Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- Duration 14 hours
- Course by Università Bocconi
-
Offered by
About
How can innovators understand if their idea is worth developing and pursuing? In this course, we lay out a systematic process to make strategic decisions about innovative product or services that will help entrepreneurs, managers and innovators to avoid common pitfalls. We teach students to assess the feasibility of an innovative idea through problem-framing techniques and rigorous data analysis labelled ‘a scientific approach’. The course is highly interactive and includes exercises and real-world applications. We will also show the implications of a scientific approach to innovation management through a wide range of examples and case studies.Modules
Welcome
-
1
Videos
1
Videos
- Welcome to the course
The scientific approach to innovation management: introduction
-
5
Videos
5
Videos
- Operation efficiency vs strategic efficiency
- What data can and cannot do
- Strategic efficiency
- What does the scientific approach do: the Galilean manager
- Inkdome case
Innovation as problem solving
-
5
Videos
5
Videos
- 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
Using the scientific approach to innovation management
-
4
Videos
4
Videos
- Antecedents of the Scientific Approach
- The Building Blocks: THEED
- Formulate and apply theories to managerial problems
- Tools: business model canvas and other tools
Additional Resources
-
3
Readings
3
Readings
- Readings & Videos
- Recap slides
- Background material (extended slides)
Review
-
1
Discussion
-
1
Assignment
1
Assignment
- Week 1
1
Discussions
- So much data, so little analysis...
The scientific approach to innovation management: tools, mechanisms and examples
-
5
Videos
-
2
Assignment
2
Assignment
- Exercise 1
- Exercise 2
5
Videos
- 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 formulate hypotheses
-
3
Videos
3
Videos
- How to derive hypotheses from a theory
- Hypotheses and their context [p values don’t always matter]
- Cases
Testing hypotheses with data
-
1
Videos
1
Videos
- Design and logic of hypothesis testing (download the attached datasets)
Testing hypotheses with experiments
-
4
Videos
4
Videos
- The use of experiments in innovation management
- Randomized Control Trials
- Split and multivariate tests
- Quasi Experimental Design
Metrics
-
4
Videos
4
Videos
- Innovation metrics
- Metrics validity and reliability
- Metrics validity
- Metrics reliability
Additional Resources
-
3
Readings
3
Readings
- Readings & Videos
- Recap slides
- Background material (extended slides)
Review
-
1
Discussion
-
1
Assignment
1
Assignment
- Week 2
1
Discussions
- Barriers to the adoption of a scientific approach to innovation management
Using regression analysis
-
5
Videos
5
Videos
- 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
Analysing real data: examples from established firms and start-ups
-
2
Videos
2
Videos
- Using data to answer important questions at Google
- How firms and startups can gather and analyze data to test hypotheses
How to make sense of results
-
1
Videos
1
Videos
- Reflection critical evaluation
Additional Resources
-
3
Readings
3
Readings
- Readings & Videos
- Recap slides
- Background material (extended slides)
Review
-
1
Discussion
-
1
Assignment
1
Assignment
- Week 3
1
Discussions
- Reflecting on uncertainty
Predicting causal links
-
4
Videos
4
Videos
- 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)
Using «big data» to make innovation management decisions
-
2
Videos
2
Videos
- Data science vs causal links
- Machine learning for innovation management decisions
Conclusions
-
1
Videos
1
Videos
- Summary, conclusions, limitations of the scientific approach
Additional Resources
-
3
Readings
3
Readings
- Readings & Videos
- Recap slides
- Background material (extended slides)
Review
-
1
Discussion
-
1
Assignment
1
Assignment
- Week 4
1
Discussions
- Discussing type 1 and type 2 errors
Final Project
-
1
PeerReview
1
Peer Review
- A Scientific Approach to Innovation Management - Final project
Auto Summary
Discover how to strategically develop and pursue innovative ideas with "A Scientific Approach to Innovation Management." This foundational course, offered by Coursera in the Business & Management domain, is designed to equip entrepreneurs, managers, and innovators with the tools to assess the feasibility of their concepts through problem-framing techniques and rigorous data analysis. Led by expert instructors, the course provides a systematic process to help avoid common pitfalls in innovation. Learners will engage in highly interactive exercises, real-world applications, and explore various examples and case studies to understand the implications of a scientific approach to innovation management. With a total duration of 840 minutes, this course is available through a Starter subscription, making it accessible and practical for those looking to enhance their innovation management skills.
Alfonso Gambardella
Arnaldo Camuffo
Chiara Spina