Information technology and outbound tourism: A cross‐country analysis
Bibliographic record
Abstract
Abstract This article provides a comprehensive investigation of the global tourism industry, combining two critical streams from the academic literature: the economic determinants of the tourism industry and the influence of technology on this industry. More precisely, this study examines the influences of technology advancement (Internet and mobile usage) on the outbound tourism (OT) in a global sample. We found interesting and consistent results by applying various panel data estimations for a sample of 126 economies composed by 3 subsamples, including (49 Low and Lower‐Middle Income Economies [LMEs], 29 Upper‐Middle Income Economies [UMEs] and 48 High‐Income Economies [HIEs]) between 2000 and 2017. Internet use has a significant positive impact on all the three aspects of OT, including total OT expenditures, international tourism expenditures for travel items and the number of international tourism departures. The effects of Internet usage are stronger than the one observed for mobile usage. Finally, the positive influences of Internet and mobile usage are found with strong consistency across the three income groups (with a stronger marginal impact in HIEs and UMEs, and lastly in LMEs). Our study invites policy‐makers to integrate digital information within the tourism sector to boost the industry and economic growth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".