Master of Science in Digital Economy [MSc(DE)] [*Subject to University’s Approval]

Frontier Theories of the Digital Economy

This course explores the cutting-edge theoretical frameworks and research questions that define the frontier of digital economics. It moves beyond established models to examine how core economic assumptions and theories are being challenged and reshaped by digital phenomena. Topics include the micro-foundations of network effects and platform dynamics, the economics of artificial intelligence and machine learning as general-purpose technologies, theoretical models of data markets and privacy, the macroeconomics of digital transformation, and the political economy of digital governance. The course emphasises critical reading of seminal and recent academic papers, theoretical modeling intuition, and the identification of open research questions for future inquiry. The course incorporates case studies from Hong Kong, China’s dynamic digital ecosystem to ground theoretical discussions in locally relevant contexts.

Digital Transformation and Economic Policy Analysis

This course examines the profound interplay between digital transformation and the formulation, implementation, and evaluation of economic policy. It moves beyond technical descriptions to critically analyse the economic logic, societal impacts, and complex governance challenges inherent in digitalisation. The curriculum investigates how digital technologies reshape core economic policy domains, including productivity and growth, labour markets and skills, competition and innovation, industrial structure, fiscal policy, and regional development. Through comparative analysis of different national and regional strategies, the course explores how policy can steer digital transitions towards inclusivity and sustainability. Students will learn to assess policy trade-offs, design evidence-based interventions, and navigate the political economy of technological change. It places a strong emphasis on the context of Hong Kong, China and the Greater Bay Area, using local cases and policy debates to ground theoretical concepts and develop students’ ability to analyse and formulate context-specific policy recommendations.

Data-Driven Economic Decision Making

This course delves into the complete process of leveraging data analytics to inform and optimise economic and business decision-making. It moves from foundational statistical and econometric methods to advanced techniques including machine learning, causal inference, Bayesian analysis, and natural language processing. The curriculum emphasises a practical, problem-solving approach, guiding students through the entire data analysis pipeline: from defining business questions and sourcing data, through rigorous data cleaning, exploratory analysis, and model building, to the final interpretation and communication of results for decision-makers. Key application areas include macroeconomic forecasting, consumer sentiment and market segmentation, algorithmic pricing and risk assessment, and policy evaluation. The course incorporates real-world datasets and case studies from Hong Kong, China and the Greater Bay Area, ensuring students can apply their skills to locally relevant economic challenges.

Platform Economics and Sharing Economy 

This course provides a focused exploration of the economic principles, business models, and societal impacts of two defining phenomena in the digital era: platform economies and sharing economies. It critically examines their interconnectedness and distinctions. The curriculum delves into the core economic theories of multi-sided markets, network effects, and transaction cost reduction that underpin platforms. It then analyses the specific application of these principles in the sharing economy context, which emphasises access over ownership and utilises underutilised assets. Through global and regional (including HKSAR, China) case studies, students will evaluate business models, pricing strategies, market dynamics, competition, innovation, data governance, and the role of trust and reputation mechanisms. The course also addresses critical regulatory challenges, labour issues in the gig economy, sustainability claims, and the future evolution of platform-mediated sharing, with particular attention to policy dynamics and governance practices in China.

Data Factor Markets and Governance  

This course provides a comprehensive analysis of the emerging market for data as a critical factor of production in the digital economy, and the complex governance frameworks required to manage it. It examines the economic characteristics of data as a non-rivalrous, partially excludable asset, and explores the institutional foundations, market designs, and pricing mechanisms for data exchange. Students will analyse key issues in data governance, including data property rights, privacy regulations, cross-border data flow regimes, and the role of public versus private institutions in shaping data ecosystems. The course also delves into the application of data markets in specific sectors, addresses challenges such as data monopolies and security, and critically evaluates policy debates aiming to foster innovation, competition, and equitable value distribution from data. With a particular focus on the Greater China region, the course incorporates case studies and policy analyses from HKSAR, China and the Chinese Mainland, enabling students to understand localised practices and regulatory developments in data factor markets.

Digital Economy and Industrial Policy 

This course applies economic principles to analyse how digital technologies reshape industries, firm strategies, and market structures. It examines the unique economics of digital industries, characterized by high fixed costs, near-zero marginal costs, network effects, and data-driven value creation. The curriculum explores core theoretical foundations such as network effects and cost economics. It systematically covers key digital industrial sectors including digital infrastructure, data factor markets, and the digital transformation of traditional sectors. The course also analyses platform economies and the digitalisation of producer services. Through global and regional (particularly Hong Kong, China and Greater Bay Area) case studies, students will evaluate industrial policies, firm strategies, and the socioeconomic impacts of digital industrial evolution. The course further investigates policy implications, including competition policy for digital giants, regulation of data markets, and the role of industrial policy in shaping regional digital competitiveness.

AI-Driven Data Mining and Econometrics

This course bridges modern artificial intelligence techniques with traditional econometric analysis to address complex economic questions with high-dimensional, unstructured, and large-scale datasets. It moves beyond standard regression models to explore how machine learning methods can enhance causal inference, policy evaluation, and forecasting in economics. The curriculum emphasises both theoretical understanding and hands-on implementation, covering topics such as regularization methods for high-dimensional inference, causal forests for heterogeneous treatment effects, neural networks for economic forecasting, and the use of transformer models for analysing economic text data. Students will critically assess the strengths and limitations of these AI-enhanced econometric tools and learn to apply them responsibly to real-world economic and financial data.

Digital Security and Digital Governance

This course provides a comprehensive exploration of the intertwined domains of digital security and governance, which are critical for trust and sustainability in the digital economy. It examines the technical, legal, economic, and organisational dimensions of securing digital assets, systems, and data, while simultaneously analysing the frameworks, policies, and institutions needed to govern cyberspace, digital markets, and technological innovation responsibly. The curriculum integrates perspectives from digital economy development and governance, covering foundational concepts of digital security, data governance, platform governance, algorithmic governance, AI governance, enterprise compliance, and global digital governance. Through global and regional (with a focus on Hong Kong, China and the Greater Bay Area) case studies, students will evaluate governance challenges and develop balanced strategies that reconcile security, innovation, economic growth, and ethical considerations.

Digital Economic Trade Research

This course provides an in-depth investigation into the rapidly evolving landscape of international trade as it is being reshaped by digital technologies and the data-driven economy. It moves beyond traditional trade in goods to focus on digitally deliverable services, e-commerce, cross-border data flows, and trade in intangibles. The curriculum is built upon a comprehensive framework covering digital trade methods, ecosystem construction, integrated services, and global rule-making. It delves into key areas such as digital trade costs, the distinction and linkage between trade digitalisation and digitalised trade, digital service trade, data governance, digital trade ecosystems, open global platforms, “born-global” firms, digital marketing, smart logistics, digital trade barriers, and the construction of global digital trade rules. Through global and regional (with a focus on Hong Kong, China's role as an international trade and financial hub, and its positioning within the Guangdong-Hong Kong-Macao Greater Bay Area) case studies and analyses, students will critically examine the economic logic, strategic implications, and policy challenges of digital trade.

Internship programme

This course integrates knowledge gained from previous studies and builds on the conceptual framework of experiential learning to provide the opportunity for programme participants to obtain the first-hand experience of frontline digital economy in the real context through an 8-week supervisory internship programme in the field. The core aim is to facilitate participants to apply contemporary digital economy theories and practices in authentic professional settings. It is expected that participants can gain exposure to work in technology firms, consultancies, government agencies, or digital transformation units, and build career networks with professional training support through this programme.

This capstone course spans two sequential phases and represents the culminating academic experience of the Master of Science in Digital Economy programme. 

Phase I: Research Methods and Proposal

Develops students as independent researchers by providing a foundation in research methodologies relevant to the digital economy and emphasising research ethics. Students learn to identify a viable research topic, conduct a critical literature review, and design a rigorous methodological approach, culminating in a comprehensive research proposal. 

Phase II: Research Report and Dissemination

Involves the execution of the approved research plan under supervisory guidance. Students collect and analyse data, interpret findings, and reflect on the research process. The primary outputs are a substantial written research report (thesis) and a formal oral presentation and defence of the findings. 

The integrated course aims to produce original scholarship that contributes to the understanding of the digital economy and demonstrates mastery of research skills and subject knowledge.