An integrative methodology for creatively exploring decision choices
Bibliographic record
Abstract
Purpose The authors translate their the concept of integrative thinking into a repeatable methodology, supported by a set of tools for thinking through difficult or “wicked“ problems, a process that offers a better chance of rejecting false choices and of finding a way through to an innovative alternative. Design/methodology/approach The authors divide their process into four phases. A case example illustrates each phase. Findings The four phases that make up the integrative thinking 10;process: articulating opposing ways to solve a vexing problem; analyzing those opposing models to truly understand them; attempting to resolve the antithetical approaches of the opposing models by creating new models that contain elements of the original alternatives but are superior to either one and testing the potential new solutions. Research limitations/implications Additional examples and detailed guidance is provided in the authors new book “Creating Great Choices: A Leader’s Guide to Integrative Thinking,” (Harvard Business School Press, 2017). Practical implications Several corporate examples of “wicked” problems to which integrative thinking might be applied are: After a merger, the combined sales organization is riven by dissension between proponents of two opposite approaches – one using direct sales and the other channel partners. The CEO of a retail bank struggling to manage the conflicting goals of increasing efficiency and improving customer service. Originality/value Applied thoughtfully, this new and tested methodology gives leaders at all levels a fighting chance at solving challenging problems and creating breakthrough choices.
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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 | 0.048 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".