Smart pattern

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.

In the past, deciding over granting loans to bank customers in Iran would be made traditionally and based on personal judgments over the risk of repayment. However, increase in demands on banking facilities by economic enterprises and families on the one side, and increased as well as extended commercial competitions among banks and financial and credit institutions in the country for reduction of facility repayment risk on the other side, have caused application of novel methods such as some statistical ones in this context. Now to predict the risk of negligence in banking facility repayment and classification of the candidates, bankers use their customers’ credit ranking. Time efficiency, cost effectiveness, avoidance from personal judgments, and further accuracy in examining the candidates who apply for various funds are of its salient merits of this new combined method. Various statistical methods including biased analysis, logistic regression, non-parametric parallelism, and also some others such as neural networks have been employed for credit ranking. In this research, given the random forest metaheuristic algorithm-based smart pattern of real bank customers’ credit risk (case study: Bank Tejarat) was presented. According to the value of skewness, the data could be stated to have a normal distribution. Based on the observed results, the lowest mean was related to the variable of type of facility and its maximum value, to the amount of facility.