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| Criterion |  Decision Making Decision Tree |  Systems Thinking Intervention Mapping |  Growth A/B Testing |  Decision Making Constraint Analysis |
|---|---|---|---|---|
Purposedifferent | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | Intervention Mapping translates a need for change into a planned, evaluable program. The method connects target group, determinants, actions, and measurement into a traceable chain. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. |
Complexitydifferent | Medium | High | High | Low |
Timedifferent | 30-90 min | 1-5 Tage | 1-4 Wochen | 30-90 min |
Participantsdifferent | 1-6 | 4-12 | 1-6 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Logic Model, Change Objectives, Intervention Components, Evaluation Plan | Experiment results, Decision log, Learning summary | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | DecisionTreeOptions | ChangeSystems thinkingCapability | ExperimentsGrowthAnalyticsValidation | ConstraintsDecisionPlanningOptions |