4 research outputs found
Understanding (Ir)rational Herding Online
Investigations of social influence in collective decision-making have become
possible due to recent technologies and platforms that record interactions in
far larger groups than could be studied before. Herding and its impact on
decision-making are critical areas of practical interest and research study.
However, despite theoretical work suggesting that it matters whether
individuals choose who to imitate based on cues such as experience or whether
they herd at random, there is little empirical analysis of this distinction. To
demonstrate the distinction between what the literature calls "rational" and
"irrational" herding, we use data on tens of thousands of loans from a
well-established online peer-to-peer (p2p) lending platform. First, we employ
an empirical measure of memory in complex systems to measure herding in
lending. Then, we illustrate a network-based approach to visualize herding.
Finally, we model the impact of herding on collective outcomes. Our study
reveals that loan performance is not solely determined by whether the lenders
engage in herding or not. Instead, the interplay between herding and the
imitated lenders' prior success on the platform predicts loan outcomes. In
short, herds led by expert lenders tend to pick loans that do not default. We
discuss the implications of this under-explored aspect of herding for platform
designers, borrowers, and lenders. Our study advances collective intelligence
theories based on a case of high-stakes group decision-making online
Emergent Influence Networks in Good-Faith Online Discussions
Town hall-type debates are increasingly moving online, irrevocably
transforming public discourse. Yet, we know relatively little about crucial
social dynamics that determine which arguments are more likely to be
successful. This study investigates the impact of one's position in the
discussion network created via responses to others' arguments on one's
persuasiveness in unfacilitated online debates. We propose a novel framework
for measuring the impact of network position on persuasiveness, using a
combination of social network analysis and machine learning. Complementing
existing studies investigating the effect of linguistic aspects on
persuasiveness, we show that the user's position in a discussion network
influences their persuasiveness online. Moreover, the recognition of successful
persuasion further increases this dominant network position. Our findings offer
important insights into the complex social dynamics of online discourse and
provide practical insights for organizations and individuals seeking to
understand the interplay between influential positions in a discussion network
and persuasive strategies in digital spaces
Hidden Influences of Crowd Behavior in Crowdfunding: An Experimental Study
Crowdfunding continues to transform financing opportunities for many across
the globe. While extensive research has explored factors related to fundraising
success, less is known about the social signaling mechanisms that lead
potential contributors to fund a project. Existing large-scale observational
studies point to non-straightforward characteristics of prior contributions
(aka "crowd signals") that forecast further contributions to a project, albeit
without theoretical support for their effectiveness in predicting fundraising
success. We translate empirical crowd signals based on variations in the
amounts and timings of contributions into mock contribution scenarios to
scrutinize the influence of essential signals on contributors' decisions to
fund. We conduct two experiments with 1,250 online participants. The first
experiment investigates whether high crowd signals, i.e., contributions of
varying amounts arriving at unequally spaced time intervals, are making people
more likely to contribute to a crowdfunding project. The second experiment
further examines the effect of basic competition on the role of the crowd
signals. Across both, we observe that high crowd signals attract 19.2% more
contributors than low signals. These findings are robust to different project
types, fundraising goals, participants' interest level in the projects, their
altruistic attitudes, and susceptibility to social influence. Participants'
unguided, post-hoc reflections about the reasons behind their choice to fund
revealed that most were unaware of their reliance on any crowd signals and
instead attributed their decision to nonexistent differences in project
descriptions. These results point to the power of crowd signals unbeknownst to
those affected by them and lay the groundwork for theory-building, specifically
in relation to the essential signaling that is happening on online platforms