Attribute-consequence-value linkages: A new technique for ... · — selecting and grouping A, C, V ... the base for the structural questionnaire ... Family Mart (796 stores), Hi-Life

effective in achieving their desiredconsumer values.

The traditional MEC methodology,however, has four main limitations.

— selecting and grouping A, C, Vvariables is a subjective process thatmay lead to the elimination ofrelevant marketing variables resultingin inappropriate product strategies

— researchers must predefine the cutoffvalue for an HVM

— the necessary process of simplifyingA/C/V variables restricts the scopeand depth of interviewee response topredetermined variables, which maynot accurately reflect consumers’ truedesires

— identifying which variable is the

INTRODUCTIONA new technique is proposed forenhancing the traditional means–endchain (MEC) methodology ofunderstanding customers’ productknowledge. Traditional MEC analysisrelies on survey data to uncover themeans-end hierarchies defined by therelationship among attributes,consequences and values. Such ananalysis leads to a tree diagram, termed ahierarchical value map (HVM), which isby nature structural and represents theserelationships. These rich and deepdescriptions of product knowledge revealdifferences in product knowledgebetween experienced and noviceconsumers. Marketers can thus use HVMto decide which attributes are most

� Henry Stewart Publications 0967-3237 (2002) Vol. 10, 4, 339-352 Journal of Targeting, Measurement and Analysis for Marketing 339

Attribute-consequence-valuelinkages: A new technique forunderstanding customers’product knowledgeReceived (in revised form): 22nd November, 2001

Chin-Feng Linis an Associate Professor in the Department of Business Administration at National Chin-Yi Institute of Technology, Taiwan.His research interests lie in the development and application of new integrated (quantitative/qualitative) methodologies for thefurther understanding of consumer behaviour. He received his PhD in marketing from National Taiwan University of Scienceand Technology. Dr Lin has published numerous marketing articles in academic journals and proceedings.

Abstract A new technique based on the means–end chain (MEC) framework isproposed to assess consumers’ product knowledge and cognitive structures. Applyinglinear regression and factor analyses to explore the relationship between productknowledge (product attributes) and self-concept (personal values) components onservices of the convenience store chains (CVS), this study provides a new analysis tohelp identify decisive attributes for satisfying customers’ value demands. The newtechnique not only addresses the limitations of the traditional MEC methodology butalso provides marketers with a new insight for developing effective marketing strategies.

Chin-Feng LinDepartment of BusinessAdministration, NationalChin-Yi Institute ofTechnology, 35, Lane 215,Sec. 1, Chungshan Rd.Taiping, Taichung 411,Taiwan.

Tel: �886 4 23924505ext 7771;Fax: �886 4 23929584;e-mail: [email protected]

Concrete attributes, such as productmodel or price, can be viewed asrelatively tangible productcharacteristics.10–12 Abstract attributes,such as product brand or style, can beconsidered as non-pictorial distinguishingfeatures. Abstractness, in this context, isdefined as the inverse of how directly anattribute denotes particular objects orevents, and is equated with thespecificity–generality of terms and thesubordination–superordination of categorylabels.13,14 Similarly, more abstract orsuperordinate category distinctionsencompass larger, more general productgroupings.

Consequence (C): many articlesemphasised only product attributes15,16 orpersonal values;17,18 only a few mentionconsequence variables. Consequence iswhat a consumer feels after consumption.The feeling can be positive or negative.That is, whether the post-consumptionfeeling can satisfy the consumers’ desiresthat will affect consumer’s willingness torepurchase the same product. Severalresearchers have used quality as areplacement of consequence,19,20 becausequality can be thought of as a measureof a product upon consumption. In otherwords, quality can function as aconsumption consequence.21,22

Value (V): several customer-orientedresearches indicated that the process bywhich consumers satisfy their valueexpectations could be used to buildconsumers’ consumption and experiencemodels. Marketers can use these modelsto clarify what consumers’ recognitionwill be, where their product position canbe, and how they can developcompetitive product strategies.23,24 In fact,consumers’ cognitive structure uncoverconsumers’ values.25–27

Personal value or characteristicclassification in LOV, VALS2 and RVSare often used to develop effectivemarketing strategies. The following are

attribute, consequence, or value isdifficult, especially when the MECmethodology is applied to developingintangible product strategies.

A new technique that integrates MECsand statistical methods such as factoranalysis and linear regression analysis cansolve these limitations of the traditionalMEC methodology. Such a methodallows the marketer to identify therelative importance of different productattributes in achieving the valuesassociated with consumer satisfaction.This expanded MEC methodologyprovides marketers with the relevantproduct information on a straightforwardA-C-V line for developing effectivemarketing strategies. The purpose of thispaper is twofold: to provide new insightsinto the A-C-V linkages and to proposea new methodology for developing andunderstanding related product strategies.

LITERATURE REVIEW

Attribute, consequence and value

Among marketing literatures, thepredominant approach to analysingproduct–consumer relevance is MECanalysis.1–7 MECs are based on thetaxonomy of consumer productknowledge involving three keyconcepts: product attributes (A),consequences (C) and values (V ). Theunderlying idea is that productattributes are means for consumers toobtain desired ends, namely valuesthrough the consequences of thoseattributes.8,9 In MEC theory, the keyconcepts are linked hierarchically incognitive structures; that is, a product’sattributes yield particular consequencesupon product consumption.

Attribute (A): product attributes can beregarded as varying along a continuumfrom the concrete to the abstract.

340 Journal of Targeting, Measurement and Analysis for Marketing Vol. 10, 4, 339–352 � Henry Stewart Publications 0967-3237 (2002)

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‘preferred end states of being’, such ashappiness or freedom.34 Prakash35 furtherdiscussed women’s segmentation by thevalue structure based mainly on theresearches of Bartos,36,37 Rokeach38 andColeman.39

Improvements in MEC analysis

New techniques for improving traditionalMEC analysis and developing usefulmarketing strategies are presented asfollows:

— APT technique: association patterntechnique (APT) is a data collectionprocedure for compelling means–endchain segmentation approach.39,40 Thismethod is built on a structured datacollection approach derived from AB(attribute–benefit) and BV(benefit–value) matrices

— LVQ technique: learning vectorquantisation (LVQ)41 is a predictiveclustering technique that can beapplied to whole means–end chains,as opposed to other specificcharacteristics

— CDA technique: cognitivedifferentiation analysis (CDA)42 can beused to identify the relationship of thepairwise preference or perceptionjudgments to the attributes,consequences and values

— graph theory and correspondenceanalysis: graph theory andcorrespondence analysis proposed byValette-Florence and Rapacchi43 allowmarketers to comprehend howconsumers translate the attributes ofproducts into meaningful associationswith respect to self-defining attitudesand values

— HVCM technique: an integratedmethodology based on cluster analysisand means–end chain method can beused to derive the hierarchical valuecluster map (HVCM), which can

the detailed descriptions of the threemodels.

Researchers28 used a scale developedby Kahle and Kennedy29 called ‘List ofValues’ (LOV) to discuss marketers’norms. Results from canonical correlationanalysis generally indicated thatmarketers’ norms could be partlyexplained by personal values. Anintegrated methodology to identifysegments in international markets basedon consumer means–end chains wasdeveloped by Hofstede et al.30 Themethodology also utilised LOV toanalyse consumers’ value systems. Thevalues used in the association patterntechnique (APT) are those from theLOV inventory,31 that is, severalresearchers think nine ‘value’ items inLOV are suitable as variables of A-C-Vlinkages.

Several research firms have developedlifestyle classification systems. The mostwidely used is SRI Consulting’s Valuesand Lifestyles (VALS2) typology.32,33

VALS2 is a psychographic system thatlinks demographics and purchase patternswith psychological attitudes. SRI, aconsulting company in USA, uses VALS2technique to differentiate eight types ofAmerican consumers, each representing aspecific market segment with adistinctive behaviour and emotionalmake-up.

Marketers need to identifysegmentation variables based ondemographics, lifestyles and values.Personal values can be an important basisfor segmentation, differing according toage, income, education, gender and socialclass. A popular methodology, RokeachValue Survey (RVS) consists of 18instrumental values and 18 terminalvalues. Instrumental values are thecognitive representations of ‘preferredmodes of conduct or behavior’, such asindependence or courage. Terminalvalues, on the other hand, represent


Attribute-consequence-value linkages

data to understand all A, C and Vvariables of CVS. The survey resultswere summarised into 48 service items(attribute variables Ai; i � 1. . .48), 36consequence variables (Cj; j � 1. . .36)and 22 value variables (Vk; k � 1. . .22).Using the total 106 A-C-V variables asthe base for the structural questionnairewith a Likert scale (scale: 1–7), 300 validsamples of data were gathered. The ratioof interviews was based on the ratio ofstores of the top five CVS, located in thecity, town and countryside. The top fiveCVS were 7-Eleven (2,248 stores),Family Mart (796 stores), Hi-Life (610stores), OK (465 stores) and SJ Express(310 stores), which account for 80 percent of total Taiwan CVS stores.

Factor analysis

Factor analysis in this research wasconducted using the Varimax Method.Forty-eight attribute variables wereclassified into eight attribute factors (AFm;m � 1. . .8) shown in Appendix 1, wherethe cutoff value of eigenvalue wasgreater than 1. The cumulativepercentage of variance for attribute factoranalysis was 68.7 per cent. Theindividual contributions of each factorare indicated in Appendix 1.

Linear relations of A-C-Vs

Multiple regression analysis is a generalstatistical technique used to analyse therelationship between a single dependentvariable and several independentvariables.47 In this study, eachconsequence variable was a dependentvariable and the attribute variables wereregarded as independent variables.Multiple regression analysis was appliedto analyse the consequence–attributerelations. The same process was used toidentify the value–consequence relations.The linear relation was considered

handle and develop strategies forpricing, product differentiation,advertising and market segmentation44

— other techniques: researchers45 usedthe concepts of statistical graphics,graphical perception theory andgraphics semiology to improve thedesign of HVM so the original dataare presented more clearly. Moreover,Lin and Fu46 utilised the mathematicalmodel on deductive technique andflow chart to demonstrate how MEC’slogic construction can be applied tocomputer programs for developinguseful marketing strategies.

METHODOLOGY

Data collection

In this study, data were collected by twoapproaches: first by conducting openone-on-one consumer interviews andsecondly interviewing respondents usinga structured questionnaire. The researcherinvestigated the top five conveniencestore (CVS) chains in Taiwan that offeredvarious patterns of service and identified39 service items. Based on those 39service items, the researcher collected100 valid samples of data by in-depthopen interviews. The sample data arebased on five questions designed todetermine the following:

— what CVS services did you preferduring your purchase

— why did you prefer these services— what are the consequences for you

after experiencing these services— what personal values did you satisfy

after experiencing these services orconsequences

— did you agree the relationshipbetween attributes, consequences andvalues?

The researcher analysed the collected


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row A01 and column ‘*’ means that tenconsequence variables have significantlinear relations with variable A01. Theseten consequence variables have significantlinear relations with 22 value variables(summing the figures in row A01) shownin row A01 and column ‘**’ of Table 1.The figures in column ‘****’ and rowA01 indicate the number of significantlinear relationships between the tenconsequence variables and their linkingvalue variables, but do not computerepeatedly those value variables. In otherwords, 15 rather than 22 value variableshave significant linear relations withvariable A01.

The number of times a single productattribute can link consequences andvalues is positively related to the strengthor weakness of consumers’ valuesatisfaction upon consumption. Forexample, linked with 11 consequencevariables ‘photo booth’ (A15) is animportant product attribute, which canattain a consumer’s positive cognition(see Table 1, column ‘*’). ‘Fun’ (C32),which linked with 17 value variables, canbe regarded as a significant means toachieving consumers’ desired ends. Whatmarketers really care about, however, ishow to design a product that satisfiesconsumers’ value demands. Of particularimportance is identifying productattributes that can yield particularconsequences upon consumption, whichcontributes an analysis to the value ofconsumer satisfaction. In this research,‘mail-order service’ (A09) and ‘photobooth’ (A15), which both linked 19value variables hierarchically (see Table 1,column ‘***’), are most importantproduct attributes to satisfy consumerwants. Therefore, marketers can use theinformation provided in Tables 1 and 2to identify the attributes that will helpthem achieve the desired consumervalues and develop the appropriateproduct strategies.

significant when the P-value (using Ttest) of beta coefficients (regressioncoefficients) of the Cj-Ai/Vk-Cj linearregression functions was less than 0.05.All significant linear relations werepositive (the negative linear relationswere eliminated) because marketers tendto focus on the positive consequences orvalue of buying and using products.48

Tables 1 and 2 show the significant linearrelations between Cj-Ai and betweenVk-Cj, respectively.

RESULTS

Analysis of A-C, C-V, A-V linkages

The number of A-C-V linkages (Tables 1and 2) was used to measure theimportance of a product’s attributes. Themore links each attribute could formwith consequence variables and eachconsequence variable could form withvalue variables, the more important theproduct attributes or consequence wereupon product consumption. The totalnumber of A-V linkages was determinedfrom each A-C and C-V linearrelationship.

In Table 1, the figures in the ‘*’column represent the total number ofAi-Cj linkages. The figures in the ‘**’column are the total number of Cj-Vk

linkages, when Ai-Cj linkages exist.Simultaneously, the figures in the ‘***’column represent the total number ofAi-Vk linkages, when A, C and Vvariables are linked hierarchically. Thefigures in the ‘**’ and ‘***’ columns inTable 1 are summarised from Tables 1and 2.

‘Xerox-copy’(A01), for example, has asignificant linear relationship with‘money-saving’(C01) (see Table 1). Thefigure ‘4’ in row A01 and column C01means that four value variables havesignificant linear relations with C01variable (see Table 2). The figure ‘10’ in



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Table 1: Profile of each AI-CJ linkage

No. C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31 C32 C33 C34 C35 C36 * ** ***

A01 4 0 1 0 2 4 1 1 3 6 10 22 15

A02 0 0 0

A03 0 6 7 3 13 10

A04 0 0 0

A05 2 5 5 3 12 11A06 0 0 0

A07 0 0 0

A08 4 0 3 4 4

A09 4 0 0 2 4 3 17 7 30 19

A10 4 5 2 9 8

A11 0 0 1 3 4 4 3A12 1 1 1 1

A13 2 1 2 2

A14 0 1 0 0

A15 0 0 0 0 0 6 1 0 4 1 17 11 29 19

A16 0 0 0

A17 4 2 0 9 5 5 20 14A18 0 1 1 1 3 5 6 6

A19 0 0 0

A20 0 0 0

A21 2 1 2 3 3

A22 0 1 0 0

A23 1 4 2 5 5A24 0 2 1 9 6 5 18 12

A25 3 1 3 3

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Notes: The figures in this Table show that the row attribute variable has a significant linear relationship withthe column consequence variable and the total number of consequence-value linkage (see Table 2).*: Total number of Ai-Cj linkages;**: Total number of Cj-Vk linkages when Ai-Cj linkages exist.***: Total number of Ai-Vk linkages, when A, C, and V variables are linked hierarchically.A: Attribute variables; C: Consequence variables; V: Value variables.

A24 0 2 1 9 6 5 18 12

A25 3 1 3 3

A26 4 1 4 4

A27 0 0 0 1 1 0 6 2 2

A28 0 0 0

A29 0 1 0 0A30 0 0 0

A31 0 3 2 3 3

A32 0 1 0 0

A33 5 0 2 5 5

A34 0 5 0 9 1 5 15 13

A35 0 0 2 0 0A36 0 9 2 9 9

A37 5 1 5 5

A38 6 1 6 6

A39 0 1 4 0 4 5 5

A40 4 5 1 0 1 5 11 7

A41 2 0 4 1 1 0 6 8 6A42 0 1 0 0

A43 0 2 5 3 7 6

A44 0 0 3 3 3 3

A45 0 1 0 0

A46 0 0 2 3 2 2

A47 0 5 2 5 5A48 0 0 0

* 5 3 3 8 5 4 5 5 2 3 4 2 4 2 4 2 3 2 1 3 2 5 3 6 4 2 5 3 4 2 1 2 2 2 2 4 119 271 216


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Table 2: Profile of each CJ-VK linkage

Note: * represents that the row consequence variable has a significant linear relationship with the column value variable.

No. V01 V02 V03 V04 V05 V06 V07 V08 V09 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 Total

C1 * * * * 4

C2 0

C3 0

C4 0

C5 2

C6 * * * * * 5

C7 0

C8 0

C9 * * 2

C10 * 1

C11 0

C12 * * * * * * * * * 9

C13 0

C14 0

C15 * * * * * * 6

C16 * 1

C17 0

C18 * 1

C19 * * * * * * * 7

C20 * * 2

C21 * * * * 4

C22 0

C23 * 1

C24 * * * * 4

C25 * 1

C26 * 1

C27 * 1

C28 * * * 3

C29 0

C30 * * * 3

C31 * * * * * * 6

C32 * * * * * * * * * * * * * * * * * 17

C33 * * * 3

C34 * * * * * * * * * 9

C35 * * * * * 5

C36 * * * * * 5

Total 3 1 5 5 4 8 6 3 5 4 6 5 4 4 5 8 5 6 5 4 3 4 103

* *

and terminal values as shown inAppendix 2. Table 3 is a profile of eachAF-C-V linkage derived from theinformation provided in Tables 1 and 2and the level classifications of Appendix2.

In this research, Table 3 shows thelinkages of each attribute factor withdifferent levels of consequence and valuevariables. AF3, for example, is the mostimportant attribute factor, involving 30AF-C linkages. Considering the numberof variables included in each attributefactor, AF6 has the highest average AF-Clinkages.

Comparing the different levels of C-Vlinkages in Table 4, ‘psychologicalconsequence’ and ‘instrumental value’have the highest linkage frequency. Thismeans to satisfy the consumers’ valuedemands, the marketer must satisfy theirpsychological consequence desires.Consequently, understanding whichproduct attributes (service items) can

Analysis of AF-C-V linkages

The purpose of factor analysis is to groupsimilar characteristics of attributevariables. The factor analysis through theVarimax Method classified 48 attributevariables into the eight attribute factors(AFs) listed in Appendix 1. In themarketing literature, two broad levels ofproduct consequences after product usewere distinguished as functional andpsychosocial.49 Functional consequencesare tangible outcomes resulting fromproduct use and more directlyexperienced by consumers. In contrast,psychological consequences of productuse are less tangible and moreindividualised outcomes, such as how theproduct makes the consumer feel. Basedon functional and psychologicalclassifications, 36 consequence variableswere divided into the two levels shownin Appendix 2. Following the Rokeach50

classifications, this study classified 22value variables into instrumental values



Table 3: Profile of each AFM-C-V linkage

Attributefactor(number ofattributevariables)

AF-C(averagelinkages)

AF-C-V(averagelinkages) AF-Cf-Vi AF-Cp-Vi AF-Cf-Vt AF-Cp-Vt

AF1(8)AF2(11)AF3(11)AF4(5)AF5(5)AF6(2)AF7(3)AF8(3)

17(2.1)21(1.9)30(2.7)13(2.6)10(2.0)10(5.0)12(4.0)6(2.0)

39(4.9)46(4.2)65(5.9)15(3.0)31(6.2)22(11.0)36(12.0)17(5.7)

85229020

7184387

13209

2010008450

4132057598

Notes: Cf: Functional Consequence; Cp: Psychosocial/Social Consequence; Vi: Instrumental Value;Vt: Terminal Value

Table 4: Profile of C-V linkages based on level categories

Value levelsConsequence levels Instrumental value Terminal value

Functional consequencePsychological consequence

1049

1331

traditional MEC methodology and thenew integrated methodology proposed inthis study. These two methodologiesbegin by collecting a particular productattribute and end by formulatingmarketing strategies. The major differencebetween these two methodologies is theway data are collected. The traditionalMEC methodology uses in-depthinterviewing to collect data. In this study,the purpose of in-depth interviewing wasto identify the differences between theattribute variables of secondary data andthe attribute preference of consumers inorder to determine the base variables fordesigning a structural questionnaire. Thetraditional MEC analysis is a qualitativeanalysis; the new integrated analysis is aquantitative analysis. Quantitative analysiscan avoid the possible response biascreated by unknown variables duringinterviewer and interviewee interaction.Furthermore, quantitative analysis can beused in various analyses, contributing tothe understanding of consumers’ productknowledge.

achieve consumer’s psychologicalconsequence desire is important. Theresearcher further analysed the linkages ofAF with different levels of C and Vvariables in Table 3. The results showthat AF3–psychologicalconsequence–instrumental value has thehighest linkage frequency. Thus, uponconsumption, the product’s attributefactors yield to psychological level ofconsequences, contributing to theinstrumental value of consumersatisfaction.

Comparison of traditional MECanalysis and the new integratedmethodology

In this study, a new integratedmethodology, based on MEC analysis andthe linear regression analysis, wasproposed to enhance traditional MECmethodology and give marketers a betterunderstanding regarding the relativeimportance of product attributes. Figure 1illustrates the frameworks of the


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Figure 1 The comparison of traditional MEC analysis and A-C-V linear regression analysis

C on tent analysis(d eciding the v alidattrib ute variab les)

D eciding A , C an dV variab les

C ollecting allre lated a ttribu te

variables

In terview in gco nsu m ers b ased

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2). That is, the CVS must adhere to the‘photo booth’ product differentiationstrategy. Again, in the A-V linkage chart,A15 also has the highest A-V linkagefrequency, meaning CVS can achieveconsumers’ value demands through the‘photo booth’ product differentiationstrategy.

Product-mix strategies

In Table 3, AF3-Cp-Vi linkage has thehighest frequency (43 times). Thus, if thetargeted consumers prefer psychologicalconsequences and instrumental values,the product-mix strategy in CVS mustfocus on the service items in AF3.

Other marketing strategy

CVS member club is a principal approachto increasing consumer brand loyalty.CVS can offer club members pricediscounts or free services. In Figure 2, forexample, CVS can provide club membershalf price for using ‘Xerox-copy’ (A01) or‘photo booth’ (A15) and no extra chargefor ‘mail-order service’ (A09) to achieveconsumers’ value satisfaction and increasebrand loyalty.

Based on the information provided inTable 3, marketers can formulateproduct–attribute (service items) mix ofthe CVS service items and identify thedifferent levels of consumers’ valuedemands upon consumption to attainconsumers’ satisfaction and furtherstrengthen the CVS competitiveadvantages.

IMPLICATIONS

Marketing strategy for CVS

Product differentiation strategy

Figure 2 is derived from the dataprovided in Table 1, which includes A-Cand A-V linkage charts. In the A-Clinkage chart, A15 has the highest A-Clinkage frequency. Providing a ‘photobooth’ (A15) machine is positivelyassociated with ‘fun’ (C32) (seeA15–C32 linkage in Table 1). Thismeans that ‘photo booth’ yields ‘fun’consequence upon consumption,contributing to 17 values of consumersatisfaction (see C32–Vi linkage in Table



Figure 2 The A-C and A-V linkage charts

0

2

4

6

8

10

12

A 01 A 03 A 05 A 07 A 09 A 11 A 13 A 15 A 17 A 19 A 21 A 23 A 25 A 27 A 29 A 31 A 33 A 35 A 37 A 39 A 41 A 43 A 45 A 47

A -C lin k age ch art: To tal num ber of tim es of each A i-C j linkage

0

4

8

12

16

20

24A -V lin k age ch art : To tal num ber of tim es of each A i-V k linkage, w hen A , C , and V variab les are linked

hierarchically

CVS or service industry, marketers cansimply follow the technique proposed inthis paper so that their service items attainthe highest positive A-C-V linkage timesand achieve consumers’ value satisfaction.

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‘Understanding consumers’ cognitive structures:Implications for advertising strategy’, in Percy, L. andWoodside, A. (eds) ‘Advertising and consumerpsychology’, Lexington Books, Lexington, MA.

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Applications of A-C-V linkage

The methodologies based on traditionalMEC methodology such as APT, LVQand CDA all exhibit a certain degree ofresearcher or interviewer subjectivity.Adopting a structural questionnaire forconsumer responses and applying statisticanalysis can eliminate the issue ofresponse bias. The researcher used linearregression to test the linear relations ofA/C/V variables, utilised A-C-V linkagesto develop effective marketing strategiesand give marketers a useful tool forunderstanding their consumers’ productknowledge.

A-C-V linkages indicate theconsumers’ product perceptions.Applying the perception analysis to CVSservice items and using factor analysis togroup those service items was effective indeveloping an appropriate product-mix.The method can help the serviceindustry develop intangible product-mixstrategies. Besides understanding whyintangible product-mix can satisfyconsumer’s value demands, marketers canutilise the analysis of A-C-V linkages toinvestigate the characteristics of targetmarkets such as demographic andgeographic variables.51 With thisinformation marketers can analyseindividual consumer’s value demand todevelop or improve CVS service itemsand strengthen competitive advantage.

Each product attribute or productattribute factor can satisfy differentconsumer consequence or value. Table 1shows that each product attribute yieldspositive consequences upon productconsumption though the linkagefrequencies are different. Simultaneously,consequence variables and value variableswere divided into two levels in Table 3.Through grouping, the number of timesthat the attribute factor linked with thatof consequences and values was different.In other words, considering thecharacteristics of target customers for the


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America and the value basis of geographicsegmentation’, Journal of Marketing, Vol. 50, pp.37–47.

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Appendix 1: Profile of each attribute factor

Attributefactors

Percent ofvariance % Attributes

AF1 19.8 (A26) Tuition remittance service, (A28) Repairs, (A29) Delivery, (A32) Valetparking, (A34) Helping customers make hospital reservations, (A35) Certificate,(A36) Ads aid, (A40) Deposited goods

AF2 11.7 (A17) Forward registered mail to post office, (A19) Remittance for various finepayments, (A20) Train ticket sales agent, (A21) Remittance for phone billpayment, (A22) Paid road ticket agent, (A27) Health insurance certificatereplacement, (A30) Remittance for public utility payment, (A31) Remittance forvarious taxes, (A37) Remittance for sundry payments, (A38) Forward registeredmail to client, (A43) Uniform invoice prize money exchange

AF3 9.0 (A05) Microwave, (A07) Locker area, (A10) Wrappings, (A11) Providing vendingmachine, (A12) Movie ticket sales agent, (A13) Music concert ticket agent, (A14)TV wall, (A15) Photo booth, (A39) Entertainment equipment, (A44) Flowerdelivery, (A48) Film processing

AF4 6.9 (A41) Handicapped friendly facility, (A42) Parking place, (A45) Rest area, (A46)Baby-sitting service, (A47) Waterworks

AF5 6.6 (A06) On-sale, (A23) Recycle, (A24) Transportation information, (A25) Motorcycleinsurance, (A33) Money-exchange

AF6 6.4 (A01) Xerox-copy, (A02) FaxAF7 5.0 (A09) Mail-order service, (A16) Missing-elder-line, (A18) Internet informationAF8 3.3 (A03) Restroom facility, (A04) Reading area, (A08) ATM

Appendix 2: Profile of each consequence/value variable based on level categories

Consequencevariables

Functional (C01) Money-saving, (C02) Easy to acquire information, (C03)Convenient store chains, (C04) Use before payment, (C05)Environmental protection, (C06) Without going out, (C07) Short distance,(C08) Air-conditioning, (C09) Bulk shopping discount, (C10)Handicapped shopping aisle, (C11) Providing life-show activity, (C12)Information available

Psychosocial/social

(C13) Handiness, (C14) Time-saving, (C15) Effectiveness, (C16)Cleanness, (C17) Comfort, (C18) Decoration, (C19) Energy-saving, (C20)Killing-time, (C21) Feeling-good, (C22) No troubles in shopping, (C23)Amusement, (C24) Usefulness, (C25) Effortlessness, (C26) Incidentalvisit, (C27) Easy to get a product or service, (C28) Pleasant, (C29)Knowledge, (C30) Warmth, (C31) Nice environment, (C32) Fun, (C33)Knowledge available, (C34) Anxiety-free experience, (C35) Tradeopportunity, (C36) Privacy

Valuevariables

Instrumental (V01) Safety, (V02) Relaxation, (V03) Energy, (V04) Feeling-great, (V05)Elegance, (V06) Activity, (V07) Helpfulness, (V08) Creativeness, (V09)Trustworthiness, (V10) Ease, (V11) High spirit, (V12) Excitements of life,(V13) Healthy

Terminal (V14) Happiness, (V15) Satisfaction, (V16) Insurance, (V17) Humanlongevity, (V18) Easygoing life, (V19) Wonder, (V20) Achievement, (V21)Healthiness, (V22) Enriched life

Documents

Attribute-consequence-value linkages: A new technique for ... · — selecting and grouping A, C, V ... the base for the structural questionnaire ... Family Mart (796 stores), Hi-Life