Sectoral entry-barriers for entrepreneurial activities – a Russian start-up between challenging global markets and local conservative path dependencies
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".