Effectuation, innovation and performance in SMEs: an empirical study
Bibliographic record
Abstract
Purpose – Ever since Sarasvathy’s (2001) seminal article, scholars have sought to test effectuation’s affect on firm performance. Although recent work has begun the arduous process of testing effectuation’s effect on entrepreneurial performance, there is still much to learn about its impact on firm performance. One such area is the relationship between effectuation and innovation. The purpose of this paper is to first, propose a scale suitable to the explication of the effectuation construct relative to innovation. Second, it proposes a more parsimonious scale for the measurement of innovation. Third, these scales are tested relative to firm performance. Design/methodology/approach – This paper develops and tests a structural model, which investigates aspects of effectuation as mediators between innovation orientation and product/service innovation. This is accomplished using a sample of 169 electronic product manufacturing-based small and medium-sized enterprises (SMEs). Subjective measures of performance are used as the dependent variable. Findings – The three most widely used measures of innovativeness were found to break cleanly into two sub-constructs, namely innovation orientation and product/service innovation. Effectuation measures included means (who I know), leverage contingencies (experimentation), pre-commitments and affordable loss. Means and leverage contingencies were found to positively mediate innovation orientation and product/service innovation leading to increased firm performance. Affordable loss did not show a mediating role, but had a direct effect on firm performance. Research limitations/implications – This study establishes two distinct sub-constructs of firm-level innovation; namely innovation orientation and product/service innovation. Second, by testing an innovation-centric effectuation model, this research establishes an empirical relationship between effectuation, innovation and firm performance. Practical implications – Practical implications include establishing a relationship between means, leverage contingencies and innovation-performance, indicating that the ways through which small and medium-sized enterprises use their innovation networks may affect innovation outcomes and ultimately firm performance. Originality/value – This research establishes an empirical relationship between effectuation, innovation and firm performance, extending effectuation theory from the entrepreneurship to the innovation literature.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".