The effects of visual congruence on increasing consumers' brand engagement: An empirical investigation of influencer marketing on instagram using deep-learning algorithms for automatic image classification.

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    • Abstract:
      Influencers are non-celebrity individuals who gain popularity on social media by posting visually attractive content (e.g., photos and videos) and by interacting with other users (i.e., Followers) to create a sense of authenticity and friendship. Brands partner with Influencers to garner engagement from their target consumers in a new marketing strategy known as "Influencer marketing." Nonetheless, the theoretical underpinnings of such remains unknown. We suggest a new conceptual framework of "Visual-Congruence-induced Social Influence (VCSI)," which contextualizes the Similarity-Attraction Model in the Social Influence literature. Using VCSI, we delineate how Influencers use visual congruence as representations of shared interests in a specific area to build strong bonds with Followers. This intimate affiliation catalyzes (i.e., mediates) the positive effects of visual congruence on Followers' brand engagement. To test these hypotheses, we conducted in vivo observations of Influencer marketing on Instagram. We collected >45,000 images and social media usage behaviors over 26 months. We then applied deep-learning algorithms to automatically classify each image and used social media analytics to disclose hidden associations between visual elements and brand engagement. Our hypothesis testing results provide empirical support for VCSI, advancing theories into the rapidly growing fields of multimodal content and Influencer marketing. • Social media Influencers post visual content congruent with Followers' interests. • Visual congruence increases followers' engagement with Influencers' posts. • Such an increase in turn augments followers' engagement with the endorsed brand. • Affiliation between Influencers and Followers mediates the above relationships. • Deep-learning algorithms can automatically classify visual posts on social media. [ABSTRACT FROM AUTHOR]
    • Abstract:
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