Providing Product Information Consumers Can Handle: Assortment and Type of Product Information in Mass Customization Environments

ABSTRACT - The strength of mass customization environments is their ability to offer large assortments. What assortments sizes are consumers still willing to search? What product information should be presented in such mass customization environments? Numerical or verbal representations are more appropriate to communicate product functionality, whereas pictorial representations are more appropriate to communicate product appearance. This study investigates these questions related to the amount and type of product information offered in mass customization environments. The results lead to implications for the design of mass customization environments.



Citation:

Niels Y. Vink and Jan P.L. Schoormans (2003) ,"Providing Product Information Consumers Can Handle: Assortment and Type of Product Information in Mass Customization Environments", in E - European Advances in Consumer Research Volume 6, eds. Darach Turley and Stephen Brown, Provo, UT : Association for Consumer Research, Pages: 76-81.

European Advances in Consumer Research Volume 6, 2003      Pages 76-81

PROVIDING PRODUCT INFORMATION CONSUMERS CAN HANDLE: ASSORTMENT AND TYPE OF PRODUCT INFORMATION IN MASS CUSTOMIZATION ENVIRONMENTS

Niels Y. Vink, Delft University of Technology, Holland

Jan P.L. Schoormans, Delft University of Technology, Holland

ABSTRACT -

The strength of mass customization environments is their ability to offer large assortments. What assortments sizes are consumers still willing to search? What product information should be presented in such mass customization environments? Numerical or verbal representations are more appropriate to communicate product functionality, whereas pictorial representations are more appropriate to communicate product appearance. This study investigates these questions related to the amount and type of product information offered in mass customization environments. The results lead to implications for the design of mass customization environments.

INTRODUCTION

Current technologies make the production of personalized products technically feasible (c.f. Lampel and Mintzberg 1996; Swaminathan 2001). Consumers show an increased interest in customizing the functionality and appearance of the products they purchase. The number of product variants that can be offered is enormous. Car dealers offer choices between different colors, types of engines, types of upholstery (for the seats and the interior), the types of wheel rims and a huge variety of other options. Consumers can choose all these options for the Volkswagen New Beetle, for example, potentially offering 10.000 different car models. Consumers may be overwhelmed by the large assortment of products that are offered through customization systems. Retailers cannot carry all these products in their assortment, because stores have limited space for showing products. In a retailer’s store, there is always limited shelf-space and the amount of different products that can be displayed is also limited. Mass customization environments are able to offer large assortments of products (Degeratu, Rangaswamy, and Wu 2000). A manufacturer can always add more products to the mass customization environment with minimal costs. Therefore, manufacturers increasingly offer products through product configurators on the Internet or at in-store computer systems. These systems allow consumers to customize products in an interactive manner by choosing preferred product characteristics and receiving direct feedback on these choices. How should these systems be designed? In this study we try to answer this question with respect to two properties of such a system: the size of the assortment (small versus large) and the type of product information (functionality versus appearance). These properties may have major consequences for the way consumers process product information (e.g. Bettman, 1979; Bettman, Luce and Payne 1998) in mass customization environments.

SIZE OF THE ASSORTMENT

The number of alternatives that is offered in mass customization environments is also called the product assortment. Various researchers have investigated the effects of increasing the size of the assortment on the way consumers process product information. An increase in assortment size generally leads to (1) an increase in the variability of the choices, (2) a decrease in the quality of choices, and (3) an increase in consumers’ confidence in their decisions (Payne, 1982; Payne, Bettman and Johnson 1993). Furthermore, the size of the assortment positively influences the amount of search (Schmidt and Spreng 1996; Srinivasan and Ratchford 1991). However, most of the assortments in these studies were limited in range. Also, no research has directly investigated the search effort that consumers invest when the size of the assortment increases.

The effort that consumers invest in searching for information in mass customization environments often consists out of two phases. In the first phase, consumers have to invest effort in finding, starting and learning the system. In the second phase, consumers search and interpret the product information. We concentrate on the second phase of this search process. Consumers can search the mass customization system until they find a product they like. However, the more products there are available for the consumer, the more effort is needed to interpret all this product information. Searching all the information when the assortment is large, takes more effort than searching all the information when the assortment is small. More effort is needed when consumers search a large assortment of products compared to a small assortment of products. In a retailer’s showroom, for example, consumers are likely to spend more effort on evaluating cars when there are two cars on display compared to one. Thus, when the assortment increases, consumers are more likely to invest effort into their search process. This indicates a linear relation between the size of the assortment and the amount of effort. On the other hand, Olshavsky (1979) found that increasing the product information by adding characteristics to the alternatives, lead to more selective processing of characteristics. Thus, consumers actually invest less additional effort when the amount of product information increases. This suggests an inverted-U-shape relation between the size of the assortment and the amount of effort. For small assortments, consumers invest increasing effort to be able to search all products in the assortment. However, when the assortment becomes large, consumers cannot search all products and they are willing to invest a limited amount of effort. Eventually, when assortments become really large, consumers are not willing to search the assortment at all, because searching such large assortments take too much effort. Because manufacturers can offer almost unlimited assortments in mass customization environments, they need to know the boundary conditions for the size of the assortment. It could be disastrous for manufacturers when they are offering too much information, because consumers do not invest effort in using the system, i.e. they do not use the system at all! Thus, consumers spend more effort when the assortment increases, but not proportionally more. Consumers are not likely, for example, to spend four times as much effort on evaluating 100 cars compared to 25 cars. The amount of effort invested per product thus decreases when the size of the assortment increases. We hypothesize a linear relationship between the relative effort (effort per product) and the size of the assortment; relative effort is large when the assortment is small and relative effort is small when the assortment is large. This leads to the first hypothesis.

H1. In a mass customization environment, consumers invest less effort per product when choosing from a large assortment of products versus choosing from a small assortment of products.

The effort that consumers are willing to invest in searching an assortment also depends on the uncertainty they experience. Moorthy, Ratchford, and Talukdar (1997) argued that uncertainty determines whether search takes place. They distinguished between two types of uncertainty: individual product uncertainty, which is the uncertainty about what a specific product offers and relative product uncertainty, which is uncertainty about whether the current product is the best available. The product uncertainty is very subjective. Consumers may believe, for example, that a certain product is the best product available, when in fact this is not the case. Individual product uncertainty of a chosen alternative is expected to be the same because consumers will gather the same amount of information on a product they are about to choose regardless the assortment size. Consumers try to reduce the relative product uncertainty before making a choice. However, when the assortment size increases, even this may take too much effort. Relative product uncertainty about large assortments of products may therefore be relatively greater compared to small assortments.

H2. In a mass customization environment, consumers experience more relative product uncertainty when choosing from a large assortment of products than when compared to choosing from a small assortment of products.

TYPE OF PRODUCT INFORMATION

Not only the size of the assortment, but also the type o product information may affect the effort and uncertainty in mass customization environments. Consumers searching for products, are often interested in both the functionality and the appearance of these products. Thus, product configurators should provide information about both the functionality of the products as well as the appearance of the products. Every product configurator will to some extent offer product information about functionality and appearance, either by numerical, verbal, or pictorial representation. There is evidence that numerical and verbal representations are more appropriate to communicate product functionality, whereas pictorial representations are more appropriate to communicate product appearance (Loosschilder, 1998). The number of pre-programmable telephone numbers for a telephone, for example, is more appropriately communicated by numerical representations, whereas the product appearance of a telephone is more appropriately communicated by pictorial representations. In the current study, functions will be represented numerically or verbally, whereas appearance will be represented pictorially.

Consumers may have to invest less effort in searching appearance related characteristics than searching functionality related characteristics. A pictorial representation tends to be perceived in an imagery system, whereas a verbal representation is received in an independent but interconnected verbal system (Holbrook and Moore 1981; Paivio 1991). Treisman (1986) argues that processing of images is primary to the processing of words. This suggests that processing of pictorial representations of product characteristics may be less effortful than processing of verbal representation of product characteristics. Thus, searching through assortments of products with characteristics represented verbally takes more effort than searching through assortments of products with characteristics represented pictorially. Searching through pictorially represented product characteristics should take less effort than searching through verbally represented product characteristics.

This is not to say that manufacturers should only provide pictorial representations of the product, just because they take less effort to process. After all, verbal representations allow evaluations of the product functionality, whereas pictorial representations allow evaluations of the product appearance. Pictorial representations often do not allow deduction of the product functionalities and verbal representations often do not allow deduction of the product appearance. Therefore, manufacturers should offer both verbal and pictorial information. In many situations all this product information cannot be displayed on one computer screen. How can the product functionalities and the product appearances of 36 products be presented on one computer screen? In such cases manufacturers have various options. Two of these options are (1) to have consumers choose the functionality first and show the appearance later and (2) to have consumers choose the appearance first and show the functionality later. It is hypothesized that it takes consumers less effort to evaluate the pictorial representations of the product appearance first compared to evaluate the verbal representations of the product functionality first. In that case, it makes sense to show consumers the pictorial representations of the products first. This leads to the third hypothesis:

H3. In mass customization environments, consumers invest more cognitive effort when evaluating the functionality of the product first, compared to evaluating appearance of the product first.

Consumers will try to reduce the (individual and relative) product uncertainty before choosing a product. Verbal representations are more likely to reduce uncertainty regarding the product functionality, whereas pictorial representations are more likely to reduce uncertainty regarding the product appearance. This applies to both individual and relative product uncertainty.

Product functions, such a the number of pre-programmable telephone numbers, are more easily conveyed by verbal than by pictorial representations. Verbal representations are often able to represent the product functions more precise and less ambiguous, especially when the functions cannot easily be deduced from the product appearance (such as with the number of pre-programmable telephone numbers). Verbal representations are therefore more likely to reduce the uncertainty of functions than pictorial representations. Verbal representations will provide precise and unambiguous information about the number of pre-programmable telephone numbers, for example. This makes verbal representation more likely to reduce both individual product uncertainty (how many pre-programmable telephone numbers has a certain telephone) as well as relative product uncertainty (which telephone has the most pre-programmable telephone numbers).

Conversely, characteristics related to product appearance, such as the color of telephone, are more easily conveyed by pictorial than by verbal representations. Pictorial representations are often able to represent the product appearance less ambiguously, but not necessarily more precise. When a telephone is grey, for example, what shade of grey is it? Is it metallic? A pictorial representation would resolve these questions in a glance. However, verbal representations of product appearance can often be more precise than pictorial representations. By a verbal representation in a hexadecimal system (for example #666666), the precise color out of 16 million different colors can be designated, whereas the human eye can distinguish far fewer colors. In many cases, verbal representations are able to define characteristics related to appearance more precise than pictorial representations. This does not mean that verbal representations of either product functionality or appearance may be more likely to reduce uncertainty than pictorial representations.

Remember though, that pictorial representations may at times be able to define product appearance less ambiguously. Manufacturers therefore should offer product information related to both product functionality as well as product appearance. When consumers are evaluating the verbally represented functionality first compared to evaluating pictorially represented appearance first, they are less likely to experience both individual product uncertainty (what color is this telephone) and relative product uncertainty (which telephone has a specific color). Thus,

H4. In a mass customization environment, consumers evaluating product functionality represented by verbal representations first compared to consumers evaluating product appearance represented by pictorial representations first, will be less likely to experience:

a. individual product uncertainty

b. relative product uncertainty

METHOD

To investigate these four hypotheses an empirical experiment was conducted. A randomized 2 x 2 factorial design was used with assortment (small assortment condition, large assortment condition) and type of product information (functionality-first condition, appearance-first condition) as independent variables.

Stimuli

The product chosen for the experiment was a telephone for home use. The Internet was used to acquire a realistic set of telephone functionality and appearance. Four functional characteristics were selected: model, price, color, and number of pre-programmable telephone numbers. The corresponding appearance of the product was represented by a picture. Respondents used a matrix of telephone characteristics on a computer, that was especially designed and programmed for the experiment.

Respondents

Ninety-eight students (58 male and 40 female) participated in the study. On average it took the respondents 6:30 minutes to complete the experiment.

Design

To test hypotheses 1 and 2, a small and large assortment of telephones was created. Respondents in the small assortment condition had the choice of 9 telephones, whereas the respondents in the large assortment condition had the choice of 36 telephones.

To test hypotheses 3 and 4, two conditions for the type of product information were created. In the functionality-first condition respondents were initially provided with the functionality of the telephones. They could click on the functionality of the telephones to investigate the appearance. In the appearance-first condition, respondents received a matrix with the product appearances. These respondents could click on the product appearance to investigate the functionality.

Procedure

Prior to the experiment all functions were explained to the respondents. They were explained how to operate the matrix; clicking on a cell in the matrix would reveal additional information (about either functionality or product appearance) in the upper left corner of the screen. The respondents were instructed to choose a telephone as if they needed a new one. They were told to search as much as they liked. When they had found the telephone they wanted, they choose this telephone by clicking the button "confirm choice" in the upper left corner of the screen. Next, respondents completed the dependent measures. After the respondents had completed the questionnaire, they indicated their gender and age, and were informed about the purpose of the study.

Measures

All questions were measured using seven-point-scales.

Control Measures

1. Assortment. To check whether respondents in the large assortment condition did indeed view the assortment larger than the respondents in the small assortment condition, respondents indicated the perceived size of the assortment. The perceived size of the assortment was measured using one item with anchors ranging from "Very small" (1) to "Very large" (7).

2. Characteristics’ importance. In order to determine whether respondents in the functionality-first condition placed different weights on the products characteristics than respondents in the appearance-first condition, they indicated to what extent their choice was determined by the provided product information with anchors ranging from "Not al all"(1) to "Completely" (7).

Dependent measures

1. Effort. Effort was measured in three ways. Next to the search time and the number of mouse clicks (Johnson and Payne 1985), the perceived effort was measured.

a. The search time was recorded from the moment a respondent started to search the matrix until the "confirm choice" button was clicked.

b. Respondents could click on different boxes to see additional information (about either functionality or appearance of the product). Each mouse click was recorded up until the respondent clicked "confirm choice."

c. Three questions measured the perceived effort, with anchors ranging from "Not a difficult choice" (1) to "Very difficult choice" (7), "Not a fast choice" (1) to "Very fast choice" (7), and "Not an easy choice" (1) to "Very easy choice" (7).

2. Uncertainty. Both individual and relative product uncertainty were measured.

a. Individual product uncertainty was measured with three items, with anchors ranging from "Don’t know much about the product"(1) to "Know very much about the product" (7), "Don’t know enough to decide whether to buy the product" (1) to "Know enough to decide whether to buy the product", and "Don’t need additional information" (1) to "Need additional information" (7). These three items were averaged to form an individual uncertainty measure (a=.70).

b. Relative product uncertainty was measured with three items with anchors ranging from "Very uncertain" (1) to "Very certain"(7) about the best product, best functionality, and best appearance. The reliability of the relative product uncertainty items was low (a=.58). Respondents apparently found the telephone a rather functional product, because the items about the best overall product and the best product functionality correlated (a=.69). Therefore, these two items were averaged to form a relative product uncertainty measure.

RESULTS

Outlier Analysis

The data was checked on any outliers. Some respondents invested a disproportional amount of time into the task. These respondents were removed from the sample using outlier analysis, because their disproportional influence would distort the results. All respondents that had a number of clicks more than three standard deviations from the mean were removed from the sample (Hair et al. 1995, p. 59). The same procedure was followed for search time. Two iterations resulted in a sample of 93 respondents (61% male and 39% female).

Control measures

Assortment. The assortment was varied to create a small assortment condition and a large assortment condition. To determine whether this manipulation had succeeded, respondents indicated the perceived size of the assortment. An analysis of variance with perceived size of the assortment as dependent variable and assortment size as independent variable revealed that respondents in the small assortment condition perceived a smaller size of the assortment than the respondents in the large assortment condition (F(1,92)=61.4, p<.001; M=2.9 versus 4.8). These results indicated that the manipulation of assortment size was successful.

Characteristics’ importance. Respondents indicated to what extend the characteristics were important in their decision-making process. Paired sample T-tests were used to determine whether the differences in the degree of importance were significantly different from each other. All pairs were significant different (all t(92)>|2.1|, p<.05), except for the model-price pair (t(92)=-.80, ns) and the appearance-price pair (t (92)=-1.9, ns). Thus, respondents’ choices were largely determined on the model, appearance, and price of the telephone and hardly on the color, number of pre-programmable telephone numbers, and the name of the telephone. Six two-way ANOVAs revealed that there were no differences in the characteristics’ importance between assortment conditions or type of product information conditions (All F(1,89)<.77, ns) and no significant interaction effects (All F(1,89)<2.6, ns).

Dependent measures

To provide tests for all hypotheses the data were analyzed by separate 2 x 2 analyses of variance with between subject-factors of assortment and product information type [No significant interaction effect were found in any of the analyses of variance (All F(1,89)<3.1, ns) and hence no interaction effects are reported.].

Effort. Three measures of effort were taken: (1) the number of mouse clicks, (2) the search time, and (3) the perceived effort. Table 1 shows the number of mouse clicks for each condition. Respondents clicked almost twice as much when the assortment was large versus small (F(1,89)=18.1, p<.001). Thus, respondents clicked more when the assortment is large compared to small. To investigate hypothesis 1, the relative effort for each condition was investigated, i.e. the amount of mouse clicks per telephone. The number of mouse clicks per telephone is smaller when the assortment is large compared to small. Respondents in the large assortment condition clicked 0.8 times per telephone, whereas respondents in the small assortment condition clicked 2 times per telephone (F(1,89)=32.4, p<.001). Thus, large assortments lead to relatively less mouse clicks compared to small assortments, providing support for hypothesis 1. The type of product information did not influence the number of clicks (F(1,89)=.002, ns). Thus no support for hypothesis 3 was found.

The search time for each condition is also shown in table 1. Respondents searched significantly longer in the large assortment condition than in the small assortment condition (F(1, 89)=21.7, p<.001). To investigate hypothesis 1, the relative effort for each condition was investigated, i.e. the amount of time spent per telephone. Respondents in the large assortment condition search 5 seconds per telephone, whereas respondents in the small assortment condition search 14 seconds per telephone (F(1,89)=69.4, p<.001). Thus, large assortments lead to relatively less search time compared to small assortments, which provides support for hypothesis 1. Also the type of product information affected the search time. Respondents in the functionality-first condition searched longer than respondents in the appearance-first condition (F(1,89)=5.3, p<.05). Per information unit respondents spent 10s per telephone in the functionality-first condition, whereas they spent 9s per telephone in the appearance-first condition. This provides support for hypothesis 3.

The results for perceived effort are presented in table 1. The analyses of variances revealed no significant results of assortment and type of product information (All F(1,89)<3.2, ns). Thus, all respondents perceived the same effort regardless the condition. Thus the results for perceived effort do not support hypotheses 1 and 3.

Uncertainty. To find support for hypothesis 2, the uncertainty measures were analyzed. The results for both relative product uncertainty and individual product uncertainty are presented in table 2. No significant effects of assortment size on individual product uncertainty were found (F(1,89)=.30, ns). The assortment size did not influence the relative product uncertainty either (F(1,89)=1.3, ns). Respondents in the small assortment condition were as uncertain they had chosen the best alternative as respondents in the large assortment condition. Thus, no support for hypothesis 2 was found.

Next the effects of type of product information on uncertainty were analyzed. No significant differences between type-of-product-information conditions on individual product uncertainty were found (F(1,89)=2.5, ns). This means that all respondents indicated they had as much information bout the products. Respondents in the functionality-first condition did not perceive more information than respondents in the appearance-first condition. Thus, no support for hypothesis 4a was found. However, significant effects of type of product information on relative product uncertainty were found. Respondents in the functionality-first condition were less uncertain they had chosen the best alternative than respondents in the appearance-first condition (F(1,89)=4.4, p<.05). Thus presenting appearance related characteristics first leads to more relative product uncertainty than presenting functionality related characteristics first. These results provide support for hypothesis 4b.

DISCUSSION AND CONCLUSIONS

Limitations

In the current study, respondents considered product information related to functionality and product appearance. However, four functional product characteristics could be considered versus one characteristic related to product appearance. The amount of effort invested could depend on the fact that respondents considered more functional characteristics than appearance related characteristics. Furthermore, these multiple (verbal) characteristics could also have lead to more confidence about the choice of a telephone. Therefore, further research should investigate which product characteristics are used, when the number of characteristics related to functionality and appearance are balanced.

Furthermore, the current study showed that increasing the size of the assortment influenced effort and uncertainty. The respondents in the large assortment condition had more product information available, because the assortment size was larger then the assortment in the small assortment condition. Are effort and uncertainty only influenced by a change in the number of products offered, or are these variables also affected by the amount of information when the number of products is held constant?

TABLE 1

MEANS FOR RESPONDENTS= EFFORT IN SEARCHING FOR A TELEPHONE

TABLE 2

RESPONDENTS= UNCERTAINTY IN SEARCHING FOR A TELEPHONE

Respondents

Students of industrial design engineering served as respondents in the experiment. There are two requirements for respondents participating in studies concerning mass customisation. First, they need certain computer skills, since the studies involve computer handling. Second, respondents need to be interested in product customization. If they are not interested in product customization, the validity of their customization choices is doubtful. These two requirements can be found in the student sample that was used. Students of industrial design engineering have learned more computer skills than are required to operate and navigate through product configurators. Furthermore, their interest in customization was high, because they have a preference for unique and personalized products. An additional advantage of using students industrial design engineering is their open mindedness towards new ideas and technologies. Mass customization is still a rather new idea for consumers and a new technology for manufactures. New ideas as such often encounter resistance, which negatively affects the validity of results. Because students industrial design engineering are trained to develop new ideas, they are less likely to resist such new ways of acquiring products, which adds to the validity of the results. In conclusion, the results can be generalized to consumers that have the skills to operate computers and are interested in product customization.

Type of product information

Respondents that were initially provided with product functionality use more time choosing a product than respondents initially provided with product appearance, but did not click more. Respondents in both conditions were able to use both product information types. Apparently, the difference in accessibility of the information lead respondents to use the product information types initially provided (c.f. Bettman and Kakkar 1977). Thus, respondents that initially received characteristics related to functionality seem to use these representations to choose a product. Respondents that initially received characteristics related to product appearance seem to use these representations to choose a product. However, all respondents based their choices on the same characteristics regardless of the information they received initially. That is, respondents based their choices largely on the model, appearance, and price of the telephone and hardly on the color, number of pre-programmable telephone numbers, and the name of the telephone. This suggests that respondents used both product information types in their choice processes. Respondents seem to use the information initially provided to select the products that look promising and then look at the additional information. Initially providing functional product information leads to more search time compared to initially providing appearance related product information, but not to more mouse clicks. Respondents do indeed seem to invest more effort when using functional characteristics to choose products compared using appearance related characteristics of the product, but they perceive the same amount of effort.

When appearance related information is shown in mass customization environments, consumers choose products more easily, compared with when functional characteristics are shown. However, consumers will also be more uncertain about whether they have chosen the product that is best for them. When the functional product characteristics are presented instead of appearance related characteristics, consumers have more difficulty choosing products, but they are less uncertain about whether they have chosen the product that is best for them. In short, presenting appearance does not demand a lot of effort, but leaves the consumer with relative product uncertainty. Presenting functionality, on the other hand, demands effort, but does not leave the consumer with relative product uncertainty.

Amount of product information

How much information should manufacturers offer to consumers in mass customization environments? When consumers are confronted with small assortments, consumers may get bored and they stop searching the product configurator. On the other hand, when consumers are confronted with large assortments, they have to invest a lot of effort to search this assortment. Consumers may get tired of searching and they will stop searching as well. Thus, the number of products offered through product configurators should be balanced. Consumers may be willing to search through these systems, but they want to invest no more than a certain amount of effort. Respondents perceived an equal investment in searching the small assortment or the large assortment. This suggests that consumers may want to invest effort in searching an assortment until the perceived effort reaches a certain level. Thus, consumers may want to invest more effort in an objective sense (e.g. mouse clicks and time), but want the perceived effort to remain the same. They might have some maximum perceived effort they want to invest in searching through mass customization environments. Thus, manufacturers should either offer a balanced assortment of products or make the mass customization environment easier to search in order to keep the perceived level of effort below this maximum level. Further research should focus on the issue of perceived effort, because determining the threshold level seems essential in providing managers with guidelines for the size and nature of the assortment provided.

CONCLUSION

Offering consumers the possibility to customize products in store allows for larger assortments, because sales personnel will be able to reduce consumers’ effort by offering assistance with the customization process. Retailers can use product configurators to increase their assortment without increasing stock. With help from retailers, consumers can choose from more variety without increasing effort. Therefore, implementing product configurators in store would be beneficial to manufacturers, retailers, and consumers.

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Authors

Niels Y. Vink, Delft University of Technology, Holland
Jan P.L. Schoormans, Delft University of Technology, Holland



Volume

E - European Advances in Consumer Research Volume 6 | 2003



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