How Does Artificial Intelligence Awareness Impact Employee Attrition: The Roles of Work Anxiety and Self‐Efficacy
Bibliographic record
Abstract
ABSTRACT Employee attrition is a significant consequence of AI awareness in the hospitality industry. However, the mechanisms linking AI awareness to employee attrition remain underexplored. This study addresses this gap by developing a model grounded in cognitive‐affective processing system theory to elucidate how and when AI awareness accelerates hotel employee attrition. Using a field survey with a sample of 216 hotel employees in China, the results demonstrate that (1) AI awareness is positively associated with hotel employee attrition, (2) the positive effect of AI awareness on employee attrition is mediated by self‐efficacy and workplace anxiety and (3) AI knowledge acts as a significant moderator such that the impact of AI awareness on increasing workplace anxiety and diminishing self‐efficacy is amplified among hotel employees with greater AI knowledge. Our research provides a comprehensive explanation of how and when AI awareness influences employee attrition from a dual pathway of self‐efficacy and workplace anxiety, thereby enriching the literature on AI and employee attrition in the hospitality management domain.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".