Business Models for the Digital Economy

Code:

M.EIC039

Acronym:

MNEC

Keywords
Classification	Keyword
OFICIAL	Quantitative Methods and Management

Instance: 2022/2023 - 1S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EIC	31	Syllabus	2	-	6	39	162

Teaching language

English

Objectives

After approval in this course unit, students should be able to develop and analyse new business models in established and emerging areas of the digital economy, leveraging the fundamental concepts and characteristics of this area of the economy.

Learning outcomes and competences

After approval in this course unit, the students should be able to:
1. Recall the fundamental concepts and characteristics of the digital economy.
2. Use those concepts and characteristics in the development and analysis of new business models in the digital economy.
3. Recall the key characteristics, typologies and success factors of platform-based, “as a service”, and data-driven business models.
4. Use this knowledge to analyze digital economy-related business areas, combining an understanding of the main technology and business issues involved.
5. Recall the state and perspectives of development, and the potential impact of emerging technologies on the digital economy.
6. Apply this knowledge to analyze the implications of emerging technologies on change in established and emerging areas of the digital economy.

Working method

Presencial

Program

1. Digital Economy
1.1. Fundamentals of the digital economy
1.2. Key features of the digital economy: e-commerce, mobility, reliance on data, network effects, platforms
2. New business models for the digital economy
2.1. Platform based business models: platform strategies and ecosystem dynamics
2.2. Products, services and software as a service: new business models for software companies, software as a service and cloud computing
2.3. Data-driven business models: strategies for acquisition, analysis and monetization of data; revenue models for data-driven businesses; Artificial Intelligence/Machine Learning-based approaches.
3. Emerging applications of business models for the digital economy: Internet of Things, Sharing Economy; Blockchain, Cybersecurity, Quantum Computing.

Mandatory literature

Dave Chaffey, David Edmundson-Bird, Tanya Hemphil; Digital Business and E-commerce Management, Pearson UK, 2019. ISBN: 1292193360, 9781292193366
Peter Weill, Stephanie Woerner; What's Your Digital Business Model?: Six Questions to Help You Build the Next-Generation Enterprise, Harvard Business Press, 2018. ISBN: 163369271X, 9781633692718
Michael A. Cusumano, Annabelle Gawer, David B. Yoffie; The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power, HarperCollins, 2019. ISBN: 0062896334, 9780062896339
Andrew McAfee, Erik Brynjolfsson; Machine, Platform, Crowd: Harnessing Our Digital Future, W. W. Norton & Company, 2017. ISBN: 0393254305, 9780393254303
Erik Brynjolfsson, Andrew McAfee; The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company, 2014. ISBN: 0393239357, 9780393239355

Teaching methods and learning activities

A1. LECTURES Lectures will focus on the fundamental management concepts and tools, which provide the basis for the applied work.

A2. CASE STUDY ANALYSIS AND DISCUSSION The practical classes will focus on case study analysis and discussion, with two types: a) Case study discussion in class of cases made available the previous week, with no evaluation; b) 3 case study analysis, report and discussion in class, which will count for the frequency evaluation.

Type of evaluation: Distributed evaluation with final exam

Terms of frequency: Students are required to complete a group assignment:

Analysis and discussion of 3 cases studies (60% of final grade)

Individual participation in case study discussion (15% of final grade)

Minimum classification of 37,5% in each of the evaluation components.

Formula of evaluation: Final grade = 0,6* (analysis and discussion of four case studies)+ 0,15*(individual participation in case study discussion) +0,25* exam.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Participação presencial	15,00
Prova oral	60,00
Teste	25,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	39,00
Estudo autónomo	58,00
Frequência das aulas	39,00
Trabalho escrito	26,00
Total:	162,00

Eligibility for exams

Presence in classes according to the school's regulations and having at least 7.5 (in 20) points in each of the distributed evaluation components.

Calculation formula of final grade

Terms of frequency: Students are required to complete a group assignment:

Analysis and discussion of 3 cases studies (60% of final grade)

Individual participation in case study discussion (15% of final grade)

Minimum classification of 37,5% in each of the evaluation components.

Formula of evaluation: Final grade = 0,6* (analysis and discussion of four case studies)+ 0,15*(individual participation in case study discussion) +0,25* exam.

Examinations or Special Assignments

a) Case study discussion in class of cases made available the previous week, with no evaluation;
b) 3 case study analysis, report and discussion in class, which will count for the frequency evaluation.

In special terms the final classification will be obtained through the following procedures:
1. Carry out an individual assignment defined by the professor;
2. Attend an oral exam about the theme of the assignment or other themes which the professor deem necessary.
3. Do a written exam.

final grade= 0.40*( assignment presentation and report) + 0.6*(exam grade)

Minimum classification of 37,5% in each of the evaluation components.

Special assessment (TE, DA, ...)

Working students who cannot attend all classes can fulfill the distributed component of the evaluation through the following procedures:
1.Individual assignment defined by Professors
2. Intermediary and final presentation of individual assignment on predefined dates.
3. The three case study presentations and discussions are mandatory.
4. Working students should contact the course professors to define the work assignment.

Classification improvement

The classification improvement of the exam follows FEUP'S regulations.
The classification improvement of the distributed component can be done at the same time as the practical assignments in the following academic year.