DEVELOPING A PRODUCT MIX DECISION MODEL FOR DIRECT MAIL IN THE DEPARTMENT STORE

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1 Internatonal Journal of Economcs, Commerce and Management Unted Kngdom Vol. II, Issue 11, Nov ISSN DEVELOPING A PRODUCT MIX DECISION MODEL FOR DIRECT MAIL IN THE DEPARTMENT STORE Hsu, P-Fang Department of Communcatons Management, Tawan pfhsu@cc.shu.edu.tw Chang, Po-Chen Department of Communcatons Management, Tawan pochen@cc.shu.edu.tw Yu, Yu-Yang Department of Communcatons Management, Tawan peggy @gmal.com Abstract Ths research develops a model for selectng the best-sellng product mx n the drect mal (DM). The research was conducted n two stages. Frstly, the evaluaton crtera of product mx are formed based on the revews of lterature and experts opnons collected va the modfed Delph method followed by the use of Analytc Herarchy Process (AHP) to assess the crtera weght. Secondly, Grey Relatonal Analyss (GRA) was performed to select the best product mx. We also used ths model to verfy the effectveness of dscount marketng strategy. The results address that product promoton, product attractveness, company collaboraton, and layout of desgn are dentfed as the most crtcal crtera. The product mx s dentfed to have the most nfluences on ndvduals product selectons durng the dscount campagns. Hence, ths research not only provdes useful gudelnes for marketers, but also gven the advantages of obtanng an effectve marketng strategy among department stores. Keywords: Drect Mal, Evaluaton model, Analytc Herarchy Process, Modfed Delph method, Grey Relatonal Analyss Lcensed under Creatve Common Page 1

2 Hsu, Chang & Yu INTRODUCTION Drect mal (DM) has been consdered the most effectve medum for acqurng potental customers n drect marketng over decades. The messages and target customers are regarded as crtcal components n promotng a successful drect mal campagn (Bult & Wansbeek, 1995). Pror studes mostly emphasze the mportance of selectng the target demographcs and channels n order to promote the effectveness of drect mal (Bawa & Shoemaker, 1987; Bult & Wansbeek, 1995; Dwyer & Evans, 1981). Whle lttle attenton s pad to the successful elements used n the drect mal that facltate the communcatons of product offerngs and persuasve messages towards ts target audence (TA). In practce, luxurous department stores n Tawan generally used drect mal to promote unque and dscount products n ther marketng actvtes. The frequency and usage of drect mal have been ncreased annually and reflected n the growth of advertsng budgets. A proper desgn of drect mal s expected to attract consumers attenton and generate purchase ntenton among customers (Bawa & Shoemaker, 1987). Storeowners mght want to use drect mal to communcate wth ther customers. For nstance, they mght wsh to promote dfferent product packages or persuade customers by advertsng nformaton along wth drect mals delvered to the households. Pror scholars have confrmed the fact that the use of drect mal would effectvely facltate customers brand awareness and purchase ntenton n the evaluaton tme spans between pre- and post-purchases (Bawa & Shoemaker, 1987; MacInns, Shapro, & Man, 1999). Nevertheless, marketers generally agreed that a success of drect mal s helpful n generatng the volume of sales revenue and compettve advantages for stores and advertsement (Bawa & Shoemaker, 1989). The storeowners are thus requred to frequently examne what product mx should be shown to ther customers va the drect mal. Such task has caused hgh pressure and burdens for them to make regular decsons n selectng the product mx contaned n the drect mal. The varety and prcng of dfferent products promoted by the stores also make t more dffcult to choose among the lst of alternatves. So far, lttle research has been found that explctly descrbes the selectons of product mx n the drect mal(bult & Wansbeek, 1995). In addton, the evaluaton of product mx should reflect on the objectves of promoton, whch present further challenges for decson makers. Therefore, ths study ams to develop a decson model that provdes nformaton for marketers to determne the deal soluton among multple crtera and optons. Lcensed under Creatve Common Page 2

3 Internatonal Journal of Economcs, Commerce and Management, Unted Kngdom LITERATURE REVIEW Drect Mal (DM) s defned as a promoton tool for communcatng wth potental customers by the delvery of postal servces (Bearden, Ingram, & Laforge, 1995; Katzensten & Sachs, 1992; T. A. Shmp, 1993; Terence A. Shmp, 2007). It can be used to contan product-samplng, coupons or cash back form, to draw mmedate customer responses (McGunness, Brennan, & Gendall, 1995). Comparng to the use of e-mal n the concerns of spam, DM stll has some advantages, such as source accountablty and flexblty n vsual presentaton (Terence A. Shmp, 2007). Hence, stll 69% of markers use DM as a promoton strategy to drve store traffc and acqure new customers, accordng to the annual report by Target Marketng n 2011 (McGee, 2011). For drect to consumer advertsng (DTCA), Menon, Deshpande, Znkhan, and Perr (2004) lsted the factors that predct the effectveness of advertsng from four dmensons, such as audence, source, message and channel. The format of a DM s an essental element n drect to customer advertsng whch contaned creatve and promotonal nformaton by marketers (Nash, 2000). The sales nformaton contaned n the DM are enclosed and dsplayed n dfferent formats, such as envelope, postcard, catalogue, brochure and coupon. The goal of usng DM s manly to stmulate customers response and provde personalzed nformaton and news (Nash, 2000). It s also promnently for marketers to use DM n precson targetng, personalzaton and callng for mmedate acton (Roberts & Berger, 1999). The effect of usng DM n retalers can also be easly evaluated through costs-to-sales relatonshp and customer responses (Hasouneh & Alqeed, 2010; Menon et al., 2004). In contrast to other meda that targeted on mass consumers, retalers can use DM more effcently wth precse nformaton extracted from customer database to attan better market segmentaton (Bauer, 1988; Hasouneh & Alqeed, 2010). Hence, t s doubtless that department store retalers adapt DM as the man tactc n promoton and advertsement. Terence A. Shmp (2007) summarzed a lst of functonalty of DM and llustrated as follows. Increase of sales and usage from current customers. Sell products and servces to new customers. Buld traffc at a specfc retaler. Stmulate product tral. Generate leads for a sales force. Delver product relevant nformaton and news. Gather customer nformaton that can be used n buldng a database. Create personalzed nformaton for target customers. Lcensed under Creatve Common Page 3

4 Hsu, Chang & Yu Although DM s vtal to the success of marketng campagns n department stores, pror research has not yet addressed the mportance of product selecton and dsplay n the DM from the retalers regards. Researchers addressed the ssues of usng DM n advertsng promoton by means of dfferent methodologes. Most pror studes put ther efforts to evaluate the effectveness of DM that nfluences customers brand choces (Bawa & Shoemaker, 1987). Some studes evoke to dentfy the relatonshp between the use of DM n advertsng and consumers purchase decson mostly through ndvduals perceptons (Bauer, 1988; Bawa & Shoemaker, 1989; James & L, 1993). Other studes ponted out the crtcal tasks of selectng DM n advertsng through dfferent econometrcal analyses (Bult & Wansbeek, 1995; Dwyer & Evans, 1981; Rao & Smon, 1983). However, lttle study was found that develops an objectve method to evaluate the product selecton n the DM. It s therefore essental for marketers to attan an optmzed decson makng process n the stage of pre-purchase before a DM s delvered to the target customers. METHODOLOGY The evaluaton of selectng the best of product mx n the drect mal s dvded nto two sectons. Frstly, a modfed Delph method s used to aggregate experts opnons and helped to from the evaluaton crtera. Analytc Herarchy Process (AHP) s chosen to determne the crtera weght based on the herarchy of crtera. However, there are many merchandzes and alternatves n the DM and AHP s not a sutable opton for handlng over seven elements n one level. Hence, after constructng the decson model, Grey Relatonal Analyss (GRA) s preferred and utlzed to verfy the selecton of products mx n the samples of department stores. Modfed Delph method Rand Corporaton orgnally developed the Delph method n the 1950s. Ths approach conssts of a survey conducted n two or more rounds. It s commonly assumed that ths method makes better use of group nteracton (Delbecq, Ven, & Gustafson, 1975; DeSancts & Gallupe, 1987; Rohrbaugh, 1979; Woudenberg, 1991). The opnons from partcpants are equally consdered and reached n consensus to solve some complcated ssues (Lnstone & Turoff, 1975; Rowe, Wrght, & Bolger, 1991). Although the Delph method has been wdely appled n dfferent felds, the tradtonal Delph method has been crtczed for low convergence n result generaton, long process of nterrogaton, and loss of valuable nformaton from expert opnons (Garrod & Fyall, 2005; Murry & Hammons, 1995; Spnell, 1982). Acknowledgng the drawbacks of the tradtonal Delph method, Murry and Hammons (1995) proposed a modfed Delph method to bypass the Lcensed under Creatve Common Page 4

5 Internatonal Journal of Economcs, Commerce and Management, Unted Kngdom complcated process n the frst round of expert query and complemented wth the results of lterature revews, or substtuted by the results from expert ntervews or collected by a structured questonnare. The advantage of ths applcaton s to save large amount of tme n obtanng the experts consensus and drect the attentons to the agreeable ssues. As for the proper number of partcpants, a number of fve to nne partcpants s recommended n conductng a Delph method (Delbecq et al., 1975). Hence, ths research chooses the prncple of modfed Delph method. A group of fve to nne people was recommended for conductng a modfed Delph method (Delbecq et al., 1975). Nne experts partcpated n ths study and the questons were derved from related lterature and suggested by experts n an open format. Analytc Herarchy Process (AHP) Analytc Herarchy Process (AHP), developed by Saaty(1980), was another tool n dealng wth uncertan stuaton of mult-attrbute decson makng(mcdm). Ths method s generally used to determne the prortes of decson alternatves va par wse comparsons of attrbutes wth respect to a common crteron. It also provdes an objectve way for reachng an optmal decson for both ndvduals and group decson makers. The AHP technque s wdely used n modelng the human judgment process (Lee, Kwak, & Han, 1995). Bryson (1996) appled ths method to assess the performance of group decsons n determnng the consensus of relevant nformaton. Dyer, Forman, and Mustafa (1992) used AHP to help advertsers n the meda selecton. Ln and Hsu (2003) also appled AHP to select Internet advertsng networks. Hsu (2006) further used AHP to select publc relaton frms for hgh-tech companes. Thus, AHP has been successfully appled to solve a wde varety of problems. The process of AHP s llustrated as follows: 1. Establshment of Parwse Comparson Matrx A Let C 1,C 2,,C n be the set of elements, whle a j represents a quantfed judgment on a par of elements C, C j. The relatve mportance of two elements s rated usng a scale wth the values 1, 3, 5, 7, and 9, where 1 stands for equally mportant, 3 for slghtly more mportant, 5 for strongly more mportant, 7 for demonstrably more mportant, and 9 for absolutely more mportant. The dgts 2, 4, 6 and 8 are used to facltate a compromse between slghtly dfferng judgments (Saaty, 1994). Besdes, Beynon (2002) compares the approprateness of the 1-9 scale wth other alternatve 9-unt scales, also used n AHP, by lookng at the probablty dstrbutons of the assocated prorty values. Lcensed under Creatve Common Page 5

6 Hsu, Chang & Yu A n -by- n matrx A s derved as follows: C 1 C 2 C n Where a = 1 and a j = 1/a j,, j = 1,2,,n. In matrx A, the problem becomes one of assgnng to the n elements C 1,C 2,,C n a set of numercal weghts W 1,W 2,,W n that reflects the recorded judgments. If A s a consstency matrx, the relatons between weghts W and judgments a j are smply gven by W /W j = a j (for, j = 1,2,,n). A a j C C C 1 2 n 1/ 1/ 1 a a 12 1n a / a 2n a1 n a 2n 1 (1) 2. Egenvalue and Egenvector Saaty (1990) suggested that the largest egenvalue max be: max If A s a consstency matrx, egenvector X can be calculated by the formula (3): max n j1 a j W W j (2) (A- I ) X =0 (3) 3. Consstency Test Saaty (1990) proposed utlzng consstency ndex (CI) and consstency rato (CR) to check the consstency of the comparson matrx. CI and CR are defned as follows: max CI = ( - n) / (n-1) (4) CR= CI / RI (5) Where RI denotes the average consstency ndex over numerous random entres of same order recprocal matrces. If CR 0.1, the estmate s accepted, and otherwse a new comparson matrx s solcted untl CR 0.1. Grey Relatonal Analyss (GRA) Grey System Theory s manly utlzed to study system model uncertanty, analyze relatons between systems, establsh models, and forecast and make decsons. GRA s used to examne the extent of connectons between two dgts by applyng the methodology of departng and scatterng measurement to actual dstance measurement (Deng, 1989). Ln and Yang (1999) Lcensed under Creatve Common Page 6

7 Internatonal Journal of Economcs, Commerce and Management, Unted Kngdom used GRA to select home mortgage loans, Sun (1999) appled GRA to rank factors nfluencng economc beneft n hosptals and thus develop economc polces, and Ln and Hsu (2003) used GRA to determne a meda agency. The followng formula dsplays the procedures for calculatng grey relatonal grades (Wen, 1999): 1. Calculatng grey relatonal grades x o Let denote the referental seres wth n enttes, x ( x0(1), x0(2),, x0( )), 0 n x and let represent the compared seres, x ( x (1), x (2),, x ( n)), 1,2, m, The grey relatonal grade for seres 0 mn max max n n x o (6) to x s then gven as: wk 0 w k 1 where k1, k 1, w k s the k-th element s weght, k=1,2,,n, 0 x0 x max max max 0 mn mn mn 0, and k, k. 2. Normalzaton (or Data dmensonless) Before the grey relatonal grades are calculated, the seres data can be treated usng the followng three stuatons and the lnearty of normalzaton to avod dstortng the normalzed data. The followng stuatons arse (Wu & Chen, 1999): 1) Upper-bound effectveness of measurement (.e., larger-the-better) x * x mn x k max x mn x k k (7) max x mn x where k s the maxmum value of entty k and k s the mnmum value of entty k 2)Lower-bound effectveness of measurement (.e., smaller-the-better) x * max x x k max x mn x k k (8) 3)Moderate effectveness of measurement (.e., nomnal-the-best) Lcensed under Creatve Common Page 7

8 Hsu, Chang & Yu x * x xob( k) 1 max max x x, x mn x k ob x where ob (k) mn x ob( k) max x s the objectve value of entty k, k k. ob k (9) Model constructon Ths model s separated nto two parts, where the frst part uses AHP to weght the evaluatve crtera, whle the second part apples GRA to select an deal product selecton n the DM. The model s detaled below: 1.Applyng AHP for determnng relatve crtera weghtngs Ths AHP model for determnng the relatve weghts of evaluatve crtera nvolves sx steps as follows: Step 1: Defne the evaluatve crtera for selectng product mx n the drect mal. Step 2: Establsh a herarchcal structure by breakng the product mx selecton problem nto a herarchy of nterrelated decson elements, ncludng the ultmate goal, crtera, and sub-crtera. Step 3: Establsh the parwse comparson matrx usng formula (1). Every expert makes a parwse comparson of the decson elements and gves them relatve scores. Step 4: Calculate the egenvalue and egenvector of each parwse comparson matrx usng formulae (2) to (3). Step 5: Test the consstency of each comparson matrx usng formulae (4) to (5). Step 6: Aggregate the relatve scores provded by all experts usng the geometrc mean method, and estmate the relatve weghts of the elements of each level. 2. Applyng GRA for selectng an deal product mx n the DM Ths GRA model for selectng the deal product mx n the DM whch nvolves seven steps, as follows: Step 1: Defne the crtera and data treatment. Step 2: Make the lower, moderate or upper bounds of the objectve value sght the referental seres. Step 3: Transform the data obtaned by arrangng the crtera for product mx selecton nto a compared seres. Step 4: Normalze ndvdual crtera values usng formulae (7) to (9) to obtan x * (k), before calculatng grey relatonal grades, n case of dfferences among ndvdual crtera unts. Lcensed under Creatve Common Page 8

9 Internatonal Journal of Economcs, Commerce and Management, Unted Kngdom Step 5: Calculate dfference seres 0. Step 6: Enter the weghts of twenty sub-crtera n the frst part nto formula (6) and then, calculate the and grey relatonal grades 0 of each compared seres. Step 7: Select the top grey relatonal grades 0, whch s the deal product mx. Model applcaton The decson model s constructed based on the selecton of product mx llustrated n the DM and adopted by the department stores n Tawan. DM s wdely used by to attract customers attentons and ther purchase ntentons. For marketers, DM s very effectve durng the dscount campagn where t not only presents the dscount nformaton wth respect to the exclusve merchandzes, promotons, and customer servces, but also plays a crtcal role n the communcaton and persuason wth ther customers. Hence, ths research ams to apply ths model to verfy the optmzed product mxed judged by a lst of experts from the department stores. Three persons formed a decson team, whch ncluded the busness drector, manager from the department of female apparel, and marketng manager. The product mx n the DM was ntally dvded by two categores: new and dscount merchandzes. The decson team members then selected twenty merchandzes and each merchandze was judged by a score based on the evaluaton crtera. The evaluatve fgures obtaned from these crtera are thus verfed, as outlned below: Frst part: Apply modfed Delph method to form the crtera and use AHP to determne the relatve crtera weght. Step 1. Defne the evaluatve crtera for selectng product mx n the DM Ths research apples the modfed Delph method to defne the evaluaton crtera by nvtng nne experts who are experenced n the busness or marketng sectors for over ten years. After two rounds of expert questonnares, the experts opnons reached the consensus and determne the herarchy of evaluatve crtera. 1. Product: fve crtera of evaluatng the product mx n the DM were formed based on the revews of pror lterature whch ncludes brand awareness (Dodds, Monroe, & Grewal, 1991; Hoyer & Brown, 1990; Kamns & Marks, 1991), fashon style (Sproles, 1979), new merchandze, word-of-mouth, and unque style. The last three crtera were suggested by experts n the query of Delph survey. 2. Promoton: four crtera were dentfed nclude the depth of dscount (Bell, Chang, & Padmanabhan, 1999; Fry & McDougall, 1974; Grewal & Krshnan, 1998), scarcty of Lcensed under Creatve Common Page 9

10 Hsu, Chang & Yu merchandze (Brannon & McCabe, 2001; Sur, Kohl, & Monroe, 2007), and campagn actvtes as suggested by experts. 3. Layout desgn: two crtera were defned nclude the color coordnaton and the varety of merchandze. 4. Revenue generaton: two crtera were dentfed nclude the revenue stem from n-store sales and the flexblty of supplers collaboraton wth respect to the quantty of merchandze, as suggested by experts. Step 2. Establsh a herarchcal structure The selecton of product mx n the DM s dvded nto three levels. The frst level s the ultmate goal n selectng the optmzed product mx n the DM, followed by the four evaluaton crtera, and fnally the 13 sub-crtera. The herarchcal structure s llustrated n Fgure 1, on page 11. Step 3. Establshng the parwse comparson matrx Nne expert respondents make a parwse comparson of the decson crtera and assgn relatve scores based on 1 to 9 scales. The relatve score provded by experts are aggregated usng the geometrc mean method, and the aggregate parwse comparson matrx for the crtera s lsted n Table 1, whle that for the sub-crtera are lsted n Table 2. Table 1. Aggregate parwse comparson matrx for crtera of level 2 Product Promoton Layout desgn Revenue generaton Product Promoton Layout desgn Revenue generaton CR CI=0.007 λ=4.022 Table 2. Aggregate parwse comparson matrx for sub-crtera of level 3 Crtera Crtera Sub-crtera Sub-crtera Overall Rankng Consstency weght weght weght Brand awareness Fashon style λ=5.067 Product New merchandze CI=0.017 Word-of-Mouth CR=0.015 Unque style Promoton Depth of dscount λ=4.100 Exclusve product CI=0.033 Lmted product CR=0.037 Campagn actvtes Layout Color coordnaton λ= CI=0.000 desgn Varety of product CR=0.000 Revenue from n-store sales λ=2.000 Revenue CI=0.000 generaton Flexblty of product supply CR=0.000 Lcensed under Creatve Common Page 10

11 Internatonal Journal of Economcs, Commerce and Management, Unted Kngdom Fgure. 1 Herarchcal structure requred to selectng the product mx n the DM Level 1. Ultmate goal Level 2. Crtera Level 3. Sub-crtera Brand awareness Fashon style Select the product mx n the DM Product Promoton New product Word-of-mouth Unque style Depth of dscount Exclusve product Lmted product Campagn actvtes Product1 Product2 Product3 Product4 Product5 Product6 Product7 Product8 Product9... Product19 Product20 Layout desgn Color coordnaton Product varety Revenue generaton Revenue from n-store sales Flexblty of supplers Lcensed under Creatve Common Page 11

12 Hsu, Chang & Yu Step 4. Calculatng the egenvalue and egenvector Usng the comparson matrx of Table 1 and Table 2, the egenvectors were then calculated usng formulae (2) and (3). Table 3 lsts the results of egenvectors for the thrteen sub-crtera and four crtera. Step 5. Consstency test The results of the consstency test, the CR of the comparson matrx from each of the nne experts, are below 0.1, ndcatng the aggregaton of experts opnons meets the requrement of consstency. Furthermore, the CR of the aggregate matrx s also blow 0.1, agan ndcatng the consstency of ths model. Step 6. Estmatng the relatve weghts of the elements of each level The relatve weghts of the elements of each level are estmated from the aggregated values of the nne experts usng the egenvector method, and Table 3 lsts the estmated results. Table 3 The score of product mx based on the evaluaton crtera K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 K13 P P P P P P P P P P P P P P P P P Second part: Applyng GRA for selectng the optmzed product mx n the DM among alternatve optons Step 1. Defne the crtera and data treatment The experts selected twenty merchandzes. Each merchandze was drectly rated by thrteen crtera based on the scales from 1 to 10. The hgher value ndcates the mpresson of merchandze based on the crtera. The scores from thrteen crtera are llustrated n Table 2. Step 2. Pck out (κ1) the hghest value s 7.667, from the brand awareness of the merchandze; (κ 2) the hghest value s from fashon style; (κ3) the hghest value s from new Lcensed under Creatve Common Page 12

13 Internatonal Journal of Economcs, Commerce and Management, Unted Kngdom merchandze; (κ 4) the hghest value s from word-of-mouth. All these crtera values are to be consdered as referental seres χ 0. That s, χ 0 = (7.667, 7.667, 7.000,, 7.667). Step 3. Consder the values of κ1, κ 2,, κ 13 as compared seres χ =(χ (1), χ (2),, χ (13)), =1,2,3. Step 4. Normalze Table 3 usng formulae (7) and (8). Step 5. Calculate the dfference seres Step 6. Calculate of ndvdual compared seres and grey relatonal grades by the formulae (6), as shown n Table 4. Table 4 Calculaton of ndvdual compared seres and grey relatonal grades K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 K13 Δ Γ 0 Rank P P P P P P P P P P P Step 7. Select 10 merchandzes from the orgnal 20 merchandzes based on grey relatonal grades n Table 5. The rankngs of the frst ten merchandzes from applyng ths approach are llustrated n Table 4. The results ndcate the top 10 merchandzes are characterzed to have the advantages wth more dscount than other dscount optons. The revenues from n-store sales and hgh brand awareness, hgh varety of product choces and styles determne the prorty of product mx whch s thus recommended to hghlght n the DM. CONCLUSION The selecton of an optmzed product mx n the DM s a complcated multple objectve decson makng process. Ths study emphaszes on the dscount strategy n department store marketng by selectng the product mx to llustrate the depth of dscount based on the decson model. The unqueness and depth of dscount n the DM are dentfed to be the most mportant crtera that can be used to convert consumers purchase ntentons nto sales revenue. To create hgh volume of sales revenue, t s also mportant to collaborate wth dstrbutors and ensure the supples and styles of the merchandzes. The weghts of selectng product mx of DM n the Lcensed under Creatve Common Page 13

14 Hsu, Chang & Yu department stores are consdered to be the prorty of promotons carred n the dscount campagns. The rankng of crtera are the unqueness of product mx, depth of dscount, flexblty of product supples, and revenues of the counter, brand awareness and dscusson topcs. Ths research provdes an objectve and systematc approach to evaluate the product mx dsplayed n the DM. Through the judgment of marketers n the department stores; ths framework not only provdes valuable nformaton for selectng the deal product categores for marketers but also facltates the process for consumers n purchase decsons. Future researchers and practtoners may follow ths framework and apply t to varous contexts, such as the applcatons n dfferent department stores or the stores from other ndustres. In addton, ths study assumes the ndependence of each crteron and elmnates the complexty of consderng the relatonshps among crtera. Future research s recommended to use Analytcs Herarchy Process (ANP) and take the relatonshps of nterdependence of crtera nto account. REFERENCES Bauer, Conne L. (1988). A drect mal customer purchase model. Journal of Drect Marketng, 2(3), Bawa, Kapl, & Shoemaker, Robert W. (1987). The effects of a drect mal coupon on brand choce behavor. Journal of Marketng Research, 24(4), Bawa, Kapl, & Shoemaker, Robert W. (1989). Analyzng ncremental sales from a drect mal coupon promoton. Journal of Marketng, 53(3), Bearden, W. O., Ingram, T. N., & Laforge, R. W.. (1995). Marketng: Prncples & perspectves. Chcago: Irwn. Bell, D. R., Chang, J., & Padmanabhan, V.. (1999). The decomposton of promotonal response: An emprcal generalzaton. Marketng Scence, 18(4), Brannon, L. A., & McCabe, A. E.. (2001). Tme-restrcted sales appeals: The mportance of offerng real value. Cornell Hotel and Restaurant Admnstraton Quarterly, 42(4), Bryson, Noel. (1996). Group decson-makng and the analytc herarchy process: Explorng the consensus-relevant nformaton content. Computers & Operatons Research, 23(1), Bult, Jan Roelf, & Wansbeek, Tom. (1995). Optmal selecton for drect mal. Marketng Scence, 14(4), Delbecq, Andre L., Ven, Andrew H. Van de, & Gustafson, Davd H. (1975). Group technques for program plannng: A gude to nomnal group and Delph processes. Chcago: Scott, Foresman and Company. Deng, J. L.. (1989). Introducton to Grey System Theory. The Journal of Grey System, 1(1), DeSancts, Gerardlne, & Gallupe, R. Brent. (1987). A foundaton for the study of group decson support systems. Management Scence, 33(5), Dodds, W. B., Monroe, K., & Grewal, D. (1991). Effets of prce, brand, and store nformaton on buyers' product evaluatons. Journal of Marketng Research, 28(8), Dwyer, F. Robert, & Evans, James R. (1981). A brand and bound algorthm for the lst selecton problem n drect mal advertsng. Management Scence, 27(6), Dyer, Robert F., Forman, Ernest H., & Mustafa, Mohammad A. (1992). Decson support for meda selecton usng the Analytc Herarchy Process. Journal of Advertsng, 21(1), Lcensed under Creatve Common Page 14

15 Internatonal Journal of Economcs, Commerce and Management, Unted Kngdom Fry, J. N., & McDougall, G. H.. (1974). Consumer apprasal of retal prce advertsement. Journal of Marketng, 38(3), Garrod, Bran, & Fyall, Alan. (2005). Revstng Delph: the Delph technque n toursm research. In B. W. Rtche, P. Burns & C. A. Palmer (Eds.), Toursm research methods: Integratng theory wth practce (pp ). Oxfordshre, UK: CABI Publshng. Grewal, D., & Krshnan, R.. (1998). The effects of store name, brand name, and prce dscount on consumer evaluatons and purchase ntentons. Journal of Retalng, 74, Hasouneh, Abdel Baset, & Alqeed, Marzouq Ayed. (2010). Measurng the effectveness of e-mal drect marketng n buldng customer relatonshp. Internatonal Journal of Marketng Studes, 2(1), Hoyer, Wayne D., & Brown, Steven P. (1990). Effects of brand awareness on choce for a common, repeat-purchase product. Journal of Consumer Research, 17(2), Hsu, P. F. (2006). Developng a new model for selectng publc relatons frms n the hgh-tech ndustry. Journal of Modelng n Management, 1(2), James, E. Lncoln, & L, Harong. (1993). Why do consumers open drect mal? Contrastng perspectves. Journal of Drect Marketng, 7(2), Kamns, Mchael A., & Marks, Lawrence J. (1991). The percepton of kosher as a thrd party certfcaton clam n advertsng for famlar and unfamlar brands. Journal of the Academy of Marketng Scence, 19(3), Katzensten, Herbert, & Sachs, Wllam S. (1992). Drect marketng. New York: Prentce Hall. Lee, Heekeok, Kwak, Wkl, & Han, Ingoo. (1995). Developng a busness performance evaluaton system: An analytc herarchcal model. The Engneerng Economst, 40(4), Ln, C. T., & Yang, S. Y.. (1999). Selecton of home morgate loans usng Grey relatonal analyss. The Journal of Grey System, 11(4), Ln, Chn-Tas, & Hsu, P-Fang. (2003). Adopng an analytc herarchy process to select Internet advertsng networks. Marketng Intellgence & Plannng, 21(3), Ln, Chn-Tsa, & Hsu, P-Fang. (2003). Selecton of Internet advertsng networks usng an analytcal herarchy process and grey relatonal analyss. Internatonal Journal of Informaton and Management Scences, 14(2), Lnstone, H. A., & Turoff, M.. (1975). The Delph study: Technque and applcatons. London: Addson- Wesley. MacInns, Deborah J., Shapro, Stewart, & Man, Gayathr. (1999). Enhancng brand awareness through brand symbols. Advances n Consumer Research, 26, McGee, Thorn. (2011). Cover story: Meda usage forecast from McGunness, Dalton, Brennan, Mke, & Gendall, Phlp. (1995). The effect of product samplng and couponng on purchase behavour: Some emprcal evdence. Internatonal Journal of Advertsng, 14(3), Menon, Ajt M., Deshpande, Aparna D., Znkhan, George M., & Perr, Matthew. (2004). A model assessng the effectveness of drect-to-consumer advertsng: ntegraton of concepts and measures from marketng and healthcare. Internatonal Journal of Advertsng, 23(1), Murry, J. W., & Hammons, J. O.. (1995). Delph: A versatle methodology for conductng qualtatve research. The Revew of Hgher Educaton, 18(4), Nash, Edward. (2000). Drect marketng: Strategy, plannn, executon (4th ed.). New York: McGrawHll. Rao, Vthala, & Smon, Julan L. (1983). Optmal allocaton of space n retal advertsements and malorder catalogues: Theory and a frst-approxmaton decson rule. Internatonal Journal of Advertsng, 2(2), Lcensed under Creatve Common Page 15

16 Hsu, Chang & Yu Roberts, Mary Lou, & Berger, Paul D. (1999). Drect marketng management (2nd ed.). Upper Saddle Rver, New Jersey: Prentce-Hall. Rohrbaugh, John. (1979). Improvng the qualty of group judgement: Socal judgment analyss and the Delph technque. Organzatonal Behavor and Human Performance, 24(1), Rowe, Gene, Wrght, George, & Bolger, Fergus. (1991). Delph: A reevaluaton of research and theory. Technologcal Forecastng & Socal Change, 39(3), Saaty, Thomas L. (1980). The Analytc Herarchy Process. New York: McGraw-Hll. Shmp, T. A.. (1993). Promoton management and marketng communcatons. Fort Worth: Dryden Press. Shmp, Terence A. (2007). Advertsng, promoton and other aspects of ntegrated marketng communcatons. Meson, OH: Thomson South-Western. Spnell, Ter. (1982). The Delph decson-makng process. The Journal of Psychology: Interdscplnary and Appled, 113(1), Sproles, George B. (1979). Fashon: Consumer behavor toward dress. Mnneapols, Mnn: Burgess Sun, M. Y.. (1999). Grey relatonal analyzng the nfluencng factor of economc beneft n hosptal. The Journal of Grey System, 11(1), Sur, Rajneesh, Kohl, Chranjeev, & Monroe, Kent B. (2007). The effects of perceved scarcty on consumers' processng of prce nformaton. Journal of the Academy of Marketng Scence, 35(1), Wen, K. L.. (1999). The cardnal form of Grey relatonal grade. Journal of The Chnese Grey System Assocaton, 2(2), Woudenberg, Fred. (1991). An evaluaton of Delph. Technologcal Forecastng & Socal Change, 40(2), Wu, J. H., & Chen, C. B.. (1999). An alternatve form for grey relatonal grades. The Journal of Grey System, 11(1), Lcensed under Creatve Common Page 16