Challenges and innovations in hotel operations in Canada
Bibliographic record
Abstract
Purpose This paper aims to analyse key challenges Canadian hotels are facing, and to suggest innovative steps to make hotel operations in Canada more successful. Design/methodology/approach The foundation for this paper was laid during a well attended Worldwide Hospitality and Tourism Themes (WHATT) roundtable discussion between industry leaders and hospitality educators in May 2012. The subject of hotel operations is discussed in the context of the theme for the 2012 Canadian WHATT roundtable and the strategic question: “What innovations are needed in the Canadian hotel industry and how might they be implemented to secure the industry's future?” Findings The paper provides valuable information on hotel management and operations, and outlines innovative solutions to key challenges Canadian hotels are facing. Practical implications The paper highlights effective approaches to managing hotel operations. The authors propose segment‐specific, tailor‐made training sessions for the diverse workforce of today. Originality/value The paper draws on the authors' experience and observations to explain how hoteliers can implement innovative change that enables them to achieve greater operational success. As the team of authors represents both academia and the industry, including a former general manager of the largest hotel in Canada, this paper will be of immense value to students, educators, and researchers, as well as industry leaders.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".