Fernandez De Arroyabe Fernandez, Juan Carlos and Arroyabe, Marta F and Fernandez, Ignacio and Arranz, Carlos FA (2023) Cybersecurity Resilience in SMEs. A Machine Learning Approach. Journal of Computer Information Systems. pp. 1-17. DOI https://doi.org/10.1080/08874417.2023.2248925
Fernandez De Arroyabe Fernandez, Juan Carlos and Arroyabe, Marta F and Fernandez, Ignacio and Arranz, Carlos FA (2023) Cybersecurity Resilience in SMEs. A Machine Learning Approach. Journal of Computer Information Systems. pp. 1-17. DOI https://doi.org/10.1080/08874417.2023.2248925
Fernandez De Arroyabe Fernandez, Juan Carlos and Arroyabe, Marta F and Fernandez, Ignacio and Arranz, Carlos FA (2023) Cybersecurity Resilience in SMEs. A Machine Learning Approach. Journal of Computer Information Systems. pp. 1-17. DOI https://doi.org/10.1080/08874417.2023.2248925
Abstract
This study investigates cybersecurity resilience in small and medium-sized enterprises (SMEs), focusing on three key aspects: the capacity to handle potential cyber incidents, the ability to recover from such incidents, and the capability to adapt in the face of possible cyber threats. Grounded in the Resource-Based View (RBV) framework, we conduct an empirical investigation utilizing a survey of 239 UK SMEs. The study makes a theoretical and methodological contribution, with significant implications for managers. First, the study highlights the lack of SMEs’ engagement with the management of cybersecurity and finds cybersecurity incidents to be the most important factor in driving resilience, as compared to cybersecurity capabilities. Moreover, the study also extends the RBV theory, emphasizing the importance of the interaction between cybersecurity capabilities affecting SMEs’ cybersecurity resilience. Second, the study showcases the potential of statistical methods, particularly machine learning techniques to identify the relationships between the factors affecting SMEs’ cybersecurity.
Item Type: | Article |
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Uncontrolled Keywords: | Cybersecurity; resilience; SMEs; cybersecurity incidents; cybersecurity impacts; cybersecurity systems |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Oct 2023 12:16 |
Last Modified: | 16 May 2024 22:00 |
URI: | http://repository.essex.ac.uk/id/eprint/36287 |
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Filename: Cybersecurity Resilience in SMEs. A Machine Learning Approach.pdf
Licence: Creative Commons: Attribution 4.0