How Mega Is the Mega? Exploring the Spillover Effects of WeChat Using Graphical Model

• 成果类型: Article
• 署名作者: Zheng, Jinyang; Qi, Zhengling; Dou, Yifan; Tan, Yong
• 署名单位: Purdue University System; Purdue University; George Washington University; Fudan University; University of Washington; University of Washington Seattle
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0865
• 发表日期: 2019
• 页码: 1343-1362
• 关键词: Causal Inference; identification; disadvantage; analytics; diagrams; demand; IMPACT; latent; apps

摘要

WeChat, an instant messaging app, is considered a mega app because of its dominance in terms of use among Chinese smartphone users. Little is known, however, about its externality in the broader app market. This work estimates the spillover effects of WeChat on the other top 50 most frequently used apps in China, using users' weekly app usage data. Given the challenge of determining causal inference from observational data, we apply a graphical model and an econometric method to estimate the spillover effects in two steps: (1) we determine the causal structure by estimating a partially ancestral diagram, using a fast causal inference algorithm; and (2) given the causal structure, we find a valid adjustment set and estimate the causal effects by an econometric model with the adjustment set for controlling noncausal effects. Our findings show that the spillover effects of WeChat are limited; in fact, only two other apps, Tencent News and Taobao, receive positive spillover effects from WeChat. In addition, we show that if researchers fail to account for the causal structure that is determined from the graphical model, it is easy to fall into the trap of confounding bias and selection bias when estimating causal effects. The findings generate managerial implications in terms of app usage patterns, strategic management of mega apps on an app platform, and app promotional strategies for app platform managers and app developers.