QUANTITATIVE METHODS IN STRATEGIC MANAGEMENT

QUANTITATIVE METHODS IN STRATEGIC MANAGEMENT

Marco Pirra

Instructional goals

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.

Intended learning outcomes

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 using 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.

Course Contents

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.

Reference Books

Recommended: Alan Agresti, Barbara Finlay. Statistical methods for social sciences. Prentice Hall, 4th edition Suggested: Darren George, Paul Mallery, IBM SPSS Statistics 27 Step by Step A Simple Guide and Reference

Teaching Methods

Lectures Computer-based sessions Workgroups

Assessment Method

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 assignments 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.

Thesis assignment criteria

Criteria for assigning the thesis are: 1) Exam results 2) Quality of the research project The project must be presented in written form (one page) introducing the main research question to be addressed and presenting a list of bibliographic references and an hypothetical index.

Does the syllabus cover sustainability topics?

YES

Week 1 Contenuto sessioni on line e on campus

In presence: How to approach an EBL project by adopting an academic style. Analysis of EBL reports and structure: Introduction, literature review, hypotheses, research questions, research design, methods, managerial insights, and conclusions. Online: Differences between an academic project and a consulting report

Week 2 Contenuto sessioni on line e on campus

In presence: Qualitative vs. quantitative methods Online: Examples of quantitative and qualitative methods.

Week 3 Contenuto sessioni on line e on campus

In presence: Introduction to SPSS. Variables and measures with data cleaning and preparation. How to start from a raw dataset. Online: Variables and measures with data cleaning and preparation.

Week 4 Contenuto sessioni on line e on campus

In presence: Sampling, descriptive, cross table analysis, control variables. Identify the best directions to setup the best firms’ strategies. Online: Applications, cases, and exercises

Week 5 Contenuto sessioni on line e on campus

In presence: Confidence intervals. The significant validity of setting successful strategies. Online: Discussion of the assignment #1.

Week 6 Contenuto sessioni on line e on campus

In presence: z-test and t-test. Comparing the firms’ strategies and identifying the most impactful decisions. Online: Applications, cases, and exercises

Week 7 Contenuto sessioni on line e on campus

In presence: Correlations, associations, and ANOVA. Understanding the links between the strategies and the eco-system. Online: Applications, cases, and exercises.

Week 8 Contenuto sessioni on line e on campus

In presence: Simple regression analysis. Predict the effect of your strategies and policies. Online: Discussion of assignment #2

Week 9 Contenuto sessioni on line e on campus

In presence: Multiple regression analysis. Online: Applications, cases, and exercises

Week 10 Contenuto sessioni on line e on campus

In presence: Introduction of complex regression models and simulation. Online: Applications, cases, and exercises

Week 11 Contenuto sessioni on line e on campus

In presence: Cluster analysis and factor analysis. Define your strategies for ad-hoc situation, identifying clear directions, and understanding big phenomena. Online: Discussion of the assignment #3

Week 12 Contenuto sessioni on line e on campus

In presence: Course closure Online: The future of quantitative methods