costs, factory overhead, variable overhead costs, and fixed factory.

(2) The method of variable costing is a pricing method that incorporates principal component costs are variable only as an element of cost of goods, including raw material costs, direct labor costs and factory overhead cost variables.

Research Method Research Type

This research is an applied research (Indriantoro & Supomo, 2002), research that aimed at solving practical problems faced by a particular institution or organization which is generally done within government or business, this study will conduct empirical research and archival research. The object of this research is P.T. Aquafarm Nusantara, is engaged in the fishing industry that have several working units, namely Hatchery Unit, Enlargement Unit, Processing Unit, and Feed Mill Factory Unit. Focus on the problem under study is using data cost of production hatchery unit and price calculation of basic seed which has been harvested tilapia.

Data Collection and Analysis Techniques The data used are secondary data obtained directly from the company that became the object of research or

the sample (i.e., the cost of data production), seed production data generated by the unit at the seedling period, the data obtained in 2006. The data analysis used to determine how to calculate the cost of production in the aquaculture industry, in particular by:

(1) Collecting production data, seed data in the form of fish, good fish that feed data feed purchases and use of fish feed and fish feed supply calculations;

(2) Collecting cost data associated with the cost of production, such as labor costs and overhead costs; (3) Conducting an interview about the business viability of aquaculture.


674

Results and Discussion Inventory Cycle Produced Tilapia

The following cycle is a cycle as a whole tilapia from automation business, which represents the flow of seed production of tilapia. The cycle is shown in Figure 1.

Figure 1. Inventory cycle tilapia.

Description of the picture: (1) Hatchery project: Tilapia hatcheries are managed intensively to produce a superior tilapia, ranging

from determining a good parent to a healthy seed must be monitored so as not to cause further delays in production. Hatchery unit is the object of this study, it can be explained again how the cycle of tilapia seed supply is produced until the exit to the growth project, namely, seeds produced from the cultivated parent will be maintained or moved to a place that has been provided, after a time will be moved to growth project. The time required for + three months or achieving proper seed crop size + 20 grams to 30 grams.

(2) Growth project: This growth project is a continuation of the cycle of tilapia supplies to be raised during the breeding units + six months or has reached a decent size + average harvest 800 grams, after which it flows the fish will be diverted to the factory project (Cold Storage Project).

(3) Factory project: The factory unit is the end of the supply of tilapia that will be processed into products that are expected by the market, namely the Meat Frozen Tilapia.

Calculate the Cost of Tilapia That Came Out/Harvest Total supply of fish feed used will be a total inventory value of tilapia, it has become a sure thing, and

after a certain time, tilapia stocks will be harvested or will be transferred to growth units. This company obtained the fish feed by purchasing the commercial feed mill. Calculating the value of inventories of harvested tilapia, should know the components that affect the calculation.

Calculation of raw materials or of harvested fish stocks. To determine the value of the raw materials contained in harvested fish stocks need to be calculated Nila the following elements:

Table 1 Biomass Harvest Species Total tail fish Average weight (gram) Biomass Mersi 0 0 Gift 8,372 29.32 245.47 Mernia 26,573 25.21 669.96 Nifi 2,800 38.22 107.02 White Aurea 2,860 34.43 98.47 Nero GS 0 0 0 Giftsi 1,829,042 25.30 46,279.03 Total 1,869,647 25.35 47,399.95

Hatchery (Hatchery project)

Growth (Grow out project)

Factory (Cold storage

project) Market


675

(1) Biomass and total of fish harvested tilapia. Biomass is the weight of tilapia harvested in a month which is calculated in kilograms and the amount of seed harvested fish tail. The data obtained in August 2006, counted 1,869,647 harvest tilapia fish weighing a total of 47,399.95 kg, average weight of 25.35 grams of fish, as seen in Table 1.

(2) FCR Tilapia. To calculate the value of fish feed conversion (FCR) is required data as shown in Tables 2 and 3.

Table 2 Number of Planted Seed Tilapia Species Planted Planting

N (Tnm) W (Tnm) BIM (Tnm) Time Nifi 11,750 0.77 9 27 Gtgt 18,000 8.00 144 18 Msnf 44,626 9.43 421 35 Giftsi 3,142,371 4.21 13,215 53 Total 3,216,747 4.29 13,789 53

Table 3 Seed Production in Growing Species Big size Small size

N (bs) W (bs) BIM (bs) ADG UB (%) N (ss) W (ss) BIM (ss) SR (%) Nifi 2,800 38.21 107 1.41 24 0 - 0 24 Gtgt 14,372 29.29 421 1.22 80 0 0 0 80 Msnf 31,785 25.01 795 0.45 71 12,000 8.00 96 98 Giftsi 2,016,606 28.30 57,068 0.46 64 666,500 8.12 5,412 85 Total 2,065,563 28.27 58,391 0.46 64 678,500 8.12 5,508 85

Table 2 shows the number of tilapia seed to be planted or spread over a period of observation and Table 3 shows the description of the total tilapia seed production during the late harvest period by seeding unit. BIM (big size) and BIM (small size) is the amount of production growth totally 63,899 kg. Furthermore, to calculate the value of FCR of fish harvested in the period of data required the use of feed, as shown in Table 4.

Table 4 The Use of Feed and FCR

Species The used of feed

NSA (2) NGA 10 (3) 788 (2) NS A-2 (C) 788 (3) PSC-22 Total FCR Nifi 134 79 75 14 - - 302 2.82 Gtgt - 268 - - - - 268 0.64 Msnf 345 547 - 91 - - 983 1.10 Giftsi 8,967 26,741 4,831 8,146 351 319 49,355 0.79 Total 9,446 27,635 4,906 8,251 351 319 50,908 0.80

FCR is the ratio between the amounts of feed used on the amount of fish produced in the harvest period. Sample period of observation in August 2006, the feed used by as many as 50,908 kg, weight of seeds during the growth was estimated 63,900 kg, the FCR of the hatchery project in the month of August 2006 amounted to 50,908 kg : 63,899 kg = 0.80 (rounded to two decimal).


676

(3) The average price of feed. The average price of feed derived from a list or inventory report fish feed in a month, the average purchase value per kilogram of seed of all types of feed tilapia. In August, 2006 (observation period) the total purchase as many as 42,330 kg of feed with total purchases amounting to IDR 205,545,511.48, it can calculate the average purchase price of seed food in August 2006 amounted to IDR 4,855.79 per kilogram (IDR 205,545,511.48 : 42,330 kg).

Wage labor. Wage labor is one component of costs to calculate the cost of fish seed overall. Wage labor is the direct labor. Direct labor includes salary, overtime pay, production bonuses, and table money. Provision of basic salary and overtime pay has become imperative for companies to comply with the government, namely by following the minimum wage in North Sumatera Province, for production bonuses granted to employees each month is calculated from the amount of production that resulted. In the period of observation in this study the total direct labor wages paid to employees amounted to IDR 156,081,397.00.

Overhead costs. Overhead costs in the cost of production of tilapia are the cost incurred other than the price of raw materials and direct labor. Overhead costs are accounted for and recognized by the company consists of indirect labor, electricity costs, water costs, the cost of gas, cost of supplies, maintenance costs, insurance premium costs, depreciation costs, rental fees, licensing fees, the cost of business travel accommodation, transportation costs, and other costs incurred in addition to an existing cost component. Of all the components of the overall production costs will become an addition to the cost of production value of fish harvested. Total overhead expenses in the period of observation in August 2006 amounted to IDR 338,152,632.08.

Cost of Production Analysis From the results, it can be calculated that the cost of production of fish harvested in the period of

observation in accordance with the elements contained in the calculation of the cost of production as follows: (1) Raw Materials: FCR = 0.80; Total weight of seed sent = 47.399.95 kg; Total weight of seed sent = 1,869,647 tail; Average purchase price of feed = IDR 4,855.79. The value of raw materials on the seeds that were harvested tilapia at: The raw materials of fish harvested = Biomass ! FCR ! Average purchase price of feed = 47,399.95 kg !

0.80 ! IDR 4,855.79 = IDR 184,131,362.57. The price of fish per head is IDR 98.48, and the price per kilogram of IDR is 3,884.63. (2) Direct labor costs in the period of observation amounted to IDR 156,081,397.00. (3) Overhead costs on the observation period amounted to IDR 338,152,632.08. (4) Total cost of production of fish that are harvested: Raw materials = IDR 184,131,362.57; Direct labor = IDR 156,081,397.00; Overhead costs = IDR 338,152,632.08; Total cost of production = IDR 678,365,391.65. The results of research conducted showed that companies use the cost method with process method, the

company does not accept orders from outside parties, but in accordance with the budget of the fish producing


677

companies that want to produce as much as possible in order to achieve the needs of raw materials in fish processing factories, while charging method used is the method of loading the full costing.

The percentage of the cost of materials contained in cost of production reached 27.14%, labor costs amounted to 23.01%, and overhead costs amounted to 49.85%. To avoid the increased costs of raw materials, it is worth noting that the FCR of fish are harvested, the higher the value of FCR production at harvest period, the higher value of raw materials, it is to be considered by the management is to minimize the consumption of fish feed when the fish have been feasible harvest, a decent harvest of fish weighing between 20 grams and 30 grams. Djarijah (1998) and Tjakrawidjaja (2001) found that the lower the value the better use of production of feed and production costs would decrease. While Christensen (1989) asserted that had the highest FCR value standard as follows:

(1) FCR above 4.5 is low; (2) FCR ranging from 2 to 4.5 is high quality; (3) FCR up to 1 indicates very good quality feed, to boost growth of 1 kg of fish, fish eat only needed one

kg of fish feed. The experimental results show that the value 0.80 can be considered to be very good, where to raise the

growth of fish weighing 1 kg is required to consume only 0.80 kg. If the calculated value of the price of feed consumed weighing 0.80 kg with an average price of feed purchased for IDR 4,855.79, then for each kilogram of fish are harvested worth IDR 3,884.63. For overhead costs are the biggest cost in the cost of production, where the period of observation there are maintenance costs for fish cages, therefore the values are quite large. To avoid a buildup of overhead costs in a particular month, the management plans to invest not only focused on a particular month.

In manufacturing companies and aquaculture industries, the cost of production is needed by the leadership in making decisions on business continuity in the product, for the purpose of determining the cost of production are:

(1) As a basis for determining the price of the product; (2) As a cost control; (3) As a basis for planning and performance measurement company; (4) To calculate the gross profit or loss on a particular period; (5) Determining the cost of products and product process presented at the balance sheet.

Conclusions (1) There is a difference between the calculation of the cost of production in the fishing industry and other

manufacturing industries, which distinguishes in determining the value of the raw materials contained in the products. If the manufacturing industry in general, calculates the raw materials in accordance with the value of the incoming raw materials without any special calculations, but the fishing industry determines the value of the raw materials of the products in its own calculation, namely:

a. Determining the biomass of fish harvested; b. Determining the value of the fish consumed the feed conversion; c. Calculates the average purchase price of food at harvest period. (2) To avoid a high increase in raw materials, the management must be able to minimize the value of


678

production, how to minimize the use of fish feed on fish that have decent harvest, the reason is that a decent harvest of fish no longer need to consume excess food for growth, but feed consumption serve only to preserve life during the waiting time of harvest.

(3) FCR decrease in value was due to good quality feed, whereby the better the feed consumed, it will accelerate the growth of fish. Rapid growth of fish will minimize amount of feed consumed by fish, so the cost of feed would be small.

(4) Other cost elements that existed at the cost of production calculations are not experiencing the difference between other manufacturers with the aquaculture industry, namely the cost of direct labor and overhead costs, whereas the cost of goods used, method of cost distribution processes, and methods of using full cost method.

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Singkep. Jakarta: LIPI, Pusat penelitian Biologi.

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HG,(F,/G"+(")(T&%1(&%+([#&%1(W>??CY(0E(&%(,M.,'',%/(."%/$0V#/0"%2(V#/(0/(+0+(%"/(+,&'(N0/G(/G,(,M"1,%"#E(,%L0$"%F,%/&'(L&$0&V',E(&%+(%"0E,EA(

Stage Two: Adjust the Original Input Variables by the Exogenous Environmental Variables 3%( /G,( E,."%+( E/&1,2( /G,( 0%P#/( E'&.hE2( /G,( "#/."F,E( )$"F( /G,( )0$E/( E/&1,2( &$,( $,1$,EE0%1( &1&0%E/( &( E,/( ")(

,%L0$"%F,%/&'( L&$0&V',EA( O,.&#E,( ")( /G,( $,1$,EE0"%2( /G,( "$010%&'( 0%P#/( L&$0&V',E( &$,( /G,%( &+^#E/,+( V=( /G,(,M"1,%"#E(,%L0$"%F,%/&'(L&$0&V',EA(-.."#%/0%1()"$(,%L0$"%F,%/&'(,)),./E(&%+(E/&/0E/0.&'(%"0E,(0%(."E/(*&'FJ#0E/(P$"+#./0L0/=(0%+,M2(/G0E(E/&1,(0E(/G,(E&F,(&E(/G,(E/".G&E/0.()$"%/0,$(&%&'=E0E(W4Z-YA(3/(.&%(V,(EG"N%(&E](

I"E/(D))0.0,%.=(i(F&%&1,$0&'(,))0.0,%.=(k(%"%:+0E.$,/0"%&$=(0FP&./E(k($&%+"F(%"0E,(

W Y0+/ +/ ; < = (

-+^#E/,+( WF&%&1,$0&'Y( ."E/( ,))0.0,%.=( 0E( l W Y m0+/ +/ ; < = A( HG,( 0%P#/( E'&.hE( EG"N%( 0%( /G,(

DJ#&/0"%( W8Y( N,$,( "V/&0%,+( )$"F( /G,( I*( 0%( /G,( )0$E/( E/&1,A( HG,( E'&.h( L&$0&V',( ."#'+( V,( /G,( $,E#'/E( ")(

,%L0$"%F,%/&'()&./"$E2($&%+"F(,$$"$E2(&%+(F&%&1,F,%/(,$$"$EA(

?>2 >2 >21 ? ? ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( W8Y(

NG,$,(?>2(i(/G,(>/G(0%P#/(")(/G,(2/G(d*Uj(1>2(i(/G,(>/G(0%P#/(E'&.h(")(/G,(2/G(d*UA(HG,(."%.,P/E(&%+(0+,&E(")(E/".G&E/0.()$"%/0,$(P$"+#./0"%(&PP$"&.G(+,L,'"P,+(V=(O&//,E,(&%+(I",''0(W6C99Y(

."#'+(V,(0%/$"+#.,+A(

W 2 Y> >>2 2 >21 @ A / ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( W7Y(

>2 >2 >2/ 4 B ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( W;Y(

NG,$,](1>2(i(/G,(>/G(0%P#/(")(/G,(2/G(d*Uj(A2(i(/G,(,M"1,%"#E(L&$0&V',Ej(>(i(#%h%"N%(P&$&F,/,$Ej(4>2(i(/G,($&%+"F(,$$"$(")(>/G(0%P#/("%("#/P#/(")(/G,(2/G(d*Uj(B>2(i(/G,(/,.G%0.&'(0%,))0.0,%.=2(%"%%,1&/0L,(,$$"$(")(>/G(0%P#/("%("#/P#/(")(/G,(2/G(d*Uj(4>2(&%+(B>2(&$,(0%+,P,%+,%/j(>(i(62(>2(<2(n2(C2(&%+(2(i(62(>2(n2(!A(

3%( DJ#&/0"%( W7Y2( /G,( W 2 Y> > >2 2@ A A ( 0E( &( )$"%/0,$( P$"+#./0"%( )#%./0"%A( -EE#F,( >o W?2 Y>2 =>4 : (

&%+( >2B ( /"(V,(E,/(&E(/$#%.&/0"%E(&/(Q,$"(")( >W 2 Y>D>: D A(HG,(P&$&F,/,$E(")( p> 2( >p=> 2( p

> 2(&%+( >pD> ( &$,(/G,(,E/0F&/"$E( ")( > 2( >

=> 2(> 2( &%+( >

D> ( WO&//,E,(X(O$".&2( 6CCBj(IG&$%,E2( I""P,$2(X(\G"+,E2( 6CB9j( K&$h(X(

40F&$2( 6CC8YA( d#,( /"( /G,( ,E/0F&/"$( ")( >2B ( 0E( pl q m>2 >2/ B / 2( /G,( ,E/0F&/"$( ")( >24 ( ."#'+( V,( +,/,$F0%,+( V=(

)"''"N0%1(DJ#&/0"%(WBY](p ppl m l m>

>2 >2 >2 2 >2 >2/ 4 / 1 A / B / ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( WBY(

HG,(0%P#/E(")(2/G(d*U(."#'+(V,(&+^#E/,+(V=(/G,()"''"N0%1(DJ#&/0"%(W9Y](

D4H3*-H35`(H[D(H[\DD:4H-`D(I_4H(*-R*fU34H(K\_dUIH3@3HT(35dDg( ;9<

r p pp plF&M W Y m lF&M W Y m> >>2 >2 2 2 2 2 >2 >2? ? A A 4 4 ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( W9Y(

Z0%&''=2(/G,(/,.G%0.&'(,))0.0,%.=(N0''(V,("V/&0%,+()$"F(/G,(&+^#E/,+(0%P#/E( r>2? 2(&%+("#/P#/(V=(0%/$"+#.0%1(

/G,(/$&+0/0"%&'(I*A(

Stage Three: Estimate the Adjusted Cost Malmquist Index by Reusing the Adjusted Inputs Z0%&''=2(&+^#E/,+(0%P#/(L&$0&V',E(P$"+#.,+(V=(/G,(E,."%+(E/&1,(&$,($,#E,+(0%(/G,(F,&E#$,F,%/(")(/G,(."E/(

*&'FJ#0E/(0%+,M(WI*YA(HG,($,&'0/=(")(I*(N"#'+(V,("V/&0%,+(V=($,F"L0%1(/G,(,M"1,%"#E(,%L0$"%F,%/&'(%"0E,(0%(/G,(/G0$+(E/&1,A(

The Source and Characteristics of Data Set HG,(E/#+=(0E('0E/,+(0%(/G,(P,$0"+(")(5&/0"%&'(d,L,'"PF,%/(K'&%2(sIG&'',%10%1(>??9t2()$"F(>??8(/"(>??B2(

&%+(/G,(E&FP',E(0%.'#+,(6<()0$FE(0%(/G,('0E/,+(E/".h(F&$h,/(&%+(67()0$FE(0%(/G,("L,$:/G,:."#%/,$(E/".h(F&$h,/A(-''($&N(+&/&(&$,()$"F(H&0N&%(D."%"F0.(!"#$%&'(d&/&(O&%h(WHD!YA(HN"("#/P#/E2(%,/($,L,%#,(WE6Y(&%+(F&$h,/(L&'#,(WE>Y2(&%+(/G$,,(0%P#/E2('&V"$E(W?6Y2(.&P0/&'(W?>Y2(&%+(F&/,$0&'E(&%+(P#$.G&E,(W?<Y(&$,(&+"P/,+(&%+(&.."#%/,+(V=(/G0E(E/#+=A(HG,(0%P#/(P$0.,E(")(?62(?>2(&%+(?<(&$,($,EP,./0L,'=(EG"N,+(&E(-F2(-!2(&%+(-,A(HG,(L&$0&V',E(")("#/P#/E2(0%P#/E2(&%+(0%P#/(P$0.,E(&$,(EP,.0)0,+(&%+(+,E.$0V,+(V,'"NA(

W6Y(_#/P#/E](5,/($,L,%#,(WE6Y(0E(+,)0%,+(&E(/G,(L&'#,(&..$#,+()$"F(&()0$FcE($,1#'&$(V#E0%,EE(&./0L0/0,E2(E#.G(&E(E,''0%1(/G,(1""+E(&%+(P$"L0+0%1(E,$L0.,EA(*&$h,/(L&'#,(WE>Y(")(&()0$F(0E(+,)0%,+(&E(/G,(/"/&'(L&'#,(")(."FF"%(E/".hE(/$&+,+(0%(/G,(E/".h(F&$h,/2(E#11,E/0%1(G"N(&(."FP&%=(0E(L&'#,+(V=(0/E(E/".hG"'+,$EA(

W>Y( 3%P#/E( &%+( 0%P#/( P$0.,E](O,.&#E,( /G,(OXOK( 0%+#E/$=( 0%(H&0N&%( G&E( /G,( P$"P,$/0,E( ")( G01G( ,%/$&%.,(V&$$0,$E2(G01G(F&/,$0&'(L&'#,:&++,+(),&/#$,E("$('"N(F&/,$0&'(."E/E2(G01G(/,.G%"'"1=:"$0,%/,+(),&/#$,E(&%+(G01G(\Xd(,MP,%+0/#$,E2(0/(0E(&EE#F,+(0%(/G,(P$,E,%/(E/#+=(/G&/(."%/&0%E(F#'/0P',(0%P#/E2('&V"$E(W?6Y2(.&P0/&'(W?>Y(&%+(F&/,$0&'E(&%+(P#$.G&E,(W?<YA(-F2(-!2(&%+(-,($,EP,./0L,'=($,P$,E,%/(/G,(0%P#/(P$0.,E(")(?62(?>2(&%+(?<A(HG,E,(0%P#/E(&%+(/G,0$(P$0.,E(&$,(+,E.$0V,+(V,'"NA(

&A(R&V"$E(W?6Y(&%+(0/E(0%P#/(P$0.,(W-FY](Z"''"N0%1(IG0#(,/(&'A(W>??<Y2(/G,(0%P#/('&V"$(W?6Y(0E(+,)0%,+(&E(/G,(%#FV,$(")(,FP'"=,,E(0%(&()0$FA(

VA(I&P0/&'( W?>Y(&%+( 0/E( 0%P#/(P$0.,( W-!Y](HG,( 0%P#/(.&P0/&'( W?>Y( 0E(+,)0%,+(&E( /G,(L&'#,(")( )0M,+(&EE,/E(&E($,."$+,+(0%(/G,(V&'&%.,(EG,,/(")(&%(&%%#&'($,P"$/A(Z"''"N0%1(IG0#(,/(&'A(W>??<Y2(/G,(0%P#/(P$0.,(")(.&P0/&'(W-!Y(0%(H&0N&%(OXOK(0%+#E/$=(0E("V/&0%,+(L0&(+0L0+0%1(/G,(+,P$,.0&/0"%(&%+(0%/,$,E/(,MP,%E,E(V=(/G,(V""h(L&'#,(")(/G,(PG=E0.&'(&EE,/E2("$()0M,+(&EE,/EA(

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H&V',(>(G"(I&>J3>="*13H3>(3>I(*L#*.H>$H5*NON-*C5'D(3&#*@%&*3K"*.>7"*-"&>%'*PQQRSPQQT*

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Conclusions

HG,(E/#+=(&+"P/E(/G,(&PP$"&.G(0%/$"+#.,+(V=(T&%1(&%+([#&%1(W>??CY(/"(,E/0F&/,(V"/G(0%P#/:"$0,%/,+(W3*Y(&%+(."E/(*&'FJ#0E/(WI*Y(P$"+#./0L0/=(0%+,M(&%+(/G,0$(."FP"%,%/E(")(>9(H&0N&%(OXOK(E&FP',()0$FE()"$(/G,(/0F,(P,$0"+(")(5&/0"%&'(d,L,'"PF,%/(K'&%2(sIG&'',%10%1(>??9t2()$"F(>??8(/"(>??BA(3%(/G,(E,."%+(E/&1,(")(/G,(F,/G"+E2( /G,( "$010%&'( 0%P#/( L&$0&V',E( &$,( &+^#E/,+( V=( )"#$( ,M"1,%"#E( ,%L0$"%F,%/&'( L&$0&V',E2( &%%#&'( \Xd(,MP,%+0/#$,(W\dY2(F&$h,/($0Eh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

D4H3*-H35`(H[D(H[\DD:4H-`D(I_4H(*-R*fU34H(K\_dUIH3@3HT(35dDg( ;9B

References O&//,E,2( À( DA2( X( O$".&2( 4A( 4A( W6CCBYA( Z#%./0"%&'( )"$FE( ")( E/".G&E/0.( )$"%/0,$( P$"+#./0"%( )#%./0"%E( &%+(F"+,'E( )"$( /,.G%0.&'(

0%,))0.0,%.=(,)),./E](-(."FP&$&/0L,(E/#+=()"$(NG,&/()&$F,$E(0%(K&h0E/&%A(8%D&5HM*%@*-&%'DI3>=>3#*05HM#(>(W*Y2(<C7:868A(O&//,E,2( À( DA2( X( I",''02( HA( !A( W6C99YA( K$,+0./0"%( ")( )0$F:',L,'( /,.G%0.&'( ,))0.0,%.0,E( N0/G( &( 1,%,$&'0Q,+( )$"%/0,$( P$"+#./0"%(

)#%./0"%(&%+(P&%,'(+&/&A(8%D&5HM*%@*/I%5%7"3&>I(W*ZY2(<9B:<CCA(O&//,E,2(À(DA2(X(I",''02(HA(!A(W6CC7YA(-(F"+,'()"$(/,.G%0.&'(0%,))0.0,%.=(,)),./E(0%(&(E/".G&E/0.()$"%/0,$(P$"+#./0"%()"$(P&%,'(+&/&A(

/7J>&>IHM*/I%5%7>I(W*PQ2(<>7:<<>A(IG&$%,E2( -A2( I""P,$2(eA(eA2(X(\G"+,E2( DA( W6CB9YA(*,&E#$0%1( /G,( ,))0.0,%.=( ")( +,.0E0"%(F&h0%1( #%0/EA(/D&%J"H5* 8%D&5HM* %@*

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Corporate Governance

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+0$,./"$F(0F(/E,(&M,`(")(0%/,$%&'(1"L,$%&%.,(F=F/,H(&%+(0F($,FM"%F0N',(/"(H"%0/"$(&%+(."HM,%F&/,(H&%&1,H,%/A(

*&%&1,$F(&$,(H"$,('0Q,'=(/"(&./(&1&0%F/(FE&$,E"'+,$FP(0%/,$,F/F(OE,%(/E,=(+"(%"/(,&$%(/E,0$(+,F0$&N',(0%/,$,F/F(

\!,%F,%(_(*,.Q'0%12(6B^;]A(XE0F("MM"$/#%0F/0.(N,E&L0"$(")(H&%&1,H,%/( .&%( ',&+( /"( $,+#.,( /E,(L&'#,(")( /E,(

)0$HA(XE,$,)"$,2(/E,(N"&$+PF(F#..,FF(0%(+0F.E&$10%1(0/F()0+#.0&$=(+#/0,F(&%+(H"%0/"$0%1($"',F(O"#'+(N,(M$,+0./,+(

/"(0%.$,&F,(/E,(L&'#,(")(/E,()0$H(&%+(,%E&%.,(/E,(FE&$,E"'+,$FP(O,&'/E(\-N+#''&E2(>??8]A(XE,(M#$M"F,(")(/E0F(

F/#+=(0F(&'F"(/"(,`&H0%,(/E,($,'&/0"%FE0M(N,/O,,%(."$M"$&/,(1"L,$%&%.,(&%+()0$H(M,$)"$H&%.,A(XE,$,)"$,2()"#$(

H&0%( .E&$&./,$0F/0.F( )"$( &( 1""+( N"&$+( ")( +0$,./"$F( /E&/( &$,( ")( 0%/,$,F/( 0%( /E0F( F/#+=( &$,( $,'&/,+( /"( N"&$+(

0%+,M,%+,%.=2(DYZ(+#&'0/=2("O%,$FE0M(F/$#./#$,2(&%+(N"&$+(F0J,A(

40%.,( /E,(N"&$+(")(+0$,./"$F( 0F( /E,(H"F/( 0HM"$/&%/(+,L0.,( /"(H"%0/"$( /E,(H&%&1,H,%/2( 0%+,M,%+,%.=(")(

N"&$+(H,HN,$F( N,."H,( &( F01%0)0.&%/( 0FF#,( \-N+#''&E2( >??8]A( C"&$+( 0%+,M,%+,%.=( H,&%F( /E,( M$"M"$/0"%( ")(

0%+,M,%+,%/(%"%:,`,.#/0L,(+0$,./"$F($,'&/0L,(/"(/E,(/"/&'(%#HN,$(")(+0$,./"$FA(3/(0F(&$1#,+(/E&/(N"&$+F(O0/E(/E,(

H"$,(%"%:,`,.#/0L,(+0$,./"$F(O0''(."%/$"'(/E,("MM"$/#%0F/0.(N,E&L0"$(")(H&%&1,$F(&%+(M$"/,./(/E,(FE&$,E"'+,$F(

0%/,$,F/F( N,//,$( /E&%( N"&$+F( O0/E( +,M,%+,%/( H,HN,$F( \a#N&0+&E( ,/( &'A2( >??B]A( 3%( &++0/0"%2( d&E=&( &%+(

*.D"%%,''(\>??7]2(d,E&,%,2(d,(@#=F/2(&%+(Z"1E,(\>??6]()"#%+(&(F01%0)0.&%/(M"F0/0L,($,'&/0"%FE0M(N,/O,,%(/E,(

$&/0"(")(0%+,M,%+,%/(+0$,./"$F(&%+($,/#$%("%(,S#0/=(\[ZY](&H"%1(C,'10&%(."HM&%0,FA(

-%"/E,$(.$#.0&'(H"%0/"$0%1(H,.E&%0FH(N&F,+("%(&1,%.=(M,$FM,./0L,(0F(/E,(F,M&$&/0"%(")(/E,($"',F(")(DYZ(

)$"H(.E&0$H&%(\!#+1,2(5&"#H"L&2(_(b"#/J,L"'2(>??<]A(gE,%(/E,$,(0F(%"(F,M&$&/0"%2(/E,(DYZ(&'F"(F,$L,F(&F(

.E&0$H&%A(XE0F(F0/#&/0"%2(Q%"O%(&F(UDYZ(+#&'0/=W2(0F(M$"N',H&/0.()$"H(&%(&1,%.=(M,$FM,./0L,(OE,$,(/E,(DYZ(

.E&0$F(/E,(1$"#M(")(M,"M',(0%(.E&$1,(")(H"%0/"$0%1(&%+(,L&'#&/0%1(/E,(DYZPF(M,$)"$H&%.,A(3%(."HM&%0,F(O0/E(

DYZ(+#&'0/=(&MM$"&.E2(/E,(.$#.0&'(S#,F/0"%(0F(UOE"(H"%0/"$F(H&%&1,H,%/VW("$(UOE"(O0''(O&/.E(/E,(O&/.E,$FVW(

\a#N&0+&E(,/(&'A2(>??B]A(XE0F(F0/#&/0"%(M$"L0+,F(DYZF(O0/E(/E,("MM"$/#%0/=(/"(E&L,(&(+"H0%&%/(0%)'#,%.,("%(/E,(

N"&$+PF(+,.0F0"%FA(XE,$,)"$,2(DYZ(+#&'0/=(O0''(O,&Q,%(N"&$+PF(0%+,M,%+,%.=(&%+(H&Q,(/E,H(#%&N',(/"(H"%0/"$(

H&%&1,H,%/(,)),./0L,'=A(

3%(&++0/0"%2(/E,(N&F0.(M$"N',H(+0F.#FF,+(0%(&1,%.=(/E,"$=(0F(/E,(F,M&$&/0"%(")("O%,$FE0M()$"H(."%/$"'(&%+(

+0)),$,%/(H,.E&%0FHF(&$,( F#11,F/,+( /"(H0/01&/,( /E,(."F/F( &FF".0&/,+(O0/E( /E,(."%)'0./(")( 0%/,$,F/F( &H"%1( /E0F(

F,M&$&/0"%( \-'N,$/"2(I0%+&+"2(_(DE&N,'&2( >??7]A(gE,%( /E,(N"&$+(")(+0$,./"$F("O%F(M&$/(")( /E,( )0$HPF( FE&$,2(

/E,0$( 0%/,$,F/F( &'01%( /E,( 0%/,$,F/F( ")( "/E,$( FE&$,E"'+,$F( &%+( /E,=( &$,( ',FF( '0Q,'=( /"( ,%1&1,( 0%( "MM"$/#%0F/0.(

N,E&L0"$( \a#N&0+&E( ,/( &'A2( >??B]A( XE,$,)"$,2( 0/( .&%( N,( ."%.'#+,+( /E&/( +0$,./"$FP( "O%,$FE0M( E&F( &( %,1&/0L,(

$,'&/0"%FE0M(O0/E(&1,%.=(."%)'0./F(&%+2(&F(&(."%F,S#,%.,2(&(M"F0/0L,($,'&/0"%FE0M(O0/E()0$H(M,$)"$H&%.,A(C"&$+(

F0J,($,),$F(/"(/E,(%#HN,$(")(+0$,./"$F("%(/E,(N"&$+A(DE,%1(\>??9](0%(E0F(&$/0.',(F#11,F/,+(/E&/('&$1,$(N"&$+F(&$,(

',FF(,))0.0,%/(&%+(F'"O,$(0%(+,.0F0"%:H&Q0%12(N,.&#F,(0/(0F(H"$,(+0))0.#'/()"$(/E,()0$H(/"(&$$&%1,(N"&$+(H,,/0%1F(

&%+()"$(/E,(N"&$+(/"($,&.E(&(."%F,%F#FA(c,(&'F"(&$1#,+(/E&/(OE,%(/E,(N"&$+(F0J,(0F(N011,$2(0/(O0''(N,(,&F0,$()"$(

DYZ( /"( E&L,( &( +"H0%&%/( "%( /E,( N"&$+( &%+( 0%.$,&F,( /E,( DYZ( M"O,$( 0%( +,.0F0"%:H&Q0%1( \!,%F,%2( 6BB<]A( 3%(

&++0/0"%2( F"H,(F/#+0,F(+".#H,%/(&(%,1&/0L,(&FF".0&/0"%(N,/O,,%(N"&$+(F0J,(&%+( )0$H(M,$)"$H&%.,( \h,$H&.Q2(

6BB;R(Y0F,%N,$12(4#%+1$,%2(_(g,''F2(6BB9]A(

Firm Performance

YHM0$0.&'( $,F,&$.E,F( "%( ."$M"$&/,( 1"L,$%&%.,( #F,( ,0/E,$( H&$Q,/:N&F,+( H,&F#$,F( "$( &.."#%/0%1:N&F,+(

H,&F#$,F(/"(&FF,FF()0$H(M,$)"$H&%.,A(b',0%(\6BB9](#F,+($,/#$%("%(&FF,/F(\[Z-](&%+(i"(\>??<](#F,+([ZY(&F(&%(

"M,$&/0%1( M,$)"$H&%.,( 0%+0.&/"$A( C$"O%( &%+( D&='"$( \>??7]( #F,+( [ZY( &%+( [Z-( &F( /E,0$( /O"( "M,$&/0%1(

CZ-[d(Dc-[-DXY[34X3D4(-5d(G3[*(IY[GZ[*-5DY(;B?(

M,$)"$H&%.,(H,&F#$,FA(g,( .&%(H,&F#$,( /E,( "M,$&/0%1( M,$)"$H&%.,( ")( &( )0$H( /E$"#1E( /E,([Z-( $&/0"(OE0.E(

FE"OF( /E,( &H"#%/( ")( ,&$%0%1F( E&L,( 1,%,$&/,+( )$"H( &%( 0%L,F/,+( .&M0/&'( &FF,/F( \YMMF( _( D,$,"'&2( >??9]A(

*&%&1,$F(&$,(+0$,./'=($,FM"%F0N',()"$(/E,("M,$&/0"%F(")(/E,(N#F0%,FF(&%+(/E,$,)"$,(/E,(#/0'0J&/0"%(")(/E,()0$HFP(

&FF,/FA(XE#F2([Z-(&''"OF(#F,$F(/"(&FF,FF(E"O(O,''(&()0$HFP(."$M"$&/,(1"L,$%&%.,(H,.E&%0FH(0F(0%(F,.#$0%1(&%+(

H"/0L&/0%1(,))0.0,%/(H&%&1,H,%/(")(/E,()0$HA(3%(/E,(M$,F,%/(F/#+=2([Z-(0F(+,)0%,+(&F(%,/(0%."H,(N,)"$,(0%/,$,F/(

,`M,%F,( )"$( /E,( )0F.&'( M,$0"+( +0L0+,+( N=( /"/&'( &FF,/F( )"$( /E&/( F&H,( M,$0"+A( Z%,( ")( /E,( M$0H&$=( $,&F"%F( )"$(

"M,$&/0%1( &( ."$M"$&/0"%( 0F( /"( 1,%,$&/,( 0%."H,( )"$( /E,(N,%,)0/( ")( /E,( ."HH"%( F/".QE"'+,$F( \YMMF(_(D,$,"'&2(

>??9]A( [ZY( 0F( &( H,&F#$,( /E&/( FE"OF( &%( 0%L,F/"$( E"O( H#.E( M$")0/( &( ."HM&%=( 1,%,$&/,F( )$"H( /E,( H"%,=(

0%L,F/,+()$"H(0/F(FE&$,E"'+,$FA(3%(/E0F(F/#+=2([ZY(0F(+,)0%,+(&F(/E,(0%."H,(N,)"$,(0%/,$,F/(,`M,%F,()"$(/E,()0F.&'(

M,$0"+(+0L0+,+(N=(/"/&'(FE&$,E"'+,$FP(,S#0/=()"$(/E&/(F&H,(M,$0"+A(

Theoretical Framework

C&F,+("%( /E,( '0/,$&/#$,2( )"#$(N"&$+(.E&$&./,$0F/0.F(E&L,(N,,%( 0+,%/0)0,+(&F(M"FF0N'=(E&L0%1(&%( 0HM&./("%(

)0$H(M,$)"$H&%.,(&%+(/E,F,(.E&$&./,$0F/0.F(&$,(F,/(&F(/E,(0%+,M,%+,%/(L&$0&N',F(0%(/E,()$&H,O"$QA(XO"(."%/$"'(

)&./"$F2(',L,$&1,2(&%+()0$H(F0J,2(&$,(0%.'#+,+(0%(/E,(/E,"$,/0.&'(H"+,'(+,F01%,+()"$(/E0F(F/#+=A(XE,F,()&./"$F(E&L,(

N,,%( Q%"O%( /"( E&L,( &%( 0HM&./( "%( )0$H( M,$)"$H&%.,2( &%+( E,%.,( %,,+( /"( N,( ."%/$"'',+( 0%( /E,( F/#+=A( XE,(

+,M,%+,%/(L&$0&N',F(&$,(/E,([Z-(&%+([ZY2(OE0.E(&$,(#F,+(/"(H,&F#$,(/E,()0$H(M,$)"$H&%.,A(XE,($,'&/0"%FE0M(

N,/O,,%(,&.E(")(/E,F,(0%+,M,%+,%/(L&$0&N',F(&%+()0$H(M,$)"$H&%.,(&$,(E=M"/E,F0J,+(&F()"''"OFT(

c6T(XE,$,(0F(&(M"F0/0L,($,'&/0"%FE0M(N,/O,,%(/E,(M,$.,%/&1,(")(0%+,M,%+,%/(%"%:,`,.#/0L,(+0$,./"$F(&%+(/E,(

)0$H(M,$)"$H&%.,A(

c>T(XE,$,(0F(&(%,1&/0L,($,'&/0"%FE0M(N,/O,,%(DYZ(+#&'0/=(&%+(/E,()0$H(M,$)"$H&%.,A(

c<T(XE,$,(0F(&(M"F0/0L,($,'&/0"%FE0M(N,/O,,%(+0$,./"$FP("O%,$FE0M(&%+(/E,()0$H(M,$)"$H&%.,A(

c8T(XE,$,(0F(&(%,1&/0L,($,'&/0"%FE0M(N,/O,,%(N"&$+(F0J,(&%+(/E,()0$H(M,$)"$H&%.,A(

Sample Selection

G$"H(/E,(UD"%F/$#./0"%(_(*&/,$0&'FW(0%+#F/$=2(/E,(N011,F/(0%+#F/$=2("%(/E,(H&0%(N"&$+(")(C#$F&(*&'&=F0&2(

<?(."HM&%0,F(&$,($&%+"H'=(F,',./,+A(XE,(."%L,%0,%/(F&HM'0%1(/,.E%0S#,(0F(&'F"(&MM'0,+(0%(/E0F(F/#+=2(OE,$,(/E,(

&L&0'&N0'0/=( ")( /E,( &%%#&'( $,M"$/F( ")( /E,( .E"F,%( ."HM&%0,F( "%( /E,( C#$F&( *&'&=F0&( O,NF0/,( &'F"( M'&=F( &(

+,/,$H0%0%1( $"',( 0%( /E,( 0%.'#F0"%(")( /E,(."HM&%=( 0%( /E,( )0%&'( '0F/A( 3%("/E,$(O"$+F2( ."HM&%0,F( /E&/(E&L,(N,,%(

.E"F,%(N=(/E,($&%+"H(F&HM'0%1()#%./0"%(N#/(+"(%"/(E&L,(&%%#&'($,M"$/F($,&+0'=(&L&0'&N',("%(/E,(C#$F&(*&'&=F0&(

O,NF0/,( &$,( ,'0H0%&/,+( )$"H( /E,( F&HM',( '0F/( &%+( /E,( $&%+"H( F&HM'0%1( /,.E%0S#,( 0F( $,M,&/,+( /"( $,M'&.,( /E,F,(

."HM&%0,FA(XE,(+&/&($,S#0$,+()"$(/E,(M#$M"F,(")(/E0F(F/#+=(0F(."'',./,+()$"H(>??^()0F.&'(=,&$(&%%#&'($,M"$/F(")(

/E,()0$HFA(XE,(+&/&(."'',./0"%(/,.E%0S#,(0F(H&0%'=(H&%#&'(F,&$.E("%(/E,(&%%#&'($,M"$/F(")(/E,(."HM&%0,FA(

Theoretical Model

G"$(/E,(M#$M"F,(")(,HM0$0.&'(&%&'=F0F2(/E0F(F/#+=(#F,F(+,F.$0M/0L,(&%&'=F0F(&%+('0%,&$(H#'/0M',($,1$,FF0"%(&F(

/E,(#%+,$'=0%1(F/&/0F/0.&'(/,F/FA(-(+,F.$0M/0L,(&%&'=F0F(")(/E,(+&/&(0F(."%+#./,+(/"("N/&0%(F&HM',(.E&$&./,$0F/0.FA(

XE,( H#'/0M',( $,1$,FF0"%( &%&'=F0F( 0F( M,$)"$H,+( "%( /E,( +,M,%+,%/( L&$0&N',F2( [Z-( &%+( [ZY2( /"( /,F/( /E,(

$,'&/0"%FE0M(N,/O,,%( /E,( 0%+,M,%+,%/(L&$0&N',F(O0/E()0$H(M,$)"$H&%.,A(X&N',(6(FE"OF( /E,(L&$0&N',F(&%+( /E,0$(

+,F.$0M/0"%( 0%( /E0F( F/#+=A( XE,( $,1$,FF0"%( H"+,'F( #/0'0J,+( /"( /,F/( /E,( $,'&/0"%FE0M( N,/O,,%( /E,( N"&$+(

.E&$&./,$0F/0.F(&%+()0$H(M,$)"$H&%.,(&$,(&F()"''"OFT(

CZ-[d(Dc-[-DXY[34X3D4(-5d(G3[*(IY[GZ[*-5DY( ;B6

? 6 > < 8 7 ;ROE Bind Dual OwnS Bsize Fsize Lev ( ( ( ( ( ( ( ( ( \6](

? 6 > < 8 7 ;ROA Bind Dual OwnS Bsize Fsize Lev ( ( ( ( ( ( ( ( ( \>](

X&N',(6(

Variables and Descriptions *,&F#$,H,%/(d,F.$0M/0"%F(@&$0&N',F(

\5"A(")("#/F0+,(+0$,./"$F]j\X"/&'(5"A(")(+0$,./"$F](k(")(0%+,M,%+,%/(%"%:,`,(+0$,./"$F(Bind 6(l(h,F2(?(l(5"(DYZ(+#&'0/=(Dual \5"A(")("$+0%&$=(FE&$,F("O%,+]j\X"/&'(5"A(")("$+0%&$=(FE&$,F](k(")("#/F/&%+0%1(FE&$,F("O%,+(N=(+0$,./"$F(OwnS 5#HN,$(")(+0$,./"$F("%(/E,(N"&$+(C"&$+(F0J,(Bsize 5&/#$&'('"1(")(/"/&'(&FF,/F(&F($,M"$/,+(0%(>??^(&%%#&'($,M"$/(G0$H(F0J,(Fsize X"/&'(+,N/jX"/&'(,S#0/=(i,L,$&1,(Lev 5,/(0%."H,(m(3%/,$,F/(,`M,%F,(\6( (X&`($&/,]jX"/&'(,S#0/=([,/#$%("%(,S#0/=(ROE 5,/(0%."H,(m(3%/,$,F/(,`M,%F,(\6( (X&`($&/,]jX"/&'(&FF,/F([,/#$%("%(&FF,/F(ROA

(

Finding and Discussion

C&F,+("%(&%&'=F0F(")(+&/&2(;<A<k(")( /E,(."HM&%0,F(."HM'=(O0/E( /E,($,."HH,%+&/0"%F(")( /E,(*&'&=F0&%(

D"+,("%(D"$M"$&/,(f"L,$%&%.,(\*DDf](\>???](N=(F,M&$&/0%1(/E,($"',F(")( /E,(.E&0$H&%(&%+(/E,(DYZ(")( /E,(

."HM&%=A( XE,( ',L,'( ")( +#&'0/=( ")( <;A^k( ")( /E,( F&HM',( +&/&( 0%( /E0F( F/#+=( 0F( E01E,$( /E&%( &( M$,L0"#F( )0%+0%1(

\66A9k(](0%(/E,(*&'&=F0&%(F,//0%1("L,$(&(M,$0"+(")()0L,(=,&$F()$"H(6BB;(/"(>???(N=([&EH&%(&%+(c&%0))&(\>??>]A(

XE,(+,F.$0M/0L,(F/&/0F/0.F(0%(X&N',(>()"$(/E,(0%+,M,%+,%/(L&$0&N',F(0%+0.&/,(/E&/(/E,(&L,$&1,(%#HN,$(")(+0$,./"$F(

"%(/E,(N"&$+(0%(/E,(F,',./,+(."HM&%0,F(0F(&N"#/(,01E/(M,$F"%FA(3/(0F(."%F0F/,%/(O0/E(/E,(F/#+=(N=(a#N&0+&E(,/(&'A(

\>??B]( 0%(C#$F&(*&'&=F0&(N&F,+("%( /E,(+&/&( )$"H(>??<A(XE,(&L,$&1,(M,$.,%/&1,(")( 0%+,M,%+,%/(+0$,./"$F("%(

N"&$+( \8>A;>k]( FE"OF( /E&/( /E,(."HM&%0,F( ."HM'=(O0/E( /E,( $,."HH,%+&/0"%F(")( /E,(*DDf( \>???]( /E&/("%,(

/E0$+( ")( /E,( N"&$+( H,HN,$F( FE"#'+( N,( 0%+,M,%+,%/A( D"%F0F/,%/( O0/E( ,`M,./&/0"%2( /E,( H,&%( )"$( +0$,./"$FP(

"O%,$FE0M(F#11,F/F(/E&/(+0$,./"$F(")(M#N'0.'=('0F/,+()0$HF(0%(*&'&=F0&(1,%,$&''=(E&L,(F0J,&N',("O%,$FE0M(F/&Q,F(

0%(/E,(."HM&%=(."HM&$,+(/"(/E,0$(."#%/,$M&$/F(0%(g,F/,$%(+,L,'"M,+(,."%"H0,F2(F#.E(&F(/E,(4O,+,%(&%+(/E,(

K%0/,+( b0%1+"H( \c"( _(g0''0&HF2( >??<]A( XE0F( 0F( M,$E&MF( +#,( /"( /E,( E01E,$( %#HN,$( ")( )&H0'=:"O%,+( &%+(

H&%&1,+(."HM&%0,F(0%(*&'&=F0&A((

X&N',(>(

Descriptive Statistics ( *0%A( *&À( *,&%( 4/+A(+,L0&/0"%(

C"&$+(F0J,( 7( 67( 9A><( >A>?9(

k(")(0%+,M,%+,%/(+0$,./"$F( >9A;( ;;A^( 8>A;>?( BA<<89(

d0$,./"$F("O%( ?A?( ^8A?( 68A><<( 6Â;B;>(

[Z-( :8A^( >6A>( 7A6B8( 7A98<;(

[ZY( :6>A>( 7>AB( 66A889( 6>A>89^(

i,L,$&1,($&/0"( 7A^( ;99A?( 68BA6;?( 67BAB79<(

iZfX-44( 9A?^( 6?A?9( 9A^>B?( ?A879;<(

(

-(I,&$F"%(."$$,'&/0"%(&%&'=F0F(0F(M,$)"$H,+("%(/E,(L&$0&N',F(/"(.E,.Q()"$(/E,(+,1$,,(")(H#'/0."''0%,&$0/=(

&H"%1( /E,( L&$0&N',F( \X&N',( <]A( YL,%( /E"#1E( /E,$,( &$,( F01%0)0.&%/( ."$$,'&/0"%F( &H"%1( F"H,( ")( /E,( L&$0&N',F2(

CZ-[d(Dc-[-DXY[34X3D4(-5d(G3[*(IY[GZ[*-5DY(;B>(

%"%,(")(/E,(.",))0.0,%/F(,`.,,+F(?A92(OE0.E(0F(#F,+(&F(&%(0%+0.&/"$(")(F,$0"#F(H#'/0."''0%,&$0/=(\f#n&$&/02(6BB>]A(

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Conclusions

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."%/$"'( L0&( "O%,$FE0M( ."%.,%/$&/0"%( &%+( $,M$,F,%/&/0"%( "%( ."HM&%=( N"&$+( &%+( H&%&1,H,%/A( c"O,L,$2( O,(

FE"#'+(."%F0+,$(/E,('0H0/&/0"%F(")(/E0F(F/#+=(N,.&#F,(FH&''(F&HM',(F0J,(&%+(&(FM,.0&'(0%+#F/$=(H&=(%"/($,%+,$(

/E,( $,F#'/F( ")( /E,( F/#+=( /"( N,( 1,%,$&'0J,+A( 3%( &++0/0"%2( /E,( +#$&/0"%( ")( F/#+=( FE"#'+( N,(H"$,( /E&%( "%,( =,&$(

N,.&#F,(/E,(,)),./(")(0%+,M,%+,%/(L&$0&N',F(O0''(N,(+#$0%1(F#NF,S#,%/(M,$0"+FA(

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"M,$&/0%1(M,$)"$H&%.,V(Critical Perspectives on Accounting, 192(66<7:6689A(f0''&%2(4A(iA(\>??;]A([,.,%/(+,L,'"MH,%/F(0%(."$M"$&/,(1"L,$%&%.,T(-%("L,$L0,OA(Journal of Corporate Finance, 122(<96:8?>A(f#n&$&/02(dA(\6BB>]A(Essentials of econometricsA(40%1&M"$,T(*.f$&O:c0''A(

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c"2( DA( -A2( _(g0''0&HF2( 4A(*A( \>??<]A( 3%/,$%&/0"%&'( ."HM&$&/0L,( &%&'=F0F( ")( /E,( &FF".0&/0"%( N,/O,,%( N"&$+( F/$#./#$,( &%+( /E,(

,))0.0,%.=(")(L&'#,(&++,+(N=(&()0$H()$"H(0/F(ME=F0.&'(.&M0/&'(&%+(0%/,'',./#&'(.&M0/&'($,F"#$.,FA(The International Journal of Accounting, 38\8]2(8;7:8B6A(

!,%F,%2(*A(\6BB<]A(XE,(H"+,$%(0%+#F/$0&'($,L"'#/0"%2(,`0/(&%+(/E,()&0'#$,(")(0%/,$%&'(."%/$"'(F=F/,HFA(Journal of Finance, 48\8]2(9<6:99?A(

!,%F,%2( *A( DA2( _(*,.Q'0%12(gA( cA( \6B^;]A( XE,"$=( ")( /E,( )0$HT( *&%&1,$0&'( N,E&L0"$2( &1,%.=( ."F/F( &%+( "O%,$FE0M( F/$#./#$,A(

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International Journal of Economic and Finance, 1\6]2(67?:6;8A(


Study on Transnational Business Operation Strategy of

China Xinhua News Network

Xiong De Wuhan University of Technology, Wuhan, China

The paper firstly discussed the strategic implication of the establishment of China Xinhua News Network Co. Ltd.

(referred to as CNC in the paper). Then, the paper explored the opportunity and threat in the CNC’s competitive

environment, and evaluated the news TV competitiveness of CNC compared with world main news network media

based on Delphi method. Based on the strategic management framework of SWOT (strength, weakness,

opportunity, and threat), the paper proposed the developmental strategies of transnational business operation for

CNC from the following three area: further expand the coverage and the number of countries entered by CNC,

enhance brand effects across the world, and build more new technology experience in the world.

Keywords: China Xinhua News Network Co. Ltd. [CNC], transnational business operation, SWOT (strength,

weakness, opportunity, and threat)

Introduction In the contemporary world with the highly information-oriented, news media has been an important tool to

reflect and strengthen the country “soft power”, and bear “soft power transmission” task for its function of information transmission and public opinion guide (Elzinga & Hogarty, 1973). It can be said that the ability of international communication is directly related to the shaping and maintenance of the country’s image.

From the international influence of the media, independent television news media, as the most influential form of transmission, plays a critical role in shaping the image of the state, which is the important means to enhance the international influence and international discourse right (United Nations Conference on Trade and Development [UNCTAD], 2005). Especially the creation and transnational dissemination of news television with high-cost, high-input access threshold, therefore, has also become the main tool of the strong states influencing and controlling the international public opinion. So building the wide coverage, advanced technology and international communication and business news television channels, and forming the international communication ability compatible with the level of China’s economic and social development and international status, have become an urgent task (Liu, 1999; Li & Zhou, 2005).

It is in this context, the Xinhua News agency founded China’s first transnational dissemination and business News TV media China Xinhua News Network Co. Ltd. [CNC]. CNC tries to break through the traditional state media business philosophy and taboos, respect the market rules, and rely on the establishment of a modern media organization management system, carry out business activities in accordance with international news television channel operating mode, improve its profitability, and form the economic basis for

Xiong De, Ph.D. candidate, School of Economics, Wuhan University of Technology.

!"#$!% % &'()$*+$,-%

!%

STUDY ON TRANSNATIONAL BUSINESS OPERATION STRATEGY

696

sustainable development. The aim of the paper is to make the transnational business strategy proposals with the strategic

management framework of SWOT (strength, weakness, opportunity, and threat). The reminder of the paper is organized as follows: Section 2 makes analysis on competitive environment, Section 3 evaluates the current competitiveness of CNC compared with other news media, and Section 4 makes development strategies proposals.

Analysis of Competitive Environment of CNC Throughout the world media development trend, the world media structure and its management

environment is occurring unprecedented adjustment and reform, which brought both huge challenges and new opportunities for newborn CNC international operations.

The Development of New Media Provides Broader Market Space for International Communication The emergence of new media, which is represented by network media and hand-held terminal, brought

about a great revolution to the global media industry, and it provides greater market space for International communication.

In the past two years, the most noticeable network media development is the rapid popularization of socialized media, including social network and micro blog. For example, in February 2004, Facebook was formally pushed forward, and it has more than 500 million active users by July 21, 2010 (Shejiao.com, 2010).

The cell phone is the most influential device of hand-held terminals. In recent years, the total amount of cell phone users is expanding rapidly. The main media phone features include: short messaging service (SMS), multimedia messaging service (MMS), mobile phone news, and mobile phone TV, etc., especially with the development and maturity of the mobile phone TV technology, mobile phone TV has more obvious influences on traditional TV because of its portability and timeliness.

The number of new media audiences is growing rapidly, it not only extend the influence of International communication, but also provide more market space for the International communication.

The Global Financial Crisis Change the Strength Contrast of the Chinese and the Foreign Media Under the impact of the financial crisis, many western media management have been severally affected. In

the U.S., for example, since 2008, 200 newspapers and 500 magazines have stopped publication, and several newspaper groups declared bankruptcy or filed for bankruptcy protection. Many famous International media have to resell to others because of their heavy losses, including the well-known “Business Week”. On August 2, 2011, the USA Harman International group bought this old brand for one dollar.

Since China has less financial crisis impact than the developed countries, and it has faster recovery speed than them, global financial crisis has smaller influence on Chinese media industry. As a result, Chinese media industry suffered slight impact. In other words, the financial crisis brought subtle change for the strength contrast between Chinese media and foreign media, which provides new opportunities to Chinese media to improve international business management ability.

The Fallen Cost of International Communication Operation Will Strengthen the Construction of Overseas Positions

In the new media era, people can send his message to every corner of the world by depending on modern communication techniques. In some places where have no reporters, the common people can also gather news


697

information. This will ensure news organizations have the first-hand reports when any place of the world produces major news. In western countries, where labor cost are expensive, free “citizen journalists” create amazing market value.

On the other hand, under the impact of financial crisis, developed countries experienced sever demand atrophy, commodity price decline, rising unemployment rate, which lead to consumption level and labor cost decrease. This situation is very good for the construction of international communications positions, especially good to overseas expansion of network communication, including construct additional overseas branches, absorb overseas collecting-and-categorizing talents, update technology systems and equipment, etc.

Development Space Become More Saturated From Cable News Network [CNN] to CNC, the development of international television news has become

saturated. At present, the well-known multinational news channel has amounted to more than 10. For a person who wanted to know the international news, the existing news channels are beyond the need. In some countries especially in English speaking countries like the U.S. and England, there are dozens of English TV channels can be chosen. From the market supply and demand aspect, it is a fact that supply has exceeded demand in news television channel market. Moreover, private TV channels leading by Chinese news have had a satisfied development, and it also add some pressure to CNC business.

Strict Industrial Regulation The ideology characteristic of culture causes the wary of entry of Chinese media in some western countries

who usually make invisible policy barriers to prevent and exclude Chinese media in quite long time. Even in the seemingly open media industry of USA, the government also makes many restrictions on the entry of foreign media. These ideological barriers have brought a lot of resistance on Chinese media’s operations oversea.

Evaluation of Competitiveness of CNC Establishment of Competitiveness Evaluation Indicator System

This research established news network media competitiveness evaluation system, based on the theory of comprehensive firm competitiveness and media firms’ management (Pan, 2003; Zhang, 2011; Wu, 2009), following the principles of objectivity, feasibility, relativity, and comparability. It reflects the competitiveness of news network media in seven areas as below:

(1) Use of internationally accepted language (defined as A1), which determines the ability of the channel to make sounds in the world. The more the internationally accepted language is used, the higher the localization of the program is realized, the more capable the program is of meeting the different needs of different audiences, and the greater influence the program can achieve in the world.

(2) Coverage area of population and the number of countries and regions entered (defined as A2). In order to have a broad impact in the world, news network media must firstly ensure that the channel meet the audience around the world. The greater coverage the channel signal is made, the larger the number of the potential audience can be achieved, the higher the contact probability with audience becomes, and the higher is the likelihood of producing influence around the world.

(3) News premier (defined as A3), the function of news programs lies in the role of information dissemination and directing public opinion, the greater the amount of news is broadcasted firstly, the more obvious the first-move advantage the news media can achieve, the stronger the first effect of media is realized.


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(4) The ratio of news programs accounted for in to the all programs of the channel (defined as A4), which measures the news productivity of media.

(5) Brand programs (defined as A5) and number of famous presenters (defined as A6), The channel brand effects in terms of the number of brand programs and the number of famous presenters play great role in establishing the channel’s international image and enhancing the channel’s influence in the world.

(6) Profitability (defined as A7), which measures the financial resources of news network media.

Evaluation Method and Evaluation Results “Delphi method” is a structured communication technique, originally developed as a systematic,

interactive market research method which relies on a panel of experts. The maximum score for each indicator in the overall evaluation system is set to be 5 and the minimum score is 1. The “Delphi method” is used to determine the weight coefficient of each indicator. Choosing seven news channel as research sample including three commercial channels and four public channels, we use the “Delphi method” to obtain the score value of each indicator, and calculate the weighted average score of seven news channels. The weights of each indicator and the evaluation results are shown in Table 1.

Table 1 Evaluation Results of Competitiveness of World Main News Media

Classification of channel Ranking Name of channel Weighted

average score

Weights (%) A1 (20)

A2 (20)

A3 (15)

A4 (10)

A5 (15)

A6 (10)

A7 (10)

Commercial 1 CNN 4.55 5 5 4 5 3 5 5 2 BBC World News 3.05 4 3 2 4 3 4 1 3 Phoenix Satellite Info-news Channel 2.05 0.5 1 2 5 3 4 1

Public

1 Deutsche Welle 2.75 4.5 3 3 4 2 1 - 2 France 24 2.5 4 3 2 4 2 1 - 3 Russia Today 2.4 4 1 4 4 2 1 - 4 NHK News 2.3 3.5 2 2 3 2 3 - 5 Jazeera Network 2.25 3.5 1 4 5 1 1 - 6 CNC 2.15 3.5 1 4 5 1 0 -

From the evaluation results in Table 1, it can be concluded the following points: (1) Compared with world-leading news media network, the main weakness of CNS lies in the following

four areas: firstly, the current coverage area of population is rather narrow and the number of countries and regions the CNC has already entered into is quite limited; secondly, the CNC’s ability to produce brand program is weak; thirdly, CNC has shortage of famous presenters, which leads to its low brand effects; fourthly, the lack of profitability cause the CNC’s low financial resource available.

(2) The main strength of CNC is its focus on news production, which will constitutes its core competitiveness.

Transnational Business Operation Strategy Suggestion of CNC From the above competitive environment analysis and competitiveness evaluation of CNC, the SWOT

analysis can be arranged in Table 2. Based on the strategic management framework of SWOT, the transnational business development strategy

is proposed as follows:


699

Table 2 SWOT Analysis of CNC

Strength (S) (1) Use of internationally accepted language; (2) Ratio of news programs accounted for in to the all programs of the channel; (3) The ability to broadcast news firstly.

Weakness (W)

(1) Narrow coverage area of population and limited number of countries entered into; (2) Short of brand program; (3) Short of famous presenters; (4) Lack of profitability.

Opportunity (O)

(1) Development of new media; (2) Global financial crisis change the strength contrast of the Chinese and the foreign media; (3) Fallen cost of international communication operation will strengthen the construction of overseas positions.

Threats (T) (1) Saturated development space; (2) Strict market entry regulation.

Use Various Foreign Channel Landing Modes to Expand the Coverage Area of CNC In short term, in order to strengthen its advantage in news production, considering the saturated market

with strict industrial regulation, it is necessary for CNC to modify its weakness of narrow coverage area of population and limited number of countries entered into. This requires CNC flexibly using various kinds of channels landing modes to enter the foreign market.

Channel lease. In this mode, CNC makes channel lease agreements with foreign channel owner. Under the agreement, CNC pays rent to foreign channel lesser; in return, all CNC programs are permitted to play on this foreign channel, and the foreign channel ownership shall not spot any content except “disaster emergency radio” and “propaganda on their own platform”. Such landing mode requires low cost and ensures the quick entry into these foreign markets.

Copyright sell or subscription-sharing. In this mode, CNC provides programs to foreign operators. Foreign operators can place commercial advertisement at interval in CNC channel in the constraints of contract, and in return, the foreign operators are required to pay royalties or subscriptions fees to CNC party.

The benefit of this model is that foreign operators must increase efforts to promote the CNC in order to make money; the difficulty of this model lies in the operation process. CNC should make suitable contract to restrain the foreign operator’s spot content. Not only must the advertisement inserted by foreign operators be in comply with local laws, but also do not harm the interests of the People’s Republic of China. At the same time, the duration of advertisement inserted need be fixed, generally no more than 12 seconds every hour. In addition, this mode requires little sunk cost and the exposure of risk is low, but it is feasible on the condition that the CNC’s programs seem to attract the audience in foreign target market.

Resource exchange. In this operating mode, the two parties, CNC and foreign channel operators, swap and use resource between each other freely, CNC provides programs without accepting a royalty fee, and foreign channel owners provide broadcasting channel without demanding channel fee; the CNC program is broadcast on the channel throughout the day, but only provide the equipment to the foreign operators which are used to receive satellite signals, and equipment property is owned by CNC. While CNC must make commitment that it not interfere the foreign party’s way to promote CNC, it is necessary to ask the foreign side to put CNC into basic package rather than into charge bags.


700

Establishment of cooperative set. This mode means that CNC has entered local transmission platform by setting cooperation set with local partner. Under this mode, CNC require its partner to play CNC programs in their channel, should fix the duration of program, and should not interfere with the integrity of CNC program. Cooperate partner can replace CNC’s add-in advertisements, but no more than 12s/hr. Also, CNC has the final adjudication about the content of advertisements. The profit sharing is limited to CNC local profits.

Direct purchase channel. With the suitable conditions, CNC can purchase directly the foreign channel. The advantage of this model is obvious: such ownership of foreign channel can definitely form valuable assets in the long-run. CNC has larger decision power in the operation of their programs in foreign target markets. When CNC changes another broadcasting platform in the future, it can resale this channel signal or rent to the third party, so this mode has some flexibility. The difficulties of this model are the high threshold for market access and high operating costs. It applies only to countries with higher market value for development, as well as the important target country.

Build Its Brand Effects Across the World In long term, in order to achieve core sustainable competitiveness, CNC must cultivate and strength its

brand effects across the world in terms of production of brand programs and fostering the famous presenters. Both the cultivation of brand programs and fostering presenters require the large investment of money and the high technology resources. As a result, the CNC should accelerate its restructuring pace into corporation and operate on the rule of market system with the aim of profit maximization. Only with quality of legalized corporation and strong profitability, can CNC have financial resources available for enhancing brand effects.

Build New Technology Experience Across the World In order to seize the opportunity of the development of new media, it is also urgent for CNC to accumulate

the new media experience. CNC can make more breakthrough in the use of new media channels to expand the global spread, for example, adding interactive functionalities of blog or micro blog in the internet news channel or mobile phone news channel, making jointly product research and launch on innovative product terminals like Apple with world terminal producers, etc.

Conclusions The establishment of China Xinhua News Network Co. Ltd. [CNC] is of greatest strategic implication.

Based on the analysis of CNC’s competitive environment, the paper explored the opportunity and threat. On one hand, the major three opportunities lie in the development of new media, the strength building compared with foreign counterpart in the global financial crisis, and also the falling international communication cost. On the other hand, saturated development space and strict market entry restriction constitute two major opportunities.

Based on Delphi method, the paper evaluated the news TV competitiveness of CNC compared with world main news network media. It is found out that the main strength of CNC is its focus on news production, which will constitutes its core competitiveness. And for the weaknesses, they lie in the narrow coverage, lack of brand, and also short of profitability.

Based on the strategic management framework of SWOT, the paper proposed the developmental strategies of transnational business operation for CNC from the coverage expanding, brand effect enhancement and new technology experience building.


701

References Elzinga, K., & Hogarty, F. (1973). The problem of geographic market definition in antimerger suits. Antitrust Bulletin, 18, 45-81. Li, X. G., & Zhou, Q. (2005). Soft power and global communication (pp. 87-89) (in Chinese). Beijing: Qinghua University Press. Liu, J. A (1999). Mass communication and international relation (pp. 162-166) (in Chinese). Beijing: Beijing Broadcasting

Institute Press. Pan, Z. G. (2003). Analysis on reportage about China made by New York Times: 1993-1998 (in Chinese). Chinese Journal of

Guizhou Normal University, 3, 82-86. Shejiao.com (2010). Comparison of number of facebook users in China and India. Retrieved from

http://www.shejiao.com/portal.php?mod=view&aid=26299 United Nations Conference on Trade and Development [UNCTAD]. (2005). Prospects for foreign direct investment and the

strategies of transnational corporations, 2005-2008. Retrieved from http://www.unctad.org/en/docs/iteiit20057_en.pdf Wu, Y. (2009). Pattern of international public opinion and China’s external communication path selection (in Chinese).

Contemporary Communications, 5, 345-348. Zhang, Y. M. (2011). Landing method and strategy of transnational TV news network (in Chinese). Journalist Front, 36, 212-215.

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Literature Review and the Information Risk Framework

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Theoretical price path of Security X

99.0000

100.0000

101.0000

102.0000

103.0000

104.0000

105.0000

106.0000

107.0000

108.0000

109.0000

110.0000

0 10 20 30 40 50 60 70

Trading day

Pri

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Event dependent

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Theoretical vs. actual price path of Security X

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100.0000

101.0000

102.0000

103.0000

104.0000

105.0000

106.0000

107.0000

108.0000

109.0000

110.0000

0 10 20 30 40 50 60 70

Trading day

Pri

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Event dependent

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The Information Risks (and the Asset Allocation Puzzle) During the Financial Crisis

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CD,(Y$,M0"#G()01#$,G(.&%(%"[(P,(."EY#/,+()"$(/0E,(',%1/D(")(/D,('&/,G/(.$0G0G(a!#'=(<2(>??;(/"(*&$.D(<62(>??\b(&%+()"$( /D,(Y$,M0"#G("%,(a5"M,EP,$(62(6\\\(/"(*&$.D(<62(>??<bA(40%.,(/D,(Y,$0"+G(&$,(.D"G,%()$"E(&(/"Y:Y,&H(/"(&()'""$:+$"Y2($,/#$%G(&$,("PM0"#G'=(%,1&/0M,A(


(

B6B

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150

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07/05/90 31/01/93 28/10/95 24/07/98 19/04/01 14/01/04 10/10/06 06/07/09 01/04/12

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C&P',(<(Return and Risks for Financial Markets in the Long Run

(-( T( -hT( J( L(o(T(r(J( -hL(Z,,H'=($,/#$%(akb( 4/+A(L(akb( akb( C"/&'(3F(akb( `&="))($0GH(akb( akb(

4llQ(3%+,U( ?A68>9( >A87B9( 7A9?98( 6A?766( 6A8?;;( 6?A689\(4`l(3%+,U( ?A6<>9( >A6>99( ;A><\8( ?A9;6?( 6A>;B9( 6?A8B;\(C`l(3%+,U( :?A?>99( >AB;;6( :6A?8?<( 6A6;\9( 6A7\;<( :6A9?>;((

C&P',(8(GD"[G(/D,($,G#'/G()"$(/D,('&/,G/(.$0G0GA(CD,(Y&="))($0GH([&G(R#0/,(,R#0'0P$&/,+(P,/[,,%(/D,(Q#$"Y,&%(&%+(/D,(W4-(*&$H,/A(CD,('"[,$(',M,'(")(0%)"$E&/0"%($0GH(',/(/D,(W4-(*&$H,/(D&M,(&(GE&'',$(G/&%+&$+(+,M0&/0"%(")($,/#$%G2(/D#G(&''"[0%1(&($,+#.,+(',M,'(")(&M,$&1,(%,1&/0M,(Y,$)"$E&%.,A((

C&P',(8(Return and Risks for Financial Markets in the 2006-2009 Crisis


4llQ(3%+,U( :?A<?7?( <A>668( :\A8\BB( 6A8<7B( 6ABB7B( :6BA6B;\(4`l(3%+,U( :?A>86( <A?89;( :BA\<<>( 6A>99>( 6AB;?<( :6<AB<9\(C`l(3%+,U( :?A<9><( <A7B>>( :6?AB??B( 6A7<?;( >A?86B( :69AB>>;((

-(M,$=(+0)),$,%/(G/"$=(/""H(Y'&.,(0%(/D,(Y$,M0"#G(.$0G0G2(&G(="#(.&%(G,,(0%(C&P',(7A(CD,(&PG"'#/,(',M,'(")(Q#$"Y,&%($0GH([&G(R#0/,(/D,(G&E,(")(/D,('&/,G/(.$0G0G(a<A>8>k(MGA(<A>66kb2(P#/(/D,(0EY&./(")(0%)"$E&/0"%($0GH([&G(M,$=('"[,$(a6A>\9k(MGA(6A8<;kb(&%+(D01D,$(Y&="))($0GH(,E,$1,+(a6A\88k(MGA(6ABB;kbA(3%(/D,(.&G,(")(/D,(W4-( $0GH2( /D,( G0/#&/0"%( 0G( ."EY',/,'=( +0)),$,%/K( CD,( 0%)"$E&/0"%( $0GH( 0%.$,&G,( /"( ',M,'( M,$=( %,U/( /"( /D"G,("PG,$M,+(0%(/D,('&/,G/(.$0G0G(a6AB?6k(MGA(6AB;?kb([D0',(/D,(Y&="))($0GH([&G('"[,$(a6A6?7k(MGA(6A>99kbA(

4"E,(."%.'#G0"%G(.&%(P,(G/$"H,("#/(&P"#/(/D,('&/,G/(.$0G0GK(a6b(CD,($,/#$%:/":Y&="))($0GH($&/0"(/,%+G(/"(P,(G/&P',(&.$"GG(/D,(E&$H,/G](a>b(CD,($,/#$%:/":$0GH($&/0"(0G(+0M,$/0%1(&.$"GG(/D,(E&$H,/G(a&'"%1([0/D($0GH(&M,$G0"%Gb](


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B69(

a<b(C"/&'($0GH(0%.$,&G,G(0%(&(D"E"1,%,"#G([&=(+#$0%1(&''(/D,(.$0G,G](a8b(CD,(0%)"$E&/0"%($0GH(0%.$,&G,G(+#$0%1(/D,(.$0G0G2(P#/(D&M,(&(G/$"%1,$(0EY&./(0%(Q#$"Y,](a7b(CD,(0%)"$E&/0"%($0GH([&G(&(G01%0)0.&%/(+$0M,$(")('&/,G/(.$0G0G(0%(Q#$"Y,(aP#/(%"/(0%(W4-bA(

(

C&P',(7(Return and Risks for Financial Markets in the 1999-2003 Crisis


4llQ(3%+,U( :?A7?6\( <A>86\( :67A89?8( 6A>\B\( 6A\88?( :>7A96;>(4`l(3%+,U( :?A>;?9( >A9?7;( :\A>\;B( 6A6?76( 6AB??7( :67A<<96(C`l(3%+,U( :?A<<<<( >A\8?8( :66A<<8\( 6A6\<9( 6AB8;;( :6\A?9>8((

CD,(+0))0.#'/0,G([,(&''(D&+(0%(.D&'',%10%1(/D,(.$0G0G(.&%(P,(,UY'&0%,+K(a6b(Z,(+"($,R#0$,(/""'G(/"(."%/$"'(/D,(0%)"$E&/0"%($0GH(P#/([,(E&0%'=(D&M,(/""'G(/"(E&%&1,(Y&="))($0GH](&%+(a>b(Y&="))($0GH(0G(&(1'"P&'(+$0M,$(P#/(/D,("YY"G0/,(0G(/$#,()"$(0%)"$E&/0"%($0GH2($,R#0$0%1(E"$,('".&'(Y"'0.0,GA(O'"P&'(&GG,/(&''".&/0"%(E01D/(P,(&)),./,+(P=(/D,('".&'0X,+(0EY&./(")(/D,(0%)"$E&/0"%($0GHA(

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kA(CD,(."$$,'&/0"%([0/D(/D,(!&Y&%,G,(*&$H,/(0G(E#.D('"[,$2(P,0%1(86A6<k()"$(/D,(Q#$"Y,&%(*&$H,/(&%+(86A\7k()"$(/D,(W4-("%,A(CD,()01#$,()"$(/D,('&/,G/(.$0G0G(0G(B\k([D0',()"$(/D,(Y$,M0"#G(.$0G0G(0G(98A7BkA(

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C&P',(;(Correlation Index Between Short-Term Total Risks and Payoff Risks in the Market Price Indexes

4llQ(0%+,U( 4`l(0%+,U( C`l(0%+,U(4D"$/:/,$E(/"/&'($0GHG(4llQ(0%+,U( 6(4`l(0%+,U( ?AB;78<;<><( 6(C`l(0%+,U( ?A>?<7B?67;( ?A6\<B?;66>( 6(

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c#$( ."%.'#G0"%( 0G( G0EY',K( CD,( 0%)"$E&/0"%( $0GH( E01D/( &)),./( /D,( &GG,/( &''".&/0"%( +,.0G0"%A( CD,( &./#&'($,'&/0M,([,01D/(")(/D,(!&Y&%,G,(0%M,G/E,%/(.&%(P,("M,$G/&/,+(P=(#%+#,(P,%,)0/GA(


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B6\

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."$$,'&/0"%(P,/[,,%(W4-(&%+(Q#$"Y,&%(E&$H,/GA((

C&P',(B(Correlation Index in Short-Term Risks During the 2006-2009 Crisis

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3)([,(."EY&$,(/D,(."EY#/,+($,G#'/G([0/D(/D"G,($,),$$,+(/"(/D,(Y$,M0"#G(.$0G0G(a0A,A2(5"M,EP,$(6\\\:*&$.D(>??<b2(G,,(C&P',(92(&(M,$=(+0)),$,%/(G/"$=(.&%(P,()"#%+K(

a6b(CD,(&PG"'#/,(',M,'("$($0GH(."$$,'&/0"%(0%(E&$H,/G(+,.$,&G,(P,'"[('"%1:/,$E(',M,'](a>b(5"($,',M&%/(.D&%1,G(.&%(P,("PG,$M,+(P,/[,,%(."EY#/&/0"%(0%(^1$"GG_(&%+(^%,/_(."EY#/&/0"%()"$(/D,(

."$$,'&/0"%(0%M"'M0%1(/D,(!&Y&%,G,(E&$H,/A((

C&P',(9(Correlation Index in Short-Term Risks During the 2000-2003 Crisis

4llQ(3%+,U( 4`l(3%+,U( C`l(3%+,U(C"/&'(GD"$/:/,$E($0GH(a0%.'#+,G(C3Fb(4llQ(3%+,U( 6(4`l(3%+,U( ?AB69>;>>6<( 6(C`l(3%+,U( ?A<??9><;>( ?A>9\BBB<98( 6(

4D"$/:/,$E(Y&="))($0GH(aC3F(,U.'#+,+b(4llQ(3%+,U( 6(4`l(3%+,U( ?AB;<;7<9<7( 6(C`l(3%+,U( ?A>\9<?\B86( ?A>9>?>\\69( 6(

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c#$( G,."%+( ."%.'#G0"%( 0G( G/0''( G0EY',K( CD,( >???:>??<( .$0G0G( .',&%,+( /D,( 0%)"$E&/0"%( $0GH( 0%( /D,( 1'"P&'()0%&%.0&'( E&$H,/G2( [D0',( /D,( >??B:>??\( [&G( #%&P',( /"( $,&.D( G#.D( &( $,G#'/A( CD,( 0%)"$E&/0"%( $0GH( [&G( &%(0EY"$/&%/(+$0M,$(")( /D,( '&/,G/( )0%&%.0&'(.$0G0G(&%+(%"(&))"$+&P',(."EY&$0G"%G(.&%(P,(+"%,([0/D"#/(."%G0+,$0%1(/D,(0EY&./(")(/D,(G"#$.,G(")(0%)"$E&/0"%($0GHA(

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C&P',(\(TIR Composition During the Crisis

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The Information Risks (and the Sector Rotation Puzzle) During the Financial Crisis

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&%&'=G0GA(British Accounting Review, 31a6b2(<7:;6A(-'',%2(NA2(g(O&',2(LA(a6\\8bA(Financial innovation and risk sharingA(*3C(`$,GGA(-E0D#+2(jA2(g(*,%+,'G"%2(eA(a6\9;bA(-GG,/(Y$0.0%1(&%+(/D,(P0+:&GH(GY$,&+A(Journal of Financial Economics, 17a>b2(>><:>8\A(-GR#0/D2(À2(g(*#''0%G2(LA(ZA(a6\9;bA(QR#0/=(0GG#,G(&%+(")),$0%1(+0'#/0"%A(Journal of Financial Economics, 152(;6:9\A(T&1%"'02(JA2(g(*&%/"M&%02(OA(*A(a>??\bA(I,(G/$&/,10,(+0(+0G.'"G#$,(M"'"%/&$0&(+,'(E"+,''"(+0(P#G0%,GG(+,'',(P'#,(.D0Y(0/&'0&%,(,+(0'(

'"$"(0EY&//"(G#'($0G.D0"(0%)"$E&/0M"A(Financial Reporting, 32(>9:9>A(T&$$=2(JA2(g(T$"[%2(4A( a6\97bA(L0)),$,%/0&'( 0%)"$E&/0"%(&%+( G,.#$0/=(E&$H,/( ,R#0'0P$0#EA(Journal of Financial & Quantitative

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The Impact of Government Spending in the RBC Model

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Simulation Results

[&G,+("%(/J,(D"+,'(&GG#DE/0"%G(/J,(0DE&./(")(&(/,DE"$&$=(0%.$,&G,(0%(1"S,$%D,%/(GE,%+0%1()0%&%.,+(V=(0%."D,(/&W,G(P&G(,W&D0%,+A(LJ,(G0D#'&/,+(D"+,'(S&$0&V',G(&%+(/J,($,&./0"%(")(D"+,'(S&$0&V',G(/"()0G.&'(E"'0.=(GJ".Q(0%(H"'0GJ(,."%"D=(P,$,(1,%,$&/,+(P0/J(`=%&$,(G")/P&$,(Y.)A(,A1A2(]$0))"'02(>??B\A(4,,(L&V',(6A(

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Summary

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LJ,$,)"$,2(/J,(.J"0.,(")(/J,(D,/J"+(")()0%&%.0%1(1"S,$%D,%/(GE,%+0%1(J&G(&(.$#.0&'(0DE&./("%(/J,(%&/#$,(")(/J,(G#EE'=:G0+,(,)),./G(")()0G.&'(E"'0.=A(*"$,"S,$(H"'&%+XG(E'&%(/"(&..,GG(/J,(,#$"(&$,&(&%+(Q,,E(/J,(V#+1,/(+,)0.0/( #%+,$( ."%/$"'2( 0%( '01J/( ")( /J,( E$,G,%/,+( G#EE'=:G0+,( D"+,'2( P0''( G01%0)0.&%/'=( +,.$,&G,( /J,( $"',( ")(1"S,$%D,%/(GE,%+0%1(&G(&(E"/,%/0&'(/""'(/"(G/0D#'&/,(,."%"D=A(

References -G.J&#,$2(À(Y6C97\A(F0G.&'(E"'0.=(&%+(&11$,1&/,(+,D&%+A(N;"&72<B942%B%;729L"F7"$19OPY6\2(66B:6>BA(-G.J&#,$2(À(Y6C99\A(LJ,(,^#0'0V$0#D(&EE$"&.J(/"()0G.&'(E"'0.=A(Q%E&B<69%=9R%B"#19C&"'7*9<B'9S<B/7B+19TUY6\2(86:;>A([&$$"2(ZA(Y6CB8\A(-$,(1"S,$%D,%/(V"%+G(%,/(P,&'/Jm(Q%E&B<69%=9.%67*72<6942%B%;#19VTY;\2(6?C7:666BA([&$$"2(ZA(Y6C96\A(d#/E#/(,)),./G(")(1"S,$%D,%/(E#$.J&G,GA(Q%E&B<69%=9.%67*72<6942%B%;#19VWY;\2(6?9;:66>6A([&W/,$2(*A2(g(N0%12(ZA(]A(Y6CC<\A(F0G.&'(E"'0.=(0%(1,%,$&'(,^#0'0V$0#DA(83"9N;"&72<B942%B%;729L"F7"$19VXY<\2(<67:<<8A([#Q"PGQ02( *A2( N"P&'2( HA2( T,P&%+"PGQ02( HA2( g( l&P0G/"PGQ02( !A( Y>??7\A( H*&E/*E&<9 79 0%Y7%;9 $#'<*/Z$9 ("/*%&<9 =7B<B(Z$9

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The Case Study Based on Real Example of Cluster CREA Hydro&Energy

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The Common Project: Penetration of International Markets

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Costs and Benefit Analysis

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