Multiplicity of alliance learning in the entrepreneurial process: strategies of early-stage biotech firms
Bibliographic record
Abstract
Entrepreneurial firms depend on knowledge variety and the ability to manage alliance-network learning for knowledge acquisition, which are both challenging. Some studies argue reliance on one key alliance partner is more effective for entrepreneurial firms with limited resources as it is less demanding than collaborations with multiple ones, while others demonstrate that alliances with different organizations significantly benefit them. Firm strategies and mechanisms of the alliance-network learning with multiple partners remain unclear, and illuminating this puzzle is relevant for understanding small and young firms creating innovations. Focusing on the early stages of human health biotech firms in Canada, this paper examines how they use the alliance-network to identify learning opportunities and pursue knowledge accumulation over their successive developmental stages. Adopting the multiple case study method and analyzing the focal firms’ collaborations and learning outcomes, this research advances a processual model of multiplicity of learning. The model identifies firm-specific strategies combining a variety of knowledge building mechanisms and multiple partners guided by organizational goals to innovate. The study contributes to the intersection of entrepreneurial development and alliance learning literatures with a novel view of early-stage firm learning strategies, and offers insights to entrepreneurs, small firms, and policymakers for innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".