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When talking about blockchain technology in academia, business, and society, frequently generalizations are still heared about its – supposedly inherent – enormous energy consumption. This perception inevitably raises concerns about the further adoption of blockchain technology, a fact that inhibits rapid uptake of what is widely considered to be a groundbreaking and disruptive innovation. However, blockchain technology is far from homogeneous, meaning that blanket statements about its energy consumption should be reviewed with care. The article is meant to bring clarity to the topic in a holistic fashion, looking beyond claims regarding the energy consumption of Bitcoin, which have, so far, dominated the discussion.

As VR technology has developed rapidly in recent years, VR has become a trendy topic in IT. This article provides a short history and conceptualization of VR, distinguishes it from related terms and acronyms, and identifies several areas of application. IS research has not yet sufficiently studied VR technology, so the article discusses two fundamental areas related to the design and use of VR that deserve IS researchers’ attention. While the development of a research agenda is outside the scope of this article, it may still inform and guide future research.

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective and efficient AI-based systems. The novel paradigm complements recent model-centric AI, which focuses on improving the performance of AI-based systems based on changes in the model using a fixed set of data. The objective of this article is to introduce practitioners and researchers from the field of Business and Information Systems Engineering (BISE) to data-centric AI. The paper defines relevant terms, provides key characteristics to contrast the paradigm of data-centric AI with the model-centric one, and introduces a framework to illustrate the different dimensions of data-centric AI. In addition, an overview of available tools for data-centric AI is presented and this novel paradigm is differenciated from related concepts. Finally, the paper discusses the longer-term implications of data-centric AI for the BISE community.

Digital nudging is the use of user-interface design elements to guide peoples behavior in digital choice environments. Digital choice environments are user interfaces such as web-based forms and ERP screens that require people to make judgments or decisions. Humans face choices every day, but the outcome of any choice is influenced not only by rational deliberations of the available options but also by the design of the choice environment in which information is presented, which can exert a subconscious influence on the outcome. In other words, what is chosen often depends upon how the choice is presented (Johnson et al. 2012, p. 488) such that the choice architecture alters peoples behavior in a predictable way (Thaler and Sunstein 2008, p. 6).