Micro-Foundations of Dynamic Capabilities in New Ventures: Founders’ Personality
Bibliographic record
Abstract
Dynamic capabilities (DCs) and their micro-foundations are recognized as strategically important for both existing organizations and de novo firms. Yet we still know little about the role of human capital in the creation and evolution of DCs in young and new ventures. In this study we investigate the impact of two lead founder personality traits – conscientiousness and openness to experience – on the development of three behaviorally-based capacities underlying DCs: (1) sensing; (2) seizing, and (3) reconfiguring. Based on a sample of 172 lead founders of new tech-based ventures and by administering seven waves of yearly surveys (2009-2016), we find that personality traits of the lead founder have distinct and significant effects on the development of sensing, seizing, and reconfiguring capacities underlying a firm’s DCs. While conscientiousness impacts development of seizing capacities, openness to experience influences the development of sensing and reconfiguring capacities. Our study contributes to the dynamic capability and entrepreneurship literatures by investigating personality-based micro- foundations of DCs in new and young ventures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".