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Can the way ads are scheduled change whether people watch or skip? In this episode, Dr. Mi Hyun Lee (Northwestern University) and Dr. Jaewon Royce Choi (Louisiana State University) join me to talk about their Journal of Advertising Research article, Acceptance Propensity of Pre-Roll Skippable Ads: An Analysis of Large-Scale Clickstream Data Using Dynamic Linear Models, coauthored with Su Jung Kim.
We dig into their concept of ad acceptance propensity — the underlying tendency to accept rather than skip an ad — and how it shifts depending on how ads are placed. Drawing on a dataset of 10,000 users and 36,000 ad exposures from a major video platform, they show that predictable, frequent exposures lower acceptance while irregular, spaced exposures boost it. We also talk about how their dynamic linear modeling approach lets researchers go beyond observed behavior to estimate hidden states, why this matters for both scholars and practitioners, and how advertisers can rethink reach and frequency.
Read the full paper here:
https://www.tandfonline.com/doi/full/10.1080/00218499.2025.2464294
Listen to the podcast here:
https://www.buzzsprout.com/2250188
And watch this and more content on our YouTube page:
https://www.youtube.com/@journalofadvertisingresearch
To keep up to date on the latest JAR news sign up for our newsletter:
https://lp.constantcontactpages.com/su/mtD04QN
And follow us on LinkedIn:
https://www.linkedin.com/company/82528291/admin/
By Journal of Advertising ResearchCan the way ads are scheduled change whether people watch or skip? In this episode, Dr. Mi Hyun Lee (Northwestern University) and Dr. Jaewon Royce Choi (Louisiana State University) join me to talk about their Journal of Advertising Research article, Acceptance Propensity of Pre-Roll Skippable Ads: An Analysis of Large-Scale Clickstream Data Using Dynamic Linear Models, coauthored with Su Jung Kim.
We dig into their concept of ad acceptance propensity — the underlying tendency to accept rather than skip an ad — and how it shifts depending on how ads are placed. Drawing on a dataset of 10,000 users and 36,000 ad exposures from a major video platform, they show that predictable, frequent exposures lower acceptance while irregular, spaced exposures boost it. We also talk about how their dynamic linear modeling approach lets researchers go beyond observed behavior to estimate hidden states, why this matters for both scholars and practitioners, and how advertisers can rethink reach and frequency.
Read the full paper here:
https://www.tandfonline.com/doi/full/10.1080/00218499.2025.2464294
Listen to the podcast here:
https://www.buzzsprout.com/2250188
And watch this and more content on our YouTube page:
https://www.youtube.com/@journalofadvertisingresearch
To keep up to date on the latest JAR news sign up for our newsletter:
https://lp.constantcontactpages.com/su/mtD04QN
And follow us on LinkedIn:
https://www.linkedin.com/company/82528291/admin/