BSc Management (International Business Economics) with Industrial/Professional Experience

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:  

• Network Industries  
• Platform Firms: Design Development and Growth  
• Predictive AI: Theory and Recent Advancements
• Generative AI: Theory and Recent Advancements
• Blockchain: Theory and general applications.
• The Economics of Data – Data as Capital
• Algorithms: Types, Design and Functions

Applications:

• AI, Pricing and Revenue Management  
• Reputation, Search and Brands, and Generative AI
• AI & Financial Technologies
• AI & Entrepreneurial Finance  
• AI, Human Resources, and Talent Selection
• AI & Healthcare

Final Thoughts:  

• AI and Future Work
• Harms of AI  

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.  

• Analyse the economic principles underpinning network industries and multi-sided platforms, including strategies related to leadership, growth, development, and pricing.
• Explore the design of modern online platforms, focusing on essential components including recommender systems, reputation systems, and governance rules.
• Explore the foundations, potentials, and limitations of artificial intelligence and cognitive automation technologies.
• Critically assess the growth models of platform and AI start-ups to formulate effective strategies for establishing and nurturing successful AI enterprises.
• Examine the ways in which cognitive automation technologies reshape firms, the nature of work, and broader economic activity.

• Develop a sound understanding of the AI revolution, Blockchain, and general-purpose technologies, assessing their potential to reshape future business operations and strategies.
• Explain the role of platform, AI, and generative AI technologies in transforming industries, with specific reference to the financial sector, Fintech start-ups and healthcare.
• Analyse the process of digital transformation within firms and industries, with a focus on key challenges and strategies. 

• Leverage economic data and analysis to guide corporate decision-making.
• Design business strategies for growing and running digital marketplaces.
• Analyse dominant AI business models and devise new AI business models. 

• Leverage generative AI tools for tasks including planning, problem-solving, ideation, and forecasting market trends.
• Train machine learning algorithms using established methods to address business challenges.
• Use programming languages such as R and Python to analyse data and support business decision-making in firms. 

Formative: 
12 short weekly mock randomised MCQ tests

Summative:
Mid-term online randomized multiple-choice test (20%)
End of term online randomized multiple-choice test (20%)
Final Individual Economic Project (60%)
 

• Prior to the exam during weekly seminars and after marks are released.
• General guidance during revision sessions, individual feedback on outlines, and comments / feedback after marks are released. 

The course mainly draws on relevant research papers, simplified and summarised to make it accessible for UG students. Typical research papers include:

• Manning, B.S., Zhu, K. and Horton, J.J., 2024. Automated social science: Language models as scientist and subjects (No. w32381). National Bureau of Economic Research.
• Felin, T. and Holweg, M., 2024. Theory is all you need: AI, human cognition, and decision making. Human Cognition, and Decision Making (February 23, 2024).
• Batista, R.M. and Ross, J., 2024. Words that work: Using language to generate hypotheses. Available at SSRN 4926398.
• Toner-Rodgers, A., 2024. Artificial intelligence, scientific discovery, and product innovation. arXiv preprint arXiv:2412.17866.
• Si, C., Yang, D. and Hashimoto, T., 2024. Can LLMs generate novel research ideas? a large-scale human study with 100+ nlp researchers. arXiv preprint arXiv:2409.04109.
• Pham, V.H. and Cunningham, S., 2024. Can Base Chatgpt Be Used for Forecasting Without Additional Optimization?. Available at SSRN 4907279.
• Alekseeva, L., Azar, J., Giné, M. and Samila, S., 2024. AI adoption and the demand for managerial expertise. FEB Research Report MSI_2412, pp.1-60.
• Luca, M., Kleinberg, J. and Mullainathan, S., 2016. Algorithms need managers, too. Harvard business review, 94(1), p.20.
• Acemoglu, D., 2021. Harms of AI (No. w29247). National Bureau of Economic Research.