From tensions to synergy: Causation and effectuation in the process of venture creation
Bibliographic record
Abstract
Abstract Research Summary This article examines previously neglected tensions between causation and effectuation in the process of new venture creation. We studied 41 episodes of new venture creation by entrepreneurs in Finland and Denmark, who we followed applying the diary method. We reveal tense relations between the respective causation and effectuation principles at multiple levels, and identify the corresponding mechanisms for their resolution, which, in turn, lead to the synergy. This study enriches the effectuation research by offering a dynamic perspective on causation‐effectuation interplay and categorizing three modes of their interaction, that is, separation, hybrid synergy, and tensions. Managerial Summary Venture creation is a complex process that involves different decision‐making logics. While combining the goal‐driven logic of causation and non‐goal driven logic of effectuation is essential for the success of a start‐up, the road to their synergy can be paved with different tensions. Our study of 41 episodes of new venture creation by entrepreneurs in Finland and Denmark shows that these tensions can occur at the individual, organizational and inter‐organizational levels. We also show four different mechanisms of how entrepreneurs can overcome these tensions within their ventures and in relations with other stakeholders.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".