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Article Excerpt
Abstract
Forming strategic alliances has become a common practice for major airlines in today's global market. The arrangement of parallel codesharing provides alliance partners serving the same route with the unique opportunity to conduct risk pooling with shared capacity and information. In this article, we develop predictive methods to analyze the effects of capacity sharing and risk pooling on the market and operational performances of airline alliance partners under parallel codesharing. We establish the interrelationship between market share and capacity through the total demands and probabilities under four different cases of demand/supply allocations between two alliance partners. We also propose methods for identifying the values of key parameters to estimate the extent of risk pooling on selected international routes. Our analyses show that risk pooling with shared capacity is a common phenomenon on the selected routes under parallel codesharing. In addition, some of the routes have rather uneven distributions of the opportunity for risk pooling between alliance partners. Several applications of the proposed methods in both the private and public sectors, such as evaluating gain-sharing by alliance partners, revising bilateral agreements, as well as fine-tuning capacity-, revenue-, and cost-sharing arrangements, are also discussed.
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In recent years, the airline industry has experienced a dramatic change in the structure of the competitive environment due to the global trend of forming strategic alliances. Airline Business (2005) reports that there are over 500 airline alliances worldwide, and the number of alliances has increased about 25 percent per year for the last decade. The predominant form of airline strategic alliances is codesharing--the use of an airline's two-letter designator on a partner's flight, producing the equivalent of an online connection and, as a result, a more favorable computer reservation system display, which allows all marketing, booking/ticketing, and baggage handling procedure to be run as if by one airline (Shaw 2004). According to Oum et al. (1996), codesharing arrangements between alliance partners can take two major forms: complementary and parallel codesharing. Figure 1 presents two typical examples of the arrangements of complementary and parallel codesharing. As shown in the figure, under complementary codesharing, EVA Air and Air Canada are alliance partners for the route between Taipei and Vancouver, but EVA Air is the sole operator on the route. In contrast, United Airlines and Lufthansa are parallel codesharing partners for the route between Chicago and Frankfurt, and both airlines operate on the same route.
The arrangement of parallel codesharing provides alliance partners with a unique opportunity to operate with shared capacity and information on a particular route, as can be observed from Figure 1. On the supply side, while explicit collusion on pricing or scheduling between alliance partners is usually prohibited since they are still considered as competitors according to most antitrust laws such as the Sherman Act in the U.S. (Schlangen, 2000), alliance partners under codesharing arrangements are permitted to coordinate on nondiscriminatory operations such as sharing ticking/ booking information and sharing excess capacity through free sales or block seat and space arrangements (Boyd 1998; Mosin 2000). (1) According to Oum et al. (1996), codeshared connecting flights are listed ahead of the interline connecting flights in computer reservation systems (CRS) and most other flight information sources. The arrangement of parallel codesharing thus allows an airline whose flights are fully booked during a particular time period to directly refer customers to its alliance partner which operates on the same route due to the advantageous position on the CRS terminal screen. (2) As shown in the theoretical and empirical analyses in Chen and Chen (2003), parallel codesharing allows two competing carriers to conduct risk pooling for reducing costs and improving operational performances. Specifically, when one airline has excess capacity while its alliance partner experiences shortage, the excess capacity can be used to satisfy the high demand faced by the partner. As a result, both alliance partners can reduce their optimal seat supplies while maintaining a satisfactory service level through cross-referring customers, which leads to higher load factors for both airlines. (3) It is interesting to note that the advantage in cross-referring customers for parallel alliance partners does not seem to disappear in the Internet age since codesharing flights still enjoy the status of priority display in CRS and most airlines' own on-line reservation systems. According to the Survey of International Air Travelers done by the U.S. Department of Commerce (2004), 55 percent of all U.S. travelers booked with travel agents/travel departments, while 21 percent booked over the Internet and 19 percent directly with airlines. In other words, airline alliance partners still enjoy the advantage of priority display at about 80 percent of points of sales in today's Internet age. (4)
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On the demand side, parallel codesharing can increase the presence of an alliance on a particular route by offering a higher flight frequency to their customer as a whole (Oum et al., 1996), which also creates strong customer loyalty through the integrated management of the frequent flyer programs by alliance partners (Gudmundsson et al. 2002, Suzuki 2003). (5) The arrangement of parallel codesharing has also been the focus of many open skies bilateral agreements between host countries which usually allows only two airlines, one from each country, to serve a new international route. For example, the open skies agreement between the U.S. and Italy in 1999 allowed Delta Airline and Alitalia to open a new gateway between Atlanta and Rome. Parallel codesharing can also result from the hub-and-hub connection between two airlines from two different host countries, such as the 1993 bilateral agreement between Northwest and KLM for the route between Detroit and Amsterdam (Mosin 2000).
One major issue concerning parallel codesharing that has received significant attention from both the private and public sectors is how to share the capacities, revenues, profits, and costs between alliance partners as a result of joint operations on a codeshared route (see, e.g., Oum and Park 1997; Boyd 1998; Li 2000; Shumsky 2006). The fact is that the negotiation of bilateral agreements (between two countries) and alliance agreements (between two airlines) is usually conducted by policy makers or airline strategic planners who determine the overall capacity shares of two alliance partners based on such considerations as access to global markets and establishing brand loyalty as opposed to how the alliance arrangements will interact with the two airlines' operations systems (Shumsky 2006). As more passengers flow between airlines and billions of dollars in revenue are generated, understanding how the demands and supplies of the two airlines interact becomes a critical issue for reaching fair and mutually agreeable capacity-, cost-, and revenue-sharing arrangements between alliance partners as well as for the long-term stability of an airline alliance (Oum and Park 1997; Li 2000).
Under the arrangement of parallel codesharing with shared capacity, the interaction between demands and supplies of two alliance partners leads to four different cases of allocations, termed Cases I, II, III, and IV in the article, which are critical to the understanding and evaluation of the effects of capacity sharing and risk pooling on the market and operational performances of the two airlines. Under Case I of demand/supply allocation, both airlines are not fully loaded so that no capacity sharing is needed between the alliance partners. Under Cases II and III of demand/supply allocations, one airline is fully loaded, and the other airline has excess capacity (and vice versa), which is the typical circumstance where capacity sharing can take place. Under Case IV of demand/ supply allocation, both airlines are fully loaded, which results in the situation where further capacity sharing is physically impossible. The analysis of the above four cases of demand/ supply allocations, which will be termed the "four cases of allocations" in this article, can thus show the extent to which capacity sharing and risk pooling activities take place on a particular route, and provide important information for airline planners to fine-tune the capacity-, cost-, and revenue-sharing agreements as well as for policy makers to evaluate or revise open-skies/bilateral agreements. (6)
Applying the existing tools for airline demand management to the analysis of the four cases of allocations, however, turns out to be a difficult task for several reasons. First of all, in most airlines' reservation systems, demand for seats is not recorded after all spaces for a particular trip have been sold out or after booking limit has been reached. Thus historical booking data kept by an airline is comprised only of ticket sales, which is also known as the censored or observed demand as opposed to the uncensored or actual demand (McGill 1995). While an airline may use different methodologies for spill analysis to uncover the uncensored demand (Schlifer and Vardi 1975; Li and Oum 2000), the analytical results can usually provide only the information about the individual airline's own lost sales without knowing specifically whether the sales were lost to its alliance partner or a competitor. Even when both alliance partners have complete data about their individual uncensored demands, simply combining the demand data of the two airlines may not provide accurate information about how risk pooling activities take place since it has been shown that demands of alliance partners, even with...
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