How Stakeholders Perceived Security Risks? A New Predictive Functional Level Model and its Application to E-Learning

A new predictive functional level security risk management model is proposed in order to quantify the security level perception and the level of risk involved. It helps in defining the assets, measuring economically the risk, managing the risk toward decisions making. It is out of implementation and based on a functional level architecture. The paper defines a simple predictive model, it relies on a few number of inputs which form the system’s security specifications and provides one output which is the average loss per unit of time ($/H) incurred by a stakeholder as a result of security threats. The obtained values represent how stakeholders perceived economically security risks and predict how it will change over time to implement in advance the needed security strategies. Our model is useful in any security context. We report it in practice originally to the level of e-Learning systems for current architectures because they lack a common measurable value and evidence of cyber security. Our model assists security experts from the early phases of system’s development to implement future safe and secure platforms

Towards sustainable e-learning platforms in the context of cybersecurity: A TAM-driven approach

The rapid growth of electronic learning (e-learning) platforms has raised concerns about cybersecurity risks. The vulnerability of university students to cyberattacks and privacy concerns within e-learning platforms presents a pressing issue. Students’ frequent and intense internet presence, coupled with their extensive computer usage, puts them at higher risk of being a potential victim of cyberattacks. This problem necessitates a deeper understanding in order to enhance cybersecurity measures and safeguard students’ privacy and intellectual property in educational environments. This dissertation work addresses the following research questions: (a) To what extent do cybersecurity perspectives affect student’s intention to use e-learning platforms? (b) To what extent do students’ privacy concerns affect their intention to use e-learning platforms? (c) To what extent does students’ cybersecurity awareness affect their intention to use e-learning platforms? (d) To what extent do academic integrity concerns affect their intention to use e-learning platforms? and (e) To what extent does students’ computer self-efficacy affect their intention to use e-learning platforms? This study was conducted using an enhanced version of the technology acceptance model (TAM3) to examine the factors influencing students’ intention to use e-learning platforms. The study involved undergraduate and graduate students at Eastern Michigan University, and data were collected through a web-based questionnaire. The questionnaire was developed using the Qualtrics tool and included validated measures and scales with close-ended questions. The collected data were analyzed using SPSS 28, and the significance level for hypothesis testing was set at 0.05. Out of 6,800 distributed surveys, 590 responses were received, and after data cleaning, 582 responses were included in the final sample. The findings revealed that cybersecurity perspectives, cybersecurity awareness, academic integrity concerns, and computer self-efficacy significantly influenced students’ intention to use e-learning platforms. The study has implications for practitioners, educators, and researchers involved in designing secure e-learning platforms, emphasizing the importance of cybersecurity and recommending effective cybersecurity training programs to enhance user engagement. Overall, the study highlights the role of cybersecurity in promoting the adoption and usage of e-learning platforms, providing valuable insights for developers and educators to create secure e-learning environments and benefiting stakeholders in the e-learning industry