TY - JOUR
T1 - How Users Drive Value in Two-sided Markets Platform Designs That Matter 1
AU - Zhou, Zhou
AU - Zhang, Lingling
AU - Van Alstyne, Marshall
PY - 2024/3
Y1 - 2024/3
N2 - Extant research has popularized the perspective that strong network effects produce “winner-take-all” outcomes, which leads platforms to invest in user growth and encourages investors to subsidize these platforms. However, user growth does not necessarily imply strong user stickiness. Without user stickiness, strong network effects in the current period may fade in future periods, thus rendering a user growth strategy ineffective. By adding a time dimension to network effects, we developed a model of cross-period and within-period network effects to explain how different types of network effects drive value. We emphasize that the cross-period same-side network effect contributes to user stickiness, while the within-period cross-side network effect persists conditional on user stickiness. We propose that one reason for platforms having heterogeneous cross-period same-side network effects is because of the “product learning” mechanism: it is expected that products with higher uncertainty have a stronger cross-period same-side network effect. Based on different drivers, we extend the customer lifetime value model (CLV2) to two-sided platform markets, allowing us to measure how different interventions drive platform value. Using Groupon data, we verify our insights and discuss platform design choices that enhance user stickiness when the cross-period same-side network effect is weak.
AB - Extant research has popularized the perspective that strong network effects produce “winner-take-all” outcomes, which leads platforms to invest in user growth and encourages investors to subsidize these platforms. However, user growth does not necessarily imply strong user stickiness. Without user stickiness, strong network effects in the current period may fade in future periods, thus rendering a user growth strategy ineffective. By adding a time dimension to network effects, we developed a model of cross-period and within-period network effects to explain how different types of network effects drive value. We emphasize that the cross-period same-side network effect contributes to user stickiness, while the within-period cross-side network effect persists conditional on user stickiness. We propose that one reason for platforms having heterogeneous cross-period same-side network effects is because of the “product learning” mechanism: it is expected that products with higher uncertainty have a stronger cross-period same-side network effect. Based on different drivers, we extend the customer lifetime value model (CLV2) to two-sided platform markets, allowing us to measure how different interventions drive platform value. Using Groupon data, we verify our insights and discuss platform design choices that enhance user stickiness when the cross-period same-side network effect is weak.
KW - Two-sided markets
KW - Customer lifetime value
KW - Experience goods
KW - Network effects
KW - Platform design
KW - Product learning
KW - User growth strategy
KW - User stickiness
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=ceibs_wosapi&SrcAuth=WosAPI&KeyUT=WOS:001222940400008&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.25300/MISQ/2023/17012
DO - 10.25300/MISQ/2023/17012
M3 - Journal
SN - 0276-7783
VL - 48
SP - 1
EP - 30
JO - MIS Quarterly
JF - MIS Quarterly
IS - 1
ER -