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

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...

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Veröffentlicht in:Computers in human behavior Jg. 112; S. 106443
Hauptverfasser: Argyris, Young Anna, Wang, Zuhui, Kim, Yongsuk, Yin, Zhaozheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elmsford Elsevier Ltd 01.11.2020
Elsevier Science Ltd
Schlagworte:
Algorithms
Bonding strength
Consumers
Deep learning
Deep-learning algorithms for image classification
Digital media
Empirical analysis
Hypotheses
Hypothesis testing
Image classification
In vivo methods and tests
Influencer marketing
Instagram
Marketing
Similarity attraction model
Social influence
Social media analytics
Social networks
Visual effects
Social influence
Similarity attraction model
Deep-learning algorithms for image classification
Social media analytics
Instagram
Influencer marketing
ISSN:0747-5632, 1873-7692
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0747-5632
1873-7692
DOI:10.1016/j.chb.2020.106443