The Dynamics of Visual Attention to Advertising Messages in Video Stories

In today’s advertising landscape, attention may has become the most scarce and valuable resource. Yet little is known about the dynamic factors that shape viewers’ attentional allocation to advertising messages that are often embedded within complex media contexts. Coupling the real-time biometrics data and the dynamic motivational activation (DMA) theoretical framework, this study conceptualizes attention to advertising messages, such as product placements (PPs), as a real-time interplay between the embedded context (e.g., storyline), the advertising message (e.g., PPs), and viewers’ internal cognitive self-feedback mechanisms. Using eye-tracking technology, we measured attention through fixation indices, providing a real-time indicator of visual attention to embedded advertising messages. We applied multilevel time-series models, grounded in the DMA framework, to examine the effects of these factors and their interactions on viewer attention over time. The findings support the DMA framework, offering both theoretical insights and practical guidance for optimizing PPs as effective brand communication tools in video content. More broadly, the study demonstrates how biometrics, coupled with dynamic theorizing and modeling, can enhance our understanding of advertising processing and its effects in complex media environments.