Discovering signals of platform failure risks from customer sentiment: the case of online P2P lending

Abstract

Purpose

Digital platforms have grown significantly in recent years. Although high platform failure risks (PFR) have plagued the industry, the literature has only given this issue scant treatment. Customer sentiments are crucial for platforms and have a growing body of knowledge on its analysis. However, previous studies have overlooked rich contextual information emb`edded in user-generated content (UGC). Confronting the research gap of digital platform failure and drawbacks of customer sentiment analysis, we aim to detect signals of PFR based on our advanced customer sentiment analysis approach for UGC and to illustrate how customer sentiments could predict PFR.

Design/methodology/approach

We develop a deep-learning based approach to improve the accuracy of customer sentiment analysis for further predicting PFR. We leverage a unique dataset of online P2P lending, i.e., a typical setting of transactional digital platforms, including 97,876 pieces of UGC for 2,467 platforms from 2011 to 2018.

Findings

Our results show that the proposed approach can improve the accuracy of measuring customer sentiment by integrating word embedding technique and bidirectional long short-term memory (Bi-LSTM). On top of that, we show that customer sentiment can improve the accuracy for predicting PFR by 10.96%. Additionally, we do not only focus on a single type of customer sentiment in a static view. We discuss how the predictive power varies across positive, neutral, negative customer sentiments, and during different time periods.

Originality/value

Our research results contribute to the literature stream on digital platform failure with online information processing and offer implications for digital platform risk management with advanced customer sentiment analysis.

Citation

Zhang, Q., Zhu, X., Zhao, J.L. and Liang, L. (2022), "Discovering signals of platform failure risks from customer sentiment: the case of online P2P lending", Industrial Management & Data Systems, Vol. 122 No. 3, pp. 666-681. https://doi.org/10.1108/IMDS-05-2021-0308