competitive reactions to market entry

7/27/2019 competitive reactions to market entry

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Competitive Reactions to Market Entry: Explaining Interfirm DifferencesAuthor(s): Hubert Gatignon, Erin Anderson and Kristiaan HelsenSource: Journal of Marketing Research, Vol. 26, No. 1 (Feb., 1989), pp. 44-55Published by: American Marketing AssociationStable URL: http://www.jstor.org/stable/3172668 .

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HUBERTATIGNON,ERINANDERSON, nd KRISTIAANELSEN*

Competitive eactionsare recognizedas a driving orce influencingmarketingdecisions.The authors eek to explainhowestablished ompetitorsn an oligopolyreact to a significant ewentry n theirmarket.Ithas beensuggested hat at leastsome establishedcompetitorswill react to a marketentrypositivelyand at leastsomecompetitorswill reactnegativelyor not at all. Both heoryand evidence ug-gest that not all firmswill react to an entryin the sameway. Theauthorspositthat interfirm ifferencesn competitiveeactions o entrycan be predictedby ob-serving, or each competitor, he elasticityof each marketingmixvariable.Com-petitorswill retaliatewith theireffectivemarketingmix"weapons" nd retreatwiththeir ineffectivemarketingnstruments. hesepredictions re tested by estimatingthe parameters f an econometricmodelof demand esponse unctions nd reactionfunctionswith data fromthe market or an over-the-counterynecologicalproductand from the airline ndustry.Results, eplicated n the two markets,are substan-

tiallyconsistentwithpredictions.

CompetitiveReact ions to M a r k e t E n t r y :Expla in ing I n t e r f i r m Difference s

In a Journal of Marketing Research editorial, Weitz(1985, p. 229) notes:The effectivenessof marketingrograms suallyde-pendson the reactionof both customersand com-petitors.However,marketingheoriesandresearchhave emphasizedissues related to customerre-sponseand have directed ess attention o competi-tive response.This lackof attentiono competitiveeffectsis surprising ecause t is difficult o imaginea marketingdecision that is not affectedby com-petitiveactivity.

Weitz goes on to call for empirical research to identifypatternsof competitive response under a variety of con-ditions.

Our research is a step in that direction. We examinecompetitive response to an event that can profoundly af-fect a firm or even an entire industry:the entry of a newcompetitor (Baumol 1982). How will established firmsreact to this potentiallymomentous event? Scherer(1980,p. 244) outlines the possibilities as either accommoda-tion (cutting back to "make room for the newcomer") orretaliation (fighting back to "make life as difficult aspossible for the interloper")and calls for economists tobuild "realistictheories" about which reactionto expect.The strategy literatureamplifies, pointing out that dif-ferent competitorsnot only choose different reactions butdiffer in how they employ specific "instrumentsof war-fare" (Kotlerand Singh 1981). For example, firm A mayreact by increasing advertising, firm B may cut price,and firm C may alter none of its marketingmix decisions(Hanssens 1980; Lambin, Naert, and Bultez 1975).In sum, each competitor decides, for each marketinginstrument, whether to respond to an entrant by coun-terattacking (raising expenditures, a positive reaction),retreating (reducing expenditures, a negative reaction),or not responding (a zero reaction). Predicting these re-actions is an importantcomponent of strategic marketing(Oxenfeldt and Moore 1978; Porter 1979; Rothschild1979). Predicting competitive reactions in detail is a

*Hubert Gatignon and Erin Anderson are Associate Professors andKristiaan Helsen is a doctoral candidate, The Wharton School, Uni-versity of Pennsylvania.The research was supported by the Center of Marketing StrategyResearch and by The Wharton School Summer Salary Support Pro-gram. The authors thank three anonymous JMR reviewers for theircomments, as well as Jerry Wind for assistance in obtaining part ofthe data for the study. They also thank an anonymous manager at amajor pharmaceutical house for considerable market research and in-dustry background information.

Journal of Marketing ResearchVol. XXVI (February 1989), 44-55

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COMPETITIVEEACTIONSOMARKETNTRY 45complex, subjectiveendeavorand often is basedon du-bious personifications f each competitor Day 1984;Miles 1980). Thoughbroadguidelineshave been sug-gested (in particular,n the industrialorganizationit-erature), hey often yield conflictingpredictions.Fur-ther, heseguidelines omefromnormativemodelswhoseapplicabilityo actualsituationshas not been assessed.The purposeof our study is to develop and test anexplanation f when andwhysomefinnrmsorbrandman-agersin the case of multiproductirms) n a given mar-ket reactnegatively o a market ntry,othersreactpos-itively, and othersdo not react at all. We surveythestrategicand industrial rganizationiteratures ndarraytheirargumentsnto reasonsfor expecting negativeorpositivereactions.We concentraten particular n theexplanation f variationsn reactionacrosscompetitorsand marketingnstruments.We then suggesta contin-gency approach asedon the principle hatcompetitorsfight only with their "bestweapons"and avoid doingbattlewiththeir"poorweapons"Kotler ndSingh1981).We propose hatgoodweaponsaremarketingmix vari-ables that the firm uses well-those for which the marketresponse is relatively elastic. Conversely, poor weaponsaremarketingmix variables hat induce a smallchangein the firm's sales or marketshareperformance.Elas-ticity,which measures hepercentage hange n the mar-ket's response o a branddue to a percentage hange none of the marketing ariablesof thatbrand, s thoughtto be an indicatorof the strengthof thatmarketingn-strument.Ourfundamental ropositions thateach establishedcompetitor eacts o an entranthatthreatensts positionby raisingexpenditures n variableswithrelativelyhighelasticities ndby lowering xpendituresn variableswithrelatively ow elasticities.Here,we considermarket e-sponse n termsof shifts n market hare.We offer a testof these ideasusing an econometricmodel of competi-tive rivalry n the market or an over-the-counteryne-cological productandwe replicate heresults n the air-line industry.

COMPETITIVEREACTIONS:CONFLICTINGTHEORIESAND CONTINGENCYPROPOSITIONSOverview

The theoretical iteraturebearingon competitivere-action comes largely from industrialorganization IO)and strategy, whereas much of the empirical researchcomes from marketing. An oft-noted feature of the IOliterature is its emphasis on structure (number and sizeof competitors), from which behavior ("conduct") is in-ferred (Scherer 1980; Weitz 1985). This literature iscovered thoroughly by Scherer (1980). In contrast, thestrategy literature is concerned directly with the actionsof each competitor (Miles 1980). These literatures are arich source of rationale for hypotheses.Perhaps because of the difficulty of obtaining com-prehensive data on competitor behavior, empirical re-

searchorcompetitiveesearchs relativelyparse Scherer1980; Weitz 1985). Empiricalmarketingresearch oncompetitive ivalryor reactionshasconcentratedn thedifficultissue of how to measure he extent of rivalry.Apartfrommarketsharemodels, the firsteconometricmodelsexplicitly ncorporatingompetition id so by in-troducing ompetitivemarketing fforts as independentvariablesBassandParsons1969;Beckwith1972;Clarke1973;Schultz1971).ThenWildt(1974)explicitlymod-eledrivalryna systemof equationswhere hemarketingdecisionswereendogenous.Further evelopmentsn themeasurementf reactionsand the effectsof competitivereaction noptimaldecisionshave been offeredby Lam-bin, Naert, and Bultez (1975), Metwally(1978), andHanssens 1980). This work has advanced he studyofcompetition o the point thata state of rivalrycan beobserved and measured Hanssens 1980), therebyov-ercominga very difficult barrier o empiricalstudyofthe impactof competitive eactions.We focus on how established irms in an oligopolyreact o the entrance f a newcompetitorn theirmarket.Thepurposeof ourstudy s to investigatewhythere aredifferences n the directionof response positiveor neg-ative) among firms in a given market.This researchquestion s motivatedby theempiricalinding hatfirmsdo not all react n the samedirection.Forexample,somefirmsmightreactto a competitor'sncrease n advertis-ing expenditures y increasingheirownor, conversely,by decreasinghem.Further, irmsmaynotreactat all,showingzero or insignificant eactionelasticities Gati-gnon 1984). Such differences n reaction to the sameevent-a marketentry-are due partially o variationsin the perceptionsof the relevantorganizationalmem-bers andin theenactment rocessesby which individualperceptionsbecome corporateviewpoints (Day 1984;Miles1980;Oxenfeldt ndMoore1978;Rothschild 979).Differences n reactionalsomaydependon the agendasand values of influential ndividualswithin the estab-lishedfirms n an industryCyertand March1963;Wil-liamson1965). Unfortunately,hese factorsaredifficultto studysystematically;heireffect maybe to introduce"noise" into the relationshipbetweencompetitiveac-tions and reactions.However, anotherfactor-differ-ences in firm abilities-operates in a more systematicway and is more amenableo theorydevelopment.Firmabilities are the focus of ourstudy.Writersn strategypointout that somefirmsare sim-ply very good at some functions (e.g., advertising) andvery poor at others (e.g., distribution). Several scholars,notably Porter (1979) and Day (1984), build their ap-proach on the identification of each competitor's "vul-nerabilities"ndstrengths competitive dvantage). uchknowledge is useful not only in deciding where a firmshould attack, but also in predicting where it will be at-tacked and what weapons each competitor is likely touse. The prevailing (and reasonable) assumption is thatfirms will use only the weapons they wield well. In thefollowing sections we first discuss reasons to expect neg-



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46 JOURNAL F MARKETINGESEARCH,EBRUARY989ative or little reaction,then reasons to expect positivereaction.Negative Reaction (Withdrawal)We suggestthatestablished ompetitorsmayrespondto entryby cuttingback resourcesdevotedto a marketfor two reasons: nabilityto formulatean effective re-sponseand thepossibility hatwithdrawals theoptimal(profit-maximizing)esponse.Inability to respond effectively. Committing resourcesto battlepresumesone knowshow to fightback.Devis-ing a counterattacks not difficult for some kinds ofcompetitiveaction, but for others the appropriatee-sponse s unclearSchmalensee 976).As Scherer1980,p. 388) puts it, ". . . any fool can match a price cut,butcounteracting cleveradvertising ambit s farfromeasy." Countering n entrant likelyto be somethingofan unknownquantity) s also far fromeasy. Hence, inthe faceof uncertaintybouthowtorespond, ome firmsmayrespondverylittle, if atall. Firmsmayeven reduceresource commitments, either permanentlyor whilewaitingto determinehow the marketwill react to theentrant Cooperand Schendel 1976;Kotlerand Singh1981).A firm also maycut back because t is certain hat itcannot countereffectively (Day 1984; OxenfeldtandMoore1978).As Rothschild1979,p. 23) states,"Ifthekey skillsaren'tavailable, hecompetitionwill have lessthanoptimalresults."Negative reaction as a profit-maximizing esponse. Oneapproacho the questionof reaction o entryis offeredby the DEFENDERmodel (HauserandShugan1983).This normativeanalyticalmodel predictsthe optimal(profit-maximizing)esponse o entry given a set of as-sumptionsaboutthe market. Under a rangeof condi-tions, some degreeof negativereactionproves optimal(profit maximizing) or the threevariablesconsidered:advertising cut expenditures),distributioncut expen-ditures),andpricing(raiseprice). One reason for theseresults s that,underHauserandShugan'sassumptions,the entrantusuallydecreasesevery other competitor'sprofit, makingthe market ess worthwhile.Hence, ex-penditures enerate owerreturnsafterentryandestab-lishedcompetitorswill have an interest n not commit-tingthesame evel of resources.An inherent ssumption,however, s that hemarketdoesnotexpandas theresultof the product ntroduction.Positive Reactions (Retaliation)

Firms may respond to entry by positive reactions (in-creasing marketing expenditures to combat an entrant)for two reasons: objectives other than profit and under-spending.Objectives other than profit. The DEFENDER mod-el's recommendation to cut back in response to marketentryfollows from two premises: firms behave rationallyand they seek to maximize profit. An extensive literaturein organization heoryestablishes the possibilitythatfirms

behave rrationallye.g. Cohen,March,andOlson1972).It is also possible that, for a given brandor strategicbusinessunit (SBU), the objective s notprofitmaxim-izationbut is insteadmarketdominance,maintenance fa niche,or some othergoal notrelateddirectly o brandor SBUprofit.Suchobjectivescan leadto a "fightback"response positive eaction) ven attheexpenseof profit.Underspending. nalyticalmodelsusuallyassume hatall firmsoperate ta level of marketingxpendituresuchthattheelasticityof eachmarketing ariable s less thanone (decreasingreturns o scale). This is also an as-sumption f the DEFENDERmodel(Hauser ndShugan1983). However, f thisassumptions violated(increas-ing returns o scale), negativereactionswill not maxi-mize profitbecause a firm is already underspending.Hence, furthercutbacksperpetuate he underspendingerror.Firmsthat are underspendingmay have positiverather hannegativereactionsbecausepositivereactionsmay be the profit-maximizingesponse or them.A firm might underspendor severalreasons, eventhoughdoing so is normatively ncorrect.First, man-agersmaybe ignorantat leastinitially)of theelasticityof a marketingmix variable Chakravarti,Mitchell,andStaelin 1981);they may not be awarethey are under-spending.Second, to find wherethey are on the salesresponsecurve,firms would needto experiment,whichtheytypicallyhesitate o do (Ackoffand Emshoff1975;Little1966;Pekelman ndTse 1980).Third, irmsmightnot be ableto increase heireffortsubstantiallyo reachthe zone of optimal eturnse.g., becauseof lack of bud-get for advertising r lack of access to channelsof dis-tribution).Fourth,as Hauserand Shugan(1983) note,some industries re "sleepy" notverycompetitive).The entrantmay "wakeup"theindustry,galvanizingcompetitors o use marketingmix instruments s theyshouldbe used. Inessence,entryheightens ompetition,which forces a firm to reexamine ts practicesand cor-rect errors,such as spending oo little on a marketingmix variable.This "error orrection"s observed as apositivereaction o market ntry.A Contingency Approach to CompetitiveReactionIf positive, zero, andnegativereactionscan varybyboth competitorand marketingnstrument,how can agiven competitor'sreactionbe predicted?Thoughwecannothopeto providea completeanswer,we suggestthat the directionof competitivereaction s contingentupon the elasticity of a firm's marketingmix variables.We propose that firms react positively (increase expen-ditures) with their best weapons-the marketing instru-ments that have relatively high elasticity. Conversely,firms react negatively (decrease expenditures) with rel-atively inelastic marketing instruments. If firms reactnegatively when elasticity is low and positively whenelasticity is high, it follows that there is a "turningpoint"-the level of elasticity where no reaction wouldoccur. In practice, given the uncertainty of the assess-ment of elasticity, there is a zone of elasticity within



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COMPETITIVEEACTIONSOMARKETNTRY 47which no reactionwouldbe expected.Theprecedingdiscussion eads to the followingprop-ositions.

PI:A firmwith owmarketingffort lasticityora givenvariableeacts o anentryby decreasingts effort orthatvariable.P2:As theelasticity f a marketingariablencreases,hefirmreacts oanentrybydecreasingtseffortbylesserincrements. t somepoint,the firm'sreaction urnspositive, hat s, thefirmreacts o entryby increasingitseffort or thatvariable.P3:A firmwithhighmarketingffort lasticityor a givenvariable eacts o anentryby increasingts effort orthatvariable.

P, proposesa zone of negativereaction,P2a zoneof noreaction,andP3a zone of positivereaction.AN EMPIRICALTEST OF THE CONTINGENCYAPPROACH TO COMPETITIVEREACTIONSOurpropositions re testedin two industries.Thoughthistestmaynotgeneralizeacrossall industries,ts rep-lication offers evidence aboutthe existenceof the hy-pothesizedphenomenonhat s notidiosyncratico apar-ticularndustry.Wefirstdescribehe data oreachmarket/industry tudied.Then,because heproceduresollowedin the two cases are identical,we discuss each step oftheprocessandthe resultsof eachstepfor both datasetsin parallel.

DataOver-the-countergynecological product. Ourfirst da-tasetis fromthe marketora women's healthcareprod-uct thatcanbe purchased ver the counter.Theproduct,which can be used once only, was formerlyavailableonly througha visit to a doctor'soffice. Onceapproved

by the Food and Drug Administrations an over-the-counterproduct, he firstbrandwas launchedn the late1970s andwas an immediate uccess. By 1982 the in-dustrywas largeand competitive.For proprietaryea-sons the natureof the productcannotbe described ngreaterdetail. We refer to the productas "OTC-gyn"(over-the-counterynecologicalproduct).Quarterly ataareavailable romJanuary 982 toJune1986, a periodof intensecompetition enteringarounda few nationalbrands, ncluding wo majorentries.Inouranalysis,we consider ourestablished randsorwhichdata are available hrough he periodand two entrants.The four established brands (brands A, B, C, and D)were the "majorplayers" in this market during this pe-riod, with a combined market share of more than 90%before the entries of two new brands, which we study.Threeof the four brands use advertisingas theirprincipalmarketing mix instrument. Because the market changesquickly, test marketingandrollouts arerare. Brands typ-ically go from concept test to national launch, which al-lows pinpointing of entry periods.We use Nielsen data for the dependent variable, mar-ket share in units. The predictor variables are repre-

sentedby proprietarydvertising ataon all majorcom-petitors.Prices were relativelystableduring he periodinvestigatedand, with the exceptionof brandC, therewere few pricedifferencesamongthe brandsavailableduring hatperiod.'BrandC is a low pricedbrand hatdid virtuallyno advertising.Giventhat mediaexpendi-turesare on a quarterly asis,we transformedhemarketsharedatafrom theiroriginalbimonthlybasis to a quar-terly basis by a straightforwardinearsmoothing ech-nique.2TheOTC-gynndustry onsistsof three ypesof prod-ucts, which have differentbenefits and drawbacksandcoexistin the market.The "firstgeneration"s effectivebut is relativelyasyto misuseunintentionally.nebrandentry(brandE), introducedn quarter of 1985, is ofthis type. "Second-generation"roducts, ntroducednthe early 1980s, areless sensitive to mishandling.Oneestablishedsecond-generationrand,A, was extendedwith the additionof a variantwe label "brandA-plus,"introducedn the firstquarter f 1984. "Third-genera-tion"productsareexceptionally asy to use andhave averydifferentappearance.Qualitative esearch ndicatesthe third-generationroducts ppeal o manyconsumerswho believethey carrya moremedically"correct" on-notation.BrandF, whoseentry s included n ourdata,is the firstbrand f this formandwas launched nquarter4 of 1984.Airlinemarket. n this industry, ompetitorsre large,the ratioof fixed to variablecosts is high (which, ac-cordingto Porter1979, would makepositivereactionsmore ikely),andtheproductine is flexible(theproductis a flightandtheflightschedule s not difficult o vary).The relativeflexibilityof the numberof flights shouldincrease he likelihoodof observingany changein thisdecision variableover time. Further, he environmentbecameparticularly ostile and uncertainafterderegu-lation(Robertson,Ward,and Caldwell1982). Indeed,the marketing iteraturehas shown the occurrenceofcompetitive eactionsn this industry, omefirmsreact-ing positively and others negatively (Gatignon1984,Hanssens1980;Wildt 1974). Empirically, he industryis relatively ractable ecause he impactof a new entryin the market s fast, as arecompetitors' eactions,so

'Thoughprice datawere not available o verify this information,which was providedby the manager f one of the brands, t is con-gruentwith the nature f theproduct,a high-involvement,ccasionalpurchase hat is not expensivein absolute terms. In addition,thegoodnessof fit of the empiricalmodelsreportedhereafterndicatesthat he variables ncluded n themodelexplainmostof thevariancesin market hare,suggesting hatrelatively ittle wouldbe left to ex-plainby additional ariables uch as price.2Though igher degreesof freedomwould be availableby disag-gregating he advertising ata,errorn measurementf the indepen-dent variables ntroduces bias in theestimatedmodelparameters,which s not the case formeasurementrror n thedependent ariable(onlyinefficiency esultsbecauseof heteroelasticity). herefore,t ispreferableo sacrificedegreesof freedom or unbiasedness f coef-ficientestimates Judgeet al. 1985).



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48 JOURNAL F MARKETINGESEARCH,EBRUARY989effects should not be confusedby lags and inertia.Asthereare few significantmarketingmix variables,the"noise"createdby the effectsof multiplechanges n themarketplaces avoided(Gatignon1984;Schultz 1971;Schultzand Hanssens1977).The market(Los Angeles-Phoenix travel) was se-lected because t met the criterion hatanentryoccurredafterderegulation,when themarketbecamemuch morecompetitive. hetime seriescorrespondso the24 months(January1979 to December1980)followingderegula-tion. This series is long enoughto estimateelasticitieswith reasonableprecisionwithout eopardizing he sta-bilityof otherenvironmental onditions.Thisperiod orrespondso thetimeof observation henGatignon 1984) demonstratedhe importance f com-petition n the lastphaseof airline ndustryderegulation(Graham,Kaplan,andSibley 1981). The time seriesisobservedbeforederegulation estabilized his industry.Therearethree ompetitors,ll withsimilarmarket hares(.342, .393, and 265), andnonein a dominant osition.Competitor is a national arrier, or whichthisroute'srevenuesconstitutea small portionof total sales, andcompetitors and2 arelargeregionalairlines.The newentrantcompetitor), which s alsoa largeregionalcar-rier,started perating nthis route n April1980.Duringthistimeperiod, hevolumeof passengerslying he routevariedgreatlybut withoutany majortrend.The entrydid not significantly ffectprimarydemand.The dependent ariableused in the study s the shareof thenumber f passengers n direct lightson the LosAngeles-Phoenixroute. Pastresearch Gatignon1984;Hanssens 980;Schultz1971;Schultz ndHanssens 977)has shown thatthenumberof flightsis the most signif-icantpredictorof the numberof passengerscarriedbyanairline.Advertising xpendituresn the city pairalsocontribute,hough o a lesserextent,to predicting itherthe numberof passengersor market hareof an airlineon a given route. In addition,afterderegulation,pricebecamea significantexplanatory ariableas well. Ga-tignon(1984) definedpriceas the "averageowest fare(one-way)weighted by the durationof that lower fareduring hemonth,"where he faresconsideredwereonlythose withoutrestrictions n typesof individualsor oncapacity.Followingthis prior iterature,we used as in-dependent ariables he shareof thecity-pairadvertisingexpendituresobtained rom the Media RecordsGreenBook andfromBroadcastAdvertisersReports, nc.), therelative number of flights, and the relative price. Thesedata were obtained from records of the Federal AviationBureau (FAB). Though no significant pattern or trendcould be observed in the numberof flights or advertisingseries, a clear trendof increasing prices occurred in thismarket during the period. However, the relative pricemeasure shows variability over the period.Model Specification

The same procedure was used for both the OTC-gynproductdata and the airline route data, which were mod-

eled separately.Our data analysisproceeded n threestages.Instage1, we estimated, or eachbrand,a modelof market hareas a functionof themarketingmixvari-ables (e.g., advertising hare,relativepriceor numberof flights, lagged marketshare, and dummyvariablesrepresentinghe two entrantsand one product ine ex-tensionforOTC-gynbrandsandone entrant or the air-lineroute).Thisstageyieldedestimates f marketingmixelasticities,as well as the impactof eachentry.In stage2, we modeled hemarketingmix decisions(advertisingfor the OTC-gynmarketand numberof flights for theairline ndustry) f each brandas a functionof compet-itive activity. In stage 3, we modified the marketingvariabledecision functions, ntroducing constraint nthe coefficients hat reflectsthe impactof anentrant ndecisionsaboutadvertisingpending r number f flights.This constraint akes the form of a process function,wherein hedegreeof reactions expressedas a functionof the brand'sadvertising lasticity OTC-gyn)or of thenumber-of-flights lasticity(airlines).The coefficientsof the processfunction orm the test of ourhypotheses.Market share equations. The market share equationswere specified, as is commonlydone in econometricmodels in marketing Beckwith 1972; Lambin 1976;Parsonsand Schultz1976),as a functionof laggedmar-ket shareand shareof marketingmix expenditures.Forthe airlinemodel, quarterlydummyvariableswere in-troduced o capture he seasonalityof the market.Thelagged dependentvariablerepresents he dynamicsofmarketshare. It is a commonlyused formulationhattends to contribute o the robustness f the market haremodel (NaertandLeeflang1978). The functional ormis linear n the logarithms, o the coefficients are inter-pretableas elasticities. Competitiveeffects were cap-turedby usingeach brand's hareof industry dvertising(OTC-gyn)or numberof flightsand relativeprice (air-line). In addition,we introduceddummyvariablestorepresenthe impactof eachentry.3In the OTC-gynmarket, here were two new entries,brandF and brandE, as well as the productine exten-sion of brandA (A-plus).Eachbrandequations there-fore of the form:

3(1) m,(t)

= ee',0m(t 1)O'a'ai(t)i2H etk+a)eUs()=13Thenew entry may influenceconsumers'responses o the mar-ketingmix variables, hereby hangingelasticitiesand cross-elastic-ities, but thereis little theoryas to why they wouldchangeand in

whichdirection.Withenoughobservations,Chow's(1960) test candeterminewhether he slopes are stablepre- andpost-entry. n ourstudy,becauseof the smallsampleof observationsn bothdatasets,we tested the stabilityof the parameterswhenthe observations fteran entryoccurredwere added(Maddala1977) for each entry.Thetests failed o reject hestability f theparameters re-andpost-entry,except n thecase of brandC. However,giventhat he branddid notadvertise, his brand'sadvertising ecisionscould not be modeled nthe rest of the analysis.Consequently,heeffect of new competitionis modeledparsimoniouslys a "main" ffect in the relevantbrandmodels of market hare.



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COMPETITIVEEACTIONSOMARKETNTRY 49where:

mi(t)= market hareof brand at time t,ai(t)= advertising hareof brand at timet,Dk(t) = dummyvariable or entryof brandA-plus (k= 1), brandF (k = 2), and brand E (k = 3),

3's= response unctionparameters, ndu,(t)= disturbanceerm.Overall, hefits confirmedhat heimportantariableswere included R2 = .94, .97, .74, and .92 respectivelyforbrandsA, B, C, andD, though hey shouldbe eval-uated with cautiongiven the relativelyfew degreesoffreedom).The OLSestimation esults ndicated he im-portanceof the lagged dependentvariablefor most ofthe brandmodels.However,variations crossbrands nthe significanceof the otherparameterswere observed.Thelack of significance ould be due to the inefficiencyof the OLSestimationf the error ermsarecontempor-aneouslycorrelatedBeckwith1972;ReibsteinandGa-tignon1984).Consequently,he marketharemodelswerereestimated simultaneously as seemingly unrelated

regressionso takeinto accountpossiblecorrelations e-tween the disturbanceerms. Given that the OLS esti-mates arenotefficient,a cutoff t-valueof 1.2 was usedfor includingeach variable n the SUR model specifi-cation accordingto a procedureused by Schultz andHanssens1977)andGatignon1984).Further,helaggedmarket harecoefficientfor brandB was constrainedo1 becausethe unconstrainedstimateof 1.033 (thoughnot statisticallydifferent rom1) could leadto inconsis-tentmarket hareresults.This constraint id not signif-icantlyaffectany of the othercoefficients.The results are summarizedn Table 1. Advertisingshare s relatedsignificantly o the market hare of thetwo largestbrands,A andD. These coefficients(.068and .136 for A and D, respectively)are in the typicalrange of advertisingelasticities (Lambin 1976). It isnoteworthy hat the advertisingof brandD is approxi-matelytwice as effectiveas the advertising f brandA,indicating ubstantial ifferences n firmabilities.In ad-dition, the market eader, brandA, is the only brandnegativelyaffectedby the two entriesof new competi-tors (-.102 and -.078 for brandsF and E, respec-tively). However,the brandA-plus product ine exten-sion hada positiveeffect on brandA's share .086). Thisadditional hare was takenfrom brandC (-.133) andbrandB (-.134). Thepositiveeffect of the brandE en-try on brand C's market share (.139) may be due to adifference in their positioning. Though both are second-generation formulations, brand E was launched as a rel-atively expensive, heavily advertised brand. Brand C'spositioning as a similar formulation but as a low pricednationalbrandmay have become clearer to the consumeras a result of the "splashy" entry of a physically similarbrand. In other words, brand E may have increasedawareness and acceptance of second-generation nationalbrands; hence consumers may have been more willingto notice brandC's price advantage and to "experiment"

Table 1MARKETHAREMODELOF OTC-GYNBRANDS:

SEEMINGLYNRELATEDEGRESSIONSTIMATIONRESULTSa

Independentvariables BrandA BrandB BrandC BrandDIntercept 2.108 -.068 1.570 .398(7.36) (1.27) (3.66) (1.20)Laggedshare .369 1.0b .385 .725(4.51) - (2.26) (5.38)Advertising hare .068 -.004 NA .136(4.44) (.44) (3.59)BrandA plus .086 -.134 -.133(5.02) (1.84) (3.17)Brand F entry -.102 - - -(6.24)Brand E entry -.078 - .139(3.46) (3.17)Numberof observa- 16 16 16 16tionsR2(basedon OLS) .94 .97 .74 .92

"t-statisticsre n parentheses.Variableswitht-values ess than 1.2in OLS estimation reomittedand ndicatedby a dash;NA indicatesno advertisingor thebrand.bCoefficientonstrainedo 1.0.

withanother econd-generationationalbrand albeitanunadvertised ame).Finally,the laggedmarket harecoefficientsof brandB's share(not significantlydifferent rom 1.0 andcon-strainedo thatvalue),combinedwiththe negativecon-stant erm(-.068), reflect the smoothlydecliningsharetrendof thebrand.BrandA and brandC haverelativelylow values for lagged share (.369 and .385, respec-tively), indicatinghat hese twobrands remorevolatileand need advertising o support heir share. BrandDbenefitsfroma relativelystrongstability witha laggedsharecoefficientof .725), in addition o the strong m-pactof advertising.By the same procedure, he marketshare model foreach airline s represented y equation2.(2) mi(t) = ee,.?m,(t

- 1)i''f;(t)s'2a,a(t)l2,pi(t)l"sD,4 'Sef.a,2t) + i,70(t +POWs4(t)e,t)where:mi(t)= market hareof airline at time t,ai(t)= advertisinghareof airline attimet,f(t) = shareof numberof flights of air-line i at time t,p,(t) = priceof airline relative o averagepriceon the route at timet,DAt)= dummyvariablefor entryof air-line 4,Q2(t),Q3(t),Q4(t)= quarterly ummyvariables,1's = response unctionparameters, ndu,(t)= disturbanceerm.



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50 JOURNAL FMARKETINGESEARCH,EBRUARY989As is similar o the fit obtainedn the OTC-gynmar-ket, theindependentariables xplainrespectively98.5,97.7, and98.2%of the variances n market hare orthethree airlines.The seeminglyunrelated egressionesti-mation esultsarereportedn Table2 (variablesorwhichthe OLS coefficient estimateshad a t-value inferior o1.2 areeliminated).Inthismarket, s typicallyound, he number f flights

is the strongestdeterminantf market hare,with shareelasticitiesof .905, .907, and .880 for eachairline,re-spectively.Advertising hare s not significant,but therelativepriceof airline3 hasa significant mpacton itsmarket hare(withan elasticityof -. 185). Though helaggedshare variablewas eliminatedafterthe OLS re-sults showed nsignificance,he market haresof airlines1 and2 havea significant easonalcomponent.Bothair-lines 1 and 3 are affectedsignificantlyby the entrant.Thenational irline,3, washardest it(-.187), whereastheregionalairlineswere affected ess (-.066 forairline1). In fact, airline 2's market hare increasedafter theentry, everything lse beingconstant. ts sales(i.e., thenumberof passengers),however, were affectednega-tively by the entry.To this pointwe have estimated he effectivenessofeachcompetitor'smarketingmix variables.We alsohaveestablished hat, in the OTC-gynmarket, he two newcompetitorsand the product ine extensionhad a dis-cernible mpacton the leader'sshareof the market.Theimpactwas similar n the airline market.We now testourpropositions ia the estimationof decisionmodels,which represent he advertisingand number-of-flightslevels (for the OTC-gynand airline markets,respec-Table2

MARKETHAREMODEL ORAIRLINE ARKET:SEEMINGLYNRELATEDEGRESSIONSTIMATIONRESULTSa

Independent Airline Airline Airlinevariables 1 2 3Intercept .428 .371 1.290(.94) (1.29) (2.76)Lagged share -Advertising share - .011(1.10)Shareof number .905 .907 .880of flights (21.52) (10.57) (13.55)Relative price -.035 - -.185(.40) (2.38)Entry -.066 .125 -.187(2.44) (2.86) (3.18)Quarter2 .045 -.073(2.04) (2.26)Quarter3 - -.061(1.98)Quarter4 .016 -.041(.68) (1.53)Numberof observations 23 23 23R2(basedon OLS) .985 .977 .982

"t-statisticsre n parentheses.Variableswith t-values ess than1.2in OLS estimation re omittedand indicatedby a dash.

tively) chosenby each competitorbeforeand afteren-tries.Marketing decision equations. The decision variableto be modeled s the advertising xpensesin the OTC-gyn market.In the airlinemarket,only the numberofflights is considered because this marketingdecisionvariables significantlymore mportanthanpriceorad-vertising,as discussedbefore. The specificationof de-

cision models is madedifficultby thecomplexityof thephenomenao incorporate.The decisionequationmustincorporatenterfirm oordination f mix variablesandreactions o competitors,bothwithlags in reactionsandanticipationof competitors'actions. Hanssens(1980)discussesthe modelingof these issues andproposesamethodbasedon time series analysisto assess empiri-callythe correctmodelspecification.However,with thismethodone must assume heavailability f a substantialnumberof observations o considerall cross (leadsandlags) correlations.The methodwe used is similar n spirit,thoughthelimitedsampleperioddid not enableus to usetime seriesanalysis.Instead,we consideredall leadsandlags in astepwisemanner, achstep introducing setof variablesentered n a stepwiseregression forward). n the firststep, the marketingmix of one brandwas specifiedas afunctionof allotherbrands'marketingmix with agsandleads of one and twoperiods. Onlyvariableswitht-sta-tisticsgreater han1.2 were retained or thesecondstepby the sameprocedure s describedbefore. In thatsec-ond step, the competitiveentrydummyvariableswereintroduced.For the OTC-gynmarket,where multipleentriesoccurred,only the currentvalue of the dummyfor the entryof brandE was specified, whereasup totwo-period eads and lags were specified and enteredstepwisefor theentryof brandF. BrandA-pluswas notincludedwitha dummyvariable, s it is nota new brand.The reactions o brandA's totaladvertising realreadymodeledin step one of the procedure. n a final step,the variables hathad beenenteredwereforced nto themodelandtheleads and ags for the lastentry brandE)wereinvestigatedn a forwardtepwiseregression.Thisprocedurewas followed for each brandthatadvertised(i.e., A, B, andD). Thehomogeneity pre/postentries)for the coefficientsof thedecisionequationswas estab-lishedby testing hestability f theparametershen newobservationswere added(Maddala1977). All the testsproved nsignificantn both datasets.This procedureed to the following specificationofthe model for each brand in the OTC-gyn market.(3) A1(t) = ea'oA2(t - 2)aA4(t - 2)''2e1'3D'+2)eelf

A2(t) = eQ2A l(t - 1)a2IA4(t)a22eoa23D2(-2)eaUD2430)e2()A4(t) = eJe3192( eag3(E )e4

where:A,(t) = advertising expenditures at time t for brand A(i = 1), brand B (i = 2), and brand D (i =4),



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COMPETITIVEEACTIONSOMARKETNTRY 51Dk(t) = value of entry dummy at period t for brand F(k = 2) and brandE (k = 3) (value is 0 beforeentryand 1 afterentry),ae's= reaction unctioncoefficients,andei(t) = disturbanceterm.

Giventhis model specificationwith simultaneity f de-cisions, we estimated he advertisingdecisionmodelbythree-stageeastsquares.Table3 reportshe results.BrandB showsthelargest eactionso bothbrandA's andbrandD's advertisingreaction lasticitiesare 5.639 and4.21,respectively) itha lag of onequartern relationo brandD advertising. randA reactsrelativelytronglyo brandD's advertising .329), whereas it avoids face-to-facecompetitionwithB (thesmallbutnegativereaction las-ticityis -.028). Both reactions ccur witha two-quarterlag. BrandD seems to make its advertisingdecisionsindependently f its competitors.These reaction coefficients(elasticities)demonstratethe asymmetryof competitivebehavior. The estimatesof the coefficients ndicate hat brandsA and B reactedto F's entryby increasing heirlevel of advertising x-penditures .794 for A and 5.767 forB). The negativecoefficientfor the effect of F's entryon the advertisinglevel of D (-1.329) mayreflecta decisionto reallocateresourcesacrossbrandswithinthe manufacturer'sort-folio. In contrast, n reaction o brandE's entry,D in-creasedits advertisingexpenditures .845) whereasBdecreasedts expenditures- 11.78)andA didnot react.The purposeof our studyis to explainthe diversityofthese reactions.It is interestingo note beforeproceed-ing,however, hatbrandA anticipated's entryandbrandD anticipatedE's entry(bothlead two quarters).4 heyboth increased heiradvertising xpenditures efore theentries.Table 1 also shows that D's marketshare wasnot affectedby E's entry,whereasF's entryhada neg-

ative effect on A's share.These outcomes can be ex-plainedpartlyby the competitiveresponseanticipationand by the fact thatD's advertising lasticityis muchlarger hanA's.Forthe airlinedata,theflightdecisionmodelresultingfrom the analysis s shown in equation4.(4) FI(t) = ea'oP2(t)a"P3(t)a2A2(t)a'3A2(t- 1)O.4

SA3(t).lSF3(t).6eal7DI(t)ell(t)F2(t)= eaIoP2(t)a2,P3(t)a"Fi(t)a3ea27D(t )ef2()F3(t) = epl(t).31A2(t)032A3(t)a33Fi(t)0.ea37Dl(t)e3()where:

Pi(t) = price of airline i at time t (i = 1,2,3),Fi(t) = number of flights of airline i at time t,A,(t) = advertising expenditures at time t,D,(t) = dummy variable for entry of airline 4,a's = reaction unctioncoefficients,andEi(t)= disturbance term.

Thethree-stageeastsquareestimation esultsaregivenin Table 4. The interpretationf the results s straight-forward.Forcompetitivebehavior n general,airline1seems to competedirectlywith airline2, which has thehighestmarket hare.Whenairline2 decreases ts priceor increases ts advertising,airline 1 (with the secondlargestshare)reactsby increasingts numberof flights(-.890 and .150). Airline 1has the opposite competitivebehavior n relationto airline3. In responseto airline3's decrease n price,increase n advertising, r increasein flights,airline1 decreases ts number f flights(.825,-.178, and -.464), suggestinga "cooperative" ehav-ior withairline3 against he leader.Airline 2 avoidscompeting n reaction o advertisingchangesby its competitors.However,it has a tendency(ao= .145) to react to increases in the number of flightsof airline2, though he coefficient s notstatistically ig-nificant.Further,airline2 offers moreflightswhen itsrivals raisetheirprices(.392 and .380).

4Interviewsf brandA managerspostanalysis)onfirmedhatbrandF's entrywas expectedandadvertisingwas boostedbeforeentry nanticipationf the new competition.

Table 3ADVERTISINGECISIONMODEL OROTC-GYNMARKET:HREE-STAGEEASTQUAREESTIMATIONESULTSaIndependentvariables Brand A Brand B Brand D

Constant 4.173 -60.522 5.963(3.73) (3.39) (71.78)BrandA advertising - 5.639 (1 = 1)(3.60)Brand B advertising -.028 (1 = 2)(1.72)BrandD advertising .329 (1 = 2) 4.21(1.78) (1.788)BrandF entrydummy .794 (L = 2) 5.767 (1= 2) -1.329(4.62) (1.87) (5.05)BrandE entrydummy -11.78 .845 (L = 2)(3.71) (3.17)

"Numbersn parentheses ret-statistics;L = n meansa leadof n periodsand I = n meansa lag of n periods.



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52 JOURNAL F MARKETINGESEARCH,EBRUARY989Table4

MODEL F NUMBER F FLIGHTS:HREE-STAGEEASTSQUAREESTIMATIONESULTSa

Independent Airline Airline Airlinevariables 1 2 3Constant 8.640 1.69 9.061(6.85) (2.69) (5.67)Price,airline - - -.425(2.08)Price, airline 2 -.890 .392(5.39) (2.83)Price,airline .825 .380(5.33) (2.78)Advertising,irline .150 - .073(5.03) (1.62)Advertising, airline 2 .054 - -(lagged) (1.88)Advertising, irline3 -.178 - -1.88(4.08) (.077)Numberf flights, - .145 -.310airline1 (1.51) (1.75)Number of flights, -.464 - -airline3 (2.65)Entry dummy -.225 .239 -.459(1.81) (4.29) (3.54)"Numbersn parentheses ret-statistics.

Airline3 showsa somewhat ess generalizable ehav-ior. It competeswith airline1 by increasingts numberof flights in response to a price cut of airline 1 (-.425),butby "cooperating"nresponse o achange nthe num-ber of flights(-.310). Airline3 responds nly to airline2's change n advertising xpenses(.073).The resultspertainingo new entryshow thatairline2, with the highestnumber-of-flightslasticity,reactedto the entrant y increasingts number f flights(.239),but the other two competitors decreased theirs (-.225and -.459).This analysisof competitiveresponses hows that re-actions n generaland reactions o a new entrydifferbycompetitor. nexplanationor thesedifferenceswastestedempiricallyn the two markets tudied.HypothesesOurprincipalnterests thecoefficientsof thedummyvariablesn the reaction unctionequations 3 and4 re-spectively or eachmarket),whichrepresent hanges nadvertisingxpenditures y firm i in response o thenewentryor entries.These coefficientsare thea,L'sandca4'sin the OTC-gynmarketand theCa,'sin the airlinemar-ket. Correspondingo the propositions, he followinghypotheses an be formulatedn termsof themodelpa-rameters.

Expressing &i with k = 3, 4 for the OTC-gyn marketandk = 7 for the airlinemarket)s a processunction(where ,3representshe reactionso theentryof brandF, a,4 representshereactionso theentryof brandE,

anda representshereactionso theentry fairline ),50ik =Y-0 + IlkNN2nablesus to expressthe followinghypotheses.Hi: Y~ok 0H2:Yk > 0

H, expresses the idea that firms retreat(oak< 0) withan inelastic market nstrumentwhen ,a is small)andattack (ask > 0) with an elastic instrument(when pi2 islarge). In this case, the marketingnstruments adver-tisingfortheOTC-gynmarket ndthe number f flightsfor the airlinemarket. notherwords, f elasticity s low,the intercept ermy dominates.We expect y to benegative, indicatingnegativereactionwith a low elas-ticity nstrumentH,). As elasticity rows,theYlk-,2 term(positiveby H2)dominates,ndicatingpositivereaction.These two hypothesesalso contain he ideathatthere sa mediumrangeof elasticitywhere no reactionscan beexpected(P2). Therefore, he two researchhypothesescompletely overthe three heoretical ropositionstatedbefore.Results. FortheOTC-gynmarket, headvertising e-cision model(3) was reestimatedwiththe constraint nthe coefficients as hypothesized.The processfunctionwas not applied o the brandF entrydummycoefficientfor brandD (c43) because both brandsare marketed ythe samecompany.Consequently, he coefficientdoesnot havea competitive nterpretation,ut insteadrep-resents he reallocationf company esources o multipleproducts.6Therefore,constraininghe coefficient (043)to a process representing competitiverationalewouldbe invalid. Theprocessfunctionconstraints reappliedacrossequations.Theestimationwas carriedout by us-ing the TSP procedureLSQ, whichappliesto our caseof simultaneousquationswith linearconstraints crossequations.Table 5 shows the estimatesof the parameters f theadvertising ecision functions orthe OTC-gynmarket,including stimatesof the coefficientsof the linearcon-straint,which constituteourhypothesis est. The signs5Theprocessfunctiondoes not contain an error erm; he EGLSestimatethat would result has unknownproperties iven the smallcross-sectionalample,because he EGLSestimators only moreef-ficient asymptotically.A specificationwithoutan error ermin theprocessfunctionactuallyprovidesa strongerest of the hypothesis,because he estimator s asymptoticallyess efficient.6The ationale f ourpropositionspplies o the direction ndextentof competitive eaction,andnot thespeedof reaction. t is clear rom

Table 3 that A's reaction o F's entryandD's reaction o E's entrywerefaster evenbefore he entriesoccurred)hanB's reaction.BrandD's coefficientrepresenting hangesin its advertising xpendituresdue to brandF'sentry s contemporaneousiththeentry,as it reflectstheportfolioplanning f thecompanymarketing othbrands.Thoughthespeedof reactions animportantimension f reactionHeil1987),its analysis s beyondthe scope of our article.Therefore, he con-straints epresentedy theprocess unctionwereapplied imilarly oall reactionsdueto anentry, regardless f the speedof the reaction.Theassumptions that heexplanationepresentedy the constraintsdoes notdistinguishwhether he reaction s fast or slow.



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COMPETITIVEEACTIONSOMARKETNTRY 53Table5

ADVERTISINGECISIONMODELN OTC-GYNMARKET ITHPROCESSQUATIONSaIndependent Brand A Brand B Brand Dvariables 1 2 3

Constant 3.517 -63.373 5.968(2.69) (2.98) (58.37)Brand A advertising 5.211 (1 = 1)(2.63)Brand B advertising -.025 (1 = 2)(1.25)BrandD advertising .044 (1 = 2) 5.234(2.04) (2.08)

BrandF entrydummy -3.575 + 61.48 p, -1.513(1.71) (2.05) (5.16)BrandE entrydummy -1.042 + 16.23p,(1.62) (2.37)Where(fromTable 1):

012 = .067022 0032 = .137"Numbersn parentheses ret-statistics; = n meansa lag of n periods.

andmagnitude f thecoefficientsof thelinearconstrainton the new entry dummyvariables,boxed in Table5,supportHi andH2.Coefficientsy arenegative -3.575and -1.042 respectively or the entriesof brandF andbrandE) andcoefficientsYlkarepositive 61.48and16.23respectively).These findingsindicatethat brands or which adver-tisinghas littleimpacton market hare(3, is low) reactto theentryby decreasingheiradvertisingxpenditures.This reaction s in accordwith the idea that inns do notfight with weaponsthatarenot highly effective. How-ever, as brandadvertisinglasticity 03,2)ncreases, inrmnscut backby smaller ncrements(at grows largeras theY1kP,2 ermcompensatesor the negative mpactof yo).Eventually, he reaction urnspositive.ForbrandF, theturningpoint is at P3,2 .0581; for greaterelasticities,the reaction s positive.ForbrandE, reactions urnpos-itiveforfirmswhoseadvertisinglasticity s greaterhan.0642. These resultsapplyto the brands n this marketacross wo entries.Though t does notinvolvemanyen-tries andmanybrands, he model is estimated imulta-neouslywith all the data n thesampleacrossbrands ndtime.Consequently,modelparametersreestimatedwithrelatively argestatisticalpower.By the sameprocedure, he airlineanalysisprovidesa replication f the OTC-gynmarket.Table 6 indicatesthatthecoefficientssupport urhypothesis.They coef-ficients are both statisticallysignificantwith the ex-pected sign (Yo7= -23.08, y17 = 25.65).This finding indicates that finnrmshose flight deci-sions affecttheirown sales very little (13,2s low) reactto the newentryby decreasingheirmarket ffort(num-berof flights), in accordwith the idea that firms do not

fight with weaponsthatarenot highlyeffective. How-ever, as flight elasticity(3,2)increases,finns cut backby smaller ncrementsa, grows arger s theya/I3,2ermTable 6NUMBER-OF-FLIGHTSECISIONMODELWITHPROCESS

EQUATIONSaIndependent Airline Airline Airlinevariables 1 2 3

Constant 5.896 1.318 7.950(5.80) (2.20) (5.85)Price,airline -.322(1.86)Price, airline 2 -.933 .501(6.43) (3.86)Price, airline3 .905 .336(6.53) (2.55)Advertising,airline2 .138 - .055(5.13) (1.42)Advertising,irline .048 - -(lagged) (1.90)Advertising,airline3 -.150 -.100(3.82) (1.88)Numberof flights, - .168 -.184airline 1 (1.81) (1.21)Number of flights, -.0001 - -airline (.0008)Entrydummy -23.08 + 25.65k,1(5.79) (5.80)Where fromTable2):

12= .90522 = .907032= .880"Numbersn parentheses ret-statistics.



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54 JOURNAL FMARKETINGESEARCH,EBRUARY989compensates for the negative impact of Yk).The turningpoint from negative to positive reaction occurs for flightelasticity greater than .90; beyond that point, firms re-taliate against the entrantby offering more flights.

DISCUSSIONAND CONCLUSIONOur results offer some supportof the hypotheses pro-posed. In two unrelated oligopolistic markets, we ob-

serve considerable variation in reaction to three entries.The effectiveness of the major marketinginstruments-advertising expenditures and numberof flights for OTC-gyn and airlines, respectively-is shown to predict howcompetitors react to a new entry: that is, whether theyincrease or decrease their efforts. In accord with the lit-eratureon competitive behavior, we find that firms reactpositively to entrants by retaliationor counterattack)withtheireffective weapons, where "effective" means havingrelatively large elasticity. Also consistent with the lit-erature, firms cut back (withdraw) their inelastic mar-keting mix instruments. Undoubtedly many factors in-fluence a competitors'sreaction. Nonetheless, our resultssuggest that reactions can be better understood and pre-dicted by observing one factor: the effectiveness of acurrentcompetitor's marketingmix instruments.The contribution of our study is to suggest when apositive reaction with a marketinginstrumentwill be ob-served and when a negative reaction will be observedinstead. The criterion, elasticity, is relatively easy tomeasure and conforms with extant theory in industrialorganization and strategic marketing. The ideas ex-pressed here are very general; they can be applied tovarious marketingmix instruments and to multiple sce-narios. We consider response to the entry of a new prod-uct, but one could substitute, for example, the reposi-tioning of a currentbrand. Repositioning, if successful,operates much as does an entry by changing the com-petition set. Other competitors may react by either in-creasing or decreasing their marketingeffort, dependingon the effectiveness of that effort.Our study has certain limitations. First, in both in-dustries studied, elasticities were not significantly al-tered by the entrant. In other settings, an entrant mayshift elasticities of competitors, thereby affecting theirreactions and complicating the prediction of competitiveresponse. Second, inferences that can be made frommodeling one industryare necessarily limited to that in-dustry. The model is merely a summarizingof the dataevidence. It is the nature of scientific research thatknowledge progresses by convergent studies and repli-cations (Zaltman,Pinson, and Angelmar 1973). We pro-vide such a replication by testing our hypothesis in twoindustries. Nevertheless, more replications are necessaryfor generalization.Methodologically, given the limited data, any signif-icance at the usual confidence level provides a strongrejection of the null hypothesis of no effect. Nonethe-less, the small sample size does limit the scope of theeffects that can be observed and measured. A natural

progression of our research therefore would be to in-crease the number of explanatory variables in order toyield a wider explanation of competitive moves andcountermoves. For example, environmental uncertaintymay affect response, perhaps blunting reactions or evenleading firms not to react. Another considerationmay bethe firm's impression of the entrant'scapabilities. A firmmay react strongly to an entrantjudged to be capableand may not react to another entrantregardedas a minorthreat. Further research also is needed to establish thegeneralizability of the ideas developed here, which area step toward a more complete theory of competitive re-actions, in particularas it discriminates between currentcompetitors' reaction strategies. Nevertheless, our studydoes present an explanation, with empirical support intwo industries, or observabledifferences n firms'/brands'reaction patterns to entries of new competitors in themarket. As such, the study enhances our understandingof a complex and crucial strategic issue-the patternofcompetitive rivalry.

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competitive reactions to market entry