Eco-systems disruption by IT

Course Objective

An ecosystem is a collection of parties who work cooperatively and

competitively to satisfy customer needs. Well-known examples are Google,

Facebook, and Netflix, but there are many other eco-systems in the

field, e.g. in Fintech, energy, and automotive industry. In this course,

we focus on digital ecosystems which are enabled by Internet- and related technologies.

Frequently, these ecosystems are disrupted by innovative information

technologies, such as machine learning, blockchain, and 5G mobile. The

overall goal of this course is to analyze disruptive opportunities for

an eco-system given a particular emerging technology. This year, that

technology is blockchain.

Upon completion of this course, students will:

- have gained knowledge and understanding of the concept of ‘eco-system’

(Knowledge and understanding);

- have gained knowledge and understanding of various methods for

designing and analyzing eco-systems and their e-services, including

e3value, trust modelling, BPMN, and UML class modelling (Knowledge and

understanding);

- have gained knowledge and understanding of the selected technology of

this year in order to analyze, design and implement a disruptive

eco-system (Knowledge and understanding);

- be able to analyze and design an eco-system offering an e-service from

different viewpoints (Applying knowledge and understanding);

- be able to elicit different e-service (business) ideas that can be

provided in an eco-system (Applying knowledge and understanding);

- be able to explore the e-service idea from a business perspective. In

this way, the student will be able to further design and develop the

e-service idea using different analysis techniques (Applying knowledge

and understanding);

- be able to transform the business perspective of the e-service design

into a design reflecting the IT perspective (Applying knowledge and

understanding);

- be able to implement (part of) the e-service using the selected

disruptive technology (Applying knowledge and understanding);

- be able to make informed decisions about eco-systems and their

e-services based on possible risks and opportunities (Making judgments);

- be able to assess the quality and correctness of the various

conceptual models created (Making judgments);

- be able to pitch a new idea for an eco-system/e-service to investors

(Communication skills);

- be able to concisely present the selected solution (Communication

skills);

- be able to write a scientifically well-founded report about the

eco-system/e-service under development in a group of students

(Communication skills);

- have learned to work in a project group and keep each other

accountable for agreed results (Learning skills);

- have learned to improve themselves based on various feedback

mechanisms (Learning skills).

Course Content

The course comprises the following topics:

- Eco-systems and platforms

- Introduction in the selected technologyof this year

- Business models and value proposition design

- The e3value method for understanding business networks

- The Business Process Modelling Notation for analysing cross-organizational networks

- The Unified Modelling Language – class modelling

- Transaction modelling

- Technological design issues

Additional Information Teaching Methods

The following methods are used:

-Lectures twice a week. These lectures include in-class workshops to practice the various methods used in this course

-Group work: teams of about 2/3 students design an eco-system. At the beginning of the course a number of eco- systems are offered as inspiration

-Group feedback sessions: Each week, students will get personal feedback in face-to-face feedback sessions. Moreover, groups will receive feedback on their submitted reports by email.

Method of Assessment

Individual exam on the theory (50%), Teamwork project assignments (50%).

To pass the course, the scores of each component must be 5.5. or higher.
Compensation: it is not possible to compensate one component with
another.
Mode of re-examination: if insufficient, each component can be revised
and resubmitted once.

Literature

A (selection of a) list of papers on the course topics. Material distributed on Canvas.

Additional Information Target Audience

Master Artificial Intelligence
Master Computer Science
Master Information Sciences

Custom Course Registration

Further information for this course will be made available online
(Canvas). All students must be enrolled in the course Canvas community.
Group enrolment takes place in Canvas.

Recommended background knowledge

The student should have sufficient knowledge about Requirements

Engineering and Conceptual Modelling and should master such a modelling

method (at least UML class or activity diagrams).