@incollection{BraunerVervierBrillowskietal.2022, author = {Brauner, Philipp and Vervier, Luisa and Brillowski, Florian and Dammers, Hannah and Steuer-Dankert, Linda and Schneider, Sebastian and Baier, Ralph and Ziefle, Martina and Gries, Thomas and Leicht-Scholten, Carmen and Mertens, Alexander and Nagel, Saskia K.}, title = {Organization Routines in Next Generation Manufacturing}, series = {Forecasting Next Generation Manufacturing}, booktitle = {Forecasting Next Generation Manufacturing}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-07734-0}, doi = {10.1007/978-3-031-07734-0_5}, pages = {75 -- 94}, year = {2022}, abstract = {Next Generation Manufacturing promises significant improvements in performance, productivity, and value creation. In addition to the desired and projected improvements regarding the planning, production, and usage cycles of products, this digital transformation will have a huge impact on work, workers, and workplace design. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these changes, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the organization dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we highlight seven areas in which the digital transformation of production will change how we work, how we organize the work within a company, how we evaluate these changes, and how employment and labor rights will be affected across company boundaries. The experts are unsure whether the use of collaborative robots in factories will replace traditional robots by 2030. They believe that the use of hybrid intelligence will supplement human decision-making processes in production environments. Furthermore, they predict that artificial intelligence will lead to changes in management processes, leadership, and the elimination of hierarchies. However, to ensure that social and normative aspects are incorporated into the AI algorithms, restricting measurement of individual performance will be necessary. Additionally, AI-based decision support can significantly contribute toward new, socially accepted modes of leadership. Finally, the experts believe that there will be a reduction in the workforce by the year 2030.}, language = {en} } @incollection{EbertSchneiderStapenhorst2022, author = {Ebert, Carola and Schneider, Tatjana and Stapenhorst, Carolin}, title = {Undergraduate Research in Architecture}, series = {The Cambridge Handbook of Undergraduate Research}, booktitle = {The Cambridge Handbook of Undergraduate Research}, editor = {Mieg, Harald A. and Ambos, Elizabeth and Brew, Angela and Galli, Dominique and Lehmann, Judith}, publisher = {Cambridge University Press}, address = {Cambridge}, isbn = {9781108869508}, doi = {10.1017/9781108869508.049}, pages = {355 -- 362}, year = {2022}, abstract = {Architecture is a university subject with educational roots in both the technical university and art/specialized architecture schools, yet it lacks a strong research orientation and is focused on professional expertise. This chapter explores the particular role of research within architectural education in general by discussing two different cases for the implementation of undergraduate research in architecture: during the late 1990s and early 2000s at the University of Sheffield, UK, and during the 2010s at RWTH Aachen University, Germany. These examples illustrate the asynchronous beginnings of similar developments, and also contextualize differences in disciplinary habitus and pedagogical approaches between Sheffield, where research impulses stemmed from within the Architectural Humanities, and Aachen with its strong tradition as a technical university.}, language = {en} }