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.
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).
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
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.
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., McDonalds.com) 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.
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., etoys.com) 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).
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 Playboy.com) (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.
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.
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.
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[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.
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.