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111 results on '"A. Ranft"'

13. Humusverlust nach Windwurf - Risiko im Bergwald?

Author

Mayer, Mathias, primary, Rusch, Silvan, additional, Didion, Markus, additional, Baltensweiler, Andri, additional, Walthert, Lorenz, additional, Ranft, Fabienne, additional, Rigling, Andreas, additional, Zimmermann, Stephan, additional, and Hagedorn, Frank, additional

Published
2022
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27. Professional Environment Matters – National Characteristics of Information Security Overconfidence.

Author

Ranft, Lukas Manuel

Subjects
DATA security, DATA security failures, CONFIDENCE, INFORMATION storage & retrieval systems, INDIVIDUALISM
Abstract

Overconfidence leads employees to take unnecessary risks. In information security, however, risk-taking is not an advantage, as it makes people susceptible to security breaches and misjudges their ability to detect potential threats. Employees overestimating their information security capabilities can result in incidents with severe financial consequences for a company. While prior research has proven that individuals’ overconfidence differs depending on their nationality, the cause and extent remain unclear and even contradictory. In this paper, we analyze the impact of national individualism on overconfidence to understand and reduce it in information security. Our large-scale survey determines overestimation, overprecision, and overplacement of 3,776 employees in an internationally operating company. Furthermore, we find different levels of overconfidence between Western European countries and the United States. Our findings will help companies to better target measures against information security overconfidence. [ABSTRACT FROM AUTHOR]

Published
2022

29. Using Machine Learning Techniques to Explore Extra-Role Security Behavior.

Author

Frank, Muriel and Ranft, Lukas Manuel

Abstract

Many contemporary organizations pit on information security policy compliance to combat information security threats originating from their own workforce. However, recent findings suggest that employees' adherence to security rules and regulations alone is insufficient to protect organizational assets. Instead, extra-role security behavior - actions that go beyond what is specified in policies and are beneficial to the firms - is needed. So far, research with regard to extra-role security behavior is meager, in particular concerning contextual determinants influencing whether employees exhibit prosocial behaviors or not. Hence, this paper uses predictive modeling, or more precisely supervised machine learning, to classify employees according to their likelihood of exhibiting extra-role security behaviors. Results indicate that informational, social, and task context factors significantly impact the performance of extra-role security behavior. [ABSTRACT FROM AUTHOR]

Published
2021

41. Overview of the FLUKA code

Author

Battistoni, Giuseppe, primary, Boehlen, Till, additional, Cerutti, Francesco, additional, Wai Chin, Pik, additional, Salvatore Esposito, Luigi, additional, Fassò, Alberto, additional, Ferrari, Alfredo, additional, Mereghetti, Alessio, additional, Garcia Ortega, Pablo, additional, Ranft, Johannes, additional, Roesler, Stefan, additional, Sala, Paola R., additional, and Vlachoudis, Vasilis, additional

Published
2014
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