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3D Printing and Sustainability: A Web Analysis Approach

dc.contributor.authorDehghan, Robert
dc.contributor.authorSchmidt, Sebastian
dc.contributor.authorSchwierzy, Julian
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:21Z
dc.date.available2023-11-29T14:50:21Z
dc.date.issued2023
dc.description.abstractBesides manifold advantages in production, 3D printing is also often touted as a green technology. However, the quantitative connection between its use and sustainability is unclear so far. In our study, we examined all companies in the DACH region applying a web analytics approach. Using web scraping and deep learning, we assessed the 3D printing and sustainability intensity for each company. Our results suggest that 3D printing companies are significantly more likely to present themselves as sustainable than other companies. However, as our analysis is purely descriptive, correlations and causality still need to be clarified in future research. Our web mining methodology is a promising approach to study economic phenomena with an environmental dimension.en
dc.identifier.doi10.18420/inf2023_152
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43077
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subject3D printing
dc.subjectSustainability
dc.subjectWeb Text Mining
dc.subjectDeep Learning
dc.title3D Printing and Sustainability: A Web Analysis Approachen
dc.typeText/Conference Paper
gi.citation.endPage1491
gi.citation.publisherPlaceBonn
gi.citation.startPage1483
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleÖkologische Nachhaltigkeit - KIU-2024

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