The Mediating Role of Job Burnout in the Relationship between Role Conflict and Job Performance: An Empirical Research of Hotel Frontline Service Employees in China
Bibliographic record
Abstract
As an increasingly fierce competition in hospitality industry?the service quality of frontline service employeesdetermine the success of hotels. Therefore, it is significant to improve the service quality of frontline serviceemployees by enhancing their job performance. According to the conservation of resources theory, frontlineservice employees are easily confronted with role conflicts, which result in job burnout and negatively influencejob performance. However, the relationship between “role conflict-burnout-job performance” is not consistentaccording to previous studies. Motivated by the theoretical concern to further understanding of this subject, thepurpose of this article is to explore whether role conflict will affect job performance through job burnout. Basedon the data of 189 frontline service employees from 18 budget hotels, this study tests the hypotheses. The resultsshow that: (1) Role conflict is positively related to burnout; (2) Both role conflict and burnout are negativelyrelated to job performance; (3) Burnout partially mediated the relationship between role conflict and jobperformance. Implications for practices are discussed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".