Exploring signal congruence: How marketer- and user-generated content congruence influences review sharing

Consumers' review sharing on review-hosting platforms provides crucial information for subsequent users. However, the content generated by marketers and users may convey incongruent information, potentially confusing readers. This study aims to operationalize the sub-dimensions of the signal congruence from two perspectives: semantic similarity and linguistic style matching, and investigate their heterogeneous effects on subsequent users' review sharing. Drawing on the signalling theory and heuristic-systematic model, we examine the impact of signal congruence on various characteristics of subsequent users' review, including review entropy, length, linguistic complexity, readability, and two-sidedness. Additionally, we investigate the moderating role of subsequent reviewer's platform activeness. Using a real-world dataset, our empirical analysis results indicate that semantic similarity positively affects subsequent review entropy, length, readability, while negatively impacting linguistic complexity. Conversely, linguistic style matching negatively influences review entropy, length, two-sidedness, and positively influences linguistic complexity. Their influences are amplified when the subsequent reviewer exhibits higher platform activeness.