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
proposé par
Université Bocconi
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
13 heures pour terminer
THE INNOVATION DECISION
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.
14 vidéos (Total 106 min), 3 lectures, 1 quiz
14 vidéos
Strategic efficiency4 min
What does the scientific approach do: the Galilean manager5 min
Inkdome case5 min
What is innovation7 min
The structure of the innovation decision15 min
Risk and Uncertainty11 min
Type I and type II errors in innovation decisions6 min
Interactive tour of the Museum of Failure3 min
The Building blocks: Theory, Hypotheses, Tests, Analysis12 min
Formulate and apply theories to managerial problems11 min
Tools: business model canvas and other tools8 min
3 lectures
Readings & Videos10 min
Recap slides10 min
Background material (extended slides)10 min
1 exercice pour s'entraîner
Week 110 min
Semaine
24 heures pour terminer
THEORY AND DATA FOR INNOVATION MANAGEMENT
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
16 vidéos (Total 140 min), 3 lectures, 3 quiz
16 vidéos
The scientific approach15 min
Using the organization to set the decision rule7 min
How to derive hypotheses from a theory6 min
Hypotheses and their context [p values don’t always matter]5 min
Cases4 min
Design and logic of hypothesis testing (download the attached datasets)12 min
The use of experiments in innovation management11 min
Randomized Control Trials7 min
Split and multivariate tests12 min
Quasi Experimental Design6 min
Innovation metrics11 min
Metrics validity and reliability5 min
Metrics validity7 min
Metrics reliability8 min
3 lectures
Readings & Videos10 min
Recap slides10 min
Background material (extended slides)10 min
3 exercices pour s'entraîner
Exercise 115 min
Exercise 215 min
Week 210 min
Semaine
32 heures pour terminer
DATA ANALYSIS
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.
8 vidéos (Total 63 min), 3 lectures, 1 quiz
8 vidéos
Interview with Mimoto: paving the way for electric mobility using a scientific approach8 min
Interview with Eni Gas and Power: leveraging big data to uncover customer preferences8 min
Using data to answer important questions at Google3 min
How firms and startups can gather and analyze data to test hypotheses6 min
Reflection critical evaluation5 min
3 lectures
Readings & Videos10 min
Recap slides10 min
Background material (extended slides)10 min
1 exercice pour s'entraîner
Week 310 min
Semaine
42 heures pour terminer
ADVANCED TOOLS FOR INNOVATION MANAGEMENT DECISIONS
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
7 vidéos (Total 53 min), 3 lectures, 1 quiz
7 vidéos
Instrumental variables: Examples (download the attached datasets)5 min
Data science vs causal links4 min
Machine learning for innovation management decisions5 min
Summary, conclusions, limitations of the scientific approach8 min
3 lectures
Readings & Videos10 min
Recap slides10 min
Background material (extended slides)10 min
1 exercice pour s'entraîner
Week 410 min
Enseignants
Alfonso Gambardella
Professor of Corporate ManagementManagement and Technology
Arnaldo Camuffo
Professor of Business OrganizationManagement and Technology
Chiara Spina
Lecturer and Ph.D. CandidateManagement and Technology
À propos de Université Bocconi
Our ambition is to develop students' potential and foster knowledge in Business, Economics and Law through innovative learning and research activities in a multicultural environment. Bocconi is a community that constantly innovates teaching and learning technologies and that strongly believes in the power of life-long learning and networking.
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