Dorwat, ShardulNamvar, MortezaAkhlaghpour , Saeed2022-12-272022-12-272023-01-03978-0-9981331-6-4https://hdl.handle.net/10125/102903This study investigates online review features that constitute review depth and assess their impacts on review helpfulness. It develops a model capturing the moderating effects of heuristic and systematic cues of an online review on the relationship between review length and its helpfulness. In particular, this study examines the moderating effects of price, product type, review readability and the presence of two-sided arguments. For testing the model, a dataset of 568,454 reviews from 256,059 different reviewers on Amazon.com were analyzed. The variables were operationalized using test processing techniques and relationships were empirically tested using regression and machine learning models. The results highlight significant moderating effects of review readability and the presence of two-sided arguments on the relationship between review length and its helpfulness. However, the results did not confirm the moderating effects of price and product type. This article discusses the significant implications for a better understanding of review depth and helpfulness in e-commerce platforms.10engAttribution-NonCommercial-NoDerivatives 4.0 InternationalData Analytics, Data Mining, and Machine Learning for Social Mediaconsumer decision-makingreadabilityreview depthreview helpfulnesstwo-sided argumenttext10.24251/HICSS.2023.272