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| Criterion |  Decision Making Decision Tree |  Innovation Design Thinking |  Product Discovery Outcome-Driven Innovation |  Innovation Design Sprint |
|---|---|---|---|---|
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. | 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. | 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. | When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. |
Complexitydifferent | Medium | High | High | High |
Timedifferent | 30-90 min | 1 Tag bis mehrere Wochen | Mehrere Wochen | 4-5 Tage |
Participantsdifferent | 1-6 | 4-10 | 2-6 researchers plus sample | 5-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Problem Statement, Prototype, Test Learnings | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | DecisionTreeOptions | InnovationPrototypeResearch | OutcomesInnovationResearchQuantitative | Design sprintPrototypeValidationInnovation |