EXPRESS: Status Downgrade: the Impact of Losing Status on a User-generated Content Platform

Non-financial incentives such as badges, ranks, and status are often used to encourage user participation on online platforms. This study focuses on the effect of one such incentive, “status,” in the context of a thirdparty restaurant-review platform. In contrast to previous research that has mainly focused on the effects of such incentives on subsequent contributions from users who gained statuses, we explore how the intrinsic and perceived quality of content generated by users is impacted after users lose their statuses. Using natural language processing (NLP) techniques to extract quality metrics from online reviews in our dataset, we exploit a quasi-experimental setting and demonstrate that even though the intrinsic quality of reviews significantly decreases after a reviewer is demoted by a platform, consumers on the platform nonetheless perceive these reviews as disproportionately useful. We draw on inequity theory and the elaboration likelihood model to theoretically support our empirical results, as well as conduct mechanism analyses to rule out alternative explanations. Furthermore, we find that temporal associations with a platform or with an elevated status do not moderate the effect of status loss on the intrinsic and perceived quality of reviews written post-demotion. The implications of our findings are significant for platform managers who manage the design of status-driven recognition systems and must determine how the change in status should be displayed on the platform.