Understanding Solo Female Travellers in Canada: A Two-Factor Analysis of Hotel Satisfaction and Dissatisfaction Using TripAdvisor Reviews
Bibliographic record
Abstract
This study aims to understand solo female travellers’ hotel experiences in Canada by analyzing online reviews from TripAdvisor. We employed keywords such as “solo female” and “single female” to identify online reviews, followed by a manual review process to confirm their relevance and eliminate duplicates. The final dataset included 240 reviews from 188 lodging establishments, totalling 49,924 words. Employing Herzberg’s two-factor theory and NVivo, we generated codes and categorized them into 29 satisfiers and 24 dissatisfiers. These were grouped into five key components impacting guests’ experiences: room, staff, hotel facilities and cleanliness, hotel amenities, and others. The top three satisfiers identified in traditional accommodations are safety, staff helpfulness, and location, while room dirtiness, insecurity, and room amenities are the primary dissatisfiers. Conversely, alternative lodgings reveal a distinct pattern, with location, room amenities, and staff friendliness as top satisfiers, and room amenities, neighbourhood, and service unavailability as leading dissatisfiers. The study found that alternative accommodations may offer a broader range of experiences, potentially due to their less-standardized nature and diversity of options. This research enhances understanding of solo female travellers, gender differences in hotel experiences, and customer satisfaction, underscoring the tourism industry’s need to address this demographic’s unique needs and concerns.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".