General data protection regulation: an individual's perspective

Marikyan, Davit, Papagiannidis, Savvas, Ranjan, Rajiv and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2021. General data protection regulation: an individual's perspective. Presented at: 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC 2021), Leicester, England, 06-09 December 2021. UCC '21: Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion. New York: Association for Computing Machinery, p. 8. 10.1145/3492323.3495620

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Abstract

Rapid digitalisation has resulted in a massive exchange of digital data between individuals and organisations, accelerating the importance of privacy-preserving legal frameworks, such as the General Data Protection Regulation (GDPR). Despite the importance of the implementation of such a framework, current research lacks evidence about how individuals perceive GDPR compliance. Given that, the objective of this study was to explore individuals' attitudes towards GDPR compliance in line with Protection Motivation Theory. This study employed a cross-sectional research design and collected 540 valid responses to test a model using structural equational modelling. The result of the analysis showed that perceived threat severity, response efficacy and self-efficacy have positive relationships with attitude towards GDPR compliance. In addition, it was found that attitude correlates with emotional empowerment. The findings of this paper contribute to the literature on privacy-preserving mechanisms by shedding light on individuals' perceptions of the GDPR. The evidence also adds to the current body of literature on information systems management by giving insights into the factors that determine the utilisation of privacy-preserving technologies. These pieces of evidence offer implications for policymakers by providing guidelines on how to communicate the benefits of the GDPR to the public.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery
ISBN:	9781450391634
Funders:	EPSRC
Last Modified:	10 Nov 2022 10:34
URI:	https://orca.cardiff.ac.uk/id/eprint/147330

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