Human resources and workforce shortages in Jeju Island due to islandness: The challenges faced by former hospitality and tourism professionals
Bibliographic record
Abstract
Workforce management is a problem in many islands. Although the Jeju tourism department and local business offer attractive promotions and opportunities, due to the demands faced by tourism professionals, many have left their positions in Jeju and returned to the mainland Korean peninsula. The purpose of this study is to explore the motivations, career decisions, and turnover behaviours of a group of former tourism professionals who had previously worked in Jeju for a period of less than two years. Based on social cognitive career and motivation theory, the study was guided by two research questions: 1) Why did the participants leave their positions in Jeju after less than two years of career development? and 2) How did the participants describe their working and living experiences in Jeju as professional tourism workers? The data from 42 participants indicated that overloaded responsibilities and unbalanced schedule, financial consideration, and personal consideration were the three major reasons for leaving Jeju. This study aims to help local businesses and governments to understand employees’ motivations, career decisions, and turnover behaviours. Further, this research seeks fill gaps in the literature regarding workforce shortages and tourism management for islands and remote regions, particularly in East Asia.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".