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Competitive Sets for Lodging Properties

dc.contributor.authorKim, Jin-Young
dc.contributor.authorCanina, Linda
dc.date.accessioned2020-09-12T21:04:08Z
dc.date.available2020-09-12T21:04:08Z
dc.date.issued2011-02-01
dc.description.abstractThis article illustrates the differences in the composition, characteristics, and performance evaluation of competitive sets of hotels determined using two methods—the common product type classification scheme and the less commonly used cluster analysis based on average daily rate (ADR) as the clustering variable. The analysis examined annual ADR, occupancy, and revenue per available room (RevPAR) for a group of hotels in a portion of a single U.S. metropolitan market. The comparison of the two methods shows the following: the average variability of ADR and RevPAR is less for the cluster-based competitors than it is for competitor groups determined using product type; most clusters contain a variety of product types (confirming that competition occurs across product types); most product types are categorized into different clusters; and the average RevPAR difference between the particular hotel and its reference competitive group is less for the ADR-cluster-based reference group than it is for the product type reference group, indicating that the performance of hotels within cluster competitive groups is more similar than in product type competitive groups. Comparing competing hotels based on the two methods can provide information regarding the extent of congruence between the hotel’s intended competitive position and its position as seen by customers.
dc.description.legacydownloadsCanina7_Competitive_sets.pdf: 813 downloads, before Aug. 1, 2020.
dc.identifier.other7075544
dc.identifier.urihttps://hdl.handle.net/1813/71672
dc.language.isoen_US
dc.rightsRequired Publisher Statement: © Cornell University. Reprinted with permission. All rights reserved.
dc.subjectcompetitive set
dc.subjectcluster analysis
dc.subjectstrategic management
dc.titleCompetitive Sets for Lodging Properties
dc.typearticle
local.authorAffiliationKim, Jin-Young: Kyunghee University
local.authorAffiliationCanina, Linda: Cornell University

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