利用机器学习预测餐厅生存状况:顾客在线评论的差异和来源重要吗?

IF 10.9 1区 管理学 Q1 ENVIRONMENTAL STUDIES Tourism Management Pub Date : 2024-09-13 DOI:10.1016/j.tourman.2024.105038
Hengyun Li , Anqi Zhou , Xiang (Kevin) Zheng , Jian Xu , Jing Zhang
{"title":"利用机器学习预测餐厅生存状况:顾客在线评论的差异和来源重要吗?","authors":"Hengyun Li ,&nbsp;Anqi Zhou ,&nbsp;Xiang (Kevin) Zheng ,&nbsp;Jian Xu ,&nbsp;Jing Zhang","doi":"10.1016/j.tourman.2024.105038","DOIUrl":null,"url":null,"abstract":"<div><p>Restaurant constitutes an essential part of the tourism industry. In times of uncertainty and transition, restaurant survival prediction is vital for deepening organizations' understanding of business performance and facilitating decisions. By tapping into online reviews, a prevalent form of user-generated content, this study identifies review variance as a leading indicator of restaurants’ survival drawing from data on 2838 restaurants in Boston and their corresponding reviews. Machine learning–based survival analysis shows that models integrating fine-grained review variance (i.e., review rating variance, overall review sentiment variance, and fine-grained review sentiment variance) outperform models that do not account for these factors in restaurant survival prediction before and during the pandemic. Furthermore, in most cases, expert reviews hold stronger predictive power for pre-pandemic restaurant survival than non-expert and all forms of reviews. This study contributes to the literature on business survival prediction and guides industry practitioners in monitoring and enhancing their enterprises.</p></div>","PeriodicalId":48469,"journal":{"name":"Tourism Management","volume":"107 ","pages":"Article 105038"},"PeriodicalIF":10.9000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Restaurant survival prediction using machine learning: Do the variance and sources of customers’ online reviews matter?\",\"authors\":\"Hengyun Li ,&nbsp;Anqi Zhou ,&nbsp;Xiang (Kevin) Zheng ,&nbsp;Jian Xu ,&nbsp;Jing Zhang\",\"doi\":\"10.1016/j.tourman.2024.105038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Restaurant constitutes an essential part of the tourism industry. In times of uncertainty and transition, restaurant survival prediction is vital for deepening organizations' understanding of business performance and facilitating decisions. By tapping into online reviews, a prevalent form of user-generated content, this study identifies review variance as a leading indicator of restaurants’ survival drawing from data on 2838 restaurants in Boston and their corresponding reviews. Machine learning–based survival analysis shows that models integrating fine-grained review variance (i.e., review rating variance, overall review sentiment variance, and fine-grained review sentiment variance) outperform models that do not account for these factors in restaurant survival prediction before and during the pandemic. Furthermore, in most cases, expert reviews hold stronger predictive power for pre-pandemic restaurant survival than non-expert and all forms of reviews. This study contributes to the literature on business survival prediction and guides industry practitioners in monitoring and enhancing their enterprises.</p></div>\",\"PeriodicalId\":48469,\"journal\":{\"name\":\"Tourism Management\",\"volume\":\"107 \",\"pages\":\"Article 105038\"},\"PeriodicalIF\":10.9000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tourism Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261517724001572\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tourism Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261517724001572","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0

摘要

餐饮业是旅游业的重要组成部分。在不确定和转型时期,餐厅生存预测对于加深组织对业务绩效的理解和促进决策至关重要。在线评论是用户生成内容的一种普遍形式,本研究通过挖掘波士顿 2838 家餐厅的数据及其相应评论,将评论差异确定为餐厅生存的领先指标。基于机器学习的生存分析表明,在大流行之前和期间的餐厅生存预测中,整合了细粒度评论差异(即评论评分差异、整体评论情感差异和细粒度评论情感差异)的模型优于未考虑这些因素的模型。此外,在大多数情况下,专家评论比非专家评论和所有形式的评论对大流行前餐馆生存的预测能力更强。这项研究为有关企业生存预测的文献做出了贡献,并为行业从业者监测和提升企业提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Restaurant survival prediction using machine learning: Do the variance and sources of customers’ online reviews matter?

Restaurant constitutes an essential part of the tourism industry. In times of uncertainty and transition, restaurant survival prediction is vital for deepening organizations' understanding of business performance and facilitating decisions. By tapping into online reviews, a prevalent form of user-generated content, this study identifies review variance as a leading indicator of restaurants’ survival drawing from data on 2838 restaurants in Boston and their corresponding reviews. Machine learning–based survival analysis shows that models integrating fine-grained review variance (i.e., review rating variance, overall review sentiment variance, and fine-grained review sentiment variance) outperform models that do not account for these factors in restaurant survival prediction before and during the pandemic. Furthermore, in most cases, expert reviews hold stronger predictive power for pre-pandemic restaurant survival than non-expert and all forms of reviews. This study contributes to the literature on business survival prediction and guides industry practitioners in monitoring and enhancing their enterprises.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Tourism Management
Tourism Management Multiple-
CiteScore
24.10
自引率
7.90%
发文量
190
审稿时长
45 days
期刊介绍: Tourism Management, the preeminent scholarly journal, concentrates on the comprehensive management aspects, encompassing planning and policy, within the realm of travel and tourism. Adopting an interdisciplinary perspective, the journal delves into international, national, and regional tourism, addressing various management challenges. Its content mirrors this integrative approach, featuring primary research articles, progress in tourism research, case studies, research notes, discussions on current issues, and book reviews. Emphasizing scholarly rigor, all published papers are expected to contribute to theoretical and/or methodological advancements while offering specific insights relevant to tourism management and policy.
期刊最新文献
AI-generated imagery in sustainable gastronomy tourism: A study from bottom-up to top-down processing Nature and adventure tourism rarely generate awe or pro-environmental behaviours: Conceptual and methodological rejoinder Digital nudging for sustainable tourist behavior in new media Construal level theory and online reviews: A search stage perspective Fresh perspectives in our understanding of induced awe in tourism contexts: A response to Ralf Buckley and colleagues
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1