How “gutsy” does an organization have to be to absorb new information?
Notice bibliographique
Résumé
Absorptive capacity was first defined in 1990 by Cohen and Levinthal as “an organization’s ability to identify, assimilate, and integrate new knowledge.”1(p.128) In 2002, Zahra and George2 expanded on this definition to emphasize the active process of transforming and exploiting that new knowledge.3 Their extended definition provided the basis for the 4 components of absorptive capacity: i) identify the new knowledge, ii) assimilate the knowledge, iii) integrate the knowledge into the existing knowledge base, and iv) use the new knowledge to change the organization in some way.3 The concept of absorptive capacity has its origins in biology4 and refers to the capacity of the gastrointestinal tract to absorb digested nutrients into the body. Somehow, this term leaked out of the gut and has been adopted by the business world where it is used to help understand and monitor an organization’s capacity to absorb new or innovative knowledge.1 In due course, the health care system also adopted this concept, hence the reason behind our review published in this issue of JBI Evidence Synthesis.3 In preparation for our scoping review, we engaged with all stakeholders and explored our understanding of the concept of absorptive capacity. Then, we examined the literature, seeking to establish how absorptive capacity is conceptualized and measured in the adoption of innovations in health care organizations. A scoping review methodology was deemed the most appropriate methodology to pursue this investigation, as it facilitated the breadth of inquiry required to inform both of these related issues. The review was conducted through the Strategy for Patient Oriented Research (SPOR) Evidence Alliance5 and was initiated by the Knowledge Translation Unit, Strategic Policy Branch of Health Canada. Colleagues from Health Canada were involved at multiple key points throughout the review.6 Most importantly, the entire team, including the Health Canada library scientist, Health Canada director, the Queen’s Collaboration for Health Care Quality research team, SPOR Evidence Alliance staff, and several graduate students, collaborated over multiple meetings to generate and refine the research question. Knowledge-user experts in the field of organizational change were also invited and included in the meeting discussions. Beyond refining the question, this integrated knowledge translation approach7 involved title and abstract screening, full-text review, and editorial contributions to the final report.6 None of the articles included in this review generated a new definition of absorptive capacity; rather, they investigated the influence of local health care contexts on this process. Each context required a different focus on the 4 components of absorptive capacity, and organizations drew attention to the factors that they found valuable to their particular process. It appears that organizations need to be more than just “gutsy” to absorb new information; expertise in each of the components of absorptive capacity is important to the success of this process, and understanding the contribution of context is essential. Measurement of the progress is crucial, and using the domains of absorptive capacity as a framework is beneficial in guiding this process of planning for and assessing the adoption of knowledge within the organization and any change that ensues as a result.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,796 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».