Effective Influencer Marketing III
(A2024-119530)

AUTHORS

Arvin Moazemi, Bocconi University; Peter Pal Zubcsek, Tel Aviv University; Rupali Kaul, INSEAD Business School

ABSTRACT

Effective Influencer Marketing III 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. “Brand Echo Chambers” by Arvin Moazemi (Bocconi University), Verena Schoenmueller (ESADE Business School), Oded Netzer (Columbia Business School): Social media echo chambers, where individuals predominantly interact with like-minded peers, pose risks to diverse idea exploration. In this research, we explore echo chambers in the context of brands, studying how consumer perceptions and behaviors are shaped by networks sharing similar brand preferences. We develop three measures to evaluate brand echo chambers based on: whether consumer networks follow the brand, the proportion of exclusive followers of the brand with those of competitors, and the brand preference similarities within these networks. Analyzing over 956,000 followers of more than 170 brands, we connect echo chamber levels to brand discussion frequency and sentiment, observing that more intense echo chambers lead to more favorable and frequent brand discussions while decreasing mentions of competitors. Furthermore, we relate echo chamber scores to brand equity, finding that brands in stronger echo chambers are seen as more independent and socially responsible, but perceived as less stylish and upper-class. “Why Firms Should Want Algorithmic Accountability” by Kalinda Ukanwa (University of Southern California), William Rand (North Carolina State University), Peter Pal Zubcsek(Tel Aviv University): The past years have witnessed growing concerns about the responsible use of artificial intelligence. Addressing these issues, European and US regulators recently introduced legislation to protect consumers from algorithmic bias. These policies hold firms accountable for the fairness of their algorithmic decisions. However – as regulators have yet to invest in the systematic monitoring of algorithmic fairness – they also rely on consumers to report when unfairness occurs. Our research shows that, paradoxically, these conditions may lead to a divergence between consumer perceptions and the judicial view regarding the fairness of firm actions – under certain conditions, a firm with a fair algorithm can be perceived by the population as less fair than a firm with a biased algorithm. “Novelty in Content Creation: Experimental Results Using Image Recognition on a Large Social Network” by Justin Huang (University of Michigan), Rupali Kaul (INSEAD), Sridhar Narayanan (Stanford University): We implement a randomized controlled experiment to induce exogenous variation in attention and recognition provided to users' content. Using a transfer learning machine learning algorithm, we convert complex images into lower-level features to analyze changes in content novelty. We find that awarded creators produce more novel content, relative to both the awarded content and their past work. This result is robust to a variety of ways in which we classify image content. Our results illustrate the importance of tools that induce attention and recognition to the creation and development of diverse content by social media creators and give insights into factors that motivate content creation.