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Running Head: The Elaboration Likelihood The Elaboration Likelihood Model Applied to Internet Advertising In just a few years, the Internet has established itself as a very powerful platform that has changed the way we communicate. The Internet, as no other medium, has given an international or a “globalized” dimension to the world. It has become the universal source of information for over 1,463,632,361 people. Cisco conducted a study, which predicted that traffic on the world’s networks would increase annually 46 percent from 2007 to 2012, nearly doubling every two years.
These results don’t come as a surprise considering that with a small investment almost anybody can have access to the World Wide Web. Among the many segments of the Internet, advertising is becoming the one with the greatest growth with an estimate of $21. 1 billion for U. S. Internet ads in 2007, a 25 percent increase over 2006. (According to two reports on the size of the Internet advertising market released in February 2008 by The Interactive Advertising Bureau). The Kelsey Group provides a global estimate of $45 billion for Internet advertising in 2007, which is 7. percent of the total $600 billion global advertising market. That compares to a 6. 1 percent share of global advertising for online ads in 2006. Researcher The Kelsey Group also has projected that online advertising will hit $147 billion by 2012. That’s worldwide advertising and it’s part of their report, “The Kelsey Group’s Annual Forecast (2007-2012): Outlook for Directional and Interactive Advertising. ” There are fundamental differences between Internet advertising and traditional advertising.
The main difference is the way Internet media is more able to be tracked in terms of success for an advertising campaign. Internet advertisers can easily track ad views and click through rates to the desired location which is usually a Website. While there are ways to track a campaign in traditional advertising methods, the Internet makes tracking results close to 100% accurate. Additionally Web users have more control over their viewing environment than television or radio.
In traditional television and radio, viewers and listeners are almost forced into watching and hearing advertisements. Internet advertising allows for ads to be placed by Web content or search results creating a more inviting advertisement environment for the user already engaged in a particular subject. The Internet has allowed advertising to also further target an audience by IP addresses pinpoint in order to place ads that offer more relevance to the potential customer.
With the introduction of the World Wide Web as a new advertising medium, understanding how people process advertising on the Internet has become the critical demand of Web advertisers. But there has been little research on advertising processes on the Web. This paper examines the Elaboration Likelihood Model of Persuasion and then explores how this theory can help explain information processing of Web advertisers. Elaboration Likelihood Model
The elaboration likelihood model of persuasion is a theory about the processes responsible for yielding to a persuasive communication and the strength of the attitudes that result from those processes. In an advertising context, the model holds that the process responsible for the effectiveness of ads is one of two relatively distinct routes to persuasion that differ according to “the extent to which the attitude change that results … is due to active thinking. ” (Petty & Cacioppo, 1996).
The first is known as the “central route,” and is “controlled, deep and systematic” (Petty & Cacioppo, 1986), and it involves effortful cognitive activity, by which individuals focus their attention on message relevant ad information, and draw on prior experience and knowledge to evaluate and develop on information that was presented. When elaboration likelihood is high, the favorability of cognitive responses generated in reaction to the ad influences the strength of attitudes.
Support arguments enhance attitude favorability, while counter arguments reduce attitude favorability. Two types of processing may occur when elaboration likelihood is high. Objective processing takes place when the individual is motivated and able to examine the message for its central merits. Biased processing occurs when the individual holds a strong previous opinion concerning the message subject and, therefore, responds to message arguments with attitude consistent cognition instead of scrutinizing the message for its quality.
If the message is consistent with prior attitudes, the individual will draw support arguments, while counter arguments will be brought forth if the message is counter attitudinal. The other route to persuasion is known as the “peripheral route” and it is “automatic, shallow, heuristic, and mindless” and “based on affective associations or simple inferences tied to peripheral cues…” (Petty & Cacioppo, 1986). When elaboration likelihood is low, individuals do not think much about message content; instead, they use noncontent elements associated with the message as a basis for attitude formation.
Peripheral cues can be the number of message arguments, music, affective reactions generated by the ad and so on.. Whether an individual will follow the central or peripheral route to persuasion is determined by the likelihood of elaboration, which, in turn, is influenced by the individual’s motivation and ability to process. Petty and Cacioppo (1986) define motivation and ability in terms of their antecedents. Some antecedents are situational factors, whereas others are individual factors.
Some variables influence the extent of information processing, whereas others tend to influence the direction of thinking. Factors that boost processing motivation include perceived personal relevance, need for cognition, and personal responsibility for evaluating the message. Similarly, factors that enhance the ability to process include low levels of external distraction, message repetition, and high message clarity. Petty and Cacioppo make it clear that they see peripheral processing as a weak advertising route, only effective if tied in to high levels of repetition.
The high involvement “active thinking” central route is favored, 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). In other words, this academic model states that emotions and unconscious process may have a part to play, but they are always subsidiary in importance to information processing and rational argument. Elaboration Likelihood Model Applied to Internet Advertising Karson and Korgaonkar (2001) tested the Elaboration Likelihood Model principles online.
Specifically, they manipulated involvement to determine if it moderated the effects of arguments and peripheral cues included in Internet advertising. The study did not find support for the Elaboration Likelihood Model principle that proposes higher levels of involvement augment the effectiveness of strong arguments in the persuasive setting and reduce the relative effect of peripheral cues. Also, it also did not support the postulate that in low involvement situations, peripheral cues are more important than argument strength.
However, an examination of Karson and Korgaonkar’s study revealed that the methodology might not appropriately reproduce offline Elaboration Likelihood Model studies. In traditional offline Elaboration Likelihood Model studies, peripheral cues activate rules of thumb to help individuals make judgments concerning attitude objects. The attractiveness of the message source is a typical cue utilized in Elaboration Likelihood Model studies. In Karson and Korgaonkar’s study, the complexity of the wallpaper of the website is used as the study’s peripheral cue.
Although background design may be an important element in the online context, similar variable operationalizations should be used when seeking to validate Elaboration Likelihood Model for Web persuasion. Additionally, the manipulation of involvement does not appear sufficient. Specifically, the difference in the participants’ high level of involvement and low level of involvement was small. Nonetheless, perhaps the manipulation of involvement was appropriate, because the Internet is a more active media environment, which increases the overall involvement of users in any situation.
Also, maybe advertisements on the Internet are not processed peripherally. In contrast with Karson and Korgaonkar’s results, Cho (1999) suggested that individuals do process peripherally while on the Internet. Furthermore, he postulated a modified elaboration likelihood model that takes into account the interactivity of the Internet. Particularly, his model suggests that if one does not have the ability or motivation to process an online ad, they will likely process the message peripherally. However, during the peripheral processing, the individual is not entirely passive.
Rather, the user interactivity is driven by cues. Particularly, if the ad has favorable cues that can maintain the curiosity of the individual, the ad may persuade the individual to interact with the banner. Such interaction will lead to a peripheral attitude change based on cues, which is not as resilient as a central attitude change. Nevertheless, perhaps an attitude shaped online utilizing cues is more resilient than a peripheral attitude change in a non-interactive environment because it is to some extent more active.
Cho’s study looked at several variables influencing banner ad click-through. Similar with the Elaboration Likelihood Model and Cho’s modified Elaboration Likelihood Model proposal, higher levels of involvement with the product forecasted higher click-through intentions when compared to those reporting less product involvement. However, when participants were less involved with the product and a cue was present like a larger banner ad or animation, lower involvement forecasted higher click-through intention than if the cue was not present.
Cho’s study testing Elaboration Likelihood Model online also sustained many of the findings previously reviewed – relevance of website content and attitude about website. Specifically, when the banner ad was relevant to the site’s content individuals were more likely to respond. Cho also found that participants’ attitude toward the website in which the banner was placed affected whether or not individuals will click-through. Cho continued his research regarding online persuasion focusing on the effect of peripheral cues.
Defining cues as banner ad size and animation, he sought to determine if the cues relationship with low involvement participants would be stronger than with high involvement participants. Utilizing 751 participants recruited from ListServ, Cho sent an actual movie review website with real banner ads randomly selected from top search engines and manipulated to adjust size and add animation. Controlling for any previous exposure to the featured advertisements in the real world, Cho found that larger versus smaller banner ads and animated versus static banner ads were clicked-through more often in low involvement conditions.
The differences were significant; the difference in the click-through rate between the banner size and animation was not significant in the high involvement condition. In the same way, Li and Bukovac (1999) determined banner size and animation affected banner ad click-through. However, their study also considered the user’s goal (information seeking and web-surfing) while online as a potential moderator of the participants’ click-through rate.
The researcher created four versions of a Levi’s ad, a brand that evoked moderately interested feelings during a pretest. Besides the manipulation of banner size and animation, all ad elements were identical. The participants in the information-seeking mode were instructed to search for something specific online while those in the web-surfing mode were asked to surf the web as they normally would. The target banner as was positioned on the home page of the test computers. Li and Bukovac found a noteworthy relationship between user mode and banner size.
Furthermore, the effect was about 23% greater for those in the web surfing condition versus the information seeking condition. Cho (1999 & 2003) as well as Li and Bukovac (1999) have contributed substantially to the literature and understanding of how the Elaboration Likelihood Model applies to the online advertising context. This will boost the case for Elaboration Likelihood Model’s application to the Internet. Petty, Cacioppo and Schumann (1983) conducted the first study applying Elaboration Likelihood Model to advertising.
This study manipulated involvement by instructing participants that the product advertised was either going to be released in a close immediacy and in the near future or vice versa. Arguments were either supported by scientific fact or anecdotal. The peripheral cue was the message source and was celebrity athletes. To that end, the peripheral cue tested was related to content not execution. In comparison, the online Elaboration Likelihood Model studies utilize execution (banner size, animation) versus content (message source) to manipulate the peripheral cue.
In my opinion, though the executional elements are important factors to consider when designing an advertising campaign, banner size and animation may capture attention more than stimulating meaningful interest suggesting that banner ads processed peripherally have little hope of affecting attitude. What is more, after such tactics to get users’ attention as banner size and animation wear out, consumers may begin to look for more substantial content when deciding to take the time to click-through a banner.
Hence, future studies are needed to apply Elaboration Likelihood Model cue operationalization to the online advertising context to further confirm the appropriateness of Elaboration Likelihood Model for the Internet. References Cho, C. (1999). How advertising works on the WWW: Modified elaboration likelihood model. Journal of Current Issues and Research in Advertising, 21, 33-50. Cho, C. (2003, April). Factors Influencing Clicking of Banner Ads on the WWW. CyberPsychology & Behavior, 6(2), 201-215. Retrieved December 8, 2008, doi:10. 089/109493103321640400 Cho, C. (2003). The effectiveness of banner advertisements: Involvement and click-through. Journalism and Mass Communication Quarterly, 80(3), 623-645. Gallagher, K. , Foster, K. D. & Parsons, J. (2001). A tale of two studies: Replicating Advertising effectiveness and content evaluation in print and on the web. Journal of Advertising Research, 41, 71-82. Karson, E. , & Korgaonkar, P. (2001, Fall2001). An Experimental Investigation of Internet Advertising and the Elaboration Likelihood Model.
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