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


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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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Journal of Electronic Commerce Research, VOL. 4, NO. 3, 2003
Page 85
Aron M. Levin
Northern Kentucky University
Irwin P. Levin
University of Iowa
C. Edward Heath
Xavier University
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
on/off, p
= Σw
× v
Here R
on/off, p
is the overall extent to which shopping online or offline is preferred for product p, w
is the
importance of attribute i, and v
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)
Health &
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
Best price: 6.35
Large selection: 7.35
Shop quickly: 8.23
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
Best Price
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
Journal of Electronic Commerce Research, VOL. 4, NO. 3, 2003
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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:, City and Pharmacy, with a very popular, though
never confirmed, rumor of an 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.
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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
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
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
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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
Type of Brand Alliance Control Diff.
online 5.46 5.30 +0.16
5.63 5.74 0.11
online 5.00 5.22 0.22
4.08 3.13 +0.95***
online 3.83 2.83 +1.00***
5.17 5.35 0.18
online 3.83 3.09 +0.74**
3.63 3.09 +0.54
online 5.46 5.57 0.11
5.63 5.65 0.02
online 5.33 5.39 0.06
3.88 3.13 +0.75*
online 3.79 3.09 +0.70*
5.38 5.96 0.58*
online 3.88 3.35 +0.53
3.96 3.22 +0.74**
online 5.33 5.67 0.34
5.42 5.52 0.10
online 5.17 5.52 0.35
4.29 3.09 +1.20***
online 4.08 3.00 +1.08***
5.29 5.96 0.67*
online 3.88 2.96 +0.92***
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.
Levin et al.: Consumer Preferences for Online and Offline Shopping Features and Retail Alliances
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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
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... In offline channels, customers can easily see-touchhandle the products, using the facilities of personal service, enjoy the shopping process and also get their product speedily. Lower price, 24/7 days shopping, large varieties of products are the main features of online shopping (Levin et al., 2003). Multi-channel shopping offers complete freedom to the consumers to meet their needs where they can shop at any time and any place according to their wish (Hsiao et al., 2011). ...
... Customers who want enjoy shopping, see-touch the products, speedy delivery, personal service, no-hassle change shop from offline retail store and those who want best price large selection, shop quickly use online channel for shopping purpose. It results that according to the type of product, customers choose from where they purchase online or offline for example highly touch products like clothing, health and sports goods are purchased from offline store and products like computer software, airline tickets which require low touch are purchased from online store (Levin et al., 2003). Bickle et al. (2007) shows the effect of uniqueness in product design on customers behaviour. ...
... Multi-channel online buyers collect information from the offline store but for taking advantage of low price, they purchase from the online store . Levin et al. (2003) state that customers who want the best price and large selection use online mode for shopping purpose and those who want to enjoy shopping and have high impulse shopping orientation, shop from the offline store. Balasubramanain et al. (2005) examined how the environmental factors affect consumer"s choice and use of channels. ...
... In addition, eye-catching promotions make consumers more price sensitive and more likely to choose products within their financial reach. Finally, the online platform allows online merchants to sell their products without the rent, utilities, and labor costs of offline physical stores, enabling them to sell at lower prices and meet consumer demand for lower-priced products [15]. ...
... In addition, for "low-touch" products, consumers do not need on-site experience to decide whether to buy them, such as amusement park tickets, airline tickets, etc. Online shopping saves consumers the hassle of going to a specific location and makes shopping convenient [15]. On the other hand, during the COVID-19 epidemic, many commercial areas and offline stores experienced brief closures due to China's epidemic prevention policies. ...
... Customers can directly access the products they are considering: clothes, shoes, accessories, etc. They can try them on and see if they fit more now, and it is also convenient for consumers to make judgments about the quality of the products in a short period [15]. For expensive products such as houses and cars, most customers prefer to experience them on-site before making a purchase decision. ...
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COVID-19, which is classified by WHO as a global pandemic and has a significant impact on all of humanity, it has been affecting different countries around the world since 2019 in terms of economic, cultural, and social aspects for a long or short period, with a deep or shallow impact. China, which has made significant achievements in controlling the development of the epidemic, completely relaxed its epidemic prevention policies that had been in place since the end of 2022, including restrictions on travel. This paper aims to study the impact of COVID-19 on the change in consumer behavior in China. According to the analysis, it is concluded that offline shopping is still irreplaceable, but more and more people are choosing online shopping as a shopping method. In particular, COVID-19 has accelerated this shift in shopping behavior. These results provide many companies with development ideas on how to capture the changing shopping style of consumers and find solutions to meet their needs in today's fully developed Internet.
... When comparing online and offline shopping environments, the relative prominence of positive and negative features vary according to products, consumers, and situation (Levin et al., 2003). The channel preference of the consumers is affected by the type of product purchased, while consumers value different features in retail environments while shopping for different products (Cho and Workman, 2015). ...
... As an extension of this situation, the products are also classified according to their tactility. When the studies in which products are classified according to their tactility are examined, it can be said that mostly clothing products are evaluated in high-touch products and books in low-touch product categories (Levin et al., 2003;Cho and Workman, 2015;Wu et al., 2015). The sensory evaluation of the tactile properties of fabrics has been studied for years and has shed light on the tactile perception process of materials. ...
... In this study, it has been ensured those both products on which the research will be carried out have similar properties in terms of concreteness and are not among service products. In the determination of the product categories, among the high-touch and low-touch products that are most frequently discussed in the literature, the ones with the highest and lowest touch need were taken into consideration, and as a result, clothing products were selected as high-touch products and books were selected as low-touch products similar to previous studies (Levin et al., 2003;Cho and Workman, 2015;Wu et al., 2015). In this way, the characteristics of online and offline retail stores are compared for the same high-touch or low-touch products. ...
... IJCHM 3. Model specification 3.1 Relative preference model Consumer preference modeling explains why a consumer prefers one product over another by developing preference functions relating the attributes of potential products to consumer preference for those products. This study follows Hauser and Shugan (1980) and Levin et al. (2003) and adopts intensity-based preference model approach, where any product alternatives can be represented by a set of value attributes (V). An attributes-based preference function [Pref=f(V)], thus, maps the value perceptions (V) into a scalar measure of preference (Pref), and the alternative with largest preference score is most likely to be chosen. ...
... Part 1 measures the relative performance of sharing accommodation versus hotels on the 18 value attributes (Dv i,sharing/hotel ). The relative measurement scale was adapted from Levin et al. (2003). For each value attributes, a brief description was provided based on the explanation in Table 1. ...
... log-transformation), this study recoded the data into a unilateral range between 1 and 7, where the central point of 4 denotes "both perform equally" (for value attributes performance) and "both are equal to me" (for relative preference). Similar recoding process was found in previous studies such as Levin et al. (2003). ...
Purpose With the rapid development of sharing economy, travelers are facing choices between conventional hotels and the peer-to-peer sharing accommodation in urban tourism. The purpose of this study is to examine how travelers form their preferences in such choice situations and whether/how their preference formation mode would change with the COVID-19 pandemic. Design/methodology/approach A relative preference model was constructed and estimated for both domestic and outbound tourists, based on two waves of survey data collected before and after the COVID-19. The results of this study were compared to derive the evolution of preference formation patterns. Findings A set of 15 key value attributes and personal traits was identified, together with their differential effects with the pandemic. Their divergent effects between domestic and outbound trips were also delineated. Based on these findings, the competitive edges and advantageous market profiles were depicted for both hotel and sharing accommodation sectors. Originality/value This study contributes to the knowledge of tourists’ preference between accommodation types and adds empirical evidences to the impact of the pandemic on tourist behavior patterns. Both hotel and sharing accommodation practitioners can benefit from the findings to enhance their competitiveness.
... In the online environment, we perceive products only through sight and sometimes through hearing, but not through touch, taste, or smell, so they remain intangible (Laroche et al., 2005). These limitations make some consumers reluctant to use online channels in their purchases (Citrin et al., 2003;Levin et al., 2003) because it is more challenging to evaluate the products and, therefore, the risk is greater (Dai et al., 2014). ...
... We consider sports shoes as experience products, while mobile phones, ballpoint pens, and hard disks are considered search products with different risk levels. Previous studies have used electronics to represent search products and clothing and shoes as experience products (Huang et al., 2014;Kim & Lennon, 2008;Levin et al., 2003;Luan et al., 2016). ...
The visual content of the product area is crucial in an e-commerce site. This paper studies the differences in attention to product images in the product area in e-commerce sites considering the effects of purchase stage and product category. Attention to product images on websites is measured using eye-tracking in two experiments with 58 students and 66 subjects, with four product categories and four purchase tasks in each one. Our results show that pictures, in general, attract attention first, before the product names and price information. Furthermore, images attract less total attention than textual information. Images attract less attention when they are not crucial for completing the task, such as when purchasing a determined product or when locating product tracking information. Younger people (less than 30) spend much less time viewing the product pictures than older age groups (50 or more). According to our results, e-retailers could improve their sites' performance by adapting the products' presentation to the purchase tasks and visitor characteristics.
... The performance of advertisement marketing will vary with the specific product type (Huettl & Gierl, 2012;Peter & Ponzi, 2018). There are a wide variety of dimensions for the product categories (e.g., durable and consumables [Srivastava & Sharma, 2013], low-touch and high-touch products [Levin et al., 2003], search and experience products [Nelson, 1974], and hedonic and utilitarian products [Holbrook & Hirschman, 1982]). Furthermore, hedonic and utilitarian products have been extensively investigated in the context of e-commerce (Huettl & Gierl, 2012;Kivetz & Zheng, 2017;Okada, 2005;Peter & Ponzi, 2018). ...
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Purpose: Online integrated marketing is arousing extensive attention from industry and academia, whereas no uniform conclusion on the effectiveness of integrated versus single marketing has been reached thus far. Accordingly, the integrated marketing effectiveness of paid search advertising and social advertising, and the moderating role of product type in it are primarily investigated in this study. Design/methodology/approach: The interaction between paid search advertising and social advertising and purchase is elucidated. Moreover, the moderating effects of product type on the relationship are examined. The hypotheses are tested using an empirical model in accordance with the natural transaction data from Taobao. Findings/results: An empirical analysis confirms a complementary relationship between paid search and social advertising on enhancing purchase. Furthermore, this study suggests that paid search advertising is more probably employed for the purchase of hedonic products, and social advertising more markedly affects the sales of utilitarian products. Moreover, the above-described two advertisements jointly increase the sales of hedonic products. Practical implications: The results provide applicable guidance for sellers’ advertising strategies on online shopping platforms. Sellers should stimulate sales by strategically using integrated marketing tools, and they should adopt different marketing strategies in accordance with different product types. Originality/value: The findings reveal the complementary relationship between paid search and social advertising. Furthermore, this study expands the application of dual-process theory and analyses the information processing of utilitarian and hedonic products.
... Due to individual differences, preferences differ some prime concern is on time-efficient shopping pool with a wide range of products and options, whereas others value first-hand interaction with sales associates and the ability to touch and feel the product prior to purchase (Levin, Levin & Wellner, 2005). According to [16] almost all the customer desire to evaluate and have direct contact with the product before purchase. Customer select how and where to purchase based upon individual preferences prior to pandemic in which most people decide to opt for traditional store for more authentic experience [17]. ...
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Aim: The study aims to analyze the shopping behaviour (Online and Offline Shopping) of the students studying in remote and hilly area during the peak of pandemic. Study Design: The study comprises of a descriptive research design. Place and Duration of Study: The study was carried out in West Garo Hills District of Meghalaya which is located distantly form the capital city, Shillong during 2020-21. Methodology: Purposive sampling technique was used to collect 60 samples form a college in Sangsangre, a small village in Tura. Results: The findings of the study reveal that respondent shop mostly during discount and offer season online, number of purchases made is more than one in a week and almost all respondent relies on online shopping to purchase almost any product namely Habiliments, Electronic gadget, Books except for groceries, furniture and jewelry. Festive ads attract respondent go for online. Amazon and Myntra services are most preferable online portal. Conclusion: It is evident that with the changing in the retail business due to digitalization and worldwide lockdown, customer behaviour also changes irrespective of the location.
... Sarkar and Das (2017) revealed that consumers' shopping methods depend on their desire. Some consumers prefer to shop in traditional land-based retail stores because they like personal interaction with sales assistants and can make physical contact with products (Levin et al., 2005), have an authentic experience (Sarkar and Das, 2017), conduct physical evaluations directly from the product they want (Levin et al., 2003). Not only that, consumers go to retail stores for socializing, diversion, utilitarianism (Jin and Kim, 2003), and recreation (Tiwari and Abraham, 2010). ...
... According to previous scholars, differences and tendencies of companies appear when consumers use offline and online channels (Chen et al., 2014;Levin et al., 2003;Levin et al., 2005) and showed differences in behaviour when consumers search for information offline and online with regard to diversified types of products such as clothing, airline tickets, computers, electronic products, and books. Some researchers have also demonstrated the differences between the preferences and attitudes of consumers when using offline and online channels (Kwon & Lennon, 2009;Diaz et al., 2017). ...
Conference Paper
Full-text available
During the COVID-19 pandemic, the routine daily habits of individuals have changed. For example, remote working has become a much more common concept, pupils carried out their classes online and various socialization habits have been moved to digital platforms. In this period, a vast number of people have become digitized. This study aimed to investigate the impact of emotions on people’s buying behaviour during the COVID-19 pandemic in Northern Cyprus. More specifically, we investigated whether emotions affected the consumer’s decision-making process. A total of 203 participants aged between 18 to 54 participated in the study. An online survey method was utilised and the PAD scale was conducted for data collection. The results revealed that dominance had a positive impact on both pleasure and arousal. Similarly, arousal showed a significant positive impact on pleasure toward online purchasing. The positive effect of pleasure on attitudes also indicated a positive impact on future purchasing intention. In this context, positive feelings about consumption led to re-buying and positive attitudes. During purchasing, on online shopping, emotional experiences are identified as a significant factor for consumers. Thus, consumer emotions were used to create an impact on the consumer and to explain the experiences of consumption. This study pinpoints that there is a gap in the literature regarding studies based on emotion in web-based shopping using further purchasing intentions on customers.
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Davranışsal iktisat yaklaşımı aracılığıyla tüketicilerin davranışları incelendiğinde karar verme süreçlerindeki psikolojik ve bireysel farklılıkların varlığı alanda yapılan araştırmalar sonucunda açık bir şekilde gözlemlenmektedir. Geleneksel iktisadın savunduğu rasyonalitenin aksine tüketicilerin, bilişsel yargıları ile hareket ettiği gözlemlenmiştir. Bu bağlamdan yola çıkarak çalışmada geleneksel ve online alışveriş açısından tüketici tercihleri davranışsal iktisat perspektifinden ele alınmıştır. Önem arz eden durum ise, tüketicilerin bilişsel çelişki, zihinsel muhasebe, beklenti teorisi, kayıptan kaçınma gibi düştüğü tuzaklar ve yanılgılar açısından geleneksel ve online alışveriş söz konuş olduğunda salt rasyonel mi yoksa irrasyonel mi? hareket ettiğinin tespit edilmesi gerekliliğidir. Söz konusu ilişkinin incelenmesi için çalışmada kolayda örnekleme yöntemi kullanılarak, online anket yöntemi aracılığıyla 750 kişi ile analiz gerçekleştirilmiştir. Elde edilen sonuçlara göre geleneksel alışverişte tüketicinin satın alma davranışının cinsiyet, yaş, eğitim düzeyi, medeni durum, meslek türü ve aylık gelire göre farklılaşmaktadır hipotezleri reddedilmiştir. Online alışverişte ise yalnızca cinsiyet faktörü hipotezi reddedilirken, diğer hipotezler kabul edilmiştir. Bu durumun temelde online alışveriş yapanların daha rasyonel davrandığı durumu ifade ettiği söylenebilir.
Globally, the characteristics of a website that are critical to increasing the likelihood that customers will shop at that site and will come back for future purchases are largely unknown. Actual shopping tasks by 299 respondents from 12 countries indicate that site quality, trust, and positive affect toward it are critical in explaining both the purchase intentions and loyalty of visitors to the site. This research indicates that the impact of these factors varies across different regions of the world and across different product categories. Results of this research highlight the need to tailor websites according to each world region and product being offered for sale.
In this article, the authors examine the circumstances in which brand names convey information about unobservable quality. They argue that a brand name can convey unobservable quality credibly when false claims will result in intolerable economic losses, These losses can occur for two reasons: (1) losses of reputation or sunk investments and (2) losses of future profits that occur whether or not the brand has a reputation. The authors test this assertion in the context of the emerging practice of brand alliances. Results from several studies are supportive of the premise and suggest that, when evaluating a product that has an important unobservable attribute, consumers' quality perceptions are enhanced when a brand is allied with a second brand that is perceived to be vulnerable to consumer sanctions. The authors discuss the theoretical and substantive implications for the area of brand management.
The authors explore the implications of considering a brand as representing a category consisting of its products. They report results of a laboratory experiment in which response times and verbal protocols were used to examine processes related to the evaluation of brand extensions. Evaluations of brand extensions were influenced both by the extension's similarity to the brand's current products (brand extension typicality) and by the variation among a brand's current products (brand breadth). An inverted U describes the relationship between brand extension typicality and evaluation process measures. Moderately typical extensions were evaluated in a more piecemeal and less global way than were either extremely typical or extremely atypical extensions. Subjects' attitudes toward brand extensions were correlated highly with their ratings of brand extension typicality.
In this article, the authors investigate the effectiveness of advertising alliances (in which two brands from different product categories are featured together in an advertisement) for introducing new brands. The authors identify degree of complementarity between the featured products, type of differentiation strategy (common versus unique advertised attributes), and type of ad processing strategy (top-down or bottom-up) as important factors in determining ad effectiveness. The conceptualization captures the effects of these factors on brand awareness, brand accessibility, brand beliefs, belief accessibility, and brand attitudes. The theory was tested in an experiment using print advertisements to manipulate the three factors in conditions of high consumer involvement. The results show an interesting pattern of interactions among the factors, which has important implications for managers of new and established brands.
The authors report two studies investigating the effectiveness of a composite brand in a brand extension context. In composite brand extension, a combination of two existing brand names in different positions as header and modifier is used as the brand name for a new product (e.g., Slim-Fast chocolate cakemix by Godiva). The results of both studies reveal that by combining two brands with complementary attribute levels, a composite brand extension appears to have a better attribute profile than a direct extension of the header brand (Study 1) and has a better attribute profile when it consists of two complementary brands than when it consists of two highly favorable but not complementary brands (Study 2). The improved attribute profile seems to enhance a composite's effectiveness in influencing consumer choice and preference (Study 2). In addition, the positions of the constituent brand names in the composite brand name are found to be important in the formation of the composite's attribute profile and its feedback effects on the constituent brands. A composite brand extension has different attribute profiles and feedback effects, depending on the positions of the constituent brand names.
A laboratory experiment examines factors affecting evaluations of proposed extensions from a core brand that has or has not already been extended into other product categories. Specifically, the perceived quality of the core brand and the number, success, and similarity of intervening brand extensions, by influencing perceptions of company credibility and product fit, are hypothesized to affect evaluations of proposed new extensions, as well as evaluations of the core brand itself. The findings indicate that evaluations of a proposed extension when there were intervening extensions differed from evaluations when there were no intervening extensions only when there was a significant disparity between the perceived quality of the intervening extension (as judged by its success or failure) and the perceived quality of the core brand. A successful intervening extension increased evaluations of a proposed extension only for an average quality core brand; An unsuccessful intervening extension decreased evaluations of a proposed extension only for a high quality core brand. Though a successful intervening extension also increased evaluations of an average quality core brand, an unsuccessful intervening extension did not decrease core brand evaluations regardless of the quality level of the core brand. The relative similarity of intervening extensions had little differential impact, but multiple intervening extensions hod some different effects than a single intervening extension.
Associations to a contextual cue were contrasted with those of an advertised object when the cognitive resources devoted to message processing were substantial and when the categories to which the contextual cue and the advertised object belonged displayed low overlap. The absence of either of these factors prompted assimilation. A two-factor theory is offered to explain these outcomes.