Three change strategies in organization development: data-based, high engagement and generative
Bibliographic record
Abstract
Purpose This article categorizes organization development (OD) approaches to change management into three categories and explains their differences and when each might be most appropriate. It focuses on the differences between two different change strategies that utilize the same methods and are associated with a Dialogic OD mindset: high engagement and generative. The generative change strategy is the newest and least discussed in the change literature. The article endeavors to alert practitioners and researchers to important differences that make the generative change strategy the most rapid and transformational catalyst for change of the three. Design/methodology/approach Descriptions of the high engagement and generative change strategies are followed by brief case examples. The differences in roles and activities of leaders (sponsors), change agents and those affected by the change are identified. Propositions about when each strategy is appropriate are offered. Findings The rate and depth of change produced by generative change is beyond what change professionals normally aspire to. High engagement strategies appear to be the most common form of dialogic organizational consulting. It is probably not coincidental that managerial control is retained while engaging the targets of change in participating on some aspect of change planning and solution finding. Generative strategies that lead to rapid transformations are based on complexity science, so are more agile, emergent and self-organizing, and thus less managerial control. A generative strategy is of limited value when high levels of interdependence or large capital outlays require central coordination of change. In such cases, high engagement is a better choice. Originality/value The authors believe this is the first article to identify the differences between high engagement and generative strategies utilized by Dialogic OD practitioners using large group interventions and propose when each may be the most appropriate. Additionally, the generative change model provides a new lens for creating a path to the agile organization.
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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 | 0.043 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".