The Impact of Banner Exposure and Clicking on Attitude Change

 

by

Chang-Hoan Cho

Assistant Professor

ccho2@unl.edu

http://jet.unl.edu/coj/advt/faculty/ccho/

43 Avery Hall

Department of Advertising

College of Journalism and Mass Communications

University of Nebraska-Lincoln

Lincoln, NE 68588-0130

 

And

 

John D. Leckenby

Everett D. Collier Centennial Chair

in Communication

john.leckenby@mail.utexas.edu

http://www.utexas.edu/coc/admedium/

Department of Advertising

College of Communication

The University of Texas at Austin

Austin, Texas 78712

 

 

 

paper to be presented to

2000 Annual Conference

American Academy of Advertising

Newport, Rhode Island

April 2000

 

The Impact of Banner Exposure and Clicking on Attitude Change

Abstract

This paper explores the impact of banner exposure and clicking on brand-attitude changes and purchase-intention changes. It is found that simple exposure to banner ads does not change people's initial brand-attitude and purchase intention, while voluntary exposure to target ads by clicking banner ads results in positive or negative brand-attitude and purchase-intention changes depending on the likability of the linked target ads from the banner ads. For methodology, this study employed a pretest-posttest control group design and used online data collection technology called Cold Fusion. A total of 961 subjects participated in this research.

 

 

Introduction

The present method of pricing online ad campaigns is moving away from CPM and towards click-through rates (PR Newswire 1998), even though a debate is arising over whether or not click-through tells advertisers anything worth knowing and whether it should be a factor in pricing Web advertising. The click-through is the first gate to entering the world of interactivity in Web advertising (Cho and Leckenby 1999). That is, the clicking of a banner initiates users’ interactivity with Web advertising. Therefore, the click-through-based pricing system is reasonable for those advertisers who recognize that interactivity is the most distinguishing and important characteristic of the Internet. Another reason for the prevailing use of click-through as a pricing base for Web advertising is that it is a concrete measure of users’ actual clicking behavior and that it is relatively easy to measure with the aid of innovative technology.

Click-through rates are influenced by many known and unknown factors (Hofacker and Murphy 1998). There have been several research studies on antecedents of banner clicking or what makes people click the banner ads: the level of involvement (Cho and Leckenby 1999); peripheral cues of banner ads such as size and animation (DoubleClick and I/PRO 1996; Cho 1998a; 1998b; Cho 1999); Clicking Motivation Profile (Cho and Leckenby 1998); action-oriented phrases such as "Click Here to…" (Hofacker and Murphy 1998), and so forth.

Recently, the study by Mbinteractive, commissioned by the Internet Advertising Bureau, found that even simple involuntary exposure to a banner ad without a click-through generated increases in advertisement awareness and brand awareness (MBinteractive 1997 at URL: http://www.mbinteractive.com/site/iab/exec.html). However, this study did not consider the real power of banner ads, i.e., the initiation of interactivity by clicking them. There has been little research on the impact of banner clicking on various advertising response functions (e.g., knowledge, attitudes, purchasing behaviors, etc.). In this paper, the researchers focus on the impact of banner clicking on people's attitude changes.

Conceptualization

Advertising Exposure on the WWW

There are two current dominant forms of Web advertising: 1) the banner ad and 2) the target ad or linked site from the banner ad (Hoffman et al. 1995; Hoffman and Novak 1996a; 1996b). Depending on these two types of Web advertising, there are two different types of advertising exposure on the WWW: 1) involuntary exposure to the banner ad and 2) voluntary exposure to the target ad (Cho 1999).

Traditional hierarchy-of-effects models assume that the very first stage of the persuasion process is awareness through advertising exposure (Preston 1982; 1985). Here, advertising exposure is mostly involuntary and/or incidental because individuals involuntarily just happen to come across an ad in traditional media. Similarly, for the banner ad on the WWW, the traditional involuntary exposure concept can be applied; that is, banner ads on the Web are nothing but the traditional passive form of non-interactive advertising unless they are clicked and move users into the separate interactivity site. If the users are only exposed to the banner ads but do not click them to open to see linked target ads, it can be said that they are not interacting with the advertising messages or the advertisers, i.e., this is traditional one-way, involuntary communication from advertisers to consumers.

In contrast, when people click the banner ad to open and see the linked target ad, it is called voluntary or sought-out exposure to the target ad. This voluntary exposure requires users to voluntarily perform an action (i.e., clicking banners) to see the content of advertising messages, which will yield more active and intensive information processing than passive exposure without voluntary action. This voluntary exposure will draw more attention to the messages and activate the consumer learning processes more intensively than will involuntary exposure.

The Effect of Banner Clicking on Attitude Changes

Various attitude measures have been used as a measure of advertising effectiveness in traditional media—persuasion-based copytesting methods. (Leckenby and Plummer 1983). Attitude toward the ad is one of the most often used persuasion-based measures and found to be superior to other measures in many aspects (Clancy & Ostlund 1976; Gibson 1983; Haley 1994; Ross 1982).

The assumption underlying the use of attitude toward the ad as a measure of advertising effectiveness is that the attitude toward the ad influences the attitude toward the brand. There have been many research studies on the effect of attitude toward the ad on brand attitude (Shimp 1981; Mitchell & Olson 1981; MacKenzie & Lutz 1983; Lutz et al. 1983; Lutz 1985; Aaker et al. 1986; Edell & Burke 1987; Holbrook and Batra 1987). This literature on attitude toward the ad is based on the traditional advertising effect resulting from incidental or involuntary advertising exposure. Here, attitude toward the ad is an outcome of involuntary exposure to the ad, which in turn influences attitude toward the brand and purchase intention. The following diagram illustrates traditional advertising effects of ad exposure on attitudes.

 

However, in Web advertising, advertising exposure can be either voluntary or involuntary, depending on the types of Web advertising. Therefore, the effect of attitude toward the ad should be explained differently in Web advertising. In Web advertising, there exist two types of attitude toward the ad: 1) attitude toward the banner ad (Abn) and 2) Attitude toward the target ad (Atarg). First, Abn is an outcome of involuntary exposure to the banner ad, which in turn affects voluntary exposure to the target ad or clicking of the banner ad. Then, voluntary exposure to the target ad by clicking the banner ad subsequently affects attitude toward the target ad (Atarg), attitude toward the brand (Abr) and purchase intention (PI). This rationale can be illustrated as follow:

 

            Here, voluntary exposure to the target ad by clicking the banner ad takes a mediating role between Abn and Abr. If the users do not click the banner ad to open and see the target ad, attitude changes may be minimal, because the banner ad itself usually does not have enough information for consumers’ cognitive processing. In other words, the banner ad itself does not fully activate consumers’ process of attitude formation unless it is clicked to be opened. Therefore, simple exposure to the banner ad without clicking it will result in minimal attitude change. This rationale leads to the following hypotheses:

H1.1: Simple exposure to a banner ad without clicking it will not influence people’s initial attitudes toward the brand.

H1.2: Simple exposure to a banner ad without clicking it will not influence people’s initial purchase intention.

Meanwhile, if the users are voluntarily exposed to the target ad and they like the target ad, their attitudes are more likely to be changed positively. This rationale leads to the following hypotheses:

H2.1: People will have a more favorable attitude toward the brand if they click the banner ad and like the target ad.

H2.2: People will have higher purchase intention if they click the banner ad and like the target ad.

In contrast, if the users are voluntarily exposed to the target ad but dislike the target ad, their attitudes are likely to be changed negatively—boomerang effects. This rationale leads to the following hypotheses:

H3.1: People will experience negative brand-attitude changes if they click the banner ad and dislike the target ad.

H3.2: People will experience negative purchase-intention changes if they click the banner ad and dislike the target ad.

Methodology

            This study employed a pretest-posttest control group design in order to measure brand-attitude and purchase-intention changes before and after exposure to banner ads. There were two subject groups in this study: 1) the experimental group and 2) the control group. The survey was conducted online using Web database technology called Cold Fusion, where responses on each survey item were automatically transmitted to a Microsoft Access database file located at the server.

Sample Banner Ads and Homepages

            According to Mitchell (1986), professionally developed ads rather than mock ads are encouraged to be used in experimental research in order to elicit a more natural response from the subjects. Following this suggestion, professionally developed Web sites and banner ads were used in this study. Stimulus materials for this study were four banner ads located on Infoseek site. The Infoseek site was selected at the researchers' discretion because of its neutral content, so that the content of the Web site does not affect the clicking of the banner ad. Four banner ads were selected from the real banner ads on three most popular search engines on the WWW, i.e., Yahoo, Infoseek, and Excite. The product categories of four sample banner ads were selected from four popular product categories of Web advertising, which include financial services, consumer brands, retailers, and travel-related products (WebTrack 1998). The final four sample banner ads included American Express (financial services), Kodak Film (consumer brands), American Airlines (travel-related products) and JCPenney (retailers) banner ad. The experimental group was exposed to four banner ads while the control group was not exposed to any banner ad.

Sampling

An electronic recruiting message for the survey was distributed via postings in various discussion LISTSERV lists. The LISTSERV lists were selected from CataList, the catalog of LISTSERV lists (URL: http://www.lsoft.com/catalist.html). This Web site provided 21,003 public LISTSERV lists on the Internet at the point of the study (1999). Among these LISTSERV lists, education- , Internet-, advertising-, and marketing-related LISTSERV lists were selected at the researchers' discretion. The study had a total of 961 participants (817 in the experiment group and 144 in the control group). To recruit these 961 subjects, the researchers posted recruiting messages on a total of 165 LISTSERV lists. For the purpose of increasing the response rate of the survey, the researchers provided a small financial incentive in the form of a sweepstake for the survey participants. A monetary incentive of $100 was given to each of 10 randomly selected survey participants.

Procedure

The online survey consisted of three parts. In Part I, two pre-banner-exposure measures were assessed for both experiment and control group. The first pre-exposure measure was each subject’s brand attitude for each brand. The second measure was pre-exposure purchase intention for each brand.

In Part II, for the experiment group, each subject was exposed to the four experimental stimuli. First, each subject was exposed to the very first banner ad and homepage (American Express banner ad located at the top of the Infoseek site). Here, each subject confronted two options: clicking the banner ad or not. If the subject clicked the banner ad, he/she was exposed to the linked target ad. After seeing the first homepage and banner ad, each subject continued to the next section—the second homepage and banner ad (the American Airline banner ad on the Infoseek site). Each subject in the experiment group followed the same procedure for the remaining two stimuli. Meanwhile, for the control group, each subject was exposed to the Infoseek site without any banner ad.

After completing Part II, each subject was asked to continue with Part III of the online questionnaire, which measured post-exposure brand attitude and purchase intention. Finally, each subject responded to the question items regarding his/her demographic information, i.e., gender, age, occupation, the purpose for Internet surfing, and average surfing hours. The participation for each subject took approximately 15 minutes.

Results

This study used a pretest-posttest control group design. To content the possibility that subjects in two groups are different enough to influence the effects of the treatment, the researchers compared the groups in terms of their demographic and Internet usage. The two groups (experiment and control group) were very similar in terms of age, gender, Internet-surfing hours, and the purpose for surfing the Internet.

First two hypotheses state that simple exposure to a banner ad without clicking it will not influence people’s initial attitudes toward the brand (H1.1) and purchase intention (H1.2). Two between-group t-tests were used to check the mean differences in brand-attitude changes and purchase-intention changes between two groups: 1) people who were exposed to banner ads but did not click them and 2) people who were not exposed to banner ads at all (the control group). The dependent variables of the present analysis were the net brand-attitude changes and the net purchase-intention changes (i.e., the changes in brand attitude and purchase intention before and after experimental treatments). For the net brand-attitude changes, the index brand attitude scores were first calculated by averaging three items measuring attitude toward the brand (I like the brand, The brand is satisfactory, and The brand is desirable). For the experimental group, the net brand-attitude changes were calculated by subtracting the index brand attitude scores before banner exposure from the index brand attitude scores after banner exposure. For the control group, the net brand-attitude changes were calculated by subtracting the index brand attitude scores before Web site exposure from the index brand attitude scores after Web site exposure. The Web site for the control group did not contain any banner ad (no treatment). The net purchase-intention changes were similarly calculated by subtracting pre-exposure purchase-intention from post-exposure purchase-intention.

Table 1 shows the relationship between banner exposure and brand-attitude / purchase-intention changes. For all four banner ads, there were no significant differences in net brand-attitude changes and purchase-intention changes between two groups: 1) those who were exposed to banner ads but did not click the banner ads and 2) those who were not exposed to banner ads at all (p > .01). Therefore, H1.1 and H1.2 are supported.

The second stream of hypotheses states that people will experience positive brand-attitude changes (H2.1) and positive purchase-intention changes (H2.2) if they click the banner ad and like the target ad. Table 2 shows the relationship between banner clicking and brand-attitude changes / purchase-intention changes between two groups: 1) people who clicked a banner ad and liked a linked target ad and 2) people who were not exposed to banner ads at all (the control group). For all four banner ads, those who clicked a banner ad and liked a linked target ad were more likely to have positive brand-attitude changes and positive purchase-intention changes than those who were not exposed to banner ads at all (p £ .01). Therefore, H2.1 and H2.2 are supported.

The third stream of hypotheses states that people will experience negative brand-attitude changes and purchase-intention changes if they click the banner ad and dislike the target ad. Table 3 shows the relationship between banner clicking and brand-attitude changes / purchase-intention changes for two groups: 1) people who clicked a banner ad but disliked a linked target ad and 2) people who were not exposed to banner ads at all (the control group). The results show that for American Express and JCPenney, those who clicked a banner ad and disliked a linked target ad were more likely to have negative brand-attitude changes and negative purchase-intention changes than those who were not exposed to banner ads at all (p £ .01). However, for the American Airlines banner ad, there were no significant differences in brand-attitude changes and purchase-intention changes between two groups. In addition, there was an unexpected positive brand-attitude change for Kodak; that is, those who clicked the Kodak banner ad had positive brand-attitude changes even though they disliked the linked Kodak target ad (p £ .05). Therefore, we can say that H3.1 and H3.2 are only partly supported.

One-way ANOVAs were used to compare the mean differences in brand-attitude changes and purchase-intention changes for all four groups at once: 1) those who were not exposed to banner ads at all (the control group); 2) those who were exposed to a banner ad but did not click it; 3) those who clicked a banner ad and liked a linked target ad; and 4) those who clicked a banner ad but disliked a linked target ad. Table 4 shows the results of one-way ANOVA for the American Express banner ad and the mean differences in brand-attitude changes and purchase-intention changes for four groups. The results overall show that: 1) there were no brand-attitude and purchase-intention changes for simple exposure without clicking of banner ads; 2) there were positive brand-attitude and purchase-intention changes for those who clicked the banner ad and liked the linked target ad; and 3) there were negative brand-attitude and purchase-intention changes for those who clicked the banner ad but disliked the banner ad (F= 3.78**, p £ .01). The results were very similar for the remaining three banner ads. Therefore, H1.1 to H3.2 overall are supported again.

Discussion

Summary and Implications

This paper examined the effect of four different types of banner-related activities—1) no exposure to banner ads at all, 2) exposure but no clicking, 3) clicking and liking, and 4) clicking and disliking—on brand-attitude changes and purchase-intention changes.

As expected in H1.1 and H1.2, people who were simply exposed to a banner ad but did not click it retained their initial brand attitude and purchase intention. That is, simple exposure to the banner ad without clicking did not change people’s initial brand attitude and purchase intention. This implies that the banner ads themselves usually do not have enough information for consumers’ cognitive processing, which is required for people’s attitude changes.

In contrast, as expected in H2.1, H2.2, H3.1 and H3.2, people who were exposed to a target ad by clicking a banner ad displayed a positive or negative brand-attitude change and a purchase-intention change depending on the likability of the target ad. In other words, if people clicked the banner ad and liked the linked target ad, their brand attitude and purchase intention were changed in the positive direction (H2.1 and H2.2). However, if people clicked the banner ad but disliked the linked target ad, there was a boomerang effect—negative changes in brand attitude and purchase intention (H3.1 and H3.2). The results imply that advertisers should encourage people to click the banner ads and at the same time make them like the target ads by providing relevant and valuable information in the target ads.

One study similarly addresses this issue of message-relatedness. According to Cho and Leckenby (1999), the extent to which messages in banner ads are related to those in target ads, and the extent to which target ads recount the relatedness of banner ads are important in generating a high degree of interactivity, which in turn influences people’s attitude changes. In other words, when people are exposed to the target ads through the clicking of banners, these people may have their own expectations about the contents of the target ads. If the expected contents are not found in the target ads, or the contents in the target ads are not related to those in the banner ads, people may click away from the target ads right away or may not interact with the advertising messages in the target ads. The researchers found that higher perceived message-relatedness between the banner ad and the target ad yielded higher subsequent interactivity in the target ad, which in turn resulted in positive attitude changes (Cho and Leckenby 1999). These results may be a warning signal to many advertisers who use fraud messages on their banner ads to make people click the banner ads and do not provide relevant information in the target ads. For example, there are many banner ads on the WWW with fraud messages such as "free money" and "you are selected as a winner of $$$," but people often find out that the contents of the linked pages from the banner ads are totally irrelevant, full of blatant promotion messages. In this case, people will display negative attitude changes.

Limitations and Suggestions for Future Research

            A weakness of this study is that the samples are not representative of the general Internet users, since they were drawn from the pool of people who subscribed to discussion LISTSERVs. It is believed that people who subscribe to discussion LISTSERVs tend to be more active and heavier users of the Internet than do general Internet users. This can be a good explanation for the reason the average click-through rate of the four banner ads used in this study (15.3 %) is significantly higher than the average industry click-through rate (2.0 %). Therefore, it would be valuable to replicate the current study with the samples drawn from general Internet users other than LISTSERV subscribers.

            Another explanation for these relatively high click-through rates of the four banner ads used in this study would be that the two banner ads were selected based on the four most popular product categories of Web advertising. In other words, we may say that the four banner ads yielded higher click-through rates than the industry average because their product categories are very popular on the WWW. Moreover, a relatively small number of sample materials were used in this study, i.e., four banner ads. Therefore, it would be valuable to replicate the current study with an increased number of banner ads for more diverse product categories.

 

Table 1

The relationship between banner exposure

and brand-attitude / purchase-intention changes

 

Banner Exposure

Net brand-attitude changes

Mean (Std. Dev)

t-value

Net Purchase-intention changes

Mean (Std. Dev)

t-value

American Express

No Exposure at all

Exposure with no clicking

.04 (.5)

.02 (.4)

.40

.16 (.6)

.04 (.8)

1.54

Kodak

No Exposure at all

Exposure with no clicking

- .05 (.3)

- .03 (.4)

.43

.02 (.4)

- .01 (.5)

.57

JCPenny

No Exposure at all

Exposure with no clicking

.01 (.4)

.01 (.6)

.09

.03 (.4)

.05 (.6)

.36

American Airlines

No Exposure at all

Exposure with no clicking

.03 (.3)

.05 (.5)

.32

- .02 (.5)

.04 (.6)

.36

** p £ .01, * p £ .05

 

 

Table 2

The relationship between banner clicking and brand-attitude / purchase-intention changes

for people who click a banner ad and like a target ad

 

Banner Clicking

Net Brand-attitude changes

Mean (Std. Dev)

t-value

Net Purchase-intention changes

Mean (Std. Dev)

t-value

American Express

No Exposure at all

Click the banner ad and like the linked target ad

.04 (.5)

.40 (1.2)

2.98**

.16 (.6)

.54 (.9)

3.06**

Kodak

No Exposure at all

Click the banner ad and like the linked target ad

- .05 (.3)

.72 (.6)

5.02**

.02 (.4)

.71 (.8)

4.15**

JCPenny

No Exposure at all

Click the banner ad and like the linked target ad

.01 (.4)

.92 (.6)

5.73**

.03 (.4)

.96 (.6)

6.19**

American Airlines

No Exposure at all

Click the banner ad and like the linked target ad

.03 (.3)

1.30 (.8)

7.23**

- .02 (.5)

1.29 (.8)

7.33**

** p £ .01, * p £ .05

 

Table 3

The relationship between banner clicking and brand-attitude / purchase-intention changes

for people who click a banner ad and dislike a target ad

 

Banner Clicking

Net Brand-attitude changes

Mean (Std. Dev)

t-value

Net Purchase-intention changes

Mean (Std. Dev)

t-value

American Express

No Exposure at all

Click the banner ad and dislike the linked target ad

.04 (.5)

-2.5 (.9)

12.95**

.16 (.6)

-2.3 (1.7)

11.24**

Kodak

No Exposure at all

Click the banner ad and dislike the linked target ad

- .05 (.3)

.14 (.6)

1.87*

.02 (.4)

.00 (.5)

.15

JCPenny

No Exposure at all

Click the banner ad and dislike the linked target ad

.01 (.4)

- .28 (.5)

2.50**

.03 (.4)

- .50 (.4)

3.50**

American Airlines

No Exposure at all

Click the banner ad and dislike the linked target ad

.03 (.3)

.00 (.4)

.31

- .02 (.5)

.09 (.6)

.72

** p £ .01, * p £ .05

 

 

Table 4

Mean differences in brand-attitude and purchase-intention changes

among four groups (American Express)

Dependent

Variables

Independent Variables

Case #

Mean (Std. Dev)

Net Brand-attitude changes

Groups

1) Control Group

2) No Click

3) Click and Like

4) Click and Dislike

117

543

83

34

 

.04 (.5)

.02 (.4)

.40 (1.2)

-2.5 (.9)

Net Purchase-intention changes

Groups

1) Control Group

2) No Click

3) Click and Like

4) Click and Dislike

122

563

87

36

.16 (.6)

.04 (.8)

.54 (.9)

-2.3 (1.7)

 

 

 

Sum of Squares

Degree of Freedom

Mean

Squares

F-ratio

Net Brand

Attitude

Changes

Main explained effects

222.8

3

74.3

152.65**

Residual

376.0

773

.49

 

Total

598.8

776

 

 

Net Purchase

Intention

Changes

Main explained effects

211.3

3

70.4

82.01**

Residual

690.6

804

.86

 

Total

901.9

807

 

 

** p £ .01, * p £ .05

 

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