Introduction to the special issue on social mining and big data ecosystem for open, responsible data science

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Big data in Financial Management a structured literature review and Opportunities for IS research

Information Systems to support Finance and Accounting functions within organisations form the backbone of modern commerce. Big data has brought a transformational change to this research space the effects of which are starting to be felt in industry and academia. This paper examines the potential research opportunities for the use of “Big data” in the cross disciplinary space of Information Systems, Accounting and Finance. We examine literature at the confluence of these three disciplines to identify current research approaches. An analysis is presented of 47 accounting and finance and information systems (IS) journals from 2007-2016 to identify key themes emerging. These are presented as a conceptual matrix and explored by means of this matrix and a theoretical framework that situates the emerging themes across the three cogent disciplines. The concept matrix reveals potential areas for further research

Responsible Knowledge Management in Energy Data Ecosystems

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data-and knowledge-driven approach for management and process-ing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces. © 2022 by the authors. Licensee MDPI, Basel, Switzerland