Marketing Canadian hotels in the future
Bibliographic record
Abstract
Purpose This paper aims to analyse literature relevant to four imperative aspects of hotel marketing, to discuss current challenges and opportunities, and to make suggestions for marketing Canadian hotels in the future. 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 marketing hotels 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 identifies innovation as the main ingredient for success in marketing Canadian hotels in the future. In the conclusion suggestions for strategic shifts in hotel marketing and tactics, which would help Canadian hoteliers in marketing their hotels in the future, are identified. Practical implications The paper reviews past concepts and industry practices as well as current practices to identify practical, effective and innovative approaches for the future. Originality/value As the team of authors represents both the industry and academia, this paper will be of immense value to students, educators, and researchers, as well as industry leaders. The paper captures significant strategy shifts, lists the top integrated digital awareness systems, and presents a new model in innovative hotel pricing empowerment for hotels.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".