relationships between management information systems and corporate performance

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

Please cite . Reland corpo iew (

ARTICLE IN PRESSG ModelRCSAR-27; No. of Pages 122 J.A. Prez-Mndez, . Machado-Cabezas / Revista de Contabilidad Spanish 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


ARTICLE IN PRESSG ModelRCSAR-27; No. of Pages 12J.A. Prez-Mndez, . Machado-Cabezas / Revista de Contabilidad Spanish Accounting Review xxx (xx) (2014) xxxxxx 3

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.



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.



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).



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%.



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). http://dx.doi.org/10.1016/j.rcsar.2014.02.001


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.



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


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



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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