Purpose

This study aims to characterize remote workers’ Airbnb experiences through the cognitive outcomes of their experiences and to consider the differences between long and short stays.

Design/methodology/approach

The structural topic model methodology was used to identify relevant topics. Data were collected from InsideAirbnb for Lisbon, Portugal and Austin, Texas, USA, for 2022 and early 2023, focusing on reviews that mentioned remote work.

Findings

The Airbnb experiences of remote workers and digital nomads are characterized as professionals who express mostly affective outcomes, but also have behavioral and nonaffective outcomes during their stay. In addition, the findings support the moderating role of length of stay and city.

Research limitations/implications

This paper contributes to the literature by exploring how length of stay affects the priorities of remote workers on Airbnb, highlighting the different needs of long-term and short-term stays, and helping to consolidate and clarify the scattered research on customers’ long-term experiences in tourism and hospitality.

Practical implications

The Airbnb experience of remote workers is the highly valued as evidenced by the high rate of commending reviews indicating a willingness to stay there again. It is suggested that Airbnb hosts continue their helpful role and ensuring the functionality and availability of essential facilities and emphasizing neighborhood amenities specific to long and short stays. ChatGPT4 was found to be valuable for extracting data and assigning topic labels.

Originality/value

This study uses a novel structural topic model, augmented with ChatGPT4, to analyze Airbnb customer reviews that mention remote work, thereby improving inferences about the characterization of remote workers.

中文翻译：

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Airbnb 长期住宿客户体验：结构化主题模型和 ChatGPT 驱动的远程工作人员评论分析

 目的

本研究旨在通过体验的认知结果来描述远程工作者的 Airbnb 体验，并考虑长期和短期住宿之间的差异。

设计/方法论/途径

结构主题模型方法用于识别相关主题。数据是从 InsideAirbnb 收集的 2022 年和 2023 年初葡萄牙里斯本和美国德克萨斯州奥斯汀的数据，重点关注提到远程工作的评论。

 发现

远程工作者和数字游民的 Airbnb 体验被描述为专业人士，他们在入住期间主要表达情感结果，但也有行为和非情感结果。此外，研究结果支持停留时间和城市的调节作用。

研究局限性/影响

本文通过探索住宿时长如何影响远程工作人员在 Airbnb 上的优先事项，强调长期和短期住宿的不同需求，并帮助巩固和澄清关于客户长期住宿的分散研究，为文献做出了贡献。旅游和酒店业的经验。

 实际意义

远程工作者的 Airbnb 体验受到高度重视，好评率很高，表明他们愿意再次入住那里。建议 Airbnb 房东继续发挥帮助作用，确保基本设施的功能和可用性，并强调针对长期和短期住宿的社区便利设施。人们发现 ChatGPT4 对于提取数据和分配主题标签非常有价值。

 原创性/价值

本研究使用一种新颖的结构主题模型（通过 ChatGPT4 进行增强）来分析提及远程工作的 Airbnb 客户评论，从而改进对远程工作人员特征的推断。