Management Research in Iran

Abstract Classification of statistical elements is one of the challenging areas in management science. This subject has changed to an interesting research areas. Although methods of cluster analysis and discriminant analysis are used as common methods in the classification, there is a doubt about their application due to high statistical errors of the methods. In this paper, it is tried to combine analysis approach of statistical discrimination and OR technique and a new method titled goal discriminant analysis is developed. Four discriminant analysis methods titled FLDF, FG, GP1 and GP2 are applied in this paper. In order to evaluate its efficiency in management science area, the fourfold technique has been employed in 5 managerial case studies. The results show that the FLDF method, which is a discriminant analysis method, is more efficient than other methods. Moreover, goal discriminant methods have more efficiency in management classification with over two groups.

Abstract Identifying Inefficient Bank Branches Using DEA and Use of Integrated Strategies in Order to Increase Performance Branches Naser Hamidi1, Reza Akbari Shemirani2, Safar Fazli3 1. Assistant Professor, Department of Managemen and Accounting, Islamic Azad University Qazvin Branch, Qazvin, Iran 2. M. A , Department of Managemen and Accounting , Islamic Azad University Qazvin Branch, Qazvin, Iran 3. Assistant Professor, Department of Social Sciences, Imam Khomeini International University, Qazvin, Iran Received: 27 /10/2010 Accept: 9/3/2011 Among the various economic and financial organizations, banks are considered and discussed as one of the most important pillars of any economic system. Therefore, considering the important role of banks in the developed countries and their multiple branches, bank branch performance measurement is important. The main purpose of this work is measure branch performance and the use of integration strategy to obtain efficient branches. Accordingly, first the efficiency of bank branches conceptual system was defined. Then using the action plan were inputs and outputs determined. In the next stage, Tehran province branch efficiency was calculated through Data Envelopment Analysis model as non-radial (SBM) to determine the inefficient branches. Based on the bank merger policy, the clusters were determined and the branches in each cluster were merged in a binary form. The final stage of the integration of non-radial (SBM) to assess the performance of branches and compare it with their initial performance.

Abstract Market segmentation by artificial neural networks has no deep root in the history. Generally, this ever developing approach has started since several years ago, and developed to other marketing areas. Now, beside statistical techniques, it is considered as one of the most popular methods in Custamer classification. In Due to the necessity of recognizing the target market for a specific company, a need for the usage of an effective approach for customers grouping was recognized, in the Present research, and finally cluster analysis with SOM neural networks, was selected, and used for customers clustering. Firstly, beneficent criteria for market segmentation were identified, and then a proper, questionnaire was designed. After gathering the questionnaires and collecting the data, using artificial neural networks, the customers were clustered, and the obtained, results were analyzed. At the end, the Findings of this method were compared with those of the traditional methods for clusteringusing K-means.