Facilitating emergence: Complex, adaptive systems theory and the shape of change
Notice bibliographique
Résumé
This study used Principal Component Analysis to examine factors that facilitate emergent change in an organization.As organizational life becomes more complex, today's dominant management paradigms no longer suffice.This is particularly true in a health care setting where multiple sources of disease interacting with each other meet with often-competing organizational priorities and accountabilities in a highly complex world.This study identifies new ways of approaching complexity by embracing the capacity of complex systems to find their own form of order and coherence.Based on a review of the literature, interviews with hospital CEOs, and my organization development practice experience in the health care sector, I identified nine constructs of interest: a strategic framework; organizational culture; work structures; CEO and executive team; leadership culture; quality control systems; accountability framework; learning structures; and feedback processes.One hundred and sixty-two senior leaders, managers, and staff at a hospital in Toronto, Canada, who had completed an eight-week leadership program, completed an Emergence Survey based on the nine constructs of interest.The survey included Likert items representing the nine constructs, as well as opportunities to provide narrative feedback.In the initial analysis of the survey results, the items taken as a whole would not converge on a clear set of components.It was also clear that the mean for most of the items was very high.I theorized that the size of the sample and possibility that they were a favorably biased convenience sample because they had self-selected as leaders may have contributed to the lack of convergence and high mean.I then theorized three clusters of constructs, based on what appeared to be natural affinities.At that point I facilitated two focus groups with people who were among the survey group.Both focus groups affirmed the importance of each of the factors in improving organizational performance indicators such as patient satisfaction, staff v engagement, and quality.I then completed a principal component analysis of each of the three clusters of constructs.From this analysis, seven components emerged.Five of these, executive engagement, safe-fail culture, collaborative decision-processes, a collaborative quality, and intentional learning processes had reliability >.70; culture of experimentation and purposeful orientation had reliability < .70.
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 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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,010 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».