The stress-innovation link: leadership and strategies of female entrepreneurs in diverse economies
Bibliographic record
Abstract
Purpose This study aims to explore the impact of occupational stress (OS) on the innovative entrepreneurial capabilities (IEC) and innovative work behavior (IWB) of female entrepreneurs operating in dissimilar economies. Canada, an advanced economy, and Pakistan, an emerging economy, provided contrasting economic backgrounds for the investigation. Design/methodology/approach Data collected from 106 female entrepreneurs (53 each from Canada and Pakistan) were quantitatively analyzed through partial least squares structural equation modeling. In addition, funnel approach (a secondary technique) was used to understand the in-depth trends and variation among contrasting economies. Findings The results from this study show that IEC and IWB are statistically significantly affected by OS (IEC = 0.001 < 0.05; p < α; IWB = 0.000 < 0.05; p < α). The causes of stress for Pakistani female entrepreneurs are commonly personal factors, while organizational factors affected Canadian female entrepreneurs frequently. Consequences of stress relating to behavioral and physical deterioration are evident among Pakistani female entrepreneurs, while emotional symptoms are evident among Canadian female entrepreneurs. Practical implications Female entrepreneurs need to understand the relationship between their economic background and the likely impact of OS on their IEC and IWB. Furthermore, appropriate measures suited to economic context are required in managing the effect of OS by female entrepreneurs. Originality/value This study contributes to the literature on entrepreneurship and effective leadership by highlighting the occupational stressors that affect female entrepreneurs operating in contrasting economies and the impact of these stressors on their IEC and IWB.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".