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Enregistrement W264246533 · doi:10.1080/08956308.2000.11671382

The Human Side: Dance With Your Collaborators

2000· article· en· W264246533 sur OpenAlex

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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. …

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,893
Score d'incertitude au seuil0,951

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,031
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