The moderating role of previous venture experience on breadth of learning and innovation and the impacts on SME performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the moderating role of previous venture experience on the relationship between learning breadth and innovation breadth, defined as the range of innovation types within a firm, and the impacts on SME performance. Design/methodology/approach A theoretical model was developed, and hypotheses were tested using step-wise multivariate regressions on survey data from 509 North American SME respondents. Findings The results demonstrate that the previous venture experience of a firm's top management plays a key role in enhancing the innovation breadth for a given level of learning breadth. There is a curvilinear relationship between innovation breadth and learning breadth, and increases in innovation breadth lead to increases in firm performance. Practical implications The results indicate that organizations seeking higher performance returns by expanding their breadth of innovations need parallel attention on higher learning breadth in order to adequately capture the value from this broader set of innovations. Originality/value The paper contextualizes learning and innovation in the SMEs and argues that the consideration of diversity (breadth) of learning and innovation can help us understand their performance implications across industries. It also extends the effect of previous venture experience (PVE) of the leadership team in explaining performance. Beyond their ability to address external factors, PVE has a moderating effect on the relationship between learning and innovation breadth across the organization. Previous venture experience serves as both a guide and catalyst for investments in learning activities that lead to a broader range of innovation activities across the firm.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".