The Effect of Expected Variability of Product Quality and Attribute Uniqueness on Family Brand Evaluations ¨ RHAN-CANLI* ZEYNEP GU This research investigates the processes by which consumers evaluate a family brand on the basis of information about its products. Findings from three experiments suggest that the expected variability of individual product quality within the brand and attribute uniqueness systematically influence information processing and family brand evaluations. On-line (vs. memory-based) processing of information to form family brand judgments is more likely when expected variability is low (vs. high) and when the attributes are shared (vs. unique) within the family brand. These different processes lead to differences in family brand evaluations due to primacy (on-line processing) and recency (memory-based processing) effects.
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revious research suggests that consumers develop family brand judgments over time, based on the attributes associated with individual products within the brand (Joiner and Loken 1998). However, relatively little empirical research has directly examined the process by which consumers evaluate family brands. Specifically, the use of individual product information in evaluating a family brand name remains unclear. In today’s economy, the importance of family brands has increased as a result of the proliferation of brand extensions and efforts to focus on a few master brands to improve efficiency and competitiveness (Keller 1998). Moreover, recent research suggests that consumers sometimes rely on brand cues at the expense of attribute cues (Van Osselaer and Alba 2000). Consequently, understanding the processes by which consumers evaluate family brands is of both theoretical and practical importance. Consumers may form family brand impressions when they initially process and encode information about individual products (i.e., on-line processing). Conversely, consumers may use individual product attributes retrieved from memory to formulate their family brand judgments when they are asked to express an opinion (i.e., memory-based processing). How do consumers process individual product information to evaluate family brands? What factors determine the extent
of on-line versus memory-based processing? These are important questions because different processes may lead to significant differences in family brand attitudes and choice. On-line processing leads to more favorable evaluations if consumers initially encounter positive (vs. negative) information (Hastie and Park 1986). Furthermore, recent research on consumer learning highlights several important implications of on-line and memory-based processing. For example, if consumer learning is characterized by cue interaction as a result of on-line processing, brands can block consumers from learning about important attributes (Van Osselaer and Janiszewski 2001). Previous research on family brands has examined the moderating effect of extension typicality on brand evaluations (Ahluwalia and Gu¨rhan-Canli 2000). Relatively little research has investigated what factors determine the degree of on-line versus memory-based processing in forming family brand impressions. In addition, processes that underlie family brand evaluations may be different from processes that underlie evaluations of single-product brands because the favorableness of attributes may vary across different products of a family brand (Kardes and Allen 1991). Therefore, new research that expands our process understanding of how consumers form family brand impressions is required. In understanding family brand impressions, an important issue relates to the consistency of a brand’s offerings in terms of a relevant dimension such as quality, reliability, or prestige. While marketers strive for a consistent core message that carries through to all products, consumers may expect varied product quality or attribute favorableness across different products of a family brand (Kardes and Allen 1991). Recent research in social cognition suggests that such expectations affect how people form impressions
*Zeynep Gu¨rhan-Canli is assistant professor of marketing at the University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI 48109–1234 (
[email protected]). The author would like to thank Rohini Ahluwalia and Yeosun Yoon for their comments on earlier versions of this article and Eric DeRosia for his assistance in data collection and analysis. The author is grateful to the editor, the associate editor, and three reviewers for their constructive feedback. This research is partially funded by the Sanford R. Robertson Assistant Professorship Chair at the University of Michigan Business School.
105 䉷 2003 by JOURNAL OF CONSUMER RESEARCH, Inc. ● Vol. 30 ● June 2003 All rights reserved. 0093-5301/2004/3001-0008$10.00
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of social groups (McConnell, Sherman, and Hamilton 1994, 1997). This research extends the social psychological theory on how people form impressions of social groups to the branding context. Specifically, it examines the effect of expected variability on both processing (i.e., on-line or memory-based) and family brand evaluations. In addition, it contributes to the previous social psychological theory by identifying attribute uniqueness (the extent to which an attribute is used to describe other members of the group) as another factor that affects how consumers evaluate family brands. While most studies on brand equity are grounded on cognitive psychology theories, this research brings in social psychological theory of impression formation to expand our process understanding of the relationship between individual products and family brand evaluations.
THEORETICAL BACKGROUND Extant research in social cognition has demonstrated that the outcomes of impressions formed of individuals and social groups differ, although these impressions are based on identical information (Hamilton and Sherman 1996). For example, in forming impressions of individuals, perceivers generally make on-line judgments when they initially process and encode target-relevant behaviors (McConnell et al. 1994). In contrast, in forming impressions of social groups, perceivers retrieve target-relevant behaviors to form their judgments about the group only when they need to form an opinion. To explain these different impression processes, Hamilton and Sherman (1996) propose a model that emphasizes the role of unity and coherence (i.e., entitativity) in forming impressions of individuals and groups. They suggest that individuals generally engage in behaviors consistent with their personality. However, members of an undefined group are not expected to behave consistently. Because of these expectations of coherence for individual targets (relative to group targets), perceivers are motivated to process information about individuals on-line, forming an integrated impression for predictability and control. In addition, integrating information about a group is more complex than integrating information about an individual, since both individual and group level information must be considered (McConnell et al. 1994). Therefore, on-line processing is more likely for information about individuals than for information about groups due to both motivational and ability factors. When information is processed on-line, the first few pieces of information are particularly important in forming evaluations, leading to primacy effects on both evaluations and recall (Hastie and Park 1986; Kardes 1994). Consistent with this reasoning, Hamilton and Sherman (1996) predicted that the evaluative primacy effects would be more likely to occur for individual (vs. group) targets. If an on-line impression is not available, impressions are formed based on information retrieved from memory. In such memory-based (vs. on-line) judgment situations, recent information has more impact on evaluations and is remembered better. Since evaluations depend on the information retrieved, the correlation between recall and judgment
should be higher when judgments are memory-based (vs. on-line; Hastie and Park 1986). This research extends the social psychological theory on how perceivers form impressions of groups versus individuals to the branding context by drawing an analogy between social groups and family brands. As Dacin and Smith (1994) note, consumer expectations about family brands involve both a perceived mean (e.g., Toyota is reliable) and a perceived variance for category members (e.g., Camry is more reliable than Tercel) on focal dimensions. If consumers expect high (vs. low) variability in performance of individual products, they should be less likely to integrate attribute information spontaneously in forming an organized impression of the family brand (Susskind et al. 1999). For example, if consumers learn that a specific model of a JVC TV set has good picture quality, they may not readily use this information to form an impression about the JVC brand, since JVC VCRs may not have good picture quality. In addition, on-line integration of attribute information seems to be more complex and resource demanding when the target is a family brand (vs. an individual product). However, extant research in social cognition (e.g., Hamilton and Sherman 1996) suggests that processing goals moderate the effect of target type on processing outcomes such that different processing goals can induce either on-line or memory-based judgments regardless of the target type. For example, McConnell et al. (1994) showed that when subjects were instructed to form an impression, they processed information on-line for both individual and group targets. This reasoning suggests that processes that underlie individual product and family brand impressions should be more alike when there is an impression formation instruction (vs. no instruction). This expectation is also consistent with recent research in consumer behavior (e.g., Joiner and Loken 1998; Van Osselaer and Janiszewski 2001), which showed that processing goals influence inferences about a family brand. Joiner and Loken (1998) found that consumers generalize from a specific product to the family brand category when their goal is to form an impression. In addition, Van Osselaer and Janiszewski (2001) showed that consumers learn brand associations in two major ways. When consumers do not have a specific processing goal, all stimulus elements get crossreferenced for later retrieval, similar to the memory-based processes discussed in this research. When consumers are motivated to learn a product benefit, primacy effects are observed such that initial learning may block the learning of other associations. In sum, previous research in both social psychology and consumer behavior suggests that, when there is no specific impression instruction, consumers are more likely to process information on-line for individual products than for family brands. This effect should be weaker when there is an instruction to form an impression. H1: When there is no specific impression instruction, more evidence of on-line processing should be observed for an individual product (vs. a family brand). This effect should be weaker when there is an instruction to form an impression.
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Hamilton and Sherman (1996) suggest that on-line processing is more likely for a group target if the group is perceived as a more (vs. less) coherent entity. McConnell et al. (1997) found direct evidence for this premise in an experiment in which the degree of variability was manipulated within group targets. Furthermore, Susskind et al. (1999) showed that the effect of target type on processing is mediated by expected variability. Applied to the family branding context, these findings suggest that, if consumers expect low (vs. high) variability within a family brand, more evidence of on-line (vs. memory-based) processing should be observed. H2: Expectations of low (vs. high) variability within a family brand name should lead to more evidence of on-line (vs. memory-based) processing. Another important issue in the context of family brands relates to the type of attribute information. While people can be described by similar or shared attributes, product attributes differ in the extent to which they can describe other products. For example, some attributes are shared by products from different categories and can be used to evaluate noncomparable alternatives (e.g., reliability; Johnson 1988). In contrast, other attributes are unique to specific products and can describe only a few products (e.g., watts per channel for a receiver). Since attribute uniqueness differs for products, it is important to examine how consumers process information in relation to a family brand name when the information features unique attributes. Previous social psychological research has not addressed this issue, presumably because people can be described along the same set of attributes. Unique attribute information is difficult to integrate across different products of the family brand. For example, if a consumer learns that a DVD player has excellent sound quality due to a unique DVD technology, s/he will not extend this information to the family brand because this technology may not apply to other products. Consequently, active integration of information (i.e., on-line processing) to form family brand impressions can be quite difficult for unique attributes. In addition, consumers may not be motivated to engage in active integration of information since unique attributes of a product may be perceived to be less diagnostic of the family brand. Therefore, while expectations of low (vs. high) variability should lead to more evidence of on-line (vs. memory-based) processing when the information features shared attributes, this effect should be weaker for unique attributes. Consumers may not have the ability or the motivation to integrate information online when the information features unique attributes regardless of expected variability. H3: Expectations of low (vs. high) variability within a family brand name should lead to more evidence of on-line (vs. memory-based) processing when the information features shared attributes. This effect should be weaker when the information features unique attributes. These predictions are tested in a series of three laboratory
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experiments. In general, the objective of the first two experiments is to extend previous social psychological theory on how people form impressions of groups versus individuals to the branding context. Another objective is to provide insight into the processes by which family brand judgments are formed, focusing on the effect of expected variability. Experiment 3 extends previous research in social psychology by examining the effect of attribute uniqueness on information processing and family brand evaluations. Since the focus is on memory-based versus on-line processes, all the experiments provide positive and negative information about the family brand and manipulate the order of positive and negative information to test for primacy and recency effects.
EXPERIMENT 1 Subjects and Procedure Two hundred and nineteen undergraduate students enrolled in a large midwestern university received either partial course credit or $10 for participating in a research study. They were randomly assigned to conditions in a 2 (target: product or family brand) # 2 (processing goal: impression formation or no explicit impression instruction) # 2 (presentation order: positive first or negative first) between-subjects design. Subjects were told that they would be reading a series of statements about a real TV set or a family brand name. Next, they were given instructions to form an impression or were given no explicit impression instruction. Then they read the attribute information. Consistent with previous research (e.g., McConnell et al. 1994; Park and Hastak 1994), each piece of attribute information was typed on a separate page to ensure that subjects would read the information in the predetermined order. Next, they responded to a personality inventory, which served as a filler task. Then, the dependent measures were assessed. Finally, subjects were thanked and debriefed.
Independent Variables Target. Subjects were told that they would be reading a series of statements about a real TV set or a family brand name. Consistent with previous research (e.g., McConnell et al. 1994; Susskind et al. 1999), subjects in the family brand condition were also told that each piece of information was associated with a different product of this consumer electronics brand. Processing Goal. The subjects in the impression formation condition were told to form an overall impression of the target. These subjects read the following instructions: “In this study, you will be reading a series of statements about a real TV set (consumer electronics brand). Please form an overall impression of this TV set (brand). This information is compiled on the basis of Consumer Reports. Please read each statement carefully. Later, we will ask you some questions about the information you have read.” The
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subjects in the no explicit instruction condition read the same instructions except for the instruction about forming an impression. This manipulation is similar to the impression instructions in the McConnell et al. (1994) study.
Presentation Order. Each subject received 13 pieces of attribute information. Similar to Park and Hastak’s (1994) procedure, the first piece of information presented was a neutral attribute, followed by either a positive block or a negative block. Each block had five positive or negative attributes and one neutral attribute. In each block, the third piece of information was neutral. These attributes were selected on the basis of Consumer Reports descriptions and a pretest (see the appendix for a description of the attributes). The attributes were selected so that they could be used to form an impression of a TV set, in particular, and electronic products, in general. A pretest was used to verify that the importance of the attributes did not differ by target. Subjects assigned to the positive (negative) first condition read the positive (negative) block first.
Dependent Variables All dependent variables except for recall were assessed using nine-point scales anchored by 1 and 9 in the following order.
Evaluations of the Target. Subjects evaluated the TV set or the family brand on the following scales: “very negative” versus “very positive,” “not at all favorable” versus “very favorable,” “very bad” versus “very good,” and “very undesirable” versus “very desirable.” These items were averaged to form an evaluation index (a p .93). Recall. In a free recall task, subjects recalled as much as they could of the information about the family brand name or the TV set. They were asked not to go back to the previous pages. The results for one subject who failed to follow this instruction were not analyzed. Two independent raters counted the total number of positive and negative attributes recalled. Ninety-eight percent of recalled attributes were classified as positive or negative by both judges. The remaining (2%) attributes were either incorrectly recalled or judges disagreed on these attributes. These attributes were not analyzed. Manipulation and Confound Checks. Subjects rated the extent to which descriptions of attributes were not at all favorable or very favorable. Five positive (a p .63), five negative (a p .62), and three neutral attribute descriptions (a p .66) were averaged to form positive, negative, and neutral information indices, respectively. Subjects rated the degree to which attribute information was very irrelevant (vs. relevant), of no use (vs. of great use), and not at all indicative (vs. very indicative) of quality. These three items were averaged to form an information relevance index (a p .78). Subjects rated the importance of attributes (not at all important vs. very important) and their subjective knowledge about TV sets and electronic products by re-
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sponding to three sentences (e.g., “My knowledge of TV sets is. . .” [anchored by “very poor” and “very good”]). These three items were averaged to form subjective knowledge indices about TV sets (a p .92) and electronic products (a p .96), respectively. Finally, subjects indicated their gender and age.
Results and Discussion All dependent measures were analyzed using a 2 (target) # 2 (processing goal) # 2 (presentation order) betweensubjects design. Gender, age, or subjective knowledge did not have significant effects as covariates.
Manipulation and Confound Checks. Paired t-tests on the positive, negative, and neutral information indices indicated that positive attributes (M p 7.37) were rated as more favorable than both neutral (M p 5.22; t(217) p 26.89, p ! .001) and negative attributes (M p 2.55; t(217) p 52.52, p ! .001). Negative attributes were rated as less favorable than neutral attributes (t(217) p ⫺38.87, p ! .001). Separate ANOVAs on these indices as well as on information relevance and attribute importance revealed no effects (p’s 1 .15). Evaluations of the Target. An ANOVA on the evaluation index yielded a main effect of presentation order (F(1, 210) p 4.95, p ! .05), a two-way interaction between presentation order and target (F(1, 210) p 7.14, p ! .01), and a two-way interaction between presentation order and processing goal (F(1, 210) p 7.03, p ! .01). More importantly, the three-way interaction of target, processing goal, and presentation order was also significant (F(1, 210) p 5.49, p ! .05). The test for simple interaction revealed that, when there was no instruction to form an impression, the interaction between target and presentation order was significant (F(1, 210) p 12.80, p ! .001). The simple effects test showed that, when there was no impression instruction, family brand evaluations were more favorable when the positive block was presented later (vs. earlier; M’s p 5.84 vs. 4.91; F(1, 210) p 7.48, p ! .01) and individual product evaluations were more favorable when the positive block was presented earlier (vs. later; M’s p 5.74 vs. 4.96; F(1, 210) p 5.41, p ! .05). In contrast, when there was a specific impression instruction, the interaction between target and presentation order was not significant (F ! 1). When there was a specific impression instruction, evaluations were more favorable when the positive block was presented earlier (vs. later; M’s p 5.80 vs. 4.97; F(1, 210) p 11.66, p ! .001), regardless of the target. Consistent with hypothesis 1, these findings indicate that brand information is less likely to be processed on-line than individual product information when there is no specific instruction. This effect is weaker when there is a specific impression instruction. Recall. An ANOVA on the number of positive attributes recalled yielded a main effect of processing goal (F(1, 210) p 4.29, p ! .05), a main effect of presentation order (F(1, 210) p 7.29, p ! .01), and a two-way interaction
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between target and presentation order (F(1, 210) p 7.15, p ! .01). These effects were also qualified by a significant three-way interaction (F(1, 210) p 5.63, p ! .05). An ANOVA on the number of negative attributes recalled yielded a main effect of presentation order (F(1, 210) p 10.33, p ! .01), and two two-way interactions between target and presentation order (F(1, 210) p 8.14, p ! .01) and processing goal and presentation order (F(1, 210) p 6.90, p ! .01). These effects were also qualified by a significant threeway interaction (F(1, 210) p 6.05, p ! .05). The test for simple interaction indicated that, when there was no instruction to form an impression, the interaction between target and presentation order was significant (F(1, 210) p 13.22, p ! .001, for positive attributes and F(1, 210) p 14.23, p ! .001, for negative attributes). The simple effects test showed that, when there was no impression instruction, subjects recalled more positive attributes and fewer negative attributes about the family brand when the positive block was presented later (vs. earlier; M’s p 2.89 vs. 2.36; F(1, 210) p 4.12, p ! .05, for positive attributes and M’s p 2.19 vs. 2.79; F(1, 210) p 5.47, p ! .05, for negative attributes). However, when there was no impression instruction, subjects recalled more positive and fewer negative attributes about the individual product when the positive block was presented earlier (vs. later; M’s p 3.29 vs. 2.46; F(1, 210) p 9.66, p ! .01, for positive attributes and M’s p 2.39 vs. 3.14; F(1, 210) p 8.95, p ! .01, for negative attributes). On the other hand, when there was a specific impression instruction, the interaction between target and presentation order was not significant for both positive and negative attributes (F’s ! 1). Subjects recalled more positive and fewer negative attributes when the positive block was presented earlier (vs. later) regardless of the target (M’s p 3.31 vs. 2.74; F(1, 210) p 9.25, p ! .01, for positive attributes and M’s p 2.46 vs. 3.21; F(1, 210) p 16.77, p ! .001, for negative attributes). Consistent with hypothesis 1, these findings imply that, when there is no impression instruction, more evidence of on-line processing (i.e., primacy effects on recall) is observed when the target is an individual product (vs. a family brand). This effect is weaker when there is an explicit impression instruction.
Evaluation/Recall Correlations. To investigate the relationship between recall and evaluations, a valenced index was created by subtracting the total number of positive attributes recalled from the total number of negative attributes recalled. Correlations are compared using Fisher’s r-to-z transformation. When there was no impression instruction, the correlation between evaluations and the recall index was higher for the family brand (vs. individual product; r’s p .58 vs. .26; z p 2.05, p ! .05). Consistent with hypothesis 1, when there was a specific impression instruction, this difference in correlations was not significant (r’s p .14 vs. .18; z p ⫺.21, p 1 .83). The results of experiment 1 suggest that, when there is no explicit instruction to form an impression, brand information (vs. individual product information) is less likely to be processed on-line. This effect is weaker when there is
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an impression instruction. These findings are consistent with the premise that the effect of product versus family brand on processing is mediated by expected variability. This implies that expected variability should have a direct effect on processing. That is, consumers are less likely to process brand information on-line as expected variability increases. Experiment 2 addresses this issue by manipulating expected variability. In experiment 2, subjects receive no explicit instruction to form an impression; in addition, the target is always a family brand.
EXPERIMENT 2 Subjects and Procedure One hundred and forty-seven undergraduate students enrolled in a large midwestern university received partial course credit for participating in a research study. They were randomly assigned to conditions in a 3 (expected variability: high, low, or no instruction about variability) # 2 (presentation order: positive first or negative first) between-subjects design. Subjects first received variability manipulation and then attribute information. Next, as in experiment 1, a filler task and dependent measures were administered.
Independent Variables Expected Variability. Subjects received instructions that manipulated expected variability (high or low) or were given no expectancies about variability. Under high (low) variability, subjects read that most of the products of this family brand do (not) differ from each other in terms of quality. Presentation Order. Attribute information was similar to the information in experiment 1. However, each statement referred to a specific product, as is shown in the appendix. Subjects assigned to the positive (negative) first condition read the positive (negative) block first.
Dependent Variables The dependent variables were similar to those used in experiment 1 and were presented in a similar order. Subjects also rated the extent to which they expected variability or differences in quality across different products of this brand on three scales anchored by “low variability” and “high variability,” “little variability” and “a great deal of variability,” and “small difference” and “big difference,” respectively. These items were averaged to form a variability index (a p .96). At the end of the experiment, subjects responded to a suspicion probe and indicated what they thought the purpose of the study was.
Results and Discussion All dependent measures were analyzed using a 2 (presentation order) # 3 (expected variability) between-subjects design. Gender, age, and subjective knowledge did not have
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significant effects as covariates. No subjects referred to the variability instructions in the suspicion probe, indicating no suspicion regarding the real purpose of the study.
Manipulation Checks. An ANOVA on the variability index yielded a main effect of variability (F(2, 141) p 28.66, p ! .001). As expected, under high variability (M p 5.44) and no instruction about variability (M p 5.66), subjects expected more variability across different products of the family brand than under low variability (M p 3.64; F(1, 141) p 37.90, p ! .001, and F(1, 141) p 48.09, p ! .001, for high variability and no instruction about variability, respectively). Paired t-tests on the positive (a p .71), negative (a p .70), and neutral (a p .60) information indices revealed that positive attributes (M p 7.23) were rated as more favorable than both neutral (M p 5.49; t(146) p 16.06, p ! .001) and negative attributes (M p 2.96; t(145) p 30.64, p ! .001). Negative attributes were perceived as less favorable than neutral attributes (t(145) p ⫺19.84, p ! .001). Separate ANOVAs on these indices revealed no effects, suggesting information favorableness does not vary as a function of experimental manipulations ( p’s 1 .15). Family Brand Evaluations. An ANOVA on the evaluation index (a p .90) yielded a significant two-way interaction between expected variability and presentation order (F(2, 141) p 5.86, p ! .01). The simple effects test revealed that positive information had a greater impact on family brand evaluations when it was presented earlier (vs. later) under low variability (M’s p 5.89 vs. 5.26; F(1, 141) p 4.49, p ! .05). In contrast, when subjects expected high variability and when there was no instruction about variability, positive information had a greater influence on evaluations when it was presented later (vs. earlier; M’s p 5.80 vs. 5.18; F(1, 141) p 4.43, p ! .05, for high variability and M’s p 5.93 vs. 5.33; F(1, 141) p 4.24, p ! .05, for no instruction about variability). Recall. Two independent raters counted the total number of positive and negative attributes recalled. Ninety-six percent of recalled attributes were classified as positive or negative by both judges. The remaining (4%) attributes were either incorrectly recalled or judges disagreed on these attributes. These attributes were not analyzed. Separate ANOVAs on the total number of positive (F(2, 141) p 6.93, p ! .001) and negative (F(2, 141) p 6.29, p ! .01) attributes recalled yielded significant interactions between the expected variability and presentation order. The simple effects test suggested that when subjects expect low variability, they recalled more positive and fewer negative attributes when positive information was presented earlier (vs. later; M’s p 2.84 vs. 2.17; F(1, 141) p 4.86, p ! .05, for positive attributes and M’s p 2.08 vs. 2.78; F(1, 141) p 4.85, p ! .05, for negative attributes). In contrast, under high variability, more positive and fewer negative attributes were recalled when positive information was presented later (vs. earlier; M’s p 2.88 vs. 2.08; F(1, 141) p 7.07, p ! .01, for
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positive attributes and M’s p 1.96 vs. 2.63; F(1, 141) p 4.53, p ! .05, for negative attributes). Similarly, when there is no instruction about variability, more positive and fewer negative attributes were recalled when positive information was presented later (vs. earlier; M’s p 2.76 vs. 2.16; F(1, 141) p 7.07, p ! .01, for positive attributes and M’s p 2.16 vs. 2.84; F(1, 141) p 4.76, p ! .05, for negative attributes). These results suggest that high variability and no instruction about variability (vs. low variability) lead to more evidence of memory-based processing (i.e., recency effects on evaluations and recall).
Evaluation/Recall Correlations. Consistent with hypothesis 2, correlation between brand evaluations and the valenced index of recall was higher under high variability (vs. low variability; r’s p .54 vs. ⫺.01; z p 2.93, p ! .01) and when there is no variability instruction (vs. low variability; r’s p .62 vs. ⫺.01; z p 3.52, p ! .001). Overall, the results of experiment 2 indicate that on-line processing of information about a family brand is more likely when expected variability is low versus high. Moreover, similar findings were observed when subjects expected high variability and when there was no instruction about variability, suggesting that subjects in this experiment expected variability when there was no instruction about variability. Experiment 3 investigates how attribute uniqueness moderates the effect of expected variability on information processing and family brand evaluations.
EXPERIMENT 3 Subjects and Procedure One hundred and ninety-eight undergraduate students enrolled in a large midwestern university received $10 for participating in a consumer research study. They were randomly assigned to conditions in a 2 (expected variability: high or low) # 2 (attribute uniqueness: unique or shared) # 2 (presentation order: positive first or negative first) between-subjects design. The procedure was similar to procedures used in experiments 1 and 2.
Independent Variables Expected Variability. In this experiment, a different manipulation of variability is used. In previous studies (e.g., Susskind et al. 1999), subjects were generally told that the members of a group are similar to each other in many ways. However, in the context of family brands, often only one attribute forms the basis that holds different products together (e.g., Hitachi is technological innovation, Kraft has quick solutions). To address this issue, subjects were told that most products under this family brand are very easy to use and consistently score very high on ease of use. A pretest indicated that consumers expect lower variability in attribute performance or in overall quality when the family brand is positioned to be easy to use as compared with a nondescript brand. Subjects received this information either before or
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after receiving the attribute information. When information about ease of use is presented before attribute information, consumers should expect less variability across different products of the family brand. However, if such an expectation is not indicated before attribute information is presented, consumers should expect some variability, as shown in experiment 1.
Attribute Uniqueness. Subjects learned about either unique or shared attributes associated with distinct products (see the appendix). The same set of products was used in both the unique and shared attribute conditions. Attribute uniqueness was verified in a pretest. Attribute importance ratings did not vary as a function of attribute uniqueness. Presentation Order. Subjects received 12 pieces of attribute information in two blocks. Each block had five positive or negative attributes and one neutral attribute. Within each block, the neutral attribute was presented in the third position. Subjects assigned to the positive (negative) first condition read the positive (negative) block first.
Dependent Variables As in experiments 1 and 2, similar dependent measures were presented in the same order. Subjects also rated the extent to which the attributes featured in the information they read can describe only specific products or can be generalized to other products on three nine-point scales anchored by “very specific” and “very general,” “very narrow” and “very broad,” and “very unlikely to be generalized” and “very likely to be generalized,” respectively. These items were averaged to form an attribute uniqueness index (a p .96). The question about expected variability was rephrased slightly. Subjects were asked the extent to which they expected variability while they were reading the attribute information.
Results and Discussion All dependent measures were analyzed using a 2 (expected variability) # 2 (attribute uniqueness) # 2 (presentation order) between-subjects design. Gender, age, or subjective knowledge did not have significant effects as covariates. No subjects referred to the variability instructions or attribute uniqueness in the suspicion probe, indicating subjects were not suspicious of the real purpose of the study.
Manipulation Checks. An ANOVA on the variability index (a p .96) yielded a main effect of variability (F(1, 190) p 4.24, p ! .05). Subjects expected lower variability when ease of use information was presented before (vs. after) the attribute information (M’s p 4.59 vs. 5.17). An ANOVA on the attribute uniqueness index indicated a main effect of attribute uniqueness (F(1, 190) p 16.22, p ! .001). Subjects perceived attribute information as more unique when the information pertained to the unique (vs. shared) attributes (M’s p 5.37 vs. 4.46). Paired t-tests on the positive (a p .73), negative (a p .79), and neutral
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(r p .56) information indices revealed that positive attributes (M p 7.53) were rated as more favorable than both neutral (M p 5.26; t(197) p 19.99, p ! .001) and negative attributes (M p 2.62; t(197) p 39.17, p ! .001). Negative attributes were perceived as less favorable than the neutral attributes (t(197) p ⫺20.32, p ! .001). Separate ANOVAs on these indices revealed no effects, suggesting that information favorableness does not vary as a function of experimental manipulations ( p’s 1 .20).
Family Brand Evaluations. An ANOVA on the evaluation index (a p .91) yielded a main effect of presentation order (F(1, 190) p 5.37, p ! .05). It also yielded two twoway interactions between expected variability and presentation order (F(1, 190) p 3.98, p ! .05) and presentation order and attribute uniqueness (F(1, 190) p 6.70, p ! .01). These effects were qualified by a significant three-way interaction (F(1, 190) p 5.22, p ! .05). The test for simple interaction revealed that, when the information featured unique attributes, the interaction between expected variability and presentation order was not significant (F ! 1). There was only a significant main effect of presentation order (F(1, 190) p 11.43, p ! .001). Consistent with hypothesis 3, brand evaluations were more favorable when the positive block with unique attributes was presented later (vs. earlier; M’s p 5.65 vs. 4.85), regardless of expected variability. In contrast, when the information featured shared attributes, the interaction between expected variability and presentation order was significant (F(1, 190) p 9.25, p ! .01). The simple effects test revealed that, consistent with hypothesis 3, positive information with shared attributes had a greater impact on brand evaluations when positive block was presented earlier (vs. later) under low variability (M’s p 5.73 vs. 4.99; F(1, 190) p 5.17, p ! .05). However, under high variability, positive information with shared attributes had a greater impact on brand evaluations when positive block was presented later (vs. earlier; M’s p 5.67 vs. 5.02; F(1, 190) p 4.12, p ! .05). Recall. Two independent raters counted the total number of positive and negative attributes recalled. Ninety-five percent of recalled attributes were classified as positive or negative by both judges. The remaining (5%) attributes were either incorrectly recalled or judges disagreed on these attributes. These attributes were not analyzed. An ANOVA on the number of positive attributes recalled yielded a main effect of presentation order (F(1, 190) p 4.96, p ! .05) and a two-way interaction between expected variability and presentation order (F(1, 190) p 4.41, p ! .05). These effects were qualified by a significant three-way interaction (F(1, 190) p 4.33, p ! .05). An ANOVA on the number of negative attributes recalled yielded a two-way interaction between expected variability and presentation order (F(1, 190) p 4.69, p ! .05). This effect was qualified by a significant three-way interaction (F(1, 190) p 6.17, p ! .05). The test for simple interaction revealed that, when the information featured unique attributes, the interaction between expected variability and presentation order was not
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significant (F’s ! 1) for both positive and negative attributes. When the information featured unique attributes, more positive and fewer negative attributes were recalled when the positive block was presented later (vs. earlier) regardless of expected variability (M’s p 2.80 vs. 2.08; F(1, 190) p 8.06, p ! .01, for positive attributes and M’s p 2.12 vs. 2.65; F(1, 190) p 4.00, p ! .01, for negative attributes). In contrast, when the information featured shared attributes, the interaction between expected variability and presentation order was significant for both positive (F(1, 190) p 9.01, p ! .01) and negative (F(1, 190) p 11.04, p ! .001) attributes. Consistent with hypothesis 3, when expected variability was high, subjects recalled more positive and fewer negative shared attributes when the positive block was presented later (vs. earlier; M’s p 2.96 vs. 2.15; F(1, 190) p 5.36, p ! .05, for positive attributes and M’s p 1.76 vs. 2.65; F(1, 190) p 6.08, p ! .05, for negative attributes). By contrast, under low variability, subjects recalled more positive and fewer negative shared attributes when the positive block was presented earlier (vs. later; M’s p 2.63 vs. 1.96; F(1, 190) p 3.72, p ! .05, for positive attributes and M’s p 2.00 vs. 2.81; F(1, 190) p 4.98, p ! .05, for negative attributes).
Evaluation/Recall Correlations. When the information featured shared attributes, the correlation between brand evaluations and the valenced index of recall was higher when consumers expected high (vs. low) variability (r’s p .56 vs. .22; z p 2.01, p ! .05). Consistent with hypothesis 3, this difference in correlations between high and low variability was negligible when the information featured unique attributes (r’s p .55 vs. .58; z p ⫺.07, p 1 .94). Overall, the results of experiment 3 suggest that attribute uniqueness and expected variability systematically influence both information processing and family brand evaluations. Expectations of low (vs. high) variability lead to more evidence of on-line processes (i.e., primacy effects on family brand evaluations and recall and lower evaluation-recall correlation) when the information features shared attributes. These effects are weaker when the information features unique attributes.
GENERAL DISCUSSION Understanding how consumers form family brand evaluations on the basis of information about individual products is an important research focus given the widespread use of family brands and consumers’ reliance on brands as predictive cues (Van Osselaer and Alba 2000). As consumers continue to face more brand extensions, additional research is required that expands our process understanding of the relationship between individual product information and family brand evaluations. This research addresses process issues that underlie family brand evaluations and contributes to prior research in the following ways. First, the results suggest that expected variability of product quality within a family brand affects information processing in relation to the family brand. Expectations of low
(vs. high) variability lead to more evidence of on-line processing for a family brand target. This finding adds to recent research, which has examined the role of processing goals in learning of brand associations (e.g., Van Osselaer and Janiszewski 2001). The results also contribute to previous literature by showing how processes that underlie family brand evaluations can be different than those that underlie evaluations of products or single-product brands. Second, this research also extends previous social psychological research on impressions of individuals versus social groups by demonstrating the effect of attribute uniqueness on information processing and group judgments. Specifically, this research suggests that on-line processing is more likely when attribute information is shared (vs. unique) because perceivers can more easily integrate such information across group members. Also, perceivers may not be motivated to integrate information on-line if they perceive that the information is idiosyncratic. Third, this research suggests that social psychological theory of impression formation can be useful in understanding how family brand impressions are formed in addition to the cognitive theories used by previous research on brand equity. This framework focuses on memory-based versus on-line aspects of information processing. These different processes can lead to significant differences in family brand evaluations as a function of the order of information acquisition. The results also suggest that consumers are likely to expect variability in performance of individual products within the family brand if no instruction about variability is provided. This finding highlights the importance of consistent positioning and management of brand portfolios as the number of products associated with the brand increases. In this research, subjects read information about multiple products, each described with a single attribute. However, it is probably more frequent that consumers receive information about only a few products described with several attributes. To the extent that consumers expect low (vs. high) variability, on-line processing should be likely in such situations. Additional research should examine the generalizability of our results by varying the number of products and the attributes. Another limitation of this research relates to the use of strong manipulation of expected variability in experiment 2. When experimental manipulations directly tell subjects that products differ from each other in terms of their quality, subjects may infer that they should not make judgments about the family brand. Consequently, it will be important to replicate these findings in contexts where such direct inferences are less likely. One way to address this issue in future research is to use real brand names and measure (vs. manipulate) expected variability. Another way is to manipulate variability in a more subtle way by focusing on other bases that hold the products together such as ease of use manipulation in experiment 3. The results of this research suggest that high expected variability discourages on-line processing of information. However, the strength of the association between individual products and a family brand name may moderate this effect.
VARIABILITY AND UNIQUENESS
Future research can test whether on-line processing is more likely when consumers are exposed to information about highly (vs. poorly) accessible products within a family brand. Consumers may expect variability in quality of individual products for both highly favorable (e.g., Sony) and less favorable brands (e.g., Sanyo). Future research is required to examine the extent to which the perceived mean of the quality distribution moderates the effects observed in this research. It is also important to explore how consumers develop expectations of variability to better understand processes that underlie family brand impressions.
APPENDIX ATTRIBUTE INFORMATION IN EXPERIMENTS 1 AND 2 Neutral: It is available in two standard sizes (clock radio). It has average weight (boom box). It has a cord wrap and hooks (stereo system). Positive: Due to its state-of the art technology that prevents interference and noise, it delivers high quality sound with sonic purity and clarity (DVD player). It comes with a wellfunctioning universal remote that can also operate other electronic equipment (HDTV set). It has an aesthetically pleasing finish and an award-winning design (cordless phone). It is highly energy efficient (portable MP3 player). It comes with a comprehensive user’s manual that is easy to understand (digital camera). Negative: Based on Consumer Reports’ 1999 annual questionnaire, it was rated poor in reliability due to the number of repairs needed (CD player). It is difficult to use because it does not have advanced automatic features (receiver). It does not have additional input/output jacks. Additional jacks provide versatility by letting you connect other equipment (camcorder). It does not have a power backup feature. Settings need to be reset after a brief power outage (VCR). Volume correction circuitry automatically lowers the volume of loud sounds and music. It does not have automatic volume correction (TV set).
ATTRIBUTE INFORMATION IN EXPERIMENT 3 Neutral and Unique: Boombox has dual cassette player. The range (area in which it can be used) of the cordless phone is standard. Neutral and Shared: Boom box is available in standard sizes, similar to the available sizes of other brands. Cordless phone has standard volume controls. Positive and Unique: Portable MP3 player has excellent music storage capacity. Clock radio has a dual alarm feature, which lets you set two separate wake-up times. Receiver is very powerful for loud listening in a large room (i.e., high watts per channel). Camcorder has a superior image stabilizer feature, which helps improve performance signifi-
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cantly. DVD player has excellent sound quality due to a unique DVD technology. Positive and Shared: Portable MP3 player is highly energy efficient based on its battery efficiency. Clock radio has an aesthetically pleasing finish and an award winning design. Receiver comes with a well-functioning remote control that can also operate other electronic equipment. Camcorder is rated as very durable. DVD player has excellent sound quality. Negative and Unique: CD player has very poor ability to cope with discs that are scratched or damaged. Based on Consumer Reports’ 2000 annual questionnaire, TV set was rated poor in reliability of the picture tube due to the number of repairs needed to fix the picture tube. VCR has very poor auto–head cleaning feature. It takes a very long time for the digital camera to be ready for the next shot. HDTV set does not deliver high picture quality due to a faulty feature found only in HDTV sets. Negative and Shared: CD player does not have a favorite track selection feature. Based on Consumer Reports’ 2000 annual questionnaire, TV set was rated poor in reliability due to the number of repairs needed. VCR lacks additional input/output jacks. Additional jacks provide versatility by letting you connect other equipment. Digital camera is very heavy to hold. HDTV set does not deliver high picture quality. [David Glen Mick served as editor and Merrie L. Brucks served as associate editor for this article.]
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