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On-line Access: 2023-11-13

Received: 2023-02-01

Revision Accepted: 2023-03-19

Crosschecked: 2023-11-14

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yilin SUN

https://orcid.org/0000-0002-8757-7261

Yinan DONG

https://orcid.org/0000-0002-6275-6175

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Correlation between travel experiences and post-COVID outbound tourism intention: a case study from China


Author(s):  Yilin SUN, Yinan DONG, Dianhai WANG, E. Owen D. WAYGOOD, Hamed NASERI, Kazuo NISHII

Affiliation(s):  College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):  dannydong@zju.edu.cn

Key Words:  Outbound tourism; Touring behavior; Travel behavior; COVID-19; Ensemble learner


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Yilin SUN, Yinan DONG, Dianhai WANG, E. Owen D. WAYGOOD, Hamed NASERI, Kazuo NISHII. Correlation between travel experiences and post-COVID outbound tourism intention: a case study from China[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2300057

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Abstract: 
The COVID-19 pandemic has devastated global tourism and recovery is proceeding very slowly. For many countries, tourism served as a major economic sector, so investigating how to recover is essential. As China was the largest source of outbound travelers before the outbreak, study of the factors influencing Chinese intentions to travel overseas in the post-COVID era is revealing. In Apr. 2022, among seven provinces (or cities) with the most outbound tourists from 2019 to 2021, 2450 individuals responded to a questionnaire on daily mobility, tourism experiences, and the shifts due to the pandemic. Light gradient boosting machine (LightGBM), a robust ensemble learning method, was adopted to quantify and visualize the impact of explanatory factors on outbound travel intention. In addition, the Optuna mechanism and Shapley additive explanation (SHAP) instruments were employed for tuning hyperparameters and interpreting results, respectively. Findings suggest neither one-day nor multi-day tours have resumed to pre-COVID levels. Higher frequency of multi-day tours with further destinations, less car utilization in daily shopping trips, and moderate pandemic restrictions can boost the intention to travel abroad. The concerns and desires of different age groups for overseas travel need different responses. This study reveals the factors affecting Chinese outbound travel intentions and provides suggestions for the recovery of tourism in the post-COVID period.

出行经历与后疫情时代出境旅游意向之间的相关性--来自中国的案例研究

作者:孙轶琳1,2,3,4,董轶男1,5,王殿海1,2,E. Owen D. WAYGOOD6,Hamed NASERI6,Kazuo NISHII7
机构:1浙江大学,建筑工程学院,中国杭州,310058;2浙江大学建筑设计研究院,中国杭州,310028;3阿里巴巴-浙江大学前沿技术联合研究中心,中国杭州,310058;4浙江大学,工程师学院,中国杭州,310015;5浙江大学平衡建筑研究中心,中国杭州,310028;6蒙特利尔工程学院,土木、岩土和采矿工程系,加拿大蒙特利尔,H3T 1J4;7流通科学大学,政策研究系,日本神户,651-2188
目的:全球旅游业在新冠肺炎疫情期间遭受了严重冲击,并与经济发展密切关联,因此,探求如何推进其相关产业的复兴十分重要。由于中国是疫情前最大的出境游客来源国,对中国人在后疫情时代的出境旅游意向及其影响因素进行研究,有利于促进国际旅游市场的复苏。
创新点:1.构建LightGBM模型,参数化各类变量及组合与后疫情时期出境旅游意向之间的关系;2.通过SHAP算法,可视化各类因素对后疫情时期出境旅游意向的影响。
方法:1.通过网络调查收集2450份有效问卷,并统计疫情期间受访者单日、多日出游次数以及出行方式等信息(图2);2.通过模型构建及参数调优,以后疫情时期的出境游意向为因变量,基于所收集数据训练LightGBM模型并评估其效能(图3);3.运用SHAP算法,对各个自变量的影响进行排序及部分依赖图分析(图4和5)。
结论:1.疫情期间的日常出行及旅游经历对后疫情时期的出境旅游意向的影响程度最大;2.多日游频率的上升和日常购物出行中小汽车利用率的增加,显著促进出境旅游意向的增强;3.后疫情时期,不同年龄段的人群在出境旅游意向及出行方式选择上的差异愈加明显。

关键词组:出境旅游;旅游行为;出行行为;新冠肺炎;集成学习

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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