Product Category Dependent Consumer Preferences for Online and Offline Shopping Features and Their Influence on MultiChannel Retail Alliances

Abstract

This paper addresses the question of how to combine online and offline services in the most complementary way for different product classes. In a series of surveys conducted for Experiment 1 it was determined that consumers' preferences for online and offline services differ for different products at different stages of th e shopping experience. These differences were accounted for by a model that weights the importance of different attributes for different products and assigns different values to these attributes depending on whether they are better served online or offline. For example, for products like clothing consumers place great value on the ability to touch and inspect the product and thus they prefer offline, bricks-and-mortar services at each stage of the shopping experience. By contrast, for products like computer software consumers place great value on the rapid dissemination of large amounts of information through Internet search, but many are concerned about speedy delivery and no- hassle exchange which leads them to make their final purchases offline. Experiment 2 was a controlled test of a particular marketing strategy for capitalizing on the complementarity of online and offline services: alliances between online and offline brands. Confirming the operation of both assimilation and complementarity effects, it was found that the images of both brands could be improved with such alliances. Other marketing strategies were also discussed.

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PRODUCT CATEGORY DEPENDENT CONSUMER PREFERENCES FOR ONLINE
AND OFFLINE SHOPPING FEATURES AND THEIR INFLUENCE ON MULTI-
CHANNEL RETAIL ALLIANCES
Aron M. Levin
Northern Kentucky University
levina@nku.edu
Irwin P. Levin
University of Iowa
irwin-levin@uiowa.edu
C. Edward Heath
Xavier University
heathc@xu.edu
ABSTRACT
This paper addresses the question of how to combine online and offline services in the most complementary
way for different product classes. In a series of surveys conducted for Experiment 1 it was determined that
consumers’ preferences for online and offline services differ for different products at different stages of the
shopping experience. These differences were accounted for by a model that weights the importance of different
attributes for different products and assigns different values to these attributes depending on whether they are better
served online or offline. For example, for products like clothing consumers place great value on the ability to touch
and inspect the product and thus they prefer offline, bricks-and-mortar services at each stage of the shopping
experience. By contrast, for products like computer software consumers place great value on the rapid dissemination
of large amounts of information through Internet search, but many are concerned about speedy delivery and no-
hassle exchange which leads them to make their final purchases offline. Experiment 2 was a controlled test of a
particular marketing strategy for capitalizing on the complementarity of online and offline services: alliances
between online and offline brands. Confirming the operation of both assimilation and complementarity effects, it
was found that the images of both brands could be improved with such alliances. Other marketing strategies were
also discussed.
1. Introduction
Within the wake of online shopping’s exponential growth, many advantages and some perceived disadvantages
of shopping online as compared to shopping offline at traditional bricks-and-mortar stores have become apparent.
Among the advantages are rapid and extensive display of information, and ease of comparison between the attributes
of different brands. On the other hand, lack of personal service, inability to inspect or handle the product, and
concern about delivery and exchange processes including giving out credit card numbers over the Internet have been
realized as perceived disadvantages. We propose that the relative advantages and disadvantages of shopping online
and offline will play out differently for different types of products, at least in the mind of the consumer.
The relative salience of such favorable and unfavorable features when comparing online and offline shopping
options undoubtedly varies across products, consumers, and situations. For example, “high-touch” products such as
clothing and “low-touch” products such as airline tickets clearly differ in this regard. “High-touch” products are
those that the consumer requires the ability to touch or experience before buying (Lynch, Kent, and Srinivasan,
2001). In contrast, “low-touch” products are those that are standardized and do not require inspection to evaluate
quality. Other products may fall at different points on the continuum. A similar distinction has been made by Chiang
and Dholakia (2003). They define “search goods” as those for which full information on dominant attributes can be
known prior to purchase (e.g., books) and “experience goods” as those for which direct experience is necessary (e.g.,
perfume). They find that online shopping intention is higher for search goods than for experience goods.
The present paper develops this theme by comparing consumer perceptions of shopping online versus shopping
offline for different products at different stages of the shopping experience. The ability to touch or feel the product is
but one of a number of features that drive the decision of whether to use online or offline sources at each of the

Levin et al.: Consumer Preferences for Online and Offline Shopping Features and Retail Alliances
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various stages. We believe that consumer needs for online and offline services vary predictably across products that
emphasize different features such as large selection, personal service and speedy delivery, as well as the ability to
see, touch, or try the product. In this paper we develop specific models and hypotheses that focus on the potential
complementarity of online and offline services as they affect shopping preferences for different products and
reactions to marketing strategies that attempt to capitalize on complementary features.
2. Specific Research Objectives
This study has two major objectives for understanding online/offline complementarity: 1) to determine the
factors that lead to differential preference for online and offline services at different stages of the shopping
experience for different products; and 2) to determine how alliances between online and offline brands impact brand
images. These two objectives are met through the use of a two-part study.
In Experiment 1 participants were asked to indicate their preferences for online and offline sources during the
search, compare and final purchase stages of shopping for different products. They were also asked to rate the
importance of different shopping attributes for each product, such as large number of selections, shopping
enjoyment, friendly service and no-hassle exchanges; and they were asked to indicate the extent to which each
attribute for each product is better provided online or offline. We then used these data to develop a model for
describing online/offline preferences at the attribute level.
Experiment 2 manipulated online/offline brand alliances and examined their impact on brand images. Some
participants were given a series of hypothetical alliances between online and offline brands and were asked to rate
each component of the alliance while other participants were asked only to rate individual brands. Comparison of
brand ratings in the Alliance and Control conditions allowed us to focus on the effects of online-offline alliances on
images of the brands comprising the alliance. Specific hypotheses were developed for Experiment 2 based on
models of assimilation/contrast and complementarity effects. To preview these hypotheses, we quote from a new
book on Consumer Behavior (Hawkins, Best, and Coney, 2004, p. 299), “Co-branding has been shown to modify
attitudes toward the participating brands. However, the effects can be positive or negative and can differ for the two
brands involved. Thus, a firm considering co-branding should be sure that its target market views the potential
partner positively and that the two brands fit together in a way that adds value.” We extend the study of the transfer
of affect across brands engaging in an alliance to the case of online-offline brand alliances.
3. Experiment One
In order to get a better understanding of the process linking the perceptions of individual attributes to overall
online and offline shopping preferences, we used a series of surveys in Experiment 1 to develop and test a simple
model of the information integration process. The model is based on Anderson’s (1981) averaging model of
information integration and states that the overall tendency to prefer shopping online or offline for a given product is
a weighted average of the values of the individual attributes comprising the product, as indicated in the following
equation:
R
on/off, p
= Σw
i
× v
ip
/Σw
i
,
Here R
on/off, p
is the overall extent to which shopping online or offline is preferred for product p, w
i
is the
importance of attribute i, and v
ip
is the rating of attribute i for product p on a scale ranging from “Shopping online is
much better” to “Shopping offline is much better.” This model is tested separately for different stages of the
shopping experience. Models of this form have been useful in describing consumers’ evaluation of product bundles
(Gaeth, Levin, Chakraborty, & Levin, 1990) as well as other forms of consumer behavior (Troutman & Shanteau,
1976). The gist of this model as applied to online and offline shopping preferences is that such preferences are
driven by consumer perceptions of whether important features of the shopping experience are better delivered online
or offline for a particular product.
3.1 Method
A multi-part survey was administered to a sample of 40 undergraduate Marketing students at a large midwestern
university. Products were chosen to be appropriate to this group and to represent a range of online and offline
shopping experiences: Airline Tickets, Books, CDs, Clothing, Computer Software, Electronic Products, Health and
Grooming Products, and Sporting Goods.
In Part 1, for each of the eight types of products, respondents were asked to consider the following steps in the
shopping process: search for options, compare options, and make a purchase. For each step for each product, the
respondents were asked to indicate whether they would prefer to complete that step online or offline. In Part 2,
respondents were asked to rate each of a set of key shopping attributes on a scale of 1 to 10 where 1 corresponds to

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“Shopping offline is much better” and 10 corresponds to “Shopping online is much better.” The attributes were
“shopping enjoyment,” “quickness of shopping,” “selection,” “price,” “tactile investigation of the product,”
“personal service,” “speed of delivery,” and “product exchange.” In Part 3, respondents were asked to rate on a scale
of 1 to 10 how important each of these attributes is for each of the eight products.
3.2 Results and Discussion
Survey responses. Results for Parts 1-3 of the survey are summarized in Tables 1-3, respectively. From Table 1
it can be seen that for about half the product categories in Part 1, online methods are preferred over offline for the
search and compare steps. Offline is greatly preferred over online for the final purchase step for most product
categories. Thus, in many circumstances, consumers would prefer to log on to the Internet to look at their possible
choices, compare those choices on their various features, but prefer to make the final purchase at a retail location.
Table 1. Online/Offline Shopping Preferences at Each Step for Each Product (Data are % who prefer online)
Airline
Tickets
Books
CDs
Clothing
Computer
Software
Electronic
Products
Health &
Grooming
Sporting
Goods
Search 92.5 50.0 55.0 22.5 80.0 50.0 12.5 30.0
Compare 95.0 47.5 37.5 15.0 77.5 52.5 12.5 20.0
Purchase 52.5 12.5 12.5 5.0 42.5 12.5 2.5 5.0
Different preference patterns emerged across products and these seemed to fall into several clusters. For
Clothing, Health and Grooming Products, and Sporting Goods, respondents preferred using offline sources for every
step in the shopping process, especially the final purchase step. When respondents considered the purchase of both
Airline Tickets and Computer Software, there was a strong preference for searching for and comparing options
online, but there was about equal preference for purchasing online or offline. Books, CDs, and Electronic Products
elicited a slightly different response. There was about equal preference for online and offline search and compare
processes, but there was strong preference for offline purchasing.
In Part 2 of the survey, respondents were asked to rate the extent to which they think shopping online or
shopping offline is better on each of a number of features. Table 2 shows that consumers see online shopping
sources as better for shopping quickly and having a large number of selections. Consumers believe that it is quicker
to shop online than it is to visit a physical retailer and that they have access to more products with a greater range of
features online. In addition, online shopping was perceived to be the source for the best prices. Considering that
most online retailers use an aggressive low price strategy to draw customers to their Web sites to shop, this result
shows that this strategy is working.
Table 2. Mean Ratings of Extent to Which Online or Offline is Better for Each Attribute
See-touch-handle: 1.25
Personal service: 2.25
Enjoy shopping: 2.88
No-hassle exchange: 3.05
Speedy delivery: 4.00
Offline
better
Best price: 6.35
Large selection: 7.35
Shop quickly: 8.23
Online
better
Note: Data are on a (1 to 10) scale where 1 = “Shopping offline
is much better” and 10 = “Shopping online is much better.”
Offline shopping sources rated higher for enjoying the shopping experience, being able to see-touch-handle the
product, personal service, no-hassle exchange, and receiving speedy delivery. This emphasizes the importance of the
physical aspects of the shopping experience and the strengths of offline retailers in providing these services. The

Levin et al.: Consumer Preferences for Online and Offline Shopping Features and Retail Alliances
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finding concerning enjoyment of the shopping experience shows that online shopping falls short of offline shopping
in creating an enjoyable experience.
Having established that, for at least most of the attributes, there was clear preference for one source over the
other, we now turn to the issue of which of these attributes is considered more important for different products. In
Part 3 of the survey, respondents rated the importance of each feature for each of the different product categories.
Table 3 shows that for every product category presented, price, selection, speedy delivery, and no-hassle exchange
were rated as being important. Regardless of the product type, consumers saw low prices, a large varied selection,
fast delivery, and the right to exchange or return the product as being very important to the shopping experience.
The ability to see-touch-handle the product is especially important for Clothing, Electronic Products, Sporting
Goods, Books, and Health and Grooming Products. Personal service is especially important for Clothing, Computer
Software, and Sporting Goods. Having a large number of selections is especially important for Clothing, Books,
Electronic Products, and Sporting Goods. Enjoying the shopping experience is more important for Clothing than for
any other product category.
Table 3. Mean Attribute Importance Ratings (1-10 scale) for Each Product
Enjoy
Shopping
Shop
Quickly
Large
Selection
Best Price
See-Touch-
Handle
Personal
Service
Speedy
Delivery
No-hassle
Exchange
Airline Tickets 3.70 7.23 7.10 9.05 2.23 6.10 7.05 7.63
Books 6.00 5.05 8.30 7.68 7.30 6.33 7.63 8.05
CDs 5.78 5.58 8.93 9.03 6.25 5.70 7.83 7.98
Clothing 7.25 5.60 8.50 8.15 8.60 7.40 7.23 8.98
Computer Software 4.18 6.60 7.40 8.55 4.43 7.43 7.00 8.33
Electronic Products 5.40 5.60 8.28 8.78 8.15 7.40 7.18 8.43
Health & Grooming 4.30 6.43 7.30 7.63 7.30 6.35 6.83 7.18
Sporting Goods 5.70 5.33 8.18 8.63 7.73 6.80 6.93 8.40
Model tests. The weighted average model uses data from Tables 2 and 3 to explain the data from Table 1 and
was applied separately to the search and purchase stages. (It was assumed that the “compare” stage could be
explained by the same factors as the “search” stage.) A priori judgments were made about which particular attributes
apply to the search stage and which to the purchase stage. “Shop quickly,” “large selection,” “enjoy shopping,” “see-
touch-handle,” and “personal service” were considered to be “search attributes.” “Best price,” “speedy delivery,”
and “no-hassle exchange” were considered to be “purchase attributes.” For each product at each shopping stage, the
mean attribute values from Table 2 were multiplied by the mean attribute importance weights from Table 3 and the
results were averaged across relevant attributes. The resultant values were then rank-ordered to predict the relative
frequency of online preferences for the different products at each stage. (The category of “health and grooming”
products was deleted from this analysis because, in retrospect, it was an anomaly. Whereas “shop quickly” and
“large selection” are generally associated with online services in Table 2, this would not be the case for this category
where large selections are readily displayed at the store.)
The predicted product ordering for preferring to search online is: Airline Tickets, Computer Software, CDs,
Books, Sporting Goods, Electronic Products, and Clothing. The predicted product ordering for preferring to make
final purchases online is: Airline Tickets, CDs, Computer Software/Sporting Goods (tie), Electronic Products,
Books, Clothing. These predicted orderings conform closely with the data reported in Table 1. The rank-difference
correlation between predicted and observed rankings was .94 for the search stage and .63 for the purchase stage. The
lower value in the latter case was due to the fact that the observed values in Table 1 are very similar for several
products so that small absolute discrepancies affect rank orderings. When the model was applied to the percentages

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in Table 1, rather than to product orderings, the correlations were .94 and .75, respectively, for the search and
purchase stages. The differential weighting of attributes that are better served online or offline for different products
appears to represent a good approximation to the process by which overall preferences for online and offline
services are formed.
Product groupings. Based on these analyses, the following product categorizations can be made. For “High-
touch” products, like Clothing, Sporting Goods, and Health and Grooming Products, traditional bricks-and-mortar
shopping methods are preferred because of the special importance of being able to personally handle and inspect the
product before purchasing. “Low-touch” products like Airline Tickets and Computer Software are products that
generally require online services because of the importance placed on shopping quickly; however, some consumers
desire personal service before making the final purchase. Books, CDs, and Electronic Products appear to be “mixed”
in that large selection is important but shopping quickly is not so important, while personal service is desired by at
least some consumers.
4. Experiment Two
Experiment 1 showed that different attributes distinguish preferences for online and offline shopping for
different products. One way to build onto these findings is to show how alliances between online and offline brands
can capitalize on the combination of favorable online and offline features. Experiment 2 investigated this. The
formation of alliances between online and offline brands has the potential of complementing the advantages of both
types of brands by allowing consumers to use both brands at different stages of the shopping experience within the
same alliance. However, it should be clear from the results of Experiment 1 that such alliances must strategically
take into account consumers’ perceptions and preferences in different product categories.
In Experiment 2, we focus on specific products that were identified as belonging to distinct categories in
Experiment 1. Clothing belongs to the “high-touch” category of products where traditional offline services are
preferred because of the special significance of being able to personally handle and inspect the product before
purchasing. Computer Software belongs to the category of “low-touch” products that generally require fewer offline
services. Books belong to the “mixed” category where many consumers prefer online services during the initial
purchasing steps but most consumers prefer making the final purchase offline. These three products thus represent
the range of products with different preferences for online and offline services. In this experiment we used these
products to test consumers’ reactions to possible alliances of online and offline brands.
While brand alliances between online and offline companies are relatively rare, there has been considerable
research with other types of brand alliances that shows that there is transfer of affect between brands that are
strategically aligned through marketing strategies such as co-branding, dual-branding, and brand extensions.
Research and theory development by Boush and Loken (1991), Keller and Aaker (1992), Levin and Levin (2000),
Levin, Davis, and Levin (1996), Prelec, Wernerfelt, and Zettelmeyer (1997), Rao and Ruekert (1994), and Shocker,
Srivastava, and Ruekert (1994) suggest that one brand’s equity can be transferred to other products with which it is
strategically linked. In other words, a brand’s good reputation can enhance the image of an alliance that includes that
brand. While the establishment of online/offline alliances has been slow to develop, several do exist:
Amazon.com/Borders, Amazon.com/Circuit City and Drugstore.com/CVS Pharmacy, with a very popular, though
never confirmed, rumor of an Amazon.com/Wal-Mart alliance in the works.
Of particular relevance to the present investigation of how online and offline sources are influenced by strategic
alliances are several models of assimilation effects in consumer evaluations. Meyers-Levy and Sternthal (1993)
indicated that assimilation in the evaluation of two products is most likely to occur when consumers evaluate two
instances of the same product class. Rao, Qu, and Ruekert (1999) show that the quality of one product signals the
quality of another when the two are allies because consumers are sensitive to the potential damage to a brand’s
reputation by forming a poor alliance. Levin and Levin (2000) specifically developed a model of the role of brand
alliances in the assimilation of product evaluations. They showed that when two brands are described by different
attributes and qualities but are strategically linked, consumers are apt to think that the two brands share common
levels of overall quality. The alliance of an online and an offline brand possesses all the features predictive of
assimilation effects: the alliance is of different components of the same product or service, each component is
described by different attributes, and each brand is risking its reputation by forming the alliance. Based on previous
research on brand alliances and Levin and Levin’s (2000) model of assimilation and contrast effects, we predict that
assimilation processes will predominate because of the non-overlapping of attributes used to describe online and
offline brands. This leads to a transfer of affect between brands based on perceptions of overall quality. Thus,
assimilation processes form the basis of the first hypotheses tested in Experiment 2.

Levin et al.: Consumer Preferences for Online and Offline Shopping Features and Retail Alliances
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H1. The rated quality of one brand will move in the direction of the perceived quality of the brand with
which it is aligned.
Experiment 1 showed that online and offline features complemented each other in the mind of the consumer,
especially for some products. For example, many consumers prefer to take advantage of the speed and selection
when searching for books online but prefer to examine the product and purchase it at a traditional store. Our second
hypothesis concerns the effects of providing complementary features through the establishment of online-offline
brand alliances. Previous studies show that consumers respond favorably to marketing strategies that provide
products that complement each other’s desirable features.
For example, Park, Jun and Shocker (1996) showed that a combination of two existing brand names (which they
called a “composite brand extension”) received more favorable consumer reactions when it consisted of two
complementary brands. In an earlier study of more traditional brand extensions, Park, Milberg and Lawson (1991)
had found that the most favorable reactions occur when there is a high degree of perceived fit between the original
brand and the new extension. Samu, Krishnan and Smith (1999) found that advertising strategies that combined two
brands led to stronger brand beliefs when the advertisement stressed brand attributes and the two brands were seen
as complementary. A. M. Levin (2002) showed that dual brands (two restaurants in the same location) were rated
higher when the two brands were seen as providing complementary services.
Taken together, these studies show that brand alliances are judged more favorably when the brands are seen as
providing complementary features. Clearly online-offline brand alliances provide such complementarity. An
additional study provides evidence that favorable evaluations of brand alliances transfer to the individual brands
composing the alliance. Simonin and Ruth (1998) report three separate demonstrations that consumer attitudes
toward the brand alliance influence subsequent impressions of each partner’s brand. They call these “spillover”
effects. Such spillover effects constitute the basis for our second hypothesis, that evaluations of product attributes
pertaining to online or offline features will be higher when the attributes are seen as complementing each other
within an online-offline brand alliance. This is because the perceived complementarity of functions provided by
online and offline brands will create a positive “halo” or “spillover” effect when evaluating each brand of an
online/offline alliance.
H2. In addition to the assimilation effect predicted in H1, there will be an overall elevation of brand
ratings in the Alliance condition compared to the Control (non-alliance) condition.
4.1 Method
Participants were 54 undergraduate Marketing students who were randomly assigned to the Alliance condition
(n = 27) or the Control condition (n = 27). In the Alliance condition, brands within a category were presented in
pairs, where each pair was described as a hypothetical alliance between an online brand and an offline brand. The
cover story mentioned an actual alliance of this type. In the Control (non-alliance) condition, brands were described
individually with no mention of alliances. The same fictitious brand names and attribute descriptions were presented
in each condition. This allowed us to compare evaluations of the same brand when it was or wasn’t part of an
alliance.
Participants in the Alliance condition judged brands with fictitious brand names within 12 hypothetical alliances
each consisting of an online brand paired with an offline brand within one of three product categories: Books,
Clothing, and Computer Software. Participants in the Control condition judged the same 12 online brands and 12
offline brands with no mention of an alliance.
To test the reactions to alliances between relatively strong and weak partners, ratings regarding select attributes
specific to either the online or the offline brand were manipulated by assigning fictitious ratings that were said to
have come from an independent consumer magazine. Attributes about the online brand, “ease of navigation,”
“selection,” and “speedy delivery” were assigned a rating of either moderate or positive. The offline brand attributes,
“store atmosphere,” “personal service,” and “exchange policy” were also assigned a rating of either moderate or
positive.
A 7-point scale with the ends labeled “Very Good” and “Very Bad” was used to rate individual brands. In the
Alliance condition, participants were asked to rate each brand “based on how well you think it will perform after the
alliance has been formed.”
4.2 Results and Discussion
Table 4 presents the mean response to each individual brand as a function of whether or not it is part of an
alliance and the type of alliance. The difference score (Diff.) column shows the extent to which that brand’s ratings
were higher (+) or lower (–) in the Alliance condition than in the Control condition. A positive difference score thus

Journal of Electronic Commerce Research, VOL. 4, NO. 3, 2003
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represents a gain for a brand when it is described as part of an alliance compared to when it is described as a
separate entity. While there were some differences between product categories, two trends can be observed across
categories: (1) ratings are, on average, higher in the Alliance condition than in the Control condition; and (2) in
alliances between a brand with moderately favorable attributes and a brand with positive attributes, ratings tend to
be increased for the moderate brand and decreased for the positive brand. This represents the assimilation effect
predicted by the Levin and Levin (2000) model and operationalized as H1 for the present study. Support for H1
holds for both online and offline brands. In order to track this in Table 4, note that in each product category an
alliance between a positive online brand and a moderate offline brand led to a negative difference score for the
online brand and a positive difference score for the offline brand, and conversely for an alliance between a moderate
online brand and a positive offline brand. Note, however, that the positive difference scores are higher in magnitude
(most of the negative differences are not significant), showing that gains outweigh losses.
Table 4. Comparison of Mean Brand Ratings (1-7 scale) for Alliance and Control Groups
Mean Rating
Type of Alliance
(online-offline)
Type of Brand Alliance Control Diff.
online 5.46 5.30 +0.16
pos-pos
offline
5.63 5.74 –0.11
online 5.00 5.22 –0.22
pos-mod
offline
4.08 3.13 +0.95***
online 3.83 2.83 +1.00***
mod-pos
offline
5.17 5.35 –0.18
online 3.83 3.09 +0.74**
Books
mod-mod
offline
3.63 3.09 +0.54
online 5.46 5.57 –0.11
pos-pos
offline
5.63 5.65 –0.02
online 5.33 5.39 –0.06
pos-mod
offline
3.88 3.13 +0.75*
online 3.79 3.09 +0.70*
mod-pos
offline
5.38 5.96 –0.58*
online 3.88 3.35 +0.53
Clothing
mod-mod
offline
3.96 3.22 +0.74**
online 5.33 5.67 –0.34
pos-pos
offline
5.42 5.52 –0.10
online 5.17 5.52 –0.35
pos-mod
offline
4.29 3.09 +1.20***
online 4.08 3.00 +1.08***
mod-pos
offline
5.29 5.96 –0.67*
online 3.88 2.96 +0.92***
Computer
Software
mod-mod
offline
3.88 3.00 +0.88***
Note: The first term in the pair represents the quality of the online brand and the second term in the pair represents
the quality of the offline brand.
*** = statistically significant at the .01 level.
** = statistically significant at the .05 level
* = statistically significant at the .10 level
While higher average ratings in the alliance condition than in the control condition is supportive of H2, direct
support for H2 is provided by a specific observation: In alliances between two brands each with moderately
favorable attributes, ratings for both brands are increased in the alliance condition compared to the non-alliance
condition. This holds for each of the three product categories. This finding, along with the generally higher ratings in
the alliance condition, represents an effect above and beyond the assimilation effect and reveals the residual
influence of forming an alliance. It attests to the perceived complementarity of online and offline features where
both types of brands benefited from the alliance.

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5. Conclusions
The most general finding of the experiment on online-offline brand alliances is that, like other types of brand
alliances, there is transfer of affect between brands. Specifically, the assimilation effects predicted by the Levin and
Levin (2000) model were found when the two brands in an alliance differed in attribute level favorability.
Evaluations of the brand with the less favorable attributes in the pair were raised in comparison to evaluations of the
same brand in the control (non-alliance) condition. Conversely, evaluations of the brand with the more favorable
attributes were lowered. Thus, any brand, whether it is an online or an offline brand, should be cautious in forming
an alliance with a brand of lesser quality that could bring down its image (see also Rao, Qu, & Ruekert, 1999).
However, there appeared to be a unique feature of online-offline brand alliances. In addition to the assimilation
effect, there was also an elevation of the ratings of each brand in an alliance between two brands with moderately
favorable attribute levels. This is hypothesized to be due to the perceived complementarity between the benefits of
the features of the online and offline brands. This makes online/offline brand alliances an especially promising
strategy that is worthy of further consideration.
Our survey data from Experiment 1 show that different products have different needs for adding an online or
offline presence. However, continued consumer exposure to online shopping may reduce concern for online
purchasing and change the current picture. Continuous surveying of the perceived advantages and disadvantages of
online shopping features should be a priority for marketers. Nevertheless, the current research suggests that even a
“high-touch” product like Clothing may benefit from an online presence and a “low-touch” product like Computer
Software can benefit from the presence of an offline service, especially if it is perceived to be of high quality.
There are, of course, other strategies beyond brand alliances that can capitalize on the perceived advantages
while overcoming the perceived disadvantages of online or offline shopping. One example involves the in-store
integration of online and offline services. Using various technological platforms, retailers are providing access to
online functions for both customers and employees alike. For example, Prada allows customers to compile the
outfits that they have tried on and create their own Web page which they can then e-mail to their friends to solicit
their opinions. A number of retailers, from Borders to REI, the outdoor specialists, have introduced Internet kiosks
to their retail space with varying degrees of success. This paper has readily described the various advantages of
having online functions within an offline environment, including extending the customer’s search capabilities,
increasing access to product information, and the ability to carry a far greater selection of products.
In summary, our major message is that some features of the shopping experience are seen to be better online
and some are seen to be better offline. For example, large selections and quick access to information are perceived to
be desirable features of online shopping while the ability to see-touch-handle the product and personal service are
perceived to be desirable features of offline shopping. Importantly, however, these features take on different
significance for different products. Our model of consumer preferences for online and offline shopping sources
focuses on attribute-level evaluations. To the extent that a consumer perceives the most important features of a
product to be delivered online, that consumer will turn to online searches and/or purchases. Conversely, a consumer
will visit a physical retailer when the most important features of a product are perceived to be best served by
traditional bricks-and-mortar stores. However, such perceptions may change, if, for example, online services could
guarantee hassle-free exchanges.
More research like the present is needed for product managers to determine which features of the shopping
experience are seen as being delivered better by online or offline sources for their particular product and at what
stage of the shopping experience these features come into play. Creating strategic alliances that capitalize on the
complementarity of online and offline services is one way to put such research to good use. Our research suggests
that product managers can strategically align online and offline brands to complement the features of either type of
brand by itself. We expect that the future will see the establishment of more online-offline brand alliances.
In addition, these research results may aid the retailer deciding whether to become a multi-channel retailer by
adding the Internet as an additional channel. A better understanding of the behavior that occurs within multi-channel
environments is a key element in making that decision (Schoenbachler and Gordon, 2002). The need for adding an
online presence differs based on product class due to the advantages and disadvantages that the consumer perceives.
According to the President of DoubleClick, David Rosenblatt: “Consumers will continue to browse in one
channel and purchase in another, reflecting their goal to find the best selection, service and pricing” (CyberAtlas,
2001). New instances of multi-channel retailing and more innovations like Internet kiosks will also continue to
change both the online and offline retailing environments, as companies move to provide customers with the
ultimate shopping experience. We hope that these new strategic developments are backed by solid research which
tracks changes in consumer perceptions of online and offline services.

Journal of Electronic Commerce Research, VOL. 4, NO. 3, 2003
Page 93
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