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Rutter, Richard, Barnes, Stuart J., Roper, Stuart, Nadeau, John and Lettice, Fiona 
ORCID: https://orcid.org/0000-0003-1304-4435
(2021)
Social media influencers, product placement and network engagement: Using AI image analysis to empirically test relationships.
Industrial Management and Data Systems, 121 (12).
pp. 2387-2410.
ISSN 0263-5577
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Abstract
Purpose: This research tests empirically the level of consumer engagement with a product via a nonbrand-controlled platform. The authors explore how social media influencers and traditional celebrities are using products within their own social media Instagram posts and how well their perceived endorsement of that product engages their network of followers. Design/methodology/approach: A total of 226,881 posts on Instagram were analyzed using the Inception V3 convolutional neural network (CNN) pre-trained on the ImageNet dataset to identify product placement within the Instagram images of 75 of the world's most important social media influencers. The data were used to empirically test the relationships between influencers, product placement and network engagement and efficiency. Findings: Influencers achieved higher network engagement efficiencies than celebrities; however, celebrity reach was important for engagement overall. Specialty influencers, known for their “subject” expertise, achieved better network engagement efficiency for related product categories. The highest level of engagement efficiency was achieved by beauty influencers advocating and promoting cosmetic and beauty products. Practical implications: To maximize engagement and return on investment, manufacturers, retailers and brands must ensure a close fit between the product type and category of influencer promoting a product within their social media posts. Originality/value: Most research to date has focused on brand-controlled social media accounts. This study focused on traditional celebrities and social media influencers and product placement within their own Instagram posts to extend understanding of the perception of endorsement and subsequent engagement with followers. The authors extend the theory of network effects to reflect the complexity inherent in the context of social media influencers.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence,image analysis,influencers,object recognition,product placement,social media,management information systems,industrial relations,computer science applications,strategy and management,industrial and manufacturing engineering ,/dk/atira/pure/subjectarea/asjc/1400/1404 |
| Faculty \ School: | Faculty of Social Sciences > Norwich Business School |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 30 Sep 2021 16:53 |
| Last Modified: | 21 Apr 2023 01:09 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/81516 |
| DOI: | 10.1108/IMDS-02-2021-0093 |
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