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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(
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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#(#
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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 $ompetitiveness ;eport <earook /97702 provides assessments of
many aspects of national factors that drive competitiveness# Here there is some
a(reement of the type of measures that should e used to measure a firm’s performance
/such as revenue (rowth% profitaility (rowth% productivity (rowth and so on2 i ut the
comparison of the performances of different companies to one another usin( these
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measures in asolute terms ecomes meanin(less ecause one company may e
operatin( a hi(h (rowth sector /such as food and drinks2 and the other in a declinin(
sector /such as electronics2# =ther studies use return on investment type measures%
particularly shareholder return /$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#
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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%
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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
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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
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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
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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#
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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#
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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
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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
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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 $amp /,0102 makes distinctions etween
internal% competitive% functional and (eneric enchmarkin(# However% these all rely on
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comparison of similar processes% functions or firms# *atson /977'2 reco(niGes the
weaknesses associated with local enchmarkin( and proposes an additional cate(ory
called global benchmarking > which attempts to e"tend the oundary of the
enchmarkin( (eo(raphically to (et over the cultural and usiness process distinctions
amon( companies# However% *atson’s /977'2 approach also does not address the cross8
industry enchmarkin( issue#
The literature also contains many studies investi(atin( how est to enchmark%
descriin( the necessary steps /$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>
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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
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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
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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
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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
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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
$ross% ,00,> 4eely% ,001> 4eely et al #% 9779> ;ucci% ,001> ;hian% 9779; *atkins and
*oodhall% 977,2 the approach shown in i(ure 9 was adopted#
Ktake in i(ure 9K
3n considerin( the question “how to measure these comparatively?: it was
decided to use a relative scorin( technique /as illustrated in i(ure 92 allowin( the
performance of each or(anisation to e scored on a five point scale over a specified time
period /,7 years2 with respect to its sector# =f course% it was reco(nised that this would
,0
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make the whole assessment su!ective# However% after much deate this was deemed
the most appropriate method% with certain qualifications% that would allow cross8
industry performance comparisons whilst ein( sensitive to environmental and
conte"tual factors within which each or(anisation operates# The aove decision was
taken with the qualification that the scores (iven to each or(anisation were trian(ulated%
as well as independently validated usin( o!ective data for each or(anisation and the
sector they operate within% consistent with previous such studies /Miller and ;oth%
,005> au(en et al % 977C2#
+. )ase ,ata and Ana!ysis
Havin( developed a framework for measurin( firms’ performances
comparatively% performance data was collected from &' SMEs across Europe# 3n the
followin( sections we provide a detailed e"planation of how the data was collected and
analysed% as well as our findin(s#
(.1 )ata Collection and )escri$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
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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,
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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
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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&
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(.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 $arlo Simulation study on the sample siGe effect
on factor analysis and concluded that sample siGe had y far the lar(est effect on factor
recovery with a sharp drop8off elow sample siGe of 97# Althou(h the (enerally
accepted appro"imation that the sample siGe must e (reater than &7 is still valid and
necessary for normality assumption% it is not sufficient for roust statistical analysis#
Measure of Samplin( Adequacy /MSA2 and artlett’s Test of Sphericity /artlett’s test2
are two different measures that are frequently used in order to check the adequacy of
factor analysis /Hair et al #% ,001> Lhao% 97702# The artlett’s test is a statistical test for
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the presence of si(nificant correlations across the entire correlation matri" /Hair et al.%
,0012# )se of artlett’s test of sphericity is recommended only if there are fewer than
five cases per variale /Taachnick and idell% 977'2# 3n this study% since the ratio of
numer of cases /&'2 to the numer of variales /12 is less than C% artlett’s test has
een used in order to check the adequacy and appropriateness of factor analysis# .aiser8
Meyer8=lkin /.M=2 test measures Samplin( Adequacy throu(h an inde" ran(in( from
7 to ,# The .M= inde" reaches towards , when each variale is perfectly predicted
without error y the other variales# .M= can e interpreted as “meritorious: when
7#17 or aove and as “middlin(: when it is etween 7#'7 and 7#17 /Hair et al #% ,0012#
Accordin( to Taachnick and idel /977'2% Hair et al,# /,0012 and Lhao /97702 a .M=
inde" over 7#67 is sufficient for estalishin( samplin( adequacy# A .M= inde" of
7#191 to(ether with the statistically si(nificant result of artlett’s test /$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
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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,> $udeck% 9777% Taachnick and
idell% 977'2#
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i(ure & illustrates the ei(envalues efore and after varima" rotation as well as
the Scree8+lot for the data# ;esults show that two principal components /$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'
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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
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(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#
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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
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• 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
&,
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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
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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
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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
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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
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• 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
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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
&'
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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
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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
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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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small usinesses in the )S# $ournal of &mall 'usiness Management vol# &0J
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+hillips -#- /,00'2 (n Action* Measuring Return on (nvestment, Ale"andria% Dir(iniaJ
American Society for Trainin( and Bevelopment#
+reacher .#-% Mac$allum ;#$ /97792 E"ploratory actor Analysis in ehavior Fenetics
;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%
$ournal of Marketing )ractice* Applied Marketing &cience% vol# 9J ,&8&C#
;eid F#$% Smith -# /97772 The impact of contin(encies on mana(erial accountin(
systems development# Management Accounting Research vol# ,,J 59'85C7#
;eider ; /97772 'enchmarking &trategies* A Tool for )rofit (mprovement *ileyJ 4ew
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;hian S /97792 Bispellin( the modern myth# (nternational $ournal of -perations
)roduction Management Dol# 99J &78C7#
;ieiro #M% $aral -#A#S /97762 A enchmarkin( methodolo(y for metalcastin(
industry% 'enchmarking* An (nternational $ournal Dol#,&J 9&8&C#
;ichard +#-% Bevinney T#M% <ip F#S% et al# /97702 Measurin( or(anisational
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#
;ucci A#-% .irn S#+% and Puinn ;#T /,0012 The employee8customer8profit chain at
Sears# 3arvard 'usiness Revie -an8eJ 1980'#
;ue #*% 3rahim 4#A /,0012 The relationship etween plannin( sophistication and
performance in small usiness# $ournal of &mall 'usiness Management vol# &6J
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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SncheG A#M% +IreG M#+ /977C2 Supply chain fle"iility and firm performanceJ A
conceptual model and empirical study in the automotive industry# (nternational
$ournal of -perations )roduction Management vol# 9CJ 61,8'77#
Schlesin(er #A% Heskett -# /,00,2 eonard A# Schlesin(er and -ames # Heskett
;espondJ $ustomer Satisfaction 3s ;ooted in Employee Satisfaction# 3arvard
'usiness Revie, vol# 60J ,518,50#
Schmider(er S% als % Hartmann E% et al# /97712 Fround handlin( services at
European hu airportsJ Bevelopment of a performance measurement system for
enchmarkin(# (nternational $ournal of )roduction 2conomics vol# ,,'J ,758
,,6#
Shirley B#-% ;eitsper(er *#B /,00,2 inkin( quality strate(y with mana(ement control
systemsJ empirical evidence from -apanese industry# Accounting -rgani0ations
and &ociet! vol# ,6% 67,86,1#
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
&trateg! vol# ,5J C&86'#
Taachnick #F% idell #S /977'2 8sing Multivariate &tatistics% oston% )SAJ
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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#
Doss $#A% $hiesa D% and $ou(hlan + /,0052 Bevelopin( and Testin( enchmarkin(
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#
'usiness &trateg! Revie vol# 0J ,8,0#
*atkins $% *oodhall M /977,2 The measurement of emplo!ee satisfaction in
3andbook of )erformance Measurement. ondonJ FEE +ulishin(#
*atson F#H /977'2 &trategic 'enchmarking Reloaded +ith &i7 &igma* (mprove :our
%ompan!?s )erformance 8sing 1lobal 'est )ractice# Hooken 4ew -erseyJ
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*orld $ompetitiveness ;eport <earook /97702 <earook 3MB# Availale atJ
httpJwww#imd#ch
<amin S% Mavondo % Funasekaran A% et al# /,00'2 A study of competitive strate(y%
or(anisational innovation and or(anisational performance amon(# (nternational
$ournal of )roduction 2conomics vol# C9J ,6,8,'9#
<asin M#M /97792 The theory and practice of enchmarkin(J then and now#
'enchmarking* An (nternational $ournal vol# 0J 9,'895&#
<e8<un #$ /,0002 Success factors of small and medium8siGed enterprises in TaiwanJ
an analysis of cases# $ournal of &mall 'usiness Management vol# &6J 5&QC6
<ip F#S% Bevinney T#M% and -ohnson F /97702 Measurin( on( Term Superior
+erformanceJ The ).Rs on(8Term Superior +erformers ,015Q977&# Long
Range )lanning. Dol# 59J &0785,&
<un( *#.#$% $han B#T#H /977&2 Application of value delivery system /DBS2 and
performance enchmarkin( in fle"ile usiness process reen(ineerin(#
(nternational $ournal of -perations )roduction Management vol# 9&J &778
&,C#
<urdakul M /977C2 Bevelopment of a performance measurement model for
manufacturin( companies usin( the AH+ and T=+S3S approaches# (nternational
$ournal of )roduction Research vol# 5&J 56708565,#
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Lairi M% <oussef M /,00C2 A review of key pulications on enchmarkin(J part 3#
'enchmarking for /ualit! Management and Technolog! vol# 9J 6C8'9#
Lairi M% <oussef M /,0062 A review of key pulications on enchmarkin(J part 33#
'enchmarking for /ualit! Management and Technolog! vol# &J 5C8C0#
Lhao 4 /97702 The Minimum Sample SiGe in actor Analysis% last ed# March 9&#
Availale
atJhttpJwww#encorewiki#or(displayVnGhaoTheWMinimumWSampleWSiGeWinW
actorWAnalysis
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i The ack(round to some of these measures and the academic deate on this area is further discussed in the ack(round literaturesection of this paper#ii Academy of Mana(ement -ournal /AM-2% Administrative Science Puarterly /ASP2% -ournal of 3nternational usiness Studies/-3S2% -ournal of Mana(ement /-=M2% and Strate(ic Mana(ement -ournal /SM-2iii 3ndependent companies employin( less the 9C7 people and with turnover not e"ceedin( XC7m or with a alance sheet total note"ceedin( X5&m#iv Here the term Birector is used as a synonym to Mana(er#v AME is a dataase that contains information for companies in the ). and 3reland# or AME and similar dataases coverin(
other re(ions httpJwww#vdinfo#com+roducts$ompany83nformation4ational#asp" vi Darima" is the most commonly used of all the rotation techniques availale and is applied to further differentiate the level ofimportance etween principal components /-ohnson and *ichern% 9779% p#C7C% =ktay8irat and Bemirhan% 977,2#vii At this sta(e several hierarchical clusterin( al(orithms were usedJ complete linka(e% avera(e linka(e% nearest nei(hour linka(emethods which use Euclidean distance /similarity2 matri"# ;esults otained from all methods were appro"imately same# *ard’smethod was considered most suitale as it minimises information losses that could arise from !oinin( two (roups /-ohnson and*ichern% 9779% p#6072#