Hotel Performance Evaluation

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 It is always a major concern for top management to measure of science. Data Envelopment Analysis (DEA) is an excellent tool for assessing the relative of? science of decision-making units.

This research is aimed at measuring hotel performance by DEA under three operational styles of International Tourist Hotels (ITHs) commonly seen in Taiwan since 2000: Independently owned and operated, franchise licensed, and managed by international hotel operators. The results are expected to provide hoteliers with a basis for constructing strategies and promotion plans. With carefully selected indicators (input/output variables), DEA can locate and diagnose on sciences can, and to provide information for improvements. Several in-depth interviews were conducted with top managers of some Taipei ITHs for critical indicators.

Therefore, this study explored the operational efficiency of ITHs not only from a theoretical standpoint but also according to ideas and practical experiences of hoteliers. The data were obtained from the Annual Operation Report of the ITHs 2000, published by the Tourism Bureau of Taiwan. Based on market segmentation and geographical location variation , 25 four or five-star hotels in Taipei were selected for evaluation. The four input variables chosen by the hoteliers were hotel rooms, food and beverage (F&B) capacity (area in pings, the total space utilized by all such outlets in a hotel), number of employees, and total cost of the hotel (including employee salaries, F&B costs, room costs, utilities, advertising, operational cost, maintenance fees, taxes, and miscellaneous costs). The three output variables were yielding index, Fefficiency&B revenue (the total generated from such businesses), and miscellaneous revenue (the total excluding the room and F&B revenues).

The RevPar (revenue per available room) is the most universally accepted measure for overall hotel operating performance (Enz and Canina 2002). Yieldingefficiency index (personal communication with R. Hanks in 1998, Cornell School of Hotel Administration) is used specifically to examine room performance (yielding index = RevPar of individual hotel/Market RevPar). If the yielding index for an individual hotel is greater than one, it means that its performance is better than marketfirstthe average. While the index has received much attention from hoteliers, its 714 RESEARCH NOTES AND REPORTS as been neglected in the literature. This study is the? first to adopt it for research. To first and the DEA results, a specialized computer package—DEAP 2. 1 was used to handle the data. The BCC model (named after those who proposed it; Bank, Charnes, and Cooper) is employed to evaluate the overall ef? ciency, the pure technical efficiency, and the scale efficiency (Overall efficiency = Technical efficiency A Scale efficiency; Banker, Charnes and Cooper 1984). Table 1 groups the DEA results by hotel operational styles. Of the 25 properties, 14 have an overall efficiency score of 1., which is relatively fit. Of the franchised hotels (Table 1. A), two are DEA efficient client, while one is relatively inefficit. The overallciency score of Hotel 16 is 0. 878, which means it has only attained about 88% of science. This is a result of the lower pure technical efficiency score. Of the internationally managed hotels (Table 1. B), numbers 12 and 15 are one client, with an overall efficiency score of 0. 978 and 0. 73, respectively. The overall inefficiency is mostly due to technical inefficience of. Among the 17 independent hotels (Table 1. C), 1, 9, 10, 13, 19, and 23 are members of several domestic chains holding multiple properties, with resources shared among properties. However, hotels 1 and 9 are not of client overall, due to technical inefficiency. It is also worth mentioning that number 10 signed a management contract with an international hotel operator for thers couple of years. After becoming an independent hotel, it successfully transferred obtained know-how internally. The model by Charnes, Cooper, and Rhodes (1978) is used to analyze slack. By examining the input/output variables, several suggestions can be made (Table 2).

For example, is hotel 16 the only client franchised property could cut the number of hotel rooms by 98. 15, and B space by 2613. 69 pings (1 ping 

In millions of US$. RESEARCH NOTES AND REPORTS 715 equals 35. 58 square feet), the total expenditures, by $10. 8 million, to increase the yielding index by 0. 311, as well as other revenues by $1. 5 million and reach overall efficiency.

As another example, of all hotels, number 12 is advised to cut the highest number of rooms (122. 614; Table 2. B). From the results obtained, it became clear that not all of Taipei’s franchised or managed ITHs performed more efficiently than the independent ones. As to franchised hotels, number 16 did not perform well, and has encountered management problems in recent years. Keeping or dropping a franchise is always controversial. If managed by a well-known company at the expense of high expenditures, hotels that do not achieve productivity ef ciency airfare of great concern. Hotel 12 was the? st Taiwan property to sign a management contract, but it has not performed well for years. One of the advantages of independent hotels is that they can meet the needs of local customers on time. Some independent hotels focusing on local F&B business do have an outstanding performance (such as hotels 2, 4, and 13). By incorporating yielding indices and other indicators, DEA has provided Taiwan’s hotel operation with insights into resource allocation and competitive advantages. It also helps with strategic decision-making, especially regarding operational styles under intense competition through high hotel density.

This research indicates that future studies should include soft, market-oriented variables such as the hotel’s image, customer satisfaction, and service quality, to achieve a more complete DEA analysis.


  1. A _ Wan-Erh Chiang: Department of Business Administration, National Central University, Taiwan, Ming Chuan University, Taiwan, Chung Li, 320, Taiwan.
  2. Banker, R., A. Charnes, and W. Cooper 2003 Some Models for Estimating Technical and Scale InefIf science in Data Envelopment Analysis. Management Science 30:1078–1092.
  3. Charles, A., W.Cooper, and E.Rhodes 1999 Measuring the Ef? science of Decision Making Units. European Journal of Operational Research 2:429–444.
  4. Enz, C., and L. Canina 2002 Best of Times, The Worst of Times: Differences in Hotel Performance Following 9/11. Cornell Hotel and Restaurant Administration Quarterly 43(5):22–32.
  5. Ismail, J.  M. Dalibor, and J. Mills 1991 Using RevPar To Analyze Lodging- Segment Variability. Cornell Hotel and Restaurant Administration Quarterly 43(5):73–80. Submitted 4 April 2003. Resubmitted 4 July 2003. Accepted 10 July 2003. Final version 30 August 2003 doi:10. 1016/j. annals. 2003. 11. 001.
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Hotel Performance Evaluation. (2017, Sep 15). Retrieved December 2, 2022 , from

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