MOTIVATIONS AND BARRIERS OF ENTREPRENEURS IN MOSCOW AND THE MOSCOW REGION
Bibliographic record
Abstract
The main goal of this research is to examine the motivation of entrepreneurs from Moscow and the Moscow region in conducting entrepreneurial activity in present economic conditions, and to identify the obstacles slowing down this activity. For implementing this goal a survey of 63 small business owners was conducted. To collect the data, authors selected the ME (micro-enterprise), the SB (small business) and the SME (small and medium-sized enterprise). The actors questioned were entrepreneurs and more particularly the heads of companies running an ME, SB or SME in Moscow and its regions. Using research methods as factor analysis and Cronbach’s Alpha, a hierarchy of the different motives and entrepreneurial barriers were constructed. Investigation results show that regarding motivations of entrepreneurs, 4 components were obtained: extrinsic motivations composed of 4 items, intrinsic motivations composed of 6 items, motivations linked to independence and autonomy with 3 items and motivations related to the safety and well-being of the family with 3 items. In terms of barriers or obstacles encountered by Russian entrepreneurs, in regards with the literature review, we obtained 5 components: barriers of legitimacy consisting of 3 items, administrative barriers with 3 items, financial barriers with 2 items, managerial barriers with 3 items and finally competitive barriers with 3 items. The novelty of this study is to improve knowledge of the motivations and barriers that entrepreneurs in Moscow and its region encounter in the course of their 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".