The Rise of Spiritual Tourism in South Asia as Business Internationalization
Bibliographic record
Abstract
Globalization and digitization are motivating organizations around the world to manage and internationalize their products and services. Adaptively, most Asian companies are internationalizing their businesses with respect to various industries; one obvious example is the tourism industry. The global tourism industry can be segmented into niche types such as heritage tourism, dark tourism, medical tourism, including spiritual tourism. The objective of this paper is to analyze the rise of spiritual tourism in South Asian countries and discusses its operations that are internationalized rather than being region-centric or locally focused. It is argued that that public and private tourism operators in South Asia have realized that spiritual tourism presents an attractive product to invest and market based on people, places, and events. However, the challenge is to internationalize multi-faith spiritual tourism in the context of people, places, and events that would be the only way to develop and sustain this niche segment of the tourism business. It is argued that there are various factors that could enable South Asian countries to effectively internationalize their spiritual tourism destinations. The paper concludes that business internationalization of South Asian spiritual tourism, will not only achieve economic development objectives, but also social and United Nations Sustainable Development Goals, and bi-lateral diplomatic goals for regional peace and prosperity
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".