An Analysis of Cognitive Responses to Comparative Advertising

ABSTRACT - This paper reports results of an initial study on the analysis of cognitive responses generated by comparative and non-comparative ads. The difference in effectiveness of these two types of ads is empirically studied from an information processing perspective. The results presented here support the notion that comparative and non-comparative advertisements create significantly different cognitive elements, both in nature and in number.


R. Dale Wilson and Aydin Muderrisoglu (1980) ,"An Analysis of Cognitive Responses to Comparative Advertising", in NA - Advances in Consumer Research Volume 07, eds. Jerry C. Olson, Ann Abor, MI : Association for Consumer Research, Pages: 566-571.

Advances in Consumer Research Volume 7, 1980     Pages 566-571


R. Dale Wilson, The Pennsylvania State University

Aydin Muderrisoglu, The Pennsylvania State University

[This research was funded by a Research Initiation Grant awarded to the first author by the Office of the Vice President for Research and Graduate Studies, The Pennsylvania State University. Thanks are in order to Professors Stewart W. Bither and Jerry C. Olson, who provided valuable comments on the design of the first author's research program in comparative advertising, and to Rajesh Kanwar, Noreen K. Moore, and Stephen C. Rood, who assisted with the data analysis. Also, the authors thank Professor Paul A. Games for his suggestions on the data analysis procedures used in the study and an anonymous reviewer for his/her insightful comments on an earlier draft of this article. Because of space limitations, this paper is an abbreviated version of the original. The complete version is available from R. Dale Wilson, Department of Marketing, The Pennsylvania State University, University Park, PA 16802.]


This paper reports results of an initial study on the analysis of cognitive responses generated by comparative and non-comparative ads. The difference in effectiveness of these two types of ads is empirically studied from an information processing perspective. The results presented here support the notion that comparative and non-comparative advertisements create significantly different cognitive elements, both in nature and in number.


The area of comparative advertising has received a great deal of attention from consumer researchers during the past few years. Since the mid-1970's, when Wilkie and Farris (1975) first drew attention to this topic as a fertile ground for testing hypotheses from the behavioral sciences, several authors have reported research findings that assess the effectiveness of comparative advertisements. Because comparative advertisements explicitly or implicitly identify one or more brands of the same product/service class and then compare "...the sponsoring brand and the other identified brands in terms of specific attributes, vague attributes, or overall product attributes" (Wilson 1978, p. 7), their unique properties set them apart from other types of consumer-directed communication and thus increase their appeal from a research viewpoint. The appeal of comparative advertising as a research area is further enhanced because of its important implications from legal and public policy, social, and managerial perspectives. These implications are discussed in detail by Wilkie and Farris (1975), Scammon (1978), and Wilson (1978).

For the most part, however, research aimed at documenting the superior effectiveness of comparative-type advertising claims when contrasted to standard, non-comparative advertising has not been successful (see Wilson and Muderrisoglu 1979, footnote 4, for a list of eight research studies in which comparative-type formats were found to be no more effective than non-comparative formats). With only a few exceptions, researchers have not been able to support the argument made by Wilkie and Farris (1975) among others that comparative advertisements should produce differential responses on brand awareness, brand comprehension, and brand preference dimensions. Numerous dependent variables have been used in an effort to uncover differential effects, but research results to date have been disappointing to those who advocate the theoretical and/or intuitive attractiveness of the concept of comparative advertising. Articles by Etgar and Goodwin (1978) and Wilson (1978) have summarized the recent research results and document the surprising lack of support for comparative advertising.

It is possible, however, that the reason for the near absence of significant findings in comparative advertising research is due simply to measurement problems. Perhaps the difficulty lies with the measurement techniques themselves and their lack of attention to psychological or cognitive elements that are induced by consumers' exposure to comparative-type communication. The usual 6- or 7-point scales used to measure subjects' attitudes about various message dimensions such as evaluation, potency, and activity (Osgood, Suci, and Tannenbaum 1957) or the usual measures of aided or unaided brand and claim recall may simply not be sensitive enough to detect subtle, albeit significant, differences between comparative and non-comparative forms of advertising. Fortunately, more sensitive measures of the mediating effects of attitude formation and change have been developed (see Wright 1973, 1974) that lend themselves nicely to monitoring the thought processes of individuals who are exposed to various types of mass communication. Although this information processing approach to investigating the influence of communication effectiveness has been successfully applied in several studies (see Olson, Toy, and Dover 1978 for a brief review), research in comparative advertising has not yet benefited from these methods.

It is the purpose of this paper to evaluate comparative advertising from a cognitive response (Wright 1973, 1974) point of view. In addition, the concept of advertising intensity (Pride, Lamb, and Pletcher 1977; Lamb, Pride, and Pletcher 1978) is used in the study to determine the various types of cognitive responses that are elicited due to the strength of the comparative and non-comparative advertisements. The results of the study point to the potential contribution of cognitive response measures in identifying and understanding the differences between comparative and non-comparative advertising stimuli.


Since the major thrust of comparative advertising research has been in testing the effectiveness of this form of communication, the cognitive and information processing effects of comparative and non-comparative ads can be contrasted. Theoretically, a more precise understanding of such effectiveness may be gleaned by analyzing those cognitive thought responses that are emitted by the receiver upon exposure to the advertising stimuli (Olson, Toy, and Dover 1978). Because of the nature of comparative forms of advertising, they would be expected to produce a differential (relative to non-comparative advertisements) and perhaps more powerful impact on the receivers' present cognitive organization and knowledge structure (i.e., amount of accumulated knowledge) for the communicated information. Current theories of the cue utilization process (e.g., Olson 1978a) lend support to the notion (first mentioned by Wilkie and Farris 1975) that comparative advertisements may evoke a different set of cognitive responses than would similar non-comparative advertisements.

Whenever comparative advertising is used to present a factual comparison over one or more attributes of a sponsoring and a competing brand (or brands), information may be more readily encoded into semantic, rather than episodic, memory. [For a more complete discussion of semantic and episodic memory, see Tulving (1972) and Olson (1978a), Tulving views episodic memory as memory for past events in one's life and semantic memory as stored knowledge about the world. Olson (1978a, p. 708) suggests that semantic memory is more likely to influence the formation of inferential beliefs about products, especially when the focus is on specific product attributes.] Because comparative advertising may be used not only to make statements about the sponsoring brand but also to make point-by-point contrasts about the sponsoring and competing brands, this type of advertisement may create more attention and involvement on the part of the receiver than would a similar non-comparative advertisement. [This view is shared by Wilkie and Farris (1975).] Thus, under normal exposure to advertising messages, the higher levels of attention and involvement may cause the receiver's mental process to be activated to a greater extent than would be expected when the receiver is exposed to a non-comparative ad. Likewise, when the receiver encodes the relationships brought forth in a comparative message, a deeper level of information processing may probably result, and the receiver may retain a more meaningful perception of the massage than would occur under a non-comparative advertising condition. This semantic encoding, in turn, will likely result in a more highly-developed memory schema than one that occurs as the result of attending to standard, non-comparative advertising. In short, comparative advertising (relative to non-comparative advertising) can be expected to build well-formed, better-organized memory schemata which should lead to better memory performances by receivers.

In order to tap the effects of comparative ads on encoding processes, memory, and cognitive structure, the cognitive response model (see Wright 1973, 1974; Olson, Toy and Dover, 1978) can be utilized. According to this model, thoughts (cognitive responses) elicited in various forms of communication can be seen as underlying factors which mediate the overall effectiveness of that communication. Unstructured cognitive responses (written or verbalized) are the outcome of the process of mediation of the cues from external stimuli and the assignment of cognitive symbols (i.e., meaning) to those cues. The general process used for obtaining cognitive responses has been discussed in detail by Wright (1973, 1974), Lutz and Swasy (1977), Petty (1977), and Olson, Toy, and Dover (1978) and will not he repeated here.

Previous research using cognitive response models suggests that the nature of the critical thoughts that come to mind during exposure actually determines the attitudinal impact of that communication. These critical thoughts can take several forms, such as counterarguments, source derogations, curiosity statements, and positive or negative message-related statements. Their mediating effect will be such that they will in some form reflect the content of the receiver's existing structure of beliefs and values that will determine the encoding of meaning for incoming information. Wright (1973), for example, found that the more counterarguments stemming from a message the less was the level of acceptance for that message. In the study of comparative advertising, this research approach is appealing since comparative ads (when contrasted to non-comparative ads) would be expected to produce differential effects in attention and involvement (see Wilkie and Farris 1975). In turn, it is expected that different types of cognitive responses would be produced by comparative and non-comparative advertisements.

In addition to the differential cognitive responses elicited from comparative ads, it would be expected that differences in the type and amount of information presented in advertisements would also affect the receiver's memory schema (and thus cognitive responses). For example, in comparative advertising, claims may range from (1) "incomplete comparisons" (Howard and Hulbert 1973, p. 56) with the unidentified "leading" brand to (2) the mere mention of the name of a competitive brand in an unsupported claim to (3) a well-documented point-by-point comparison on several specific product attributes. The notion that comparative advertising can vary widely according to the type and amount of information provided recently served as a basis for an expository article and an empirical study by Pride, Lamb, and Pletcher (1977; Lamb, Pride and Pletcher 1978). Here, these authors suggest that advertising intensity, which is defined as the degree of specificity in a comparative ad, can vary widely and that comparative advertisements can be categorized into high, moderate, and low levels of intensity depending upon the strength of claims made in the ad, illustrations, and frequency at which the claims are made.

While the concept of advertising intensity was suggested by Pride, Lamb, and Pletcher in an attempt to categorize comparative advertisements, this concept can also be used to explain differences in cognitive responses across non-comparative advertisements. Elsewhere, it has been argued that standard, non-comparative advertisements can also be categorized into high, moderate, and low intensity levels depending upon such features as strength of claims, presence or absence of pictorial demonstrations, length of the copy, specificity of product attributes mentioned, and inclusion of documented product performance data (Wilson and Muderrisoglu 1979). Hence, it is argued that "advertising intensity" is a viable concept that can be generalized to all advertising regardless of the type of claims made (i.e. comparative vs. non-comparative) and the medium used to transmit the information to consumers.

From an information processing point of view, the concept of advertising intensity may be used to explain why receivers may respond differentially to advertising stimuli having different characteristics. In line with Olson (1978b), an advertisement that provides a large amount of high quality information which is of great value to consumers (for example, a high intensity ad) would seem more likely to be linked to semantic memory than would an advertisement that provides lower quality and lesser amounts of product-relevant information (for example, a moderate or low intensity ad). If so, various levels of intensity in advertisements should be reflected in cognitive responses elicited from receivers.

By viewing both comparative and non-comparative advertisements as having varying degrees of competitive intensity, one should be able to match comparative and non-comparative ads in a research design that would determine those differences in receivers' cognitive responses to those ads. By doing do, it should be possible to evaluate the effect of any additional information that may be available in a comparative ad but not available in a similar non-comparative ad. If significant differences are found among cognitive responses within a particular level of intensity, these differences should be due to the inclusion of comparative-type claims. Secondly, careful attention to intensity can be viewed as a method to assess differences in cognitive responses for ads containing various amounts of information within a particular type of communication.


Selection of the Experimental Stimuli and Subjects

The methodology was designed to test the theoretical notions described above by controlling as many extraneous factors as possible while, at the same time, providing as much external validity as possible. The experimental stimuli used in the study were actual print advertisements that were chosen from a large battery of magazine ads collected over a two-year period. Approximately two-thirds of the ads were comparative while the remainder were non-comparative. After being given the definition of the three levels of intensity, five judges (each of whom worked independently) were instructed to separate the ads into high, medium, and low intensity categories and then to rank the ads from the most highly intense ad to the least intense ad. Groups of comparative and non-comparative ads were evaluated separately so as to avoid confounding the judges' rankings.

A brief description of the advertisements used in the research is contained in Table 1, which indicates that twelve ads (four in each level of intensity) were selected as experimental stimuli. Nine of the ads (three in each level of intensity) were comparative, and three of the ads (one in each level of intensity) were non-comparative. As the means and standard deviations for the judges' rankings indicate, the final group of ads could be easily classified into levels of intensity with one exception (the Pendleton Woolen Mills ad). Here, one judge evaluated the Pendleton ad as being much more intense than the other four judges. Because of the substantial amount of agreement among the remaining four judges, this ad was classified as a low intensity ad and was included in the study.



After the twelve ads were chosen, slides were prepared so that the ads could be shown on a large screen in a laboratory setting. The order of presentation of the slides was determined by a random numbers table. The slides were then shown to 44 paid subjects who were recruited randomly from an introductory marketing class. Since all of the ads had appeared in general interest magazines during the six-month period prior to the data collection, and since most students would have at least some familiarity with all the product classes being advertised, it was felt that student subjects constituted a suitable population from which to draw the experimental sample.

Data Collection and Analysis

All of the data were collected during three time periods on the same day in the fall of 1978. When the experiment began, subjects were told merely that they would be asked to provide information about a group of magazine ads; and each subject was given twelve blank (but numbered) sheets of paper. Ss were told that they would be shown slides of actual magazine ads one at a time with a blank slide after each ad. They were asked to look at and study the ad carefully and then write on one of the pages all the thoughts that came to mind as they looked at each ad. The subjects were told that they could write their thoughts during the time each ad was to be projected on the screen and/or during the blank slide that followed each ad. Each ad was shown for one minute and the blank slide following each ad was shown for one minute and fifteen seconds.

The cognitive responses that were elicited from each subject were then identified and classified by three judges. Each judge had been trained extensively for the task, and each worked independently from the others. First of all, Judge 1, the most experienced of the three judges, identified the number of concepts elicited from each subject for each ad. Judges 2 and 3 then reviewed Judge l's work and indicated their agreement or disagreement with this identification process. In this phase of the analytical procedure a total of 2346 concepts were identified, and there were no three-way disagreements among the judges. When one judge disagreed with the others, the experimenters' rule was to accept the majority decision, Then, each judge (working separately) determined the nature of each concept by classifying each concept into one of seven categories: counterarguments, support arguments, positive ad-related statements, negative ad-related statements, source derogations, curiosity statements, and neutral statements. [Formal definitions were provided to each judge prior to the beginning of the analysis even though each judge had had previous experience in classifying concepts into these seven categories. Definitions for these responses can be found in Wright (1973). The set of definitions used in the present research were similar to those used by Olson, Toy, and Dover (1978).] All three judges agreed as to the nature of the concept in 1473 of 2345 cases (62.79%) and two of the judges agreed in 817 of the cases (34.83%). Since a majority rule was used, 2290 concepts (97.61%) were classified with no further analysis. In those 56 cases (2.39%) in which all three judges classified the concept differently, Judge 1 reevaluated all three judges' original classification and resolved the disagreement.

Dependent Variables and Hypotheses

Each of the seven categories of cognitive responses served as a dependent variable in the analysis of the data, and the data input consisted of the raw number of each type of concept elicited from each subject. For each comparative ad/intensity level combination, the number of concepts elicited for each type of cognitive response was averaged in order to simplify the analysis. In this way, the averages for the comparative ad data could then be compared to the baseline data obtained from the non-comparative advertisements. A repeated measures, within-subjects analysis of variance (ANOVA) routine was used to analyze the data. Thus the final ANOVA design was a two types of advertisement (comparative and non-comparative) X three levels of intensity (high, medium, and low) repeated measures design.

Specific hypotheses were developed for six of the seven dependent variables. It was expected that the comparative ads would generate more counterarguments, source derogation statements, and curiosity statements, but fewer support arguments, positive ad-related statements, and negative ad-related statements than would the non-comparative ads. These hypotheses are justified by the expectation that comparative ads would be more likely than non-comparative ads to trigger attention to and involvement with sponsors' claims, thus activating a more meaningful relationship between these claims and semantic memory. Thus, more counterarguments, source derogations, and curiosity statements (and fewer support arguments) would be expected. On the other hand, a non-comparative ad would more readily lend itself to positive and negative ad-related statements since Ss will find less reason to create meaningful linkages between claims made in these ads and their existing knowledge structure. However, there was no reason to believe that differential responses across ad types would occur for neutral statements. For the three levels of advertising intensity, it was not exactly clear how Ss might alter their cognitive responses according to the specificity of information presented. Consistent with previous empirical research on the cognitive response model (e.g., Olson, Toy, and Dover 1978), no formal hypotheses were developed for testing the intensity concept since it is not entirely clear how increased amounts of information affect cognitive responses. [Although it seems premature at this point in time to attempt to specify precise hypotheses regarding Ss' responses to varying levels of intensity across all forms of advertising, it seems much easier to do so for comparative ads. A review of empirical findings (Wilson 1978) as well as individual empirical articles (see especially Wilson 1976; Pride, Lamb, and Pletcher 1976) suggests that, in general, comparative ads that are "non-factual" (i.e., low intensity) may elicit a more negative response than highly factual comparative ads. Thus, low intensity comparative ads would likely generate more source derogation, counterarguments, etc. than high intensity comparative ads, especially for users of the named competing brand.]


Major Findings

The major results of the data analysis are presented in Tables 2 and 3. Table 2 presents the mean number of cognitive responses per advertisement and the mean number of responses per advertisement per subject for each type of cognitive response. These data point to several interesting differences and trends in Ss' cognitive responses across the types of ads and levels of intensity. One interesting finding is that the 44 Ss provided slightly more responses to the non-comparative ads (an average of 203.00 responses for each ad) than for the comparative ads (an average of 193.00 responses per ad). Major differences are found in the totals for the type of ad across the cognitive responses columns (e.g., counterarguments, support arguments, positive-ad related statements, and negative-ad related statements). Further, it is interesting to note that some variations in the distribution of specific cognitive responses does occur across levels of intensity (and within the type of ad), but monotonic (i.e., constantly increasing or decreasing) relationships are encountered in only 50 percent of the cases (seven of 14).

Table 3 presents the ANOVA results and, thus, a formal test of the hypotheses. The data in Table 3 result from separate ANOVA runs for each dependent variable. The F-ratios indicate that main effects (significant at the .05 level or less) were found for the type of ad for five dependent variables and that significant type of ad/level of intensity interactions were detected for support arguments and negative ad-related statements. No main effects for the level of intensity were significant at the .05 level.

An inspection of Table 2 and 3 indicates that of the six specific hypotheses developed for testing, the hypotheses were supported in five of the cases. As expected, more counterarguments and source derogations and fewer support arguments and positive ad-related statements were generated by the comparative ads than by the non-comparative ads. Also as expected, no main or interaction effects were found for neutral statements. Contrary to expectations, however, no ad-type main effects were found for curiosity statements. Interestingly, only a total of 35 curiosity statements were elicited by the 44 subjects for all twelve ads (only 1.49% of the total concepts elicited); and these were distributed fairly equally across the two types of ads and three levels of intensity. What is even more surprising, however, is the results for negative ad-related statements. Although Table 3 indicates a significant main effect (F = 12.40, p < .01), the trends in the data are not in the expected direction. Table 2 indicates that more negative ad-related statements were elicited from the comparative ads than from the non-comparative ads, which is contrary to a priori expectations.


On the whole, the results presented in Tables 2 and 3 provide new insights into the effectiveness of comparative advertising. The significant main effects for five of the dependent variables (counterarguments, support arguments, positive and negative ad-related statements and source derogations) point out that information processing measures may be capable of detecting the differential effects of comparative advertising when other, less-sensitive attitude measures may not be capable of doing so. Since receivers' cognitive responses are thought to mediate attitude formation and change, the finding that the comparative ads generated more within-subjects counterarguments, fewer support arguments, fewer positive ad-related statements, more negative ad-related statements, more source derogations, and essentially the same number of neutral statements, may indicate that comparative advertising is less effective overall than is traditional non-comparative advertising.

One specific finding worth mentioning is the relationship between counterarguments and the intensity levels for comparative ads. As Table 2 indicates, the medium intensity comparative ads generated more counterarguments (an average of 48.00 for each ad and an average of 1.09 per ad for each subject) than any other level of intensity. Yet, other researchers (Pride, Lamb, and Pletcher 1977) have found that moderate intensity in comparative advertising created greater perceived informativeness and product feature awareness than did high or low intensity. Based upon this finding, Wilson (1978) concluded that the chances of successfully implementing a comparative advertising strategy can be improved when the ad includes moderately intense associations. However, if the results presented in Table 2 can be generalized, moderately intense comparisons would seem to cause more counterarguing and, if the cognitive response model is correct, less favorable attitudes and behavioral intentions (see Wright 1973). Further research is needed to resolve this conflicting evidence over the most appropriate level of competitive intensity in comparative-type advertisements.

Limitations of the Research

It should be noted that the research presented in this paper is not without its limitations. In particular, two limitations are apparent that (1) serve to limit the generalizability of the research and (2) provide direction for future research in this area. First, the findings presented here may be strictly a function of the specific advertisements chosen as experimental stimuli. As stated previously, however, the three intensity levels were used to partially control for variations in the type and amount of information presented in the ads. Still, variations in copy design, headlines, etc. could account for variations in the data as could the fact that Ss responded to nine comparative ads, but only three non-comparative ads. Differences across experimental subjects, especially with regard to their familiarity with the experimental ads and prior interest in the brand or product class, also could have affected the outcome of this study. Secondly, the research is limited because additional measures such as subjects' brand usage and involvement, attention, and recall data were not taken. These measurements, when combined with the appropriate statistical procedures, could be used to probe deeper into the questions surrounding the effectiveness of comparative advertising. Despite these limitations, however, the research presented here provides a first step at applying the cognitive response model to the study of comparative advertising. Although methodological questions can be raised, the results provide a preliminary indication that rather strong differences can be expected to occur for comparative and non-comparative advertising. Further, the cognitive response data is well in line with past research, both in terms of the percentages of agreement among judges who categorized the cognitive responses and the number of concepts elicited from subjects.






The primary conclusion that follows from the research presented in this paper is that the cognitive response model can be used to evaluate the relative effectiveness of comparative and non-comparative advertising. If the results presented here can be confirmed by additional research, the claim that comparative advertising may be more effective (from an advertiser's point of view) will be in jeopardy. In this study, comparative ads were found to produce significantly more counterarguments, source derogations, and negative ad-related statements, but fewer support arguments and positive ad-related statements. These results should be of great interest to behavioral researchers, advertising practitioners, and public policy officials.


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R. Dale Wilson, The Pennsylvania State University
Aydin Muderrisoglu, The Pennsylvania State University


NA - Advances in Consumer Research Volume 07 | 1980

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