Identifying and Prioritizing Successful e-Business Models in Iranian Dot-Coms by Using Machine Learning Techniques

Vakilisadeghi, M.; Noori, R.; Ebadati, O.

doi:10.22061/jte.2017.731

Document Type : Original Research Paper

Authors

¹ Department of Information Technology Management ,Faculty of Management, Kharazmi University, Tehran, Iran

² Information Technology Management Department, Management Faculty, Kharazmi University, Tehran, Iran

https://doi.org/10.22061/jte.2017.731

Abstract

Today, business, economics and society has been transformed by information technology. Many traditional ways of earning money have evolved and new methods and values have come to the fore. In this regard, Study of the e-business system in today's complex and turbulent world is essential. Despite the fact that some businesses succeed in their field of work, there are many businesses that fail, because of selecting inappropriate business model. So, this study which has been done to identify successful e-business models using machine learning techniques. Quantitative survey was used to doing research. 105 businesses with a eTrust were selected to find the best successful electronic business model. The instrument used to collect data was a questionnaire. Analyzing collected data shoes the best business model for the success of businesses in Iran, are e-shop model and the advertising. The results of the k-means algorithm and ID3 show that of the 12 criteria considered in choosing the best model for success, two criteria; including the development of IT tools and company strategy have the most important role for the success of trusted businesses.

Keywords

20.1001.1.20080441.1396.11.4.4.7

Main Subjects

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Technology of Education Journal (TEJ)

Identifying and Prioritizing Successful e-Business Models in Iranian Dot-Coms by Using Machine Learning Techniques

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Volume 11, Issue 4 - Serial Number 44
October 2017
Pages 323-335

Identifying and Prioritizing Successful e-Business Models in Iranian Dot-Coms by Using Machine Learning Techniques

References

References

Send comment about this article

Volume 11, Issue 4 - Serial Number 44October 2017Pages 323-335

Volume 11, Issue 4 - Serial Number 44
October 2017
Pages 323-335