Global Staycation Trends: A Comparative Analysis of Consumer Interest Across Time and Regions
Bibliographic record
Abstract
Staycations (i.e., vacations close to one’s home) have surged in popularity recently, significantly impacting travel patterns and destination management. In line with Construal Level Theory, staycations uniquely satisfy the need for psychological distance while maintaining spatial proximity to home. This study uses Google Trends data to examine consumer search behavior related to staycations over 7 years from 2016 to 2022. Our analysis reveals a noticeable staycation interest increase, which began before the pandemic and grew exponentially during travel restrictions and lockdowns. A key finding is that staycation searches are highest in Asia, Europe, and the Americas, reflecting international travel patterns. However, staycation queries are a global phenomenon, with significant interest observed across multiple regions. As interest in staycations has surged, a new lexicon of search terms has emerged, offering insights into specific factors influencing consumer decision-making. Initially, the searches were more general, but they have become more targeted, focusing on travel products and services such as hotels, booking platforms, and discounts. This research uses a visualization-driven approach to analyze global, regional, and national staycation trends. The article concludes with implications for destination resilience, contributing to the growing literature on staycations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".