Overview of article about online offline shopping


Schroder and Zaharia (2008) looked at the buying habits of retail consumers in Germany, in particular how consumers behaved with respect to multi-channel shopping. They had noticed a trend that retailers were running multiple channels, and that there were times when these channels would complement one another. An example would be a company that has an online store and bricks-and-mortar stores. A consumer might research the product in one, and then purchase in another. The authors looked at the flow of information from company to consumer, and how consumers used that information in their purchasing habits.

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Multi-Channel Retailing

One of the core concepts of the paper is multi-channel retailing. Retailers have utilized multiple channels for decades, but the issue has become more prominent with the advent of online retailing. Online retailing has lowered the barrier to entry to retailing, so that most offline retailers now have an online shop of some sort. The strength of the offline brand helps to draw traffic to the online shop, for example. Moreover, online shops carry a number of other benefits for companies — they allow the company to sell outside of normal shop opening hours, which in Germany are restricted by law. Furthermore, they allow brands to expand their service area — people in smaller centers can still partake of a shop, even if that shop has no physical location near that customer.

For the consumer, the ease of shopping online represents a much lower barrier to shopping than going to offline stores. A user can quickly and easily from the comfort of his or her own home comparison shop. The cost of acquiring critical information on which to base purchase decisions has decreased significantly with online retailing. The authors were interested in how these changes have manifested themselves in consumer behavior with respect to multi-channel retailers.

Purpose of the Study

With this basis, the authors saw their study as contributing to the body of knowledge that retailers can use to design their multi-channel retailing systems. The study focused on the behavior of consumers and how they approached multi-channel shopping. The field is relatively young, so the full understanding of consumer behavior with respect to online/offline retailing is relatively new, and the body of knowledge is just being built. Larger companies might have their own research on this, but smaller ones will not, so there is a direct practical application for this research as well.

The focal point of the study was the determination of consumer behavior with respect to two key activities — information gathering, and purchasing. The focal point was to determine the factors that contribute to consumers either being of the type to utilize a single channel for both tasks or the type to use multiple channels to perform these tasks. There were three research questions at the heart of the survey, as elaborated on page 453:

What are the shopping motives of customers who shop through a multi-channel retailer?

Over which channels do the customers of a multi-channel retailer spread the “information prior to purchase” and the “purchase” stages?

Can the different patterns of behavior be explained by different shopping motives?

The Buying Process

A critical theoretical underpinning of the study is the buying process, as the study separates two different elements of the buying process. The authors outline Engels’ five different stages of the buying process: need recognition, information search, pre-purchase alternative evaluation, purchase decision and post-purchase evaluation. This study is focused primarily on steps two through four. These stages are conducted with differing degrees of intensity, along a spectrum of low involvement purchases to high involvement purchases. The authors apply this theory to the concept of multi-channel retailing, recognizing that the information search and purchase decision can be made via any of the different channels that are available.


The researchers conducted both focus groups and one-on-one interviews with the subjects for this study. The authors placed limitations on their study, however. For example, they “decided to limit the research to one channel for the information stage in order to avoid overextending the respondents.” This wording is vague, and does lend clarity as to the specific constraint that the researchers imposed or why they imposed it –“overextending the respondents” means almost nothing unless the phrase is defined. The first step was a preliminary pre-test, where the authors tested the questions that they planned to use, to make sure that the audience was able to understand them. From a large initial sample set, there were 36 customers who took part in the focus groups, of which there were four. Another 30 customers took part in one-on-one interviews.

Links Between Research and Literature

The first hypothesis was that customers spread the information and purchase stages over different channels according to different shopping motives. Their search of the literature revealed four basic shopping motives: convenience orientation, independence orientation, recreation orientation, and risk aversion. The convenience orientation is defined as seeking a product with the minimal amount of investment in time and effort. The independence shopper is one with a preference for freedom from constraints, in this study most particularly the constraints of geography and time. The recreation orientation sees shopping as a source of entertainment and pleasure, so values those elements of a shopping experience. The risk aversion shopper seeks to minimize the negative consequences of a purchase.

Research Outcomes

There were some biases that emerged from the survey, based on self-selection of those who were willing to participate in the study. Bias is also introduced in the form of a telephone survey — younger people often do not have landlines, and many people are not available during a normal work day. As such, during the telephone survey, 80% of the respondents were women, with an average age of 46. While the median age in Germany is 46.5, the male-female population split is obviously not 20-80. Of the respondents, 89% lived in a household with two or more persons and 59% lived in a household without any children under 18. This highlights that the typical respondent was an older female living with a partner in a two-person household. Most were educated, and earned a living wage. The demographic bias might be related to the specific retailer, as only one retailer was studied. Since the retailer was anonymous, it is difficult to say if the respondent profile fits with the demographic of that retailer’s target market or not, so it is not known what the reason for the bias on the demographics of the respondents was. The researchers present the argument that one retailer reduces bias because different retailers use different marketing tactics, but these authors did not seem to understand that they a) that argument makes no sense and b) that they introduced significant bias to their study. Not picking up on that conceptual gap is a fault in the paper.

The respondents were asked to rank their responses on a scale from one to five, with one being “applies completely” and five means “does not apply at all.” The respondents were asked about a specific purchase that they had made at this retailer, to recall the buying process that they underwent at the retailer. All told, which the focus groups and one-on-one interviews had small sample sizes, there were 525 respondents for the telephone survey.

The first element of the study was that of the shopping motive. Because of the structure of the questionnaire, multiple motives could be recorded for a single individual. The strongest scores were for the risk aversion orientation, followed by the recreational orientation and the convenience orientation.

Single channel users were the most common among respondents, with 67.4% of buying processes. Table 2 highlights this part of the responses. The largest single category was that for information and purchase at the shop. The second-largest, however, was information at the shop and then purchase online. Third most common was “shop within grocery store” for both, which is an unusual differentiation between channels. But catalog for both information and purchase was the fourth most-popular mode. Just 7.6% of respondents were single channel online.

Having determined the shopping patterns of the users, the authors then sought to determine relations (if any) between the method of shopping and the orientation of the consumer. The most common (n=157) was single channel at the chain store. This was most strongly correlated with convenience orientation, recreational orientation and risk aversion (payment). It was weakly correlated with risk aversion (delivery) and independence orientation. Catalog-catalog was most closely associated with convenience, risk aversion (payment) and weakly associated with risk aversion (delivery). The online-online grouping was most closely associated with convenience and independence, and weakly associated with risk aversion on delivery.

These findings are reasonable. In general, risk aversion on delivery is not a big motivator for any mode of shopping, but almost nobody shops online to mediate this risk. Risk aversion on payment is a motivator, and online purchasing scores relatively poorly on that motivator. Online information + purchase has the best independence score, indicating that this is a significant motivator, especially given the strength of convenience as a motivator as well. Convenience is a significant motivator for all channels, indicating that the consumer chooses to gather information and make a purchase based on whatever is most convenient for her/him.

Online shop for information and chain store for purchase was the main multi-channel shopping mode. This was viewed as convenient for the respondents. This was a strong performer on the risk aversion (payment) scale, which makes sense when considering what the implications of using this multi-channel method are. The consumer can research the product extensively online, for example looking at consumer reviews, and checking prices at other shops, but then gains safety on payment by buying at the store, once the purchase decision has already been made. The apparent time difference between making the purchase decision and the actual purchase does not appear to be an issue for customers choosing this shopping mode. However, the fact that this shopping mode was the 6th popular indicates that it might be a risk for people who use other shopping modes.

The authors also sought explanatory factors for some of the differences that they noted. Having the benefit of actually knowing what the store was, they note that the in-store shopping experience (CS+CS) scored higher for recreation, which they speculated might relate to the fact that some of the stores have an in-store coffee bar.

The authors noted that their second hypothesis is partially upheld. The convenience orientation is higher for non-store shopping (catalog or online) in a single channel. The use of a single channel should be more convenient, and the ability to do this from one’s own home should score higher for convenience than shopping at the store.

They note that their third hypothesis is also partially upheld. The independence score, which relates particularly to removing geographical and time constraints on shopping, was found to be strongest for the online+online consumer. The catalog method was not found to be more convenient than many other channels, other that store+store. This ran somewhat contrary to the expectations of the researchers, but it is not stated anywhere whether the catalog ordering system has any hours. If there are only certain hours for ordering, the catalog would not be expected to score any higher than other time-restricted methods.

The fourth hypothesis does not hold. The recreational shopping motive was stronger for in-store shoppers. Again, the researchers speculate that coffee shops and sales staff are responsible for this distinction, not that they offer any support for this.

Table 5 highlights the differences between different single channel groups. The most significant differences were found between in-store shoppers and online shoppers. The differences in all orientations were statistically significant. Between the chain store shoppers and catalog shoppers, convenience, independence and recreation were all significant differences in orientation. The shop within grocery store mode did not express very many differences with other categories, especially not versus shop within bakery, highlighting that perhaps these modes were always redundant.

An interesting finding from Table 5, however, is that the difference between online shoppers and other single mode shoppers besides chain store were only significant on a few dimensions. The significance existed on independence in all cases and on delivery-related risk aversion. This indicates that risk aversion on payment is a driver of consumers away from the online channel, not surprising given that many consumers are somewhat untrusting with respect to the security of online transactions. But the online mode scored better for independence, and this was a significant difference versus all other channels. Any bricks-and-mortar channel would be expected to score poorer on independence than the online channel, but the difference was noted between online and catalog as well (albeit a smaller difference). Online consumers are essential those for whom the trade off on payment and deliver y risk versus independence swings towards independence. This is consistent with a lot of other studies about online shopping habits.

Table 6 compares the multichannel shopper with CS+CS and OS+OS. There is no significant difference on independence between OS+CS and CS+CS, but there is with the other pairing. This perhaps indicates that the convenience of online is related more to the information gathering stage than it is to the shopping stage. There was a difference between OS+CS and CS+CS in convenience and recreation, but not between OS+CS and OS+OS. This indicates perhaps that consumers still view the bricks-and-mortar store as delivering more on the convenience and recreation scales than online shopping.

In conclusion, there are a number of interesting findings here, tempered by the somewhat unrepresentative demographic of the survey respondents. Consumers shop online for independence, but have a certain amount of risk aversion that prevents more from using online shopping. Consumers still prefer the bricks-and-mortar shopping experience, find it convenient, and tend to view it as less risky. Online shoppers who buy in-store tend to be those who prefer a low acquisition cost of information, that they can do at any point in time, but still prefer the store for risk aversion, faster delivery and the overall shopping experience.


Schroder, H. & Zaharia, S. (2008). Linking multi-channel customer behavior with shopping motives: An empirical investigation of a German retailer. Journal of Retailing and Consumer Services. Vol. 15 (2008) 452-468.

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