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Survey of the venture business development in different countries

2013· article· ru· W2238116646 sur OpenAlexaboutno aff
Alfiya R. Gaisina

Notice bibliographique

RevueСборники конференций НИЦ Социосфера · 2013
Typearticle
Langueru
DomaineBusiness, Management and Accounting
ThématiquePrivate Equity and Venture Capital
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésVenture capitalSocial venture capitalGovernment (linguistics)Investment (military)BusinessDeveloping countryFinanceCapital (architecture)Economic growthEconomicsPolitical science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Summary. This article is devoted to the experience of establishing venture business systems in both developed and developing countries. The paper also covers key issues of main supporting institutions and related challenges. Key words: venture business; venture capital; investments; innovations. Venture capital is an American invention, and so far the United States is home to the largest venture capital industry. This paper presents a study of a number of coun-tries that have tried to develop their own venture capital industries, and issues fre-quently faced by developing and developed nations willing to enhance their domestic venture capital industries. Let’s fi rstly review the experience of developed countries.Canadian government policies have resulted in its venture capital system being based on funds sponsored by labor unions. Recent growth in Canada’s VC market [4] has weakened the union funds’ grip on VC funding, however, and total investment has notably grown. Canada was the world’s fi fth largest recipient of VC fi nancing. Canadian strength in telecommunications technology has been a particular boonto VC investment.In a relative sense, Israel has achieved even greater success, since it was the sixth largest recipient of VC and the world’s largest recipient expressed as venture capital funding as a percent of GDP. At least part of Israel’s success can be traced to deliberate policy decisions by the Likud government in the early 1990s, which took concrete steps to commercialize defense-related technology developed with public funding [2].In Japan, direct investment is not popular, thus there are very few individual inves-tors who invest in start-up or early stage venture enterprises. Because of the lack of individual investors and the conservative investment attitude of venture capital fi rms, the entrepreneur in Japan has to provide a signifi cant portion of start-up capital com-pared with other countries.In contrast with other countries, Sweden was an early mover in venture capital. Although investments were made in Swedish companies earlier, 1973 is considered the year in which the venture capital industry started in a more organised form. Continued focus on R&D in big businesses and in universities mainly resulted in rather limited at-tention being allocated to innovation. The fi rst institutional private equity and venture capitalist, Foretagskapital, was established as a joint venture between the state and merchant banks in Sweden. Soon more funds followed [1].Scoreboard, Denmark, has been the best performer among the global innovation leaders in recent years, although the country is still lagging Sweden somewhat in this area as a whole. A number of Danish venture capitalist companies invested in young start-ups in the early 1980s, thus beginning Denmark’s venture capital tradition [3].Opportunities for entrepreneurs in developing countries are broader in scope than in developed markets, allowing fi rms to pursue a portfolio approach to strategy that can effi ciently manage the higher levels of business and market risk. Entrepreneurs in developing countries face a different set of circumstances than their counterparts in developed economies. While Western entrepreneurs operate at the fringes of the economy, emerging market entrepreneurs operate closer to the core – the needs and opportunities are more widespread.Russia seems to be the worst positioned among the BRICS countries on the innova-tion front. The current problems are far more emphasized by negative demographics

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0070,014
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,207
Écart entre enseignants0,187 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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