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High-Performance Research Organizations: Here Are Ten Attributes That Help Managements Do the Right Things to Turn Their Visions into Reality

2001· article· en· W240041370 sur OpenAlexaboutno aff
George A. Neufeld, Peter A. Simeoni, Marilyn Taylor

Notice bibliographique

RevueResearch-Technology Management · 2001
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueResearch, Science, and Academia
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBest practiceSet (abstract data type)VisionAuditBusinessQuality (philosophy)Government (linguistics)Work (physics)Perspective (graphical)Public relationsKnowledge managementProcess managementComputer sciencePolitical scienceEngineeringSociologyAccounting
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This article has two messages. First, it is both useful and possible to develop a set of performance ideals or attributes for a notoriously difficult area to manage--research organizations. Second, it would be both useful and practical to apply the same approach to developing attributes for other types of organizations, such as software firms, manufacturers, insurance companies, government agencies, and universities. The concept of attributes comes from the question, How can you tell if an organization is well-managed? In other words, is there a set of performance ideals that could be used to assess the quality of management of any organization? To be most useful, these attributes would have to be end-results-oriented questions that go beyond the development and implementation of best practices. From a CEO's perspective, best practices are means to the end in mind. So, our initial question leads to several others: What are the intended results of the best practices? What should Boards and CEOs be looking for to be sure that best practices are in fact moving the organization toward the intended results? Can these results be stated in terms that are useful, observable and, preferably, measurable? The development of attributes of high-performance research organizations serves several purposes: * Over a six-year period, the Office of the Auditor General of Canada undertook work that pointed to the need for a description of what a well-managed research organization looks like. We used the guidance provided in the federal government's and Technology Strategy and Framework for the Human Resources Management of the Federal Science and Technology Community, and other sources to create a set of ideal outcomes of research management. We call these ideal outcomes attributes. The extent to which an attribute is demonstrated by an organization is an indication of the quality of management. * Companies are increasingly dependent upon the results from research for new and improved products in order to maintain competitive advantage. Governments view industry-driven science and technology as economic engines, and are increasingly dependent upon their own science and technology program for dealing with public policy issues such as climate change and the impacts of toxic substances. Furthermore, governments are placing more emphasis on achieving results, e.g., Results for Canadians, an initiative of the federal government, and the Government Performance and Results Act in the United States. * Assessing the performance of, and return on investment (ROI) from, research is a challenge faced by private and public sector executives as well as politicians. Research is a risky activity; not all research activity leads to expected results. Furthermore, the benefits from research sometimes take years to materialize. * The information available to Boards and CEOs for assessing the performance of research organizations is inadequate. The information tends to focus on past performance (e.g., published papers and patents) and on process and activities. However, high past performance does not guarantee the same in the future; today's executives need better and more current information. Furthermore, performance assessments that focus on process beg the question: What has happened as a result of having implemented good practices? Approaches to assessing the performance and ROI of research organizations generally fall into three categories: (1) Retrospective evaluation (examining the relevance and impact of research completed in the past); (2) Current evaluation (examining the organization's vision, strategies, target clients, practices, and people); and (3) Future evaluation (examining planned research, its relevance, potential benefits, and likelihood of success). Methodologies are most advanced for retrospective evaluations, and tend to involve costly studies that are conducted by third parties. …

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)0,059
score de la tête « metaresearch » (Gemma)0,054
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,941
Score d'incertitude au seuil0,311

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0590,054
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,005
Études des sciences et des technologies0,0110,023
Communication savante0,0400,038
Science ouverte0,0020,014
Intégrité de la recherche0,0060,011
Charge utile insuffisante (le modèle a refusé de juger)0,0060,004

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 Opus0,157
Tête enseignante GPT0,449
Écart entre enseignants0,292 · 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.

Devis d'étudeSans objet
DomaineIncitatifs
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

Citations13
Publié2001
Routes d'admission1
Résumé présentoui

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