Seyed Mehdi Sadat Rasoul; Sepideh Shafi'ah; Mohsen Khodakarami
Abstract
Hundreds of thousands of new businesses are created every year around the world, and it is estimated that about half a billion people worldwide are actively trying to start new businesses. Therefore, creating a new business is very important; because it creates new job opportunities, produces new technology ...
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Hundreds of thousands of new businesses are created every year around the world, and it is estimated that about half a billion people worldwide are actively trying to start new businesses. Therefore, creating a new business is very important; because it creates new job opportunities, produces new technology and creates wealth and value in society. However, a large percentage of small businesses fail in the early years of their existence. Meanwhile, in the field of superior technologies, due to the high dependence on changing technology and also the need for high initial capital, firms are facing a more difficult situation. The purpose of this study is to provide a rule based database to determine the success and failure of IT startups in Iran. For this purpose, using research conducted in this field and also receiving the opinions of experts through a semi-open questionnaire, we identified 36 factors affecting the success and failure of startups and classified them into 5 categories. In this research, the method of fuzzy inference system by Mamdani method has been used to analyze the factors. The statistical community is the founders of Iranian startups as well as professors and business professionals;30 founders of successful Iranian startups have been randomly selected as a sample. The results show that team-related factors and team characteristics have the greatest impact on the success and failure of startups.
Imani Raeesi Vanan; Mohammad Reza Taghva; Delnia Amir Ashayeri
Abstract
In this research, for the purpose of evaluating the performance of knowledge management systems as an improving infrastructure for organizational learning and performance in software development industry, a fuzzy inference system is designed and evaluated. At first, input variables were extracted ...
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In this research, for the purpose of evaluating the performance of knowledge management systems as an improving infrastructure for organizational learning and performance in software development industry, a fuzzy inference system is designed and evaluated. At first, input variables were extracted as the performance evaluators of knowledge management system. Then, if-then rules were identified through the utilization of experts’ opinions and inserted to the fuzzy rule-base. The output of inference system was also designed for performance evaluation of knowledge management system. The designed system, through a comprehensive assessment of knowledge management systems, can enable organizations to identify the strengths, weaknesses, current condition, and future decisions making for the purpose of performance improvement. For the validation of fuzzy inference system, a comparison was made between system outputs and experts viewpoints. Considering the very small difference between the average of experts’ opinions and system output, it can be stated that the system has an appropriate precision and validity for future assessment.
