Electronic copy available at: http://ssrn.com/abstract=1017608

VISUAL COMPLEXITY IN BANNER ADS: THE ROLE OF COLOR, PHOTOGRAPHY,

AND ANIMATION

Bruce A. Huhmann, Ph.D. Department of Marketing

College of Business New Mexico State University

P.O. Box 30001 Las Cruces, NM 88003

N.B. THIS IS ANOTHER VERSION OF A PAPER THAT APPEARED AS:

Huhmann, Bruce A. (2003) “Visual Complexity in Banner Ads: The Role of Color, Photography, and Animation,” Visual Communication Quarterly, 10 (Summer), 10-17.

I would like to thank the editor, Julianne Newton, and the three anonymous reviewers for all their helpful comments on this manuscript.

Electronic copy available at: http://ssrn.com/abstract=1017608

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VISUAL COMPLEXITY IN BANNER ADS: THE ROLE OF COLOR, PHOTOGRAPHY,

AND ANIMATION

Abstract

Two studies investigate visual complexity in banner ads and support predictions based on

the resource-matching perspective, which suggests that processing is optimized when the

resources required to process a stimulus match the resources available. A content analysis of

actual banner ads indicates that Internet advertising tends to rely on a moderate degree of visual

complexity. This implies that advertisers’ experience may be intuitively encouraging them to

combine visual elements, such as color, line art, photography, animated text, and animated

visuals for moderate complexity, which should improve message processing. A follow-up

experiment verifies that, when processing resources are constrained, memory is better for images

with moderate visual complexity than for more simple or more complex images.

VISUAL COMPLEXITY IN BANNER ADS: THE ROLE OF COLOR, PHOTOGRAPHY,

AND ANIMATION

The principles of visual communication are beginning to be tested in an Internet context.

This article focuses on one of these principles, visual complexity. Visual complexity is

determined by the elements comprising an image. Images with more elements or detail (e.g.,

colors, animation/movement, or photography instead of line art) are more visually complex.

Thus, for example, an image with even just two colors against a background is more complex

than a monochrome image in which one color is placed against a background.

The influence of visual complexity on communication effectiveness of pictorial stimuli

has long been of interest to scholars (e.g., Berlyne 1960, 1974; Cox and Cox 2002; Morrison and

Dainoff 1972; Rimmer 1984; Vitz 1966). Increases in the complexity of a visual stimulus have

been found to encourage a variety of desirable audience responses, such as greater attention

(Berlyne 1974; Lang, Zhou, Schwartz, Bolls, and Potter 2000; Morrison and Dainoff 1972) and

better memory (Berry 1991; Gilbert and Schleuder 1990; Lang et al. 2000).

Research on visual complexity has primarily involved print media (e.g., Gilbert and

Schleuder 1990; Micklos 1982; Morrison and Dainoff 1972) or television programming (e.g.,

Lang et al. 2000; Thorson and Lang 1988; Welch and Watt 1982). However, audience responses

to visual complexity in an Internet context are in need of exploration. Thus, the purpose of this

paper will be to investigate visual complexity in banner ads through two studies, a content

analysis that documents the visual complexity of actual banner ads and an experiment that tests

the impact of visual complexity on processing images similar to those found in banner ads.

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Visual Complexity and Banner Ads

Since the first banner ad spanned the top of the HotWired web site in 1994, Internet

advertising expenditures have seen triple digit growth to approximately $5.6 billion in 2002

(Boland 2002). Although limited in size to 468 by 60 pixels, banner ads are the main form of

advertising on the Web. While designed to elicit a direct response, measured by click-through

rates, banner ads also stimulate other communication effects, such as message recall and attitude

change (Li and Bukovac 1999; Briggs and Hollis 1997).

While banner ads are obviously becoming an important form of visual communication,

little is known of the role played by visual complexity in banner ads. However, there has been

some research into one visual element, animation, from which some effects of visual complexity

might be inferred. Animated banner ads often use a GIF89 file, which repeatedly cycles through

a number of still images to create the illusion of movement similar to kinetoscopes of the late

1890s (e.g., see Figure 1). Unfortunately, findings of studies on animated banner ads are

contradictory. Li and Bukovac (1999) found that animated banner ads led to quicker click-

through responses and better recall than ones without animation. However, Rae and Brennan

(1998) found that both prompted and free recall were higher for banner ads without animation.

Visual Complexity in Print and Televised Images

While not much research has investigated the effects of visual complexity in an Internet

context, some implications for audience responses can be derived from research in other media

on visual complexity or elements that can be used to create visual complexity, such as color,

photography, or animation. In comparison to simple stimuli, complex visual stimuli have

previously been found to encourage greater reader attention (Berlyne 1974; Lang et al. 2000;

Morrison and Dainoff 1972). Complexity can also aid the memory for visuals (Gilbert and

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Schleuder 1990). Improved memory has also been demonstrated with research on individual

elements that could be used to create visual complexity. In comparison to monochrome images,

for example, color has been found to improve recognition of newspaper photographs (Josephson

1996) and recall of photographs (Berry 1991). Similar improvements in memory have been

observed for photographs, which are typically more visually complex, in comparison to line art

(Berry 1991; Finn 1988; Tversky and Baratz 1985).

Study 1

Study 1 was conducted to determine the level of visual complexity in actual banner ads.

A content analysis examined the visual elements that banner ads typically use and the resultant

level of visual complexity. Complexity increases as more detail (e.g., photography rather than

line art, color rather than monochrome, or animated rather than still images or text) is added to a

banner ad visual (e.g., see Figure 2). Given that prior research in other media has found that

increased visual complexity improves audience attention and memory, it is hypothesized that:

H1: There will be a greater number of banner ads that contain a higher degree instead of

a moderate or lower degree of visual complexity.

Method

A content analysis was conducted on 137 banner ads from the collection of the Banner

Ad Museum (http://www.banneradmuseum.com). The ads were selected using a random number

table from the 1,712 banner ads in the museum’s collection. These banner ads were submitted

by their designers; so, there is some self-selection bias in the sample. Only banner ads (468 by

60 pixels) were included in the sampling universe; other forms of Internet advertising in the

museum’s collection, such as microbuttons (88 by 31 pixels), half banners (234 by 60 pixels),

and square banners (125 by 125 pixels) were not included. The 137 banner ads analyzed

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represent a wide range of product categories (e.g., business-to-business, auto dealerships,

fashion, consumer electronics, home repair, pet care, etc.) While most of the banner ads were for

U.S. web sites and products, some were for web sites and products of other countries (e.g.,

Australia, Canada, Israel, Japan, Thailand, etc.)

Each banner ad was coded as to the image type (text only, line art, or photography), use

of color (monochrome or color), and use of animated (i.e., moving) images or texts.1 Since

visual complexity has been defined as the number of elements, such as these, used in an image,

an overall visual complexity score was created to reflect that number. In regard to image type, a

banner ad’s visual complexity score received no additional points if only text was present, one

point if a line art image was present, and two points if a photographic image was present.

Similarly, one point per element was added to a banner ad’s visual complexity score for the

presence of each of the following elements: color, animated visuals, or animated text. Thus, the

visual complexity score could range from a low of zero (i.e., a banner ad comprised of only text

in monochrome without any animated visuals or text) to a high of five (i.e., a banner ad with a

photographic image in color with both animated visuals and text).

Results

Table 1 shows the number of banner ads fitting each condition. In regard to image type,

24 banner ads had text only with no visual, 49 used line art, and 64 used photography. Of banner

ads with visuals, 22 were monochrome and 91 were color. In regard to animation, 45 had

animated visuals, 35 had animated text, and 6 had both animated visuals and text.

Hypothesis 1 predicted that banner ad designers would use higher levels of visual

complexity to achieve the positive effects on audience attention and memory found in prior

research in other media contexts. Thus, more banner ads with higher visual complexity scores

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were expected. Contrary to the prediction of hypothesis 1, designers were more likely to employ

a moderate rather than a higher or lower degree of visual complexity in the sample of banner ads

analyzed (χ2 = 12.124, df = 2, p < .001). In fact, a majority (57%) of banner ads contained a

moderate degree of visual complexity (i.e., an overall visual complexity score of two or three) as

shown in Figure 3. Lower levels of visual complexity (i.e., scores of zero or one) were found in

23% of banner ads analyzed. Higher levels of visual complexity (i.e., scores of four or five)

were found in 25% of the banner ads in the sample.

Discussion

While various elements can be used to create visual complexity in banner ads (e.g., see

Figure 2), a majority of banner ad visuals appear to use a moderate degree of complexity. Over

half of all banner ads analyzed had scores of two or three points out of overall visual complexity

scores that ranged from zero to five points. The mean overall visual complexity score of 2.54

points and median of three points provide further evidence that a moderate level of complexity is

most common in banner ad visuals.

Given the benefits of more visually complex stimuli for audience attention and memory

as documented in previous research, it was expected that banner ad designers would employ a

high level of visual complexity. Also, designers could have felt compelled to use color,

photography, and animation to appear technologically savvy and up-to-date, in much the same

way as newspapers feel compelled to use color photographs (Detenber and Winch 2001).

However, experimenting with the visual design of banner ads is relatively simple and

inexpensive compared to experimenting with visuals in print or television media. Therefore, it

should not take as long for banner ad designers to learn how to create visual, or verbal, messages

that receive the best response in terms of click-through rates (Hanson 2000). The greater use of

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banner ads with moderate visual complexity may imply that such banner ads are the ones best

received by web users.

Also, although most prior research has established a positive linear relationship between

visual complexity and processing (e.g., Berry 1991; Gilbert and Schleuder 1990; Lang et al.

2000), this linear relationship is unable to explain certain findings. For example, Welch and

Watt (1982) found that pre-schoolers’ recall for segments of the television show Sesame Street

was better when less visual complexity was present. Also, Ritchey (1982) found that, if the

items were categorized (e.g., animals), detailed drawings with labels as well as outlines with

labels were better recalled than just the labels alone. However, if the items were uncategorized,

recall of outlines with labels outperformed recall of either detailed drawings with labels or the

labels alone.

The resource-matching perspective, which takes into account the viewer’s limited

processing capacity, may be able to explain why some studies have found a positive impact of

visual complexity and others have found a negative impact. While the concept of limited

cognitive capacity is not new (e.g., Broadbent 1958), the resource-matching perspective suggests

that processing will be optimal when available resources match the resources required to

adequately process a stimulus. When a stimulus requires more resources (e.g., cognitive

capacity, attention, information-processing faculties, etc.) than an individual has available or

when more resources are available than required to process a stimulus, processing will not be as

good (e.g., Anand and Sternthal 1990; Meyers-Levy and Peracchio 1995). Thus, if the

information load of a visual stimulus is less than the resources available to process that

information, the viewer is expected to devote the extra resources to some other cognitive task to

the detriment of processing. If the information load of a visual stimulus matches the resources

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available, processing will be optimal. But if the information load of a visual stimulus is greater

than the available resources, the viewer will not be able to adequately process the visual

stimulus.

Resources available for processing may be constrained by limiting the motivation,

opportunity, or ability to process information (MacInnis, Moorman, & Jaworski, 1991). For

example, the less interest someone viewing a newspaper photo has in the subject matter, the less

likely he or she will be to devote available attention, cognitive capacity, or information-

processing faculties to understanding that photo. Also, the cognitive resources available may be

limited by someone’s opportunity to process the photo, such as when time is limited (e.g.,

someone is rushing past a newsstand and only has a second or two to view the photo) or when

distraction is present (e.g., someone is trying to comprehend the newspaper photo while riding a

busy subway, watching television, listening to someone else speak, etc.). The cognitive

resources may also be limited by one’s ability. For example, if person is very young (e.g., five

years old), they may not possess the information-processing faculties or the prior knowledge of

the subject matter needed to comprehend the newspaper photo. Alternatively, photographers or

editors can reduce the resources required to process a photo by cropping or digitally removing

extraneous elements from the photograph.

These constraints on available processing resources viewed within the framework of the

resource-matching perspective can explain why increasing visual complexity has not always

been found to increase processing. For example, in Ritchey’s (1982) experiment, opportunity to

process was limited by the time viewers were exposed to the visual stimuli. Ritchey (1982)

exposed his subjects to a slide containing 54 items for a total of eight seconds. Since the

cognitive resource required to process the images was greater when the items were

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uncategorized (i.e., had no common categorical origin), recall was higher for outlines labeled

with the names of the items, which represent a moderate level of visual complexity, than for the

more visually complex detailed drawings with labels. Whereas the resources available to

process the items within eight seconds remained the same, categorizing the items lessened the

resources required to process, which improved the recall of the more visually complex detailed

drawings. Regardless of whether or not the items were categorized, far more resources were

available than required to process the more visually simple set of labels alone; therefore, subjects

devoted excess resources to some other processing task (e.g., thinking about their weekend

plans), which hampered recall.

Similarly, rather than Welch and Watt’s study contradicting other research on visual

complexity’s effect on processing, the resource-matching perspective would suggest that

memory was best when the resources required by visually simple or complex stimuli matched

the resources available as constrained by the subjects’ age. Research on how age influences

processing ability suggests that subjects aged 15 and under or 51 and older would be expected to

have more limited cognitive capacity than subjects between the ages of 16 to 50 (e.g., Maitland

et al. 2000; McGinnis and Zelinski 2000; Small et al. 1999). Thus, Welch and Watt’s (1982)

study, which was conducted on preschool children whose processing resources are not yet fully

developed, found that visual simplicity improved memory. On the other hand, research

conducted on undergraduate or graduate students (Berry 1991; Gilbert and Schleuder 1990; Lang

et al. 2000) found that visual complexity improved memory.

Similarly, the resource-matching perspective explains the contradictory findings on

animated banner ads. Li and Bukovac’s (1999) used undergraduate subjects between the ages of

17 to 25 in a lab setting free of distractions. Rae and Brennan’s (1998) subjects were drawn

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from the general population; thus, some may have been outside the peak age range for cognitive

resources. Also, their subjects participated outside a lab setting where distractions were likely

present. Given these differences in the ability and opportunity to process, animation hurt recall

by increasing resources required beyond the resources available to Rae and Brennan’s subjects,

but helped recall by increasing resources required nearer to the resources available to Li and

Bukovac’s subjects.

Thus, applying the resource-matching perspective to the relationship between visual

complexity and memory would mean that the relationship is nonmonotonic (i.e., an upside-down

U shape). Memory for visual stimuli should rise with complexity up to the point at which the

resources required to process exceed the resources available for processing.

Study 2

A second study was conducted to specifically test the predictions derived from resource-

matching perspective in regard to visual complexity. Memory, as an outcome of processing, was

used to measure how much subjects in study 2 processed images with differing degrees of

complexity. If there is a nonmonotonic relationship between visual complexity and memory, it

could explain the predominate use of moderate visual complexity in banner ad design found in

study 1.

Cognitive resources available for processing banner ad visuals may be limited by the

time someone looks at a banner ad before clicking on another web link or scrolling down past

the banner ad, the alternative task (i.e., most people do not go to a particular web page to view

banner ads, but to perform some other task), or the existence of distractions (e.g., music listening

or television viewing while surfing the Internet). Thus, according to the resource-matching

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perspective, optimal processing will occur when the resources required to process an image

match the resources available; therefore,

H2: Under conditions of limited cognitive resources, images with a moderate degree

of visual complexity should lead to better processing than:

(a) images with a lower degree of visual complexity or

(b) images with a higher degree of visual complexity.

Also, whereas gender should not affect the resources available for processing, the

subject’s age may constrain processing ability since subjects aged 15 and younger or 51 and

older may possess more limited cognitive resources than subjects aged 16 to 50 (e.g., Maitland et

al. 2000; McGinnis and Zelinski 2000; Small et al. 1999). Therefore:

H3: Recall should be lower for subjects aged 15 and younger or 51 and older than for

other subjects.

Method

An experiment with a repeated-measures design compared four levels of visual

complexity -- monochrome line art, line art with two colors, line art with four colors, and full-

color photographs. Since adding colors to an image or switching from line art to photography

increases visual complexity, processing resource requirements would be expected to increase

from monochrome line art to line art with two colors to line art with four colors to full-color

photographs.

Fifty subjects were shown four sets of ten computerized images. Set order was rotated to

avoid order effects. Each set of images appeared against a plain white background without any

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accompanying text or other elements on the page. Subjects viewed the stimuli in the same

laboratory setting.

To constrain resources available, opportunity to process each set was limited to fifteen

seconds. Fifteen seconds was chosen because Web users typically view Web site home pages

for an average of 10-15 seconds (Young 1996). After viewing a page, subjects had one minute

to recall the images in that set.

To enhance generalizability to the population of Internet users overall, student subjects

were not used. The subjects ranged in age from pre-teens to 70 years of age. Sixteen of the

subjects were under 16 years of age. Six subjects were between 16 and 30 years of age.

Twenty-one subjects were between 16 and 50 years of age. Seven subjects were over the age of

50. Of the subjects, 28 were male and 22 were female. Subjects were recruited from a variety of

church and civic organizations.

Results

Because subjects were asked to recall the images in all four conditions, the responses are

related. Hence, a repeated measures multivariate analysis of variance (MANOVA) was used to

test hypothesis 2 with two levels of gender and three levels of age. If responses fit with the

predictions based on the resource-matching perspective, a quadratic pattern should be observed,

which would reflect a non-monotonic, curvilinear relationship between recall and visual

complexity. If the resource-matching perspective does not apply to this data, a monotonic

relationship should exist between visual complexity and recall, resulting in a linear pattern.

Figure 3 shows the mean recall for each of the four conditions and the tests of the linear,

quadratic, and cubic patterns. As expected based on the resource-matching perspective, a

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quadratic pattern of contrasts in the mean number of images recalled was observed, which

supports hypothesis 2.

Paired comparisons show that recall of line art with two colors is significantly higher

than of monochrome line art (t = -2.385, df = 49, p = .021). No difference was observed in recall

of line art with two or four colors (t = 0.732, df = 49, p = .467) or between monochrome line art

and line art with four colors (t = -0.990, df = 49, p = .327). Full-color photos were recalled

significantly less than line art with either two colors (t = 3.344, df = 49, p = .002) or four colors

(t = 2.319, df = 49, p = .025), but not monochrome line art (t = 1.516, df = 49, p = .136).

Tests of the effects of gender and age showed that the mean number of images recalled

did not differ significantly by gender (F1, 43 = 0.002, p = .966) nor by the interaction between

gender and age (F2,43 = 1.139, p = .330). However, in support of hypothesis 3, the mean number

of images recalled did differ by age (F3,43 = 3.616, p = .021). The best recall occurred in the 16

to 30 and the 31 to 50 year-old age groups.

Multiple comparisons show that the number of images recalled by subjects in the 16 to 30

age group (mean = 6.458) did not differ from the number recalled by the 31 to 50 age group

(mean = 6.2024; p = 0.629). But subjects in both the 16 to 30 age group and the 31 to 50 age

group recalled more images than those in the 15 and under age group (mean = 5.063, p’s < .02)

and marginally more images than those in the 51 and older age group (mean = 5.357; p’s < .10).

The number of images recalled by the 51 and older age group did not differ from the number

recalled by the 15 and under age group (p = .570).

Since the task of recalling 10 images that appeared for only 15 seconds is cognitively

demanding itself, it is not surprising that the greatest differences between the age groups with

more available cognitive resources (16 to 30 and 31 to 50 year olds) and less available cognitive

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resources (15 and younger and 51 and older) occurred with the least complex and, hence, least

cognitive demanding images (i.e., monochrome and two-color line art) as shown in Table 2.

Univariate tests showed no significant difference in the mean number of images recalled

in any of the four conditions by gender or the gender by age interaction. However, as shown in

Table 2, age had an effect on the mean number of images recalled for monochrome and two-

color line art. With regard to monochrome line art, paired comparisons show that subjects age

16 to 30 recalled more images than subjects 15 and under (p = .001), but only marginally more

than subjects 51 and older (p = .087). There was no significant difference in the recall of

monochrome line art between subjects 16 to 30 and those 31 to 50 (p = .458). Subject 31 to 50

recalled more monochrome line art than subjects 15 and under (p < .001), but not those 51 and

older (p = .158). With regard to two-color line art, subjects 16 to 30 recall marginally more

images than those 15 and under (p = .067) or 51 and older (p = .061). There was no difference in

the recall of two-color line art between subjects 16 to 30 and those 31 to 50 (p = .860). Subject

31 to 50 recalled more two-color line art than those 15 and under (p = .018) or 51 and older (p =

.029).

Discussion

The results of study 2 supported prediction based on the resource-matching perspective

as recall increased as visual complexity increased up to a point and then began to decrease. The

simplest images, monochrome line art, were not recalled as well as moderately complex line art

with two colors. Recall decreased as visual complexity increased from line art with two or four

colors to full-color photos, since opportunity to process limited available resources. Also,

subjects whose ability to process was not yet fully developed or whose processing speed was

beginning to decline, did not perform as well overall as subjects with peak processing ability.

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In study 2, memory for images from the four sets was used to measure information

processing. For almost 75 years, memory has been used as a surrogate for attention and depth of

processing (e.g., Starch 1923). And, in fact, previous research suggests better remembered

information has been more extensively processed information (Anderson 1985). If the memory

for images does, in fact, translate into better processing of banner ad visuals, it is not surprising

that designers were found to be more likely to use moderate visual complexity in study 1.

General Discussion

As with print and television media visuals, the use of color, photography, and animation

undoubtedly benefit the processing of banner ads. However, increasing visual complexity by

adding more detail or more elements increases the ability of a banner ad to communicate only up

to the point at which the resources required to process the message match those available to the

audience. This point at which the resources required match those available depends not only on

the visual complexity of the banner ad, but also on the individual audience member’s motivation,

opportunity, and ability to process the banner ad. As indicated in study 2, if the processing

demands of a visually complex banner ad surpass or the processing demands of a visually simple

banner ad fall short of the processing resources available to the audience then processing appears

to be hindered. Study 1 suggests that banner ad designers are, at least, intuitively aware of this

through their experience and tend to design banner ads with moderate visual complexity. Since

experimenting with the elements that comprise banner ads is simple and inexpensive, banner ad

designers quickly learn how to create successful banner ads (Hanson 2000).

Many in the Internet community view the Internet as a unique medium in which mass

communication principles developed from past research on print, radio, and television do not

apply (e.g., Sustain-Ability.net’s (2002) Web Users Design Guide). Such a view is not limited to

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practitioners; some researchers have also expressed similar opinions (e.g., Grossman 1994;

Leong, Huang, and Stanners 1996). However, other researchers are beginning to demonstrate

that many communication principles can be successfully applied to the Internet. For example,

Gallagher, Foster, and Parsons (2001) demonstrated that traditional mass media principles apply

to verbal processing of Internet texts, as measured by similarities in the unaided recall of

information from print and Internet texts. The current two studies demonstrate that visual

processing principles developed for traditional mass media can likely be applied to new media,

such as the Internet, as well. These studies support in an Internet banner ad context hypotheses

derived from previous research on visual complexity in other media. The nonmonotonic

(upside-down U-shaped) relationship between visual complexity and unaided recall of the

images presented in study 2 is similar to findings on memory of visual structural complexity

(i.e., the number of cuts, edits, pans, dollies, zooms, etc.) in television images (e.g., Gunter 1987;

Thorson and Lang 1988). Unlike the previous studies, this study makes an added contribution of

theoretically explaining the nonmonotonic relationship between visual complexity using the

precepts of the resource-matching perspective.

Since the current studies represent preliminary investigations into responses to visual

complexity in banner ads, a variety of other avenues for future research are also suggested.

While the results of study 1 indicate that banner ads often use moderate visual complexity, which

suggests that advertisers may be experiencing better direct response (i.e., click-throughs) with

these ads, a specific test of this variable for banner ads with different levels of visual complexity

would be valuable. Study 2 measured the recall of sets of images. While memory is an outcome

of processing and, therefore, has often been used as a surrogate for processing; research is

needed to explicitly measure depth of processing, as well as other responses of interest to

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researchers and practitioners alike (e.g., attention, comprehension, etc.). Also, the number of

subjects in the 51 and older group of study 2 was small. More research investigating the

response of this age group to visuals of various degrees of complexity is sorely needed. This is

the fastest growing age group in the overall population; yet, almost all studies of visual

complexity focus on subjects 25 years of age or younger. The experiment also did not test

reactions to the use of animated images or texts in creating visual complexity; this is an area

worthy of further research given the frequent use of animation in banner ads. Finally, while the

design of the experiment created four sets of images that increased in visual complexity (and,

hence, the resources required to process), it did not allow for the individual effects of color and

photography on the visual complexity and processing resources required to be teased out. This

would also be an interesting avenue for future research.

According to an article in New Media Age (2002), the web advertising industry tends to

have few guidelines in regard to designing successful banner ads. The current studies should

assist designers in more confidently identifying the types of visuals and visual elements (e.g.,

color or animation) to include in a banner ad. As an additional benefit, banner ads with

moderate visual complexity should not require as much computer download time as banner ads

with high visual complexity; thus, reducing web user frustration at slow download times. These

studies also provide some research to support designers in convincing clients who may request a

banner ad that will be too simple or too complex to opt for a potentially more successful one

with moderate visual complexity. To enhance recall and processing, the processing requirements

of a banner ad should match the web user’s available resources. Otherwise, a lack of attention,

frustration, or annoyance may result. Perhaps, the high visual complexity of rich media ads with

“such a mass of moving, singing, beeping images you can get motion sickness sitting at your

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desk” is partially responsible for the greater level of annoyance these ads engender (cf. Schorow

2002, 34). User frustration and annoyance stemming from the added complexity of Java applets,

Shock, Flashware, or interactive features is part of the reason online newspapers do not use or

have discontinued their use (Dibean and Garrison 2001; Thompson and Wassmuth 2001).

While the results of studies reported here suggest that moderate visual complexity

generally results in the best processing of a banner ad, exceptions undoubtedly exist. The goal

for a banner ad designer, or indeed any communicator using visual images, should be to create a

match between the resources required to process a visual image and the resources available to

the audience. In cases in which the audience’s available resources are even more constrained

than in the experiment reported here, such as a web page teaming with a multitude of banner ads

or other visual images competing for attention, visual simplicity (which requires fewer resources

to process) should communicate better than even moderate visual complexity. In other cases,

such as when frequent repetition or numerous exposures increase the opportunity to process,

higher visual complexity may lead to better processing in the long run as the audience might

quickly tire of simpler images. As with any type of communication, it is important for the

communicator to understand the audience. In the case of visual complexity, it is important to

estimate the processing resources available to the audience and design one’s message

accordingly.

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Footnotes

1. Codebook available from the author upon request (e-mail bhuhmann@nmsu.edu).

2. According to the National Park Service website “All photographs and images on this web page are ‘public domain’ images. You are free to

use these images without a release from the National Park Service” (see http://photo.itc.nps.gov/storage/images/help.html for more information).

The line art image (i.e., the lower complexity banner ad) is my own creation.

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Table 1. Visual, Color, and Animation Use in Banner Ads No Text Animation Text Animation Visual

No Visual Animation

Visual Animation

No Visual Animation

Visual Animation

Total

Text Only 14 N/A 10 N/A 24 Monochrome Line Art 8 1 -- 2 11 Multi-color Line Art 14 15 9 -- 38 Monochrome Photo 8 2 1 -- 11 Full-Color Photo 19 21 9 4 53 Total 63 39 29 6 137

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Table 2. Mean Number of Images Recalled From Each Ten-Image Set By Age Group Mean Number of

Images RecalledStandard Deviation

Univariate Results

Monochrome Image Set F3, 43 = 5.534, p = .003 15 and under 4.44 1.55 16 - 30 6.83 0.98 31 - 50 6.33 1.32 51 and over 5.43 1.72 Two-Color Image Set F3, 43 = 3.150 p = .034 15 and under 5.44 1.71 16 - 30 7.00 1.41 31 - 50 6.86 1.71 51 and over 5.14 2.34 Four Color Image Set F3, 43 = 0.918, p = .440 15 and under 5.50 1.46 16 - 30 6.50 2.66 31 - 50 6.10 1.61 51 and over 6.00 1.91 Full-Color Photograph Image Set F3, 43 = 0.388, p = .762 15 and under 4.88 2.19 16 - 30 5.50 1.52 31 - 50 5.52 1.36 51 and over 4.86 1.95

26

Figure 1. Still Frames From an Animated Banner Ad

Note: This line-art banner ad with animated visuals and text was composed of 30 still frames. To conserve space, only 20 are presented here. Designed by Angelina Barreras.

27

Figure 2. Different Levels of Visual Complexity in a Banner Ad Simple - Text only on a plain background

Keep Liberty’s Torch Burning.Support a Free and Critical Press.

Lower Complexity -- Text with line art visual

Keep Liberty’s Torch Burning.Support a Free and Critical Press.

Higher Complexity -- Text with photographic visual

Keep Liberty’s Torch Burning.Support a Free and Critical Press.

Note: The addition of other elements, such as color, animated (i.e., moving) visuals, or animated text would also increase a banner ad’s visual complexity. Photo: National Parks Service.2

Figure 3. Results of the Content Analysis of Banner Ads by Overall Visual Complexity Score

1418

23

48

30

4

0

10

20

30

40

50

60

0 1 2 3 4 5

Number of Banner Ads

Overall Visual Complexity Score Note: The overall visual complexity score was computed by giving one point each for the use of line art, color, animated visuals, or

animated texts; two points were given for the use of photography.

2

Figure 4. Mean Number of Images Recalled by Level of Visual Complexity

5.94

5.22

6.18

5.66

4.6

4.8

5

5.2

5.4

5.6

5.8

6

6.2

6.4

Monochrome line art Two-color line art Four-color line art Full-color photo

Note: Linear contrast F1, 43 = 1.959, p = .169; Quadratic contrast F1, 43 = 11.112, p = .002; Cubic contrast F1, 43 = 0.210, p = .649.