Turning strain into gain: leveraging manager compassion to promote team innovation and customer satisfaction in response to teams’ emotional exhaustion
Bibliographic record
Abstract
Although emotional exhaustion is generally regarded as a threat to individual and team functioning, research on the effects of team emotional exhaustion is scant and inconsistent. The present research offers a novel perspective on the implications of team emotional exhaustion for team innovation and customer satisfaction. By integrating conservation of resources theory and the literature on compassion, we identified manager compassion, reflecting managers’ engagement with teammates’ suffering and actions aimed at relieving it, as a moderator of the relations among teams’ emotional exhaustion, team innovation, and customers’ service satisfaction. Using hierarchical linear modelling, the results of a three-wave multisource study conducted on 56 retail stores of a consumer electronics and household appliances company showed that manager compassion strengthened the positive relationship between team emotional exhaustion and team innovation and between team innovation and customer satisfaction with teams’ services. The team emotional exhaustion-team innovation-customer satisfaction chain of relationships was also stronger when managers exhibited high levels of compassion. A post-hoc multisource study on 38 units of a healthcare centre further supported the moderating role of manager compassion between team emotional exhaustion and team innovation. We discuss the theoretical and practical implications of these findings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".