Shopping For Durables: Some Observations

ABSTRACT - This paper offers some general observations on consumer information search behavior, particularly in the context of shopping for durables. This is followed by specific comments on the papers by Cattin and Punj, Bloch and Richins, Cox, Granbois and Summers, and Curry. These papers are treated in the order in which they were listed in the program.


Naresh K. Malhotra (1983) ,"Shopping For Durables: Some Observations", in NA - Advances in Consumer Research Volume 10, eds. Richard P. Bagozzi and Alice M. Tybout, Ann Abor, MI : Association for Consumer Research, Pages: 406-408.

Advances in Consumer Research Volume 10, 1983      Pages 406-408


Naresh K. Malhotra, Georgia Institute of Technology


This paper offers some general observations on consumer information search behavior, particularly in the context of shopping for durables. This is followed by specific comments on the papers by Cattin and Punj, Bloch and Richins, Cox, Granbois and Summers, and Curry. These papers are treated in the order in which they were listed in the program.


Consumer information seeking and processing has emerged as a popular approach to examining consumer choice behavior. However, the results of several consumer information seeking and processing studies have not been clear cut or precise (Malhotra 1982a, 1982b). Particularly sensitive in this respect has been the area of consumer information search (Newman 1977). To no small extent, these inconsistencies in the empirical findings have resulted from varying and inappropriate conceptualizations of the consumer decision process. Hence, we first offer a framework for consumer decision making in the context of shopping for durables. In the light of this framework we show how past research on information search has fallen short. We then comment on each of the four papers presented in the session on "Shopping for Durables: Consumer and Competitor Perspectives".


The latest edition of a well known text on Consumer Behavior characterizes the high involvement decision process as consisting of five phases. These phases have been labeled as problem recognition, search, alternative evaluation, choice and outcomes (Engel and Blackwell 1982). I would like to add an initial preproblem phase. It is important to realize that information seeking occurs not only in the search phase, but also in the earlier stages denoted by preproblem and problem recognition phases. Moreover, information could be obtained in an active or passive way. Examples of active information search include dealer visits and consulting information sources such as Consumer Reports. Passive search for information may be described by browsing or exposure to advertisements while watching evening news or a favorite T.V. program. The problem with the literature on information search behavior has been the almost exclusive focus on information obtained by active seeking at the "search" phase of the consumer decision process. The substantial amount of information obtained passively has been largely ignored. The assertion that significant information may be sought even at the preproblem phase also receives support from one of the four papers in this session. Bloch and Richins found "that significant numbers of people do browse in retail outlets without an upcoming purchase in mind." Thus, the extent of search reported in literature has been substantially underestimated, misleading some to even question the concept of consumer decision making (Olshavsky and Granbois 1979).

Only one of the four papers in this session has explicitly dealt with passive information search. Next, we offer specific comments on each of these papers. The papers are considered in the order in which they were listed in the program.


A major strength of the Cattin and Punj (1983) is the large sample size on which the analysis is based. Particularly, noteworthy is their attempt to sample three different SMSA's. Also to be appreciated are the several checks these authors have carried out to ascertain the quality of data before they proceed ahead with the analysis. This constitutes good, although sadly rare, research practice. The authors are commended for it.

However, reservations must be expressed about some of their hypotheses and the way in which these were examined. Cattin and Punj state that a consumer is more likely to purchase a car from the first dealer he visits if he had more purchase experience with cars recently. They, accordingly, examine this hypothesis in a linear fashion.

I have, elsewhere, reviewed the literature on information search and prior experience and shown that this relationship is curvilinear. It would be sufficient here to quote Bettman and Parks (1980) who concluded that, "the effects of prior knowledge and experience give a fairly consistent picture. The moderate group appears to do more processing of the currently available information and relies on prior knowledge to a lesser extent than the high and low groups." The reason is that those with low experience lack the ability whereas those with high experience lack the motivation to acquire and process much information.

Their hypotheses that a consumer is more likely to purchase a car from the first dealer he visits if he is more educated, and has a high income or is a professional are also questionable based on past research on the extent of information search. In his review Newman (1977, p. 87) notes, "several studies support the proposition that education represents interest in and ability to seek information, although the evidence is not unanimous. Education's correlate, income also has been found to-be positively related to search but to a lesser extent." Occupation, too, one would expect, is correlated with education and income. Hence, the hypotheses with respect to education, income, and occupation should be the opposite of that postulated by Cattin and Punj. Indeed, their results support my contention for they found that "education was significant in the wrong direction."

Some comments on the measurement and coding of some of the variables are also in order. The authors measure a feasible set of cars which was found to average 30.70 and 17.83 for the two groups. This is unrealistic in terms of number of cars a consumer would actually consider buying. A more appropriate measurement would have been the evoked set size. The size of the evoked set is known to be small, typically less than five (Malhotra 1982a). Also, the coding of four of the five demographic variables is inappropriate. Age, education, income and occupation were measured as categorical variables but were assigned integer values. The appropriate procedure is to code a K category variable with K-1 dummy variables. It is then not too surprising that in their discriminant analysis, all the demographic variables explained less than 5: of the variance.


The concept of browsing or shopping without a current intent to buy has not received due attention in consumer behavior so the effort of Bloch and Richins in this area is applauded. The authors' finding "that significant numbers of people do browse in retail outlets without an upcoming purchase in mind," is, indeed, important. The literature on information seeking has not taken into account such behavior. This may be yet another reason why the extent of information search reported in literature may be underestimated.

Bloch and Richins (1983) report a significant positive relationship between browsing and product interest, readership of product related magazines, product knowledge and word-of-mouth activity concerning the product. However, these findings must be interpreted with caution as all-the variables are self reported measures obtained at the same point in time. Thus, a natural tendency on the part of respondents to give consistent answers would have inflated these relationships. Our confidence in these findings would have been enhanced if:

a. product interest was also measured in terms of whether a respondent belonged to a local sports car club or was a customer of fashion clothing boutiques. The authors did have this information but it was not used in the analysis.

b. information on product related magazines read was obtained by unaided as opposed to aided recall.

c. product knowledge was also measured by some sort of a quiz.

d. specific questions were designed to measure word-of-mouth activity.

The relationship of demographic variables to browsing is weak, particularly when viewed in the light of discriminant analysis results. Also, these variables have been inappropriately examined. The Spearman rank correlations reported in Table 2 are based on a very large number of ties. In addition, the ordinally scaled variables were treated as interval data in performing the discriminant analysis.

A better strategy for determining the differences between browsers and nonbrowsers would be to divide the sample into quartiles based on the browsing score, and then examine the differences between the two extreme quartiles.

The authors have not examined the overlap between browsing for automobiles and browsing for clothes. This is an interesting question which could be explored. The relationship of browsing to store characteristics also deserves attention and should be emphasized in future studies.


The paper by Cox, Granbois and Summers (1983) is an attempt investigating a new classification of major durables acquisition. Such attempts in the discipline should indeed be encouraged and I would like to commend the authors for undertaking such a study. I would also laud the authors for adopting a longitudinal design and for a relatively large sample size. Longitudinal studies of this type are desperately needed but sadly lacking. I agree with the authors' observations that the distinction between planned and unplanned observations is more a methodological artifact than a behaviorally meaningful division.

While I appreciate the authors' attempt to investigate the four acquisition categories, I am much less enthusiastic about the results they obtain. The results they report in Table 5 are weak and not clear cut. Also, I would observe that the ANOVAS are complicated by small, unequal cell sizes. The chi square results should also be interpreted cautiously. I would suspect that in quite a few cases the cell sizes were less than 5. Consider, for example, the chi square analysis of satisfaction with the four acquisition categories. Here we have 20 cells and only 128 observations which yields an average of only 6.4 observations per cell. The reason for concern for sparse cells is even stronger when we look at the marginal distributions. The authors do not indicate how the sparse cells were treated. Perhaps a more useful classification may be simply whether the purchase is a first acquisition or not. We note from the authors' Table 5 that certainty is the lowest and satisfaction is the highest for first acquisitions, both of which make sense. If a further classification of purchases of previously owned products is desired, it would be done on the basis of the degree of prior experience with the product.


I commend Curry's (1983) use of secondary data. These days we hear a lot of people complaining about how difficult it is to collect primary data, given the time and costs involved. In this context, Curry illustrates how, instead of merely lamenting the lack of primary data. one can usefully exploit secondary data.

The author makes a statement that it is quite likely that consumers formulate opinions on a broadline or corporate level rather than line-by-line basis. I would like to qualify his statement by adding that this would depend on the branding strategy adopted by the marketer. If a blanket brand name is adopted for all the items and lines in the product mix, as is the case in the major appliance industry, then this is likely to be true. However, consumers are much less likely to form generic evaluations of manufacturers where the product items or product lines in the mix carry a different brand name. The use of Thurstone's scaling procedures, such as case V, in general, has several advantages, for aggregating individual level nonmetric data to develop an aggregate level interval scale. For example, Jain, Malhotra and Mahajan (1979) have shown this to be a desirable, indeed preferred, method of aggregation in conjoint analysis to develop group level part worth functions. However, this procedure is appealing only when the input-data is nonmetric, typically rank order or paired comparison. Typically, case V procedure processes individual level ordinal input data to produce aggregate level interval scale.

Curry has, however, used the case V to process individual level interval scale input data to produce an aggregate level interval scale. Where the input data is interval scaled, aggregation can be achieved more directly by other simpler procedures. Curry has indeed used such a simple procedure for developing an alternative aggregate price scale and shown it to be very similar to that obtained by case V. The utility of case V procedure in specific context examined by Curry (1983) is, thus, questionable.


The area encompassing shopping for durables is fascinating and will continue to attract attention of consumer researchers. It is hoped that neglected issues in this area, particularly information acquisition during preproblem and problem recognition phases, will receive greater emphasis. One could also conceive of a unified theory emerging ultimately. In this vein the four papers presented in this session are welcome.


Bettman, James R., and Park, C. Whan (1980), "Effects of Prior Knowledge and Experience and Phase of the Choice Process on Consumer Decision Processes: A Protocol Analysis," Journal of Consumer Research, 7, 234-248.

Bloch, Peter H. and Richins, Marsha L. (1983) "Shopping Without Purchase: An Investigation of Consumer Browsing Behavior," in Advances in Consumer Research, Vol. X, Richard P. Bagozzi and Alice M. Tybout, eds., Ann Arbor, MI: -Association for Consumer Research.

Cattin, Philippe, and Punj , Girish (1983), "Identifying the Characteristics of Single Retail (Dealer) Visit New Automobile Buyers," in Advances in Consumer Research, Vol. X, Richard P. Bagozzi and Alice M. Tybout, eds., Ann Arbor, MI: Association for Consumer Research.

Cox, Anthony, Granbois, Donald, and Summers, John (1983), "Planning, Search, Certainty and Satisfaction Among Durables Buyers: A Longitudinal Study," in Advances in Consumer Research, Vol. X, Richard P. Bagozzi and Alice M. Tybout, eds., Ann Arbor, MI: Association for Consumer Research.

Curry, David J. (1983), "Measuring Price and Quality Competition Among Conglomerates: Methodology and an Application to the Major Appliance Industry," in Advances in Consumer Research, Vol. X, Richard P. Bagozzi and Alice M. Tybout, eds., Ann Arbor, MI: Association for Consumer Research.

Engel, James F., and Blackwell, Roger D. (1982), Consumer Behavior, Chicago: The Dryden Press.

Jain, Arun K., Malhotra, Naresh K., and Mahajan, Vijay (1979), "Aggregating Conjoint Data: Some Methodological Considerations and Approaches," Proceedings, American Marketing Association, 1979, 75-77.

Malhotra, Naresh K. (1982a), "Information Load and Consumer Decision Making," Journal of Consumer Research, 8, 419-430.

Malhotra, Naresh K. (1982b), "Multi-Stage Information Processing Behavior: An Experimental Investigation," Journal of the Academy of Marketing Science, 10, 54-71.

Malhotra, Naresh K., Jain, Arun K., and Lagakos, Stephen W. (1982), "The Information Load Controversy: An Alternative Viewpoint." Journal of Marketing, 46, 27-37.

Newman, Joseph W. (1977), "Consumer External Search: Amount and Determinants," in Consumer and Industrial Buying Behavior, eds., Arch G. Woodside, Jagdish N. Sheth, and Peter D. Bennett, Amsterdam, Holland: North Holland Publishing Company, 79-94.

Olshavsky, Richard W., and Granbois, Donald H. (1979), "Consumer Decision Making-Fact or Fiction," Journal of Consumer Research, 6, 93-100.



Naresh K. Malhotra, Georgia Institute of Technology


NA - Advances in Consumer Research Volume 10 | 1983

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