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Teaching Chinese R&D Managers

2007· article· en· W1598901840 sur OpenAlexaboutno aff
Tom Clarke

Notice bibliographique

RevueResearch-Technology Management · 2007
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueInnovation and Knowledge Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChinaSurpriseMandarin ChinesePsychologyHistoryLinguisticsSociologyPolitical scienceSocial psychologyLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Letter from Shanghai The culture that defines effective and ineffective R&D management practices is universal and to a great degree independent of country culture, at least as far as managing R&D in China is concerned. I base this conclusion on my recent experience in teaching four groups of industrial R&D managers in Shanghai, China, in December 2005 and June 2006. When I was first approached to put on a workshop, I had several concerns. The main one was how much of the R&D management best practices that I teach in North America would be applicable in the Chinese culture. Another was the language barrier. I don't speak or understand Mandarin. Thus it was with some trepidation that I accepted the invitation to teach. The language barrier did not prove as difficult as I thought it might. A majority of the workshop participants had a working knowledge of English and I was provided with two-way simultaneous interpretation. Thus when participants were asked to post on flipcharts the results of their group discussions in response to the open-ended questions posed during the workshop, it was not uncommon for the answers to be written in English. The biggest surprise, however, was the answers to those questions. To a great degree, they were identical to the responses I hear from my American and Canadian participants. After a while, I had to keep reminding myself that I was in China, not North America. Thus my primary concern over the transferability of North American/Western European R&D management best practices to China did not prove to be a problem. The following are some of the flipchart questions and answers from the Chinese R&D managers: What gives you the greatest satisfaction on the job? * Having a new product you worked on be successful in the market place. * Being rewarded for working hard. * Receiving recognition and respect from leaders and subordinates. * Receiving promotions and salary increases. * Doing a job you like very much. * Making friends at work-having good relations with co-workers. * Having the right to make decisions. These responses more or less match the answers to a 1999 survey by R&D Magazine on the satisfying aspects of a researcher's job, which they reported to be: * Solving challenging problems. * Quality of the people they work with. * Doing interesting work. * Doing what they are good at. * Ability to work independently. What are the factors that discourage idea generation and creativity? * Organization is too bureaucratic, with a strict hierarchy. * Not enough recognition for the development of intellectual property. * Top management focuses too much on key performance indicators that don't take creativity into account. * People are not given enough time to be creative due to work load. * Company focuses too much on short-term return on investment. * Culture is to always say yes to the boss when the answer should be no. * Fear of failure is too high. * Customers prefer safe solutions, not creative ones. * Motivation system does not support creativity. These answers are clearly in line with the many research studies conducted in North America and Western Europe on what inhibits creativity. The only distinct difference is the difficulty that many Asians have in saying no, when no is the appropriate response to a request to do something. Describe the most effective R&D manager you ever had who motivated you to work to the best of your ability. * Was a good role model. * Was involved, appreciated good work and provided rewards. * Was fair, and open in his dealings with subordinates. * Understood the nature of R&D work (e.g., risky, uncertain). * Fought for subordinates' benefits and compensation. …

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)0,004
score de la tête « metaresearch » (Gemma)0,005
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 enseignants0,041
Score d'incertitude au seuil0,081

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0070,002
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0050,006
Charge utile insuffisante (le modèle a refusé de juger)0,0230,005

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 Opus0,033
Tête enseignante GPT0,348
Écart entre enseignants0,315 · 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
Publié2007
Routes d'admission1
Résumé présentoui

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