Toward a social capital theory of technology‐based new ventures as complex adaptive systems
Bibliographic record
Abstract
Purpose Technology‐based new ventures (TNVs) – which rely on entrepreneurial activities based on science and technology applications in newly created organizations to be successful – are important to current economic growth and innovation. Past research has looked at the importance of networks and social capital to TNV performance. Yet these studies rarely provide theoretical predictions of the attributes of network ties. This paper aims to bring TNV theory up to date with respect to twenty‐first century adaptation and complexity conditions. Design/methodology/approach The paper draws on new developments in complexity science (specifically scalability and scale‐free theories) and long‐standing first principles of efficacious adaptation to develop TNV‐relevant theory offering an alternative perspective on the impact of network ties on the performance of TNV. Findings It is argued that TNVs can achieve superior performance by developing and building moderate numbers of short‐term (and thereby weak) network ties. The theorizing calls for a new research agenda pertaining to TNVs, which are delineated. The paper also develops four propositions as part of setting forth an agenda for future research. Originality/value The paper updates the entrepreneurship and social network literatures by reshaping them with respect to the nonlinear order‐creation dynamics of complexity theory and scale‐free dynamics of econophysics. It focuses on the aspects of network theory that are especially likely to set in motion the complex adaptive systems dynamics essential to TNV performance. Therefore, the conceptual framework contributes to TNVs as a guide to achieving higher performance, effectiveness, and longevity in a rapidly changing world.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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".