Can all sectors of the hospitality and tourism industry be influenced by the innovation of Blockchain technology?
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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