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.
A Scientific Approach to Innovation Management
A Scientific Approach to Innovation Management
Instructors: Alfonso Gambardella
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Gain insight into a topic and learn the fundamentals.
4.6
(77 reviews)
13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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Assessments
6 assignments
Taught in English
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There are 5 modules in this course
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
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
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
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
What's included
1 peer review
Instructors
Instructor ratings
4.6 (18 ratings)Offered by
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4.6
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Showing 3 of 77
FC
4
Reviewed on Apr 9, 2020
course well done in terms of pace and content.as improvement point teachers could schedule few times boxed windows with studens for Q&A more interactive
WC
5
Reviewed on Dec 1, 2019
Establishing fundamental Ideas about making a solid decision in the world of uncertainty and ambiguity.
JW
5
Reviewed on Apr 10, 2022
Really good course that helped me de-mystifying several areas within innovation management.
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