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This study examines how firms’ use of competitor-focused accounting information, specifically competitor monitoring information, impacts their pricing, demand, and overall revenue performance. The monitoring activities examined are the scope of monitoring, monitoring above and below one’s own hotel class (i.e., market segment), and the extent of reciprocity of monitoring. Competitor analysis is a central element in strategic management accounting (SMA), yet little empirical research has been done since companies do not disclose competitor monitoring activities. Proving the value of competitive monitoring provides strong support for SMA. Archival, proprietary monitoring information regarding pricing, demand, and revenue were obtained from one of the largest hotel markets in the United States. Using regression, we modeled the relationships between performance measures (pricing, demand, and revenue) and monitoring behaviors, while controlling for quality (hotel characteristics and management skill), competitive intensity, hotel class, geographic location, and ownership type. Our results indicate that two aspects of competitor monitoring impact hotel pricing that, in turn, impacts hotel demand and revenue performance. Specifically, a hotel monitoring more competitors (what we refer to as Scope) achieves higher prices with unchanged demand, resulting in higher revenue performance. Most hotels monitor within their class. However, deviating from one’s class has profound outcomes: looking at lower (higher) quality hotels results in a hotel setting lower (higher) prices, resulting in higher (unchanged) demand and lower (higher) revenue performance. Surprisingly, we did not find support for the reciprocity of monitoring. That is, whether the competitors monitored by a hotel, in turn follow the target, has no impact on hotel revenue performance outcomes. While the SMA literature notes the importance of competitor monitoring, this study fills a gap in an important, under-researched area by documenting the link between competitor monitoring behaviors and organizational revenue performance. This may help promote greater diffusion of SMA practices.

Unlike manufacturing firms where the production of goods can be adjusted according to the demand of customers, hospitality firms do not have the ability to alter the capacity of the changing demand of guests in a short period of time. Given the relatively fixed capacity or supply, maximizing revenue through inventory control is essential for hospitality operations. This chapter covers operations inventory control extracted from the field of revenue management. First, the concept of capacity management and planning is enclosed and various capacity management tactics and inventory control strategies are explored. Next, the management and principles of space inventory through inventory-based restrictions, strategic pricing, displacement analysis, and distribution channel management are addressed. Finally, the respective applications of these principles, strategies, and tactics in several hospitality sectors are discussed.

Successful revenue management programs are found in industries where managers can accurately forecast customer demand. Airlines, rental car agencies, cruise lines, and hotels are all examples of industries that have been associated with revenue management. All of these industries have applied revenue management, whether it be complex overbooking models in the airline industry or simple price discrimination (i.e., having a tiered price system for those making reservations ahead of time versus walk-ups) for hotels.

An accurate tourism forecast is critical to destination countries as a foundation for tourism-related decision making and efficient tourism planning. The purpose of this study is to evaluate how Taiwan's inbound tourism was affected by the September 21st Earthquake in 1999 and the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003, one of the mega earthquakes in the 20th century and most catastrophic health hazard in the past hundred years in Taiwan, respectively. According to the empirical findings, Taiwan's inbound tourism was brutally devastated by the two calamities, particularly during the SARS outbreak. The inbound tourism was more heavily influenced by the SARS epidemic and recovered from the SARS shadow was greater compared with the recovery after the September 21st Earthquake.

This chapter examines the pricing, demand (occupancy), and revenue per available room (RevPAR) dynamics of European hotels for the period 2006–2007. The importance of understanding the pricing behavior of direct competitors is critical to effective strategy formulation and meaningful industry analysis. Nevertheless, existing demand studies miss a critical link to local market dynamics. This study offers an alternative approach to examining competitive set pricing behavior that yields insights into the inelasticity of lodging demand. The results of this study of over 3,000 European hotel observations reveal that hotels that offered average daily rates (ADRs) above those of their direct competitors had lower comparative occupancies but higher relative RevPARs. The observed pattern of demand and revenue behavior was consistent for hotels in all market segments from luxury to economy. Country-specific analyses reveal a similar pattern, with more volatility in the results for hotels in Spain and Italy. Overall, the results suggest that the best way for a hotel to have higher revenue performance than its competitive group is to maintain higher rates. The results of this study support the position that hotel operators who resist pressures to undercut competitor's prices may be better served with higher revenues.

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and arranging facilities, based on the predicted demand and supply. This chapter covers three main concepts related to forecasting: it provides an understanding of hospitality demand and supply, it introduces several forecasting methods for practical application, and it explains yield management as a function of forecasting. In the first section, characteristics of hospitality demand and supply are described and several techniques for managing demand and supply are addressed. In the second section, several forecasting methods for practical application are explored. In the third section, yield management is covered. Additionally, examples of yield management applications from airlines, hotels, and restaurants are presented.

This chapter analyzes the pricing policy of hybrid P2P lodgings versus traditional hotels in relation to seasonality. A quantitative comparative analysis of room prices in Airbnb and hotel lodgings in the city of Barcelona was conducted across four different pairs or dates (peak versus low season) in one year. Overall, the study shows that Airbnb prices are significantly lower than those of hotels and that the former’s prices fluctuate very little, whereas hotels use yield or revenue management techniques to adjust prices in high and low seasons. This implies that relevant purchase behavioral changes may occur in terms of customers waiting until the last minute to book.

This study explores the short- and medium-term effects generated by the Milan Expo 2015, adopting a microeconomic approach. The focus is on the hospitality sector. The study embraces nine years, identifying three intervals: pre- (2011–2014), during- (2015) and post-Expo (2016–2019). The time span does not include the Covid-19 pandemic period, which started in 2020. The dataset is composed of daily data. Three research questions are explored. First, an overall evaluation of the short- and medium-term effects is performed. Second, the seasonal effects are measured. Finally, the impacts for different classes of hotels are considered. The findings are supportive for the legacy generated by the Milan Expo. The results confirm the ability of the Milan Expo to strengthen the leisure segment. Positive results have been observed for all classes of hotels, relevantly augmenting the real revenue per available room (RevPAR). Luxury hotels achieved the highest increase of RevPAR, while economy class hotels registered the highest percentage of increase of RevPAR.