A New Analysis on Identifying Entrepreneurial Success Patterns in Small and Medium Industries Using Data Mining.

Document Type : Research Paper

Authors

1 Department of Management, College of Humanities , Hamedan Branch ,Islamic Azad University, Hamedan, Iran

2 Faculty Member of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran

3 Faculty Member of Management , Hamedan Branch , Islamic Azad University, Hamedan ,Iran

4 Assistant Professor, Faculty of Management and Accounting, University of Bu-Ali Sina

Abstract

Entrepreneurship in small and medium-sized industries has always been of great importance, as it will play a key role in the employment of a country as well as boost economic growth and increase GDP. The purpose of the present study is to examine the factors that can make entrepreneurship successful in small and medium-sized industries and to suggest a way for industrial entrepreneurs to become more aware of this important economic field. This research is a case study of industrial entrepreneurs in Iran who obtained initial permission to establish a factory (establishment permit) between 2011 and 2014 and some of them managed to exploit the factory (%100 physical progress) And some others have failed. The raw data of 4580 cases of investor licenses were examined. In order to determine existing and determinant patterns of entrepreneurship success, using data mining concepts. The information is pre-processed for subsequent analyzes and by reducing some of the variables that had less impact on the results. Finally through specialized analyzes using neural networks, Multilayer perceptron, Rough set algorithm and decision tree, best rules were extracted and the results were analyzed in detail. Based on the results, factors such as Industrial background, education related to the industry, and being native entrepreneur can play an important role in the success of this entrepreneurship.

Keywords

References

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History

  • Receive Date: 04 August 2019
  • Revise Date: 07 December 2019
  • Accept Date: 10 December 2019

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