SATISFACTION, CITIZENSHIP BEHAVIORS, AND PERFORMANCE IN WORK UNITS: A META-ANALYSIS OF COLLECTIVE CONSTRUCT RELATIONS
Bibliographic record
Abstract
This paper offers theoretical development clarifying the structure and function of collective job satisfaction and uses meta-analytic methods (k = 73) to examine the satisfaction–performance relationship when both constructs are construed at the work unit level. Overall, our results suggest that the relationship between unit-level job satisfaction and unit-level performance is significant (ρ= .34). Specifically, significant relationships were found between unit-level job satisfaction and unit-level criteria, including productivity, customer satisfaction, withdrawal, and organizational citizenship behaviors (OCB). Furthermore, the satisfaction-performance relationship was moderated by the strength of unit consensus, performance criteria, industry type, and whether the sample was U.S. based. Although these moderators were identified, collective satisfaction positively predicted performance across all levels of moderators. In addition, results indicate that unit-level OCB has a moderately strong relationship with unit-level performance. Only limited support was found for the notion that OCB is a route through which satisfaction has an impact on performance. We elaborate on these findings and attempt to provide a more clear direction for future research in this area.
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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 | 0.039 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.022 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".