The challenge of sustaining organizational hybridity: The role of power and agency
Bibliographic record
Abstract
Hybrid organizations harbor different and often conflicting institutional logics, thus facing the challenge of sustaining their hybridity. Crucial to overcoming this challenge is the identification process of organizational actors. We propose a theorization of how power relations affect this process. More specifically, we argue that an actor’s power influences their own professional identity: an increase [decrease] in their power, via the heightened [diminished] control that this power provides them over organizational discourse, boosts [threatens] their identity. Our theorization has implications for the longevity of a newly adopted logic within an organization. If the new logic modifies incumbent power relations, the identities of (formerly and newly) powerful individuals are influenced, which may lead these individuals to promote or resist the new logic, thereby affecting the odds that the logic will survive within the organization. We illustrate our theorization with a case study in a professional service firm. Our study contributes to nascent research on hybrid organizations by emphasizing the role of power and agency in the longevity of hybridity.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".