The Interactive Advertising Model:
How Users Perceive and Process Online Ads

Shelly Rodgers

University of Minnesota

Esther Thorson

University of Missouri-Columbia


The authors provide an integrative processing model of Internet advertising, which incorporates the functional and structural schools of thought. The model begins with the functional perspective, which attempts to identify reasons for Internet use. Since most individuals enter cyberspace with some goal, or agenda, in mind, the authors argue that a model of online processing should begin with consideration of Internet functions. These functions, according to the authors, operate conjointly with the user’s mode–ranging from highly goal-directed to playful–to influence the types of ads web users will attend to and process. A number of mediating variables, such as skill level, are offered as reasons to switch motives. These variables are conceptualized as having either a deleterious effect, as in the case of low skill and high anxiety, or beneficial effect, as in the case of high skill and low anxiety, on ad processing. Last, the authors incorporate a structural perspective, which seeks to identify and classify Internet ads. The authors offer a broad scheme in which to classify most Internet ads, as well as a number of common features unique to these ads. The authors conclude by offering a number of hypotheses suggested by the model.


Although the Internet [1] provides an efficient medium for advertising (Hoffman & Novak, 1996), practitioners are trying to figure out how to maximize this new medium (Eighmey & McCord, 1998). Scholars are attempting to do the same. Their approach, however, generally differs from practitioners in the way rigorous theories, methods and models are built, used, tested and re-tested. Our purpose here is to offer an interactive model of ad processing that can be used, tested and re-tested by anyone interested in Internet advertising research. This model differs from other online models in the way it incorporates several paradigms, or schools of thought. These paradigms include a functional conception of how people come to Internet advertising, an information processing conception of what they do when they get there, with emphasis on stimulus structure of Internet ads. This broad-based view of interactive advertising is discussed both in terms of the new concepts of the Internet and in terms of the important knowledge that has already been accumulated about how advertising works in traditional media channels. Our argument is that much of what we already know about traditional advertising is relevant to Internet advertising, but that because of the additional complexities of interactivity and the greater proximity to “reality” available via the web, additional specifications of functions and structure are necessary.

A great deal of research on interactive advertising has focused on the structures of advertising, whereby scholars have identified and classified characteristics of the message (e.g., Bucy et al., 1998; Doyle, 2000; Frazer & McMillan, 1999; Lee & Lee, 2000; Li, Kuo, & Russell, 1999; Miranda & Ju-Pak, 1998; Wassmuth & Thompson, 1999), the receiver (e.g., Pashupati & Raman, 1999; Rodgers & Cannon, 2000), and in very few cases, the interaction of the message and receiver (Oh, Cho, & Leckenby, 1999). In contrast to examining the structural elements of Internet ads, a fairly large number of Internet scholars have used the functionalist approach to identify reasons for Internet use (e.g., Abels, White, & Hahn, 1997; Eighmey, 1997; Katz & Aspden, 1997; Korgaonkar & Wolin, 1999; Maignan & Lukas, 1997; Papacharissi & Rubin, 2000; Raman, 1997; Rodgers & Sheldon, 1999; Stafford & Stafford, 1998; Stafford & Stafford, 2000). Information processing, the third approach, also has been used by a number of scholars to examine how individuals perceive and process online ad-based messages (e.g., Bezjian-Avery, Calder, & Iacobucci, 1998; Chen & Wells, 1999; Cho, 1998; Cho & Leckenby, 2000; Cho, Lee, & Tharp, 2000; Coyle & Gould, 2000; Leong, Huang, & Stanners, 1998; Li & Bukovac, 1999; Rodgers, 2000; Rodgers & Cannon, 2000; Sundar et al., 1998).

The overall assumption of our model is that information processing in an interactive environment is dependent on both function and structure. After all, the reason researchers attempt to classify Internet ads is to determine what effect differing features will have on consumer’s memory, attitude and behavior. For example, Rodgers (2000) has examined how the association between an electronic sponsorship and news story relate to information processing effectiveness, and Cho and Leckenby (1999) have looked at the relationship between website interactivity and processing effects.

Thus, Internet researchers have already begun the task of identifying structural features of Internet ads–some that are unique to the Internet and some that are not–and testing these features in an information processing framework. We hope to extend this research by taking a systematic look at what research on traditional ad features has taught us. The past 50 years of advertising research certainly will provide an excellent starting point for this task.

Despite the utility of approaching Internet research from a structuralist frame of reference, it should be obvious to the reader that structure alone cannot explain what drives individuals to enter cyberspace, and how they react to the physical features of Internet ads once the cyber journey has begun. This is where functionalism fits in. According to Thorson and Leavitt (1986), “unless advertising is understood in terms of consumers and their goals, there is no adequate base for dealing with complex behavior such as responding to persuasive communication”(p. 11). After all, consumers are not simply reacting to Internet ads, they are using these ads to accomplish their goals.

As can be seen from the discussion above, information processing is closely related to the function of the Internet and structure of Internet ads. As noted by Rodgers and Cannon (2000), what would be the point of classifying online ads and features of online ads if not for the purpose of understanding the cognitive and conative effects of those features? Likewise, what use would a scheme of Internet motives serve if not for the purpose of understanding their role in creating and changing consumer’s attitudes and behaviors?

In short, we propose an interactive information processing model of Internet advertising that incorporates both function and structure. Our rationale for doing so is simple: an integrated medium (Sheehan & Doherty, 2000) requires an integrated processing model. Of course, other advertising scholars have proposed an integrated model for understanding processing of traditional advertisements (e.g., MacInnis & Jaworksi, 1989). It is our belief, however, that these models cannot be applied to the Internet merely because they were not created with the Internet in mind. For one, these models do not take into account the reasons consumers use the Internet. Because consumers actively seek out the Internet to fulfill a variety of goals, we believe the functions the Internet serves must be included in a processing model. Secondly, although past advertising models have accounted for processing in a broadcast and/or print medium, they do not include aspects unique to the Internet–namely, interactivity and virtual reality. These are just some of the reasons a new processing model is being offered to predict and understand how Internet ads affect online consumers.

With these assumptions in mind, we now look at the details of the model introduced here, the Interactive Advertising Model (IAM). As Figure 1 illustrates, the basic components of the IAM broadly include: consumer-controlled aspects, such as Internet uses and information processes; advertiser-controlled aspects, like ad structures; and, finally, the array of responses that result from the encounter of a functioning, information processing individual with the structures of Internet advertising. We turn now to a discussion of these components.

Consumer-Controlled Aspects of the Internet

Perhaps one of the most basic ways to think about how individuals process advertisements in an interactive environment is to distinguish between aspects of the Internet that are consumer-controlled and those that are advertiser-controlled. Traditionally speaking, advertisers have controlled which ads consumers see, when and how. Of course, consumers always have the option of not paying attention to, becoming involved with or ignoring the ad. In the case of the Internet, however, the control has switched (for the most part) from advertiser to consumer.

In fact, a number of researchers and practitioners argue that consumers have more control on the Internet than do advertisers (Roehm & Haugtvedt, 1999). Some have gone so far as to argue that interactive marketing and advertising techniques will not work unless practitioners “step into the shoes” of and approach the Internet from the consumer’s vantage point (Cross & Smith, 1997). This makes sense if we consider the fact that most Internet users typically log onto the Internet with some sort of plan, or goal, in mind (Cannon, Richardson, & Yaprak, 1998). Thus, initiation of Internet use is completely under the consumer’s control. Add to this the fact that users are in the driver’s seat throughout the entire online experience–interacting with websites, ads, advertisers, other consumers and so on (Hoffman & Novak, 1996)–and it becomes even easier to imagine why an Internet processing model must delineate which aspects of the Internet are consumer- versus advertiser-controlled, and how this control ultimately influences consumer responses.

That is the goal of this next section. The functional and information processing paradigms are useful toward this end. To orient the reader, we begin by offering a brief historical account of each perspective, as well as underlying assumptions. Then we discuss how these assumptions relate to our model. Last, we “walk” the reader through consumer-controlled aspects of our model and attempt to explain how these various components work individually and conjointly to influence information processes and responses. We begin with a functional approach.


A number of scholars studying the Internet have used a functionalist perspective to identify primary motives of the Internet (e.g., Eighmey, 1997; Eighmey & McCord, 1998; Flaherty, Pearce, & Rubin, 1998; Hammond, McWilliam, & Diaz, 1998; Isabelle & Lukas, 1997; Katz & Aspden, 1997a; Korgaonkar & Wolin, 1999; Papacharissi & Rubin, 2000; Rodgers & Cannon, 2000; Rodgers & Sheldon, 1999; Stafford & Stafford, 1998). Central to this approach is the argument that, before we can begin to understand how people process web-based ads, we must understand why individuals visit cyberspace in the first place (Cannon, Richardson, & Yaprak, 1998; Rodgers & Cannon, 2000).

Of course, this kind of functionalist theorizing about human motivation has a long and distinguished history in the psychological (Snyder & Cantor, 1998) and mass communication (Rayburn, 1996) literatures. With mass communication, the themes of functionalism derive in part from Lasswell’s (1948) findings on the specific reasons people actively attend to the media. Known as the uses and gratifications approach, functions of mass communication–such as surveillance (Lasswell, 1948) and entertainment (Wright, 1960)–have served as the basis for empirical studies that have investigated gratifications received in pursuit of information found in a variety of mediated contexts (Rayburn, 1996).

Although the uses and gratifications (U&G) model has been useful for identifying reasons for Internet use (e.g., Eighmey, 1997; Eighmey, 2000; Stafford & Stafford, 1998; Stafford & Stafford, 2000), we believe the general functionalist paradigm provides a more “precise” conceptualization of this phenomenon. In fact, in his chapter on U&G, Rayburn (1996) highlights a number of criticisms and shortcomings inherent in the U&G approach–the least of which is a “lack of precision in major concepts” (p. 145). This is one reason we prefer to use the broader functional paradigm, which emphasizes not only why people use the Internet, but how well they do it and whether their strivings bring the desired outcome (Snyder & Cantor, 1998). Put another way, a functionalist approach allows us to articulate the “why” and “how” of users’ Internet motives–not just the “why,” as evidenced by the U&G approach. In effect, functionalism helps explain the motivational basis of Internet user’s goals, as well as plans and actions that are set and carried out in pursuit of those goals.

Historically speaking, John Dewey is often considered the founder of functionalism. Dewey (1896) suggested that activity does not start with a stimulus, go through some central process in the organism, and then end up creating a response. Instead, Dewey argued that the process should be represented as a “reflex circuit” where the responses become the stimuli. This premise was built on the basic assumption of adaptation. That is, the stimulus adapts to the response, which then feeds back into the stimulus. This can be represented as a “feedback loop,” whereby the response and stimulus continue changing and adapting to one another until the activity either changes or ends altogether.

Dewey’s notion of a reflex circuit provides a broad conceptualization of how we believe individuals perceive and process interactive ads. That is, the activity of using the Internet does not start with the stimulus, or advertising message. Rather, Internet use begins with a response to some “drive,” for example, the need to shop. Once this response occurs and users visit the Internet, they are likely to find an ad that satisfies that drive (i.e., to shop) and helps them make a decision (i.e., to buy or not to buy). This, in turn, satisfies the motive (i.e., shopping), and the original state of that motive changes–perhaps to satisfy another motive or undertake some other activity altogether.

The first assumption we begin with, then, is that information processing in an interactive environment begins with the individual. The second assumption, which is implied in Dewey’s approach and then later built upon by Angell (1907), is that individuals constantly adapt to their environment in an effort to satisfy some need, or goal. In other words, we assume the individual is an active initiator and participator in the online experience (see Hoffman & Novak, 1996).

Our third and final assumption is based on not just the “why” of Internet use, but the “how” as well (see Angell, 1907). For the early functionalists, the “why” of human behavior pertained to motivation to solve a problem (i.e., motives) and the “how” was the cognitive effort (i.e., information processes) needed to solve the problem. In short, the assumptions of functionalism provide a rationale for structuring the IAM, beginning with motives and followed by information processes. In fact, the early functionalists assumed that motives could not be carried out without the memory processes.

On a practical level, knowing how and why people use the Internet may equip researchers with information about the factors that attract audiences to the Internet and prompt them to return. Such an understanding should help scholars and practitioners develop more sophisticated models and make better predictions about users’ web-related attitudes and behaviors. Consistent with this line of reasoning, our model begins with web motives, or more precisely, the reasons people use the Internet, as demonstrated by the beginning component of our model (see Figure 1).

Figure 1.


Advertising Feature Variables

Internet Motives

A motive is an inner desire to actively fulfill a need or want (Deci & Ryan, 1985; Papacharissi & Rubin, 2000). This definition highlights the importance of an active audience–a central supposition of the functional approach. Thus, an Internet motive can be defined as an inner drive to carry out any online activity. The term “drive” is used to signify effort on the user’s part. Scholars assume that the Internet requires at least a minimal amount of effort, or involvement, on the part of Internet users (Hoffman & Novak, 1996). The term “any” in the definition above includes motives already identified in the literature, as well as any future motives that might be identified once the Internet and its uses have been solidified (see Korgaonkar and Wolin,1999).

A dozen or so scholars have attempted to identify uses of the Internet (Eighmey, 1997; Eighmey & McCord, 1998; Isabelle & Lukas, 1997; Katz & Aspden, 1997a; Korgaonkar & Wolin, 1999; Rodgers & Cannon, 2000; Rodgers & Sheldon, 1999; Stafford & Stafford, 1998; Stafford & Stafford, 2000). These motives range from shopping (Wells & Chen, 1999), information-seeking (Raman, 1997) and surfing (Rodgers & Sheldon, 1999), to communicating (Katz & Aspden, 1997), social escapism (Korgaonkar & Wolin, 1999) and relaxation (Papacharissi & Rubin, 2000). Although more than 100 web motives have been identified across these studies, Rodgers and Sheldon (1999) argue that the bulk of these motives fall into four primary categories, including: researching, communicating, surfing (i.e., entertaining) and shopping.

These motives are highlighted, not to defend nor define a taxonomy of Internet motives, but rather to orient the reader to the range and possibility of reasons for going online. No other medium, in fact, provides consumers with such an array of opportunities to fulfill motives. Nevertheless, the important point here is that, because Internet use is initiated with some specific goal in mind (Cannon, Richardson, & Yaprak, 1998), an information processing model must begin with these motives. These motives, we believe, are antecedents to any ad processing that takes place once the motive is pursued. Ultimately, then, we expect to find that Internet motives influence consumer responses to Internet ads.

In fact, Papacharissi and Rubin (2000), in their investigation of five primary motives of Internet use, found that Internet motives were the most significant predictor of consumer responses. For example, the “information seeking” and “entertainment” motives predicted whether individuals used e-mail. Likewise, Rodgers and Sheldon (1999) found through regression analysis that Internet motives predicted how consumers responded to online ads. Surfers, for instance, were more positive toward online ads than researchers.

Knowing what motivates individuals to use the Internet also provides insights into the types of ads and ad appeals that will attract attention and prompt click-throughs. Clary and his colleagues (1998) have examined this issue in an off-line context, using a brochure as the stimulus. The authors hypothesized that, by knowing what motivates individuals to become volunteers, appropriate messages can be designed to entice volunteerism. Support was found for this hypothesis. Individuals who were motivated to volunteer for career reasons, were more likely to say they would volunteer when shown brochures that emphasized a career-related motive.

Although this exact hypothesis has not been tested online, it seems likely that it would apply there as well. For example, it has been shown that people who are driven to use the Internet for a particular reason (e.g., surfing) are more likely to express favorable attitudes toward banner ads that “promote” that motive (Rodgers & Sheldon, 2000).

In effect, these findings demonstrate that Internet motives make a difference in terms of the cognitive “tools” individuals use in pursuit of their goals. Knowing what motivates people to use the Internet also may provide hints about the kinds of ads and messages to create. According to our model, Internet motives should influence attention, memory and attitude toward interactive ads encountered while attempting to fulfill a need or want in cyberspace.

In a test of this very hypothesis, Li and Bukovac (1999) conducted an experiment and manipulated whether the Internet motive was researching or surfing. Information seekers were hypothesized to outperform web surfers in terms of recall and the number of ads clicked. Although the mean scores were in the expected direction, the findings were not statistically significant. As noted by the authors, this may have been due to motive-switching among the participants (p. 352). That is to say, the participants who were information-seekers, switched to a surfing motive while in pursuit of information (for a similar argument, see Rodgers & Cannon, 2000).

Motive-switching is an important assumption of our model. First, we expect to find individuals entering cyberspace with one or more motives in mind and, throughout the course of carrying out that motive, switch motives perhaps because they became bored or frustrated with the initial motive, or because some other activity captured their attention (Hoffman & Novak, 1996). It is easy to imagine, for example, an individual who enters cyberspace with a research motive. Throughout the course of researching, the individual switches motives and decides to purchase a CD or some other item that was advertised or made available adjacent to the material being read. The individual might maintain this motive until the purchase is complete before switching to another motive or exiting the web completely.

This is an important element of our model, and one that is not easily understood nor conceptualized. We mention this switching process, however, to demonstrate the complex nature of an interactive environment. An individual who enters the web with a shopping motive, but then becomes discouraged because a search engine does not produce the necessary results, might find a shopping-oriented advertisement interesting one moment and not the next. One way to conceptualize this motive-switching process is to think about the broader concept of user mode.


A mode, as represented in the second component of our model, is defined as the extent to which Internet activities are goal-directed. Walters, Apter and Svebak (1982) have conceptualized goal-directedness along a continuum ranging from “telic” to “paratelic,” where telic refers to high goal-directedness and paratelic refers to low goal-directedness (i.e., “playfulness”). The authors purport that individuals in a telic mode tend to be more serious-minded and focus more on the future than the present. In contrast, individuals in a paratelic mode tend to be more playful and lighthearted, orienting to the present rather than the future (see Walters, Apter, & Svebak, 1982, p. 197).

Thus, it may be that researchers are more naturally inclined than surfers to enter cyberspace with a highly goal-directed agenda (see Rodgers & Sheldon, 1999). Presumably, this goal-directed state would make the online experience more “serious” and less “playful” (see Walters, Apter, & Svebak, 1982). A more serious, goal-oriented mode might then translate into more cognitive effort being placed on reaching the goal (i.e., a “futuristic” outlook) and less cognitive effort being devoted to other tasks, such as attending to online ads (i.e., a “present” orientation).

Surfers, on the other hand, presumably are more “present-oriented” and, as a result, are more likely to demonstrate curiosity and exploration in cyberspace (see Katz & Aspden, 1997a). Our model predicts that these individuals would be more likely than researchers to click on ads found along their cyber journey. This is not to say that researchers ignore and/or are uninfluenced by online ads. It may be, in fact, that researchers are more likely than surfers to click on ads that are directly relevant to the task at hand (see Cho, 1998). Our point here is that the motive influences the mode in which users enter the Internet. Thus, these two components work conjointly to influence whether and which types of ads are attended to, as well as the extent of processing.

As noted by the up-and-down arrows in our model, we conceptualized the user’s mode as constantly “switching,” possibly even moment by moment. This is consistent with the work of Walters, Apter and Svebak (1982), who define this type of switching as psychological reversal. They argue that individuals switch from serious to playful modes throughout the course of a day and even moment by moment. The same appears to be true of the Internet. Either way, we expect to find that Internet motives and modes will influence information processing of interactive ads.

We shift our focus now to a brief discussion of the information processing school of thought, its assumptions and how it applies to our model.

Information Processes

Information processing, which developed in psychology, has its roots in functionalism. Like functionalism, this approach assumes that there are inputs to and outputs from humans. Between the two lies “information processing.” Three areas of research in psychology came together to create information processing approaches and “cognitive” psychology. The first area is artificial intelligence, which involved efforts to create computer programs that mimicked human activities like game playing (e.g. Feigenbaum & Simon, 1962), problem solving (e.g. Simon & Kotovsky, 1963; Evans, 1963), and verbal learning (Simon & Feigenbaum, 1964). The second area of influence was information theory (Evans, 1963; Feigenbaum & Simon, 1962; Simon & Feigenbaum, 1964; Simon & Kotovsky, 1963), which conceptualized communication tasks in terms of mechanical processes like information flow, signals, filters and bandwidths. The third area was linguistics (e.g., Chomsky, 1957), which contributed by proposing the idea that rule-governed processes are helpful in understanding the complexities of language acquisition and use.

During the 1960’s, psychologists like Neisser (1967) and Berlyne (1975) came to call this approach “cognitive psychology.” Cognitive psychology was importantly different from the behaviorist paradigm in that the tasks assigned to people were more complex than those used by behaviorists, such as learning simple lists of words or letter combinations. In addition, cognitive psychologists posited “mental” activities like attending, attitude formation, memory storage and decision making.

Out of this rich tradition that combined empirical research with the idea of mental processing came the modern “stage models” of advertising processing (.e.g., McGuire, 1978; Preston, 1982). In these “hierarchies,” consumers gather information from commercials they “attend” to, comprehend that information, link it with what they already know, evaluate the information, form attitudes and intentions to purchase, and as a function of these processes, consumer behavior is created. As this literature developed (see Thorson, 1989 for a review of information processing models of advertising), the models became increasingly complex. Additional processes like involvement (e.g., Krugman, 1965) central and peripheral processing (e.g., Petty & Cacioppo, 1986), systematic and heuristic processing (Chaiken, 1980) and affect (Batra, 1984) became articulated. A fairly recent and broadly defined information processing theory of advertising was outlined by MacInnis and Jaworski (1989). Their model includes needs, motivation, ability, attention, cognitive and emotional processing, and attitude formation (see Figure 1 in MacInnis & Jaworski). What we are suggesting here is that the research and conceptualizations developed for traditional advertising are certainly relevant to a theory of interactive advertising. In fact, we expect that the information processing models developed in the last twenty years or so of advertising research will be highly applicable in the interactive world. Although we do not try to explicate information processing further here, we include those processing stages in what we have labeled “cognitive tools” in the model of Figure 1. Once we have determined what the motives are for Internet use and user mode, we then assume a set of information processing tools. That is, the individual must attend to Internet ads, remember them and develop attitudes based on them before making a response. Before we ask in more detail how the information processing tools operate in an interactive environment, however, we must specify the structure of the that environment.

Advertiser-Controlled Aspects of the Internet

In the previous section, we outlined which aspects of the Internet are consumer-controlled. These components, as represented in our model, included Internet motives and modes, as well as cognitive tools–which always come under the consumer’s control. Now we turn to a discussion of Internet aspects that are under the control of the advertiser. Most of these variables include structural elements, such as ad types, formats and features, which we will define in a moment. This does not mean that consumers never control the structure of the interactive ads. In fact, a number of websites (e.g., allow consumers to alter the structural elements to personalize a webpage, as well as messages found within that page. In general, however, we assume that any control the advertiser can exert in an interactive environment will take place on a structural level.


Although the functionalist approach is useful for explaining why and how individuals use the Internet, it cannot account for environmental factors, such as the physical features of ads. More importantly, a strict functional approach ignores a host of theoretical possibilities concerning the interaction between user’s goals and how these goals operate in the face of ad stimuli. According to Snyder and Cantor (1998), the structural features themselves afford opportunities and impose constraints as individuals take action to carry out their motives, or goals. They argue that we need to consider how features of motives and features of message stimuli work “dynamically and interdependently” to guide and direct information processing in a variety of contexts (Snyder & Cantor, 1998, p. 644).

In fact, it has been argued that by knowing the characteristics of a stimulus environment, we are more likely to predict the behavior that occurs in response to that environment (e.g., Bandura, 1986). Psychologists are among the social scientists who have spent considerable effort in describing the stimulus environment in which people have to negotiate. For example, Brunswik (1950) presents a lens model that relates peoples’ perceptions of the stimulus array (i.e., environmental factors) to what mediated their senses. The stimulus environment is important because, as noted by Stewart and Furse (1985), it evokes people’s responses and it constrains them by variables like how reliable, unambiguous and complex it is. Obviously, the Internet is a complex choice situation where choices can occur for consumption, entertainment, and information or knowledge gathering–to name a few. Therefore, if we are to understand this complex environment, we must know the functions with which people enter an online environment, as well as the controlling features of that environment.

Three basic structural components have been conceptualized as part of the IAM, which include ad types, formats and features. Our primary argument is that information processing, as well as consumer responses, will hinge on the presentation of the interactive ad itself. This presentation, we believe, also interacts with the individual’s motive for using the Internet, as well as the mode in which the motive is carried out. Last, it is possible that the three general structural components will interact with one another to influence memory, attitude and behavior. We begin our discussion with a fairly detailed description of each of these three structures, starting with ad types.

Ad Types

The primary controlling feature of any ad is its general structure, or type. According to Thorson (1996), all advertisements can be classified into one of five basic categories, including: product/service, public service announcement, issue, corporate and political (for a definition of each, see Thorson, 1996). This is a useful taxonomy because it can be applied to any medium, including the Internet. It is not unusual, for example, to see an online ad for a product (e.g., Palm IV), service (e.g., free e-mail), corporation (e.g., or health issue (e.g., give blood to the American Red Cross).

Each of these ad types represents the general structure in which an ad is seen. That is, the ad type itself provides an indicator of the types of possible consumer responses. For example, we would expect in the case of a political ad that an individual will either take action and vote, or not vote. We would not expect to see an individual respond to a political ad by purchasing a product. A purchase response would be expected for ads that actually sell a product or service. So distinguishing among ad types is critical to the extent that practitioners and scholars expect to make accurate predictions about the sorts of outcomes as a result of the ads.

A second reason to include ad type in an interactive processing model is because ad type often will determine the types of cognitive tools, as outlined earlier, that individuals will use and to what extent. For example, attention may be heightened by ads that promote a political candidate who is strongly favored by the consumer. In the same vein, memory for an issue ad may be poor in instances where the ad promotes a health or public message that is irrelevant to the user. On a more general level, the structure of the ad itself might prompt different responses, regardless of whether the message is perceived as favorable or not. For example, we know from traditional advertising research that PSAs outperform other types of ads in terms of credibility and perceptions of social responsibility (Haley & Wilkinson, 1994). We assume the same would be true in an online environment.

But ad type is important from a functionalist perspective as well. As intimated in the previous section, we expect to find that the general type of ad interacts with the user’s motives to influence responses to the ad. Ads that are intended to promote a social issue presumably will fail in the face of a user who is attempting to shop for a particular item. The same may be true of individuals in a particular mode. For example, if the ad is intended to “draw” a person away from a goal-directed task by requesting a great deal of attention or effort, as in the case of a political ad, then it is likely that this type of ad would be least effective when a consumer uses the Internet for a goal-directed task, such as researching or shopping.

Thus, it is our belief that the general ad type will predict whether and how much cognitive effort is devoted to the task of processing online ads. As demonstrated by the example above, ad type also will interact with the user’s motives to influence outcomes, or consumer responses. But simply knowing the ad type may not be enough to predict the more microscopic sorts of processes and reactions to interactive ads. A number of studies, for example, have found that the format of an interactive ads makes a difference in terms of how people perceive and process it (e.g., Cho, 1998; Li & Bukovac, 1999).

Ad Formats

The format of the ad simply refers to the manner in which it appears. In traditional media, ads are generally formatted in the same, basic fashion. For example, TV commercials generally are formatted in 30- or 60-second spots, whereas magazine ads have a half- or full-page format. Although not as common, these formats are also seen online (e.g., Proctor & Gamble’s 30-second, fully-animated “Bounty” commercial).

At the same time, the Internet has the capacity to support a number of additional ad formats, some of which we do not find in traditional media. According to the Internet Advertising Bureau (1999), 55% of all online ads are formatted as banners, 37% are sponsorships and 8% are formatted as hyperlinks, interstitials and pop-ups. With the exception of sponsorships, all of these formats are certainly unique to the Internet. Even in the case of sponsorships, it has been argued that the manner in which they are formatted online is often unique compared to the format found in traditional media (Rodgers, 2000). Knowing how Internet ads differ from traditional ads in terms of formatting should add understanding to the manner in which the ad stimuli affects Internet users. Therefore, we begin with a brief definition of each of these interactive ad formats, incorporated with a discussion of how each format relates to our model. The reader should keep in mind that this list is by no means exhaustive of the ad formats found online. Rather, this list should be viewed as examples of the major types of formats found on the Internet.

Banners. Banner ads are those rectangular-shaped graphics, usually located at the top or bottom of a web page (Zeff & Aronson, 1997). Although banners generally appear in a horizontal position, advertisers have also experimented with vertical banners, which appear in the left- or right-hand side of the screen. Vertical banners, however, are more expensive because they take up space where most websites position an index or menu (i.e., on the left-hand side of the screen). Larger banners are about 7 inches wide by 1 inch deep (Zeff & Aronson, 1997), and smaller banners are about half as wide by 1 inch deep.

A number of findings from various studies demonstrate the importance of including banner formats in a processing model of interactive ads. First, banner ads have been shown to increase awareness even without click-throughs (e.g., Briggs & Hollis, 1997; IAB, 1997). When banners are clicked on, however, attitudes appear to become more positive and purchase intentions stronger (Brill, 1999) than unclicked banners. Banner size has certainly been shown to make a difference in terms of information processing. Larger banners have almost always shown higher click-throughs than smaller banners (e.g., Cho, 1998; Li & Bukovac, 1999), as have animated (versus static) banners, which we will discuss in the next section.

Interstitials and Pop-ups
. The terms “interstitial” and “pop-up” are often used interchangeably in trade articles and/or books. Yet, each represents different formats. Interstitials are usually full-screen ads that run in their entirety between two content pages. Pop-ups, on the other hand, appear in a separate window on top of content that is already on the user’s screen. This distinction is important for a number of reasons. First, unlike pop-ups, interstitials do not interrupt the user’s interactive experience because they tend to run while the user waits for a page to download. Users, however, have less control over interstitials because there is no “exit” option to stop or delete an interstitial, which is common among pop-ups. In other words, with interstitials, users have to wait until the entire ad has run.

We presume that these ad formats will have different effects on the Internet user. Ads that interrupt the user’s flow of work probably will be perceived as less favorable and more frustrating to the user, as in the case of a pop-up, than an ad that runs in between the user’s activity, as in the case of an interstitial. In addition, we would expect an interstitial to have a greater effect on memory, considering the ad takes up the entire computer screen, whereas a pop-up takes up maybe one-tenth of the screen. We will return to the topic of size as a structural feature of interactive ads in the next section. For now, suffice it to say that interstitial and pop-up ads are expected to exert differential processing on Internet users.

We also expect the format of pop-ups and interstitials to interact with Internet motives and modes. Individuals who are highly goal-directed, as in the case of researching a specific topic, presumably will find pop-ups and interstitials frustrating. This may particularly be the case with ads that get in the way of completing the goal (e.g., pop-ups). In contrast, individuals who are less goal-directed, such as surfers, may find pop-ups appealing, interesting or possibly even fun to explore.

Sponsorships. Although no common definition of sponsorship in a traditional medium has been accepted up to this point (Cornwell & Maignan, 1998), a sponsorship in an online context can be defined as “an indirect form of persuasion that allows companies to carry out marketing objectives by associating with key content” (Rodgers, 2000, p. 1). In traditional media channels, most sponsorships tend to be simple and are limited to brand name identification (e.g., “Sponsored by Kraft Foods”) or, in some cases, the brand name and a brief slogan (Hansen & Scotwin, 1995) (e.g., “Kraft Foods: Feeding the hungry one person at a time”).

Although the same is true of online sponsorships, they also can appear as part of the content of a webpage, or as part of a list of sponsors (Rodgers, 2000). In addition, electronic sponsorships can be interactive, such that a click of the mouse sends a visitor to the homepage of the sponsor (Rodgers, 2000). So we can see that even for online sponsorships, the interactive format is quite different than what we would find in a non-interactive environment. Another difference with sponsorships is that they almost always take up little space and, as a result, demonstrate more “consideration” of screen space, as well as the user’s time (i.e., interstitials require longer download time, whereas sponsorships require almost no download time). Rodgers (2000) has speculated that this difference alone may account for sponsorship’s popularity and seeming high credibility (compared to other ad formats) among Internet users.

In terms of psychological processing, sponsorships have been shown to outperform other ad formats, such as traditional (Rajaretnam, 1994) and advocacy ads (Haley & Wilkinson, 1996), in terms of recall and credibility. Although no study has tested this proposition in an interactive environment, we assume the same would be true of, say, sponsorships versus banners and/or sponsorships versus pop-ups.

The IAM also predicts that sponsorships will interact with Internet motives to yield some response. It may be, for example, that researchers are more responsive to sponsorships than, say, surfers, simply because sponsorships are more considerate of the user’s time and space. Because sponsorships are often embedded in the content of a webpage, it would be logical to think that researchers would be more likely than surfers or, perhaps, shoppers, to “stumble across” an embedded sponsorship while reading an article or researching. Either way, we believe the consumer’s response to sponsorships depends partly on the format of the sponsorship and partly on the motive pursued at the time the sponsorship is encountered.

Hyperlinks. A hyperlink, also sometimes referred to as a “hypertext link,” is simply a highlighted word, phrase or, sometimes graphic, that allows users to link to another website by simply clicking on the hyperlink. Hyperlinks are similar to sponsorships in that they generally take up less space than other ad formats, such as banners or pop-ups, and are generally embedded in the content itself. Several differences worth noting, however, is that there are no limits to the number of hyperlinks that can appear on any one webpage (Lewis & Lewis, 1997), and hyperlinks are often reciprocated, especially among popular websites (e.g., ESPN. com and (Thorson, Wells, & Rodgers, 1999). No study we know of has actually tested the psychological effects of hyperlinks as ad formats, but one study in particular demonstrates that too many clickable surfaces decreases the attractiveness, friendliness and usefulness of a website or webpage (Coyle, 1997). Too many hyperlinks might yield similar results.

Websites. Until recently, a website generally has been considered a “carrier” of ad formats, such as the ones highlighted above. Singh and Dalal’s (1999) recent conceptualization of the website as ad, and Chen and Wells’ (1999) recent measure for attitude toward the website, demonstrate the importance of placing a website in a category with other ad formats. This seems logical when we consider that many corporations, at least initially, created their websites using “shovelware” (Brill, 1999). (i.e., “shoveling” brochures and promotional pieces onto the website). So the corporate homepage has traditionally served the function of a “communications message” (Singh & Dalal, 1999, p. 92), much like the function of any online ad.

But websites have a greater number of options than any other online ad format. For example, unlike banner ads, the length of a persuasive appeal located on a webpage has no bounds (Brill, 1999). Websites also afford greater opportunities to create an emotional experience (Rodgers & Frisby, 1998) than, say, sponsorships. Perhaps the most important difference between websites and other ad formats, however, is the manner in which they are used and visited. Users, for instance, almost always seek out a website of their choice, presumably to fulfill a motive. In contrast, other interactive ad formats are often stumbled upon accidentally, as in the case of “trick” banners (Thompson & Wassmuth, 1999), or received unwantedly, as in the case of pop-ups. One way to think about this difference, then, is to consider this “push/pull” dichotomy, where push refers to ads the advertiser controls and pull refers to ads the consumer controls. Websites almost always require the user to “pull” its contents, and only in a few instances (e.g., pornographic websites) are websites “pushed” onto a user without her consent.

These differences between a website and other ad formats are important for several reasons. First, we expect information processing of websites to be much more complex than information processing of sponsorships, banners, pop-ups, etc. For one, users visit a website with a wider variety of motives. Some users may want to play a game, whereas others may want to socialize in their favorite chatroom. Thus, the length of time that is spent with a website might be longer than what might be spent glancing or even clicking into another ad format. As such, we expect to find higher retention of, at least, the brand name (i.e., website).

Given the complex nature of websites, we also expect to find a wider variety of user responses. Hoffman and Novak (1996) have offered a Network Navigation Model that conceptualizes response activity in terms of the user’s ability to overcome obstacles, such as dead-end websites, and challenges, such as low computer skill. Responses can range from boredom, in cases where the user loses interest in a website, to excitement, where the user becomes highly involved with content found in the website (see Hoffman & Novak, 1996). In cases where boredom sets in, we would expect the user to switch motives or, perhaps, exit the Internet altogether. Here is a case where the format of the ad interacts with the user’s motive and/or mode. We presume that poorly designed or unengaging websites will irritate most users, but particularly those who are highly goal-directed.

In short, it is important to articulate the format in which an interactive ad is seen because, as our model predicts, different ad formats result in differential processing and outcomes. In addition, we expect to find that the user’s motive and/or mode will “interact” with the ad format to influence consumer responses.

Ad Feature

What we need now is to move beyond the more general structure of an interactive ad to the particular features that can be found within each of these types and formats. Fortunately, we do not have to start from scratch. Advertising theorists have been working for years to describe the critical stimulus features of print, broadcast, outdoor, and other advertising media environments and their content. While not all of the features identified in these contexts is relevant to the Internet, many of them are. Figure 2 shows a modified model from Thorson and Leavitt (1986) that attempts to organize the stimulus structure variables from the primary media of print and broadcast advertising, and compare them with features from the Internet advertising environment.


On the x-axis of the figure, we see the three media: print, television, and the Internet. On the y-axis we see the dimension of whether the measurement of the structure is based on responses from people (subjective advertising structure) or directly from the stimulus itself (objective advertising structure). To make this figure clearer and to remind ourselves of the kinds of variables that advertising researchers have been studying for years, we looked for studies that examined one or more of the six cells represented here. What we found is that, as we moved up the channels from print to broadcast to the Internet, the structural features found in each medium became more complex. In fact, it could be argued that broadcast subsumes aspects of print, whereas the Internet subsumes aspects of broadcast and print. We will return to this discussion in a moment.

Right now, we begin with objective advertising structure. For print, the obvious variables have been structural dimensions such as color, size, typeface, number of words in the headline or body copy, size of the illustration, and other such variables that can be determined with pointer readings. Other examples of variables in this cell include product class and type of message appeal (Holbrook & Lehman, 1980).

For television, these objective variables may include some of the print variables (e.g., presence of color), but are, because of the increased complexity of television, more extensive than what has been studied for print. In one of the earliest studies, Simon (1981) defined ten creative strategies that might characterize television commercials. These strategies included: information presentation, argument, motivation with psychological appeals, repeated assertion, command, brand familiarization, symbolic association, imitation, obligation and habit starting.

Haller (1972) described TV commercials by whether they had visuals that aided the audio in getting the message across, the clarity of the message and whether it spoke personally to the consumer. Aaker and Norris (1981) looked at whether commercials had a hard or soft sell, whether a problem was posed, whether price or product tests were mentioned and how much information was in the commercial. In the most extensive analysis of television commercial structure, Stewart and Furse (1985; 1986) described 153 variables for which good interobserver reliability was possible and then used these variables to predict how well the commercials performed on Research Systems Corporation’s (ARS) three measures of advertising effectiveness: recall, key message comprehension and persuasion. In a psycholinguistic approach, Thorson and Snyder (1984) described television commercials in terms of semantic idea units, and their grammatical connections with each other.

When we look at the subjective advertising structure approach to describing print and television commercials, we see that consumers can provide direct evaluations of commercials that are important dimensions. An early example was developed by Leavitt (1970) and Wells, Leavitt and McConville (1971). They began with a large group of words that could be applied to commercials. After a number of iterations, these words were boiled down to four factors: stimulating, relevant, gratifying and familiar.

In a similar approach, McEwen and Leavitt (1970) used phrases that might be applied to advertising and came up with 12 factors: empathetic project integration, announcer integrated into ad, demonstration by people, pleasant/unpleasant, liveliness, confusion, new product introduction, structured product story, problem/solution, animation, persuasive and opening suspense.

A number of researchers developed what are known as adjective checklists. For example, Schlinger (1974) started with 600 descriptive statements derived from consumer playback and eventually identified seven factors: entertainment, confusion, relevant news, brand reinforcement, empathy, familiarity and alienation. These approaches are linked in that they attempt to identify specific features of ads, but do so based on measurement of people’s responses to the ads.

We argue that most of the research developed in this tradition can be relevant to Internet advertising. In fact, on the Internet, a television commercial can be simulated. So too can a print ad. Therefore, the ways of describing these advertising message structures can often be quite relevant. For example, the adjective checklists, like that one developed by Schlinger (1974), could certainly be employed to describe any online ad (e.g., banners) or combinations of ads (e.g., banners, sponsorships and pop-ups). Consistent with print and broadcast, Internet researchers have worked from a structural framework to attempt to characterize stimulus ad features present in cyberspace (e.g., Bucy et al., 1998; Frazer & McMillan, 1999; Miranda & Ju-Pak, 1998). Not surprising, many of the structural features identified and studied by earlier researchers also have been identified online.

For example, Bucy et al. (1998) content analyzed 496 of the most popular websites and found the most common structural features included animation, color and graphics. Miranda and Ju-Pak (1998) content analyzed 200 banner ads found among the top 50 websites. Structural features that were examined ranged from advertising appeals (e.g., rational versus emotional), the presence of a headline, as well as banner and font size. So here again, we see that Internet researchers are using a structural framework that has been common among traditional advertising research in the past. As demonstrated by this brief overview, in many cases, the structural features that are present online also have been found in ads appearing in broadcast and print mediums.

But of course, Internet ads can add new levels of complexity beyond what is available in print and broadcast. Probably the most salient feature of Internet advertising is that of interactivity. Interactivity has been described as an important feature that distinguishes the Internet from every other medium (Roehm & Haugtvedt, 1999), and the presence of interactivity as a structural feature within websites has been shown to boost Internet traffic (Ghose & Dou, 1998). Although interactivity has been defined and conceptualized in communication (Heeter, 1989) and interpersonal (Rafaeli, 1988) contexts, we prefer a definition that is specific to the Internet. Steuer’s (1992) definition does just that. He defines interactivity as “the extent to which users can participate in modifying the form and content of a mediated environment in real time” (p. 84).

Thus, this particular feature of the Internet is unique in the way it allows the user to participate in the persuasion process by changing the structural elements themselves. Never before have consumers had this ability when confronted with ads from either the broadcast or print genres. Online, a consumer can choose to click on a banner or not. She can search out advertising websites. And when she’s at a website, she can select pages to look at–some she’ll spend little or no time with. Web advertising can be personalized to her, referring to her by name or indicating that she is known to have particular interests.

According to Roehm and Haugtvedt (1999), the consumer benefits more from an interactive (versus non-interactive) environment, because it allows them to be actively involved in the persuasion process. For one, an interactive environment allows consumers to select ads that directly benefit and are relevant to them by providing consumers with the ability to customize ads to their own liking.

A number of studies have also attempted to assess subjective advertising structure on the Internet. For example, Eighmey (1997), using a U&G approach, identified subjective structures of websites by asking participants to rate their online experience after visiting several websites. Some of the adjectives used to describe the websites were similar to those identified in earlier studies looking at traditional media (e.g., credible). Yet many of the subjective features were quite different than what we might find in a traditional advertising environment. For example, the “playfulness” of the website, the “informativeness” of the content and the “effort” it took to navigate the website.

The primary purpose, of course, in identifying either objective or subjective features of online ads is to enable predictions of potential responses to these features. Returning to the findings of Li and Bukovac, the objective structure of size and animation made differences in terms of people’s responses to them when viewed in the context of banner ads. Larger, animated banners, for instance, were recalled and clicked on more often than smaller, static banners. Likewise, Chen and Wells (1999) have demonstrated how subjective features of a website (e.g., navigation) can influence user’s ultimate response to that website. Interestingly, Cho (1998) has found that features of the ad (e.g., relevance) and features of the website in which the ad is viewed (e.g., relevance) interact to influence differential processing.

It seems likely that subjective and objective features also would interact with the user’s motives in yielding different responses. Animation, for example, might be viewed as an irritation by individuals attempting to carry out specific goals. This may particularly be the case for veteran users who understand persuasion strategies (e.g., larger sizes, animation, color, etc.) used online (see Friestad & Wright, 1994).


Like in the other areas we have examined, the responses that people make to traditional advertising remain relevant to the interactive world, but, again, there are new sets of responses that must be defined and included. Our information processing model suggests a basic set of responses (see Wells, 1997, for a thorough examination of responses that are used to evaluate the effectiveness of traditional advertising).

The first question is whether an ad breaks through its surrounding environment to register with the consumer. Recalling and recognizing ads or ad cues are most commonly used measures for indicating attention and perception of the ad has occurred. But there are more direct measures of attention, including eye movements (Brown, 1968), and eye gaze (e.g., Anderson, 1987), which Thorson and Zhao (1997), when using the measure for television, called “eyes on screen.” There is also some use of psychophysiological measures, at least for attention to television (e.g., Thorson & Lang, 1992; Rothschild et al., 1988). And also for television is the measure of response time, usually response to a secondary task which the viewer must respond to while viewing the ad (Anderson, 1987; Rothschild et al., 1988; Thorson & Lang, 1992; Thorson, Reeves, & Schleuder, 1987; Thorson & Zhao, 1997). And, of course, there is self-reported attention to ads, which is often the easiest measure to employ.

All of these measures would be potentially applicable to measuring attention to interactive advertising. But of course, there are additional alternatives for this new medium. For example, the time spent at a website (actually a response time, but of a much longer duration than has been employed for television commercials) is likely to be informative about how much attention is being devoted to the information in that interactive advertising format. There has also been some initial work using response time to banner ads (Brill, 1999). Another measure that can be used to measure attention to interactive advertising is the “click,” which is a mouse response to a location on the Internet screen, or the “click-through,” which is a series of mouse selection responses to the pages in an Internet ad or website. Attention can also be indexed by “hits” to a website or other interactive format. A “hit” is a visit by an individual to that particular site. Many advertising websites are programmed with ways to automatically track who visits that site. Information can then be gained from this individual by asking them questions directly. It is also possible to deposit a “cookie” with the individual so that future visits can be identified as coming from the same person, at least from the same URL.

Memory, either in terms of recall or recognition, is a response to interactive advertising, which seems likely to take the same kind of measurements that have been used with traditional advertising. However, instead of using paper and pencil measures, it is much easier to index recall and recognition by questions asked directly on the computer screen.

Attitude toward the ad, which we have noted earlier to be an important component of many information processing models of advertising, is a response easily applied to interactive advertising, and indeed the features of this measure are described in the Wells article in this journal issue.

Of course, purchase and trial are key responses examined in advertising research, and are particularly easy to measure for Interactive advertising as well. In fact, behavioral responses to products or services advertised interactively, because they can be direct responses, can be measured in the same way as direct mail. This makes possible the same kind of ad structure testing that is used so effectively in direct mail to see what message variables have the highest impact.

Exploratory behavior is a response fairly unique to interactive advertising. For websites, this involves tracking and recording which pages consumers examine and how long they remain on those pages. This can also involve observing the order in which pages are chosen, and with the use of cookies and log files, studying return visits for further exploration.

Finally, all of the attitudinal measures that have been developed for traditional advertising are likely applicable to interactive advertising. Attitude toward the brand, liking for characters, or for message features as described above, are all likely to provide insight into attitudes that develop in response to the advertising. In addition, one can examine how “realistic” websites are, how much people feel they are able to “experience” the product or elements of the advertising itself (e.g., Coyle, 1999).

In summary, then, all of the response measures used for traditional advertising can be applied to interactive advertising. But there are also some important new types of measures, including: hits, click-throughs, time spent at websites, exploration patterns and the pattern of online purchasing. Clearly, a processing model must account for these new responses in order to better understand and predict consumer behavior in an interactive environment.

Applications of the Integrative Processing Model of Interactive Advertising

It is our contention that the model outlined here is useful in a number of important ways. First, the IAM takes some first steps in linking models of advertising in traditional media to a model of interactive advertising. Second, the IAM links many areas of advertising research into its components, and articulates how that research can continue to be relevant. And third, the IAM shows how in every case, interactive advertising involves the same variables, measures, and human processes as traditional advertising, but always in more complex ways.

In other words, the interactivity feature means that, for this new form of advertising, there are additional and different structural features of interactive ads as compared to traditional ads. In addition, there are additional and different kinds of responses to the advertising that people can make in an interactive (versus non-interactive) environment. Because people have at least four major kinds of motives for being on the Internet, these motives are critically important to how interactive advertising is effective. In addition to describing in-depth how interactive advertising is similar and different from traditional advertising, our model provides several sets of hypotheses about how and when interactive advertising will be most effective. We look briefly at some example hypotheses.

Probably most important to how interactive advertising operates are the motives and modes (serious versus playful) with which people enter cyberspace. When an individual logs onto the Internet intending to shop, those items on the shopping list are going to do well with attention, attitude and intent to purchase. Other items will be less effective, regardless of the structures of the interactive ads. Of course, the focused shopper may react negatively to ad features that make getting necessary information or ordering the product/service more difficult. But features of the ad message itself (color, animation, presence of trade characters, and so on) will likely be less critical. Presumably, the opposite will be true when consumers are motivated to use the Internet to surf or entertain themselves. In this case, it is more likely that advertising features will be better predictors of whether attention and selection of that advertising occurs (for a similar argument, see Rodgers & Sheldon, 1999).

For advertising websites, the model suggests that in addition to the traditional ad features affecting responses, interactivity variables will have great impact. Coyle (1997) posited that the number of clickable surfaces on a website, and the presence of sound and animation would all affect attitude toward the ad and intention to purchase. Interestingly, he found that positive responses increased at first with clickable surfaces, but then decreased. Sound and animation had no direct effect on attitudes.

Other applications of the IAM include examining how Internet motives influence information processes and consumer responses. Are users with different motives attracted to different types, formats and/or features of interactive ads, and how do motives interact with the various structural elements to influence information processing? Does the mode in which the user enters cyberspace make a difference in terms of the cognitive tools and responses used?

Although a good deal of research has looked at the psychological effects of banner ads, more research is needed to understand how other ad formats influence audience processing. The same can be said of ad types. Do political interactive ads, for example, affect users differently than social ads? Are there some motives that are more inviting of certain ad types than others? In addition, how does ad type interact with ad format to influence consumers’ reactions? Are political banner ads, for example, more or less successful at gaining attention and prompting a behavioral response than, say, a political website?

These are just some of the uses of this interactive model. We believe the IAM offers a broad enough framework that any researcher interested in consumer behavior in an interactive environment should find it useful. It is our hope that this model will promote additional thinking and ignite excitement among scholars as we attempt to test and advance theory and solve practical problems in this interactive medium.


Aaker, D. A., & Norris, D. (1981). Characteristics of television commercials perceived as information. Working paper, University of California, Berkeley.

Abels, E. G., White, M. D., & Hahn, K. (1997). Identifying user-based criteria for web pages. Internet Research: Electronic Networking Applications and Policy, 7 (4), 252-262.

Anderson, D. (1987). Now you see them–Now you don’t: Frequency and duration of exiting behavior in a home viewing environment. Paper presented at the conference of the Association for Consumer Research, Boston.

Angell, J. R. (1907). The province of functional psychology. Psychological Review, 14, 61-91.

Allport, G. W. (1937). Personality: A psychological interpretation. New York: Holt, Rinehart and Winston.

Banudra, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.

Batra, R. (1984). Affective advertising: Role, processes, and measurement. In R. A. Peterson, W. D. Hoyer, & W. R. Wilson (Eds.), The role of affect in consumer behavior. Lexington, MA: Lexington Books.

Berlyne, D. A. (1975). Behaviorism? Cognitive theory? Humanistic psychology?–To hull with them all! Canadian Psychological Review, 16 (2), 69-80.

Bezjian-Avery, A., Calder, B., & Iacobucci, D. (1998). New media interactive advertising vs. traditional advertising. Journal of Advertising Research, 38 (4), 23-32.

Boring, E. G. (1950). A history of experimental psychology, 2nd ed. New York: Appleton-Century-Crofts.

Brady, R., Forrest, E., & Mizerski, R. (1997). Cybermarketing: Your interactive marketing consultant. Lincolnwood, IL: NTC Business Books.

Briggs, R., & Hollis, N. (1997). Advertising on the web: Is there response before click-through? Journal of Advertising Research, 37 (2), 33-45.

Brill, A. M. (1999). Online newspaper advertising: A study of format and integration with news content. In D. W. Schumann & E.

Thorson (Eds.), Advertising and the World Wide Web (pp. 159-173).Mahwah, NJ: Lawrence Erlbaum.

Brown, B. B. (1968) Visual recall ability and eye movement. Psychophysiology, 4, 300-306.

Brunswik, E. (1950). The conceptual framework of psychology. Chicago: University of Chicago Press: International Encyclopedia of Unified Science.

Bucy, E. P., Lang, A., Potter, R. F., & Grabe, M. E. (1998). Structural features of cyberspace: A content analysis of the World Wide Web. Paper presented at the 1998 Conference of the Association for Education in Journalism and Mass Communication, Theory and Methodology Division, Baltimore.

Burns, K. S. (2000). Branding in cyberspace: Using the congruity of consumer and web site personality to unravel online user satisfaction. Proceedings of the 2000 Conference of the American Academy of Advertising, 9-18.

Cannon, H., Richardson, T., & Yaprak, A. (1998). Toward a framework for evaluating Internet advertising effectiveness. Paper presented at the 1998 Conference of the American Academy of Advertising, Lexington, KY.

Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39 (5), 752-756.

Chen, Q., & Wells, W. D. (2000). A new look at traditional measures: Attitude toward the site. Proceedings of the 2000 Conference of the American Academy of Advertising, 231.

Cho, C. H. (1998). How advertising works on the WWW: Modified Elaboration Likelihood Model. Paper presented at the 1998 Conference of the American Academy of Advertising, Lexington, KY.

Cho, C. H., & Leckenby, J. D. (2000). Banner clicking and attitude changes on the WWW. Proceedings of the 2000 Conference of the American Academy of Advertising, p. 230.

Cho, C. H., & Leckenby, J. D. (1999). Interactivity as a measure of advertising effectiveness: Antecedents and consequences of interactivity in web advertising. Proceedings of the 1999 Conference of the American Academy of Advertising, pp. 162-179.

Cho, C. H., Lee, J. G., & Tharp, M. (2000). Advertising responses to different forced exposure levels on the WWW. Proceedings of the 2000 Conference of the American Academy of Advertising, p. 148.

Chomsky, N. (1957). Syntactic structures. The Hague: Mouton.

Clary, E. G., Ridge, R. D., Stukas, A. A., Snyder, M., Copeland, J., Haugen, J., & Miene, P. (1998). Understanding and assessing the motivations of volunteers: A functional approach. Journal of Personality and Social Psychology, 74 (6), 1516-1530.

Cornwell, T. B., & Maignan, I. (1998). An international review of sponsorship research. Journal of Advertising, 27 (Spring), 1-21.

Coyle, J., & Brill, A. (1998). Examining metaphor use in on-line advertising. Paper presented to the Association for Education in Journalism and Mass Communication, Advertising Division Special Topics, Baltimore.

Coyle, J. R., & Gould, S. J. (2000). Exploring the clickstream: How Internet users navigate web sites. Proceedings of the 2000 Conference of the American Academy of Advertising, p. 147.

Coupey, E. (1999). Advertising in an interactive environment: A research agenda. In D. W. Schumann & E. Thorson (Eds.), Advertising and the World Wide Web (pp. 197-215). Mahwah, NJ: Lawrence Erlbaum.

Coyle, J. (1997). The effects of progressive levels of interactivity and vividness in on-line advertising on attitude-behavior consistency. Unpublished doctoral dissertation, University of Missouri, Columbia.

Cross, R., & Smith, J. (1997). Customer-focused strategies and tactics. In R. Brady, E. Forrest, & R. Mizerski (Eds.), Cybermarketing: Your Interactive Marketing Consultant (pp. 55-78). Lincolnwood, IL: NTC Business Books.

Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19, 109-134.

Dervin, B., Nilan, M. S., & Jacobson, T. L. (1982). Improving predictions of information use: A comparison of predictor types in a health communication setting. In M. Burgoon (Ed.), Communication Yearbook, 5, (pp. 806-830). New Brunswick, NJ: Transaction Books.

Dewey, J. (1896). The reflex arc concept in psychology. Psychological Review, 3, 357-370.

Ducoffe, R. H. (1996). Advertising value and advertising on the web. Journal of Advertising Research, 26 (5), 21-35.

Edwards, S., & La Ferle, C. (2000). Empathy based persuasion: Flow and telepresence as experiential aspects of cyberspace. Proceedings of the 2000 Conference of the American Academy of Advertising, 232.

Edwards, S., & LaFerle, C. (1998). Cross media promotion of Internet sites in television advertisements. Paper presented at the 1998 Conference of the American Academy of Advertising, Lexington.

Eighmey, J. (2000). The relationships among informativeness, user enjoyment, and marketing perceptions of commercial sites on the Internet. Proceedings of the 2000 Conference of the American Academy of Advertising, 231, 232.

Eighmey, J. (1997). Profiling user responses to commercial web sites. Journal of Advertising Research, 37 (3), 59-66.

Eighmey, J., & McCord, L. (1998). Adding value in the information age: Uses and gratifications of sites on the World Wide Web. Journal of Business Research, 41, 187-194.

Evans, T. C. (1963). A heuristic program for solving geometric analogy problems. Unpublished dissertation, Massachusetts Institute of Technology, Cambridge. As referenced in Hilard, E. R., & Bower, G. H. (1966). Theories of learning. New York: Appleton-Century-Crofts.

Feigenbaum, E., & Simon, H. A. (1962). A theory of the serial position effect. British Journal of Psychology, 53, 307-320.

Flaherty, L. M., Pearce, K. J., & Rubin, R. B. (1998). Internet and face-to-face communication: Not functional alternatives. Communication Quarterly, 46 (3), 250-268.

Fortin, D. R. (1999). “New and improved!” Advertising in cyberspace: Using specific conduits to access browsers and seekers. In D. W. Schumann & E. Thorson (Eds.), Advertising and the World Wide Web (pp. 63-69). Mahwah, NJ: Lawrence Erlbaum.

Frazer, C., & McMillan, S. J. (1999). Sophistication on the World Wide Web: Evaluating structure, function, and commercial goals of web sites. In D. W.

Schumann & E. Thorson (Eds.), Advertising and the World Wide Web (pp. 119-134). Mahwah, NJ: Lawrence Erlbaum.

Fredin, E. S., & David, P. (1998). Browsing and the hypermedia interaction cycle: A model of self-efficacy and goal dynamics. Journalism & Mass Communication Quarterly, 7 (1), 35-54.

Friestad, M., & Wright, P. (1994). The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research, 21 (June), 1-31.

Ghose, S., & Dou, W. (1998). Interactive functions and their impacts on the appeal of Internet presence sites. Journal of Advertising Research, 38 (2), 29-43.

Ha, L., & James, E. L. (1998). Interactivity in business web sites: A content analysis. Paper presented at the 1998 Conference of the American Academy of Advertising, Lexington.

Haley, E., & Wilkinson, J. (1994). And now a word from our sponsor: An exploratory concept test of PSAs vs. advocacy ads. The proceedings of the 1994 conference of the American Academy of Advertising, 79-87.

Haller, T. B. (1972). Predicting recall of TV commercials. Journal of Advertising Research, 12, 43-46.

Hammond, K., McWilliam, G., & Diaz, A. N. (1998). Fun and work on the web: Differences in attitudes between novices and experienced users. Advances in Consumer Research, 25, 372-378.

Hansen, F., & Scotwin, L. (1995). An experimental inquiry into sponsoring: What effects can be measured? Marketing and Research Today, 23 (3), 173-181.

Heeter, C. (1989). Implication of new interactive technologies for conceptualizing communication media use. In J. Savaggio & J. Bryant (Eds.). The information age: Emerging patterns of adoption and consumer use (pp. 53-75). Hillsdale, NJ: Lawrence Erlbaum.

Hilard, E. R., & Bower, G. H. (1966). Theories of learning. New York: Appleton-Century-Crofts.

Holbrook, M. B., & Lehmann, D. R. (1980). Form versus content in predicting Starch scores. Journal of Advertising Research, 20, 53-62.

Internet Advertising Bureau (1997). IAB online advertising effectiveness study. Internet Advertising Bureau.

Katz, J. , & Aspden, P. (1997a). Motivations for and barriers to Internet usage: Results of a national public opinion survey. Internet Research: Electronic Networking Applications and Policy, 7 (3), 170-188.

Katz, J. E., & Aspden, P. (1997b). A nation of strangers? Communications of the ACM, 40 (12), 81-86.

Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public Opinion Quarterly, 37, 509-523.

Korgaonkar, P. K., & Wolin, L. D. (1999). A multivariate analysis of web usage. Journal of Advertising Research, March/April, 53-68.

Krugman, H. E. (1965). The impact of television advertising: Learning without involvement. Public Opinion Quarterly, 29, 349-356.

Lasswell, H. (1948). The structure and function of communication in society. In L. Bryson (Ed.), The Communication of Ideas (pp. 37-51). New York: Harper & Row.

Leavitt, C. (1970). A multidimensional set of rating scales for television commercials. Journal of Applied Psychology, 54 (5), 427-429.

Lee, S. J., & Lee, W. N. (2000). The Internet as a potential political communication tool: A content analysis of political web sites. Proceedings of the 2000 Conference of the American Academy of Advertising, 162-168.

Lewis, H. G., & Lewis, R. D. (1997). Selling on the Net: The complete guide. Chicago, IL: NTC Business Books.

Li, H., & Bukovac, J. L. (1999). Cognitive impact of banner ad characteristics: An experimental study. Journalism and Mass Communication Quarterly, 76 (2), 341-353.

Li, H., Kuo, C., & Russell, M. G. (1999). Demographic differences in perceived channel attributes: A comparison of the web, catalogs, and retail stores. Proceedings of the 1999 Conference of the American Academy of Advertising, p. 237.

Lin, C. A. (1999). Online-service adoption likelihood. Journal of Advertising Research, 39 (2), 79-89.

MacInnis, D. J., & Jaworksi, B. J. (1989). Information processing from advertisements: Toward an integrative framework. Journal of Marketing, 53 (4), 1-23.

Maddox, L. M., & Mehta, D. (1997). The role and effect of web addresses in advertising. Journal of Advertising Research, 37 (2), 47-59.

Maignan, I., & Lukas, B. A. (1997). The nature and social uses of the Internet: A qualitative investigation. The Journal of Consumer Affairs, 31 (2), 346-371.

McDonald, S. C. (1997). The once and future web: Scenarios for advertisers. Journal of Advertising Research, 37 (2), 21-28.<

McGuire, W. J. (1978). An information-processing model of advertising effectiveness. In H. L. David & A. J. Silk (Eds.), Behavioral and management science in marketing. New York: Ronald.

McMillan, S. J. (2000). Interactivity is in the eye of the beholder: Function, perception, involvement, and attitude toward the web site. Proceedings of the 2000 Conference of the American Academy of Advertising, 71-78.

Miranda, B., & Ju-Pak, K. H. (1998). A content analysis of banner advertisements: Potential motivating features. Paper presented at the 1998 Conference of the Association for Education in Journalism and Mass Communication, Baltimore.

Mitchell, A. A., & Olson, J.C. (1981). Are product attribute beliefs the only mediator of advertising effects on brand attitudes? Journal of Marketing Research, 18, 318-332.

Morris, M., & Ogan, C. (1996). The Internet as mass medium. Journal of Communication, 46 (1), 39-50.

Morrison, M. A. (1998). A look at interactivity from a consumer perspective. Developments in Marketing Science, 21, 149-154.

Neisser, U. (1967). Cognitive psychology. New York: Appleton-Century-Crofts.

Newhagen, J. E., & Rafaeli, S. (1996). Why communication researchers should study the Internet: A dialogue. Journal of Communication, 46 (1), 4-13.

Nowak, G. J., Shamp, S., Hollander, B., & Cameron, G. T. (1999). Interactive media: A means for more meaningful advertising? In D. W. Schumann & E.

Thorson (Eds.), Advertising and the World Wide Web (pp. 99-117). Mahwah, NJ: Lawrence Erlbaum.

Oh, K. W., Cho, C. H., & Leckenby, J. D. (1999). A comparative analysis of Korean and U.S. web advertising. Proceedings of the 1999 Conference of the American Academy of Advertising, pp. 73-86.

Papacharissi, Z. & Rubin, A. M. (2000). Predictors of Internet use. Journal of Broadcasting & Electronic Media, 44 (2), 175-196.

Pashupati, K., & Raman, P. (1999). Web banner ads on India’s online newspapers: Who’s talking to whom. Proceedings of the 1999 Conference of the American Academy of Advertising, pp. 87-88.

Petty, R. E., & Cacioppo, J. T. (1986). Attitude change: Central and peripheral routes to persuasion. In Advances in experimental social psychology. New York: Springer-Verlag.

Pittman, T. S., & Heller, J. F. (1988). Social motivation. Annual Review of Psychology, 38, 461-489.

Preston, I. L. (1982). The association model of advertising communication process. Journal of Advertising, 11 (2), 3-15.

Rafaeli, S. (1988). Interactivity: From new media to communication. In R. Hawkins et al. (Eds.), Advancing communication science: Merging mass and interpersonal processes, 16 (pp. 110-134). Newbury Park, CA: Sage Publications.

Rafaeli, S. (1986). The electronic bulletin board: A computer-driven mass medium. Computers and the Social Sciences, 2, 123-136.

Rajaretnam, J. (1994). The long-term effects of sponsorship on corporate and product image: Findings of a unique experiment. Marketing and Research Today, 22 (February), 62-74.

Raman, N. V. (1997). Wandering on the web. In M. C. Macklin (Ed.), Proceedings of the 1997 Conference of the American Academy of Advertising (pp. 224-228). Cincinnati, OH: University of Cincinnati.

Rayburn, J. D., II (1996). Uses and gratifications. In M. B. Salwen & D. W. Stacks (Eds.), An Integrated Approach to Communication Theory and Research (pp. 145-163). Mahwah, NJ: Lawrence Erlbaum.

Rodgers, S. (2000). Predicting sponsorship effects using the Sponsorship Knowledge Inventory. Unpublished dissertation. School of Journalism, University of Missouri-Columbia.

Rodgers, S., Cameron, G. T., & Brill, A. M. (1999). This page is brought to you by…: An experimental test of sponsorship credibility in an online newspaper. Paper presented to the Advertising Division of the Association for Education in Journalism and Mass Communication, New Orleans.

Rodgers, S., & Cannon, H. M. (2000). The many faces of web users: An exploratory study of functionally-based web-usage groups. Paper presented at the 2000 Conference of the American Academy of Advertising, Rhode Island.

Rodgers, S., & Frisby, C. (1998). Not on target: Effects of Gender-Targeted Web Sites on Liking and Visit Intent. Paper presented at the 1999 Conference of the Association for Education in Journalism and Mass Communication, New Orleans, LA.

Rodgers, S., & Sheldon, K. M. (2000). Predicting banner effects by knowing web motives. Working paper, University of Minnesota, School of Journalism and Mass Communication.

Rodgers, S., & Sheldon, K. M. (1999). The Web Motivation Inventory: A new way to characterize web users. Paper presented at the 1999 Conference of the American Academy of Advertising, Albuquerque, NM.

Roehm, H. A., & Haugtvedt, C. P. (1999). Understanding interactivity of cyberspace advertising. In D. W. Schumann & E. Thorson (Eds.), Advertising and the World Wide Web (pp. 27-39). Mahwah, NJ: Lawrence Erlbaum.

Rothschild, M., Hyun, Y. J., Reeves, B., Thorson, E., & Goldstein, R. (1988). Hemispherically lateralized EEG as a response to television commercials. Communication Research, 15 (2), 185-198.

Schlinger, M. J. (1974). A profile of responses to commercials. Journal of Advertising Research, 19 (2), 37-46.

Sheehan, K. B., & Doherty, C. (2000). IMC on the WWW: An exploration. Proceedings of the 2000 Conference of the American Academy of Advertising, p. 156, 157.

Simon, H. A., & Feigenbaum, E. (1964). An information-processing theory of some effects of similarity, familiarization, and meaningfulness in verbal learning. Journal of Verbal Learning and Verbal Behavior, 3, 385-396.

Simon, H. A., & Kotovsky, K. (1963). Human acquisition of concepts for sequential patterns. Psychological Review, 70, 534-546.Simon, J. L. (1981). The management of advertising. Englewood Cliffs, NJ: Prentice-Hall.

Singh, S. N., & Dalal, N. P. (1999). Web home pages as advertisements. Communications of the ACM, 42 (8), 91-98.

Snyder, M., & Cantor, N. (1998). Understanding personality and social behavior: A functionalist strategy. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The Handbook of Social Psychology (pp. 635-679). New York: Oxford University Press.

Sperling, G., & Reeves, A. (1980). Measuring the reaction time of a shift of visual attention. In R. S. Nickerson (Ed.), Attention and performance (pp. 347-360). Hillsdale, NJ: Lawrence Erlbaum.

Stafford, M. R., & Stafford, T. F. (2000). Identifying the uses and gratifications of web use. Proceedings of the 2000 Conference of the American Academy of Advertising, p. 70.

Stafford, T. F., & Stafford, M. R. (1998). Uses and gratifications of the World Wide Web: A preliminary study. Proceedings of the 1998 Conference of the American Academy of Advertising, 174-181.

Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42 (4), 73-93.

Stewart, D. W., & Furse, D. H. (1985). Analysis of the impact of executional factors on advertising performance. Journal of Advertising Research, 24 (6), 23-26.

Stewart, D. W., & Furse, D. H. (1986). Effective television advertising. Lexington, MA: Lexington Books.

Sundar, S. S., Narayan, S., Obregon, R., & Uppal, C. (1998). Does web advertising work? Memory for print vs. online media. Journalism & Mass Communication Quarterly, 75 (4), 822-835.

Thompson, D. R., & Wassmuth, B. (1999). Do they need a trick to make us click? A pilot study that examines a new technique used to boost click-through. Paper presented at the 1999 Conference of the Association for Education in Journalism and Mass Communication, New Orleans.

Thorson, E. (1996). Advertising. In M. B. Salwen & D. W. Stacks (Eds.), An Integrated Approach to Communication Theory and Research (pp. 211-230). Mahwah, NJ: Lawrence Erlbaum.

Thorson, E. (1989). Processing television commercials. In B. Derwin, L. Grossberg, B. O’Keefe, & E. Wartella (Eds.), Rethinking communication: Volume 2: Paradigm exemplars. Newbury Park: Sage.

Thorson, E., & Coyle, J. (1994). The third person effect in three genres of commercials: Product and greening ads, and public service announcements. In K. W. King (Ed.), Proceedings of the 1993 Conference of the American Academy of Advertising (pp. 103-111).
Utica, GA: University of Georgia, School of Journalism.

Thorson, E., & Lang, A. (1992). The effects of television videographics and lecture familiarity on adult cardiac orienting responses and memory. Communication Research, 19 (3), 346-369.

Thorson, E., & Leavitt, C. (1986). Probabilistic functionalism and the search for a taxonomy of commercials. Unpublished paper.

Thorson, E., Reeves, B., & Schleuder, J. (1987). Local and global complexity and attention to television. In M. L. McLaughlin (Ed.), Communication Yearbook, 10. Sage.

Thorson, E., & Snyder, R. (1984). Viewer recall of television commercials: Prediction from the propositional structure of commercial scripts. Journal of Marketing Research, 21 (May), 127-136.

Thorson, E., Wells, W. D., & Rodgers, S. (1999). Web advertising’s birth and early childhood as viewed in the pages of Advertising Age. In D. W. Schumann & E. Thorson, Advertising and the World Wide Web (pp. 5-25). Mahwah, NJ: Lawrence Erlbaum.

Thorson, E., & Zhao, X. (1997). Television viewing behavior as an indicator of commercial effectiveness. In Wells, W. D. (Ed.), Measuring advertising effectiveness (pp. 221-237). Mahwah, NJ: Lawrence Erlbaum.

Wassmuth, B. L., & Thompson, D. R. (1999). Banner ads in online newspapers: Are they specific? Proceedings of the 1999 Conference of the American Academy of Advertising, pp. 263-271.

Wells, W. D. (Ed.) (1997). Measuring advertising effectiveness. Mahwah, NJ: Lawrence Erlbaum.

Wells, W. D., & Chen, Q. (2000). Internet users’ attitudes toward advertising and related institutions: An update and two new segmentations. Proceedings of the 2000 Conference of the American Academy of Advertising, 19-29.

Wells, W. D., & Chen, Q. (1999). Surf’s up–Differences between web surfers and non-surfers: Theoretical and practical implications. Proceedings of the 1999 Conference of the American Academy of Advertising, 115-126.

Wells, W. D., Leavitt, C., & McConville, M. (1971). A reaction profile for TV commercials. Journal of Advertising, 11 (6), 11-17.

Woodworth, R. S. (1906). Imageless thought. Journal of Philosophical and Psychological Scientific Methods, 3, 701-708.

Wright, C. (1960). Functional analysis and mass communication. Public Opinion Quarterly, 24, 605-620.

Yoon, D. (2000). Use of endorsers in Internet advertising: A content analysis of top 100 American advertisers’ web pages. Proceedings of the 2000 Conference of the American Academy of Advertising, 169-175. Zeff, R., & Aronson, B. (1997). Advertising on the Internet. New York: John Wiley & Sons, Inc.

End Notes

[1] We intentionally used the term “Internet” (versus web) to include all components germane to the Internet. Since the World Wide Web (web) is just one of those components, we will use the term Internet when we mean all components, and web when referring to just that component.

About the Authors

Shelly Rodgers is Assistant Professor in the School of Journalism and Mass Communication at University of Minnesota at Twin Cities.

Esther Thorson is Professor in the Department of Advertising and Associate Dean for Graduate Studies at University of Missouri-Columbia.