Understanding Consumers’ Visual Attention in Mobile Advertisements: An Ambulatory Eye-Tracking Study with Machine Learning Techniques

Wen Xie, Mi Hyun Lee*, Ming Chen, Zhu Han

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

As mobile devices have become a necessity in our daily lives, mobile advertising is also prevalent. Accordingly, it is critical for practitioners to understand how consumers visually attend to mobile advertisements. One popular way of doing so is via eye-tracking methodology. However, scant eye-tracking research exists in mobile settings due to technical challenges, e.g., cumbersome data annotation. To tackle these challenges, the authors propose an object-detection machine learning (ML) algorithm—You Only Look Once (YOLO) v3—to analyze eye-tracking videos automatically. Moreover, we extend the original YOLO v3 model by developing a novel algorithm to optimize the analysis of eye-tracking data collected from mobile devices. Through a lab experiment, we investigate how two types of ad elements (i.e., textual vs. pictorial) and shopping devices (i.e., mobile vs. PC) affect consumers’ visual attention. Our findings suggest that (1) textual ad elements receive more attention than pictorial ones, and such differences are more pronounced in ads on mobile devices than those on PCs; and (2) mobile ads receive less attention than PC ads. Our findings provide managerial insights into developing effective digital advertising strategies to improve consumers’ visual attention in online and mobile advertisements.

Original languageEnglish (US)
Pages (from-to)397-415
Number of pages19
JournalJournal of Advertising
Volume53
Issue number3
DOIs
StatePublished - 2024

ASJC Scopus subject areas

  • Business and International Management
  • Communication
  • Marketing

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