Mitigating work interference with family by leveraging an entrepreneurial strategic posture
Bibliographic record
Abstract
This article adds to entrepreneurship research by detailing the mediating role of work-related emotional exhaustion in the connection between the extent to which women entrepreneurs experience work interference with family—defined as the degree to which the quality of their personal lives is compromised by work demands—and the performance of their businesses. It also predicts a buffering role of the entrepreneurial strategic posture of their businesses in this process. Survey data collected among women entrepreneurs in Chile indicate that the depletion of entrepreneurs’ work-related energy resource reservoirs is an important reason that increasing levels of work interference with family diminish business performance. This mediating role of emotional exhaustion is less prominent when they run their businesses entrepreneurially, which might help them find innovative solutions for the negative spillovers of work stress into the family domain. This research therefore reveals a critical challenge for women entrepreneurs who suffer in their personal lives due to pressing work demands: the associated emotional drainage compromises the success of their business endeavors, which eventually can generate even more hardships. This study also shows how women entrepreneurs can address this challenge, that is, by drawing from the novel insights that arise from an entrepreneurial strategic posture.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".