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The Role of Direction of Comparison,Attribute-Based Processing, and Attitude-Based Processing in Consumer PreferenceSUSAN POWELL MANTELFRANK R. KARDES*

Preference formation involves comparing brands on specific attributes (attribute-based processing) or in terms of overall evaluations (attitude-based processing).When consumers engage in an attribute-based comparison process, the uniqueattributes of the focal subject brand are weighed heavily, whereas the uniqueattributes of the less focal referent brand are neglected. This is because theattributes of the subject are mapped onto the attributes of the referent, ratherthan vice versa. This direction-of-comparison effect is reduced when consumersengage in attitude-based processing or when high involvement increases motiva-tion to process accessible attributes more thoroughly and systematically. Thepresent research investigates a personality variable, need for cognition, that in-creases the likelihood of attribute-based (i.e., high need for cognition individuals)versus attitude-based processing (i.e., low need for cognition individuals) andtherefore, also affects the magnitude of the direction-of-comparlson effect. Thedirection-of-comparison effect is observed only when attribute-based processingis likely (i.e.. vi'hen need for cognition is high) and v ihen thorough and systematicprocessing is unlikely {i.e., when involvement is low). Mediational analyses involv-ing attribute recall and a useful new measure of analytic versus intuitive pro-cessing support this dual-process model.

Consumers use a wide variety of information-pro-cessing strategies to judge products and to arrive at

decisions. One of the most fundamental distinctionsamong the various strategies that have been identified isthe distinction between stimulus-based versus memory-based processing (Alba. Hutchinson, and Lynch 1991;Ha-stie and Park 1986; Kardes 1986; Lynch and Srull1982). In stimulus-based processing, all relevant infor-mation is directly observable in the judgment context, andconsumers can readily and directly compare all brands onall ailributes. By contrast, in memory-based processing,information about brands and attributes must be retrieved

*Susan Powell Mantel is assi.stant profe.s.sor in lhe Department ofMarkeiing, College of Busineiis Administralion. 3020 Stranahan Hall.University of Toledo. Toledo, OH 43606-3309 (419-530-4737 [office]:41*)-5.30-7744 (fax)); e-mail; [email protected]. Frank R,Kiirdcs is professor in Ihe Depiinment of Markeiing. College of BusinessAdiiiinislraiion. 433 Carl H. Linder Hall. University of Cincinnali, Cin-cinnati. OH 45221-0145 (513-556-7101 ). This research is based on the(irsi aiiihor's dissertation under the second author's supervision. Theauthors wish to thank James Kellaris and Dan Langmeyer for servingon the dissertation committee and providing comments on earlier ver-sions iif this article. In addition, the authors wish to thank Chris Alienand Jeen Lim as well as three JCR reviewers tor their comments onearlier drafts of this article.

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from memory before judgment-relevant comparisons canbe performed. Such processing is constrained by the limi-tations of memory and is generally considered to yieldless optimal judgments (Hutchinson and Alba 1991).

In stimulus-based processing, the amount of informa-tion and the extent to which this information is scruti-nized, weighed, and carefully integrated depends on theindividual's motivation to form an accurate judgment(Chaiken and Maheswaran 1994; Maheswiu-an. Mackie.and Chaiken 1992). When concem about accuracy ishigh, people tend to focus heavily on relevant informationand tend to be quite "data driven" and analytic (Albaand Hutchinson 1987; Hutchinson and Alba 1991). Asconcem about accuracy decreases, however, people be-come more "theory driven." or more reliant on stereo-types, preconceptions, and heuristics (Wyer and Srull1989).

Although the distinction between analytic, data-drivenprocessing versus intuitive, theory-driven processing hasbeen applied mainly to stimulus-based judgment (e.g..Fiske and Neuberg 1990; Kruglanski and Frcund 1983;Sujan 1985). we suggest that the distinction may alsoapply to memory-based judgment. If the individual isable to retrieve specific attribute information, then thejudgment will be data driven, but when only summary

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336 JOURNAL OF OONSUMER RESEARCH

impressions are retrieved, the judgment will be theorydriven (Sanbonmatsu and Fazio 1990). Tlie ability toretrieve specific attributes or general impressions maydepend on many important situational and individual dif-ference variables f Hutchinson and Alba 1991).

The Direction-of-Comparison Effect

Tversky's (1977) feature-matching model suggeststhat attributes are often compared in an asymmetric man-ner. When two brands are compared, one brand is typi-cally the more focal subject of comparison, the otherbrund is the less focal referent of comparison. The focalbrand tends to elicit more thoughts than the less focalbrand when a judgment is made between the two brands(Dhar and Simonson 1992). One important factor thatdetermines which brand serves as the subject of compari-son and which brand serves as the referent is the orderof brand presentation. Several studies have shown thatthe most recently observed brand serves as the subject ofcomparison and the earlier observed brand serves as thereferent (Houston and Sherman 1995; Houston, Sherman,and Baker 1989. 1991; Kardes and Sanbonmatsu 1993;Sanbonmatsu, Kardes. and Gibson 1991). According toTversky's (1977) model, the distinction between sharedand unique attributes is crucial for understanding the com-parison processes. Shared attributes are used to describeboth brands, whereas unique attributes are used to de-scribe one brand but not the other. The attributes of thesubject of comparison serve as a checklist against whichthe attributes of the referent are compared. Attributesunique to the subject are highlighted by the directionalcomparison process and carry the most weight in judg-ments of the two brands, Conversely, attributes unique tothe referent are downplayed by the directional comparisonprocess.

For example, a person may be considering two brandsof equivalent value {e.g.. two calculators) sold at differentstores. The brand encountered second becomes the subjectof comparison because the comparison process cannotbegin until two brands have been encountered. Also, as-suming both brands are recalled from memory (e.g., theconsumer has left both stores and is making the decisionat some later time), the memories associated with thesecond more recently encountered brand should be moreaccessible, thus making the second brand likely to be thefocal brand in the comparison process.

In a memory-based judgment task, when the uniqueattributes of both brands are positive, the subject is pre-ferred, but when the unique attributes of both brands arenegative, the referent is preferred. This is because theunique attributes of the subject are more focal and areweighed more heavily than the unique attributes of thereferent. As Figure 1 shows, there is a directional biassuch that when brand A is the subject of comparison, thecompari.son process highlights brand A"s unique attributesand downplays brand B's unique attributes. By contrast,when brand B is the subject, the comparison process high-

lights brand B's unique attributes and downplays brandA's unique attributes. Consequently, when the unique at-tributes of both brands are positive (as in Fig. 1 ). thesubject of comparison is preferred, whereas when theunique attributes of both brands are negative the referentis preferred. The manipulation of the valence of theunique attributes is necessary to show that the systematicdifferences are due to greater attention to the attributes ofthe subject of comparison (i.e.. direction-of-comparisoneffect) rather than a simple primacy or recency effect.

Attribute-Based versus Attitude-BasedComparison Processes

Another important factor for understanding comparisonprocesses is the type of information that is used. Attribute-based strategies require the knowledge and use of specificattributes at the time the judgment is rendered and involvethe use of attribute-by-attribute comparisons acrossbrands. For example, if brand A shampoo leaves hairshiny and brand B shampoo does not. brand A should bepreferred on this specific attribute.

However, preferences are not always based on specificattribute-by-attribute comparisons. In some cases, anoverall evaluation or attitude-based strategy is used. Atti-tude-based processing involves the use of general atti-tudes, summary impressions, intuitions, or heuristics: nospecific attribute-by-attribute comparisons are pertbrmedat the time of judgment. Of course, attributes may be usedas heuristic cues or as cues to form general attitudes be-fore the preference judgment is formed. However, at thetime of preference judgment, general attitudes and im-pressions are used instead of specific attribute-by-attributecomparisons.

Another distinction between attribute-based and atti-tude-based judgment strategies is the time and effort asso-ciated with each. An attribute-based judgment requiresthe comparison of specific attributes associated with eachbrand. Therefore, this process will be more time consum-ing, effortful, and usually more accurate than the globalcomparisons made for an attitude-based judgment. Mo-tivation and opportunity to process information worktogether to determine whether attribute-based or attitude-based processing will be used in a given situation (San-bonmatsu and Fazio 1990). Specifically, as motivation tomake a correct judgment increases and specific attributesare accessible from memory, individuals tend to use ana-lytic, data-driven, attribute-based processing. Conversely,when attributes are inaccessible from memory, the judg-ment will be based on global attitudes and impressionsderived from separate noncomparative evaluations ofeach brand formed during brand exposure. These globalattitudes are infiuenced by both common and unique attri-butes (Sanbonmatsu et al. 1991) and by attributes thatare difficult to compare directly across brands (Nowlisand Simonson 1997).

Although attribute-based processing is often more ac-curate, we suggest that the use of attribute-based pro-

ATTRIBUTE- AND ATTITUDE-BASED PROCESSING 337

FIGURE 1

DIRECTION-OF-COMPARISON PROCESS

Brand A comoared

Brand A

Small display •

Easy to use ^

to Brand B

Brand B

Small display

?

(Dependable)

Brand B compared

Brand A

Small display ^

? .4

(Easy to use)

to Brand A

Brand B

Small display

Dependable

NOTE, — Left panel. When A is the subject ot comparison, the comparison process highlights A's unique attributes and downpiays B's unique attributes. Rightpanel. When B is fhe subject of comparison, the companson process highlights B's unique attributes and downplays A's unique attributes.

cessing may actually increase rather than reduce system-atic biases in preference judgment due to the order inwhich the brands are considered. When judgments aremade between brands with hoth common and unique attri-butes, the direction-of-comparison effect is strongestwhen atlribute-bused processing is encouraged and weak-est when attitude-based processing is encouraged (San-bonmatsu et al. 1991). Therefore, the use of infonnationin the judgment process is dependent on when an evalua-tion is formed. It is either formed separately for eachbrand at the time of exposure (attitude-based processing).or comparatively when the preference judgment is made(attribute-based processing). The order in which thebrands are encountered appears to influence the decisionmore when attribute-based processing is used.

In order to understand this effect more completely, weneed to understand when attribute-based decisions arehkciy and when attitude-based decisions are likely. Forexample, personal or situational characteristics that en-courage attribute-based processing may aiso influence themagnitude of the direction-of-comparison effect by in-lluencing when the evaluation process lakes place. Be-cause attribute-based processing is influenced by the orderof compari.son while attitude-based processing is not(Sanbonmatsu et al. 1991 ), it seems reasonable that anyindividual charactcri.stic that tends to increase elaborationat the lime thai the brands are presented will encouragethe storage and the subsequent accessibility of specificattributes. This intemal motivation to process may simplyprovide the means by which attributes are made accessiblefor consideration, hut having the information may notalways improve decision making because of the limitedtime people can devote to this effortful, data-driven analy-sis. Without the external motivation to process all of thisinformation completely, asymmetric recall may producesystematic preference reversals.

Two new moderator variables relating to the direction-of-comparison effect are investigated in the present study:one personal characteristic, need for cognition (Cacioppoand Petty 1982). and one contextual variable, involve-

ment (Celsi and Olson 1988; Richins and Bloch 1986;Richins, Bloch, and McQuarrie 1992; Swinyard 1993).These variables were selected because need for cognitioninfluences the extent to which people encode and subse-quently retrieve attribute information and involvement in-fluences processing effort during the comparison process.

The Role of Need for CognitionCacioppo and Petty (1982) developed a need for cogni-

tion scale that can distinguish groups based on the extentto which individuals "engage in and enjoy thinking" (p.1). High need for cognition individuals (i.e.. those whoare more likely to enjoy thinking) have been shown toprocess and evaluate advertising information more thor-oughly than low need for cognition individuals. They tendto be influenced by message-relevant thoughts rather thanperipheral cues such as endorser attractiveness (Haug-tvedt. Petty, and Cacioppo 1992), spokesperson credibil-ity (Petty and Cacioppo 1986), humor (Zhang 1996), orthe number of arguments {even weak arguments) pre-sented (Cacioppo. Petty, and Morris 1983 ). Further, highneed for cognition individuals tend to make more optimalin-store purchase decisions because they tend to react toa promotion signal (e.g., feature advertisement) onlywhen a significant price reduction is offered. Conversely,low need for cognition individuals react when the productappears to be on special regardless of the amount of pricereduction offered (Inman. McAlister, and Hayer 1990).This research implies that high need for cognition individ-uals simply make more carefully thought-out and specificdetail-oriented judgments. By extension, it could be ar-gued that individuals high in need for cognition may beless likely to fall prey to a host of judgment and decisionbiases, including the direction-of-comparison effect. Wesuggest, however, that high need for cognition individualsmay be more susceptible to the direction-of-comparisoneffect, relative to low need for cognition individuals.

The level to which message order influences judgmenthas been shown to be dependent on the level of message

338 JOURNAL OF CONSUMER REEIEARCH

elaboration (Haugtvedt and Wegener 1994; Haugtvedt,Wegener, and Warren 1994; Petty. Haugtvedt, and Smith1995). It has also been shown that individuals high inneed for cognition tend to process information in a moreelaborate manner and are better able to recall specificattribute information at a later time (Srull, Lichtenstein,and Rothbart 1985). Therefore, in preference judgments,they are more likely to have the attribute informationaccessible from memory and more likely to engage inattribute-based processing than are their low need for cog-nition counterparts. Thus, it appears that message elabora-tion and need for cognition can shape the decision processby influencing how information is encoded, stored inmemory, and used in making a preference judgment.

Research has shown that individuals who are high inneed for cognition are more likely to encode, retain, andrecall attribute information (Srull et al. 1985, experiment2). and are likely to engage in attribute-based processing(Lynch, Marmorstein. and Weigold 1988). Therefore, be-cause attribute-based (vs. attitude-based) processing in-creases the direction-of-comparison effect (Sanbonmatsuet al. 1991), high need for cognition individuals shouldbe tnore prone to the direction-of-comparison effect. Con-versely, attribute information is less likely to be accessiblefrom memory during preference formation for individualslow in need for cognition, and consequently, low need forcognition individuals are less likely to engage in atttibute-based processing and are less susceptible to the direction-of-comparison effect.

Hla: Need for cognition will interact with directionof comparison such that individuals who scorehigh (low) on the need for cognition scale willbe more (less) likely to use attribute-basedprocessing during preference formation, andthus will be more (less) likely to be influencedby direction of comparison.

Hlb: Individuals who are high in need for cognitionwill be likely to encode and store the specificattributes associated with the available optionswhile those who are low in need for cognitionwill encode an overall impression of the vari-ous options. The attendant variation in levelof attribute recall will mediate preference for-mation.

The Role of Involvement

A second important factor that may affect an individu-al's tendency to be influenced by the direction of compari-son is involvement. Involvement can be defined as per-sonal relevance (Petty, Cacioppo. and Schumann 1983)or an individual's subjective feeling of the importance ofthe judgment process or importance of the object aboutwhich the judgment is being made. There are many poten-tial manipulations that would increase the level of per-sonal relevance in a judgment. One specific manipulation.

"concem about accuracy." has been shown to increasemotivation to process without necessarily increasing thelikelihood that the decision maker will have valid infor-mation with which to make an accurate judgment (John-son and Eagly 1989; Kruglanski 1989).

A large number of studies have reported that individu-als process information more thoroughly with higher lev-els of involvement (e.g., Celsi and Olson 1988; Haugtvedland Wegener 1994; Johnson and Eagly 1989; Mahesw-aran et al. 1992; Petty et al. 1983; Ratneshwar andChaiken 1991; Richins and Bloch 1986; Swinyard 1993;Zaichkowsky 1985). Individuals having a high level ofinterest in a judgment may be more likely to spend timeconsidering and evaluating any accessible attributes dur-ing the judgment process (Ceisi and Olson 1988). Thismotivation to process, however, may not be sufficient,by itself, to encourage attribute-based processing. Rather,high levels of involvement tnay increase the likelihoodof reflection and deliberation on the judgment if and onlyif the attributes are already accessible from memory. Inthe case where attitude-based processing is likely to occur(i.e., among low need for cognition individuals), the mo-tivation provided by the involvement manipulation maydo little to influence preferences because lhe individualslack the infonnation (i.e., accessible attributes) neededto make an attribute-based preference (Sanbonmatsu andFazio 1990).

Involvement and need for cognition should interactsuch that need for cognition influences the likelihood ofattribute- versus attitude-based processing, whereasinvolvement influences processing effort regardless ofwhich type of processing task has been employed. Ourhypotheses suggest that both the high and low need forcognition groups have similar involvement effects suchthat each group experiences an increased motivation toprocess with no change in the underlying processing strat-egy (i.e.. attribute- vs. attitude-based processing). Thus,we predict a change in preference due to the change inmotivation to process the accessible attributes among highneed for cognition subjects and no change in preferencedue to the inaccessibility ofthe attributes for low need forcognition subjects. For low need for cognition subjects,infonnation that was not encoded into long-term memoryis lost (Wyer and Srull 1989), and it does no good toincrease motivation to process information that is notavailable in memory.

H2a: Individuals with low levels of involvementand

i. high need for cognition are likely to usean attribute-based processing strategy tomake the judgment and hence bestrongly influenced by direction of com-parison.

ii. low need for cognition are likely to usean attitude-based processing strategyand should not be influenced by the di-rection of comparison.


H2b: Individuals with high levels of involvementshould be motivated to process the availableinformation more fully but will be constrainedby the processing strategy determined by theirrelative need for cognition. Therefore, thosewith high levels of involvement and

i. high need for cognition are likely to usean attribute-based processing strategy,but to more fully consider all accessibleattributes (both unique and common) inassessing preference. Therefore, theyare less likely (than their low involve-ment counterparts} to be influenced bydirection of comparison,

ii. low need for cognition are likely to usean attitude-based processing strategyand, because the attributes are not avail-able during the judgment phase, shouldnot be influenced significantly by the di-rection of comparison.

Attribute Recall as a Mediator of Preference

Whereas a moderator variable specifies conditions un-der which a given effect is likely to occur (vs. not occur),a mediator identifies the process by which independentvariables influence a dependent variable (Baron andKenny 1986). Many ofthe previous studies that examinedthe direction-of-comparison effect have assumed (but notdirectly tested) recall to be a mediator of asymetricchoice. Because the previous experiments produced re-sults that were consistent with those that would have beenexpected if Tversky's feature-matching model was used,prior research assumed that the participants were asym-metrically considering only the unique attributes of thefocal brand during preference formation (Houston et al.1989, 1991; Sanhonmatsu et al. 1991). In the presentstudy, recall is hypothesized and tested as a mediator ofpersonal and situational influences on preference judg-ments. Experiment 1 focuses on the impact of need forcognition and involvement on the direction-of-compari-son effect, and experiment 2 is a conceptual replicationand extension of experiment I using a different measureof preference and investigates the proposed cognitive pro-cesses via the presumed mediator: relative attribute recall.Measuring recall of attributes permits the determinationof when consumers engage in attribute-based processing.

EXPERIMENT 1

Overview

A 2 X 2 X 2 mixed factorial design was used. Thedesign included one within-subject factor (high vs. lowinvolvement), and Iwo between-subject factors (high vs.low need for cognition and valence of the unique attri-

butes). Preference was examined as the dependent mea-sure.

Stimuli 1The stimuli included four brands of pens and four

brands of calculators described by lists of attributes. Thelists contained five positive attributes and four negativeattributes to portray each pen or calculator (see ExhibitI ) . Each participant saw one pair of product descriptionsfor each category containing either shared good attributesand unique negative attributes (unique negative condi-tion) or unique good attributes and shared negative attri-butes (unique positive condition).

The pen descriptions were adopted from Sanhonmatsuet al. (1991), and the two sets of unique attributes wereequal in mean desirability.' Brand descriptions for a handcalculator were developed to match the pen descriptionsin terms of attribute valence and importance. The specificattributes for the hand calculator brands were selectedusing the Consumer Reports (1991) evaluation of thecategory. These attributes were then pretested to deter-mine the proper combinations of attributes to describe twobrands of calculators that meet the requisite conditionsof equal desirability, equal importance, shared positive(negative) attributes, and unique negative (positive) attri-butes.

Pretest iA pretest was conducted to construct the four brand

descriptions for the hand calculators. Pretest data werecollected in a marketing class at a large midwestern uni-versity. Eighteen students, who received class credit forparticipating, filled out a questionnaire during lhe classperiod. First, respondents rated 24 generic attributes (e.g..programmable, price, etc.) of a hand calculator on a 10-point scale from "not at all important" to ''extremelyimportant." Next, they rated specific positive or negativeattributes of a calculator (e.g.. "Comprehensive program-ming," "Friend says it is a good value for the price,""Number pad uncomfortable" ) on a lO-point scale from"extremely bad" to "extremely good." These responseswere evaluated to construct two pairs of hand-calculatordescriptions such that the two brands were equally goodwith shared positive (negative) attributes and unique neg-ative (positive) attributes.'

'The pen stimuli were created by Sanbonmatsu et al. (1091) and wereeach described by nine attributes (four were negaiive aliribuies, andfive were po.sitive attributes). The two pens cither had ( I ) shared posi-tive and unique negative attributes or (2) shared negative and uniquepositive attributes. The attributes were pretested so that both optionswere of equal value.

'The combination of attributes selected for the two brands of calcula-tors produced goodness and importance rating,s lhat indicate lhat thetwo brands were perceived as equal. For example, the HHH brand andJJJ brand calculator with unique positive attributes produced goixJncssratings of 54 and 53.2, respectively, and importance ratings of 55.3 and55.9. respeciively. Similarly, the HHH brand and the JJJ brand withunique negative attributes produced goodness ratings of 52.9 and 52.3,respectively, and importance ratings of 63.1 and 64.5, respectively.

340 JOURNAL OF CONSUMER RESEARCH

EXHIBIT 1

DESCRIPTION OF STIMULI

Unique Positive

Circle Brand PenDoes not skipAvailable in a wide range of colorsFriend says it is slightly overpricedBecomes uncomfortable with prolonged useRefillableNonsmear inkTip breaks down with excessive pressureNot long lastingFriend says it has a nice feel to it

HHH Brand CalculatorVery durableMinimal programming abilityPrinter makes a lot of noiseBattery lasts a long timePrinter output is easy to readFour-month warrantyKeys are silent when pressed15 built-in functionsKeypad is slightly uncomfortable

Dot Brand PenFriend says it is slightly overpricedGuaranteed to write every timeAvailable in a wide range of colorsNot long lastingGrip can become slipperyRefillableWrites on a variety of surfacesInk smears readilyFriend says it has a nice feel to it

HHH Brand CalculatorDependableDoes not have automatic shut-offFive built-in functionsEasy to programKeypad designed for rapid entryPrinter speed is slower than mostPrinter output is easy to readDisplay will accommodate 16 digitsLarge and bulky (not portable)

Dot Brand PenSpecial grip to ensure precision and controlTip breaks down with excessive pressureFriend says it is slightly overpricedElegantly styledWrites on a variety of surfacesBecomes uncomfortable with prolonged useFriend says it writes nicelyGuaranteed to write every timeNot long lasting

JJJ Brand CalculatorAutomatic shut-off to preserve the batteryMinimal programming abilityPrinter makes a lot of noiseUnit is small and compactEasy to programFour-month warrantyPrinter speed is faster than mostHas many featuresKeypad is slightly uncomfortable

Unique Negative

Circle Brand PenWrites on a variety of surfacesTip breaks down with excessive pressureSkips on occasionAvailable in a wide range of colorsRefiilabieBecomes uncomfortable with prolonged useGuaranteed to write every timeFriend says it has a nice feel to itNot attractively styled

JJJ Brand CalculatorPrinter output is easy to readSmall memory bufferBattery needs to be replaced oftenDependableEasy to programSmall numbers in the displayKeypad designed for rapid entryDisplay will accommodate 16 digitsHas only the basic feature

SubjectsSubjects were 317 students recruited from a subject pool

at three large midwestern universities. Of those, 16 nonna-tive English-speaking students and three students who didnot follow directions were excluded from the analysis."* Thefinal, usable sample consisted of 298 students. Subjects werenaive to the purpose ofthe study, and each student receivedextra course credit as an incentive to participate.

'Three of the students refused to return the first ix>oklet to the enve-lope and referred back to the prtKiuct descriptions when answering thepreference question. These students were dropped from the sample.

Independent VariablesThe individual characteristic, need for cognition, was

measured and the two situational variables, involvement,and valence of the unique attributes, were manipulated.The valence of the unique attributes is used to determinethe magnitude ofthe direction-of-comparison effect. Needfor cognition was measured via the 18-item scale devel-oped by Cacioppo, Petty, and Kao (1984) to identify aperson's inherent desire to engage in elaborate processing.Responses to the 18 statements (e.g., "I would prefercomplex to simple problems," "Thinking is not my ideaof fun" [reverse coded], etc.) were scored on a scaleranging from -1-4 (very strong agreement) to —4 (very


strong disagreement). As in previous studies, a mediansplit was used to separate participants into high and lowneed for cognition groups based on the summed tneasure(e.g., Cacioppo and Petty 1982; Cacioppo et al. 1996).The resulting mean composite need for cognition scoreswere signihcantly different between the high and the lowneed for cognition groups (Xi., ^ d for c>.gm<io., = 1-1.high need for cogniiion = 35.6. p < .0001) and the scale exhib-

ited a high degree of reliability ( a = .89).

Two categories were used to operationalize high/lowinvolvetnent, and each subject evaluated both categories.The design was counterbalanced so that each category,pens and hand calculators, was evaluated as the high (orlow) involvement category by half of the subjects. Thelow involvetnent category was the first category evaluatedby each subject and was deseribed as having "no correctanswer." The high involvement category was describedto the subject as a "calibration" category in which thetwo options presented had been rated by a panel of expertjudges and that they would be rewarded for accuracy (i.e..matching the panel's response) by being entered in alottery for a cash prize. The prizes offered included a firstprize of $25, a second prize of $10, and a third prize of$5. It was necessary to order the questions in the question-naire such that the high involvement category (financialincentives for accuracy) was positioned after the lowinvolvement category (no financial incentives) to avoidpossible carryover etfects.

Involvement has been described to incorporate morethan product class involvement (Johnson atid Eagly 1989)and can be extended to include involvement with thejudgment. This manipulation of involvement "links theissues to anticipated outcomes." and it creates "an ex-plicit personal goal that one expects to obtain relativelysoon, mainly by one's own efforts" (Johnson and Eagly1989, p. 293). Moreover, the manipulation increases thelikelihood that participants will be "motivated to evaluatethe brands featured" (Petty and Cacioppo 1990, p. 372).The current involvement manipulation has personal rele-vance, control over the outcome, and the financial rewardor risk that is directly related to something controllableby the subject (i.e., correctly answering the questions).

Order of presentation was counterbalanced such thathalf of the subjects were presented with the descriptionof each brand first. In addition, half of the subjects sawbrand pairs where the unique attributes were positive, andhalf saw pairs where the unique attributes were negative.The brand descriptions are presented in Exhibit 1.

Dependent VariablesThe dependent variable for this experiment, preference,

was tiieasured via a relative quality measure using twosemantic differential scales (i.e.. one for each brand)ranging from "extremely low quality" (1) to "extremelyhigh quality" {10). This measure is consistent with pa.stresearch that has used both product perceptions (Hochand Ha 1986J and quality difference measures (Ha and

Hoch 1989) to measure preference. In addition, by usingthis composite relative-quality measure, the analysis canfocus on the extremity of preference rather than on asimple yes or no choice. For the present study, the prefer-ence measure was created by subtracting the reportedquality for the first brand presented (i.e.. the referent)from the quality reported for the second brand presented,(i.e., the subject). Hence, negative values on this indexindicate a preference for the referent, and positive valuesindicate a preference for the subject of comparison.

I

ProcedureEach subject was presented with a set of booklets con-

taining descriptions of four brands (two from one cate-gory, pens, and two from another category, calculators).Each description was presented on a separate page andconsisted of the brand name as well as a list of the attri-butes for which some attributes were shared while otherswere unique. For the positive unique cell, the unique attri-butes were positive and the shared attributes were nega-tive; and for the negative unique cell, the unique attributeswere negative and the shared attributes were positive. Thepreference measure was administered after the respectivebooklets describing the brands were removed. Finally,manipulation checks and need for cognition scales wereadministered.

Manipulation Checks

A manipulation check was performed by measuring theparticipants' level of involvement with each of the twocategories via a six-item, seven-point scale similar to thatused by Swinyard (1993). The bipolar adjectives in-cluded important/unimportant. relevant/irrelevant,means a lot to me/means nothing to me. valuable/worth-less, matters to me/does not matter to me. undesirable/desirable. This involvetnent scale was reliable for boththe low and the high involvement categories ( a = .89and a = .93, respectively). These results indicate thatthe financial incentive manipulation of involvement was

htghly effect ive (AIQ^ mvolvcmcm = 22.1 vs . .A i h involvcmcm= 243, p < .0001).

In addition, a check was performed to ensure that needfor cognition was not confounded with the measured levelof involvement. Neither involvement measure was sig-nificantly correlated with need for cognition scores.

The product categories (pens and calculators) werecounterbalanced such that each served as the low involve-ment category for some subjects and the high involvementcategory for others. A test was conducted to determinewhether the two product categories could be collapsedwithin each involvement condition. No main effect ofproduct category on preference and no interactions werefound (F ' s < I ) . Moreover, the presence ofthe categoryorder variable in the MANOVA did not change the statis-tical relationships between the independent variables andthe dependent variables. Thus, all subsequent analyses


TABLE 1

MEANS AND STANDARD DEVIATIONS: EXPERIMENT 1

TABLE 2

MANOVA: EXPERIMENT 1

Total (N = 298)

High involvementLow need for cognition:

Positive valenceNegative valence

High need for cognition:Positive valenceNegative valence

Low involvementLow need for cognition:

Positive valenceNegafive valence


Preference

K

.319

.493

.262

.580-.103-.027

-.203-.350

.901-.574

SD

2.681

2.4172.661

2.6972.9332.256

2.0972.334

2.1662.139

Source of variation

Between-subjects effects:ConstantValenceNFCValence x NFC

With in-subjects effects:InvolvementValence x involvementNFC X involvementValence x NFC x involvement

NOTE.—NFC = need for cognition.

MS

9.3959.473.36

29.33

19.664.64

12.427.08

Preference

F

1.509.51

.544.69

3.42.81

2.161.23

SignificanceOfF

.22<.OO1

.46

.03

.06

.36

.14

.26

aggregate the data across categories within each level ofinvolvement (Keppel 1982).

EXPERIMENT 1 RESULTS

Preference

Because one within-subjects factor (involvement) andtwo between-subjects factors (valence and need for cogni-tion) were employed, a mixed ANOVA was performed(see Tables I and 2 for descriptive statistics and MA-NOVA results). Consistent with Hypothesis la, the datashow that individuals who score high on the need forcognition scale are more likely to be influenced by thedirection-of-comparison manipulation than are those whoscore low on the need for cognition seale. To show adirection-of-comparison effect, individuals who wereasked to choose between objects with unique positiveattributes should prefer the subject of comparison, andthose who were asked to choose between objects withunique negative attributes should prefer the referent ofcomparison. Groups that do not exhibit a direction-of-comparison effect will show no difference in preferencebased on unique attribute valence. A significant interac-tion between need for cognition and unique attribute va-lence was found on preference (F{\. 294) = 4.69,p - .03, partial Tl = .015) and a significant main effectfor unique attribute valence was found (F{\, 294) = 9.51.p = .001, partial Ti" = .03). consistent with Hypothesisla. The significant interactive effect of need for cognitionand valence on preference shows that those with highneed for cognition have a systematic bias in their prefer-ence. For items with unique positive attributes, the prefer-ence is significantly greater than the no-preference point(p = .003) showing a preference for the subject of com-parison. When considering items with unique negativeattributes, the preference is significantly less than the no-

preference point (p = .05) showing a preference for thereferent of comparison. On the contrary, low need forcognition individuals had an equal preference for bothalternatives (i.e.. preference scores were not significantlydifferent from zero; p > .2; see Fig. 2).

The planned contrasts suggest that the involvement hy-pothesis. Hypothesis 2, is supported. First, among lowinvolvement individuals, need for cognition moderatesthe direction-of-comparison effect (Hypothesis 2a). Thatis, those who scored high on the need for cognition scaleexhibit a direction-of-comparison effect, while those whoscored low on the need for cognition scale exhibit nosuch effect. Using planned contrast analysis (Rosenthaland Rosnow 1985). the preference score reported for thelow involvement/high need for cognition individuals whochose between two items with unique negative attributesis significantly lower than the same category of individu-als who chose between two items with unique positive

dliriUUlCS \ A]ow involvemenl, high need Tor cognilion. uniiiuc piiNilivc . "U I ,

"low involvcmcni. high need for cognilioii, unique nepalive . . J / H , i\ Z^H- )

= 4.16. /7 < .01). Conversely, the preference scores re-ported by low involvement individuals who scored lowon the need for cognition scale were not significantlydij ferent across the unique attribute valence conditions

1 ,I '^low involvemenr. low need for cognilion, unique posiUve

•^low jnvolvemeni, low need for cogniiion, unique negaiive .JJ, *

Fig. 2).Further, among high involvement individuals (Hypoth-

esis 2b). the direction-of-comparison effect is nonsig-nificant for both low and high need for cognition groups.The planned contrast analysis showed that no cell in Ihchigh involvement condition was significantly differentfrom any other high involvement cell (planned contrast/(294) = 1.63, p = .19)."

''When inlcraclions involving differences between specific cells arepredicted, planned comparisons should be conducted instead of evaluat-


FIGURE 2

PREFERENCE: EXPERIMENT 1

1.00

U

'S3(0

a.

0.00 -

0)

Q.

-1.00

0.49

•

<

0.26 • \ . . ^ ^ ^ , ' '

-0.20 A'

X - . ^

-0,35

>0.90

___- .: ^0.581

1

-0.10

-0.57

LoNFC Hi NFC

High involvement/ unique positive attributesLow involvement/ unique positive attributes

-«—High involvement/ unique negative attributes,•X- Lovtr involvement/ unique negative attributes

DiscussionThe data provide insight into the predicted person-by-

situation interaction on the preference judgment processand support the contention that person-by-situation influ-ences are important in behavioral decision research. Spe-cifically, it appears that individuals who are high in needfor cognition are influenced by the direction-of-compari-son effect, whereas those who are low in need for cogni-tion are not influenced by this effect. The interaction be-tween involvement and need for cognition suggests thatan increased level of involvement in a judgment will in-

ing overall A-iesis (Hays 1981; Keppel 1982: Kirk 1982). In faci.Roscnlhal and Rosnow ( 198f>) suggest ihal "there are relatively fewcircumstances under which we will want to use omnibus F-tesls. Whatwe will be getting in return tor the small amount of computation requiredlo employ contrasts is {n) very much greater statistical power and (/>)very much greater clarity of substantive interpretation of research re-sults" (p. 4) . Nevertheless, the reader may be interested in the resultsof otnnibus F-tests. Ttie /•-value for the main etfect of involvement onthe preference measure was F(\. 294) - 3.42 ip - .065, partial Ti*= .012). The F-values for the interactive effects that include involve-ment were F{\, 294) = .81 (N.S.), 2.16 (N.S.), and 1.23 (N.S.) forthe two-way interactions (i.e., with valence and need for cognition) andthe three-way interactions, respectively.

crease the motivation to produce a correct response.Therefore, for high need for cognition individuals,involvement appears to moderate the direction-of-com-parison effect. It is hypothesized that individuals who"enjoy thinking" (i.e., high need for cognition individu-als) use an attribute-based processing strategy in formu-lating preferences. Conversely, we hypothesize that thelow need for cognition group uses an attitude-based pro-cessing strategy. Because attributes are not accessible atthe time of judgment for this group, involvetnent doesnot affect the processing strategy or infiuence preference.Conversely, high need for cognition individuals, forwhom the attributes are accessible at the time of judg-ment, may be motivated to consider more attributes underconditions of high involvement. The planned contrastsshow a significant direction-of-comparison effect for highneed for cognition individuals under low involvementconditions but a nonsignificant effect under high involve-ment conditions. The inference that can be drawn fromthese planned comparisons is that the involvement manip-ulation tnay motivate the individual to increase consider-ation of all infonnation that is accessible at the time ofjudgment, but may not motivate increased accessibilityof the attributes. This inference will be tested in experi-ment 2.


EXPERIMENT 2

OverviewExperiment 2 replicates and extends experiment 1 by

investigating the proces.sing strategies that may mediatethe interactions identified. Experiment 2 employed a dif-ferent measure of preference to permit the use of converg-ing operations. In addition, attribute recall was measuredto investigate its influence as a mediator, and a measureof the thought process used (i.e., analytic vs. intuitiveprocessing) was included. A 2 X 2 X 2 mixed factorialdesign was employed.

SubjectsThe sample for this study was similar to that used

in experiment 1. Two hundred forty-four undergraduatestudents were recruited from a subject pool at two largemidwestern universities. Subjects were naive to the pur-pose of the study and received extra course credit forparticipation. All of the subjects followed the directions;however, 12 subjects declined to indicate a product pref-erence on at least one of the preference questions. There-fore, analyses using these questions were run on the re-maining 232 subjects.

Stimuli, Procedure, and Independent VariablesThe brand descriptions for all brands in both categories

and all experimental procedures used are identical to thosein experiment 1. Also, the same independent variableswere used {i.e., the individual characteristic, need forcognition, and the two situational variables, involvementand valence of the unique attributes). All manipulationchecks and pooling tests were conducted just as describedin experiment I and indicated that all subsequent analysescould be aggregated across categories within each of thehigh and low involvement classifications (Keppel 1982).

Dependent MeasuresA converging operations approach was used by em-

ploying a new preference measure on a scale from"strongly prefer the [referent] brand" (1) to "stronglyprefer the [subject! brand" (12). A line was drawn be-tween points 6 and 7 on the scale, and each half of thescale was clearly marked as to which brand was beingchosen. A low score on this forced-choice measure indi-cates a preference for the referent, and a high score indi-cates a preference for the subject of comparison.

In order to determine which attributes were accessibleduring preference formation, attribute recall was assessedfor each brand. The order of brand attribute recall wascounterbalanced such that each brand was assessed firstwithin its category for half of the participants and theother brand was assessed first for the remaining partici-pants. Two judges coded these open-ended responses in-dependently. An attribute received a one if it was recalled

and a zero if it was not recalled. Ninety-six percent ofthe coded responses matched between the two judges anddiscrepancies were resolved via discussion.

Finally, a recall index was calculated using the equationdescribed below. This recall index was constructed tocapture the asymmetric recall described by Tversky's(1977) feature-matching model within a single index thatcould then be used as a mediator. Specifically, Tversky's(1977) feature-match ing model suggests that a respon-dent will heavily weigh the unique attributes of the subjectand neglect the unique attributes of the referent. Asym-metric attribute recall should mediate preference for thesubject in the unique positive condition and mediate pref-erence for the referent in the unique negative condition.The following index was employed: RI = (PAs - PAH)+ (NAR - NAs). where RI ^ recall index, PAs = percentpositive attributes recalled from the subject, PAR = per-cent positive attributes recalled from the referent, NAR= percent negative attributes recalled from the referent,and NAs = percent negative attributes recalled from thesubject. A positive score on this index indicates asymmet-ric attribute recall favoring the subject of comparison. Anegative score indicates asymmetric recall favoring thereferent of comparison. A score of zero indicates no asym-metry. As the recall index deviates from zero, the magni-tude of the direction-of-comparison effect should in-crease.

Relative attribute recall is important in invesligatiiig theunderlying thought process used: however, this measure issimply a count of attributes recalled from the stimuli. Inorder to ascertain the relative worth of the items recalled,and thus the usefulness of those items, subjects wereasked to rate each recalled item on the "importance to[their] decision." This importance of recall has beenshown to affect the relationship between recall and atti-tude (Chattopadhay and Alba 1988). Subjects were askedto turn back to the page on which they listed the attributesand place a number next to each item. The four-pointscale ranged from "very important to my decision" to"very unimportant to my decision." These rankings wererecoded such that "very important" attributes were as-signed a rating of 4. "very unimportant" attributes wereassigned a rating of 1, and attributes nol explicitly ratedwere excluded from the analysis. Fewer than 4 percentof recalled attributes were left unrated.

A six-item measure of analytic versus intuitive pro-cessing was also developed: "The answer just came tome" (reversecoded): "In making my decision,! focusedmore on my personal impressions and feelings rather thanon complex tradeoffs between attributes" (reversecoded): "I tried to use as much attribute information aspossible": "I carefully compared the two brands on sev-eral different attributes"; "My decision was based onfacts rather than on general impressions and feelings";"My decision was based on careful thinking and reason-ing." Each statement was rated on a six-point scale rang-ing from "strongly disagree" (1) to "strongly agree"(6). The scale was validated using confirmatory factor


TABLE 3

MEANS AND STANDARD DEVIATIONS: EXPERIMENT 2

Total (W = 298)

High involvementLov\/ need for cognition:



Lovtf involvementLow need for cognition:



Preference

6.10

6.625.77

7.005,256.37

6.656,28

8,004.94

SD

2.73

2JXA2J^

2.T32i30

iMi

3.923.02

Relative recall

-.0023

.0206 1-.0150

.2717-.2110-.0100

,0302-.0320

.2660-.2301

SD

,4809

.3898

.3489

,4588.5535.4750

.4777

.4712

.4163

.4075

Analytic/intuitivethought

43.9

39.345.3

45.545.739.5

35.040.2

40.242.4

SD

13,2

12.713.0

13,313.112.9

12.012.3

13.912.4

analysis performed via LISREL 8.12 (GFI = .98. AGFI= .95, RMSR = .065; x ' = H-IO, df = 9, p = .27;Cronbach's a ^ .75).

To ensure that the analytic/intuitive processing con-stnact measures a different underlying dimension thanneed for cognition, discriminant validity was assessed(Peter 1981). To do this, two methods were employed.First, both factor structures were evaluated via LISREL8.12 using a model with two latent variables (i.e., ana-lytic/intuitive thought and need for cognition). The twolatent variables are not signihcantly correlated (O = .10,N.S.). Second, the average variance extracted (AVE)method was used (Fomell and Larcker 198!). Both ana-lytic/intuitive processing and need for cognition show agreater variance captured by the construct than capturedby measurement error as shown by the AVE (.34 and. 13. respectively) being greater than the squared structurallink between the two constructs (<i>- = .01). Therefore,lhe constructs are empirically distinct. This discriminantvalidity is confirmed using the confidence interval proce-dure (Gerbing and Anderson 1988) showing a high likeli-hood that two distinct constructs are measured.

EXPERIMENT 2 RESULTS

Moderating Influence of Need for Cognition

A mixed ANOVA with one within-subjects factor(involvement) and two between-subjects factors (valenceand need tor cognition) was preformed on preferencejudgments. The data replicate the results found in experi-ment 1 and support the hypothesis that preference is mod-erated by need for cognition. {See Tables 3 and 4 forMANOVA results.) As suggested by Hypothesis la, indi-viduals who score high on the need for cognition scalewere more likely to be influenced by direction of compari-

son than were those who scored lower. That is, high needfor cognition individuals preferred the subject of compari-son when the decision was between two brands withun ique pos i t ive a t t r ibutes (.^|,,ph need for cognition, unique positive= 7.0 and 8.0 for high and low involvement groups, re-spectively ) and the referent of comparison when the deci-sion was between two brands with unique negative attri-butes (^highnc«lfnr c<.gnitmn..,niq.,oncgi.Iivc = 5.3 and 4.9 for highg g q g

and low involvement groups, respectively). The meanswere significantly different (p < .001 ) for both high andlow involvement groups (see Fig. 3). Low need for cogni-tion individuals did not report a significant difference inpre fe rence {Xi,,* n cd for cognition, unique ,v.siii>L- = fi-^ anti 6 .7 .

^low n..K) f.,, cognilion. unique ncgaiivc = 5.8 and 6.3 foi" high and

low involvement, respectively; p > .14). The significantinteractive effect of need for cognition with valence onpreference ( F ( I . 227) = 11.038. p < .001, partial r]~= .046) was consistent with Hypothesis la and suggeststhat need for cognition did indeed moderate the direction-of-comparison effect.

Also, the significant three-way interaction betweeninvolvement, need for cognition, and tinique attribute va-lence (F( l , 227) = 3.763, p = .05. partial n ' = -02)supported Hypothesis 2. Among low involvement individu-als, the direction-of-comparison effect was not evident forthe low need for cognition group but was evident for thehigh need for cognition group. The planned contrast analy-sis (Rosenthal and Rosnow 1985) showed that the low needfor cognition (low involvement) group reported preferencescores that were not significantly different from each other(/ < I) and not significantly different from the no-prcfer-ence point (i.e.. a value of 6.5; see Fig. 3). The high needfor cognition {low involvement) group, in contrast, reportedpreference scores that were both significantly different fromeach other (; ^ -5.53. p < .001) and signihcantly differentfrom the no-preference point (p < .(X)l).

TABLE 4


Source of variation

Between-subjects effects:ConstantNFCValenceValence x NFCRecall (low involvement)Recall (low involvement)

Within-subjects effects:InvolvementNFC X involvementValence x involvementValence x NFC x involvementInvolvement x recall {low

involvement)Involvement x recall (high

involvement)

DV

MS

1,812.60 2.20

254.9190.27

11.48.24

5.4823.87

= Preference

F

,223.00.03

31.1711.04

1.81.04.86

3.76

SignificanceOfF

<.OO1.87

<.OO1<.OO1

.18

.84

.35

.05

DV

MS

.075

.0658.7005.800

.0075,0024.0116.0011

= Relative recall

F

.32

.2736.9124.70

.05

.02

.07

.01

SignificanceOfF

.57

.60<.OO1<.OO1

.83

.90

.79

.93

DV =(recall

MS

Preferencecontrolled)

SianificanceF

17,910.6 3,304.111.33

32.931.74

188.92333.35

12.24.22.01

7.68

135.16

17.21

.256.08

.3234.8561.50

2.12.04.01

1.22

23.38

2.98

OfF

<.OO1.620.014.572

<.OO1<.OO1

.147

.845

.970

.270

<.OO1

.086

NOTE.—NFC = need for cognition; DV = dependent variable.

FIGURE 3

PREFERENCE: EXPERIMENT 2

.13

CO

IQ.

Lo NFC Hi NFC

High involvement/unique positive attributes

- Lovt> involvement/ unique positive attributes- • — High involvement/ unique negative attributes

•X- Low involvement/ unique negative attributes


FIGURE 4

RELATIVE RECALL: EXPERIMENT 2

0.30

0.00

Lo NFC Hi NFC

— High involvement/ unique positive attributes- Low involvement/ unique positive attributes

-•—High involvement/ unique negative attributes•X- Low involvement/ unique negative attributes.

Although the direction-of-compari.son effect was stillevident among high involvement/high need for cognitionindividuals, the effect was not as strong as in the lowinvolvement cell. The planned contrasts indicated thatalthtiugh the preference responses were significantly dif-ferent for positive unique attribute condition {X = 7.0)and negative unique attribute condition {X = 5.3;/ = 3.61, p <.OO1), the preference response for the posi-tive unique attribute condition (X = 7.0) was not signifi-cantly different from the no-preference point (/ = 1.203./) ^ .24, compared to 6.5). These results suggest a weakerdirection-of-comparison effect and are consistent withboth experiment 1 and Hypothesis 2.

Test for Mediation

Baron and Kenny's (1986) framework for combiningmediation and moderation was used to test HypothesisIb. First, as shown in the previous section, the interactionof the independent variables, need for cognition andunique attribute valence, significantly influence the de-pendent variable, preference. The mediator (recall index)is significantly affected by the interaction ( f {1, 240)= 24.69. /? < .001; see Fig. 4, Table 4). Also, the media-tor significantly affeeLs preference, in both low {F {\,225) = 34.852. p < .0001) and high <F (1 , 225)= 61.495, p < .0001) involvement groups. Finally, thepreviously significant interaction becomes nonsignificantwhen the recall index is included as a covariate {p = .57).

Additional mediational analyses were performed on re-call for common attributes and recall for unique attributesseparately. The results show that asymmetric recall forunique attributes (but not for common attributes) medi-ates the effect of the need for cognition x unique attributevalence interaction on preference. Specifically, two newindices were calculated to capture the common attributerecall (CI) and the unique attribute recall (UI). For theunique positive attribute condition the unique recall indexmeasured the relative recall of positive attributes andcotnmon recall index measured the relative recall of nega-tive attributes. For the unique negative condition, the indi-ces were reversed. Next, Baron and Kenny's (1986) me-diation analysis was repeated for each new subindex. Forthe unique recall index, the interaction (between needfor cognition and unique attribute valence) affects themediator ip < .001). Then, when the original MANOVAis run with the unique attribute recall as a covariate. theunique attribute recall significantly (p < .001) affectspreference, and the two-way interaction becomes nonsig-nificant (p = .19). Thus unique recall mediates the effect.Conversely, the common recall index does not show me-diation because, when the original MANOVA is run withthe comtnon index as the covariate, the two-way interac-tion remains significant [p = .008). Thus, asymmetricattribute recall (specifically unique attribute recall) medi-ates the interactive influence of" need for cognition andunique attribute valence on preference.

The hypotheses slate that the involvement interaction


Analytic

Intuitive

FIGURE 5

ANALYTIC VERSUS INTUITIVE PROCESSING: EXPERIMENT 2

48.00

46.00

44.00

42.00

40.00

38.00

36.00

34.00

45.31

40.2139.34

35.04,

Lo NFC Hi NFC

•—*—High involvemant/ unique positive attributes — • — H i g h involvement/ unique negative attributes

- -A- Low involvemant/unique positive attributes - -X- Low invoivement/ unique negative attributes

with need for cognition and unique attribute valence isdriven by a difference in extent of processing rather thanasymmetry in processing. According to Baron and Kenny(1986), for recall to be a mediator ofthe involvementX need for cognition X valence three-way interaction onpreference, recall must show a similar significant relation-ship to the moderating variables. This is not the case. Notonly is the three-way interaction nonsignificant (F < 1).all main effects and interactions including involvementare nonsignificant {F\ < I ) .

Second, the analytic/intuitive processing scale is testedagainst the hypotheses. As expected, participants indi-cated that they engaged in higher levels of analytic think-ing in high (vs. low) involvement ( F ( l , 240) = 47.307,p < .0001, r|" = .20) and unique negative (vs. positive)attribute conditions (F (1, 240) = 4.9. p = .02, TI - = .02).Also, high (vs. low) need for cognition individuals indi-cated a higher level of analytic thought {F (1, 240)= 5.3, p = .022, ri^ = .02). See Figure 5 and Ta-ble 5.

The three-way interaction on preference was testedwhile controlling for analytic/intuitive processing. Theanalytic/intuitive thought associated with the highinvolvement judgment is significantly related to prefer-ence iF (1, 225) = 4.172, p = .04), but the analytic/intuitive thought associated with the low involvementjudgment is nonsignificant (F ( I, 225) = 2.078,p = .15).At the same time, the three-way interaction significancelevel changes to .08. Further, there are no other main orinteractive effects of involvement. The valence main ef-fect and the interaction between unique attribute valenceand need for cognition remain significant (F (1 , 225)= 3\, p < .0001, partial T]' = .13; F ( l . 225) = 9.76,p = .002. partial rj" = .04, respectively). This suggests

that the three-way interaction is partially mediated byanalytic/intuitive thought for the high involvement deci-sion.

To further investigate the underlying processing differ-ences between high and low need for cognition subjects,the average importance of the recalled attributes was in-vestigated along with the absolute level of recall. Thosesubjects who were using attribute-based processing (i.e..high need for cognition subjects) should have recalled arelatively greater number of important attributes, andthose subjects who were using attitude-based processing(i.e., low need for cognition subjects) should have re-called fewer and less important attributes. In order to testthis, two planned /-tests were employed. The percentageof attributes recalled and the average importance of thoseattributes were tested between high and low need forcognition groups. The data showed that low (vs. high)need for cognition subjects recalled significantly fewer

attributes ( Xu,w nc«l for mgnili-n = 3 7 percen t , X^,^h ,,cc<l tof ^.y^nUwu— 53 percent, p < .001) overall, and low need for cogni-tion subjects assigned a significantly lower averageimportance rating to each of the recalled attributes'•^]i)w need for cognilkin ~ *--O a n d Ahigh nctil lor cognilion — 2 . 8 .p - .05). These pattems of recall are consistent for boththe high and low involvement categories. We can inferfrom this that while the low need for cognition subjectscan recall some attributes when explicitly asked to do so.the attributes recalled are relatively few and not particu-lariy important to the judgment. Conversely, high needfor cognition subjects recalled significantly more attri-butes that were considered important to the preferencejudgment.

Finally, in order to engage in attribute-by-attributecomparisons, comparable attributes from both brands


TABLE 5


Source of variation

Between-subjects effects:ConstantNFCValenceValence x NFCAnalytic/intuitive (low involvement)Analytic/intuitive (high involvement)

Within-subjects effects:InvolvementNFC X involvementValence x involvementValence x NFC x involvementInvolvement x analytic/intuitive (low)Involvement x analytic/intuitive (high)

DV-

MS

836,292.01.469.81,384.8

567,1

2,470.63.3

10J58.1

Analytic/Intuitive Thought

F

3,015.05.34.92.1

47.3.6.2

1.1

SignificanceO f F

<.OO1.02.02.15

<.OO1.80.65.29

DV - Preference(analytic/intuitive thought i

MS

1.167.700251.407

.31079.08316.83033.794

4.8167.179

.00219,148

.85811.362

F

144.15031.036

.0389.7632,0784.172

.7601.133

.0033.024

.1351.794

controlled)

SignificanceO f F

<.OO1<.O01

.845

.002,157,042

.384

.285

.957

.083

.713

.182

NOTE.—NFC = need for cognition; DV = dependent variable.

must be accessible at the time the judgment is made.Therefore, high (vs. low) need for cognition .subjectsshould be more likely to recall common attributes to bothbrands because common attributes are directly compara-ble on an attribute-by-attribute basis. As expected, for thelow and high involvement categories, respectively, 62percent and 79 percent of high need for cognition subjectsrecalled more than two common attributes (out of fouror five possible) for both brands under consideration. Thispattern is reversed for low need for cognition subjects,where 67 percent and 52 percent recalled one or fewercommon attributes for both brands. This pattern is sig-nificantly different (x^ = 20.32, p < .001; x^ = 25.62,/? < .001) in both involvement categories.

Discussion

Asymmetric recall for unique attributes mediates theeffect of direction of comparison on preference. The dif-ferences in processing strategy across need for cognitiongroups may cause the direction-of-comparison effect toshape preference among high need for cognition individu-als u.sing an attribute-based processing strategy withoutinfluencing preference among low need for cognition in-dividuals using an attitude-based processing strategy. Al-though this mediation hypothesis has been suggested inthe past, the direct test of asymmetric recall as the media-tor provides evidence for the hypothesized underlyingprocess. Thus, decision makers who exhibit the direction-of-comparison effect are more likely to recall (and thusconsider) the unique (vs. shared) attributes ofthe subjectand neglect the unique attributes of the referent. Whenthe unique attributes are positive, these unique attributesof the subject of comparison are weighted heavily, and.

consequently, the subject is preferred. Conversely, whenthe unique attributes are negative, these negative attri-butes are weighted more heavily, and the referent is pre-ferred.

Further, an increased level of involvement appears toincrease motivation to process (i.e., increased analyticthought) without increasing the accessibility of attributesunder consideration (i.e., recall). This increased motiva-tion minimizes the direction-of-comparison bias amonghigh need for cognition individuals while having no in-fluence on the reported attribute recall. Thus, the higherlevel of analytic thought by high (vs. low) involvementindividuals may be the impetus for reduction in direction-of-comparison bias among high need for cognition indi-viduals. Although the data appear to confimi a similarinvolvement effect among low need for cognition subjects(i.e., increased motivation with no change in altitude-based processing), the lack of change in observed prefer-ence makes the testing of this finding difficult at best.Because we do not expect a change in preference amongthis group (i.e., we expect the null hypothesis to be sup-ported), we are focusing our evaluation on the processindicators and the differences between high and low needfor cognition groups. The relatively low level of recalland the low importance attached to that recall suggeststhat the low (vs. high) need for cognition group is usingthe one or two attributes recalled as a heuristic cue duringthe judgment process and thus they are probably engagingin attitude-ba.sed processing. In future research, this issuecould be more fully evaluated if a .specific measure ofattitude-based processing is included in addition to theattribute-based processing indicator (i.e.. recall). Withthis one caveat, these data appear to confirm the theorythat those who have the attributes accessible at the time


preference is expressed can be motivated to better usethose attributes; however, if the personaHty type is suchthat the decision maker is prone to attitude-based pro-cessing, the extra motivation of a high itivolvement taskdoes not influence preference.

IMPLICATIONS AND CONTRIBUTION

Evidence from two experiments suggests that memory-based judgments can be influenced by attitude- or attri-bute-based processing strategies depending on theperson-by-situation influences that are operative. Both ex-periments support the hypothesis that persons who arehigh in need for cognition are more hkely to use attribute-based processing and be influenced by the direction-of-comparison effect whereas those who are low in need forcognition are not. It also appears this difference betweenindividuals is mediated by asymmetric recall for uniqueattributes. The analyses executed in both experiments sug-gest that involvement in the judgment, which is inducedby a concem about accuracy, increases effort but has littleeffect on the ability to use an attribute-based (vs. attitude-based) judgment strategy. Consequently, low need forcognition individuals (i.e., those who did not have theattributes accessible in memory during the judgment task)were not affected by the involvement manipulation. Theseresults suggest that need for accuracy may motivate theindividual to increase consideration of all accessible in-formation but may not increase the likelihood that moreinformation will be accessible.

Acknowledging the interplay between the person andthe situation is requisite to understanding human behavior(see, e.g.. Bem and Allen 1974; Lewin 1951; Mischel1968; and Punj and Stewart 1983). In fact, some evidencesuggests that although cognitive styles do not vary acrosssituations, people who have different cognitive styles willinterpret (and respond to) the situations differently (Bemand Allen 1974). Recent research in this area has shownthat person-by-situation interactions not only produce ob-viously different behaviors (Haugtvedt et al. 1992). butalso may influence the long-term behaviors even when theimmediate responses appear to be similar across groups(Haugtvedt and Petty 1992). Thus, person-by-situationinteractions will account for more of the interesting vari-ance in behavior than either person or situation effectsalone, and it is only by considering these interactions thatwe can fully make sense of past findings and advanceconsumer decision research (Bagozzi 1993).

With knowledge about when the direction-of-compari-son effect is likely (i.e., when an attribute-based pro-cessing strategy is used), the marketer may decide todiscourage attribute-based processing by encouraging at-titude-based processing when his or her brand is unlikelyto be the focus of comparison. That is, the brand managercould design the marketing campaign to stress the imageof the product and encourage the consumer to considerthe big picture rather than focusing on the individual attri-butes within the advertisements. Conversely, the marketer

may try to encourage attribute-based processing evenamong the low need for cognition individuals by specifi-cally requesting an attribute-based judgment (i.e., "evenif you never remember details, remember these"). Thismay be effective if the brand is likely to be the focus ofcomparison.

Funber. tbe marketer may be able to influence a brand'sposition as the focal object in a preference decision byvarious techniques. Being the first major brand in a newcategory (i.e., pioneering advantage), obtaining a promi-nent position on the retail shelf, or achieving a large shareof voice in the advertising schedule are all likely to makethe brand the focus of comparison for many consumers.It seems reasonable to assume that a prominent shelf posi-tion would be more effective in becoming the focus ofcomparison among the low need for cognition individualswhereas a large share of voice would be more effectivefor the high need for cognition group. The direction-of-comparison effect appears to be asymmetrical in that thehigh need for cognition individuals are influenced by itbut the low need for cognition individuals are not. There-fore, the marketer should choose a strategy that is mostlikely to make the brand the focus of comparison for thehigh need for cognition group.

Further, manufacturers should consider the relative su-periority of their brands before embarking on an increasedinvolvement strategy. It appears that involvement onlymotivates more analytic thought and consideration of theinformation available when preferences are generatedrather than directly influencing the direction-of-compari-son effect among all consumers. For this reason, market-ers may want to influence the involvement associated withthe decision (e.g., "You don't buy a car very often, makesure you've made the best choice") only if the brand isclearly superior to other brands under close scrutiny.

This work fills a gap in the current literature becausethere is a need for research to explore the interactionbetween persona! characteristics and situational variablesthat may moderate decision biases. The two studies re-ported above offer the marketer potentially valuable in-sights into consumer decision making and the moderatingroles of need for cognition and involvement on the direc-tion-of-comparison effect.

The present research raises an important caveat in thecurrent research and theory on the key factors that influencethe level of bias and error that is likely to creep into humanjudgment. Individuals high (vs. low) in need for cognitionare more likely to engage in effortful, data-driven, attribute-based information processing, and less likely to engage inheuristic, theory driven, attitude-based processing. Al-though effortful processing can decrease the level of judg-ment bias and error observed in many cases (see. e.g.,Arkes 1991; Cacioppo et al. 1996). the present researchshows that effortful, attribute-based processing can actuallyincrease bias when directional comparison processes giveprominence to the unique attributes of one brand and de-crease attention to the unique attributes of a less focalbrand. The asymmetric treatment of the unique features


of two competing brands and the direction-of-comparisoneffect in consumer preference is less likely to be observedwhen effortful. attribute-ba.sed comparisons are unlikely tobe perlbrmed. Effort carries no guarantee of accuracy: therelationship between effort and accuracy depends on theinteraction between the nature of the information used as abasis for judgment and the manner of cognitive operationsperibmied on this infonnation.

\ Received September 1996. Revised August 1998.Robert E. Burnkrant served as editor, and BarbaraLoken served as associate editor for this article.]

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