Sustaining authenticity while enabling adaptation: Discursively navigating strategy-identity tensions over time
Bibliographic record
Abstract
Over time, environmental pressures may push organizations to engage in strategic actions that diverge from their foundational identity claims. In such circumstances, organizations experience tensions between pressures for authenticity on the one hand, and pressures for adaptiveness on the other. Such pressures are likely to be of particular importance for organizations that have historically laid claim to a strong social mission. Drawing on a longitudinal study of a large cooperative financial services organization, this study examines how leaders discursively navigate strategy-identity tensions when strategic actions appear inconsistent with historically valued identity attributes. We identify three types of discursive practices leaders may engage in to navigate strategy-identity tensions: (i) pacing; (ii) sensegiving; and (iii) revising. In so doing, we show how leaders may work to enable strategic actions that might be perceived as contrary to key organizational identity attributes, with each of the practices serving different and complementary roles. At the same time, we show how successive recalibrations of the grounds for authenticity enacted in practices of pacing, sensegiving and revising can result in claims of organizational distinctiveness that while continued and insistent, could also become increasingly contestable.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".