Navigating the emerging market context: Performance implications of effectuation and causation for small and medium enterprises during adverse economic conditions in Russia
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".