Prior research on peer-to-peer (P2P) lending and electronic word-of-mouth (eWOM) has overlooked how eWOM valence ambiguity and the external environment influence the relationship between eWOM volume and platform performance. Based on signaling and information processing theories, this study differentiates various types of eWOM valence ambiguities and examines how these ambiguities, along with the external environment, affect the impact of eWOM volume on P2P lending platform performance.
Using data from Chinese P2P lending platforms (January 2018–July 2019), econometric analyses evaluate the connection between eWOM volume and platform performance as well as the moderating effects of valence ambiguity and external uncertainty.
The findings confirm that eWOM volume significantly influences platform performance. However, this positive effect diminishes with high inconsistencies between star ratings and textual eWOM or increased external uncertainty.
This study’s scope is limited to Chinese P2P lending. Future research should explore diverse markets and user engagement through experimental designs.
This study offers practical strategies for P2P lending platforms in emerging markets, emphasizing the importance of managing inconsistencies between star ratings and textual reviews and addressing lenders’ concerns in uncertain signaling environments.
This study advances the understanding of P2P lending and eWOM dynamics by analyzing the interplay between eWOM volume and internal and external factors related to eWOM diagnosability. It highlights how inconsistencies in star ratings and textual eWOM, coupled with external uncertainty, exacerbate decision-making challenges for potential lenders and reduce the positive impact of eWOM volume.
