Abstract:

In the era of mobile Internet, the in-feed native advertising, which is more acceptable in the mobile news context, has become one of the primary forms of online advertisements. However, with the rapid development of in-feed native advertising, the ad avoidance problem has become prominent and brings unignorable harm to the platforms, advertisers, and users. Recent studies on in-feed native ad avoidance mainly focus on users' static, stable, and historical characteristics but neglect the dynamic, native, and reactive nature of in-feed platforms. The lack of a dynamic view on in-feed native advertising will inevitably lead to a wider gap between academic research and marketing practice. By applying the dynamic information processing perspective, this research breaks the limitation of traditional studies on in-feed native advertising that mainly focus on users’ static characteristics and explores the mechanisms of users’ ad-skipping and ad-blocking behaviors under varied information processing modes (convergent vs. divergent). The subsequent re-targeting strategies by the platform and their effectiveness are also discussed. This research consists of the following three sub-studies.

In study 1, the authors propose a theoretical framework from a dynamic perspective and discuss the antecedents of in-feed native advertising avoidance based on users’ dynamic information processing states. First, following previous research on online advertising, the authors identify two typical ad avoidance types of ad-blocking and ad-skipping and delineate their differences and associations. Second, they explore the mechanisms through which the users avoid the in-feed native ads under different information processing states (i.e., convergent vs. divergent). Finally, they construct a multi-state hazard model to demonstrate the dynamic process of the users’ in-feed native ad avoidance. In study 2, the authors discuss how the platform should apply re-targeting strategies after the users actively block the in-feed native ads. The extant studies on ad avoidance have demonstrated that the ad-blocking behavior requires high cognitive and behavioral effort and therefore brings "residual impact" on improving the memory and cognition toward the blocked ads. Based on this theorem, the authors examine the signal value from the "residual effect" of the ad-blocking. Besides, the authors demonstrate whether and how the platform can leverage the "guided attribution" strategy to improve the users’ attitudes toward the subsequent ads. However, the authors further argue that the effectiveness of such re-targeting strategy is contingent. In study 3, the authors focus on the other parallel but fundamentally different ad avoidance behavior, i.e., the ad-skipping. The platform’s responsive re-targeting strategy is also discussed in this study. Due to the "learning effect" in the users’ continuous information processing, repetitive ad-skipping is more likely for users who have just skipped the ads. The increased arousal level can break such momentum during the users’ online browsings. Based on this theorem, the authors demonstrate whether and how the platform can apply the "prominence strategy" to decrease the likelihood of subsequent ad-skipping by the users.

This research contributes to the present literature and marketing practice in several ways. First, it enriches the literature of ad avoidance by distinguishing two fundamentally different types of ad avoidance behaviors (i.e., ad-blocking vs. ad-skipping) in the in-feed native advertising context. The authors further discuss the varied mechanisms of ad avoidance based on users’ dynamic information processing states. Second, compared with the traditional and relatively static view on the in-feed native advertising, the authors build a theoretical framework from a dynamic perspective and discuss the mechanisms regarding how and when the dynamics of users’ online browsings affect their ad avoidances. Third, this research provides valuable and actionable suggestions on how the in-feed native advertisers and platforms should respond to the users’ ad avoidance appropriately to improve the overall market communication efficiencies.

Key words: in-feed native advertising, dynamic information processing mode, advertising avoidance, advertising re-targeting