Endorsement Effectiveness on YouTube: How Social Capital Theory Can Aid in the Field of Endorsement Theory

Abstract

Today, YouTube influencer marketing poses many lucrative opportunities for companies, both in the form of increased brand awareness as well as being able to tap into a platform with millions of potential new customers. However, due to the novelty of social media influencer marketing, no guidelines for best practice have been established, and many companies waste large amounts of advertising budget on endorsements with a questionable outcome. Empirical cases suggest that the subscriber growth of social media influencer endorsers (SMIEs) takes a hit when they become overly commercialised, which negatively impacts the endorsement effectiveness.
However, the field of endorsement theory is ill-equipped to explain this phenomenon. Therefore, the purpose of this thesis has been to identify the shortcomings of the existing endorsement theory and build on it by means of social capital theory, in order to understand the endorsement effectiveness of SMIEs on YouTube. More specifically the research question guiding this thesis has been: “How can the interrelation between company social capital (CSC) and subscriber social capital (SSC) aid in the field of endorsement theory, in explaining endorsement effectiveness of beauty and lifestyle SMIEs on YouTube?”
In order to answer this question, a thorough literature review of endorsement theory and social capital theory was conducted, based on which the SMIEE model (social media influencer endorser effectiveness model) was developed. The model seeks to account for how the interplay between the SMIE’s CSC and SSC can explain endorsement effectiveness throughout the SMIE’s lifecycle. More specifically, it claims that the SMIE’s third life cycle stage is characterised by a negative correlation between CSC and SSC, as the SMIE’s increasing number of commercial relations have a negative impact on the subscriber growth, which in turn impacts the endorsement effectiveness negatively. Moreover, the model claims that the SMIE’s first, second and fourth lifecycle stages are characterised by a positive correlation between CSC and SSC, as the interplay between the two variables generate SSC growth, which impacts endorsement effectiveness positively. According to the model, the second lifecycle phase is particularly promising for effective endorsements.
To test the SMIEE model, data from 80 different SMIEs, regarding SSC and CSC, was collected and statistically analysed. While the assumed negative and positive correlations were not statistically proven, a scatterplot of the data indicated that such interrelations do in fact exist. In addition, it showed different clusters of SMIEs with different endorsement characteristics, which were analysed, named and accounted for. These clusters are: “Shapeshifters”, “The Uninvested”, “First Movers”, “Ones To Watch”, “Money Makers” and “Sell-outs”. Among the clusters it was found that the “Sell-outs” cluster corresponds with the third critical lifecycle stage of the SMIEE model, that the “MoneyMakers” cluster corresponds with the second, most prosperous life cycle stage of the SMIEE model, that the “Ones To Watch” cluster corresponds to the first and fourth lifecycle stages of the SMIEE model, while the remaining clusters cannot be explained by the model. In conclusion, we found that social capital theory can aid in explaining endorsement effectiveness, as it comprehends the facets of the network ties surrounding the SMIE, as well asthe interplay between these network ties, which affects endorsement effectiveness. Moreover, the findings based on the development and testing of the SMIEE model can potentially guide best endorsement practice, as it identifies the most effective cluster of SMIEs, i.e. “MoneyMakers”, and the most risky cluster of SMIEs, i.e. “Sell-outs”.