A Scientific Approach to Innovation Management
Ends soon! Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
A Scientific Approach to Innovation Management
11,135 already enrolled
Included with
Ask Coursera
104 reviews
104 reviews
Skills you'll gain
- Regression Analysis
- Statistical Hypothesis Testing
- Analysis
- Statistical Analysis
- Complex Problem Solving
- Data-Driven Decision-Making
- Entrepreneurship
- Innovation
- Case Studies
- Machine Learning
- Strategic Thinking
- Decision Making
- Big Data
- Performance Metric
- Data Analysis
- General Science and Research
- Strategic Decision-Making
- Correlation Analysis
Details to know
See how employees at top companies are mastering in-demand skills
There are 5 modules in this course
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.
This video is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101021061)
We provide a general discussion of innovation as problem-solving and we link the discuss the building blocks of the scientific approach to innovation decisions – from how to formulate the problem, to how to formulate the hypotheses and the theory, and how to test them. The whole discussion will be framed and applied to concrete managerial problems, including a discussion of the specific managerial tools that facilitate the application of a scientific approach to innovation management.
What's included
15 videos3 readings1 assignment1 discussion prompt
15 videos•Total 108 minutes
- Welcome to the course•6 minutes
- Operation efficiency vs strategic efficiency•3 minutes
- What data can and cannot do•4 minutes
- Strategic efficiency•5 minutes
- What does the scientific approach do: the Galilean manager•6 minutes
- Inkdome case•6 minutes
- What is innovation•7 minutes
- The structure of the innovation decision•16 minutes
- Risk and Uncertainty•11 minutes
- Type I and type II errors in innovation decisions•7 minutes
- Interactive tour of the Museum of Failure•4 minutes
- Antecedents of the Scientific Approach•9 minutes
- The Building Blocks: THEED•4 minutes
- Formulate and apply theories to managerial problems•11 minutes
- Tools: business model canvas and other tools•8 minutes
3 readings•Total 30 minutes
- Readings & Videos•10 minutes
- Recap slides•10 minutes
- Background material (extended slides)•10 minutes
1 assignment•Total 10 minutes
- Week 1•10 minutes
1 discussion prompt•Total 10 minutes
- So much data, so little analysis...•10 minutes
We provide more details about the scientific approach and we introduce probabilities to understand how and why certain decisions lead to some outcomes instead of others and how to make better decisions. We also focus on how to formulate and test hypotheses in practice, and how to interpret these tests. We finally discuss how to design and run experiments. NB: some videos may contain a downloadable database; please, download it and follow the in-video instructions
What's included
17 videos3 readings3 assignments1 discussion prompt
17 videos•Total 140 minutes
- Basic tools: probabilities•7 minutes
- Conditional probabilities and the Bayes Theorem•8 minutes
- The Scientific Approach: Theory and Mechanisms•11 minutes
- Using the organization to set the decision rule•7 minutes
- The Scientific Approach: summary and its use in practice•9 minutes
- How to derive hypotheses from a theory•6 minutes
- Hypotheses and their context [p values don’t always matter]•6 minutes
- Cases•4 minutes
- Design and logic of hypothesis testing (download the attached datasets)•12 minutes
- The use of experiments in innovation management•11 minutes
- Randomized Control Trials•8 minutes
- Split and multivariate tests•12 minutes
- Quasi Experimental Design•6 minutes
- Innovation metrics•11 minutes
- Metrics validity and reliability•5 minutes
- Metrics validity•7 minutes
- Metrics reliability•8 minutes
3 readings•Total 30 minutes
- Readings & Videos•10 minutes
- Recap slides•10 minutes
- Background material (extended slides)•10 minutes
3 assignments•Total 40 minutes
- Week 2•10 minutes
- Exercise 1•15 minutes
- Exercise 2•15 minutes
1 discussion prompt•Total 10 minutes
- Barriers to the adoption of a scientific approach to innovation management•10 minutes
We cover the basics of data analysis, beginning with the distinction between correlation and causality in the analysis of data. We also teach how to make predictions using regression analysis and link these methods to the scientific approach, showing what role these analyses play, how they help to make scientific decisions and why. We complement this with real examples of companies using data to make innovation decisions. We close by discussing how to interpret these analyses and results critically to make sure we understand what we really learn from the analyses and when, how and why we should interpret our results cautiously and critically.
What's included
8 videos3 readings1 assignment1 discussion prompt
8 videos•Total 63 minutes
- Correlation vs causality•9 minutes
- Regression analysis: Theory•12 minutes
- Regression analysis: Application•10 minutes
- Interview with Mimoto: paving the way for electric mobility using a scientific approach•9 minutes
- Interview with Eni Gas and Power: leveraging big data to uncover customer preferences•8 minutes
- Using data to answer important questions at Google•4 minutes
- How firms and startups can gather and analyze data to test hypotheses•6 minutes
- Reflection critical evaluation•5 minutes
3 readings•Total 30 minutes
- Readings & Videos•10 minutes
- Recap slides•10 minutes
- Background material (extended slides)•10 minutes
1 assignment•Total 10 minutes
- Week 3•10 minutes
1 discussion prompt•Total 10 minutes
- Reflecting on uncertainty•10 minutes
This is s a more advanced part in which we discuss causality and provide the students with some broad exposure to big data and machine learning, and we discuss what they can do for managerial decisions.We provide a general wrap-up and conclusion of the course, including a discussion of when the scientific approach is most appropriate or has limitations. This helps to see when to apply it, or when to apply other approaches, including our own gut feelings. NB: some videos may contain a downloadable database; please, download it and follow the in-video instructions
What's included
7 videos3 readings1 assignment1 discussion prompt
7 videos•Total 53 minutes
- Difference-in-difference approach: Theory (download the attached datasets)•10 minutes
- Difference-in-difference approach: Examples (download the attached datasets)•10 minutes
- Instrumental variables: Theory (download the attached datasets)•9 minutes
- Instrumental variables: Examples (download the attached datasets)•5 minutes
- Data science vs causal links•4 minutes
- Machine learning for innovation management decisions•6 minutes
- Summary, conclusions, limitations of the scientific approach•9 minutes
3 readings•Total 30 minutes
- Readings & Videos•10 minutes
- Recap slides•10 minutes
- Background material (extended slides)•10 minutes
1 assignment•Total 10 minutes
- Week 4•10 minutes
1 discussion prompt•Total 10 minutes
- Discussing type 1 and type 2 errors•10 minutes
What's included
1 peer review
1 peer review•Total 240 minutes
- A Scientific Approach to Innovation Management - Final project•240 minutes
Instructors
Offered by
Explore more from Business Strategy
- Status: PreviewU
University of Leeds
Course
- Status: Free Trial
Course
- Status: Free TrialU
University of Maryland, College Park
Course
- Status: Free TrialT
Tecnológico de Monterrey
Course
Why people choose Coursera for their career
Learner reviews
- 5 stars
75.96%
- 4 stars
18.26%
- 3 stars
3.84%
- 2 stars
1.92%
- 1 star
0%
Showing 3 of 104
Reviewed on Apr 4, 2026
This course is very useful in scientific as well as for teaching
Reviewed on Nov 8, 2020
When intuition is not enough and data are almost everywhere, managers should use these techniques.
Reviewed on Apr 1, 2026
Exceptional one. I was completely in agreement with all the course contents. 100% human brain is used in this. Brilliant one.
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
More questions
Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.