Questions
1. The NHS COVID-19 mobile phone app was introduced in England and Wales in September 2020 by NHS Test and Trace to facilitate tracking and tracing of Covid-19 infections. It includes features such as contact tracing, local area alerts and venue check-in. (Word limit: 1000 words in total for this question.)
(a) The UTAUT (Unified Theory of Acceptance and Use of Technology) model (Venkatesh et al., 2003) considers performance expectancy, effort expectancy, and social influence as among the factors that influence an individual’s intention (behavioural intention) to use technology. Explain how each of these factors might influence the acceptance of the NHS COVID-19 app.
(b) Stamper’s Organisational Containment Model (sometimes called the “organisational onion model”) stipulates that automated systems (such as IT systems) are contained within formal systems which in turn are contained within informal systems.
Apply the Organisational Containment Model to the NHS COVID-19 app. Considering the app as the automated system, identify both the informal system and the formal system that contain it. Then assess potential issues in the operationalisation of the app.
(c) Among the seven key principles of GDPR (General Data Protection Regulation) are purpose limitation, data minimisation, and storage limitation. Suggest how each of the three principles should be addressed in contact tracing apps such as the NHS COVID-19 app.
From a semiotic point of view, an information system can be considered as a system that manages and processes signs (Word limit: 700 words in total for this question.)
(a) Using an example, explain why an information system can be considered as a system that manages and processes signs.
(b) Stamper’s Semiotic Framework contains six aspects of a sign system – the physical world, empirics, syntactics, semantics, pragmatics, and the social world.
Explain why the first three are aspects of the IT platform, while the remaining three are characterised as part of human information functions. Then discuss how such characterisations can benefit the design of IT systems.
Data-driven decision-making can be defined as the practice of basing decisions on the analysis of data rather than purely on intuition. Tools and techniques such as data analytics (predictive analytics, prescriptive analytics, etc.), data mining and data visualisation support data-driven decision-making (Word limit: 700 words in total for this question.)
(a) Using an example, explain the process of data-driven decision- making using the DIKAR (data-information-knowledge-action- result) model.
(b) Supply-chain management can be considered as the coordination of all supply activities of an organisation from its suppliers and partners to its customers.
Identify THREE aspects in supply-chain management that can be improved by data-driven decision-making and its tools and techniques. Then, using examples, explain how they can be improved. Finally, discuss the limitations of data-driven decision-making in this context.
