Changing Brand Attitudes through Influencer Marketing
2018
- 1,428Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage1,428
- Abstract Views1,169
- 1,169
- Downloads259
Artifact Description
Influencer marketing is a subtle product endorsement by ordinary individuals who have sizable following on their social networking sites. Leveraging these individuals’ established influences on their social networks, brands attempt to portray favorable images. Eventually, followers’ likeability towards the influencer serves as a vehicle towards building stronger and positive brands. In contrast to its popularity, no systematic way of assessing their effectiveness has been proposed to date. Based on the source-congruence approach and electronic word of mouth literature, we hypothesize that when there is congruence between the influencers’ and followers’ posts, followers will like the influencers’ posts more; as a result, the followers’ attitudes towards the brand will improve. In this emergent research paper, we tested the first hypothesis and propose a method to test the second. Specifically, we downloaded 90,000 images posted by influencers and their followers from Instagram and employed an automatic image classification algorithm. The results support the first hypothesis that the congruence between influencers’ posted images and the followers’ positively influence the linking behaviors of followers.
Bibliographic Details
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