Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Leveraging consensus effect to optimize feed sequencing in online discussion platforms
Joseph Carlstein1, Gad Allon1, Yonatan Gur2
1The Wharton School of the University of Pennsylvania; 2Stanford Graduate School of Business
We will use data from a structured online discussion forum to understand what the key engagement drivers in online discussions are, and how we can leverage these drivers to improve the operations and performance of online discussion platforms. We will present both empirical and theoretical results characterizing strategies to guide discussions optimally - a crucial feature in an age where communications in both business and educational settings are increasingly moving to online settings.
Pricing strategies for online dating platforms
Titing Cui, Michael Hamilton
University of Pittsburgh, Katz Graduate School of Business
Online dating apps are the most common way for couples to meet. Many of these dating apps use subscription based pricing (SP), where subscriptions to the app are sold at a fixed price. In online dating (SP) is controversial as it misaligns the incentives of the platform and its users. Another strategy is contract pricing (CP), where the dating app is contracted at a one time price. We study the profit and welfare trade-offs associated with either pricing strategy for online dating platforms.
Herding, learning and incentives for online reviews
Rajeev Kohli1, Xiao Lei2, Yeqing Zhou3
1Graduate School of Business, Columbia University; 2Industrial Engineering and Operations Research, Columbia University; 3Eindhoven University of Technology
We study the herding and learning effects on the incentives for online reviews. We model the evolution of sales and reviews for a seller by a generalized Polya urn process and evaluate the profit for three incentive policies: incentivize before purchase, after purchase, or only if they write positive, possibly fake, reviews. We obtain conditions that each type of incentive is profitable and optimal. The results imply that platforms can curb fake reviews if allowing pre-purchase incentives.