Time‐based differences in the effects of positive and negative affectivity on perceived supervisor support and organizational commitment among newcomers
Bibliographic record
Abstract
Summary Building on the broaden‐and‐build theory and research on the negativity bias, this study examines how trait affectivity, as a stable predisposition predicting the pattern of emotional responding, shapes newcomers' perceptions of supervisor support and experience of organizational commitment. Using latent growth modeling and data collected at four points in time from newcomers ( N = 158), we found the initial level of perceived supervisor support to mediate a negative relationship between negative affectivity and the level of commitment. Moreover, although newcomers experienced a general decrease in perceived supervisor support and a related decrease in commitment, those with high positive affectivity experienced a weaker decrease in perceived supervisor support, which led to a weaker decrease in commitment. Incidentally, positive affectivity was also positively related to the initial level of commitment. Two post hoc studies indicated that positive and negative affectivity exerted their effects controlling for state affect and replicated the relationship between change in perceived support and commitment. We discuss how these findings inform our understanding of trait affectivity's influence on newcomers' work attitudes.
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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) | 0.001 | 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 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".