Institutional Equivalence: How Industry and Community Peers Influence Corporate Philanthropy
Bibliographic record
Abstract
This paper explores how organizations respond to simultaneous institutional influences from two distinct sources: the industry in which they operate and the local geographic community in which they are headquartered. We theorize that the existence of institutional equivalents—other organizations at the same intersection of different fields, such as the same industry and the same community—provides a clear and well defined reference category for firms and thus shapes which subset of peers the focal organization imitates most closely. We develop hypotheses about how the presence or absence of institutional equivalents affects organizations’ responses to behavioral cues from different peer groups, how these effects vary when peers in different fields exhibit inconsistent behaviors, and how organizational characteristics, such as size and performance, strengthen or weaken the influence of institutional equivalents. We test our propositions through a longitudinal analysis of philanthropic contributions by Fortune 1000 firms from 1980 to 2006. Our framework illuminates how simultaneous presence in multiple fields affects organizations and introduces to institutional theory the concept of institutional equivalence, which we argue is a critical factor in determining how organizations respond to multiple institutional cues.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".