MétaCan
Menu
Retour à la cohorte
Enregistrement W255448933

Do Innovative Organisations Survive Longer Than Non-Innovative Organisations? Initial Evidence from an Empirical Study of Normal Organizations

2013· article· en· W255448933 sur OpenAlex
Eleanor D. Glor

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

venuePublié dans une revue dont le pays d'attache est le Canada.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

Notice bibliographique

Revue˜The œinnovation journal · 2013
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueEntrepreneurship Studies and Influences
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPopulationMortality rateOrganizational ecologyOrganizational performanceOrganizational studiesBusinessOrganizational cultureEconomicsSociologyPublic relationsMarketingPolitical scienceDemographyManagement
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

ABSTRACTThe literature focuses on innovation as an adaptive mechanism. From functionalist and evolutionary adaptation perspectives, innovative organizational populations should be expected to survive longer than normal ones, because innovative organizations and populations should be more adaptive than normal ones. The mortality rates of innovative organizational populations have not been identified, however, and the proposition of lower levels of mortality has not been tested. While the mortality rates of a number of organizational populations are known from the literature, the normal mortality rate for organizational populations has not been established either. This paper sets the stage for comparison of innovative organizations and populations to normal organizational population by identifying normal organizational population mortality rates. It concludes by discussing the basics of determining the mortality rate of innovative organizational populations.The approach to identifying normal organizational population mortality is demographic and the methodology a research synthesis of organizational population mortality studies described in the academic literature. It identifies the range of normal (mean) population mortality rates for all organizational populations assessed, and for the private (PS), non-profit (NPS) and public sectors (PSE) separately. A search of the literature for organizational population mortality studies found 33 published studies including one usable database (on the Internet). To assure only appropriate studies were included in the analysis, two criteria (screenings) were first applied to them: (1) unbiased study, covering a full population, and (2) not an outlier population. Twenty-eight studies met the standards set for the first screening and of these 21 survived the second screening. The expectation was that mortality rates would be highest in the PS; surprisingly, the highest mean mortality rates were discovered in the PSE (in the American federal government) followed by the PS. Should researchers be interested in studying innovative organizational populations using an organizational demography approach, recommendations are made as to how this could be done.Key words: Organizational demography, organizational ecology, organizational mortality, organizational population mortality, public sector innovation, innovative organization, mortality of innovative organizational populationsIntroductionInnovation has been promoted for all organizational sectors for two generations. We know little, however, about the impact of innovativeness on the survival of organizations or innovative organizational populations in any of the three societal sectors-the private sector (PS), non-profit (NPS) and public sector (PSE). To determine whether innovative organizational populations have different mortality rates than normal populations requires creation of a theory linking the effect of innovation on the mortality of its organization and its population, a methodology for tracking this link and the identification of a normal organizational population mortality rate against which to compare the results for innovative populations. This paper addresses the third element. First, normal organizational population rates are established. Then, suggestions are made for a methodology for researching innovative organizational populations. To discuss these issues effectively requires clarity of concepts, so the paper begins with a discussion of key concepts in organizational demography.DefinitionsAn organizational population is all or almost all of the organizations in a population, a population being, for example, an industry or all of the newspapers in a country or all of the trade associations in a country or all of the departments and agencies in a government. It is proposed that in the public sector (the publicly-owned sector) a population is a government responsible for a wide territory and a wide range of programs and services, such as a provincial or federal or a large local government but likely not a small local government. …

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.

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)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,032
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,012
Études des sciences et des technologies0,0010,000
Communication savante0,0010,005
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,049
Tête enseignante GPT0,318
Écart entre enseignants0,269 · 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