Gender Based Advertising

Market Lett (2006) 17: 5–16 DOI 10.1007/s11002-006-3545-8

The effects of gender on processing advertising and product trial information DeAnna S. Kempf · Russell N. Laczniak · Robert E. Smith

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Springer Science + Business Media, Inc. 2006

Abstract Consistent with past research and theory explaining gender differences in information processing, the empirical study reported here showed that men process two forms of marketing information (advertising and product trial) differently than women. Specifically, women are more sensitive to the comprehensiveness of the trial information, recognizing manipulated differences in trial diagnosticity. In contrast, men tend to use readily available information to form brand judgments and are less likely to notice that other attribute information is unavailable in the product trial. Keywords Product trial . Gender effects . Diagnosticity There is a rich literature base in both psychology and marketing suggesting that men and women process information differently. One much-cited theory explaining gender differences in information processing is the “selectivity hypothesis” (Meyers-Levy, 1989). This theory states that, in general, women tend to engage in more detailed, elaborative, and comprehensive processing of information than do men, unless extrinsic motivations are present that prompt men to do so (Meyers-Levy, 1989; Meyers-Levy and Maheswaran 1991). Moreover, absent such extrinsic motivations, women are more likely to attempt to assimilate all available information, while men tend to rely on a single cue (or multiple cues that imply a single inference) that is readily available during processing.

D. S. Kempf () Middle Tennessee State University, Department of Management and Marketing, P.O. Box 440, Murfreesboro, TN 37132-0001 e-mail: [email protected] R. N. Laczniak Iowa State University, Department of Marketing, College of Business, Iowa State University, 3183 Gerdin Business Building, Ames, Iowa 50011 R. E. Smith Indiana University, Kelley School of Business, 426A, Department of Marketing, 1309 E. Tenth Street, Bloomington, IN 47405 Springer

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Putrevu (2001) offers a more detailed explanation of gender differences in information processing that is complementary to the predictions of the selectivity hypothesis. He suggests that men are more likely to perform item-specific processing (Einstein and Hunt, 1980), whereby they tend to focus on individual attributes or message cues in ads, and do not attempt to decipher the interrelationships between them. On the other hand, he suggests that women are more likely to engage in relational processing (Einstein and Hunt, 1980), in which they look for interrelationships, similarities, and differences between multiple attributes and/or message cues, as they process information. Therefore, Putrevu (2001) describes in more detail the nature of the detailed, comprehensive processing that women are more likely to engage in than men. Prior studies investigating gender and processing of marketplace information have focused almost exclusively on advertising as the information source (e.g., Darley and Smith, 1995; Kempf et al., 1997; Meyers-Levy and Maheswaran, 1991). Yet, advertising is only one source of product information available to consumers, and is not necessarily the most powerful one. Indeed, research suggests that other sources of product information, such as word-of-mouth communication (Herr et al., 1991; Laczniak et al., 2001) and product trial (e.g., Smith, 1993; Smith and Swinyard, 1983, 1988) are superior to advertising in generating strong and confidently held brand beliefs and attitudes. These findings highlight the need to study gender-based differences in consumer processing of non-advertising product information sources. Given this need in the literature, the objective of the present research is to test predictions suggested by extant gender-difference theories by comparing men’s and women’s integration and evaluation of information from two distinct marketplace sources—advertising and product trial. Not only does this research extend the context of past theory testing beyond an advertising context, in many respects it represents a more comprehensive test of the selectivity hypothesis and item-specific versus relational processing theories. This is the case since advertising provides multiple cues that potentially conflict with information acquired in subsequent product trials. For example, an ad may say that a diet cola does not have a bitter aftertaste, but a subsequent trial of the product may indicate otherwise. Given that both the selectivity hypothesis and relational versus item-specific information processing theory suggest that males and females process multiple-inference information differently than single-inference information, the study of gender information processing differences in this context provides an important contribution to the literature in this area.

1. Literature review and conceptual framework 1.1. Product trial research Product trial is a powerful source of product information for consumers because it leads to the development of strong and confidently held brand beliefs and attitudes (e.g., Fazio and Zanna, 1978; Smith and Swinyard, 1983, 1988). Its power is especially evident for experiential attributes (i.e., those for which product experience provides direct information). Examples of experiential attributes include taste, weight, fit, etc. Nonexperiential attributes (i.e., those for which product experience provides little or no direct evidence) include characteristics such as nutritional content and long-term reliability. Kempf and Smith (1998) demonstrated that consumers process product trial information pertaining to experiential and nonexperiential attributes differently. That study introduced the concept of trial cognitions, which represent a consumer’s evaluation of the trial as an Springer

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information source, to demonstrate processing differences. According to Kempf and Smith, consumers engaging in a product trial may generate three types of trial cognitions: (1) perceptions of the validity of the trial (the consumer’s perceptions of whether the product trial offered a fair and valid test of the brand), (2) subjective perceptions regarding the consumer’s own ability to judge the trial (perceived expertise), and (3) evaluations of the overall diagnosticity of the trial (the perceived usefulness of the product trial in evaluating the brand). Moreover, Kempf and Smith contend that trial diagnosticity is a function of the level of trial validity, perceived expertise, and the proportion of the salient product attributes that are experiential (as compared to nonexperiential). All else being equal, a trial should be relatively more diagnostic when a product’s salient attributes are mostly experiential and relatively less diagnostic when a product’s salient attributes are primarily nonexperiential. Trial should also be more diagnostic when perceived expertise and validity are higher. 1.2. Gender research As noted above, theory suggests that females are comprehensive processors who consider a broad scope of information, potentially including that which is related but unobservable in the immediate processing environment (Meyers-Levy, 1989, p. 232) and relevant information held in memory. This is also consistent with Putrevu’s (2001) view that females are more likely to use relational processing, whereby they consider the potential relationships among items of information received in the immediate environment, and information that is held in memory. Theory suggests that males, on the other hand, tend to focus on one or a few salient attributes that are readily available in the environment, and use them independently as cues or heuristics to achieve processing efficiency. These predictions have been tested in an advertising context and have generally been confirmed (e.g., Meyers-Levy, 1989; MeyersLevy and Maheswaran, 1991). Empirical research (Meyers-Levy and Maheswaran, 1991) suggests that when attribute information supplied by an ad is available for subsequent use, men are less likely than women to access or use the information in subsequent tasks. Women, as part of their greater elaborative processing of information, tend to attempt to integrate newly encountered information with previously learned information available in memory. Such a finding is relevant to a context where consumers receive ad information, and then subsequently, product trial information (which is common in the marketplace—consumers will try a brand after seeing an ad for it). Relational processing theory would similarly predict that females are likely to access ad-based information during subsequent trial processing and will try to relate information from both sources of information. Males, in contrast, will more likely focus exclusively on attribute information readily available in the immediate trial environment and will be less likely to access and integrate prior ad-based information. Much of the strength of product trial stems from the fact that it is self-generated information. That is, the information gleaned from the trial is derived from a consumer’s own interactions with the physical product (Fazio and Zanna, 1978). Advertising, on the other hand, represents an other-generated information source. Past research shows that women are more likely to use information from others when forming judgments than are men (MeyersLevy, 1988). This notion provides additional support for the prediction that women will be more likely to attempt to integrate and use information from both sources (ad and trial) to evaluate a brand. It is important to note, however, that in some cases, attribute information supplied in the ad will not be available during trial processing. This will be the case for information about nonexperiential attributes, which, by definition, cannot be provided via trial. Since theory Springer

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suggests that females will attempt to comprehensively process all attribute information during the trial, a side effect of this attempt is that females should also be more sensitive to what the trial is not telling them about the product. Therefore, differences in the information value of trial for products that differ greatly in their proportion of experiential versus nonexperiential attributes may be noted by females but not by males. In addition, when asked directly about product attributes individually, women will be more likely to consciously note that the trial was less useful for evaluating these nonexperiential attributes. Men are less likely to note these significant differences in trial diagnosticity between attribute types. Men will focus more on what is present in the product trial, rather than thinking about what is not present. The observed and predicted gender differences described above collectively suggest several important hypotheses about how women and men will process ad and trial information differently, with differential results in terms of the cognitions produced during a product trial, and post-trial evaluations of trial’s diagnosticity, both on an overall product-level level and on the individual attribute level. 2. Hypotheses As comprehensive processors, women can be expected to produce a greater number of cognitions (of all types, including cognitions about the trial itself and about the brand) following exposure to ad and trial information than men. Women, according to the selectivity hypothesis, tend to focus on a greater number of attributes during exposure to ad and trial data and process the information in a more elaborative fashion than men, resulting in a larger number of total cognitions produced. More specifically, as comprehensive processors, women should produce a greater number of brand cognitions when experiencing a highly diagnostic trial that provides information about multiple experiential attributes of the brand, and comparably fewer brand cognitions when experiencing a trial of a product dominated by nonexperiential attributes. In the latter case, there is simply less brand information available to process. In contrast, according to the selectivity hypothesis, men will tend to focus on a small number of highly salient cues, regardless of the total volume of attribute information available in the trial, and thus the number of brand cognitions produced will not be significantly affected by the experientiality of the product being tried. H1a: Following exposure to ad and trial information, women will produce a greater number of total cognitions than will men. H1b: Women will produce more brand cognitions following the trial of a product dominated by experiential attributes, compared to the trial of a mostly nonexperiential product. Men will produce an equal number of brand cognitions following trial, regardless of product experientiality. When trial is not highly diagnostic (e.g., the product is dominated by nonexperiential attributes, the trial is considered invalid for some reason, or the consumer believes he/she does not have the requisite expertise to understand the data from the trial), women will produce fewer brand cognitions as predicted in H1b, but will be more likely to generate trial cognitions regarding the lack of informativeness or diagnosticity of the trial. Kempf and Smith (1998) describe the production of such trial cognitions as being governed by a “management by exception” rule. A consumer’s default judgment of a product trial is that it will provide useful information about the brand, and it is only when a trial noticeably fails to Springer

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provide useful information for some reason that conscious thoughts will be produced about perceived trial diagnosticity. Extending this reasoning, and considering the gender effects suggested by the selectivity hypothesis and women’s greater propensity to perform relational processing, we predict that women are more likely than men to produce trial cognitions, and that women will produce more of these cognitions when the product is dominated by nonexperiential attributes. Men will focus on the information that is present in the trial, and will therefore be less likely to produce a significant number of trial cognitions regarding the trial’s diagnosticity, even when trial is not highly diagnostic. H2: Following exposure to ad and trial information, women will produce a significant number of cognitions regarding perceived trial diagnosticity when the trial is of a product dominated by nonexperiential attributes. Men will not produce a significant number of trial diagnosticity cognitions, regardless of product type. When a trial is preceded by an ad, the selectivity hypothesis (Meyers-Levy, 1989) and women’s higher probability of using relational and elaborative processing (Putrevu, 2001) suggest that women will attempt to evaluate all brand attribute information during the trial, including information about both the experiential and nonexperiential attributes mentioned in the ad, while men will tend to focus exclusively on information that is immediately available in the trial (i.e., information regarding only the experiential attributes, by definition). Women should therefore be more sensitive to the presence or absence of direct physical evidence offered by the trial for each attribute. Further, women are more likely to access the prior ad information (Meyers-Levy and Maheswaran, 1991) during trial processing and will therefore be likely to notice what information the trial did and did not provide. Thus, women, when asked directly, will report significantly lower levels of overall perceived trial diagnosticity for the mostly nonexperiential product than for the more experiential product. Men, on the other hand, will use the available, self-generated information from the trial (about experiential attributes), and be less cognizant of the attribute information presented in the ad that is not available in the trial (Meyers-Levy, 1988). Therefore, men will be less likely to note differences in trial diagnosticity between the experiential and nonexperiential product types. H3: Following exposure to ad and trial information, women will rate the trial of the product whose salient attributes are primarily experiential in nature as more diagnostic than the trial of the product whose salient attributes are primarily nonexperiential in nature. Men will show no significant difference in perceived diagnosticity between the two types of products. Hypothesis 3 involves consumers’ ratings of the trial’s diagnosticity on an overall basis. However, one can also conceptualize and measure perceived trial diagnosticity on a perattribute basis. Attribute-level trial diagnosticity is the perceived usefulness of the product trial for evaluating a particular product attribute. Furthermore, when considering attributelevel differences in diagnosticity in addition to the product-level differences described above, one would expect a similar pattern of gender effects. That is, across both products, when asked about the diagnosticity of the trial for judging each specific attribute, women will rate the trial as being less diagnostic for the nonexperiential attributes (that trial cannot provide information on) than will men. For the experiential attributes, there should be no significant gender differences in attribute-level perceived trial diagnosticity judgments because trial will, in fact, be diagnostic for these attributes and will be perceived as such by both genders. Springer

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H4: Following exposure to ad and trial information, across products, women will report lower levels of attribute-level perceived trial diagnosticity for the nonexperiential attributes than will men. There will be no significant gender differences in perceived trial diagnosticity for the experiential attributes.

3. Method Seventy-five subjects (40 women and 35 men) participated in the experiment. Subjects were randomly assigned to one of two product conditions (between-subjects design) that were selected to significantly manipulate the information value of the trial experience. For the highly informative trial, a product was selected that is dominated by experiential attributes, and for the low-information trial, a product was selected that is dominated by nonexperiential attributes. Participants were drawn from a subject pool consisting of introductory marketing students at a major Midwestern university. To increase motivation and involvement with the study tasks, participants received class credit for their participation and were entered into a lottery for a $100 first prize and two $50 second prizes. The experimental sessions were conducted in a “technology classroom” containing 36 personal computers. Participants were told they would be receiving information about a new computer software product and would be asked questions about the ad and the advertised brand. Subjects were first shown a print ad for one of the two software packages and were asked to carefully read the ad and form a judgment of both the software package and the ad itself. These instructions were designed to create a brand processing goal during the ad exposure consistent with that used in past ad/trial studies. Participants were then asked to fill out the ad-related dependent measures. Then, participants experienced a short hands-on trial of the software package featured in the ad. Next, they completed a thought-listing task and dependent measures pertaining to the trial and the brand. The use of student consumers is appropriate because they are a major target segment for the types of computer software used in the experiment (a virus scanner and a grammar evaluation software program—see below). Software marketers often target students through on-campus promotions and educational discounts on software. Thus, students are likely to be somewhat familiar with these products and see them as meaningful for their own lives. 3.1. Product selection Two stimulus products were needed to test the hypotheses in this study: one dominated by experiential attributes, and one dominated by nonexperiential attributes, to create a more informative trial and a less informative trial, respectively. The criteria for the stimulus products also included: (1) they must permit a realistic trial in a laboratory setting, (2) they must have brand names unfamiliar to participants, and (3) they must be as equivalent as possible in all ways except the relative experientiality of their salient attributes. A series of pretests, including one in which participants (n = 63) experienced a hands-on trial of (one of) three software products, narrowed down the stimulus products to two software programs: a virus scanner and a grammar evaluation software package. The salient attributes for these product categories were identified through a free-elicitation task where pretest subjects were asked to list all product attributes that would affect their purchase decision for these types of software packages. Pretest results suggested that a majority of the attributes of the virus scanner program were nonexperiential (i.e., they could not be directly judged by product trial), while the grammar Springer

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evaluation software was perceived to be dominated by experiential attributes. Pretest subjects (n = 63) rated overall trial diagnosticity (1–7 scale) for the virus scanner at 4.29, and the grammar evaluation software scored a 5.18 in overall trial diagnosticity (t61 = 1.99, p < .05). Similarly, when pretest subjects were asked to rate how useful the trial was in judging each of the products’ attributes individually, the average for the virus scanner was 3.71, and the average for the grammar software was 5.08 (t61 = 4.64, p < .0001). Specifically, the nonexperiential attributes for the virus scanner were: (1) effectiveness at detecting viruses, (2) ability to clean detected viruses, and (3) comprehensiveness of the list of viruses the software scanned for. The virus scanner also had two experiential attributes: speed and ease of use. For the grammar evaluation software, the experiential attributes were: (1) ease of use, (2) speed, (3) usefulness of correction tips, and (4) accuracy. One attribute was rated as nonexperiential—the adequacy of the dictionary used by the software. In addition, pretest results indicated that participants perceived the two products to be equivalent in terms of their hedonic versus functional nature (7-point semantic differential scale, 1 = functional, 7 = hedonic, virus scanner: 1.48, grammar software: 1.23 (t61 = 1.12, p < .27)), thus minimizing this potential source of extraneous variation. In the final study’s sample, the two products were also shown to be perceived as equivalent in terms of their perceived cost and perceived risk levels. 3.2. Stimulus advertisements Professionally produced, full-page, color print ads were developed for both products that included claims about each of the five salient attributes for each product. The layout and style of the print ads were developed by a professional graphic artist. The ads were designed to be similar to software print ads found in personal computing magazines, both in terms of content and layout. The ads for the two products were designed to be as similar as possible in color, layout, graphics, length, etc., to minimize extraneous variance in the results. In both ads, positive attribute claims were presented in bullet points surrounding the graphic of the box in the center of the page. 3.3. Trial procedure Participants experiencing the trial of the grammar evaluation software (experiential product) were presented with a short paragraph containing several grammatical and spelling errors. They were told to mark on the page any grammar, spelling, or style problems that they saw. After doing so, they ran the grammar software on this same paragraph. For the virus scanner, participants were asked to review a listing of the major commands available in the virus scanning software. They were then instructed to scan the hard drive of the computer they were working on, and to clean any detected viruses using the cleaning command of the software. They were then instructed to follow this same procedure on a floppy disk already inserted into the a: drive. The decision was made to not intentionally insert a virus on the computer or the floppy drive the participants would be scanning for the following reasons: (1) the vast majority of the time, when using a virus scanner no virus is detected, and the user has to trust that there were in fact, no viruses to be found, and (2) inserting a virus would tend to artificially overstate the diagnosticity of the trial of the virus scanner, compared to most real-world trials of such software. Since we wanted to create a trial that was, in fact, low in trial diagnosticity in order to test our hypotheses about participants’ perceptions, it was not desirable to artificially inflate the diagnosticity of the trial. Springer

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3.4. Dependent measures Perceived diagnosticity. This variable was measured using both an overall product-level diagnosticity measure and attribute-level measures, as in Kempf and Smith (1998). Overall diagnosticity was assessed by asking respondents: “Overall, how helpful would you rate the trial experience you just had in judging the quality and performance of the software?” The five attribute-level trial diagnosticity items (one for each of the five salient product attributes) were: “To what extent did your trial experience with the software enable you to directly judge whether the package (possessed attribute X)?” Cognitions. Cognitions were collected via a thought-listing task immediately following the product trial. The recorded cognitions were coded by two judges into one of four subject categories: ad cognitions, brand cognitions, trial cognitions, or other. The thoughts were also coded for valence (positive, negative, or neutral). The inter-judge agreement rate was 91.3%. All disagreements in codes assigned to individual thoughts were resolved by discussion.

4. Results Before testing the individual hypotheses regarding the effects of gender and product type, an overall MANOVA was performed to control the overall experiment’s Type II error rate. The results of this analysis indicate that gender and product type had a highly significant interactive effect on the set of dependent variables being studied: total cognitions, brand cognitions, trial cognitions, and overall product-level perceived trial diagnosticity (Wilks’ Lambda for Sex × Product interaction = .83, F4,67 = 3.55, p < .01). The mean patterns specified in the hypotheses will be tested using t-tests or planned contrasts, as outlined below. Hypothesis 1a states that women, because of their more comprehensive processing style, will report a greater number of total cognitions following product trial than will men. The mean number of total cognitions recorded by women was 3.6 and the mean for the men was 3.0 (t73 = 1.59, p = .05, one-tailed). However, an ANOVA revealed a significant gender x product interaction indicating that women produced more total cognitions only when the trial was for an experiential product. This finding is logically consistent with H1b, which predicts this same pattern of interactive effects for brand cognitions. Since brand cognitions are a subset of total cognitions, this finding is not surprising. Thus, H1a was supported only for the experiential product. H1b predicts that women will produce more brand cognitions during the trial of the experiential product, compared to the trial of the nonexperiential product. The number of brand cognitions produced by men, on the other hand, was predicted to be unaffected by product type. This hypothesis was supported. Using planned mean comparisons, the predicted mean pattern was found (see Figure 1). For the grammar evaluation software (experiential product), women produced an average of 4.16 brand cognitions, and for the virus scanner (nonexperiential product), they produced only 2.05 brand cognitions, on average (F1,73 = 24.85, p < .0001). For men, the mean number of brand cognitions did not differ by product (MeanGrammar Software = 2.71, MeanVirus Scanner = 2.41; F1,73 = .41, p < .52). To test H2, the mean number of cognitions produced regarding the trial’s diagnosticity (or more specifically, the lack thereof) was compared across gender and product type. It is important to note here that the trial cognitions recorded by subjects were always negative in nature. This supports the statement made by Kempf and Smith (1998) that trial cognitions are consciously elicited only when the trial is noticeably lacking in diagnosticity for some reason. Springer

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Brand Cognitions

Fig. 1 Cell means for brand cognitions

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Fig. 2 Cell means for trial cognitions

Trial Cognitions

We predicted that women would produce a significant number of trial diagnosticity cognitions for the nonexperiential product, but not for the highly experiential product. Men were not expected to produce a significant number of trial cognitions, regardless of product type. Although the absolute number of trial cognitions recorded by subjects across the entire sample was small, H2 was fully supported. For the virus scanner, women produced a statistically significant number of thoughts about the trial’s lack of diagnosticity, on average (mean = .43, Student’s t = 3.29, p < .002), while men produced only .24 thoughts, which was not significantly greater than zero, Student’s t = 1.73, p < .10). For the grammar checker, neither men nor women recorded a significant number of conscious thoughts about the trial’s diagnosticity (MeanWomen = 0.0, Mean Men = .05 (Student’s t = 1.0, p < .33)). See Figure 2. Hypothesis 3 predicted that women would be more sensitive than men to differences in trial diagnosticity due to the products’ relative proportion of experiential and nonexperiential attributes. To test this hypothesis, planned orthogonal contrasts were performed based on the predicted cell mean differences in H3. Specifically, the perceived trial diagnosticity means between the experiential and nonexperiential products for women were found to be significantly different (5.58 for the grammar tool vs. 4.33 for the virus scanner, F1,71 = 6.13, p < .02), as predicted, while the cell means for men were not significantly different (4.94 for the grammar software vs. 4.76 for the virus scanner, F1,71 = .11, p < .74). The cell means are depicted graphically in Figure 3. These results are supportive of H3. To test H4, we examined subjects’ trial diagnosticity ratings by individual attribute type (experiential vs. nonexperiential), across products. Although the theoretical rationale for H3 and H4 are identical, H4 is perhaps a finer-grained test of the theory in that it is not subject to any potential confounding of inter-product differences unrelated to trial diagnosticity. The 0.5 0.4 0.3

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Fig. 3 Cell means for overall perceived trial diagnosticity

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results support H4. Specifically, the mean trial diagnosticity rating for the nonexperiential attributes was 3.52 for the women and 4.23 for the men, a statistically significant difference (t73 = 1.87, p < .033). As predicted, there was no significant gender difference in perceived trial diagnosticity for the experiential attributes (MWomen = 5.25, MMen = 5.32, t73 = .30, p < .77 .)

5. Discussion and conclusions The intent of this research was to examine the manner in which gender influences consumers’ processing of advertising and trial information. While past studies in the gender area have focused primarily on the processing of verbal and pictorial information, which is descriptive of the type of information that is commonly available in an advertisement (e.g., Meyers-Levy and Maheswaran, 1991), the present study focused on advertising information combined with a self-generated source of information—hands-on product trial. The study results provide support for the notion that women are more sensitive than men to the comprehensiveness of the information that they are processing during a product trial (as evidenced by their greater sensitivity in terms of perceived trial diagnosticity judgments). Men tend to rely on available information to make judgments (Meyers-Levy, 1989) and are less likely to notice that other information was unavailable. That is, while women noticed the lack of information about advertised nonexperiential attributes in the trial, this did not appear to be the case for men, who rated the trials of both products as relatively (and equally) diagnostic. Even when questioned directly about the diagnosticity of the trial for each specific attribute, which would have drawn the men’s attention to the nonexperiential attributes, men reported significantly higher levels of trial diagnosticity for the nonexperiential attributes than did the women. In general, our results provide support for the selectivity hypothesis proposed by Meyers-Levy (1989), and also provide some indirect support for Putrevu’s (2001) hypothesis that women are more likely to engage in relational processing, and men are more likely to perform item-specific processing. When exposed to ad and trial information, it appears that women engage in more detailed processing, and are more discriminating than men in terms of evaluating the information content of the trial when making brand judgments. Consequently, marketing practitioners implementing integrated marketing communications in the form of trial-inducement promotional programs (e.g., sampling or trial purchase inducement) should ensure that information regarding nonexperiential attributes is provided (from a nontrial source such as advertising) when targeting women (but only when positive claims can be made about these attributes, of course). However, when targeting men, marketers using Springer

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trial-based promotions should focus more on ensuring that their brand performs in a superior manner for the experiential attributes that men will focus on during the trial. Further, it is perhaps more important to provide women with pre-trial ads than it is for men. Since women seem to desire full information and notice when it is not available, it is important to provide access to data that may be better communicated via an ad than a trial (i.e., nonexperiential attribute data). The study reported here suggests numerous opportunities for future research in the area of gender differences in processing market information. For instance, the results reported here are consistent with both the selectivity hypothesis and the item-specific versus relational processing styles that are used differentially by men and women. Future research should strive to use measures that will allow us to determine the exact nature of the information processing styles of men and women, perhaps using verbal protocols. In our study, we exposed subjects to an ad prior to the product trial. The ad contained claims about both the experiential and the nonexperiential attributes. One explanation given for the observed gender differences was that women may have accessed the ad-supplied information regarding the nonexperiential attributes during trial processing, and thus the lack of trial information regarding these attributes may have been made salient. Future research should include an ad present/absent manipulation in combination with the trial so that it can be determined whether, without the ad exposure, females still note the lack of trial diagnosticity regarding the nonexperiential attributes.

Acknowledgment The authors would like to acknowledge the financial support of Indiana University for this project.

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