Insegnamento a.a. 2018-2019

20567 - INNOVATION MANAGEMENT AND STRATEGIES

Department of Management and Technology

Course taught in English
Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-P/08) - M (6 credits - II sem. - OP  |  SECS-P/08) - IM (6 credits - II sem. - OP  |  SECS-P/08) - AFC (6 credits - II sem. - OP  |  SECS-P/08) - CLEFIN-FINANCE (6 credits - II sem. - OP  |  SECS-P/08) - CLELI (6 credits - II sem. - OP  |  SECS-P/08) - ACME (6 credits - II sem. - OP  |  SECS-P/08) - DES-ESS (6 credits - II sem. - OP  |  12 credits SECS-P/08) - EMIT (6 credits - II sem. - OBS  |  12 credits SECS-P/08) - GIO (6 credits - II sem. - OP  |  SECS-P/08)
Course Director:
ALFONSO GAMBARDELLA

Classes: 31 (II sem.)
Instructors:
Class 31: ALFONSO GAMBARDELLA


Prerequisites

To feel comfortable in this course, students should have a basic knowledge of innovation management and data analysis.

Mission & Content Summary

MISSION

This course focuses on how firms manage innovation and the development of new technologies. Since these decisions are typically decisions under uncertainty, the course studies how to make effective decisions in these contexts. As a result, the course focuses on how and when to make these decisions, under what conditions, what are the tools to make these decisions in the most informed way, and the use of data to make them. The overarching goal is to provide the students with an analytic framework to make decisions like the launch of a new product, a new business idea, an innovation, a start-up. The approach is very practical and it involves concrete uses of data to make managerial decisions, as well as the realization of concrete projects in class by groups of 2-4 students about real managerial problems. The performance in the projects counts as part of the student’s evaluation for the course.

CONTENT SUMMARY

  • A scientific approch to innovation management.
  • Developing theories about the innovation process and testing them with data.
  • How the approach works in practice.
  • Using data to make innovation decisions: from correlations and tests of hypotheses to regressions and causality.
  • Building experiments to make innovation decisions.
  • Innovation theories.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Learn how to make managerial and entrepreneurial decisions about whether to launch and develop innovations.
  • Learn how to use theory and data to build and test analytical frameworks to make these innovation decisions.
  • Learn how to formulate in practice theories that produce testable implications to assess the outcome of innovation decisions.
  • Learn methods and instruments to test and predict the results of these decisions.
  • Learn in particular how to build experiments for these decisions.
  • Learn how and why this analytical approach leads to better managerial decisions about innovation than more generic managerial approaches.
  • Learn how to make these innovation decisions in very practical contexts.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Make decisions about whether to launch or develop an innovation in very practical contexts by developing frameworks and by using data to test the outcomes of these decisions.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
  • Interactive class activities (role playing, business game, simulation, online forum, instant polls)

DETAILS

  • Lectures.
  • Practical activities: formulation of theories about innovation decisions, and test with actua data using Stata or other relevant software.
  • Class project developed by groups of students and discussed at different points in time in class with the instructor and the other students.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Group assignment (report, exercise, presentation, project work etc.)
     x
  • Active class participation (virtual, attendance)
     x

ATTENDING STUDENTS

  • Group project:  60%
  • Final exam:  35%
  • Class participation:  5% (this is a totally discretionary bonus assigned by the instructors).

An attending student is a student who participated in no less than 20 classes. Class attendance is strongly encouraged.


NOT ATTENDING STUDENTS

Not attending students are evaluated only on the basis of a final written exam.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • Lecture slides, and references to articles indicated at the end of the lecture slides.
  • General textbooks for some of the more technical material (not required for the exam, but useful for consultation).
Last change 11/06/2018 19:26