THE INNOVATION DECISION-Welcome
Welcome to the course
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THE INNOVATION DECISION-The scientific approach to innovation management: introduction
Operation efficiency vs strategic efficiency
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What data can and cannot do
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Strategic efficiency
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What does the scientific approach do: the Galilean manager
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Inkdome case
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THE INNOVATION DECISION-Innovation as problem solving
What is innovation
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The structure of the innovation decision
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Risk and Uncertainty
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Type I and type II errors in innovation decisions
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Interactive tour of the Museum of Failure
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THE INNOVATION DECISION-Using the scientific approach to innovation management
Antecedents of the Scientific Approach
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The Building Blocks: THEED
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Formulate and apply theories to managerial problems
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Tools: business model canvas and other tools
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THE INNOVATION DECISION-Additional Resources
Readings & Videos
Recap slides
Background material (extended slides)
THEORY AND DATA FOR INNOVATION MANAGEMENT-The scientific approach to innovation management: tools, mechanisms and examples
Basic tools: probabilities
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Conditional probabilities and the Bayes Theorem
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The Scientific Approach: Theory and Mechanisms
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Using the organization to set the decision rule
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The Scientific Approach: summary and its use in practice
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THEORY AND DATA FOR INNOVATION MANAGEMENT-How to formulate hypotheses
How to derive hypotheses from a theory
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Hypotheses and their context [p values don’t always matter]
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Cases
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THEORY AND DATA FOR INNOVATION MANAGEMENT-Testing hypotheses with data
Design and logic of hypothesis testing (download the attached datasets)
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THEORY AND DATA FOR INNOVATION MANAGEMENT-Testing hypotheses with experiments
The use of experiments in innovation management
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Randomized Control Trials
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Split and multivariate tests
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Quasi Experimental Design
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THEORY AND DATA FOR INNOVATION MANAGEMENT-Metrics
Innovation metrics
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Metrics validity and reliability
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Metrics validity
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Metrics reliability
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THEORY AND DATA FOR INNOVATION MANAGEMENT-Additional Resources
Readings & Videos
Recap slides
Background material (extended slides)
DATA ANALYSIS-Using regression analysis
Correlation vs causality
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Regression analysis: Theory
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Regression analysis: Application
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Interview with Mimoto: paving the way for electric mobility using a scientific approach
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Interview with Eni Gas and Power: leveraging big data to uncover customer preferences
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DATA ANALYSIS-Analysing real data: examples from established firms and start-ups
Using data to answer important questions at Google
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How firms and startups can gather and analyze data to test hypotheses
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DATA ANALYSIS-How to make sense of results
Reflection critical evaluation
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DATA ANALYSIS-Additional Resources
Readings & Videos
Recap slides
Background material (extended slides)
ADVANCED TOOLS FOR INNOVATION MANAGEMENT DECISIONS -Predicting causal links
Difference-in-difference approach: Theory (download the attached datasets)
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Difference-in-difference approach: Examples (download the attached datasets)
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Instrumental variables: Theory (download the attached datasets)
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Instrumental variables: Examples (download the attached datasets)
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ADVANCED TOOLS FOR INNOVATION MANAGEMENT DECISIONS -Using «big data» to make innovation management decisions
Data science vs causal links
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Machine learning for innovation management decisions
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ADVANCED TOOLS FOR INNOVATION MANAGEMENT DECISIONS -Conclusions
Summary, conclusions, limitations of the scientific approach
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ADVANCED TOOLS FOR INNOVATION MANAGEMENT DECISIONS -Additional Resources
Readings & Videos
Recap slides
Background material (extended slides)