Introductory description

In the new era of the "data economy", the value of high-quality, granular, and rich data assets is a critical success factor for transformative decision making in many industries and the leveraging of artificial intelligence (AI).

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Strategic nature of data and relevant organizational practices and business models emerging from these.
Management of data and relevant technologies (big data, AI, business intelligence practices, etc.)
Business Analytics techniques including data analytics (use of software) and use in organisations for decision-making (business performance management, dashboards, etc.)
Introduction of artificial intelligence, techniques, and applications (supervised/unsupervised learning, machine learning, text mining, clustering, etc.) Issues around the quality of data and bias, risks, and business opportunities.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a comprehensive understanding of the core concepts and theories of data analytics and artificial intelligence and their implications for contemporary enterprises
• Demonstrate a comprehensive understanding of methodology for developing data visualizations and analytical models, and the managerial issues associated with the selection and adoption of such data analytics technologies
• Develop a comprehensive understanding of the managerial and organisational issues associated with the use of data and artificial intelligence practices
• Demonstrate critical analytics skills in the information- and data-driven business environment

Indicative reading list

Books:

• Akerkar, R. (2019). Artificial intelligence for business. Springer.
• Corea, F. (2019). Applied artificial intelligence: Where AI can be used in business. Springer International Publishing.
• Barrow, M. (2009). Statistics for economics, accounting and business studies. Pearson Education.
• Bramer, M. (2007). Principles of data mining (Vol. 180). London: Springer.
• Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.
• Stock, J. H., & Watson, M. W. (2020). Introduction to econometrics. Harlow, England : Pearson
• Ertel, W. & Black, N. T. (2011). Introduction to Artificial Intelligence. London: Springer
• Kwartler, T. (2017). Text mining in practice with R. John Wiley & Sons.

Publications:

• Brynjolfsson, E., Hui, X., & Liu, M. (2019). Does machine translation affect international trade? Evidence from a large digital platform. Management Science, 65(12), 5449-5460.
• Kokkodis, M., & Ipeirotis, P. G. (2021). Demand-aware career path recommendations: A reinforcement learning approach. Management Science, 67(7), 4362-4383.
• Senoner, J., Netland, T., & Feuerriegel, S. (2021). Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. Management Science.
• Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research.
• Jussupow, E., Spohrer, K., Heinzl, A., & Gawlitza, J. (2021). Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Information Systems Research, 32(3), 713-735.
• Schanke, S., Burtch, G., & Ray, G. (2021). Estimating the impact of “humanizing” customer service chatbots. Information Systems Research, 32(3), 736-751.
• van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: an ethnography of developing AI for hiring. MIS Quarterly, 45(3).
• Lou, B., & Wu, L. (2021). AI on drugs: can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms. MIS Quarterly, 45(3).
• Teodorescu, M. H., Morse, L., Awwad, Y., & Kane, G. C. (2021). Failures of fairness in automation require a deeper understanding of human-ML augmentation. MIS Quarterly, 45(3).
• Lebovitz, S., Levina, N., & Lifshitz-Assaf, H. (2021). Is AI ground truth really “true”? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quarterly.
• Li, J., Li, M., Wang, X., & Thatcher, J. B. (2021). Strategic directions for AI: the role of cios and boards of directors. MIS Quarterly, 45(3).
  Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI. MIS Quarterly 45

Interdisciplinary

This module is the intersection between business, statistics and computer science, and the goal of the module is to introduce the business applications of AI in different sectors. However, to ensure students’ full understanding of the AI models, some basic knowledge in statistics and computer science will be involved

International

Data analytics and AI is a global trend. In many case studies, the students will need to consider how cultural difference influences the implementation and applications of AI in organisations

Subject specific skills

• Evaluate management practices and recommend measures to enable an organisation to exploit data assets and AI-related information technologies effectively
• Evaluate and exploit real-world datasets and make business recommendations using data analytics tools

Transferable skills

Written communication