EFFECTIVE ONLINE ADS: THE ROLE OF PLACEMENT AND ANIMATION

EFFECTIVE ONLINE ADS: THE ROLE OF PLACEMENT AND ANIMATION Research-in-Progress Sameh Al-Natour American University of Sharjah Sharjah, United Arab Emirates salnatour@aus.edu Andrew Gemino Simon Fraser University Burnaby, BC, Canada gemino@sfu.ca Robert Krider Simon Fraser University Burnaby, BC, Canada rkrider@sfu.ca Abstract Online advertising has experienced an unparalleled growth over the last decade. Yet, despite the increased spending on this form of advertising, there is a lack of understanding of what makes an online ad effective. This study helps to fill in this gap. Specifically, in an experimental study, we examine the effects of ad placement and animation on ad content recall and recognition, as well as customers attitudes towards the ad, brand, website and their purchase intention. The results reveal significant effects of ad placement on recall and recognition and two of the attitudes. Surprisingly, animation had no effects on attitudes, and only modest and inconsistent effects on ad content recall and recognition. Keywords: Consumer behavior, electronic commerce, interface design, advertising Thirty Fourth International Conference on Information Systems, Milan 2013 1

General IS Topics Introduction Despite the recent financial meltdown, online advertising has been growing at a rapid rate over the last several years. With more than $102 billion spent worldwide in 2012, and a projected $118 billion in 2013, online advertising has surpassed advertising in print newspapers and magazines, and equals more than half of the amount spent on TV advertising (emarkerter 2013). This growth in spending has been accompanied by significant improvements in technology and broadening in online advertising techniques. Yet, a consistent question has always been whether online advertisements, in their differing forms, represent a good investment on the part of the advertiser. Hence, it has become increasingly important for advertisers and marketers to examine the factors influencing the effectiveness of online advertising. To that goal, scholars have proposed that many variables play a role in influencing an online ad s effectiveness. Past studies investigated a plethora of product, customer and host site factors that affect the effectiveness of online ads, amongst others. Collectively, this research has confirmed that many variables could affect the extent to which an online ad is liked and is perceived to be informative, relevant, and useful. For instance, factors such as the level of involvement of the product advertised (e.g., Yoon 2001), the familiarity of the brand (e.g., Dahlen 2001), the relevance, reputation and complexity of the host site (e.g., Shamdasani 2001), and the viewer s Internet experience (e.g., Bruner 2000), were all shown to play important roles in affecting the extent to which the customer has favorable attitudes towards the ad, the host website, the product/brand advertised, and their purchase intention (e.g., Gallagher 2001). Other researchers have focused on examining factors concerning the design of the ad itself, such as its placement (Benway and Lane 1998) and the use of animation (Burke et al. 2005; Hong et al. 2007). The findings of past research, however, have been inconsistent; especially in relation to the role of different factors in making online ads more effective. This has led to what has been termed the distinctiveness-blindness debate. In essence, this debate revolves around the contradictory views of whether online ads, often designed with salient features, would attract users attention or be ignored by them (Sun et al. 2013). Furthermore, despite this large body of research, very little attention has been directed at examining the other important measures of effectiveness, such as ad recall and recognition. This is mainly caused by the lack of models and metrics for evaluating online advertising effectiveness, where researches seem to disagree about which measures are to be used, and which factors need to be considered (Bruner 2000). While studies have focused on what is being advertised, and where it is advertised, no attention has been paid to understanding the influence of how an ad is presented on its effectiveness. Thus, these factors which are controlled by the ad developer, e.g., its format and placement on the host site, are markedly absent from our collective knowledge. More recently, Sun, Lim, and Peng (2013) have introduced a variance model, where a number of semantic and structural factors were proposed to influence an online ad s salience. These were suggested to subsequently enhance the attention paid to it, often resulting in higher recall, recognition and arousal. This study performs a test of the effects of some of the structural factors that have been proposed in this new variance model. Specifically, this study fills in the two research gaps described above. First, it operationalizes a metric for measuring online ad effectiveness that includes the objective measures of ad recall and recognition. Second, it investigates two design characteristics of online ads and examines their impact on its effectiveness. Specifically, this study investigates the effects of the use of animation and different ad placements on ad effectiveness measured through content recall, content recognition, as well as the traditional perceptual measures included in online ads hierarchy-of-effects (Bruner 2000). In this hierarchy-of-effects, it is suggested that the attitude-toward-the-ad has a direct impact on the attitudetoward-the brand, which positively impacts purchase intention. Research Model: The Effects of Placement and Animation Although, in all of the literature studied, the term effectiveness was never fully defined, there is an agreement that customers responses to online ads could be considered a measure of their effectiveness 2 Thirty Fourth International Conference on Information Systems, Milan 2013

Al-Natour et al. / Effective Online Ads: Animation and Placement (Gallagher 2001). Bruner (2000) further suggests that a study of the hierarchy of effects for online ads, and the individual responses to each effect, could constitute a measure for the effectiveness of online ads. In this study, four research hypotheses are developed. The first and third are in relation to the effects of the two independent variables (placement and animation) on the individual constructs of the hierarchy of effects. In addition, we propose that content recall and recognition constitute an objective measure of ad effectiveness. Therefore, we develop two additional hypotheses that concern the effects of animation and placement on recall and recognition. Placement Effects Online advertising can take many forms and shapes. However, banner, skyscraper and pop-up ads are considered by most researchers to be the most prevalent forms of online advertising (Cho 2001, Dahlen 2001). Further, industry research suggests that pop-up ads for example, are expected to generate higher response than other forms of ad placements. While in traditional media, most exposure to advertisements occurs at the same level of forced or incidental exposure conditions, for the web, different levels of forced exposure can occur. This means that even the same advertisement can be presented to the audience with different degrees of forced exposure. Most common of these types of forced exposure are pop-ups and pop-unders. Pop-up ads automatically launch in a separate window, and remain viewable until the customer elects to minimize or close them, or until they automatically disappear after a predetermined length of time if they were programmed to do so. Pop-unders are less intrusive, and typically load in another browser behind the one being viewed by the customer (Edwards et al. 2002). Past research has been mixed in regards to the effects of forced exposure on customers advertising perceptions, including clicking behavior, attitudes toward the ad and the brand, as well as purchase intention (Sun et al. 2013). Some have argued that because pop-up ads increase the distinctiveness of the ad, they are more noticeable, and therefore are more memorable and arousing (Cho 2001). Yet, others have suggested that because forced exposure may include elements of unwelcomed interruption, customers maybe irritated. This could lead to avoidance behavior, and more importantly, negative affect that negatively influences customer attitudes (Edwards et al. 2002). Regardless of the type of forced exposure employed, it is logical to think that with forced exposure, the customer s attention is forcibly drawn to the ad. This should be expected to result in a higher likelihood that the ad is noticed by the customer, especially compared to skyscraper and banner ads which could be easily ignored (Sun et al. 2013). This increased attention and possibly longer viewing time should increase the memorability of the ad content, and thus, result in higher recall and recognition. The effects of forced exposure on attitudes, however, are more contentious. Nonetheless, we believe that forced exposure, despite its potential to cause interruptions and possible irritation, causes positive affect that enhances the customer s attitude towards the ad and the brand. This should subsequently, as described earlier via the ad hierarchy-of-effects (Bruner 2000), enhance the attitude toward the website and increase purchase intention. We anchor our hypothesis in the concept of perceptual fluency, which refers to the ease with which a person perceives and identifies the physical characteristics of a stimulus (Lee and Labroo 2004). Perceptual fluency can be enhanced via a number of factors, of which increased contrast is one. Increased contrast, which could be caused by forced exposure, will make the object more understandable and visually clear, thus reducing memory misattribution and generating positive affect (Reber et al. 2004). Specifically, it is suggested that high fluency elicits positive affect because it is associated with successful recognition of the stimulus and easier processing (Reber et al. 2004). Hence, we expect that the increased fluency caused by forced exposure would increase positive affect and result in positive attitudes toward the ad itself, and as suggested by past research, also towards the brand advertised (Lee and Labroo 2004). H1: Compared to other placements, pop-up ads will have positive associations with attitude-toward-thewebsite, attitude-toward-the-ad, attitude-toward-the-brand, and purchase intention. H2: Compared to other placements, pop-up ads will have the highest recall and recognition. Thirty Fourth International Conference on Information Systems, Milan 2013 3

General IS Topics Animation Effects Since their introduction in the mid nineteen nineties, standards and norms for online ads have progressively changed. Tools such as animation and multimedia have been widely adopted (Stevenson 2000). Nonetheless, research examining the effects of animation on online ad effectiveness has been scant (sun et al. 2013). In this study, we propose that animation will have positive effects on customers attitudes, their purchase intention, as well as their recall and recognition of the ad s content. Similar to the case of forced exposure, we propose that animation increases the ad s distinctiveness. This increases the attention paid to the ad, and subsequently enhances the customer s memory of its content. We further propose that animation will positively impact customers attitudes. More specifically, animating an ad increases its figure-background contrast and introduces visual priming. This will increase the ad s perceptual fluency, subsequently enhancing judgments regarding its aesthetics (Reber et al. 2004). The resulting aesthetic response will likely translate into pleasure and positive affect that enhances attitudes towards the ad and the brand. H3: Compared to static ads, animated ads will have positive associations with attitude-toward-the-website, attitude-toward-the-ad, attitude-toward-the-brand, and purchase intention. H4: Compared to static ads, animated ads will have higher content recall and recognition. Method Overview The research study used repeated measures to test for placement effects. The different ad contents were rotated around the different ad placements (a Latin Square rotation) to guard against order effects. A fractional factorial was used to test for animation effects, where only one of the different ad placements was treated with both animated and static ads. The full experimental treatment consisted of the respondents viewing a website that contained the treatment online ads. It was necessary that the six treatment websites were prepared with the ads embedded being the only variable that was intentionally manipulated. All fonts and design components were exactly the same. Participants were asked to review a reading comprehension section from a GMAT exam and answer some questions. The time needed to complete this task was estimated at 10-15 minutes. Treatments A total of twelve ads were created and embedded in six websites. Each website included three different ads that differed in their placement (one banner ad, one skyscraper ad, and one pop-up ad). Each ad advertised a different event, where the ad content included four distinct pieces of information, namely the event name, date, time and location. The three advertised events chosen were: 1) film festival, 2) MP3 player sale, and 3) sporting goods swap. Of the twelve ads, there were three different banner ads (three different contents), three different pop-up ads (three different contents), and six different skyscraper ads (for each of the three contents, there was an animated and a static version). The same ad dimensions were used for both banner and pop-up ads. The dimensions for the skyscraper version of each ad were modified to fit the shape of a skyscraper ad. The banner and skyscraper ads were placed in frames to ensure that they are continuously visible. The pop-up ad appeared a number of times during the task based on pre-specified movements of the subject s mouse. The six treatment groups are outlined in Table 1. As could be observed in the table, to guard against specific ad contents confounding ad placement effects, the different ad contents were rotated across the three placements for odd numbered treatment groups. To test for animation effects, each skyscraper ad was presented as both static or animated in one of the six treatment groups. 4 Thirty Fourth International Conference on Information Systems, Milan 2013

Al-Natour et al. / Effective Online Ads: Animation and Placement Table 1. Treatment Conditions Ad Placement Banner Skyscraper Pop-up Treatment Group 1 2 3 4 5 6 (static) (animated) (static) (animated) (static) (animated) Sample and Measures One hundred and fifteen undergraduate and graduate business students participated in the study via email invitations. Data was collected through an online questionnaire. The questionnaire was identical for the six treatment groups. The study used established multi-item Likert scales to measure the subjective exogenous variables (attitude-toward-the-ad, attitude-toward-the-brand, attitude-toward-the-website, and purchase intention) adapted from Gallagher (2001). For example, the attitude-toward-the-ad was measured using the items: 1) I like this ad, 2) I believe this ad is good, 3) I believe this ad is irritating, and 4) I believe this ad is interesting. Purchase intention was measured using the following items: 1) I am likely to attend the event, 2) It is probable I will attend the event, and 3) It is possible I will attend the event. All items loaded as expected on their respective factors, and the scales had a high level of reliability. Recall and recognition were measured objectively by asking the subject to recall the contents of each ad (recall), or choose from among options (recognition). Gender and web experience were also measured and used as control variables. Subsequent analysis revealed that both variables had statistically insignificant effects on all of the exogenous variables, and hence, they were removed from the model. Results Effects of Placement and Animation on Attitudes and Purchase Intention To test for Hypothesis 1, which concerns the effects of placement on attitudes and purchase intention, a three-way within subject repeated measure ANOVA was performed. The results (shown in Table 2) revealed an effect of placement on the attitude-toward-ad (F = 5.80, p<0.01, significant quadratic contrast p<0.05) with skyscrapers having the highest mean, and on purchase-intention (F = 5.41, p<0.01, significant linear contrast p<0.05) with pop-ups having the highest mean. Due to these inconsistencies in the effects of placement on attitudes and purchase intention, hypothesis 1 is rejected. Table 2. Placement Effects Construct F Sig. Contrast Attitude-Ad 5.80 <0.01 Quadratic Attitude-Event (brand) 0.32 0.73 None Attitude-Website 5.41 0.01 Linear Purchase Intention 2.08 0.13 None Thirty Fourth International Conference on Information Systems, Milan 2013 5

General IS Topics In order to test for hypothesis 3, a set of one-way ANOVAs were performed to examine the difference in the attitudes and purchase intention means between the group that was treated with an animated skyscraper ad, and the group that was treated with a static one. The results (shown in Table 3) suggest that animation has no statistically significant effect on any of the dependent variables. To ensure that the insignificance in mean differences among the groups is not a product of the different ad contents, each test was re-performed with the treatment number as a covariate. The results (also shown in Table 3) confirm that animation does not have an effect (p > 0.05), and that the treatment effect sizes were small. Hence, hypothesis 3 is rejected. Table 3. Animation Effects No-Covariate With Covariate Construct F Sig. F Sig. Attitude-Ad 2.52 0.12 1.27 0.26 Attitude-Event (brand) 2.01 0.16 0.63 0.43 Attitude-Website 0.04 0.85 0.54 0.56 Purchase Intention 3.19 0.08 1.40 0.24 Effects of Animation and Placement on Recall and Recognition To test for hypotheses 2 and 4, which concern the effects of placement and animation (respectively) on ad content recall and recognition, we computed recall and recognition scores for each ad in each treatment. Each of the three ads in each treatment condition was given a unique code. A response was judged as a recall hit if it matched any of the keywords for the event title and place. Event times and dates were judged a hit if the response contained at least a correct start or end time, day, or month. Table 4 shows the recall scores for each ad in each treatment. Table 5 shows the recognition data (frequency and % of responses) for each ad in each treatment group. To test for hypothesis 2, we examined the results in Tables 4 and 5. The results in Table 4 reveal that overall, pop-up ads have much higher rates of recall than those of banner and skyscraper ads. A finding that was further confirmed by Chi-square tests examining the difference between observed and expected recall frequencies for the three groups. Similarly, the results in Table 5 reveal that pop-up ads have higher content recognition of the four content components. The level of content recognition of banner and skyscraper ads is close, but in three out of four content components (i.e. event name, date, and place) banner ads have higher recognition. Skyscraper ads have a slightly higher recognition of the event s time. The results of Chi-square tests indicated a high level of significance when testing for the difference in means. Hence, hypothesis 2 is fully supported. 6 Thirty Fourth International Conference on Information Systems, Milan 2013

Al-Natour et al. / Effective Online Ads: Animation and Placement Ad Type Banner Treatment Total Resp. Ad Code Table 4. Recall Data Event Date Time Place Event % Date % Time 1 23 1 2 1 2 2 9% 4% 9% 9% 2 16 4 2 0 0 1 13% 0% 0% 6% 3 21 7 4 2 1 5 19% 10% 5% 24% 4 19 10 0 0 0 1 0% 0% 0% 5% 5 18 13 4 1 0 4 22% 6% 0% 22% 6 18 16 3 0 1 2 17% 0% 6% 11% Total 115 15 4 4 15 13% 3% 3% 13% Average Recall Percentage (average of recall of the four ad information points) = 8% Skyscraper 1 23 2 3 1 1 0 13% 4% 4% 0% 2 16 5 1 1 0 1 6% 6% 0% 6% 3 21 8 1 2 0 0 5% 10% 0% 0% 4 19 11 4 0 1 0 21% 0% 5% 0% 5 18 14 0 0 0 0 0% 0% 0% 0% 6 18 17 2 0 0 0 11% 0% 0% 0% Total 115 11 4 2 1 10% 3% 2% 1% Average Recall Percentage (average of recall of the four ad information points) = 4% Pop-up % Place 1 23 3 8 4 2 7 35% 17% 9% 30% 2 16 6 6 3 1 3 38% 19% 6% 19% 3 21 9 11 2 2 8 52% 10% 10% 38% 4 19 12 7 0 2 2 37% 0% 11% 11% 5 18 15 7 3 2 6 39% 17% 11% 33% 6 18 18 9 0 0 1 50% 0% 0% 6% Total 115 48 12 9 27 42% 10% 8% 23% Average Recall Percentage (average of recall of the four ad information points) = 21% % Thirty Fourth International Conference on Information Systems, Milan 2013 7

General IS Topics Ad. Type Banner Skyscraper Pop-up Table 5. Recognition Data Treatment 1 2 3 4 5 6 Total Event 3 (13%) 3 (19%) 3 (14%) 0 (0%) 5 (28%) 6 (33%) 20 (17%) Date 1 (4%) 1 (6%) 1 (5%) 0 (0%) 1 (6%) 2 (11%) 6 (5%) Time 1 (4%) 1 (6%) 0 (0%) 0 (0%) 0 (0%) 2 (11%) 4 (3%) Place 3 (13%) 3 (19%) 4 (19%) 0 (0%) 4 (22%) 3 (17%) 17 (15%) Event 4 (17%) 3 (19%) 3 (14%) 4 (21%) 2 (11%) 2 (11%) 18 (16%) Date 1 (4%) 1 (6%) 0 (0%) 1 (5%) 0 (0%) 1 (6%) 4 (3%) Time 2 (9%) 2 (13%) 0 (0%) 1 (5%) 0 (0%) 2 (11%) 7 (6%) Place 4 (17%) 3 (19%) 1 (5%) 3 (16%) 1 (6%) 0 (0%) 12 (10%) Event 8 (35%) 4 (25%) 13 (62%) 11 (58%) 7 (39%) 8 (44%) 51 (44%) Date 2 (9%) 2 (13%) 3 (14%) 3 (16%) 0 (0%) 1 (6%) 11 (10%) Time 2 (9%) 1 (6%) 3 (14%) 1 (5%) 2 (11%) 0 (0%) 9 (8%) Place 5 (22%) 3 (19%) 12 (57%) 8 (42%) 4 (22%) 1 (6%) 33 (29%) Avg. = 10% Avg. = 9% Avg. = 23% To test for hypothesis 4, which concerns the effects of animation on recall and recognition, we focused on skyscraper ads. Due to the way the experiment was designed, it is possible to determine which of the treatments included an animated skyscraper ad, namely treatments 2, 4 and 6. The remaining treatments, (1, 3 and 5) had a static skyscraper ad. The recall and recognition data for skyscraper ads was dissected from Tables 4 and 5 and compiled into Table 6. Table 6. Skyscraper Ad Recall and Recognition Data Ad Type Treatment Total Resp. Event Date Time Place Static Animated 1 23 13% (17%) 4% (4%) 4% (9%) 0% (17%) 3 21 5% (14%) 10% (0%) 0% (0%) 0% (5%) 5 18 0% (11%) 0% (0%) 0% (0%) 0% (6%) Total 62 6% (15%) 5% (2%) 2% (3%) 0% (1%) 2 16 6% (19%) 6% (6%) 0% (13%) 6% (19%) 4 19 21% (21%) 0% (5%) 5% (5%) 0% (16%) 6 18 11% (11%) 0% (6%) 0% (11%) 0% (0%) Total 53 13% (17%) 2% (6%) 2% (9%) 2% (11%) Note: Recognition data are listed inside the parentheses. The results in Table 6 reveal that, on average, animated skyscraper ads have higher ad content recall and recognition than static ones. The only exception is ad event recall which is higher in static ads (5%) than animated ones (2%). Additionally, event ad time recall is similar for both static and animated ads (2%). On the other hand, ad content recognition is significantly higher than ad recall, and exhibits larger differences between static and animated ads. The animated skyscraper ads event name content 8 Thirty Fourth International Conference on Information Systems, Milan 2013

Al-Natour et al. / Effective Online Ads: Animation and Placement recognition is 17% versus 15% for static ads. Event date, time and place recognition scores are 6%, 9% and 11% respectively versus 2%, 3% and 10% for static skyscraper ads. The average recognition rate for animated ads is 11% versus only 8% for static ones. Despite some inconsistency in the effects of animation on ad recall, collectively, the results provide support for hypothesis 4. Discussion and Future Research The results from this study provide evidence that pop-up ads are more effective than skyscraper and banner ads in terms of recall and recognition of the ads contents, as well as generating higher purchase intentions. These findings lend support to the notion that forced exposure could have positive effects in terms of enhancing ad effectiveness. Specifically, pop-up ads appear to increase ad distinctiveness, and in so doing decrease their relative blindness. Nonetheless, pop-up ads have been shown in prior research to give rise to reactance-based mechanisms and enhance feelings of intrusiveness (Edwards et al. 2002). This is evidenced in this study by the finding that pop-up ads did not enhance any of the attitudes, and only skyscraper ads, which are often considered the least intrusive of the three examined ads, had positive effects on the attitude-toward-the-ad. Future research should focus on better delineating the positive and negative effects of forced exposure, including examining other types of forced exposure. Surprisingly, animation had no effect on customers attitudes, albeit it had modest and inconsistent effects on their recall and recognition of ad content. This adds another perspective on the distinctivenessblindness debate. Given their generally higher production costs, animated ads do not seemingly appear to provide a good return on investment. One could further reason that unless the advertiser can find a way to make the advertisement itself the object of the customer s focus, possibly by making it more relevant and/or engaging, animated ads remain to be a questionable investment. Garnering customer attention and focus is especially important given that animated ads require longer viewing time on average, since the information contained is displayed over a number of frames. Future research should focus on examining means for making animated ads more relevant and arousing, so as to enhance their effectiveness. Social media advertising contexts offer a promising avenue for this research. In general, this study provides early evidence that online ad effectiveness should not only be measured via psychologically-based measures, but also through objective ones such as recall and recognition. Further, it highlights the need to examine a host of relevant factors that may affect how ads are viewed and responded to. This, we consider to be, one of the study s main limitations; the exclusion of other variables that may affect customers responses to online ads. Product and host site factors were excluded completely, as well as the set of semantic factors proposed by (Sun et al. 2013). Future research should focus on emerging trends of online advertising, such as the use of dynamic content, as these could enhance some of the semantic factors such as interactivity and entertainment. Acknowledgments We would like to thank the Associate Editor and the two reviewers for their valuable feedback. This research was supported by a grant from the Social Sciences and Humanities Research Council of Canada. References Benway, J. P., and Lane, M. 1998. Banner blindness: Web searchers often miss obvious links, Internetworking (1:3), pp. 1-4. Bruner, G. C., 2000. Web commercials and advertising hierarchy-of-effects, Journal of Advertising Research(40:1/2), pp. 35-42. Burke, M., Hornof, A., Nilsen, E., and Gorman, N. 2005. High-cost banner blindness: Ads increase perceived workload, hinder visual search, and are forgotten, ACM Transactions on Computer- Human Interaction (12:4), pp. 423-445. Cho, C. 2001. Different forced-exposure levels to banner advertisements, Journal of Advertising Research (41:4) pp. 45-57. Dahlen, M. 2001. Banner advertisements through a new lens, Journal of Advertising Research (41:4), pp. 23-30. Thirty Fourth International Conference on Information Systems, Milan 2013 9

General IS Topics Edwards, S. M., Li, H., and Lee, J. 2002. Forced exposure and psychological reactance: Antecedents and consequences of the perceived intrusiveness of pop-up ads, Journal of Advertising (31:3), pp. 83-95. emarketer. 2013. Digital to Account for One in Five Ad Dollars, Available at: http://www.emarketer.com/article/digital-account-one-five-ad-dollars/1009592 Gallagher, K. 2001. The medium is not the message: Advertising effectiveness and content evaluation in print and on the Web, Journal of Advertising Research (41:4), pp. 57-70. Hong, W., Thong, J. Y. L., and Tam, K. Y. 2007. How do web users respond to non-banner-ads animation? The effects of task type and user experience, Journal of the American Society for Information Science and Technology (58:10), pp. 1467-1482. Lee, A. and Labroo, A. 2004. The Effect of Conceptual and Perceptual Fluency on Brand Evaluation, Journal of Marketing Research (41:2), pp. 151-165. Reber, R., Schwarz, N., and Winkielman, P. 2004. Processing Fluency and Aesthetic Pleasure: Is Beauty in the Perceiver's Processing Experience? Personality and Social Psychology Review (8:4), pp. 364-382. Shamdasani, P. N., 2001. Location, location, location: Insights for advertising placement on the Web, Journal of Advertising Research (41:4), pp. 7-21. Stevenson, J. 2000. Webpage background and viewer attitudes, Journal of Advertising Research (40:1/2), pp. 29-35. Sun, Y, Lim, K., and Peng, J. 2013. Solving the Distinctiveness Blindness Debate: A Unified Model for Understanding Banner Processing, Journal of the Association for Information Systems (14:2), pp. 49-71. Sundar, S. 1998. Does Web advertising work? Memory for print vs. online media, Journalism and Mass Communication Quarterly (75:4), pp. 822-835. Yoon, S. 2001. Is the Internet more effective than traditional media? Factors affecting the choice of media, Journal of Advertising Research (41:6), pp. 53-60. 10 Thirty Fourth International Conference on Information Systems, Milan 2013