The course aims at providing both theoretical and practical knowledge on how to evaluate stakeholders’ acceptability towards innovations linked to digitalisation thus supporting public and private decision-making.
The course focuses on the definition, design and deployment of rigorous and robust techniques, based on sound microeconomic theory, capable of ex-ante determining the acceptability of potential alternative policy interventions and predicting the behavioural change that might materialise when actually implemented.
Students will learn how to apply cutting hedge methods and will, through a case study-based approach, be familiar with data collection, modelling, analysis, and interpretation.
The course focuses on the definition, design and deployment of rigorous and robust techniques, based on sound microeconomic theory, capable of ex-ante determining the acceptability of potential alternative policy interventions and predicting the behavioural change that might materialise when actually implemented.
Students will learn how to apply cutting hedge methods and will, through a case study-based approach, be familiar with data collection, modelling, analysis, and interpretation.
teacher profile teaching materials
Module 1: Introductory Concepts: Digitalization (examples, relevance); Preferences (methods, innovations); Intervention policies (public, private).
Module 2: Data acquisition: theory, exercise, practical case studies
Module 3: Data analysis: theory, exercise, practical case studies
Module 4: Implications: theory, exercise, practical case studies
The exercises and practical case studies are developed with the software R.
• Furlan R., Martone D. (2011). La conjoint analysis per la ricerca sociale e di marketing. Franco Angeli, Milano, Italia.
• Marcucci (2005). I modelli a scelta discreta per l'analisi dei trasporti. Carocci, Roma
Articles/book chapters provided during the course
Programme
The course foresees 4 modules.Module 1: Introductory Concepts: Digitalization (examples, relevance); Preferences (methods, innovations); Intervention policies (public, private).
Module 2: Data acquisition: theory, exercise, practical case studies
Module 3: Data analysis: theory, exercise, practical case studies
Module 4: Implications: theory, exercise, practical case studies
The exercises and practical case studies are developed with the software R.
Core Documentation
• Acquati E., Bellini C. (2016). Digital Italy 2016. Per una strategia nazionale dell'innovazione digitale. Maggioli Editore• Furlan R., Martone D. (2011). La conjoint analysis per la ricerca sociale e di marketing. Franco Angeli, Milano, Italia.
• Marcucci (2005). I modelli a scelta discreta per l'analisi dei trasporti. Carocci, Roma
Articles/book chapters provided during the course
Reference Bibliography
Hideo Aizaki, Tomoaki Nakatani, Kazuo Sato, 2014, Stated Preference Methods Using R, CRC Press. A.H. Studenmund, Using econometrics: a practical guide, 2011, Pearson. A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models, prepared for U.S. Department of Transportation, Federal Transit Administration, F.S. Koppelman and C. Bhat, 2006.Type of delivery of the course
The course includes class lectures (online if needed) and exercises with the use of the PC. Students will make presentations in class and participate in discussions related to practical case studies.Attendance
Attendance, although not mandatory, is strongly recommended.Type of evaluation
For attending students: the final mark depends on the outcome of the final orale exam (50%) and on the activities carried out during the course, that is report and presentation on a practical case study, in class discussions and participation (50%). The oral exam consists of questions aimed at verifying the knowledge of the contents discussed during the course. For non-attending students: the final mark depends on the outcome of the final written exam. The written exam lasts 2 hours and consists of open-ended questions aimed at verifying the knowledge of the contents of the following books: • Acquati E., Bellini C. (2016). Digital Italy 2016. Per una strategia nazionale dell'innovazione digitale. Maggioli Editore • Furlan R., Martone D. (2011). La conjoint analysis per la ricerca sociale e di marketing. Franco Angeli, Milano, Italia. • Marcucci (2005). I modelli a scelta discreta per l'analisi dei trasporti. Carocci, Roma, Chapters 1 and 4.