Cyber-security, Privacy and Ethics of IS
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Paper Number
1514
Paper Type
Completed
Description
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.
Recommended Citation
Frank, Muriel and Ranft, Lukas Manuel, "Using Machine Learning Techniques to Explore Extra-Role Security Behavior" (2021). ICIS 2021 Proceedings. 6.
https://aisel.aisnet.org/icis2021/cyber_security/cyber_security/6
Using Machine Learning Techniques to Explore Extra-Role Security Behavior
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.
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07-Security