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Enregistrement W1479926022

R&D on the Fast Track to Globalization

2011· article· en· W1479926022 sur OpenAlexaboutno aff
Eric R. Larson

Notice bibliographique

RevueResearch-Technology Management · 2011
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic Growth and Productivity
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGlobalizationIBMRevenueInvestment (military)Quarter (Canadian coin)ChinaBusinessMarketingManagementPolitical scienceEconomicsGeographyFinanceMarket economyPoliticsLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Views and News of the Current Research-Technology Management Scene Whether one calls it a polycentric R&D strategy, a multinodal approach, or something else altogether, recent studies have made it clear: the globalization of R&D has begun. a 2010 Ernst & Young survey of 1,050 company executives, U.S. companies indicated that they will increasingly shift more R&D dollars and capacity to places like China, India, and Brazil. Currently, according to the Ernst & Young survey, fewer than 11 percent of the U.S. companies surveyed said they currently spend more than 25 percent of their R&D budget in emerging markets. But by 2015, the number of companies at that level of investment is projected to double. speed of the trend is even more dramatic for Western European companies, which may see a tripling of the number of companies with at least a quarter of their R&D investment in emerging markets (from 6.9 percent today to 18.8 percent in fi ve years, according to the E&Y report). Firsthand observers of the trend, such as Vivek Wadhwa, a Duke University business school professor, say it's hard to overplay its signifi cance. The United States needs to wake up and realize that the world has changed, says Wadhwa. Top companies like IBM, GE, and HP now get the majority of their revenue from abroad, and they need to be close to their markets. This approach makes the most sense for them. Like Wadhwa, University of Maryland professor Anil Gupta has been studying and writing about emerging markets for years. author of Getting China and India Right says there are a few key reasons why Western companies are decentralizing their R&D at an increasing rate. First, many Western-designed products need to be redesigned to lower costs if they are to be successful with the growing middle class who represent the biggest increase in purchasing power in these new markets. Emerging markets are becoming bigger quite rapidly. But many products are designed for richer customers, not the middle income customer. When companies relocate R&D to an emerging market, they challenge themselves to create lower-cost solutions to meet the needs of consumers in that market. Sometimes developing lower-cost alternatives for emerging markets brings benefits back home, as well. India, General Electric (whose 5,000-person R&D center in Bangalore is bigger than GE's largest R&D center in the United States) developed a portable ECG machine that costs a fi fth as much as the baseline U.S. model. Now it's selling the device in niche markets in the United States. In the future, we will see more innovations being developed abroad and brought to the United States, says Gupta. Whether or not a given product yields such a reverse innovation effect, having R&D capacity close to the new market is essential if the product is to hit its mark. A second key driver of the move to overseas R&D: dollar for dollar, higher returns on the R&D investment. China and India, for example, market-rate compensation for scientists and engineers is about a quarter what it is in the United States. Venture capitalists and corporations alike see their R&D investment stretching farther overseas. That's a good sell right now, especially for industries like, for instance, pharmaceuticals, in which only a small percentage of trialed products make it to market. Lower costs allow these companies to hedge their bets more effectively as they search for the rare product that really takes off. And, the sheer number of scientists and engineers being produced by universities in emerging markets allows a quicker scaling up of R&D. This is true even when the quality factor is weighed, argues Gupta. While the average researcher in the United States is better trained than the average researcher in an emerging market, there are enough scientists in the top stratum of foreign researchers that companies can draw from. …

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)0,007
score de la tête « metaresearch » (Gemma)0,015
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,113

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,015
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0030,008
Communication savante0,0150,020
Science ouverte0,0010,007
Intégrité de la recherche0,0070,012
Charge utile insuffisante (le modèle a refusé de juger)0,0340,011

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 Opus0,128
Tête enseignante GPT0,283
Écart entre enseignants0,155 · 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'étudeSans objet
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
Publié2011
Routes d'admission1
Résumé présentoui

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