Team psychological capital and process improvement: an interactionist perspective
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the relationship between team psychological capital (PsyCap) and team process improvement (TPI) by focusing on the mediating role of team self-managing behaviors (TSMBs) and the moderating effect of the team reward system. Design/methodology/approach Data were collected from 514 members and their immediate superiors nested in 135 action teams working for a Canadian public safety organization. Hypotheses were tested using a path analytic procedure. Findings Team PsyCap was positively related to TPI, and this relationship was mediated by TSMBs. In addition, the team reward system positively moderated the first stage of this relationship. Research limitations/implications This study highlights the important role that motivational factors play in the effectiveness of action teams. Specifically, the present study reveals that the psychological resources of action teams interact with the level of recognition and reward they receive to predict members’ engagement in self-managing behaviors and in improvement processes. Practical implications Findings suggest that to promote the capacity for process improvement of actions teams, managers should focus on their positive psychological resources, their capacity to self-manage and on the level of recognition and reward they receive. Originality/value Considering the dynamic and complex environments within which action teams operate, the finding that team PsyCap promotes their optimal functioning is particularly noteworthy. Furthermore, this study clarifies why and when team PsyCap enhances TPI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".