TY - RPRT A1 - Zerfaß, Ansgar A1 - Stieglitz, Stefan A1 - Clausen, Sünje A1 - Ziegele, Daniel A1 - Berger, Karen T1 - Communications trend radar 2023 BT - state revival, scarcity management, unimagination, augmented workflows & parallel worlds T2 - Communication insights N2 - How do social changes, new technologies or new management trends affect communication work? A team of researchers at Leipzig University and the University of Potsdam (Germany) observed new developments in related disciplines. As a result, the five most important trends for corporate communications are identified annually and published in the Communications Trend Radar. Thus, Communications managers can identify challenges and opportunities at an early stage, take a position, address issues and make decisions. For 2023, the Communications Trend Radar identifies five key trends for corporate communications: State Revival, Scarcity Management, Unimagination, Parallel Worlds, Augemented Workflows. KW - public relation KW - trend KW - country KW - stakeholders KW - bottleneck KW - resilience KW - artificial intelligence KW - virtual reality Y1 - 2023 UR - https://hdl.handle.net/10419/270993 U6 - https://doi.org/10419/270993 SN - 2749-893X VL - 17 PB - Academic Society for Management & Communication CY - Leipzig ER - TY - CHAP A1 - Grum, Marcus ED - Shishkov, Boris T1 - Managing human and artificial knowledge bearers BT - the creation of a symbiotic knowledge management approach T2 - Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings N2 - As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences. KW - knowledge management KW - artificial intelligence KW - neuronal systems KW - design of knowledge-driven systems KW - symbiotic system design Y1 - 2020 SN - 978-3-030-52305-3 SN - 978-3-030-52306-0 U6 - https://doi.org/10.1007/978-3-030-52306-0_12 SP - 182 EP - 201 PB - Springer International Publishing AG CY - Cham ER - TY - RPRT A1 - Gagrčin, Emilija A1 - Schaetz, Nadja A1 - Rakowski, Niklas A1 - Toth, Roland A1 - Renz, André A1 - Vladova, Gergana A1 - Emmer, Martin T1 - We and AI BT - living in a datafied world : experiences & attitudes of young Europeans KW - sociology & anthropology KW - technology (applied sciences) KW - sociology of science KW - sociology of technology KW - research on science and technology KW - technology assessment KW - artificial intelligence KW - digitalization KW - educational technology KW - decision making KW - data security KW - monitoring KW - data protection KW - automation KW - Europe KW - attitude KW - young adult KW - technological change KW - new technology Y1 - 2021 U6 - https://doi.org/10.34669/wi/1 PB - Weizenbaum Institute for the Networked Society - the German Internet CY - Berlin ER - TY - CHAP A1 - Clausen, Sünje A1 - Brünker, Felix A1 - Stieglitz, Stefan T1 - Towards responsible augmentation BT - identifying characteristics of AI-based technology with ethical implications for knowledge workers T2 - ACIS 2023 proceedings N2 - Artificial intelligence (AI)-based technologies can increasingly perform knowledge work tasks, such as medical diagnosis. Thereby, it is expected that humans will not be replaced by AI but work closely with AI-based technology (“augmentation”). Augmentation has ethical implications for humans (e.g., impact on autonomy, opportunities to flourish through work), thus, developers and managers of AI-based technology have a responsibility to anticipate and mitigate risks to human workers. However, doing so can be difficult as AI encompasses a wide range of technologies, some of which enable fundamentally new forms of interaction. In this research-in-progress paper, we propose the development of a taxonomy to categorize unique characteristics of AI-based technology that influence the interaction and have ethical implications for human workers. The completed taxonomy will support researchers in forming cumulative knowledge on the ethical implications of augmentation and assist practitioners in the ethical design and management of AI-based technology in knowledge work. KW - artificial intelligence KW - augmentation KW - taxonomy KW - human-AI interaction KW - ethics Y1 - 2023 UR - https://aisel.aisnet.org/acis2023/123/ PB - Australasian Association for Information Systems CY - Wellington ER - TY - CHAP A1 - Thim, Christof A1 - Grum, Marcus A1 - Schüffler, Arnulf A1 - Roling, Wiebke A1 - Kluge, Annette A1 - Gronau, Norbert ED - Andersen, Ann-Louise ED - Andersen, Rasmus ED - Brunoe, Thomas Ditlev ED - Larsen, Maria Stoettrup Schioenning ED - Nielsen, Kjeld ED - Napoleone, Alessia ED - Kjeldgaard, Stefan T1 - A concept for a distributed Interchangeable knowledge base in CPPS T2 - Towards sustainable customization: cridging smart products and manufacturing systems N2 - As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization. KW - learning KW - distributed knowledge base KW - artificial intelligence KW - CPPS Y1 - 2021 SN - 978-3-030-90699-3 SN - 978-3-030-90702-0 SN - 978-3-030-90700-6 U6 - https://doi.org/10.1007/978-3-030-90700-6_35 SP - 314 EP - 321 PB - Springer CY - Cham ER -