The role of effectuation and causation for SME survival amidst economic crisis
Bibliographic record
Abstract
Purpose Economy-wide crises create major challenges for small and medium enterprises (SMEs). Existing studies emphasize the crucial role of contrasting behavioral strategies, effectuation and causation in SMEs' adaptation to crisis conditions. Yet, prior literature concentrated predominantly on exploring the impact of effectuation and causation on firm performance rather than survival. The authors present and empirically test a theoretical model explaining how behavioral strategies affect SME survival during an economy-wide crisis under different levels of environmental dynamism. Design/methodology/approach The authors propose a theoretical framework based on the combination of the effectuation literature and the emerging variance-based perspective on entrepreneurial actions. The theoretical model is then tested using a sample of Russian SMEs during a period of economic adversity and recovery (2015–2019). Findings The empirical results reveal that causation reduces the probability of firm survival in dynamic environments, while effectuation increases the chance of survival irrespective of the state of the environment. In a nutshell, the study provides evidence that the effectuation logic serves a viable way for SMEs to increase the chances of survival through the economic shock and subsequent recovery period. Originality/value For the first time in the literature, the authors demonstrate the role of behavioral strategy (effectual and causal) as a crucial antecedent of SME survival in the short and medium term, particularly during an economy-wide downturn. Furthermore, the study demonstrates the power of variability-based theorizing for explaining and predicting the survival/failure implications of entrepreneurial actions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".