The role of price knowledge in consumer product knowledge structures

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Psychology & Marketing, Vol. 19(6):551–568 (June 2002)Published online in Wiley InterScience (www.interscience.wiley.com).� 2002 Wiley Periodicals, Inc. ● DOI: 10.1002/mar.10024

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The Role of PriceKnowledge in ConsumerProduct KnowledgeStructuresRobert LawsonWilliam Paterson University

Parimal S. BhagatPhiladelphia University

ABSTRACT

In this study price knowledge is viewed as a necessary component ofa product knowledge structure, in which it is inferred from domain-specific relations among product features. Consumers’ knowledge oftuition rates of colleges and universities was investigated in twotuition-estimation experiments. In Experiment 1, providingparticipants with seed knowledge of a small subset of listed schoolsimproved the mapping properties of the estimates and reducedreliance on using the availability heuristic. In Experiment 2, similareffects were observed when instructions contained informationabout tuition magnitudes of school groupings, information abouthow to categorize schools into these groups, and, especially, bothtypes of information combined. Results were interpreted within aframe structural view of consumer knowledge, in which priceknowledge occupies an obligatory slot in a richly structured frameas opposed to being derived from memory of isolated exposures tospecific prices. � 2002 Wiley Periodicals, Inc.

How much a product costs is a basic and important aspect of consumers’product knowledge. Regardless of the product category or particular

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brand under consideration, consumers will almost always have someidea of its price, even if they have never been directly exposed to thatinformation. For example, knowing that a new model of an automobileis a sporty BMW convertible would lead a casual observer to infer thatits price is around $40,000. In addition, knowing that a product sells fora particular price implies that it has a bundle of other characteristics.Knowing that a pair of sneakers is priced at $19.99 is likely to lead aconsumer to infer that the shoes are also uncomfortable, lack durability,and are not made of leather. In other words, a consumer’s knowledgestructure of a product domain not only is likely to contain price infor-mation, but also to integrate price information with the full set of knownfacts about other relevant attributes. The purpose of this article is toexplore the structure of consumer knowledge about a richly structuredproduct domain by using a price-estimation research methodology.

Price Knowledge

As a consumer-behavior topic, issues surrounding price have focused onprice-perception phenomena, especially the hypothesis that price sig-nals product quality (e.g., Rao & Monroe, 1989; Zeithaml, 1988). In thecontext of deciding about a purchase, classical economic theory statesthat consumers’ decisions are tied to a process involving a comparisonbetween the perceived price and a stored reference price (Gabor &Granger, 1969). Research concerning the sources of reference prices hastaken the approach that price awareness emerges from memory tracesof past purchase experiences of particular brands (e.g., Krishna, Cur-rim, & Shoemaker, 1991; Urbany & Dickson, 1991; Wakefield & Inman,1993). Yet to the authors’ knowledge no research exists about howknowledge of reference prices, distributions of prices across a productcategory, or, for that matter, prices of individual brands may be repre-sented in the context of consumers’ product knowledge structures.

It is apparent that price researchers typically have regarded priceknowledge as arising somehow from past purchase experiences. Thus,the issue of pricememory has received considerable attention. The mostsalient fact emerging from this literature is consumers’ surprisinglypoor memory for prices (e.g., Krishna et al., 1991; Mazumdar & Monroe,1992; McGoldrick & Marks, 1987). Monroe and Lee (1999) have re-viewed this research and point out that it has been dominated by mea-sures of explicit memory. They go on to suggest that price knowledgemay be better understood as based on the concept of implicit memory,in which qualitative nonnumeric memory traces may only be detectedin subtle ways, including vague feelings of familiarity (see Jacoby, 1991on the explicit/implicit memory issue). That is, observations of consum-ers reveal that although they may not be able to recall or recognizeprices of recently encountered items, they will nevertheless know if aproduct is too expensive or fairly priced.

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Although the idea that explicit memory does not capture the conceptof price knowledge is well taken, it does not follow that implicit memorynecessarily provides a better answer. The view here is that a consumer’sknowledge of price is not so much a matter of memory of past experi-ences, whether explicit or implicit, as it is a construction based on one’sknowledge of the target product domain. Therefore, in order to under-stand knowledge of prices, it is first necessary to understand how gen-eral knowledge about a product domain is structured, and then askabout the role of price within that structure. In other words, price knowl-edge is regarded as deriving more from semantic memory than fromepisodic memory (Tulving, 1972).

Frame Structure and Price Information

According to the prevailing associative network view of consumerknowledge structures, knowledge about a product category or brand isequivalent to the totality of other concepts that have become linked toit through experience (e.g., Anderson, 1983; Collins & Quillian, 1969;Mitchell & Dacin, 1996; Olson, 1978). That is, what a consumer knowsabout a product is the list of features that have become associated withit. Barsalou (1992) has criticized feature list theories as inadequate de-scriptions of knowledge structure on several grounds. Most importantly,associative networks fail to capture the underlying stability and orga-nizational structure of knowledge.

In contrast to feature list-based models of knowledge, Barsalou (1992)outlined the basic characteristics of the frame structure. Foremost isthe requirement that associated features be related to the target conceptvia more abstract nodes instead of the direct connections prescribed inassociative networks. Thus, in describing the conceptmen’s dress shoes,instead of simply noting that the shoe is uncomfortable, a knowledgeframe requires an attribute node, comfort level and the different values(comfortable, uncomfortable, painful) that the attribute could take on.A knowledge frame for a concept then consists of sets of attribute–valuepairings. According to Barsalou, the linkages that develop in a frameare constrained by invariant relations between attributes (chairs musthave backs that are above seats), values of those attributes (highly du-rable shoes are more likely to cost more), and the consumer’s goals (pref-erence for a beach vacation will lead to the selective development ofbeach-related knowledge).

In applying frame theory to knowledge about products, Lawson (1998)proposes that consumer knowledge frames consist of a combination ofproduct-specific attributes and universal marketing attributes. The lat-ter describe characteristics that would hold for any entity when consid-ered as a product, and thus would be included in each and every frame.Examples include purchase-related (with more specific attributes likeretailer type, payment mode), performance, and brands.Although a com-



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plete listing of universal marketing attributes has not been attempted,it is clear that the attribute of price would have to be included as a coreattribute because it is a conceptually necessary part of any product con-cept. Because all products must have a price, any product frame mustallow for a slot for price, regardless of whether price is exactly or im-perfectly remembered, totally forgotten, or has never been encounteredby the consumer.

Not only is price an obligatory core attribute in product frames, ittypically occupies a central position within the structure. For example,much of the price-perception literature is concerned with exploring theidea that price knowledge serves as a signal to product quality (e.g., Rao& Monroe, 1985), especially in cases in which product knowledge is lessthan perfect. In terms of frame structure, this means that values of theprice attribute constrain values of product quality. In addition, becauseprice is often integrated into consumers’ purchase goals, it further con-strains knowledge. A consumer whose goal is to buy a good pair of shoesfor less than $80 will temporarily deactivate access to known expensivebrands.

In general, it is reasonable to assume that the attribute of price islinked to a host of other product attributes in ways that organize, con-strain, and reflect a consumer’s knowledge structure of a product do-main. If this description of the role of price in a product knowledge frameis accurate, then price knowledge need not result from remembered pur-chase experiences, as much of the price-perception literature suggests.Instead consumers may readily infer price based on where that productfits within the frame. In this approach, price knowledge is viewed as amatter of numerical estimation, rather than memory.

Price Estimation and the Plausible Reasoning Framework

In their analysis of numerical estimation tasks, in which participantssupply estimates of numerical values from large and complex domains,Brown and colleagues (Brown & Siegler, 1993; Friedman & Brown,2000) distinguished between three different bases for judgments: con-sumers’ metric knowledge of the statistical properties (central tendencyand variability) of the domain, their mapping knowledge reflecting howthe various instances compare with one another, and their use of heu-ristics. Errors in estimation can be grounded in any or all of thesesources. For example, a person may have a good grasp of the range ofvalues (metric knowledge), but not how the different instances line up(mapping knowledge). Or, a consumer may lack both domain mappingand metric knowledge, and rely upon feelings of familiarity (availabilityheuristic; Tversky & Kahneman, 1973).

In order to make more fine-tuned inferences about the underlyingstructure of domain-specific knowledge, Brown and Siegler (1993) mod-ified the numerical estimation task by employing knowledge seeds. In



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this procedure, following an initial set of estimates, participants aregiven seed facts as a subset of the full stimulus set before generatingthe estimates for a second time. At issue are the effects of the seed factson the estimates for the remaining items in terms of measures of metricknowledge, mapping knowledge, and use of the availability heuristic.They found that seed facts evenly distributed over the range of nationalpopulations improved metric, but not mapping knowledge. Friedmanand Brown (2000) demonstrated that the seed methodology was usefulin understanding the basis for various “illusions” in people’s mentalmaps of world cities. For instance, most North Americans believe thatRome is much further south than Chicago, when, in fact, both are lo-cated at about 42� north latitude. This method allowed them to concludethat people have some mapping knowledge of cities’ locations within aparticular region, such as North America or southern Europe, and thatthe Chicago–Rome illusion stems from the erroneous belief that Europeis further south than it really is.

In general, this line of research has been successful in demonstratingthat coming up with numerical values associated with objects that be-long in complex, real-world domains depends on a process of plausiblereasoning carried out over one’s overall knowledge structure of the do-main. In addition, the use of knowledge seeding can lead to more ac-curate estimates by selectively improving either the metric or mappingcomponents of the domain.

In this research the plausible reasoning hypothesis is extended toknowledge about price; the most central numerically based aspect ofconsumer knowledge. Its role in the domain of colleges and universitiesis explored by having undergraduate university students estimate an-nual tuition rates. Although one might expect that students would beespecially knowledgeable in this domain, it was selected it because itwas not considered very likely that their tuition knowledge primarilyresulted from episodic memory opportunities. Instead, it was hoped thatusing this domain with this population would reveal how tuition knowl-edge is related to the broader knowledge frame of colleges and univer-sities.

EXPERIMENT 1

The first experiment was intended to get a handle on what studentsknew of college tuition rates for different categories of schools, thesource of their knowledge, and the effects of presenting them withknowledge seeds. It was anticipated that students’ responses would in-deed be estimates, based on their general knowledge structures of thedomain, rather than recollections of previous encounters with actualtuition information. With these objectives in mind, a list of 50 collegesand universities was constructed, drawn from different broad categories



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covering the full range of tuition rates. Students first supplied theirnumerical estimates, as well as knowledge ratings and knowledgesource judgments for each school. Two weeks later they were givenknowledge seeds from either the lower-priced schools, the higher-pricedschools, or evenly drawn from all categories, prior to supplying a secondset of numerical estimates.

This design allowed the assessment of different aspects of students’price knowledge according to the plausible reasoning framework. First,their reliance on the availability heuristic was reflected by the correla-tion between knowledge ratings and estimates. To the extent that stu-dents lacked solid knowledge about what schools mapped into what cat-egories, and how tuition rates were related to those categories, it washypothesized that they would base their estimates upon a simple avail-ability heuristic: the greater one’s familiarity with the school’s name,the higher its estimated tuition. It wasfurther anticipated that theknowledge seeds would decrease participants’ dependence on availabil-ity. Second, the metric component of their estimates was measured bythe median overall deviation (MOD) measure (Brown & Seigler, 1993),which simply compares each student’s median estimate with the truemedian of the set of schools. It was not known whether the low and highseed conditions would confine their effects to the categories given orwould generalize across all categories, or whether the balanced seedcondition would minimize MOD. Finally, the mapping component wasmeasured by the rank-order correlation coefficients between each par-ticipant’s estimates and the actual tuition rates. The hypothesis herewas that because knowledge seeding provides clues about pricing pat-terns, it would improve this measure across the board, and that thebalanced seed would provide the greatest benefit because it provides themost complete information.

Method

Materials. The list of 50 colleges and universities was structured toinclude 10 examples from each of five categories. The categories wereselected to reflect the entire range of tuition rates, sizes, and fundingsource. They were: (1) small public schools, (2) large flagship publicschools, (3) small local private schools, (4) small elite private schools,and (5) national elite private schools. The complete set of schools usedand tuition rates for the 2000–2001 academic year (selected from theCollege Board website: www.collegeboard.com) are shown as the barsin Figure 1. They were arbitrarily selected with the restriction that noschool in the home state or immediate geographical area was included.This structure revealed three distinct levels of tuition rates. The publicschools in the first two categories had the lowest prices, ranging fromabout $2K to $7K (in-state rates). The small local privates that tend tobe less selective formed a second price group, with rates between $10K



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and $20K. The elite privates of the last two categories charged the high-est tuition, with rates generally between $20K and $26K. The mediantuition for the entire set of schools was $14,212. It is noted that amongthe state schools and among the elite privates, size did not matter asfar as tuition was concerned. Thus, knowing whether a school is publiclyor privately funded, and if it is private, whether it is local or elite, ac-counts for much of the variance in tuition.

Participants. Participants were 30 undergraduate students enrolledin business classes at a midsized state university in the Northeast.These students typically attend university full-time, are employed part-time, and are at least partially responsible for paying their own tuitionbills. They participated in exchange for course extra credit. Three stu-dents did not do the second estimation task, resulting in repeated-mea-sures analyses involving only 27 participants.

Procedure. In a classroom setting, participants were instructed to sup-ply the current academic year (2000–2001) tuition and fees for the setof 50 schools. They were told that no one is expected to know the exactvalues, that some of the schools were private and some public, and, forthe public schools, to supply the in-state tuition rates. They also wereasked to rate their knowledge of each school on a 10-point scale, an-chored at 1 � “never even heard of it” and 10 � “know a lot about it.”In addition, they classified each estimate according to whether they re-membered (R) having learned the amount from direct experience, theyinferred (I) it, based on their general knowledge of colleges and univer-sities, or their estimate was a pure guess (G). Following instructions,participants worked through a test booklet, in which the 50 schools werepresented, one to a row with columns for their estimates, knowledgeratings, and source (RIG) judgments. All participants received the samelist order, which was arranged in blocks of 5, with each block containingone school from each category.

Two weeks later participants were given the knowledge seed andasked to provide a second set of estimates for the same 50 schools. In-structions stated “to help you in your (estimation) task, please read andthink about the sample of actual tuition rates presented below.” Theknowledge seed consisted of the true tuition rates for six of the schools.In the low seed condition, two schools from each of the small public,flagship public, and local private categories were provided. In the highseed condition, prices of the same two local private schools, in additionto two schools each from the two private elite categories were listed. Thebalanced seed condition presented amounts of the same two local pri-vates, plus one each from the other four categories. The mean tuitionamounts of the seeds were $7.8K, $14.9K, and $21.9K for the low, bal-anced, and high seed conditions, respectively.



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Table 1. Summary of Key Tuition Estimation Measures

Condition nMedian

Estimate

MeanAbsolute

Error (MAE)

Median ofDeviations

(MOD)Mapping

rho

Experiment 1

First estimate 30 $12,377 $9,294 $4,480 .25Second estimate 27 10,986 8,465 5,137 .34

Low seed 10 6,545 9,237 7,667 .38High seed 9 16,742 8,434 3,029 .27Balanced seed 8 10,063 7,533 4,347 .32

Experiment 2

Control 16 $15,076 $9,283 $4,068 .32Magnitude only 13 13,077 7,964 4,096 .50Categorization only 14 16,214 9,061 5,214 .47Combined 14 14,286 6,076 3,029 .63

Results

First Estimates. Overall, it is fair to say that the students found thatestimating tuition amounts was a difficult task. Figure 1 compares theirmean estimates to actual amounts, with the schools arranged by cate-gories and ascending rates within categories. Some systematic discrep-ancies reveal interesting aspects of the underlying knowledge structure.First, the tuition rates for the two categories of state schools are decid-edly and uniformly overestimated, with the effect exaggerated for theschools in the flagship category. To take the most blatant example, in-state tuition for UCLA is $3.7K, but yielded an estimate of $20.9K. Sec-ond, estimates for the small elite category are uniformly underesti-mated. For example, the most expensive school in that category,Swarthmore, whose tuition is $25.2K, is estimated at only $11.3K, oneof the lowest mean estimates in the entire set.

Table 1 summarizes key measures of different aspects of participants’estimates. Overall mean absolute error (MAE) in the first estimate(M� $9294) was relatively large, amounting to 67% of the actual tuitionrates. According to the participants’ own judgments about the source oftheir responses, only 2.5% were thought to have come directly frommemory (versus 21.9% inferences and 75.7% guesses). Interestingly,these “remember” responses (M � $9438) were no more accurate thanfirst estimates in general. This result confirms the suspicion that thesetasks were indeed exercises in estimating, not remembering. First es-timate measures of the metric and mapping components are discussedbelow in conjunction with differences in second estimate measures.

Metric Component. This component of the numerical estimates is con-



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cerned with the statistical properties of the numbers generated. Themost direct measure of this property was each participant’s median es-timate. Table 1 shows the means of those medians. Overall, the seedsaffected estimate magnitudes, F(2,24) � 32.08, p � .001, primarily byreducing estimates for the low seed participants, t(9) � 3.06, p � .01.

Following Brown and Siegler (1993), MOD was calculated for eachparticipant by subtracting that person’s median estimate from the me-dian of the actual amounts. A mixed-effects analysis of variance re-vealed that there was no consistent main effect of presenting seeds,F(1,24) � 1. Instead seeding had more selective effects, increasing MODfor the low (�MOD � $3595) and balanced (�MOD � $979) seed con-ditions and decreasing MOD for the high seeds (�MOD � �$2725; in-teraction F(2,24) � 6.88, p � .004. These findings reflected the fact thatlow seeds drove down initially fairly accurate estimates for privateschools, whereas high seeds tended to correct the initial underestimatesfor the lesser-known privates.

Mapping Component. This aspect of the set of estimates focuses spe-cifically on the correspondence between the rank ordering of the esti-mates and the true rank ordering. Table 1 shows the means of eachparticipant’s Spearman rank-order correlation coefficients (rho) for thefirst estimates and for the second estimates, excluding seed schools, ac-cording to seed condition. Overall, seeding resulted in improved rhos for21 of 27 participants (Z � 2.81, p � .005). In particular, the low (Z �1.99, p � .05) and balanced (Z � 2.10, p � .04) seed conditions wereassociated with higher rhos, whereas the high seed condition (Z � 0.65)was not. Apparently, providing knowledge seeds improved how well peo-ple are able to figure out the relative price tags for the different schools.

Availability Heuristic. When people have sketchy knowledge of col-leges and universities, and have to come up with estimates of tuition, itbecomes reasonable to base those estimates on familiarity or subjectiveknowledge. To do so would be using a form of availability heuristic.Taken across the set of schools (N � 50), the median first estimates oftuition are indeed highly correlated with mean knowledge rating (Pear-son r � 0.82, p � .001). By comparison, the correlations between actualand estimated tuition (Pearson r � 0.35, p � .006), and between actualtuition and knowledge ratings (Pearson r � 0.07; ns) are much lower.

Table 2 shows the results of regression analyses that predict esti-mated tuition from actual tuition and knowledge ratings. For the firstestimates, before receiving any knowledge seeds, participants reliedmore heavily on availability than true tuition (0.64 versus 0.09 uniquevariance accounted for). In separate regression analyses for each seedcondition excluding seed schools (N � 44), the extent of participants’dependence on availability could be gauged. The low seed and balancedseed groups showed reduced dependence on availability, whereas the



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Table 2. Predictors of Tuition Estimates

Condition TotalR2

Unique Variance Accounted For

Availability True Tuition

Experiment 1

First estimate 0.77 0.64 0.09Second estimate (all seed groups) 0.58 0.15 0.40

Low seed 0.55 0.16 0.32High seed 0.62 0.50 0.07Balanced seed 0.63 0.29 0.24

Experiment 2

Control 0.74 0.58 0.17Magnitude only 0.69 0.29 0.38Categorization only 0.71 0.25 0.42Combined 0.70 0.02 0.68

influence of actual tuition amounts increased. However, the high seedcondition was more similar to the first estimate pattern than to theother seed groups.

Discussion

The results of this experiment identified three distinct influences onconsumers’ estimates of tuition rates for colleges and universities: met-ric knowledge, domain-specific mapping knowledge, and the availabilityheuristic. All three components need to be assessed in order to under-stand how people reason about the prices of products within a particularproduct domain. The heuristic of availability was shown to be the pri-mary basis for estimating price when no additional information is pro-vided. Knowledge seeding was successful in reducing reliance on thisheuristic for the low and balanced seed conditions, but not for the highseeds. This differential effect probably occurred because the high seedsdid not provide participants with any help in adjusting their overly highestimates of state schools, whereas the low and balanced seeds did.

Unaided knowledge of the metric properties of these prices was notthat far off the mark, with the mean MOD missing the actual mediantuition by about 31%. The effect of knowledge seeds on MOD dependedupon the seed condition. It appeared that providing information aboutthe relatively low prices of state schools in the low and balanced con-ditions led students to underestimate too many schools, which resultedin lowering the overall level of their (too low) estimates, whereas theopposite effect occurred in the high seed condition.

However, it is the mapping properties that reflect consumers’ domain-specific knowledge. To the extent that participants are able to categorizeschools according to funding source and prestige/selectivity while ignor-



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ing factors such as size and whether they have famous sport teams, theyshould be able to reason intelligently about their relative prices. Al-though the initial level of mapping knowledge indicates a tenuous graspof these aspects of their knowledge structures, providing the knowledgeseeds resulted in a modest, but significant improvement in mappingknowledge.

The knowledge seeds provide an opportunity for consumers to bothcategorize the various schools and get a sense of the magnitude of thenumbers. However, in the context of this particular domain, if consum-ers do not know that state schools are clearly less costly than privateschools, and that, within privates, prestige and selectivity matter, thenthe knowledge seeds would not be very helpful. Furthermore, even ifconsumers knew these relationships, their estimates would be ham-pered if they could not use this knowledge to categorize various in-stances. In the present study, consistently wild estimates of particularinstances, like the UCLA–Swarthmore illusion, indicate that knowl-edge seeds in the absence of additional supporting information abouthow to use that information are not the most effective way to improveestimates. Therefore, Experiment 2 was conducted with the use of moredescriptive information about crucial relations among domain attrib-utes in an effort to further distinguish among the metric and mappingproperties and heuristics. In addition, because Experiment 1 employeda within-subjects design, the effects of seeding were confounded withpractice and specific memory effects. Accordingly, Experiment 2 utilizeda between-subjects design.

EXPERIMENT 2

In this experiment, the type of information given about the domain ofcolleges and universities was manipulated in an effort to improve esti-mates and to separate more clearly the different influences on estima-tion performance. Instead of knowledge seeds, either information aboutthe magnitude of tuition amounts associated with different categoriesof schools or guidelines useful in placing instances into those tuition-relevant categories were provided.

Method

Design and Materials. Two types of information were manipulated ina 2 � 2 factorial design. One factor was whether or not participantsreceived information about themagnitude of the tuition amounts sortedaccording to three tuition-sensitive categories: publicly funded, local(less selective) privates, and prestigious (more selective) privates. Par-ticipants who received this information were informed that schools inthose categories had tuition rates in ranges of $2K–6K, $10K–20K, and



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$20K–26K, respectively. The second factor was whether or not studentsreceived information that would help them categorize instances intothose price-sensitive categories. Participants who received this infor-mation were told (a) that state schools can be identified if their name isthe name of a state, contains the name of a state, or contains the wordState, and (b) that private schools tend to be named after people, or havereligious connotations. Four examples of public schools, two examplesof smaller, less selective privates, and two examples of more selectiveprivates were given. Thus four groups of participants received differentinstructions for the tuition-estimation task: a magnitude-only group, acategorization-only group, a combined group that received both, and acontrol group that received neither.

The list of colleges and universities was a subset of 40 drawn fromthe set used in Experiment 1. There were eight schools from each of thesame five categories. A smaller set was used because of the increase ininformation supplied per school. One university, from the small publicset, was inadvertently omitted from the estimation task.

Participants and Procedure. Participants were 58 undergraduatebusiness students drawn from the same population as Experiment 1.They were randomly assigned to the four different instruction condi-tions. Data from one student were not included in any of the analysesbecause of failure to follow instructions.

In a setting similar to that of Experiment 1, participants first pro-vided two types of information about their knowledge of the listed col-leges. They rated their knowledge of each school on the same 10-pointscale as in Experiment 1, and they supplied various pieces of objectiveknowledge (not analyzed in this report), such as location, size, and pub-lic versus private funding. They were then engaged in unrelated activityfor about 1 hour. Next, the participants were randomly assigned to oneof the four treatment conditions described above, read the instructionscorresponding to their treatment, and then estimated the annual tuitionrates for each of the 39 colleges on the list.

Results

Overall, the initial pattern of price estimations was similar to that ofthe first experiment: Control-group participants overestimated the stateschools, while underestimating the small elite privates. The differenttypes of information provided in the different instruction conditions gen-erally had the predicted effects on the estimates. These effects are as-sessed with the use of separate analyses on the measures of MAE, MOD,and mapping rho. The relevant measures are summarized in the lowerpanel of Table 1. Reliance on the availability heuristic was assessed withthe use of a regression analysis, which is summarized in the lower panelof Table 2.



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Instructions had a significant effect on MAE, the measure of overallaccuracy, F(3,53) � 3.38; p � .025. Contrasts revealed that receivingsome form of information about the domain (M � $7702) resulted in amarginally lower MAE, t(53) � 1.78; p � .08, than in the control con-dition (M � $9283). Also, the combined instruction condition (M �$6076) resulted in a lower MAE, t(53) � 2.45; p � .02, than the meanof the magnitude and categorization conditions separately (M� $8533).Although these results indicate that supplying students with domain-specific information about the pricing structure reduces overall esti-mation errors, it does not pinpoint the effects in terms of the metric andmapping properties of distribution.

Instructions did not have a consistent effect on the metric proper-ties, as measured by MOD. Neither the overall effect of instructions,F(3,53) � 1.11, nor any of the contrasts were significant. The apparenttendency of the combined instructions (M � $3029) to lower MOD morethan the separate instructional sets (M � $4676) did not reach signifi-cance, t(53) � 1.56; p � .13. Surprisingly, being given the magnitude-only instructions, which provided information about range of typicaltuition rates, failed to reduce MOD. On the other hand, instructions didhave the expected effect on mapping properties. A Kruskal-Wallis testperformed on mapping rhos, chi-square (3) � 9.97, p � .02, reflected apattern of results that showed a beneficial effect of either instructionalset alone, along with an enhanced effect for the combined condition.

In addition, instructions had the predicted effects on participants’ re-liance on the heuristic of availability. In a series of regression analyses,in which students’ mean knowledge ratings and actual tuition amountswere used to predict median estimates, inspection of Table 2 reveals apattern of decreasing reliance on availability and increasing accuracyof estimate as instruction condition changes from control, to magnitudeand categorization separately, and finally to combined. In fact, in thecombined instructional condition, knowledge ratings account for a non-significant 2% of the variance. Thus, when participants are presentedwith relational information about the domain of colleges and universi-ties, they are able to utilize this information as they abandon the avail-ability heuristic.

Discussion

Experiment 2 showed that providing people with domain-specific infor-mation about colleges and universities that helps them build a coherentknowledge structure, has in turn predictable and specific beneficial ef-fects on their tuition estimates. In particular, consistent with the resultsof Experiment 1, it was found that participants rely heavily on the heu-ristic of availability when they know little about the domain. Instruc-tions that provided either magnitude or categorization information re-



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duced that reliance, whereas instructions that combined both types ofinformation were successful in virtually eliminating the heuristic. Fur-ther analyses revealed that providing information increased the overallaccuracy of estimations, and that the beneficial effects of providing ei-ther or both types of information were concentrated on the mapping, asopposed to the metric properties of the estimates. In general, the in-structional condition that combined both types of information was themost successful in reducing reliance on availability, reducing the overallerror of estimation, and improving the mapping component of the esti-mates.

GENERAL DISCUSSION

Together these experiments provide clear evidence that what peopleknow or think they know about the price of instances within a broadproduct category is tied to their general knowledge of that domain. Al-though it is doubtless true that some knowledge of price comes about asa result of direct experience in purchasing or deciding about purchasingparticular products, this study makes the point that other aspects ofprice knowledge may arise from reasoning and inference making. Con-sumers were able to express what they knew about the tuition rates ofcolleges and universities despite their (undoubtedly accurate) percep-tion that they had never run across that information for the overwhelm-ing majority of schools that they estimated. If their price knowledge didnot arise from memory, then just what were the sources? These twoexperiments attempted to answer that question by distinguishing be-tween alternate sources of knowledge: the availability heuristic, themetric properties of their estimations, and the mapping knowledge thatlinks tuition amounts to individual schools.

Across both experiments, it was clear that in conditions in which par-ticipants were not given any information to help them arrive at a co-herent knowledge structure of the domain, they relied greatly on avail-ability. In the absence of extensive domain knowledge, they operated bythe heuristic that if they had heard of the school, then it was more likelyto be famous, and if it were famous, then it would likely be expensive.In conditions in which additional information was not provided, avail-ability accounted for the majority of the variance in predicting esti-mates. However, it was possible to reduce the extent to which consumersdepended on this heuristic by providing them with various types ofknowledge before they estimated. Providing knowledge seeds withoutcomment (Experiment 1), information about the magnitude of theamounts (Experiment 2), or information about mapping instances totuition-relevant categories (Experiment 2) resulted in varying degreesof improvement in estimation accuracy and lessened reliance on avail-



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ability. The condition that led to the best estimation performance acrossboth experiments was the condition that presented a combination ofmagnitude and categorization information.

Additional understanding of the factors behind these price estima-tions came from exploring the distinction between metric and mappingproperties (Brown & Siegler, 1993). Overall, these participants had areasonably good handle on the metric properties of tuition levels fromthe beginning. Neither experiment was able to provide interventionsthat led to a significant decrease in the MOD measure. On the otherhand, these consumers had very sketchy knowledge of which schoolswere expensive and which were inexpensive. The various treatmentsgenerally resulted in improving the mapping component of the esti-mates, with the most striking improvement coming in the combinedinformation condition of Experiment 2. It seems that providing consum-ers with a few key interrelated facts about the domain gives them abasis for making more plausible estimations. In particular, the knowl-edge that private schools cost more than state schools, that more selec-tive private schools cost more than less selective privates, the ballparkamounts for these categories, and some hints about how to place par-ticular schools into these categories, all combined to improve the map-ping property of the estimates.

Beyond the immediate task of numerical estimation as it applies toprices, this study makes contributions to the price perception, consumerknowledge, and conceptual cognition literatures. With regard to priceperception and memory, the widespread observation that consumersstore inaccurate price information is upheld. However, the prevailingperspective that views such inaccuracies as arising from poor episodicmemory does not fit the price estimation paradigm. Consumers’ priceknowledge was shown to be linked to their knowledge of the productdomain and to their ability to make reasonable inferences from it. It hadlittle to do with failing to remember prior exposures to that information,because those exposures had never occurred in the first place. Althoughthis description seems reasonable in the domain of colleges and univer-sities, in which direct, purchase-related experiences are unlikely, itsgenerality is unknown. In general, the degree to which consumers wouldrely upon memory of specific product encounters rather than upon se-mantic memory would seem to depend upon the particular domain. Forexample, an experienced supermarket shopper would probably rely ondirect experience to recover prices of brands of peanut butter, ratherthan make inferences based on knowledge structure. It would be inter-esting to investigate further these two bases for knowing about price.

With regard to consumer knowledge structures, this study makes acase that price knowledge is a central feature of any product category,rather than simply an isolated attribute that may or may not becomepart of an associative network. The results showed that price knowl-edge, at least in the domain of colleges and universities, is constrained



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by the relations inherent in the particular structure. In that domain, afew broad subcategories of school imply a certain level of price. Whenpeople who are not initially knowledgeable about these categories aregiven key pieces of information, they are able to infer the correct ap-proximate prices. Although this study did not try to provide a completedescription of a knowledge structure, the results fit nicely with a framestructural approach (Barsalou, 1992; Lawson, 1998). The central role ofprice in this structure is revealed by the primary result that price es-timates improved when structural information was provided.

More generally, this research is in tune with recent trends in concep-tual cognition by focusing on the relations among features in richlystructured, domain-specific categories, and how reasoning occurs withinthose structures (see reviews by Medin & Coley, 1998; Medin, Lynch, &Solomon, 2000). In particular, the improvement in tuition estimates fol-lowing certain instructions can be viewed as another instance of thecategory use effect (Ross, 1996), in which learning how to use the do-main-specific relations changes the way participants make subsequentcategorizations. When colleges and universities are construed as prod-uct concepts, consumers view them in ways that bring to light theirspecial attributes in conjunction with attributes that are common to allproducts. In this way, if consumers are generally knowledgeable abouta product domain, they will likely be reasonably well informed aboutthe prices of the different instances.

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Correspondence regarding this article should be addressed to: Robert Lawson,Department of Marketing and Management Sciences, William Paterson Uni-versity, Wayne, NJ 07470 ([email protected]).

The role of price knowledge in consumer product knowledge structures

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