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Title: | Average booking curves draw exponential functions |
Authors: | Shintani, Masaru Umeno, Ken |
Author's alias: | 新谷, 健 梅野, 健 |
Keywords: | Applied mathematics Civil engineering Scientific data Statistical physics |
Issue Date: | 22-Sep-2023 |
Publisher: | Springer Nature |
Journal title: | Scientific Reports |
Volume: | 13 |
Thesis number: | 15773 |
Abstract: | The booking curve time series in perishable asset industries, including hotels, has been studied to manage a demand-supply condition or revenue management (RM). However, due to changing times, e.g., economy and technology, many RM practitioners have put their efforts into catching on to peoples’ booking pattern shifts, representing macroscopic changes in booking curves. We investigate macroscopic aspects of booking curves with actual sales data across six properties in the hotel and car-rental industries for two years, considering the difference in the economic environment characterized before and during the COVID-19 epidemic. We explain a new cross-industry and cross-economic-environment universal statistical law: average booking curves draw exponential functions (the ABCDEF law). We provide a basis for the ABCDEF law from three perspectives; data validation, modeling in the statistical physics framework, and empirical justification for the causality of the model. The ABCDEF law derives informative statistics to quantitatively measure peoples’ buying behavior even in time or society changes, which is expected to contribute to management in various industries. |
Description: | 人の“予約パターン”の中に普遍的な数理法則を発見 --変動価格制の公正性評価の基準の確立へ--. 京都大学プレスリリース. 2023-09-29. |
Rights: | © The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
URI: | http://hdl.handle.net/2433/285297 |
DOI(Published Version): | 10.1038/s41598-023-42745-3 |
PubMed ID: | 37737293 |
Related Link: | https://www.kyoto-u.ac.jp/ja/research-news/2023-09-29-1 https://doi.org/10.57723/276374 |
Appears in Collections: | Journal Articles |
This item is licensed under a Creative Commons License