Complex data in tourism analysis: A stochastic approach to price competition

IF 3.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Giovanni Angelini , Michele Costa , Andrea Guizzardi
{"title":"Complex data in tourism analysis: A stochastic approach to price competition","authors":"Giovanni Angelini ,&nbsp;Michele Costa ,&nbsp;Andrea Guizzardi","doi":"10.1016/j.bdr.2025.100520","DOIUrl":null,"url":null,"abstract":"<div><div>This study examines pricing strategies and decision-making processes in the hospitality industry by analyzing “ask” prices on online travel agencies (i.e., the rates at which hoteliers are willing to sell their rooms). We face the challenge of modeling a continuous flow of big data organized as “time series of time series,” where daily seasonality and advance bookings intersect. Our research combines insights from tourism, quantitative methods, and big data to improve pricing strategies, contributing to both theory and practice in revenue management. Focusing on Venice, we analyze price competition as a multivariate stochastic process using a Structural Vector Autoregressive (SVAR) approach, aligning with modern dynamic pricing algorithms.</div><div>The findings show that time-based pricing strategies, which adjust based on the day of arrival and booking, are more important than room features in setting hotel prices. We also find that price changes have a non-linear and decreasing effect as the booking date approaches. These insights suggest that hotels could create more advanced pricing strategies, and policymakers should consider these factors when addressing the challenges related to overtourism.</div><div>We study the complex competitive relationships among heterogeneous service providers with an approach applicable to any market where consumption is delayed relative to purchase time. However, we highlight that the quality and accessibility of information in the tourism sector are key aspects to be considered when using big data in this industry.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"40 ","pages":"Article 100520"},"PeriodicalIF":3.5000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214579625000152","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

This study examines pricing strategies and decision-making processes in the hospitality industry by analyzing “ask” prices on online travel agencies (i.e., the rates at which hoteliers are willing to sell their rooms). We face the challenge of modeling a continuous flow of big data organized as “time series of time series,” where daily seasonality and advance bookings intersect. Our research combines insights from tourism, quantitative methods, and big data to improve pricing strategies, contributing to both theory and practice in revenue management. Focusing on Venice, we analyze price competition as a multivariate stochastic process using a Structural Vector Autoregressive (SVAR) approach, aligning with modern dynamic pricing algorithms.
The findings show that time-based pricing strategies, which adjust based on the day of arrival and booking, are more important than room features in setting hotel prices. We also find that price changes have a non-linear and decreasing effect as the booking date approaches. These insights suggest that hotels could create more advanced pricing strategies, and policymakers should consider these factors when addressing the challenges related to overtourism.
We study the complex competitive relationships among heterogeneous service providers with an approach applicable to any market where consumption is delayed relative to purchase time. However, we highlight that the quality and accessibility of information in the tourism sector are key aspects to be considered when using big data in this industry.
求助全文
约1分钟内获得全文 求助全文
来源期刊
Big Data Research
Big Data Research Computer Science-Computer Science Applications
CiteScore
8.40
自引率
3.00%
发文量
0
期刊介绍: The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信