
后疫情时代下短租民宿用户购买行为研究——基于BERT衡量的网络口碑
Research on the Influencing Factors of Online Short-term Rental Users' Consumption Behavior Based on the eWOM Measured by BERT in the Post-pandemic Era
[目的/意义] 挖掘疫情后网络口碑和用户购买行为影响因素,从而在后疫情时代更好地吸引用户,促进在线短租行业经济回暖甚至实现超越。[方法/过程] 获取Airbnb上北京地区的公开数据,利用BERT算法构建在线短租网络口碑的衡量指标,基于ELM模型选取4个房源属性和6个房东属性构建在线短租用户购买行为影响因素模型并进行实证研究。[结果/结论] 利用BERT模型计算的评论情感得分均值来衡量在线短租网络口碑,为确认其替代变量提供思路。同时负二项回归结果表明,除房源周边环境描述所在影响路径外,网络口碑在其他房源和房东属性作用于用户购买行为过程中具有中介作用。然而,与研究假设相反,房东持有房源数量将负向显著影响房源口碑与销量,这可能是因为单套房源房东参与度更低;而房源与市中心距离越远,网络口碑越好,与疫情后周边游特征相关联。最后基于本研究结果为房东和平台提出相关运营建议,后疫情时代房源描述应当更加着墨于房源防疫措施、退订政策、室内活动等,但应与周边环境描述区别开来,同时可以抓住周边游热潮将疫情挑战转变为发展机遇,提升在线短租房源的竞争实力和业务量。
[Purpose/Significance] Exploring the influence factors of electronic word-of-mouth (eWOM) and user purchase behavior after the epidemic, so as to better attract users in the post-epidemic era, which is crucial for promoting the economic recovery of the online short-term rental industry. [Method/Process] This research obtained public data in Beijing area on Airbnb, and constructed a measurement index of eWOM based on BERT. Then based on the ELM, a model of influencing factors of online short-term rental users' purchase behavior was constructed with 4 listing attributes and 6 landlord attributes. Finally, the empirical research was carried out. [Result/Conclusion] Using the average sentiment score of reviews calculated by the BERT model to measure online short-term rental eWOM provided ideas for confirming its substitute variables.
online short-term rental / electronic word of mouth / ELM / BERT
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黄茜:设计研究方案,分析数据与撰写论文;
贺超城:提出选题和研究思路,进行数据分析;
李欣儒:收集并处理数据;
吴江:提出研究方向,确定选题。
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