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The purpose of this paper is to examine factors affecting the engagement metrics of the hotel Facebook page. Such factors include time-of-day, day-of-week, age, gender and distance between the hotel and users’ origin of residence. Another purpose is to assess the impact of Facebook engagement on electronic word-of-mouth (eWOM), to better understand the importance of the engagement metrics within the hotel Facebook context.

This study uses secondary data from the Facebook page of a 147-room hotel in Northeast America. A total of 181 observations reflecting primary Facebook metrics are adopted via Facebook Insights between January 2014 and June 2014.

The number of daily-engaged users positively affects the number of daily people talking about the page (eWOM). Moreover, the number of engaged users differs significantly by the external factors (time-of-day, day-of-week, age, gender and distance).

Hotel Facebook developers should post the most important promotions on Monday afternoon, targeting females aged between 25 and 34 years living within 50 miles of the hotel. Posting on hotel Facebook a few hours before “traffic” to avoid competition and gain visibility is important. Marketers should focus on giving feedback during peak times.

This empirical study extends prior studies on social media metrics to the effects of external factors on the engagement metrics within the hotel Facebook context. Increasing the number of engaged users improves the effectiveness of eWOM for a hotel, which lacks empirical evidence.

This study aims to provide an in-depth understanding of the complex nature of Airbnb user experience by analyzing the pattern and sentiment of online reviews and assessing the relationships among review scores.

Big data analysis is conducted using Airbnb users’ online reviews of 16 US cities; correlation is run on review scores.

The key themes of Airbnb users’ online reviews are “clean,” “location,” “stay,” “home,” “place,” “host,” “neighborhood” and “recommend” and users have positive Airbnb experiences in general. The score of “cleanliness” significantly affects the “overall review” score.

This study is exploratory in nature; mixed methods should be used in the future to measure the relationship between user experience and extracted themes. As the context is in the USA in the current study, comparisons of review patterns across different countries and regions are necessary for later studies. Furthermore, future studies should consider Airbnb users’ demographics, personality and lodging preferences.

It is important for Airbnb hosts to maintain a clean and accessible property. Both Airbnb hosts and hoteliers should enhance the attributes that generate positive customer reviews. Each city should develop different strategies based on the performance of “cleanliness” and “overall review.”

This study supplements the existing literature in Airbnb user experience by analyzing online reviews in 16 US cities via Leximancer 4.0.

分析Airbnb网络评论：论美国16城市的用户体验

本论文旨在深入了解Airbnb用户体验的复杂属性, 通过分析网络评论以及网络评分之间的关系来找寻规律和判断用户情感偏差。

本论文采用大数据方式, 样本为美国16个城市的Airbnb用户网络评论; 本论文还对网络评分做出相关性分析。

研究结果表明, Airbnb用户网络评论的关键主题为“清洁性”、“地理位置”、“居住体验”、“家”、“地点”、“招待主人”、“邻里”、以及“推荐”和用户总体来说对Airbnb有着积极体验。“清洁度”分数对“整体评论”有显著影响。

本论文属于开拓性研究作品; 未来项目可以采用混合方法来衡量用户体验与主题的关系。本论文的研究背景是美国, 未来研究可以做不同国家和区域的评论模式对比分析。此外, 未来研究还应该考虑Airbnb用户的统计数据指标、个性、以及住宿设施偏好。

对于Airbnb住宿提供者来说, 保持设施的清洁和可使用性是非常重要的。对于Airbnb住宿主人和酒店经营者来说, 加强服务属性来提高网络好评是非常重要的。每个城市都应该基于“清洁度”和“整体评论”来开发不同的战略。

本论文补充了现有Airbnb用户体验的文献, 通过Leximancer 4.0 软件来分析美国16个城市的网络评论