Discrete choice experiments with multiple price vectors for products sold in a wide price range

Method

Article

Discrete

choice

experiments

with

multiple

price

vectors

for

products

sold

in

a

wide

price

range

Caterina

Contini

*

,

Fabio

Boncinelli,

Caterina

Romano,

Gabriele

Scozzafava,

Leonardo

Casini

UniversityofFlorence,Italy

ABSTRACT

Thediscretechoiceexperimentisawidelyusedmethodologyinconsumerstudies.However, applyingthis methodtoinvestigatethemarketofproductssoldinawidepricerangecouldpresentissuesastothequalityof theestimateofpreferences.Infact,forthis`typeofproduct,frequentlyconsumersmayhavedifferentbehaviours whenfaced withdifferentprice levels.Forexample,somemarketsegments mayrefrain frompurchasing productsbelowcertainpricethresholds,consideringthemofanunacceptablequality,whileotherschooseonly belowcertainprices.Toworkaroundthisproblemarea,weproposeamethodologyinwhicheachrespondent declareshisownpriceintervalofreferenceandconsequentlyparticipatesinachoiceexperimentwithaprice vectorcoherentwith hishabits. In thismanner, we areabletograsp andinclude in theestimations the heterogeneityofconsumerswithrespecttopriceandthusobtainmoreaccuratewillingnesstopayestimates.

Themethoddescribesaproceduretobypassissuesrelatedtoidentifyingthepricevectorindiscretechoice experimentsthatinvolveproductssoldinawidepricerange.

Weproposeadiscretechoiceexperimentwithdiff`erentpricevectorsforconsumersegmentswithdifferent pricepreferences.

©2019TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).

ARTICLE INFO

Methodname:Discretechoiceexperimentswithmultiplepricevectors

Keywords:Pricepreferenceheterogeneity,Wine,Experimentaldesign,Willingnesstopay,Qualitycue

Articlehistory:Received6March2019;Accepted24July2019;Availableonline31July2019

*Correspondingauthor.

E-mailaddress:caterina.contini@unifi.it(C.Contini).

https://doi.org/10.1016/j.mex.2019.07.026

2215-0161/©2019TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://

creativecommons.org/licenses/by-nc-nd/4.0/).

ContentslistsavailableatScienceDirect

MethodsX

SpecificationsTable

SubjectArea: AgriculturalandBiologicalSciences

Morespecificsubjectarea: Consumerbehaviour

Methodname: Discretechoiceexperimentswithmultiplepricevectors

Nameandreferenceoforiginalmethod: Discretechoiceexperiment

Resourceavailability: NA

Methoddetails

TheDiscreteChoiceExperiment(DCE)isamethodthatiswidelyusedintheambitofapplied economicsandmarketinginordertostudyconsumerpreferences.Thismethodconsistsofcreating hypotheticalmarketsbymeansofquestionnairesinwhichapurchasingdecisionissimulatedamong variousproductalternativesthatdifferwithrespecttoqualitycharacteristicsandprice[1]Although theprocedurestoapplythismethodarewell-establishedandsharedamongscholars,thechoiceofthe pricevectorpresentsvariouselementsofcomplexityandhasstillnotbeencodified[2].Indeed,to date,the selection ofprice levelstoutilise in theexperiment implies that researchersmake an educatedguessbasedonananalysisofthepricesofproductsavailableonthemarketandsimilarto theonebeingevaluated.

Thisprocedurebecomesparticularlyproblematicwhentheproductsubjectedtotheexperiment presentsaverybroadpricerangeontherealmarket,asinthecaseofelectronicdevices,healthcare, and hospitality services.In fact,for these products, therecould bediversified behavioursof the consumerswithrespecttoprice,inrelationtomanyfactorssuchasincome,habits,andinvolvement. Someconsumers,forexample,mightassociatelowpricestolowquality,whichcandetermineatrend oftheutilityfunctionalongpricevaluesfirstincreasingthendecreasing[3,4].Inthiscase,usingaprice

vector that covers the entire surveyed range on the market would determine an estimate of

compromisethatwouldfailtograspthedifferentbehavioursofconsumers[4].

Weproposetoimplementaprocedurethatbridgesthelimitsoftheconventionalapproachinorder toidentifypricevectorstoutiliseinconductingDCEs.Inparticular,theproposedmethodstrivesto improvetheestimationofconsumerpreferencesbymeansofcodifyingaproceduretodefinetheprice vectorthatcanassistDCEpractitionersinbuildingtheexperimentaldesign.Thismethodmakesit possibletoverifythepresenceofheterogeneousbehavioursofconsumerswithrespecttodifferent pricelevelsandthusobtainmorereliableestimatesofconsumerpreferencesaswell.Inthismanner, wecanindeedconsideranimportantfactorofpreferenceheterogeneityintheestimationsthatwould otherwiseremainconfusedintherandomcomponentoftheestimationmodel.

Theapproachweproposeisbasedontheideathateachrespondentstateshisownpriceintervalof referenceandconsequentlyparticipatesinachoiceexperimentwithapricevectorcoherentwithhis elicitedhabits.Theapproachcanbedividedinto4steps.

Step1–marketanalysisandidentifyingpricerangeofproducts

Inthefirststep,theresearcheriscalledtomakeacarefulmarketanalysisofthegoodbeingstudiedin ordertoidentifythepricerangeinwhichtheproductissold.Thisphaseisnecessarytodeterminethe number andbreadthofthepriceintervalsthatthemarketisdividedinto.Thechoice willhavetobeeffected basedonthehypothesisthatthevariousintervalsalsoincludedifferentbehavioursandthateachofthese insteadcontainshomogeneity.Thenumberofintervalsclearlydoesnotdependsolelyonthepricerangein themarket,butalsoonthesamplesizethatonewishestoobtainandonthegeneralobjectivesofthestudy. Step2–translatingthepriceintervalsidentifiedintopricevectors

Instep2,itisnecessarytodefineapricevectorforeachpriceintervalidentifiedintheprevious phase. Translatingthepriceintervalsintopricevectors presentsacritical aspectthat consistsin identifyingtheupperlevelofthepricevector.Thislevelmustindeedbedefinedinordertobeableto

graspthemonetarysacrificetheconsumeriswillingtomaketoobtaincertainproductcharacteristics, soastoavoidundervaluingthepriceattribute.

Step3–identifyingthemethodforelicitingrespondents’preferencesonpriceintervals

Inthisstep,eachrespondentmustbeattributedapriceintervalthatcorrespondstohishabitual purchasing behaviour. Phrasing thequestion in the questionnaire to collectthe information on behaviourpresentsseveral pointsofattention.Inparticular,theresearchermustevaluatevarious optionsthatconcernboththemodalityofresponse(openorclosed),andthedifferentphrasingsofthe questionpertainingtotheintervalofprices.Thisquestioncaninfactbedeclinedindifferentwaysso thatfortheproductbeingstudied,theconsumershaveareferencepriceorarangeofprices.Inthefirst case,theresearcherscouldaskforthepriceatwhichtherespondentsgenerallypurchasetheproduct, aswellasthepreferredprice,ortherealmarketprice.Inthesecondcase,thequestionscouldinstead concerntheprevalentinterval,theaverageprice,theminimum,and/ormaximumpriceatwhichthe respondentsmaketheirpurchase.Eachoftheseoptionshasdifferentimplicationsontheresults,and theirchoicedependsonthespecificobjectofinvestigation.Forexample,choosingtheminimumprice couldmakeitpossibletoavoidphenomenaofattributingapositiveutilitytoincreasingpriceswhen dealingwithproductsforwhichpriceisaqualitycue.

Informationgatheredinthismanneralsoallowsustoverifytheneedtobreakdowntheprice range.Infact,thepresenceofasignificantpercentageofthesampleinthedifferentpriceintervals provestheheterogeneityinconsumerbehaviourwithrespecttoprice.

Step4–makingDCEswithmultiplepricevectors

Basedontheinformationgathered,eachrespondentwillperformthechoiceexperimentwitha pricevectorthatrepresentshishabitualbehaviour.Inotherwords,theDCEwillbeequalfortheentire samplewithrespecttothelevelsofalltheattributesexceptforthepriceattributelevels.Asafinal result,weshallhaveasmanysub-samplesaswehavepricevectorsutilisedintheexperiment.Wecan therebyanalysethevarioussub-samplesandfinddifferentiatedpreferencesandwillingnesstopayfor eachpriceinterval.

Methodvalidation

Inordertovalidateourmethodology,wecarriedoutaDCEonasampleof400Italianconsumersof redwinein0.75Lbottles.Wineaffordsavaluablecasestudysinceitpresentsextremelyvariable prices[5].Themarketstudyontheproductpointedoutanoverallpricerangebetweens2ands14, withamedianvalueofapproximatelys5.00(IRI-Infoscandata).

Onthebasisofthisinformation,weaskedrespondentstoindicateinwhichoftwopriceintervals, theyhabituallyorienttheirwinepurchases:(i)“lessthanorequaltos5.00perbottle”or(ii)“morethan s5.00perbottle”.Thisis,ofcourse,oneofthepossiblewaystoelicitconsumerbehaviourwithrespect toprice.Identifyingtwopriceintervalsisindeedfunctionaltothepresentcasestudy,butinother contextsitmightbemoreappropriatetodefineagreaternumberofintervalsand/ortophrasethe questionsothatitpointsoutbehaviourwithrespecttopricedifferentlyfromthewaywehaveused. Inthefollowingstep,thetwosub-samplesparticipatedinanidenticalDCEbutwithtwodifferentprice vectorswithfourlevelseach.Thefirstone,called“lowprice”withthelevelss2.00,s3.50,s5.00ands 6.50 was assigned to those that stated they habitually purchased wine at prices lower thans5.00.Thesecond vector,“highprice”withthelevels,s5.00,s8.00,s11.00,ands14.00wasassignedtoalltheothers.Theother attributesincludedintheexperimentare:organic,nosulphitesadded,andwinegeographicalindications. Thegroupthatchosetheintervalwithpriceslowerthans5.00represents62.5%ofthesample (250individuals),whiletheothergroupmeasures37.5%(150individuals).Thisresultconfirmsthe presenceoftwoconsistentgroupsofconsumerswithheterogeneousbehaviourswithrespecttothe differentpricelevels,and it showsthe effectivenessof thepriceintervalswe haveidentifiedin representingconsumerbehaviour.

Inorder toallowforpreferenceheterogeneity,weestimateda RandomParameterLogit (RPL) model.Accordingly,theutilityofthei-thconsumerwhenheselectsabottleofwinejinthechoicetask tcanbewrittenas:

Uijt=ASC+αPRICEijt+β’iXijt+

e

WhereASCisanalternative-specificconstantthatrepresentstheno-buyoption.PRICErepresentsthe pricelevelsofferedtotherespondenttopurchaseabottleofwine;_βiisthevectoroftheutility

parametersforparticipanti;Xijtisthevectorofwineattributes,

e

Thehypothesisthatwehavesignificanteffectsinestimatingtheparametersbychangingtheprice vectorusedinthechoiceexperimentistestedwithalikelihoodratio(LR)test:

LR¼2 LLj XM i¼1 LLi ! ð2Þ whichisdistributedχ2_{withK(M1)degreesoffreedom,whereLL}

jistheloglikelihoodvalueforthe

pooleddata(lowprice+highprice),LLiaretheloglikelihoodvaluesforthedifferentrestrictedmodels

(lowprices,highprices),Kisthenumberofparameters(10inourcase),andMisthenumberofgroups (twoinourcase).

Table1showstheloglikelihoodofthetwomodelsplusthatofthepooledmodel.TheLRvalueis 134.40with10degreesoffreedom.Thisvalueishigherthanthecriticalvaluefortheχ2

statisticat5%, equal to18.31.We thusrejectthenull hypothesisand acceptthealternativehypothesis, i.e.the parametersofthetwomodelsarestatisticallydifferent.Therefore,thechoiceofpricerangeaffectsthe estimation of preferences.Moreover,to assessthe robustnessof the results ontheeconometric specificationandtoworkaroundpotentialscaleissues[6],werepeatedtheLRtestspecifyingthe utilitymodel in WTP spaceinsteadof inpreference space.Using this differentspecification, we confirmthattheresultsforthetwosub-samplesaredifferent.

Table2showstheresultsoftheRPLmodels.Theparametersareallsignificantforthelowandthe highpricemodels.Inbothmodels,theorganic,nosulphitesadded,andthedesignationsoforigin parametershaveapositivesign,whiletheparametersoftheno-buyoptionalwayshaveanegative sign.

Themagnitudesof theparameters ofeach attribute aresubstantially similarin thetwo sub-samplesexceptforthatofprice.Ingeneral,thetwomodelsdescribeanalogouspreferenceswith respecttothechosenattributes,inlinewiththeliterature[7–9].

Thepriceparametersarenegativeandverysignificantinbothmodels.However,themagnitudeof thetwoparametersisquitedifferent,thatis0.34(95%confidenceintervalequals[0.40;0.27])for themodelwiththelowpricesand0.08(95%confidenceintervalequals[0.11;0.04])forthemodel withthehighprices.Theseresultsshowthattherespondentsthatdeclaredanorientationtowards lowerpricesaremoresensitivetovariationsinpricethantherespondentsthatprefertopurchase wineinthehigherpricerange.

Table3reportsthewillingnesstopayforthetwosub-samples.The95%confidenceintervalsare estimatedwiththedeltamethod.ThedifferencesbetweenattributeWTPsareallsignificantaccording toaMann-WhitneyUandKolmogorov-Smirnovtest.

In conclusion, the proposed procedure has made it possible to point out two structures of significantlydifferentpreferencesinpricefortwoimportantmarketsegments,thuspermittinga betterunderstandingofthebehaviourofconsumersand,inparticular,oftheirwillingnesstopay.

Table1

Loglikelihoodandobservationsforbothscenariosandthepooledmodel.

Model Observations Loglikelihood

(Preferencespacemodel*)

Loglikelihood

(WTPspacemodel**)

Lowprice 6000 1546.792 1522.536

Highprice 3600 897.744 886.939

Pooledsample 9600 2511.735 2463.654

Anin-depthdiscussionoftheresultsandfurtherdetailsaboutourcasestudyarepresentedinan articlewrittenbyContinietal.[10].

References

[1]W.Adamowicz,J.D.Swait,Discretechoicetheoryandmodeling,in:J.L.Lusk,JuttaRoosen,J.F.Shogren,J.Shogren(Eds.), TheOxfordHandbookoftheEconomicsofFoodConsumptionandPolicy,2012,pp.119–151.

[2]N.Hanley,W.Adamowicz,R.E.Wright,Pricevectoreffectsinchoiceexperiments:anempiricaltest,Resour.EnergyEcon. 27(3)(2005)227–234.

[3]U.SlothuusSkjoldborg,D.Gyrd-Hansen,Conjointanalysis:thecostvariable.AnAchilles’heel?HealthEcon.12(6)(2003) 479–491.

[4]L.Casini,C.Contini,N.Marinelli,C.Romano,G.Scozzafava,Nutraceuticaloliveoil:doesitmakethedifference?Nutr.Food Sci.44(6)(2014)586–600.

[5]C.Contini,C.Romano,G.Scozzafava,F.Boncinelli,L.Casini,Wineconsumptionandsalesstrategies:theevolutionofMass RetailTradinginItaly,WineEcon.Policy4(2)(2015)116–127.

[6]K.Train,M.Weeks,Discretechoicemodelsinpreferencespaceandwillingness-to-payspace,in:R.Scarpa,A.Alberini (Eds.),ApplicationsofSimulationMethodsinEnvironmentalandResourceEconomics,Springer,Dordrecht,2005,pp. 1–16. [7]M.Costanigro,C.Appleby,S.D.Menke,Thewineheadache:consumerperceptionsofsulfitesandwillingnesstopayfor

non-sulfitedwines,FoodQual.Prefer.31(2014)81–89.

[8]N.Mtimet,L.M.Albisu,Spanishwineconsumerbehavior:achoiceexperimentapproach,Agribusiness:Int.J.22(3)(2006) 343–362.

[9]I. Schäufele, U. Hamm, Consumers’ perceptions, preferences and willingness-to-pay for winewith sustainability

characteristics:areview,J.Clean.Prod.147(2017)379–394.

[10]C.Contini,F.Boncinelli,C.Romano,G.Scozzafava,L.Casini,Pricevectorissueinachoiceexperiment:amethodological proposal,FoodQual.Prefer.75(2019)23–27.

Table2

EstimatedparametersfromtheRPLmodelforbothscenariosandthepooledmodel.

LowPrice (N=250) Highprice (N=150) Pooledsample (N=400)

Coef. [95%C.I.] Coef. [95%C.I.] Coef. [95%C.I.]

Randomparametersinutilityfunctions

Organic 0.32* 0.06;0.59 0.49** 0.20;0.78 0.34** 0.16;0.53

Nosulphitesadded 1.98** 2.27;1.70 1.56** 1.90;1.22 1.81** 2.02;1.59

PGI 1.08** 0.76;1.41 1.20** 0.78;1.62 1.58** 1.32;1.83

PDO 1.19** 0.90;1.48 1.25** 0.93;1.57 1.40** 1.19;1.61

Non-randomparametersinutilityfunctions

Price 0.34** 0.40;0.27 0.08** 0.11;0.04 0.07** 0.10;0.04

No-buy 2.04** 2.45;1.62 1.54** 2.05;1.03 0.92** 1.19;0.64

Standarddeviationsofparameterdistributions

Organic 0.92* 0.67;1.16 0.63** 0.33;0.92 0.76** 0.57;0.95

Nosulphitesadded 1.41** 1.13;1.70 1.45** 1.11;1.80 1.41** 1.20;1.63

PGI 0.83** 0.41;1.26 0.69** 0.19;1.19 0.92** 1.22;0.62

PDO 0.98** 0.73;1.24 0.37 0.18;0.92 0.80** 0.60;1.00

Note:(**)and(*)denotestatisticalsignificancerespectivelyat1%and5%;N=Samplesize;Coef.=Coefficients;C.I.=95%

confidenceinterval.

Table3

Willingnesstopayinsforbothscenarios.

Variable Willingnesstopayestimates(95%confidenceinterval) Mann-Whitney

UZ-score

Kolmogorov-SmirnovD

Lowprice Highprice

Organic 0.97(0.18;1.76) 6.45(2.05;10.86) 13.03*** 0.74***

Nosulphitesadded 5.90(7.16;4.64) 20.66(31.53;9.79) 9.94*** 0.73***

PGI 3.23(1.91;4.54) 15.85(4.36;27.34) 16.75*** 0.99***

PDO 3.55(2.40;4.70) 16.52(6.93;26.11) 16.75*** 1.00***