Preempting online review helpfulness-an elaboration likelihood perspective

The Information Systems literature has substantially advanced understanding of online review helpfulness in both its antecedents and consequences. Despite the rich understanding, existing studies predominately focused on the impact of coarser-grained characteristics on consumer’s helpfulness perception. The possibility of evaluating review’s helpfulness based on both fine-grained textual information along with multiple general factors of reviewer and products challenged existing understanding. While textual features such as latent topics and semantic traits of a review have been considered as effective predictors for identifying helpful reviews, the causal effects of these predictors on review’s perceived helpfulness still remain largely unclear. Drawing on the Elaboration Likelihood Model (ELM), this proposal focuses on understanding how coarse-grained general characteristics and fine-grained textual characteristics jointly affect the perceived helpfulness of online review. In particular, following the spirit of ELM, we propose a model to disentangle the types of online review characteristics through a dual information processing perspective, and investigate how consumer’s motivation moderates effect of the two information processing routes. The proposed research model shall be operationalized by employing a panel data analysis.