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Applying double loop learning to interpret implications for information systems security design

Authors: Angela Mattia; Gurpreet Dhillon;

Applying double loop learning to interpret implications for information systems security design

Abstract

The security of information systems (IS) continues to be one of the most serious issues of the twenty-first century. Past research indicates human factors to be the prime reason for IS security breaches. Human factors are repertoires of behavior that evolve from the reasoning and actions that individuals follow. These actions become the 'theories of action' individuals espouse and their 'theories-in-use', which are the actions they actually use. We argue that IS security problems occur when an organization's 'espoused theory' and their 'theory-in-use' (what they actually do) are contradictory. It is important, therefore, that human factors be addressed in (IS) security. The current focus on technological methods alone is an incomplete solution. The purpose of this paper is to present double loop learning as a strategy for designing and implementing security actions that bring an organization's 'espoused theory' and their 'theory-in-use' (what they actually do) into congruence . Indeed, by doing so double loop learning is a proactive security method that never becomes outdated.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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