Purpose

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Design/methodology/approach

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Findings

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

Practical implications

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

Originality/value

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

中文翻译：

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住宿特征偏好的动态：探索使用在线评论的时间序列分析来分解时间效应

目的

本研究旨在探索如何利用时间序列分析，通过解开季节性、趋势和 COVID-19 大流行的一次性破坏性影响来检查旅行者对住宿特征的偏好变化。

设计/方法论/途径

纵向数据是通过西班牙加那利群岛七年（2015 年至 2022 年）的在线旅行者评论（n = 519,200）检索的。时间序列分析分解了六大住宿特色（景观、露台、泳池、商店、位置和房间）的季节性、趋势和破坏性影响。

发现

单一的住宿特征揭示了不同的季节模式。趋势分析表明 Covid-19 造成的长期趋势影响和短期破坏影响。相比之下，没有发现大流行的长期影响。

实际影响

研究结果强调需要在单一住宿特征层面解决季节性问题。除了针对不同客人群体的特定功能外，新方法还可以实现动态价格优化。实时洞察可用于平台提供商和住宿业主的定向营销。

原创性/价值

时间序列视角的新颖应用揭示了旅行者住宿特征偏好的趋势和季节性变化。研究结果有助于更好地满足旅行者对 P2P 产品的需求。