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Please citeand corpo
ARTICLE IN PRESSG ModelRCSAR-27; No. of Pages 12Revista de
Contabilidad Spanish Accounting Review xxx (xx) (2014) xxxxxx
REVISTA DE CONTABILIDADSPANISH ACCOUNTING REVIEW
www.elsev ier .es / rcsar
Relationship between management information systemsand corporate
performance
Jos AntoLecturer Unive
a r t i c l
Article history:Received 31 JuAccepted 25 FAvailable onlin
JEL classicatioM1M4
Keywords:Management iROIClusterPLSNew managem
Cdigos JEL:M1M4
Palabras clave:Sistemas de inROIClusterPLSNuevas herram
CorresponE-mail add
http://dx.doi.o1138-4891/ this article in press as: Prez-Mndez,
J. A., & Machado-Cabezas, . Relationship between management
information systems
nio Prez-Mndez, ngel Machado-Cabezas
rsity of Oviedo (Spain), Facultad de Economa y Empresa, Oviedo,
Spain
e i n f o
ly 2013ebruary 2014e xxx
n:
nformation systems
ent techniques
a b s t r a c t
The literature review on the success of management information
systems (IS) provides empirical evidencethat mere investment in IS
and New Management Tools (NMTs) does not guarantee better
businessresults. Aiming to contribute to the knowledge of the
factors explaining the success of IS implementation,this paper
classies them through cluster analysis, with a sample of Spanish
companies according to thevaluation given by their nance directors
(CFOs) to the quality of such systems and their use for
strategicpurposes. This classication helps to answer three
questions: do companies that better rate their ISimprove their
performance? How do IS quality and strategy affect results? Is
there a positive relationshipbetween the use of NMTs and
improvement in performance?
Through the non-parametric KruskalWallis test and a partial
least squares (PLS) model results areyielded that support the rst
question and show the positive effect of the IS quality and
strategy onimproving corporate protability. Logistic regression
showed an interaction between the use of NMTsand the IS strategic
approach with positive effects on improving protability.
The results of this study have signicant implications for
companies, suggesting that investment innew IS and NMTs must be
coupled with a clear sense of strategy.
2013 ASEPUC. Published by Elsevier Espaa, S.L.U. All rights
reserved.
Relacin entre los sistemas de informacin de gestin y el
resultadoempresarial
formacin de gestin
ientas de gestin
r e s u m e n
La revisin de la literatura sobre el xito de los sistemas de
informacin de gestin (IS) aporta evidenciaemprica que senala que la
mera inversin en IS y en nuevas herramientas de gestin (NMT) no
garan-tiza la mejora de los resultados empresariales. Con el n de
contribuir al conocimiento de los factoresexplicativos del xito de
los IS, este trabajo realiza una clasicacin de los mismos a travs
de un anlisiscluster para una muestra de empresas espanolas en
funcin de la valoracin realizada por los directoresnancieros (CFOs)
sobre la calidad de tales sistemas y su uso con nes estratgicos.
Esta clasicacin con-tribuye a responder a tres cuestiones: mejoran
ms su rentabilidad las empresas con mayor valoracinen su IS?, cmo
afectan la calidad de los sistemas de informacin y su enfoque
estratgico a los resultadosempresariales?, existe una relacin
positiva entre el uso de NMT y la mejora de los resultados?
A travs del test no paramtrico de Kuskal-Wallis y de un modelo
Partial Least Squares (PLS) los result-ados dan soporte a la
primera cuestin, al igual que muestran un efecto positivo de la
calidad de los IS yde su enfoque estratgico sobre la mejora de la
rentabilidad empresarial. La regresin logstica encuentrauna
interaccin entre el uso de NMT y el enfoque estratgico del IS con
efectos positivos sobre la mejorade la rentabilidad.
Los resultados de este trabajo presentan implicaciones
relevantes para las empresas, ya que la inversinen nuevos IS y NMT
debe realizarse con sentido estratgico.
2013 ASEPUC. Publicado por Elsevier Espaa, S.L.U. Todos los
derechos reservados.
ding author.ress: [email protected] (. Machado-Cabezas).
rg/10.1016/j.rcsar.2014.02.0012013 ASEPUC. Published by Elsevier
Espaa, S.L.U. All rights reserved.rate performance. Revista de
Contabilidad Spanish Accounting Review (2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
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Accounting Review xxx (xx) (2014) xxxxxx
Introduction
The objective of management accounting is to provide timelyand
value-relevant information to managers to help them takeshort and
lo
Nowadaalised, and effective ansuccessfullyperformancLibby &
Wa
In recentchallenges agement acneeds if it is1998a).
Maintroduced
Traditiontions costs, combined wThere is ntute New
MNeverthelestechniques:ment (ABMquality manment accoutheory of
cindicates thmanagemechanging ne
Researchunlikely to atheir rmsment informand, as a corate their
mgreater extimproving follows theaccounting cessful if it its
nancial
Internalterms of quaccounting agement tethe evaluatto
nancialment in NMthrough whexamined. Tanalysing timprove rNMTs has
o
This studrms on thegive in two (IS strategysis. We use identies
thment IS. Thiquestions:
- Do rms wformance
- How do IS strategy and IS quality affect rms performance?-
Does a positive relationship exist between the use of NMTs and
increased protability?
folloith ecforthe mee varcal ssions
ure
s artinancanc
IS, fonto a
s of in
s wos; he
cont evalee, &g wh
ans s, meor exe casculariatio
one IS aness, eth
e and with
quatisfay IS re
r sats (Urt (Liv
have Wa
are 92).
for e fac
posf its hieve
difd spe
prolt to ountves oterm this article in press as: Prez-Mndez, J.
A., & Machado-Cabezas, rate performance. Revista de
Contabilidad Spanish Accounting Rev
ng-term decisions (Gupta & Gunasekaran, 2005).ys, the
environment is extremely competitive and glob-technologies are
evolving constantly. Firms need mored sophisticated management
accounting systems to
face the new conditions and improve their nanciale (Al-Omiri
& Drury, 2007; Gupta & Gunasekaran, 2005;terhouse, 1996;
Mia & Clarke, 1999).
years, increasing global competition has intensied thefaced by
managers, and many experts warn that man-counting needs to adapt to
meet managers changing
to maintain its relevance (Chenhall & Langeld-Smith,ny
innovations in management accounting have beenin response, in an
attempt to improve its utility.al techniques in management
accounting, such as sec-budgets, standard costs, and direct costs
have beenith more recent techniques over the last three
decades.
o universal consensus on which techniques consti-anagement Tools
(NMTs) (Cadez & Guilding, 2008).s, most authors consider as
NMTs or non-traditional
activity-based costing (ABC), activity-based manage-), balanced
scorecard (BS), just in time (JIT), totalagement (TQM), target
costing (TC), strategic manage-nting (SMA), lifecycle costing
(LCC), benchmarking andonstraints (TOC). The prevalence of these
techniquesat rms need increasingly accurate and sophisticatednt
information systems (IS) that adapt to managerseds.ers assume that
managers, as rational agents, aredopt a management IS that does not
help them improve
nancial performance (Chenhall, 2003). Thus, manage-ation will
conceivably help improve decision-making
nsequence, nancial performance. Likewise, rms thatanagement IS
highly will conceivably adopt NMTs to a
ent, with the ultimate objective of maintaining and/ortheir
nancial performance. The current piece of work
approach of the abovementioned contributions to theliterature
and considers that a management IS is suc-enables the rm to take
better decisions and improve
performance. accounting IS differ between companies, for
example, inality, level of use and strategic relevance. Studies in
theliterature tend to focus on the impact of specic man-chniques on
nancial performance, while few look ation rms make of their own IS
and the relation of these
performance. Empirical evidence shows that invest-Ts does not
guarantee better results. The mechanismsich IS affect a rms
performance are therefore under-his study aims to contribute to
this line of research by
o what extent quality and the strategic approach of ISms
performance, evaluating the effect that the use ofn performance.y
evaluates the management IS of a sample of Spanish
basis of the scores that their nancial directors (CFOs)areas:
quality of IS (IS quality) and strategic use of the IS), which are
identied in a principal components analy-these elements to
accomplish a cluster analysis, whichree different types of rms
depending on their manage-s typology of rms is then used to answer
the following
hose management IS scores highly improve their per-?
Theship w(henceand thand thempiriconclu
Literat
Thiof IS, performcess intaken i
Succes
Thiof NMTin this
TheBanerjdecidinno me(2008)Thus, fis in tha
partiapprec1997).
DeLcess ofof succation mDeLonmodelmationuser saused bof IS.
Usesuccesconcepity, to (Wu &with ISYip, 19ductedof
thesCFOs).
Onemine ihas acThis isdeneas to imdifcu
Accobjectiered inationship between management information
systems2014). http://dx.doi.org/10.1016/j.rcsar.2014.02.001
wing section analyses the success of IS, their relation-onomic
results and the effect of new tools or techniques
techniques, NMTs). Then, the research hypothesesthodology
followed are described, including the sampleiables used. The fth
section presents the results of thetudy, while the nal section
offers the most important
of the research and its limitations.
review
cle deals manly with three basic concepts: the successial
performance, and the relation between NMTs ande. First, we will
analyse the literature dealing with suc-cusing on its effect on
corporate results. NMTs are alsoccount.
formation systems
rk aims to evaluate the success of management IS andnce, the rst
step is to dene what is meant by successext.uation of IS is a
difcult task for researchers (Limayem,
Ma, 2006; Serafeimidis & Smithson, 2000). Similarly,ether an
IS or management technique is successful is byimple either.
According to Petter, DeLone, and McLeanasurement of IS success is
both complex and illusive.ample, it is extremely difcult to dene
what successe of ABC (Shields, 1995), and some apparent failures
of
technique may in fact be a consequence of a limitedn of the uses
for which it was put into practice (Malmi,
and McLean (1992) examine the literature on the suc-d conclude
that researchers do not use a single measurebut various. These
authors established a success evalu-od from 6 different and
interrelated dimensions. Later,
McLean (2003) updated and improved the previous 7 variables or
dimensions to measure IS success: infor-lity, service quality,
system quality, intention to use, use,ction and net benets. These
models have been widelysearchers for understanding and measuring
the success
isfaction is one of the most important measures of ISbach &
Mller, 2012); it remains, however, an uncertainari, 2005). IS users
expect the system to be of high qual-
quality information and to provide substantial benetsng, 2006).
The main determinants of user satisfactionrelevance, content,
accuracy, and timeliness (Seddon &These elements were all
gathered in the IS survey con-this study. It is therefore
understood that a high scoretors is related to high IS user
satisfaction (in this case,
sible way of evaluating the success of an IS is to
deter-objectives have been met. In other words, if the rmd the
benets that theory suggests it would achieve.cult to decide because
such systems often lack clearlycic objectives. The objectives are
usually generic, suchve the process of decision-making, which is
extremelytest a posteriori.ancy literature has not reached a
consensus about thef IS. In a global context, most objectives can
be consid-ediate. That is, they are not the nal goals but
rather
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stepping-stones on the road to the rms ultimate objective. This
isgenerally assumed to be to ultimately obtain the greatest
possibleprot, or more specically to achieve sustainable
improvements inprotability (Chenhall, 1997). This amounts to saying
that no rmwould wantthe system performancobjectives
oimplementibecause somas improved2013).
Information
As has bobtained bywith this idshould be tomance. For better
decis1993); the not to obtaian innovatinancial pethe main obrole of
the smance (Ran
Using nfailure of ISmeasuremeunderstand(Gunasegarnancial perms
IS anto evaluatecial data ha2000), whilsince they
dnon-nanci(Anderson &
Given thnancial peand NMTs.
New manag
Firms admaking proto improveSadik, 2012ies attemptNMTs. The
ticular manin results.
Some aumanagemenif rms folloSmith, 199studies ndtechniques &
Langeld
Abernetincreasing does not imcial perform
of a particular information technology (IT) on nancial
performanceconsidering ve types of strategy, and in all the
strategies they ndimprovements in nancial performance when rms use
advancedITs. Therefore, there are difculties providing evidence on
a positive
nship (Ism
threS, a
le rel; althdor &nshipposie rmll, 1uthobene
stude of
to leonal res (j, & Oat th
the t posiers o
obtthat . Destree,h reanaliffer
BC haof bo, 201r stuestmher mman th
Innessed Ailed t
has abos unent
stenlfssoutio
n thed, an
hese
carriple
intro draan be theabiruate this article in press as: Prez-Mndez,
J. A., & Machado-Cabezas, rate performance. Revista de
Contabilidad Spanish Accounting Rev
to implement a new management IS if it did not expectto
ultimately generate an improvement in its nanciale, even if the rm
adopts the system with some specicf management improvement. When a
rm commits tong, using, and supporting an IS, the rm often does soe
type of positive organisational impact is desired, such
protability or productivity (Petter, DeLone, & McLean,
systems and performance
een suggested previously, the success of the IS can be measuring
its effect on results. Various authors agreeea, and afrm directly
that the aim of a management IS
achieve an improvement in the rms nancial perfor-instance,
authors say that ABC should help rms takeions or improve their
nancial performance (Dopuch,objective of ABC is to improve nancial
performance,n more exact costs (Cooper & Kaplan, 1992); rms
adopton to achieve benets that directly or indirectly
affectrformance indicators (Cagwin & Bouwman, 2002); orjective
of an IS is to improve and enhance the potentialystem in improving
the rms overall nancial perfor-ganathan & Kannabiran,
2004).ancial performance as an indicator of the success or
has various advantages. On the one hand, performancent is
critical to the success of the rm because it createsing, shapes
behaviour, and improves competitivenessan, Williams, &
McGaughey, 2005). On the other hand,rformance represents a common
objective of all thed/or management techniques, which makes it
easier
their utility. Finally, despite their limitations, nan-ve the
advantage of being precise and objective (Parker,e intermediate,
non-nancial goals are often subjective,epend on personal opinions.
Hence, the evaluation ofal goals may depend on the job held by the
respondent
Young, 1999).e above advantages, the current study uses the
rmsrformance to measure the success of management IS
ement techniques and performance
opt NMTs with the purpose of improving the decision-cesses,
their exibility and output costs, and, ultimately,
results (Henry & Mayle, 2003; Hatif AlMaryani &).
Despite the limitations, a number of empirical stud-
to relate nancial performance to management IS ormajority of
them analyse the individual effect of a par-agement technique,
albeit with a degree of divergence
thors nd that a set of management techniques andt accounting
practices improve nancial performancew certain strategic priorities
(Chenhall and Langeld-
8b; Naranjo-Gil, 2004). In contrast, other empirical that rms
that use traditional management accountingare more protable than
those that use NMTs (Chenhall-Smith, 1998a).hy and Bouwens (2005),
citing various studies, observeevidence that innovation in
management accountingprove either the decision-making or the rms
nan-ance. Theodorou and Florou (2008) analyse the effect
relatiomance
TheTQM, Bpossibmance(Correrelatiothat a and th& Jarresome
aBoujel
Fewthe usshowntraditimeasuMooragest thOnly ifhave amembBS
andthose use BS& Grab
Witsulted using dtors. Aterms SilvolaAnotheon invand ot&
Bouwbetwee1999; have ustill fasystem
Theremaininvestma consi(Brynjocontribplays iadopte
Hypot
Weto a samin the may be
It cenhanc& Kannto evalationship between management
information systems2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
between IT investments and rms nancial perfor-ail, 2007; Mahmood
& Mann, 1993).e NMTs that are most used by the sample rms arend
ABC. Various empirical studies have analysed theationship between
applying TQM and nancial perfor-ough some nd no relation between
the two variables
Goni, 2011; Ittner & Larcker, 1995), or only a partial
(Samson & Terziovski, 1999), the majority concludetive
relationship exists between the TQM techniques nancial performance
(Choi & Eboch, 1998; Easton
998; Lam, Lee, Ooi, & Lin, 2011; Sila, 2007), althoughrs
consider that such a relationship is negative (Wali &,
2011).ies have investigated the possible relationship betweenBS and
nancial performance. This system has beenad to superior nancial
performance in comparison toresults measurement systems based only
on nancialChi & Hung, 2011; Davis & Albright, 2004; De
Geuser,yon, 2009). Braam and Nijssens (2004) ndings sug-e use of BS
does not automatically improve results.echnique complements the
strategy does the techniquetive impact on nancial performance. The
majority off the Institute of Management Accountants (IMA) useain
improvements in operational performance, whiledo not improve
operational performance tend not topite this, many applications of
this system fail (DeBusk
2006).gard to the ABC system, the various studies con-yse the
effects of using ABC on nancial performanceent methodologies and
nancial performance indica-s been found to improve rms relative
protability inth accounting and market-based measures (Jnkl &2;
Kennedy & Afeck-Graves, 2001; Raq & Garg, 2002).dy nds a
positive association between ROI (returnent) and ABC, and that
synergies exist between ABCanagement techniques such as JIT and TQM
(Cagwin
n, 2002). In contrast, other studies nd no associatione use of
ABC and rm performance (Gordon & Silvester,
& Mitchell, 1995; Ittner, Lanen, & Larcker, 2002).
FirmsBC now for more than 20 years, but the literature haso nd
sufcient empirical evidence that adopting the
an effect on nancial performance (Gosselin, 2006).ve discussion
means that the productivity paradoxresolved. According to this
paradox, despite the massive
in new IS, researchers have still failed to demonstratet
correlation between this investment and productivityn & Hitt,
1996). The current study offers an empiricaln that analyses this
correlation, highlighting the role it
success of IS, taking into account the strategic approachd the
quality and implementation of NMTs.
s
ed out an empirical study based on a questionnaire sent of
Spanish rms to try to respond to the questions raisedduction. Based
on this information certain hypotheseswn and are presented below.e
said that the main aim of an IS is to improve and
overall performance of the organisation (Ranganathanan, 2004);
this is the reason why this criterion is used
the IS in this study.
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Prez-Mndez, . Machado-Cabezas / Revista de Contabilidad Spanish
Accounting Review xxx (xx) (2014) xxxxxx
The measure of IS user satisfaction provides a useful
assess-ment of the systems success (DeLone & McLean, 1992;
EscobarPrez & Vlez Elorza, 1997; Raymond, 1987). The degree of
IS utilityperceived by users is similar to the expectations of
future bene-ts to be relikely to be feel the sysquently
imprelationshiptheir IS andof the inforthey are sacess modelused by
reset al., 2013lishes, amonin order toand Aronsoknowledge not
always mance (Lee
Bearing exists betwof IS qualityas follows:
H1. Informated with th
The possevaluated inperformancnancial pethe issues cimplementbe
studyingperformanc
Since thetems is sumHypothesisH1.1 and H
IS stratof all organSabherwal, found IS strformance (CJarvenpaa
&following h
H1.1. IS improveme
Theoretiimprove nallow betteresult in imfound a posin
performqiang & Ze-plan is esseimproves thhypothesis
H1.2. IS improveme
Organizatechniques Schoch, & Y
The literature review suggests the possibility of a positive
rela-tionship between the use of NMTs and nancial performance.
Theadoption of recent management accounting changes are growingdue
to their contribution to overall performance of organisations
& Fred, 2008; Vera-Munoz, Shackell, & Buelner,
2007).anisawiths
Topniqual peempial pel of tn, Koe, &
line sequ
he uce im
dolo
ore tis1 anampriabluesti
m thnt ISntiat
test na2004ram
Kru wher corelat, thes. Th
pairoughat a
en od toeme
ndedd valtion-rsonr ind
to cemetest H
allowmpanriable
de
19.0 this article in press as: Prez-Mndez, J. A., &
Machado-Cabezas, rate performance. Revista de Contabilidad Spanish
Accounting Rev
alised by using the system (Rai et al., 2002). Users aresatised
with their rms IS and rate it highly when theytem will help them
improve their decisions and conse-rove the rms nancial performance.
Thus, a positive
conceivably exists between the score managers give to the rms
nancial performance. Obviously, the usersmation obtained with an IS
will rate it highly whentised with the system. DeLone and McLeans
IS suc-
(DeLone & McLean, 2003) is the method most widelyearchers,
both at theoretical and empirical levels (Drr); this model, as has
been previously explained, estab-g others, the user satisfaction
and net benets variables
evaluate the IS. Following this, Halawai, McCarthy,n (2007) nd a
relation between user satisfaction andmanagement systems success.
However, IS success doesimply a signicant improvement of the rms
perfor-, 2012).all this in mind, in order to clarify whether a
relationshipeen user satisfaction (measured by the users
evaluation
and strategy) and performance, the rst hypothesis is
ation systems with high scores are positively associ-e rms
nancial performance improvement.
ible effect of rms IS on nancial performance can be two ways:
rst, by studying the change in the nanciale over a period of time;
and second, by examining therformance observed at a particular
moment in time. Asovered in the survey refer to the characteristics
and theation of the IS during the last analysed period, we will
the effect the IS has on the improvement of the rmse.
valuation given by the CFOs regarding information sys-marised in
two main factors, IS strategy and IS quality,
1 has been augmented with two additional hypotheses:1.2.egy
alignment is assumed to facilitate the performanceisations,
regardless of type or business strategy (Chan,& Thatcher, 2006:
27). Some empirical studies haveategy alignment to inuence the rms
nancial per-han, Huff, Barclay, & Copeland, 1997; Chan et al.,
2006;
Ives, 1993; Teo & Ang, 1999). Thus, we propose
theypothesis:
strategy is positively associated with performancent
cally, it seems fairly clear that quality information mayancial
performance, given that this information shouldr management
decisions to be made, which may in turnproved nancial performance.
Some researchers haveitive correlation between IS quality and
improvementance (Byrd, Thrasher, Lang, & Davidson, 2006;
Xing-jiang, 2009). Byrd et al. (2006) nd that an IS qualityntial
for the success of an IS, particularly since the plane quality of
the IT system. Consequently, the followingis presented:
quality is positively associated with performancent.
tions dissatisfaction with their traditional accountingis a
major motivation for the diffusion of NMTs (Beng,ap, 1994;
Gosselin, 2006).
(AdamOrg
cantly the rm2005).or technancimany nanciseveraFeriduMcKonin
this
Con
H2. Tforman
Metho
Befanalysin the sThe vatype qrms.
Froagemediffere
Weof the(1996non-pa
Thetestingused foor not resultssamplesample
Thrtions thbetwestructeimprovommetest anpredic
Peaation orelatedmanag
To whichthe cothe vafactors
1 SPSSationship between management information systems2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
tions have increased their investments in IS signi- the
expectation that these investments will improvenancial performance
(Ravichandran & Lertwongsatien,
managers use new management accounting systemses when they
believe that they will improve the rmsrformance (Abernethy &
Bouwens, 2005). There arerical studies that analyse the effect of
using a NMT onrformance, but few studies have been done
consideringhese techniques simultaneously (Kannan & Tan,
2005;rhan et al., 2005; Al-Khadash & Feridun, 2006; Cua,
Schroeder, 2001). Therefore, more research is neededof
study.ently, we advance the following hypothesis for testing:
se of NMT has a positive effect on rms nancial
per-provement.
gy
esting the hypotheses, we ran a principal componentsd obtained
three factors relating to the management ISle rms (use of cost
systems, IS quality and IS strategy).es used to form the factors
were obtained from Likert-ons in a questionnaire sent to the CFOs
of the sample
e factors identied, which dene and evaluate the man-, we ran a
cluster analysis. This led to three types of rmed by the scores
given to their management IS.ed the rst hypothesis by studying the
evolutionncial performance variables in the period analysed), using
the non-parametric KruskalWallis test, the
etric MannWhitney test and partial least squares
(PLS).skalWallis analysis is a non-parametric method forther
samples originate from the same distribution. It ismparing more
than two samples that are independented. When the KruskalWallis
test produces signicantn at least one of the samples is different
from the othere MannWhitney test is useful for analysing the
specics for signicant differences.
PLS, which is a technique based on structural equa-llows the
building of models with complex relationshipsbservable and latent
variables, a model was con-
analyse the effects of IS quality and IS strategy in thent of
corporate performance. PLS path modelling is rec-
in the early stage of theoretical development in order toidate
exploratory models, being particularly suitable fororiented
research (Henseler, Ringle, & Sinkovics, 2009).s chi-square (2)
test is used to determine the associ-ependence of two qualitative
variables, such as thoseluster membership and the use or not of a
particularnt tool.ypothesis 2, we use the logistic regression
technique,s the identication of characteristics that
differentiateies that have improved their nancial results. Amongs
that explain the improved economic results are the
ning the IS and use of NMTs.
and SmartPLS 2.0 were used for the statistical analyses.
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Review xxx (xx) (2014) xxxxxx 5
Table 1Characteristics of sample.
Mean 25thpercentile
75thpercentile
Revenue from ordinary activitiesin 2004 (D 000s)
44,962 20,602 68,028
Total assets in 2004 (D 000s) 41,055 16,226 52,890Number of
workers in 2004 198 70 284Operational
Table 2Use of new ma
Managemen
ABC cost sysBalanced scoValue chain Just in time Business
proTotal qualityComputer-in
Sample
Using inInforma, whchose 450 the followin
- Spanish fo- Revenues
During 2to inform tparticipatiowho agreedagain to rmresponses
w
The 56 r
- Industry: - Commerc- Services:
Table 1 rcentiles for
To test respondingber of wortotal assetsthese variahas a lowetry
(D 35,09there were non-respon
Of the 5Table 2). Th(35.7%), and
2 Before elabcompanies, ascases, the answthe CFO is the
Dependent variables
The dependent variables are ratios to facilitate
comparisonbetween the rms. They are all based on objective data
from rmsbalance sheets, not on the respondents opinions. They all
measurenancial performance, and are as follows:
- MARGIN 1. Resources generated by ordinary activities over rev-
fromeciatGIN 2ratio. Opt fromt.. Pro
ations fro. Opal prce shC. R
labouS/OI.
stud2004eivabal pee beearsal pe1997periorderhangwe rdianes ar.3
Ths sho
rm ector
houlangther prot/Total assets (%) 8.3 2.7 11.9
nagement techniques (NMT).
t technique No. rms % sample
tem 10 17.9recard (BS) 20 35.7analysis (VCA) 2 3.6(JIT) 6
10.7cess reengineering (BPR) 7 12.5
management (TQM) 20 35.7tegrated manufacturing (CIM) 7 12.5
formation from the SABI database, from the rmich holds
accountancy data on Spanish companies, werms as the object of
analysis. The rms complied withg requisites:
r-prot rms, operating, and founded before 1996. from ordinary
activities exceeding D 10 million in 2004.
006, we contacted the CFOs of the rms by phonehem of the
objectives of the study and request theirn.2 The questionnaire was
sent by e-mail to those CFOs
to receive the survey. The questionnaire was sents that had not
initially responded. Eventually, 56 validere received, which
represent a response rate of 12.4%.
espondent rms are distributed by sector as follows:
75%e: 10.7%14.3%.
eports on the mean values and the 25th and 75th per- some of the
variables in the sample.
enuedepr
- MARties:
- ROI 1proshee
- ROI 2operasset
- ROI 3ationbalan
- ROI Hting
- COST
To 1996it concnancithat ththree ynanci1996of the
In otheir cables, the mevariablmedianlated a
Final Final s
It ssure chbut ra this article in press as: Prez-Mndez, J. A.,
& Machado-Cabezas, . Relrate performance. Revista de
Contabilidad Spanish Accounting Review (
for non-response bias, we compared by sectors the and
non-responding rms revenue, total assets, num-kers and the ratio of
operational prot divided by
in 2004. There were no signicant differences acrossbles (at p =
0.05) with the exception of revenue, thatr value in the case of
non-respondents in the indus-1,900 vs. D 40,994,870). It was then
understood thatno fundamental differences between respondents
anddents.6 rms analysed, 58.9% apply at least one NMT (seee
following are the most widely used: BS (35.7%), TQM
ABC (17.9%).
orating the nal questionnaire, a pilot questionnaire was sent to
veking who the most appropriate person to answer it was. In all
theer was the CFO. In the SMEs, as with the companies of the
sample,
person in charge of the IS as its main user.
period 199nomic deverms in the
In applymeasures tthe period, ROI 1, ROI 2
Control vari
This worstudies: rm
For the is similar
3 This proceure (Izan, 1982000; Fernndationship between
management information systems2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
ordinary activities: ratio of operational prot plusion to
revenue from ordinary activities.. Operational prot over revenue
from ordinary activi-
of operational prot to revenue from ordinary
activities.erational prot over total assets: ratio of
operational
prot and loss account to total assets from balance
t from ordinary activities over total assets: ratio ofal prot
plus nancial prot (less nancial costs) to totalm balance
sheet.erational prot over operational assets: ratio of oper-ot from
prot and loss account to total assets fromeet less nancial
investments.
OI of human capital: operational prot before subtrac-r costs
divided by labour costs.
Operating costs over ordinary income.
y the change in the results, we chose the period. The reason for
this relatively long time period is thatly takes time for the
effects of changes in the IS on therformance to become evident.
Researchers have foundnets of new IS may not become apparent for
two or
(Brynjolfsson, Gurbaxani, & Kambil, 1994). The
initialrformance is measured as the mean value of the period, and
the nal nancial performance as the mean valued 20032004.
to analyse the initial and nal relative positions ande in the
period 19962004 for the protability vari-e-calculated these
variables dividing their values by
of the rms sector (Cagwin & Bouwman, 2002). Thee interpreted
as their relative distance from the sectore change in performance
variables over time is calcu-wn in the following formula:
protability protability
Initial rm protabilityInitial sector protability
d be made clear that this expression does not mea-es in
protability of each company in absolute terms,evaluates the
relative performance change for the62004 by sector position,
irrespective of macroeco-lopments, since such inuence will be the
same for all
same sector.ing the PLS technique, a latent variable is used
thathe change in the ROI from the beginning to the end ofand is
constructed via the changes in three indicators:
and ROI 3.
ables
k uses two control variables commonly used in similar size and
sector.
development of the PLS model, the chosen approachto that taken
by Serrano-Cinca, Fuertes-Calln, and
dure has been used earlier, for example in studies predicting rm
fail-4; Platt and Platt, 1990), or analysing protability (De Andrs
Surez,ez Snchez, Montes Pen, & Vzquez Ords, 1996).
-
Please citeand corpo
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Accounting Review xxx (xx) (2014) xxxxxx
Table 3Factors obtained in principal components analysis.
Factor Items Scale validation
F1Use of cost s
-C1. Cost data is used to aid in cost reduction
vestments
Cronbach alpha: 0.79Factorial: 1 factorVariance explained:
63.9%Sig. Bartlett: 0.00KMO: 0.72
F2IS quality
s integrated with systems
able
Cronbach alpha: 0.87Factorial: 1 factorVariance explained:
66.6%Sig. Bartlett: 0.00KMO: 0.85
F3IS strategy
t
Gutirrez-Nsize of the number of e
When thby a dichotoordinary ac1 otherwisand manag
Various Nijssen, 200of rms fromthree initial(commerce
Independen
In orderLikert-typedisagree, toysis.
This techmaking up
A brief ewith their s
F1, Use omanagemein turn shothat informquestions foByrd et al.
(adequatelyvalued, howit negativel
F2, IS quinformationconceivablytion and imfor this factoDue to
issuOperations
F3, IS string superioimportanceand developbased on C(2004).
The
ccuradatio
s
gy of
rderS, weis. Clserveermieredtincton, Tis caentiff the
1998 useding ay, bed qun bet), it wter gistin
calcuemesed oystem -C2. Cost data is used in price decisions-C3.
Firm carries out many special cost studies-C4. Cost of acquisition
and maintenance is considered in capital in
-Q1. Information system for one area (e.g. sales, production,
etc.) ifor other areas-Q2. Information system allows user to get
answers easily-Q3. Detailed sales and operations data from recent
years are avail-Q4. Many perspectives on costs and performance are
available-Q5. Cost management system is currently excellent
quality
Non-cost management information:-S1. Is useful in planning and
setting strategy-S2. Is important for maintaining and improving
competitiveness-S3. Includes aspects from rms internal and external
environmen
ieto (2007), in that it uses a construct expressing thecompany
through the variables: total assets, sales andmployees.e logistic
regression is applied, rm size is measuredmous variable that equals
0 if the rms revenue from
tivities in the nal year (2004) is below the median ande. Larger
rms have more resources, professionalsement experts to apply new
techniques (Finch, 1986).authors use sector as a control variable
(Braam &4; Cagwin & Bouwman, 2002). Due to the small
number
the commerce and services sectors in the sample, the sectors are
re-grouped into industry and non-industry
and services).
t variables
to identify the main factors underlying the set of variables
obtained in the questionnaire (from 1 = totally
5 = totally agree), we used principal components anal-
nique identied three factors. Table 3 reports the itemseach
factor, along with their validation values.xplanation of the
questions in each factor follows, alongource.f cost system. Using
information about costs for variousnt objectives will facilitate
management thereof, whichuld conceivably enhance the managers
perception ofation and improve the rms nancial performance. Ther
this factor are adapted from Krumwiede (1998) and
2006). In the questionnaire, the item Product costs are
than ato vali
Result
Typolosystem
In oment Ianalyses obpredetconsidbe disAndersanalysthat ideffect
oSmith,
Wesis, takstrateguse anrelatioqualityof clusrms d
Wemanagter. Ba this article in press as: Prez-Mndez, J. A.,
& Machado-Cabezas, . Relrate performance. Revista de
Contabilidad Spanish Accounting Review (
assessed to be able to compete in the market is alsoever this
has not been included within the F1 factor as
y affects its validation.ality. Using quality internal
information means that the
will be more relevant and timely, which in turn will enhance the
perception of the quality of the informa-prove the rms nancial
performance. The questionsr are based on Krumwiede (1998) and Byrd
et al. (2006).es relating to the validation of the F2 factor, the
item
data are updated in real time has been omitted.ategy. Given the
importance of the strategy for achiev-r nancial performance, it is
useful to measure the
of the internal information for the implementationment of the
strategy. The questions for this factor are
agwin and Bouwman (2002), and Braam and Nijssen item Timeliness
and relevance are more important
medium (1a high valulow group ity; and theIS strategy.dent
samplbetween th
Table 4Mean of factor
No. rms IS quality (FIS strategy (
*** DifferenceCronbach alpha: 0.72Factorial: 1 factorVariance
explained: 64.6%Sig. Bartlett: 0.00KMO: 0.62
cy has not been taken into consideration in the F3 duen
issues.
rms according to their management information
to analyse the heterogeneity in the rms manage- produced a
typology of the sample rms using clusteruster analysis is a
multivariate technique that classi-d cases into homogenous groups
with respect to somened selection criterion. The cases in each
cluster can be
similar, while the different clusters are assumed to from each
other (Aldenderfer & Blasheld, 1984; Hair,atham, & Black,
1999). Researchers argue that cluster
n be used to show different combinations of variablesy the
management IS, which is useful for testing the
system on nancial performance (Chenhall & Langeld-b).
the k-means technique to carry out the cluster analy-s
classication variables two factors, IS quality and IScause they
indicate the managers satisfaction with theality of the management
IS. Since there is a strong cor-ween the factor F1 (use of cost
system) and factor F2 (ISas decided not to include the rst one in
the production
roups. The cluster analysis resulted in three groups ofguished
by the values of these two dimensions (Table 4).lated the mean of
the two factors that characterise the
nt IS for each rm, and then the mean for each clus-n that value,
the groups were labelled: high (26 rms),ationship between
management information systems2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
3), and low (17). The high group contains rms withe in the two
dimensions of the management IS; thecontains rms with the worst
mean value in IS qual-
medium group contains rms with the lowest value in The
non-parametric KruskalWallis test for k indepen-es shows that
statistically signicant differences existe three clusters in the
two factors.
s by cluster.
Low Medium High
17 13 262) (p = 0.00)*** 1.16 0.16 0.67F3) (p = 0.00)*** 0.09
1.10 0.61
signicant at 1%.
-
Please citeand corpo
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. Machado-Cabezas / Revista de Contabilidad Spanish Accounting
Review xxx (xx) (2014) xxxxxx 7
Table 5Change in nancial performance.
Low Medium High
MARGIN 1 (pMARGIN 2 (pROI 1 (p = 0.0ROI 2 (p = 0.0ROI 3 (p =
0.0ROI HC (p = 0COSTS/OI (p
** Difference*** Difference
Hypothesis t
Hypothesis 1This hyp
ment IS higperformancchange in 19962004
We useddent samplbetween thperformancall the narms givinggroup
achiehave a low
We also ters taken idifferences clusters. Wnicant diffagainst
med
Hypotheses In order
the cluster business peused. The m
PLS is a tbuilding of mand latent vis a constru(formative
interest fortechnique hvariables ob
The mostructs. Theobservable turn responROI changecators that
indicators, rise to obseare factors principal co
Firm size
- FS 1. Ln of- FS 2. Ln of- FS 3. Ln of
Table 6Relationships between constructs.
Relationships between constructs Beta t statistic
tegy IS quality 0.244 1.60tegy ROI change 0.419 4.05***lity ROI
change 0.201 1.77*ize IS quality 0.086 0.52ize IS strategy 0.179
0.95ize ROI change 0.303 2.27**
icant difference at 10%.icant difference at 5%.icant difference
at 1%.
change. The change in nancial performance throughout the,
integrated by ROI 1 change, ROI 2 change and ROI 3 change.the annex
it may be seen that the requirements ensur-ernal consistency
(unidimensionality, reliability, convergenty and discriminant
validity) were met. Latent variables cane used to test the
relationships in the model.
uctural model. R2 and Betas
is usartPLrdiseobtaesesance
of th R2 ae to rdise
con boot
of theses
just
ationordine n
are ming o
whion oossibrd, Tsionssearc = 0.02)** 0.22 0.57 0.31 = 0.00)***
0.16 0.98 0.790)*** 0.14 0.75 0.830)*** 0.11 0.89 0.711)*** 0.32
0.74 0.65.02)** 0.13 0.15 0.30
= 0.00)*** 0.01 0.06 0.04 signicant at 5%. signicant at 1%.
ests
othesis postulates that rms that score their manage-hly achieve
superior improvements in their nanciale than the rest of the rms.
Table 5 shows the meanthe relative protability indicators over the
period
for the three groups of rms identied. the non-parametric
KruskalWallis test for k indepen-es to investigate the existence of
signicant differencese three clusters of rms in the change in the
nanciale. The results show that signicant differences exist inncial
performance variables in favour of the group of
the highest score to their management IS. The mediumves the
lowest changes. This may be because these rmsscore in terms of
their IS strategy.ran a non-parametric MannWhitney U test on the
clus-n pairs. The results show that statistically signicantexist
between the high cluster and the low and mediumhen comparing the
low and medium clusters, only sig-erences are observed in the
change in ROI 1 and ROI 2ium group.
1.1 and 1.2 to analyse the effect that the variables that
determinegroups (IS strategy and IS quality) have on
improvingrformance, the partial least squares (PLS) technique
wasodel also included the rm size factor.echnique based on
structural equations that allows theodels with complex
relationships between observable
ariables. A latent variable is not directly observable; itct
made from other variables that theoretically formindicators) or
reect (reective indicators) a factor of
the study (represented by the latent variable). Thisas been
widely used to analyse relationships betweentained from survey
responses.
del shows six relationships between factors or con- factors
represented by circles in Fig. 1 are not directlyvariables; they
are obtained from indicators that are inses to different questions
in the questionnaire (except
and rm size). The constructs employed and the indi-comprise them
are presented next. We use reective
IS straIS straIS quaFirm sFirm sFirm s
* Sign** Sign
*** Sign
ROIperiod
In ing intvaliditthen b
The str
PLSthe Smstandato be hypothof variresults
Thevariablstandaing thewith a
Outhypothwill be
The relAcc
improvrms regard1997),sicatiwith p(Goddaconcluand re this
article in press as: Prez-Mndez, J. A., & Machado-Cabezas, .
Relrate performance. Revista de Contabilidad Spanish Accounting
Review (
which implies that the non-observed construct givesrved
indicators. The four constructs used in the modelF2 (IS quality)
and F3 (IS strategy), identied in themponent analysis (Table 3),
and the following two:. Formed by three indicators:
total assets at end of period. sales at end of period. number of
employees at end of period.
0.032
IS strategyed to estimate the structural equations with the aid
ofS software (Ringle, Wende, & Will, 2005), which allowsd Beta
regression coefcients called path coefcientsined. These coefcients
test whether the proposed
are supported or not. R2 values measure the amount of the
construct that is explained by the model. Thee estimation are shown
in Fig. 1 and Table 6.re shown in Fig. 1, within the circles. The
R2 of the latentbe explained, ROI change, is 0.306. Table 6 shows
thed path coefcients (these are also on the lines connect-structs
in Fig. 1) and the Students t values (obtainedstrapping procedure
with 500 samples).e 6 path coefcients of the model, two correspond
to the
H1.1 and H1.2 already mentioned, while the remainderied
ahead.
ship between rm size and ROI changeg to several studies,
increased rm size can helpancial performance for a number of
reasons: largerore able to take advantage of economies of
scale,
perating costs and the costs of innovation (Hardwick,le greater
size means the possibility of more diver-f activities, allowing rms
to cope more successfullyle market changes, as well as with high
risk situationsavakoli, & Wilson, 2005; Winter, 1994). However,
the
of the various studies do not coincide in this respect,hers have
yet to establish a clear relationship between
0
0.3030.179
0.419
Firm sizeationship between management information systems2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
0.059
0.306
0.2010.244
0.086ROI change
IS quality
Fig. 1. Model estimated using SmartPLS.
-
Please cite . Relationship between management information
systemsand corpo iew (2014).
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ARTICLE IN PRESSG ModelRCSAR-27; No. of Pages 128 J.A.
Prez-Mndez, . Machado-Cabezas / Revista de Contabilidad Spanish
Accounting Review xxx (xx) (2014) xxxxxx
protability and size. Gonzlez Prez, Rodrguez, and Acosta
Molina(2002) provide a review of the various Spanish studies
groupedaccording to their conclusion regarding the relationship:
positive,negative, or non-existent.
RelationshipThe larg
formalised,systems (Ma greater dcoordinatio(Hendricks,
RelationshipIn order
based on qto implicitlbe strategicmation techwith quality
RelationshipLarge r
are forced tsystems in managemeeffectivenesisfaction is
organisatio
There armeasuring relationshipbetween r
As the rresults indi
IS strateg IS quality In the ana
The resugic approacperformancthe differen
In orderanalysis shing the diffthe two seindustrial of
non-induted. The PLsubset (42 the total sam IS quality ROI
chang
New manag
Table 7 aclusters, asthe average(2) enableexpressed arespond to
tthe non-par
Table 7Percentage use of new management techniques (NMT).
% Low Medium High
ABC 11.8 23.1 19.2 0.00)
= 0.03
p = 0.0
t one0.05)*
T (p =years
rencerencerence
resumoresing
yed pr of tly hrder
therm, aisticriabl
esis
otheen usviouch thntribhigh ng Nve a
to behan trms chnies anc
, per theie, Chic pu, andes is
resue add
tion of logistic regression
ing found that the margins and protabilities differ depend- the
characteristics of the management IS that rms use, thew is to
determine if the different dimensions identied for
and the use of NMTs help explain the different margins andbility
change obtained by the rms. For this analysis, we used
regression. sample is ranked for each nancial
performance-changele in increasing order, and the 28 cases with the
lowest valueen 0, and the 28 cases with the highest value, 1
(exceptrational costs over operational income, where the criterion
this article in press as: Prez-Mndez, J. A., & Machado-Cabezas,
rate performance. Revista de Contabilidad Spanish Accounting
Rev
between rm size and IS strategye rms are generally more complex
and require more
decentralised, specialised and integrated informationintzbert,
1979). These systems provide the rms withegree of functional and
organisational structure andn that aids in effective managerial
decision-making
Hora, Menor, & Wiedman, 2012).
between IS strategy and IS quality to properly serve its
purpose, IS strategy needs to beuality information. In fact, an IS
strategy may be saidy entail a quality IS, because otherwise it
would hardly. Kearns and Sabherwal (2006) found business
infor-nology strategic alignment to be positively associated
information technology.
between rm size and IS qualityms are organised in more complex
ways, such that theyo use more sophisticated and better quality
informationorder to be able to meet their greater coordination
andnt needs. The rm size is an essential factor affecting thes of
an IS (Mahmood, Hall, & Swanberg, 2001). IS sat-greater in
organisations that are large because smallerns tend to be less
mature (Lees, 1987).e three non-signicant path coefcients, namely
thosethe relationship between IS strategy and IS quality, the
between rm size and IS quality and the relationshipm size and IS
strategy.emaining path coefcients are signicant, the modelcate
that:
y has a positive effect on ROI change. positively affects ROI
change.lysed sample, size negatively affects the ROI change.
lts found with the PLS technique show that the IS strate-h is
the most striking factor in improving the businesse, which was
previously mentioned when interpretingce in results between the
high and medium clusters.
to analyse the effect of sector variations, a multigroupould be
carried out with the objective of identify-erences in the proposed
PLS model results betweenctors that have been identied: industrial
and non-(services and commerce). Given the small samplestrial rms
(14), the multigroup analysis has been omit-S model has been
replicated for the industrial rmsrms) and the results are similar
to those obtained for
ple, though it must be pointed out that the IS strategy relation
is found to be signicant, while the IS strategye positive effect
also increases.
ement techniques and cluster groups
llows us to check the level of use of NMTs in the three well as
the average number of techniques used and
years of use of these techniques. The chi-square tests us to
identify signicant differences for the variabless a percentage of
use of different techniques, which cor-he rst 8 rows of Table 7,
while for the last two variables,ametric KruskalWallis test
applies.
BS (p =VCA (pJIT BPR TQM (CIM At leas
(p = No. NMMean
* Diffe** Diffe
*** Diffe
TheNMTs rms uemplonumbenican
In oculatedeach no statthis va
Hypoth
Hypbetwethe prein whican coof the
Usinot haseemsmore tMost tive teimprovperformchosentary inthis
linstrategmancepurpos
Theprovid
Applica
Having ontask nothe IS protalogistic
Thevariabare givfor ope*** 17.6 15.4 57.7)** 0.0 15.4 0.0
5.9 0.0 19.25.9 7.7 19.2
8)* 17.6 30.8 50.05.9 23.1 11.5
new management technique*
35.3 61.5 73.1
0.03)** 0.53 1.17 1.83of use of management technique 6.8 6
6.3
signicant at 10%. signicant at 5%. signicant at 1%.
lts indicate that the rms from the high cluster use than the
rest. This result holds both for percentage of
at least one technique and for number of techniqueser rm. The
rms from the low cluster use the least
techniques. In particular, the use of BS and TQM is sig-igher in
the high cluster than in the other two clusters.
to consider the rms experience in using NMTs, we cal- mean
number of years each technique had been used innd then the mean for
each cluster. But the results showally signicant differences among
the three clusters ine.
2
sis 2 tests whether a positive relationship existse of NMTs and
nancial performance change. In view ofs results, if the NMTs form
part of a management systeme information has strategic relevance,
these techniquesute to improved nancial performance. This is the
casecluster.MTs on their own, without a strategic perspective,
maypositive effect on nancial performance. This is what happening
with the medium cluster, which uses NMTshe low cluster but has the
worst nancial performance.in the high cluster use BS, which seems
to be an effec-que for implementing and controlling a strategy
thatnancial performance. The effect of the IT on nanciale is not
the same for all rms. It depends on the strategyhaps due to the
fact that IT and strategy are complemen-r effect on rms nancial
performance (Shin, 2006). Inan et al. (2006) nd empirical evidence
that use of IS forrposes has a positive effect on a rms nancial
perfor-
Teo and Ang (1999) conclude that using IT for strategic one of
the key success factors in management.lts of the logistic
regression that are presented belowitional empirical evidence
concerning Hypothesis 2.
-
Please citeand corpo
ARTICLE IN PRESSG ModelRCSAR-27; No. of Pages 12J.A. Prez-Mndez,
. Machado-Cabezas / Revista de Contabilidad Spanish Accounting
Review xxx (xx) (2014) xxxxxx 9
Table 8Logistic regression of protability change variables.
MARGIN 1 MARGIN 2 ROI 1 ROI 2 ROI 3 COSTS/OI ROI HC
Constant 0.51P = 0.21
0.55P = 0.20
0.74P = 0.09
0.53 0.64 0.51 0.88
NMTF2: IS qualityF3: IS strategy
F2 NMTF3 NMT 1.42
P = 0.002.03P = 0.00
2.10P = 0.00
Size (1: large, 0: small) 1.54 1.85 2.31
Sector (1: in% cases class
NMT: 1: use at
adopted is teach of the as depende
The logisculating thedependent 1999).
We dividvalue of a the rms w(0). Thus a fsample intoacteristics
othe best cha
Results of lo
The follochange. Wi(1: use at lea minus sigthat the effIS
strategy whad a negatthe results about Hypo
Conclusion
In this aspects of mrms. We rathat differ iIS: IS qualit
The grouconsidered over the peNMTs more
4 In variousauthors removextreme proand low protas high- and
lvarious percenet al., 1996). Tcases), so chos
grou is ofs ofe ry.
PLS nanproant a
resuof NM
an ISose
Teo & resu
as ine wiy, wpanito mand
plan theiy (Bylly, tssiblresea
sampion.re reugh t theort oP = 0.02 P = 0.00 P = 0.00dustrial,
0: non-industrial)ied correctly 71.4 75 78.6
least one, 0: do not use any.
he reverse).4 This results in a dichotomous variable fornancial
performance-change variables, which are used
nt variables in the subsequent logistic regression.tic
regression is a conditional probability model for cal-
probability of obtaining each value of a dichotomousvariable
given a set of predictor variables (Hair et al.,
e the sample rms into two groups depending on theparticular
variable of performance change: half ofith high values (1) and the
other half with low valuesunctional relation can be established for
classifying the
each of the two groups. The aim is to identify the char-f the IS
that serve to characterise the rms that obtainnge in the nancial
performance.
gistic regression
wing variables explain the nancial performance ratioth a plus
sign, IS strategy, interaction between NMTsast one; 0: do not use
any) and IS strategy; and withn, the rm size variable (Table 8). It
should be notedect of the interaction between the NMTs variable
andould be negative if NMTs were applied and IS strategy
ive value (little relevance). These results are in line
withabove in the cluster analysis and with what was saidthesis
1.
s
study we have obtained valuations about differentanagement IS
from the CFOs of a sample of Spanishn a cluster analysis which
identied three types of rmn the scores given to two factors that
characterise the
Thefactorsin termthat thstrateg
Therms with imimport
Theeffect part ofwith th2006;
Thepaniesbe donstrategof comutility
(Ravichproperensurestrateg
Finaare pofuture
The caut
Fututhroaffecsupp this article in press as: Prez-Mndez, J. A.,
& Machado-Cabezas, . Relrate performance. Revista de
Contabilidad Spanish Accounting Review (
y and IS strategy.p of rms with the highest scores in the two
dimensionsobtains the best improvement in relative protabilityriod
analysed. At the same time, these rms also use
than the rest.
studies that examine rms based on their level of protability
thee the middle 50% of the sample, and run their analysis on the
two
tability quartiles in an attempt to dene the characteristics of
highability (De Andrs Surez, 2000). In other studies the
researchers takeow-protability groups the areas outside an interval
of plus/minustage points around the mean sector protability
(Fernndez Snchezhe current authors are working with a relatively
small sample (56e to not omit any cases.
employeemore-sopagement a dynamiof new va
The workeconomictigation forder to affect the
Conicts o
The authP = 0.20 P = 0.12 P = 0.21 P = 0.04
0.94P = 0.01
1.72P = 0.00
1.40P = 0.00
1.63P = 0.00
1.69P = 0.01
1.42P = 0.02
1.54P = 0.02
2.44P = 0.00
75 66 71.4 78.6
p of rms with intermediate scores in the set of two particular
interest since these rms perform the worst
nancial results change. This has to do with the factms in this
cluster have the lowest score in terms of IS
model shows the positive effect that IS strategy has oncial
results. The IS quality also has a positive associationved nancial
results, but the effect of IS strategy is morend signicant.lts from
a logistic regression analysis show a positiveTs on protability
improvement as long as they form
with a high strategic relevance. These results are in lineof
previous studies (Braam & Nijssen, 2004; Chan et al.,
Ang, 1999).lts of this study have signicant implications for
com-vestment in new IS and management techniques shouldth strategic
direction, aligning said tools with businesshich requires a high
level of involvement on the partes managers. The protability of the
IS depends on itsanage and improve key strategic areas of the
businessran & Lertwongsatien, 2005). In this sense, it
requiresning when designing and investing in IS, in order tor
quality and relevance to the development of businessrd et al.,
2006).his work suffers from a number of limitations and theree
lines of development that should be considered inrch:
le is small, so the conclusions should be taken with
search should include other variables not availablethe
questionnaire used here, and which conceivably
rms internal management system, such as: level off top
management; resistance to change among users;ationship between
management information systems2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
s educational background; and perceived need forhisticated
management IS among managers and man-accountants. Additionally,
given that companies are inc environment, studies are needed to
collect the effectsriables of IS and their evolution.
refers to a time period (19962004) prior to the current crisis.
It would be of great interest to carry out an inves-or the period
immediately afterwards until present, inknow how the different
variables that make up the IS
rms performance.
f interest
ors declare no conict of interest.
-
Please cite . Relationship between management information
systemsand corpo iew (2014).
http://dx.doi.org/10.1016/j.rcsar.2014.02.001
ARTICLE IN PRESSG ModelRCSAR-27; No. of Pages 1210 J.A.
Prez-Mndez, . Machado-Cabezas / Revista de Contabilidad Spanish
Accounting Review xxx (xx) (2014) xxxxxx
Acknowledgement
The authors wish to thank anonymous reviewers for their
com-ments and suggestions.
ANNEX. Measurement model internal consistency
The measurement model includes the relationships betweeneach
construct and its indicators and is based on the calculationof the
principal components. The constructs must full certaininternal
consistency properties: unidimensionality, reliability, con-vergent
validity and discriminant validity.
Unidimensionality. A principal component analysis is carried
outfor each construct, subsequently applying Kaisers criterion
(Kaiser,1960), such that the eigenvalue is greater than 1 only for
the rstprincipal component. A different principal component
analysis wascarried out for each construct. Another important
factor is the per-centage of variance explained: the rst component
being requiredto explain most of the variance. Table A.1 shows that
the require-ment of unidimensionality is met for all the constructs
analysed.
Reliability. This measures the consistency of the indicators
thatmake up the construct; i.e., the indicators should be measuring
thesame concept. Cronbachs alpha (Cronbach, 1970) and the
compos-ite reliability (Werts, Linn, & Jreskog, 1974) are
calculated, rangingfrom 0 (absence of homogeneity) to 1 (maximum
homogeneity).Cronbachs alpha assumes a priori that each indicator
of a constructcontributes in the same way, while the composite
reliability usesthe loadinging of reliabindices shoindices exce
Convergereect the calculated, ance can be1981). The 1988),
whicis due to itsvariables ex
A seconvalidity is component
Table A.2Factorial loadings matrix.
IS quality IS strategy ROI change Firm size
Q1 0.845 0.244 0.252 0.008Q2 0.893 0.214 0.296 0.025Q3 0.799
0.208 0.260 0.138Q4 0.714 0.089 0.099 0.079Q5 0.797 0.110 0.279
0.057S1 0.136 0.874 0.353 0.209S2 0.250 0.844 0.373 0.106S3 0.160
0.689 0.251 0.117ROI 1 change 0.317 0.417 0.992 0.246ROI 2 change
0.280 0.447 0.988 0.216ROI 3 change 0.319 0.346 0.983 0.240FS1
0.157 0.096 0.254 0.885FS2 0.025 0.040 0.173 0.795FS3 0.055 0.266
0.154 0.807
(Jreskog & Srbom, 1993) or that they are above 0.7 according
tosome authors (Chin, 1998). The last column of Table A.1 shows
thatthe aforementioned criterion is met in all cases.
Discriminant validity. This means that each construct should
besignicantly different from the other constructs. A factorial
load-ings matrix was obtained to analyse the discriminant validity,
aswell as the cross-factor loadings. The factorial loadings are
Pearsoncorrelationstruct. The c
en thouldors sthaned meconthat e cor
Tablucts. nditin.
thermnt faoccu
Table A.1Unidimension
Constructs a R
Ca
IS strategy 0S1 S2 0.844S3 0.689
IS quality 0.87 0.91 0.66Q1 0.846Q2 0.893Q3 0.799Q4 0.714Q5
0.797
Firm size 0.78 0.87 0.69FS1 0.885FS2 0.796FS3 0.807
ROI change 0.97 0.99 0.97ROI 1 chang 0.992ROI 2 chang 0.988ROI 3
chang 0.983 this article in press as: Prez-Mndez, J. A., &
Machado-Cabezas, rate performance. Revista de Contabilidad Spanish
Accounting Rev
s of items as they exist in the causal model. When speak-ility,
the usual requirement is that the values of both
uld be above 0.7. It can be seen in Table A.1 that theseed this
minimum threshold in all cases.nt validity. This is the degree to
which the indicatorsconstruct. The Average Variance Extracted (AVE)
waswhich indicates the extent to which the construct vari-
explained by the chosen indicators (Fornell &
Larcker,minimum recommended value is 0.5 (Baggozi & Yi,h means
that over 50% of the variance of the construct
indicators. Table A.1 shows that the AVE of all the latentceeds
the value of 0.5.d approach to analysing the fullment of
convergentto check that the factorial loadings of the principal
matrix are greater than 0.5 for each of the indicators
betweings shindicatstruct propos
A scheck than th1998).constrThe cocriterio
Furdiffere0.8, as
ality, reliability and convergent validity.
nd indicators Unidimensionality
Eigenvalue for the 1st and2nd component
Variance explained by the1st and 2nd component
1.95 0.68 65.16% 22.65%
3.32 0.58 66.47% 11.69%
2.09 0.61 69.69% 20.42%
2.96 0.05 97.53% 1.70% e e e coefcients between the indicators
and their own con-ross-factor loadings are Pearson correlation
coefcientse indicators and the other constructs. The factorial
load-
be greater than the cross-factor loadings. Therefore, thehould
be more closely correlated with their own con-
with the other constructs. This criterion is met in theodel, as
shown in Table A.2.
d criterion for verifying the discriminant validity is tothe
square root of the AVE of the construct is greaterrelation between
that construct and all the others (Chin,e A.3 shows the correlation
coefcients between theThe square root of the AVE is shown on the
diagonal.on of discriminant validity is also met following this
ore, for Bagozzi (1994) the correlations between thectors that
make up the model should not be higher thanrs in this case.
eliability Convergent validity
ronbachslpha
Compositereliability
AVE Loadings
.73 0.85 0.650.874
-
Please citeand corpo
ARTICLE IN PRESSG ModelRCSAR-27; No. of Pages 12J.A. Prez-Mndez,
. Machado-Cabezas / Revista de Contabilidad Spanish Accounting
Review xxx (xx) (2014) xxxxxx 11
Table A.3Correlations between constructs and the square root of
the AVE (on the diagonal).
IS quality IS strategyROI change Firm size
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