<p>In this dissertation, I investigated price dynamics in the hotel room-night market and attempted to explain pricing decisions from a market perspective. Since market dynamics of the hotel room-night market can be paralleled to those in the financial market, financial market models allowed for examination of various aspects of hotel room pricing decisions.</p><p>In the first study, advance-purchase discounts were estimated through application of an option pricing model considering property-specific attributes. Non-refundable advance-purchase discounts are a commonly used rate fence. One challenge to their implementation, however, is deciding upon the precise magnitude of the discount. Quan’s (2002) study on the price of room reservations is a good starting point, but it is a conceptual model that assumes away other property-specific factors. This study thus tested the idea that advance-purchase discounts are affected by various components, including the value of the right to cancel a reservation (e.g., cancelation option value) and the room- and property-specific factors in the hotel room-night market (e.g., uncertainty, reviews, and seasonality). The analysis supported this hypothesis and additionally revealed that advance-purchase discounts are smaller for rooms with high review ratings in a high-demand period. Interestingly, the divergence between advance-purchase discounts and cancelation option value components widened in a high-demand period, which implies a tendency by hotels to adjust their room rates rather than the amount of discount for customers who book their stay well in advance. Theoretically, this study thus contributes to finance literature by extending the application of the option pricing model to real options for non-financial assets. This study also contributes to the hospitality literature by demonstrating the effects of property-specific attributes on advance-purchase discount magnitude. The results also have implications to the hospitality industry by providing an analytical framework by which hoteliers can estimate property-specific advance-purchase discounts.</p><p>The second study concentrated on rate parity agreement’s effect on the hotel room-night market’s efficiency at reflecting product characteristics in room rates. This study examined the impact of rate parity agreement between hotels and online travel agencies by comparing hotel rates between Europe and the United States. This study found that room rates were less sensitive to property quality attributes under rate parity clauses. The reflection of property quality on room rates were less efficient when hotels have rate parity agreement with OTAs. Furthermore, the results supported the claim that rate parity exacerbates price increase in periods of high demand, which indicates possible collusion between suppliers (hotels) and distributors (OTAs). The findings provided theoretical implications by testing the market efficiency of the hotel room-night market and confirming the impact at the property level. This study also provided a perspective on pricing decision makers to understand how rate parity agreement influence their pricing decisions. Last, the findings provided support for recent policies in Europe that restrict rate parity agreements between hotels and OTAs.</p><p>The third study empirically examined hoteliers’ response to the demand by observing the price movement of two rates with different cancelation policies—free cancelation rates and non-refundable rates. By modifying Hasbrouck’s (1995) information share approach, this study examined the non-refundable rates’ contribution to the price discovery process. The perceived quality of accommodation by customers, one of the primary determinants of the price discovery process, was included in analysis. The results suggested that non-refundable rates were contribute more to the information variance than free cancelation rates did. The findings also suggested that consumers’ perceived quality and volatility influence non-refundable rates’ contribution to the price discovery process. The results also have practical implications for market participants, as they help to build an understanding of aggregated demand and its impact on pricing. Non-refundable rates are generally regarded as just one of many kinds of discounted rates, but the results of this study suggest that hoteliers should carefully consider the role that non-refundable rates play in their pricing strategy.<br></p>

Les systèmes de recherche ont facilité l’accès à l’information disponible sur le web à l’aide de mécanismes de collecte, d’indexation et de stockage de contenus hétérogènes.Ils génèrent des traces résultant de l’activité des internautes. Il s’agit ensuite d’analyser ces données à l’aide d’outils de data mining afin d’améliorer la qualité de réponse de ces systèmes ou de la personnaliser en fonction des profils des utilisateurs. Certains acteurs, comme la société Marketshot, se positionnent comme intermédiaires entre les consommateurs et les professionnels. Ils mettent en relation les acheteurs potentiels avec les grandes marques et leurs réseaux de distribution à travers leurs sites Internet d’aide à l’achat. Pour cela, ces intermédiaires ont développé des portails efficaces et stockent de gros volumes de données liées à l’activité des internautes sur leurs sites. Ces gisements de données sont exploités pour répondre favorablement aux besoins des internautes, ainsi qu’à ceux des professionnels qui cherchent à comprendre le comportement de leurs clients et anticiper leurs actes d’achats. C’est dans ce contexte, où on cherche à fouiller les données collectées du web, que se placent mes travaux de recherche. L’idée est de construire des modèles qui permettent d’expliciter une corrélation entre les activités des internautes sur les sites d’aide à l’achat et les tendances de ventes de produits dans la « vraie vie ». En effet, ma thèse se place dans le cadre de l’apprentissage probabiliste et plus particulièrement des modèles graphiques « Topic Models ». Elle consiste à modéliser les comportements des internautes à partir des données d’usages de sites web. / Research systems have facilitated access to information available on the web using mechanisms for collecting, indexing and storage of heterogeneous content. They generate data resulting from the activity of users on Internet (queries, logfile). The next step is to analyze the data using data mining tools in order to improve the response’s quality of these systems, or to customize the response based on users’ profiles. Some actors, such as the company Marketshot, are positioned as intermediaries between consumers and professionals. Indeed, they link potential buyers with the leading brands and distribution networks through their websites. For such purposes, these intermediaries have developed effective portals, and have stored large volumes of data related to the activity of users on their websites. These data repositories are exploited to respond positively to the needs of users as well as those of professionals who seek to understand the behavior of their customers and anticipate their purchasing actions. My thesis comes within the framework of searching through the data collected from the web. The idea is to build models that explain the correlation between the activities of users on websites of aid for the purchase, and sales trends of products in « real life ». In fact, my research concerns probabilistic learning, in particular Topic Models. It involves modeling the users’ behavior from uses of trader websites.