Restaurant survival prediction using machine learning: Do the variance and sources of customers’ online reviews matter?

IF 10.9 1区 管理学 Q1 ENVIRONMENTAL STUDIES
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引用次数: 0

Abstract

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.

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?
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。
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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.
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