Airbnb customer experience in long-term stays: a structural topic model and ChatGPT-driven analysis of the reviews of remote workers

Abstract

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.