علیرضا تمجید یامچلو

Qualitative information security risk assessments are somewhat subjective and the high degree of subjectivity associated with the perception of risk means that management is often skeptical of risk analysis results, and is unwilling to make important decisions based on that. Besides, the process of information security risk assessment is quite complex and rife with uncertainty and without taken into account the uncertainty of information security risk assessment the results can be misleading. Therefore, in this paper, the Fuzzy Multi Criteria Group Decision Making (FMCGDM) model is proposed to address the above-mentioned problems. The focus group method used to identify risk parameters and the Delphi method is used to construct a hierarchy for risk parameters. The findings of this research would be useful for the information security department to become more capable in analyzing the InfoSec risks and reducing the consequences of subjective assessment. A case study involving an actual information security risk management project was presented to illustrate the use of the proposed model. Computational results demonstrated the efficiency and effectiveness of the presented model that can assist InfoSec risk analyst to better evaluate InfoSec risk.

Purpose: This study uses TAM theory to better understand variables that are indicative of MOOCs’ courses’ completion rates; furthermore, this paper scrutinizes the quantitative relationship between MOOC platform usage and learning satisfaction. Methodology: This was an applied research in a virtual community called Coursera where people all over the world participate. The research design was quantitative and the questionnaire link was posted on the Coursera, with convenience sampling. Data collection process started from November 12, 2020 until February 23, 2021, and 234 users of the selected MOOC platform (Coursera) participated to evaluate the proposed model. A multivariable systematic technique (PLS) was applied to analyze the model. To conduct the reliability test, individual item loadings and internal consistency were consulted. Convergent validity of the model was measured via the Structural Equation Model (SEM). The examination of the SEM was incorporated an evaluation of the path coefficients and R2 values. Findings: The loadings of all measurement items are larger than 0.792, indicating sound internal reliability of the dataset. Moreover, the Cronbach's alpha values are all > 0.7 which proves the internal consistency of the research model. In this research, the range of CR is 0.838 to 0.947 and the range of AVEs 0.634–0.857, both exceeding the threshold values for desirable convergent validity. To obtain discriminant validity, the square root of AVE should be larger than the correlation among the constructs. The value of each AVE’s square root is greater than the off-diagonal components. The model explains 21.1% of the variance in perceived usefulness, 20.9% in perceived ease of use, 26.9% in attitude to use, 20.0% in MOOC platform actual usage and 24.2% of the variance in learning satisfaction. The path coefficient from quantity and quality of knowledge (β=0.261, p<0.01) and perceived feedback (β=0.215, p<0.01) to PU are positive. The results show that perceived feedback (β=0.275, p<0.01), perceived complexity (β=-0.367, p<0.01) significantly and meaningfully affect PEOU. The PU showed a positive and strong effect on attitude to use (β=0.271, p<0.01), MOOC platform actual usage (β=0.360, p<0.01), and learning satisfaction (β=0.277, p<0.01). Conclusion: The results indicated that the quantity and quality of the knowledge and perceived feedback have a positive and significant impact on the perceived usefulness of MOOCs. The perceived complexity as a negative construct was found to be an important indicator of MOOC platforms usage.