Social Dynamics on Online Platforms
(A2020-59765)

AUTHORS

Jing Niu, HEC Paris

ABSTRACT

Online platforms open opportunities for practitioners and researchers to explore previously unobserved phenomena, test predictions, and derive better-informed managerial insights. This special session focuses on work that investigates social dynamics occurring such platforms, which impact marketing outcomes.

“Social Dynamics in Crowdsourcing: Imitation and Return on Originality in Online Design Contests” by Reto Hofstetter (University of Lucerne), Sanjog Misra (Chicago Booth School of Business), and Harikesh Nair (Stanford Graduate School of Business): Open innovation platforms are proliferating despite their open nature that invites imitation and free-riding. We analyze data from such platforms and find that seekers tend to reward more original designs and avoid picking as winners those that seem to be imitating. A result that suggests that market behavior on such platforms may have a self-policing component that disincentivizes excessive imitation, rewards originality and prevents unraveling.

“Status for Sale: Premium Accounts in Freemium Networks” by Simon Stolz (WHU), Christian Schlereth (WHU), and Andreas Lanz (HEC Paris): Career networks such as LinkedIn or Xing offer paid premium memberships, which help users to build and capitalize on network status. In this study we explore the role of this paid option for status-seeking users: (1) Do low-status users consider premium membership a means to build status and (2) does it really pay off? Our preliminary results of an assessment of large-scale discount voucher campaigns suggest that status has a negative effect on the conversion decision.

“The wisdom of the few: Predicting success by tracking key individuals” by Manuel Sebastian Mariani (U of Zurich), Yanina Gimenez (Grandata), Jorge Brea (Grandata), Martin Minnoni (Grandata), René Algesheimer (U of Zurich), and Claudio Juan Tessone (U of Zurich): Drawing on a large-scale credit card record, we find small sets of individuals, called discoverers, whose early purchases in physical stores are persistently predictive of the visited store’s success. Differently from most studies on influencers and word-of-mouth processes, we detect the discoverers from the transaction history, without using social network data.

“Influencer Marketing Effectiveness” by Jing Niu, Peter Ebbes, Kristine de Valck, and Andreas Lanz (HEC Paris): Previous research in influencer marketing suggests firms to seed macro influencers who have high (in-degree) connectivity in social networks––while in practice more and more marketers engage micro influencers (with lower in-degree). Analyzing a large dataset of sponsored posts on Instagram, we find that micro influencers are indeed more effective in terms of ROI, i.e., when considering both audience engagement with posts and endorsement compensations.