Bibliographic record
Abstract
Purpose The purpose of this paper is to theorize and test a model concerning the role of complaining behaviors in work teams. Despite the prevalence of workplace complaining, there is no consensus in the literature regarding the consequences of those behaviors and the extent to which they are harmful. Design/methodology/approach Using a multisource approach and a team-level design, the authors collected data from 82 teams (i.e. 394 members and their 82 immediate superiors) working in a Canadian public safety organization. Findings The results show that complaining behaviors are negatively related to two effectiveness outcomes (i.e. team performance and team process improvement) and that meaningfulness mediates these relationships. The results also reveal that task interdependence moderates the relationship between complaining behaviors and meaningfulness. More specifically, complaining behaviors have a stronger relationship with meaningfulness when the level of task interdependence is high. Originality/value The present study contributes to the literature on counterproductive behaviors by deepening the understanding of emergent states and outcomes stemming from workplace complaining, particularly in work teams. The findings of this study highlight the negative consequences that complaining behaviors may have in a team setting, the underlying mechanism involved in these relationships, and the moderating role of task interdependence.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".