a typology of individual search strategies among purchasers of new automobiles

8/2/2019 A Typology of Individual Search Strategies Among Purchasers of New Automobiles

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Journal of Consumer Research, Inc.

A Typology of Individual Search Strategies Among Purchasers of New AutomobilesAuthor(s): David H. Furse, Girish N. Punj, David W. StewartReviewed work(s):Source: Journal of Consumer Research, Vol. 10, No. 4 (Mar., 1984), pp. 417-431Published by: The University of Chicago PressStable URL: http://www.jstor.org/stable/2488911 .

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A Typology o f Individual Search StrategiesAmong Purchasers o f N e w AutomobilesDAVID H. FURSEGIRISH N. PUNJDAVIDW. STEWART*

Clusteranalysis of questionnaire data was used to identify six distinctive externainformationsearch patterns among purchasers of new automobiles. Two of thshopper clusters had not been clearly specified in prior research-namely, aadvisor-assisted shopper group and a highly self-reliant shopper group. An effoto cross-validate the typology using data obtained from automobile sales personnel was partially uccessful. It s hypothesized that these strategies are reflectionof heuristic decision processes which reflect both individual difference characteristics and the purchase situation.

Previous researchon consumerbehavior has foundthatconsumers employ information search strategies whichcan be distinguished by the amount of external search effortand decision time. Early efforts to identify distinct groupsof consumers characterized by differing information searchstrategies used unidimensional measures of aggregatesearch activity (Katona and Mueller 1955, Newman andStaelin 1972). Such aggregate measures have been criti-cized for failing to adequately define differences in patternsof information gathering (Claxton, Fry, and Portis 1974).Very different patterns of search activity may produce sim-ilar aggregate search measures, with the result that poten-tially important differences in search activity may bemissed.More recently, Newman and Staelin (1973), Claxton etal. (1974), Westbrook and Fornell (1979), and Kiel andLayton (1981) have sought to more fully examine the rich-ness of the pattern of information search. This literaturesuggests that there are ratherdistinct patternsof informationsearch, at least among purchasers of durable goods. All ofthe research to date has examined search for high-cost itemssuch as appliances and automobiles. Despite differences inmeasurement, samples, and method of data analysis, thereis a remarkable degree of agreement in the findings. Pat-terns of search emphasizing retail outlets, interpersonalsources, little active search, extensive search of many

sources, and a more modest amount of search of severasources have been identified in one or more previous studies. Yet much of the prior research has been restricted tan examination of search activities, and so has not providea detailed characterization of individuals who use a particular search strategy on other, non-search-related variablesThe present study was designed to consolidate and extenprevious efforts in three ways:1. First, we attempt ofocus more extensivelyon the participation of others in the decision-makingunit. Previostudies have not consideredthe involvement of anyonbut the principaldecisionmakern the information earc

process, although othershave been examinedas sourcof information.2. Second, we attempt o validatefindingsbasedon the anaysis of consumerself-reportwith data gathered ndepedently rom sales personnel. While studies to date havrelied on data providedsolely by the buyer, characterissearchpatterns hould also be evident to sales personnNot all search activities of shoppers may be visible tsales personnel,but evidence of prominent hoppingactivity patterns houldbe obtainable.3. Our thirdobjectiveis to cross-validateand synthesize hfindings of previous research and offera speculativeexploratory framework for the processes which produccharacteristic earch patternis.The larger samplesize othe present study providedan opportunity or split-havalidationof the derivedsearchpatternsand for detaileanalysesof the characteristics f smaller, yet significanconsumerclusters.We also examined a very largeset odata on non-search-relatedharacteristics f the clusterwhichallowed for a richerand morecompletedescriptiof the types of individualswho use particular atternsosearch. This additionaldescriptive nformationprovida basis for theorizingabout the determinants f searstrategiesamongconsumers.

*David H. Furse is President, Nashville Consulting Group, 2200 Hills-boro Road, Nashville, TN 37212. Girish N. Punj is Assistant Professorof Marketing, University of Connecticut, Storrs, CT 06268. David W.Stewart is Associate Professor of Management, Owen Graduate School ofManagement, Vanderbilt University, Nashville, TN 37203. This studywas supported by a doctoral dissertation grant from the American Mar-keting Association, a grant from the University of Maine at Orono, andthe 1981 and 1982 Dean's Funds for Faculty Research of the Owen Grad-uate School of Management, Vanderbilt University.417 ?) JOURNAL OF CONSUMER RESEARCH e Vol. 10 e March 198


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418 THE JOURNALOF CONSUMER RESEARCThe study was carried out in two stages: (1) an extensionof earlier findings using buyer-generated data, and (2) aneffort at consensual validation of the findings from buyer-generated data using data obtained from sales personnel.

NEW CAR BUYER STUDY'Subjects/Data CollectionData for the study were generated by consumers who hadpurchased a new automobile during the period of Septemberto November 1978, in the cities of Buffalo, Milwaukee,and Phoenix. Stratified random samples taken from a com-plete census obtained from R.L. Polk of all households thathad purchased new automobiles during a specified time pe-riod were used in the study. Most respondents were con-tacted by telephone and asked to participate in the study.The individuals identified as the principal decisionmakerswere mailed a questionnaire between late February and May

1979. Average time from purchase to receipt of the ques-tionnaire was 28 weeks. The questionnaire solicited infor-mation on the activities and decisions undertaken by therespondent or others in selecting their new automobile. Thequestionnaire requested information such as the number ofdealer visits, activities at each dealership visited, out-of-store information search behavior (e.g., talking to friends,reading magazines), number and types of previous carsowned, satisfaction with car purchased, and so on. Infor-mation was also requested concerning the automobile pur-chased, the dealership from which it was purchased, andthe price paid for the automobile. Demographic informationconcerning age, sex, education, income, and so on was alsocollected.The variables of primary interest were those concernedwith the amount of time the purchaser or someone elsespent on a variety of information search activities, rangingfrom reading advertisements in the newspaper to test driv-ing automobiles. There were 18 of these items. A six-pointscale was employed to obtain estimates of the time spenton each activity: (1) no time; (2) up to '/2 hour; (3) morethan 1/2 hour but less than 2 hours; (4) more than 2 hoursbut less than 5 hours; (5) more than 5 hours but less than10 hours; and (6) more than 10 hours. Otherquestions askedfor the total number of visits to dealers and the number ofdifferent dealers visited.Twenty-four items were used in the classification phase

of the study. To be consistent with earlier studies, the itemsselected for classification were related to search activities,rather than to the content or outcome of the search. Itemselection was based on the findings of previous studies ofsearch patterns and on other studies of search behavior.

Table 1 provides a comparison of the variables used in thclassification phase of the present study with those used ithe Claxton et al. (1974) and Kiel and Layton (1981) studies, which are the two studies most similar to the presenone in methodology.Data were obtained from 1,056 respondents, and 1,03responses were actually used. The survey response rate wa38 percent. However, 61 percent of respondents who werreached by telephone returned a completed questionnairewhile only 13 percent of households that could not be contacted by phone responded. Twenty-five respondents wereliminated because of excessive missing and/or improbabresponses. For other respondents, missing responses werreplaced by the mean for the entire sample. To ensure thinternal validity of the data, a series of preliminary analysewas carried out.First, responses to separate items that requested essentially the same information were compared. This multipleresponse feature was deliberately included in the questionnaire to allow a check for internal consistency. The detaileresults of these comparisons are too numerous to reporther(about 15 consistency checks were performed), but in eaccase better than 98 percent of the respondents displayeconsistency across the multiple responses. These checkincluded determining actual agreement between severameasures of the same event-e.g., number of decisionmakers, whether the consumer was a first-time buyer-aswell as the more frequently used reliability coefficientamong multiple measures of time spent, preference, and son.To analyze the effect of "forgetting," a variable labele"duration" was constructed to measure the elapsed calendar time between the purchase of the car and the return othe questionnaire. The total sample was split into four subsamples corresponding to the four quartiles on this variableThe Kruskal-Wallis test (one-way analysis of variance branks) was used to compare the four subsamples on a selective set of variables related to information search. Thnull hypothesis was that the four subsamples were drawfrom four identically distributed populations. A chi-squarestatistic was used to determine rejection/nonrejection of thnull hypothesis. Using a 0.05 confidence level, the nuhypothesis could be rejected for only five of the 23 variableexamined. Although this number is greaterthan what woulbe obtained by chance, an examination of the mean rankshowed no systematic directional relationships. The lack oassociation of the variable "duration" with the distributioof responses seems to indicate that respondents were abto recall the sources used-or at least that if there waforgetting, this forgetting did not take place systematicallduring the period when any of the respondents were asketo fill out the questionnaires.Next, a set of analyses compared the sample distributionfor the variables "dealer visits," "total purchases," an"total makes" with those reported in previous studies (seStaelin 1969; U.S. News and World Report 1974, 1975and Newsweek 1977a, 1977b). No appreciable differencewere found. Finally, an analysis similar to the one reporte

'A preliminary report of the findings of this study was made at the 1981meeting of the Association for Consumer Research. The present reportrepresents a reanalysis of earlier findings using a larger data set and ad-ditional variables. Donald Granbois, Roger Layton, and two anonymousreviewers of the earlierreport made useful suggestions for furtheranalyses,which we have tried to incorporate in the current report.


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SEARCH STRATEGIES 419TABLE 1

RELATIONSHIPS BETWEEN SEARCH ACTIVITIES EXAMINED IN THREE DIFFERENT STUDIESaClaxton, Fry and Portis (1974) Kiel and Layton (1981) Furse, Punj, and Stewart (1984)

1. Considered alternate use of money Number of ads recalled Time spent talking to salespersons(hours)b2. Considered alternate brands Number of owners contacted Time spent looking at ads (hours)b3. Considered alternate price levels Number of opinion leaders contacted Time spent talking to others (hours)b4. Salesmen mentioned as source of Number of trips to dealers Purchase pal help (hours)cinformation5. Stores mentioned as source of Number of retailers visited Total number of visits to dealerinformation6. Advertisments mentioned as source Time spent at retailers Number of different dealers visitedof information7. Family mentioned as source of Total introspection time Time spent walking around dealerinformation showrooms (hours)b8. Friends mentioned as source of Total search time Total number of different searchinformation activities9. Other sources mentioned *Introspection and search time factor Time spent reading books and

magazines (hours)b10. Price mentioned as important Number of makes considered Time spent reading manufacturer'sfeature pamphlets (hours)b11. Brand mentioned as important Number of phone calls made Time spent reading about car ratingsfeature (hours)b12. Style mentioned as important Number of items of written information Regularly reads consumer magazines,feature used such as Consumer Reportsd13. Quality mentioned as important Number of other dealers considered Time spent test driving cars (hours)bfeature14. Size mentioned as important *Retailer search factore *Retailer visits factorefeature

*15. Total store visits *Media search factore *Out-of-store search factore16. Number of stores visited *Interpersonal search factore *Interpersonal search factore17. Maximum visits to single store *ln-store search factore

*18. Deliberation time (duration of *Involvement of otheresearch)19. Number of alternatives considered(sum of 1-3 above)

*20. Number of sources used (sum of4-9 above)21. Number of features considered

important (sum of 10-14 above)aAsterisks indicate variables serving as the basis for classification.bThese activities included separate measures for the respondent and others in the household; responses ranged from 1 (no hours) to 6 (more than 10 hours).cPurchase pal help involved specific assistance of others within or outside the household who had specific expertise; responses ranged from 0 (no assistance) to 5 (more than 10 hoursdDichotomous variable.eVariables derived from factor analysis.

for "duration" was performed by comparing the responsesfor the telephone precontacted and noncontacted subsam-ples. The null hypothesis was rejected for only one of the23 variables, a number just about equal to chance.In light of the above validity and reliability checks, itwas felt that the data were of high enough quality to merittheir use in the analysis. Two key assumptions concerningthe data should be noted, however. First, it is assumed thatthe amount of information sought is related to time spentin each of the search activities. Second, it is assumed that

auto buyers can provide reasonable estimates of the amounof time spent in various search activities. Previous studiehave made similar assumptions, although it is not cleawhether they are strictly true. Obviously, some consumermake more efficient use of their time and obtain greateinformation than others. Time spent appears to be a reasonable measure of effort expended, but it is not a perfecone. Since the emphasis of the present study is on the reative amounts of time spent on various search activitierather than on absolute time spent, even if consumers disto


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420 THE JOURNAL OF CONSUMER RESEARCtime estimates, the same results will hold unless that dis-tortion varies with each activity.

AnalysisAs a prelude to cluster analysis for identifying searchpatterns, a factor analysis was carried out on the 24 itemsrelated to various search activities. The initial factor anal-ysis was designed (1) to reduce the problem of multiplemeasures of similar constructs being more heavily weightedthan constructs measured by fewer items, and (2) to attemptto replicate dimensions of search activity identified in ear-lier research. A principal components analysis was carriedout and a five-factor solution was indicated by both theeigenroots > 1.0 criterion and a plot of the roots. Thesefive factors were submitted to both varimax and obliquerotations. Although the oblique rotation (direct oblimax,delta = 0.2) did not appreciably change the hyperplanecount (the number of factor loadings between ? 0.10) ob-

tained with the VARIMAX rotation, it did serve to reducemoderate factor loadings and to increase those loadings al-ready high. For this reason the oblique rotation was retainedin the final solution. Table 2 identifies items loading eachfactor.Factor 1 is characterized by items related to dealershipvisits: time spent driving to dealerships, the number of dif-ferent dealers visited, total dealer visits, and the number ofhours spent at dealer showrooms. This factor is very similarto the retailer search factor identified by Kiel and Layton(1981) and was probably represented in the Claxton et al.(1974) and Westbrook and Fomell (1979) studies by thecomposite measure "total store visits." Factor 2 is asso-ciated with the level of personal participation in out-of-storesearch activities, particularly time spent reading ads, carratings, and manufacturer brochures. This factor is alsorelated to the total number of different search activities. Itwas probably represented in the Claxton et al. study by acomposite measure of number of sources used. Kiel andLayton also identified a media search factor which is similarto the present one.Factor 3 is associated with the participation and involve-ment of others in out-of-store search activities. Factor 3 hasnot been identified previously, because earlier studies havenot explicitly considered the active involvement of personsother than the principal decisionmaker in particular searchactivities. Factor 4 is an interpersonal search factor char-acterized by the amount of time the principal decisionmak-ers reported spending in talking to others about cars and bythe involvement of a "purchase pal" in the search process.2This search dimension was also identified by Kiel and Lay-ton. Factor 5 is characterized by amount of search activitywhile at the dealership. This factor is related to the searchprocess at dealers: time spent looking around showrooms,

TABLE 2INDIVIDUALTEMSRELATEDTO DIMENSIONSOF SEARCHSHOPPER DATA

Factor 1Number of different dealers visited (.89)Number of hours spent at dealers (.82)Number of visits to dealers (.81)Time you spent drivingto and from dealerships (.60)

Factor 2Time you spent reading books and magazine articles (- .81)Time you spent reading about car ratings in magazines (-.80)Number of different search activities (-.75)Time you spent reading advertisements in newspapers andmagazines (-.74)Time you spent reading automobile manufacturerbrochures andpamphlets (-.59)Regularly reads consumer magazines such as Consumer Report(-.52)Time you spent talkingto friends/relatives about new cars ordealers (-.40)

Factor 3Time others spend reading about car ratings in magazines (.70)Time others spent reading books and magazine articles (.66)Time others spent talking to friends/relatives about new cars ordealers (.59)Time others spent reading automobile manufacturerbrochures anpamphlets (.55)Time others spent reading advertisements in newspapers andmagazines (.46)Factor 4Purchase pal help (.63)Time you spent talking to friends/relatives about new cars ordealers (.59)

Time others spent talking to friends/relatives about new cars ordealers (-.48)Factor 5

Time others spent test driving cars (-.76)Time others spent talkingto salespersons (- .71)Time others spent looking around dealer showrooms (-.61)Time you spent test driving cars (-.54)Time you spent talkingto salespersons (- .51)Time you spent looking around dealer showrooms (-.39)NOTE: Factor pattern coefficients are in parentheses.

talking to sales personnel, and test driving automobiles botby the principal decisionmaker and by others. It appears tdiffer from Factor 1 in that it is related to the amount otime spent in activities while at dealerships, rather than tthe process of finding dealerships.Factor scores3 for each of the 1,031 subjects were computed and provided the basis for a clustering procedure.

2A purchase pal is someone assisting in the search process who is knownto the principal decisionmaker and is believed to have some expertise inevaluating automobiles (see Bell 1967 for the role played by purchase palsin automobile purchasing).

3The use of oblique factors does not fully eliminate the problem multiple measures of the same construct. However, a cluster analysis usinorthogonal factors produced almost identical results to those reportedherThe results of the oblique rotation are presented because we believe thprovide a richer and more realistic picture of the dimensions of searactivities.


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SEARCH STRATEGIES 42TABLE 3

CLUSTER MEANS FOR DERIVEDFACTORS (SELF-REPORT DATA)Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster

Factor 1-Dealer visits -.70 .24 1.07 1.19 1.38 -.40Factor 2-Out-of-store activity (- 1.10 .25 -.72 -1.50 .34 -.43Factor 3-Other involvement -.26 -.27 3.42 -.29 -.13 -.01Factor 4-Interpersonal search - .50 .97 .48 - .03 .19 - .27Factor 5-In-store activity (_)a .53 .01 -1.29 -.30 -2.10 .22aBoth factors 2 and 5 are negatively loaded factors; negative factor scores indicate high activity for clusters.

procedure suggested by Punj and Stewart (1983a) was fol-lowed. Ward's hierarchical clustering method with Euclid-ean distances was used with half of the respondents to ob-tain an initial description of potential clusters within thedata. This initial analysis suggested five to seven clusters.A k-means clustering procedure (BMDP-KM with Euclid-ean distances) was then used to develop five-, six-, andseven-cluster solutions based on seed points suggested bythe earlier hierarchical clustering. These solutions were de-veloped for two independent subsets of the data, whichwere generated by random assignment of cases to subset.Group centroids obtained from one subset of the datawere used to classify cases in the other subset, and viceversa. This cross-validation procedure was carried out forthe five-, six-, and seven-cluster solutions. Coefficients ofagreement (Kappa) were then computed. Kappa is a chance-corrected measure of agreement for nominal scales that maybe interpreted as an intraclass correlation coefficient (Fleissand Cohen 1973). The six-cluster solution produced aKappa coefficient of 0.83. Coefficients of agreement ob-tained for the five- and seven-cluster solutions were smallerand were particularly poor for the seventh cluster. A highdegree of confusion among cluster assignments existed inthe five-cluster solution. On the basis of these findings, thesix-cluster solution was accepted. A final six-cluster solu-tion based on all 1,031 cases was then developed. Clustermeans for the derived factor scores are in Table 3.

FindingsBased on the variables from which they were derived,the six clusters were labeled as follows:

1. A low-searchgroup, classifying 26 percentof respon-dents, with below averagesearchon all factors-espe-cially out-of-storesearchactivities

2. A purchase-pal-assisted search group, classifying 19 per-cent of respondents3. A high-searchgroup, withonly 5 percentof respondents,characterized y aboveaverageactivityon all search ac-tors-especially the participation f others4. A high-self-search group, classifying 12 percent of re-spondents, who are above average on all out-of-storesearch activities in addition to the total numberof visitsto differentcardealers

5. A retail-shopper group with heavy other-involvementclassifyingabout5 percentof respondents6. A moderate-search group-the largest group-with 3percentof respondents, haracterizedy moderate ctivion all search factors, althoughslightly above average oout-of-storesearch activities and slightlybelow averaon numberof visits to car dealers.

Cluster 1 is similar to the low-search groups identifiein prior studies. These studies have also identified a highsearch pattern similar to Cluster 3 and an in-store shoppepattern similar to Cluster 5, but none have isolated thheavy involvement of others in these shopper patternsCluster 6, the moderate shopper, has also been identifiein earlier studies (it resembles the "balanced, thorougshopper" of Claxton et al. 1974). The other two searcpatterns-Cluster 2, the purchase-pal-assisted shopper, anCluster 4, the highly self-reliant shopper-have not beeclearly identified in prior research. Earlier studies havpointed to the existence of additional clusters beyond thosreplicated here (Clusters 1, 3, 5, and 6)-e.g., "selectivsearchers" (Kiel and Layton 1981) and "balanced, thoough" shoppers (Claxton et al. 1974)-but limitations osample size and the type of variables measured made difficult to define these additional clusters clearly. BotClaxton et al. and Kiel and Layton did find some evidencof an interpersonal search pattern, but it was not as clearldefined as in the present study, nor was the involvement oa purchase pal associated with this pattern.Comparisons among the six clusters were conducted oa variety of descriptive variables, including demographcharacteristics and past purchase experience. Table 4 prvides a thumbnail sketch of the characteristics that diffeentiate each cluster from other clusters, based on tests ostatistical significance.4Cluster 1, the group involved in the least informatiosearch, is the most experienced of the groups. Members othis group are older and have, on average, owned more caand been more satisfied with previous purchases than members of other groups. They are more likely to know iadvance the manufacturer and dealer from whom they wanto purchase, and they spend less time than any other grouin search-related activities. This group appears to exhib

4A complete listing of the variables by cluster is available from tauthors upon request.


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422 THE JOURNALOF CONSUMER RESEARCTABLE 4

THUMBNAIL KETCHES OF CLUSTERSDERIVEDFROM CONSUMERSELF-REPORT DATA

Cluster 1 (Low Search) Cluster 4 (Self-Reliant Shopper)Spend least time of all clusters in search-related activities Spend the greatest amount of own time in search process but dGreatest priorpurchase experience not involve others in searchHave owned more cars than average Consider a large number of automobile makes and modelsMore satisfied with previous purchases Less likely to know in advance the dealer from whom car is puMost certain would get a good deal without information earch chasedMore likely to know in advance the manufacturerand dealer from Less likelyto have a trade-inwhom they wish to purchase Well educated with moderate to high incomeReason for purchase more likely to be feeling that it is good to trade Malecars every few years Most likelyto be purchasing new car for fuel efficiency or becausOlder they want a car for a differentpurpose fromthat of previous caHighest income of all clusters Most likelyto consider subcompacts and compactsSearch for and purchase cars in a higher price range Most likelyto consider imports, Ford products, and DodgeMost likely of clusters to consider full-sized, four-doormodels Less likelythan average to consider General Motors productsMore likely to consider products made by Ford and General Motors(e.g., Cadillac)Less likelyto consider Chryslerproducts or imports Cluster 5 (Retail Shopper)

Largest number of decisionmakers involved-especially the wiwhen she is not the principal decisionmaker (this group has thCluster2 (Purchase Pal Assisted) highest percentage of marriedindividuals)Least experienced car shoppers Unlikelyto know dealer in advanceHave owned the fewest cars previously Less likelyto have a trade-inMost likely to indicate a father was involved in decision Consider a large number of makesTend to involve another who is perceived as knowingcars (purchase Large amount of "other" nvolvement in the search processpal) Well educated but not necessarily high incomeExpress little confidence in their ability to judge cars Common occupations are managers, government officials, or prLikelyto be less satisfied with most recent car purchase prietorsMay know manufacturer butnot dealer fromwhom they willpurchase Principal reasons for new car purchase are desire for greater fuLargest percentage of single respondents in this cluster efficiency or the fact that the old car quit working and neededTend to work in clerical and sales jobs replacementMost likely to be buying because had no car or because they feel Pay highest average price of all clusters for carit is good to trade cars every few years Prefer intermediate-sized sedans made by GMor Ford (OldsmobiMore likely to purchase a two-door model and Pontiac are particular avorites)Car purchased more likelythan for any other cluster to be outside More likely to buy outside of initial manufacturerset but less likeof original size and price set of models considered at the outset to buy outside of original price setof formal search

Cluster 6 (Moderate Search)Cluster 3 (High Search) Devote below-average amount of time to search activitiesSpend the greatest amount of time (their own and others') in search Highcertaintythat they could obtain a good deal withoutinformatiactivity searchHave lowest confidence of any cluster in theirability to judge cars Very likelyto know manufacturer n advance but not necessarily thBelieve extensive information earch is necessary to get a good buy dealerLeast satisfied of all clusters with previous purchase Least likely to involve others in search processPost-purchase satisfaction with new car is below average Tend to be older males with higher income than averageTend to involve others in search activities, but these other individuals Most likelyto receive a high trade-in pricemay have no particularexpertise Principal reasons for purchase are desire for greater fuel efficiencCar actually purchased has the lowest average sticker price of all and feeling that it is best to trade cars every few yearsclusters Most likely to consider four-doormodelsLess likelyto have a trade-in or get a high trade-in price Most likely to buy outside of initialprice setBest educated of clusters but of moderate income Preferences for manufacturers well distributedamong members oMore likely than other groups to be female (although over half are this groupmale)More likely to consider subcompacts, compacts, and hatchbacksLikelyto consider popular importsLeast likelyof the clusters to select General Motors as the preferredmanufacturer, although a majoritystill prefer GM products


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SEARCH STRATEGIES 423a shopping pattern similar to what Bettman and Zins (1977)have labeled "preprocessed choice." These individuals arethe most certain that they would make a good car purchasewithout any information search. They are also more likelythan members of other groups to say that their reason forpurchase was that they trade cars every few years. Thisgroup has, on average, the highest income, and they searchfor and purchase cars in a higher price range than do othernew car buyers. They are the most likely to consider a full-size, four-door automobile-not subcompacts or hatch-backs. Cluster 1 is more likely to consider favorably theproducts of Ford and General Motors. They are most likelyto consider buying a Cadillac, and are less likely to considera Datsun, Honda, Toyota, Volkswagen, Chrysler, Plym-outh, Pontiac, or Ford.Cluster 2, the group that depends on the assistance of apurchase pal, is the least experienced of new car shoppers.They have owned the fewest cars previously, and are themost likely to indicate that a father was involved in helpingmake the decision. They express little confidence in theirability to judge cars, and they are likely to indicate thatthey are less satisfied with their most recent car purchase.This group is more likely to know the manufacturer (al-though not necessarily the dealer) from whom they willpurchase, perhaps reflecting the influence of the purchasepal (probably the father). This group has the highest per-centage of single respondents and those working in clericaland sales jobs. The reason for purchase is more likely tobe the fact that they currently have no car-or that it is bestto trade cars every few years, perhaps reflecting the influ-ence of an older purchase pal, such as a parent. The cartype purchased is more likely to be a two-door, and thefinal purchase is more likely than for other groups to beoutside the original size and price set of models considered.Cluster 3, the most involved shopper group, has thegreatest number of hours (their own and others) involvedwith search activities. They are less likely to have a trade-in and to get a high trade-in price. They also buy, on av-erage, the new car with the lowest sticker price comparedto the purchases of other groups. They have the highestnumber of total hours devoted to search activities, both bythemselves and others, but have the lowest confidence intheir ability to judge cars. They are less satisfied than othergroups with their previous car, and they are less certain thatthey would get a good buy without extensive informationsearch activity. This group appears to be compensating forlack of prior positive experience by devoting a large numberof hours to search. They do not appear to particularly enjoythe search process, and their post-purchase satisfaction withtheir current automobile is below average. They appear tocompensate by involving others in the decision, althoughnot necessarily others with any particular expertise. Theymay be attempting to limit their risk by purchasing a newcar with the lowest sticker price. This group is the besteducated (largest percentage with post-graduate study) andof moderate income. They are more likely than other groupsto be female, although over half are male. The car types

most likely to be considered are subcompact and compacof any body type, but especially hatchback. They are leaslikely to select General Motors as the preferred manufacturer, although the majority still prefer GM products. Thspecific makes they are most likely to consider are DatsunToyota, Volkswagen, Dodge, and Mercury.Cluster 4, the self-reliant shopper group, considerslarger total number of automobile makes and is higher ototal number of personal hours devoted to search than othegroups. A member of this group is likely to be male, is leslikely to know in advance the dealer from whom he wilpurchase, and is less likely to have a trade-in. He is weleducated, has a moderate to high income compared to members of other groups, and is more likely to be employed aa craftsman or foreman. He is less likely to involve othersexcept for his wife, in the search process. The reason fopurchase is more likely to be that new cars are more fuelefficient or that he wanted a car for a different purpose. Catypes most likely to be considered are subcompact or compact, and he is less likely than members of other groups tconsider General Motors products. He is most likely tconsider Datsun, Fiat, Honda, Toyota, Volkswagen,Dodge, Ford, and Mercury.Cluster 5, the in-store shopper group, has the largesnumber of decisionmakers involved-especially the wifewhen she is not the principal decisionmaker. This grouhas the highest percentage of currently married individualsThey are less likely than other shoppers to know in advancthe dealer from whom they will purchase or to have a tradein. They are higher than other groups (except Cluster 3) othe number hours that others spend in the search processmore likely to consider a large number of makes, and morlikely to buy a new car with a higher sticker price. Thigroup is relatively well educated, but does not necessarilearn a large income. They are more likely than other groupto be employed as managers, government officials, or proprietors. The principal reasons for purchase are that newcars are more fuel-efficient or that the old car quit workinand needed replacement. The car types they consider arintermediate-sized sedans; the makes they consider favorably are Chevrolet, Oldsmobile, Pontiac, Buick, and Fordbut not Datsun, Volkswagen, or Honda. Oldsmobile anPontiac, in particular, are disproportionately favored by thigroup. Cluster 5 shoppers are more likely to buy outsidof their original manufacturer set, possibly due to their higlevel of dealer search, but they are less likely to buy outsidof their original price set.Cluster 6, the moderate-search group, devotes a belowaverage number of total hours to search activities, anmembers of this group exhibit higher certainty on averagthat they could get a good buy without prior informatiosearch. They are more likely to know the manufacturer iadvance of purchase, but not necessarily the dealer. Theare also more likely to receive a high trade-in value. Alonwith Cluster 1 (the low-search group), Cluster 6 exhibithigher income (31 percent with incomes over $30,000).This group is older, has the second highest proportion o


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424 THE JOURNAL OF CONSUMER RESEARCmales, and is less likely than other groups to involve otherindividuals in the search process. The principal reasonsgiven for purchase are that it is best to trade cars every fewyears and that new cars are more fuel-efficient. The cartype most likely to be considered is a four-door, and thisgroup, along with Cluster 1, is more likely to consider Fordbut not GM products. Members of Cluster 6 are less likelyto specify particular makes that they would consider favor-ably, and more likely than other groups to buy outside theiroriginal price set.The results of the self-report data are generally consistentwith prior research. It is worth noting that no simple rela-tionship was found between the amount and type of searchand such variables as confidence in one's ability to judgethe product, product experience, and satisfaction with pre-vious or current purchases. Consistent with the findings ofDuncan and Olshavsky (1982), there does appear to be arelationship between the amount of search activity and con-sumers' certainty about their ability to get a good deal with-out information search. This finding reinforces the impor-tance of consumer beliefs as mediators of search activity.Although the study is cross-sectional, there is little in theresults to suggest a longitudinal development of shoppersthrough the patterns-except that younger, less experiencedconsumers appear to rely more heavily on the expertise ofothers, while more experienced buyers do not. This is par-ticularly evident in the purchase-pal-assisted cluster (Clus-ter 2). A purchase pal is another individual perceived bythe purchaser to be knowledgeable about the product cat-egory. Information obtained from a purchase pal is clearlyinterpersonal communication, but it is not merely opinionor prior experience which is being communicated; it is alsoa statement of how the purchase process should be carriedout. While other clusters may have involved others in thesearch and decision process, these other interpersonalsources are not perceived as more experienced or knowl-edgeable than the primary purchaser. Purchase-pal-assistedshoppers employ a strategy that appears to substitute theexpertise of another person for their own lack of expertise.The effect is a rather modest amount of search activity.This strategy may be contrasted with that employed by thehigh-search group (Cluster 3). These purchasers have lowconfidence in their ability to judge cars but believe exten-sive information search is required to get a good buy. Thus,this high-search cluster spends a great deal of time (theirown and that of others) in search activity. Interpersonalsources of information are important for this group, butthey are only one of many sources of information.Few empirical studies to date have examined the devel-opment of these strategies. Prior studies do seem to suggestthe existence of characteristic search strategies similar tothose identified here, but few efforts have been made toexplain why these patterns may develop. Further under-standing of this developmental process may be possible byexamining the perspective of the seller. Accordingly, thesecond phase of this research studied consumer search pat-terns as viewed from the other side of the buyer-sellerdyad-a sample of automobile dealer sales personnel.

NEW CAR DEALERSALESPERSON STUDYIf consumers use different search strategies in the automobile purchase process, car salespersons should be awarof at least some of these strategies. Obviously, much searc

behavior cannot be observed directly by sales personnel, sosome of the dimensions of search behavior apparent fromconsumers' self-reports may not be reported by sales personnel. Nevertheless, salespersons provide the best opportunity for cross-validation of at least some of the characteristics of different shopper types.To gain an initial perspective on salespersons' perceptions of their customers, a series of personal interviews waconducted with sales personnel and sales managers at cadealerships. These initial interviews suggested the searcbehaviors that would be observable and replicable. Basedon these interviews, a structured questionnaire was developed for use with sales personnel.Method

Questionnaire. A set of 74 items formed the basis othe salesperson questionnaire. These items described potential characteristics of car buyers-e. g., "first-timbuyer," "visit prompted by advertisement," "comes tshowroom alone." Each item was measured on a sevenpoint scale ranging from "definitely does not apply" t"definitely applies." The questionnaire instructions requested that the salesperson respondents think of a partiular customer or type of customer with whom they hapersonal experience. They were asked to describe that customer briefly in their own words in the space provided athe top of the questionnaire and then describe that customeusing the 74-item scale. Respondents were also asked tindicate whether or not they liked to sell to this type ocustomer. Upon completion of this description, respondenwere asked to think of another customer or type of customewhom they felt was different in some important aspect, anto describe that customer in their own words. Again, respondents were asked to describe this customer using th74-item scale and to indicate whether or not they liked tsell to that type of customer. This procedure was continueuntil the respondent indicated that he or she could think ono other types of customers to describe. Each respondenalso provided personal demographic and sales experiencdata.

Respondents. Forty-eight sales personnel representineight Nashville, Tennessee automobile dealerships particpated in the study. Three of the dealerships had separaimport and domestic sales staffs, which were treated aseparate dealerships, bringing the total number of "dealerships" represented in the study to 11. These dealershiprepresented AMC, Buick, Cadillac, Chevrolet, DatsunFiat, Honda, Jaguar, Mercedes-Benz, Oldsmobile, PontiacRenault, Subaru, Toyota, and Volvo. The number of salespersons participating in the study per dealership range


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SEARCH STRATEGIES 425from two to 10, and was largely a function of the size ofthe dealership and the proportion of experienced sales per-sonnel employed. Only the "most experienced" sales per-sonnel, as identified by the sales manager, were asked toparticipate in the study. Respondents' experience sellingcars ranged from five to 31 years.

Procedure. Each respondent was approached by one ofthe authors and invited to participate in the study. Thisinitial contact provided an opportunity to give verbal in-structions for completing the questionnaire and to answerany questions respondents might have. Respondents wereleft with a copy of the questionnaire and were requestednot to discuss their responses with others. Questionnaireswere picked up from respondents at a prearranged timeseveral days later.Salespersons described from two to eight different cus-tomer types; typically, each one described three or fourtypes. For purposes of data analysis, each description pro-vided by a respondent was treated as a separate observation.A total of 194 such observations formed the basis for dataanalysis.Analysis and Findings

Factor Analysis. Fourteen of the 74 total items werespecifically related to information search behavior. Thesewere submitted to a factor analysis, which resulted in threefactors that accounted for 50 percent of the total variance.These factors were submitted to both VARIMAX and OB-LIMIN rotations, and the VARIMAX solution was retainedwhen the oblique solution failed to increase the hyperplanecount or to make the results of the orthogonal rotation moreinterpretable. Table 5 indicates which items loaded specificfactors.Not surprisingly, the factor solution is considerably sim-pler than the one obtained from the consumer self-reportdata. Factor 1 appears to represent the participation, or lackof participation, of multiple decisionmakers in the purchaseprocess. In the self-report data, self- and other-in-storesearch loaded on the same factor and appeared to vary to-gether, but car sales personnel seem to treat self- versusother-involvement as bipolar. This difference is probablydue to differences in frames of reference and objectivesbetween customers and sales personnel. Customers are in-formation gatherers and undoubtedly use as sources (per-haps extensively) other persons whose influence will not beapparent to salespersons. Sales personnel attempt to closethe sale as expeditiously as possible; they are likely to beaware of and react to only those other persons who arephysically present in the showroom or otherwise activelyinvolved in the final decision.Factor 2 represents a retail search pattern and is loadedby items related to both the number of visits to dealershipsand the amount of time spent at the dealership. This factorappears to correspond to two retail search factors in theconsumer self-report data-one related to the number ofvisits to dealer showrooms, and the other related to time

TABLE 5INDIVIDUALTEMS RELATEDTO DIMENSIONSOF SEARCHSALESPERSON DATA

Factor 1Making decision alone (-.88)More than one person makes decision to buy (.84)Others (family, etc.) involved in decision (.81)Comes to showroom alone (-.75)Has advisor (spouse, friend, parent, etc.) along (.74)

Factor 2Spent a lot of time looking at models on the showroom floor (.76)Returns to dealership several times before purchasing (.75)Spent a lot of time test drivingcars (.75)Spent a lot of time talkingto sales personnel (.74)Obtained manufacturers'brochures describing cars (.55)Has visited many other dealerships (.47)

Factor 3Visit prompted by advertisement (.70)Telephones for informationon model availability,prices, etc. (.62)Referred by previous customers (.58)NOTE: Factor loadings are in parentheses.

spent on in-store activities. (However, these two factorwere positively correlated.) Given that sales personnel caonly infer the number of total visits to different dealershipand time spent at other dealerships, the differences amonthe retail dimensions in the two solutions is not surprisinFactor 3 is a general out-of-store search factor. Unlikthe results obtained from the consumer data, where threseparate out-of-store search factors emerged, only onemerged from the salesperson analysis. This is not unreasonable, since sales personnel have limited opportunity tlearn of out-of-store search activities.

Cluster Analysis. Factor scores for each observatiowere computed and used as the basis for cluster analysisAn initial hierarchical clustering procedure was employeto obtain a candidate number of clusters and seed points foa k-means cluster analysis. Although the limited number oobservations prohibited split-sample cross-validation, thsame six-cluster solution was obtained with random annonrandom seed points, lending additional credibility to thfinal solution. Table 6 provides the cluster means on eacfactor. Clusters were then compared on all 74 variablemeasured in the questionnaire. Table 7 is a thumbnail summary of these results.5 Only variables for which statisticadifferences among groups were obtained are reported.Cluster 1 appears to be a retail search group seekinggood price, hence the label "the negotiator." Cluster 2 composed of inexperienced shoppers who may be assistein the purchase process by an "advisor." Cluster 3 is composed of shoppers who engage in little search activity anmake their purchase decision alone. Cluster 4 is also madup of low-search shoppers, but appears to involve the family in what in-store search does take place. Cluster 55A complete set of results is available from the authors upon request.


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426 THE JOURNAL OF CONSUMER RESEARCHTABLE 6

CLUSTER MEANS OF DERIVEDFACTORS:SALESPERSON DATACluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster

Factor 1-Participation of others .94 .44 -1.10 1.24 -.86 -.76Factor 2-In-store activities .80 .42 -.21 -1.07 .94 -1.21Factor 3-Out-of-store activities -1.17 .75 -1.22 - .24 .19 .62

TABLE 7THUMBNAILDESCRIPTIONOF CLUSTERS DERIVEDFROMSALES FORCE DATA

Cluster 1 (The Negotiator) Cluster 4 (The FamilyShopper)Spends much time at dealers (talkingto salespeople, looking at Most likely to have other family members involved in the decisionmodels, test drivingcars, etc.) Likely to have small children along when visits the showroomReturns to dealership several times before purchasing Spends relatively littletime at dealershipGets a "good deal" Looks at fewest models of any clusterHigher income Not a hard bargainer but does not get a "bad deal"Works as manager or professional Above average likeabilityBetter educatedA "hard sell"Least liked of all clusters by sales personnel Cluster 5 (The Pain-in-the-Neck)Likelyto be marriedand have wife involved in purchase decision Hardest bargainerLookingfor a familycar Hardest to pleaseSeeks good gas mileage, good dealer service department, and Least likely to make a purchase (at a given dealership)good price Not well liked by sales personnelVisits many dealers (and communicates this to sales personnel)

Cluster 2 (The Inexperienced Shopper) Returns to dealerships several timesMore likely than average to order a car rather than buy off the lotLikelyto be a first-timebuyer Middle-agedEqually likelyto be female as male Most likely of all clusters to have an unrealistic, ideal car in mindNot an efficient shopper Product attributes of concern are gas mileage and dealer serviceHas an unrealistic, ideal car in mind departmentVisits many dealerships and returns several times before Does not necessarily get a "good deal"purchasingLooks at many differentmodels, attains many price quotes, testdrives many cars Cluster 6 (The Moderate-Search Shopper)Unlikelyto make a purchase (at a given dealership) Most likely to make a purchase (from a given dealership)Telephones for information Visits few dealershipsLikelyto be referredto dealership by previous customer Easy to pleaseObtains manufacturerbrochures Spends least time on in-store search activities of any clusterMost likelyto be assisted by an advisor Snds east time on nanyuConernd bou fnanin Knows exactly what he wantsoncerned about financing Upper incomeLower income, less educated, younger than average Well educatedMore likelyto be single WeledatedHas trouble qualifyingfor purchase MieagedNeither liked nor disliked by sales personnel Well dressed

Comes to showroom aloneCluster 3 (The Lone Shopper) Self-assuredVisit most likelyto be triggered by an advertisementMakes purchase alone Engaged in out-of-store searchKnows exactly what he wants Best liked of all clusters (tie with Cluster 3)Does not visit many dealers or consider many modelsMost experienced shopperEngages in littleinformationsearch (does not telephone, obtainbrochures)Visit not prompted by either a referralor an advertisementMost concerned about dealer service departmentOlderAn "efficient"shopperSales personnel like this shopper best (tie with Cluster 6)


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SEARCH STRATEGIES 427FIGUREA

RELATIONSHIPOF CUSTOMERSELF-REPORTCLUSTERSAND CLUSTERS BASED ON SALES PERSONNEL REPORTS

Self-Report Sales-PersonnelPurchase-Pal-Assisted Shopper (2) Inexperienced Shopper (2)Moderate-Search Shopper (6) Moderate-Search Shopper (6)Retail Shopper (5) Negotiator (1)

amily Shopper (4)Low-Search Shopper (1)

Lone Shopper (3)High-Search Shopper (other) (3

Pain-in-the-Neck Shopper (5)Self-Reliant Shopper (4)NOTE: Numbers in parentheses are the cluster numbers (see Tables 4 and 7).

composed of high-search individuals who are difficult tosell and hard to please. Members of Cluster 6 manifest amoderate amount of search activity, relying more heavilyon out-of-store information sources, such as advertising,than on in-store sources of information.It is not possible to compute an objective measure ofcongruence between the clusters obtained from the self-re-port data in Phase 1 and the sales personnel data in Phase2, but there does appear to be a reasonable degree of con-gruence between the two independent sets of clusters. Fig-ure A is a comparison of the clusters with the most apparentsimilarities. There are definitely differences (many of themprobably due to differences in perspective), but both datasets yield six clusters that are surprisingly similar. Both setsof data manifest low-search clusters, retail shoppers, in-experienced shoppers, high-search shoppers, and moderate-search shoppers, confirming what has been found in pre-vious studies. Yet the two sets of factors are complemen-tary, in that differences do exist in the two sets of clusters.For example, the cluster that appears as a low-search clusterin the self-report data seems to be composed of two of theclusters derived from the sales personnel: a family shopperwho may find it difficult to shop because there are smallchildren present, and a lone shopper who knows exactlywhat he or she wants. The two high-search clusters in theself-report data-one entailing heavy involvement of others

and the other confined to the principal decisionmaker-ap-pear to collapse into a single high-search cluster in the salepersonnel data. From the perspective of sales personnel,probably matters little how many persons are actively involved in the search process; these are simply difficult customers. The sales personnel data also shed further light othe retail shopper and the moderate-search shopper in thself-report data. It appears that the strictly retail shopper iprice shopping, seeking the best buy at the lowest priceThis strategy requires visiting numerous retailers, comparing prices, and actively negotiating price. The moderatshopper is apparently less concerned with price and morlikely to use out-of-store search, particularly advertising.It is interesting to note that the sales personnel who paticipated in the study did not tend to readily offer types ocustomers based on search strategy. Rather, they appeareto respond to specific characteristics of customers. Informdiscussions with sales personnel in the early phases of thstudy suggested that they tended to use cues such as "pipesmoker," "carries a clipboard," and so on as indicators oa time-consuming or difficult sale, while recognizing thpeople with small children tended to be rathereasy or quicsales.Additional study of how sales personnel process and usinformation would be interesting. Based on anecdotal evdence, it appears that sales personnel do not make the mo


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428 THE JOURNAL OF CONSUMER RESEARCefficient use of the information about consumers that isavailable to them. Potentially useful information about con-sumers appears to be rather nonsystematically processed.This obviously has important normative implications forsales management. Also of significance is the degree towhich sales personnel reported liking or not liking particularcustomer groups. It is quite clear that sales personnel preferselling to customers who are involved in lesser amounts ofsearch activity and who have a clearer idea of the productthey wish to purchase. Least liked are those customers whoactively engage in negotiation. Such customers appear toconsume large amounts of a salesperson's time.The results of the sales personnel data provide additionalsupport for the existence of systematic search patterns sim-ilar to those identified in prior research. The fact that theresults are not a perfect replication suggests either differ-ences in frame of reference, considerable noise in both datasets, or both. Further work on the seller side of this puzzle,as well as on the buyer side, would be illuminating.

DISCUSSIONWhile it now seems clear that systematic search strategiesare common among consumers and identifiable both fromthe self-report data of shoppers and from sales personnel,it is less clear why such patterns emerge. The amount ofsearch activity a consumer actually engages in is a functionof numerous factors. Newman (1977), Bettman (1979), andMoore and Lehmann (1980) have all suggested categoriesof variables that are related to amount of external search.These categorizations generally include product knowledgeand experience, including satisfaction with prior purchases;individual differences, such as ability; situational variables,

such as time pressure; and product importance. Punj andStewart (1983b) suggest that an interaction of situationaland individual difference characteristics may produce dis-tinctive patterris of search and decisionmaking. Recently,Chaiken (1980, 1982) has suggested that a distinctionshould be made between heuristic and systematic process-ing in choice behavior, in order to understand how variousfactors influence decisionmaking and information searchstrategies.In the systematic processing mode, decisionmakers ac-tively attempt to comprehend and critically evaluate infor-mation about relevant attributesof alternatives. In the heu-ristic mode, decisions are based on a more superficialassessment of cues. The systematic mode requires detailedprocessing of information content, whereas the heuristicapproach emphasizes the role of simple schemas or cog-nitive heuristics. Chaiken (1982) has suggested that heuris-tics are most likely to be used by individuals with lowinvolvement in the decision, by those who do not have theability or expertise to engage in systematic processing, andby those faced with tasks or distractions that are difficultfor them. This view is consistent with the interaction frame-work of decisionmaking proposed by Punj and Stewart(1983b). Research by Chaiken (1980) and by Caccioppo,

Petty, and Schumann (1982) has supported the suggestiothat heuristic processing is more likely in low involvemensettings. Wright (1974) and Chaiken and Eagly (1976) havfound increased heuristic processing under conditions odistraction.This work on heuristic versus systematic processing provides a framework for explaining some of the search paterns identified in the two studies reported here. In the absence of significant prior experience and when there is lowself-confidence in one's ability to judge a product, a simplheuristic may be employed, such as asking for advice fromsomeone perceived as knowledgeable about the producThis appears to be the process at work among members othe purchase-pal-assisted cluster identified in the self-repodata. When shoppers are under time pressure or are distracted, heuristics are more likely to be employed. Thimay explain the low level of search among members of thfamily-shopper cluster identified in the sales personnel dataIn contrast, the moderate-search groups spend relativellittle time on active search because they are more experenced car buyers who have been satisfied with prior puchases. Their most involved (i.e., systematic) activityfocused on out-of-store search, particularly advertisingAdvertising appears to trigger a dealer visit for this groupAnderson (1980, 1982), Neves and Anderson (1981) anothers (Shiffrin and Dumais 1981; Schiffrin and Schneide1977; Schneider and Shiffrin 1977) have argued that, witexperience, cognitive processes may be overleamed anbecome automatic. This automaticity occurs as a result ointegration of information and procedures in a "chunking"process. In effect, more elaborate processing of earlier experiences is replaced by cognitive shortcuts, which requiless attention and function as heuristics. This notion hafound support in studies demonstrating that experiencedconsumers tend to use fewer attributes to evaluate producand rely more heavily on more global evaluations (Bettmaand Park 1980; Edell and Mitchell 1978; Johnson and Russ1980, 1981; Park and Lessig 1981; Russo and Johnso1980). It is reasonable to assume that the two lowest-searcgroups found here are exhibiting automaticity of choiceIndeed, both groups' extensive experience with the produclass is consistent with this hypothesis. In addition, thlow-search group reports a greater likelihood of knowinin advance both make and dealer, while the moderatesearch group is more likely to know only the make(s) ointerest in advance of the search process. This may suggesthat the low-search group is further along in the development of automated decisionmaking. This hypothesis, whilspeculative, is more appealing than the notion that lowsearch among these consumers is due to lack of ability tprocess information or to laziness. The extensive searcexhibited by two of the clusters identified here may battributed to (1) a high degree of involvement with thproduct class, (2) a nonautomatic decision process-i.e.,systematic processing, due possibly to lack of experiencor to a conscious effort to avoid the use of decision heuristics and thereby satisfy oneself that one has done all on


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SEARCH STRATEGIES 429FIGURE B

PATTERNS OF CONSUMERDECISIONMAKING S A FUNCTIONOF INVOLVEMENT, BILITY,DISTRACTION,AND LEVELOF AUTOMATICITY

High Involvement | Low InvolvementT | (e.g. durable products) s | (e.g., package goods)

Inability to Distraction Ability to Process

Heuristic Heuristic Automaticity Systematic HeuristicProcessing Processing ("chunking") Processing ProcessingAdvisor- Family Low-SearchAssisted (in-store) Shopper -Shopper Shopper &Moderate-Search Preferred Mode(s)Shopper of Information(out-of-store) Acquisition

out-of-store involvement in-storesearch of others search

Moderate-Search Shopper (in-store);Self-Reliant Shopper; &High-Search Shopper

could to understand the alternatives by a thorough search,or (3) the existence of preferred modes of information ac-quisition.Punj and Staelin (1983) have offered an empirical test ofthe proposition that product knowledge in memory consistsof two unique components: (1) knowledge of specific attri-butes associated with product alternatives, as well as gen-eral shopping procedures for the particularproduct, and (2)a general knowledge structure about the product and/or pur-chase decisions in general. The former construct would tendto decrease external search, while the latter would tend toincrease external search-at least up to a point, since itprovides a frame of reference for new product information.In the context of a consumer decision task, these findingsadd further weight to the systematic/automatic processingexplanation of the results of the present study.A significant amount of empirical evidence now existson consumer search strategies. This empirical evidence hasyet to be integrated within an explanatory framework. It isnot premature to offer such an explanation, even if specu-lative. The automaticity/systematic processing notion ad-vanced by Chaiken (1982) appears to have promise, al-though it remains to be tested and other theories may beput forth. Figure B provides a schematic diagram of therelationship among the patterns of search behavior identi-fied here and the constructs of involvement, ability, dis-traction, and automaticity. This figure represents but oneof many possible schematics that might capture the inter-

action of situational and individual difference characteritics. Several of the patterns identified in the present studhave been observed by other researchers, not only for automobiles, but for a variety of durable goods. Since all sucgoods tend to carry high costs and risks, it would be unreasonable to assume that the same patterns of search obtained in research with durable goods would necessarily bmanifest with other types of products that are less involving. Indeed, it is likely that heuristic processing is evemore dominant for low-cost, low-risk, frequently purchaseconsumer goods.

SUMMARY AND CONCLUSIONThis paper replicates and extends previous research oconsumer search patterns and lends additional empiricaevidence to support the existence of such patterns. It suggests that there is considerable similarity in the perceptionof buyers and sellers regarding search strategies employedby consumers, but identifies enough differences in thesperceptions to warrant further exploration of their sourcesWe have also presented a tentative framework that integrates and suggests an explanation for observed patterns osearch. A number of questions for future research are suggested by this framework:

* Do the heuristics used by consumers under the variobranchesof FigureB differ?If so, how andwhy?


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430 THE JOURNAL OF CONSUMER RESEARC* How do preferredmodes of information earch arise?* What changes occur in the decision processover time?Does an automated,high-involvementprocesseventuallybecome a low-involvementprocess?Howdo searchstrat-egies change with situations-for example, whathappensif the distractedconsumer s not distracted n otherpur-chase occasions?* To what extent do heuristicsdevelopedunderone set ofconditions, suchas inability o processordistraction, en-eralizeto otherpurchase ituations?

An interesting and potentially useful finding of the stud-ies reported here is the ability of experienced sales person-nel to identify types of shoppers, although they are currentlyless systematic about this process than might be desirable.Informal discussions with sales personnel suggested thatthey tend to use different sales approaches with differentshopper types. This has potential relevance for sales train-ing and management. It should be possible to teach salespersonnel to recognize characteristic search patterns andadapt their sales approaches to these types. This could takethe form of formal training rather than "learning bydoing." In addition, sales productivity may be increasedby identifying those customer types that engage in pro-tracted information search and deliberation prior to pur-chase. To the extent that alternative modes of informationpresentation may be substituted for the salesperson, the timeof the sales personnel could be used more productively.Further, it may prove unnecessary to provide certain typesof information to particular sets of customers. These man-agerially relevant hypotheses are equally worthy of pursuitin future research.

[Received March 1983. Revised October 1983.]

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a typology of individual search strategies among purchasers of new automobiles

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