During the COVID-19 pandemic, the bidirectional relationship between policy and data reliability has been a challenge for researchers of the local municipal health services. Policy decisions on population specific test locations and selective registration of negative test results led to population differences in data quality. This hampered the calculation of reliable population specific infection rates needed to develop proper data driven public health policy. https://doi.org/10.1007/s12508-023-00377-y
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This research investigates growth inhibitors for smart services driven by condition-based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under- or over-maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.
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Through artistic interventions into the computational backbone of maternity services, the artists behind the Body Recovery Unit explore data production and its usages in healthcare governance. Taking their artwork The National Catalogue Of Savings Opportunities. Maternity, Volume 1: London (2017) as a case study, they explore how artists working with ‘live’ computational culture might draw from critical theory, Science and Technology Studies as well as feminist strategies within arts-led enquiry. This paper examines the mechanisms through which maternal bodies are rendered visible or invisible to managerial scrutiny, by exploring the interlocking elements of commissioning structures, nationwide information standards and databases in tandem with everyday maternity healthcare practices on the wards in the UK. The work provides a new context to understand how re-prioritisation of ‘natural’ and ‘normal’ births, breastfeeding, skin-to-skin contact, age of conception and other factors are gaining momentum in sync with cost-reduction initiatives, funding cuts and privatisation of healthcare services.
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This project researches risk perceptions about data, technology, and digital transformation in society and how to build trust between organisations and users to ensure sustainable data ecologies. The aim is to understand the user role in a tech-driven environment and her perception of the resulting relationships with organisations that offer data-driven services/products. The discourse on digital transformation is productive but does not truly address the user’s attitudes and awareness (Kitchin 2014). Companies are not aware enough of the potential accidents and resulting loss of trust that undermine data ecologies and, consequently, forfeit their beneficial potential. Facebook’s Cambridge Analytica-situation, for instance, led to 42% of US adults deleting their accounts and the company losing billions. Social, political, and economic interactions are increasingly digitalised, which comes with hands-on benefits but also challenges privacy, individual well-being and a fair society. User awareness of organisational practices is of heightened importance, as vulnerabilities for users equal vulnerabilities for data ecologies. Without transparency and a new “social contract” for a digital society, problems are inevitable. Recurring scandals about data leaks and biased algorithms are just two examples that illustrate the urgency of this research. Properly informing users about an organisation’s data policies makes a crucial difference (Accenture 2018) and for them to develop sustainable business models, organisations need to understand what users expect and how to communicate with them. This research project tackles this issue head-on. First, a deeper understanding of users’ risk perception is needed to formulate concrete policy recommendations aiming to educate and build trust. Second, insights about users’ perceptions will inform guidelines. Through empirical research on framing in the data discourse, user types, and trends in organisational practice, the project develops concrete advice - for users and practitioners alike - on building sustainable relationships in a resilient digital society.
