Impacts of Employee Empowerment and Organizational Commitment on Workforce Sustainability
Bibliographic record
Abstract
Building and maintaining a sustainable workforce in the hospitality sector, where demand for talent consistently exceeds supply across the globe, has only been exacerbated by COVID-19. The need to sustain this workforce behooves the industry to unpack core drivers of employee commitment in order to retain top talent. This paper explores how dimensions of employee empowerment increase organizational commitment and, in turn, reduce turnover intention—leading to a more sustained workforce. Drawing on the results of 346 surveys within the Canadian lodging industry, structural equation modeling was undertaken to examine the influence of empowerment on organizational commitment and organizational commitments influence on turnover intention. Findings suggest that the development of meaning through employee empowerment, particularly when the ideals and standards between workers and their organization are aligned, creates a strong emotional commitment which appears to strongly reduce an employee’s intention to leave. Feelings of emotional connection or duty towards an organization show clear positive relationships with reduced intentions to leave. For an industry struggling with higher-than-average turnover intention and labour costs, focusing on creating work with meaning, and instilling a sense of belonging in the workforce will enable organizations to reduce their employee’s turnover intentions.
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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 | 0.003 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".