by and 2000/16/MKT * Assistant Professor of Marketing at INSEAD ...

THE INFLUENCE OF CHOICE JUSTIFICATION AND STIMULUS MEANINGFULNESS ON THE ATTRACTION EFFECT: AN INFORMATION-PROCESSING PERSPECTIVE by P. MALAVIYA* and K. SIVAKUMAR** 2000/16/MKT

*

Assistant Professor of Marketing at INSEAD, Boulevard de Constance, 77305, Fontainebleau Cedex, France.

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Associate Professor of Marketing, Department of Managerial Studies (MC 243) College of Business Administration, University of Illinois at Chicago, 601 South Morgan Street, Chicago, IL 60607, USA.

A working paper in the INSEAD Working Paper Series is intended as a means whereby a faculty researcher’s thoughts and findings may be communicated to interested readers. The paper should be considered preliminary in nature and may require revision. Printed at INSEAD, Fontainebleau, France.

THE INFLUENCE OF CHOICE JUSTIFICATION AND STIMULUS MEANINGFULNESS ON THE ATTRACTION EFFECT: AN INFORMATION-PROCESSING PERSPECTIVE

Prashant Malaviya* K. Sivakumar**

January 2000

* Assistant Professor, Department of Marketing, INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France. Phone: (33) (0)1 60 72 42 48; E-mail: [email protected] ** Associate Professor of Marketing, Department of Managerial Studies (MC 243), College of Business Administration, University of Illinois at Chicago, 601 South Morgan Street, Chicago, IL 60607. Phone: (312) 996-6229; Fax: (312) 996-3559; E-mail: [email protected].


ABSTRACT The “attraction effect” refers to the phenomenon in which preference for a target product increases relative to a competitor when a choice option that is dominated by the target brand but not by the competitor brand is included in the choice set. This finding reflects decision making that is inconsistent with principles of IIA and utility maximization. The present article offers an explanation for this nature of decision making in terms of fundamental principles of information processing. The findings implicate two factors, data deficiency and cognitive resource deficiency, in the observation of the attraction effect. Specifically, the results show that when the product information is relatively “meaningless” and thus, data deficient, making respondents allocate greater cognitive resources to the decision task by asking them to justify their decision, increases the attraction effect. However, when the product information is quite “meaningful” and thus, not data deficient, increasing resource allocation by instructing respondents to justify choice, decreases the attraction effect. We propose that when the stimulus is less meaningful, the additional processing effort that justification induces is used to generate heuristics to justify choice, which enhance the attraction effect. However, when the stimulus is more meaningful, justification induces the effort necessary to engage in value maximization decisions that are devoid of context effects and this diminishes the attraction effect. The implications of these findings and future research directions are highlighted.


In today’s highly competitive marketplace, consumers are often faced with an excessive number of choices. Some options are positioned as high quality brands; others are low quality price leaders, while yet others are somewhere else on this continuum. Making decisions and choices after careful consideration of all the available choice options is usually not feasible. In fact, a consumer is most likely to be exposed to only some subset of these options. Considerable research has attempted to examine how the nature of the choice set that is considered by the consumer will influence the choice outcome (Payne, Bettman, and Johnson 1992; Ratneshwar, Shocker, and Stewart 1987). Consider a situation in which a consumer is faced with three different choice situations. In each case, the consumer notices various brands of orange juice and needs to decide which brand to purchase. In the first situation, the consumer encounters one brand of orange juice, Brand A, that is priced at $3.99 and is thought to be of high quality, and a second brand, Brand B, that is priced at $2.99 and known to be of moderate to average quality. In the second situation, in addition to these two brands, the consumer is exposed to a third brand, Brand C’, which is slightly more expensive that Brand A at $4.09 and appears to be of a high quality that is just about, but not quite, comparable to the quality of Brand A. In the third scenario, in addition to the first two brands, the consumer is exposed to a third brand, Brand C”, that is priced slightly higher than the price of Brand B at $3.09 and is perceived to be of a little less quality than Brand B. What will be the choice of the consumer in each situation? Conventional economic models based on standard utility theory will predict that the consumer’s choice will be the same in all scenarios. This is because Brand C in the latter two scenarios is inferior to one of the two original brands. Thus, choice in these two scenarios should become identical to the choice in the first situation. Nevertheless, recent research shows that, compared to the

first scenario, in the second scenario consumers are more likely to choose Brand A, whereas in the third scenario consumers are more likely to choose Brand B. In other words, the presence of a choice option that is inferior on all considered attributes to another (target) brand, increases the preference for the target brand. This nature of decision making is known as the attraction effect (Huber, Payne, and Puto 1982), and is the subject of this paper. Interest in the attraction effect stems from the observation that the presence or absence of an obviously inferior alternative influences the consumer’s decision. In other words, the attraction effect represents a violation of the “independence of irrelevant alternatives (IIA)” principle (Luce 1977). One would expect that a choice alternative that is dominated by another option would simply be ignored because it is not a viable alternative, and would have no impact on how the respondent weighs the other two alternatives. Yet, the findings reliably show that although the dominated alternative may not be preferred, its presence (versus absence) increases preference for the dominating target brand relative to the competitor, thus prompting a biased decision making process and violating the principles of IIA and utility maximization (Burton and Zinkhan 1987; Heath and Chatterjee 1996; Huber, Payne, and Puto 1982; Huber and Puto, 1983; Lehmann and Pan 1994; Malaviya and Sivakumar 1998; Mishra, Umesh, and Stem 1993; Pan and Lehmann 1993; Ratneshwar et al. 1987; Simonson 1989; Simonson and Tversky 1992). Quite naturally, these findings have spawned research into the cause of the attraction effect. Generally, these investigations have addressed three issues: (i) why is a respondent unable to adhere to the principles of IIA and value maximization and instead adopts a biased decision-making process (e.g., Ratneshwar et al. 1987); (ii) what is the specific decision making process that is adopted and that results in a consistent bias in decisions (e.g., Simonson and Tversky 1992); and (iii) what specific decision rules or heuristics are invoked that result in a bias in favor of the dominating alternative (e.g., Wedell

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1991). Of the three issues, the one that has been examined the least and has proven most immune to significant progress is the first issue of why does decision making deviate from the norms of rational thinking in the first place. As Kardes, Herr, and Marlino (1989) note, investigations of the "process which might be responsible for producing these violations" of the IIA assumptions have been limited. The present paper addresses this issue. Thus, the most important contribution of this research is theory development. It offers an explanation for the attraction effect that is built on fundamental notions of information processing theory (Bettman 1979; Petty and Cacioppo 1986). Based on this explanation, we offer predictions regarding the conditions that might exacerbate such biased decision making and enhance the attraction effect, and conditions under which decisions would be less biased and the attraction would diminish. Toward this goal, we manipulate two factors that allow us to examine important information processing concepts. Though these factors – choice justification and stimulus meaningfulness – have been examined in isolation in past research, investigating their joint effect allows us to make substantive theoretical progress. Finally, understanding the mechanism contributing to the attraction effect and the joint effect of stimulus meaningfulness and choice justification will provide useful guidelines for product positioning and presentation. Thus, this research also makes important managerial contributions. The rest of the article is organized as follows. In the next section, we discuss the relevant literature to build the conceptual framework and derive our research hypotheses. Following this, a study to test the hypotheses is described and the results are presented. The paper concludes by discussing the significance and implications of the findings and future research directions.

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CONCEPTUAL BACKGROUND AND HYPOTHESES As a starting point in our discussion, consider the nature of a typical attraction effect study. Respondents are given the task of deciding which brand they prefer from among two or three brands that are described on two attributes. In the three-brand condition, the configuration of the brands represents asymmetric domination, that is, one brand (the “decoy”) is clearly dominated by the target but not by the third brand, the competitor. In the two-brand condition, the decoy brand is not presented. The reliable finding is that compared to the two-brand scenario, in the three-brand situation, the target brand is more likely to be preferred. That is, the presence of a dominated decoy makes the target more attractive. This outcome has been explained to occur because respondents use a heuristic to make their decisions (Huber et al. 1982; Ratneshwar et al. 1987; Simonson and Tversky 1992). This heuristic is constructed from the available product information. Because this information contains an asymmetric domination relationship among the choice options, the heuristic is biased in favor of the target brand. As Simonson and Tversky (1992) explain, relative to the decoy brand, the target brand presents a more favorable attribute trade-off contrast than the competitor brand, and therefore the target is judged more favorably. Further, because of the asymmetric nature of the choice alternatives, the comparison between the target and the decoy would yield an unambiguous and straightforward preference of the target, while the competitor would not present such a clear choice (Huber et al. 1982). These decision processes offer the respondent a reasonable heuristic for making the choice. Use of these heuristics as a decision rule gives rise to the attraction effect because the target would always appear to be more attractive than the competitor. It follows from this that a factor that fosters the use of these heuristics in decisions should enhance the magnitude of the attraction effect, whereas a variable that decreases reliance

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on these heuristics and instead prompts value maximization decisions would diminish the attraction effect. Justification of choice (Simonson 1989) might be one variable that prompts greater heuristic-based decisions because it has been found to enhance the magnitude of the attraction effect. Simonson reports that the attraction effect was enhanced when respondents were informed before they made their choice that they would be asked to justify their decision to another person. Respondents who believe they will have to defend their decision are likely to spend considerable effort looking for an alternative for which they can offer a rationale that they believe would be acceptable to the other person. In the context of asymmetric domination, the asymmetrically dominating brand (the target) possesses an obvious justification in terms of its clear superiority to the dominated brand. This suggests that respondents who are asked to justify their choice will be more inclined to use a heuristic, thus enhancing the attraction effect. Although Simonson’s (1989) research suggests that justification enhances attraction effect presumably because it prompts greater use of heuristics, additional research casts doubt on this conclusion, and instead suggests that justification should have the opposite influence on the attraction effect. Tetlock and his colleagues report that decision accountability1 (i.e., asking people to justify their decisions) makes respondents better decision makers and reduces reliance on various decision biases and heuristics (Tetlock 1985; Tetlock, Skitka and Boettger 1989). When respondents are made accountable for their decisions, they are more thorough in their consideration, think in a more complex, multi-dimensional, and flexible way, and therefore are less susceptible to biases, are less likely to overlook and ignore information, and are more likely to consider several perspectives.2 These observations suggest that the attraction effect should be diminished when respondents are asked to justify their choice because the more careful and thorough consideration of product data would make the decision-making process and the search for a

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rationale more utility based and less heuristic based. Yet, the finding reported by Simonson (1989) is inconsistent with this possibility. One way to resolve this apparent conflict in research findings might be to consider the nature of the information that is available for the decision-maker. The stimuli presented in a typical attraction effect study consists of fictitious brands are described in terms of their quality and price. The quality of the product is indicated numerically in the form of a quality index. For instance, a brand of orange juice might be described as having a quality index of 76. Describing the quality of a product in terms of some index numbers, without providing any contextual anchor for these numbers, is likely to make it difficult to assess the quality of the brand and in turn compare its utility to that of another brand. As Ratneshwar et al. (1987) argue, this nature of product data is not very meaningful and does not reveal the utility of the product options. Even if people wanted to, they would be unable to engage in utility based decision making because the stimulus is not conducive for making such inferences. Indeed, the best a person can do is to construct meaning of the product data within the context of the other brands (Ratneshwar et al. 1987; Stewart 1989). Such within context consideration of product data involves trade-off contrasts and the use of asymmetric domination as a heuristic, which in turn produces the attraction effect (Simonson and Tversky 1992). These observations pertaining to the nature of the available product information offer a possible explanation for why justification might not have diminished the attraction effect as suggested by Tetlock’s research, and instead may have enhanced its magnitude in the study by Simonson (1989). Consistent with Tetlock’s (1985) theorizing, justification would prompt more careful and thorough decision-making, and search for a viable decision rationale. However, whether such processing will induce utility considerations or whether decisions will be based on heuristics is likely to be influenced by the nature of available product data. If the available product data is deficient and not very meaningful, that is, it

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does not offer much insight into the utility of each choice option, the best a decision maker can do is intensify their search for an acceptable rationale, which in this context would mean constructing a heuristic from the asymmetric domination cue. Thus, because of the meaningless nature of the product data, use of the asymmetric domination heuristic would actually increase among respondents who are asked to justify their choice. Use of this heuristic in decision making would make the target appear more attractive and thus the attraction effect would be enhanced. These observations also suggest conditions under which justification may lead to a decrease in the attraction effect. If the available product data were more meaningful so that a thorough and careful scrutiny would yield an assessment of the value and utility of the product independent of the context of the choice set, value maximization decisions might be prompted (Ratneshwar et al. 1987). For instance, the description of the choice options might include information that a brand of orange juice that has a quality index of over 70 has “fresh orange character and is quite flavorful”, whereas a brand with an index rating of 50 has a “faint processed-orange taste”. This information would facilitate comprehension of the product’s utility independent of the choice context and allow value maximization considerations. To the extent, the availability of meaningful product information prompts people to use utility considerations in their decision-making instead of asymmetric domination as a heuristic, the attraction effect would be diminished. Based on these observations, the hypotheses for the study can be formally stated as follows: H1: When product stimulus information is less meaningful, instructions to justify choice will increase the attraction effect (compared to the "no justification" condition). H2: When product stimulus information is more meaningful, instructions to justify choice will diminish, the attraction effect (compared to the "no justification" condition). In summary, our explanation of the attraction effect is based on a consideration of two factors – the data that is available for decision making and the resources that are

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allocated for this task. For data deficient stimuli, increasing resources for the decision task enhances the use of heuristics and in turn, the attraction effect. For data sufficient stimuli, increasing resource allocation prompts more careful utility-based considerations that diminish the attraction effect. In the next section, we present a study that tests this theorizing.

METHOD Stimulus Material The stimuli for the study were patterned after material used by Ratneshwar et al. (1987). This was done to provide continuity and comparability across studies, and also to use stimuli that have proven successful in producing the attraction effect. Three product categories (TV, orange juice, and car) were used in this research. Brands in each category were described on two attributes: percent distortion and reliability for TV, price and quality rating for orange juice, and mileage and ride quality for car. The stimulus was assembled in the form of a booklet. Two factors were varied in the presentation of the stimulus. The meaningfulness of product information was varied by presenting the attribute information either numerically or by accompanying this numerical information with a verbal description explaining what the numbers implied with respect to the product’s quality. These verbal descriptions were identical to those used by Ratneshwar et al. (1987). Another factor manipulated was the instructions to respondents. All respondents were asked to report their choice of brand based on the information provided. In addition, before they made their choice, half the respondents were also informed that they would be required to justify their choices by providing a reason for their rating of the brands. A question asking them to indicate a justification for their choice was included in the booklet to complete the manipulation. The two factors (stimulus meaningfulness and justification) were

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crossed to obtain four treatment conditions. A separate booklet was assembled for each condition.

Procedure Respondents in the study were undergraduate students enrolled in an introductory marketing course in a large university. A total of 254 students participated in the experiment for course credit. Respondents were randomly assigned to one of four conditions described earlier and were given the appropriate version of the booklet. Each booklet contained eight pages. The first page of the booklet informed respondents that they were being asked to make choices between several brands in a product category on the basis of the information provided. The second page of the booklet described brands in the TV category on the two attributes, percent distortion and reliability. Instructions regarding the need for justification of choices as well as elaborated product information were added in the questionnaire for the appropriate groups (none for the first version, meaningfulness only for the second version, justification only for the third version and both for the fourth version). On this page, only the target and competitor brands were described. Respondents were asked to indicate their preference for the brands by dividing a total of 100 points between the alternatives, so that the preferred brand was assigned the maximum points and the other brands assigned fewer points in proportion to their preference (e.g., Mishra et al. 1993). For the conditions requiring justification of choice, respondents also answered a question asking them to briefly explain the rationale for their preferences. On the next page, a decoy brand was added to the two previous brands in the TV category. Respondents were again given the constant sum choice question for this three-brand scenario. Thus, the attraction effect was assessed within subjects (Mishra et al. 1993). The preference task was followed by a

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question, in the conditions applicable, asking respondents to indicate the justification for their choice. Pages four and five contained similar material for the orange juice category and pages six and seven contained questions on the car category. Finally, page eight included general demographic questions, including gender, age, school status, major, and so on. These were gathered as potential covariates in the analysis. The entire task took about twenty minutes and respondents were allowed to pace themselves through the booklet.

RESULTS Because the attraction effect was assessed within subjects, it was calculated by taking the difference between the target brand’s share of the 100 points in the presence and absence of the decoy brand. In case the decoy brand was assigned any share points, the share of the target brand was adjusted to a percent of the total share of the target and competitor brands. For instance, if the respective shares of the target, competitor, and decoy brands were 65, 30, and 5 points each, the adjusted share of the target was calculated to be ((65/95)*100). Similar measures for the attraction effect have been used by Malaviya and Sivakumar (1998), Mishra et al. (1993), Sen (1998), and Sivakumar and Cherian (1995). Each respondent provided information on all three product categories. Response on each product category was treated as a separate data point. Thus, each respondent provided three data points, one for each product category. Because the pattern of the responses was similar for the three product categories, they were entered into the analysis together and this analysis is presented below3. Table 1 presents the attraction effect for the various experimental conditions at the aggregate and category level. Figure 1 is a graphical representation of the aggregate findings. -----------------------------------------------------Table 1 and Figure 1 about here ------------------------------------------------------

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The effect of justification and stimulus meaningfulness on the magnitude of the attraction effect was examined using a 2-way ANOVA. This aggregate analysis revealed a significant interaction effect (F1,691 = 7.31, p < .007). As expected (H1), for less meaningful stimuli, the attraction effect was greater when respondents were asked to justify their choice (X = 4.39), than when such justification was not required (X = 1.01; F1,691 = 3.33, p < .06). By contrast, as per H2, for more meaningful stimuli, the attraction effect was eliminated when respondents were asked to justify their choice (X = -0.16), but it remained significant when justification of choice was not required (X = 3.51; F1,691 = 4.00, p < .04). Further, when respondents were not instructed to justify their choice, the attraction effect was not significantly different when the stimulus was more meaningful than when the stimulus was less meaningful (F1,691 = 1.89, p < .16). By contrast, when respondents were asked to justify their choices, the attraction effect was significantly less when the stimulus was more meaningful than less meaningful (F1,691=5.94, p < .01). These findings offer support for the theorizing presented in the paper. When the product data is less meaningful and does not facilitate assessments of product utility, people are likely to base decisions on a heuristic. Justification induces greater reliance on this heuristic, thus magnifying the attraction effect. In contrast, when the product data is meaningful and allows respondents to assess the utility of each choice option, justification prompts greater use of utility maximization in choice and consequently diminishes the attraction effect.

DISCUSSION The findings of this study offer an explanation for the attraction effect that can be interpreted in terms of general notions of information processing. Specifically, these findings implicate two factors in the attraction effect: data deficiency and resource

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deficiency. Data deficiency refers to a lack of adequate information to make an informed choice. As has been suggested by Ratneshwar et al. (1987), if the product data that is made available to respondents is sparse and does not clarify the product’s utility, value maximization decisions are not likely to occur. Instead, respondents tend to base decisions on heuristics constructed from the available data in the choice context. If however, data deficiency is removed and more diagnostic and informative product data is presented to respondents, it induces utility-based considerations and consequently, diminishes the attraction effect (Ratneshwar et al. 1987). In contrast to data deficiency, resource deficiency suggests that the attraction effect occurs because the decision maker does not allocate an adequate amount of cognitive resources to the choice task for engaging in value maximization decision making. A lack of adequate processing resources has been known to prompt the use of heuristics in decisionmaking (Petty and Cacioppo 1986). In the present context, inadequate resource might increase reliance on the asymmetric domination cue that would enhance the attraction effect. Respondents might allocate an insufficient amount of resources because the choice task appears deceptively simple leading them to erroneously judge the task as requiring minimal resources. It is also possible that a value maximization procedure demands considerable resources for the weighting and averaging of the utilities of the various attributes and options, a level of resources that respondents might be unable or unwilling to allocate (Malaviya and Sivakumar 1998). Resource deficiency could be overcome by asking people to justify their choice, a manipulation that appears to influence resource allocation (Tetlock and Boettger 1989; Tetlock et al. 1989). Respondents who are asked to justify choice, devote greater resources to the choice task and these resources prompt a more careful and thorough examination of the product data, and decrease reliance on heuristics and remove biases in decision making (Tetlock and Boettger 1989; Tetlock et al. 1989).

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Although both data and resource deficiency are implicated in the attraction effect, the findings in this article suggest that neither factor alone can fully account for the attraction effect. Instead, a consideration of both factors is necessary to explain these findings. When the available data is deficient in that it does not facilitate utility-based inferences, the allocation of greater resources increases the magnitude of the attraction effect. By contrast, when the available product information is not data deficient, increasing the allocated resources for the choice task diminishes the attraction effect. These observations are consistent with the findings reported by Tetlock and Boettger (1989). These authors found that as expected, accountability made people more receptive to diagnostic data and lead to better decisions. However, when people were presented with additional data that was non-diagnostic, accountability lead to decision making that was more biased. Respondents who were asked to be accountable for their decisions apparently took into consideration this non-diagnostic information more so than did respondents who were not asked to be accountable. These findings parallel those for the attraction effect, where presenting respondents with the irrelevant decoy alternative makes them consider this data, more so when they are asked to justify their choice. In turn, consideration of the decoy information increases bias in decisions and enhances the attraction effect. Thus, the conclusion one can draw about the effect of justification is that while it makes people more thorough decision-makers, it does not make them better decision-makers because justification does not help overcome data deficiencies (Simonson and Nye 1992; Tetlock and Boettger 1989). One issue that these findings still do not address is why do people consider irrelevant or non-diagnostic information in the first place. After all, if the decoy brand is completely dominated by the target, it should simply be ignored. Yet, it is not. The answer to this issue might lie in Grice’s norms of conversation (1975). Grice’s argument is that in most social interactions people assume that any information that is provided by another person is

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of some value. Why else would the other person go through the trouble of presenting that information, if it were not important to carrying out the conversation? It might be that experimental participants make a similar assumption, that the experimenter would not present information that is irrelevant to the experimental task (Hilton 1995). If such an assumption is made by the respondent and if this is part of the reason why biases such as the attraction effect creep into decision making, alerting the respondent to the presence of spurious information might alleviate their inclination to consider all the data that is presented and make them more discriminating decision makers. Future research is needed to examine this issue. Our findings also have several managerial implications. Stimulus meaningfulness is a variable under the control of the manager. Justification of choice could be construed as a factor under managerial control (by means of a commercial that rhetorically asks consumers “can you give us a single reason for preferring our competitor’s product?”) as well as an individual difference factor (some people might not care to adequately justify their decisions to themselves or to others because they are willing to cope with more uncertainty). Thus, our results have important implications for marketing strategy as well as for understanding differences between consumers. For a manager who is introducing a decoy for her (his) brand with a view to influencing customer choice, the strategy is to either give details of product attributes or create a situation that motivates the customer to justify the choice. Doing both will result in diminished attraction effect and would negate the very purpose of introducing the decoy. For the competitor, the strategy will be reversed. If a company introduces a decoy, the competitor could provide additional information about product attributes and induce consumers to justify their choices. This observation should alert managers to carefully assess the business conditions and the type of consumers they are likely to attract if the decoy brand is to produce a desirable effect on the target brand's market share.

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Alternatively, our findings suggest that as long as the consumer is willing to expend sufficient resources toward assessing the value of a brand and for comparing alternatives before making a decision, a brand need not worry about a competitor trying to seek perceptual advantage by introducing products that create a context conducive to an attraction effect.

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ENDNOTES

1

Justification of choice (Simonson 1989) and Accountability to decisions (Tetlock 1985) are

very similar constructs and have been used interchangeably in the literature (e.g., Simonson and Nye 1992). In both cases the decision maker is required to take into account how another person would accept his/her decision. To avoid confusion, only the term justification will be used here. 2

In addition to making the decision making process more complex and thorough, Tetlock et

al. (1989) find that accountability can make decision makers engage in two additional decision making strategies: conform to the views of the other person, if these are known with reasonable certainty; or bolster arguments for a position to which the decision maker is committed. Since in the experimental setting of a typical attraction effect study, the respondent does not have any insight into the experimenter’s views, nor is the respondent likely to be a priori committed to any particular choice option, the only possibility is that decision justification would prompt respondents to become more thorough and complex in their thinking and decision making. That is, when respondents are asked to justify their choice they should put in more effort in trying to identify an acceptable rationale for their decision. 3

The effect of stimulus meaningfulness and justification of choice on the attraction effect

was also analyzed for each category separately (Table 1). The findings were similar to that obtained with the aggregate data. However, the significance of the effects for each category was diminished due to the smaller sample sizes. Specifically, the stimulus meaningfulnessjustification of choice interaction for each category was as follows: (i) TV: (F1,222 = 4.16, p < .04); (ii) Orange juice: (F1,229 = 2.62, p < .10); (iii) Car: (F1,232 .35).

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Figure 1. Effect of Justification and Stimulus Meaningfulness on the Attraction effect

4.5 4

Attraction effect

3.5 3 Low stimulus meaningfulness

2.5 2

High stimulus meaningfulness

1.5 1 0.5 0 -0.5 No

Yes Justification

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Table 1. Attraction Effect: The Influence of Stimulus Meaningfulness and Justification _____________________________________________________________________ Not meaningful Meaningful No Justification Justification No Justification Justification _____________________________________________________________________ Overall Attraction Effect (%)

1.01

4.39

3.51

-0.16

The Attraction Effect for Individual Categories TV 2.04 5.62 Orange Juice 1.20 3.18 Car -0.18 4.45

5.36 4.30 0.92

-1.37 -0.96 1.78

Cell Size

181

175

169

170

_____________________________________________________________________

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