عباس طلوعی اشلقی

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: Evaluation of employee performance plays an effective role in organizational productivity and effectiveness. As a result, the purpose of this study was identification the components of evaluation of employee performance smart pattern. Methodology: This study in terms of purpose was applied and in terms of implementation method was qualitative. The research population was specialist managers and experts evaluation of employee performance of the bank of industry and mine in 2020 year, which according to the principle of theoretical saturation number of 16 people was selected by purposeful sampling method. The research tool was a semi-structured interview, which whose validity was confirmed by the triangulation method and its reliability was calculated by the agreement coefficient method between the two coders 0.82. Finally, data were analyzed based on open, axial and selective coding methods. Findings: The findings showed that components of evaluation of employee performance smart pattern had 50 open codes in 12 axial codes and 3 selective codes. The selective codes included the factors of functional (with 4 axial codes of optimal function, quality of work, active and effective participation in meetings and planning, forearm and follow-up), job behavior (with 7 axial codes of observing administrative regulations and discipline, proper behavior and encounter with clients and colleagues, cooperation and accountability, analysis of issues and providing appropriate solutions, self-propulsion and innovation, increased job information and skills and information transfer) and ethical behavior (with 1 axial code of observing ethics and Islamic behavior). Finally, according to the identified components, the evaluation of employee performance smart pattern was painted. Conclusion: The results of this research have practical implications for the specialists and officials of the Bank of Industry and Mine, and they with the help of identified components for evaluation of employee performance smart pattern can provide the ground for improving the performance of the organization.