Job Burnout and Job Engagement Dimensions Among Hotel Employees in Sarawak: What Is the Relationship?
Bibliographic record
Abstract
Hotel employees are constantly working in an increasingly stressful work environment. As hotel employees working in a demanding working environment, they may eventually face job burnout dimensions (emotional exhaustion, cynicism, reduced professional efficacy) due to the day-to-day operations which lead to burnout phenomenon. Despite the limited study investigating burnout dimensions particularly revealing the research finding in the non-western context which may have adverse effects on their relationship with their job engagement phenomena. The study aims to explore the correlation between job burnout dimensions and job engagement dimensions among hotel employees in Sarawak, Malaysia. In more specified, each of the burnout dimensions will be tested on its significant relationship with job engagement dimensions (vigour, dedication, absorption). Cross-sectional research design with a total of 201 valid responses were obtained which involved descriptive and inferential statistic with high reliability scoring while exploratory factor analysis values met the benchmark. This study can be utilized by the hotel industry to develop effective strategies to minimize job burnout of each dimension while enhancing job engagement among the hotel employees in Sarawak.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".