The Organization of Product Information in Memory Identified By Recall Times

ABSTRACT - A chronometric technique is used to examine whether consumers' memory structure for product information reflects the input organization of that information, or whether people will reorganize into a preferred structure, either brand-based or attribute-based. The evidence clearly favors congruence between memory structure and input organization, although some reorganization did occur.


Eric J. Johnson and J. Edward Russo (1978) ,"The Organization of Product Information in Memory Identified By Recall Times", in NA - Advances in Consumer Research Volume 05, eds. Kent Hunt, Ann Abor, MI : Association for Consumer Research, Pages: 79-86.

Advances in Consumer Research Volume 5, 1978      Pages 79-86


Eric J. Johnson (student), Carnegie-Mellon University

J. Edward Russo, University of Chicago

[The authors would like to thank John Herstein for introducing them to the problem, Patrick Langley for helpful discussions, and Jeff Godliss for running subjects.]

[Order of authorship is alphabetical; the authors' contributions are equal.]


A chronometric technique is used to examine whether consumers' memory structure for product information reflects the input organization of that information, or whether people will reorganize into a preferred structure, either brand-based or attribute-based. The evidence clearly favors congruence between memory structure and input organization, although some reorganization did occur.

This report has two purposes: (1) to call attention to the role played by memory for product information in purchase decisions, and (2) to introduce a chronometric technique to the study of the structure of remembered product information. When consumers make purchases, they use product information from two sources. The external environment contains package labels, point-of-purchase displays, posted prices, etc., relevant to the purchase decision. However, most information needed for the purchase decision is retrieved from memory. Information like use experience, family preferences, word-of-mouth communications, and advertising (except point-of-purchase advertising) must all be mediated by memory. Further, if all brands are not available from a single vendor, then information about the missing ones must come solely from memory. The point is that product information retrieved from memory plays an essential role in almost all purchase decisions. Unfortunately, research into consumer decision making has largely ignored this role (Olson, 1977).


A processing history of product information can be outlined as follows:

1. encoding of product information from external sources.

2. retention of product information in memory.

3. retrieval and use of product information in the purchase decision process.

The second stage, retention in memory, is the focus of this paper. It will be useful, however, to consider the interactions between retention and the other two stages.

The Effect of Retention on Decision Strategies

Does the nature of the stored information determine the type of decision process? For example, if product information is organized around brand names, as many researchers have suggested, will the decision rule have to be compatible with this information structure? Two studies suggest that the answer may be yes. Simon and Hayes (1976) used different cover stories for the same problem to induce different internal representations of the problem information. The subjects then selected problem solving strategies that were compatible with their specific internal representations of the information. Different representations led to different strategies. If purchase decisions are seen as a type of problem, the Simon and Hayes result implies that different representations of this same product information may well lead to different decision strategies. Further, this implication should hold whether the representations are internal or external.

The last assertion has been explicitly confirmed by Bettman and Kakkar (1977), except that they relied entirely on externally available rather than remembered information. Each of their subjects was shown product information in one of three formats: a brand-organized display, in which information was available most easily by brand name; an attribute-organized display, in which the same information was available most easily by attribute; and a matrix display, which equated the availability of the information. Although consumers could process the available information however they wished, Bettman and Kakkar found that the decision rule was usually chosen to be compatible with the format of the product information. Thus, decision rules that required the information to be processed by brand were most frequent with the brand-organized format. This study provides direct evidence that consumer decision rules are flexible and will depend on the structure of the available product information.

The Effect of Encoding on Retention

Just as the structure of product information can influence the subsequent decision strategies, the process used in encoding the information can determine how the information is stored. For example, if product information is presented in a brand-organized fashion, will it be remembered in a brand-organized structure? Alternatively is there a preferred memory structure into which product information is always organized no matter what the original presentation and encoding format?

Unfortunately, the authors know of no studies that have investigated the relation between the encoding of product information and its organization in memory. The experiment presented in subsequent sections was designed to address this issue.

The Realworld versus the Laboratory

The existing research literature has largely ignored the role of memory in purchase decisions. Most laboratory studies attempt to bypass any memory stage. Instead, all product information is provided externally.

We believe that researchers in consumer behavior have not deliberately shunned memory issues per se. Rather, this omission follows naturally from the information acquisition paradigm, which dominates research on consumer choice behavior. Within this paradigm experiments are designed to rely as much as possible on externally provided information because the information acquisition sequence provides the necessary data base. Since acquisition from memory is unobservable, it can provide no insight into the purchase process.

This information acquisition paradigm is, of course, unrepresentative of realworld purchases. Most such purchases rely primarily on remembered information.

Thus, an unfortunate consequence of a generally successful research paradigm has been a systematic gap in our investigation of consumer behavior.

Representation of Knowledge

Two alternative structures have been proposed to represent a consumer's product knowledge. This knowledge can be stored by brand or by attribute (Bettman, 1978; Payne, 1976). Storage by brand assumes that a person's knowledge about a product class is organized around each brand. To take peanut butter as an example, a verbal statement of brand-organized knowledge might be: "Skippy tastes very good, contains preservatives and costs $.99 for an 18 oz. jar." In contrast, attribute-organized knowledge could be verbalized as: "Price, let's see, Skippy costs $.99, Jif costs $.99, but Peter Pan costs only $.95." Note that whether the product information is stored by brand or by attribute, the information itself is identical. The price of Skippy is stored as $.99 in either structure. The concern is not with the content of memory but with its structure or organization.

This conceptualization of product knowledge should be distinguished from such theories as those underlying multidimensional scaling and semantic differential scales. The emphasis of the present research is not on the content of the stored information, but with the organization of the information itself. Although not everyone will share the same sets of brands and attributes, especially across different products, it is suggested that preferred organization is independent of content.

Representations in Cognitive Psychology

A major concern of cognitive psychology is the representation of knowledge. There are several highly related formalisms for representing the structure of long-term memory, including list structures (Simon and Newell, 1974) and semantic networks (Anderson, 1976). References to these and other readings in the representation of knowledge can be found in Norman (1976, pp. 197-198). Because list structures can be most easily simplified, Figure 1 presents the previous example in a list structure format. Both the brand-organized and attribute-organized versions are shown.



The essence of the list structure representation is the use of the "next" relation to connect items. Thus, in Figure 1A Skippy is followed by a list of its attributes, whereas in Figure 1B, Skippy is followed by the other brands. The links created by the next relation can be traversed to get from any item in the complete list structure to any other item. Of course, the nature of the list structure can either facilitate or hinder such activity. In the brand-organized list (Figure 1A), if one is at Skippy-Preservatives-Yes, it is easier to reach Skippy-Size 18 oz. than to reach Jif-Preservatives-Yes (not shown).


The verification of structural representations such as those depicted in Figure 1 is often accomplished by using chronometric techniques. In essence, these techniques assume a direct relation between the amount of processing and the time taken to perform a task. If the task is recall, the time to recall is assumed to measure the "distance" in the memory structure between the recall cue and the target item.

It is important to realize that it is not just access time that makes up time to recall. The subject may, in fact, have the needed information in memory, but in a form different from that necessary for the correct response. Therefore, some time must be spent converting the internal representation to a more useful form. However, whether more cognitive processing is required by greater "distance" or by the conversion of "near" information, the longer recall time accurately indicates the greater effort needed to recall the given item.

Chronometric analysis has been developed into a sophisticated methodology. Although only the basic principles will be used in this report, it is worth noting two elaborations. In most tasks, it is possible to trade speed for accuracy. Such a trade-off threatens the validity of recall times in identifying cognitive structure. In practice, this trade-off is avoided by enforcing minimal error rates through instruction and training. However, more powerful techniques are available for situations with large or unstable error rates or when the retrieval dynamics must be known in detail (e.g., Dosher, 1976). A second elaboration is the partitioning of the total response time into segments that measure the durations of several, nonoverlapping stages of processing. This version of chronometric analysis was developed by Sternberg (1969), and many successful applications could be cited (see Chase, 1977).

The analysis of retrieval times is one of a surprisingly large number of techniques for investigating memory representations (for a review, see Bower and Tulving, 1974). A more common technique is to ask the subject to respond to a presented item as the "same" as or "different" from some member of the memorized set of items. Both techniques require the recall of a remembered item. However, the verification task seems more removed from most purchase situations. Occasionally a consumer may search memory to verify a manufacturer's claim, but usually the consumer is recalling the relevant product knowledge as rapidly as possible. Thus, the recall time technique not only enables the discrimination between brand-organized and attribute-organized memory structures, but also relies on behavior that is similar to the retrieval of information in real shopping situations.


The focus of our experiment is the relation between the first two stages of the 'processing" of product information, namely encoding and retention. Specifically, we inquire whether the encoding format, brand-based or attribute-based, determines the storage organization or whether there is a systematic tendency to reorganize into a preferred memory structure regardless of input format.

The basic design had subjects learning a booklet of product information. The total information set was a complete brand x attribute matrix with four brands and four attributes. Each page of the booklet consisted of a column of information headed by either a brand name or an attribute name. The column itself contained, respectively, either the four attributes and their values for that brand or the four brands and their values for that attribute. These two input organizations were called brand input and attribute input, respectively.

The information in a complete brand x attribute matrix is rarely available in the real world. However, it was important to the success of this experiment to treat brands and attributes equally. That is, any preferences for brand-based or for attribute-based memory structures had to be separable from encoding factors that favored one over the other. For this reason a complete, symmetric brand x attribute matrix was used. Future studies can address the effects of encoding factors, especially as they occur in real world sources of product information.

After learning a booklet, the subjects performed a cued speeded recall task. They were presented with one of the brands or attributes and asked to recall, as quickly as possible, all the information associated with that cue. For example, if the cue was Skippy, a subject had to report each of the four attributes followed by its value for Skippy.

The cued speeded recall task was the same whether the information had been learned by brand or by attribute. The response times, however, should be different. In the brand input condition, the links between attributes within a brand should permit relatively fast recall of information to a brand cue. In contrast, a cue like price is separated from the necessary information by a larger set of links. Thus, our experiment depends on this simple principle: if information is stored by brand, it can be recalled more quickly by brand than by attribute; if it is stored by attribute, it should be recalled faster by attribute than by brand.



Two brand-attribute matrices were used, one for air conditioners and one for cooking oils. These products were selected to represent both an infrequently purchased major durable and a common nondurable with a high repurchase rate. For air conditioners the brands were Admiral, Carrier, Frigidaire and Westinghouse, and the attributes were average price, circulation, energy use, and indoor noise. All 16 values took on one of three levels, high, medium or low. This matrix was adapted from evaluations of air conditioners published in Consumer Reports (July, 1973; and July, 1975).For cooking oils the four brands were Crisco, Kroger, Planters, and Wesson, and the four attributes were fat count, flavor, smoke point, and unit price. All 16 values took on one of three levels, fair, good, or excellent. This matrix was adapted from an evaluation of cooking oils published by Consumer Reports (September, 1973). Accompanying the attributes for each product category was a description of the meaning of the attribute. Subjects began the learning of product information by studying these attribute descriptions until they felt comfortable in their understanding of all of them.

The selection of particular brands and the phrasing of attribute labels were designed to maintain equal syllable counts for all rows and columns of a product matrix. This insured that response time was a measure of retrieval difficulty and not the output (speaking) time. Preliminary tests of the time needed to read each row and column within a matrix confirmed the approximate equality of the time to speak the eight files in a matrix.


There were three main factors: A, input organization (brand or attribute); B, product (air conditioners or cooking oil); and C, probe type or cue type (a brand name or an attribute name). A fourth factor (D) is the specific cue, e.g., a specific one of the four brand names of air conditioners. This factor is nested within combinations of B and C. Each of the factors was fixed.

The three main factors were fully crossed (i.e., factorial) with one critical exception. Subjects could not be expected to learn information about the same product (Factor B) twice, once in each of the two input organizations (Factor A). Instead, each subject memorized both booklets once, alternating input formats (with order counter-balanced). The design for Factors A and B was a 2 x 2 Latin square fractional factorial design (see Kirk, 1968, p, 407). The main import of this design is the loss of all information about the A x B and A x B x C interactions. The only possible alternative design required a single subject to memorize both booklets in the same input organization. This split-plot design, by confounding group and input format effects, made the main effect of input (Factor A) indeterminate, which was even less desirable than losing an interaction effect.

The 2 x 2 Latin square divided the subjects into two groups (with 10 subjects in each group). Because 8 recall times (4 attribute cues and 4 brand cues) were obtained from both products and from each of the 20 subjects, a total of 320 observations formed the data base.


Twenty-four undergraduates at Carnegie-Mellon University participated in the experiment. They were either volunteers (2) or were fulfilling a course requirement for introductory psychology (22). Two subjects were eliminated because they made more than three errors on the sixteen possible trials. Additionally, two subjects took an excessive amount of time to learn the information booklet for the first product. They were not asked to finish the experiment and their data were discarded. Thus, a total of twenty subjects provided data for the experiment.

When the response time data were analyzed, the means of 19 subjects ranged between 8.8 sec and 16.4 sec. The remaining subject's mean was 33.4 sec. Because this subject's responses were so unique and because they would thoroughly disrupt any pattern of results that held over the other 19 subjects, he was dropped from the study. One of the two subjects who failed to pass the error criterion had participated in the same condition as the dropped subject. This former subject was returned to the sample to maintain ten subjects per group.


Subjects were told that they were participating in a study examining consumers' knowledge of various products. They were then asked to learn the contents of the first booklet. Instructions emphasized that the material in the booklet had to be remembered accurately. Subjects were also asked not to switch between pages of the booklet but rather to look at the book one page at a time. After they had informed the experimenter that they thought they had learned the booklet, subjects were administered a criterion test. Subjects were asked to name the value of four different product-attribute combinations. If a subject could not successfully name the four values, he was given the booklet and told to try again. Subjects who passed the criterion test went on to the cued speeded recall task. The total time until the subject passed the test was recorded.

In the recall task subjects were presented either an attribute or a brand name. If the probe was an attribute name, they were told to recall all of the appropriate brand names and their values. For a brand name probe, they recalled the various attributes and values for that brand. The recall time was measured (in msec) from the onset of the probe to the end of the list. For each booklet the subject received four attribute probes and four brand probes. Order was randomized across subjects.

After completion of the first recall test, subjects were given the second booklet, this time with the opposite organization. The procedure for criterion testing and cued speeded recall was repeated.

Finally, subjects were presented an empty matrix for the first product class. The four attributes and the four brands were given but the sixteen values were missing. The subjects were asked to recall these values, and to write them down, in whatever order they wished. The output order was recorded. This was done for the second product as well. Then the subjects were debriefed and released.

The written free recall measure is not necessarily an independent measure of memory structure. Inherent in the cued memory task is recall by both brand and attribute. This task demand could cause reorganization which could well affect subsequent free recall. Also the time between learning the information and recall varies and the structure as therefore measured may be different than that measured with the cued recall. High convergence between these two recall methods may well wait until they are tried under a different experimental procedure.


Encoding Time and Error Rate

A preliminary analysis tested for differences in the time necessary to learn the product information. An analysis of variance showed that learning times did not differ between the types of input organization, the different products or the two groups of subjects. Also, no interactions among these factors were significant. This precludes the possibility that differences in recall time could be due to differences in how well the lists were initially learned.

In the same way, differences in recall time could be confounded with differences in error rates across conditions. If speed is traded off for accuracy, then a difference in recall time can be due to different error rates as well as to the effect of an independent variable. Error rate was defined as the proportion of cued recalls for which at least one of the four required values (belonging to a single brand or a single attribute) was incorrect. The overall error rate was 7.8% (25 of 320). Using individual X2-tests for the equality of two proportions, no significant differences in error rate were found between levels of input organization, product category, or cue type. Taken together, these analyses imply that differences in error rates or thoroughness of learning can be ruled out as explanations for observed differences in recall speed.

Recall Times

Differences in recall times were tested by an analysis of variance. An examination of the residuals of the appropriate model indicated that a log transformation was needed to stabilize the variance over different levels of recall time. The results of the analysis of variance on the transformed recall times are reported in Table 1. The total degrees of freedom, 294, reflect the removal of the 25 recall trials on which an error occurred. Note that there are no replications in the design. The error term was estimated from the Group x Probe and Subject x Probe interactions. If these terms include any systematic efforts, the resulting F tests will be conservative.



The critical prediction concerned the A x C interaction. This interaction captures the effect of storage organization (as induced from input organization) on the cued recall. It was predicted that recall would be faster when the probe type matched the structure stored in memory. Attribute probes would result in faster recall with attribute-organized information, and brand probes would be faster for brand-organized information. The "incompatible" probes would lead to slower recall.

As predicted, the A x C interaction is statistically significant and, furthermore, is the largest effect (in terms of mean squares) in the model. Before pursuing this and related effects, the subject and cue differences are noted. The presence of significant subject differences is typical in chronometric analysis. They are potentially attributable to many factors including motivation and strategy differences. There was no difference between groups, indicating that subject differences were not systematically related to group. Note that in this design the A x B interaction is confounded with group. Therefore, the lack of a significant group effect validates the assumption of zero A x B interaction. The only other possibility is that both effects were of equal magnitude and opposite direction, thus canceling each other out. Individual cue effects (Factor D) were also significant. This result was explored with a series of a posteriori contrasts. These tests revealed only one significant difference: for cooking oil the cue "cost" was faster than the cue "fat count." In general, the differences among cues exhibited no systematic relation, and they are of little intrinsic interest. The observed differences may be due to any articulation time or to irrelevant factors affecting memory, such as word frequency or concreteness.

To communicate the results involving the major independent variables, means over subjects and cues were computed. These values are plotted in Figure 2. The two pairs of crossing lines represent the A x C interaction, separately for the two product categories. Using a priori contrasts, both interactions were found to be significant. For air conditioners F(1, 203) = 47.30, p < .001; for cooking oils the interaction was less dramatic, F(1, 203) = 5.93, p < .05. Both the contrasts and an inspection of Figure 2 reveal that, though the predicted effect of compatibility between cue type and input organization is present for both product categories, the size of this effect differs across products.



Returning to Table 1, it can be seen that both Factors B (product category) and C (cue type) show significant effects. As shown in Figure 2, cooking oil information was recalled faster than air conditioner information, holding all other factors constant. One possible explanation for this difference is articulation time. Though speaking times were approximately equalized across all brands and sizes within a product category, there were differences between categories. The total number of syllables over the eight brand or attribute names is 27 for air conditioners and 16 for cooking oil. Thus, the articulation time would be expected to be longer for air conditioners, as confirmed by the means in Figure 2. The other possibility is that cooking oil information is genuinely easier to recall. However, both products were learned as quickly and both yielded equal error rates during recall. These results suggest that the difference in recall times is not due to memory retrieval but rather to an output factor like articulation.

Differences between cue types are also apparent in Figure 2. The mean recall times were 12.97 sec for attribute probes and 13.70 sec for brand probes. This difference, though relatively small (2.7%), is intriguing. Although one can only speculate given the present data, this effect might be related to a more general preference for attribute information. Factor A, input organization, exhibited no significant effect. The equality of recall times over brand and attribute inputs is compatible with the absence of differences in learning time or error rate.

The B x C and G x C interactions are probably attributable to differences in large A x C interaction for each product combined with the nature of the design. As noted earlier, any A x B interaction is confounded with G. Thus, G x C is confounded with A x B x C, and the G x C interaction may well be explained by the product differences (Factor B) in the A x C interaction that are so apparent in Figure 2. In a similar way, the B x C interaction, which is smaller, seems to have no natural interpretation. It may be related to product differences or to the large A x C effect combined with the confounding of Factors A and B within groups. In sum, the G x C and the B x C interactions show that product category affects the strength and nature of the effect of display format on memory structure. However, these differences are small when compared to the main A x C interaction, accounting for less than a fourth of its variance.

Finally, the interactions with subjects (S x A, S x C and S x A x C) should be acknowledged as statistically significant. Since these effects are not large and reflect only individual differences, they are not particularly meaningful. For completeness, it is noted that the error term is composed of the G x D and S x D interactions.

In summary, the pattern of recall times indicates a strong effect of congruence between input organization and cue type. Attribute probes led to faster recall when the input organization was attribute-based, and similarly for brand cues with brand-organized information. This result confirms the main prediction of the study. In addition, marked product differences were found when the input x probe (A x C) interaction was examined separately for each product.

Results for Individual Subjects

Chronometric studies are usually based on data analyses that are complete within individual subjects. This requires the collection of many data points to enable reliable estimates of mean response time and statistical tests with adequate power. Collecting this large amount of data becomes more costly as the task becomes more cognitively complex. In such cases, it is not unusual to find a subject participating for 20 or more hours in one experiment (e.g., Dosher, 1976). In the present study only 16 response times are available for each subject. Thus, although statistical tests of within-subject differences can be performed, some real differences may be obscured by variability.

The 16 recall times were classified by input organization and cue type (Factors A and C), creating four groups with four observations each. For each input organization, there are two mean response times, one for attribute cues and one for brand cues. The difference between the latter two means can be used to measure congruence between memory structure and input organization. This congruence will be termed input bound-ness. If the product information was learned in an attribute format, the mean for attribute probes is subtracted from the mean for brand probes. The larger this difference, the greater the input boundness. These then can be thought of as estimates of the degree to which a subject's internal representation is congruent with the external source. For each subject two such measures exist, one for attribute-organized input and one for brand-organized input. By summing these two measures, individual estimates of input boundness are obtained.

Using the estimate of MSE obtained from the analysis of variance, a t-test was performed for each subject. The significance level for this t-test (one-tailed) was set at .10 to favor the detection of differences in spite of the small number of data points. Using this criterion, 8 of 20 subjects exhibited significant input boundness. Thus, the main prediction and finding of this study, the congruence between input organization and memory structure, may be characteristic of less than half the subjects.

The reduced number of subjects showing an effect of input boundness could be due, in part, to the reduced statistical power of the individual tests. Additionally, the individual estimates of input boundness are confounded with product category. The observed differences across products may weaken comparability between mean response times for a single subject. Finally, it must be remembered that typical chronometric analyses of individual subjects require much larger sample sizes than available here.

Written Free Recall

After both sets of product information had been learned and probed via the cued speeded recall task, a written free recall was collected. For each product class, an empty 4 x 4 matrix was shown. The row and column labels were provided, and subjects were required to write in the 16 missing cell values. Each such output sequence consists of 15 intercell transitions.

Each of these transitions can occur within a column, within a row, or both to a different row and a different column. Since the booklets arranged information by column, a column transition was judged to be congruent with the original display. Row transitions could only occur if the memory structure is dissimilar to the original display. Therefore, each transition was coded as congruent (column) or incongruent (row or other) with the original input organization. At the end of a column or row, when all entries are filled, no more transitions within that file are possible. Thus, transitions from filled files could not be used to measure congruity with input, and they were excluded from the analysis. This left a data base of 12 transitions for each protocol. The overall proportion of congruent transitions is .72 (344 of 480). This value indicates a strong tendency for the written free recall to be congruent with the input organization. This confirms the major finding from the recall time data, the significant tendency for memory structure to be determined by input organization.

The proportion of congruent transitions were partitioned by the two possible independent variables, input organization and probe type. These data are reported in Table 2. The striking aspect of the pattern of proportions is their uniformity except for the brand-cooking oil cell. The other three proportions are all quite high, indicating input boundness. But when cooking oil information had been learned in a brand-organized format, considerable reorganization occurred. That is, subjects tended to reorganize from a brand-based input to an attribute-based memory structure. Again, this result confirms the findings from the recall time data.



At this point it is worth recalling the problems associated with the written free recall data that were discussed in the method section. The problematical nature of this recall technique, as it was implemented in this experiment, implies that all findings must be tempered with qualification. It is comforting that the free recall data averaged over subjects agree with the results based on the cued speeded recall task. However, a stricter test of the convergent validity between these two recall techniques can be based on the data of individual subjects. The proportion of congruent transitions was computed for each subject and correlated with the Bound statistic that is based on recall times and discussed earlier. Both are measures of input boundness. The correlation over the 20 subjects was .33, a value significantly greater than zero (p = .02) but not very high. It should be concluded that the free recall transitions are not as reliable as they should be--or as they could be with a different experimental procedure. However, when averaged over subjects, they probably present a satisfactorily accurate picture of memory organization.


Summary of Results

The main finding was the congruence between input organization and memory structure. Averaged over the twenty subjects, both cued recall times and the output order of the written free recall supported this conclusion. Individual subject analysis of recall times suggested that 10 of the 20 subjects were significantly input bound. Input boundness was sensitive to type of product, with air conditioners inducing relatively consistent input boundness and cooking oil exhibiting more reorganization of the memory structure.

An important question is whether people prefer attribute-based or brand-based memory structures. A very preliminary result of the present study is that attribute structures were preferred. This result is as intriguing as it is tentative.


The findings of this study, although clearly supporting the view that memory structure is input bound, should be considered only in light of product differences. There was much greater reorganization (less input boundness) for cooking oil than for air conditioners. Possible factors that can influence the input boundness of a product class need to be isolated. It is interesting to note, for example, that both the brand names and the attribute names were more familiar for cooking oil. Also, at least one of its attributes (polyunsaturated) fat count is health-related and apt to be especially salient. Clearly, a wider range of products will have to be examined before any relation between reorganization potential and product type can be established.

Because the subjects in this experiment were undergraduates, the findings may not generalize to representative shoppers. Bettman and Kakkar (1977) argue that such shoppers would be more brand conscious than undergraduates. A shopper's experience within a particular product category may contribute to brand consciousness or, alternatively, to the ability to reorganize by attributes. Questions of this nature can only be answered by studies designed to test the generality of the present findings over a much wider sample of products and subjects.

The Chronometric Technique

One of the positive results of this study is the demonstration of the usefulness of chronometric analysis. This is especially true for the study of the representation of product information in memory. The present use of chronometric analysis to identify the memory structure of product information parallels the recent recognition of the importance of this topic (e.g., Olson, 1977 and Bettman, 1978). However, chronometric analysis can be applied to a wide range of other consumer research problems. See Gardner, Mitchell and Russo (1978) for a discussion of this point and an application of this technique to low involvement advertising. As a final methodological note, the reader should be reminded that the chronometric technique used here is among the least sophisticated. More powerful methods are available and, hopefully, will be brought into the study of consumer behavior. However, even the simple chronometric techniques may be adequate given the size of the effects observed here which are considerably larger than those reported in most chronometric studies.

Implications for Policy

Memory structure for product information, and its relation to input format on the one hand and decision rules on the other, has important policy implications. We give one example. To the extent consumers are input bound, their memory structures will reflect the organization of product information in the environment. As just pointed out, this organization is largely brand-based (advertising, etc.). This is all well and good if consumers make better purchase decisions using brand-based strategies. But if attribute-based strategies are superior, and a growing body of evidence suggests this (e.g., Russo and Dosher, 1976), then brand-organized storage contributes to less accurate and more effortful purchase decisions. We may then find a conflict between superior decision rules and the organization of currently available product information.

This suggests a potential need for public policy intervention. More importantly, it suggests that any policy of information provision may want to alter the current format of product information. If the ultimate policy goal is to improve consumer decisions through information disclosure, the format of the information should be designed for compatibility with the preferred decision rules.

Future Research

A major direction for future research is to examine this relation between memory structures and decision rules. Does the structure of stored product information determine the decision rule(s) that can be employed? Or, conversely, does a preferred decision rule lead to the reorganization of product information into a compatible memory structure? The relative strength of influence between memory structure and decision rules is an important question in consumer research.

A related issue concerns the conditions surrounding the acquisition of knowledge about products. Consumers may intentionally retain information with the conscious goal of making a purchase decision. Knowledge can also be retained, possibly incidentally, for more genera], future use (Greeno. 1976).

A second topic for future research is the influence of different external organizations of product information on memory structure. What is the preferred memory organization if a complete brand x attribute array is available? Suppose the externally available information is very unstructured, possibly in a discursive format or only accessible in a random order. Will shoppers attempt to organize an unstructured external display into a consistently preferred internal structure? Finally, what if product information is available as it typically is in the market place: from several sources (advertising, use experience, word of mouth, published ratings, etc.) that are not naturally comparable and that may include inconsistencies? Further, most real-world product information is brand-centered. Does this imply that memory structures are usually brand-organized whether or not attribute-based structures ere preferred?

In the real world, memory of product information is the link between information acquisition and the purchase decision. This topic is a major understudied area of consumer behavior. There are many important research questions associated with it, only a few of which have been posed above. By actively studying memory of product information, it may be possible to close a major gap between current laboratory research and the real world consumer setting.


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James R. Bettman and Pradeep Kakkar, "Effects of Information Presentation Format on Consumer Information Acquisition Strategies," Journal of Consumer Research, 3(March, 1977), 233-240.

William G. Chase, "Elementary Information Processes," in W. K. Estes, ed., Handbook of Learning and Cognitive Processes, Vol. 5 (Hillsdale, NJ: Lawrence Erlbaum Associates, in press).

Barbara Anne Dosher, "The Retrieval of Sentences from Memory: A Speed-Accuracy Study," Cognitive Psychology, 8(July, 1976), 291-310.

James G. Greeno, "Indefinite Goals and Well-Structured Problems," Psychological Review, 83(November, 1976), 479-491

Meryl Gardner, Andrew A. Mitchell and J. Edward Russo, "Chronometric Analysis: An Introduction and an Application to Low Involvement Perception of Advertisement," in H. Keith Hunt (ed.), Advances in Consumer Research, Vol. 5, Association for Consumer Research, 1978.

Roger E. Kirk, Experimental Design: Procedures for the Behavioral Sciences (Belmont, CA: Brooks/Cole, 1968).

Donald A. Norman, Memory and Attention: An Introduction to Human Information Processing, Second Edition (New York: Wiley, 1976).

Jerry C. Olson, "Theories of Information Encoding and Storage: Implications for Marketing and Public Policy," presented at the Conference on the Effect of Information on Consumer and Market Behavior (Carnegie-Mellon University, May, 1977).

John W. Payne, "Heuristic Search Processes in Decision Making," in Beverlee B. Anderson, Ed., Advances in Consumer Research, Vol. 3, Association for Consumer Research, 1976.

Herbert A. Simon and John R. Hayes, "The Understanding Process: Problem Isomorphs," Cognitive Psychology, 8(April, 1976), 165-190.

Herbert A. Simon and Alan Newell, "Thinking Processes," in David H. Krantz, R. Duncan Luce, Richard C. Atkinson, and Patrick Suppes, Eds., Contemporary Developments in Mathematical Psychology, Vol. 1, (San Francisco: Freeman, 1974).

Saul Sternberg, "Memory Scanning: Mental Processes Revealed by Reaction Time Experiments," American Scientist, 57(1969), 421-457.

Endel Tulving and Gordon H. Bower, "The Logic of Memory Representations," in Gordon H. Bower, Ed., The Psychology of Learning and Motivation, Vol. 8 (New York: Academic Press, 1974).



Eric J. Johnson, (student), Carnegie-Mellon University
J. Edward Russo, University of Chicago


NA - Advances in Consumer Research Volume 05 | 1978

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