1. Voice Analytics of Online Influencers—Soft Selling in Branded Videos
- Author
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Kannan Srinivasan, Serim Hwang, and Xiao Liu
- Subjects
History ,Polymers and Plastics ,media_common.quotation_subject ,Advertising ,Commission ,Industrial and Manufacturing Engineering ,Influencer marketing ,Identification (information) ,Honesty ,Regression discontinuity design ,Social media ,Consumer confidence index ,Speech analytics ,Business ,Business and International Management ,media_common - Abstract
Influencer marketing has been growing rapidly on social media, such that the influencer marketing industry is now worth $15 billion. As more consumers are eager to rely on influencers’ recommendations for shopping, the trustworthiness of influencers’ opinions becomes a key question. Specifically, consumers increasingly question the honesty of influencer opinions when influencers advertise sponsored products from brands. To help consumers make informed shopping decisions, the Federal Trade Commission (FTC)’s endorsement guidelines require influencers to disclose sponsorship. However, little is known about how influencers respond to the FTC’s endorsement guidelines. In this paper, we study how influencers change their content delivery strategies upon sponsorship and how these strategies affect consumer sentiment. Employing both the Instrumental Variable (IV), and Regression Discontinuity (RD) identification strategies, we find that influencers choose a soft selling strategy and tone down their voice loudness significantly in sponsored videos. This voice modulation strategy can help influencers win back favorable consumer sentiment. Above all, influencers who are more popular (have more view counts) are more likely to modulate their voice in their favor. We discuss implications for the four stakeholders: the FTC, brands, influencers, and consumers.
- Published
- 2021