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EMAC 2024 Annual


Effective Influencer Marketing I
(A2024-119421)

Published: May 28, 2024

AUTHORS

caio vieira, HEC; Francesca Sotgiu, Vrije Universiteit Amsterdam, School of Business and Economics; Dingli Wu, KU Leuven; Yanting He, Imperial College London

ABSTRACT

Consumers use user-generated content (UGC) networks to serve a range of purposes in their day-to-day lives. Along these lines, this special session focuses on research projects that explore different aspects to increase the effectiveness of influencer endorsements. “Seeding Bandits” by Caio Vieira (HEC Paris), Andreas Lanz (University of Basel), Gui Liberali (Rotterdam School of Management, Erasmus University Rotterdam): We investigate content creators who promote themselves and their content, particularly the trade-off faced between acquiring new followers directly (follow to get a follow-back) or indirectly through the activation of the current follower base to solicit endorsements (follow to get a repost, and thereby additional followers due to this endorsement). Our analyses suggest that low outdegree users who are highly active are associated with a superior indirect return, while high-outdegree users that are highly active are associated with superior direct returns. We develop a multiarmed bandit model to build an understanding of this trade-off. “Monetization of Knowledge Videos” by Yuhua Liu (Renmin University of China), Francesca Sotgiu (Vrije Universiteit Amsterdam), and Xia Wang (Renmin University of China): This paper aims to address the research question: Are visual, verbal, and vocal features in knowledge videos able to stimulate both social media engagement and product sales? To address these questions, we analyze over 120,000 videos from Kuaishou featuring 1,000 knowledge influencers between January 1, 2023, to July 23, 2023. The preliminary results indicate that visual, verbal, and vocal features significant influence product sales and social media engagement. “How Much Influencer Marketing is Undisclosed? Evidence from Twitter” by Daniel Ershov (University College London), Yanting He (Imperial College London), and Stephan Seiler (Imperial College London): In this paper, we quantify the prevalence of undisclosed influencer posts on Twitter across a large set of brands based on a unique data set of over 100 million posts. We develop a novel method to detect undisclosed influencer posts and find that paid partnerships are rarely disclosed. Despite stronger enforcement of disclosure regulations, the share of undisclosed posts decreases only slightly over time. Compared to disclosed posts, undisclosed posts tend to be associated with younger brands with a large Twitter following and are posted from smaller accounts that generate higher engagement per follower. Through this study, we hope to provide insights that support more transparent practices in the industry and help consumers make informed decisions. “Spreading Seeds: Using Community Detection to Improve Viral Marketing Campaigns” by Dingli Wu (KU Leuven), Ralf van der Lans (Hong Kong University of Science and Technology), and Xi Chen (Erasmus University Rotterdam): To address the potential overlap of friends of seeds, we propose to spread seeds in the social network, such that each seed is assigned to a different part in the network. To do so, we propose using the community detection algorithms (see e.g., Fortunato 2010) to divide the social network into smaller subnetwork.