EXPLORING CONSUMERS' PERCEPTIONS OF AUTOMOBILE BRANDS IN TURKEY THROUGH MUL TIDIMENSIONAL SCALING

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1 97 EXPLRING CNSUMERS' PERCEPTINS F AUTMBILE BRANDS IN TURKEY THRUGH MUL TIDIMENSINAL SCALING Meltem Öztürk Pamukkale University Hnaz Vcatinal Schl Dç. Dr. Ayşe Akyl Trakya University Faculty f Ecnmics and Administrative Sciences Department f Business Selin Küçükkancabaş Bğaziçi University Faculty f Ecnmics and Administrative Sciences Department f Management The purpse f this study is t investigate the cnsumers' preferences twards varius autmbile brands in Turkey. Multidimensinal Scaling (MDS) algrithm knwn as ALSCAL and a number f ther cmplementary techniques (PREFMAP and PRFITj is perfrmed t transfrm cnsumers' preference evaluatins int gemetric distance fr a multidimensinal cnfiguratin fr studying the subjects' perceptin f autmbile brands. Subjects are sampled frm cnsumers wh use r have an pprtunity t use cars, and wh ges t car galleries. The main results f this studyare summarized as fllws: (1) safety and advertising campaigns are the tw imprtant attributes in cnsumers' aut brand preferences (2) Mercedes perceivedfavrably in bth dimensins and the ideal pint is directed at the lcatin f Mercedes. Keywrds: Multidimensinal scaling, PREFMAP, PRFIT, autmbile industry. Özet: Bu çalışmanın amacı Türkiye'deki tüketicilerin tmbil markalarına yönelik tercihlerinin araşurılmasıdır. Tüketicilerin tmbil marka tercihlerini belirlemek için, tüketicllerin tercih değerlendirmelerini çk byutlu bir yapılandırma için gemetrik uzaklıklara çeviren Çk Byutlu Ölçekleme Algritmalarından ALSCAL ve diğer tamamlayıcı teknikler lan PREFMAP ve PRFIT uygulanmıştır. Çalışmanın örneklemini tmbil sahibi lan veya tmbil kullanma şansına sahip lan veya t galerilerine giden tüketiciler luşturmaktadır. Çalışmanın temel snuçları şu şekildedir: 1) Tüketicilerin tmbil markası tercihlerinde önemli lan iki özellik güvenlik ve reklam kampanyalarıdır 2) Mercedes bu her iki byutta da iyi larak algılanmıştır ve analiz snucunda elde edilen ideal nkta da Mercedes markasına işaret etmektedir. Keywrds: Çk Byutlu Ölçekleme Analizi, PREFMAP, PRFIT, tmbil endüstrisi, Türkiye. EUL Jurnal f Scial Sciences (I: 1) LAÜ Ssyal Bilimler Dergisi

2 98 i Explring Cnsumers ' Perceptins f Autmbile Brands in Turkey ı. IXTRDL"CTIX Autmbile industry is ne f the majr industries in Turkey. Cmpetitin amng firms in the Turkish autmbile market has been grwing since the ı990s as a result f new firms entering the market. With this ccurrence in mind, it has becme imperative fr autmbile firms t develp apprpriate marketing strategies t meet the challenges f the emerging cmpetitive market. T effectively create a marketing strategy, it is imprtant t understand hw ne's brand and thse f cmpetitrs are perceived. ne apprach t identifying cnsumer perceptins is Multidimensinal scaling. Multidimensinal scaling (MDS) refers t a set f spatial mdels and assciated numerical techniques fr estimating these mdel s t btain a multidimensinal spatial representatin f the structure in varius types f data (e.g, prximity data, such as brand switching data). Fr the past decades, MDS has assisted marketing managers and researchers in depicting market structure, market segmentatin (Cper, ı983), reslving issues in prduct design and psitining, and understanding relatinships amng cnsumer perceptins and chice. Green, Carmne, and Smith (1989), and Carrll and Green (1997) have summarized the majr applicatin areas f the varius frms f MDS in marketing research, incıuding such areaslstudies as cmpetitive market structure, prduct/service psitining, market segmentatin, pricing, branding and image, advertising, new prducts, and s frth. Here, we identify hw prducts are perceived n tw r mre "dimerısins," allwing us t plt brands against each ther. it may then be pssible t attempt t "mve" ne's brand in a mre desirable directin by selectively prmting certain pints. The verall purpse f this study is t investigate the Turkish cnsumers' preferences twards varius autmbile brands by using a particular Multidimensinal Scaling (MDS) algrithm knwn as ALSCAL and a number f ther cmplementary techniques (PREFYtAP and PRFIT) 2. METHDLGY MDS enables us t map bjects (brands) spatially, s that the relative psitins in the mapped space reflect the degree f perceived similarity between the bjects (the clser in space, the mre similar the brands). When the map has been generated, the relative psitining f the brands, tgether with knwledge f the general characteristics f the brands, allw the analyst t infer the underlying dimensins f the map. There are tw main appraches t multi-dimensinal scaling. In _the apriri apprach, market researchers identify dimensins f interest and then ask cnsumers abut the ir perceptins n each dimensin fr each brand. This is useful when (1) the market researcher knws which dimensins are f interest and (2) the custmer's perceptin n each dimensin is relatively clear. In the similarity rating apprach, respndents are nt asked abut their perceptins f brands n any specific dimensins. Instead, subjects are asked t rate the extent f similarity f different pairs f prducts (e.g., Hw similar, n a scale f ı-7, is BMW's t Mercedes). Using a cmputer algrithm (ALSCAL), the cmputer then identifies psitins f each brand n a map f a given number f dimensins. The EVL Jurnal f Scial Sciences (1: 1) LA Ü Ssyal Bilimler Dergisi

3 Meltem Öztürk, Ayşe Akyl, Selin Küçukkancabaş i 99 cmputer des nt reveal what each dimensin means that mu st be left t human interpretatin based n what the variatins in each dimensin appears t reve al.since in this study it was nt elear what the variables f difference are fr the brand categry we used MDS algrithm knwn as ALSCAL and a number f cmplementary methds PRFIT t attribute meaning MDS dimensins and PREFMAP t find the ideal pint/vectr in the same MDS space Sampling The assumptins f MDS deal primarily with the cmparability and represen-tativeness f the stimuli and the respndents. The bjects chsen in this analysis shuld be quiet cmparable, and shuld have a high level f representativeness cnsidering the diversificatin. The respndents shuld als be aware f the stimuli and the bjectives f the study. Keeping all these in mind, 130 peple wh use r have an pprtunity t use cars, and wh ges t car dealers ("galleries" in Turkish cntext) were chsen frm Aydın, Kuşadası, İzmir, Denizli, İstanbul, and Bursa. The cnvenience sampling methd was emplyed based n participant availability. ö" ;; ] t: '" - " ~ u, i- ~ i;: " > ~ en BMW,00 Frd Tyta Renault pel Fiat VW 1,89,00 2,1! 3,42,00 1,76 3,65 3,21,AA 2,51 3,93 4,03 3,45,00 Mli 3,57 2,40 4,36 3,02,00 3,70 3,30 3,59 2,90 3,95 2,14,00 Mercedes.2& 2,88 2,32 1,77 2,93 1,82 3,40 Table 1: Average Perceived Similarity Between Autmbile Brands 2.2. Questinnaire Design The survey instrument was made up f three sectins. The first seetin is a similarity frm. In this seetin respndents were asked t rate the similarity f a pair f car brands n a 7-pint Likert scale with 1 representing the "least similar" and 7 representing 'the mst similar". Since the survey frms were prepared fr the cmparisn f eight different car EUL Jurnal f Scial Sciences (/: I) LAÜ Ssyal Bilimler Dergisi December 20 i Aralık

4 100 i Explring Cnsumers' Perceptins f Autmbile Brands in Turkey brands (Mercedes, BMW, Vlkswagen, Fiat, pel, Frd, Renault, Tyta) there were 28 (n (n-l)/2 where n is equal t the number f bjects) pairwise similarity judgments t be cmpleted by the respndents. The secnd seetin f the questinnaire cntains questins abut particular attributes that have generally been used t identify cars. In this seetin respndents ask t evaluate the car brands n a scale 1 t 7 (1 being very bad and 7 being very gd) based n the brand attributes like safety, prestige, price, after sale services, secnd hand sales, spare part, and advertising campaigns. In the last seetin respndents are expected t prvide a rank rder f the eight car brands İn terms f preference. 3. RESULTS 3.1. Cmmn Space As Seen by the Average Cnsumers In rder t see the cmmn space as seen by the average cnsumer, the 130 respnses frm the custmers were averaged in rder t get an average value f perceived similarity between thecars. Table 1 shws the average perceived similarities between the cars in the lwer rectangular frmat. All f the 28 pssible pairs are presented here. Table 1 shw that the respndents perceived Fiat and BMW (1.48) as the least similar and Mercedes and BMW (5.43) as the mst similar car brands. In rder t get a tw-dimensinal perceptual map f the cars as seen by the custmers, ALSCAL (Yung & Harris, 1990) prgram (a part f SPSS 13.0 data analysis package) was used. Since ALSCAL nly accepts dissimilarities and this research data is riginally similarities, the 7 pint scale was reverted such that 7 crrespnded t mst dissimilar and 1 crrespnded t mst similar. After this necessary transfrmatin, ALSCAL was used t btain a tw-dimensinal slutin. The resulting map is given in Figure 1. it is evident here the mst safety cars frm a distinct grup at the tp right cmer f the map. Fiat, which is actually fund t be very dissimilar frm all ther cars, ccupies a distinct lcatin n the upper left cmer. The next seetin utlines a mre frmal apprach in naming the dimensins. it is wrthwhile t pint ut any reflectin, rtatin r translatin f the pints wuld nt change the Euclidean distances hence wuld give essentially the same slutin. The fıt f the slutin is generally gd. There are a number f ways f determining hw well the tw-dimensinal slutin suits the average similarities calculated frm the 130 subjects (Table l). The seatter plt f distances calculated frm the slutin given in Figure 1 against the dissimilarities used as input t the ALSCAL prcedure (generally called "disparities", these wuld be the value s given in Table l, except the scale is reversed). The smthness f the graph given in Figure 2 indicates gd fıt. The square f the crrelatin between the disparities and the distances (R 2 ) indicates hw much f the variatin in the disparities is explained by the distances calculated frm the cnfıguratin fund by the MDS prcedure. The R 2 value here is %, which is reasnably high. There are tw ther measures, ften called "badness f fıt" functins (Kruskal & Carrll, 1969), as lwer value s indicate better fıt, namely Kruskal's STRESS frmula 1 (Kruskal, 1964a,l964b), and Yung's SSTRESS (the measure that ALSCAL- EUL Jurnal f Scial Sciences (I: 1) LAÜ Ssyal Bilimler Dergisi

5 Meltem Öztürk, Ayşe Akyl, Selin Küçükkancabaş i 101 Derived Stimulus Cnfiguratin Euclidean distance mdel d i Fiat L f- ıi 5'1 jı ~' i Dlmensln 2 Frd Mercedes BMW, C, - c! i {enault Vlkswagen I,C Teyta 1,9-, , r ~ Dimensin ı Figure 1: Tw-dimensinal Perceptual Map f Average Similarities Scatterplt f Linear Fit Euclidean distance mdel 3,, 3,a' ~ it 2~ 2, a 00 1,' 1,~ ci),, 000 ci) 0,5 1, 1,5 2, 2,5 3, 3,5 Disparities Figure 2: Distances vs. Disparities EVL Jurnal f Scial Sciences (I: I) LAÜ Ssyal Bilimler Dergisi

6 102 i Explring Cnsumers ' Perceptins f Autmbile Brands in Turkey prgram tries t minimize). Fr bth measures "" represents perfect fit. Yung's SSTRESS in this study turns ut t be STRESS 1 value is smewhere between "gd" and "excellent" fıt accrding t Kruskal's rule f thumb (Lattin et al.,2003). When it is lked at these SSTRESS values, it can be als seen that relatively best imprvement is achieved when mving frm ne t tw dimensins after which the imprvement diminishes smewhat and remains cnsistent as increased in the number f dimensins. Iteratin S-Stress Imprvement 1, ,31549, ,31345, ,31286,00059 Stress,25199 RSQ =, Attributing Meaning t Dimensins Using PRFIT Apprach PRFIT uses the crdinates f car brands n the perceptual map and the average attribute ratings f 130 respndents fr each car brands as an input data and prvides bth riginal ratings and prjectin value s fr each attribute tgether with the plt f these values n the X and Y axes as utput. Table 2 prvides the average ratings f the 8 cars n the 7 attributes. In rder t name the dimensins, it is needed t lk fr the directin csines f fıtted vectrs in nrmalized space. These values will he Ip t give name s t dimensins. The fırst dimensin is mst highly crrelated with safety, prestige, and price. The secnd dimensin is negatively crrelated with advertising campaigns and spare parts. If the secnd dimensin is multiplied by -1 (a reflectin) these crrelatins will be psitive. The cmputer prgram PRFIT -shrt fr prperty fıtting-(chang & Carrll, ı989b) is helpful in determining the dimensins that are highly crrelated with the attribute ratings. Safe Pres Pric Af.S Se.H Spa, Adv. BMW S.82 S Frd S S Tyta S S.31 Renault S.02 pel 4.2S S S Fiat 3.S vw S.08 S.17 S.I S4 S.28 Merced. S S.II Table 2: Averages Attribute Ratings fthe Car EVL Jurnal f Scial Sciences (I: 1) LAÜ Ssyal Bilimler Dergisi

7 Meltem Öztürk, Ayşe Akyl, Selin Küçükkancabaş i 103 DI D2 Safetv Prestize Price Af ter S Sec.H SpareP Advert Table 3. PRFIT utput: Directinal Csines f Fitted Vectrs it emplys a mre sphisticated technique than the ne explained in the previus paragraph. PRFIT takes the crdinate value s frm the ALSCAL utput and als the average attribute rating s n the five attributes as input. The highest value fr the first dimensin is safety. As fr the secnd dimensin the highest value is advertising campaign. Figure 3 prvides a plt f the riginal stimulus cardinates and the directinal vectrs. The directins f the 2 vectrs (safety and prestige) are twards the first quadrant (the quadrant where Mercedes is lcated). The directins f the ther 2 vectrs (after sale and secnd hand) are twards the BMW. Spare parts are related with the pel. Advertising is attributed t Renault and Vlkswagen. Price is als attributed t Vlkswagen. 1.50"",,",,~ j fia Dlmensinz.;!!9 frd brnlll aftersal. secndha pres!i&!,. mer safety price spare advertis. ~ rnl. -i.]j ~..jy!,_ r ~ -;' L9'J DimensinI Figure 3 Directin Vectrs f Attributes and Universities (PRFIT) 3.3. Preference Scaling Using PREFMAP The cmputer prgram PREFMAP (Chang & Carr II, 1989a) takes preference data, and the stimulus crdinates btained frm an MDS analysis as input, and prvides the EVL Jurnal f Scial Sciences (/: /) LAÜ Ssyal Bilimler Dergisi

8 ı04 i Explring Cnsumers ' Perceptins f Autmbile Brands in Turkey "ideal pint" in the same crdinate space as utput. Hence, the s called "ideal car" can be visualized in terms f the crdinate space already generated fr the perceptual map f the cars. The prgram inputs include the crdinates f universities in the aggregate perceptual map (ALSCAL utput) and the average preference values fr each car cmputed frm the answers t 3 rd seetin f the survey. The average preference value s fr the universities are given in Table 4. Vlkswaaen Preferenee 3.42 Rank 3 Fiat Merc Renault BMW 2.68 Tvta Frd pel Table 4. Average Preferenee Values f Cars it is seen here (Table 4) that BMW has the highest average preference. Mercedes, Wlkswagen, Tyta, pel, are all arund the same average preference rating and Frd, Renault and Fiat are the least preferred cars. There are a number f different mdels (pint based and vectr based) that PREFMAP uses t display the ideal pints relative t the MDS prduced crdinates. In this study, pint representatins are decided t use, that result in the derivatin f ideal pints fr the respndents' average preferences, plus an ideal pint fr the average f these preferences. The results are shwn in the Figure 4 belw. it is seen frm the figure that, Mercedes is the mst clsets car brand t the ideal pint. Anther finding f this analysis is that, mst f the cars are quiet far away frm the average ideal pint..69 T yta i.46 i.35 Fi at i Vlkswagen.23 i.12 i pel BMW Renault Mercedes i Frd ide i Figure 4: Autmbile Brands and Ideal Pint EUL Jurnal f Scial Sciences (I: 1) LAÜ Ssyal Bilimler Dergisi

9 Meltem Öztürk, Ayşe Akyl, Selin Küçükkancabaş i C:\,CLUSI:\, The aim f this study is t prvide a better understanding f the cnsumers' perceptins abut autmbile brands that help autmbile industry manager s t establish mre effective marketing strategies. Using multidimensinal scaling methd, the prpsed study prmtes marketers t analyze their brands' psitin in the market and mdify the marketing-mix based upn the current cnsumer preference. This study starts with the similarity judgments f cnsumers as t their preferences fr autmbile brands. The aim f this analysis is t identify the underlying dimensins behind these judgments. The results f this analysis highlight the imprtance f safety and advertising campaigns in cnsumers' aut brand preferences. In the secnd part f the study respndents asked t evaluate ne brand t anther in terms f certain attributes. Results shwed that B.Y1W,Frd and Mercedes perceived favrably in safety. In terms f advertising campaigns Mercedes, Tyta and Vlkswagen cnsidered t be gd. These findings als cnsistent with the cnsumers' preferences map that shws the cnsuıner ideal pint. Since Mercedes perceived favrably in bth dimensins in the previus analysis it is nt surprising that the ideal pint is directed at the lcatin f Mercedes. REFERENCES Allyn, B. and V.R. Ra (1972), Applied Multidimensinal Scaling: A Cmparisnf Appraches and Algritlıms. New Yrk: Hlt, Rinehart, and Winstn Cper, L.G. (1983), "A Review f Multidimensinal Scaling in Marketing Research," Applied Psychlgical Measurement, 7 (4), Carrll, J. D. and Paul E. Green (1997), "Psychmetric Methds in Marketing Research: Part 11, Multidimensinal Scaling," Jurnal f Marketing Research, 34 (May), Chang, II and Carrll, ld. (1989a), "Hw t Use PREFMAP - A Prgram that Relates Preference Data t Multidimensinal Scaling Slutins," in P.E. Green, F.J. Carmne, and S.M. Smith (eds.), Multidimensinal Applicatins: Newtn, MA: Allyn and Bacn. Scaling: Cncepts and Chang, lj (1989b), "Hw t Use PRFIT - A Cmputer Prgram fr Prperty Fitting by ptimizing Nnlinear r Linear Crrelatin," in P.E. Green, F.l Carmne, and S.M. Smith (eds.), Multidimensinal Scaling: Cncepts and Applicatins: Newtn, MA: Allyn and Bacn. Green, P. E. (1975), "Marketing Applicatins f Marketing, 39 (January), Green, FJ. Carmne Jr., and S.M. Smith (1989), Multidimensinal Applicatins. Bstn: j f MDS: Assessment and utlk, " Jurnal Scaling: Cncepts and Kruskal, J.B. (1964a), "Multidimensinal Scaling fr ptimizing a Gdness f Fit Metric t a Nnmetric Hypthesis," Psychmetrika, 29: EUL Jurnal f Scial Sciences (/: 1) LAÜ Ssyal Bilim/er Dergisi December 20/0 Aralık

10 106 i Explring Cnsumers ' Percepıins f A utmbile Brands in Turkey K.ruskal, LB. (l964b), "Xnmetric Multidimensinal Scaling: A :'\umerical Methd," Psychmetrika, 29: K.ruskal, lb., and Carrll, J.D. (1969), "Gernetrical Mdels and Badness-f-Fit Functins," in P.R. Krishnaiah (ed.), Multivariate Analysis, 2: "\"ew Yrk: Academic Press. Lattin, lm., Carrll, J.D., and Green, P.E. (2003), Analyzing Multivariate Data. Pacific Grve: Brks/Cle. Wind, Y. (1982), Prduct Plicy: Cncepts, Methds, and Strategy. Reading, MA: Addisn Wesley. Yung, F.W. and Harris, D.F. (1990), "Multidimensinal Scaling: Prcedure ALSCAL," in Ll.Nrusis (ed.), SPSS Base System User's Guide: Chicag, IL: SPSS Ine. Reprinted in J.J. Nrusis (ed.), SPSS Prfessinal Statistics, (1994): Chicag, IL: SPSS Ine. Meltem zturk is a Leeturer f Marketing at Pamukkale University, Denizli. She has BA Degree in Business Administratin frm Anadlu University, Eskisehir; MBA Degree frm Eurpean University f Lejke, KKTC Currently, she entinues her detral studies at Pamukkale University. Her researeh interests are marketing and sales management, sales techniques, marketing cmmunicatins, brand management and emmunieatins. Meltem Öztürk Pamukkale Üniversitesi/nde pazarlama dalında kutman larak görev yapmaktadır. Eskişehir Anadlu Üniversitesi iş Yönetimi Bölümü/nden mezun lduktan snra KKTC LejkeAvrupa Üniversitesi/nde iş Yönetimi yüksek lisansı yapmıştır. Dktra çalışmalarına Pamukkale Üniversitesi/nde devam etmektedir. Araştırma knuları pazarlama ve satış yönetimi, satış teknikleri, pazarlama iletişimi, marka yönetimi ve iletişimidir. Selin Kueukkaneabas is aresearch Assistant at Bgaziei University, IstanbuL. She has a BA Degree in Business Administratin frm Yildiz Teknik University, Istanbul; a MA Degree in Management frm Trakya University, Edirne. Her research interests are educatin marketing, relatinship marketing, marketing research. Selin Küçukkancabaş halen Bğaziçi Üniversitesi/nde araştıma görevlisidir. Yıldız Teknik Üniversitesi iş Yönetimi Bölümü/nden mezun lduktan snra Trakya Üniversitesi/nde yüksek lisans yapmıştır. Araştırma alanları eğitim pazarlaması, ilişki pazarlaması, pazarlama araştırmalarıdır. EVL Jurnal f Scial Sciences (I: J) LAÜ Ssyal Bilimler Dergisi

11 Meltem Öztürk, Ayşe Akyl, Selin Küçükkancabaş i 107 Ayse Akyl is an Assciate Prfessr f Marketing at Trakya University, Edirne. She has BA Degree in Business Administratin frm Dkuz Eylul University, ızmir; MA Degree in Persnnel Management frm Istanbul University, Istanbul; MSc Degree in Business Research and Cnsultaney, and a Ph.D Degree in Marketing bth frm University f Prtsmuth, UK; and pst dctral experience at University f Suth Flrida, USA. Her research interests are internatinal marketing, freign trade, marketing ethics, wine marketing. Currently, she is wrking at Eurpean University f Lejke, Nrth Cyprus as Vice Rectr. Ayşe Akyl Trakya Üniversitesi/nde pazarlama alanında dçent öğretim üyesi larak gôrevlidir. Dkuz Eylül Üniversitesi İş Yönetimi Bölümü/nden mezun lduktan snra İstanbul Üniversitesi/nde persnel yönetimi knusunda yüksek lisansını tamamlamış, ardından Prtsmuth Universitesi'nde eşzamanlı larak İş Araştırmaları ve Danışmanlığı yüksek lisansı ile, pazarlama alanında dktra yapmıştır. Araştırma knuları uluslararası pazarlama, dış ticaret, pazarlama etiği, şarap pazarlamasıdır EVL Jurnal f Scial Sciences (I: 1) LAÜ Ssyal Bilimler Dergisi

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