Can all sectors of the hospitality and tourism industry be influenced by the innovation of Blockchain technology?
Bibliographic record
Abstract
Purpose This paper aims to provide a general introduction to Blockchain technology and how it can be used within the global hospitality industry. In particular, this paper speaks to three industry sectors where Blockchain technology is currently in use. Design/methodology/approach This paper draws on the perspective of an academic who also continues to serve as an industry practitioner within the field of hospitality technology. To this end, the paper provides several examples as to how Blockchain technology can be used to further advance the hospitality profession within a number of different industry sectors. Findings Blockchain technology is being used now within the hospitality industry for both practical and strategic purposes. It can be used in most sectors of the profession and will continue to be used within the hospitality industry for many years ahead. The technology is still relatively new and will continue to become more advanced and sophisticated with the passage of time. Practical implications Many hospitality industry examples are provided as to how Blockchain technology can be used to improve operational effectiveness, efficiencies and overall profitability. Originality/value This paper adds value and contributes to the literature relating to Blockchain technology applications in the international hospitality industry. It represents current and future use that can and should be taken into consideration by both the hospitality industry and academia.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".