Business and well being : the experience of entrepreneurs
Bibliographic record
Abstract
This study examined the role of self-regulation moderating the effects of business stressors on quality of life for Canadian entrepreneurs. Research finds the inability to make progress towards goals can negatively influence a person's quality of life (Carver & Scheier, 1998). It was predicted that business difficulties would deplete the emotional and physical resources of entrepreneurs. Challenges to the business were expected to affect the owner's experience of business regrets, and in turn, these would affect their well-being. Building on work demonstrating that self-regulation capacities involved in the adjustment of personal goals serve adaptive functions (Wrosch, Scheier, Miller, Schulz, & Carver, 2003), a theoretical model was elaborated, implying that the relations between business struggles, regrets and entrepreneurial well-being would be moderated by the entrepreneur's goal adjustment abilities. Findings are based on a cross-sectional sample of 140 entrepreneurs from across Canada. Analyses suggest the entrepreneur's health and well-being are adversely affected by negative business outcomes only for those who are not able to adjust their business goals. Further, it was found that among entrepreneurs facing business struggles, those who could reengage in new goals suffered fewer intrusions about their business regrets. Finally, the intensity of the negative emotions about a regret was a predictor of aversive outcomes for quality of life measures, and this was also moderated by the goal adjustment ability of the entrepreneur. The implications of the findings for adaptive self-regulation of business goals are discussed.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".