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Cites:

Information Journal Paper

Title

Design and Implementation of the Rules for Detecting Customer Loyalty in E-commerce Systems Using Fuzzy Techniques

Pages

  137-170

Abstract

 Nowadays, the importance of studies in Customers’ behavior and attitude has been proved. Customer Loyalty and its influencing factors in the world of E-commerce are being evaluated from different point of view. Nevertheless, little awareness of E-commerce factors from Customers or service providers is a major barrier to the adoption of this business in Iran. On the other hand, the frequent use of vague terms such as "High quality websites" which does not have accurate and standard meanings between different people is another problem in understanding the factors affecting electronic Loyalty. In this research, firstly factors influencing e-Loyalty was studied and then fuzzy inference engine was designed using Fuzzy Logic and fuzzy rules were extracted. Comparing the rules with 2 websites as a real-world example showed the correctness of the proposed rules. The results showed that in the field of Customer Loyalty, it is necessary to consider the principles and concepts of Herzberg Health and Motivation theory in order to adopt the correct strategy in electronic business management.

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  • Cite

    APA: Copy

    HASANZADEH, ALIREZA, & KHORASANI, AMIR. (2018). Design and Implementation of the Rules for Detecting Customer Loyalty in E-commerce Systems Using Fuzzy Techniques. JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES, 6(24 ), 137-170. SID. https://sid.ir/paper/268122/en

    Vancouver: Copy

    HASANZADEH ALIREZA, KHORASANI AMIR. Design and Implementation of the Rules for Detecting Customer Loyalty in E-commerce Systems Using Fuzzy Techniques. JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES[Internet]. 2018;6(24 ):137-170. Available from: https://sid.ir/paper/268122/en

    IEEE: Copy

    ALIREZA HASANZADEH, and AMIR KHORASANI, “Design and Implementation of the Rules for Detecting Customer Loyalty in E-commerce Systems Using Fuzzy Techniques,” JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES, vol. 6, no. 24 , pp. 137–170, 2018, [Online]. Available: https://sid.ir/paper/268122/en

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