Conference Agenda

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).

 
 
Session Overview
Session
TC6 - PF6: Online platforms
Time:
Tuesday, 28/June/2022:
TC 14:00-15:30

Session Chair: Yeqing Zhou
Location: Forum 10


Show help for 'Increase or decrease the abstract text size'
Presentations

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.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: MSOM 2022
Conference Software: ConfTool Pro 2.8.101+TC
© 2001–2024 by Dr. H. Weinreich, Hamburg, Germany