If sorting and customization affect brand choice, then CMEs with those features may change constructs, such as loyalty, that are important to brand managers. For example, suppose a household has a given brand switching matrix in a product category. What does the ability to sort via Peapod do to that matrix? A concern of marketing managers in the last decade is that brand loyalty has been eroding due to the proliferation of price-oriented promotions and the growth of private labels (although both have slowed somewhat recently). With CMEs, different forces work in different directions. Brand loyalty may be enhanced with the ability of households to press a button with the mouse and simply reorder from the previous set of choices. One study comparing realistic, laboratory simulated choices to actual supermarket choices by the same people found the latter behavior to be more zero order than the former (Burke, et.al. 1992). In addition, less exposure to in-store promotions and communications should enhance loyalty. Alternatively, with more product information available and fewer distractions, brand choices could become less routinized therefore exhibit more switching.
Changes in brand loyalty have important implications for brand equity. A powerful brand name is one that survives the kind of information onslaught characterized by a Peapod or Shopper?s Advantage environment. In fact, with CMEs, we can reexamine the whole area of brand equity. An important motivator for the construction of Web sites is to enhance brand equity. Are they working? What do customers learn about brand names from Web sites? Are brand names less important in a CME environment? This latter topic relates to the research on memory versus stimulus-based decision-making (Lynch and Srull 1982); clearly, a customer using a CME is at least exposing him or herself to more stimuli in terms of advertisements and attributes and potentially increasing the weight given to those stimuli relative to short or long-term memory components.
A current area of research interest is the formation of consideration sets (Roberts and Lattin 1991). The predictive accuracy of choice models is greatly improved when the set of brands is reduced from the total set in the category to those in the household?s consideration set. Using Peapod or Shopper?s Advantage, it is easier to construct consideration sets by noting for which brand attributes were examined. The use of ?personal lists? in Peapod allows consumers to construct and customize shopping lists that only contain products and brands of interest (interestingly, brands which are purchased but not added to the ?personal list? can also be identified). Useful research topics would be how the sets change over time, what factors affect brands in the sets, and predicting brand choice conditioned on brands in the set. Lower decision costs implied by the use of at-home CMEs like Peapod would predict that consideration sets would be larger (Hauser and Wernerfelt 1990) and, relevant to our discussion above about loyalty, would provide more opportunity for brand switching.
There are a variety of other research topics related to brand choice. For example, satisfaction with and confidence in purchases made using CMEs can be studied. These variables are not only interesting from a research perspective but are managerially important as well and relevant to the long-term commercial potential of CMEs. CMEs tend to separate choice from purchase, especially in high-involvement categories such as cars, where consumers obtain comparative information from a CME (e.g., www.edmunds.com) and then purchase from a dealer offering the lowest price. In this environment, consumers may attribute poor brand choices to their own poor judgment in incorrectly utilizing available information rather than to persuasion or manipulation by a dealer. In addition, while conventional wisdom suggests that the use of CMEs would decrease impulse purchases, the opposite could occur when a consumer sorts a product category on a selected attribute or pursues a set of hypertext links. 4. Brand Communities
In the discussion of communication theory, we noted that a characteristic of the CME environment is the way customers can form groups or communities around brands (Muniz and O?Guinn 1996). For example, Saab owners are notorious for their fierce loyalty to the brand and their high degree of interest in new product offerings and other matters related to the company. Saab owners have their own ?chat rooms? where they discuss the cars with each other and communicate their feelings to the company via e-mail. Muniz (1997) has reported several similar CME brand communities each with different interests but very similar communal characteristics. Clearly, this hybrid form of interpersonal and mass communication afforded by CMEs increases the probability that communities will form around brands. There are many research questions that emerge around these communities.
One important research question is how communities affect member attitudes toward the target brand and their purchasing behavior. It is important to note that communications among communities are largely outside of the control of the marketing manager, and that the size and interconnectedness of these communities shifts power from the marketer to the consumer. How then should a marketer?s communications be targeted to brand communities as well as to individual consumers? While it is important for a manager to monitor community communications, traditional advertising may have a decreasing impact on member attitudes and behavior. Particularly influential and knowledgeable users may have a much more profound effect than when interested consumers were more geographically and temporally isolated from one another, and thus less powerful. Now Whyte?s (1954) ?web of word of mouth? takes on a whole new meaning. CMEs may actually enable the field to revisit the essential but ultimately frustrating work into mass opinion leadership (Katz and Lazarsfeld 1955, Robertson 1971). That is, we may now have the technology to study the interplay between word-of-mouth and marketer produced information in diverse contexts and in near real time.
Another question would be: around which products are communities more or less likely to form? There is a CME brand community for Saab, but there is unlikely to be one for Crest toothpaste. Controlling for price and frequency of purchase, what are the factors leading to community creation? What kinds of people are likely to be members of communities for different kinds of products?
A potentially exciting way to view a community is as a market segment. This kind of segment has common interests but may cut widely across demographic and socioeconomic lines. How does this kind of segmentation fit with more traditional approaches to forming market segments? Do community-defined segments respond similarly to marketing mix variables? How can marketers influence these segments? Does this change our definition of target marketing? 5. Pricing
Clearly, the impact of price in these attribute-rich environments leads to many interesting questions. Unlike the prominent role it takes at point-of-purchase, price is simply another attribute in a list of attributes (much like in a conjoint analysis experiment). Therefore, its role in the choice process could be diminished. Thus, an interesting question relates to price sensitivity in a CME environment.
The area of reference price (Kalyanaram and Winer 1996) has been extensively studied over the last 10 years. Some researchers (e.g., Rajendran and Tellis 1994) have categorized references prices as being temporal, formed on the basis of past prices, and contextual, formed at point of purchase based on other observed prices. The empirical evidence so far is mixed on which kind seem to most influence brand choice. In a Peapod or Shopper?s Advantage kind of environment, a reasonable hypothesis would be that contextual prices would dominate. However, in the price recall domain (e.g., Dickson and Sawyer 1990), it would be interesting to study if prices are remembered any better or worse based on computer transactions versus conventional store transactions.
Different pricing mechanisms can be easily used with CMEs. For example, occasionally, Cathay Pacific airline holds auctions of available business class seats on the Internet. Lands End sells closeouts in a special ?room? at its Web site where the prices steadily decrease until the merchandise is cleared. Another possibility is that multiple types of prices can be offered including financing; for example, in purchasing a car, the CME can suggest prices for a 3-year fixed loan, 5-year adjustable loan, 5-year lease, etc. A potential area of research is when and where different mechanisms can be most profitably applied and if new approaches to pricing can be developed.
6. Conclusion
The diffusion of computer-mediated environments useful for customer transactions is occurring rapidly. While forecasts of the dollar amounts of sales that will occur through CMEs are wildly different, it is safe to say that the next 5 years will see a significant increase in sales made through these channels. At the same time, marketers will spend an increasing amount of money on customer communications to improve their relationships with customers, increase brand equity, and to simply provide information to current or prospective customers.
It is important for marketing researchers to examine these emerging technologies. At the very minimum, we view CMEs as an exciting new laboratory for testing existing theories. Such a laboratory is relevant to research on decision processes, brand choice, and marketing mix variables such as advertising and price. However, we also believe that some new research areas will emerge from CMEs. Clearly, CMEs point to new questions for communications theory and ideas about community. We believe that new theories are needed to handle the unique environment. Thus, we see the CME environment as providing both a new context in which to study existing theories and an entirely new phenomenon, both of which merit research by marketing academics and other social scientists.
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