The Elaboration Likelihood Model Applied to Internet Advertising

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The Internet has revolutionized global communication and information sharing, with over 1,463,632,361 individuals depending on it. It is widely recognized as the ultimate knowledge center. Cisco’s research forecasts a yearly growth in network traffic of 46 percent from 2007 to 2012, leading to a doubling every two years.

Due to the growing affordability of internet access, there has been a significant rise in internet advertising. In February 2008, The Interactive Advertising Bureau released two reports stating that U.S. Internet ads were projected to reach $21.1 billion in 2007, reflecting a 25 percent growth compared to the previous year.

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According to The Kelsey Group’s research, online advertising is expected to reach $45 billion in 2007, accounting for 7. percent of the global advertising market worth $600 billion. This represents an increase from the previous year when online ads held only a 6.1 percent share of global advertising. The Kelsey Group predicts that online advertising will continue to grow and reach $147 billion by 2012 as stated in their report titled “The Kelsey Group’s Annual Forecast (2007-2012): Outlook for Directional and Interactive Advertising.” It is important to note that there are significant differences between Internet advertising and traditional forms of advertising.

One significant difference is that Internet media allows for easy monitoring of advertising campaigns. Internet advertisers can conveniently track the number of ad views and click-through rates, which usually lead users to a specific website. Traditional advertising methods also offer tracking mechanisms, but the internet provides much more precise measurements of results. Additionally, web users have more control over their viewing experience compared to television or radio.

Compared to traditional TV and radio, internet advertising offers a more user-friendly experience. It allows ads to be shown alongside web content or search results that are relevant to the user’s interests. Moreover, advertisers can use IP addresses on the internet to precisely target specific audiences with highly relevant ads for potential customers.

Web advertisers have a critical demand for understanding how people process advertising on the Internet since the emergence of the World Wide Web as a new advertising medium. However, there has been limited research conducted on advertising processes specifically on the Web. This paper intends to analyze the potential of the Elaboration Likelihood Model of Persuasion in explaining information processing by Web advertisers.

The elaboration likelihood model of persuasion is a theory that explains the processes behind persuasive communication and the strength of resulting attitudes. According to this model, in the context of advertising, the effectiveness of ads is determined by two distinct routes to persuasion, which vary based on the level of active thinking involved in producing attitude change (Petty & Cacioppo, 1996).

The initial route, referred to as the “central route,” is a cognitive process that requires concentrated effort and systematic thinking. This involves individuals focusing on relevant information in a message, utilizing their previous knowledge and experience to evaluate and build upon the provided information. When the likelihood of elaboration is high, the favorability of cognitive responses towards the advertisement impacts the strength of attitudes.

Support arguments increase favorability of attitudes, while counter arguments decrease it. High elaboration likelihood leads to two types of processing. Objective processing occurs when individuals are motivated and capable of evaluating the central merits of a message. Biased processing happens when individuals have pre-existing strong opinions about the message subject and respond to arguments in line with their attitudes instead of critically examining the message’s quality.

If individuals’ existing attitudes align with the message, they will actively search for arguments that support it. Conversely, if the message contradicts their attitudes, counter arguments will emerge. Another method of persuasion is known as the “peripheral route,” which involves automatic, shallow, heuristic and mindless thinking. This route relies on affective associations or simple inferences tied to peripheral cues (Petty & Cacioppo, 1986). When individuals are not inclined to deeply analyze the message’s content, they instead rely on noncontent elements related to the message to shape their attitudes.

Peripheral cues, such as the number of message arguments, music, and affective reactions generated by the ad, can influence an individual’s decision to choose between the central or peripheral route to persuasion. This decision is affected by the likelihood of elaboration, which depends on the individual’s motivation and ability to process information. Petty and Cacioppo (1986) state that situational or individual factors determine one’s motivation and ability.

Various variables influence the level of information processing and the direction of thinking. Motivation for processing is increased by factors such as perceived personal relevance, need for cognition, and personal responsibility for evaluating the message. Furthermore, processing ability is enhanced by minimal external distraction, message repetition, and clear message content. Petty and Cacioppo emphasize that peripheral processing in advertising is only effective when accompanied by significant repetition.

The high involvement “active thinking” central route is preferred because “Attitude changes via the central route appear to be more persistent, resistant, and predictive of behavior than changes induced via the peripheral route” (Petty & Cacioppo, 1986). This means that emotions and unconscious processes may have some role, but they are always less important than information processing and rational argument. Karson and Korgaonkar (2001) applied the Elaboration Likelihood Model principles to internet advertising and conducted tests online.

The study aimed to investigate the role of involvement in moderating the effects of arguments and peripheral cues in Internet advertising. However, the findings of the study did not support the Elaboration Likelihood Model principle which states that high levels of involvement enhance the impact of strong arguments and diminish the influence of peripheral cues in persuasive situations. Additionally, the study also did not support the hypothesis suggesting that peripheral cues play a more crucial role than argument strength in low involvement situations.

A closer look at Karson and Korgaonkar’s research suggests that the way they conducted their study may not accurately replicate traditional offline Elaboration Likelihood Model studies. These studies typically rely on peripheral cues to guide individuals in forming judgments about attitude objects. One commonly used cue is the attractiveness of the source of the message. However, in Karson and Korgaonkar’s study, the complexity of the website’s wallpaper is used as the peripheral cue for their research.

Despite the significance of background design in the online domain, it is crucial to employ consistent operationalizations when validating the Elaboration Likelihood Model for Web persuasion. Moreover, the effectiveness of involvement manipulation seems inadequate. Specifically, the distinction between participants’ high and low levels of involvement was minimal. Nevertheless, the involvement manipulation may have been suitable as the internet serves as a more dynamic media platform, heightening users’ overall engagement in any given scenario.

While Karson and Korgaonkar found that advertisements on the Internet may not be processed peripherally, Cho (1999) contradicted this and proposed that individuals do process peripherally while online. He also presented a modified elaboration likelihood model that considers the interactivity of the Internet. According to his model, if someone lacks the ability or motivation to process an online ad, they will probably process the message peripherally. However, this peripheral processing does not make the individual completely passive.

Instead of relying on user interactivity, the cues present in an ad drive the level of interactivity. If an ad includes favorable cues that pique the individual’s curiosity, they may be persuaded to interact with the banner. This interaction can lead to a peripheral attitude change based on cues, which is not as enduring as a central attitude change. However, it is possible that an attitude formed online through cues is more durable than a peripheral attitude change in a non-interactive setting because it is somewhat more proactive.

Cho’s research examined various factors that impact click-through rates of banner ads. Like the Elaboration Likelihood Model and Cho’s modified proposal of the Elaboration Likelihood Model, a greater degree of product engagement predicted higher intentions to click on the ad, compared to those with lower product involvement. However, in cases where participants had lower levels of product engagement and were presented with a cue like a larger banner ad or animation, their intention to click was higher compared to when the cue was absent.

Cho’s online study, which tested the Elaboration Likelihood Model, corroborated several previously reviewed findings related to website content relevance and attitude towards the website. Notably, if the banner ad was relevant to the site’s content, individuals were more inclined to respond. Furthermore, Cho discovered that participants’ attitude towards the website where the banner was placed influenced their likelihood of clicking through. Cho further examined peripheral cues’ impact on online persuasion in his research.

Defining cues as banner ad size and animation, Cho aimed to investigate whether the relationship between cues and low involvement participants would be stronger compared to high involvement participants. To conduct the study, Cho enlisted 751 participants from ListServ and provided them with an authentic movie review website containing randomly selected banner ads sourced from prominent search engines. The size of the ads was adjusted and animation was added through manipulation. Cho ensured that participants had not been previously exposed to the featured ads in the real world. The results showed that larger banner ads and animated banner ads were clicked-through more frequently in low involvement conditions, after controlling for any previous exposure to the featured advertisements.

The click-through rate disparity was notable, but it was not significant in the high involvement condition regarding the difference between banner size and animation. Likewise, Li and Bukovac (1999) found that banner size and animation influenced banner ad click-through. However, they also explored the user’s goal (either seeking information or web-surfing) while online as a potential moderator for the participants’ click-through rate.

The researcher conducted a study using four variations of a Levi’s ad. The brand had initially evoked moderate interest in a pretest. The ad elements remained the same, except for changes in banner size and animation. Participants were divided into two groups – one focused on information-seeking and the other engaged in regular web-surfing activities. The information-seeking group searched for something specific online, while the web-surfing group browsed as usual. The target banner appeared on the home page of the test computers. Li and Bukovac discovered a significant correlation between user mode and banner size.

The effect was significantly higher, approximately 23%, for participants who were in the web surfing condition compared to those in the information seeking condition. Studies by Cho (1999 & 2003) and Li and Bukovac (1999) have made significant contributions to the understanding of how the Elaboration Likelihood Model applies to online advertising. These findings further support the application of the Elaboration Likelihood Model to the Internet. The first study that applied the Elaboration Likelihood Model to advertising was conducted by Petty, Cacioppo, and Schumann (1983).

In this study, involvement was manipulated by informing participants that the advertised product would either be released soon or at a later time. Arguments used in the study were either based on scientific facts or personal stories. The peripheral cue used in the study was celebrity athletes as the message source. It should be noted that the peripheral cue tested in this study focused on the content of the message rather than how it was presented. In contrast, online Elaboration Likelihood Model studies use factors like banner size and animation to manipulate the peripheral cue, instead of focusing on the message source and content.

In my opinion, the executional elements play a crucial role in designing an advertising campaign. However, the size and animation of a banner might attract attention more effectively than generating meaningful interest. This suggests that banner ads that are perceived peripherally may struggle to influence attitudes. Furthermore, once the initial tactics of capturing attention with banner size and animation become less effective, consumers may seek more substantial content before deciding to click on a banner.

Therefore, further research is necessary to determine if the Elaboration Likelihood Model is suitable for Internet advertising by applying cue operationalization from the model in an online advertising context.


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  7. Pacheco, E. (2008, February 25). Interactive Advertising Revenues to Reach US$147 Billion Globally by 2012, According to The Kelsey Group’s Annual Forecast. Retrieved December 02, 2008, from The Kelsey Group Web site: http://www. kelseygroup. om/press/pr080225. asp
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The Elaboration Likelihood Model Applied to Internet Advertising. (2018, Feb 08). Retrieved from

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