Identity Struggles in Merging Organizations
Bibliographic record
Abstract
Mergers as a type of organizational change call attention to questions of identity. In this article, the authors ask: How do people collectively reconstitute their group identities for themselves and others, and in particular, how do they renegotiate understandings of sameness and difference called into question by merging? The authors draw on qualitative case data from two different merger contexts within the health care sector to develop rich descriptions and a deeper understanding of the identity struggles of four groups of employees. They identified four patterns of identity work ranging from more proactive forms of positioning as “mavericks” or fighters” to more passive forms as “adapters” or “victims” as each group struggled to navigate an altered, fluid, and emerging landscape of potential resources for self-understanding and affiliation. The authors show how identity regulation and identity work manifest themselves in three domains of language, practices and space, and how identity regulation and identity work mutually interact. Thus, the negotiation of identity in merging is a dialectic process in which managerial identity regulation aimed at enhancing convergence across groups may be undermined both by groups’ attempts to reestablish differences and by a countervailing managerial need to accommodate (and thus sustain) differences in order to enable groups to locate themselves in the emerging entity.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.036 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| 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".