Will Self-Service Technologies Be Widely Adopted in Travel, Tourism, and Hospitality Industries During and after COVID-19?
Bibliographic record
Abstract
Travel, Tourism, and Hospitality (TTH) sectors had been believed to continue experiencing constant growth before the unexpected COVID-19 outbreak. Although the Severe Acute Respiratory Syndrome (SARS) and Middle-East respiratory syndrome (MERS) outbreak had occurred before, COVID-19 is causing a vast number of fatalities and raised social and economic issues in many countries. TTH sectors were under heavy pressure compared to other industries during COVID-19. This article aims to systematically review the critical role of SSTs to be adopted in the TTH companies during and after COVID-19. How business transformation is possible in the TTH companies will also be discussed. For that purpose, this study carried out a meta-analysis from previous studies. The results revealed TTH companies must re-shape business strategy by adopting and adjusting service delivery using Self-Service Technologies (SSTs) to stay competitive and survive tough times. TTH ecosystem needs to employ Augmented reality, Virtual reality, Blockchain, Robot, and Autonomous Service which have become a reality today and become a necessity for many industries in the future. The originality or novelty of this research is that this research contributes to the body of knowledge by elaborating on how TTH sectors will adopt SSTs.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".