Navigating the emerging market context: Performance implications of effectuation and causation for small and medium enterprises during adverse economic conditions in Russia
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Research Summary This study aims to broaden the understanding of effectuation and causation by investigating their effectiveness for small and medium enterprises (SMEs) in the emerging market context during adverse economic conditions. We embrace a holistic view of the performance implications of these behavioral logics, theorizing and empirically testing their impact not only on the level of firm performance but also on its variability. The findings suggest that emerging market conditions create significant contingencies in the relationships between effectuation, causation, and firm performance, substantively affecting their effectiveness. In particular, we demonstrate that for the firms affected by adverse conditions, causation brings marginal performance improvements while also making it highly unreliable (variable), whereas effectuation leads to performance improvements coupled with higher reliability. Managerial Summary Entrepreneurial actions can be based on one of two behavioral logics: causation (rigorous forward‐looking analysis, relying on well‐prepared plans, pre‐defined goals, and required resources) or effectuation (leveraging the existing resources and controlling the environmental uncertainty through creating new markets, products, and opportunities). We investigate the effectiveness of these logics for Russian SMEs navigating adversity in the emerging market context. The results suggest that causation leads to performance improvements, yet these become marginal and highly unreliable if a firm finds itself in adverse conditions. Effectuation, on the other hand, is a costly and unreliable strategy in stable times, yet leads to reliable performance improvements in volatile contexts.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it