Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews

摘要

Consumer recommendations of products and services are important performance indicators for organizations to gain feedback on their offerings. Furthermore, they are important for prospective customers to learn from prior consumer experiences. In this study, we focus on user-generated content, in particular online reviews, to investigate which service aspects are evaluated by consumers and how these factors explain a consumer's recommendation. Further, we investigate how recommendations can be predicted automatically based on such user-driven responses. We disentangle the recommendation decision by performing explanatory and predictive analyses focusing on a sample of airline reviews. We identify core and augmented service aspects expressed in the online review. We then show that service aspect-specific sentiment indicators drive the decision to recommend an airline and that these factors can be incorporated in a predictive model using data mining techniques. We also find that the business model of an airline being reviewed, whether low cost or full service, is also an applicable consideration. Our results are highly relevant for practitioners to analyze and act on consumer feedback in a prompt manner, along with the ability of gaining a deeper understanding of the service from multiple aspects. Also, potential travelers can benefit from this approach by getting an aggregated view on service quality.