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.
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A Scientific Approach to Innovation Management
Università Bocconi
About this Course
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Approx. 14 hours to complete
Suggested: 6 hours/week
English
Subtitles: English
Offered by
Università 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.
Syllabus - What you will learn from this course
3 hours to complete
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.
3 hours to complete
14 videos (Total 106 min), 3 readings, 1 quiz
14 videos
Operation efficiency vs strategic efficiency3m
What data can and cannot do4m
Strategic efficiency4m
What does the scientific approach do: the Galilean manager5m
Inkdome case5m
What is innovation7m
The structure of the innovation decision15m
Risk and Uncertainty11m
Type I and type II errors in innovation decisions6m
Interactive tour of the Museum of Failure3m
The Building blocks: Theory, Hypotheses, Tests, Analysis12m
Formulate and apply theories to managerial problems11m
Tools: business model canvas and other tools8m
3 readings
Readings & Videos10m
Recap slides10m
Background material (extended slides)10m
1 practice exercise
Week 110m
4 hours to complete
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.
4 hours to complete
16 videos (Total 140 min), 3 readings, 3 quizzes
16 videos
Conditional probabilities and the Bayes Theorem12m
The scientific approach15m
Using the organization to set the decision rule7m
How to derive hypotheses from a theory6m
Hypotheses and their context [p values don’t always matter]5m
Cases4m
Design and logic of hypothesis testing (download the attached datasets)12m
The use of experiments in innovation management11m
Randomized Control Trials7m
Split and multivariate tests12m
Quasi Experimental Design6m
Innovation metrics11m
Metrics validity and reliability5m
Metrics validity7m
Metrics reliability8m
3 readings
Readings & Videos10m
Recap slides10m
Background material (extended slides)10m
3 practice exercises
Exercise 115m
Exercise 215m
Week 210m
2 hours to complete
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.
2 hours to complete
8 videos (Total 63 min), 3 readings, 1 quiz
8 videos
Regression analysis: Theory11m
Regression analysi: Application9m
Interview with Mimoto: paving the way for electric mobility using a scientific approach8m
Interview with Eni Gas and Power: leveraging big data to uncover customer preferences8m
Using data to answer important questions at Google3m
How firms and startups can gather and analyze data to test hypotheses6m
Reflection critical evaluation5m
3 readings
Readings & Videos10m
Recap slides10m
Background material (extended slides)10m
1 practice exercise
Week 310m
2 hours to complete
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.
2 hours to complete
7 videos (Total 53 min), 3 readings, 1 quiz
7 videos
Difference-in-difference approach: Examples (download the attached datasets)9m
Instrumental variables: Theory (download the attached datasets)9m
Instrumental variables: Examples (download the attached datasets)5m
Data science vs causal links4m
Machine learning for innovation management decisions5m
Summary, conclusions, limitations of the scientific approach8m
3 readings
Readings & Videos10m
Recap slides10m
Background material (extended slides)10m
1 practice exercise
Week 410m
Reviews
4.4
TOP REVIEWS FROM A SCIENTIFIC APPROACH TO INNOVATION MANAGEMENT
by GRFeb 3, 2020
My idea of approaching decisions really changed thanks to this course. Professors are good at accompanying you along the way. Also very useful for those who don't deal with innovation.
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