The emergence of team helping norms: Foundations within members' attributes and behavior
Bibliographic record
Abstract
Summary This research examined the antecedents of organizational citizenship behavior helping norms in teams, specifically with regard to how members' personality, values, beliefs, and helping behavior predict the emergence of helping norms in newly formed project teams. We drew from theory on emergent phenomena and team composition research to propose and test a compilation model of how helping norms are influenced by having at least one member with particularly low (minimum) or high (maximum) levels of attributes that may influence helping‐norm development (i.e., conscientiousness, agreeableness, other‐oriented values, personal helping beliefs). We further examined the extent to which members' helping behaviors, as rated by peers, predicted helping norms and whether these behaviors mediated the relationship between individual attributes and helping norms. The results of a longitudinal study of 47 student project teams revealed that teams' minimums on agreeableness, other‐oriented values, and personal helping beliefs had direct relationships with helping‐norm emergence, and the effects of agreeableness were mediated through mean helping behavior. By contrast, teams' maximums on these attributes showed no relationships with helping norms, and only a team maximum on agreeableness was associated with teams' mean helping behavior. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".