Sectoral entry-barriers for entrepreneurial activities – a Russian start-up between challenging global markets and local conservative path dependencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Sectoral foresight activities often identify technological opportunities but leave the question open who will pursue them. Entrepreneurial activities have become increasingly important for the introduction and commercialization of new technological solutions. The same is true for Russia’s oil and gas industry, which requires a major technological upscaling to stay competitive. Promising start-ups, however, often face high barriers and fail to commercialize superior technological solutions. The purpose of this study is to show how industry-specific entry barriers can hamper start-up activities. Design/methodology/approach This paper discusses the experiences of a Russian oilfield service start-up in commercializing a self-developed technology for increasing the productivity of oil wells. Findings The start-up faced conservatism from corporate decision-makers, declining oil prices and suboptimal protection of intellectual property rights. The company overcame most barriers through moving into other markets outside of Russia, as closing a deal with customers in the USA and Canada went much faster than the extended business cycles of national oil companies. Originality/value This paper connects sectoral foresight activities to the real-life experience of a start-up. The findings suggest that entry barriers need to be addressed by the planning process to really pave the way for a greater impact of entrepreneurial activity.
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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 | 0.000 |
| 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 it