The impact of 'incubator' organizations on opportunity recognition and technology innovation in new, entrepreneurial high-technology ventures
Bibliographic record
Abstract
The increasingly important role of SMEs in both regional and national economic development has been widely acknowledged in the economic and entrepreneurship literature.The development of an economic and policy environment supporting new, high-growth, high-technology ventures has become common strategy adopted by many policy makers, as a critical means of promoting future economic growth and job creation. Many of these high-technology, economic development programmes embrace enabling technologies, such as micro- and nanotechnology- based firms.Although nascent entrepreneurs have numerous sources of advice and support as they embark on the venture creation pathway, aspects of an entrepreneur's past and present experience exert a central and often pivotal influence on their ability to engage effectively in opportunity recognition and exploitation of innovative new product technologies.This article argues that the professional/social environment in which an entrepreneur lives and works has a fundamental impact upon their ability to recognize and exploit opportunities.The work explores the innovation process in young high-technology firms and focuses upon the long-term impact of previous employment, in 'incubator' organizations, in influencing opportunity recognition and product innovation processes. Findings suggests that incubator organizations fundamentally shape entrepreneurs' technical and commercial experience of markets, strongly influence their attitudes to risk and personal achievement, help develop an intricate network of social capital and resources and, finally, provide critical knowledge of the existence, availability and applicability of technology solutions in new and emerging markets. Issues are explored through in-depth interviews in 31 companies in Aberdeen, Scotland, and Ottawa, Canada, cities with established strengths in technology development in academic, commercial and government research organizations, and a history of successful high-technology firm creation. Implications for theory and policy are discussed, with particular reference to the lessons to be learned to assist the development of sectors based upon newly emerging technologies.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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".