Analyzing the Behavior of Social Network Users in E-Business Using Data Mining

Document Type : Original Article

Authors
Mohammad Reza Omrani Tabarestani 1
Nader Naghshineh 2
Sepideh Fahimifar 2
1 Ph.D. Candidate, Department of Information Science and Epistemology, International Campus, Tehran University, Kish, Iran
2 Associate Prof., Department of Information Science and Epistemology, Faculty of Management, University of Tehran, Tehran, Iran
10.22034/aimj.2024.189578
Abstract
Continuous advances in e-business have put on the agenda of businesses the approaches that lead to value creation and long-term communication with users. Therefore, many IT managers believe that the future of business depends on "user experience management". The main purpose of this study is to explain the behavior of social network users in e-business. In this study, CLEMENTINE software was used to perform data mining based on Bayesian network techniques and artificial neural networks. The data of this research was based on a sample of a thousand documents and reports of Elite online store during the years 1395 to 1400, seasonally. The findings of the present study showed that it was important that based on the initial model and the proposed research, after conducting a qualitative case study in the Elite online store, the final model was developed according to the following variables: "Business users profile" component based on indicators such as: Education Users; Age of users; Users' revenue; Gender of users; Was determined. The component of "managing the behavior of business users" based on indicators such as: the number of likes of business users; Number of business users sharing; Number of comments from business users; Number of business user posts; Was determined. The component of "managing relationships with e-business users" based on indicators such as: user surveys; Customizing products and services; Adherence to the promise with the user; Ways of communicating with the user; Was determined. The component of "business social network management" based on indicators such as: the amount of social media feedback, the quality of content in the CMS; Information on business products and services; Features of a business website in SEO; Was determined.

Keywords

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  • Receive Date 06 June 2022
  • Revise Date 27 August 2022
  • Accept Date 02 February 2024