Managing history: How New Zealand's Gallagher Group used rhetorical narratives to reprioritize and modify imprinted strategic guideposts
Bibliographic record
Abstract
Abstract Research Summary Imprinting theory predicts that organizations are imprinted with multiple intersecting imprints that persist. Evidence suggests, however, that imprints are sometimes reprioritized or modified, implying that they can be strategically managed. We draw upon rhetorical history research and an in‐depth historical case study of New Zealand's Gallagher Group to describe how one firm managed its imprints. Our inductive theorizing links historically imprinted strategic guideposts to decision‐making via two rearranging processes —that is, prioritizing and suspending—wherein managers use narratives to rearrange guideposts' influence and two scope modifying processes —that is, constraining and expanding—wherein managers change where guideposts apply. As a first explanation of how imprints are managed, these processes add nuance to existing theory and open new research avenues regarding additional processes and boundary conditions. Managerial Summary Imprints are elements of culture, strategy, structure, or decision‐making that emerge when the firm is founded or during times of turmoil. Imprints resist change and make organizational adaptation difficult. This study explains one way that managers manipulate imprinted decision‐making rules so that organizations can adapt. Using an in‐depth historical case study of New Zealand's Gallagher Group from 1938 to 2015, we follow four imprinted decision‐making rules that we call strategic guideposts and show how managers rhetorically revised these rules to adapt organizational decision‐making to changing environments. Managers prioritized some decision‐making rules while deemphasizing others or they changed their claims about the kinds of decisions where a decision‐rule applied. Knowing these rhetorical processes can help managers leverage their organization's history to facilitate necessary organizational change.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".