Integrating Protection Motivation Theory With Cultural Context: A Framework for Cybersecurity Education

Integrating Protection Motivation Theory With Cultural Context: A Framework for Cybersecurity Education

Eiman Tamah Al-Shammari (Department of Information Science, College of life Sciences, Kuwait University, Kuwait)
Copyright: © 2025 |Pages: 27
DOI: 10.4018/JCIT.368146
Article PDF Download
Open access articles are freely available for download

Abstract

This study integrates Protection Motivation Theory (PMT) with cultural dimensions to design a cybersecurity education framework tailored to Kuwait. The study uses advanced analytical techniques like K-means clustering and Association Rule Mining (ARM) to find important patterns in how age, education level, and cultural norms (like religious adherence and family reputation) affect how people act online safely. The findings highlight key gaps in awareness and coping mechanisms, informing a framework that incorporates gamification, microlearning, and culturally relevant scenarios. We design these strategies to enhance engagement and foster meaningful behavioral change. Although we developed the framework for Kuwait, its scalable design ensures applicability to other culturally diverse regions, offering a replicable model for improving global cybersecurity awareness and resilience.
Article Preview
Top

Introduction

The exponential growth of digital technologies has transformed the cybersecurity landscape, necessitating nuanced approaches to fostering cybersecurity awareness. Conventional education strategies, often uniform and standardized, inadequately address the cultural and demographic factors shaping online behaviors, particularly in diverse societal contexts like Kuwait (Abokhodair & Vieweg, 2016; Othman, 2023). Deeply rooted cultural values, such as family reputation and religious adherence, heavily influence individual perceptions and practices, underscoring the need for culturally adaptive frameworks that integrate technical knowledge with socio-cultural realities.

This study fills in that gap by using the protection motivation theory (PMT) as a base and adding cultural aspects to look at how threat perception, coping mechanisms, and social norms interact (Lie et al., 2021; Rippetoe & Rogers, 1987; Rogers, 1975). Advanced statistical methods, like K-means clustering and association rule mining (ARM), find patterns in people’s knowledge of cybersecurity and how they deal with problems (Abu et al., 2020; Agrawal et al., 1993). These insights help create a complete plan that uses gamification, microlearning, and scenario-based learning to help people change their behavior in a way that fits in with Kuwait’s culture.

Beyond addressing Kuwait’s specific needs, the proposed framework offers scalability and adaptability to other culturally diverse regions, providing a replicable model for enhancing global cybersecurity resilience.

This paper continues with “Cybersecurity Awareness and Cultural Context in Kuwait,” while “Theoretical Framework” reviews Kuwait’s national cybersecurity initiatives, providing a contextual background. “Technical Definitions” elaborates on the theoretical framework, integrating PMT with cultural dimensions. “Literature Review” defines key analytical concepts underpinning the hybrid model. “Data Collection and Description” details the methodology, including data collection and validation techniques. “Compliance” discusses the results and their implications for cybersecurity education. “The Hybrid Model” introduces a data-driven framework that combines K-means clustering and ARM to identify patterns in cybersecurity awareness and cultural sensitivity, enabling the development of targeted, culturally adaptive interventions. Finally, the “Conclusion” offers concluding insights and recommendations for future research, emphasizing the broader applicability of the proposed framework.

Complete Article List

Search this Journal:
Reset
Volume 27: 1 Issue (2025)
Volume 26: 1 Issue (2024)
Volume 25: 1 Issue (2023)
Volume 24: 5 Issues (2022)
Volume 23: 4 Issues (2021)
Volume 22: 4 Issues (2020)
Volume 21: 4 Issues (2019)
Volume 20: 4 Issues (2018)
Volume 19: 4 Issues (2017)
Volume 18: 4 Issues (2016)
Volume 17: 4 Issues (2015)
Volume 16: 4 Issues (2014)
Volume 15: 4 Issues (2013)
Volume 14: 4 Issues (2012)
Volume 13: 4 Issues (2011)
Volume 12: 4 Issues (2010)
Volume 11: 4 Issues (2009)
Volume 10: 4 Issues (2008)
Volume 9: 4 Issues (2007)
Volume 8: 4 Issues (2006)
Volume 7: 4 Issues (2005)
Volume 6: 1 Issue (2004)
Volume 5: 1 Issue (2003)
Volume 4: 1 Issue (2002)
Volume 3: 1 Issue (2001)
Volume 2: 1 Issue (2000)
Volume 1: 1 Issue (1999)
View Complete Journal Contents Listing