L
Lennart Hofeditz
Researcher at University of Duisburg-Essen
Publications - 21
Citations - 181
Lennart Hofeditz is an academic researcher from University of Duisburg-Essen. The author has contributed to research in topics: Computer science & Social media. The author has an hindex of 6, co-authored 9 publications receiving 75 citations.
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Ethical Management of Artificial Intelligence
TL;DR: In this paper, the authors propose an ethical management of AI (EMMA) framework, focusing on three perspectives: managerial decision making, ethical considerations, and macro- as well as micro-environmental dimensions.
Journal ArticleDOI
Understanding Collaboration with Virtual Assistants – The Role of Social Identity and the Extended Self
Milad Mirbabaie,Stefan Stieglitz,Felix Brünker,Lennart Hofeditz,Björn Ross,Nicholas R. J. Frick +5 more
TL;DR: Results highlight that employees who identify VAs as part of their extended self are more likely to identify with team members and vice versa, and are combined into the proposed construct of virtually extended identification explaining the relationships of collaboration with VAs.
Journal ArticleDOI
When are researchers willing to share their data? - Impacts of values and uncertainty on open data in academia.
Stefan Stieglitz,Konstantin Wilms,Milad Mirbabaie,Lennart Hofeditz,Bela Brenger,Ania López,Stephanie Rehwald +6 more
TL;DR: It was found that researchers’ assumptions about effort required during the data preparation process were diminished by awareness of e-science technologies, which also increased their tendency to perceive personal benefits via data exchange.
Strategies and Influence of Social Bots in a 2017 German state election
TL;DR: In insight in the use of these bots in the run-up to the German Bundestag elections, a dataset from Twitter consisting of tweets regarding a German state election in May 2017 was collected and 61 social bots were identified.
Posted Content
Strategies and Influence of Social Bots in a 2017 German state election - A case study on Twitter
TL;DR: In this article, the authors collected a dataset from Twitter consisting of tweets regarding a German state election in May 2017 and analyzed the strategies and influence of social bots based on relevant features and network visualization.