Interorganizational Collaboration and Professional Diversity: A Mixed-Methods Investigation of Disagreement in the Context of Disaster Risk Management
Notice bibliographique
Résumé
Disasters such as major floods and heat waves are taking an increasing toll on societies. Like other pressing policy issues, they are complex and cut across sectors, jurisdictions, and professional fields. Addressing these problems requires interorganizational collaboration between heterogeneous organizations and thus, interactions between representatives who may have different professional views and identities. Successful collaboration partly hinges on their capacity to integrate perspectives and develop sustainable working relationships despite differences. This thesis aimed to improve our understanding of the role played by professional differences in perspectives and identities in public-sector interorganizational collaboration. Three specific objectives were pursued in a multilevel approach: 1) To document the role of professional diversity for interorganizational collaboration when considered outside of sectoral or jurisdictional differences; 2) To investigate how salient differences in professional identity affect perceptions and reactions following task disagreement; and 3) To investigate the cognitive and relational pathways by which emotions, conflict perceptions, and information processing can predict decision quality and relationship quality following disagreement. Study 1 examined the experience of interorganizational collaboration in disaster management based on qualitative interviews with professional- and executive-level public servants from relevant Canadian federal organizations. Findings suggested that professional diversity was not by itself a salient issue. The most disempowering type of diversity was differences in mandates, especially when combined with differences in expertise or identities. Study 2 examined whether group composition based on professional identity was associated with differential perception of and reaction to disagreement during interorganizational problem solving. It was based on a small sample of experienced senior risk managers involved in a quasi-experimental simulation. In terms of disagreement perception, nonparametric analyses indicated that interprofessional teams reported more disagreement than homogeneous ones even if observed disagreement did not differ. In terms of reaction, disagreement showed consistent negative associations with reported measures of effectiveness, performance, and relationship quality in homogeneous teams. In contrast, these associations were either positive or nonsignificant in interprofessional teams. Study 3 experimentally tested in a disciplinary-defined university sample whether salient group professional composition affected how people perceived and reacted to a scripted task disagreement. Findings indicated that after experiencing the exact same task disagreement, participants in interprofessional teams were significantly more satisfied with their team than those in homogeneous teams. Path analyses supported the two hypothesized pathways linking emotion following disagreement to integrative decision making and satisfaction: a) a cognitive pathway whereby surprise predicted beneficial outcomes through increased reported task conflict and increased information processing and b) a relational pathway whereby negative emotions predicted detrimental outcomes through increased reported relationship conflict and decreased information processing. As a whole, the thesis improves our understanding of the cognitive and relational roles played by professional diversity in interorganizational collaboration. It provides evidence on the beneficial effects of salient diversity for group cohesion in the face of disagreement. It documents intervening cognitive and relational processes predicting performance and relational quality following task disagreement. Finally, it proposes research avenues whereby social psychology can be leveraged to support the adaptation of public-sector organizations to contemporary challenges in public policy.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,043 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».