Contributions in Merlin

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The prevailing patterns of consumption and production are not sustainable because they are based on increasing extraction of non-renewable resources (such as fossil fuels or scarce metals) from the Earth’s crust and overuse of life-sustaining ecosystem services (such as CO2 assimilation or the water cycle). One strategy to direct consumption to a sustainable pathway is the circular economy. The goal of the circular economy is to slow down the flow of material resources through the anthroposphere and to return them back to nature in a form that is as compatible as possible with the ecosystem processes. We focus on the first aspect, which means that each unit of material resource that enters the economic system should satisfy as much human needs as possible until it is considered waste. We ask the question if and how the emerging “sharing economy” can contribute to this specific goal. We see the phenomenon of sharing economy as a transformation of sharing practices with means of digital Information and Communication Technology (ICT). The resulting Digital Sharing Economy (DSE) can therefore be considered an important special case of ICT impact on sustainable development. We open up an argument on how sharing in the DSE can be either supportive or counter-productive with regard to the circular economy goals. We present a first framework that provides a guideline for the qualitative assessment of new sharing practices with regard to their potential contribution to a circular economy.

In the past decade, the proliferation of digital technologies such as social media, internet platforms, algorithms, artificial intelligence and ‘big data’ have gained interest from businesses and academic researchers alike. Practitioners and scholars have revealed far-reaching organizational and managerial implications of these new technologies. At the same time many risks have also been identified including matters such as the loss of individual rights and the difficulty of resisting new forms of organizational control; increasing power imbalances between corporations, employees, and customers; the intensification of organizational surveillance; the opacity and biased nature of machine based processes and decisions; and many unexpected externalities that have yet to be explored. With this call for papers, we draw on and extend the emerging critical debate on the economic, ethical, social, and political implications of digitalization. We encourage scholars to critically explore the dark side and unexpected consequences of digitalization, i.e. issues that have (so far) been overlooked, ignored, or suppressed with regards to the impact of digital technologies on organization and organizing. The aim of this call is to span the boundary between multiple disciplines, to fertilize the discourse amongst critical scholars, and to develop the necessary theoretical and empirical groundwork around the implications of digitalization for organizations and organizing processes.

Stream Processing (SP) is an important Big Data technology enabling continuous querying of data streams. The stream setting offers the opportunity to exploit synergies and, theoretically, share the access and processing costs between multiple different collaborators. But what should be the monetary contribution of each consumer when they do not trust each other and have varying valuations of the differing outcomes? In this article, we present Collaborative Stream Processing (CSP), a model where the costs, which are set exogenously by providers, are shared between multiple consumers, the collaborators. For this, we identify three important requirements for CSP to establish trust between the collaborators and propose a CSP al- gorithm, ENCSPA, adhering to these requirements. Based on the collaborators’ outcome valuations and the costs of the raw data streams, ENCSPA computes the payment for each collaborator. At the same time, ENCSPA ensures that no collaborator has an incentive to manipulate the system by providing misinformation about her/his value, budget, or time limit. We show that ENCSPA can calculate payments in a reasonable amount of time for up to one thousand collaborators.

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We study the intra-horizon value at risk (iVaR) in a general jump diffusion setup and propose a new model of asset returns called displaced mixed-exponential model, which can arbitrarily closely approximate finite-activity jump-diffusions and completely monotone Levy processes. We derive analytical results for the iVaR and disentangle the risk contribution of jumps from diffusion. Estimating the iVaR for several popular jump models using on S&P 100 option data, we find that option-implied estimates are much more responsive to market changes relative to their historical counterparts. Moreover, disentangling jumps from diffusion, jump account for about 90 percent of iVaR on average.

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