Calibrating 30 years of experimental research: a meta-analysis of the atmospheric effects of music, scent, and color

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Research

Note

Calibrating

30 Years

of

Experimental

Research:

A

Meta-Analysis

of

the

Atmospheric

Effects

of

Music,

Scent,

and

Color

Holger

Roschk

a,∗

,

Sandra

Maria

Correia

Loureiro

b

,

Jan

Breitsohl

c

a_Department_of_Service_Management,_{Alpen-Adria-Universität}_Klagenfurt,_{Universitätsstraße}_65-67,₉₀₂₀_Klagenfurt,_Austria

b_Marketing,_Operations_and_General_Management_Department,_Instituto_{Universitário}_de_Lisboa_(ISCTE-IUL),_Business_Research_Unit_(BRU/UNIDE),_Av._For¸cas

Armadas,1649-026Lisbon,Portugal

c_School_of_Management_&_Business,_Aberystwyth_University,_Ceredigion_SY23_3AL,_United_Kingdom

Availableonline27October2016

Abstract

Atmosphericin-storestimulihavebeenthesubjectofconsiderableempiricalinvestigationforover30years.Thisresearchpresentsa meta-analysisof66studiesand135effects(N=15,621)calibratingtheatmosphericeffectsofmusic,scent,andcoloronshoppingoutcomes.Atan aggregatelevel,theresultsrevealthatenvironmentsinwhichmusicorscentarepresentyieldhigherpleasure,satisfaction,andbehavioralintention ratingswhencomparedwithenvironmentsinwhichsuchconditionsareabsent.Warmcolorsproducehigherlevelsofarousalthancoolcolors, whilecoolcolorsproducehigherlevelsofsatisfactionthanwarmcolors.Theestimatedaveragestrengthoftheserelationshipsrangedfromsmall tomedium.Effectsizesexhibitedsignificantbetween-studyvariance,whichcanbepartlyexplainedbythemoderatorsinvestigated.Forinstance, largereffectsizeswereobservedfortherelationshipbetweenscentandpleasureinthosesampleswithahigher(vs.lower)proportionoffemales. Dataalsoindicatedatendencytowardstrongermusicandscenteffectsinservicesettingsascomparedtoretailsettings.Theresultsofthisanalysis, basedondataaggregatedacrosstheresearchstream,offerretailersaguidetoenhancecustomers’shoppingexperiencethroughjudicioususeof in-storeatmosphericstimuli.

Keywords: Music;Scent;Color;Meta-analysis;In-storeatmospherics;Retailcustomerbehavior

One of the key success factorsfor any retailer or service providerispresentingcustomerswithapleasurable

consump-tion environment (Pan and Zinkhan 2006). A well-designed

storeenvironmentmaypositivelystimulatecustomers’senses, enhance their shopping experience, and ultimately translate intolargersalesrevenues(DoucéandJanssens2013;Sullivan 2002).Thesubtletyofatmosphericeffectsoftenresultsin cus-tomersbeingunawareoftheir exposuretothem,eventhough theirbehaviorisaffected(MorrinandRatneshwar2000).

Aca-demic researchers have explored how environmental stimuli

affect customers’ shopping behavior for more than 30 years

(Bellizzi, Crowley,and Hasty1983; Ludvigson andRottman 1989;Milliman1982).Inparticular,scholarshaveinvestigated

how music, scent, and color influence shopping outcomes,

∗_{Corresponding}_author._Fax:₊₄₃₄₆₃₂₇₀₀_994091.

E-mailaddresses:holger.roschk@aau.at(H.Roschk),

sandra.loureiro@iscte.pt(S.M.C.Loureiro),jab99@aber.ac.uk(J.Breitsohl).

affectingemotionalreactions,satisfactionandpurchase inten-tion,andhaveproducedavoluminousliteraturewithsubstantial variation in sample composition, industry context,and study design(BellizziandHite1992;MattilaandWirtz2001;Sayin etal.2015).

This body of work hasproduced mixed results, including

significant and non-significant findings, as well as effects in opposingdirections,evenforthesamerelationship(Andersson et al. 2012; Cyr, Head, and Larios 2010; Michon, Chebat, and Turley 2005; Morrin and Ratneshwar 2000; Yalch and Spangenberg1988).Furthermore,estimatesofthestrength of therelationshipshaverangedfromsmalltolarge(Jacob,Stefan, and Guéguen2014; Morrisonet al.2011),rendering conclu-sionsabouttheelasticityof atmosphericeffects,animportant questionforretailexecutives,uncertain.Generalizableestimates of effect sizes are therefore badly needed. Previous reviews of atmospheric effects have, however, been limited to narra-tive or vote-counting methods(Bone andEllen1999; Turley and Milliman 2000), and generalized estimates among the

http://dx.doi.org/10.1016/j.jretai.2016.10.001

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relationshipshereinvestigatedhavebeenreportedonlyforthe effectofmusiconpleasure(GarlinandOwen2006).In addi-tiontothelackofaggregatedeffectsizeestimates,theseearly summariesdatebackmorethan10years.

Thefirstcontributionofthisstudyisthereforetopresenta meta-analysis attemptingto calibrate the size of atmospheric effects onshopping outcomes.The second contribution isan attempttoaccountforbetween-studyvarianceineffectsizesand toinvestigateanumberofmoderatorsthatreflectstudydesign choicesmadebytheresearchers.Themoderatorsinclude samp-lingframe(studentsversuscustomers),gendersplit(lowversus highproportionoffemalesinasample),industrysetting(retail versusserviceversusonlinesettings),andexperimentaldesign (fictitiousversus actualenvironments).Through thesemeans, weaimtopresentretailerswithareliableguidetotheeffectsof atmosphericstimulionshoppingoutcomesbasedonananalysis ofdataaggregatedacrosstheresearchstream.

TheoreticalBackground

Theframeworkfor thismeta-analysis isdepictedinFig.1

togetherwith the investigated variables. It follows the extant literature in using the so-called stimulus-organism-response

paradigm (Mattila and Wirtz 2001; Mehrabian and Russell

1974).

ShoppingOutcomes

Frequently studied shopping outcomes at the organism

levelincludecustomers’emotionalreactionsandjudgmentsof satisfaction.Emotionalreactionsareconceptualizedasa combi-nationofarousalandpleasure.Arousalrepresentstheactivation dimensionandcanbedefinedastheperceiveddegreeof stimula-tion,whilepleasurerepresentsthevalencedimensionandrefers tothe perceived degreeof enjoyment(Donovan andRossiter 1982).Satisfactionreflectsanoverallevaluativejudgmentabout theshoppingexperience(MattilaandWirtz2001).Satisfaction isdistinct from pleasurein that it relatesto outward-looking judgmentsaboutexternalentitiessuchasastore’satmosphere (“Shoppinginthisstoreis apositiveexperience”,as adapted fromSayinetal.2015,p.5);whilepleasurereflectsasubjective, inwardfocus(“I’mexperiencingpleasantfeelings”).

Attheresponselevel,themostcommonlystudiedvariables includepurchase(Fiore,Yah,andYoh2000),visiting(Doucé and Janssens 2013), shopping (Broekemier, Marquardt, and Gentry2008),orpatronageintention(Grewaletal.2003). Stud-iesalsocaptureactualexpenditures(Sullivan2002).Together, thesevariablesreflecttheunderlyingobjectiveofcustomersto dobusinesswithanorganization,andareheresubsumedunder

behavioralintentions. AtmosphericStimuli

Theintegrationofpriorfindingsintoacommonframework necessitates a concentration on the most frequently studied variables.Amongthewidevarietyofinvestigatedatmospheric stimuli,music,scent,andcolorhavereceivedthemostresearch

attentionandarethereforethefocusofthisanalysis(Bellizzi, Crowley, andHasty 1983; Bone andEllen 1999; Garlin and Owen2006).

Music

Asanatmosphericstimulus,musicreferstohuman compo-sitionsfunctioningasanambient elementinthe consumption environment(GarlinandOwen2006).Atthemostbasiclevel,

musichasbeenstudiedbycomparingtheeffectsofthepresence andabsenceofmusic;that iscustomeremotions,satisfaction, andbehavioralintentionsarecomparedacrossconditionswhere musicispresentandwheremusicisabsent(e.g.,Grewaletal. 2003).

Authorsarguethat musiccanbeseenasacomplementary

productorservicefeaturethatisconsumedduringthepurchase processandisthereforelikelytoinfluenceshoppingoutcomes (Hui,Dubé,andChebat1997).Anotherexplanationfortheeffect ofmusiccomesfromoptimalarousaltheory,whichpositsthat peopleseektoaligntheircurrentlevelofarousaltoalevelthey findpersonallyoptimal(Berlyne1971).Customerswhoarein an“understimulated”statewillbeseekingheightenedarousal, whichtheymayrealizethrough the presence ofmusicin the shoppingenvironment(MattilaandWirtz2001).Sincearousal operatesasanamplifierofpositivein-storeexperiences(Oliver, Rust, and Varki 1997),downstream positive effects on plea-sure,satisfaction,andbehavioralintentionscanbeanticipated. Therefore,wehypothesize:

H1. The presence (versus absence) of music has apositive

effecton(a)arousal,(b)pleasure,(c)satisfactionand(d) behav-ioralintentions.

Scent

Ambientscentreferstoascentpresentintheenvironment thatdoesnotemanatefromaparticularobject(BoneandEllen 1999).Scenthasbeenemployedasanaturallyoccurring stimu-lus(e.g.,inbakeries)aswellasanartificiallyinducedstimulus toenhancestoreambience (Spangenbergetal.2006).Similar tomusic,scenteffectsareusuallymeasuredbycomparing

cus-tomers’ shoppingexperiences ina scentedenvironment with

thoseinascent-freeone(e.g.,DoucéandJanssens2013). Researchsuggeststhat,relativetoothersensorycues,scent isprocessedinamoreprimitiveportionofthebrain(Herzand Engen1996),andscentthereforerequireslittleornocognitive effort to enhance alertness, improve in-store experience, and promote positiveshopping outcomes (Bone andEllen1999). Studies havealso founda privilegedneural link between the olfactorynerveandthearearesponsibleforemotionalmemory (Herz2004).Thisisunderstoodasthephysiologicalexplanation for whysmell evokessignificantlystrongeremotional memo-riescomparedtothosetriggeredbyauditoryandvisualstimuli (Herz 2004).Therefore, when evoked in-store, such memory associationsmaystimulatepositiveemotionsandleadtoamore enjoyableshoppingexperience(BoneandEllen1999).Hence,

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Fig.1.Meta-analyticframework.

H2. Thepresence(versusabsence)ofscenthasapositiveeffect on(a)arousal,(b)pleasure,(c)satisfactionand(d)behavioral intentions.

Color

Asanatmosphericvariable,colordescribesthevisual appear-anceof theconsumption environment(Bellizzi,Crowley,and Hasty1983).Scholarsusuallycomparenon-whitecolorsasthese canbeorderedbywavelength,i.e.,fromlongtoshort(Crowley 1993).Researchdefinesthosewithalongerwavelength—such asred,orange,andyellow—aswarmcolors,whilethosewith

a shorter wavelength—such as green, blue, and violet—are

describedascool(Crowley1993).Althoughwhiteisgenerally regardedasneutral,itissometimesascribedtothecoolcolor cat-egory(ChebatandMorrin2007).Accordingly,researchershave predominantlystudiedcolorbycomparingcustomers’internal dispositionsandbehavioralintentionsinresponse towarmor cool conditions in amanner analogous tothe present versus absentcomparisonusedinstudiesonmusicandscent(e.g.,van Rompayetal.2012).

Forconceptualizing coloreffects inretail settings, studies drawonphysiologicalandpsychologicalfindingsreportedfor human behaviorin general inresponse tocolor (Bagchi and Cheema2013;BellizziandHite1992).Physiologically,red (rel-ativetoblue)hasbeenfoundtobemoreactivatingintermsof bloodpressure,respiratoryrate,andskin-conductance. Psycho-logically,warmcolors(especiallyred)areseenasemotionally arousing,exciting,anddistracting,whilecoolcolors(especially blue)arelinkedtofeelingsofrelaxation,peacefulness,calmness, and pleasantness(for moredetailed summaries, see Bellizzi, Crowley,andHasty 1983 andLabrecque,Patrick, andMilne 2013).

Findings in the atmosphericsdomain mirror these results. Red(comparedtoblue)hasbeenlinkedtogreaterarousal,and

blue (compared to red) is reported to be more relaxing and

pleasantandtofacilitatepurchaseincidencewithinashopping

environment (Bagchi and Cheema 2013; Bellizzi and Hite

1992;Crowley1993).However,theoverallempiricalevidence remainsinconclusive,withsomestudiesbeingunableto iden-tifydifferencesinresponsetowarmandcoolcolors,andothers revealingeffectsoppositetothedirectionhypothesized(Babin, Hardesty,andSuter2003;BagchiandCheema2013;Bellizzi andHite1992;Cyr,Head,andLarios2010).Forourhypothesis, wefollowthemaintheoreticalperspectiveandpropose: H3. Warm (versuscool)colorshaveapositiveeffecton(a) arousal andanegativeeffecton(b) pleasure,(c)satisfaction, and(d)behavioralintentions.

Moderators

SubstantiveInﬂuences

Substantive influences relate to boundary conditions that describehowastimulusshouldbedesignedinordertomaximize theimpactofitspresence.Variationsinstimuliinvestigatedin thecontextofmusic,scent,andcolorcanbecategorizedas struc-turalandcongruitycharacteristics(BoneandEllen1999;Mattila andWirtz2001).Althoughthenumberofempirical operational-izationsofthesecharacteristicswasinsufficientforthemtobe includedandcomparedinthemeta-analysis,wedescribethem herebrieflyastheydofigureintheresearchwearereviewing.

Structural characteristics describe the ‘components’ of a stimulusandincludevalence,intensity,andcomplexity.Valence reflectswhetherastimuluscarriesapositiveornegative conno-tationandreflectsdistinctionssuchashappyversussadmusic, the bright-to-darkgradationof color,andthehedonicor utili-tariantoneofascent(Cheng,Wu,andYen2009;Hui,Dubé, andChebat 1997;Knasko1995).Stimulus intensity captures, forexample,thetempoandvolumeofmusic,theconcentration ofscentintheair,andthedegreeofsaturationofagivencolor (BoneandEllen1999;Cheng,Wu,andYen2009;Herrington

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andCapella1996).Complexitydescribesthesimplicityversus elaborationofastimulus,including,forinstance,howeasyitis foracustomertofollowapieceofmusic(NorthandHargreaves 1996),toidentifyascent(plainversusblendedodors;Herrmann etal.2013),ortounderstandacolorscheme(plainversus gra-dientcolorcompositions).

Congruityisdefined as thefit of astimulusto theoverall storeambience.Sinceaperceivedincongruenceinterfereswith customers’informationprocessing,studiesprovide consistent evidencethatthefitofastimulustoeitherapart(e.g.,a prod-uctassortment)or toanentirestoreenvironmentenhancesits impactonshoppingoutcomes(MattilaandWirtz2001;Mitchell, Kahn,andKnasko1995;Spangenbergetal.2006).The same also applies tothe fit of a stimulustoindividual preferences (Broekemier,Marquardt, and Gentry 2008). In consequence, researcherstypicallyemploystimuliwithstructural character-isticsthat suitthe shoppingenvironment,as for instancewas implementedbyGrewaletal.(2003,p.262),whousedclassical music“becauseit‘fits’thecontextofluxurygoods”.

MethodologicalInﬂuences

Study designs used in research on atmospheric effects

reflect the differences in methodological decisions made by researchers. One objective of this research was to consider

the impact of these methodological decisions on the effect

sizes observedin the primary studiesand touse coded vari-ablesreflectingthesemethodologicaldecisionsinanattemptto accountforbetween-studyvarianceineffectsizes.Four poten-tialmoderatorswereincluded:samplingframe(studentversus customer),gendersplit(lowversushighproportionoffemalesin asample),industrysetting(retailversusserviceversusonline), andexperimentaldesign(actualversusfictitiousenvironments). Variationsbetweenstudentandcustomer(i.e.,non-student) samples maybe ascribed todifferences inpersonality

devel-opment. Specifically, students are regarded as “unfinished

personalities”(Carlson1971,p.212)withlessdefined prefer-ences.Peterson’s(2001)secondordermeta-analysisshowsthat effectsizesfromstudentsamplesfrequentlydifferfromthose derivedfromnon-studentsubjects.However,nosystematic pat-ternwithrespecttoeffectsizesordirectionofeffectsbetween students and customers could be firmly established. Expect-ingasimilarunsystematicvariation,we useanon-directional hypothesisandpropose:

H4. Theeffectsizesofatmosphericstimulionshopping out-comesvarybetweenstudent-andcustomer-basedsamples.

Although some authors have made suggestions regarding

musicandcolor(Anderssonetal.2012;vanRompayetal.2012), empiricalfindingsongendereffectshavebeenreportedmainly inrelationtoscent(BoneandEllen1999).Physiological evi-dencepointstowomenasbeingemotionallymoresensitiveand responsivetoscent(HerzandEngen1996).Yousemetal.(1999)

forinstancefindthatscentactivatesalargerareainawoman’s brainthaninaman’s.Intheatmosphericsdomain,Lehrneretal. (2000)indicatethatthepresenceofanambientorangescentin thewaitingroomof adentalsurgeryevokedmorepleasurein

womenthaninmen.Consequently,wepropose:

H5. The effectsizeof scenton(a)arousaland(b) on plea-sureislargerinsampleswithahigherproportionoffemalesas comparedtosampleswithalowerproportionoffemales.

Industrysetting(retail,service,oronline)mayaccountfor between-studyvarianceintheeffectsizeofatmosphericstimuli. Among investigations into settings, the differences between servicecontextsandproductretailsettingshavebeen particu-larlydiscussed(Bitner1990).Relativetoproducts,servicesare definedbytheirinherentlylargerdegreeofintangibility(Bitner 1990).Itissuggestedthat,intheabsenceof tangibleproduct features,environmentalcuesregardingservicesserveassubtle messagesthathelpcustomerstoknowwhattoexpectfroma cer-tainoffering(BoomsandBitner1982).Accordingly,customers maydependtoalargerextentonatmosphericstimuliwhenin contactwithaserviceenvironmentthaninaretailsetting.This leadstothefollowinghypothesis:

H6. Theeffectsizesof music,scent, andcolorarelargerin servicethaninretailsettings.

Finally, research designs inthe literature on experimental

atmospherics can be grouped into those based on fictitious

versusactual(i.e.,in-store)environments.Extantresearch pro-videsmixedfindings.Ontheonehand,BatesonandHui(1992)

find evidencefor the ecologicalvalidity of fictitious environ-ments. Onthe otherhand,fictitiousenvironmentscanleadto inflatedeffectsizesbecausetheyallowresearchersmore con-trolinisolatingextraneousfactorsthatmayhavebearingonthe hypothesizedrelationshipsandindirectingtheattentionofthe studyparticipants(Shadish,Cook,andCampbell2010).Thus, wehypothesize:

H7. Theeffectsizesofatmosphericstimulionshopping out-comesarelargerinfictitiousthaninactualenvironments.

Method

DatabaseDevelopment

Abibliographickeywordsearchwasconducted toidentify studies reporting on customers’ reactions tothe atmospheric stimulusvariablesasspecifiedinFig.1.Databasesusedincluded

EBSCO Business Source Complete, Science Direct, Emerald ManagementXtra,ABI/Inform,PsycINFO,GoogleScholarand theSocialScienceCitationIndex.Inaddition,areference analy-siswasconductedonprevioussummaries(BoneandEllen1999; GarlinandOwen2006;TurleyandMilliman2000),anda cita-tionanalysisofpertinentarticleswasmade(Bellizzi,Crowley, andHasty 1983; Milliman1982; Spangenberg, Crowley,and Henderson 1996). Unpublished work was also obtained by searchingtheSSRNdatabaseandGoogleScholar.

Astudywasincludedinthefinaldatasetif(1)music,scent, andcolorweremanipulatedexperimentally,(2) thestudywas independent (i.e., if the results of two different studies were derivedfromthesamesample,thestudywhichprovidedmore detail wasused), (3) themeasurementitem(s) of anoutcome variableaccuratelyreflectedourconstructspecification,and(4) aneffectsizeorsufficientstatisticalinformationwasprovided

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foratleastoneoftherelationshipsspecifiedinFig.1.Astudy wasexcludediftheexperimentaldesignpreventedtheisolation ofeffectsforasinglestimulus(i.e.,multiplemanipulationswere notfullycrossed).

Thesearchidentified66studies(64articlespublishedin

aca-demic journals, 2 unpublished working papers) that met the

criteriaforinclusion.Theincludedstudiesreferredto74

inde-pendent samplesfrom thetimeperiod 1982to2016 (March)

witha combined totalN of 15,621 respondents. Overall, the averagesamplehadamedianageof33.2years(student sam-ples=22.0years,generalcustomersamples=39.1years)anda mediangendersplitof 61.7%infavorof femalerespondents. Followingtheremovaloftwooutliers,1weobtainedafinaldata setof135studyeffects.

EffectSize Computation

Common meta-analytic guidelines were followed for the

experimentalstudiesreviewed inthemeta-analysis. First,the standardizedmeandifferences(Cohen’sd)werecomputed, fol-lowed by conversion of these to correlation coefficients (De Matos, Henrique, andRossi 2007). The choiceof using cor-relationcoefficientsastheeffect-sizemetricwasbasedonthe relative ease with which these can be interpreted, and their generalutility as astandardmeta-analytic metric inthe mar-keting literature(Gelbrich and Roschk2011; Palmatier etal. 2006).

When calculating r, a positive (negative) value indicates

that—inthecaseof musicandscent—thepresencecompared

totheabsenceconditionincreases(decreases)thevalueofthe outcome variable. Likewise, in the case of color, a positive (negative)valueindicatesthatthewarm(i.e.,red,orangeand yel-low)comparedtothecool(i.e.,green,blue,violet,andwhite) color schemeincreases (decreases) the value of the outcome variable.

Integration

Correlationcoefficientsweredirectlyderivedfromthe stud-iesathand orwerecalculatedthroughstatistical datasuchas Student’st,η2,andF-ratios(Cohen1988;Glass,McGaw,and Smith1981).In somecases,the studyauthorsprovidedmore thanonemeasure of correlationfor the samerelationship by analyzingdifferentresponsemeasuresormultipleexperimental comparisonsbetweenastimulus-absentgroup(e.g.,nomusic) andastimulus-presentgroup(e.g.,differentstylesofmusic).If thiswasthecase,weaveragedtheeffectsizesinordertoavoid bias from the overrepresentationof samples (Palmatieret al. 2006).

Theeffectsizeswerenextadjustedforreliabilitytocorrect

for attenuation from randommeasurement error (Hunter and

Schmidt2004).Forstudiesthatdidnotprovidereliabilityindices orused single-itemmeasures,themeansample size-weighted

1 _The_outliers_were_excluded_because_they_lay_outside_of_the_range_of_three

standarddeviationstotherespectivesample-sizeweightedmeaneffectsize.

reliability across allstudieswasused. Wethen computedthe sample-sizeweightedmeansofallavailableeffectsizeestimates foreachrelationship,whichwerefertoasr(HunterandSchmidt 2004).

CalculationofAssociatedStatistics

Wecalculatedthe‘fail-safeN’ statistictofind theaverage numberofdiscardednullresultsnecessarytorenderthe relation-shipsnon-significant.Forameaningful,robustresultRosenthal (1979)suggeststhattheobtainedfail-safeNshouldbegreater thanorequaltofivetimesthenumberofobservationsplus10 (referredtoas‘requiredfail-safeN’).Heterogeneityofthe inte-grated effectsizes(i.e.,between-studyvariance)wasassessed via theQ-Statistic(HedgesandOlkin1985).Moderator anal-yses are justifiedwhen between-study variance issignificant, indicating that resultsof extant studiesdo not convergeona

commonpopulationvalue.

Coding

Two judges independentlycoded the dependent and

mod-erating variables based on the theoretical definitions of the constructs.Thetwojudgesconcurredonmorethan95percent of independentlydetermined codingdecisions;disagreements wereresolvedbydiscussion.Atableofthecodingsisprovided intheAppendix.

Results

AtmosphericEffects

Theresultsoftheimpactofatmosphericstimulionarousal, pleasure, satisfaction,andbehavioral intentionsare shownin

Table1.FollowingCohen’s(1988,p.82)criteria,aneffectsize of .10canbeconsideredas small,.30asmedium,and.50as large.

Ourfindingsindicatedthatthepresenceofmusic(compared toitsabsence)wassignificantlyandpositivelyrelatedtopleasure (r=.098),tosatisfaction(r=.226),andtobehavioralintentions (r=.130).Fail-safeNvaluesexceededtherequiredfail-safeN

(see Table 1). Presence of musicdid not significantly affect

arousal.Hence,H1b,H1c,andH1dweresupportedwhileH1a

wasnot.

Scentwassignificantlyrelatedtoalloutcomevariables. Pres-enceofscentledtohigherarousal(r=.079),pleasure(r=.093), satisfaction (r=.183), and behavioral intentions (r=.054) as comparedtoscent-absentconditions,supportingH2a,H2b,H2c, andH2d.Theeffectofscentonarousalshouldbetreatedwith cautionbecausetheobtainedfail-safeNwasbelowthenormative value.

Color schemessignificantly affectedbotharousal and

sat-isfaction. As hypothesized, these effects were in opposing

directions.Warmascomparedtocoolcolorschemesproduced higherarousal(r=.157)butlowersatisfaction(r=−.254).Both effectshadrobustfail-safeNs.Colorwasnotsignificantlyrelated toeitherpleasureorbehavioralintention.Thus,H3aandH3c

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Table1

Meta-analyticresultsfortheinfluenceoftheatmosphericstimuliontheshoppingoutcomes.

95%CI Fail-safeN

k N r Lowerbound Upperbound SD p Obtained Required Min Max Q-Statistica

Music(present=1,absent=0)

Arousal 11 2441 .042 −.021 .106 .108 .193 – – −.272 .265 28.87**

Pleasure 12 2489 .098 .043 .153 .097 <.001 77 70 .000 .334 24.47*

Satisfaction 5 877 .226 .170 .282 .064 <.001 63 35 .151 .354 4.03ns

Behavioralintentions 19 3116 .130 .078 .181 .114 <.001 314 105 −.059 .395 43.03***

Scent(present=1,absent=0)

Arousal 14 2763 .079 .033 .124 .087 <.001 49 80 −.071 .277 20.96ns

Pleasure 18 3793 .093 .039 .148 .117 <.001 184 100 −.193 .331 53.36***

Satisfaction 4 938 .183 .099 .268 .086 <.001 30 30 .031 .293 7.40ns

Behavioralintentions 21 5280 .054 .013 .094 .094 .009 129 115 −.154 .458 49.32***

Color(warm=1,cool=0)

Arousal 9 1724 .157 .020 .294 .210 .025 156 55 −.198 .522 85.69***

Pleasure 8 1499 .031 −.129 .192 .232 .704 – – −.380 .446 88.46***

Satisfaction 7 947 −.254 −.299 −.210 .060 <.001 79 45 −.320 −.105 3.52ns

Behavioralintentions 7 1225 −.057 −.190 .076 .179 .402 – – −.292 .301 40.47***

Note:k,numberofeffects;N,totalsamplesize;r,samplesize-weightedmeancorrelationcoefficientadjustedforreliability;SD,standarddeviation.Obtained/required fail-safeNarenotcalculatedforinsignificantrelationships(indicatedbyadashedline).Figuresinboldindicatesignificanceand,withregardstothefail-safeN,

robustcorrelations.ns,notsignificant.

a _df₌_k₋_1.

* _p_<_.05.

** _p_<_.01.

***_p_<_.001.

ModeratingEffects

ThelastcolumninTable 1depicts the Q-statistic.Results

indicated that between study variance was non-significant

for four relationships (music→satisfaction, scent→arousal, scent→satisfaction, color→satisfaction). Forthe remaining eightrelationships,theresults weresignificant,indicatingthe appropriatenessofmoderatoranalysesinattemptingtoexplain between-studyvariance.Moderatoranalyseswereconductedby partitioningstudiesaccordingtocodedlevelsofstudy character-isticsandcalculatingrwithinsubgroups.Forgender,weusedthe medianproportionoffemalestocreatetwosub-groups(≤61.7% versus >61.7%). Subsequently, a t-test for small samples, as describedbyWiner(1971),wasused(Palmatieretal.2006).2 Itshouldbenotedthatthesmallnumberofstudyeffectsrender thesetestslowinstatisticalpower,andthereforeTypeIIerror ismorelikelythanTypeIerror(Brown1996).Theresultsof thesemoderatoranalysesaredepictedinTables2and3,andare reportedbelow.

For most relationships, effect sizes did not differ

signifi-cantlybetweenstudentandnon-studentsamples.Only music

hadstrongereffectsonbehavioralintentionsinnon-student sam-ples(r=.169)thaninstudentsamples(r=.069).Overall,H4was notconfirmed.

Withregardtogendereffects,significantlylargereffectsizes

were found for the relationship between scent and pleasure

2 _The_small_sample _t-statistic_is_based _on _the_single_reliability _corrected

correlationcoefficientsandiscalculatedbyt= (¯xa−¯xb)

(s2 a/na)+(s2_b/nb) ,withdf= (V+W)2 [V2_/(n a−1)]+[W2/(nb−1)],whereV=s 2 a/naandW=s2b/nb.

amongsampleswithahigherproportionoffemales(r=.176) comparedtothosewithalowerproportion(r=.069).Asimilar between-groupdifferencewasevidentforarousal,butwasnot statisticallysignificant.Hence,H5bwassupportedwhileH5a

wasnot.Additionally,thepresenceofmusichadasignificantly strongereffectonbehavioralintentionamongsampleswitha lowerproportionoffemales(r=.165)ascomparedtothosewith ahigherproportion(r=.006).

The industry settinganalyses indicatedatendency toward

stronger music and scent effects in service as compared to

retailsettings.However,theresultsweresignificantonlyforthe effectofmusiconbehavioralintentions(servicer=.200,retail

r=.112),andtheyweremarginallysignificantfortheeffectof musiconpleasure(servicer=.263,retailr=.073)andfor the effectofscentonbehavioralintentions(servicer=.342,retail

r=.041).Thus,H6waspartiallysupportedformusicandscent. Theresultsalsoshowedsignificantlylargereffectsizesinonline comparedtoretailsettingsfortherelationshipsbetweencolor andarousal(onliner=.401,retailr=.040)andbetweencolor andpleasure(onliner=.208,retailr=−.059).

Finally,sevenrelationshipshadasufficientnumberof avail-ableeffectstomakeitpossibletocompareeffectsizesbetween experimentscarriedoutinactualversusfictitiousenvironments. Resultsshowedthat noneoftheanalyzeddifferenceswereof statisticalsignificanceandH7wasnotsupported.

GeneralDiscussion

Inthelight ofthevolumeoftheliteraturethatisuncertain abouttheimpactofatmosphericstimulionshoppingoutcomes, thefirst objectiveofthismeta-analysiswastocalibrateeffect sizes. On an aggregate level, the results revealed predictable

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Table2

Meta-analyticmoderationresultsfortheinfluenceofthemoderatorsontheeffectsizesbetweenatmosphericstimuliandshoppingoutcomes.

Samplingframe Gendersplit(%female)

Level r k LB UB t df p Level r k LB UB t df p

Arousal Customer .045 6 −.063 .152 0.53 8 .305 ≤61.7 .081 4 −.037 .199 1.29 4 .133

Student .041 5 −.032 .114 >61.7 .033 5 −.028 .094

Pleasure Customer .099 6 −.011 .208 0.01 7 .496 ≤61.7 .065 4 −.043 .173 0.05 6 .481

Student .098 6 .055 .140 >61.7 .115 5 .037 .194

Satisfaction

Behavioralintentions Customer .169 10 .102 .235 1.76 17 .048 ≤61.7 .165 8 .089 .240 3.78 9 .002

Student .069 9 .006 .132 >61.7 .006 3 −.044 .056

Arousal Customer .084 9 .030 .138 0.25 8 .404 ≤61.7 .059 7 .004 .115 1.25 8 .124 Student .058 5 −.028 .144 >61.7 .132 4 .061 .203 Pleasure Customer .087 13 .025 .149 0.69 8 .255 ≤61.7 .069 8 .000 .138 3.00 11 .006 Student .130 5 .016 .245 >61.7 .176 5 .124 .227 Satisfaction Customer .158 2 .148 .168 0.04 1 .489 ≤61.7 .213 2 .046 .381 0.04 1 .489 Student .213 2 .046 .381 >61.7 .158 2 .148 .168

Behavioralintentions Customer .042 14 −.011 .094 0.67 19 .254 ≤61.7 .091 5 .018 .165 1.47 7 .093

Student .096 7 .054 .138 >61.7 −.002 6 −.048 .044

Arousal Customer .130 5 −.041 .301 0.37 7 .362 ≤61.7 .286 3 .073 .499 0.90 4 .209 Student .237 4 .013 .462 >61.7 .050 4 −.104 .204 Pleasure Customer .087 3 −.077 .250 1.45 6 .098 ≤61.7 .239 2 −.069 .547 1.80 2 .107 Student −.038 5 −.294 .219 >61.7 −.035 4 −.146 .077 Satisfaction Customer −.260 3 −.317 −.203 0.05 5 .483 Student −.233 4 −.315 −.151

Behavioralintentions Customer −.075 4 −.210 .060 0.49 3 .330 ≤61.7 .087 2 −.102 .276 2.29 2 .074

Student −.025 3 −.286 .235 >61.7 −.150 4 −.280 −.020

Note:r,samplesize-weightedmeancorrelationcoefficientadjustedforreliability;k,numberofeffects;LB/UB,lower/upperboundofthe95%CI.Duetomissing information,thecombinednumberofobservationacrosssubgroupscanbelowerthanthetotalfromTable1.Subgroupcomparisonsareconductedifatleasttwo observationsineachsubgroupcouldbeidentified.Significantdifferencesarehighlightedinbold.

patternsfor theeffectsof music(presencevs.absence),scent (presencevs.absence)andcolors(warmvs.cool)onshopping outcomes.Theeffectsizesforthesignificantrelationshipswere estimated atan average rthat spanned from.054 to.254 (in absoluteterms). Thesevalues indicatedthat the relationships weresmall-to-mediuminstrength,whichcanbeseenas reflect-ing the subtlenature of the atmosphericstimuli. Overall, the resultsprovideresearcherswithaquantitativesummarythat fig-uresthepatternoftheeffectsthathavebeenincludedwithinthe researchstream.Specificinsightsformusic,scent,andcolorare asfollows:

Formusic,theresultsrevealedsignificantandpositive aggre-gateeffectsonpleasure,satisfaction,andbehavioralintentions. A link witharousal could not be established. It appearsthat musicis,againstabackgroundnoisesuchascustomerchatter, toosubtletotriggercustomers’arousal.Hence,musical stimula-tionmaybeseenasapleasure-inducingsubstitutefordistracting in-storesounds,whichwillenhancetheshoppingexperience. Furthermore,thegeneralizedestimatesreportedhereaddtoand updatethoseprovidedby GarlinandOwen(2006),thus

pro-vidingresearcherswithtwocomplementarysummarieswhich

maybeusedasreferencesforfurtherresearchintotheeffectsof atmosphericmusic.

At a meta-analytic level, scent positively influences cus-tomers’ pleasure, satisfaction, and behavioral intentions. A positive effectof scenton arousalwas also evident, withthe limitationthatitsrobustnessagainstdiscardednull-resultscould notbeverified.Thesefindingssuggestaneedtoreconsiderthe conclusioninBoneandEllen’s(1999)review,whichstatesthat “countingonsuch [scent]effects isanunwise strategyatthis point in time” (p. 259). Instead, scentmay be regarded as a reliablemeansforenhancingtheshoppingexperience.

The integratedeffects showcolortobebipartiteinnature. Whilewarmcolorscausehigherlevelsofarousalthancool col-ors,theoppositeistrueforsatisfaction.Researcherscandrawon theseresultswhenconceptualizingcoloreffectsforspecific in-storebehavioralphenomena.Forinstance,Labrecque,Patrick, andMilne(2013)discussthearousing yetdissatisfyingeffect of warm(vs.cool)colorsontheperceptionofcustomerswho arewaitinginthecheck-outline.Ourdatadidnotconfirma sig-nificanteffectofcoloronpleasureoronbehavioralintentions. Opposingcoloreffectsmay,duetointerrelationshipsamongthe

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Table3

Meta-analyticmoderationresultsfortheinfluenceofthemoderatorsontheeffectsizesbetweenatmosphericstimuliandshoppingoutcomes.

Industrysetting Experimentaldesign

Level r k LB UB ta _df _p _Level _r _k _LB _UB _t _df _p

Arousal Retail .022 5 −.084 .129 1.31 2 .160 Actual .041 7 −.055 .138 0.82 8 .219

Service .142 2 −.032 .316 .37 2 .374 Fictitious .043 4 −.040 .126

Online .043 4 −.040 .126 1.26 7 .124

Pleasure Retail .073 6 −.020 .166 2.79 2 .054 Actual .104 7 .005 .203 0.41 8 .346

Service .263 2 .152 .374 2.45 2 .067 Fictitious .093 5 .049 .138

Online .094 4 .044 .145 .76 8 .234

Satisfaction Retail .236 2 .200 .272 .51 1 .349

Service .273 2 .153 .393

Behavioralintentions Retail .112 8 .031 .194 2.03 13 .032 Actual .169 10 .102 .235 1.53 16 .073

Service .200 7 .142 .258 4.40 7 .002 Fictitious .076 8 .007 .144

Online .022 3 −.036 .081 1.64 9 .067

Arousal Retail .107 9 .057 .158 .14 6 .446 Actual .089 7 .029 .149 1.17 9 .135

Service .037 4 −.042 .116 Fictitious .051 6 −.024 .126

Pleasure Retail .078 12 .008 .148 .11 6 .459 Actual .090 11 .023 .157 0.09 12 .466

Service .120 5 .031 .208 Fictitious .115 6 .011 .219

Satisfaction Actual .158 2 .148 .168 0.04 1 .489

Fictitious .213 2 .046 .381

Behavioralintentions Retail .041 17 .006 .076 3.88 1 .080 Actual .051 13 .000 .101 0.27 15 .395

Service .342 2 .191 .493 Fictitious .064 7 −.012 .140

Arousal Retail .040 4 −.096 .177 .78 2 .260 Service .088 2 −.122 .297 1.89 1 .155 Online .401 3 .283 .518 3.63 5 .008 Pleasure Retail −.059 4 −.219 .101 Service Online .208 3 −.095 .511 2.33 4 .040 Satisfaction Retail −.237 2 −.279 −.196 .67 1 .311 Service −.277 2 −.346 −.207 .27 2 .405 Online −.179 3 −.292 −.066 1.11 3 .174

Behavioralintentions Retail −.094 5 −.216 .027 Service

Online .037 2 −.280 .354 1.14 1 .229

Note:r,samplesize-weightedmeancorrelationcoefficientadjustedforreliability;k,numberofeffects;LB/UB,lower/upperboundofthe95%CI.Duetomissing information,thecombinednumberofobservationacrosssubgroupscanbelowerthanthetotalfromTable1.Subgroupcomparisonsareconductedifatleasttwo observationsineachsubgroupcouldbeidentified.Significantdifferencesarehighlightedinbold.

a _Subgroup_comparisons_in_the_following_sequence:_retail_vs._service,_service_vs._online,_online_vs._retail.

shoppingoutcomes,indirectlyexhibitajointeffectonpleasure andonbehavioralintentions,whichthuscanceleachotherout. Thesecond objective of thismeta-analysis wastoattempt toexplainbetween-studyvarianceineffectsizeestimates.The resultsofprimarystudiesshowsignificanteffectsizevariations forthe majorityof relationships,indicatingthat the effective-nessofatmosphericstimuliislikelytodependonthespecific contextinwhichtheyareemployed.Partofthisvariationcanbe accountedforbytheresearchers’studydesignchoices. Specif-ically,gendersplitandindustrysettingprovide the following insights.

Forgender,themoderationresultsindicatethatwomenderive morepleasurefromscentthanmen.Thisconfirms physiolog-ical research on scent (Yousem et al. 1999) and encourages researcherstopayattentiontogenderdifferencesinscenteffects.

Itfurthersuggeststhatpriorresearchusingfemale-onlysamples (Morrison etal.2011) may haveexperienced inflatedeffects for this particular relationship. Additionally, our data shows that musichas a weakerimpact on the behavioral intentions ofwomenthanonthoseofmen,confirmingrecentfindingsin theatmosphericsliterature(Anderssonetal.2012).

With regard to industry setting,the effect sizes for music andscenttended tobestronger inservice comparedtoretail settings. Thisfindingisinlinewithpriorresearchsuggesting thatatmosphericstimuli,as subtlesignalsforcustomers,gain greatersaliencewhentangibleproductcuesareabsent(Bitner 1990). Asimilarlogic mayalso explain the largereffects of coloron arousalandpleasureinonline as comparedtoretail settings.Additionally,alackofdataregardingscenteffectsin online settingsreflects theneed for abetterunderstandingof

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how virtual environmentscan leveragescent effects (e.g., by scentingproductpackages).

Between-studyvarianceofeffectsizescouldnotbeexplained byeithersamplingframeorbyexperimentaldesign.Thus,the effectsizesappearnottovarysystematicallyinrelationtothese methodologicalspecifications.Oneexceptionwasaweakerlink betweenmusicandbehavioralintentionsinstudentthanin cus-tomersamples,whichmaybeseenasapreliminaryindication thatthereislessexplicablevarianceinstudent(versuscustomer) behavioralintentions.

In addition to the heterogeneity of effect size estimates, moderatoranalysisalsoindicatedthatbetween-studyvariance wasnotsignificantforfourrelationships:music→satisfaction, scent→arousal,scent→satisfaction,andcolor→satisfaction. Fortheserelationships,extant primarystudiesappearto con-verge of what is tentatively, and pending further research, consideredtorepresentapopulationestimateofthe elasticity oftheatmosphericeffects.

Managerially, establishing reliable effect patterns has the potential to provide retail executives withthe necessary

pre-dictability over the uncertainty currently conveyed by the

literatureand thustoenable themto purposefullyimplement music, scent,andcolortoenhancetheir customers’shopping

experience. In doing this, two aspects need consideration.

First, congruence of the stimuli with the environment needs tobeensuredotherwisethestimulimaycauseadverseeffects (Mitchell, Kahn, and Knasko 1995). Second, the small-to-medium size of the effects suggests that the stimuli are of a subtlenatureandthusshouldbeconsideredaslong-term strate-gies.Specifically,musicandscentfostertheshoppingoutcomes withtheexceptionofarousal.Musicoffersthegreatest poten-tialforbeingtailoredtothepurchasesetting,whilescentbears the advantage that pleasant scentsmay occur naturally (e.g., roastedcoffeebeans)andcanbeventedtoaligningstoreareas. Further,warmcolorscausehigher levelsof arousalthancool colors, while the opposite is true for satisfaction. To benefit fromtheseeffects,warmcolorsmaybefavoredfornewproduct aisles,leveraging ontheir arousal property,while coolcolors maybepreferredforcomplainthandlingdesks,leveraging on theirsatisfyingproperty,forinstance.

Finally,two mainlimitationsthatofferpotentialfor future researcharediscussed.First,considerablevariationin concep-tualframeworksandinconsistentevidencepreclude definitive conclusionsonthecausalpriorityamongarousal,pleasure, sat-isfaction,andbehavioral intentions(BabinandDarden 1996; ChebatandMichon2003;DonovanandRossiter1982;Morrison et al. 2011). Therefore, our analysis is based on bivariate

correlations. This choice was due to the main study focus,

i.e., calibrating effect sizes to understandtheir direction and

strength, and accounting for moderators to understand

spe-cificboundaryconditions.Therefore,futureworkisencouraged to test for causal effects using larger meta-analytic datasets

as downstream sequential effects relate to many additional

types of exogenous influences besides those of atmospheric

stimuli.

Second, the meta-analytic design relies on secondary

data and thus only those relationships for which there was

a sufficient quantity of empirical evidence were included.

In particular, substantive moderators (valence, intensity, and complexity) could not be integrated and therefore represent an under-researched field worthy of future investigation and meta-analytic integration. Further, examining the extent to whichatmosphericstimulivariablesaffectnon-purchaserelated behavior, like in-store product trial participation, would be worthexploring.

Executivesummary

Retailers use subtle atmospheric stimuli to purposefully

enhance customers’shopping experience. Academicresearch

hasstudiedatmosphericstimuliwithaparticularemphasison music, scent, and color in a large variety of study designs. Thefindingsofthisvoluminousbodyofwork,spanningmore than 30 years, are however inconclusive, rendering reliable predictions about the stimuli’s effects on shopping outcomes uncertain. Wetherefore synthesizedthe quantitativeevidence from66experimentalstudiesandexaminedtheeffectsofmusic (presence vs.absence),scent(presencevs.absence),and col-ors(warm:red,orange,andyellowvs.cool:green,blue,violet, andwhite)onshoppingoutcomes.Shoppingoutcomescomprise customers’arousal(emotionalstimulation),pleasure(emotional enjoyment), satisfaction (evaluation of the shopping experi-ence), and behavioral intentions (e.g., purchase- or visiting intentions).

Resultsoffertwo keyinsightsrelevant toretailexecutives. First,inthelightofthecurrentuncertaintyaboutthestimuli’s atmosphericeffects,thefindingsshowpredictablepatternsfor

how music, scent, and color impact on shopping outcomes.

Morespecifically,environmentsinwhicheithermusicorscent is present, yield higher pleasure, satisfaction, andbehavioral

intention ratings compared withenvironments inwhich such

conditions are absent. Warm colors produce higher levels of arousalthancoolcolors,whiletheoppositeistruewithrespect tolevelsofsatisfaction.Itisimportanttonotethatthestimuli typically shouldfit theconsumptioncontext.Retailmanagers can use these findings as a guide,based as theyare on data aggregated acrossthe researchstream,toenhancecustomers’ shoppingexperience.Asonesuchstimulus,musicoffersa flex-ible tool that easily can be tailored tothe shopping context, pleasantscentsmayoccurnaturallyandcanbeventedto align-ingstoreareas,andcolorscanbeusedspecificallyinthoseareas whereeither arousal (e.g.,newproduct aisles)or satisfaction (e.g.,complainthandlingareas)shouldbetriggered.

Second,thesizeoftheeffectofstimulionshoppingoutcomes variedacrossstudies.Weattemptedtoexplainthesevariations

by conducting subgroupanalyses using coded moderators to

reflectthestudydesigndecisions.Resultsshowed,forinstance, atendencytowardlargermusicandscenteffectsinserviceas comparedtoretailenvironments.Wealsofoundevidence indi-catingthatwomenderivemorepleasurefromscentthanmen. Effect sizevariationspointtowardthe contextdependenceof atmospheric effects, justifying the efforts of marketing intel-ligence todesignandimplement thestimuliaccording tothe

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individual consumption environment. Overall, the average strength of the relationships across contexts was small-to-medium,whichcanbeinterpretedasareflectionofthesubtle nature of stimuli. Therefore, their implementation should be consideredasalong-termstrategy.

FinancialSupport

Theauthorsreceivednofinancialsupportortechnical assis-tancefortheresearchand/orauthorshipofthismanuscript.

Acknowledgments

Theauthorsareverygratefultothreeanonymousreviewers andtheeditorfortheirhelpfulandconstructivecomments.The firstauthoralsolikestothankWendyTraboldforherconstant supportduringthedevelopmentofthisproject.

Appendix. Codedcharacteristicsofincludedsamples

Sample(inalphabeticalorder) Independentvariables Dependentvariables Moderatingvariables Music Scent Color Arousal Pleasure Satisfaction Behavioral

intentions Sampling frame Gender split(% female) Industry setting Experimental design

1 AlpertandAlpert(1990) × × × Student NA Retail Fictitious

2 Anderssonetal.(2012–Study1) × × × × Customer 43.3 Retail Actual

3 Anderssonetal.(2012–Study2) × × × × Customer 56.5 Retail Actual

4 Babin,Hardesty,andSuter(2003) × × × × Customer 100.0 Retail Fictitious

5 BagchiandCheema(2013– Study3)

× × Customer 59.0 Online Fictitious

6 Barlietal.(2012) × × Customer 52.0 Retail Actual

7 Baron(1997) × × Customer NA Retail Actual

8 BellizziandHite(1992–Study 1)

× × Customer 100.0 Retail Fictitious

9 BellizziandHite(1992–Study 2)

× × × × × Student NA Retail Fictitious

10 Bouzaabia(2014) × × × × Customer NA Retail Actual

11 Bramley,Dibben,andRowe (2016)

× × Student 64.2 Online Fictitious

12 ChebatandMichon(2003) × × × Customer 57.0 Retail Actual

13 ChebatandMorrin(2007) × × × Customer 64.6 Retail Actual

14 Chebat,Morrin,andChebat (2009)

× × Customer NA Retail Actual

15 Cheng,Wu,andYen(2009) × × × Customer NA Online Fictitious

16 Cyr,Head,andLarios(2009– Canadiansample)

17 Cyr,Head,andLarios(2009– Germansample)

18 Cyr,Head,andLarios(2009– Japanesesample)

19 deWijkandZijlstra(2012) × × × Customer 59.1 NA Fictitious

20 Dijkstra,Pieterse,andPruyn (2008–Study2)

× × Student 63.6 Service Fictitious

21 DoucéandJanssens(2013) × × × × Customer 89.7 Retail Actual

22 Fiore,Yah,andYoh(2000) × × × × Student 100.0 Retail Fictitious

23 Grewaletal.(2003) × × × Student 47.0 Retail Fictitious

24 GuégenandPetr(2006) × × Customer NA Service Actual

25 Harrington,Ottenbacher,and Treuter(2015)

× × Customer NA Service Actual

26 HerringtonandCapella(1996) × × Customer 20.0 Retail Actual

27 Herrmannetal.(2013–Study1) × × Customer NA Retail Actual

28 Herrmannetal.(2013–Study3) × × Student NA Retail Fictitious

29 Hui,Dubé,andChebat(1997) × × × Student 49.1 Service Fictitious

30 Jacob,Stefan,andGuéguen (2014)

× × Customer NA Service Actual

31 KimandLennon(2012) × × × Student 100.0 Online Fictitious

32 Kim,Kim,andLennon(2009) × × × Student 100.0 Online Fictitious

33 Knasko(1995) × × × Student NA Retail Fictitious

34 Lehrneretal.(2000–Female sample)

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Appendix(Continued)

Sample(inalphabeticalorder) Independentvariables Dependentvariables Moderatingvariables Music Scent Color Arousal Pleasure Satisfaction Behavioral

intentions Sampling frame Gender split(% female) Industry setting Experimental design

35 Lehrneretal.(2000–Male sample)

× × × Customer 0.0 Service Actual

36 Lehrneretal.(2005) × × × × Customer 49.5 Service Actual

37 Lorenzo-Romero,Gómez-Borja, andMollá-Descals(2011)

× × × × × Student NA Online Fictitious

38 LudvigsonandRottman(1989) × × Student 70.8 NA Fictitious

39 Madzharov,Block,andMorrin (2015–Study3)

40 MattilaandWirtz(2001) × × × × × × Customer 75.0 Retail Actual

41 McDonnell(2007) × × Customer 45.0 Service Actual

42 McGrath,Aronow,andShotwell (2015)

43 MichonandChebat(2007) × × Customer 62.0 Retail Actual

44 Michon,Chebat,andTurley (2005)

45 Middlestadt(1990) × × Student 100.0 Retail Fictitious

46 Milliman(1982) × × Customer NA Retail Actual

47 Mitchell,Kahn,andKnasko (1995–Study2)

× × × Customer NA Retail Fictitious

48 Moore(2014) × × Student 54.3 Retail Fictitious

49 MorrinandChebat(2005) × × Customer 61.7 Retail Actual

50 MorrinandRatneshwar(2000) × × × Student NA Retail Fictitious

51 Morrisonetal.(2011) × × × × × Customer 100.0 Retail Actual

52 NorthandHargreaves(1996) × × Student 62.0 NA Fictitious

53 North,Shilcock,andHargreaves (2003)

× × Customer 50.0 Service Actual

54 Novak,LaLopa,andNovak (2010)

× × × Student 62.8 Service Actual

55 Parsons(2009) × × Customer NA Retail Fictitious

56 Poon(2014) × × × Student 54.3 Retail Fictitious

57 Price(2010) × × × Student 71.5 Online Fictitious

58 Sayinetal.(2015–Study2) × × Student NA Service Fictitious

61 SchiffersteinandBlok(2002) × × Customer NA Retail Actual

62 Schifferstein,Talke,and Oudshoorn(2011)

× × × Customer 50.1 Service Actual

63 Spangenberg,Crowley,and Henderson(1996)

× × × Student 46.0 Retail Fictitious

64 Spangenbergetal.(2005) × × × × × Student 50.7 Retail Fictitious

65 Sullivan(2002) × × Customer NA Service Actual

66 vanHagenetal.(2008) × × × Student 50.0 Service Fictitious

67 vanRompayetal.(2012) × × × × Customer 61.8 Retail Fictitious

68 VinitzkyandMazursky(2011) × × Student 29.8 NA Fictitious

69 Wilson(2003) × × Customer 54.6 Service Actual

70 Wu,Cheng,andYen(2008) × × × × × Student 49.0 Online Fictitious

71 YalchandSpangenberg(1988) × × × × Customer NA Retail Actual

72 YalchandSpangenberg(1993) × × × Customer 68.6 Retail Actual

73 Yildirimetal.(2012) × × Customer 100.0 Service Fictitious

74 Yildirim,Akalin-Baskayab,and Hidayetoglu(2007)

× × Customer 51.0 Service Actual

Note:NA,notavailable(i.e.,characteristicsthatwereeitherabsentfromornotcodeableinthestudies).Individualstudiesmaycontaindependentvariableswhich arenotconsideredinthisanalysissincethestatisticaldatawasnotsufficienttocalculateaneffectsize.Areferencelistoftheincludedstudiesisavailablefromthe authorsuponrequest.

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References

Andersson,PernilleK.,PerKristensson,ErikWästlundandAndersGustafsson (2012),“LettheMusicPlayorNot:TheInfluenceofBackgroundMusicon ConsumerBehavior,”JournalofRetailingandConsumerServices,19(6), 553–60.

Babin,BarryJ.andWilliamR.Darden(1996),“GoodandBadShoppingVibes: SpendingandPatronageSatisfaction,”JournalofBusinessResearch,35(3), 201–6.

Babin,BarryJ.,DavidM.HardestyandTracyA.Suter(2003),“Colorand ShoppingIntentions:TheInterveningEffectofPriceFairnessandPerceived Affect,”JournalofBusinessResearch,56(7),541–51.

Bagchi,RajeshandAmarCheema(2013),“TheEffectofRedBackground ColoronWillingness-to-Pay:TheModeratingRoleofSellingMechanism,” JournalofConsumerResearch,39(5),947–60.

Bateson,JohnE.G.andMichaelK.Hui(1992),“TheEcologicalValidityof Pho-tographicSlidesandVideotapesinSimulatingtheServiceSetting,”Journal ofConsumerResearch,19(2),271–81.

Bellizzi,JosephA.,AynE.CrowleyandRonaldW.Hasty(1983),“TheEffects ofColorinStoreDesign,”JournalofRetailing,59(1),21–45.

Bellizzi,JosephA.andRobertE.Hite(1992),“EnvironmentalColor, Con-sumerFeelings,andPurchaseLikelihood,”Psychology&Marketing,9(5), 347–63.

Berlyne,DanielE.(1971),AestheticsandPsychology,NewYork,NY:Meredith.

Bitner,MaryJ.(1990),“EvaluatingServiceEncounters:TheEffectsof Physi-calSurroundingsandEmployeeResponses,”JournalofMarketing,54(2), 69–82.

Bone,PaulaF.andPamS.Ellen(1999),“ScentsintheMarketplace:Explaining aFractionofOlfaction,”JournalofRetailing,75(2),243–62.

Booms,BernardH.andMaryJ.Bitner(1982),“MarketingServicesby Man-agingthe Environment,” CornellHotel and Restaurant Administration Quarterly,23(1),35–40.

Broekemier,Greg,RayMarquardtandJamesW.Gentry(2008),“AnExploration ofHappy/SadandLiked/DislikedMusicEffectsonShoppingIntentionsin aWomen’sClothingStoreServiceSetting,”JournalofServicesMarketing, 22(1),59–67.

Brown,StephenP.(1996),“AMeta-AnalysisandReviewofOrganizational ResearchonJobInvolvement,”PsychologicalBulletin,120(2),235–55.

Carlson,Rae(1971),“WhereIsthePersoninPersonalityResearch?,” Psycho-logicalBulletin,75(3),203–19.

Chebat,Jean-CharlesandRichardMichon(2003),“ImpactofAmbientOdorson MallShoppers’Emotions,Cognition,andSpending:ATestofCompetitive CausalTheories,”JournalofBusinessResearch,56(7),529–39.

Chebat, Jean-Charles and Maureen Morrin (2007), “Colors and Cultures: ExploringtheEffectsofMallDécoronConsumerPerceptions,”Journal ofBusinessResearch,60(3),189–96.

Cheng,Fei-Fei,Chin-ShanWuandDavidC.Yen(2009),“TheEffectofOnline StoreAtmosphereonConsumer’sEmotionalResponses:AnExperimental StudyofMusicandColour,”Behaviour&InformationTechnology,28(4), 323–34.

Cohen,Jacob(1988),StatisticalPowerAnalysisfortheBehavioralSciences, 2nded.Hillsdale,NJ:LawrenceErlbaumAssociates.

Crowley,AynE.(1993),“TheTwo-DimensionalImpactofColoronShopping,” MarketingLetters,4(1),59–69.

Cyr,Dianne,MilenaHeadandHectorLarios(2010),“ColourAppealinWebsite DesignWithinandAcrossCultures:AMulti-MethodEvaluation,” Interna-tionalJournalofHuman-ComputerStudies,68(1–2),1–21.

DeMatos,CelsoAugusto,JorgeL.HenriqueandCarlosA.V.Rossi(2007),

“ServiceRecoveryParadox:AMeta-Analysis,”JournalofServiceResearch, 10(1),60–77.

Donovan,RobertJ.andJohnR.Rossiter(1982),“StoreAtmosphere:An Envi-ronmentalPsychologyApproach,”JournalofRetailing,58(1),34–57.

Doucé,LieveandWimJanssens(2013),“ThePresenceofaPleasantAmbient ScentinaFashionStore:TheModeratingRoleofShoppingMotivationand AffectIntensity,”EnvironmentandBehavior,45(2),215–38.

Fiore,AnnM.,XinluYahandEunahYoh(2000),“EffectsofaProductDisplay andEnvironmentalFragrancingonApproachResponsesandPleasurable Experiences,”Psychology&Marketing,17(1),27–54.

Garlin,FrancineV.andKatherineOwen(2006),“SettingtheTonewiththe Tune:AMeta-AnalyticReviewoftheEffectsofBackgroundMusicinRetail Settings,”JournalofBusinessResearch,59(6),755–64.

Gelbrich,KatjaandHolgerRoschk(2011),“AMeta-AnalysisofOrganizational ComplaintHandlingandCustomerResponses,”JournalofServiceResearch, 14(1),24–43.

Glass,GeneV.,BerryMcGawandMaryL.Smith(1981),Meta-Analysisin SocialResearch,NewburyPark,CA:SagePublications.

Grewal,Dhruv,JulieBaker,MichaelLevyandGlennB.Voss(2003),“The EffectsofWaitExpectationsandStoreAtmosphereEvaluationson Patron-ageIntentionsinService-IntensiveRetailStores,”JournalofRetailing,79 (4),259–68.

Hedges, LarryV.and IngramOlkin (1985), StatisticalMethods for Meta-Analysis,Orlando,FL:AcademicPress.

Herrington,J.D.andLouisM.Capella(1996),“EffectsofMusicinService Environments:AFieldStudy,”JournalofServicesMarketing,10(2),26–41.

Herrmann,Andreas,ManjaZidansek,DavidE.SprottandEricR.Spangenberg (2013),“ThePowerofSimplicity:ProcessingFluencyandtheEffectsof OlfactoryCuesonRetailSales,”JournalofRetailing,89(1),30–43.

Herz,RachelS.(2004),“ANaturalisticAnalysisofAutobiographicalMemories TriggeredbyOlfactoryVisualandAuditoryStimuli,”ChemicalSenses,29 (3),217–24.

Herz,RachelS.andTryggEngen(1996),“OdorMemory:ReviewandAnalysis,” PsychonomicBulletin&Review,3(3),300–13.

Hui,MichaelK.,LauretteDubéandJean-CharlesChebat(1997),“TheImpact ofMusiconConsumers’ReactionstoWaitingforServices,”Journalof Retailing,73(1),87–104.

Hunter,JohnE.andFrankL.Schmidt(2004),MethodsofMeta-Analysis: Cor-rectingErrorandBiasinResearchFindings,2nded.ThousandOaks,CA: SagePublications.

Jacob,Céline,JordyStefanandNicolasGuéguen(2014),“AmbientScentand ConsumerBehavior:AFieldStudyinaFlorist’sRetailShop,”The Inter-nationalReviewofRetail,DistributionandConsumerResearch,24(1), 116–20.

Knasko,SusanC.(1995),“PleasantOdorsandCongruency:EffectsonApproach Behavior,”ChemicalSenses,20(5),479–87.

Labrecque,LaurenI.,VanessaM.PatrickandGeorgeR.Milne(2013),“The Marketers’PrismaticPalette:AReviewofColorResearchandFuture Direc-tions,”Psychology&Marketing,30(2),187–202.

Lehrner, Johann,ChristineEckersberger,PeterWalla,G.PötschandLüder Deecke(2000),“AmbientOdorofOrangeinaDentalOfficeReduces Anx-ietyandImprovesMoodinFemalePatients,”Physiology&Behavior,71 (1–2),83–6.

Ludvigson,WayneH.andTheresaR.Rottman(1989),“EffectsofAmbient OdorsofLavenderandClovesonCognition,Memory,AffectandMood,” ChemicalSenses,14(4),525–36.

Mattila,AnnaS.andJochenWirtz(2001),“CongruencyofScentandMusicas aDriverofIn-storeEvaluationsandBehavior,”JournalofRetailing,77(2), 273.

Mehrabian,AlbertandJamesA.Russell(1974),AnApproachtoEnvironmental Psychology,Cambridge,MA:TheMITPress.

Michon,Richard,Jean-CharlesChebatandL.W.Turley(2005),“Mall Atmo-spherics:TheInteractionEffectsoftheMallEnvironmentonShopping Behavior,”JournalofBusinessResearch,58(5),576–83.

Milliman,RonaldE.(1982),“UsingBackgroundMusictoAffecttheBehavior ofSupermarketShoppers,”JournalofMarketing,46(3),86–91.

Mitchell,DeborahJ.,BarbaraE.KahnandSusanC.Knasko(1995),“There’s SomethingintheAir:EffectsofCongruentorIncongruentAmbientOdor onConsumerDecisionMaking,”JournalofConsumerResearch,22(2), 229–38.

Morrin,MaureenandS.Ratneshwar(2000),“TheImpactofAmbientScenton Evaluation,Attention,andMemoryforFamiliarandUnfamiliarBrands,” JournalofBusinessResearch,49(2),157–65.

Morrison,Michael,SarahGan,ChrisDubelaarandHarmenOppewal(2011),

“In-StoreMusicandAromaInfluencesonShopperBehaviorand Satisfac-tion,”JournalofBusinessResearch,64(6),558–64.

North,AdrianC.andDavidJ.Hargreaves(1996),“ResponsestoMusicina DiningArea,”JournalofAppliedSocialPsychology,26(6),491–501.

(13)

Oliver,RichardL.,RolandT.RustandSajeevVarki(1997),“CustomerDelight: Foundations,Findings,andManagerialInsight,”JournalofRetailing,73 (3),311–36.

Palmatier,RobertW.,RajivP.Dant,DhruvGrewalandKennethR.Evans(2006),

“FactorsInfluencingtheEffectivenessofRelationshipMarketing:A Meta-Analysis,”JournalofMarketing,70(4),136–53.

Pan,Yueand GeorgeM.Zinkhan(2006),“Determinantsof Retail Patron-age: A Meta-Analytical Perspective,” Journal of Retailing, 82 (3), 229–43.

Peterson,RobertA.(2001),“OntheuseofCollegeStudentsinSocial Sci-enceResearch:InsightsfromaSecond-OrderMetaanalysis,”Journalof ConsumerResearch,28(3),450–61.

Rosenthal,Robert(1979),“The‘File-DrawerProblem’andToleranceforNull Results,”PsychologicalBulletin,86(3),638–41.

Sayin,Eda,AradhnaKrishna,CarolineArdelet,GwenaëlleB.DecréandAlain Goudey(2015),“‘SoundandSafe’:TheEffectofAmbientSoundonthe PerceivedSafetyofPublicSpaces,”InternationalJournalofResearchin Marketing,32(4),343–53.

Shadish,WilliamR.,ThomasD.CookandDonaldT.Campbell(2010), Experi-mentalandQuasi-ExperimentalDesignsforGeneralizedCausalInference, Belmont,CA:WadsworthCengageLearning.

Spangenberg, EricR., AynE. Crowleyand Pamela W.Henderson (1996),

“ImprovingtheStoreEnvironment:DoOlfactoryCuesAffectEvaluations andBehaviors?,”JournalofMarketing,60(2),67–80.

Spangenberg,EricR.,DavidE.Sprott,BiancaGrohmannandDanielL.Tracy (2006),“Gender-CongruentAmbientScentInfluences onApproachand AvoidanceBehaviorsinaRetailStore,”JournalofBusinessResearch,59 (12),1281–7.

Sullivan,Malcolm(2002),“TheImpactofPitch,VolumeandTempoonthe AtmosphericEffectsofMusic,”InternationalJournalofRetail& Distribu-tionManagement,30(6),323–30.

Turley,L.W.andRonaldE.Milliman(2000),“AtmosphericEffectsonShopping Behavior:AReviewoftheExperimentalEvidence,”JournalofBusiness Research,49(2),193–211.

vanRompay,ThomasJ.L.,KarinTanja-Dijkstra,JoostW.M.Verhoevenand Annemiek F.van Es(2012),“OnStore Designand Consumer Motiva-tion:SpatialControlandArousalintheRetailContext,”Environmentand Behavior,44(6),800–20.

Winer,BenJ.(1971),StatisticalPrinciplesinExperimentalDesign,2nded. NewYork,NY:McGraw-Hill.

Yalch,RichardF.andEricR.Spangenberg(1988),“AnEnvironmental Psycho-logicalStudyofForegroundandBackgroundMusicasRetailAtmospheric Factors,” inEfﬁciencyandEffectiveness inMarketing,FrazierGaryed. Chicago,IL:AmericanMarketingAssociation,106–10.

Yousem,DavidM.,JosephA.Maldjian,FaezSiddiqi,ThomasHummel,David C.Alsop,RenaJ.Geckle,WarrenB.BilkerandRichardL.Doty(1999),

“GenderEffectsonOdor-StimulatedFunctionalMagneticResonance Imag-ing,”BrainResearch,818(2),480–7.