Diversification and innovation in tourism development strategy : the case of Abu Dhabi
Bibliographic record
Abstract
Through its diversification thrust, Abu Dhabi has succeeded in reaping the benefits both of oil and gas and of the services sector, including tourism, through deliberate government action. Diversification and innovation have been key drivers in all sectors of the economy, including tourism, and innovations in one sector have spawned innovations in others. Drawing on literature sourced from secondary data, including government documents and publications available in the public domain, this article argues that innovation in various sectors can facilitate tourism diversifi-cation, innovation, and product/market development. The authors propose a diver-sification and innovation model, arguing that these aspects are inter-related and that their relationship is cyclical. Diversification and innovations are generated from and influenced by any economic, social, or institutional milieu and by entities such as government, business, and non-governmental organizations The article contends that Abu Dhabi’s new growth path should be geared towards small and medium enterprises in the boutique and lifestyle lodging business, so as to spread the benefits of its success in the tourism industry to the broader population through diversifying the emirate’s “traditional” hotel market.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".