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Enregistrement W264246533 · doi:10.1080/08956308.2000.11671382

The Human Side: Dance With Your Collaborators

2000· article· en· W264246533 sur OpenAlexaffabout
Richard Smith, Mohi Ahmed

Notice bibliographique

RevueResearch-Technology Management · 2000
Typearticle
Langueen
DomaineChemistry
ThématiqueCatalytic Alkyne Reactions
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésDanceArtVisual artsComputer sciencePsychology

Résumé

récupéré en direct d'OpenAlex

Many commentators suggest that the basis of management is (1). But, when the management problem covers more than one organization, and those organizations are of different sizes or have different core competencies, there is good reason to believe that they have different cultures and, consequently, sense is not necessarily common (2). Based on our studies of 13 Japanese and Canadian firms and their partners, it seems clear that when dissimilar organizations work together, they need concrete collaboration skills to guide their actions. Specific collaboration skills, which can be taught, are necessary in this delicate and sensitive aspect of management. To understand the role and importance of these skills, we suggest a metaphor from another activity that requires sensitivity and delicacy: dancing. The process of learning to is our metaphor for learning to collaborate for technological innovation. We call the organized, deliberate (but fun) process of acquiring these skills dance lessons. The metaphor is intended as a heuristic, a better way to understand the steps that an individual or an organization takes as it learns to be a better collaborator. The metaphor can help us to recognize why and when training in technological collaboration is appropriate. Why Dance Lessons? One reason why a firm might seek to increase its skills in technological collaboration would be to put more into its collaborative activities. The potential from the collaboration (here, the prospect of developing hot new technology), can actually be a significant motivator. There is a small but growing literature on the role of humor and fun in the workplace (3-5). Although not without its critics (6, for example), the beneficial role of fun in management training seems to be gaining credibility. One of the reasons for this is that in a learning environment, humor has the ability to break down barriers and increase learner involvement and information retention. Another explanation is that technical people find fun in learning new skills and they seek out projects or employers that provide plenty of opportunity to so. It doesn't hurt that in a dynamic, technology-driven economy, those new skills are highly marketable. Given the great cultural and core competence divides that may separate innovation teams engaged in collaboration, the ability to surmount resistance to change is an important aspect of any training program. For this reason, we suggest, there may be more than metaphorical importance to the use of the dance concept when deploying skills training for technological collaboration. It may be that tim, as in dancing, becomes the mechanism for inspired-not merely acceptable-performance. Other reasons include learning how to lead, how to follow, how to communicate, how to select the partner (customer, supplier), how to build trust, how to manage risk and avoid danger, how to negotiate with a partner from a different culture, how to communicate effectively and efficiently, how to share risk and benefits, how to collaborate for sustainability, how to make collaboration sustainable and how to collaborate on the global stage. A thread in these reasons is the focus on having the skills needed to make choices before collaborating. When Are Lessons Appropriate? We believe firms should consider taking dance at any time. Instead of initiating training immediately before starting to collaborate or negotiate on an issue, we suggest a firm consider lessons as early as possible. While there is merit in postponing training in cross-cultural communication until one is about to travel on the assumption that this just in time training will be fresher and more relevant, training for technological collaboration is presumably about more than doing things right in an existing or established plan. Instead, training should include significant attention to the selection of partners and projects, or doing the things. …

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)0,044
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: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil0,120

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,044
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0170,009
Communication savante0,0060,010
Science ouverte0,0040,005
Intégrité de la recherche0,0180,021
Charge utile insuffisante (le modèle a refusé de juger)0,0360,014

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 Opus0,031
Tête enseignante GPT0,347
Écart entre enseignants0,316 · 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
GenreCommentaire

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

En bref

Citations2
Publié2000
Routes d'admission2
Résumé présentoui

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