Information and communication technology (ICT) brings about numerous advantages across various domains of our lives. However, alongside these benefits, there is a growing awareness of its potential negative ethical, social, and environmental impacts. Consequently, stakeholders ranging from conceptual modellers to policy makers often find themselves grappling with ethical considerations stemming from ICT engineering and usage. This paper presents a review of 10 ethical reasoning methods suitable for the ICT domain. We have employed a method engineering technique to author metamodels for the methods, which were subsequently subjected to validation by experts proficient in the respective methods. Following a situational method engineering approach, we have also characterised each ethical reasoning method and validated the characterisation with the experts. This has allowed us to develop a tool that helps select the method that is most suitable for a given ethical reasoning situation. Furthermore, we deliberate on the practical application of ethical reasoning methods within conceptual modelling contexts. We are confident that we have laid the groundwork for further research into ethical reasoning of ICT, with a specific emphasis on its role during conceptual modelling.

The field of process mining has evolved from discovering single work processes towards providing broad insights into peoples’ work practices. Existing techniques can be used to analyse such work practices, but this can be problematic if the available data is limited to the use of a single IT system or is not captured at the right level of granularity. We propose the use of a personal informatics technique, called Active Window Tracking (AWT), as a new way of gathering data for mining work practices. In this study, we identify the opportunities that this technique brings through a case study within our research group. In particular, we show how AWT helps to: capture previously-unrecorded work activities, expose the relations between work processes, and navigate between different levels of data granularity. The technique, which allows for generating new data as well as complementing existing data, is a valuable asset for the community when it comes to better understanding people’s work practices across individual systems and processes.