As consumers increasingly research and purchase hospitality and travel services online, new research opportunities have become available to hospitality academics. There is a growing interest in understanding the online travel marketplace among hospitality researchers. Although many researchers have attempted to better understand the online travel market through the use of analytical models, experiments, or survey collection, these studies often fail to capture the full complexity of the market. Academics often rely upon survey data or experiments owing to their ease of collection or potentially to the difficulty in assembling online data. In this study, we hope to equip hospitality researchers with the tools and methods to augment their traditional data sources with the readily available data that consumers use to make their travel choices. In this article, we provide a guideline (and Python code) for how to best collect/scrape publicly available online hotel data. We focus on the collection of online data across numerous platforms, including online travel agents, review sites, and hotel brand sites. We outline some exciting possibilities regarding how these data sources might be utilized, as well as discuss some of the caveats that have to be considered when analyzing online data.
Han, S., & Anderson, C. K. (2021). Web Scraping for Hospitality Research: Overview, Opportunities, and Implications. Cornell Hospitality Quarterly, 62(1), 89–104. https://doi.org/10.1177/1938965520973587