Bibliographic record
Abstract
Purpose – Voluntary employee turnover can be a challenge for all industries but high employee turnover has been a special concern in the hospitality industry, which is the context of this paper. The purpose of this paper is to incorporate a “trickle-down” perspective into the conventional research on turnover intention and satisfaction with supervision. The authors assess whether mid-level managers’ satisfaction with senior managers’ supervision is related positively to line employees’ satisfaction with mid-level managers’ supervision and, in turn, line employees’ turnover intentions. Further, the authors examine whether the strength of this “trickle-down” effect is affected by the middle managers’ gender. Design/methodology/approach – The authors tested our theoretical argument using a sample of 1,527 full-time employees in 267 different departments at 94 hotels in the USA and Canada. Hierarchical linear modeling was employed to analyze the data. Findings – The authors found a trickle-down effect of satisfaction with supervision, as predicted, and the effect was stronger for female than male middle managers. These findings open new avenues for addressing turnover issues for organizations and managers. Originality/value – This study extends the line of research on leadership and turnover in three ways. First, it shows how senior managers, who have no direct contact with line employees, can affect turnover intentions of line employees. Second, this research helps the authors know where to target efforts at intervention; by connecting middle managers’ satisfaction with supervision with employees’ turnover intentions, the authors know to target interventions to reduce turnover not just at line employees and supervisors but also at senior-level managers as well. Third, this study sheds light on the ongoing debate over “female advantage” in leadership (Eagly and Carli, 2003a, b; Vecchio, 2002, 2003) by examining not just how women are treated, but how their experience may reshape managerial dynamics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".