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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| Vydáno v: | Computers in human behavior Ročník 112; s. 106443 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elmsford
Elsevier Ltd
01.11.2020
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0747-5632, 1873-7692 |
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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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 106443 |
| Author | Yin, Zhaozheng Argyris, Young Anna Kim, Yongsuk Wang, Zuhui |
| Author_xml | – sequence: 1 givenname: Young Anna surname: Argyris fullname: Argyris, Young Anna email: argyris@msu.edu organization: Department of Media and Information, Michigan State University, 404 Wilson Road, East Lansing, MI, 48823, USA – sequence: 2 givenname: Zuhui surname: Wang fullname: Wang, Zuhui email: zuhui.wang@stonybrook.edu organization: Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA – sequence: 3 givenname: Yongsuk surname: Kim fullname: Kim, Yongsuk email: yongskim@skku.edu organization: SKK Business School, Sungkyunkwan University, Seoul, South Korea – sequence: 4 givenname: Zhaozheng surname: Yin fullname: Yin, Zhaozheng email: zyin@cs.stonybrook.edu organization: Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA |
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| Keywords | Social influence Similarity attraction model Deep-learning algorithms for image classification Social media analytics Influencer marketing |
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| SubjectTerms | 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 Marketing Similarity attraction model Social influence Social media analytics Social networks Visual effects |
| Title | 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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