Quantitative data analysis is becoming increasingly important in the social sciences and in Strategic Management studies. The use of these methods (e.g. opinion polls, aggregate analysis of company data, evaluation of management strategies) is crucial nowadays not only in the field of academic research, but also in business and public institutions. The ability to properly understand and critically evaluate the results of quantitative analyzes is a key ingredient for social scientists as well as for professionals. The course introduces students to quantitative data analysis world with the aim of providing a solid conceptual and operational basis to learn how to read the data and apply statistical analysis models.

Knowledge and understanding At the end of the course, the students will be able to critically analyze quantitative data and to apply the main data analysis techniques. The level of mathematical formalization (use of complex formulas and calculations) will be reduced. Students will instead be required to be able to evaluate the main methodological complexities inherent in data analysis and to use the most appropriate empirical tools to evaluate Strategic Management choices. Applying knowledge and understanding The course will provide students with the tools necessary to apply the quantitative analysis techniques discussed during the course through the use ofusing statistical software. Making judgments The course aims to provide students with the tools to effectively evaluate and use the main techniques of quantitative data analysis. Furthermore, the course aims to stimulate the ability to read and interpret possible strategies through the use of statistical analysis. To this end, the course combines introductory lectures with computer-based sessions, group work and expert testimony. Communication skills One of the main objectives of the course is to provide students with the necessary skills to be able to communicate the results obtained from quantitative data analysis in an appropriate and effective way. Learning skills The course will be conducted mainly in seminar / workshop form. The activities will therefore be characterized by a practical and dynamic approach, to favor the development of technical skills that can be used both in the world of business and in the academic career.

The course will provide the necessary skills to properly use the main statistical techniques of data analysis. Alongside the main descriptive techniques (measures of central tendency and dispersion), the course will introduce simple techniques of bivariate analysis (cross-tabulation and correlation), clarifying the concepts of control variable and spurious relationships. Students will subsequently be introduced to the concepts of inference and statistical significance. The course will then focus on regression analysis, which forms the foundation for the application of more sophisticated statistical techniques. Finally, students will be introduced to the main data reduction techniques (i.e. cluster and factor analysis) The course will have a mainly practical orientation: students will be required not only to understand the main data analysis techniques, but to put them into practice through using main statistical software. The introductory sessions of the main data analysis techniques will be followed by sessions in which students will be asked to carry out quantitative analyzes.

Recommended: Alan Agresti, Barbara Finlay. Statistical methods for social sciences. Prentice Hall, 4th edition Suggested: Discovering Statistics Using IBM SPSS

- Lectures - Lab activitiesComputer-based sessions - Workgroups

The assessment will be done through: 1) a continuous assessment approach based on 3 deliverables; 2) analysis of a project done with the course of Organization Design; 3) Final written exam. The assignment will consist of applications on topics done over the weeks. Each assignment will have a weight of 15% of the final grade (the total of the 3 assignmentassignments will then be 45% of the final grade) The EBL done with the course of Organization Design will have a weight of 35% of the final grade. The final exam will be written and will consist of exercises. It will have a weight of 20% of the final grade. All deliverables will be evaluated on a 30-point scale.

Students are very welcome to pursue their thesis in the area of operations and supply chain with interfaces between marketing and operations using the quantitative tools learnt during this course. Students will have to present a proposal that will be carefully assessed. A good proposal is the key to formalize the thesis process. Two types of thesis projects will be evaluated: 1. Projects suggested by the students, which must be either based on empirical work. 2. Projects that the professor is pursuing, for which some datasets will be made available for the students in the area of operations, marketing, sustainability, and digital transformation. Other projects may be available within the X.ite Research Center in Luiss, to pursue both professional projects with companies or empirical works. In the latter case, the thesis will be submitted for publications in international journals. Hereby some examples of recent publications achieved with MSc students: 1. Maranesi, C., & De Giovanni, P. (2020). Modern Circular Economy: Corporate Strategy, Supply Chain, and Industrial Symbiosis. Sustainability, 12(22), 9383. 2. Naclerio, A. and De Giovanni, P. (2021) Improving the operational performance through Logistics, Blockchain, and Omnichannel, International Journal of Production Economics, to appear. 3. Prencipe, M.P. and De Giovanni, P. (2021) The use of Blockchain at Cantina Placido-Volpone. Chapter in the book: Blockchain Technology Applications in Businesses and Organizations, IGI-Global, London.

When possible, the dataset to be analyized will involve sustainable issues and climate changes.