Interorganizational Collaboration and Professional Diversity: A Mixed-Methods Investigation of Disagreement in the Context of Disaster Risk Management
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".