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| Criterion |  Innovation Design Thinking |  Decision Making Constraint Analysis |  Product Discovery Outcome-Driven Innovation |  Decision Making Decision Tree |
|---|---|---|---|---|
Purposedifferent | For complex user problems with an uncertain cause, the method connects observation, interpretation, and experiment into a learning cycle. It keeps the focus on real needs and avoids premature solution language. This produces robust decisions for product and service. | 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. | Outcome-Driven Innovation clarifies customer problems, solution opportunities, and evidence. It separates jobs, desired outcomes, importance, satisfaction, and opportunity, and captures results as desired outcome statements, an opportunity landscape, and segments by underserved needs. | 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. |
Complexitydifferent | High | Low | High | Medium |
Timedifferent | 1 Tag bis mehrere Wochen | 30-90 min | Mehrere Wochen | 30-90 min |
Participantsdifferent | 4-10 | 2-8 | 2-6 researchers plus sample | 1-6 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Problem Statement, Prototype, Test Learnings | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Decision Tree, Option Map, Assumption List |
Tagsno overlap | InnovationPrototypeResearch | ConstraintsDecisionPlanningOptions | OutcomesInnovationResearchQuantitative | DecisionTreeOptions |