Global Staycation Trends: A Comparative Analysis of Consumer Interest Across Time and Regions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it