The Relevance of Tourism in Financial Sustainability of Hotels

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w w w . e l s e v i e r . e s / e r m b e

The

relevance

of

tourism

in

ﬁnancial

sustainability

of

hotels

Pedro

Ribeiro

Mucharreira

a

_,

_Marina

_Godinho

_Antunes

b,∗

_,

_Nuno

_Abranja

c

_,

Maria

Rosário

Texeira

Justino

b

_,

_Joaquín

_Texeira

_Quirós

d

a_ISCE_–_Higher_Institute_of_Educational_Sciences,_and_UIDEF,_Institute_of_Education,_University_of_Lisbon,_Portugal b_Lisbon_Accounting_and_Business_School,_Lisbon_Polytechnic_Institute,_Portugal

c_ISCE_–_Higher_Institute_of_Educational_Sciences,_University_of_Lisbon,_Portugal d_Faculty_of_Economics_and_Business,_University_of_Extremadura,_Spain

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received18March2019

Receivedinrevisedform5July2019 Accepted19July2019

Availableonline18August2019 JELclassiﬁcations: F230 L250 L830 Keywords: Tourism Financialsustainability Hospitalityindustry

a

b

s

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Themacroeconomiccontextisanextremelyimportantfactorforthegrowthanddevelopmentof compa-nies,andforthehotelsector,beingexpectedthattheperformanceofhotelcompaniesshouldbestrongly dependentontheconditionsandthemacroeconomicenvironmentwheretheyareinserted.

Usingapaneldatamethodology,thisresearchanalyzedthegrowthofhotelcompanies,thesizeof hotelcompanies,totalnumberofguestsinthesector,totalrevenues,andtotalincomeofthesector,with thecorporateindebtednessvariable,givenbytotalliabilities/totalassetsratio.Itisconcludedthat91.5% oftheaveragevariationinthecorporateindebtednessisdeterminedbytheremainingvariablesofthe study,withtheremaining8.5%variationexplainedbyotherfactorsnotspeciﬁed.Itisalsoconcludedthat thereisnostatisticallysigniﬁcantdifferencebetweenthevaluesofthecorporatesizevariablethroughout thestudy,existinganegativerelationbetweenthisvariableandthevariablescorporatesize,numberof guests,andtourismrevenue,andapositiverelationwiththevariablescorporategrowthrateandtotal incomeofthehospitalityindustry.

Thisresearchprovidesagreatcontributionand enrichmentofexistingliteraturebecausewitha detailedknowledgeconcerningthesetopics,managers’canbasetheirdecisionmakingonthesecause andeffectrelationships,lookingforthebestdecisionsthatwillprovidethehighestproﬁtability.

1. Introduction

Tourismisconsideredthemostcovetedactivityintheworld,

consideringthegrowingnumberofdestinationsaroundtheworld

thathavebeeninvestinginthisactivityoverthepastsixdecades,

makingitthemajordriverofsocioeconomicprogressthroughthe

creationofjobsandenterprises,exportrevenuesandinfrastructure

development,leadingtoacontinuousexpansionanddiversiﬁcation

thatrevealsitasoneofthefastestgrowingeconomicsectorsinthe

growingworld(UNWTO,2017).

Themacroeconomiccontextisanextremelyimportantfactorfor

thegrowthanddevelopmentofcompanies,andforthehotel

sec-tor.Chen(2010)pointsoutthattourismsectororganizationsare

particularlyexposed toeconomic cycles.Economicperformance

anditsconditioningfactorsareakeyissueinthedevelopmentof

∗ Correspondingauthor.

E-mail addresses: prmucharreira@ie.ulisboa.pt (P.R. Mucharreira), maantunes@iscal.ipl.pt (M.G. Antunes), nuno.abranja@isce.pt (N. Abranja), mrjustino@iscal.ipl.pt(M.R.T.Justino),jtexeira@unex.es(J.T.Quirós).

thehospitalityindustry(Assaf&Josiassen,2012).Inthissense,the

hotelindustryisseenasacyclicalindustry,beingconsideredan

industryhighlysensitivetothestateoftheeconomy(Bodie,Kane,

&Marcus,2008;Chen,2010).Oneofthereasonspointedoutfor

thisrealityisthathotelcompanieshavealargeexpressionofﬁxed

costsintheirtotalcosts,translatingintoagreateroperationalrisk

forthesecompanies.Assuch,withhighﬁxedcosts,hotel

compa-niesareverysensitivetobusinessconditions,becauseinperiodsof

economicrecessionorcontractionthesalesrevenueswill

neces-sarilydecreaseandevensmalldecreasesinsalesvolume,willhave

effectsmuchmorethanproportionalontheirresults,becauseof

lowerrevenues(Chen,2010,Graham&Harris,1999).Givenawide

rangeofeconomicandﬁnancialindicatorsthatcanbeused,itis

essentialtounderstandtheweightofindebtednessintourism

sec-tororganizations,particularlyinacontextofﬁnancialcrisisandin

countrieswhosesectorcontributessigniﬁcantlytotheaddedvalue

oftheeconomy(Farcnik,Kuscer,&Trobec,2015).

Undoubtedly,corporate performanceiscloselyrelatedtothe

macroeconomiccontext(Bodieetal.,2008;Mucharreira&Antunes,

2015).Thechangesthattakeplaceinthemarketsinwhichthese

https://doi.org/10.1016/j.iedeen.2019.07.002

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companiesoperatewillhaveamajorimpactonwhatconcernsthe

expansionorcontractionoftheirbusinesses.Inthisway,the

per-formanceofhotel companiesisalsostrongly dependentonthe

conditionsandthemacroeconomicenvironmentwheretheyare

inserted.Thus,thestateoftheeconomywillaffecttheperformance

oforganizations,andthesecompanieswillhavetoorganize

them-selvesinfunctionofmarketdynamicsfarbeyondthenational

con-text,strategicallypreparedfortheconstantchangesthatcomefrom

anincreasinglyglobalizedcontext(Antunes&Mucharreira,2015).

Theexpansionandgrowthoftourismcanalsohaveastrong

inﬂuenceonthebusinessperformanceofthehotelindustry.Onthe

onehand,theexpansionofindustryortourismactivitiesdirectly

increasesthedevelopmentofthehotel industry,increasingthe

occupancyrate and, consequently,sales revenue. On theother

hand,thedevelopmentoftourismcansigniﬁcantlyimprovethe

businessenvironment,whichhasanindirecteffectonthe

busi-nessperformanceofhotelcompanies.Researchescarriedoutby

severalauthorshave shown that theexpansion oftourism can

boosttheeconomyandthattourismgrowthleadstoabetter

ﬁnan-cialperformance(Dritsakis,2004;Fayissa,Nsiah,&Tadasse,2008;

Lee&Chang,2008;Proenca&Soukiazis,2008;Chen,2010).The

studydevelopedbyChen(2007)showedthatthegrowthoftourism

improveseconomicconditionswhich,consequently,increasesthe

performanceofcompanies.

Theperformanceofcompaniesisasubjectofenormous

impor-tanceinallstudiesthattargettheorganizations,duetotheinterest

thatexistsintheanalysisofthefactorsthataffectthevariationsof

theorganizationalperformance(Hoopes,Madsen,&Walker,2003).

Financialperformance,whichallowstoevaluatethefulﬁllmentof

theeconomicobjectivesoftheorganizations,hasbeenthefocusof

severalinvestigationsoftheperformanceofcompanies(Barney,

2002; Combs,Crook, &Shook, 2005; Hult etal., 2008; Richard, Devinney,Yip,&Johnson,2009).

Followingthisframework, this researchaims toanalyzethe

relationshipbetweenseveralindependentvariables,suchasthe

indexofgrowthofthehotelcompanies,thedimensionofhotel

companies,the total number of guests in the sector, thetotal

revenues of the sector (using data from the Portuguese

Bal-ance of Payments), and the total income of the sector (using

information from thePORDATA database), with thedependent

variableof theresearch,describedbytheﬁnancialperformance

ofPortuguesehotelcompanies,attheleveloftheirindebtedness,

beingthis indicatorobtainedby thetotal liabilities/total assets

ratio.

2. Theoreticalframework

2.1. Literaturereview

The prospects for the future of tourism worldwide,

includ-ing its contribution to economic and social development, are

increasingly important. There is a signiﬁcant volume of

esti-mateddemandbytheincreaseindisposableincome,demographic,

social and technologicalchanges, thediversiﬁcationof

destina-tionsandtheincreasingliberalizationofthesector.Inthisway,

itis consideredthat tourismgrowthcouldhave astrong

inﬂu-ence on the organizational performance of the hotel industry.

On the one hand, the expansion of industry or tourism

activ-ities directly improves the development of the hotel industry,

increasingtheoccupancyrateand,consequently,salesrevenue.

Ontheotherhand,thedevelopmentoftourismcansigniﬁcantly

improvethebusinessenvironment,whichhasanindirecteffect

on the organizational performance of hotel companies (Chen,

2010). Several studies carried out previously showed that the

expansionoftourismcouldpromotetheeconomicdevelopment

of organizations (Dritsakis, 2004; Fayissa et al., 2008; Kim,

Chen, & Jang, 2006; Lee & Chang, 2008; Proenca & Soukiazis, 2008).

The concept of performance has been described as the

desiredoutcomethatcanbeachievedthroughmultiplemeasures

(Wheelen,Hunger,Hoffman,&Bamford,2015).Inthisway,

organi-zationalperformanceemergesasamultidimensionalvariableand

remainsuncertaininthestrategicorientationoftheorganizations,

regardingacausallink,consideredinaglobalvision,andinquality

managementpractices,inparticular(Gomez-Gras&Verdu-Jover,

2005;Hasan&Kerr,2003).Lusthaus,Adrien,Anderson,Carden,and Plinio(2002)deﬁneorganizationalperformanceintermsof

effec-tiveness,thatis,theachievementofthemission,efﬁciencyand

permanentrelevance,analyzingtheextenttowhichthe

organi-zationadaptstochangesinenvironmentalconditions.Therefore,

for performance evaluation the use of ﬁnancial indicators will

notbeenough,butalsoindicatorsofeffectiveness,efﬁciencyand

relevance.However,ﬁnancialperformanceismeasuredprimarily

throughaccountingheadingswhicharecommontoallcompanies,

andotherindustry-speciﬁcindicatorsofwhichthecompanyisa

part.

Severalstudieshavebeencarriedout basedonseveral

indi-catorsthatmeasuretheﬁnancialperformanceofhotels,suchas,

total grossproﬁt orthedaily occupancyrateof hotels(

Claver-Cortés,Molina-Azorín, &Pereira-Moliner,2007; Min,Min,Jong, &Kim,2009;Pereira,Claver,&Molina,2011),totalrevenuesand

revenues per room(Chen, 2011; Chung &Kalnins, 2001; Xiao,

O’Neill,&Mattila,2012),theratiobetweennetoperatingincome

andoperatingexpenses(Chen&Lin,2012),returnonassets(ROA)

(Athanasoglou, Brissimis,&Delis,2008;Tavitiyaman,Qiu,&Qu,

2012),returnonequity(ROE)andstockreturns(Chen,2007,2011),

netproﬁt(Tavitiyamanetal.,2012),andnetproﬁtmargin(Jae&

Jang,2007).

Althoughtourismdevelopmentisexpectedtohaveadirect

pos-itiveimpactonhotels,thiscanalsoaffectthehotelindustrythrough

itsabilitytoimprovethestateoftheeconomy,thusstrengthening

hotelbusinessperformance.Severalempiricalstudieshave

sup-portedthisevidencethattheexpansionoftourismcanimprove

theeconomicconditionofthehotels(Balaguer&Cantavella-Jorda,

2002;Dritsakis,2004;Gunduz&Hatemi-J,2005;Kimetal.,2006).

Inthisway,tourismgrowthcanpromotebusinessandboostsales

andproﬁtsofcompanies,thusstrengtheningtheﬁnancial

perfor-manceofhotelcompanies(Chen,2010).Itisexpected,therefore,

thatthevariableofthetotalnumberoftouristshasanimpactonthe

businessperformanceofthehotelindustry.Fromabusiness

perfor-mancestandpoint,Chen(2010)arguesthattourismdevelopment

caninﬂuencetheperformanceofthecompanybecausetourism

growthcanimproveproﬁtsandsalesand,thus,boosttheﬁnancial

performanceofhotels.Theresultsobtainedbytheauthor,ina

pre-viousresearch,supportapositiverelationbetweentheincreaseof

foreigntourismandﬁnancialperformanceinthehotelindustryof

Taiwan.

On the otherhand, other internal variables related to hotel

corporationsmayalsoberelevantfor theanalysisofhotel

per-formance.Severalresearchershavestudiedthemostsigniﬁcant

indicatorsofﬁnancialperformance(Chung&Kalnins,2001;Gursoy

& Swanger, 2007; Okumus, 2002; Reichel & Haber, 2005). For

example,Claver-Cortésetal.(2007)haveshownthathotel

man-agement,hotelcategory,andhotelsizearestrategicdeterminants

ofperformance.Thereis,likewise,avastliteraturethatseeksto

identifytherelationshipbetweenthesizeofthehotelandits

ﬁnan-cialperformance.ResearcherssuchasChungandKalnins(2001),

Israeli(2002), Chenand Tseng (2005),Barros and Mascarenhas (2005),Claver-Cortésetal.(2007),andRodríguezandCruz(2007)

presented evidence of the positive relationship between the

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relationshipwasalsoconﬁrmedbyPineandPhillips(2005),and

Kim,Cho,andRobert(2013).

2.2. TheimportanceoftourisminthePortuguesebusinesssector

Tourismisastrategiceconomicactivityfortheeconomicand

socialdevelopmentofPortugal,concerningemploymentandthe

growthofexports.Thenaturalandclimaticconditions,thegreat

competitivenessinrelationtoPortugueseprices,hospitalityand

culture,in addition tothelandscape and nationalheritage, are

aspectsthatfavorthedevelopmentoftourismandeconomic

activ-ity.TourismisoneofthemainsectorsofthePortugueseeconomy,

ifnotthemostimportant,sinceithasaveryimportant

contribu-tiontoGrossDomesticProduct(GDP),exports,investmentandjob

creation.

Thetourismsectoriscurrentlythemainengineofthenational

economy,recordingconsistentgrowthlevelsinrecentyearsand

reachinghistoric highs in the main indicators: income, guests,

employmentandexports,andisconsideredthecountry’slargest

exportactivity,accountingfor16.7%oftotalexports.Portugalis

consideredoneofthemostcompetitivecountriesintheworldin

thetourismsector,havingreceived,in2016,fortheﬁrsttime,more

foreigntouriststhanthetotalPortugueseresidentpopulation–

11.4million.Therevenuesgeneratedbytourismhavealso

regis-teredstronggrowthand,currently,thesectorhasaweightinGDP

ofalmost7%(PORDATA,2016).

3. Empiricalresearch

3.1. Deﬁnitionofvariablesandresearchhypotheses

This research aims to study the performance of hotels in

a ﬁnancial perspective, using theconcept of indebtedness. The

indebtednessrepresents,inthisway,theﬁnancialsustainabilityof

thehotelsstudied.Theconceptofsustainabilityisseenasoneofthe

mainconcernsofanorganization,whichisrelatedtotheprocesses

oftransformationofthemainareasofactivityofacompany,with

respecttothebusinessmodel,itsstrategyandoperations,which

aimstomaketheorganizationmorecompetitiveandachieveits

ﬁnancialgoals(DeGrosbois,2012;VanPassel,VanHuylenbroeck,

Lauwers,&Mathijs,2009).However,theanalysisdevelopedinour

researchrelatestothesustainabilityconceptinaﬁnancial

perspec-tive,lookingtoanalyzewhichvariablesmayinﬂuencetheﬁnancial

sustainabilityofthehotelenterprises,beinganalyzedinthe

per-spectiveoftheirindebtedness(totalliabilitiesversustotalassets).

Thisissueassumesgreatrelevance,sincetheﬁnancialperformance

ofabusinessdependsonitsabilitytogeneraterevenuesand

main-tainstabilityinbusinesscontinuity.

Consideringthepresenttheoreticalframework,which

demon-stratestheneedtotakeintoconsiderationtheeconomiccyclesin

theﬁnancialperformanceoforganizations,aswellasthedata

pre-sentedthatreﬂecttheweightofthetourismsectorinPortuguese

GDP,thepurposeofthisstudyistoanswerthefollowingresearch

question:’RQIstherearelationshipbetweenthecorporategrowth

rateofhotel companies,thecorporate sizeof hotel companies,

thenumberofguestsofhotelcompanies,thetourismrevenuesof

hotelcompanies,thetotalincomeofhospitalityindustry,andthe

indebtednessofthecompaniesofthehotelsector?

Thegrowingtouristdemandregisteredin Portugal,overthe

lastyears,ledtosigniﬁcantadjustmentsininstalledcapacityand

thenecessaryinvestmentsthatmayhavecontributed,despitethe

undisputedbeneﬁttotheGrossCapitalFormation,tohigher

lev-elsofindebtedness.Theobjectiveofthisstudyistoanalyzethe

possiblerelationshipsbetweenthesevariablesandtheﬁnancial

performanceofPortuguesehotelcompanies,namelythelevelof

their indebtedness, obtained by the total liabilities/total assets

ratio.

The various stakeholders of a company, for example,

man-agers and investors, usevarioustypesof indicators toevaluate

companyperformanceandhighlightareasforimprovement.The

studydevelopedbySainaghi(2010),inwhichthisauthorreviewed

aroundahundredinvestigationsonhotelmanagement,identiﬁed

threedimensionsontheperformanceofthehotelindustry,namely,

ﬁnancial, operational, and organizational. In this sense, several

typesofﬁnancialand operationalindexeshavebeendeveloped

andwidelyusedbyresearchers.Themeasuresthatbestrepresent

theﬁnancialperformanceofthehotelindustryareliquidity,

sol-vency,activity,proﬁtabilityandoperatingindexes(Jagels,2007,

Andrew&Schmidgall,1993).While liquidityratiosmeasurethe

company’sabilitytomeetitsshort-termobligations,activityratios

measuretheﬁrm’sefﬁciencyinmanagingitsassets.Ontheother

hand,proﬁtabilityratiosevaluatetheoverallperformanceofthe

company’sabilitytogeneraterevenueanditsreturnonrevenue

andinvestment.

Thus,thisresearchconsideredasadependentvariablethetotal

liabilitiesversustotal assetsratio,tohighlighttheﬁnancial

sus-tainabilityofcompanies,representingthissustainabilityadirect

relationshipwiththeirﬁnancialperformance.Thisindicatorshows

theproportionoftotalassetsthatareﬁnancedwithliabilities,and

thelowerthisindicator,thegreatersecurityittransmitstoits

credi-tors,sinceitwillshowlessﬁnancialrisk.AccordingtoJagels(2007),

thehotelsectorpresentsvaluesbetween0.60and0.90.

Thus,ﬁveindependentvariableswereconsidered.Twoofthose

variables refer tothe internal perspective oforganizations and

theremainingthree toexternal and macroeconomicindicators.

Regardingtheinternalindicators,thecorporategrowthrate(CGR)

wasconsidered,givenbythetotalrevenuesofoneyearinrelationto

thepreviousone,andthecorporatesizevariable(CS),givenbythe

totalsalesofeachhotelcorporation.Regardingexternalvariables,

thefollowingindicatorswereconsidered:

• Tourism revenue (TR), being considered in this variable the

tourismrevenuesofthe ¨TravelandTourism¨itemoftheBalance

ofPayments,throughthedataprovidedbyPORDATA(2016).The

travelandtourismbalanceincludesthegoodsandservices

pur-chasedfromaneconomybytravelers,duringvisitsoflessthan

oneyeartothateconomy.Theexportismainlythesaleofgoods

andservicesabroad.Expensesincurredbyforeigntouristsinthe

country,suchasthoseincurredinhotels,restaurantsorleisure

establishments,areconsideredasexports.

• Numberofguests(NG),thatis,individualswhospendatleast

one night in a tourist accommodation establishment, being

consideredin this category the hotelsfrom oneto ﬁvestars,

hotels-apartments,touristvillages,touristapartments,and

oth-ers,throughthedataprovidedbyPORDATA(2016).

• TotalIncome of theHospitality Industry (TIHI)refers tototal

incomeofhotel establishments,thatis,establishmentswhose

mainactivityis theprovisionof accommodationservicesand

other ancillary or support services. The hotel establishments

areclassiﬁedinhotels,pensions,inns,hotels,andaparthotels.

Forstatisticalpurposesholidayvillagesandapartmentsarealso

included(PORDATA,2016).

Inthissense,ﬁvehypothesesofinvestigationweredeﬁned:

H1. Thecorporategrowthrateofhotelcompanieshasasigniﬁcant

impactontheindebtednessofthecompaniesinthesector.

H2. Thecorporatesizeofhotelcompanieshasasigniﬁcantimpact

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H3. Thenumberofguestsofhotelcompanieshasasigniﬁcant

H4. Thetourismrevenuesofhotelcompanieshaveasigniﬁcant

H5. Thetotalincomeofthehospitalityindustryhasasigniﬁcant

Completedthedescriptionofthefundamentalsthattooktothe

constructionoftheinitialissuesthroughtheliteraturereview,the

nextsectionwilldescribethestudy.

3.2. Descriptionofthestudy

Thisresearchintendstoevaluatetheimpactofsomevariables,

deﬁnedinthisstudyasindependentvariables,intheperformance

ofsomePortuguesehotelcompanies,beingthisperformance

repre-sentedbyanindicatorofindebtedness,intheperiodbetween2007

and2014.Independentvariablesincludethegrowthrateofhotel

companies,thesizeofhotelcompanies,thetotalnumberofguests,

totaltourismrevenues,andtotalrevenuesofthesector,whilethe

dependentvariableconsistsoftheindebtednessofthecompanies

inthesector,givenbytheratiototalliabilities/totalassets.

Data on the independent variables were obtainedfrom the

PortugueseStatisticalInstitute,BankofPortugal,andTourismof

Portugal,availableonthePORDATA(2016)database.Regarding

theinformationaboutthedependentvariable,isusedthe

indica-torrelatedtotheindebtednessoftourismcompanies,givenbythe

ratiototalliabilities/totalassets.Thedatareferringtothe

compa-niesweretakenfromthedatabaseSistemadeAnálisisdeBalances

Ibéricos(SABI),andthesamplewasrestrictedtocertaincriteriato

obtainseveralcompaniesfeasiblefortheinvestigation.Theﬁrst

criterionconsistedintheselectionofcompaniesfromcodes551

¨

HotelEstablishments ¨and552 ¨Residencesforholidays andother

short_stay¨,takingintoconsiderationthePortugueseclassiﬁcation

ofeconomicactivities(CAE-Rev.3).Inthisframework,theSABI

databaseprovidedinformationon503companies,however,forthe

researchsample,onlythecompanieswithﬁnancialinformation

availablebetween2007and2014,withallknownvaluesavailable

forconsultationwereselected.Inthisway,theﬁnalsample

con-sistedof275companiesandthedataprocessingwasperformed

withthestatisticaltreatmentsoftwareSPSS(StatisticalPackagefor

theSocialSciences).

3.3. Estimationoftheresearchmodel

Thisresearch,takingasreferencethatthecharacteristicsthat

distinguishthecompanies,understoodas ¨basic statistical_units¨,

mayormaynotbeconstantovertime,wasmadewiththe

esti-mationwithpaneldata.Baltagi(2005)andHsiao(1986)argued

that thepanel data methodology can control for an individual

corporation’sheterogeneity,toreduceproblemsassociatedwith

multicollinearityandestimationbiasandspecifythetime-varying

relationbetweendependentandindependentvariables.Thistype

ofstudysuggeststheexistenceofindividualcharacteristics,since

theuseoftemporalorsectionalstudies,whichdonotconsider

het-erogeneity,willalmostalwaysproduceskewedresults.Itcanalso

beconsideredthatthepaneldataprovideagreateramountof

infor-mation,greatervariabilityofthedata,lowercollinearityamongthe

variables,thegreaternumberofdegreesoffreedomandgreater

efﬁciencyintheestimationofthemodel.Inthesemodels,adouble

assignmentdataset(twoindices)isconsidered.Thedatarelateto

eachofseveralcross-sectionunitsobserved,overseveralperiods

oftime.Inthestudy,thedifferentsectionalunitsarethecompanies

(totalof275companies)andthetimeperiodsaretheyears(8years,

from2007to2014).Thismeansthat,fortheobservablevariables

presentinthemodel,dataareobtainedwhicharethenumerical

valuesthateachofthe275companiesregistersover8years.

Inthestudyofthisdatastructure,emphasiswillbeplacedonthe

sectionalunits,inthesensethatdifferentbehaviorsareallowedfor

thedifferentsectionalunits,butwhosebehavior,however,willnot

varywithtime–is,inthisrespect,constant.Itshouldbenotedthat

thenumberofperiodsisrelativelyshortinrelationtothenumber

ofcompanies.AccordingtoGreene(2008),apaneldatasetconsists

ofnsetsofobservationsofthesubjectswhicharedescribedasi=1...

N.Aboutthecharacterizationofthepaneldatatype,itisveriﬁed

thateachsubjectinthedatasetisobservedthesamenumberof

times,usuallydescribedasT(yearsofthestudy),sothedatasetis

presentedasabalancedpanel.Wecanalsoexplainthatthesample

ofthisstudyisprovidedtobestudiedasabalancedpanelmodel

withﬁxedeffects.Still,accordingtoGreene(2008),theﬁxedeffects

regressionmodelarisesfromthefactthat,althoughtheintercept

maydifferbetweenindividuals,eachindividualinterceptdoesnot

changeovertime,thatis,itisconstantintime.

3.4. Characterizationofthevariablesusedinthemodel

Table1presentssomedescriptivestatisticalmeasures

charac-terizingthevariablesusedinthestudy.Thestandarddeviationis

highintheCI,CGRandCSvariables,evidencingthatthesample

includescompanieswithverydifferentdimensions,beingvery

het-erogeneous.However,theNG,TIHIandTRvariablesshowasmaller

variation.

InTable2itispossibletoobservethatthereisnostatistically

signiﬁcantrelationshipbetweentheCIvariableandtheremaining

variables.Itisveriﬁedthat,exceptfortheCGRvariable,the

remain-ingvariableshaveanegativeassociation,whichindicatesthatthe

increaseofoneimpliesadecreaseintheother.Inthiscase,higher

valuesintheCS,NG,TIHIandTRvariableswillresultinlower

val-uesintheCIvariable.Table2alsoallowstoverifytheexistence

ofthreelinearcorrelations,statisticallysigniﬁcant,namely,

TIHI-NG;TR-NGandTR-TIHI,andastrongpositivelinearassociation

betweenTRandNG.

The existenceof multiple outliers in thedifferent variables,

forces the statistical analysis of equality of averages over the

timeofthestudy.Thus,thereisnostatisticalevidencetoassert

that themeanof CIis signiﬁcantly differentbetweentheeight

yearsunderanalysis(F(7,2192)=0.1517;p-value=0.9937),thatis,

althoughtherearediscrepantvalues,theaverageofCIissimilar

throughout the study, not standing out any of the years.

Ver-iﬁcation of the homogeneity of variances was made using the

LevenetestandtheBrown–Forsythetest,whichcertiﬁesthatthere

are nostatisticallysigniﬁcant differences, becauseit is veriﬁed

thatLevene (7,2192)=0.721;p-value=0.653and Brown–Forsythe

(7,2191)=0.514;p-value=0.824,beingveriﬁedthatthevariance

ofCIisidenticalintheeightyears(Table3).

In the study of the CGR variable it is veriﬁed that there is

statisticalevidencetoassertthatitsmeanissigniﬁcantly

differ-entovertimeunderanalysis(F(7,2192)=2.97;p-value=0.0042),

which means that there are statistically signiﬁcant variations

betweentheyears2007and2014.ThevarianceintheCGRvariable

presentsstatisticallysigniﬁcantdifferences,becauseitturnsout

thatLevene (7,2192)=7.047;p-value=0.000and Brown–Forsythe

(7,2191)=2.044;p-value=0.046,denotingthatitisnotconstant

in theeight years. Also, in theCS variable there is no

statisti-calevidencetoassertthatthemeanissigniﬁcantlydifferentin

theeightyearsunderanalysis(F(7,2192)=0.526;p-value=0.815).

In the study of the variance behavior of the CS variable, it

was found that there is statistical evidence to assert that it

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Table1

Descriptivestatisticsofthevariablesusedinthestudy.

CI CGR CS NG TIHI TR Mean 67.71360 21.61644 4,137,151. 13,944,349 1,913,167. 8,218,244. Median 62.41450 0.337506 2,079,251. 13,691,230 1,924,798. 7,873,415. Maximum 821.5990 11,432.29 66,635,840 16,057,142 2,108,627. 10393920 Minimum 1.170000 −96.36670 5648.750 12,927,907 1,763,954. 6,907,843. Std.Dev. 55.06576 319.0274 6,392,858. 896,748.5 100,135.4 1,079,711. Skewness 6.143428 27.47996 4.948061 1.401788 0.376120 0.790412 Kurtosis 62.10027 875.0322 35.13931 4.106148 2.614381 2.528404 Observations 2200 2200 2200 2200 2200 2200 Table2

Pearson’scorrelationbetweenthevariablesunderstudy.

CI CGR CS NG TIHI TR CI 1.000000 CGR 0.043485 1.000000 CS −0.058106 −0.001364 1.000,000 NG −0.010946 0.057863 0.020098 1.000000 TIHI −0.011166 0.074704 0.035335 0.818803** _1.000000 TR −0.006528 0.043440 0.014161 0.959761** _0.743003** _1.000000

**_Correlation_is_signiﬁcant_at_the_0.01_level_(2-tailed)

Table3

TestforequalityofmeansandvariancescategorizedbyvaluesofDATEID.

TestforequalityofmeansofCI TestforequalityofvariancesofCI

Sample:20072014 Sample:20072014

Includedobservations:2200 Includedobservations:2200

Method df Value Probability Method df Value Probability

AnovaF-test (7,2192) 0.151723 0.9937 Levene (7,2192) 0.721343 0.6539

WelchF-testa _(7,_936.514) _0.146964 _0.9943 _{Brown–Forsythe} _(7,₂₁₉₂₎ _0.514362 _0.8243

TestforequalityofmeansofCGR TestforequalityofvariancesofCGR

Sample:20072014 Sample:20072014

TestforequalityofmeansofCS TestforequalityofvariancesofCS

Sample:20072014 Sample:20072014

a_Test_allows_for_unequal_cell_variances.

resultsLevene(7,2192)=0.706;p-value=0.666andBrown–Forsythe

(7,2191)=0.373;p-value=0.918.

3.5. Implementationofthemodel

Theconﬁrmationoftheexistenceofhighvariationinthedata,in

somevariables,suggeststheuseofatransformation,more

specif-icallyalogarithmictransformation.Considerthattheequationin

whichthedependentvariable–inthestudyistheneperian

log-arithmofcorporateindebtedness–isdeterminedbyﬁvemajor

factors:thesetofexplanatoryvariables(assumedas

determinis-tic)groupedinvectorX,whichhasassociatedtheparametervector

ˇ’;thespeciﬁceffectforeachunobservablesectionalunit

(compa-nies),alsocalledlatentvariable,˛i;andthetermofdisturbance

withrespecttounobservableerrorsεit.

Thus,theeconometricmodelisasfollows:

Yit= ␤’Xit+␣i+␧it

Itbeingunderstoodthat:

Y_it =Ln(CI_it),neperianlogarithmofcorporateindebtednessiin

yeart;i=1,2,...,n_b;theindexirepresentseachofthesectional

units,thatis,eachofthecompanies,whichareintotaln_b=275

companies;t=1,2,...,8;theindext representsthetime,inthis

casetheyeartowhich theinformation relates,inthestudyare

considered8years,from2007to2014.

Inthisformulation,theunobservable(orlatent)speciﬁceffect

thatplaysanimportantroleinthemodellogicandwhichhas

conse-quencesonestimationproceduresisconsidered.Itshouldbenoted,

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factorscharacteristicofeachcompany.Thismeansthat,byhaving

thisspeciﬁceffectinconsiderationandeconometricpointofview,

anerrorofomissionofrelevantexplanatoryvariablesisavoided,

whichmeansthat theconventional estimation bytheOrdinary

LeastSquares(OLS)methodwouldnotbeadequate,considering

thebias,inefﬁciencyandinconsistencyoftheOLSestimators,by

notconsideringtheunobservableeffect.

However,aquestionarisesonhowtoincorporatethisspeciﬁc

latenteffectnotobservableinthemodel.Thus,thetwomodelswill

beassumed:theﬁxedeffectsmodelandtherandomeffectsmodel,

asisusualintheliterature.

Intheﬁxedeffectsmodel,weconsiderthefollowingmodel:

Yit= ␣i+␤’Xit+␧it

Itisassumedthatthespeciﬁcsectionalfactor˛iiscorrelated

withtheobservableexplanatoryvariablespresentintheˇ’Xit.The

modelconsidersforeachsectionalunit(eachcompany)aspeciﬁc

constantterm,thatdoesnotvarywiththetime.

3.6. Estimationofthemodelasapanelofconstantcoefﬁcients

Theresults of thedata paneladjustment, without theﬁxed

effectsorweights (Table4), showa lowlevelofjointand

indi-vidualsigniﬁcanceoftheestimatedcoefﬁcients,notrevealingthe

model statistically signiﬁcant. The statistic F=1.388 presents a

p-value=0.226>0.05.Themodelpresentsitselfwiththeconstant

C(p-value=0.0403)andthevariableLNG(p-value=0.0418)with

a statistically significant significance, a very low coefficient of

determination (R2₌_0.006186), _and _Durbin _Watson _statistic _also

low(DWstat≈0.09).

Table5showstheresultsoftheveriﬁcationofthe

heteroskedas-ticitybetweenthecross-sections,observingthattheequalityof

residualvariancesinthedifferentcross-sectionsisnotaccepted

(p-value<0.05),whichmeansthatthereisheteroscedasticitybetween

thecross-sections.

In addition to heteroscedasticity problems, the model also

presentsautocorrelationproblems.Toovercometheproblemsof

heteroscedasticity and autocorrelation, an AR(1) structure was

insertedintheresidues.Theeffectsobtained,showninTable6,

revealanacceptableDurbinWatsonstatisticveryclosetotwo,a

reasonablejoint signiﬁcance ofthevariables, and a good value

ofR2₌_0.880572. _Although_individual_{signiﬁcance}_has_been_lost,

thespeciﬁccase ofthevariableLNG,themodelwithastatistic

F=517.3566andap-value=0.000<0.05revealsitselfstatistically

signiﬁcant.

The panel of estimated constant coefﬁcients, without

het-eroscedasticityandautocorrelation,isthefollowing:

LCI= 0.00704∗LCGR−0.01013∗LCS−0.75578∗LNG

+0.68312∗LTIHI−0.05481∗LTR+7.56616+[AR(1)

=0_.94415]

3.7. Estimationofthegeneralmodelofﬁxedeffects

Theestimationofthepanelwiththeeffectsofcross-sections

(effectsofthecompanies)andwithoutﬁxedeffectsoftimewas

madeusingthestructureAR(1)intheresidues.Themodelpresents

itself with a low individual signiﬁcance, but with a value of

F=22.385andap-value=0.000<0.05whichimpliesagoodjoint

signiﬁcance. The statistical values shown in Table 7 reveal an

acceptablestatisticofDurbinWatson(2.95),areasonablejoint

sig-niﬁcanceofthevariablesandagoodvalueofR2₌_0.958188.

Table 8 shows that there is no heteroskedasticity between

thecross-sections(companyeffects)andnoﬁxedeffectsoftime,

observingthatonlytheBartletttest(p-value=0.0477≈0.05)reveals

itselfstatisticallysigniﬁcant.However,theresidualequalityof

vari-ancesinthedifferentcross-sectionsisaccepted.

The typiﬁcation of the ﬁxed effects of the companies, of

beingornotbeingequal,wasmadeusingthetestofmaximum

likelihoodfortheredundancyofﬁxedeffects.Thus,with95%

con-ﬁdence,itisveriﬁedthatp-valuessmallerthan0.05(Cross-section

F(274.841)=13.292; p-value=0.000) and (Cross-sectionChi-square (274)=1875.97; p-value=0.000) lead to conclude that the ﬁxed

effectsofenterprisesaredifferent(Table9).

According tothe results of Tables 7–9, the equation of the

adjustedﬁxed-effectsmodelisasfollows:

LCI=0.0041∗LCGR–0.0231∗LCS–0.3248∗LNG

+0.2484∗LTIHI–0.1273∗LTR+8.1371–0.3939∗d1

+0.1364∗d2+0.2662∗d3...+[AR(1)= 0.6465]

(d_i=1forobservationsofenterprisei,andd_i=0otherwise).

3.8. Finalconsiderations

Inmacroeconomicstudies,sinceitisimpossibletohavea

sam-pleof Nenterprisesasa randomselectionofapopulationwith

dimensiontendentiallyinﬁnite,ageneralmodelofﬁxedeffectsis

used,sinceitisindifferenttoconsiderthesampleasrandomornot.

Thisresearchtookasareferenceagroupof275companies,

imply-ingthatallinferenceofthestudyisconditionedtothespeciﬁcgroup

underobservation,beingpossibletopointoutsomeconclusions,

bothfromtheperspectiveofacademicresearch,aswellasforthe

orientationofthecompaniesunderstudy.

Thus,theresultsoftheeconometricestimationsarehighlighted:

themodelshowsaweakindividualsigniﬁcance.However,thevalue

ofF=22.385andap-value=0.000<0.05indicateagoodoverall

sig-niﬁcance,fromwhichitcanbeconcludedthat91.5%oftheaverage

variationinthevariableCIisdeterminedbytheremainingvariables

ofthestudy,withtheremaining8.5%variationexplainedbyother

factorsnotspeciﬁed.Itisconcluded,quiteconsistently,thatthere

isnostatisticallysigniﬁcantdifferencebetweenthevaluesoftheCI

variablethroughoutthestudy,existinganegativerelationbetween

thisvariableandthevariablesCS,NG,andTR,andapositiverelation

withthevariablesCGRandTIHI.Theseeconometricresultsallow

tocorroboratetheoneobservedinpreliminarygraphicalanalyzes.

Amongthecoefﬁcientsestimatedinthemodel,itisworthnoting

thatamongthemostrelevanttransformedvariables,thevariable

LNGhasthelargestnegativecontribution,thatis,istheonethat

mostwillhelptoreducecorporateindebtednessandthevariable

LTIHIapositivecontribution,indicatingthatthegreateritsvalue

thegreaterthecompany’sindebtedness.

Sincethedependentvariableandtheexplanatoryvariablesare

logarithmictransformationsofthestudyvariables,thecoefﬁcients

oftheindependentvariablescanbeinterpretedasthe

percent-agevariationofthedependentvariableforavariationofoneunit

inabsoluteterms inanexplanatoryvariable,keepingtheother

variables x’s constant or withtheir effects controlled.Thus, by

increasingoneunitinthevariableLCGR,thecompanyincreases

itsindebtednessby0.41%.Thenegativecoefﬁcientandnot

statis-ticallysigniﬁcantofthevariablesLCS,LNGandLTRindicatesthat

theincreaseinoneunit,separatelyandkeepingtheothers

con-stant,reducesthecompany’sindebtednessby2.32%,by32.48%,

andby12.7%,respectively.IntheanalysisofthevariableLTIHI,its

increaseinoneunit,althoughnotstatisticallysigniﬁcant,increases

thecompany’sindebtednessby24.8%.

ThecoefﬁcientoftheartiﬁcialvariableofresiduesAR(1)hasonly

asafunctiontocorrecttheestimatedcoefﬁcientsandthe

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Table4

Modelasapanelofconstantcoefﬁcients. Dependentvariable:LCI

Method:panelleastsquares Sample:20072014 Periodsincluded:8 Cross-sectionsincluded:275

Totalpanel(unbalanced)observations:1121

Variable Coefﬁcient Std.error t-Statistic Prob.

LCS −0.028965 0.020666 −1.401583 0.1613 LNG −2.734694 1.404764 −1.946729 0.0418 LTIHI 0.870421 0.720173 1.208628 0.2271 LTR 1.011487 0.626719 1.613940 0.1068 LCGR 0.008641 0.013767 0.627630 0.5304 C 20.70212 10.08179 2.053417 0.0403

R-squared 0.006186 Meandependentvar 3.994550

AdjustedR-squared 0.001730 S.D.dependentvar 0.680812

S.E.ofregression 0.680223 Akaikeinfocriterion 2.072546

Sumsquaredresid 515.9145 Schwarzcriterion 2.099426

Loglikelihood −1155.662 Hannan-Quinncriterion 2.082706

F-statistic 1.388113 Durbin-Watsonstat 0.089929

Prob(F-statistic) 0.226024

Table5

Testofequalityofvariancesofresiduesinapanelofconstantcoefﬁcients. TestforequalityofvariancesofRESID

CategorizedbyvaluesofRESID Sample:20072014

Includedobservations:1121

Method df Value Probability

Bartlett 3 159.9381 0.0000

Levene (3,1117) 52.66513 0.0000

Brown–Forsythe (3,1117) 33.41071 0.0000

Categorystatistics

Meanabs. Meanabs.

RESID Count Std.dev. Meandiff. Mediandiff.

[−4,−2) 18 0.560304 0.477969 0.460674

[−2,0) 423 0.457063 0.362259 0.343886

[0,2) 675 0.267401 0.198013 0.196535

[2,4) 5 0.235691 0.200050 0.179880

All 1121 0.678703 0.264494 0.256304

Bartlettweightedstandarddeviation:0.356436.

intheregressionequationtoindicatetheorderofthemodel,butit

isnotconsideredwhenitmakesaprediction.

4. Conclusions

Thispaperintendstocontributetothereinforcementofthisline

ofresearch,analyzingsomemacroeconomic variableswith

eco-nomicandﬁnancialindicatorsthatcanassess,inanaccurateway,

theﬁnancialperformanceofcompaniesinthehotelsector.Inthis

sense,aresearchmodelwasproposedtorespondtotheresearch

questions.

Thetourismandhotelsectorisanincreasinglyglobal,dynamic

andcompetitivearea,whichhasledtoanincreaseininternational

competitiveness.Thus,thehotelindustry,ingeneral,isimmersed

inahighlycompetitiveenvironmentanditsmanagersneed

accu-rateandreliableinformationforthepropermanagement ofthe

activities.Hotel companies,likeanyotherorganization,canuse

ﬁnancialmeasuresorindicatorstogenerateinformationforthe

decision-making processoftheirmanagersin ordertoimprove

business performance. The results obtainedshould allow

com-panies in thehotel industry topromote an adequatereturn to

theirshareholders and investors, pay creditors and adequately

rewardemployees,andsustaingrowth.Consequently,tomaintain

a level ofperformance that providestheexpectedresult,

man-agers needa broadknowledge ofoperationalvariables and the

macroeconomicenvironmentthatallowsthemtomakethebest

decisions.

Althoughtherole oftourism isundoubtedly relevantin any

economy,thestudiesdevelopedpreviouslyhavebeenscarceand

insufﬁcientinwhatconcernstheanalysisoftheperformanceofthe

tourismindustryandthemainvariablesthatdeterminethe

ﬁnan-cialsustainabilityofhotels.Inthisway,thisresearchanalyzedthe

relationshipofcauseeffectsamongseveralindependentvariables,

someinternalandreferringtothecompaniesstudied,andothers

referringtomacroeconomicvariables.

Themacroeconomiccontextisanextremelyimportantfactor

forthegrowthanddevelopmentofcompanies,andforthehotel

sector.So, thechangesthattakeplacein themarketsinwhich

thesecompaniesoperatewillhaveamajorimpactonwhat

con-cernstheexpansionorcontractionoftheirbusinesses(Dritsakis,

2004;Fayissaetal.,2008;Lee&Chang,2008;Proenca&Soukiazis, 2008;Chen,2010).Inthisway,itisexpectedthattheperformance

ofhotelcompaniesshouldbestronglydependentontheconditions

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Table6

ModelasapanelofconstantcoefﬁcientswithstructureAR(1). Dependentvariable:LCI

Method:panelleastsquares Sample(adjusted):20082014 Periodsincluded:7 Cross-sectionsincluded:211

Totalpanel(unbalanced)observations:428 Convergenceachievedafter7iterations

LCGR 0.007044 0.005142 1.369839 0.1715 LCS −0.010137 0.022914 −0.442412 0.6584 LNG −0.755787 0.473766 −1.595277 0.1114 LTIHI 0.683127 0.405965 1.682722 0.0932 LTR −0.054819 0.358304 −0.152995 0.8785 C 7.566164 3.406923 2.220820 0.0269 AR(1) 0.944151 0.017052 55.36782 0.0000

S.E.ofregression 0.196301 Akaikeinfocriterion −0.402119

Sumsquaredresid 16.22279 Schwarzcriterion −0.335732

Loglikelihood 93.05349 Hannan–Quinncriterion −0.375900

F-statistic 517.3566 Durbin–Watsonstat 1.714710

InvertedARroots .94

Table7

Generalmodelofﬁxedeffects. Dependentvariable:LCI Method:panelleastsquares Sample(adjusted):20082014 Periodsincluded:7 Cross-sectionsincluded:211

Totalpanel(unbalanced)observations:428 Convergenceachievedafter7iterations

LCGR 0.004120 0.006376 0.646220 0.5188 LCS −0.023200 0.028393 −0.817079 0.4148 LNG −0.324872 0.616836 −0.526674 0.5990 LTIHI 0.248431 0.401150 0.619298 0.5364 LTR −0.127343 0.366760 −0.347210 0.7288 C 8.137111 3.937681 2.066473 0.0400 AR(1) 0.646533 0.053087 12.17865 0.0000 Effectsspeciﬁcation Cross-sectionﬁxed(dummyvariables)

S.E.ofregression 0.164067 Akaikeinfocriterion −0.470332

Sumsquaredresid 5.679685 Schwarzcriterion 1.587682

Loglikelihood 317.6510 Hannan–Quinncriter. 0.342470

F-statistic 22.38590 Durbin–Watsonstat 2.952008

InvertedARroots .65

Basedontheliteraturereview,thedevelopmentoftourismcan

signiﬁcantly improve the business environment, which has a

direct and an indirect effect on the business performance of

hotel companies (Balaguer & Cantavella-Jorda, 2002; Dritsakis,

2004; Gunduz & Hatemi-J, 2005; Kim et al., 2006). Following

theseideas underlyingthe assumptions of hotels performance,

this research analyzed the relationship between the index of

growth of the hotel companies, the size of hotel companies,

the total number of guests in the sector, total revenues, and

thetotal income of thesector, withthe dependentvariable of

the research, described by the ﬁnancial performance of

Por-tuguesehotelcompanies,consideringtheirindebtedness,beingthe

indebtednessindicatorcalculatedbythetotalliabilities/totalassets

ratio.

Concerningtheresultsobtained,itispossibletoconcludethat

themodelshowsaweakindividualsigniﬁcance.However,thevalue

ofF=22.385andap-value=0.000<0.05indicateagoodoverall

sig-niﬁcance,fromwhichitcanbeconcludedthat91.5%oftheaverage

variationinthevariableCIisdeterminedbytheremainingvariables

ofthestudy,withtheremaining8.5%variationexplainedbyother

factorsnotspeciﬁed.Itisconcludedthatthereisnostatistically

sig-niﬁcantdifferencebetweenthevaluesoftheCIvariablethroughout

thestudy,existinganegativerelationbetweenthisvariableandthe

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Table8

Testofequalityofvariancesoftheresiduesinthegeneralmodelofﬁxedeffects. TestforequalityofvariancesofRESID

CategorizedbyvaluesofRESID Date:11/30/15time:16:05 Sample(adjusted):20082014

Includedobservations:428afteradjustments

Method df Value Probability

Bartlett 2 6.554176 0.0477

Levene (2,425) 3.128222 0.0548

Brown–Forsythe (2,425) 1.218169 0.2968

Categorystatistics

Meanabs. Meanabs.

RESID Count Std.dev. Meandiff. Mediandiff.

[−0.5,0) 202 0.097989 0.071252 0.061972

[0,0.5) 225 0.082186 0.057331 0.051635

[0.5,1) 1 NA 0.000000 0.000000

All 428 0.115332 0.063767 0.056393

Bartlettweightedstandarddeviation:0.090007.

Table9

Testsofredundantﬁxedeffects. Testcross-sectionﬁxedeffects

Effectstest Statistic d.f. Prob.

Cross-sectionF 13.292353 (274,841) 0.0000

Cross-sectionChi-square 1875.970467 274 0.0000

Cross-sectionﬁxedeffectstestequation: Dependentvariable:LCI

Method:panelleastsquares Sample:20072014 Periodsincluded:8 Cross-sectionsincluded:275

Totalpanel(unbalanced)observations:1121

LCGR 0.008641 0.013767 0.627630 0.5304 LCS −0.028965 0.020666 −1.401583 0.1613 LNG −2.734694 1.404764 −1.946729 0.0418 LTIHI 0.870421 0.720173 1.208628 0.2271 LTR 1.011487 0.626719 1.613940 0.1068 C 20.70212 10.08179 2.053417 0.0403

CGRandTIHI.Amongthecoefﬁcientsestimatedforthemodel,itis

worthnotingthatamongthemostrelevanttransformedvariables,

thevariableLNGhasthelargestnegativecontribution,thatis,isthe

onethatmostwillhelptoreducecorporateindebtednessandthe

variableLTIHIapositivecontribution,indicatingthatthegreaterits

valuethegreaterthecompany’sindebtedness.Byincreasingone

unitinthevariableLCGR,thecompanyincreasesitsindebtedness

by0.41%.Thenegativecoefﬁcientandnotstatisticallysigniﬁcant

ofthevariablesLCS,LNG,andLTRindicatesthattheincreaseinone

unit,separatelyandkeepingtheothersconstant,reducesthe

com-pany’sindebtednessby2.32%,by32.48%andby12.7%,respectively.

IntheanalysisofthevariableLTIHI,itsincreaseinoneunit,although

notstatisticallysigniﬁcant,increasesthecompany’sindebtedness

by24.8%.

Weconsiderthatthisresearchprovidesagreatcontributionand

enrichmentofexistingliterature,andalsoinapracticalperspective

becausewithadetailedknowledgeconcerningsome

macroeco-nomicvariables and characteristicsof thehotels, analyzingthe

impact that those variables have on the ﬁnancial performance

of those corporations, managers’ can base theirdecision

mak-ingonthesecauseandeffectrelationships, lookingfor thebest

alternativesanddecisionsthatwillprovidethehighest

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