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作 者:刘俊 余云云[1] 胡思可 李潇涵 何默其 LIU Jun;YU Yunyun;HU Sike;LI Xiaohan;HE Moqi(Institute for Disaster Management and Reconstruction,Sichuan University,Chengdu 610207,China;Tourism School,Sichuan University,Chengdu 610207,China)
机构地区:[1]四川大学灾后重建与管理学院,四川成都610207 [2]四川大学旅游学院,四川成都610207
出 处:《旅游科学》2025年第1期1-23,共23页Tourism Science
基 金:四川大学研究基金“世界文化与自然遗产地环境与社会系统演化模拟研究”(SKSYL2022-04)。
摘 要:系统地回顾大数据在旅游研究中的应用,对于理解旅游研究范式的转型,响应新涌现的科学问题和实践应用问题具有重要意义。对Web of Science、Archive和中国知网3个数据库中的2477篇旅游大数据文献进行了综述。研究发现:(1)从2010年开始,旅游大数据研究文献数量逐年增长。中国研究者发表了838篇旅游大数据研究论文,占文献总量的33.83%。(2)接近50.00%的论文以会议和学位论文的形式发表,超过70.00%的文献发表在非旅游类期刊。(3)62.65%的论文利用了TripAdvisor、携程旅行网、马蜂窝等的UGC数据。(4)预测旅游需求、旅游推荐、旅游消费行为、游客流动模式、旅游目的地形象、游客满意度、景观评价和方法创新是当前研究聚焦的八大场景。(5)目前旅游研究领域对大数据应用方法的创新贡献不足,主要通过迁移数据科学与信息科学等已经发展较为成熟的方法,结合旅游情景和数据开展研究。A systematic review of the application of big data in tourism research holds significant importance for understanding the paradigm shift in tourism research and addressing emerging scientific and practical challenges.This study examined 2477 articles on tourism big data sourced from three major databases:Web of Science,Archive and CNKI.The key findings are as follows:(1)Since 2010,the volume of research on tourism big data has exhibited a consistent upward trajectory.Chinese scholars have contributed 838 research papers,accounting for 33.83%of the total literature.(2)Nearly half of the articles were published through conference proceedings and dissertations,while over 70.00%appeared in nontourism journals.(3)62.65%of the studies leveraged user-generated content(UGC)data from prominent platforms such as TripAdvisor,Ctrip,and Mafengwo.(4)The current research is mainly focus on eight major application scenarios:tourism demand prediction,tourism recommendation systems,tourism consumption behavior analysis,tourist flow pattern identification,tourism destination image analysis,tourist satisfaction evaluation,landscape assessment,and methodological innovation.(5)Presently,the field of tourism research has made limited contributions to the innovation of big data application methods.Instead,it primarily relies on the adaptation of relatively mature methodologies from data science and information science,integrating them with tourism-specific contexts and datasets to advance research.
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