Although a hotel’s basic purpose of providing a temporary place of lodging has not changed fundamentally over the course of history, the industry has continuously evolved with the newest innovations in architecture, technology, and culture. The most recent evolution is the digitization of the hotel marketplace. This thesis investigates two areas heavily influenced by the digital marketplace – online reputation management and dynamic pricing. The first study of this dissertation addresses one important facet of reputation management. How do managers’ responses to online reviews alter the opinion of subsequent reviewers? By analyzing a dataset of approximately 17 million hotel reviews, we demonstrate that managers’ responses can change the opinion of subsequent reviewers, but not always in a positive way. Responses to negative reviews generally improve subsequent opinion but responses to positive reviews can sometimes negatively influence subsequent opinion. A deep learning topic analysis of response and review texts reveals that tailored responses to positive reviews can actually negatively impact subsequent opinion. The findings in this study are shown to be consistent with the predictions of reactance theory. The second study seeks to uncover the degree to which managers’ pricing heuristics are optimal. Analyzing a year’s worth of spot prices for a focal hotel and its two competitors in the Las Vegas market, we show that managers do not price optimally in two peculiar ways. First, managers are able to set close-to-optimal average prices during off-season but dramatically underprice during peak-season. This result is consistent with agency theory that suggest the observable binary outcome of selling out the hotel may attenuate managers’ aggressiveness in setting prices. Second, managers, like untrained experimental subjects in prior literature, tend to make price changes that are too small. Furthermore, this study investigates the revenue gains due anticipating competitors’ pricing behavior and mean reversion tendencies in online reviews.