Gender Differences in Responding to Management Supports, Work Engagement, and Service Recovery Performance: A Test of Moderated Mediation Model
Bibliographic record
Abstract
This study investigates the moderating role of gender in the relationship between management supportiveness and service recovery performance mediated by work engagement. Little attention has been given to understand the work engagement on service recovery performance and the influence of individual differences; therefore, the study responded to the call for additional research to improve service behavior. The data set was analyzed based on 1,232 call centers from three service companies, located in Bangkok, Thailand. The results showed that work engagement mediates the relationship between management supports and service recovery performance for both males and female differently. Particularly, the results reveal that work engagement fully mediated the relationship between management supports (high performance work practices and perceived supervisory support) and service recovery performance for male, unlike for female. As to the implication, the study contributes to the boundary condition that may influence the manifestation of management supports and work engagement on employee behavior through the inclusion of gender as a moderator of the exchange relationship between management and employees. Thus, managers should take into account on both work engagement and management supports by providing supportive people policies and emotional support to improve employee service delivery. Especially for male employees, management should ensure that necessary level of resources is being made available for the implementation of all HR practices. This should go along with the emotion support by supervisor and is delivered synthetically in order to enhance employees’ work engagement that provides the highest benefit to the process of recovery service failures.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".