In this study, we investigate the sales impact of different types of online word-of-mouth (WOM) based on their source (user versus critic) and form (structured versus unstructured). We propose a model by adopting the heuristic-systematic perspective and empirically test it using online movie reviews collected from Rotten Tomatoes, a review-aggregation website for film and television. We construct a unique dataset that matches critic reviews and user reviews, with metadata such as weekly box-office sales and weekly advertisement spending for 90 movies released in the U.S in 2014. In addition, we use a text-mining method to extract valuable sentiment information from the textual contents of both user and critic reviews. This dataset allows us to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings, and critic text reviews. The results reveal that critic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives. Our study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to lift sales.