Predicting and interpreting digital platform survival: An interpretable machine learning approach

被引：0
作者：
Zhu, Xinyu [1 ]
Zhang, Qiang [2 ,3 ]
Ma, Baojun [1 ]
机构：
[1] Shanghai Int Studies Univ, Sch Business & Management, Key Lab Brain Machine Intelligence Informat Behav, Minist Educ & Shanghai, Shanghai 201620, Peoples R China
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518066, Peoples R China
[3] BYD Auto Ind Co Ltd, BYD Auto Engn Res Inst, Shenzhen 518118, Peoples R China
来源：
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS | 2024年 / 67卷
基金：
中国国家自然科学基金;
关键词：
Digital Platform Survival; Online Content; Interpretable Machine Learning; XGBoost; Causal Forest; WORD-OF-MOUTH; USER; PRODUCT; FIRMS; BEHAVIOR; REVIEWS;
D O I：
10.1016/j.elerap.2024.101423
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
Despite the substantial economic impact of digital platforms, research on platform risk evaluation has been sparse. In this study, we investigate whether online content can serve as leading indicators of digital platform survival. We employ machine learning techniques to extract features from three types of online content, that is, user generated content, platform generated content, and third party generated content and examine their utilities in predicting platform survival. Using a predictive XGBoost algorithm and data crawled from a leading web portals of digital platforms for online lending in China, we find online content are strong predictors of platform survival. Furthermore, we use casual forest models to reveal the differences among the three type of online content in terms of predictive utility. Interestingly, we find the presence of third-party generated content indicates lower probability of platform survival while the platform with more user generated content has higher chance to survive. The relationship between platform generated contents and platform failure is not significant. Based on the results, we provide practical implications for market managers and platform owners.
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页数：10
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