Digital Economy
- Credits: 4
- Ending: Examination
- Range: 2P + 2C
- Semester: summer
- Year: 1
- Faculty of Business Economics with seat in Košice
Teachers
Included in study programs
Teaching results
The aim of the course is to introduce students to the main features of the "digital economy", as well as with several issues related to it. The central questions across the various themes is whether business practices and contracts in the "digital economy" may deviate from efficient behavior and whether they can be beneficial or detrimental to society. Case studies that are specific to digital markets will be also analyzed. First, students will study the economic characteristics of the platforms and two-way markets that often emerge in the digital industries. Second, the course will cover the applications of the information economy to various digital sectors. In particular, students will learned the theory of auctions, as auctions are commonly used to sell advertising services and e-commerce. Third, the course will cover economic innovation, security, privacy and GDPR, which are an essential part of the "digital economy".
Knowledge:
• Knowledge of the digital economy
• Knowledge of B2B and B2C platforms
• Knowledge of trust building mechanisms and building reputation in electronic markets
• Knowledge of the basic tools of dataminig and extraction of information from data
• Knowledge of security, privacy and GDPR in the digital environment.
Competences:
• Orientation in different types of electronic markets
• Operating in the environment of B2B and B2C trading platforms
• Evaluate business data using database and basic data mining tools.
• Evaluate the trustworthiness of business partners and building trustworthiness in electronic markets
• Introduce digital innovation in enterprises
• Management of Business Intelligence systems in the company
Skills:
• Buying and selling on electronic markets using B2B and B2C platforms,
• Adjusting the settings in the functionalities of market platforms
• Creation and parameterization of electronic product lists and electronic catalogues
• Reporting, control and management of Business Intelligence systems
• Creating a strategy for building reputation in electronic markets.
Indicative content
Lectures:
1. Nature of Data and data collection
2. Data analytics
3. Digitalization of economy
4. Digital goods and services
5. B2B Platforms
6. Digital Markets
7. Trust building mechanism
8. Reputation on the Internet
9. Digital business, strategy, and innovation
10. Business Intelligence
11. Introduction to Datamining
12. Datamining methods
13. Security, privacy and GDPR
Seminars:
1. Selected case studies and examples related to data types
2. Selected case studies and examples related to data preparation
3. Selected case studies and examples related to visualization multivariable datasets
4. Selected case studies and examples related to data analytics
5. Selected case studies and examples related to preparation of electronic auction
6. Selected case studies and examples related to realisation of electronic auction
7. Selected case studies and examples related to evaluation of electronic auction
8. Selected case studies and examples related to evaluation of reputation mechanisms
9. Selected case studies and examples related to basic datamining methods I (e.g. basic classification trees)
10. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network modelling)
11. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network training)
12. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network validating)
13. Midterm assignment
Support literature
1. ZHOU, Hong. Learn Data Mining Through Excel. Apress, 2020.
2. OVERBY, Harald; AUDESTAD, Jan Arild. Digital Economics: How Information and Communication Technology is Shaping Markets, Businesses, and Innovation. Sp, 2018.
3. PEITZ, Martin; WALDFOGEL, Joel (ed.). The Oxford handbook of the digital economy. Oxford University Press, 2012.
4. POCHIRAJU, Bhimasankaram; SESHADRI, Sridhar (ed.). Essentials of Business Analytics: An Introduction to the Methodology and Its Applications. Springer, 2019.
5. HODEGHATTA, Umesh R.; NAYAK, Umesha. Business analytics using R-a practical approach. Apress, 2016.
Syllabus
Lectures: 1. Nature of Data and data collection 2. Data analytics 3. Digitalization of economy 4. Digital goods and services 5. B2B Platforms 6. Digital Markets 7. Trust building mechanism 8. Reputation on the Internet 9. Digital business, strategy, and innovation 10. Business Intelligence 11. Introduction to Datamining 12. Datamining methods 13. Security, privacy and GDPR Seminars: 1. Selected case studies and examples related to data types 2. Selected case studies and examples related to data preparation 3. Selected case studies and examples related to visualization multivariable datasets 4. Selected case studies and examples related to data analytics 5. Selected case studies and examples related to preparation of electronic auction 6. Selected case studies and examples related to realisation of electronic auction 7. Selected case studies and examples related to evaluation of electronic auction 8. Selected case studies and examples related to evaluation of reputation mechanisms 9. Selected case studies and examples related to basic datamining methods I (e.g. basic classification trees) 10. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network modelling) 11. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network training) 12. Selected case studies and examples related to basic datamining methods II (e.g. simple neural network validating) 13. Midterm assignment
Requirements to complete the course
Midterm written assignment
Final written exam
Midterm evaluation:
Midterm written assignment: 40% of course (total points 40)
Final written exam:
Final written exam 60% of course - 60 points
Student workload
104 hours in total, of which:
• 26 hours of participation in lectures
• 13 hours preparation for lectures
• 26 hours of participation in seminars
• 13 hours preparation for seminars
• 13 hours preparation for midterm written assignment
• 13 hours preparation for the final exam
Language whose command is required to complete the course
Slovak
Date of approval: 16.02.2023
Date of the latest change: 22.02.2023