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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1919||2013||22 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Annals of Tourism Research, Volume 41, April 2013, Pages 20–41
This paper aims to find the optimal pricing strategy for tourism hotels when they operate their online distribution channel by cooperating with a third party website. The paper first gives the first-best solution when all the participants are integrated as a single system, and then leads to the second-best one under the decentralized scenario through a non-cooperative game model composed by a Stackelberg game between the hotels and the website and a Nash game among the hotels. Through a numerical example, we analyze the decision making process of the players; and give the service providers some useful suggestions for operating their cooperative relationship successfully.
Motivation and Research Questions Along with the growth of e-commerce, business and marketing models have invaded into a wide variety of industries. On the one hand, in order to reduce marketing cost and improve revenue, more and more traditional providers begin to establish internet channel to sell their products or services to customers directly. On the other hand, as retailers, taking the example of Bloomingdales and Best Buy etc., they open internet channel to meet the demands of those consumers who like shopping online. For example, in order to increase its operational efficiency and for the convenience of the tourists, airline industry provides web service, through which tourists may search information on all available flights and book it online whenever they like. Faced with fierce competition and increasing online booking requirements, hospitality industry also turns to web channel. However, unlike the traditional industries, most hotels are not well-known (Bastakis, Buhalis, & Butler, 2004) so they seek to cooperate with famous third party website like online tour operators such as Expedia (http://www.expedia.com), Ctrip (http://hotels.english.ctrip.com/), Kuoni (http://www.kuoni.com/) etc., or a website has a mass of visitors and their customers who have purchased some services here can give some reviews about the service provider, like dianping.com (http://www.dianping.com/) in mainland China. The cooperation mechanisms between provider and website are diverse (Clemons, Hann, & Hitt, 2002), among which the most famous two are the agent model and the merchant model. For instance, the cooperation between Target (http://www.target.com) and Amazon (http://www.amazon.com) is the agent one: Target determines the price of its products on Amazon, and pays Amazon a commission fee for each product sold there. However, since the information online can be searched and confirmed conveniently, the merchant model with different retail prices among different online distribution channels (such as the service provider’s own website and online travel agencies’ website) cannot be adopted for cooperation in the tourism and hospitality industries. In our agent model between tourism hotels and third party websites, the hotels, as principal players, determine the commission fee for the agent websites; and then the websites determine how much effort will be done for each hotel, including but not limit to the ranking position on the webpage, picture views and video shows, etc., which influences the room sales directly. For this cooperative relationship, this paper tries to answer the following questions: What is the equilibrium of the cooperation between the tourism hotels and their third party website? How does a hotel make its decisions considering its own profits and the actions of the other hotels? Is the cooperation based on agent model inefficient with regard to the total revenue compared with centralized scenario, and why? Overview of the Cooperation Mechanism and Key Findings Although in practice the online distribution channel of tourism hotels can be operated in various forms, we employ an agent model through the cooperation with a third party website. Under the cooperation, the hotels provide commission fees to the cooperative website for each hotel room sold through the website. Then the website determines the effort level for the corresponding hotels, which influences the room sales directly, according to their commissions respect to its maximal profit. As a result, there are two “sources” of consumers, the traditional tourists and the website consumers, for the hotels under the cooperation. The traditional tourists for a hotel are those who know the hotel and always book hotel rooms from the hotel directly, no matter the cooperation (between the hotel and third party website) exists or not, that is to say, the traditional tourists are loyal to their corresponding hotels. While the website consumers get the hotels’ information from the third party website, choose a favorite hotel among the cooperative hotels of the website, and then make their reservations through the website. Through a composed game model, we show that there is a first-best solution to gain the maximum profit for the tourism supply chain when the hotels and the third party website are integrated as a single player in the centralized decision scenario. Basing on this centralized model as a benchmark, we analyze the players’ actions in the decentralized scenario, and explain how the hotels make their decisions considering their profits and the actions of the other hotels, and show how the website determines the effort levels for each hotel with respect to its maximal profit and finite effort capacity. The results show that the hotels with high available room capacities and high average room rates have much stronger motivation to organize the cooperative relationship with a third party website in order to make the best use of their room capacity and gain more profits. In addition, the findings tell that the highest commission fees are not generally provided by the most luxury hotels, because the available capacities for the luxury hotels are often small, and they have no motivation to seek high effort level from the website. That’s why the most expensive hotels are not always ranked at the first position on the websites of the online travel agencies. Moreover, the optimal effort levels for the hotels are more sensitive to the available capacities than the commission fees. This suggests that the website prefers to provide online distribution service to hotels with large capacity and low occupancy rate and gives them a marvelous effort level. The rest of this paper is organized as follows. After reviewing some related literature in the next section, we present the model assumptions afterwards. Then in the following section, we analyze the first-best solution in the centralized scenario and also the second-best case. A numerical study is presented to explain the decision process of the players, and to show how our model can be applied in practice. Finally, in the conclusion section we summarize the managerial implications of our model and present the further research issues.
نتیجه گیری انگلیسی
This paper studies the cooperation contract between tourism hotels and third party websites. The website receives commission fees from the hotels for selling room reservations online, and determines the level of sales effort (such as ranking position of hotels’ information on its webpage, customer evaluations, picture and video show, etc.) to maximize its profit with a finite effort capacity. We give the first-best solution in the centralized scenario when the hotels and website are integrated as a single player, as well as the second-best solutions showing each player’s equilibrium actions in the decentralized scenario. Intuitively, the hotels’ optimal commissions would increase with the room capacity of the hotels. However, through the analysis and numerical studies of this paper, we find that the optimal commission fee is not only depends on the room capacity of the hotels, but also the average room rate and the expected number of t-tourists. And this finding gives the hotel managers a suggestion that they should decide the commission for the cooperative website considering both the average room price and the available capacity for the w-tourists (i.e. the desired demand of the w-tourists). For the website effort, the properties have been shown in Proposition 2 and Corollary 1. The website decides the effort levels considering the commission fees and the available capacities provided by the cooperative hotels. And the findings show that the website prefers to cooperate with hotels with high capacity and low occupancy rate and provides them high effort levels even a low commission is paid (as shown in Fig. 3). Meanwhile, from the numerical studies, we can find that the cooperation contract in this paper can provide a high operational performance in the decentralized scenario. Although there is double marginalization effect in the decentralized situation, the operational performance maintains high (the performance suffers only 0.08% loss comparing with the centralized case) which implies that channel coordination is not a necessary mechanism in the cooperation. Finally, the model of this paper is limited by some necessary restrictions in scope, and can be extended to a number of interesting further studies. Firstly, we assume that all the hotel rooms are coessential and the tourists’ choice is induced by the website through the website effort. If the demand of the w-tourists for a hotel depends on both the website’s effort and the hotel’s attributes (such as location place, traffic convenience), then how will the pricing policy of the hotel and the responses of the website be decided? This extension may need an additional assumption on the demand function with respect to the hotel properties. Secondly, this model can be extended to the scenario that the information of the players is unobservable to each other. And then an asymmetric information game will be presented. Thirdly, cancellations and no-shows are very common in the hospitality industry, and accordingly, an overbooking strategy can be adopted to fix this problem, and this would certainly be worth working on. Finally, a dynamic pricing policy depends on the reservation date and room inventory for a hotel would yield some interesting insights for the service operators, although this may be a great challenge.