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Курс: A Scientific Approach to Innovation Management. Чтобы вернуться, нажмите здесь.
- Welcome to the course
- Operation efficiency vs strategic efficiency
- What data can and cannot do
- Strategic efficiency
- What does the scientific approach do: the Galilean manager
- Inkdome case
- What is innovation
- The structure of the innovation decision
- Risk and Uncertainty
- Type I and type II errors in innovation decisions
- Interactive tour of the Museum of Failure
- The Building blocks: Theory, Hypotheses, Tests, Analysis
- Formulate and apply theories to managerial problems
- Tools: business model canvas and other tools
- Basic tools: probabilities
- Conditional probabilities and the Bayes Theorem
- The Scientific Approach: Theory and Mechanisms
- Using the organization to set the decision rule
- The Scientific Approach: summary and its use in practice
- How to derive hypotheses from a theory
- Hypotheses and their context [p values don’t always matter]
- Cases
- Design and logic of hypothesis testing (download the attached datasets)
- The use of experiments in innovation management
- Randomized Control Trials
- Split and multivariate tests
- Quasi Experimental Design
- Innovation metrics
- Metrics validity and reliability
- Metrics validity
- Metrics reliability
- Correlation vs causality
- Regression analysis: Theory
- Regression analysis: Application
- Interview with Mimoto: paving the way for electric mobility using a scientific approach
- Interview with Eni Gas and Power: leveraging big data to uncover customer preferences
- Using data to answer important questions at Google
- How firms and startups can gather and analyze data to test hypotheses
- Reflection critical evaluation
- Difference-in-difference approach: Theory (download the attached datasets)
- Difference-in-difference approach: Examples (download the attached datasets)
- Instrumental variables: Theory (download the attached datasets)
- Instrumental variables: Examples (download the attached datasets)
- Data science vs causal links
- Machine learning for innovation management decisions
- Summary, conclusions, limitations of the scientific approach