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The use of consumer'sprice informationsearch behaviour forpricing differentiation inretailingCarmen Berne , Jose M. Mugica , MartaPedraja & Pilar RiveraPublished online: 15 Apr 2011.

To cite this article: Carmen Berne , Jose M. Mugica , Marta Pedraja& Pilar Rivera (1999) The use of consumer's price information searchbehaviour for pricing differentiation in retailing, The InternationalReview of Retail, Distribution and Consumer Research, 9:2, 127-146, DOI:10.1080/095939699342606

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The use of consumer’s priceinformation search behaviourfor pricing differentiation inretailing

Carmen Berne, Jose M. Mugica, Marta Pedraja andPilar Rivera

Abstract

The purpose of this paper is to analyse the potential of price-information-seekingbehaviour (PISB) as a basis for pricing differentiation in the retail grocery market.Based on a model developed by Urbany et al. (1996), we analyse the presence ofconsumer clusters showing differences in their PISB and the possibility of identifyinga particular pro� le for each of the clusters. The cluster analysis identi� ed two clearlyseparated clusters: high and low intensity price-information seekers. Based on thisclustering, a discriminant analysis revealed that some of the independent variablesproved to be good descriptors of the consumer pro� le of the clusters. The jointconsideration of the clusters and their pro� les allows us to provide some managerialimplications for retailers.

Keywords

Retail, pricing, prices, search, segmentation

Introduction

Driven by pro� t-optimization goals, retail companies frequently adopt price-discrimination decisions across their stores; this interstore price dispersion

Carmen Berne, Business Department, Universidad de Zaragoza, C/Pedro Cerbuna 12,50009 Zaragoza, Spain (e-mail: cberne@posta,unizar.es).

Jose M. Mugica, Professor of Marketing, Departamento de Gestion de Empresas,Universidad Publica de Navarra, 31006 Pamplona, Spain (tel: (34) 4816 9400; fax: (34)4816 9404; e-mail: [email protected]).

Marta Pedraja, Business Department, Universidad de Zaragoza, C/Pedro Cerbuna 12,50009 Zaragoza, Spain.

Pilar Rivera, Business Department, Universidad de Zaragoza, C/Pedro Cerbuna 12,50009 Zaragoza, Spain.

Copyright © Routledge 1999 0959–3969

The International Review of Retail, Distribution and Consumer Research 9:2 April 1999 127–146D

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usually re� ects differences among the demand functions of the customer bases(Hoch et al. 1995). Differences in the price elasticity then become a primarycornerstone of their pricing decisions. However, pricing is a more complexdecision; retailers need not only to � x a certain price but also to communicate itto their target customers. The mix of price � xing and communication results invaried policies, such as hi-lo pricing or every-day-low-prices. As shown in arecently published work, price discrimination may be also made effectivethrough the use of merchandising (Dhar and Hoch 1996).

One alternative in differential pricing is to segment the customer baseaccording to their information-seeking behaviour about prices. The informationthat consumers have about retail prices will have a certain impact on the majorbehavioural dimensions which retailers try to in� uence: store selection (where),product/brand choices (what), quantity (how much) and timing (when). Then, ifconsumers in a particular market show different patterns of price-information-seeking behaviour (PISB), retailers may guide their pricing decisions, whetherby focusing on a certain segment or by serving the market with two or moredifferentiated pricing policies.

The retail grocery market is an interesting ground to analyse differences inPISB patterns. Consumers supposedly utilize a very limited amount of informa-tion in their grocery shopping process. A � rst explanation for this lack of searchhas been provided by economists (Stigler 1961), who maintain that consumersfollow cost-bene� t criteria in deciding the amount of information to search for.As the bene� t potential of information search is limited primarily by the pricelevel, low-price products such as non-durables would require less search forinformation. But, from a marketing perspective, the acquisition of informationprior to the purchase is a more complex problem: it is not just the amount ofsearch but, also, the information sources utilized, the heuristics developed tomanage overload information problems and the inferences made under in-complete information.

As noted by Urbany et al. (1991), consumers may have good reasons forvarying their propensity to search for prices in different retail stores, but thediscipline of marketing has experienced some dif� culty in both understandingand explaining the most elemental characteristics of consumer search in marketsin which price search is an ongoing activity. In general, there has been anoverestimate of the proportion of consumers who actively search for prices andrespond to promotions. This observation may be due to the fact that there are nostudies which adequately explain PISB in, for example, day-to-day shoppingproduct categories, since the tendency has been to examine relatively small setsof predictors and to use dependent measures that do not directly re� ect thesearch for price information (Lichtenstein et al. 1993).

The purpose of this paper is to analyse the potential of PISB as a basis forpricing differentiation in the retail grocery market. To achieve this objective wewill adapt the model used by Urbany et al. (1996) to explore a very similar setof activities included in price-information seeking. Based on this model, we willanalyse the presence of consumer clusters showing differences in their PISB andthe possibility of identifying a particular pro� le for each of the clusters. Finally,the discussion of the results will lead us to provide some managerial implicationsfor pricing differentiation in the retail grocery market.

128 The International Review of Retail, Distribution and Consumer ResearchD

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Price-information-seeking behaviour

Generally, the information which consumers need in order to assess thealternatives that are available in the market can be obtained at two levels, namelyinternal and external search (Beales et al. 1981). The former involves the initialsearch being carried out by the individual, often subconsciously, drawing on pastexperiences and learning structures. If the result of this search is not suf� cientfor decision-making purposes, then the consumer moves onto the second level,making use of external information sources. This external information-seekingdecision will be based on how the individual envisages the return to be obtainedas opposed to the costs (monetary and non-monetary) to be borne in his/hersearch.

Some studies have focused on the antecedents and factors explaining theeffort and time devoted to the activities involved in search (Kleimenhagen1966–7; Newman and Staelin 1971; Kiel and Layton 1981; McLelland andTurner 1983). Other studies have analysed a great variety of aspects of externalinformation seeking. To name a few: the number of establishments visited(Newman and Lockeman 1975; Claxton et al. 1974; Midgley 1983); the numberof brands assessed (Dommermuth 1965; Furse et al. 1984); the informationresources used (Newman and Staelin 1972), including advertisements (Bucklin1965; Udell 1966; Thorelli 1971; Kiel and Layton 1981) and word-of-mouth(Udell 1966; Thorelli 1971; Kiel and Layton 1981; Mugica and Yague 1993); orthe individual’s belief on how a particular market behaves (Duncan andOlshavsky 1982). Very recently, Grewal et al. (1998) have provided an additionalperspective by exploring this issue in terms of behavioural intentions rather thanpast or present behaviour.

To assess the relative importance of these activities in PISB for non-durables,Table 1 provides the self-reported measures found in a survey for the differentinformation sources used by consumers. About half of the respondents reportedno use of external information sources, showing a predictable pattern for thistype of low-price item. But more interesting for our research purposes is that,among the different external sources used, consumers relied more on thosemediated by retailers. Actually, more than half of the sample said that theylooked for price information in mailings from retailers and in the different in-store information devices. In this direction, the ability of retailers to in� uenceprice perception by customers is con� rmed and the PISB becomes then apotential criterion for effective price differentiation.

The model and the hypotheses

The model

The model applied in this empirical study is an extension of the one used byUrbany et al. (1996), to analyse price search in the retail grocery market. Themodel speci� es that the overall behaviour concerned in seeking price information

Berne et al.: The use of consumer’s price information search behaviour 129D

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is explained by � ve different sets of factors:

PISB 5 f ((Economic Returns: 1 PPD, 1 BC); (Search Costs: 2 TIME, 2 MOB,2 DIFF, 2 YC); (Human Capital: 1 K, 2 IS, 1 TM); (Demographics:1 AGE, 1 EDU); (Psychosocial Returns: 1 MM, 1 SE))

where,

Economic Returns : PPD 5 perceived pricedispersion;

BC 5 budget constraints

Search Costs : TIME 5 time constraints; MOB 5 mobilityconstraints;

DIFF 5 dif�culty of storecomparison

YC 5 presence of youngchildren

Human Capital : K 5 market knowledge; IS 5 investment search;TM 5 time management skills

Demographics: AGE 5 age; EDU 5 educationPsychosocial Returns: MM 5 market maven

motivation;SE 5 shopping enjoyment

Moreover, Urbany et al. distinguish two large components of the PISB: theactivities involved in price comparisons and those targeting the search for pricespecials.

PISB: Price-Comparison-Seeking Behaviour (PCSB) 1Price-Specials-Seeking Behaviour (PSSB)

Table 1 Self-reported measures of the use of information sources

No Yes Totaln % n % n

1 Use external sources Detergent 186 (42) 259 (58) 445Milk 226 (51) 217 (49) 443

2 Type of source:2.1 Interpersonal sources Detergent 288 (65) 158 (35) 446

Milk 310 (70) 133 (30) 443

2.2 Neutral sources Detergent 396 (89) 50 (11) 446Milk 389 (88) 54 (12) 443

2.3 Retailer sourcesRetailer mailing Detergent 204 (46) 242 (54) 446

Milk 243 (55) 200 (45) 443

Media ads Detergent 305 (68) 141 (32) 446Milk 335 (76) 108 (24) 443

In-store Detergent 182 (41) 264 (59) 446Milk 224 (51) 219 (49) 443

3 Visits to stores Detergent 270 (61) 176 (39) 446Milk 301 (68) 142 (32) 443

Source: adapted from Mugica and Yague (1993).


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As far as the components of the model were concerned, i.e. the collection ofitems included in the dependent variable and in the different sets of factors, ourstudy included only some minor modi� cations required to adapt the ques-tionnaire to the Spanish grocery market (Table 2). The inclusion of thesemodi� cations does not affect the initial premises of the model concerning theoverall price-information-seeking behaviour. These minor modi� cations mightbe summarized as follows; � rst, given the importance of specialized foodstores(butchers, � sh and sea-food, and frozen products), three more items wereincluded in the PCSB to re� ect price comparisons at these types of stores.Second, the PSSB was slightly extended to cover not only price specials butprices in general and, by adding another information source, price enquiry in thestore.

The second set of activities concern information sources mostly under thecontrol of retailers. When consumers rely on these it is because they areconceding them some credibility or because they cannot afford to get involved inmore costly price-search activities. If the pro� les of consumers using one oranother type of PISB more intensively are different, then there would be a goodground for effective price differentiation policy between segments.

Model hypotheses

The model is based on the premises of some basic hypotheses which relate PISBto � ve kinds of potentially determinant factors: economic returns, search costs,human capital, demographic characteristics and psychosocial returns (Figure 1).The role of each of these � ve factors is discussed in the following text includingthe hypotheses describing the expected relationships included in Figure 1.

Economic Returns Within the purchasing process, and considering the pricevariable, one of the factors faced by consumers is that a particular product mightbe sold at different prices, depending on the point of sale,1 the time and theterms of payment. In fact, price dispersion across stores and along time is theorigin of most of the activities included in PISB. Economic returns derivingfrom PISB are identi� ed with the acquisition of the product at a lower price, soit is expected that consumers will increase their search for prices whenperceiving larger ranges in the distribution of prices (Urbany 1986; Bucklin1969). But, also, PISB is a function of the impact that the cost of a product mayhave on the budget of one individual or household (Stigler 1961). That is, theeconomic returns have to be measured in relative terms, framed within thebudget constraints of the consumer.

H1: The greater the economic returns perceived in purchasing by aconsumer, the greater the probability of being an intensive price-information seeker.

H1.1: The greater the price dispersion perceived among stores, the greaterthe probability of being an intensive price information–seeker.

H1.2: The greater the budgetary constraints, the greater the probability ofbeing an intensive price-information seeker.


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Table 2 Independent and dependent variable

Factor Subfactor Items

Economicreturns

Perceived pricedispersion

c A cart full of the same groceries bought fromeach of my local grocery stores will cost aboutthe same (PPD1)

c Some grocery stores in Zaragoza have a lot lowerprices than others (PPD2)

c The price of meats and produce varies a lotbetween Zaragoza grocery stores (PPD3)

c The price of individual items often varies a lotbetween stores (PRICEDIF)

Perceivedbudgetconstraints

c I frequently have problems making ends meet(PBC1)

c My budgeting is always tight (BUDGET)c I often have to spend more money than I have

available (PBC2)Income perfamily

c Intervals of income (IPF)

Search costs Perceived timeconstraints

c I am time poor, I never have enough time(PTC1)

c Everything I do is rushed (TIME)c Hours per week paid employment (PTC2)c Number of children less than 8 years of age

(CHILDREN)Perceiveddif� culty ofstorecomparison

c It is very dif� cult to compare the prices ofgrocery stores (PDSC)

c It is very dif� cult to compare the quality ofproducts between grocery stores (DIFFICULTY)

Mobilityconstraints

c I have a lot of energy to do things (MC1)c My health restricts my activities (HEALTH)c I do have reliable transportation to get out and

about (MOBILITY)c To have to use transportation to shopping is not

a problem for me (MC2)

Human capital Marketknowledge

c I know a lot about Zaragoza grocery storesc I know which stores have the best pricesc I know which stores have the best price specialsc I know which grocery stores have the best meat

departmentc I know which grocery stores have the best � sh

departmentc I know which grocery stores have the best

greengrocery departmentInvestmentsearch

c I shop back and forth between several differentstores before choosing where I now do most ofmy grocery shopping

c I compare the prices of different stores before� nally deciding where to do most of my groceryshopping

c I made an extra effort in the beginning to learnabout different stores so as to simplify thegrocery shopping I do now


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Search costs PISB activities involve confronting a series of costs or timeconstraints, and undergoing a decision-making process based on personalperceptions of the complexity of the search activity, as well as possible physical

Table 2 Continued

Factor Subfactor Items

Perceived timemanagementskills

c I am very good at organizing and schedulingacitivities

c I am very good at time management

Demographic Age c Age rangecharacteristics Education c Levels of education

Psychosocialreturns

Market mavens c I like introducing new brands and productsc I like helping people by providing them with

information about many kinds of productsc I like it when people ask me for information

about products, places to shop or salesc I like it when someone asks me where to get the

best buy on several types of productsc I think of myself as a good source of information

for other people when it comes to new productsor sales

c I know a lot of different products, stores andsales and I like sharing this information

c I like giving people information about pricesShoppingenjoyment

c I view grocery shopping in a positive wayc I enjoy grocery shoppingc Shopping is amusingc Shopping is funnyc Shopping is a pleasure

DependentvariablesPCSB c I compare the prices of different stores (PI)

c I often compare the prices of fruit and vegetables at two or moregrocery stores (P2)

c I often PCSB the prices of meat at two or more grocery stores(P3)

c I often PCSB the prices of � sh at two or more grocery stores (P4)c I often PCSB the prices of frozen food at two or more grocery

stores (P5)c How often do you PCSB the speci� c prices of grocery stores? (P6)

INFOR c I take into account the information about prices of substituteproducts or specials showed in the store, before shopping (P7)

c Regularly read ads or � iers to PCSB prices and/or check pricespecials (P8)

c I decide to visit some stores before shopping (P9)c Decide where to shop based upon ads/� iers I receive at home

(P10)c Often talk to friends about price specials before shopping (P11)c Regularly shop for the price specials at one store and then the

price specials at another store (P12)


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constraints on the consumer. These costs and restrictions are unevenly dis-tributed and perceived among consumers, so it is expected that some of thevariance in PISB could be explained by differences in search costs. The generalhypothesis and the subhypotheses proposed are as follows:

H2: The higher the costs perceived in information seeking, the less theprobability of being an intensive price-information seeker.

H2.1: The less time available, the less the probability of being an intensiveprice-information seeker.

H2.2: The greater the perception of dif� culty in comparison, the less theprobability of being an intensive price-information seeker.

H2.3: The greater the constraint on mobility, the less the probability of beingan intensive price information seeker.

Human capital The human capital dimension refers basically to the knowledgeaccumulated by the individual over time. In this study, human capital ismeasured in the same way as in Urbany et al. (1996): a composite of (1) thedegree of present knowledge of the competing stores, (2) the previous invest-ment made in price search and (3) the time-management skills. The impact ofknowledge on price search is not clear as there are two implicit con� ictingin� uences: knowledgeable consumers are less dependent on external search butare also more motivated to seek information. However, a stronger associationwith the latter is expected, so a positive relationship is hypothesized. Previousinvestments in price search are expected to have a negative impact as consumerswith an a priori knowledge of prices and their distribution tend to limit theirfuture searching, on the supposition that prices are correlated over time (Stigler1961). Finally, consumers self-reporting better time-management skills will bemore determined to get involved in price-information seeking as they are morecon� dent of getting positive results.

H3.1: The greater the knowledge of local stores, the greater the probability ofbeing an intensive price-information seeker.

H3.2: The greater the previous investment in search, the less the probabilityof being an intensive price information seeker.

H3.3: The greater the time-management skills, the greater the probability ofbeing an intensive price-information seeker.

Demographics The determining role of demographic characteristics is usuallyexplored in consumer behaviour since they are an excellent basis for effectivedifferentiation. However, very often their estimated effects in consumer behav-iour models are not signi� cant or are contradictory. Demographics, such as age,sex or education, include many implications operating in opposite directions.The role of these variables when modelling price-information seeking is not anexception: older consumers are more experienced and would need less externalprice search but they also have fewer competing activities; educated consumersmight be more skilful and ef� cient in information seeking, resulting in areduction of the search effort intensity, but they also have the potential of being


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more search extensive using a wider variety of information sources. As noted byUrbany et al. (1996), the impact of demographics on price-search activities isuncertain, but they observed signi� cant correlation of age and education withother independent variables. In a further analysis of the age variable they foundthat older consumers reported more extensive price search. In this direction, ourinitial premise is that both age and education have a positive effect.

H4: The probability of being an intensive price-information seeker ishigher for older and more educated consumers.

Psychosocial returns Psychosocial returns are the enjoyment and self-esteembene� ts associated respectively with the activities and outcomes of the price-information-seeking effort. In this context they are associated, � rst, with howmuch enjoyment consumers get when seeking for price information. Thisattitude, which may be found in those consumers who enjoy shopping in general,re� ects an incremental utility that is primarily associated with the possibility of� nding lower prices.

H5.1: The greater the self-reported enjoyment when shopping, the greaterthe probability of being an intensive price-information seeker.

Second, some consumers might take some interest in taking a leading role in thegathering of marketplace information and sharing it with others, with theintention of proving their worth as experts on the subject among their circle offriends or acquaintances, a concept which has come to be known as being a‘market maven’.2 This behaviour will in� uence price search, especially throughits importance as an information transmission mechanism in the retail grocerymarket (Urbany et al. 1996).

H5.2: The greater the self-reported market mavenism, the greater the proba-bility of being an intensive price-information seeker.

Research method

Data-gathering procedure

A survey was conducted in 1997 in a large town in the northeast of Spain(Zaragoza), an urban setting with a large supply of alternatives for groceryshopping. After a pretest, it was decided to use the personal interview mode forthe gathering of information, using a questionnaire based on that employed byUrbany et al. (1996). The respondents were the primary grocery shoppers intheir respective households. A geographic criterion was used in the allocation ofthe sample so as to preserve the distribution of the population across thedifferent districts. In the � eldwork 231 grocery shoppers were interviewed,although only 196 of the questionnaires were � nally considered valid.

Characteristics of the sample

Given the requirement of being the main grocery shopper in the household,most of respondents in the sample were women (85.3 per cent). Since sex


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differentiation was deemed to be non-existent, this variable has not beenincluded in the determinants of PISB in this study. Other descriptive elementsof the sample are: 50 per cent of the sample was aged between 36 and 50; 60.5per cent self-reported a low–medium or medium gross annual income. Most of

Figure 1 Model of the determinants of price-information-seeking behaviour


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the respondents (90.4 per cent) had some degree of education, with an evendistribution between those with primary, secondary and further education (31per cent, 28 per cent and 31 per cent respectively).

Measures

As described in Table 2, according to the previous discussion, the measurementof the dependent variable (PISB) is a composite of twelve items about the price-information-seeking activities in which consumers may eventually engage. Thetotal information-seeking effort has been separated into two components. The� rst one, PCSB (6 items: P1 through P6), is equivalent to the one used inUrbany et al. and re� ects the consumer’s tendency to make effective pricecomparisons across stores. These initiatives by the consumer in acquiringmarket information through price comparison across stores imply high degreesof involvement and physical effort. The second, PSSB (6 items: P7 throughP12) is a different concept as it includes the rest of information sources availableto consumers.3 It is important to note that most of the activities included inPSSB are related to information sources which require less physical effort andtime than the activities included in PCSB. In order to check reliability, the alphacoef� cients for the two measurements of the price-search dependent variablewere calculated, which gave values of 0.82 for PCSB and 0.74 forPSSB.

The measurement of the independent variables involved the self-reportedanswers about forty-three statements (Table 2) using a Likert-type scale with 5points, ranging from ‘strongly disagree’ to ‘strongly agree’. The forty-threeitems make up the � ve initial factors of the mode: eight items for economicreturns, ten for search costs, eleven for human capital, two for demographics andtwelve for psychosocial returns.

However, this initial structure of the independent variables was altered after afactor analysis was made. For our model, in which some of the variables were acomposite measurement of different items, we calculated the alpha coef� cientsfor those with more than two items, and the correlation coef� cients for thosewith only two items (Table 3).4 The calculated coef� cients, or correlation, for thevariables economic returns (price dispersion, budgetary constraints and house-hold income) and search costs (time constraints, dif� culty of comparison andmobility constraints) are not shown in Table 3 as they did not reach acceptablevalues.

To uncover whether there was indeed a common structure between the itemsforming the variables, both dependent and independent, an exploratory factoranalysis was carried out. It was noticeable that the independent variable ‘humancapital: market knowledge’, which was originally unidimensional, gave rise to atwo-factor structure5 which held 80 per cent of the variance. The � rst factor(HCMK1), was formed by the items which indicated an ‘overall knowledge ofstore prices’ and the second factor (HCMK2) by those which measured theknowledge of prices in ‘special sections in the stores (� sh department, green-grocery and meat department)’.


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The � nal structure of the independent variables is:

Economic returns (perceived price dispersion) PPD1PPD2PPD3PRICEDIF

Economic returns (perceived budget constraints) PBC1BUDGETPBC2

Economic returns (income per family) IPFSearch costs (perceived time constraints) PTC1

TIMEPTC2CHILDREN

Search costs (perceived dif� culty of store comparison) PDSCDIFFICULTY

Search costs (mobility constraints) MC1HEALTHMOBILITYMC2

Human capital (market knowledge) HCMK1HCMK2

Human capital (investment in information) HCSIHuman capital (time management) HCTMDemographics (Age) AGEDemographics (education) EDUPsychosocial returns (market mavens) PRMMPsychosocial returns (shopping enjoyment) PRSE

Results

To achieve the purpose of this paper, i.e. to provide some basis for theidenti� cation of market segments and suggest subsequent managerial implica-tions for price differentiation, the method should focus on analysing thediscriminatory power of the independent variables in a market segmentationbased on PISB. In this direction, we � rst applied a cluster of cases for thedependent variables, and, second, we applied a discriminant analysis to explorethe capacity of the independent variables in this way.

Table 3 Alpha coef� cients, independent variables

Variable Alpha/corr.

Human capital: market knowledge (HCMK) 0.84Human capital: investment in information (HCSI) 0.8Human capital: time management (HCTM) 0.75Psychosocial returns: market maven (PRMM) 0.91Psychosocial returns: shopping enjoyment (PRSE) 0.87


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Cluster analysis

The cluster analysis was made for three different speci� cations of the dependentvariable:

1 the total effort in price-information seeking (PISB)2 the effort in information seeking through price comparisons (PCSB)3 the effort in information seeking through other sources (PSSB).

A relevant market partition for price differentiation purposes should not resultin too many segments; pricing is a mix of prices and communication which is noteasily delivered differently to groups of consumers. In our analysis6 we restrictedthe number of clusters to two.

The results for the three alternative speci� cations of the dependent variableindicate the existence of two well-differentiated clusters in each of them: a groupof intensive price-information seekers and a group of light information seekers(Table 4). For the PISB variable, the intensuve seekers group has signi� cantlyhigher measurements for all the thirteen components of the variable. The samecircumstance is true for the two partitions of PISB: PCSB and PSSB.7

The size of each cluster and their crossed relationships are provided in Table5. For the PISB variable, the size of the heavy seekers group is forty-fourconsumers; one-third (33.3 per cent) of the consumers in the sample ascribed toany cluster might qualify as intensive price-information seekers while two-thirds(66.6 per cent) show a low-intensity pro� le in price-information seeking.Observing the clusters obtained with the PCSB and PSSB criteria (Table 6), wemay get a more de� ned picture of the distribution of price-information seeking.For PCSB, the segment of intensive seekers is � fty-eight; this is the 44 per centof all consumers ascribed to any cluster. For PSSB, the segment of intenseseekers goes up to seventy-three; that is, 55 per cent of the consumers. Pricecomparison across stores is a much more time-and-energy-consuming activitythan any of those included in the PSSB measurement. Very likely, this is thereason why the cluster size of intensive seekers is larger in PSSB than inPCSB.

Discriminant analysis

The following step in our procedure is to explore the discriminant capacity ofthe independent variables of our model; that is, their in� uence in allocating oneconsumer to any of the previously identi� ed clusters. With this step we aretrying to provide a picture of the consumers in each cluster so to enable theretail companies to design an effective pricing-differentiation policy.

Using the enter method, the � ltered results of the discriminant analysis areshown in Table 7. The procedure used to select the variables was based on twocriteria: (1) the correlation between variables (variables with low correlationcoef� cients were excluded) and (2) a joint consideration of the variable coef� -cient and its correlation with the discriminant function; the variables selectedhad high or medium coef� cients and high or medium correlation with the


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Table 4 Characterization of cluster variables

ANOVA PCSB-PSSB PCSB PSSBME-L ME-2 F p-value ME-1 ME-2 F p-value ME-1 ME-2 F p-value

P1 3.35 4.66 43.43 0.000 3.26 4.47 40.48 0.000P2 3.12 3.84 17.53 0.000 3.08 3.72 15.45 0.000P3 2.58 4.27 56.89 0.000 2.45 4.03 54.92 0.000P4 2.10 4.27 135.26 0.000 1.84 4.09 200.41 0.000P5 2.02 4.18 142.78 0.000 1.82 3.91 154.71 0.000P6 2.32 3.59 33.62 0.000 2.08 3.59 60.77 0.000P7 3.94 4.64 14.22 0.000 3.80 4.48 15.48 0.000P8 2.16 2.86 7.14 0.008 1.88 2.81 14.49 0.000P9 2.43 3.14 8.27 0.005 1.86 3.31 51.10 0.000P10 2.60 4.09 44.78 0.000 1.90 4.07 199.68 0.000P11 1.83 2.80 20.49 0.000 1.56 2.63 29.74 0.000P12 3.61 4.66 24.41 0.000 3.07 4.68 94.42 0.000

MANOVA Value Exact F Hypoth. DF Error DF p-value Value Exact F Hypoth. DF Error DF p-value Value Exact F Hypoth. DF Error DF p-value

Test Wilks 0.33 20.45 12 119 0.000 0.30 49.74 6 125 0.000 0.30 48.94 6 125 0.0000

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discriminant function (variables in bold characters in Table 7 have highcoef� cients and high correlation).

The variables which discriminate jointly are not the same for the differentspeci� cations of the dependent variable. When analysing information seeking ingeneral (PISB), the pro� le of an intensive price-information seeker would be:knowledgeable consumer (market knowledge 1 investment in information) per-ceiving a wide range of price variations across stores, and with a high degree ofeducation, and, although with less intensity, they are people facing a tight budgetbut neither mobility nor time restrictions. When the analysed behaviour isintensive PSSB, the pro� le is detailed with fewer variables: knowledgeableconsumers who enjoy sharing and delivering price information (market mavens)and with young children at home. The pro� le for intensive PCSB behaviour is:knowledgeable consumer, perceiving a wide range of price variations acrossstores, with health limitations, but no mobility restrictions and a high degree ofeducation and perceiving dif� culties in differentiating the quality of products.

Most of these results are consistent with the hypotheses embedded in ourmodel about the individual relationships. However, the roles of two componentsof the human capital factor need to be discussed. First, although the self-

Table 5 Dependent variables: percentages in the clusters

PISBLow High

PCSB PSSB PCSB PSSB

Low 74 (84.1%) 55 (62.5%) 0 4 (9.1%)High 14 (15.9%) 33 (37.5%) 44 (100.0%) 40 (90.9%)Total 88 (100.0%) 88 (100.0%) 44 (100.0%) 44 (100.0%)

Table 6 Cluster crossing with PCSB and PSSB

Comparison across storesOther information sources High Low Total

High 44 (33.3%) 29 (22.0%) 73 (55.3%)Low 14 (10.6%) 45 (34.0%) 59 (44.6%)Total 58 (44.0%) 74 (56.0%) 132 (100.0%)

Table 7 Discriminant variables in price-information seeking behaviour

PISB PSSB PCSBVariable Coef� c. Variable Coef� c. Variable Coef� c.

PRICEDIF 0.55 HCMK1 0.68 PRICEDIF 0.52HCMK1 0.48 PRMM 0.40 HCMK1 0.52HCSI 0.33 HCSI 0.20 HCSI 0.49EDU 0.54 CHILDREN 0.41 EDU 0.47MOBILITY 0.38 MOBILITY 0.37BUDGET 0.33 DIFFICULTY 0.32TIME 2 0.29 HEALTH 0.26


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reported measurement of market knowledge was initially considered to have anuncertain effect on PISB, it has shown to be probably the most discriminatorycharacteristic. Second, contrary to the initial argumentation, previous invest-ment in seeking has a positive relationship with the probability of being anintensive price-information seeker. A very plausible explanation for these con-� icting � ndings lies in the nature of the retail grocery market. In these marketsinformation becomes obsolete within very short periods of time. In a simulationin which consumers had to select a neighbourhood store for shopping for abasket of grocery products, the use of previous-week price information resultedin inef� cient choices. In an eight-week period, random choices of stores resultedin a lower bill at the end of the simulation than the one resulting from the useof previous-week price information (Mugica and Deike 1991). This misleadinguse of information about past prices poses the need to update the informationconstantly. In this direction, market knowledge and investment in informationare just part of PISB, there seems to be no causality, they are not an antecedentof PISB.

These different pro� les suggest that there are some relevant differencesbetween intensive PCSB seekers and intensive PSSB seekers. Basically, con-sumers with an intensive use of in-store and at-home information sources aredriven in their information-mode choice by their maven orientation and theirtime restrictions. This result is coherent with previous � ndings in the studyabout store loyalty in the grocery market (Berne et al. 1996) in which timerestrictions were identi� ed as a major barrier to inter-store visits or to multi-store shopping.

Discussion and managerial implications

The � rst main purpose of this paper was to identify different clusters ofconsumers according to their behaviour in price-information seeking. Theempirical study allowed us to identify two clearly separated clusters for each ofthe three alternative speci� cations of the dependent variable: high and lowintensive price-information seekers. Based on this clustering, some of theindependent variables proved to be good descriptive devices allowing us to givesome hints about the consumer pro� le of the clusters.

Market knowledge and investment in information, ‘human capital’ followingUrbany et al.’s (1996) terminology, seems to have the greatest discriminantpower in ascribing consumers to the intense information seekers cluster, both forprice comparison activities and for other information sources. However, they arenot relevant discriminatory variables in the retail grocery market since, � rst, theyare among the dependent variables and, second, they are not effective indicatorsfor any differentiation policy as they do not provide external signals toretailers.

The need to establish a sound pricing policy is reinforced by the � ndings ofour cluster analysis in which only forty-� ve consumers (34 per cent of oursample) self-reported a low intensity pro� le in price-information seeking. Therest of consumers are not just concerned about prices but, more than that, they


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show some explicit initiatives in gathering information from the various sourcesin the market-place.

Retailers use a wide variety of ways to communicate their prices: in-storecommunication – promotions and all varieties of merchandising devices such asshelf-tags (regular or special) and electronic panels; local media – newspaper ads,magazines, local TV and radio; direct marketing – mail, home delivery, phone.Should retail companies use these communication means disregarding the PISBpatterns of their market target?

There follow two examples of proposals for pricing differentiation.

1 Pricing policy for intense PCSB seekers (44.0 per cent of the market). Theexternal signals of these consumers are degree of education and transportavailability, and they are associated with limited budgets for shopping forgroceries. If the store manager believes that this is the market segment servedby the retail outlet, the pricing policy should incorporate:

c competitive price � xing for meat and produce; these consumers PCSBprices across stores based on the belief that there are signi� cant differencesin these type of products. Maintaining a competitive advantage on theseprices might be a decisive factor in attracting these customers.

c the competitive price � xing, with claims such as matching the lowest pricein the market or 10 per cent below our competition, should be communi-cated according to the pro� le of the cluster. These consumers are educatedand ef� cient in their information effort; the communication should em-phasize the price advantages with subtle and far from deceptive guide-lines.

2 Pricing policy orientated to intense PSSB seekers (55.3 per cent of themarket). The only external signal of these consumers is that they will haveyoung children, something basically associated to time restrictions. If thetarget market has a relevant share of these consumers, the pricing policyshould incorporate:

c consistent price specials in children products: very likely, the cluster will bevery sensitive to prices for these products so that price � xing will have toconsider a promotion planning with the continuous presence of pricespecials for some of these products.

c communication orientated to market mavens: these consumers enjoy com-municating to other consumers their knowledge about good prices. Thecommunication from the store to these individuals should include appeal-ing arguments which they could replicate in their communication withfriends or neighbours.

c communication orientated towards establishing an identi� cation of thecustomer with the store: while knowledgeable about market prices, theseconsumers face some restrictions which will result in some behaviouralloyalty to the store. The store should include in its communication policysome relationship marketing considerations: frequency programmes orsome kind of price advantages based on loyalty.


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Finally, the authors feel that it would be of interest to extend the study invarious directions. On the one hand, it is necessary to be aware that this presentstudy has been undertaken without differentiating price dispersion types. Thus,it contains information on inter-type and intra-type differences which will be thesubject of examination in future research. In addition, other types of variablescould well be used as explanatory variables in search behaviour, such as intrinsicconsumer variety seeking or the division of the different search costs (percep-tion, prospecting and evaluation) which are borne by consumers.

Notes

1 With respect to the point of sale, price differences may be inter-type and intra-type,inter-channel and intra-channel, or inter-chain and intra-chain. Inter-type and intra-type price differences take the level of service offered as a classi� cation criterion. Forthe former, price differences arise from the different types of establishment (super-markets, hypermarkets, etc.). Intra-type differences refer to the fact that no twoestablishments of the same type are ever exactly the same: the mere siting of anestablishment is in itself a service. Inter-channel and intra-channel divisions refer toprice differences arising from the type of relationship the establishments have withtheir suppliers, which is re� ected in their purchasing costs, and the form in whichthese are passed on to the consumer. Finally, the inter-chain and intra-chain divisionis due to the differences existing in the � rms’ marketing policies, as well as the variouslevels of price control exercised by individual establishments (Mugica and Yague1993).

2 ‘Market maven’ refers to ‘those who have information on many types of product,places to buy and other facets of the marketplace, and who share such information withother consumers’ (Feick and Price 1987).

3 In Urbany et al. (1996), this second component is named INDEX and is a measure-ment of the consumer effort only in the search for price-specials.

4 In the variables included in psychosocial returns, two items were eliminated. Speci� -cally, it was deemed expedient to leave out the item: ‘I like introducing new brands andproducts’, a component of the market maven independent variable, and the item: ‘Iview shopping in a positive way’, a component of shopping enjoyment. This decisiongave rise to a slight increase in alpha values by 0.04 and 0.06. Although, at � rst, theelimination of the items did not appear relevant, basically because of our intention notto lose information, it was � nally decided to exclude them since an exploratory factoranalysis con� rmed an increase of the explained variance with one factor, from 64 percent to 73.3 per cent (with the elimination of item ‘I like introducing new brands andproducts’) and an increase in the retained variance from 70.1 per cent to 82 per cent(with the elimination of item ‘I view shopping in a positive way’, whose structure wasunidimensional). In addition, the communality of these variables was 0.11 and 0.28respectively, while the other variables, in their respective factors, showed communal-ities higher than 0.6 and 0.7, respectively.

5 This result coincides with that obtained by Urbany et al. (1996).6 The methodology used was the agglomerative hierarchical cluster analysis applied to

the matrix of the squared euclidean distances between cases.7 For the PISB analysis, the criterion used for case combinations in each step was the

average linkage between groups. For PCSB and INFORM the criterion used was theaverage linkage within groups.


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