Individual Search Strategies in New Automobile Purchases

David R. Furse, Owen Graduate School of Management, Vanderbilt University
Girish N. Punj, Owen Graduate School of Management, Vanderbilt University
David W. Stewart, Owen Graduate School of Management, Vanderbilt University
ABSTRACT - The research identified typologies of external information search strategies employed by purchasers of new automobiles. Cluster analysis was used to isolate five distinct search strategies, which were related to routinized response, limited problem solving, and extensive problem solving decision strategies.
[ to cite ]:
David R. Furse, Girish N. Punj, and David W. Stewart (1982) ,"Individual Search Strategies in New Automobile Purchases", in NA - Advances in Consumer Research Volume 09, eds. Andrew Mitchell, Ann Abor, MI : Association for Consumer Research, Pages: 379-384.

Advances in Consumer Research Volume 9, 1982      Pages 379-384


David R. Furse, Owen Graduate School of Management, Vanderbilt University

Girish N. Punj, Owen Graduate School of Management, Vanderbilt University

David W. Stewart, Owen Graduate School of Management, Vanderbilt University


The research identified typologies of external information search strategies employed by purchasers of new automobiles. Cluster analysis was used to isolate five distinct search strategies, which were related to routinized response, limited problem solving, and extensive problem solving decision strategies.


The principal focus of this research is to identify characteristic information search strategies employed in purchasing a new automobile. Previous research has focused on how much total time is spent in information search and processing to isolate decision strategies (e.g., routinized response, limited problem solving, and extensive problem solving), while the more relevant focus would appear to be how an individual goes about information search. Knowing what an individual actually does (e.g., how much time they spend in different search activities) is more useful than knowing the aggregate amount of time spent in search. The decision strategy which eventually emerges is more likely to be a function of the types of information sources used than of an aggregate measure of search time.

In fact the decision strategy employed by consumers in making choices is as likely to be influenced by the information sources available to and chosen by the consumer as are sources of information sought to be influenced by decision heuristics. Although information processing patterns can be identified, these patterns change frequently. For example, Bettman and Zins (1977) have suggested the importance of constructive decision making processes in which subjects may change their processing strategy as they gather data during the task.

It is likely too that information search strategies and decision strategies are interdependent. Decision rules may influence search but search activity may alter decision rules. Thus the notion that search strategies are predetermined by a decision strategy may not hold. Research on the characteristics of particular choice situations and individual differences is crucial, then, to an understanding of how decision strategies develop. Brucks and Mitchell (1980) have suggested that the goal of future research in consumer decision making should center on predicting what decision strategy an individual will use in a particular situation, and that the achievement of this goal will require the identification of the critical elements of the situation and the individual which cause the selection of a particular decision strategy. A first step in this direction would be the determination of an empirical typology of search strategies employed by consumers. This research employs cluster analysis to identify typologies of information search strategies of new automobile purchasers. Other methods have been employed to isolate consumer choice strategies - for example, judgmental methods using consumer protocols (e.g., Payne 1976, Bettman and Zins 1977), eye movements (e.g., Russo and Rosen 1976), and information monitoring techniques (e.g. Bettman and Kakkar 1977). Each of these methods requires careful and cumbersome content analysis of the decision process sequence of consumers. While these are useful techniques they to not provide the efficiency of clustering methods for large sample research.

It should be noted that cluster analytic methods do not identify search strategies in the sense of some overall search plan or heuristic. Rather, it identifies groups of individual consumers who are similar to one another in the amount of time spent in various information seeking activities. Strategies of search are inferred from these groupings of consumers. Rather than following a relatively few consumers through the search process and explicitly asking for the search strategy employed, clustering methodology seeks to provide a means for identifying common behavioral patterns which may represent common underlying strategies.

Previous work in consumer research has noted that consumers employ search strategies which can be distinguished based on amount of external search effort and decision time (Robinson, Faris, and Wind 1967, Howard and Sheth 1969, Hansen 1972). For example, Howard and Sheth distinguish routinized response behavior, limited problem solving, and extensive problem solving. These strategies have also been characterized as stages which consumers pass through as they evolve toward routinized response. Bettman and Zins (1977) have developed similar notions in their distinction between preprocessed choice, analytic implementation processes, and constructive processes. The expectation in the current research is that the specific search patterns identified among new car buyers would roughly correspond to these general search strategies. The current research employs factor analysis and cluster analysis of time spent in various search activities ranging from talking to friends and relatives to visiting dealer showrooms and test driving cars. Comparisons are then made among the clusters of search patterns on a variety of exogenous variables which might influence or be influenced by search activities.

This research is distinguishable from prior research on external search processes for consumer durables in that it emphasizes a multidimensional profile of search activities rather than studying any single aspect of search behavior or some aggregate measure. For example, Newman and Staelin (1972) looked at an aggregate measure of total information seeking and Katona and Mueller (1955) described buyers based on a single deliberation score by summing across search measures. Claxton, Fry, and Portis (1974) used an approach similar to the current study, employing numerical taxonomic analysis to summarize ways in which shoppers gather information in purchasing furniture and appliances, and relating these to potential causes for search pattern differences. Kiel and Layton (1981) have also reported a study similar to the present one using an Australian sample.

There are several features which distinguish this research from the research conducted by Claxton, Fry and Portis. First, factor analysis was employed to reduce the problem of multiple measures of similar constructs and to provide more stable measures for clustering. Second, the clustering solutions obtained were submitted to a more elaborate cross-validation procedure than was used by Claxton, Fry, and Portis to assure that the identified clusters were not artifacts of the research. Third, the sample size in the current research is over twice as large, yielding more reliable results. Finally, the current research extends the findings of Claxton, Fry, and Portis to a new automobile purchase situation.

Although both studies used a similar research design, there are also several differences between the present study and that reported by Kiel and Layton. The sampling frame is different from that which Kiel and Layton used on the Australian sample. The present study used a sample of automobile buyers in the United States. A second difference in the two studies is in the nature of the information seeking variables employed. Kiel and Layton obtained measures of search behavior by requiring respondents to recall the number of times a behavior was undertaken between the time the purchase was first contemplated and the purchase was actually completed. A global measure of total search time in weeks was also obtained. The present study sought estimates of the total amount of time actually spent on each of the information search tasks regardLess of the frequency the behavior occurred or the length of time from initiation of search to purchase completion. The Kiel and Layton study was restricted to an examination of information search behavior of the principal purchaser while the present study sought to identify search behavior of both the principal purchaser and others who may have assisted in the purchase process. Only 194 consumers were involved in the Kiel and Layton study while the present study employed more than three times that number. Thus, the cluster analysis result was cross-validated in the present study but not in the Kiel and Layton information search study. Finally, the behaviors examined in the present study differed somewhat from those used in the Kiel and Layton study. Table 1 compares the two sets of variables



Identifying consumer information search strategies can be of practical value to marketing managers and consumers. For managers it can be an important characteristic in specifying target markets requiring different marketing strategies, and it can be useful in helping the manager decide how to influence the search process. Knowledge of consumer search strategies can also provide a basis on which to develop consumer education programs and public policy.

Finally, it should be noted that consumer information search is undoubtedly characterized by both external and internal search of past experience in memory (Bettman 197% 116). Although a comprehensive set of measures of both internal and external search are desirable in completely specifying routinized response, limited problem solving, and extensive problem solving decision processes, it is typically impractical for the manager to evaluate internal search in large sample market analysis. External search activities currently provide the most accessible and appropriate measurement for managers to use in identifying consumer search strategies. This is likely to remain so until some future date when more reliable, valid, and efficient measures of internal search processes are available


Data for the study were generated by consumers who had purchased a new automobile during the period of September to November, 1978, in the cities of Buffalo, Milwaukee, and Phoenix. All respondents were contacted by telephone and asked to participate in the study. Respondents received a questionnaire by mail. Questionnaires were received by respondents from 2 weeks to 4 months after the purchase of the new automobile. The average elapsed time from purchase to receipt of the questionnaire was two months. The questionnaire solicited information on variables associated with their automobile purchase. These variables were related to the decision process employed by consumers in making the selection of their new automobile. The questionnaire requested such information as the number of dealer visits, activities at each dealership visited, information search behavior (e.g., talking to friends, reading magazines, etc.), number and types of previous cars owned, satisfaction with car purchased, etc. Information was also requested concerning the automobile purchased, the dealership from which it was purchased, and the price paid for the automobile. Demographic information concerning age, gender, education, income, etc. was also solicited. The variables of primary interest in the present study were eighteen items concerned with the amount of time the purchaser or someone else in the household spent on a variety of information search activities ranging from reading advertisements in the newspaper to test driving automobiles. These items used a six point scale to obtain the time spent on each activity: l) No time; 2) Vp to i hour; 3) More than i hour but less than 2 hours; 4) More than 2 hours but less than 5 hours; 5) More than 5 hours but less than 10 hours: and 6) More than 10 hours.


As a prelude to cluster analysis an initial factor analysis was carried out on eighteen items related to time spent in various search activities. This initial factor analysis was designed to reduce the problem of multiple measures of similar constructs being more heavily weighted than constructs measured by fewer items. Only the 602 subjects responding to all eighteen items were included in the analysis. A principal components analysis was carried out and a three-factor solution was indicated by both the eigenroots >1 criterion and a plot of the roots. These three factors were submitted to both varimax and oblique rotations. Although the oblique rotation (direct oblimax, delta = .4) did not appreciably change the hyperplane count obtained with the VARIMAX rotation it did serve to reduce moderate factor loadings and increase those loadings already high. For this reason the oblique rotation was retained as the final solution. The factor pattern matrix is given in Table 2 and the factor structure matrix is provided in Table 3.

The factor analysis identified three significant search activities factors (Table 2). Factor 1 is essentially characterized by others in the household than the actual purchaser engaging in all the search activities. Factor 2 is an out-of-store search factor and is principally characterized by high loadings for time spent by the purchaser talking to friends and relatives, reading articles, advertisements, brochures, etc. It is not characterized by time spent visiting dealerships, talking to salespeople, or test driving cars. Factor 3 is an in-store search factor with the highest loadings (negative) on time spent by the purchaser and others in the household visiting dealer showrooms, talking to salespeople, and test driving cars.

Factor scores for each of the 602 subjects were computed. These factor scores provided the basis for a clustering procedure. A modification of a clustering procedure suggested by Hartigan (1975) was followed. Ward's hierarchical clustering method was used with half of the respondents to obtain an initial description of potential clusters within the data. This initial analysis suggested five to seven clusters. A k-means clustering procedure was then used to develop five, six, and seven cluster solutions based on seed points suggested by the earlier hierarchical clustering. These solutions were developed for two independent subsets of the data.

The several clustering solutions were cross-validated by using group centroids obtained from one subset of the data to classify cases in the other subset and vice-versa. Coefficients of agreement (Kappa) were then computed. The five cluster solution produced a Kappa coefficient of .91. Coefficients obtained for the six and seven cluster solutions were smaller and were particularly poor for the sixth and seventh cluster. On the basis of these findings the five cluster solution was accepted. A final five cluster solution based on all 602 cases was then developed.

Table 4 provides cluster means for each of the original eighteen variables from which the factors were derived.

Five distinct search patterns were identified based on the cluster analysis of respondents' factor scores (See Table 4). Cluster 1 purchasers were the most extensively involved in all search activities. They were the most likely to have others in the household as active participants in the search process (Factor 1) in addition to extensively engaging themselves in both out-of-store (Factor 2) and in-store (Factor 3) [Note that Factor 3 is a negative factor. Below average scores are indicative of greater time spent in search ; activities at automobile dealerships.] search activities. Thus, Cluster 1 has been labeled the "constructive shopper" after Bettman's notion of constructive information search (Bettman 1979). Cluster 2 is more likely than all other clusters except the first to have other household members involved in the search process while their own involvement in both in-store and out-of-store search is about average. Consequently, Cluster 2 has been labeled the "surrogate shopper" pattern. Cluster 3 purchasers are more likely than any other except Cluster 1 to engage in out-of-store search activities. They are also less likely to use other household members in the search process, but this is undoubtedly due to the fact that these respondents are predominantly single. Cluster 3 has been labeled the "preparatory shopper" due to the greater likelihood to engage in out-of-store information search. Cluster 4 is characterized by below average amount of time spent in out-of-store search activities. This is probably due to the fact, which will be developed later, that purchasers in Cluster 4 were more likely than those in other clusters to know in advance the manufacturer from whom they intended to purchase. Therefore, this cluster was labeled the "brand loyal shopper" pattern.







Purchasers in Cluster 5 are distinguished by below average involvement in all search activities. This shopping pattern appears to exhibit what Bettman (Bettman and Zins 1977) has described as pre-processed choice. As will be discussed later, Cluster 5 purchasers are more likely to have prespecified both the manufacturer of the car that they will buy and the dealer from whom they will buy it. Cluster 5 has been labeled the "routinized response shopper" pattern.

Comparisons among the clusters on a variety of exogenous variables were carried out. Table 5 provides a summary of means and/or percentages of response for each of the clusters on these exogenous variables.

The "constructive shopper" (Cluster 1) is more likely to spend a large number of hours searching for information relating to their new car purchase - 66 total hours compared to 24 hours for the sample as a whole - and they are more likely to make a large number of dealer visits (Table 5). They are more likely to have a larger number of household members involved in the purchase decision - most notably the wife. [The negative relationship with husband involvement is an artifact, since Cluster 1 is predominantly male.]



Also, the relationship of the number of decision makers involved is a direct function of marital status and number of adults and children in the household. So this is less likely to indicate an individual difference in search strategy than to be merely the result of a situational effect (e.g., larger households have more decision makers).

They were less likely than other clusters to be confident that they would have made a good purchase initially without going through the information search process (see Initial Certainty, Table 5), and were less likely to know in advance the manufacturer or the dealer from whom they wanted to buy. They were also less satisfied than other shoppers with their previous car. They are more likely to read consumer-oriented magazines and to have someone in the household who is knowledgeable about cars. Demographically, they are more likely to be married and to have children in the household. Differences across all five of the clusters for education and income are nonsignificant.

The "surrogate shopper" (Cluster 2) is like the "constructive shopper" in that (l) they are likely to be from households with more than three members and consequently have more decision makers involved, (2) their prior expectations of what they would pay for a new car were lower than for other clusters, (3) they were less satisfied with their previous car purchase and (4) they are less likely to know in advance the manufacturer or dealer from whom they wanted to purchase. They differ from the "constructive shopper" in that they spend less total hours engaged in external search activities (although still above average for all shoppers) and most of these hours are spent by others in the household. They are also less likely to read consumer-oriented magazines or to have someone in the household who is knowledgeable about cars, but they are about average on these characteristics for the sample as a whole.

The "preparatory shopper" (Cluster 3) is less likely to be married or to have children and is more likely to spend time personally in the search process. Like the "constructive" and "surrogate" shopper patterns, the "preparatory shopper" is less likely to know in advance the manufacturer or dealer from whom they wish to purchase, but they are somewhat more likely to be satisfied with their previous car. They are more likely than other shopper patterns to read consumer-oriented magazines and newspaper columns. The "brand loyal shopper" (Cluster 4) is more likely than all other clusters, except the "routinized response shopper" (Cluster 5), to be satisfied with their previous car and to know in advance the manufacturer of the car that they want to purchase. Like the "constructive" and "surrogate" shoppers they are more likely to be married and to have other members of the household involved in the purchase decision, but they are less likely to be personally involved in search activities. They are also less likely to read consumer-oriented magazines, such as Consumer Reports. To the extent that these consumers shop, they are shopping for a dealer rather than Z make

The "routinized response shoppers" (Cluster 5) have already made up their minds. They know in advance both the manufacturer and dealer from whom they intend to purchase, and they are more likely to have been satisfied with their previous car purchase. They spend less time engaged in information search activities than any other group - just over six hours compared to 24 hours for the total sample. "Routinized response shoppers" are also more likely than any other cluster to expect to pay a higher price for their new car. They are more likely to be older and in a single person household.


The five search patterns identified in this research generally correspond to the search strategies identified in previous consumer behavior research. Kiel and Layton (1981) identified a high search group, a low search group, and three clusters they collectively labelled selective information seekers. Cluster 5. the "routinized response shopper" pattern, fits the profile for the decision making strategies of routinized response behavior (Howard and Sheth 1969) and preprocessed choice (Bettman and Zins 1977). They expend a minimum of effort in information search activities, presumably because the cost/ benefits of new information is high since they have already made up their minds about the manufacturer and dealer from whom they want to buy. Cluster 5 purchasers' lack of information search may also be explained by their greater satisfaction with their previous purchase. Newman and Staelin (1971) show that satisfied users take less time to make a decision and in a later study (Newman and Staelin 1972) that buying the same brand as before is associated with less external search. Bennett and Mandell (1969), Westbrook (1977), and Sheth and Venkatesan (1968) report similar findings. Bettman (1979,p.119) argues that satisfaction with previous purchases is an important determinant of the suitability of previously stored information for the current decision. The greater the amount of suitable information that is available in memory, presumably the less external information search that is necessary.

At the other extreme, Cluster l, the "constructive shopper" is active in pursuing both out-of-store and in-store information sources. He makes a large number of visits to dealer showrooms and reads consumer-oriented publications, such as Consumer Reports. He is also the least likely to be satisfied with his previous car purchase. This segment is the smallest of the five search patterns identified - just over 11 percent of the sample. This is consistent with the finding of Claxton, Fry, and Portis (1974) that only five percent of furniture and appliance buyers were "thorough" shoppers. This finding is also consistent with findings in earlier studies citing the general lack of external search in consumer durable purchasing (Katona and Mueller 1955, Newman and Staelin 1972). Alternative explanations to a conclusion that consumers are irrational or lazy may be that reported search substantially understates true search activities (Newman and Lockeman 1975), that internal search of memory is compensatory with external search (Bettman 1979,p 129), or that the cost/benefit relationship for additional information is higher for experienced buyers (Hansen 1972).

Clusters 2, 3, and 4 all appear to exhibit characteristics of limited problem solving. Cluster 4, the "brand loyal shoppers", have already made up their minds about the manufacturer, so they need to process less information in making a choice. Clusters 2 and 3, the "surrogate" and "preparatory" shoppers appear to be closer to the "constructive shopper" (Cluster 1), although both report substantially fewer hours spent in search activities and differ in their specific search patterns. For the "surrogate shopper" the ratio of self to other total search hours (Table 5' is very close to that of the "constructive shopper", but they are somewhat less dissatisfied with their previous purchase.

"Preparatory shoppers" are second only to the "constructive shopper" in total personal hours devoted to search, but they are less likely to be dissatisfied with their previous automobile purchase and are somewhat more likely to know in advance the manufacturer and dealer from whom they want to purchase. Furthermore, fewer others in the household may tend to simplify the choice decision.

Finally, one additional finding should be noted because it differs from the findings of prior research. More educated and affluent customers are thought to have superior information processing ability and thus to engage in more information search (Thoreli, Becker, and Engeldow 1975, Miller and Zikmund 1975, Claxton, Fry, and Portis 1974, Katona and Mueller 1955). The results of this research do not support this conclusion. Although the analysis did not address this question directly, there were no significant differences in education or income among the five information search clusters. The reasons for this could be due to differences in the information search measures and analysis employed in this research and to differences in the product purchased. It may also be that education and income have an influence at a different level of behavior than was examined in the present study. An analysis of the effects of education and income within clusters might reveal the influence of the two variables.

The present results have identified five types of information search. However, these types may not be discrete. Rather, they may represent stages in a developmental, or learning, process. The present results to not resolve this important issue. Future research might address the development of search strategies in a longitudinal design.

The present research may well suggest to marketing managers the need to segment consumers on the basis of the information search strategy employed. Given the importance of prior satisfaction for determining the amount of search activity, a marketing strategy designed to increase dissatisfaction with competitors' models and to increase satisfaction with the marketers on brand may be necessary. Other marketing strategies will be specific to the particular segments. The surrogate shopper seems to place a great deal of emphasis on the opinion of significant others. Selling to surrogate shoppers probably means directing marketing and sales efforts at these significant others rather than the actual purchasers. Preparatory shoppers engage in a significant amount of out-of-store shopping and spend little time with dealers. Advertising and other forms of information such as Consumer Reports would probably be most effective in reaching these consumers. Indeed, advertising seems most likely to have an impact on the constructive and preparatory shoppers. Brand loyalty and dealer loyalty appear separate phenomena with some consumers being brand loyal but not dealer loyal. Dealers may need to build on the reputation of the brands they carry and offer unique services to develop a loyal group of customers. This approach to segmenting consumers is unlike typical segmentation studies which place emphasis on subjective values of product features. The emphasis here is on segmentation on the basis of shopping behavior and the development of marketing strategies to match particular consumer search strategies.


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