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Computer mediated evnvironments

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Computer mediated evnvironments



In the last several years, the increased diffusion of computer and telecommunications technologies in businesses and homes has produced new ways for organizations to connect with their customers. These computer mediated environments (CMEs) such as the World Wide Web raise new research questions. In this paper, we examine the potential research issues associated with CMEs in five areas: (1) decision processes, (2) advertising and communications, (3) brand choice, (4) brand communities, and (5) pricing.

In the last several years, the world of the marketing has changed dramatically with the rapid diffusion of computer and information technologies throughout businesses and homes. The two most notable changes that have increased potential of linking buyers and sellers are the number of households owning personal computers (over 33% in the U.S.) and the exponential growth of applications of the Internet, most notably the World Wide Web (WWW).

With increased penetration of computers, particularly multi-media computers equipped with CD-ROM drives and modems, subscription-based services such as America Online (AOL), Prodigy, and Compuserve, where consumers are able to check airline flight schedules and make reservations, purchase a wide variety of products, and discuss product performance with other consumers, are becoming very popular. Households in Chicago and San Francisco can purchase groceries from home using computer software marketed by Peapod, Inc. This latter service allows consumers to search within a product category using attributes such as price, calories, sugar content, and package size. Car manufacturers such as BMW regularly mail CD-ROMs to potential customers with video shots of the cars and data both about the cars and their competitors. Durable goods can be purchased through CUC International's Shopper's Advantage membership-based service ( 'Virtual' shopping experiences (Burke 1996) enable marketing researchers to simulate a buyer's actual experience in a supermarket with remarkable accuracy.

While the Internet has been around since the 1960s, only since the development of the WWW (Berners-Lee et. al. 1993) has its potential for electronic commerce become apparent. The WWW is essentially a network of 'home pages' where companies (and other organizations and individuals) can place information about themselves, communicate with customers, receive communications from customers, make transactions, and deliver customized messages, products, and services to customers. Through the use of layers of information called hypertext, 'hot links' that permit easy flow from one WWW site to another, web search engines that permit a user to search for information by simply typing in a keyword or phrase, and various methods of payment including credit cards and 'E-cash,' the WWW has become a hot area of marketing.

These technologies are examples of what we refer to as computer-mediated environments (CMEs) . In general, we define a CME as a link between a 'sponsor' (e.g., a seller) and 'users' (e.g., a customer) involving

? information technology

? feedback (i.e., interactivity)

? customization.

A CME may or may not allow open access and communications among the users. For example, AOL is a subscription-based service while, in most cases, the WWW permits open access. The WWW and AOL both allow for communications among users while Peapod does not.

In this paper, we focus on the research implications of CMEs for better understanding consumer choice. Because of their interactive, dynamic nature, CMEs provide a rich environment for studying choice. In particular, we examine the following different areas for research using CMEs: (1) decision processes, (2) advertising/communications, (3) brand choice, (4) 'communities' around brands, and (5) pricing. An important question that we explore is whether or not the CMEs give rise to new research issues, i.e., are CMEs generating new theories, or are they simply a different (albeit exciting) new laboratory for testing existing theories.

1. Decision Processes

While CME environments permit users to perform many tasks such as accessing product-related information, many of the environments permit customers to search for and evaluate alternative products and brands by their attributes. Thus, the CME environment is a sophisticated version of the old information board or computer-based Mouselab (Johnson, Schkade, and Bettman 1988) experimental environments used to test decision processes and strategies in the 1970s and 1980s. The main differences between Mouselab and CMEs are that with CMEs, there are more attributes, there is more information available through hypertext links, the purchasing situation is real rather than artificial, and there may be interactions between sellers and customers or customers and customers.

One area of research in consumer behavior that can be tested using CMEs is how sorting capabilities affects decision rules. First, does the use of CMEs affect the size of the consideration set? Second, different kinds of decision rules or data 'combination' hypotheses such as compensatory, conjunctive, and lexicographic have been tested before on experimental data (cf. Wright 1973). However, it has not been previously possible to examine this topic in actual purchase situations; as a result, we have a good idea of the alternative processing strategies used in lab experiments, but we do not have a good understanding of the strategies that are used in practice. Given a large number of product attributes (calories, carbohydrates, cholesterol, fat, etc.) and the ability to sort brands easily on any of these attributes via a service like Peapod, we can examine whether decision makers use the capabilities, and if so, how. These new sorting capabilities may affect decision rules and ultimately brand choice (see '3 below). Different kinds of decision rules might result since the effort required relative to improvements in decision accuracy is low. Sorting can also affect accessibility of attitudes and/or past behavior which can lead to increases in purchase incidence and ultimate shifts in market share (Morwitz, Johnson, and Schmittlein 1993).

In the last 20 years, a large body of research has emerged on context effects, in particular, attraction (asymmetric dominance) and compromise effects (Huber, Payne, Puto 1982, Mellers and Cooke 1996, Simonson 1989). This research has shown that, among other things, the inclusion of a dominated alternative (in terms of product attributes) in a choice set can materially affect the choice probabilities of extant brands. CMEs provide the opportunity to move this research from the lab into actual choice contexts. Given the availability of attributes including price, consumers would now have the ability to form their own efficient frontiers (Hauser and Shugan 1983). In theory, one would predict that inefficient brands, i.e., those on the interior of the frontier, should have no buyers since they are dominated by other alternatives. In the current shopping environment, it is very difficult to form such frontiers and make efficient choices. However, with CMEs (where we assume that such data manipulation could actually be performed by the software), the nature of the marketplace would change dramatically as inefficient brands are either driven out or re-manufactured to be on the frontier.

CMEs also provide the opportunity to study the dynamics of processing strategies, i.e. learning, forgetting, customization, etc. This can happen in two ways. First, the CME can be viewed as an intervention and we can explore differences in behavior before and after the intervention. Suppose we could observe a household's behavior prior to adopting a CME (e.g. using Nielsen or IRI scanner data in a packaged goods context) and then follow the household's behavior afterwards (e.g. using Peapod). Do consumers make fewer/more efficient choices? Do they change their decision strategies from, say, brand-based to attribute-based? Do they become less/more price/promotion sensitive? Is less/more variety sought? Do brand names have more or less effect on purchase decisions? Second, during the intervention, behavior could change. People might use different decision strategies immediately after vs. some time after the intervention. Consumers might not change their decision strategies at the early stages of adoption of the new technology, but as the consumer learns and adapts to the CME, the decision strategies would differ greatly (Alba, Hutchinson, and Lynch 1991, Bettman, Johnson, and Payne 1991, and Lynch, Marmorstein, and Weigold 1988).

CMEs give researchers the opportunity to examine both segmentation schemes and individual differences. Prior research in marketing has used actual purchase data to develop segmentation schemes based upon the implied decision processes (e.g., Currim and Schneider 1991). Since the decision processes have been only inferred from purchase (scanner) data, the number of processes and the ability to determine the processes used has been limited. The opportunity to better examine how consumers make brand choices in the CME environment will improve our ability to develop market segments based on these processes which should be useful to marketing decision makers.

There are undoubtedly individual differences in how people use CMEs. Researchers should begin to examine these differences which range from very light usage to strong personal relationships with Web pages, discussion groups, etc. Researchers should develop a better understanding of these relationships and integrate them into their of the underlying choice process. This area of research could be linked to the work delineating acquisition and transaction utility (Thaler 1985); some customers will get added utility from making an electronic purchase (e.g., time savings, part of transaction utility) which might be offset by the loss of being able to actually see and touch merchandise in a retail store (acquisition utility).

An interesting innovation in the WWW is the construction of so-called 'smart agents.' These are decision rules encoded by a user where future visits to a site make predictions about the user's preferences based on previous choices (see, for example, the WWW site Firefly,, which can be used for music or movie choices) of the user and similar users. Methodological research issues which are apparent are how do we develop these predictions, i.e. how do we combine a user's data and other users' data? Can we construct different agents for different occasions, moods, etc.? How accurate are these predictions? Can these agents actually affect customer preferences and can their recommendations affect future product choices? (Morwitz and Pluzinski 1996, West 1996).

Many other research topics emerge on the topic of decision processes. For example, a topic relevant to public policy is if and how consumers use specific attributes such as nutrition and/or construct and use unit prices from the CMEs. Also, mediators such as product class knowledge (Brucks 1985) can be studied for their impact on information sought in Peapod or Shoppers' Advantage-like environments. Other dependent measures such as decision accuracy and customers' perceptions of the CME as a decision can be studied (Widing and Talarzyk 1993).

2. Advertising/Communications

Perhaps the most important research issue in this area is in terms of communications theory. Marketing researchers have traditionally relied on source-sending-message-to-receiver models of communications dating back to Lasswell (1948) and Shannon and Weaver (1949), and popularized by Osgood (1954) and Schramm (1954) in the 1950's. The reality of CMEs may bring a long overdue re-thinking of marketing communication specifically, and 'mass communication' in general. Traditional mass communication models assume a one-to-many form where the sender (e.g., a firm with a TV or radio ad) sends messages to its audience with no little or no real interactions between audience and sender. This is clearly not the case with most CMEs such as the WWW. The CME feedback loop from 'receiver' to 'sender' is much more direct, immediate, and much more likely to yield a significant response from the sender than with traditional mass media (e.g., television programming). In the case of CMEs, we see active communication between audience and sender, as well as the ability for audience members to communicate with one another, and to form important communication collectives which have their own voices as well.

In the new media things are so different that it is not even always clear who is the sender and who is the receiver. Communication theories have always revealed a tension between the form and functions of interpersonal and mass communication (Anderson and Meyer 1988). Here, in the case of CMEs, we may have something which really exists at the border of the two: audience members may actually participate (to varying degrees) in the production of mediated content. There can be (and often is) significant mutual participation in 'message' content, and in the construction of meaning. This alone makes the kind of communication that occurs in CMEs fundamentally different than that which occurs in traditional mass media environments. This calls for new theory.

What then are the most significant research implications of the new media? First, there is more work to be done in developing communications models which can adequately account for this new form. How do we account for a communications network in which the distinction between sender and receiver is unclear as in interpersonal communications settings, yet also retains many of the aspects of mass communication as well? Second, what predictions would come from models representing the ability of so many consumers to communicate with each other so rapidly? How will we account for and predict the actions of the CME groups or 'communities' which now form around brands? How these communities interact with each other and firms can have a substantial impact on brand equity, customer satisfaction, and overall brand performance. Third, would one predict that the dynamic nature of CMEs to yield a more dynamic environment for advertising copy? If customers can virtually interact with advertising, a fixed campaign such as might be run on TV might become inappropriate, or on the other hand, might endure due to the unwillingness of audience members to consistently interact due to their essential passivity. The audience may not care to 'interact' as much as the technology will allow. In a McLuhan (1964) sense, will the nature of the medium itself fundamentally alter the nature of the marketing message? What are the public policy implications of a more decentralized 'information store' (Bordewijk and Van Kaam 1986) and a more active consumer?' At present, these are all open questions, and they are questions largely without the benefit of appropriate theory.

An important use of CMEs, particularly the WWW, is as an advertising medium. As any casual browser of the Web knows, most non-corporate sites are peppered with advertisements, many of which are tailored to the individual user who submits a brief profile before being allowed access to all of the site's features. In addition, the sponsored Web sites themselves act as communication devices to customers and to potential customers.

This is obviously a new area for marketing. Thus, there are some basic questions that have to be answered: what constitutes an advertisement in this new medium? Are existing tools for creating ads applicable? How do we evaluate the effectiveness of Web-based advertising?

One perspective on evaluating the effectiveness of Web-based advertising relates to advertising objectives. The most common framework for thinking about objectives is the classic 'hierarchy of effects' model, or AIDA, Awareness - Interest - Desire - Action. Are these appropriate objectives for Web-based advertising? How should a site visit be counted? Conventional measures of 'eyeballs,' i.e. exposure are not really useful because users jump quickly from one 'page' of hypertext to another as well as from one site to another. At the same time, better measures of 'interest' can be obtained from 'clickstream' data ...

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Keywords: mediated environment meaning, computer- mediated communication, computer mediated communication in the workplace, computer mediated synonym, computer mediation

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