D
2019
MEASURING PRICE ELASTICITY AND ITS APPLICATION INTO THE REVENUE OPTIMIZATION PROCESS
PETŘÍČEK, Martin and Štěpán CHALUPA
Basic information
Original name
MEASURING PRICE ELASTICITY AND ITS APPLICATION INTO THE REVENUE OPTIMIZATION PROCESS
Name (in English)
MEASURING PRICE ELASTICITY AND ITS APPLICATION INTO THE REVENUE OPTIMIZATION PROCESS
Edition
Prague, 11th INTERNATIONAL CONFERENCE PROCEEDINGS - HOSPITALITY, TOURISM AND EDUCATION, p. 87-94, 8 pp. 2019
Publisher
Institute of Hospitality Management in Prague 8.
Other information
Type of outcome
Stať ve sborníku
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
Organization unit
University College Prague – University of International Relations and Institute of Hospitality Management and Economics, Ltd.
Keywords in English
Demand, Elasticity, Price
Tags
International impact, Reviewed
V originále
The paper focuses on various possibilities of measuring price elasticity of demand with the application of log-log regression analysis. The elasticity measured can be than an input for the optimization process. The critical element of this optimization is the value of the coefficient of price elasticity, which was in the given contribution (for the selected accommodation facility) to the value -0.297, which indicates a price inelastic demand. This approach also has some limitations. It assumes a regression function that is linear in parameters, and also it is only a simple regression. Despite this fact, the results are comparable to other approaches.
In English
The paper focuses on various possibilities of measuring price elasticity of demand with the application of log-log regression analysis. The elasticity measured can be than an input for the optimization process. The critical element of this optimization is the value of the coefficient of price elasticity, which was in the given contribution (for the selected accommodation facility) to the value -0.297, which indicates a price inelastic demand. This approach also has some limitations. It assumes a regression function that is linear in parameters, and also it is only a simple regression. Despite this fact, the results are comparable to other approaches.
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