Bibliographic record
Abstract
Purpose The purpose of this study is to investigate whether and how leadership practices that facilitate a positive emotional climate (the “PEC practices”) are related to organizational outcomes in terms of performance (increase in revenue), strategic growth, and outcome growth. Design/methodology/approach A panel study was conducted to test the hypotheses. Data were collected from 229 entrepreneurs and small business owners operating in Greater Vancouver, British Columbia, Canada. In the first wave of the study, the authors collected data regarding the PEC practices. The data on outcome variables, i.e. revenue, strategic growth, and outcome growth, were collected in the second wave, 18 months later. Findings The regression analyses showed that the PEC practices were positively related to company performance, revenue growth, and outcome growth, providing support for the hypotheses in the study. Originality/value This study provides valuable insights about the role of emotional factors in organizational‐level outcomes, a relatively unexplored area in emotions research. Analyzing a set of panel data, the study has shown that leadership practices that facilitate a positive emotional climate in an organization make a difference in organizational‐level outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".