Digital Transformation in Tourism and Hospitality: Towards Sustainable and Innovative Management
Bibliographic record
Abstract
Abstract This chapter explores how tourism and hospitality businesses can effectively integrate digital technologies into their management strategies to achieve operational sustainability while enhancing long-term customer experiences. In an era where technology is revolutionizing business practices, it is crucial for these companies to adapt to remain competitive and address environmental sustainability challenges. The chapter examines the use of digital tools, such as property management systems, online booking platforms, and data analytics solutions, to streamline operations and personalize customer interactions. Additionally, it addresses the challenges associated with adopting these technologies, including costs and complexity, while highlighting opportunities for eco-friendly and efficient management practices. By focusing on how these digital strategies can support sustainability goals while striving for operational excellence, the chapter provides insights into the critical role of technology in long-term management. The successful integration of these technologies will enable companies in the sector to balance sustainable performance with competitiveness, paving the way for innovative and responsible business practices, as exemplified by the case of Marriott International.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".