- UCAS course code
- N248
- UCAS institution code
- M20
Bachelor of Science (BSc)
BSc Management (International Business Economics) with Industrial/Professional Experience
- Typical A-level offer: AAA
- Typical contextual A-level offer: ABB
- Refugee/care-experienced offer: BBB
- Typical International Baccalaureate offer: 36 points overall with 6,6,6 at HL
Course unit details:
Digital Economy: Platforms, AI and The Business
Unit code | BMAN31952 |
---|---|
Credit rating | 20 |
Unit level | Level 6 |
Teaching period(s) | Semester 2 |
Available as a free choice unit? | No |
Overview
This advanced course leverages the latest research to equip you with tools for analysing the economics of AI and digital transformation, preparing you for the evolving digital economy.
We begin by examining the foundations: exploring the economic theories of network industries and multi-sided platforms. The course delves into platform strategies, design principles, business models, competition dynamics, and advanced pricing algorithms. We will also examine how big data analytics drives success in these ecosystems.
The course then takes a deep dive into the AI revolution, with a particular focus on generative AI (GenAI) and cognitive automation. You will develop a robust theoretical framework to understand AI's potential business applications and critically assess its profound impact on the economy, labour markets, and the future of work. We will examine AI-driven business models and the trajectory of AI start-ups.
We also cover the dynamics of FinTech, payment systems, blockchain applications, the process and challenges of digital transformation for firms, strategic adoption of digital technologies by established players, the economics of automation, and evolving competition policies for a digital world.
Drawing extensively on recent economic and firm data, the course emphasises empirically informed analysis, enabling you to develop critical insights into AI technologies. It is well suited for students aiming for careers in tech strategy, digital entrepreneurship, or policy related to the digital economy, or for those considering starting a business or joining a start-up.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Economic Principles : Microeconomics | BMAN10001 | Pre-Requisite | Compulsory |
This course is available to third year students on BSc Management and Management (Specialisms), BSc International Management and BSc ITMB.
The unit aims to provide a solid understanding of foundational concepts, theories and technologies that are essential for understanding the digital economy. It offers a thorough review of platform strategies / competition and covers the emerging literature on corporate digital transformation: the process by which traditional firms adopt digital and AI technologies (including generative AI) to adapt to changes in the market. The course also aims to demonstrate how emerging technologies such as AI, generative AI, machine learning algorithms, and Blockchain, joined with platform technologies, have begun to transform industries such as the financial sector, retail, advertising, healthcare, manufacturing, services, and transportation. By examining a rich list of cases and data and using recent theories, the course aims to help students to form a systematic view of how digital technologies are likely to shape corporations and industries and change the nature of competition. Finally, by examining numerous young online firms from different sectors, the course will seek to explain the process of start-up formation and show how to set up an online business. The course combines recent economic theories with a rich selection of case materials to provide both an analytical and applied understanding of the digital economy / AI, supported by up-to-date data on each topic. The course uses a rich mix of theory and practice, with a strong emphasis on recent advances in AI and platform technologies, to help students understand the complex changes occurring in almost all industries, from retail and health to finance. In today’s competitive job market, understanding these shifts is increasingly vital. The ability to assess emerging AI capabilities and their business applications, and to formulate effective strategies, offers students a significant advantage. A rich list of recent cases will equip students with a pragmatic approach to running businesses. Foundations: Applications: Final Thoughts: Methods of delivery: Lecture/seminars /computer aided learning Lecture hours: 30 (3 hours per week over 10 weeks) plus 3 hours overall course revisions (synchronous) Seminar hours: 8 (1 hour per week) (asynchronous, pre-recorded videos) 6 hours optional revision sessions for students needing help with economics (synchronous) 8 hours optional coding sessions for students aiming to improve their knowledge of machine learning and data analysis. Formative: Summative: The course mainly draws on relevant research papers, simplified and summarised to make it accessible for UG students. Typical research papers include:Aims
Learning outcomes
Syllabus
Teaching and learning methods
Knowledge and understanding
Intellectual skills
Practical skills
Transferable skills and personal qualities
Assessment methods
12 short weekly mock randomised MCQ tests
Mid-term online randomized multiple-choice test (20%)
End of term online randomized multiple-choice test (20%)
Final Individual Economic Project (60%)
Feedback methods
Recommended reading
Study hours
Scheduled activity hours
Lectures
33
Seminars
8
Independent study hours
Independent study
159
Teaching staff
Staff member
Role
Mohammad Salehnejad
Unit coordinator