The COVID-19 pandemic has created a need for rapid, population-wide digital contact tracing. One solution, Bluetooth-enabled digital proximity tracing using smartphones, promises to preserve individual privacy while helping to contain society-wide viral outbreaks. However, this digital solution works effectively only if adopted by the majority of the population. This poses a collective action problem: everyone would benefit from widespread proximity tracing, but the benefits for the individual are indirect and limited. To facilitate such collective action at the societal level, this paper conceptualises the option space of IT governance actions for proximity tracing adoption along two dimensions: decision-making entities (who will govern the roll-out) and accountability enforcement (how strictly will adoption and use be enforced). Examining coherent governance approaches that arise from the framework, we show that there are no globally ideal approaches but only locally contextualised ones that depend on immediate health risk, prior experience with pandemics, societal values and national culture, role of government, trust in government and trust in technology in each society. The paper contributes specific propositions for governing digital contact tracing in the COVID-19 pandemic and general theoretical implications for IT governance for collective action at the societal level.

In the context of education, "open(ness)" has become the watermark for a fast growing number of learning materials and associated platforms and practices from a variety of institutions and individuals. Open Educational Resources (OER), Massive Open Online Courses (MOOC), and more recently, initiatives such as Coursera are just some of the forms this movement has embraced under the "open" banner. Yet, ongoing calls to discuss and elucidate the "meaning" and particularities of openness in education point to a lack of clarity around the concept. "Open" in education is currently mostly debated in the context of the technological developments that allowed it to emerge in its current forms. More in-depth explorations of the philosophical underpinnings are moved to the backstage. Therefore, this paper proposes a historical approach to bring clarity to the concept and unmask the tensions that have played out in the past. It will then show how this knowledge can inform current debates around different open initiatives.

Social media platforms, such as Facebook, are today’s agoras, the spaces where public discourse takes place. Freedom of speech on social media has thus become a matter of concern, with calls for better regulation. Public debate revolves around content moderation, seen by some as necessary to remove harmful content, yet as censorship by others. In this paper we argue that the current debate is exclusively focused on the speaking side of speech but overlooks an important way in which platforms have come to interfere with free speech on the audience side. Rather than simply speaking to one’s follower network, algorithms now organise speech on social media with the aim to increase user engagement and marketability for targeted advertising. The result is that audiences for speech are now decided algorithmically, a phenomenon we term ‘algorithmic audiencing’. We put forward algorithmic audiencing as a discovery, a novel phenomenon that has been overlooked so far. We show that it interferes with free speech in unprecedented ways not possible in pre-digital times, by amplifying or suppressing speech for economic gain, which in turn distorts the free and fair exchange of ideas in public discourse. When black-boxed algorithms determine who we speak to the problematic for free speech changes from ‘what can be said’ to ‘what will be heard’ and ‘by whom’. We must urgently problematize the audience side of speech if we want to truly understand, and regulate, free speech on social media. For IS research, algorithmic audiencing opens up entirely new research avenues.

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