Innovation And Technology Management Milano

Taking action to combat climate change, reduce social inequalities, and develop sustainable practices has become imperative for corporations. Fortunately, the United Nations 2030 Agenda with its 17 goals charted the path forward. To be sustainable, a corporation needs to find new operating models which allow it to generate value for the benefit not only of shareholders but also of other corporate stakeholders. Based on this perspective, the course aims to provide basic knowledge about circular economy and sustainability. The course answers key questions such as: Who do companies create value for? What are the most appropriate models for contributing to sustainable development? How to manage relationships with suppliers if you want to be respectful of the natural environment and human rights? How to develop a sustainable marketing plan? How to use sustainable marketing techniques to create value and improve customer satisfaction? How to communicate corporate sustainability commitment to customers? How to promote social sustainability? How to promote diversity and inclusion? Which tools to adopt to measure and report on sustainability performance?
Upon completion of the course, students will understand the various implications arising from corporate activity on the natural environment and society. In addition, students will know the main circular economy and sustainability strategies which can enable a business to amplify positive impacts and reduce negative ones. Lastly, students will be able to communicate using the specific vocabulary of circular economy and sustainability. To reach this aim, the course is structured around two modules, each with a specific focus area.

Module I
The aim of module I is to introduce the topics of circular economy and sustainability, with a focus on sustainable marketing planning. Particularly, the topics covered by this module are the following: sustainable marketing; sustainable marketing research; the marketing mix; sustainable product and process development; sustainable entrepreneurship; sustainable branding.
Module II
Module II aims to focus on key theories and concepts related to sustainability and circular economy strategies. The topics covered by this module are the following: environmental sustainability; social sustainability; circular economy; stakeholder management; social and environmental sustainability along the supply chain; corporate welfare and work-life balance; social impact; diversity and inclusion; strategic philanthropy; sustainability reporting; new technologies for sustainability; sustainable governance and management.
The teaching method adopted in this course is highly interactive. It includes lectures, analysis and discussion of case studies, guest lectures from the corporate world, and group activities. A prerequisite for active participation is that students prepare assigned case studies in advance for the class.

For attending students, the final assessment method consists of a mix of elements: i) a written mid-term exam on the contents covered by the module I (40%); ii) a group assignment to put into practice the contents covered by the module I (10%); iii) a written end-term exam on the contents covered by the module II (50%).
The written exam consists of both multiple-choice and open questions. The written exam aims at assessing the students’ familiarity with the main concepts and theories related to the two modules of the course. Particularly, the objective of the written exam is to understand if the student has learned the conceptual and methodological tools, as well as if the student can interconnect the various concepts related to sustainability and circular economy.
The evaluation criteria adopted are as follows: knowledge and mastery of the discipline; use of discipline-specific vocabulary; clarity of exposition; practical application of the theoretical concepts. Further details will be provided at the beginning of the course and made available on Blackboard.
For non-attending students, the final assessment method consists of a single element: a written exam. The exam focuses on the contents of the book listed in the reading list section. A detailed examination program for non-attending students will be made available on Blackboard.

Module I
Richardson, N. (2020). Sustainable Marketing Planning (1st Edition). New York: Routledge. (Selected chapters)
Module II
Rasche, A., Morsing, M., Moon, J., & Kourula, A. (Eds.). (2023). Corporate Sustainability: Managing Responsible Business in a Globalised World (2nd ed.). Cambridge: Cambridge University Press. (Selected chapters)

The objectives of the course are threefold. The first aim is to provide students with knowledge and understanding of cultural dimensions that identify people’s thought, reasoning, communication and behaviour. The second aim of the course is to provide students with an essential Negotiation framework applicable to different multicultural and organizational contexts. Third, the course aims to provide students with the ability to effectively manage intercultural and distant teams and understand complex international business organizations.
At the end of the course, students will be able to:

• identify and interpret the theoretical and conceptual foundations of Communication, Negotiation and Lateral Leadership models in different multicultural and organizational contexts (knowledge);.
• identify and interpret the theoretical and conceptual foundations of cultural dimensions and dynamic cultural frameworks in global contexts and support ethical and sustainable decision making (knowledge);
  apply proven theoretical and conceptual knowledge to the design and use of managerial processes in managing intercultural, cross-functional and distant teams in complex multicultural organizational settings (practical skill);
  develop theoretical and conceptual knowledge on the design and implementation of managerial practices in international organization structures (knowledge);
• apply proven theoretical and conceptual knowledge to manage complex international commercial negotiations (practical skill);

Module I: Cultural dimensions

• Hofstede, Hall, Trompenaars and Schwartz theories.
• Dynamic theories on cultural dimensions.
• The Cultural Quotient (CQ).
• The Yin & Yang Perspective.
  Module II: Business Negotiation
• Leading an International Organization.
• Organizational Turnaround.
• Leading successfully across cultures.
• How to lead cross-functional and cross-cultural teams.
  Module III: Commercial Negotiation.
• The 4Ps Framework.
• Key elements of a negotiation.
• Preparing for a negotiation.
• Power dynamics in negotiation.
  Module IV: Intercultural Negotiation
• Definition and analysis of the communication and negotiation process.
• Leading an intercultural negotiation.
• Examples of political negotiations.
• Examples of M&As.
  The course will be taught through a combination of instructional methods: lectures, case studies, group and individual projects, video tutorials, role plays, and presentations by managers.

Attending Students (the presence will be registered)
30% Multiple choice questions on classroom training program contents. Slides are uploaded on Blackboard. .
50% Final Project Presentation: group work based on a real and current organizational undertaking. Detailed instructions are provided during the classroom sessions.
20% Written test with two open questions on classroom training program contents. Slides are uploaded on Blackboard.
Non-attending Students
100% Written test with four open questions on the following materials:
Y.S. Rana, The 4Ps Framework: Advanced Negotiation and Influence Strategies for Global Effectiveness, CreateSpace Independent Publishing Platform, 2014 (Chapters 2, 3, 5, 6, 7, 12, 13).
R. Guida-D. Trickey-E. Fregnan D.S. Pugh, Managing Challenges Across Cultures, McGraw-Hill Education, 2015.

Rana, Y.S. (2022). A turning points analysis of Cross-Border M&A negotiations, Negotiation and Conflict Management Research. https://ncmr.lps.library.cmu.edu/article/id/397/
Y.S. Rana, The 4Ps Framework: Advanced Negotiation and Influence Strategies for Global Effectiveness, CreateSpace Independent Publishing Platform, 2014 (Chapters 2, 3, 5, 6, 7, 12, 13).
R. Guida-D. Trickey-E. Fregnan D.S. Pugh, Managing Challenges Across Cultures, McGraw-Hill Education, 2015.
Additional materials throughout the course, including supplemental reading materials, articles and cases, will be provided by the Professor.

The course studies the economics of innovation and technological change from three perspectives: macro, micro and industry level. The first part of the course (macro) examines the relationship between technological change and economic transformation (e.g. productivity growth, unemployment, structural change). Particular attention is paid to measurement of innovation activities and the relationship between innovation and places and why some places prosper while others stagnate. Secondly the course studies at the micro level the different sources of innovation and the main appropriablity strategies. It studies the dichotomy big state vs. free markets and asks which works best, for whom and when. It addresses the issue of skills and innovation and how the rewards form innovation activities are distributed. At the industry level the course analyzes the forces that drive the diffusion of new processes and products, industry and market lifecycles, the role of design(s), network effects and standards and how to address the technological lock-in and lock-out. In addition, two themes are central in the analysis of technological innovation. Intellectual property (patents, copyrights and trademarks) and scientific progress. The course examines the economic justification for intellectual property and how different possible institutional design can stimulate or hinder innovation. It addresses the efficiency of the international regulation of intellectual property with a specific regard to developing countries (diffusion of knowledge, access to medicines etc. etc.). It studies how intellectual property can be used for technology transfer between university and industry. Secondly, the course analyzes the economics of science, the determinants of scientific progress and its economic impact. Some sectoral or firm-level case studies are proposed in some particularly relevant industries as the so called ‘industry 4,0’ or biotechnology, software and telecommunication.
Upon successful completion of the requirements for this course, students will be able to:
1. understand the relationships between innovation and the most important economic processes like growth, unemployment and structural change.
2. evaluate different competitive strategies in different the different innovative and emerging industries in the economy.
3. master the microeconomic principles driving innovation.
4. understand the different sources of innovation for the firm and the various appropriability strategies.
5. evaluate critically the use and impact of intellectual property.
6. think critically about the opportunities and challenges in the relationship between scientific progress and innovation.

1. Introduction to the course: stylized facts - basic concepts and definitions
2. Sources of innovation
3. Research and Development, and its management
4. The external organization of innovation: “Open Innovation”
5. Services and Innovation
6. Platforms and Business Model Innovation
7. Innovation and Growth: Artificial Intelligence and General Purpose Technologies
8. Measurement of innovation: Indicators and data
9. Diffusion of Innovation
10. Intellectual property rights and Profiting from Innovation
11. Economics of Science
12. Environmental Innovations: Drivers and Effects.
13. Technology and employment.
The course uses a mixture of readings, exercises, cases and guest speakers. Traditional lectures will be associated to a group assignment (the economic analysis of sources and impact of an existing innovation) and to discussion of case studies. Examples of the most relevant dynamics and controversies in high tech sectors are discussed through case studies, with a specific attention to the sources of innovation, firms’ strategy, intellectual property and knowledge transfer policies.

For attending students grades are based on:

• Group assignment (50%).
• Two written exams (1 h): the mid-term exam and the final exam on the second and third parts of the course to be held at the end of the course (2 out of 3 questions on various course topics, 50%). Only in the first exam session it will be possible to take the final exam only on the second and third part of the course.
  Non-attending students:
  Written exam (1h 30) to be held at the end of the course (3 questions on various course topics. The program is a set of compulsory readings posted on Blackboard).
  The group assignment is the analysis of a specific innovation developed in the last few years. The students will have to form a group of four/five people using the e-learning platform BLACKBOARD. Each group will deliver 3 intermediate assignments of two pages each. The nature of the assigment will be specified in class. The final project will contain the three assignments plus the additional considerations each group is free to put forward (e.g. prospects, competitors, welfare and policy implications, health and environmental issues).

The course is based on various reading material and slides provided at the beginning of the course and available on the Blackboard Platform.
Course Materials
Posted on BlackBoard.Unicatt: case studies, exercises, and library links to readings.
Library resources
The Università Cattolica del Sacro Cuore’s Library provides a range of learning resources including texts, journals, periodicals, magazines, and access to online databases and information services. It also offers a virtual library which is accessible via the University’s website.
Online learning
Icatt is the Università Cattolica del Sacro Cuore's online learning environment. It is used to support traditional face-to-face lectures, tutorials and workshops at the University. Icatt provides access to various features including announcements, course materials, discussion boards and assessments for each online course of study.

Territorial/national competitiveness is becoming a crucial driver for firms’ performance in a globalized world. Most of the firm level decisions are for different reasons related to the territorial context where the firm is located. And to changed geopolitical situations. The objective of the course is to identify the most important challenges that firms and ecosystems face and the elements in decisions made by firms in establishing a presence in Europe and internationally.
The first half of the course will present, examine and analyze some of the most relevant challenges that firms and ecosystems have to face, such as: climate change, the growth of startups and of innovative technologies, an aging population, digitalization and AI and the space economy. In this part of the course some responses to these challenges will be presented and analyzed through theoretical models and practical examples.
In the second half of the course, we start with an in-depth discussion of globalization, both in trade, capital mobility and FDI from an empirical and a theoretical perspective. We will also analyze the current debate on the limits of globalization. We then investigate international competitiveness looking at absolute, relative, and competitive advantage. Then we analyze comparative country case studies and propose a series of practical tools for analyzing national economies and their potential in the global world. The main points of reference are the instruments proposed by European and international organizations and by the principal agencies for rating the business environment of the points of destination for FDI. We then put competitiveness in perspective by looking at the exchange rate as a main instrument to leviate competitive pressure with special reference to the Krugman critique. Last but not least, we use big data and machine learning algorithms to study sovereign default risk and its implications for foreign investors.
Upon successful completion of the requirements for this course, students will be able to:
Understand the driving forces of globalization, its consequences, and its limits.
Understand the driving forces of innovation.
Compare national performances based on different competitiveness rankings.
Define the business environment of a given territory in terms of competitiveness and respond to the demand of firms regarding the evaluation of location strategies.
Assess the impact of innovation on labour and employment.
Comprehend the relationship between competitiveness, terms of trade and exchange rates.
Identify the elements that cause an increase in country risk, including from an empirical profile.
Define the innovation strategies for companies and banks.

(8 hours of lecturing): Globalization
(8 hours of lecturing): Driving Forces of Innovation
(8 hours of lecturing): Theories of Competitiveness and Empirical Indicators
(6 hours of lecturing): Competitiveness and the Krugman critique
(2 hours of lecturing): When things go wrong - Assessing Country Risk
(6 hours of student presentations/case study)
(8 hours of lecturing): Assess the impact of innovation on labour and employment
(8 hours of lecturing): Innovation Strategies for companies and banks
(3 lessons x 2 hours): Testimonials from industry experts
The course uses frontal lectures and student presentations to discuss readings, exercises, cases.

Students will be evaluated on the basis on a written exam with open questions and optional group work (up to 30% of the final grade).
The answers to the questions, aimed at testing the understanding of fundamental theoretical issues, will be evaluated by looking at the degree of the candidate’s knowledge, as well as at her/his ability to convey key messages in conceptually consistent ways through a clear and precise exposition.
The goal of the work group is the applications of learned theoretical issues to a case study, and it will be evaluated by looking at the degree of the analysis and class presentation.

Climate Change and the Circular Economy
Ourworldindata.org
Ruggerone, L. (2017) More Sustainability, Higher Growth Potential and Lower Risk: why the Global Financial System should support the Circular Economy, in Quaderni della Fondazione Brodolini numero 59, Roma 2017
Reina, D. (2025) La catena di smontaggio, EGEA, Milano 2025
The Challenges of an aging Population
Ruggerone, L. et alii (2025) “Tom and Pepper Lab”. Robotics for cognitive stimulation and social skills: a preliminary study. Asian Journal of Psychiatry, January 2025
Financing Innovation
Morse, A. (2024) Venture Debt, NBER WP number 32183, Cambridge Mass. 2024
The AI and Automation Challenges
Acemoglu, D. and Restrepo, P. (2019) Automation and New Tasks: How Technology Displaces and Reinstates Labor, Journal of Economic Perspectives – Volume 33, number 2, Spring 2019, pp. 3-30
Acemoglu, D. (2024) The Simple Macroeconomics of AI, NBER WP number 32487, Cambridge Mass. 2024
Mish, F. et alii. (2025) Artificial Intelligence and Productivity in Europe, IMW WP/25/67
Globalization
Esteban Ortiz-Ospina, Bertha Rohenkohl, Veronika Samborska, Simon Van Teutem, Diana Beltekian, and Max Roser (2025), Trade and Globalization: How have international trade and globalization changed over time? What is the structure of global trade today, and what is its impact? Our World in Data.
Dani Rodik, Symposium on Globalization in Perspective, Journal of Economic Perspectives, Vol 12(4), 1998. https://www.aeaweb.org/articles?id=10.1257/jep.12.4.3
Robert C. Feenstra, Integration of Trade and Disintegration of Production in the Global Economy, Journal of Economic Perspectives, Vol 12(4), 1998. https://www.aeaweb.org/articles?id=10.1257/jep.12.4.31
J. Richard Huber, Effect on Prices of Japan's Entry into World Commerce after 1858, Journal of Political Economy, Vol 79(3), May/June 1971. https://www.journals.uchicago.edu/doi/abs/10.1086/259771
Davies, Ronald B., Deriving the Trump Tariffs (April 06, 2025). Skatteforsk Working Paper No. 21, Available at SSRN: https://ssrn.com/abstract=5711044 or http://dx.doi.org/10.2139/ssrn.5711044.
Federle, Jonathan, André Meier, Gernot J. Müller, Willi Mutschler, and Moritz Schularick. 2026. "The Price of War." American Economic Review 116 (3): 791–827.
Driving Forces of Innovation
Innovation score European union, Innovation indicators for regions and sectors (Startup Genome)
Theories of Competitiveness and Empirical Indicators
Robert Feenstra and Alan M. Taylor (2021), International Economics, 5th edition, Macmillan, Chapter 2.
Porter, M. E. (1990). "The Competitive Advantage of Nations." Harvard Business Review, 68(2), 73–93.
The Global Competitiveness Report, World Economic Forum, last year available. https://www.weforum.org/reports/how-to-end-a-decade-of-lost-productivity-growth/
The World Bank Group (2020), Doing Business 2020: Comparing business regulation in 190 economies.
International Institute for Management Development (IMD), (2025), IMD World competitiveness yearbook 2025, IMD Business School.
J. Stiglitz-A. Sen-J.P. Fitoussi, Report by the Commission on the Measurement of Economic Performance and Social Progress, 2009.
S. Lall, Competitiveness indices and developing countries, an economic evaluation of Global Competitiveness Report, World Development, Vol. 29, N.9, pp. 1501-1525, 2001.
Rapporto sulla competitività dei Settori Produttivi, ISTAT, last year available. https://www.istat.it/it/competitivita (only in Italian).
The future of European competitiveness, Draghi Report, 2024, European Commission Report.
Competitiveness and the Krugman critique
Robert Feenstra and Alan M. Taylor (2021), International Economics, 5th edition, Macmillan, Chapter 13.
Desai, Mihir and Mark Veblen (2006), Foreign Exchange Hedging Strategies at General Motors: Competitive Exposures, Harvard Business School Case 9-205-096. https://www.hbs.edu/faculty/Pages/item.aspx?num=32133
Paul Krugman, Competitiveness: A Dangerous Obsession, Foreign Affairs, Vol 73(2), March/April 1994. https://www.jstor.org/stable/20045917?seq=1
When things go wrong - Assessing Country Risk
P. Manasse, N. Roubini,"Rules of Thumb" for Sovereign Debt Crises, IMF working paper, WP/05/42. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Rules-of-Thumb-for-Sovereign-Debt-Crises-17889
Carmen Reinhart, Kenneth Rogoff, 2008, This Time is Different: A Panoramic View of Eight Centuries of Financial Crisis, NBER Working Paper No. 13882. https://www.nber.org/system/files/working_papers/w13882/w13882.pdf
10. Assess the impact of innovation on labour and employment
Various papers by Daron Acemoglu and others
11. Innovation strategies for companies and banks
Technology transfers examples
Library Resources
The Università Cattolica del Sacro Cuore’s Library provides a range of learning resources including texts, journals, periodicals, magazines, and access to online databases and information services. It also offers a virtual library accessible via the University’s website.
Online Learning
Icatt is the Università Cattolica del Sacro Cuore's online learning environment. It is used to support traditional face-to-face lectures, tutorials and workshops at the University. Icatt provides access to various features including announcements, course materials, discussion boards and assessments for each online course of study.

The course aims at analysing the relationships between innovation and economic growth from the first industrial revolution to the recent technological development grounded on the diffusion of the Internet. In this context the term innovation is considered in its wider meaning: it involves invention, but also organizational change, the transformation of production and distribution processes, financial innovation, the emergence of new institutions and regulations. In considering the advances in science and technology the course will pay particular attention to the evolution of markets, to the role played by enterprises and to the development of public policies related to innovation.
Moreover, innovation always entails an ethical dimension. Innovation is never morally neutral, as it affects labour organisation, consumption habits and lifestyle, human relations, health, and the environment. The multifaceted nature of innovation can be better understood in the context of the Responsible Research and Innovation paradigm adopted by the EU since 2010 as a development of Hans Jonas’ Principle of responsibility. Rejecting both hostility towards technology and blind trust in science, Jonas relies on the middle way, the project of an ethic for technological civilization.
Upon successful completion of the requirements for this course, students will be able to:
1. understand innovation as a complex process that emerges from networks of people and institutions that include inventors, entrepreneurs, researchers, workers, consumers, investors, firms and governments;
2. improve their understanding of complex socio-technical processes, reflecting on feedback provided during cases and class discussions;
3. evaluate the possible social and environmental effects of innovation in order to asses its desirability and shape its development in line with a specific set of human values and objectives;
4. develop research skills, primary source analysis and evidence-based writing.

The first part of the course will be dedicated to the concept of innovation, to the relationships between innovation and economic growth and to the structuring of innovation processes. These topics will be analysed both in theorical and historical perspective, highlighting the relationships between technological paradigms and the structure of the processes of innovation, paying attention to the characteristics of the institutions involved in the innovation processes.
The second part will be focused on the Responsible Research and Innovation paradigm. Faced with a world such as the one we live in today, in which digital, demographic and environmental transitions are deeply intertwined, reflecting on Jonas (who in 1979 published The Principle of Responsibility) means addressing the issue of Responsible Research and Innovation (RRI).
Attending students will be asked to read one of the fundamental texts in the history of innovation, selected from a reading list posted by the teacher on Blackboard, and offer a critical reading of it to their classmates as a research assignment.
Students will also have the opportunity to participate, on a voluntary basis, in group projects and workshops organized by the Giannino Bassetti Foundation schola, called "Sandbox," conducting foresight exercises on some of the key trajectories of contemporary innovation: life sciences, a society for all ages, poiesis Intensive Innovation, technosciences, and public decision-making. Students who attend will earn up to 2 additional points on the final exam.
The course uses a mixture of readings, case studies and primary source analysis.

Grading will be based on the evaluation of a group assignment and a final written exam.
Percentage of total mark
Written examination (on the first part)
50%
Written examination (on the second part)
20%
Research assignment (presentation and report)
30%
The output of the work group will be a report on one of the topic of the course and its presentation to the class (the grade of the work group will be valid till the end of the academic year). Students attending to the 70% of the entire course are expected to take a mid-term exam and an end-term exam. Non-attending students will have a general exam on their specific program.

Attending students
First part. The History of Innovation: technology, institutions and organizations
Bruland, Kristine, and David C Mowery. “Innovation Through Time.” In The Oxford Handbook of Innovation, edited by Jan Fagerber, David C Mowery, and Richard R. Nelson, 349-379. Oxford: Oxford University Press, 2004.
Mokyr, Joel. “Evolution and the Dynamics of Technological Change.” In The Lever of Riches, 273–300. Oxford: Oxford University Press, 1992.
Nelson, Richard R, and Nathan Rosemberg. “Technical Innovation and National System.” In National Innovation Systems, edited by Richard R Nelson, 3–21. Oxford: Oxford University Press, 1993.
Mowery, David C. “Military R&D and Innovation.” In Economics of Innovation, edited by Bronwyn H Hall and Nathan Rosemberg, 1219–56. Amsterdam: North-Holland, 2010.
Lazonick, William. “The Innovative Firm.” In The Oxford Handbook of Innovation, edited by Jan Fegerberg, David C Mowery, and Richard R Nelson, 29–55. Oxford: Oxford University Press, 2004.
O’Sullivan, Mary. “Finance and Innovation.” In The Oxford Handbook of Innovation, edited by Jan Fegerberg, David C Mowery, and Richard R Nelson, 240–65. Oxford: Oxford University Press, 2004.
Granstrand, Ove. “Innovation and Intellectual Property Rights.” In The Oxford Handbook of Innovation, edited by Jan Fegerberg, David C Mowery, and Richard R Nelson, 266–90. Oxford: Oxford University Press, 2004.
Second Part: The Responsible Research and Innovation paradigm
Collingridge, David, The Social Control of Technology, Palgrave MacMillan, 1981
Additional reference materials will be posted on Blackboard.
Not attending students

• Same as reading list for attending students
  Lamoreaux, Naomi R., Daniel M. G. Raff, and Peter Temin. Learning by Doing in Markets, Firms, and Countries”, Chicago: The University of Chicago Press, 2007.

This course takes you to the cutting edge of how our legal systems are grappling with rapid technological change. We'll be exploring how digital tools and technologies, from AI-powered robots on the factory floor to algorithmic management systems, are fundamentally challenging normal legal understandings in labour law. A central theme is the digital transformation at the firm level, particularly in the context of manufacturing and the production of the future. But this isn't happening in a legal vacuum; different countries and regions are responding with diverse regulatory strategies, creating a fascinating landscape for comparative analysis. We'll examine the national and transnational impact of technology on labour law, comparing approaches across jurisdictions like the European Union and the United States. While the EU has tended towards a more regulated, risk-based framework like the AI Act, classifying workplace AI use as high-risk, the US has often favoured a self-regulatory or lighter-touch approach, though this is evolving. Crucially, this course will delve into how these challenges are addressed within different industrial relations systems, focusing on collective bargaining (CBAs) and worker representation. While formal law provides a foundational framework, CBAs are essential tools for regulating the introduction and use of AI and robotics in specific workplace contexts, offering context-specific approaches that law may struggle to capture. In the European Union, the Information and Consultation Directive and the Directive on Transparent and Predicable Working Conditions provide general rights that apply to algorithmic management systems, especially when they significantly change work organization. Furthermore, EU employment law mandates that employers introducing new technologies, including AI, shall consult with workers or their representatives in conformity with national procedures.
We'll see how this plays out differently across specific countries like Germany, France, Italy, and the United States. We will analyse critical cross-cutting themes, such as how law and technology affect the balance of power and responsibility between workers and AI machines. This includes grappling with liability when AI systems cause harm, considering concepts like the "third element" in the employment relationship (the intelligent machine itself) and the adequacy of existing social insurance systems. We'll explore the legal challenges of incorporating values into AI decision-making frameworks at the firm level, addressing issues like algorithmic bias and the need for transparency, fairness, and even internal ethics codes. Finally, we will dissect the complex interplay of norms and formal norms, recognizing that a balanced mix of law, collective bargaining, market forces, and technology is needed to regulate AI effectively at the workplace, often mediated through procedures and consultation. This course aims to equip you to critically assess these challenges and contribute to the development of legal frameworks, including potentially a new branch of law like Robot Labor Law (RLL), that address the dynamic interaction between AI/robots and workers.
Students who complete this class will be able to (i) know and understand through the likely legal and labour implications of technologies with new ideas and critical thinking; (ii) apply complex reasoning and formulate successful legal and labour strategies, with problem solving method, in relation to artificial intelligence, workplace process transformation, big data, encryption, blockchain, job tasks, vocational training, collective bargaining in tech/firms: (iii) clearly communicate the contents of their research themes, and, on the basis of the acquired knowledge in tech and labour, interlock with experts and not experts.
The aim of this module is to provide students with theoretical and practical knowledge of the main legal issues regarding the interplay between Intellectual Property Law and New Technologies. During the lessons and through discussion of cases we will study how intellectual property rights – patents, trade secrets, copyright, trade marks and designs – can be used to protect and foster the development of new technologies and digital tools. We will also study whether, how and to what extent these technologies and tools make it necessary to rethink and possibly reshape the traditional rules governing Intellectual Property Rights.
During the course, we will focus mainly on issues relating to Artificial Intelligence and how AI is having a significant impact across all areas of Intellectual Property. We will discuss topics such as: whether there are exclusive IP rights – and if so, who holds them – over works created by generative AI or with the aid of AI systems; what issues arise regarding the use of IP-protected material in AI training; whether the outputs of AI systems may constitute an infringement of third-party IP rights; what forms of protection exist against unlawful uses of AI, such as deepfakes; how AI may affect market dynamics and consumer purchasing decisions.
By the end of the course students will have acquired
basic legal notions regarding intellectual property protection for new technologies and digital tools;
the ability to apply these notions in order to understand whether, how and to what extent intellectual property law can be used to protect the business investments needed to create and develop such technologies and tools;
the ability to critically assess whether, how and to what extent it is necessary to adapt and modify the traditional foundations and rules of intellectual property law due to the ongoing development of such technologies and tools.

The areas we will explore are below indicated:
The Robot Labour Law and the Tech Jurisdictions. Should the law ensure that human-robot interactions occur in ways that are safe at workplace level? What happens when a robot causes an accident at work? What happens when robots interact with workers, at firm level, patrolling and organizing the work to perform? Can we define such workers as cyborg-workers? How will the law respond to such change? Is tech itself a jurisdiction, a place that could have laws of its own, a cyberspace with its own regulation? Tech is a global network, so jurisdictional questions are inevitable. This area is all about conflicts: there is the conflict between (radical) markets and consumers, tech users and the governments who disapprove of what they are doing, conflicts between different governments with different policies, conflicts between unions and corporations dealing with transnational robotics, platforms and digital infrastructures. A tech network brings together people in different places. Its aim is to bridge geographic divisions. When those divisions are transnational, the network raises jurisdictional issues just by being a network. We will explore different facets of jurisdiction. We will also explore the problem of overlapping national laws on a global network and how EU/USA laws deals with the question of jurisdiction over online activity.
The Collective Labour Law and the AI/Robots Regulations in EU/USA Legal Frames. Collective Labour Law refers to the laws governing unions, employers’ organisations, and collective negotiations, also in the most advanced tech frames. Governments regulate unions and collective negotiations for several reasons, including protecting the right to freedom of association, promoting positive economic development, and avoiding the negative social impact of labour relations problem. All collective labour law systems perform at least two functions. In its constitutive function, collective labour law provides the rules for organizing unions and establishing collective bargaining policies. This includes rules about union membership, internal union activities, policies that obligate employers to recognise union representation. In its power broker function, collective labour law fixes rules that influence the bargaining power of the parties and industrial conflicts. Can we refer to these two functions of the collective labour law to better protect the cyborg-workers? If yes, why? Which are the possible new functions of the collective bargaining at national and decentralised levels in the field of AI/Robot legal frame? To what extent are the forthcoming AI, data, platforms, and digital markets regulations in line with the fundamental rights that the collective labour law intends to protect?
Students are requested to: (i) do the assigned readings (see the materials published on blackboard and the related timetable); (ii) participate in class discussions on one or two principal problems that will be listed by means of blackboard; (iii) keep materials in digital folders. Our class discussions will be directed and focused to solve cases and to come up with a collective answer to the problem. Most of the readings consist of excerpts from casebooks, doctrines, judicial opinions, case law, statutes, law, CBAs.
1. Notion of Intellectual Property Rights (IPRs). Fundamental principles and rules.
2. IPRs and New Technologies: issues and challenges.
3. IPRs and Artificial Intelligence.
4. Patents. AI-made and AI-assisted inventions.
5. Trade secrets. Secrecy, disclosure obligations and AI.
6. Copyright. Copyrighted works and AI training. AI outputs and copyright infringement. Protection of AI-assisted and AI-generated works. Copyright-related issues and emerging technologies.
7. Protection of IPRs on Software and Databases.
8. Trade marks. AI and trade mark infringement. AI and consumer behaviour.
9. Designs. CAD and AI-generated design. AI and design infringement.
Classroom lectures. Analysis of legislative acts, documents and position papers from international organisations and IP offices, scholarly articles, judicial decisions. Exercises on practical cases. Classroom discussions with students on the course topics and materials.

Written exam. For each module the grade will be based on the answers to queries related to a legal case (i.e. students will receive a set of facts that have legal implications, and will be required to provide someone - a client, a judge, a legislator, etc. - with good advice on what to do in light of those facts). Students should identify the legal questions those facts raise and do the best to answer those questions based on the law they learned in the course (pursuant to the IRAC Template - link). The student’s capacity will be assessed on the basis of the attitudes to (i) synthetize the case, (ii) identify and apply relevant principles, (iii) conduct legal research, (iv) communicate effectively the legal concepts.
The overall grade will be based on the answers to the queries related to the Labour Law module (1/2) and on the answers to the queries related to the Intellectual Property Law module (1/2).
Written exam. Students will be presented with a practical case that raises legal issues related to the topics covered in the course and will be asked to address and discuss these issues, providing an opinion and indicating a possible solution. The assessment of the answers and the grade for the IP module will depend on the ability to (i) identify the issues raised by the case, (ii) identify the relevant legal principles and apply them to the case, (iii) develop proper legal reasoning in support of the proposed solution, (iv) use effective and appropriate legal language.
The overall grade will be based on the answers to the queries related to the Labour Law module (1/2) and on the answers to the queries related to the Intellectual Property Law module (1/2).

Most readings will be taken from these casebooks and from materials I developed during my recent researches (see blackboard):
M. Faioli, Assessing Risks and Liabilities of AI-Powered Robots in the Workplace. An EU-US Comparison, 2025, Available at: https://journals.uniurb.it/index.php/dsl/article/view/4922
M. Faioli, Artificial Intelligence: The Third Element of the Labour Relations, in The Future of Work (A. Perulli, T. Treu eds.), Wolters Kluwer, 2021
U. Liukkunen, Collective Bargaining in Labour Law Regimes, Springer, 2019 [for the related chapters concerning the USA, France, Germany and Italy]
F. G'sell, Regulating under uncertainty: governance options for generative AI, 2025, Available at: https://ssrn.com/abstract=4918704
J. Grimmelmann and A. J. Windawi, Blockchains as Infrastructure and Semicommons, 2023, Available at: https://ssrn.com/abstract=4152068
All readings, materials and case studies will be published on Blackboard.

The strong and quick development of digital technologies requires professional profiles capable of operating in the new economic and business context, stemmed from the fourth industrial revolution.
In this context the course aims to pursue two macro objectives addressed in two modules
The first module focuses on an introduction to cloud computing solutions and emerging trends in the technology landscape by providing both basic cloud computing programming capabilities and hands-on experience on a real cloud infrastructure. In addition, the students will be introduced to cloud-based solutions for natural language processing with an overview on popular use cases.
In order to understand how projects with a strong component of innovation / experimentation can be managed and governed, agile and extreme programming concepts/best practices will be investigated during the second module.
At the end of the first module of the course students will be able to:
have a basic understanding of the different technologies and capabilities that can be leveraged in a cloud environment
understand the pros and cons of each scenario, alongside a working knowledge of the most popular development tools and phases of a development project.
The second module is about the achievement of an in-depth knowledge related to organizational contexts characterized by digital transformation, , strong relationships, high integrated processes, innovation oriented internal and external pressures, and the development of creative skills and empathic ability as well flexibility and openness of thought, seeking to acquire reflexive and critical thinking.
In details the aims of the module are as follows:
to understand the importance of human factors & work organizational psychology into the digital transformation.
to acquire the main methodologies & tools for digital innovation in the company with a special focus on needs & requirements collection.
to experience a proposal development for digital innovation, addressing specifically the practical, social and institutional implications.
At the end of the second module the students will be able to:
Use a basic glossary about the psychological drivers embedded in the innovative technological contexts.
Use self and peer-evaluation tools for assessing the more relevant digital competencies.
Define and articulate a project draft related to specific organizational needs.
Acknowledge clues and cues of the workplace needs embedded in specific organizational environments.

Module 1:
Introduction to Cloud Computing
Service models on Cloud: IaaS, PaaS and SaaS
Cloud services: Storage, Infrastructure, Computing, Data, etc.
Cloud native development
Microservices
Containers and orchestration (Kubernetes)
Hands-on experiences on cloud infrastructures (create and develop applications and services deployment)
Development lifecycle: waterfall and agile methodologies applied to technical projects
Tools and resources on the cloud for data science projects
Module 2:
Smart working in a digital scenario
Organizational change and new way of working: the agile work and its features
Digital transformation & human factors in technology evolution.
How to detect new competencies in recruitment and development process: assessment exercise.
Organizational needs & requirements collection for digital innovation
Theories method & tools for digital innovation in the organization.
Function owner: needs & requirements collection.
Develop a proposal for a digital innovation in a Company function.
Presentation of the project.
The module 1 will include lectures and class exercises based on traditional teaching and teach by example principles. It is strongly advised to attend lectures for working on case studies and examples, and for revising materials. The module also involves hands-on activities with a cloud based integrated environment.
The module 2 is highly interactive and includes use of case studies, assessment meeting with company’s manager, exercises (including on the field observation of technological campus) and frontal lessons.

At the end of each module there will be a mid-term exam. The final exam score is the average score of Module 1 and 2 results.
The exam of Module 1 consists of a series of open-ended questions and students will be also asked to produce an essay on a topic related to the course.
The exam of Module 2 consists of Questionnaire made by open and multiple-choice question related to the topic of the course (60%) and Observation, with an evaluation grid, on the presentation of the project work (40%).
If the students don’t attend the project or the presentation, there will be an interview on the content of the training.
The evaluation criteria will focus on the acquisition of the basic content, on the ability to process and to connect between the contents proposed in the learning process and the material acquired during the interaction with the firm. For non-attending students, the criteria will concern the acquisition of basic contents and their critical analysis and elaboration, with associations and connections between the various contents, referring to the reading list adopted.
The final mark, expressed in thirtieths, will evaluate the above knowledge and skills according to the following ranges: - incomplete or insufficient knowledge and skills:

During the course instructors will provide link to public available online material and textbook references.

The course aims to provide students with a general understanding of computer programming, computational thinking, and data science in the context of modern data-driven organizations. Its objective is to introduce the main concepts, methods and tools required to support digital transformation processes and to enable the effective use of data in decision making activities.
This course also seeks to provide students with the foundational knowledge of the Python programming language and its application to problem solving, data manipulation, and basic analytical tasks. Attention is devoted to the role of programming and data science techniques in real world organizational and business contexts.
The course is structured in two complementary modules.
Module A focuses on fundamentals of programming, starting from basic coding and progressing to elementary data structures and algorithms (30 hours), supported by practical laboratories sessions using Python (20 hours).
Module B covers all stages of a real-world data science or machine learning project, including problem formulation, data exploration, and the application and evaluation of commonly used machine learning models (30 hours of lecture and 10 hours of laboratory).
Overall, the course aims to equip students with conceptual and operational tools necessary to contribute to data analysis and machine learning projects, and to effectively interact with both technical and non-technical stakeholders in organizational settings.
At the end of the course, the student will be able to:
Describe the fundamental concepts of computer programming
Explain the main features and basic syntax of the Python programming language
Recognize key data structures, algorithms, and the main phases of a data science project
Develop simple Python scripts for data analysis and basic modelling tasks
Apply standard techniques for data exploration, cleaning, and preprocessing
Use basic machine learning models and evaluate their performance
Select appropriate methods, tools, and libraries according to the problem context
Recognize common limitations, risks, and pitfalls in data science and machine learning projects
Present analytical results in a clear, structured, and accessible way
Communicate effectively with both technical and non-technical stakeholders
Independently expand their knowledge of programming and data science tools
Adapt to new technologies, analytical methods, and evolving data-driven contexts

Module A
Introduction to computational thinking
Python basics: data types, operators, variables, expressions
Flow control: conditional and iterative constructs
Functions and modules
Collections: strings, lists, tuples, sets, dictionaries
File I/O
Classes and objects
Introduction to main Python libraries: NumPy, Matplotlib, and Pandas
Module B
The value of data for organizations
Data science project lifecycle
Problem formulation
Data quality
Data exploration
Modelling
Model evaluation
Change management
Risks and common issues
The course combines lectures and practical sessions. Teaching includes theoretical explanations, guided exercises, and learning-by-examples. Students actively work on coding tasks and data analysis examples. Active participation and regular individual study are strongly encouraged.
The course involves lectures and exercise sessions using personal notebook.

The final assessment consists of two written exams covering Modules A and B respectively. Each exam includes open-ended questions, multiple-choice questions and practical coding exercises and contributes 50% of the final grade.
Assessment is designed to verify both theoretical knowledge and practical application skills. If evidence emerges suggesting the use of AI tools in the written examination, an additional oral examination may be required to further assess the student’s actual knowledge and understanding.
Honors (Laude) are awarded to students achieving excellent performance across both components.

Downey, A. (2012), Think Python, O’Reilly
McKinney, W. (2022), Python for Data Analysis, 3rd ed., O’Reilly
James, G. et al. (2023), An Introduction to Statistical Learning with Applications in Python, Springer
Lecture notes and online materials

The aim of the course is to provide participants with a critical understanding of the main issues related to management control systems and performance measurement as key factors of organizational success. Students will learn how control systems and performance measurement tools drive organizational decision-making and behaviors, with a particular emphasis on innovation management. Upon successful completion of the course, students will be able to:
understand the general aims of management control systems and performance measurement, as well as the roles such systems can play in relation to innovation management;
assess the design, implementation, use and review of management control systems and performance measurement;
apply the key principles and tools illustrated throughout the course (e.g., responsibility centres, EVA, transfer prices, compensation systems, balanced scorecard) in organizational settings in general and to innovation management more specifically;
critically interpret the information produced by management control systems, formulating independent judgments, also in medium and large organisations and in complex environments;
communicate clearly their conclusions gained from the interpretation of the information produced by management control systems and performance measurement, so as to support organizational decision-making with appropriate arguments;
advance in management control systems and performance measurement studies in a mostly self-directed and autonomous way.

The course provides basics in management control systems and performance measurement. The main topics which will be handled are:
introduction to management control systems and performance measurement (organizational control mechanisms, design of management control systems);
the behavioral dimension of management control systems (responsibility centres, transfer prices, compensation systems);
the relationships between management control systems and IT systems/Big Data;
asset allocation systems;
multidimensional performance measurement systems (building and using a balanced scorecard, measuring innovation);
management control of projects.
The course uses a mixture of frontal lectures and case studies analysis, discussions and presentations. Teaching methods will stimulate learning by doing and will require an active and full participation by students. The course may include expert testimonials.

• Attending students
  The course is heavily based on students’ class participation and it therefore highly assesses such active participation. More specifically, the final grade will be composed as follows:
  1. Group assignments (30%);
  2. Final written test (70%);
  3. Active class participation (0-2 additional marks).
  The final written test will be based on open-ended, theoretical questions on the whole contents of the course.
• Non attending students
  Non attending students will have to sit for a written exam (three open-ended theoretical questions and one short business case/quantitative exercise) on the whole contents of the course. This exam will account for the 100% of the final grade.
  The student's preparation is evaluated according to the following criteria: in-depth knowledge and understanding of the systems and models presented during the course; originality, appropriateness and depth of the exemplifications proposed in response to the request for applicative arguments; awareness of the business and environmental characteristics that influence the problems object of study; rigour in framing the problems under study and in identifying and arguing original solutions located in the real context; appropriateness and correctness in the choice and application of qualitative and quantitative analysis models in response to the problems proposed; autonomy and rigour in interpreting and selecting the relevant information from among those proposed in relation to the problems under study, as well as in highlighting possible information gaps, thus identifying further information hypotheses on which to base the proposal of appropriate solutions; critical approach to the subject, also by arguing the advantages/disadvantages of the models studied from a conceptual and applicative point of view and by highlighting the links between the various elements of the management control system; clarity, completeness, coherence and linguistic mastery in communication.

• Attending students
  Attending students are required to prepare for the exam by studying the lecture materials that will be made available by the instructors on the Blackboard page of the course.
• Non attending students
  Non attending students are required to prepare for the exam by studying the lecture materials that will be made available by the instructors on the Blackboard page of the course, as well as the following textbook: Hartmann, Kraus, Nilsson, Anthony, & Govindarajan, Management Control Systems. Second Edition, McGraw-Hill, 2020 (chapters 1-6, 8-10, Appendix on Variance Analysis). Acquista da VP

The course will provide students with the conceptual framework to access the realm of novel technologies based on the exploitation of materials properties. A survey of the basic physical properties is provided to help classifying materials according to possible applications. Devices developed on the basis of selected materials will be presented, focusing on working principles and their outcome on the marketplace. In particular, materials related to nanotechnologies will be considered, including smart materials, materials for energy harvesting and generation, materials for ICT, for biomedical applications, and environmental remediation.
At the end of the course, students will be able to:
Understand the working principles, at the basis of the most important every-day technologies;
Capture the possible technological impact of the outcomes of fundamental and applied research in the fields of materials science, electronics, optoelectronics, ICT, energy harvesting, optics, nanotechnologies, biomedical;
Demonstrate operational knowledge in the field of application of innovative and disruptive technologies.

The course will cover the following topics:
Module 1 (1st quarter)
1. The structure of matter. Basic physical properties.
2. Reactions, transformations, and phase diagrams.
3. Materials classification: metals and metallic alloys, insulators, semiconductors, polymers, composites.
4. Mechanical, optical, magnetic, thermal and electrical properties of materials
Module 2 (2nd quarter)
5. Introduction to semiconductors and electronics
6. Introduction to optics, photonics and lasers
7. Devices and applications: electronic devices, photonics, photovoltaics, energy harvesting, sensing.
8. Nanomaterials, nano- and quantum-technologies.
The teaching method is mainly based on frontal lectures and case-studies discussions. Expert testimonials on specific subjects will be possibly invited during the course.

The final exam will aim to assess the acquired knowledge, in a simple form, of the physical principles behind the many different technologically relevant device/material classes discussed during the course.
The final assessment will be based on a written exams divided into two parts, one for each module. Each of the two parts of the exam will consist in a multiple choice test (13 questions, corresponding to 26/30 maximum marks) and the open discussion of a case-study on a technologically relevant device/material class (4/30 maximum marks), to be chosen among a portfolio of topics previously discussed. The marks obtained in the two parts will be averaged to attribute the final marks, which can range from 18/30 to 30/30. Full marks cum laude will be attributed to outstanding cases in which the student demostrates a clear independence in the application of the concepts learned by presenting the case-study in a personal and original way.

Text book:
W.D. Callister Jr.-D.G. Rethwisch, Materials Science and Engineering. An Introduction, John Wiley & Sons, Inc. This text book mainly covers Part 1 to 4 of the program and will be completed by lecture slides (to be downloaded from Blackboard). For Part 5 and 6 additional reading materials will be provided before lectures and will be posted on Blackboard.

In today's business landscape, customers are often described as omnichannel, meaning they expect seamless interactions with brands and retailers across various platforms. Consequently, companies must adapt by reevaluating and restructuring their approach to consumer-brand relationships and retail systems. This course aims to provide an overview of the latest trends in customer-brand interactions and connections with products and marketing offerings.
Throughout the course, participants will gain insights into the needs and journeys of omnichannel customers, as well as the business and managerial processes necessary for companies to align their marketing and branding strategies with an omnichannel approach. Key concepts in brand management, such as brand identity, positioning, equity, customer journey, and relationship management, will be explored and examined. Hints about legal branding protection and financials complete the course.
By the end of the course, participants will be able to:
identify the distinctive features of multichannel and omnichannel models;
understand the complexities of implementing an omnichannel strategy and current challenges;
recognize the role of technology and innovations in enabling omnichannel strategies and creating a frictionless brand experience;
manage the sources of brand identity and equity to create a seamless brand experience;
analyze how customers use channels and touchpoints to interact and establish connections with their preferred brands in an omnichannel ecosystem;
design projects aimed at nurturing positive consumer-brand relationships by leveraging various touchpoints and technological innovations to create an omnichannel journey.

First Module: Prof.ssa Silvia Biraghi
The channel ecosystem in consumer-brand connections
Players in the retail ecosystem
From single channel to multichannel approach
From Multichannel to Omnichannel management
Managing a brand to create a seamless brand experience
Conveying a coherent brand identity across all channels
Brand identity, brand positioning and differentiation
The role of technology and innovations in delivering a frictionless customer experience
Pure digital players, phygital and high-tech ecosystems
The online shopping experience in a connective and interactive environment
Technocapitalism and platform economy in action: owing the digital last mile
Critical junctures in omnichannel customer experience management
Second Module: Prof. Alberto Dal Sasso
Building brand equity in an omni channel environment.
Customer Based Brand Equity model.
How to reach the brand resonance with customers.
Brand value chain to get the market value
Brands element and choosing criteria
Designing and implementing Brand architecture strategies
The media environment
Integrating media communication.
Branding in the digital era
Customer Journey and Customer Delight
Touch points
Elements to create brand value
The concept of customer delight (theory and practice)
The course is based on lectures in order to provide a conceptual framework of the topics listed above, on critical discussions/forums, and on group assignments in order to facilitate and activate the learning process. Guest speakers will be involved in case study presentations to provide an overview of how omnichannel strategies work across different business sectors. Students will also be involved in team projects that are designed to enable them to face the challenges of omnichannel customer management in practice.

The evaluation is based on ongoing assessment or, alternatively, on a final summative assessment.
Ongoing Assessment:
For the first module, ongoing assessment (50% of the final grade) is determined as follows:
Group project (40% of the first module assessment). The project will be presented and discussed in class. Students will be evaluated based on their critical understanding of omnichannel challenges and their ability to envision creative and managerial solutions to handle omnichannel customers and projects.
Individual written exam for students who have participated in the lessons and the group project of the first module (60% of the first module assessment)
At the end of the first module, students can take a test based on two open-ended theoretical-application questions related to the topics covered in class and the study material provided by the instructors.
Students who have completed the project but have not passed the test or wish to retake it can answer to two questions related to the topics covered in the first module in the exams offered in the March or April sessions.
For the second module, ongoing assessment (50% of the final grade) is determined as follows:
Students who successfully achieve the first module project and exam will complete their evaluation process as follows:
Students will be involved in a team project focused on the branding techniques and branding models discussed in class (40% of the second module grade)
A final exam that consists of a written test related to the topics covered in the second module during the official exam sessions scheduled in March and April (60% of the second module grade). Students who will sit for the second module exam in March, can take again the second module exam in April in case of failure and withdrawal.
For both modules, students must achieve a positive assessment in both the group projects and individual exams to record their grade. If students pass only one of the two modules, the assessment remains valid only within the same exam session. Otherwise, students will have to retake the entire exam following the same procedures outlined below for students who have not completed the project and who face the final summative assessment mode.
Final Summative Assessment
For students who do not attend classes and will not carry out the group project, the course includes a final individual written exam, consisting of four theoretical-application questions related to the various sources listed in the bibliographic material in the reading list and on Blackboard.
Make sure to check Blackboard for updates about the reading list.

Presentation decks and readings will be available on Blackboard for attending students. Professors will post course materials after each class.
For non-attending students the reading list includes the following books:
First module:
M. Zaki, Data-Driven Customer Experience Transformation Optimize Your Omnichannel Approach. Kogan Page, 2025. (ch. 1-2-3-10-12-conclusion).
P. Myerson, Omni-channel retail and the supply chain. Working together for a competitive advantage. Routledge, 2021. (selected excerpts will be available on BlackBoard).
Second module:
K.L Keller, Strategic Brand Management, Pearson, 5th ed. 2020. (Introduction, 1-8, 12, 13) Acquista da VP
M. Zaki, Data-Driven Customer Experience Transformation Optimize Your Omnichannel Approach. Kogan Page, 2025. (ch. 4, 5).

The Organizing and Leading Change class will address the soft and hard components of change management, more specifically covering topics like: Change mgmt. strategy; Change Mgmt. models, roles and processes; Change mgmt. and culture; Behavioral design; Leadership and Change Management; Change Mgmt. in digital environment. The course also deals with organization design principles, structures and models, smart working and new ways of working. The journey from strategy to transformation and change mgmt. in technology driven projects will be deeply discussed and analyzed. The class will also cover the most updated people centric frontiers in change mgmt., as behavioral design, agile and the change capability Platform.
Upon successful completion of the requirements for this course, students will be able to:
Understand the why, what, how of Change Mgmt.
Apply Change Mgmt. techniques and tools
Select the appropriate approach to be followed in the different transformation cases
Make BPM, BPR and organizational impact analysis and design
Choose the appropriate Change Mgmt. KPIs and KAIs (Key adoption index)
Recognize the most innovative trends in change mgmt.

The Why of change mgmt.
The What: change mgmt. definition The How: change mgmt. approaches, roles and processes
Setting the business strategy
One Page Integrated Strategy;
Business Model Canvas;
Fragility, Robustness, Resiliency and Anti-fragility
Materiality & Integrated Thinking Strategy
Sustainability Strategy & Impact
ESG, SDG, Theory of change
Human, Green and Digital enablers
Organizational structure models and design
Hierarchical
Product/Market/Customer centric
Knowledge centric
Process centric
Matrix centric
Project centric
Product centric
Liquid
Culture in change Management
What culture is?
The cultural hacks
The Mindset as a key cultural building block
Leadership and Change Management
Leader centric Theories
The Leadership traits, competencies & behaviors
The Leadership Grid
Followers centric Theories
The Situational Leadership
The Servant Leadership
New leadership models
Behavioral design: tips, nudges and coaching plan
From Strategy to Change: the journey
Strategy methods application
Kick-off launch
Business Process Mgmt.
Business Process Re-engineering
The Waterfall Model
The Agile Philosophy and Methods
Change mgmt. in technological and digital projects
The SAP Activate example
Change mgmt. and communication plan
Governance
Change Management roles
Role Catalogue,
Responsibilities and RACI matrix
Organizational impact
Change adoption
Change Mgmt. in the HR Transformation
HR Processes
Agile HR
Performance Management and OKR
The today agenda:
People centric change mgmt.
AI, Change Management & User Adoption
Change capability platform
The measurement of change: KPIs, KAIs and KRIs
The course uses a mixture of face-to-face lectures, theoretical focus, discussions on experiences and cases, lab exercises, guest speakers’ talks and case studies discussions and reconceptualization.

Attending Students
G. Sgalambro evaluation grades in based on:
Case studies; Group assignments (30%); two hours written examination to be held at the end of the course (60%); Class discussions and questions are considered an important component of the course (10%). The written exam of 1.5 hours consists of closed questions with multiple choices and open questions finalized to understand if the student has absorbed the conceptual and methodological tools and is able to develop complex lines of reasoning, interconnecting the different concepts and developing advisory suggestions.
Not Attending Students
The exam consists of a 2 hours examination with both closed and open questions and a change case practical application.

Kotter, Leading Change.
Reading:
S. Johnson: “Who Moved my cheese”
Gary Hamel; Domenico Zanini, Mc Kynsey: Build a Change Platform, not a Change Program
Other material is posted on BlackBoard.Unicatt

The aim of the course is to make students aware of the current importance of personal data protection, privacy and security, introducing the key principles and legal institutes in this area. The goal to acquire up-to-date tools to understand this developing sector cannot disregard, firstly, the consideration of the recent changes introduced at the European level which will be examined on both a theoretical and operational level, with the indispensable recall of the fundamental principles and legal institutes of constitutional and private law governing the field. In addition, the course intends to let students appraise the risks related to technological innovation: in this respect, after the study of the essential grammar of criminal law and cybercrime, the class will be constantly challenged – also through the analysis of several business cases written by leading scholarships – in trying thinking critically towards today’s tensions between privacy and security, the numerous perils posed by blockchain technologies and the law and ethics of artificial intelligence systems. At the end of the course, students will be thus able:
to thoroughly manage the rules of the General Data Protection Regulation 16/679 (GDPR), whose compliance has become a top-priority for both private and public companies around the world;
to properly balance, from both a legal and a managerial standpoint, benefits and threats of technological innovation, which will increasingly be a fundamental skill to foster in order to firmly navigate the «industry 4.0».

Module I – Private Law (Prof. Vittorio Bachelet)
This first part of the course introduces to privacy and data protection rules within the framework of private law. The rights to privacy and the protection of personal data, as fundamental rights, will be explored against the backdrop of the European Union law, as the General Data Protection Regulation 16/679 (GDPR) largely replaced national laws, ensuring uniform rules across the EU. The module will focus, among other things, on the key characteristics of the EU data protection regime; the dual objectives of European data protection Regulation; the link between data protection and privacy in the EU legal order; the relationship between data protection and other rights or interests; the role of individual control over personal data in EU data protection law: tangible and intangible harms of data processing; the limits of individual control over personal data: the property approach.
Module II – Criminal Law (Prof. Ennio Alagia)
The second part of the course will then focus on the criminal facets related to privacy, security, and technological innovation. Specifically, after a brief introduction on the basic principles of criminal law, the module will concentrate on analyzing the criminal protection of the right of privacy and the realm of cybercrime and security. Attention will be then paid to the perils of technological innovation, exploring, among others, the risks of mass surveillance post COVID-19, the phenomenon of “outsider” trading, the new forms of algorithmic market manipulation, the legal threats of blockchain technologies and the connections between cryptocurrencies and money laundering, and the law and ethics of artificial intelligence systems. This Module will also include a ten-hour focus on the criminogenic features of digital interactions and on specific digital threats to the art and antiquities markets.
The modules will be delivered by means of interactive frontal lectures with discussion of case studies. To succeed in the exam, attendance it is thus strongly recommended.

Students must obtain the pass mark (sufficienza) in the end-of-course oral exam to qualify in this course. Attendance and class participation will also receive particular attention.
A separate assessment for each module is envisaged: the final grade will be the average score of the two parts. Specific instructions will be provided at the beginning of each module.

There are no mandatory textbooks for the course.
Students might find anyhow helpful hints browsing the following references.
As for Module I
Reference book:
O. Lynskey, The Foundations of EU Data Protection Law, Oxford University Press, 2015.
Other relevant publications:
Ch. Kuner-L.A. Bygrave-Ch. Docksey, The EU General Data Protection Regulation: A Commentary, Oxford University Press, 2021.
B. Van Alsenoy, Data Protection Law in the EU: Roles, Responsabilities and Liability, 2019.
S. Zuboff, The Age of Surveillance Capitalism. The Fight for a Human Future, Profile Books, 2019.
S.D. Warren-L.D. Brandels, The Right to Privacy, Harvard Law Review, Vol. 4, No. 5, 1890, pp. 193-220.
As for Module II
Reference books:
G.P. Fletcher, Basic Principles of Criminal Law, Oxford University Press, 1998.
J. Clough, Principles of Cybercrime, 2nd ed., Cambridge University Press, 2016.
R. Abbot, The Reasonable Robot. Artificial Intelligence and the Law, Cambridge University Press, 2020.
Other relevant publications:
D. Gunkel, The Machine Question. Critical Perspective on AI, Robots, and Ethics, MIT Press, 2012.
N. Bostrom, Superintelligence. Paths, Dangers, Strategies, Oxford University Press, 2014.
G. Hallevy, Liability for Crimes Involving Artificial Intelligence Systems, Springer, 2015.
S.J. Russel, Human Compatible: Artificial Intelligence and the Problem of Control, Viking Press, 2019.
Interpol – United Nations Interregional Crime and Justice Research Institute, Artificial Intelligence and Robotics for Law Enforcement, March 2019.
Interpol – United Nations Interregional Crime and Justice Research Institute, Towards Responsible AI Innovation. Second Interpol-Unicri Report on Artificial Intelligence for Law Enforcement, May 2020.
European Commission’s High-Level Expert Group on Artificial Intelligence, Ethics Guidelines for Trustworthy AI, April 2019.
European Commission, White Paper on Artificial Intelligence – A European approach to excellence and trust, February 2020.
M.D. Dubber-F. Pasquale-S. Das, The Oxford Handbook of Ethics and AI, Oxford University Press, 2020
Details on the readings for the specific subjects are provided in the Syllabus, whilst the course study materials will be published on the area of Blackboard reserved for students enrolled in the course.

The course aims at providing students with a comprehensive knowledge regarding the rationale, models, and techniques behind the management of complex projects and of people in work and project settings. The underlying logic is that managing project (activities) and human resources (people) can be separated only analytically, requiring an integrated approach in practice in order to swiftly execute projects and reach excellent results.
The course aim is to let students learn and discuss the basic principles and models that form the pillars of Project Management. Furthermore, the course is intended to provide the basics of people’s behavior within projects and organizations, with a focus on the project team level. To this aim, the course is intended to be a primer on how to develop behavioral competencies both in self-management and in managing others, that are needed to work in complex projects and organizations.
At the end of the course, students will be able to:
Understand the importance of planning, managing, executing projects and learn principles of people management within projects, and a swift execution thereof.
Acquire knowledge on the most internationally spread techniques of Project Management.
Learn the relevance of people’s attitudes and behaviors for well executed projects and results thereof.
Diagnose basic issues in managing projects and people, and devise the required actions in order to attain outcomes such as people’s engagement, swift execution, high quality output, and balanced cost management, ultimately delivering excellent results.
Apply their knowledge and skills in order to address issues and devise solutions in managing projects and people.
Effectively share their knowledge on project and people management and communicate their considerations on how to deal with project management issues.
Progress autonomously in learning advanced knowledge on project and people management.

The course starts with an execution challenge, aimed to let students touch with hands the complexities of project management and coordination of people within a project aimed to achieve a negotiated outcome. Then, the course moves to provide in-depth familiarity with Project Management principles and techniques for swift planning, execution, and reporting of complex projects.
The second part of the course is devoted to let students understand the complexities of managing people in challenging contexts, where pressure on time and results is high, and excellence in delivering outcomes is a must. In the people management module, topics such as self-management capabilities, competences for managing others, and effective leadership are discussed.
Both modules give the chance to observe and ‘approach’ project and people management experiences in a company environment.
The last session is devoted to the presentation of a ‘Masterwork Project’ developed by students. Such task allows them to apply the knowledge and to train the behavioral competences developed throughout the course.
More specifically, sessions are dedicated to:
Execution challenge: building prototypes under tight customer requests.
Project Management methodology and tools.
People Management: Human Resource Management practices and managerial tools to manage employees.
People Management: self-management and drivers for personal improvement.
People Management: employee motivation and team work.
Integrating Project Management and People Management: drivers for delivering excellent results.
Presentation of the team assignments
Teaching is based on an interactive and involving approach. The whole course revolves around an inductive, hands-on approachbased on several tools such as role-playing, simulations of real organizational situations, guest lectures, case studies, business challenges and games.
Collaborative learning, through frequent teamwork activities, is a pillar of the course.

Attending students:
Students’ evaluation method is coherent with the goal of developing knowledge as well as problem solving skills. To this aim, a final individual exam is the key moment for assessing the students’ understanding of the course topics, their ability to organize the set of knowledge, and their ability to apply that knowledge to solve real problems related to managing projects & people. Moreover, team assignments will stimulate students to practice collaborative learning and consolidate the acquired knowledge.
More specifically, the course evaluation will be based on:
Final exam (70%): closed-books exam based on the topics covered in class. Students will be required to aptly refer to class activities (e.g. simulations, case studies, etc.) in order to reach excellent grades.
Team assignment (30%): students will be required to carry out an assignment in team, which will give them the opportunity to reflect on the meaning of the topics and the approaches covered in class and their managerial implications.
Non-attending students:
Non-attending students will be evaluated on their knowledge of the topics covered in the books listed in the readings list. The exam will be composed of open questions referring to the books theories and approaches.

Attending students:
Slides, case studies, role playing and readings provided by instructors.
Complementary book (not mandatory): M. Fenzi-D. Pinto-E. Fregnan, Project & People Management, McGraw-Hill, 2012. Acquista da VP
Non-attending students:
Mandatory readings for non-attending students:
M. Fenzi-D. Pinto-E. Fregnan, Project & People Management, McGraw-Hill, 2012 (whole book). Acquista da VP
J. R. Meredith, S. J. Mantel Jr., S. M. Shafer, Project Management: A Managerial Approach, 9th Edition International Student Version, Wiley, 2015 (Chapters 1, 3, 4, 5, 6, 7, 8, 10, 11. The section: ‘Project management in practice’ of chapters 1 and 6 are suggested readings. The same section of the other chapters is not included in the program). Acquista da VP

The aim of the course is to provide students with a first approach to companies that are operating in the field of novel technologies dealing with materials, devices, and processes. For this reason, the course will be mainly based on the development of a case study in close connection with a company tutor, through visits to the company, web-meeting, and periodical assessments of the project with the course instructors.
At the end of the course, students will be able to:
Develop team working skills to cope with the specific tasks agreed with the company tutor and course instructors;
Identify the main features of the company and the degree of innovation in its industrial activity. Relate, where possible, the innovation aspects concerning materials, devices, processes to team work topic;
Apply operational knowledge in the field of innovative and disruptive technologies;
Propose an original contribution or develop ideas that can reinforce, support, or boost the degree of innovation of the company.

1. Classroom presentation of the host companies.
2. Project/case study assignment to students groups.
3. Visit to the company, to be followed by meetings (3) with the company tutor.
4. Discussion and progress assessment with course lecturers.
5. Classroom presentation of the case study. Final assessment.
The course uses a mixture of company speakers’ presentations, on-site visits, meeting with company tutors and course lecturers.

For attendees, the final assessment will be based on:
Preparation of a team document illustrating the case study (up to marks 14/30)
Team presentation of the case study (up to marks 8/30)
Individual colloquium (up to marks 8/30)
All the students who didn’t attend the course and didn’t join one of the teams (non-attendees), can take the final exam after having fulfilled the following conditions:
The students must autonomously find an external partner (company/industry) and agree upon the project with the teachers. The subject of the project must be related to one of the topics of the MATERIALS PHYSICS AND TECHNOLOGY course. At least one visit to the company should be scheduled. The company must also provide the student with materials and a tutor.
The students must finalize a written report to be submitted before the final exam.
During the scheduled exam sessions, the students can take the final exam, which will include a detailed evaluation of the report and of the project
For non-attendees, the final assessment will be based on:
Preparation of a document illustrating the case study (up to marks 14/30)
Individual colloquium, starting with a slide presentation of the project (up to marks 16/30)
Full marks cum laude will be attributed to the cases in which the student demostrates a clear outstanding persoal contribution to the team work.
The evaluation of the written report of the activities carried out during the project will be based on the achievement of the following goals:
to demonstrate a proper understanding of the company business, in particular of the aspects related to the specific application ($) treated in the project
to provide an overall view of the market (present status, trends, competitors,…)
to capture the technological aspects at the basis of the application considered in the project
to highlight the innovation aspects related to the application
to be able to work autonomously (both as individual or within a restricted team) in order to prepare a report with a proper information content, aimed to exhaustively present the work carried out during the project
($) Here APPLICATION is meant to be, depending on the project, a material, device, process, product with relevant links to the MATERIALS PHYSICS AND TECHNOLOGY course.
The evaluation of the final presentations of the project will be based on the achievement of the following goals:
to demonstrate the capacity of presenting a specific aspect of the work in a clear way
to manage questions related to the subject of the presentation
to demonstrate the comprehension of the topic and properly communicate specific aspects of the technology driven business of the partner company
The evaluation of the individual oral examinations will be based on the achievement of the following goals:
to demonstrate a relevant personal contribution to the specific topic tackled during the work.
to demonstrate a deep comprehension of the specific topic tackled during the work.
to demonstrate a general comprehension of other topics tackled by the other group members during the work.

Depending on the project theme, materials will be posted on Blackboard by the lecturers and by the company tutors.

This course will give you the fundamentals of probability, and methods for statistical inference, with focus on estimation and tests of hypotheses. Students will learn how to perform regression analysis for datasets with quantitative and qualitative variables, especially in the context of applied works in business analytics. More advanced statistical techniques based on the partition of the covariate space, and unsupervised learning methods for clustering will be presented.
The course deals with concepts, methods and techniques of multivariate analysis of data for estimating and predicting purposes. The focus is on analyzing real data, using the software R.
The following learning abilities are provided and expected to be achieved by participants at the end of the course:
Knowledge of concepts, terms and methods of statistical inference, regression models, tree-based methods and clustering techniques (DD1- Knowledge and understanding);
Ability to correctly apply methods of estimation and prediction through the use of the statistical software R. (DD2- Applying knowledge and understanding);
Quantitative thinking addressed to make independent evaluations, driven by estimations and predictions (DD3- Making judgements);
Ability to read and interpret data and communicate results, through the extraction of qualitative information from quantitative data (DD4-Communication);
Mastery of tools useful for quantitative analyses required in future careers involving management of data, rigorous reasoning and data-driven decision-making (DD5- Lifelong learning skills).

Module I (Prof. Rosario Barone)

• Introduction: Business Analytics and data mining (the CRISP-DM methodology).
• Basics of probability theory: random variables, distribution theory.
• Point estimation for population mean, proportion and variance. Interval estimation for the population mean (in the normal case) and for the population proportion (binomial model). Extension to two sample problems.
• Hypothesis testing for population mean and proportion. Extension to two sample problems.
• Linear regression: inference and prediction, related t and F tests.
• Logistic regression: inference and prediction.
  Module II (Prof. Emilio Gregori)
• Introduction: machine learning algorithms and artificial intelligence in business analytics
• Supervised methods: tree-based algorithms (regression and classification trees), neural networks.
• Unsupervised methods: factor analysis and clustering techniques (K-means cluster analysis, hierarchical algorithms).
  The course involves lectures, exercise sessions using PC and discussion of use cases.

Two alternative type of exams are foreseen:
Option 1 - Two written partial exams, one mid-course exam and one final exam at the end of the course. Moreover, students can present a group-assignment about carrying out a research project through the use of R-Studio on a real dataset. The first partial exam will cover approximately 60% of the topics of Module I, the second partial exam will cover the rest of Module 1 and the topics of Module II. Both the first partial and the second partial exam will foresee close-ended and open-ended questions about theoretical concepts and about analyzing and interpreting results. The maximum grade for each of the two partial exams is 28. The assignment will foresee an autonomous activity of analysis on a real dataset through the software R, according to some basic instructions; the work is carried out in groups and must be submitted before the first exam session. The maximum grade for the assignment is 3 points.
The aim of the exams is to assess reasoning analytic abilities on the course subjects; language properties and presentation communication abilities are also assessed. The final grade is given by the weighted average between the grade of the first partial exam (50%) and the grade of the second partial exam (50%) plus the grade of the assignment.
Option 2 - One general written exam, covering both the topics of Module I and the topics of Module II. Moreover, students can present a group-assignment about carrying out a research project through the use of R-Studio on a real dataset; the work is carried out in groups and must be submitted before the first exam session. The exam will foresee close-ended and open-ended questions about theoretical concepts and about analyzing and interpreting results. The aim of the exam is to assess reasoning analytic abilities on the course subjects; language properties and presentation communication abilities are also assessed.
The maximum grade for the general exam is 28. The assignment will foresee an autonomous activity of analysis on a real dataset through the software R, according to some basic instructions; the work is carried out in groups and must be submitted before the first exam session. The maximum grade for the assignment is 3 points. As alternative to the assignment, students can attend an oral exam with maximum grade of 3 points, immediately after the general exam.
The final grade is given by the grade of the general exam plus the grade of the assignment, or of the oral exam.
For the exam sessions other than the first, only Option 2 is applied; the grade of the assignment will be valid for the entire academic year.
Other details will be provided on Blackboard.

The textbook of the course is:
A. Agresti, Statistical Methods for the Social Sciences, Pearson, 2018, 5th edition. Acquista da VP
A selection of notes and other materials, available in the course reserve on Blackboard.
Suggested optional material for an introduction to R: W.J. Owen, The R Guide, CRAN, 2010.

Decision making often coincides with the design of policy interventions. As decisions translate in the allocation of resources, the demand for professionals evaluating the effectiveness of interventions blending economic theory with robust quantitative tools is high. The main challenge for policy evaluation is to establish a causal link between interventions and outcomes. The objective of this course is to introduce the main approaches used in policy evaluation, both public policies but also firm policies in human resources or corporate governance, illustrating how to use econometric tools to assess policy effectiveness.
At the micro level, the course will focus on randomized evaluations, natural experiments, discontinuity designs, selection on observables and difference-in- differences. The course presents strengths and weaknesses of each approach in terms of internal and external validity. Each approach will be presented and illustrated with specific policies in the areas of labor, health, education, development and firm organization.
At the macro level the focus we will be on the application of econometric tools tailored to understand the effectiveness of fiscal and monetary policies and how economic activity reacts to policy stimulus over the business cycle.
In the lab sessions, students themselves replicate the results of a widely-cited published study for each evaluation approach.
At the end of the course, students will:
have acquired knowledge and understanding of the econometric techniques and statistical softwares needed to evaluate the impact of policy interventions both at the micro and macro level;
be able to apply the econometric methodologies developed during the course to the evaluation of real policy scenarios using both micro and macro data;
be able to critically assess the effectiveness of different policy measures based on the outcomes of their econometric analysis;
have developed a technical language to communicate policy prescriptions based on their analysis to policymakers;
have developed learning skills that will enable them to pursue further advanced studies.

Part I: Evaluation of microeconomic policies (Prof. Lorenzo Cappellari)
Randomized Control Trials.
Application: Business Literacy and Development
Selection on Observables and Propensity Score Matching.
Application: An Evaluation of Active Labour Market Programmes
Difference -in-Differences.
Application: Evaluating the Effectiveness of Selective College Admissions
Regression Discontinuity Designs.
Application: Evaluating the Effect of an Antidiscrimination Law
Synthetic Control.
Application: Evaluating the Costs of Organized Crime
Part II: Evaluation of macroeconomic policies (Prof. Emiliano Santoro)
Stationary ARMA Processes.
Application: The Unemployment Rate
Unit Roots.
Application: Filtering GDP
VAR and Structural VAR
Application: Evaluating the Impact of Monetary Policy
Cointegration and Error Correction Mechanisms.
Application: Money Demand, Inflation and Policy Rates
Bayesian VAR.
The course is based on classroom lectures (60 hours) and is complemented by econometric laboratory sessions (20 hours).

Students will be evaluated through a written examination, which will include both theory and empirical questions. Questions will be aimed at proving the student’s understanding of the topics discussed during the lectures, but also, and importantly, ability to apply appropriate estimation methods and to provide the correct economic interpretation to estimation results. Each question will be evaluated out of thirty and the final mark will be the average of the marks obtained in each question.
Students presentations will also be assessed during the course and will contribute to the final mark.

J.D. Angrist-J.S. Pischke, Mostly Harmless Econometrics: An Empiricist's Companion, Princeton University Press, 2009.
M. Verbeek, A Guide to Modern Econometrics, Wiley (4th ed.). Buy from VP
Detailed reading lists of papers, class notes, and additional teaching material will be uploaded on teachers web pages and/or Blackboard (http://blackboard.unicatt.it/).

The course aims to provide students with knowledge and understanding of design methods specific to the so-called Design Thinking (DT) approach and methods and instruments for creating a Business idea and writing a Business Plan.
“Module 1 - Design Thinking for responsible innovation” will introduce the theoretical framework of the DT approach and process, illustrating the “schools of thought”. As a diversified approach to innovation, the teaching of DT will train students to break down the traditional management cognition for framing, defining, and developing a design mindset for identifying business opportunities and developing new value-creating logics and processes.
In “Module 2 - Developing a business plan for a new venture” students will develop and evaluate a new entrepreneurial project. Throughout the module, students will be required to follow the start-up process till the development of a business plan.
At the end of Module 1, students will be able to:
dive into a design mindset with critical thinking, merging DT methods in their activities for elaborating an iterative and explorative process.
experiment with design tools for research, ideation and prototyping through an iterative and explorative approach
acquire know-how on the creative process and operational plan, a necessary skill in multidisciplinary working environments.
articulate and communicate project research roads with a critical thinking capability.
At the end of Module 2, students will be able to:
analyze and assess the potential of a new business opportunity.
develop a concise and compelling value proposition.
design the basic characteristics of the new venture structure.
engage in basic financial modelling and financial assessment for the new venture.
write and present a Business Plan.

The course aims to provide students with the basic knowledge of:
primary design methodologies and evolution of design thinking (overview).
tools and methods used within the DT approach for the entrepreneurship curriculum (perspectives).
design value proposition and the business model
new venture development issues.
business planning.
financial structuring and fluency related to the assessment and launch of a new project.
Module 1 will guide teams of students through a design and creative process to frame business opportunities and develop a business idea through teamwork; Module 2 will guide the same teams of students towards developing a business plan on the selected project.
Module 1: the phases of the process will be:
design research: a human-centered way to collect research data
creative reframing: developing a novel insights that reframes the problems and redefines opportunities for innovative business solutions
concept generation and ideation: converging to focus on generating innovative solutions.
development of entrepreneurial ideas.
refining and final presentation, also with possible further directions
Module 2: the module will be based on training students for the development of a business plan for a new business project. Classes are articulated in lectures and exercises (teamwork sessions and tutorials, project work and open discussions).
Specifically, this part will deal with the following topics:
role, relevance, and content of a business plan.
value proposition design.
market opportunity sizing.
business structure development.
evaluation of venture profitability and financial viability.
project communication.
The course embraces the studio format, and it aims to sustain students' capabilities in applying their competencies and expertise to foster the investigation of how to learn and develop a foresight and creative process.
The course will be based on class-related activities: frontal lectures (also by guest lectures), class discussions, teamwork, and oral presentations.
Students are expected to come to each class having read and prepared the required material (articles, cases, or project advancement) and prepared to present and defend their analysis of the material. Students are also expected to generate discussion by questioning both the professor and presentations by their peers.
Course content will be delivered in the following methods:
Lecture/Introduction of the central theme for each session;
Tutorials (review sessions) on team project advancement
Required readings;
Class exercises and discussions;
Case analyses;
Student presentations and peer feedback.
Professors will use visual presentations to introduce the subject matter for each session and stimulate class discussion. Copies of each session presentation will be available on Blackboard immediately after the session.

We define “Attending students” as those who attend all the course lectures (both modules) and are included in a working team (the same for both modules) for project development, with no exceptions.
For Module 1, the assessment method is structured as follows.
For attending students:
40% based on the team project (project reviews during class sessions and final oral presentation through slides)
60% based on the individual oral exam: open questions based on the lecture slides and bibliography.
For students not attending the course lectures; for students rejecting the final grade:
100% based on an individual oral exam: open questions based on the lecture slides and bibliography.
For Module 2, the assessment method is structured as follows:
For attending students:
40% based on the team project (Business Plan in written form – max 25 pages, A4), with financials included in the final oral presentation
60% based on the individual oral exam: open questions based on the bibliography. A written test on the quantitative elements of the business plan can precede the oral exam
For students not attending the course lectures; for students rejecting the final grade:
100% based on an individual oral exam: open and closed questions based on the bibliography. A written test on the quantitative elements of the business plan can precede the oral exam
The final grade will result from 50% final evaluation of Module 1 + 50% final evaluation of Module 2.

Here follows a list of the compulsory readings for the exam preparation and of the recommended readings about the course’s contents.
Module 1
Compulsory readings for the individual exam of attending students:
Books:
Martin, R. L. (2009). The design of business: Why design thinking is the next competitive advantage. Harvard Business Press.
Class lectures
Contents provided on Blackboard during the course.
Compulsory readings for the individual exam of not attending students:
Books:
Martin, R. (2009). The design of business: Why design thinking is the next competitive advantage. Harvard Business Press.
Brown, T. (2009). Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. Harper Collins.
Papers:
Kolko, J. (2010). Abductive Thinking and Sensemaking: The Drivers of Design Synthesis. Design Issues, 26(1), 15–28. https://doi.org/10.1162/desi.2010.26.1.15
Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006
Lynch, M., Kamovich, U., Longva, K. K., & Steinert, M. (2021). Combining technology and entrepreneurial education through design thinking: Students' reflections on the learning process. Technological Forecasting and Social Change, 164, 119689.
Klenner, N. F., Gemser, G., & Karpen, I. O. (2022). Entrepreneurial ways of designing and designerly ways of entrepreneuring: Exploring the relationship between design thinking and effectuation theory. Journal of Product Innovation Management, 39(1), 66-94.
Module 2
Compulsory readings for the individual exam of attending students:
Books:
Parolini C., Business Planning, 2021, KDP
Compulsory readings for the individual exam of not attending students:
Books:
Parolini C., Business Planning, 2021, KDP
Papers:
Blank S. (2013). Why the Lean Start-up Changes Everthing, Harvard Business Review.
Braun M., Scott L. and Cannatelli B. (2019). Strategy and business models: why winning companies need both, Journal of Business Strategy.
Hinterhuber A. (2005). Customer value‐based pricing strategies: why companies resist, Journal of Business Strategy
Kim C.W. and Mauborgne R. (2005). Blue Ocean Strategy: From Theory to Practice, California Management Review.
Sawhney M., Wolcott R.C., and Arroniz I. (2006). The 12 different ways for companies to innovate, MIT Sloan Management Review.

The aim of the course is to learn how to plan, design and conduct controlled experiments in order to collect informative data that may lead to conclusive results. Both design and statistical analysis issues are discussed. Particular attention will be paid to: a) understanding the opportunity of planning an experiment through factorial or fractional factorial designs, eventually including a blocking factor; b) examining how a factorial design allows cost reduction, increases efficiency of experimentation, and may reveal the essential nature of a process. The theoretical presentation of the methods is supported by examples and real applications with the R statistical software.
By the end of the course, students are expected to:
having acquired advanced knowledge of concepts, terms and methods of experimental designs, which constitute a basis for identifying causal relationship between variables in the research context (DD1- Knowledge and understanding);
be able to select the most appropriate experimental design for the given problem and to correctly interpret the results from a statistical point of view (DD2- Applying knowledge and understanding);
be able to make independent judgments about experiments’ performances and analyses according to different real-world applications (DD3- Making judgments);
be able to communicate in a clear and objective way to specialist and non-specialist audience the assumptions behind an experimental design, the correct way to perform it and to analyze its results (DD4-Communication);
be able to autonomously design and implement experiments to solve a research problem (DD5- Lifelong learning skills).

Experiments with a single factor - The fixed effects model: analysis of variance and regression model; the random effects model. Choice of sample size. Exercises with R
Factorial experiments - The two-factor factorial design with fixed effects: analysis of variance and regression model. The general factorial design. Choice of sample size. Exercises with R
Restriction to the randomization: completly randomized block designs. Exercises with R
The 2k design - The 22 design, the 23 design, the general 2k design; a single replicate of the 2k design; the addition of center points to the 2k design; the central composite design; follow-up design. Exercises with R
The fractional factorial design - The 2k-1 design, the 2k-2 design and the general 2k-p design: construction and resolution. Sequential Experimentation. Exercises with R
Design Optimality criteria and computer generated design - Exercises with R
Teaching method will include:
Frontal lectures
PC-labs using the software R
Case study discussion

The assesment is based on two sequential parts:
a) a first part – that accounts for 50% of the final grade – in which students will be asked to provide an experimental design and a suitable model to answer a given research question;
b) a second part - that accounts for 50% of the final grade – in which students will be asked to analyse a dataset provided coherently with a specific design and to discuss the results.
The final grade is an average of the grades on the two parts.
Students can use their pc with their R codes to prepare a pdf document containing code, outputs and comments through a Rmd file.
Aim of the exam is to assess knowledge, reasoning and analytic abilities on the course subjects.

Main textbooks:
D.C. Montgomery (2019) “Design and analysis of experiments”. Nine Edition. John Wiley and Sons, Singapore. Chapters 1-3-4-5-6-8-9.6
J. Lawson (2015). "Design and Analysis of Experiments with R", Boca Raton: CRC Press, Taylor & Francis Group. Chapter 1-2-3-4-5-6
Further support material (lecture slides, coding and further material) will be available on the course’s Blackboard page.

This course starts by providing an introductory survey of the field of finance, and examining the agents, instruments, and institutions that make up the financial system of a modern economy. Along the way, standard concepts and tools of financial analysis are introduced: time value of money, present value, and the efficient markets hypothesis. It will then focus on the investment and financing decisions of firms. Recent developments in the field – in particular, the application of psychology to financial markets (called behavioral finance) – are also discussed within the various topics. Moving to the second module, students will be exposed to a genuine grasp of risk management techniques. In recent years, pure financial risk management has been increasingly embracing enterprise-wide risk management. Individuals and firms confront risk in nearly all decisions they make. We live in a turbulent and complex world, full of vulnerabilities which – through an educated mindset and wise use of financial tools – can be turned into opportunities: risk management discipline is therefore critical for the success of any business. The course aims at teaching students the essential financial tools, while showing them how to recognize, measure and manage risks in various settings. Alongside the intuition and theoretical framework around finance and risk management more broadly, the course will address traditional practices at banking institutions, as well as the most common techniques employed in a corporate context.
At the completion of the course, students will be able to:
Understand the background and tools to support financial decisions within a company.
Estimate the appropriate cost of capital to support investment and financing decisions.
Critically analyse the impact of alternative financing solutions.
Construct appropriate financial valuation models to support firms’ investment decisions.
Critically discuss how corporates and financial institutions make use of the insights and tools of risk management.
Broaden their view on the major risk drivers, with a framework for measuring and monitoring it.
Recognize potential threats, risks, and vulnerabilities in an organization.
Frame risks through scenario planning in a given context, by employing acquired know-how and own intuition towards sensible risk management practices.
Discuss risk profiles with internal / external stakeholders, within an international work set-up.
Master the financial jargon and key technicalities of the risk management practice.

Module I
The first module will discuss the following topics:

• Introduction to financial management.
• Financial statements analysis, cash flow analysis.
• Time value of money, discounted cash flow valuation.
• Interest rates and bond valuation.
• The investment decision: net present value and criteria for selecting investments.
• Equity markets and stock valuation.
• Risk and return, the cost of capital, and capital structure decisions.
• Raising capital: equity issues and debt issues.
• Mergers and acquisitions.
  Module II
  The second module is organized around four sub-sections:
  The concept of risk
• The risk matrix: introduction to risk and its measurement, old and new risks.
• The role of insurance and optimal decision making under risk.
  Asymmetric information and psychological considerations affecting risk management
• Exploration of barriers to risk management.
• Incentive problems arising from asymmetric information, moral hazard and adverse selection. Behavioural finance in executive decision making.
• Typical mistakes in risky scenarios.
• Real-life examples and case studies.
  Firms facing trade-offs in managing risk: investigating the potential benefits vs. costs incurred
• Corporate risk management: how firms can manage their own risk for value creation.
• Prevention, resiliency planning and crisis management.
• Scenario-based strategic planning & risk mitigants.
• The costs of a poor risk management culture.
• Real-life examples and case studies.
  Managing risk in financial markets and within financial institutions
• Threat or opportunity? Interest rate risk, equity risk, credit risk, FX risk, and others.
• The use of derivatives as a powerful tool.
• Introduction to portfolio management and risk-modelling.
• Real-life examples and case studies.
  Interactive face-to-face lectures, exercises and case studies, guest speakers.

Final grades for the course are determined by one comprehensive written exam, which will include both theoretical and numerical exercises. A mock exam will be made available on Blackboard at the end of the course, to help students understand the format of the exam.

Lecture notes, readings, case studies, problem sets, and surveys will be posted on Blackboard. Students are responsible for checking Blackboard regularly for updates.

The course provides students with conceptual frameworks and analytical tools for marketing decision making in businesses leveraging on new technologies to develop and implement their products and services. The continuous innovation of products and services is at the basis of companies’ market competitiveness and one of among the most complex challenges faced by managers. However, innovation is risky, and a large extent of new products and services fail in the marketplace. Managers must own the skills and techniques to intercept and analyze market opportunities, to generate new product and service concepts whose value is perceived high by customers and to successfully position and launch the offering in the market.
Upon successful completion of the requirements for this course, students will be able to:
Define target market opportunities and search out high potential ideas
Understand new product development processes and service innovation and develop the concept of a new offering;
Develop an approach to innovation that is based on the alignment between customer’s value expectations and perceptions and new products and services’ development;
Handle the main steps to bring a new product or service to the market;
Develop a marketing plan in a technology market
Provide a critical analysis of the strategy, planning and new product development processes

Marketing management and the marketing process in new market contexts
Understanding and marketing innovation in the context of new forms of globalization, servitization and industrial revolution
Marketing and innovation in consumer and business markets
Product planning at the business unit level
The front end of innovation
Product development
Product management
Marketing plan for innovations
The course uses a mixture of class lectures, guest speakers’ talks, theories or case studies discussions and/or projects works.

Attending Students (registered presence to at least the 80% of the lessons)
Group Project work (40% of the grade); The group project work consists of an assignment given by a company and assessed by the company, jointly with the professors;
Written examination to be held at the end of the course (60% of the grade) in the dates of the exam. The exam consists of 8 multiple choice questions (8 points) and 3 open questions (24 points), general and specific. General open questions are finalized to understand if the student has absorbed the conceptual and methodological tools and is able to develop complex lines of reasoning interconnecting the different concepts and developing management suggestions. Specific questions are finalized to understand the level of study depth. Examples and applications will be also requested, finalized to understand if the student is able to apply the concepts by using real world examples. The exam lasts 60 minutes.
Non-attending Students
The exam consists of a written examination. In particular, the exam consists of: 10 multiple-choice questions (10 points) and 5 open questions (22 points), general and specific. General open questions are finalized to understand if the student has absorbed the conceptual and methodological tools and is able to develop complex lines of reasoning interconnecting the different concepts and developing management suggestions. Specific questions are finalized to understand the level of study depth. Examples and applications will also be requested, finalized to understand if the student is able to apply the concepts by using real-world examples. The exam lasts 80 minutes.

The program is different for attending and not-attending students.
Attendance will be recorded during lessons according to the methods established by the Blackboard platform.
Attending Students ( participating at least to the 80% of the lessons and activities)
Kenneth B. Kahn & Mayoor Mohan, (2021), Innovation and new product planning Routledge (chapters 1, 3, 4, 5, 6, 7, 9, 10, 11).
Acquista da VP
A collection of texts compiled by the instructors and made available on Blackboard in the folder for attending students
Other material posted on BlackBoard.Unicatt: integrative slides, articles, case studies, exercises and library links to readings
non-attending students
Kenneth B. Kahn & Mayoor Mohan, (2021), Innovation and new product planning Routledge (all chapters).
Acquista da VP
Other book chapters and readings posted on BlackBoard.Unicatt in the folder for non-attending students
Library resources
The Università Cattolica del Sacro Cuore’s Library provides a range of learning resources including texts, journals, periodicals, magazines, and access to online databases and information services. It also offers a virtual library which is accessible via the University’s website.
Online learning
Blackboard is the Università Cattolica del Sacro Cuore's online learning environment. It is used to support traditional face-to-face lectures, tutorials and workshops at the University. Blackboard provides access to various features including announcements, course materials, discussion boards and assessments for each online course of study.

Innovation encompasses multiple fields within the social sciences, including management and economics. The Project Financing module consists of an introduction to the mechanism of project financing. After reviewing the main market frictions faced by firms when raising capital for new projects, the module outlines the difference between corporate and project financed-investments and the ways in which project finance can address the above frictions. Then, the module focuses on the identification and allocation strategies of the main risks associated with project finance. Finally, it addresses the setup of the financial model, the capital structure decision, and the main valuation techniques used in project finance. The Open Innovation module consists of an extensive analysis of the main barriers firms face when innovating, in its different phases moving from invention towards commercialization of a new product or process or organizational set up. Also, the module assesses the concept of “Open Innovation” as a competing innovation model (with respect to existing and more traditional models) which would partly allow overcoming the financial barriers a firm faces when innovating/or when willing to innovate.
Upon successful completion of this course, students will acquire:
1. The knowledge and understanding of the logic underlying project financing, as well as the ability to outline and understand the open innovation model and the main innovation barriers, including the financial ones.
2. The ability to apply such knowledge to real and unfamiliar issues associated with the financing of new projects and the strategies firms may pursue in terms of open innovation.
3. The elements necessary to autonomously and critically develop their own judgment about the opportunities and challenges of project financing and open innovation, depending on the characteristics of the players involved.
4. The lexicon associated with the topics of the course and the ability to communicate with both specialist and non-specialist interlocutors clearly and unambiguously by means of dedicated activities throughout the course.
5. The learning skills that will allow them to further delve into the interaction between finance and innovation in an autonomous way.

The contents of the Project Financing module are the following:

• Capital market frictions: agency costs, information asymmetry.
• Introduction to project financing: main features and players, corporate vs. project finance, advisors and financial intermediaries.
• Risk in project financing: risk identification, analysis, and allocation.
• Financial structure and valuation: financial model, capital structure choice, project valuation and cover ratios.
• Case studies.
  The contents of the Open Innovation module are the following:
• General introduction to innovation
• Main barriers to innovation, both financial and non-financial.
• Conceptualization of the open innovation model and implications for firm boundaries, networks, and digitalization.
• Opportunities and challenges of open innovation.
  The course uses a combination of frontal lectures, case study discussions, and project works to be presented in groups.

The assessment method is different for attending and non-attending students.
Attending students: the Project Financing module is evaluated in an interim test during the mid-term break. Students who pass the interim test on the Project Financing module are then evaluated on the Open Innovation module in a completion test at the end of the course. Students who fail the interim test on the Project Financing module have to retake the full exam.
The Project Financing interim test is written and composed of multiple choice questions and numerical exercises. The Open Innovation completion test is written and composed of multiple choice and open-ended questions. The final grade is the arithmetic average of the grades of the two modules.
Non-attending students: the exam is written and composed of two parts, one for each module. Both parts consist of open-ended questions, multiple choices and numerical exercises. Non-attending students are not allowed to take the interim exam.
IMPORTANT: please note that the status of attending and non-attending student applies only to the first two exam dates following the end of the course (i.e., the winter session up to the end of December). In all other sessions, the exam is written and composed of two parts (one for each module), and is the same for all students. Both parts consist of open-ended questions, multiple choice questions, and numerical exercises.

Project Financing module:
S. Gatti, Project Financing in Theory and Practice, 3rd edition, Elsevier, 2018, chapters 1, 3, 5, 6.
Open Innovation module:

• H. Chesbrough-W. Vanhaverbeke-J. West (eds). Open innovation: Researching a new paradigm. Oxford University Press on Demand, 2006.
• H. Hottenrott-B. Peters (2012), Innovative capability and financing constraints for innovation: more money, more innovation?, Review of Economics and Statistics, 94(4), 1126-1142.
• G. Pellegrino-M. Savona (2017), No money, no honey? Financial versus knowledge and demand constraints on innovation, Research Policy, 46(2), 510-521.
  For both modules, additional teaching material will be made available on Blackboard.

The aim of this course is to learn how to describe and analyze network data. To this end, we discuss theory, methodology, and applications of networks and network- indexed data. In particular, students should be able to: a) understand how network data is generated, represented, and stored for efficient computations; b) summarize and visualize information in network data to interpret connectivity patterns; and c) model network data to obtain solutions to real-world problems. The theoretical presentation of the methods is supported by examples and real applications with the R statistical software.
By the end of the course, students are expected to:
have acquired advanced knowledge of concepts, terms and methods of network data analysis (DD1- Knowledge and understanding);
be able to select and apply suitable statistical models to summarize and interpret connectivity patterns in network data (DD2- Applying knowledge and understanding);
be able to use network statistical methods and models to perform inference, make predictions, and ultimately take decisions in real-world applications (DD3- Making judgments);
be able to communicate in a clear and objective way to specialist and non- specialist audiences the assumptions behind a network model and method and the correct way to perform analyses (DD4-Communication);
be able to autonomously apply and potentially extend statistical network methods and models to solve research problems (DD5- Lifelong learning skills).

Introduction and definitions - Introductory concepts from graph theory, network data interpretation and representation. Lab: introduction to R/RStudio, Rmarkdown, igraph package, network data examples.
Network statistics I - Descriptive statistics for networks, connectivity, clustering, transitivity, degree distribution. Lab: descriptive statistics for network examples, exercises.
Network statistics II - Graph linear algebra, eigen statistics, visualization. Lab: descriptive statistics and visualization for network examples, exercises.
Network statistics III - Sampling and estimation in networks: Horvitz-Thompson estimation, network sampling designs. Lab: estimation in network examples, exercises.
Network models I - Erdos-Renyi, Barabasi-Albert, Watts-Strogatz, preferential attachment. Lab: simulating networks and computing descriptive statistics, parametric bootstrap.
Network models II - Random dot product models, stochastic blockmodels. Lab: simulation and exercises.
Network models III - Exponential random graph models, community detection.
Lab: simulation and exercises.
Lectures and PC-labs using the software R/RStudio.

The assessment is based on one take-home exam in which students will apply network descriptive statistics and statistical models covered in the course topics. The exam will cover both theoretical and applied questions and will be based on a real-world network data problem, requiring computations using R and a discussion of the results. The aim of the exams is to assess knowledge, reasoning and analytical abilities on the course topics.

Slides, coding and further material will be posted on the University platform Blackboard. Useful readings are:
E. D. Kolaczyk (2019) “Statistical Analysis of Network Data”, Springer.
E. D. Kolaczyk and G. Csardi (2014) “Statistical Analysis of Network Data with R”, Springer.
M. E. J. Newman (2010). "Networks”, Oxford University Press.

The course will provide students with an overview of the main supply chain design and management principles, focusing on the role that up-to-date information systems, as well as hardware and digital technologies can play on the improvement of procurement, manufacturing and logistics processes. Nowadays being able to deliver the product in the timing, location and quantities requested by the customer is becoming more and more important to successfully compete in both BtoB and BtoC markets. In this setting, technological solutions as Internet of Things, Cloud computing, Big Data and Analytics, Robotics, Artificial Intelligence to mention a few, can be leveraged to improve the performance of supply chain processes. Furthermore, the experience of best-in-class companies shows that such technologies can also be used to pursue a servitization strategy, offering value-added services to the clients, on top of the physical products. Thus, managers must be able to identify improvement opportunities and to revise operational processes accordingly, through the selection and adoption of suitable technological solutions.
At the end of this course, students will be able to:
Master the main concepts and theories concerning supply chain design and management and with the main technological solutions that can be leveraged in order to improve manufacturing and logistics performances;
Implement their knowledge in this field in order to analyze the supply chains of manufacturing companies, to identify improvement opportunities that the availability of up-to-date technologies can offer and to suggest appropriate actions;
Autonomously address and solve managerial problems in the context of supply chain management, collecting relevant data and analyzing it through sound methodologies suitable for developing tailored solutions; they will be able to express their personal assessment on complex problems, providing insights also on the ethical aspects of the issue under analysis;
Communicate in a clear and effective way their knowledge, ideas and improvement suggestions to both managers and novices of this field;
Keep on learning the topics of supply chain management and of technology adoption in this field, widening their knowledge and understanding of this subject through the reading of further materials and the real life experience in challenging contexts.

The main topics addressed during this course are:
Supply Chain Management: strategies and performance
Overview of the main supply chain processes
Design of production processes
Robotics and automation in manufacturing and logistics processes
E-commerce and last mile logistics
Physical distribution and omnichannel logistics
Leveraging technologies to boost social and environmental sustainability
Leveraging technologies to pursue collaboration along the supply chain.
The teaching method will be highly interactive. Case-studies, simulations and site visits will be used to boost class participation and discussion among students, through a learning-by-doing approach.

Attending students will be assessed through a weighted average, as follows:
30% group assignments and presentations; during the course two group assignments will be carried out. They will consist of case-studies or companies’ supply chains assessments and will require the preparation of a report, in which students will provide a detailed description of the way they addressed and solved the problem. These assignments will aim at developing and assessing students’ ability to: 1) communicate their knowledge and ideas in an effective way; 2) solve managerials problems collecting relevant data, analyzing it through the concepts and theories addressed during this course; 3) keep on learning the topics of supply chain management, widening their knowledge in contexts of the two assignments.
70% final written exam, consisting of both multiple-choice and open questions. Multiple choice questions will be exclusivelly on theoretical topics. Open questions will be on mini-cases and on theoretical topics. Multiple choice questions will aim at assessing students’ familiarity with main concepts and theories concerning supply chain management. Open questions will require the writing up of short essays (some of them also with calculations) and the solution of quantitative exercises in order to assess students’ ability to analyze supply chain processes through the frameworks and concepts learnt during the course.
Attendance (not less than 75% of class sessions) is mandatory for taking the exam as an attending student.
Non attending students will be assessed through a written exam consisting of multiple choice and open questions, referred to the entire textbook. Open questions will be theoretical or will consist of numerical exercises.
At the end of the course, a mock-exam will be uploaded on Blackboard.

Attending Students
All materials uploaded on Blackboard will be considered mandatory.
Grando A., Belvedere V., Secchi R., Stabilini G. (2021), Production, Operations and Supply Chain Management, Bocconi University Press. (selected chapters) Acquista da VP
Non attending students
Grando A., Belvedere V., Secchi R., Stabilini G. (2021), Production, Operations and Supply Chain Management, Bocconi University Press. (chapters 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 20) Acquista da VP

The course is intended to provide the managerial skills for implementing efficient and sustainable growth strategies based on emerging technologies. Successful integration of new technologies requires a strategic approach that leads to rethinking the company's aims, structure, and processes to gain efficiency and support the company's competitiveness and growth. The course reports on experiences of corporate strategies both in firms operating in tech-based industries, and in firms operating in mature industries.
Students are taught to view these industries under the perspectives of top managers. They will learn how to understand and consider the point of view of various industry players, how to evaluate strategic options and their consequences, and how to predict possible behaviors under different circumstances.
The course will provide students with the skills for an effective problem-solving attitude, decision-making approach as well as knowledge on strategic management in a technologically dynamic environment.
Upon successful completion of the requirements for this course, students will:
be aware of the most important theories and frameworks in the corporate strategy field;
be able to understand existing corporate strategies implemented by firms, and to make recommendations;
be aware of decision-making abilities and tools required to strategizing in multi-business firms;
be aware of the most important technological trends that are occurring in the global markets;
develop and reinforce a problem-solving approach;
develop the capability to work in team;
improve the capacity to present in public business cases and industry analyses;
be equipped with the knowledge basis and capacity to collect additional information that is needed to tackle more advanced strategic topics.

The course consists of lectures, guest speakers, case-study and case-problem discussions, and a group project.
Attending Students are required to participate actively in class; to discuss business cases; and to deliver presentations.

The two modules converge to a unique final mark. The latter is the result of the following evaluations:
Attending students (Multiple Assessment route):
For attending students, the final grade will be based partially (50%) on a final exam in written form with a combination of questions with open-ended and closed-ended answers, analyses of cases and written essays, based on the reading list for attending students. The exam will assess the knowledge of most important theories and frameworks in the business strategy and the ability of their application.
The remaining part of the final grade (50%) will be determined by a group work aimed at assessing the strategic skills and capacities and communication skills. More details will be communicated at the beginning of the course and made available on Blackboard. Students will be qualified as attending students only for the exam sessions in March/April. Afterwards, students will have to take the exam as not attending students.
Not attending students (Single Assessment route):
For not attending students, the final grade will be based on a final exam in written form with a combination of questions with open-ended and closed-ended answers, analyses of cases and written essays, based on the reading list for non-attending students. The exam will assess students’ knowledge of most important theories and frameworks in the business strategy and students’ability to apply such theories and frameworks to real cases.

Attending students (Multiple Assessment route):
Pedrini M., Minciullo M., Molteni, M.M. The corporate strategy hourglass. Multi-stakeholder and multi-business value creation, McGraw-Hill, 2023 (Selected chapters: 1-3; 8-14; 16-19; 21). Acquista da VP
Additional readings and other integrating teaching materials provided through the Blackboard platform and through Harvard Business School Publishing.
Not attending students (Single Assessment route):
Pedrini M., Minciullo M., Molteni, M.M. The corporate strategy hourglass. Multi-stakeholder and multi-business value creation, McGraw-Hill, 2023 (All chapters). Acquista da VP
Additional readings and other integrating teaching materials provided through the Blackboard platform and through Harvard Business School Publishing.