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EMAC 2024 Annual


Effective Influencer Marketing V
(A2024-119434)

Published: May 28, 2024

AUTHORS

Jan Klostermann, University of Cologne; Renana Peres, HEBREW UNIVERSITY OF JERUSALEM SCHOOL OF BUSINESS ADMINISTRATION MOUNT SCOPUS CAMPUS; Daniel Ringel, University of North Carolina at Chapel Hill

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. “Influencer Topic Variety and Consumer Engagement” by Alida Volkmer (TUM School of Management), Jan Klostermann (University of Cologne), and Martin Meissner (TUM School of Management): Our research contributes to both influencer marketing practice and theory. First, we contribute to research on influencer marketing by investigating a novel influencer trait, topic variety. Topic variety is a strategic decision made by influencers and as such our research helps to understand when high variety is more or less effective to increase engagement. While topic variety has a negative effect on engagement on average, we find that influencers who have more knowledge about their followers’ interests are less likely to lose engagement due to high topic variety. “Data Trading and Social Equity – How do Influentials Evaluate their Data?” by Renana Peres (The Hebrew University Business School), and David Schweidel (Goizueta Business School, Emory University): This research aims to advance initial conceptual research on data marketplaces (Peres et al. 2022; Peres and Schweidel 2023) by shedding empirical light on customer acceptance of offers from firms for their data. Specifically, we ask how does the social capital, that is, the number of social network contacts of consumers influence their valuation of various types of their personal data. “Distilling Brand Alliance Opportunities from Information Networks” by Pankhuri Malhotra (University of Oklahoma), Daniel Ringel (University of North Carolina Chapel Hill), Keran Zhao (Pennsylvania State University), and Yaxin Cui (Northwestern University): The aim of this research is to identify the most promising brand alliance opportunities from information networks by considering both network structure and network content. Such undertaking is, however, challenging for two reasons. First, information networks comprise vast amounts of unstructured user-generated content (UGC) that needs to be collected, processed, and distilled to separate signals from noise. Second, while there has been a growing interest in using information networks to solve marketing problems, little is known about the validity of their structural elements for brand analysis.