杏吧论坛

Prof Jannis Kallinikos and Dr Zhi Cheng

Dr Cheng is known as 'Aaron'.

This course is available on the CEMS Exchange, Global MSc in Management, Global MSc in Management (CEMS MIM), Global MSc in Management (MBA Exchange), MBA Exchange, MSc in Management (1 Year Programme), MSc in Management of Information Systems and Digital Innovation and MSc in Media and Communications (Data and Society). This course is available with permission as an outside option to students on other programmes where regulations permit.

The course is about the significance of digital data and the role they play in the current economy and society. The course reviews the technological arrangements, organisational forms and business models through and by means of which data are being produced and used. More specifically, the course pays attention to the role social media and commercial platforms play in engineering user participation to produce a computable data footprint that is subsequently used to develop a range of data-based resources and services.

The course also deals with the state-of-the-art data analytics techniques and methods used by social media and digital platforms to deploy personalisation strategies as a means of boosting user platform engagement and generating data. It covers the current and emerging approaches in data extraction, aggregation, predictive computing, personalisation and recommender systems, which shape the future of digital business strategy that builds on big data, machine intelligence and analytical thinking.

Overall, the course takes a unique approach to social media and commercial platforms by examining the data work they perform — encoding, aggregating, and computing — from the organisational, managerial and technical perspectives. The course blends theories, ongoing research insights, data analytics concepts and techniques, as well as real-life examples to analyse the socio-economic implications of these significant developments.

After students complete the course, they shall be able to:

• Explain the drives behind the evolution of social computing
• Understand data practices that underpin social computing and the digital economy
• Understand data infrastructures and ecosystems and the role they play in sustaining the operations of social media platforms and the digital economy
• Analyse business models of social media platforms
• Develop data-analytic thinking for decision-making in management and business
• Design a digital business strategy using the platform and algorithmic thinking
• Critically assess data-driven information services (e.g., personalisation, recommender systems) and their socio-economic implications.

20 hours of lectures, 9 hours of seminars and 3 hours of workshops in the WT.

The workshops will be dedicated to essay development. There is a Reading Week in Week 6. There will be no teaching during this week.

In its Ethics Code, 杏吧论坛 upholds a commitment to intellectual freedom. This means we will protect the freedom of expression of our students and staff and the right to engage in healthy debate in the classroom.

Written formative feedback is provided on the 500 words proposal for the summative essay.

1. Alaimo C. and Kallinikos J., (2017). Computing the everyday, The Information Society 33/4: 175-191.
2. Alaimo, C. & Kallinikos, J. (2019). Recommender Systems, in Beyes, T., Holt, R. and Pias, C. (eds.), The Oxford Handbook of Media, Technology and Organization Studies. Oxford University Press, pp. 401-411
3. Alaimo, C., Kallinikos, J., & Valderrama, E. (2020). Platforms as Service Ecosystems: Lessons from Social Media. Journal of Information Technology, 35(1), 25-48.
4. Aaltonen, A., Alaimo, C., & Kallinikos, J. (2021). The Making of Data Commodities: Data Analytics as an Embedded Process. Journal of Management Information Systems, 38/2: 401-429.
5. Anderson, C. (2009). Free: The Future of a Radical Price. Random House.
6. Arthur, B. (2011). The Second Economy, McKinsey Quarterly, October 2011.
7. Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2016). Transformational Issues of Big Data and Analytics in Networked Business. MIS Quarterly, 40(4), 807–818.
8. Baldwin, C. Y. & Woodard, C. J. (2009). The Architecture of Platforms: A Unified View. In A. Gawer, (ed.) Platforms, Markets and Innovation. Edward Elgar, Cheltenham, pp. 19–44.
9. Burkov, A. (2019). Machine Learning Engineering. LeanPub.
10. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4).
11. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73.
12. Helmond, A. (2015). The Platformization of the Web: Making Web Data Platform
13. Hosanagar, K. (2019). A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control. Viking.
14. Kitchin, R (2014). The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage.
15. Konstan, J and Riedl, J. (2012) Recommended for you. Spectrum, IEEE, 49(10), 54-61.
16. Lemahieu, W., vanden Broucke, S., & Baesens, B. (2018). Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data. Cambridge University Press.
17. Parker, G, G, Van Alstyne, M. and Choudary, S. P. (2016). Platform Revolution. London: Norton.
18. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.
19. Swanson, B. E. (2021). When Data Becomes Infrastructure and our Lives Depend on it. Twenty-Ninth European Conference on Information Systems (ECIS) 2021.
20. Van Dijck, J. (2013). The culture of connectivity: A critical history of social media. Oxford: Oxford University Press.

Essay (100%, 3000 words) in the WT.