Understanding Electronic Word-of-Mouth Helpfulness Using Review, Hotel, and Reviewer Characteristics

A customer provides their feedback on the online platform through textual and numeric formats. The textual reviews are also known as electronic Word-of-Mouth (eWOM), which contain the elaborated experience of the customer. It enables potential customers in making informed decisions. These also act as tools for the service providers, by informing them about key areas of customer interest. Nowadays, eWOM has emerged as a business intelligence tool that enables optimum strategy making. eWOM helpfulness is important in choosing useful reviews from a corpus of humongous data available on the internet. In this study, we propose a framework to identify the impact of various characteristics related to the reviewer who posted the review, eWOM itself, and the hotel for which a review is posted on eWOM helpfulness. We used a dataset from Tripadvisor.com which contains 16699 reviews written by 1099 reviewers. Variables belonging to all three categories have been extracted and have been evaluated using econometric modeling and different machine learning techniques. The findings of this study report a positive significant relationship between hotel and reviewer features and eWOM helpfulness. It also reports that eWOM polarity and recency share a negative significant relationship with eWOM helpfulness. However, all other eWOM features have been reported to share a positive significant relationship with their helpfulness. We also discuss theoretical and practical implications of our research at the end.