Performance Comparison in SMEs - Bititci Et Al - 2010-03-29 %283%29

8/13/2019 Performance Comparison in SMEs - Bititci Et Al - 2010-03-29 %283%29

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Performance comparison in SMEs

Abstract

This paper seeks to address the question “How to measure different SMEs’ performances

comparatively? An initial review reveals that the literature does not provide o!ective and

e"plicit deate on the su!ect# $onsequently% an approach is developed% informed y the

literature% which is used to compare the performances of &' SMEs# The consistency and

reliaility of the approach is tested% resultin( in a rankin( of the &' firms accordin( to their

performances#

)sin( cluster and factor analysis the paper demonstrates that leadin( indicators are

somewhat redundant% and that la((in( indicators have (reater si(nificance for the purpose

of comparative measurement of different SMEs performances# *hilst the approach adopted

here withstood internal and e"ternal validity tests and can e seen as a roust way of

comparin( SMEs performances% these results may e limited to this particular study#

Keywords: SMEs% +erformance% measurement% comparisons% enchmarkin(

,



1. Introduction

Ever since -ohnson and .aplan /,01'2 pulished their seminal ook% entitled

Relevance Lost – The Rise and Fall of Management Accounting % performance

measurement (ained increasin( popularity oth in practice and research# 3n fact 4eely

/,0002% havin( identified that etween ,005 and ,006 over &677 articles were pulished

on performance measurement% has coined the phrase the performance measurement

revolution# Today% performance measurement and performance mana(ement practices

are common place in all sectors of industry and commerce as well as the pulic sector%

includin( (overnment departments% non8(overnmental or(anisations and charities#

Alon( with this increased interest in performance measurement at all levels of an

or(anisation% we have also witnessed an increasin( interest in comparin( the

performance of or(anisations in order to identify the performance (aps and

improvement opportunities# $onsequently% we have seen a numer of articles from

practitioners and researchers on the su!ects of performance measurement% performance

comparisons and enchmarkin(# 3n parallel to these theoretical developments we have

also seen an increase in performance comparison and enchmarkin( practices and

services# An early review of some of these enchmarkin( services was reported y

$oulter et al. /97772# A quick search of the internet reveals a plethora of services /e#(#

9



www#pm(enchmarkin(#com and www#estfactoryawards#co#uk 2 for performance

comparison and enchmarkin(#

2. The esearch Prob!em

The research prolem we are tryin( to address in this paper is simply “How to

measure different SMEs’ performances comparatively?: *e have come across this

prolem when we were tryin( to compare the mana(erial practices% activities and

processes of a numer of different SMEs with different levels of performance#

3n pursuin( this line of research our first challen(e was how to measure the

performance of different SMEs operatin( in different sectors so that we can o!ectively

classify them accordin( to their performance# 3n seekin( an answer to this challen(e and

havin( reviewed the literature in this area% we identified a ran(e of studies that compare

the performances of different firms#

The *orld $ompetitiveness ;eport <earook /97702 provides assessments of

many aspects of national factors that drive competitiveness# Here there is some

a(reement of the type of measures that should e used to measure a firm’s performance

/such as revenue (rowth% profitaility (rowth% productivity (rowth and so on2 i ut the

comparison of the performances of different companies to one another usin( these

&

http://www.pmgbenchmarking.com/

http://www.bestfactoryawards.co.uk/


http://www.pmgbenchmarking.com/




measures in asolute terms ecomes meanin(less ecause one company may e

operatin( a hi(h (rowth sector /such as food and drinks2 and the other in a declinin(

sector /such as electronics2# =ther studies use return on investment type measures%

particularly shareholder return /$ollins% 977,> .ratchman et al #% ,0'5> @nsiluoto et al #%

9775> 4ohria et al #% 977&> +hillips ,00'; <amin et al #% ,00'2# There are two prolems

with this approach# irstly% return on investment% whilst ein( an appropriate hi(h level

measure for lar(er companies% it fails to provide an o!ective assessment of smaller

companies that may e owner8mana(ed /;ue and 3rahim% ,001> +erry% 977,> Benison

and McBonald% ,00C> uller8 ove% 9776> *esthead and Storey% ,0062# Secondly% it still

relies on comparin( performance of similar firms within their sector and does not allow

for cross8sectoral comparisons# urthermore% to compare the performances of firms

within the same sector seems to require comple" approaches# Here we also found

performance comparisons of firms in different countries /Andersen and -ordan% ,001>

Samson and ord% 9777> Doss and lackmon% ,0062 or performance comparisons

etween (roups of firms% such as SMEs v local firms v lar(e firms /Frando and

elvedere% 97762# 3n all these cases% lar(e comple" questionnaires were used to collect

the necessary data and the comparison did not consider the differences in different

usiness sectors#

5



There are indeed other studies that focus on comparin( the performances of

firms from different sectors# However% the ma!ority of these focus on particular

processes or function such as supply chain performance /AkyuG and Erkan% 9770> ynes

et al #% 977C> Funasekaran et al #% 977,> .im% 977'> .o!ima et at#% 9771> ewis and 4iam%

,00C> SncheG and +IreG% 977C> <urdakul% 977C2 or manufacturin( performance

/onvik et al #% ,00'> ukchin% ,001> Miller and ;oth% ,005> au(en et al.% 977C2

without payin( much attention to overall performance of the firm# or e"ample% the

3nternational Manufacturin( Strate(y Survey pro!ect /au(en et al.% 977C2 attempts to

identify the manufacturin( practices of hi(h performin( manufacturin( companies

/lar(e and small2 y usin( ,' different quality% fle"iility% speed and cost indicators#

The prolem here is that it is the manufacturin( performance that is ein( compared

rather then the overall performance of the firms concerned#

3t appears that althou(h there are many studies measurin( and comparin( the

performances of different firms from different perspectives /such as marketin(%

operations% finance% human resource mana(ement2 and for different purposes% there is

little or no informed scientific deate as to which measures are appropriate and how

these measures should e comined and used in order to compare the usiness

performance of different firms operatin( in different sector% whilst accountin( for the

industry specific factors /Hawawini et al #% 977&% Ellis and *illiams% ,00&> ;eider%

C



9777% ;ichard et al #% 97702# 3n fact% ;ichard et al # /97702% havin( reviewed performance

measurement related pulications in five of the leadin( mana(ement !ournals ii /'99

articles etween 977C and 977'2% su((est that past studies reveal a multidimensional

conceptualisation of or(anisational performance with limited effectiveness of

commonly accepted measurement practices# They call for more theoretically (rounded

research and deate for estalishin( which measures are appropriate to a (iven research

conte"t#

3n short% with this paper we seek to make a contriution to this (ap y

identifyin( the appropriate measures and how they should e comined and used in

order to measure different firms’ performance comparatively#

". esearch Method

3n order to investi(ate the research question posed aove% we carried out a

ri(orous research pro(ramme summarised in i(ure ,# The research process was ased

on three main phases# irst% we reviewed literature% includin( oth SMEs and lar(e

companies and% covered a road ran(e of overlappin( fields% includin( performance

measurement% mana(ement control systems% enchmarkin( and performance

mana(ement and we synthesised it in two main research streamsJ the performance

measurement literature in (eneral% and the cross8industry enchmarkin( literature in

6



particular# Secondly% (iven the lack of scientific deate on measurement of different

firms’ performances comparatively% we used a focus (roup to review the literature

conclusions and identify how and what to measure# As a result we identified nine key

performance measures that would enale assessment of a SME’s overall performance

to(ether with an approach for accountin( for intersectoral differences# Thirdly% we

empirical tested the proposed approach on a (roup of &' SMEs operatin( in different

sectors# inally% ased on the empirical evidence we could validate our approach and

have an informed deate on what measures to use and how to use these to measure

firms’ performances comparatively# The methodolo(ical details of each phase of the

research are further e"plained in the followin( sections#

Ktake in i(ure ,K

#. $ac%&round 'iterature

The literature review presented in this section was conducted to estalish the

current knowled(e pertinent to the research question posed aove# 3n order to identify

the relevant papers% specific mana(ement dataases% such as usiness Source +remier%

*e of .nowled(e% Emerald 3nsi(ht% Mana(ement and =r(anisation Studies% A3

3nform and Science Birect% were searched usin( search phrases such as performance

'



comparison% performance measurement in SMEs% enchmarkin(% and performance

enchmarkin(# ;elevant articles were identified after a review of astracts followed y

full te"t reviews# The selected papers were analysed and inte(rated with key ooks on

the areas of interest# or the purposes of this literature review% pulications in

conferences proceedin( were omitted#

The resultant literature covered a road ran(e of overlappin( fields% includin(

performance measurement% mana(ement control systems% enchmarkin( and

performance mana(ement# The followin( sections provide a synthesis of the

performance measurement literature in (eneral% and the cross8industry enchmarkin(

literature in particular#

4.1 Performance Measurement Literature

Burin( the ,017s% with the reco(nition of the limitations associated with

traditional performance measurement systems% the interest in the theory and practice of

performance measurement started to (row# The main issues associated with traditional

performance measurement may e summarised asJ lack of ali(nment etween

performance measures and strate(y> failure to include non8financial and less tan(ile

factors such as quality% customer satisfaction and employee morale> mainly ackward

1



lookin(% thus poor predictors of future performance> encoura(in( short8termism> insular

or inwards8lookin( measures (ivin( misleadin( si(nals for improvement and innovation

/-ohnson and .aplan ,01'> ynch and $ross% ,00,> Eccles% ,00,> 4eely et al #% ,005>

Fhalayini and 4ole% ,0062# $onsequently% out of reco(nition of the inappropriateness

of traditional approaches to performance measurement% in a (loaliGed% hi(hly dynamic%

market focused and stakeholder driven economy% the contemporary approaches to

performance measurement were orn /.aplan and 4orton% ,009% ,006> Eccles% ,00,>

3ttner and arcker% ,001> 977&> 4eely% ,0002#

$ontemporary approaches to performance measurement include the intan(ile

dimensions% such as pulic ima(e and perception% customer satisfaction% employee

satisfaction and attrition% skills levels% innovations in products and services investments

into trainin( and new value streams and so on /see for instance Ahire et al.% ,006>

Atkinson et al #% ,00'> lynn et al #% ,005> orslund 977'> rancisco et al #% 977&>

ullerton and *empe% 9770> Maskell% ,00,> McAdam and HaGlett% 9771> .asul and

Motwani ,00C2#

Today% there is a (eneral consensus that the old financial measures are still valid

and relevant /<ip et al #% 97702% ut these need to e alanced with more contemporary%

intan(ile and e"ternally oriented measures#

0



The discourse on contemporary approaches to performance measurement

hi(hli(hts how shorter term operational measures affect usiness performance and

measures in the lon(er term# This deate led to the development of the notion of leadin(

and la((in( indicators where the leadin( indicators are the indicators that provide an

early warnin( of what may happen in the future and the la((in( indicators communicate

what has actually happened in the past /Anderson and McAdam% 9775> auly% ,005>

ourne et al #% 9777> .aplan and 4orton% 977,> Manoochehri% ,000> 4eely et al #% ,00C>

4i"on% ,001> =lve et al #% ,0002#

The literature identifies a numer of leadin( indicators that serve to predict

future performance of an or(anisation# These include customer oriented operational

indicators such as delivery performance% lead times% fle"iility and quality performance

/Bi(alwar and San(want 977'; eamon% ,000> ynn et al #% ,00'> Maskell> ,00,2 as

well as human resource oriented indicators such as employee satisfaction and morale

/Aott% 977&> urke et al #% 977C> Heskett et al #% ,005> Schlesin(er and Heskett% ,00,>

Simmons% 9771> TuGovic and ruhn% 977C2# 3n fact% authors such as itG8EnG /,00&2%

;ucci /,0012% ;hian /97792 and *atkins and *oodhall /977,2 hi(hli(ht the stron(% and

comple"% relationship etween employee satisfaction% customer satisfaction and overall

performance#

,7



The notion of creatin( performance measures that are predictive adds an

important characteristic to the thinkin( ehind performance measurement in (eneral# 3n

order for any performance indicator /leadin( or la((in(2 to e predictive a sin(le point

of measure would e meanin(less and that prediction would need to e ased around a

time series of measures indicatin( how performance is chan(in( in time% thus allowin(

one to predict what may lie in the future# 3t is thou(ht that leadin( and la((in(

indicators% when used in a time series format% rin(s or(anisations one step closer to

havin( predictive performance measurement systems /ourne et al #% 9777> 4eely et al #%

,00C2#

The literature also contains many empirical studies that call for contin(ency

ased approaches to performance measurement /3ttner and arcker% ,001> 4anni et al #%

,009> Shirley and ;eitsper(er(% ,00,2# Here the importance of the internal /such as

strate(y% o!ectives% structures and culture2 and e"ternal /such as customers%

competitors% suppliers% le(al% political and social2 conte"t of the or(anisation is

reco(nised /$henhall% 977&> Faren(o and ititci% 977'2# This emphasis on a

contin(ency approach hi(hli(hts the need to consider the contin(ency variales when

comparin( performance results of different companies# 3n short% company performance

,,



could not e considered in isolation from the characteristics and the needs of the

industry and the environment in which a company operates /;eid and Smith% 97772#

3n the conte"t of SMEs% the performance measurement literature hi(hli(hts the

characteristics of SMEs that differentiates them from lar(er or(anisations# These

characteristics include% lack of formalised strate(y% operational focus% limited

mana(erial and capital resources% and misconception of performance measurement

/routhers et al #% ,001> uller8ove% 9776> Fhoadian and Fallear% ,00'> Hudson et al #%

977,> Hussein et al #% ,001> -ennin(s and eaver% ,00'> Faren(o et al # 977C22# This

literature also su((ests that SMEs require simple measures that provide focused% clear

and useful information /Hussein et al # ,001> aitinen% 97792# As SMEs lack the

resources needed to implement comple" measurement systems a key requirement is that

the numer of measures used should e limited /$ook and *olverton ,00C> Hussein et

al # ,001> <e8<un ,0002 without compromisin( the inte(rity of the performance

measurement system /McAdam and ailie% 97792#

3n summary% the performance measurement literature emphasises the need for

adoptin( a alanced approach to performance measurement and the need for usin(

leadin( and la((in( indicators in a coordinated way# Althou(h different scholars may

use different words to descrie this% all the performance measurement models developed

after the mid8,017s take a alanced approach to performance measurement% where the

,9



use of leadin( and la((in( measures are coordinated /itG(erald et al # ,00,> .aplan and

4orton% ,009> .ee(an et al #% ,010> ynch and $ross% ,00,> 4eely% ,001> 4eely et al #%

97792# Althou(h the alanced approach to(ether with the notion of leadin( and la((in(

indicators provide useful (uidance on what to measure and how to use these measures%

it provides little (uidance on how these measures and the firm specify contin(ency

factors /such as sector characteristics2 could e used to measure the performance of

different firms comparatively#

4.2 Cross-Industry Benchmarking Literature

Here the literature contains diverse interpretations that reflect the level of

interest in enchmarkin(# Bespite this diversity one common theme that inds this field

to(ether is that meanin(ful measurement is relative /Fre(ory ,00&2# That is% in order to

e si(nificant% each measurement needs to e compared a(ainst a point of reference or

enchmark /$Guchry et al #% ,00C> Battakumar and -a(adeesh 977&> Di(% ,00C> <asin%

9779> Lairi and <oussef% ,00C> ,0062#

Althou(h the literature proposes a variety of approaches to enchmarkin(% the

widely accepted classification proposed y $amp /,0102 makes distinctions etween

internal% competitive% functional and (eneric enchmarkin(# However% these all rely on

,&



comparison of similar processes% functions or firms# *atson /977'2 reco(niGes the

weaknesses associated with local enchmarkin( and proposes an additional cate(ory

called global benchmarking > which attempts to e"tend the oundary of the

enchmarkin( (eo(raphically to (et over the cultural and usiness process distinctions

amon( companies# However% *atson’s /977'2 approach also does not address the cross8

industry enchmarkin( issue#

The literature also contains many studies investi(atin( how est to enchmark%

descriin( the necessary steps /$amp% ,010> $odlin(% ,001> reyta( and Hollensen%

977,> .arlof and =stlom% ,00&> Spendolini% ,00&> Doss et al #% ,0052# However% none

of these studies propose approaches to facilitate cross8industry enchmarkin(# Many of

the enchmarkin( pro!ects found in the literature focus onJ

• enchmarkin( within a specific sector 8 such asJ manufacturin( /Miller and ;oth%

,005> au(en et al % 977C2> construction /$osta et al # 97762> transportation and

lo(istics /Be .oster% and *arffemius 977C> Feerlin(s et al #% 9776> Huo et al.,

97712% water supply /aadaart% 977'2> metal8castin( /;ieiro and $aral% 97762>

automotive /Belrid(e et al #% ,00C> SncheG and +IreG% 977C2> human resources

/;odri(ues and $hincholkar% 97762> information services /Ho and *u% 97762# These

include international enchmarkin( networks such asJ www#enchnet#com>

,5

http://www.benchnet.com/

http://www.benchnet.com/



www#enchmarkin(network#com> www#apqc#or(> www#capsresearch#or( /Andersen

and -ordan% ,0012#

• enchmarkin( a specific cross8industry measure% such asJ days8sales 8 outstandin(

/www#icc#co#uk2> annual8asset8ased8lendin( /www#cfa#com2 and financial

performance /.ratchman et al #% ,0'5> ansiluoto et al #% 97752#

• enchmarkin( a sin(le industry% to assess the competitiveness of that industry

/raadaart% 977'> Belrid(e et al #% ,00C> owler and $ampell 977,> Feerlin(s et

al #% 9776> Hwan( et al #% 9771> ;ieiro and $aral 97762

• enchmarkin( a specific process% such as supply chain performance /ewis and

4iam% ,00C> Schmider(er% et al #% 9771> <un( and $han% 977&2#

+erformances enchmarkin( refers mainly to quantitative comparisons of

performance variales /such asJ costs% quality% customer satisfaction% productivity and so

on2 to identify (aps in performance and thus identifyin( improvement opportunities#

$learly performance enchmarkin(% as such% is most useful when it is used for dia(nosis

and comparison amon( companies and industries# The literature also raises an important

point concernin( performance versus practice enchmarkin(# Fiven the o!ective of our

study% we restrict our interest to (loal performance enchmarkin(# As our interest in

enchmarkin( is to compare usiness results of companies elon(in( to different

sectors% *atson’s /977'2 findin(s from enchmarkin( studies were considered

,C

http://www.benchmarkingnetwork.com/

http://www.apqc.org/

http://www.capsresearch.org/

http://www.cfa.com/

http://www.benchmarkingnetwork.com/

http://www.apqc.org/

http://www.capsresearch.org/

http://www.cfa.com/



particularly noteworthy# =ne of the main limitations of (loal performance

enchmarkin( seems to e the need for focusin( mainly on financial results ecause% at

this level% the o!ective is to determine which or(aniGation performs est accordin( to

an o!ective standard that is typically financial 8 like return on investment# Also% whilst

enchmarkin( is considered to work well as a method of identifyin( the est

performance in a specific industry% it is also reco(nised that it does not work well across

industries as the comparisons ecomes meanin(less due to conte"tual factors /Ellis and

*illiams% ,00&> Hawawini et al #% 977&> ;eider% 97772#

4.3 Literature Conclusions

3t seems that there is a plethora of literature on performance measurement%

mana(ement control systems% enchmarkin( and performance mana(ement# These

ran(e from measurement and control of or(anisational performance as a whole to

mana(ement and control of people performance% and includes or(anisational functions%

usiness processes% activities% teams% as well as supply chains and SMEs#

The literature does contain studies where the performances of different firms

from different sectors have een compared usin( a scale /e#(# aove8avera(e% avera(e%

elow8avera(e2 to account for intersectoral differences /e#(# au(en et al % 977C> Miller

,6



and ;oth% ,0052# However% the ma!ority of these studies use these approaches as a

research instrument and there seems to e little scientific research and deate to

enhance our understanding of !"hich measures to use# and !ho" to comine and

use these measures# to com$are the o%erall $erformance of different &M's.

Bespite this lack of specific deate% there is some (eneral (uidance% in that the

measures we use to assess and compare the performance of different firms shouldJ

• e alanced% includin( financial and non financial measures#

• include oth la((in( /such as traditional financial measures2 and leadin( measures

/such as employee satisfactions% investments in new equipment% personnel% markets

and so on2#

• e ased on a time series /e#(# indicatin( how profitaility of an or(anisation has

chan(ed over a period of time2#

• e sensitive to the conte"tual and environmental conditions the firms operate within

and assess firms’ performances within this conte"t#

(. Measurin& SMEs Performances )omparati*e!y: An Approach

,'



aced with the plethora of opinions and approaches with little or no informed

scientific deate on how to measures firms’ performances comparatively it was decided

to develop an approach that would allow us to measure SMEs performances

comparatively and then allow us to classify these into hi(h% medium and low

performance cate(ories# A focus (roup was formed comprisin( of academics with

varyin( ack(rounds /operations mana(ement% manufacturin(% human resource

mana(ement% mana(ement science% strate(ic mana(ement and psycholo(y2 as well as

industrial memers /two mana(in( directors and two operations directors from four

different SMEs2# The conclusions of the literature were reviewed with the focus (roup

that identified two areas where decisions had to e madeJ “*hat to measure: and “How

to measure these comparatively:#

3n focusin( on what to measure% it quickly ecame apparent that% as the focus of

our main study was SMEs iii in Europe% we would e well advised to consider the key

usiness measures these companies would use to assess their own performance# The

measures identified lar(ely comprised of traditional financially focused la((in(

indicators% as followsJ ;evenue /sales2> +rofits or profitaility> $ash8flow and Market

share#

3n a wider conte"t% in makin( comparison etween different countries or sectors%

productivity is also a commonly used measure# 3n fact% any chan(e pro(rammes would

first show improvements in productivity efore the results are seen in sales% profits or

,1



cash8flow# Thus the focus (roup considered productivity to e a leadin( indicator for

;evenue% +rofit and $ash8low measures% a view that is also supported y the literature

/see for instance Maskell% ,00,> Misterek et al #% ,009> lynn et al #% ,005> Fhalayini and

4ole% ,006> +arker% 9777> Bi(alwar and Metri% 977C> Harter et al #% 97792# rom a

customer perspective% the focus (roup considered it important to measure customer

satisfaction as an all encompassin( indicator of customer facin( performance of firms

operatin( in different sectors and to different operatin( strate(ies#

$onsiderin( the emphasis on leadin( indicators su((ested in the literature% such

as introduction of new value stream% new investments% as well as employee satisfaction

/itG(erald et al #% ,00,> .aplan and 4orton% ,009> .ee(an et al #% ,010> ynch and

$ross% ,00,> 4eely% ,001> 4eely et al #% 9779> ;ucci% ,001> ;hian% 9779; *atkins and

*oodhall% 977,2 the approach shown in i(ure 9 was adopted#

Ktake in i(ure 9K

3n considerin( the question “how to measure these comparatively?: it was

decided to use a relative scorin( technique /as illustrated in i(ure 92 allowin( the

performance of each or(anisation to e scored on a five point scale over a specified time

period /,7 years2 with respect to its sector# =f course% it was reco(nised that this would

,0



make the whole assessment su!ective# However% after much deate this was deemed

the most appropriate method% with certain qualifications% that would allow cross8

industry performance comparisons whilst ein( sensitive to environmental and

conte"tual factors within which each or(anisation operates# The aove decision was

taken with the qualification that the scores (iven to each or(anisation were trian(ulated%

as well as independently validated usin( o!ective data for each or(anisation and the

sector they operate within% consistent with previous such studies /Miller and ;oth%

,005> au(en et al % 977C2#

+. )ase ,ata and Ana!ysis

Havin( developed a framework for measurin( firms’ performances

comparatively% performance data was collected from &' SMEs across Europe# 3n the

followin( sections we provide a detailed e"planation of how the data was collected and

analysed% as well as our findin(s#

(.1 )ata Collection and )escri$tion

Fiven that we were seekin( to understand the $erformance of each firm

relati%e to its sector we decided to adopt a qualitative case study methodolo(y ased on

97



structured interviews /Eisenhardt% ,010> Eisenhardt and Fraener% 977'2# A case study

protocol was developed that (uided researchers throu(h the case study interviews#

etween -anuary and 4ovemer 9776 performance data was collected throu(h face to

face interviews from &' European SMEs operatin( in different sectors% such as ood

and evera(es% Electronics% Electrical Equipment% +lastic $omponents% +rocess and

Heavy En(ineerin(# 3n selectin( the case study or(anisations% SMEs with less than C7

people were avoided as accordin( to Doss et al. /,0012 they represent different levels of

mana(erial capailities# 3n fact the &' cases e"amined all had etween ,77 and 9C7

employees#

The interviews were conducted in pairs y a team of si" researchers# or

trian(ulation purposes secondary data in the form of internal reports and media

pulications were also used /Eisenhardt% ,010> Miles and Huerman% ,0052# 3n each

company the mana(in( director(eneral mana(er or hisher equivalent was interviewed

as well as hisher direct reports# Typically% these included an =perations Birector%

inance Birector% Sales$ommercial Birector% +roduct Bevelopment Birector iv#

As the data collection interviews pro(ressed it ecame apparent that only a few

of the &' case study or(anisations collected and reported customer satisfaction data# 3t

also ecame evident that a lot of the interviewees were not ale to score their customer

9,



satisfaction performance relative to their sector# Thus the customer satisfaction indicator

was aandoned durin( the early sta(es of the research#

Eventually data was collected from &' firms a(ainst 1 performance variales# 3n

each company these performance variales were rated y the C to 1 mana(ers

considerin( their own or(anisation’s performance over the past ,7 years% consistent with

the (rowin( coalition that a ,78year timeframe is the minimum appropriate timeframe to

overcome random variation /see .iry% 977C and ;ichard et al.% 97702#

(.2 )ata reliaility and %alidation

The reliaility and validity of the data collected was tested usin( e"ternal and

internal consistency checks usin( an independent researcher# E"ternal consistency of the

performance ratin( (iven y the mana(ers interviewed was tested a(ainst actual

performance of these or(anisations# This was done y takin( a sample of five firms

from the research sample of &' case studies# The actual performance information for the

sample of five firms was otained from the AME and similar dataasesv% as well as the

companies own internal accounts# The team also had access to local industry dataases%

as well as news stories% to (ather o!ective information a(ainst each of the ei(ht

performance variales# The actual values for the performance variales were compared

99



to other companies in the same sector# As the consistency etween self and independent

assessment of each performance variale is aove '&N /Tale ,2% it is considered that

the data used is e"ternally reliale and valid#

Ktake in Tale ,K

3nternal consistency was tested usin( $ronach8alpha statistics /Salkind% 9776%

p#,,9% =ktay8irat and Bemirhan% 97792# This approach indicates whether or not the

performance ratin(s used are helpful in e"plainin( the performance of the firms y

providin( information aout the relationships etween individual performance variales

in the scale# or the ei(ht performance variales used% $ronach’s alpha value was

(reater than 7#0,0, for all variales in the overall scale# This value indicates that

performance ratin(s used have stron( internal consistency% as reliaility coefficients

aove 7#1 are considered reliale /Salkind% 9776% p#,,72# Also as e"plained in the ne"t

section% $ronach’s alpha values were calculated for oth first and second components

/factors2 in order to determine reliailities of la((in( and leadin( indicators and found

as 7#096, and 7#1C95 respectively#

These results confirm that the performance ratin(s otained from the &' case

studies are oth internally and e"ternally reliale and consistent#

9&



(.3 *hich indicators are most useful+

Althou(h it is now estalished that the performance variales and the ratin(

system used have stron( internal and e"ternal consistency% the question as to whether all

the ei(ht indicators are required for e"plainin( the performance of a firm still remains#

Accordin( to Hair et al # /,0012% factor analysis% in addition to servin( as a data

summarisin( tool% may also e used as a data reduction tool as it assists with the

reduction of the numer of variales# However% there are several su((estions concernin(

the appropriate sample siGe for applyin( factor analysis# $omrey and ee /,0092

su((est that a sample siGe of C7 is very poor% ,77 is poor and so on# +reacher and

Mac$allum /97792 conducted a Monte $arlo Simulation study on the sample siGe effect

on factor analysis and concluded that sample siGe had y far the lar(est effect on factor

recovery with a sharp drop8off elow sample siGe of 97# Althou(h the (enerally

accepted appro"imation that the sample siGe must e (reater than &7 is still valid and

necessary for normality assumption% it is not sufficient for roust statistical analysis#

Measure of Samplin( Adequacy /MSA2 and artlett’s Test of Sphericity /artlett’s test2

are two different measures that are frequently used in order to check the adequacy of

factor analysis /Hair et al #% ,001> Lhao% 97702# The artlett’s test is a statistical test for

95



the presence of si(nificant correlations across the entire correlation matri" /Hair et al.%

,0012# )se of artlett’s test of sphericity is recommended only if there are fewer than

five cases per variale /Taachnick and idell% 977'2# 3n this study% since the ratio of

numer of cases /&'2 to the numer of variales /12 is less than C% artlett’s test has

een used in order to check the adequacy and appropriateness of factor analysis# .aiser8

Meyer8=lkin /.M=2 test measures Samplin( Adequacy throu(h an inde" ran(in( from

7 to ,# The .M= inde" reaches towards , when each variale is perfectly predicted

without error y the other variales# .M= can e interpreted as “meritorious: when

7#17 or aove and as “middlin(: when it is etween 7#'7 and 7#17 /Hair et al #% ,0012#

Accordin( to Taachnick and idel /977'2% Hair et al,# /,0012 and Lhao /97702 a .M=

inde" over 7#67 is sufficient for estalishin( samplin( adequacy# A .M= inde" of

7#191 to(ether with the statistically si(nificant result of artlett’s test /$hi8SqO9,7#1'1>

dfO91#777> pO7#772 su((est that the sample chosen and the set of variales are

conceptually valid and appropriate to study with factor analysis#

Thus factor analysis /Taachnick and idell% 977'% =ktay8irat and Bemirhan%

97772 and Darima" ;otationvi

/-ohnson and *ichern% 9779% p# C7C> =ktay8irat and

Bemirhan% 977,2 was applied to the ei(ht performance variales to identify a

comination of variales that est e"plain the performance of these firms# The

correlation matri" in Tale 9 illustrates that most of the variales are stron(ly related to

9C



each other# Althou(h the weakest relationship is etween +rofitaility and 4ew value

streams% this correlation still remains si(nificant at the level of 7#,#

Ktake in Tale 9K

The de(ree to which the model descries data and the interpretaility of the

solution are perhaps the most difficult part of a factor analysis# Methods such as

“Ei(envalues8(reater8than8unity rule: /Everitt and Bunn% 977,> $udeck% 9777> .aiser%

,0672 and the Scree8+lot are common methods to decide which factors est descrie the

data# However% the choice ultimately involves a certain amount of su!ective evaluation

on the part of the investi(ators and it is su((ested that personal opinion is not only

unavoidale% it is also desirale /$udeck% 97772# Accordin( to the Ei(envalues8(reater8

than8unity rule% the components havin( ei(envalues (reater than , are considered

si(nificant and all factors with ei(envalues less than , are considered insi(nificant and

are disre(arded /Everitt and Bunn% 977,2# Scree8+lot approach su((ests that the numer

of factors should e decided y the numer of ei(envalues that are appreciale in siGe

compared to the others in the distriution and this usually corresponds to the “elow: in

the Scree8+lot /$attell% ,066> Everitt and Bunn% 977,> $udeck% 9777% Taachnick and

idell% 977'2#

96



i(ure & illustrates the ei(envalues efore and after varima" rotation as well as

the Scree8+lot for the data# ;esults show that two principal components /$omponents ,

and 9 with ei(envalues (reater than ,2 e"plain ''#0' N /cumulatively2 of the total

variaility of performance in the sample# As a result% there are two main dimensions in

the performance data% these are the first principle component and the second principle

component that e"plains C7#75N and 9'#09N of the total variaility respectively# This

result can also e visually oserved from Scree8+lot.

Ktake in i(ure &K

i(ure 5 shows the contriution of each performance variale towards each one

of the two principal components and illustrates% throu(h the $omponent +lot% how the

performance variales relate to one another ased on coefficients of each principal

component#

Ktake in i(ure 5K

As to the laellin( of the components% accordin( to Hair et al. /,0012 the

decision is ased primarily on the su!ective opinion of the researchers% ut if a lo(ical

name can e assi(ned that represents the underlyin( nature of the factors% it usually

9'



facilitates the presentation and understandin( of the factor solution# )sually variales

with hi(her loadin(s influence to a (reater e"tent the lael or name selected to represent

a factor#

This analysis shows that the two principal components are sufficient to represent

all performance variales and that these results are consistent with the performance

measurement literature% i#e# the irst +rinciple $omponent may e primarily laelled as

“a((in( 3ndicators: with the e"ception of Employee Satisfaction indicator# Similarly%

the Second +rinciple $omponent may e laelled as “eadin( 3ndicators:#

The literature on factor analysis also su((ests that the researcher has the option

of e"aminin( the factor matri" and selectin( the variale or variales with the hi(hest

factor loadin( on each factor to act as a surro(ate variale that is representative of that

factor /Hair et al., ,001> Taachnick and idell% 977'2# $onsiderin( these results it can

e concluded thatJ

• a((in( performance variales included in the first principal component are more

important than others to indicatemeasure the performance of the companies in the

sample#

• +rofitaility variale is the most important performance indicator to e"plain

performance levels of the companies in the sample# As its coefficient is si(nificantly

91



(reater than others% it can e used as a sin(le “surro(ate: performance indicator y

i(norin( other variales#

• The factor analysis literature su((ested that as a rule of thum the first one% two or

three variales that have hi(hest loadin( /variales with loadin(s of 7#&9 and aove2

in the principal component can e used to represent all the remainin( variales

/Taachnick and idell% 977'% p #69C> =ktay8irat and Bemirhan% 977,> 97792#

Therefore% providin( an opportunity to avoid comple"ity y reducin( the numer of

variales that are required to measure the performance of an or(anisation# 3n this

case% for the followin( reasons% it would e possile to omit Employee Satisfaction

from the irst +rinciple $omponentJ

/,2 3n the performance measurement literature Employee Satisfaction is

classified as a eadin( 3ndicator# Thus it does not naturally fit with the rest

of the la((in( indicators in this (roup of performance variales#

/92 The Employee Satisfaction variale has one of the lowest loadin(

coefficients#

/&2 urther analysis conducted usin( Hierarchical $lusterin( usin( *ard’s

methodvii

to discover natural (roupin(s of performance variales /-ohnson

and *ichern% 97792 shows that Employee Satisfaction is clearly distant from

other variales in irst +rinciple $omponent /i(ure C2#

90



Ktake in i(ure CK

• $onsiderin( also the results of i(ure 5 it may e ar(ued that la((in( indicators

+rofitaility% $ash low and Dalue Added +roductivity may e used e"clusively to

measure and assess company performance with a reasonale de(ree of reliaility

and consistency#

• +erformance variales /4ew Dalue Streams and 3nvestments2 included in the second

principal component are less important% ut these variales measure different

dimensions of performance% i#e# the eadin( 3ndicators% and would serve to predict

future performance rather than past performance#

The o!ective of this section was to determine useful indicators that would

facilitate a reliale and consistent assessment of firms’ performances# The analysis

presented su((ests that althou(h all the indicators used are capale of representin(

firms’ performances in a reliale and consistent way% it also presents an opportunity to

reduce comple"ity y reducin( the numer of performance indicators# Thus% the

proceedin( comparative performance analysis will e ased on the performance

indicators included in the irst +rinciple $omponent with the e"ception of the

Employee Satisfaction indicator% for reasons discussed aove# Therefore% the most

useful indicators for purposes of performance comparison amon(st firms areJ

&7



• Frowth in +rofitaility

• Frowth in Dalue Added +roductivity

• Frowth in $ash flow

• Frowth in ;evenue

• Frowth in Market Share

(.4 ,o" can "e grou$ firms according to their $erformance+

Havin( identified the most useful indicators for purposes of performance

comparison the ne"t research challen(e was how to rank or (roup firms accordin( to

their performance# This was achieved y usin( three different approaches% namelyJ

• Total performance scores% where the scores /, to C2 a(ainst each performance

variale were simply added to determine the total score#

• Hierarchical clusterin( /i#e# the *ard method2 applied to the data8set usin( S+SS

,6#7 software# The dendro(ram shown in i(ure 6 illustrates the results of the

hierarchical clusterin( usin( *ard’s method#

• .8Means /Puick $lusterin(2 also usin( S+SS ,6#7 software#

Ktake in i(ure 6K

&,



Tale & illustrates the rankin( otained from three different approaches used to(ether

with the final clusterin( decision# 3n compilin( these results% the clusterin( results

otained from three different methods were interpreted as followsJ

• $ompanies with a total performance score of equal or (reater than 97 were

classified as Hi(h performers#

• $ompanies with a total performance score of less than ,7 were classified as ow

performers#

• $ompanies with a total performance score etween ,7 and 97 were classified as

Medium performers#

The final clusterin( decision was reached y comparin( the clusters across the

three sets of results% i#e# Total Score% .8Means and Hierarchical clusterin(# Althou(h

there were hi(h de(rees of consistency etween the three sets of results% in three cases

/numers ,&% 9& and 62 there were conflicts as hi(hli(hted in Tale &# These conflicts

were resolved y lookin( at the ma!ority (roupin(# or e"ample% in the case of company

,& .8Means and Hierarchical clusterin( techniques oth place the company into the 9nd

cluster% which is associated with medium performers /see total performance clusters2#

Thus% even thou(h the Total +erformance clusterin( approach places them into the hi(h

performance cate(ory with a score of 97 they were classified as a medium performer#

&9



Ktake in Tale &K

-. ,iscussion

3n this paper our o!ective was to e"plore how we can measure firms’

performances comparatively# *e had a real need to do this o!ectively as in a wider

pro!ect we were seekin( to identify mana(erial practices that differentiated hi(h

performin( SMEs from others# Therefore% we needed to measure the performance of

different firms from different sectors in such a way that we could compare them to one

another% rank them accordin( to performance and then (roup them accordin( to their

performance in an o!ective way#

*e started our research with a review coverin( performance measurement and

mana(ement control systems literature to identify what measures we should e usin( to

o!ectively compare the performances of different firms# *e also studied the

enchmarkin( literature in order to identify ways of comparin( the performances of

different firms operatin( in different sectors# Althou(h the literature provided quite a lot

of (uidance on what measures should e used and how they should e used to measure

firms’ performances comparatively% we were unale to identify ri(orous theoretically

(rounded deate on the su!ect# ;ather we found a numer of mana(ement researchers

&&



usin( a variety of performance measures assumin( that the measures they use

adequately e"plain performance# $onsequently% informed y the literature% a focus

(roup was formed to develop the approach presented earlier in the paper /i(ure 92#

This approach was applied to measure and compare the performances of &' different

SMEs across Europe# 3n doin( this we have evaluated and tested the consistency and

reliaility of approach usin( different tools and techniques# These tests led us to modify

our approach resultin( in a classification of the &' firms accordin( to their

performances# As a result the followin( insi(hts have een (ained#

The literature (ives clear (uidance as to the nature of the measures that should

e used to measure a firm’s performance# 3t is clearly stated that there should e a

alance etween financial and non8financial indicators% as well as a alance etween

la((in( and leadin( indicators# However% in contrast to the advice (iven in the literature

we discovered that in order to measure SMEs performances comparativelyJ

• The distinction etween la((in( and leadin( indicators% hi(hli(hted y the literature%

emer(es spontaneously# a((in( indicators are hi(hli(hted as the most relevant to

measure performance# Althou(h this findin( appears to conflict with the views that

emer(e from the literature% this result was somewhat e"pected as the o!ective of the

e"ercise was to compare firms’ performances comparatively ased on results

&5



achieved over the precedin( years rather than to predictin( the potential future

performances of these firms#

• 3n order to descrie the performance of firms adequately we only need to focus on

five financial indicators# These are ;evenue% Market Share% +rofitaility% $ash low%

Dalue Added +roductivity# However% ased on the results further simplification

would e possile y focusin( on only three measures /i#e# Dalue Added

+roductivity% $ash low and +rofitaility2 or even y !ust focusin( on +rofitaility#

This result su((ests that% dependin( on the conte"t of research /;ichard et al.,

97702% it is possile to simplify the performance measurement prolem for

comparative purposes to one% three or five performance indicators as appropriate#

These areJ

• +rofitailit

y

• +rofitaility

• Dalue Added

+roductivity

• $ash low

• +rofitaility

• Dalue Added

+roductivity

• $ash low

• ;evenue

• Market Share

&C



• eadin( customer oriented indicators such as> delivery% lead8time% quality and

responsiveness were considered to e meanin(less when comparin( across different

conte"tual settin(s with different operational strate(ies#

• The only leadin( customer oriented indicator was susequently aandoned as a

result of SMEs’ inailities to score or position their customer satisfaction

performance with respect to their sector# 3t is envisa(ed that the same prolem alsomay have applied to other leadin( customer oriented indicators should we have tried

to collect this data% unless of course the firms were part of a sector wide

enchmarkin( clu utilisin( these measures#

• The leadin( indicators were identified as relevant ut surplus to purpose as the

analysis showed that this (roup of measures% with the e"ception of employee

satisfaction% only made a mar(inal contriution towards descriin( the performances

of the firms# As discussed aove% (iven the aim to compare companies’

performances over the precedin( years% the la((in( indicators are ale to provide

adequate comparative data without the need for more predictive future focused

leadin( indicators#

The literature also su((ests that the performance of firms should e compared

over a period of time and e sensitive to conte"tual factors% such as sectoral and

operational differences# Althou(h this was achieved usin( a scorin( system% this scorin(

&6



system was considered to e su!ective and required e"ternal auditin(# 3n this study% the

scores were deemed a reliale assessment of actual performance# The quality of data

was ensured throu(h the involvement of a numer of mana(ers from each firm% alon(

with the independent e"ternal validation of the data#

*ith respect to the time period over which performance should e compared%

the literature /;ichard et al.% 9770 and .iry% 977C2 su((ests a ,78year timeframe as a

minimum# As this research was not lon(itudinal in nature /a key limitation of the

research method employed2% when collectin( data% the firms were asked to evaluate their

performance over a ,7 year timeframe# Althou(h this was possile in many of the cases%

there were few cases where the mana(ement teams were not capale of assessin( the

performance of the firm over a ,7 year timeframe as none of them had een with the

firm that lon( and they were not ale to provide a comparative !ud(ement in relation to

their sector% ased on the information availale#

*hilst this paper demonstrates that it is indeed possile to measure different

SMEs performances comparatively% there are some questions over the reliaility and

repeataility of these types of comparative measures# The approach adopted here

withstood internal and e"ternal validity tests and can e seen as a roust way of

&'



comparin( SMEs’ performances# However% these results may e limited to this

particular study and the repeataility of the study remains to e seen#

The empirical data used in this research that led to the classification of

companies into hi(h% medium and low performance cate(ories was collected etween

-anuary and 4ovemer 9776# At the time of sumission of this paper for review% three

of the four low performin( companies had already (one out of usiness% mainly due to

the credit8crunch and the (loal economic recession e"perienced in the second half of

9771 and early 9770# 3n contrast% durin( the same period% some of the hi(h performers

in pursuit of their (rowth strate(ies were makin( strate(ic investments into new markets

andor usinesses# This anecdotal% ut si(nificant% insi(ht serves to further stren(then

the validity of the findin(s presented in this paper#

. )onc!usion

The purpose of this paper was to report our findin(s on “what measures to use:

and “how to use these measures: when comparin( overall performance of different

SMEs operatin( in different sectors# Also% as the sample or(anisations were all in the

,7789C7 employees cate(ory it could e ar(ued that the findin(s may also e valid for

lar(er or(anisations% i#e# or(anisations with (reater then 9C7 employees# A key

&1



limitation of the research was the non8lon(itudinal nature of the research methodolo(y

employed#

;ichard et al # /9770J'5C2 in summarisin( their research challen(es in

performance measurement state that “ithout the abilit! to link managerial

prescriptions based on theor! to practical and observable and "ustifiable performance

outcomes, management research ill be little more than informed speculation# # 3n fact

they su((est that performance measurement is a multi8disciplinary issue spannin(

across all disciplines of mana(ement /such as finance% marketin(% operations and human

resources2# They ar(ue that various researchers workin( in their own disciplines usin(

functional performance measures /such as market share in marketin(% schedule

adherence in operations and so on2 need to link their discipline focused performance

measures to overall or(anisational performance#

This paper contriutes to this deate y identifyin( the most si(nificant five

performance indicators that enale us to articulate and compare SMEs overall

performance% thus providin( a framework for linkin( functional performance measures

and indicators to firms’ overall performance# 3t also su((ests that these five indicators

may e further reduced to three or even down to a sin(le profitaility indicator# $learly%

profitaility is hi(hli(hted as the most important performance indicator to e"plain

&0



performance of the SMEs investi(ated# Bespite the emphasis placed on soft measures in

today’s literature% this findin( is consistent with Bawkins et al. /977'2 findin( that

(rowin( relevance is ein( placed on the profit measure#

urthermore% this paper% in seekin( to rank firms’ performances comparatively%

contriutes to the deate on how overall performance may e conceptualised in a

comparative conte"t y identifyin( the appropriate indicators and how they should e

comined and used in order to measure different SMEs performance comparatively#

Acknowled(ements# To e inserted after the review process

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Applied Multivariate &tatistics and Mathematical Modeling # )SAJ Academic

+ress#

$Guchry A#-% <asin M% and Barsch - /,00C2 A review of enchmarkin( literature#

(nternational $ournal of )roduct Technolog! vol# ,7J 9'85C#

Battakumar ;% -a(adeesh ; /977&2 A review of literature on enchmarkin(#

'enchmarking* An (nternational $ournal vol# ,J ,'68970#

Bawkins +% eeny S% and Harris M#4 /977'2 enchmarkin( firm performance#

'enchmarking* An (nternational $ournal vol# ,5J 60&8',7#

Be .oster M##M% *arffemius +#M#- /977C2 American% Asian and third8party

international warehouse operations in EuropeJ A performance comparison#

(nternational $ournal of -perations )roduction Management vol# 9CJ '698

'17#

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Belrid(e ;% owe -% and =liver 4 /,00C2 The process of enchmarkin( A study from

the automotive industry# (nternational $ournal of -perations )roduction

Management vol# ,CJ C7869#

Benison T% McBonald M% /,00C2 The role of marketin( past% present and future#

$ournal of Marketing )ractice* Applied Marketing &cience vol# ,J C58'6

Bi(alwar A#.% Metri #A% /977C2 +erformance measurement framework for world classmanufacturin(# (nternational $ournal Applied Management and Technolog! vol#

9J 1&8,79#

Bi(alwar A#.% San(want .#S /977'2 Bevelopment and validation of performance

measures for *orld $lass Manufacturin( +ractices in 3ndia# $ournal of

Advanced Manufacturing &!stems vol# 6J 9,Q&1#

Buhachek A% $au(hlan A#T% and acoucci B /977C2 ;esults on the Standard Error of

the $oefficient alpha 3nde" of ;eliaility# Marketing &cience vol# 95J 9058&7,#

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Eisenhardt .#M% Fraener M#E /977'2 Theory uildin( from casesJ opportunities and

challen(es# Academ! of Management $ournal vol# C7J 9C8&9#

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Ellis -% *illiams B /,00&2 %omparative financial anal!sis. %orporate &trateg! and

Financial Anal!sis* Managerial, Accounting and &tock Market )erspectives

ondonJ inancial Times+itman +ulishin(#

Everitt #S% Bunn F /977,2 Applied Multivariate ata Anal!sis% Sec#Ed# ondonJ

Arnold#

itG8EnG - /,00&2 'enchmarking staff )erformance. 3o staff epartment can enhance

their value to the customer # San ranciscoJ -ossey8ass +ulishers#

itG(erald % -ohnson ;% and ri(nall S% et al# /,00,2 )erformance Measurement in

&ervice 'usinesses% ondonJ $3MA#

lynn #% Schroeder ;#F% and Sakakiara S /,0052 A framework for quality

mana(ement research and an associated measurement instrument# $ournal of

-perations Management vol# ,,J &&08&66#

orslund H /977'2 The impact of performance mana(ement on customersR e"pected

lo(istics performance# (nternational $ournal of -perations )roduction

Management vol# 9'J 07,80,1#

owler A% $ampell B /977,2 enchmarkin( and performance mana(ement in clinical

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rancisco M% ;oy ;% *e(en % et al /977&2 A framework to create key performance

indicators for knowled(e mana(ement solutions# $ournal of 4noledge

Management vol# 'J 56Q69#

reyta( D% Hollensen S /977,2 The process of enchmarkin(% enchlearnin( and

enchaction# The T/M Maga0ine vol# ,&J 9C8&5#

uller8ove 4 /97762 Mana(ement development in small firms# (nternational $ournal

of Management Revies Dol# 1J ,'C8,07

ullerton ;#;% *empe *# /97702 ean manufacturin(% non8financial performance

measures% and financial performance# (nternational $ournal of -perations

)roduction Management vol# 90J 9,58957#

ynes % Doss $% and Be urca S /977C2 The impact of supply chain relationship

quality on quality performance# (nternational $ournal of )roduction 2conomics

vol# 06J &&08&C5#

Faren(o +% iaGGo S% ititci ) /977C2 +erformance Measurement Systems in SMEsJ a

review for a research a(enda# (nternational $ournal of Management Revies vol#

'J 9C85'

Faren(o +#% ititci ) /977'2 Towards a contin(ency approach to +erformance

MeasurementJ an empirical study in Scottish SMEs# (nternational $ournal of

-perations and )roduction Management Dol# 9'J 179819C#

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Feerlin(s H% .lementschitG ;% and Mulley $ /97762 Bevelopment of a methodolo(y for

enchmarkin( pulic transportation or(anisationsJ a practical tool ased on an

industry sound methodolo(y# $ournal of %leaner )roduction vol# ,5J ,,&8,9&#

Fhalayini A#M#% 4ole -#S /,0062 The chan(in( asis of performance measurement#

(nternational $ournal of -perations and )roduction Management vol# ,6J 6&8

17#

Fhoadian A% Fallear B /,00'2 TPM and or(anisation siGe# (nternational $ournal of

-perations and )roduction Management vol# ,'J ,9,Q,6&

Frando A#% elvedere D /97762 Bistrict’s manufacturin( performancesJ A comparison

amon( lar(e% small8to8medium8siGed and district enterprises# (nternational

$ournal of )roduction 2conomics vol# ,75J 1C800#

Fre(ory M#- /,00&2 3nte(rated performance measurementJ A review of current practice

and emer(in( trends# (nternational $ournal of )roduction 2conomics vol# &7%

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Funasekaran A% +atel $# and Tirtiro(lu E /977,2 +erformance measures and metrics in a

supply chain environment# (nternational $ournal of -perations and )roduction

Management vol# 9,J ',81'#

Hair -;% Anderson ;#E% Tatham ;#% et al# /,0012 Multivariate ata Anal!sis. 4ew

<orkJ +rentice8Hall 3nternational% 3nc#

5'



Harter -#.% Schmidt #% and Hayes T# /97792 usiness8unit8level relationship

etween employee satisfaction% employee en(a(ement% and usiness# $ournal of

Applied )s!cholog!# vol# 1'J 96189'0#

Hawawini F% Suramanian D% and Derdin + /977&2 3s performance driven industry8 or

firm8 specific factors? A new look at the evidence# &trategic Management

$ournal vol# 95J ,8,6#

Heskett -#% -ones T#=% oveman F#*% et al# /,0052 +uttin( the service8profit chain to

work# 3arvard 'usiness Revie March8AprilJ ,6&8,'5#

Ho $hien8Ta% *u <un8Shan /97762 enchmarkin( performance indicators for anks%

'enchmarking* an (nternational $ournal. vol# ,&J ,5'8,C0#

Hudson M% Smart +#A% and ourne M /977,2 Theory and practice in SME performance

measurement systems# (nternational $ournal of -perations and )roduction

Management vol# 9,# ,7068,,,6

Huo % Selen *% <an <eun( -# H% et al# /97712 )nderstandin( drivers of performance

in the &+ industry in Hon( .on(# (nternational $ournal of -perations

)roduction Management vol# 91J ''98177#

Hussein M% Funasekaran A% and aitinen E#. /,0012 Mana(ement accountin( system

in inish service firms# Technovation vol# ,1J C'Q6'#

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Hwan( <#B% in <#$% and yu - /97712 The performance evaluation of S$=; sourcin(

process 8 The case study of TaiwanRs TT8$B industry# (nternational $ournal

of )roduction 2conomics vol# ,,CJ 5,,859&#

3ttner $#B% arcker B#% /,0012 3nnovations in +erformance MeasurementJ Trends and

;esearch 3mplications# $ournal of Management Accounting Research vol# ,7J

97C89&'#

3ttner $#B% arcker B# /977&2 $omin( up short on nonfinancial performance

measurement# 3arvard 'usiness Revie 4ovJ 1180C

-ennin(s +% eaver F% /,00'2 The performance and competitive advanta(e of small

firmsJ A mana(ement perspective# (nternational &mall 'usiness $ournal vol# ,CJ

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-ohnson H#T% .aplan ;#S% /,01'2 Relevance lost – The rise and fall of management

accounting. oston% MAJ Harvard usiness School +ress#

-ohnson ;#A% *ichern B#* /97792 Applied Multivariate &tatistical Anal!sis. ondonJ

+earson Education 3nternational#

.aiser H# /,0672 The application of electronic computers to factor analysis#

2ducational and )s!chological Measurement vol# 97J ,5,8,C,#

.aplan ;% 4orton B /,0092 The alanced ScorecardJ The Measures that Brive

+erformance# 3arvard 'usiness Revie -an8eJ ',8'0#

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.aplan ;% 4orton B /,0062 )sin( the alanced Scorecard as a Strate(ic Mana(ement

System# 3arvard 'usiness Revie -an8eJ 'C81C#

.aplan ;% 4orton B /977,2 The &trateg!5Focused -rganisation, 3o 'alanced

&corecard %ompanies Thrive in the 6e 'usiness 2nvironment. $amrid(e%

MAJ Harvard usiness School#

.arlof % =stlom S /,00&2 'enchmarking 5 A &ignpost to 27cellence in /ualit! and

)roductivit! ondonJ -ohn *iley Sons#

.asul ;#A% Motwani -#F /,00C2 +erformance measurements in world 8 class operations#

'enchmarking for /ualit! Management and Technolog!# vol# 9J 978&6#

.ee(an B#+% Eiler ;#F# and -ones $#; /,0102 Are your performance measures

osolete?# Management Accounting vol# '7J 5C8C7#

.im *#S /977'2 =r(aniGational structures and the performance of supply chain

mana(ement# (nternational $ournal of )roduction 2conomics vol# ,76J &9&8&5C#

.iry - /977C2 Toward a theory of hi(h performance# 3arvard 'usiness Revie vol# 1&J

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.o!ima M% 4akashima .% and =hno . /97712 +erformance evaluation of S$M in -3T

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Revie vol# 50J 6198610#

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innish technolo(y companies# &candinavian $ournal of Management vol# ,1J

6CQ00#

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financial performance comparison usin( self8or(aniGin( maps# 'enchmarking*

An (nternational $ournal vol# ,,J 96'8916#au(en #T% Acur 4% oer H% et al# /977C2 est manufacturin( practices 8 *hat do the

est8performin( companies do? (nternational $ournal of -perations

)roduction Management vol# 9CJ ,&,8,C7#

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)lanning %ontrol vol# 6J 9C18960#

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$amrid(e% Massachusetts* lackwell +ulishers 3nc#

ynn #% Schroeder ;#F% lynn E#-% et al # /,00'2 *orld8class manufacturin( pro!ectJ

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)roduction Management vol# ,'J 6',861C#

Manoochehri F /,0002 =vercomin( ostacles to developin( effective performance

measures# +ork &tud! vol# 51J 99&89&0#

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McAdam ;% ailie /97792 usiness +erformance measure and ali(nment impact on

strate(y Q The role of usiness improvement models# (nternational $ournal of

-perations and )roduction Management vol# 99J 0'98066#

McAdam ;% HaGlett S#A /97712 Bevelopin( a conceptual model of lead performance

measurement and enchmarkin(# (nternational $ournal of -perations

)roduction Management vol# 91J ,,C&8,,1C#

Miles M#% Huerman A#M /,0052 /ualitative ata Anal!sis* 1rounded Theor!

)rocedures and Techni;ues# ondonJ Sa(e +ulications#

Miller -F% ;oth AD /,0052 A Ta"onomy of Manufacturin( Strate(ies# Management

&cience% vol# 57# 91C8&75#

Misterek S#B#A% Booley .#-% and Anderson -#$ /,0092 +roductivity as a +erformance

Measure# (nternational $ournal of -perations )roduction Management vol#

,9J 9085C#

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mana(ement accountin( to support the new manufacturin( realities# $ournal of

Management Accounting Research vol# 5J ,8,0#

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(nternational $ournal of -perations and )roduction Management vol# ,0J 97C8

991#

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4eely A% Adams $# and .ennerley M /97792 The performance )rism* the scorecard for

measuring and managing stakeholder relationship. ondonJ +rentice Hall#

4eely A% Fre(ory M% and +latts . /,00C2 +erformance measurement system desi(n% a

literature review and research a(enda# (nternational $ournal of -perations

)roductions Management vol# ,CJ 178,,6#

4eely A% Mills % +latts .% Fre(ory M% et al# /,0052 ;ealisin( strate(y throu(h

measurement# (nternational $ournal of -perations and )roduction Management

vol# ,5J ,578,C9#

4i"on /,0012 ;esearch and development performance measurementJ a case study#

Management Accounting Research vol# 0% &908&CC#

4ohria 4% -oyce *% and ;oerson /977&2 *hat ;eally *orks# 3arvard 'usiness

revie% vol# ,1J 598CC#

=ktay8irat S# % Bemirhan A /97772 Analysis of the +erformance of the anks Un ,001

y )sin( Multivariate Statistical Methods% (nternational %onference (n

2conomics (< # Septemer ,&8,6% MET)8Ankara% Turkey#

=ktay8irat S# % Bemirhan A /977,2 Analysis of the +erformance of the $ommercial

anks% (nternational %onference (n 2conomics <. Septemer ,78,&% MET)8

Ankara% Turkey#

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=ktay8Urat S##% Bemirhan A /97792 The financial performance of the commercial

anks transferred to the Savin( Beposit 3nsurance und /SB32J Analysis and

$omparison# =ktisat =>letme ve Finans ergisi vol# ,'J 1'8,77#

=lve 4#F% ;oy -% and *etter M /,0002 A )ractical 1uide to 8sing the 'alanced

&corecard % En(landJ *iley#

+arker $ /97772 +erformance measurement# +ork &tud! vol# 50J 6&866#

+erry S#$ /977,2 The relationship etween written usiness plans and the failure of

small usinesses in the )S# $ournal of &mall 'usiness Management vol# &0J

97,8971#

+hillips -#- /,00'2 (n Action* Measuring Return on (nvestment, Ale"andria% Dir(iniaJ

American Society for Trainin( and Bevelopment#

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;esearchJ actor ;ecovery with Small Sample SiGes# 'ehavior 1enetics vol# &9J

,C&8,6,#

;afiq M% +allett ;#A /,0062 Marketin( implementation in the ). en(ineerin( industry%

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;hian S /97792 Bispellin( the modern myth# (nternational $ournal of -perations

)roduction Management Dol# 99J &78C7#

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industry% 'enchmarking* An (nternational $ournal Dol#,&J 9&8&C#

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performanceJ Towards methodolo(ical est practice# $ournal of Management

vol# &CJ ',18175#

;odri(ues #;% $hincholkar A#M /97762 enchmarkin( the H; practices of an

en(ineerin( institute with pulic sector industry for performance enhancement#

(nternational $ournal of Training and evelopment vol# 0J 6897#

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Sears# 3arvard 'usiness Revie -an8eJ 1980'#

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958&9#

Salkind 4#- /97762 27ploring Research% Si"th Ed# +earson +rentice Hall#

Samson B% ord S /97772 Manufacturin( practices and performanceJ $omparisons

etween Australia and 4ew Lealand# (nternational $ournal of )roduction

2conomics vol# 6CJ 95&89CC#

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conceptual model and empirical study in the automotive industry# (nternational

$ournal of -perations )roduction Management vol# 9CJ 61,8'77#

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,,6#

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Simmons - /97712 Employee si(nificance within stakeholder8accountale performance

mana(ement systems# T/M Maga0ine vol# 97J 56&85'C#

Spendolini M#- /,00&2 How to uild a enchmarkin( team# $ournal of 'usiness

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+earson#

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TuGovic S% ruhn M /977C2 3nte(ratin( customer orientation% employee compensation

and performance mana(ementJ a conceptual framework # (nternational $ournal

of 'usiness )erformance Management vol# 'J 9CC89'5#

Di( S#4 /,00C2 enchmarkin(J a select ilio(raphy# )roductivit! vol# &6J C9,8C&5#

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and Self8assessment rameworks in Manufacturin(# (nternational $ournal of

-perational )roduction Management vol# ,5J 1&8,77#

Doss $% lackmon . /,0062 The impact of national and parent company ori(in on

world8class manufacturin(J indin(s from ritain and Fermany# (nternational

$ournal of -perations )roduction Management vol# ,6J 018,,C#

Doss $% lackmon .#% $a(liano ;% et al# /,0012 Made in EuropeJ Small $ompanies#

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-ohn*iley Sons#

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+erformanceJ The ).Rs on(8Term Superior +erformers ,015Q977&# Long

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Lairi M% <oussef M /,00C2 A review of key pulications on enchmarkin(J part 3#

'enchmarking for /ualit! Management and Technolog! vol# 9J 6C8'9#

Lairi M% <oussef M /,0062 A review of key pulications on enchmarkin(J part 33#

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Performance Comparison in SMEs - Bititci Et Al - 2010-03-29 %283%29

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