![]() ![]() static photographs) are encoded using salience variables, such as size, colour and brightness. Traditional theory, such as feature integration theory ( Treisman and Gelade, 1980), suggests that scenes (e.g. This research investigates whether explaining viewer attention to video advertising content requires new theory specifically devised for dynamic stimuli, rather than theory originally developed for attention to static stimuli. For example, the viewer decides how long to look at a print ad, where all objects are constantly present, whereas the advertiser decides how long an object is present to be looked at in a video ad scene by scene. Extrapolating theory from static to dynamic media, as a basis for designing effective video ads for example, is problematic because the stimuli are more complex and the viewing experience differs. Theories about what draws attention to advertising have predominantly been developed for and researched with static media stimuli (for an overview of eye-tracking research specifically Wedel and Pieters, 2017). There are numerous approaches to measure attention, but a most direct and popular method is to measure visual attention with eye-tracking. Attention is one construct advertisers use to assess advertising effectiveness, including for dynamic ads, as a precursor to other downstream effects such as recall and behaviour. Television and online video are forecast to jointly attract around 40% of global advertising media spend in 2022 ( Dentsu, 2021). print, billboards and online display ads) to dynamic media (e.g. The full terms of this licence may be seen at Īdvertising expenditure has steadily shifted over time from static media (e.g. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Copyright © 2022, Brooke Wooley, Steven Bellman, Nicole Hartnett, Amy Rask and Duane Varan. ![]()
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