Strategic entrepreneurship in VUCA environment: the competing forces of outcome variability
Bibliographic record
Abstract
Purpose Scholars have extensively studied the concept of strategic entrepreneurship (SE), shedding light on its antecedents, dynamics and outcomes. However, a notable gap exists in understanding the reliability of its performance implications, which explains the inherent risks as well as the possibility of yielding outliers (instances of exceptionally high or low performance). Addressing this gap, this study aims to present a detailed analysis of the implications of SE for the variance of resulting performance distribution. Design/methodology/approach This conceptual study uses the deductive theory-building approach to dissect the four dimensions of SE (entrepreneurial mindset, entrepreneurial leadership and culture, managing resources strategically and applying creativity and developing innovations) as presented by Ireland et al.’s (2003) model, offering theoretical propositions on how each of them influences the variability of resulting performance distribution. Findings This study demonstrates that the strategic entrepreneurship (SE) dimensions have distinct impacts on the reliability/variability of performance outcomes, acting as boosters or attenuators in the volatile, uncertain, complex and ambiguous (VUCA) context. Originality/value The study uniquely links each component of SE with outcome variability in VUCA environments, thereby shifting the focus from traditional performance metrics to outcome variability. This approach complements the existing body of knowledge on the performance implications of the SE construct by integrating a previously neglected critical perspective on the reliability of resulting performance distribution. These insights allow subsequent investigation of SE’s outcomes, including explaining the likelihood of obtaining positive outlier performance or firm failure.
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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 | 0.016 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".