Analyzing the Online Word of Mouth Dynamics: A Novel Approach

Abstract

At least three challenges hinder the analysis of online WOM. First, the process of transform unstructured online WOM data can generate many variables, increasing the need for dimension reduction. Second, the volume and valence of online WOM may dramatically change in a short period before and after an incident. Third, important events might trigger symmetric or asymmetric reactions in online WOM across rival products or services. We introduce a new method—multi-view sequential canonical covariance analysis to solve the myriad WOM conversational dimensions, detect WOM dynamic trends, and examine its concurrent effects across multiple firms. This new method also provides greater computational efficiency, and thus can be referred as a more advanced manifold optimization approach. We illustrate the advantages of this new method through an empirical example—the 2017 United Express Flight 3411 incident. We find that United Airlines and its rivals all experienced a sudden increase of negative emotions and a sudden decrease of positive emotions because of the Incident, yet the magnitudes of the changes were more dramatic for United Airlines. This new method provides a novel insight into the online WOM dynamics and can contribute to a wide range of fields.