A Scientific Approach to Innovation Management

Università Bocconi

Discover how to make strategic decisions about innovative products or services in the course "A Scientific Approach to Innovation Management." Through problem-framing techniques and rigorous data analysis, this course equips entrepreneurs, managers, and innovators with the tools to assess the feasibility of their ideas. It offers a highly interactive learning experience, including exercises and real-world applications.

The course delves into different modules, covering topics such as the innovation decision, theory and data for innovation management, data analysis, advanced tools for innovation management decisions, and a final project. Each module provides comprehensive insights and practical examples, enabling learners to avoid common pitfalls and understand the implications of applying a scientific approach to innovation management. The course is designed to empower individuals to make informed decisions and navigate the complexities of innovation.

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A Scientific Approach to Innovation Management
Course Modules

This comprehensive course encompasses modules covering the innovation decision, theory and data for innovation management, data analysis, advanced tools for innovation management decisions, and a final project, providing learners with practical insights into innovation management.


The first module, "THE INNOVATION DECISION," introduces learners to the strategic efficiency and the scientific approach to innovation management. It covers topics such as risk and uncertainty, problem-framing techniques, and the use of theories and tools in managerial decision-making. Through interactive exercises and real-world examples, learners gain a solid foundation in making strategic innovation decisions.


"THEORY AND DATA FOR INNOVATION MANAGEMENT" explores the application of probabilities, hypothesis testing, and innovation metrics in decision-making. Learners delve into the use of experiments and randomized control trials, gaining practical insights into leveraging data to drive innovation. This module aims to equip learners with the tools to derive hypotheses from theories and make informed decisions based on data-driven evidence.


The third module, "DATA ANALYSIS," delves into correlation versus causality, regression analysis, and the use of data to answer critical questions in innovation management. Real-world interviews and case studies provide learners with valuable insights into using data to test hypotheses and evaluate uncertainty. This module aims to enhance learners' data analysis skills and critical evaluation capabilities in the context of innovation management.


"ADVANCED TOOLS FOR INNOVATION MANAGEMENT DECISIONS" introduces learners to advanced approaches such as difference-in-difference methods, instrumental variables, and machine learning for innovation management. This module aims to broaden learners' understanding of causal links, data science, and the limitations of the scientific approach. Through practical examples and theoretical insights, learners develop a deeper appreciation for leveraging advanced tools in innovation management.


The final module, "FINAL PROJECT," provides learners with an opportunity to apply the knowledge and skills acquired throughout the course. It encourages learners to synthesize their understanding of innovation management and demonstrate their ability to make strategic decisions using a scientific approach. The final project allows learners to showcase their innovative insights and practical application of course concepts.

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