MoneyLab is a network of artists, activists, and geeks experimenting with forms of financial democratization. Entering the 10th year of the global financial crisis, it still remains a difficult yet crucial task to distinguish old wine from its fancy new bottles. The MoneyLab network questions persistent beliefs, from Calvinist austerity, growth, and up-scaling, to trustless, automated decision making and (anarcho-)capitalist dreams of cybercurrencies and blockchained solutionism.We consider experiments with digital coops, internet-based payment and network-based revenue models as spaces of political imagination, with an equally important aesthetic program. In this second MoneyLab Reader the network delves into topics like the financialization of art; love as a binary proposition on the blockchain; the crowdfunding of livelihood; the cashless society; financial surveillance of the poor; universal basic income as the real McCoy or a real sham; the cooperative answer to Airbnb and Uber; the history of your financial dashboard; and, Hollywood’s narration of the financial crisis. Fintech rushes through our veins, causing a whirlwind of critical concepts, ideas and imaginaries. Welcome to the eye of the storm.
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We propose an approach to enhancing knowledge sharing and connectedness in distributed teams. Termed ‘Narrating Your Work’ (NYW), the approach involves members of distributed team using a microblogging tool to post regular updates about their current work, accomplishments, and issues. The NYW approach was evaluated within a geographically and temporally distributed team at Shell International for a period of one month, using a mixed-method research design. Methodology comprised of a quantitative survey, followed by semi-structured interviews and analysis of microblogging updates posted during the month in which the approach was being trialled. The evaluation results suggest that NYW was viewed as a valid and practical approach to enhancing knowledge sharing and connectedness. A range of barriers and enablers that could impact the future application and embedding of the approach are identified and recommendations for implementation are outlined.
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