An Exploratory Study on Sustainable ICT Capability in the Travel and Tourism Industry: The Case of a Global Distribution System Provider
Bibliographic record
Abstract
Climate change is one of the biggest challenges facing humanity today. Environmental values have spread globally and consumer beliefs are pressurizing firms in almost all industries to comply with green regulations. Sustainability has become such an important part of business strategy that almost every major company now has an executive with “sustainability” in their title. The travel and tourism industry produced 14 percent of global greenhouse gas emissions in 2010. Policy makers have responded with ambitious targets. The European Union aims to achieve a 60 percent reduction in transport sector emissions by 2050. This exploratory study draws on the sustainable ICT capability maturity framework (SICT-CMF) and the case of the Amadeus IT Group, a large travel and tourism industry corporate enterprise that specializes in IT solutions. The study investigates the current capability maturity level of sustainable ICT in the company. The findings suggest that the company is a market leader in terms of sustainability initiatives and that it demonstrates an “advanced” level of sustainability capability. We discuss the lessons learned from Amadeus’ experience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".