ASSESSING THE EFFECTS OF ANIMATION IN ONLINE BANNER
ADVERTISING: HIERARCHY OF EFFECTS MODEL
Chan Yun Yoo, Kihan Kim, and Patricia A. Stout
Abstract: The present study attempts to examine the effects of animated banner ads, as well as the moderating effects of
involvement, on each stage of the hierarchy of effects model, and to explore the applicability of the hierarchy of effects model to
the banner advertising environment through an online experiment. The results provide support for the notion that animated
banner ads prompt better advertising effects than do static ads. Animated banner advertising has better attention-grabbing
capabilities, and generates higher recall, more favorable Aad, and higher click-through intention than static ads. Furthermore, an
individual's product involvement moderates the effects of animated banner advertising on recall, Aad, and click-through intention.
However, the study does not provide solid evidence of the feasibility of the traditional hierarchical model (Cognition -> Affect ->
Behavior) in the online banner advertising environment. Several implications and limitations of these results are discussed, and
future research is suggested.
While online advertising has grown dramatically during the
past several years (Low 2000), attracting individuals' attention
and persuading them remains one of the critical issues for the
practitioner. As competition for individuals' limited attention
is of concern in the online advertising environment, animation
is one innovation widely used by practitioners (Sundar et al.
1999); such ads substitute for static ads (Cleland and
Carmichael 1997). The increased use of animation in online
advertising is based on the belief that dynamic images have
superior attention-grabbing potential over static images
(Beattie and Mitchell 1985; Heo, Sundar, and Chaturvedi 2001;
Reeves and Nass 1996), thus enhancing the effectiveness of
persuasion (Ellsworth and Ellsworth 1995).
For advertising scholars, the applicability of the traditional
advertising theories to online advertising has been of great
concern since the advent of online advertising. Traditional
approaches remain quite relevant to the online advertising
environment, because not only do the fundamental goals of
online advertising tend to be similar to the goals of traditional
advertising (Pavlou and Stewart 2000), but also the theoretical
models developed for traditional advertising have successfully
been applied to online advertising (Cho 1999; Rodgers and
Thorson 2000).
The century-old advertising approach, the hierarchy of effects
model, has received widespread attention from both the
practitioner and academic communities as a specific
description of the way advertising works, and in turn, as a
basis for measuring the effects of advertising (Barry and
Howard 1990; Weilbacher 2001). Because of its simplicity and
logic, the hierarchy of effects model provides information on
where advertising strategies should focus, and in turn provides
for good advertising planning because the model acts as a
conceptual tool to predict consumer behavior (Barry 2002).
However, little academic research has dealt with the effects of
animated banner advertising in terms of this well-known
theoretical framework. Thus, the dearth of academic work in
this area calls for further research.
The present study attempts to assess the effects of animated
banner ads over static ads within the framework of the
hierarchy of effects. For the empirical examinations,
hypotheses were postulated and tested through an online
experiment. Specifically, this experiment examines the effects
of animated banner ads, as well as the moderating effects of
involvement, on each stage of the hierarchy of effects model,
and explores the applicability of the hierarchy of effects model
to the banner advertising environment.
CONCEPTUAL BACKGROUND
Hierarchy of Effects Models in Advertising
From the well-known AIDA (attention-interest-desire-action)
model, which originated with St. Elmo Lewis in the late 1800s,
to the recent 'association model' posited by Preston and
Thorson (1984), hierarchy of effects models have been around
in the advertising literature for more than a century. The
traditional hierarchy framework asserts that consumers
respond to advertising messages in a very ordered way. The
frequently cited hierarchy model posited by Lavidge and
Steiner (1961) suggests that consumers move over time
through a variety of stair-step stages, beginning with product
'unawareness' to actual purchase. These researchers' view of
Journal of Interactive Advertising, Vol 4 No 2 (Spring 2004), pp. 49‐60. © 2010 American Academy of Advertising, All rights reserved ISSN 1525‐2019 50 Journal of Interactive Advertising Spring 2004
the advertising hierarchy is implicitly a causal relationship
from cognition to affect, and from affect to conation. The
recent 'association model' (Preston and Thorson 1984)
supports the traditional hierarchy of effects framework, and
focuses on a comprehensive advertising process that takes into
consideration advertising research techniques (e.g., syndicated
data, surveys, experimentation) and concepts consistent with
behavioral intentions models.
While there is fundamental agreement regarding the
importance of the three stages of the hierarchy among
advertising researchers (Barry and Howard 1990), there has
been significant discrepancy regarding the order of the three
stages. For example, Krugman (1965) suggested a cognitionconation-affect sequence as an alternative model in low
involvement situations. On the other hand, Zajonc and
Markus (1982) suggest an affect-conation-cognition sequence,
in which preferences do not require a cognitive basis, but
instead are mainly affectively based. Ray et al. (1973) suggested
another alternative sequence (i.e., conation-affect-cognition),
in which consumers' purchasing behavior comes first,
attitudes are then formed to reinforce their choice, and
selective learning follows to further support purchase
decisions. The several alternatives to the original Lavidge and
Steiner's model (1961) suggest that advertising researchers
have developed different hierarchical models for various
consumer decision making situations, but agree on the
importance of the three basic tenets (i.e., cognition, affect, and
conation) of the hierarchy of effects model.
Conceptually, cognition has generally been viewed as "a
system of beliefs structured into some kind of semantic
network" (Holbrook and Batra 1987). On the other hand,
affect is typically treated as feelings and emotions which are
physiologically based or have some physiological component
(Barry and Howard 1990; Peterson, Hoyer, and Wilson 1986).
Finally, conation has usually been referred to as either
intentions to perform a behavior or the performance of the
actual behavior. However, criticism concerning the hierarchy
of effects indicates that the operationalizations of each stage
have been a problem among many researchers (Barry and
Howard 1990). In other words, there does not appear to be a
universally accepted means of distinguishing between
cognition and affect.
With regard to this issue, we have followed Barry and
Howard's (1990) suggestions on the operationalizations of
cognition, affect, and conation. They suggested memory, such
as various recall, recognition, and key comprehension scores
for the operationalization of cognition; attitude toward the ad
(Aad), measured by a unidimensional bipolar continuum
(Holbrook and Batra 1987; Homer 1990; MacKenzie and Lutz
1989; MacKenzie, Lutz, and Belch 1986) for the
operationalization of affect; and finally behavioral intention
and actual product purchase for the operationalization of
conation (Barry and Howard 1990; Holbrook and Batra 1987).
Based on Barry and Howard's (1990) operationalizations, in
this study, we measured recall and recognition for memory
(cognition), Aad (affect), and click-through intention
(conation) to assess the effects of animated banner advertising.
Animation in Online Banner Advertising
Animation is one of the unique innovational features of
banner advertising, carrying moving images and graphics to
simplify or enhance the presentation of persuasive messages
(Ellsworth and Ellsworth 1995). Several technological
developments including plug-ins, JAVA script, Flash, and
streaming media have contributed to improving the design
and interactivity of online banner advertising. Motion is often
considered to be a critical component of animated banner ads
(Reiber 1991), because most animated banner ads are a series
of static images superimposed on one another to create an
illusion of motion (Kalyanaraman and Oliver 2001).
Researchers studying motion effects have suggested that
motion elicits responses based on the actual image, per se, as
well as on the implied relationships, such as a "slow moving"
or "fast-moving image" (Rieber 1991; Sundar et al. 1999). The
characteristic distinguishing animated banner ads (i.e.,
motion) from static ads is related to the effects of animated
banner ads.
In the subsequent section, how the effects of animation in
banner ads are related to each stage of the traditional hierarchy
of effects model will be explored, and furthermore, we will
propose the hypotheses for empirical tests based on the
discussion.
HYPOTHESES
Attention
Virtually all hierarchy-of-effect models assume attention
responses as an antecedent to actual processing. The term
"attention" refers to the amount of mental effort or cognitive
capacity allocated to a task (Kahneman 1973), and the concept
is considered to have both direction (i.e., the focus of mental
effort) and intensity (i.e., the amount of mental effort focused
in a particular direction) (MacKenzie, Lutz, and Belch 1986).
Traditionally, one of the common means of attracting an
51 Journal of Interactive Advertising Spring 2004
individual's attention is by creating a distinctive or unusual ad
execution (Shimp 2000). Since animated banner ads are
regarded as more distinctive and unusual than static ads, it is
reasonable to suggest that animated banner ads may have
better attention-getting potential than static ads. Furthermore,
Reeves and Nass (1996) noted that "when objects or people in
pictures move, attention will be higher than during segments
with no motion" (p. 220). This suggests that an image with
animation will be perceived as representing motion, relative to
the static version of the same image, thus inducing greater
attention in the online advertising environment. The
discussion leads to the following hypothesis:
H1: An animated banner ad will have greater attentiongetting capability than a static banner ad.
Memory
Memory plays a critical role in guiding an individual's
advertising perception process. Of the massive amounts of
advertising information available on the Web, an individual
can be selectively exposed to only a limited amount. Of the
information to which the individual is exposed, only a
relatively small amount is attended to and passed on to the
systematic processing part of the brain for interpretation.
Studies examining both visual and verbal stimuli suggest that
distinctive stimuli are more likely to be remembered (Gati and
Tversky 1987). Additionally, Childers and Houston (1984)
noted that more recall occurs as the access to the features that
are distinctive in the stimulus increases. Accordingly, a
stimulating visual image on a calm background, or an
animated object on a still background would be considered
distinctive, and such distinctive images are theoretically
presumed to develop unique memory traces, making them
easier to locate in memory (Li and Bukovac 1999).
Furthermore, in their 'Flow of Effect Model,' Watt and Welch
(1983) noted that the increased attention as a result of using
dynamic visual images may affect further information
processing and an individual's memory (i.e., recall or
recognition). Thus, in addition to the increased attentiongetting potential, animated banner ads are likely to result in
better memory performance than are static ads. Thus:
H2: An animated banner ad will result in better memory
than will a static banner ad.
Attitude toward the ad (Aad)
Historically, the construct, attitude toward the ad (Aad) has
been conceptualized in different ways. In the unidimensional
view, Aad is purely affect and not consisting of a cognitive or
behavioral component (Holbrook and Batra 1987; Lutz,
MacKenzie, and Belch 1983; MacKenzie, Lutz, and Belch
1986), while the proponents of the multidimensional
viewpoint propose that Aad may consist of two (Batra and
Ahtola 1991: hedonic and utilitarian; Shimp 1981: cognitive
and affective) or three dimensions (Fishbein and Ajzen 1975:
cognitive, affective, and behavioral). In this study, we are
primarily concerned with the former tradition of defining Aad
as a unidimensional bipolar construct, which consists solely of
an affective dimension. Specifically, we believe that this
practice is not compounded by cognitive and behavioral
responses, and, in turn, it has exerted a potentially restrictive
effect of Aad in our theoretical framework.
The importance of Aad has been studied extensively over the
last few decades in advertising. Review of various literature
revealed that one of most common sets of relationships is that
Aad tends to have a strong direct impact on attitude toward the
brand (Ab), which in turn tends to have a strong positive effect
on purchase intention (i.e., Aad -> Ab -> PI). Furthermore, Aad
has been considered an efficient indicator for measuring the
effects of advertising.
Babin and Burns (1997) considered imagery as a mediator of
eliciting stronger attitude formation for visual stimuli, because
imagery is a process by which sensory information is
represented in active memory (MacInnis and Price 1987).
Thus, imagery incorporates sensory processing, resulting in
greater impact on attitude formation (Babin and Burns 1997).
In general, animated images contain more identifiable ad
elements than do static images, thus provoking stronger visual
imagery processing (Rossiter and Percy 1978; 1983), which
further affects individuals' attitude formation. Distinctive
advertising cues such as pictures and motion trigger more
vivid imagery that, in turn, generates more favorable attitudes
toward the ad and the brand (Babin and Burns 1997).
Furthermore, a single exposure to a banner ad without clickthrough generates favorable attitudes, and inflates the
likelihood of inclusion of the brand into a consideration set
(Briggs and Hollis 1997). Given the expectation that animated
banner ads will result in stronger and positive attitudes than
static ads, we propose the following hypothesis:
H3: An animated banner ad will generate more favorable
Aad than will a static banner ad.
Click-Through Intention
Along with the belief that banner advertising is supposed to be
more accountable than its traditional counterparts, one of the
52 Journal of Interactive Advertising Spring 2004
most frequently used indicators of ad effectiveness is the clickthrough rate. In the advertising industry, click-through rate is
an important factor in online advertising, with many firms
billing based on clicks generated rather than on the
conventional cost-per-thousand exposures (CPM) model.
Click-through refers to the process of clicking through a
banner advertisement to the advertiser's destination. In the
hierarchical advertising model, the click-through means the
behavioral response to an advertisement. Given the
expectation that animated banner ads will result in higher
attention, memory, and Aad, it is reasonable to expect that they
would be more likely to initiate individuals' behavioral
response in the form of clicking on the animated banner than
would static ads. This expectation leads to the following
hypothesis:
H4: An animated banner ad will have higher clickthrough intention than will a static banner ad.
Moderating Role of Involvement
Consumer researchers have employed several different
conceptualizations and operationalizations of involvement
(Muehling, Laczniak, and Andrews 1993). For instance, Batra
and Ray (1983) suggested that most prior research has used
the term involvement to describe one of two phenomena:
involvement with a product class (Zaichkowsky 1985), or
involvement with an advertising message (Greenwald and
Leavitt 1984; Petty, Cacioppo, and Schumann 1983). In either
case, personal relevance seems to be an important factor in
determining individuals' level of involvement with products
and/or advertising messages (Petty and Cacioppo 1986). In an
ad-processing context, researchers have found that the level of
involvement is positively related to individuals' cognitive
engagement in the ad (Petty, Cacioppo, and Schumann 1983).
Thus, individuals with higher product involvement pay more
attention to advertising stimuli and spend more time
processing advertisements than those with lower product
involvement (Celsi and Olson 1988). For example, Gardner,
Mitchell, and Russo (1985) have found that higher
involvement increases memory for an advertising message,
because higher involvement increases the accessibility of
message details, which leads to better recall (Hawkins and
Hoch 1992). Furthermore, in attitude formation and change
process, those with high involvement are believed to
elaborately process ad messages and form enduring attitudes
toward the ad and brand (Petty, Cacioppo, and Schumann
1983).
Recently, the concept of involvement has been employed in
banner ad effectiveness studies (e.g., Briggs and Hollis 1997;
Cho and Leckenby 2000) as well, and found to affect
individual's click-through, and attitudes toward the banner ad
and brand. For example, Briggs and Hollis (1997) found that
high-involvement products were remembered better than lowinvolvement products. Furthermore, Cho and Leckenby
(2000) noted that individuals with high product involvement
are more likely to click through banner ads than are those with
low product involvement, and higher click-through rates, in
turn, lead to more favorable attitudes toward the banner ad
and brand.
Based on the above discussion, we suggest that animated
banner advertisements can affect each stage of the hierarchy of
effects model, but the mechanism driving the effects is
believed to be different under conditions of low versus high
involvement (Greenwald and Leavitt 1984; Petty and Cacioppo
1986). Therefore, we suggest the following hypotheses:
H5: Product involvement will play moderating roles in
the effects of animated banner ads on (a) attention, (b)
memory, (c) Aad, and (d) click-through intention.
METHOD
A 2 (Level: animation vs. static) x 2 (Involvement: high vs.
low) between subjects design was used in the study. A pre-test
preceded the main study in order to select a proper product
category for ad stimuli (banner ads). Books were selected as an
appropriate product category, based on the results of the pretest.
Product Category Selection. Two principal considerations
guided the selection of a product category to be used in the
study: The product category should 1) demonstrate a strong
appearance in the online advertising, and 2) be appropriate for
use as a stimulus for a population of available subjects.
Students were determined to be appropriate subjects in the
study because college students make up a significant
proportion of the Internet population (GVU' s 10th Survey
1998), and appealing to college students is of importance to
the broader societal acceptance and potential success of online
advertising (Davis 1999). Furthermore, Calder, Phillps, and
Tybout (1981) supported the use of college students as subjects
in consumer research when the objective of the study was
theoretical in nature.
Informal in-depth interviews with 55 undergraduate students
(21 male and 34 female) were conducted. The students were
asked to list 1) top of the mind product categories when it
53 Journal of Interactive Advertising Spring 2004
comes to banner advertising, 2) appropriate product categories
that well fit the banner advertising format, and 3) the most
frequently encountered banner advertising while surfing the
Web. The results of the in-depth interviews showed six
product categories - books, credit cards, DVD rentals, online
gambling, music CDs, and Web cams - are highly visible in the
Web environment. Among the six product categories, the two
most frequently listed product categories, as well as those
listed by fewer than 10 percent of the participants, were
eliminated to avoid ceiling and floor effects. Two product
categories from the six categories were then selected. The
categories included books and DVD rentals. We selected
'books' as an appropriate product category, because some
students indicated that they do not own DVD players, so they
were not interested in any ads about DVD rentals.
Main Study
Stimulus Material. One target ad (i.e., ebooks.com) and two
filler ads (filler ads A and B) were developed by a professional
Web designer (see Appendix A). Each advertisement was
designed to have two levels (i.e., static vs. animated) with the
identical creative style in terms of the layout, the number of ad
elements, and the size (550 x 100 pixels). However, three
advertising cues (i.e., one visual and two ad messages) in the
stimuli sets were differentiated in terms of motion. For the
manipulation purpose, the animated banner ad contained
three moving advertising cues, which looped one time each ten
seconds, while the static one did not include any moving
advertising cues. In addition to the creation of banner ads,
three different online newspaper-type Websites (see Appendix
B) were created to use as background Web sites for the banner
ads. Three sports - tennis, figure skating, and golf - were
selected as the main themes of each site to avoid subjects'
different responses to different Web site themes. In order to
control for possible effects from the amount of information
contained in the Web sites, each Web site contained the same
number of stories and visuals. The target and filler banner ads
were placed at the top of each site, and the types of Web sites
and filler ads were counterbalanced to guarantee a balanced
distribution of background Websites to the target ad, and
prevent any order effects.
Sampling and Procedure. A total of 50 subjects (29 male and 21
female) were recruited from an introductory marketing class
at a major southwestern university. Each subject was randomly
assigned to one of two experimental conditions (static vs.
animated ad). They were exposed to one target ad and the two
filler ads, posted on one of the three Web sites as the orders of
types of Web site and filler ads were properly counterbalanced.
Each subject was given online instructions that provided a
fictitious study objective (i.e., Web site design evaluation) and
the general online experiment procedure. By clicking the
'Next' button at the bottom of the instruction page,
participants were subsequently exposed to the three different
newspaper-type Web sites (i.e., two Web sites with filler ads
and one Web site with a target ad). Each site remained on the
screen for 45 seconds, the average duration of a page-viewing
(Nielsen/NetRatings 2002), as the sites were automatically
refreshed by the function of JAVA scripts. By manipulating
the 45-second exposure to each site, we were able to generate
in the experiment a condition more similar to the natural Web
surfing environment, and furthermore, we provide an equal
opportunity for all subjects to process the advertising
information. The Web sites contained no active links to limit
subjects to surfing only to the experimental sites. The site
containing the target banner ad was preceded by one filler site
and followed by the other filler site to limit primacy and
recency effects. After being exposed to all three Web sites,
subjects were directed to the questionnaire site and asked to
answer a series of questions measuring Web site evaluations
(i.e., fillers), level of product involvement (i.e., independent
variable) as well as dependent variables (i.e., level of attention,
recall, recognition, attitude, and click-through intention), and
then were thanked.
Measures. There were two independent variables: the level of
animation in banner ads (static vs. animated), and the product
involvement (high vs. low product involvement). The level of
animation was manipulated as described above. Personal
product involvement (See Zaichkowsky 1985) was measured
by a three-item, seven-point semantic differential scale. The
items were anchored by "important/unimportant,"
"appealing/unappealing," and "interested/uninterested." The
scores of the three items were averaged to obtain an index
score of product involvement (Cronbach alpha = .95), and,
using a median split, we divided the subjects into two groups
(high vs. low product involvement groups).
There were four dependent variables of primary interest: level
of attention, memory (measured by recall and recognition),
attitude toward the banner ad, and click-through intention.
The level of attention paid to the banner ads was measured by
two items, which were modified from Duncan and Nelson's
(1985) measure: a seven-point scale anchored by "paid no
attention" and "paid a lot of attention," and a seven-point
54 Journal of Interactive Advertising Spring 2004
Likert-type scale ("The banner ad was eye-catching") anchored
by "strongly disagree" and "strongly agree." The scores of the
scales were averaged to derive an index score of attention
(Cronbach alpha = .88). In order to measure recall, a
retrospective thought-listing procedure was used. Subjects
were asked to list all of the brand names from banner ads they
saw during the experiment. For the recognition measure,
subjects were asked to select the banner ad they were exposed
to during the experiment from among three choices including
one target banner ad, and two additional banner ads that were
not presented during the experiment. The designs of the three
banner ads for the recognition measure were very similar.
Both recall and recognition were coded as dichotomous
variables (1 = yes and 0 = no). Then, Aad was measured on a
four-item, seven-point semantic differential scale, which was
borrowed from the prior research studies with the
unidimensional view on Aad (See Homer 1990; MacKenzie and
Lutz 1989; MacKenzie, Lutz, and Belch 1986). The items were
anchored by " pleasant/unpleasant," " good/bad," "
favorable/unfavorable," and " likable/unlikable." The scores of
the four items were averaged to generate an index score of Aad
(Cronbach alpha = .93). Finally, subjects indicated their clickthrough intention, measured by one seven-point Likert-type
scale (" I would like to click-through the banner
advertisement") anchored by " strongly disagree" and "
strongly agree."
As shown in Table 2, multivariate statistics (Wilks' Lambda)
for the animation level, product involvement, and the
interaction (animation level x involvement) were significant at
α=.05 level. Therefore, there were statistically significant
effects of the animation level, product involvement, and the
interaction between the animation level and product
involvement on the three different dependent variables.
Detailed relationships will be examined separately as they
relate to the hypothesis in the following section.
RESULTS
animation = 3.83, S.D. = .96, vs.
static = 3.06, S.D. = 1.16, F
(1, 46) = 6.60, p < .05). Therefore, the subjects exposed to
animated banner ads paid more attention to the ad than those
exposed to static ads.
Hypotheses Testing
A MANOVA test was conducted with attention, Aad , and
click-through intention as dependent variables to test
Hypotheses 1, 3, 4, 5a, 5c, and 5d (see Table 1 for means and
standard deviations and Table 2 for MANOVA results).
Where necessary, a series of t-tests followed, as specified by the
hypotheses. Two logistic regressions were also conducted to
test Hypotheses 2 and 5b, since both recall and recognition
were coded as dichotomous variables (1 = yes and 0 = no).
Results regarding each of the hypotheses are presented in the
subsequent section.
Table 1. Descriptive Statistics
Table 2. Effect of Animation and Product Involvement on
Attention, Aad,
** p < .05
*** p < .01
Hypothesis 1: Effect of Animation on Attention. Hypothesis 1
stated that subjects exposed to animated banner ads would pay
more attention to the ad than those exposed to static ads.
Consistent with the hypothesis, the analysis revealed a
significant main effect of animation on individuals' attention (
Table 3. Effect of Animation and Product Involvement
on Recall (Logistic Regression)
** p < .05
*** p < .01
55 Journal of Interactive Advertising Spring 2004
Table 4. Effect of Animation and Product Involvement on
Recognition (Logistic Regression)
showed an insignificant Animation Level x Involvement
interaction effect, F (1, 46) = 1.60, p = .21. However, we found
a significant main effect of product involvement (
* p < .10
** p < .05
*** p < .01 Hypothesis 2: Effect of Animation on Memory.
Hypothesis 2 expected that subjects exposed to animated
banner ads would have better recall and recognition of the
target ad (i.e., ebooks.com) than those exposed to static ads.
The model assessing the probability of recall was statistically
significant (χ2df=3 =16.06, p < .01), but not for recognition
(χ2df=3 =5.26, p = .15). The results of logistic regressions
showed a significant effect of animation on ad recall (b = -7.81,
Wald χ2 = 9.15, p < .01). Therefore, the results indicated that
the subjects exposed to animated banner ads had better ad
recall than those exposed to static ads, while there was no
significant effect of animation on ad recognition (b = -1.54,
Wald χ2 = .63, p = .43) over static ads. Therefore, Hypothesis 2
was partially supported.
Hypothesis 3: Effect of Animation on Aad. Hypothesis 3
predicted a positive effect of animation on Aad. Consistent with
the hypothesis, the results showed a significant main effect of
animated banner ads on Aad over static ads (
animation
= 4.24,
S.D = 1.07, vs.
static = 3.42, S.D = 1.19, F (1, 46) = 6.87, p <
.05). Therefore, subjects exposed to animated banner ads had
more favorable Aad than those exposed to static ads,
supporting Hypothesis 3.
Hypothesis 4: Effect of Animation on Click-Through Intention.
Hypothesis 4 expected that those exposed to animated banner
ads would have higher click-through intention than those
exposed to static ads. As shown in Table 2, consistent with the
hypothesis, the results showed the significant main effect of
animation on click-through intention (
animation
= 4.07, S.D =
1.45, vs.
static = 3.26, S.D = .96, F (1, 46) = 5.46, p < .05).
Thus, subjects exposed to animated banner ads had higher
click-through intention than those exposed to static ads.The
results supported Hypothesis 4.
Hypothesis 5a: Moderating Effect of Involvement on Attention.
Hypothesis 5a predicted that the level of product involvement
moderates the effects of animation on attention. The results
high-
low-involvement = 3.11, S.D = 1.12,
involvement = 3.78, S.D = 1.05, vs.
F (1, 46) = 4.88, p < .05), which indicated that the level of
product involvement affects an individual's attention
independently. Therefore, Hypothesis 5a was rejected at the p
= .05 probability level.
Hypothesis 5b: Moderating Effect of Involvement on Memory.
Hypothesis 5b predicted that the level of product involvement
moderates the effects of animation on memory. The analyses
of logistic regressions revealed a significant interaction
between animation level and product involvement on ad recall
(b = -4.35, Wald χ2 = 8.19, p < .05), but not for an interaction
effect on recognition (b = -1.64, Wald χ2 = 1.41, p = .24). The
results showed a significant moderating effect of involvement
on ad recall, indicating that the impact of animation on ad
recall was greater under high (χ2 = 4.89, p < .05) rather than
low involvement (χ2 = 3.95, p < .10). However, we were not
able to find any moderating effect of involvement on ad
recognition. Therefore, Hypothesis 5b was partially supported.
Hypothesis 5c: Moderating Effect of Involvement on Aad.
Hypothesis 5c expected that the level of product involvement
moderates the effects of animation on Aad. Consistent with the
hypothesis, the results revealed a significant interaction effect
between animation level and product involvement (F (1, 46) =
4.57, p < .05), indicating that animation had a significant
impact on Aad only under high involvement (t (24) = 2.97, p <
.01), but not under low involvement (t (24) = .43, p =.68).
Therefore, Hypothesis 5c is strongly supported. However,
there was no main effect of product involvement on Aad (
= 4.07, S.D. = 1.24, vs.
1.12, F (1, 46) = 2.14, p = .15).
high-involvement
low-involvement
= 3.59, S.D. =
Hypothesis 5d: Moderating Effect of Involvement on ClickThrough Intention. Hypothesis 5d predicted that the level of
product involvement moderates the effects of animation on
click-through intention. The results showed a marginally
significant interaction effect between animation level and
product involvement (F = 3.09, p < .10), indicating that
animation had a marginally significant impact on clickthrough intention under high involvement (t (24) = 1.76, p
<.10), but not under low involvement (t (24) = .56, p = .58).
There was no main effect of product involvement on clickthrough intention (
high-involvement
= 3.87, S.D. = 1.43, vs.
56 Journal of Interactive Advertising Spring 2004
low-involvement = 3.46, S.D. = 1.11, F = 1.24, p = .27). Therefore,
marginal support for Hypothesis 5d was found.
MODEL TESTING
In order to assess the applicability of the hierarchy of effects
model to the banner advertising environment, one of the basic
premises of the hierarchical model -- causal influences -- was
tested through a series of regression analyses.
Table 5. Causal Relationships between Dependent Variables
* p < .10
** p < .05
a. Logistic regression As Table 5 shows, the series of regression
analyses revealed one significant causal relationship between
attention and click-through intention, and three marginally
significant causal relationships (attention ->click-through
intention, and click-through intention ->Aad). The results do
not provide strong evidence of the applicability of the
traditional hierarchical model (C->A->B) to the banner
advertising environment. However, interestingly, a marginally
significant causal relationship between Aad and click-through
intention illustrated the importance of affective responses
rather than cognitive responses (recall and recognition) in
predicting click-through intention (conation).
DISCUSSION AND CONCLUSIONS
Given the importance of online advertising, many advertisers
try to attract consumers' attention and to persuade them
through various advertising executions. Animated banner
advertising is one alternative to conventional static banner ads.
This study empirically examined the effects of animated
banner advertising within the framework of the hierarchy of
effects model, and explored the applicability of the hierarchical
advertising model to the banner advertising environment
through an online experiment.
The results of the analyses offer support for the notion that
animated banner ads prompt better advertising effects than do
static ads. In other words, animated banner advertising has
better attention-grabbing capabilities, and generates higher
recall, more favorable Aad, and higher click-through intention
than do static ads. Furthermore, an individual's product
involvement moderates the effects of animated banner
advertising on recall, Aad, and click-through intention.
However, the study does not provide solid evidence of the
feasibility of applying the traditional hierarchical model in the
banner advertising environment.
The findings in this study have several implications, as well as
limitations. The main implication found here is that animated
banner advertising is a better alternative to traditional static
ads in terms of each stage of the hierarchy of effects model.
However, we assume that this relationship between advertising
effectiveness and animation (or motion) is subject to the
phenomenon of the inverted U-shaped curve. At some point,
too much animation or motion may reduce the advertising
effectiveness due to the individual's limited cognitive
capacities or some negative affective responses (such as
irritation or annoyance), even though those banner ads are
eye-catching. Thus, additional research is needed to determine
the underlying process and generalizability of this argument.
The results showed that the effect of product involvement on
attention was independent from animation effects. This result
indicates that, as an individual' s product involvement
increases, the level of attention to banner advertising will also
rise, regardless of the level of animation in banner ads. In
addition, the hypothesized effects of the animation and/or
involvement on recognition did not find empirical support. It
is believed that a recall measure is required to have relatively
higher cognitive efforts than a recognition measure (Du Plessis
1994). The present study employed relatively short time
intervals between the actual ad exposure and subjects
recognition task (i.e., less than 10 minutes), which may have
led to easily reminding subjects of a stimulus banner
advertisement and have precluded any deeper cognitive
processing, and consequently, we believe this may have
overwhelmed the effects of animation or involvement on
recognition measures.
This study could not provide solid evidence of the applicability
of the hierarchy of effects model to the banner advertising
environment. The traditional hierarchy of effects model
suggests that advertising is essentially a 'long-term' process
(Barry and Howard 1990), so that a causal influence between
stages must occur over the long-run. However, because this
study was conducted in the experimental setting with one-
57 Journal of Interactive Advertising Spring 2004
time ad exposure, it is not feasible to examine the long-term
effects of the banner advertising, in which causal relationships
between the three basic tenets of the hierarchy of effects model
are expected to be identified.
The preceding results and interpretations are limited by the
nature of our stimuli and respondents. The present study used
one product category (e.g., books) as an advertising stimulus.
However, using different product categories (e.g., highly
emotional products or highly habitual products), one may find
different results. The FCB planning model (Vaghn 1980; 1986)
classified 'books' as a rationally-based, high involvement
product. Therefore, the results may not hold for other product
categories due to the effects of emotion and involvement
engaged in banner advertising processing.
Although the choice of student subjects seemed appropriate
for the study, the small convenience samples limit the
generalizability of the findings to the general Internet
population. Thus, future research with a larger and more
diverse sample is imperative to expand the scope of the present
study.
A series of regression analyses illustrated the importance of
affective responses to banner ads when it comes to predicting
behavioral responses. However, at this point, it would be
premature to conclude that affective responses are more
important than cognitive responses in the banner advertising
environment, partly due to the aforementioned limitations in
the study, and partly due to a marginally significant reciprocal
relationship between Aad and click-through intention. Thus,
future research is required to examine the underlying process
of this phenomenon and the generalizability of our findings.
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APPENDIX A
Stimulus Material: Target and Filler Ads
•
Target Ads.
•
Filler Ads.
60 Journal of Interactive Advertising Spring 2004
APPENDIX B
•
Stimulus Material: Web Sites
•
Figure Skating Web Site
AUTHORS
Chan Yun Yoo is currently a doctoral candidate in the
Department of Advertising at the University of Texas at
Austin. His research focuses on interactive advertising,
consumer behavior on the Web, advertising media planning,
and agenda-setting effects of new media. His research appears
in the International Journal of Advertising and several
academic conferences.
Kihan Kim is a doctoral candidate in the Advertising
Department at the University of Texas at Austin. He got his
MA from Missouri School of Journalism in 2001. His research
areas include source effects in advertising, branding,
sponsorship communication, new communication technology,
and agenda-setting studies.
•
Tennis Web Site
•
Golf Web Site
Patricia A. Stout, Ph.D., is Professor of Advertising and John
P. McGovern Regents Professor in Health and Medical Science
Communication in the College of Communication at The
University of Texas at Austin. She is a co-director of the
Center for Public Health Promotion Research in the School of
Nursing at The University of Texas at Austin.