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Mostafa Bahrami,Mahdi Salehi,Mohsen Akbarzadeh,Alireza Morsali 한국유통과학회 2013 The Journal of Industrial Distribution & Business( Vol.4 No.1
Purpose - Labor productivity is extremely important to the profitability and competitive advantage of organizations that provide services to customers, such as banks. This study investigates the factors driving labor productivity in Iran’s Melli Bank. Research design, data, methodology - Five managerial, psychosocial, cultural, and individual factors are identified and their relative importance for labor productivity prioritized using AHP. The required data are then collected through a questionnaire designed for a pairwise comparison of the driving factors of labor productivity and their subcategories. Results - The study outcomes reveal that the managerial and individual factors are the most important. Specifically, the most important factors in increasing labor productivity in the branches of Melli Bank are having a competent supervisor, promotion opportunities, fair working conditions, conscientiousness, the right tools, and a correspondence between skills and work. Conclusions - Implementing AHP using Expert Choice software revealed that, among the driving factors of labor productivity (i.e., managerial, psychosocial, cultural, environmental, and personal), managerial factors were considered the most important by the respondents.
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Predicting Audit Reports Using Meta-Heuristic Algorithms
Hashem Valipour,Fatemeh Salehi,Mostafa Bahrami 한국유통과학회 2013 유통과학연구 Vol.11 No.6
Purpose - This study aims to predict the audit reports of listed companies on the Tehran Stock Exchange by using meta-heuristic algorithms. Research design, data, methodology - This applied research aims to predict auditors reports’ using meta-heuristic methods (i.e., neural networks,the ANFIS, and a genetic algorithm). The sample includes all firms listed on the Tehran Stock Exchange. The research covers the seven years between 2005 and 2011. Results - The results show that the ANFIS model using fuzzy clustering and a least-squares back propagation algorithm has the best performance among the tested models, with an error rate of 4% for incorrect predictions and 96% for correct predictions. Conclusion - A decision tree was used with ten independent variables and one dependent variable the less important variables were removed,leaving only those variables with the greatest effect on auditor opinion (i.e., net-profit-to-sales ratio, current ratio, quick ratio, inventory turnover, collection period, and debt coverage ratio).
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Prediction of Auditor Selection Using a Combination of PSO Algorithm and CART in Iran
Mahdi Salehi,Sharifeh Kamalahmadi,Mostafa Bahrami 한국유통과학회 2014 유통과학연구 Vol.12 No.3
Purpose - The purpose of this study was to predict the selection of independent auditors in the companies listed on the Tehran Stock Exchange (TSE) using a combination of PSO algorithm and CART. This study involves applied research. Design, approach and methodology - The population consisted of all the companies listed on TSE during the period 2005-2010, and the sample included 576 data specimens from 95 companies during six consecutive years. The independent variables in the study were the financial ratios of the sample companies, which were analyzed using two data mining techniques, namely, PSO algorithm and CART. Results - The results of this study showed that among the analyzed variables, total assets, current assets, audit fee, working capital, current ratio, debt ratio, solvency ratio, turnover, and capital were predictors of independent auditor selection. Conclusion - The current study is practically the first to focus on this topic in the specific context of Iran. In this regard, the study may be valuable for application in developing countries.
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