Cornell Hospitality Quarterly

Using an event study methodology and data from 3,494 new entrants in the U.S. lodging industry, this paper examines how quickly new hotels ramp up their performance after opening. For the years 2006 through 2009, new entrants entered with average daily rates (ADRs) above incumbents, and took seven quarters (1.75 years) to ramp up occupancies to the levels of comparable incumbent hotels. These averages include performance behavior of brand-managed, franchisee-managed, and unaffiliated independent hotel new ventures compared with incumbent hotels in similar geographic markets, locations, and price segments. Overall, new hotels reached comparable revenue per available room (RevPAR) performance by the second quarter of the second year of operation. RevPAR ramp-up was earlier for brand-managed hotels (first quarter of the second year), an outcome primarily attributable to higher occupancies and lower initial ADRs. Independent hotels took substantially longer than other new entrants to reach the RevPAR performance of existing hotels. Based on the faster ramp-up of new branded properties, the chief implication is that hotel developers should consider affiliating with a brand for quicker stabilization and short-term gain. The speed of hotels’ ramp-up also calls into question the conventional view that new hotels represent a relatively risky investment.

Contrary to conventional wisdom, loyalty may be a driver of hotel guests’ favorable behavior when they are satisfied with a hotel’s service recovery effort. Instead of having satisfaction with service recovery directly influencing guests’ supportive actions, loyalty acts as a precondition to consumers’ positive citizenship behavior. Moreover, the factors that drive such favorable behavior may be independent of those that cause guests to offer favorable word of mouth after a hotel stay. Based on a study of 288 guests in seven high-end hotels in Spain’s Canary Islands, satisfaction with service recovery has a direct effect on loyalty, which in turn has a strong effect on customer citizenship behaviors. However, loyalty plays its mediating role only on the effects of satisfaction with service recovery on favorable citizenship behavior. That is, the fact that a guest is loyal helps to explain why a guest decides to help the hotel after satisfactory service recovery. On the other hand, loyalty does not enter into the equation when a guest is not happy with the service recovery and elects to behave dysfunctionally, including trashing the room.

This study addressed the relationship between the innovativeness of hotels and their profitability. The authors propose that innovativeness and the visibility of the benefits to customers from innovative activities both serve as endogenous variables. Competitive market advantage, sales growth, and capacity utilization serve as mediators. Both key informants and financial statements provided data for a sample of 298 hotels. The results show that the relationship between innovativeness and profitability is positive and fully mediated.

In the first study of its kind, a conjoint analysis of 530 Chinese leisure travelers visiting Hong Kong analyzed the relative value that these travelers assigned to a variety of hotel rate fences or rate restrictions. Not surprisingly, the attributes with the highest utility scores were also the least restrictive. That is, the respondents preferred the lowest price (947 Hong Kong dollars [HKD]), the least restrictive advance reservation or purchase requirement (seven days in advance), and an advance reservation requirement that included a full refund and unlimited changes without any restrictions. Of all the rate fences presented, price and refundability were consistently the most valuable to these travelers. The implication for hotel operators is that Chinese travelers will accept hotel rate restrictions, but they must be carefully constructed and avoid stringent restrictions, particularly advance purchase and refundability, unless those restrictions are offset by favorable price.

The fundamental booking decision for a cruise line involves how many reservations to accept to avoid two outcomes—either sailing with empty cabins or denying some customers’ bookings. Cruise lines accept reservations that overbook a cruise because they anticipate a number of cancellations and no-shows. The question is how many overbooked reservations to accept such that no one is denied boarding. This article applies a real options approach to formulate a risk decision model for cruise line dynamic overbooking. The analysis includes multiple cabin types and allows upgrading to reduce and avoid the two reservation risks. This article illustrates the way to find the best overbooking level vector. The analysis models a series of real options contained in the joint overbooking decision. Furthermore, the dynamic joint overbooking decision is analyzed and discussed from the view of various types of real options. Finally, numerical examples are used to demonstrate how to solve the joint overbooking problem with two given decision schemes using the real options analysis (ROA) in real-time.

A study of 124 consumers found distinct differences in the consumers’ responses to a policy once used by Orbitz, in which prices were determined according to the computer platform used to search for bookings. The policy arose when Orbitz noticed that Macintosh users were generally willing to accept higher prices than PC users. This observation was converted into a price discrimination mechanism. The study found that Mac users took a dim ethical view of this rate fence and, moreover, indicated that they were less willing to use Orbitz as a result. Men and women in the study reacted differently from each other, however. Men expressed more outrage when they were Mac users, while women were more likely to consistently view this form of price discrimination as unreasonable and of questionable ethics. Since the hospitality industry uses many price discrimination rules, two implications of this study are choose rate fences carefully and consider explaining the rationale for any price discrimination policies.