Does Positive Emotional Climate Matter? - A Look at Revenue, Strategic and Outcome Growth
Bibliographic record
Abstract
This paper investigates a taken-for-granted assumption in the emotions literature that taking care of emotional needs of employees contributes to company performance. Although previous studies demonstrated the impact of emotions on individual performance, the linkages between emotions and company performance still remain to be empirically examined. In an attempt to shed light onto this relatively unexplored area, we analyzed the role of positive emotional climate for overall company performance. Specifically, we hypothesized that an entrepreneur's intention to maintain a positive emotional climate would have a positive impact on the company's a) revenue, b) strategic growth, and c) outcome growth. To test these hypotheses, we analyzed a longitudinal data collected from entrepreneurs and small business owners operating in Greater Vancouver, British Columbia. In the first wave of our study, we asked respondents a series of questions regarding their intention to maintain a positive emotio...
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.111 | 0.015 |
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; both teacher heads agree on what is shown here.
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".