基于用户动态信息加工的信息流广告回避机制与重定向策略

s the in-feed native advertising is growing fast, ad avoidance from the users is  becoming more prominent. However, traditional knowledge about ad avoidance is no  longer adequate for this new context. This research, based on users’ dynamic  information processing states, seeks to (1) explore the mechanisms of users’  ad-skipping and ad-blocking behaviors under varied information processing modes  (convergent vs. divergent); (2) apply the re-targeting strategy of "guided  attribution" to leverage the signal value brought by the "residual effect" from  users’ ad blockings; (3) apply the re-targeting strategy of "prominence" to break  the ad-skipping momentum caused by the "learning effect" from users’ continuous ad-skippings. While enriching extant theories, this research provides theoretical  and managerial support for the responsive optimization of in-feed native advertising.