ID:
PLG20
Duration (hours):
36
CFU:
6
SSD:
ECONOMIA POLITICA
Located in:
PESCARA
Url:
DIGITAL MARKETING/CORSO GENERICO Year: 2
Year:
2026
Course Catalogue:
Overview
Date/time interval
Primo Quadrimestre (16/09/2026 - 20/12/2026)
Syllabus
Course Objectives
The course contributes to the overall objective of the Digital Marketing degree programme of training graduates able to understand the economic functioning of the digital markets in which firms, platforms and consumers operate. The main objective of the course is to develop students' ability to read the strategies, prices and market structures of digital goods with the tools of information economics, and to recognise to what extent generative artificial intelligence confirms or overturns those logics. At the end of the course students will be able to:
KNOWLEDGE AND UNDERSTANDING: describe the economic properties of digital goods (non-rivalry, cost structure, economies of scale) and how they differ from the cost structure of AI; explain the main pricing strategies and business models for digital goods; understand network effects, lock-in, standards and multi-sided markets; know the fundamentals of information asymmetries and online reputation systems; know the regulatory framework of digital markets (antitrust, DMA, AI Act).
JUDGEMENT SKILLS: critically assess the pricing strategy or business model of a digital product or AI service; analyse the competitive structure of a digital market and anticipate the effects of the entry of AI; distinguish evidence-based claims from purely promotional ones.
COMMUNICATION SKILLS: present the topics covered with appropriate terminology; present and discuss the results of the group project in class.
LEARNING SKILLS: autonomously update their knowledge in a very fast-moving field, through the critical reading of papers, institutional reports and qualified sources, making critical and informed use of the very AI tools studied in the course.
KNOWLEDGE AND UNDERSTANDING: describe the economic properties of digital goods (non-rivalry, cost structure, economies of scale) and how they differ from the cost structure of AI; explain the main pricing strategies and business models for digital goods; understand network effects, lock-in, standards and multi-sided markets; know the fundamentals of information asymmetries and online reputation systems; know the regulatory framework of digital markets (antitrust, DMA, AI Act).
JUDGEMENT SKILLS: critically assess the pricing strategy or business model of a digital product or AI service; analyse the competitive structure of a digital market and anticipate the effects of the entry of AI; distinguish evidence-based claims from purely promotional ones.
COMMUNICATION SKILLS: present the topics covered with appropriate terminology; present and discuss the results of the group project in class.
LEARNING SKILLS: autonomously update their knowledge in a very fast-moving field, through the critical reading of papers, institutional reports and qualified sources, making critical and informed use of the very AI tools studied in the course.
Course Prerequisites
Basic knowledge of microeconomics (supply and demand, costs, perfect competition and monopoly). No programming experience is required.
Teaching Methods
The course combines three teaching methods: (i) lectures and interactive class discussions with a narrative structure: each lesson presents a classical concept of the economics of digital goods, illustrates it with real cases and tests it against the case of artificial intelligence (the "AI lens"); (ii) three hands-on labs with no programming prerequisites, based on browser-based interactive simulations, pre-built spreadsheets and the guided, critical use of large language models (cost analysis of a digital good and of an AI model; audit of the business model and pricing of a digital service; simulation of a regulatory case); lab materials can be used both in class and at home, with individual homework assignments; (iii) discussion seminars on applied cases and recent papers. For each lesson, students receive a study handout (theory, examples, glossary and self-assessment questions) and the presentation material used in class. An intermediate test on Part I is held mid-course; the course ends with the group project presentations.
Assessment Methods
Student assessment is structured into the following components:
1) Intermediate written test (week 3, lesson 6), worth 20% of the final grade, with closed and open questions covering Part I of the course.
2) Group project with final presentation (30%): an economic analysis of a digital good, a platform or an AI service of the students' choice (pricing strategy, business model, competitive structure of the market), with the requirement to explicitly compare the logic of traditional digital goods with that of AI. Assessment rubric: rigour of the economic analysis 35%; quality of evidence and sources 25%; originality and critical thinking 15%; effectiveness of the presentation 15%; individual contribution (peer assessment) 10%.
3) Final written exam (50%), 90 minutes: three open questions (one for each part of the course) and a short applied case on a digital market or an AI service. Students may bring one page of personal notes ("cheat sheet") to encourage active synthesis during preparation.
4) Lab assignments: the three take-home lab assignments are assessed on a pass/fail basis; students who pass all three obtain up to 2 bonus points on the final grade.
The final grade is expressed out of thirty and results from the weighted average of the components, according to criteria of coherence, completeness, command of the analytical tools and argumentative ability. A student is considered as attending if they sit the intermediate test, take part in the group project and submit at least 2 of the 3 lab assignments; no attendance record is required. For students who do not meet these conditions (non-attending students), assessment is based on a written exam covering the entire syllabus, supplemented by an oral discussion of the reference texts.
1) Intermediate written test (week 3, lesson 6), worth 20% of the final grade, with closed and open questions covering Part I of the course.
2) Group project with final presentation (30%): an economic analysis of a digital good, a platform or an AI service of the students' choice (pricing strategy, business model, competitive structure of the market), with the requirement to explicitly compare the logic of traditional digital goods with that of AI. Assessment rubric: rigour of the economic analysis 35%; quality of evidence and sources 25%; originality and critical thinking 15%; effectiveness of the presentation 15%; individual contribution (peer assessment) 10%.
3) Final written exam (50%), 90 minutes: three open questions (one for each part of the course) and a short applied case on a digital market or an AI service. Students may bring one page of personal notes ("cheat sheet") to encourage active synthesis during preparation.
4) Lab assignments: the three take-home lab assignments are assessed on a pass/fail basis; students who pass all three obtain up to 2 bonus points on the final grade.
The final grade is expressed out of thirty and results from the weighted average of the components, according to criteria of coherence, completeness, command of the analytical tools and argumentative ability. A student is considered as attending if they sit the intermediate test, take part in the group project and submit at least 2 of the 3 lab assignments; no attendance record is required. For students who do not meet these conditions (non-attending students), assessment is based on a written exam covering the entire syllabus, supplemented by an oral discussion of the reference texts.
Texts
- Shapiro C., Varian H.R., Information Rules: A Strategic Guide to the Network Economy, Harvard Business School Press, 1999, selected chapters.
- Goldfarb A., Tucker C., "Digital Economics", Journal of Economic Literature, 57(1), 2019.
- Agrawal A., Gans J., Goldfarb A., Power and Prediction: The Disruptive Economics of Artificial Intelligence, Harvard Business Review Press, 2022, selected chapters.
- Carlton D.W., Perloff J.M., Modern Industrial Organization, Pearson, chapter 13 (Information).
- Akerlof G.A., "The Market for 'Lemons'", Quarterly Journal of Economics, 84(3), 1970.
- Rochet J.-C., Tirole J., "Platform Competition in Two-Sided Markets", Journal of the European Economic Association, 1(4), 2003 (selected sections).
- Brynjolfsson E., Li D., Raymond L., "Generative AI at Work", Quarterly Journal of Economics, 2025 (selected sections).
- Xu F., Wang X., Chen W., "The Economics of AI Foundation Models: Openness, Competition, and Governance", working paper, 2025 (selected sections).
- Up-to-date institutional reports on digital markets and AI (European Commission, OECD, Italian Competition Authority, Stanford AI Index), indicated in class.
- Lecture notes prepared by the instructor, distributed lesson by lesson, with lesson-specific further readings (articles, case studies, recommended video materials).
- Goldfarb A., Tucker C., "Digital Economics", Journal of Economic Literature, 57(1), 2019.
- Agrawal A., Gans J., Goldfarb A., Power and Prediction: The Disruptive Economics of Artificial Intelligence, Harvard Business Review Press, 2022, selected chapters.
- Carlton D.W., Perloff J.M., Modern Industrial Organization, Pearson, chapter 13 (Information).
- Akerlof G.A., "The Market for 'Lemons'", Quarterly Journal of Economics, 84(3), 1970.
- Rochet J.-C., Tirole J., "Platform Competition in Two-Sided Markets", Journal of the European Economic Association, 1(4), 2003 (selected sections).
- Brynjolfsson E., Li D., Raymond L., "Generative AI at Work", Quarterly Journal of Economics, 2025 (selected sections).
- Xu F., Wang X., Chen W., "The Economics of AI Foundation Models: Openness, Competition, and Governance", working paper, 2025 (selected sections).
- Up-to-date institutional reports on digital markets and AI (European Commission, OECD, Italian Competition Authority, Stanford AI Index), indicated in class.
- Lecture notes prepared by the instructor, distributed lesson by lesson, with lesson-specific further readings (articles, case studies, recommended video materials).
Contents
The course provides the tools of economic analysis to understand digital goods and online markets, from classical information economics (Shapiro-Varian, Akerlof) to generative artificial intelligence, treated as a sui generis digital good that challenges many of the regularities that hold for software, content and platforms. AI is the common thread of the whole course: each classical concept is first presented and then tested against the case of AI models.
The course is organised into three parts. Part I (foundations: from digital goods to AI) analyses the economic nature of information goods (non-rivalry, high fixed costs and near-zero marginal costs, economies of scale) and contrasts it with the cost structure of generative AI, where training is a huge fixed cost but inference carries a positive and significant marginal cost (compute, energy), so that average costs are not necessarily decreasing; it then covers pricing strategies for digital goods (price discrimination, versioning, bundling, freemium, subscriptions, usage- and token-based pricing, the return of the advertising model in chatbots) and the problems of imperfect and asymmetric information in online markets (adverse selection, moral hazard, signalling, reputation systems, search costs and shopping agents). Part II (networks, platforms and market power) deals with network effects, critical mass, lock-in, standards and standards wars, multi-sided markets and the long tail, advertising- and data-based business models, asking whether and how these mechanisms operate in AI (weak network effects, data flywheels, multi-homing, open-weight models, agent protocols, training data and copyright). Part III (strategy, regulation and the frontier) analyses the AI value chain (chips, cloud, models, applications), antitrust and regulation of digital markets (Microsoft and Google cases, DMA, AI Act), the impact of generative AI on digital marketing (generative search, agentic commerce, zero-cost content and the scarcity of attention and trust) and frontier scenarios. The course includes three hands-on labs with no programming prerequisites and a group project.
The course is organised into three parts. Part I (foundations: from digital goods to AI) analyses the economic nature of information goods (non-rivalry, high fixed costs and near-zero marginal costs, economies of scale) and contrasts it with the cost structure of generative AI, where training is a huge fixed cost but inference carries a positive and significant marginal cost (compute, energy), so that average costs are not necessarily decreasing; it then covers pricing strategies for digital goods (price discrimination, versioning, bundling, freemium, subscriptions, usage- and token-based pricing, the return of the advertising model in chatbots) and the problems of imperfect and asymmetric information in online markets (adverse selection, moral hazard, signalling, reputation systems, search costs and shopping agents). Part II (networks, platforms and market power) deals with network effects, critical mass, lock-in, standards and standards wars, multi-sided markets and the long tail, advertising- and data-based business models, asking whether and how these mechanisms operate in AI (weak network effects, data flywheels, multi-homing, open-weight models, agent protocols, training data and copyright). Part III (strategy, regulation and the frontier) analyses the AI value chain (chips, cloud, models, applications), antitrust and regulation of digital markets (Microsoft and Google cases, DMA, AI Act), the impact of generative AI on digital marketing (generative search, agentic commerce, zero-cost content and the scarcity of attention and trust) and frontier scenarios. The course includes three hands-on labs with no programming prerequisites and a group project.
Course Language
Italian (study materials and readings in Italian and English).
More information
Attendance is strongly recommended, particularly for lab activities and the group project. No formal attendance record is kept: for assessment purposes, a student is considered as attending if they sit the intermediate test, take part in the group project and submit at least 2 of the 3 lab assignments. Students are advised to bring a laptop or a browser-enabled device to class for lab sessions. The use of generative AI tools is allowed and encouraged in lab activities and in the project, provided that it is disclosed and that the student is able to explain and defend every result produced. Teaching materials (handouts, slides, lab materials and assignment sheets) are distributed lesson by lesson through the University e-learning platform. ERASMUS students are invited to contact the lecturer to agree on their course programme and, if needed, on materials and tests in English.
Degrees
Degrees
DIGITAL MARKETING
Master’s Degree
2 years
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