Native advertising relevance effects and the moderating role of attitudes toward social networking sites

Journal of Research in Interactive Marketing

Hye Jin Yoon, Yan Huang, and Mark Yi-Cheon Yim (2022), “Native advertising relevance effects and the moderating role of attitudes toward social networking sites,” Journal of Research in Interactive Marketing

Abstract: Native advertising on social media continues to be a popular ad placement for marketers. With native ad-content relevance in need of further exploration regarding individual differences and social media public sentiment waning, this study set out to test the effects of ad-content relevance of native ads varying by users’ pre-existing social networking site (SNS) attitude. Two experiments were conducted with native ads relevant and irrelevant to the surrounding media content with SNS attitude as a measured moderator and perceived ad importance as the mediator. Across Instagram and Twitter, SNS attitude moderated the effect of ad relevance on ad responses. Users that had lower SNS attitudes had significantly higher ad ratings that were relevant (vs. irrelevant) to the media content, while those that had higher SNS attitudes did not show differences. The lower SNS attitude individuals showed a greater appreciation for the relevant ads through the mediating effect of perceived importance. Marketers need to look beyond traditional ad attitude models and craft ad content strategies that consider target audiences’ SNS preferences. Layering targeting tactics on Instagram and Twitter such as “interests” and “life events” with demographic targeting could help increase the chances of ad-content relevance. SNSs should advance their ad placement tools and utilize image, speech, and text recognition algorithms to help achieve ad-content relevance for greater ad effectiveness. This study adds to the literature by identifying SNS attitude as a qualifier of ad-content relevance effect for SNS native ads and uncovering perceived ad importance as the underlying psychological mechanism. 

Hye Jin Yoon 

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