A time to imitate, a time to explore: roles of performance relative to aspirations and interorganizational learning processes in diversity of Ontario hospitals, 1971--1992
Bibliographic record
Abstract
Although research on organizational diversity has made significant progress in the past two decades, most studies have drawn predominantly on environmental perspectives. The role of organizational adaptive capacity in organizational diversity has been largely ignored. In my dissertation, I link the behavioral theory of the firm with interorganizational learning theories to examine whether performance relative to aspirations and interorganizational learning processes affect organizational diversity. My theoretical accounts and empirical findings reveal that the multifaceted learning processes of organizations shape the heterogeneity of organizations in a population. Specifically, two fundamental themes emerge from the empirical analysis of Ontario hospitals, 1971--1992. First, organizations, like the hospitals examined here, respond to increases in the discrepancy between their performance and aspirations by not imitating comparable, large, and successful organizations. As a result, performance relative to aspirations gives rise to interorganizational heterogeneity. Second, not until that performance feedback triggers decision makers to explore new routines can organizations rely upon the experience of other organizations to cope with environmental uncertainty and reduce exploration costs. Consequently, interorganizational learning processes increase the homogeneity of organizations. Overall, my theoretical development illustrates that performance feedback and interorganizational learning processes are important factors contributing to organizational diversity. My empirical findings highlight that interorganizational learning processes do not necessarily lead to the homogeneity of organizations, conditional upon performance feedback and the histories and experiences of other organizations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".