Data mining and application of social e-commerce users based on big data of internet of things

Author:

Xie Chao1,Xiao Xiaoyong2,Hassan Dina K.3

Affiliation:

1. Business School, Wuchang University of Technology, Wuhan, Hubei, China

2. School of Economics, Wuhan Donghu University, Wuhan, Hubei, China

3. Accounting Department, Faculty of Commerce, Kafr Elsheikh University, Egypt

Abstract

Social media has accumulated a large number of users by its community, which has greatly changed and affected people’s lifestyles. Social media not only provides convenience for users to make friends, entertainment, information acquisition and other activities, but also provides an ideal way for the development of e-commerce with the advantages of fast transmission speed and accurate audience. The content and behavior of social e-commerce platforms are mostly generated and dominated by users, who are the key subjects that determine the development of platforms and the profitability of enterprises. The main purpose of this study is to enrich the theoretical system of data mining for social e-commerce users and provide a theoretical basis and reference for platform and business management and operation of social e-commerce. First, based on the information ecology and information dissemination perspective, this paper constructs the model of information flow in social e-commerce. Second, based on the social network analysis method, analyzes the social network of social e-commerce users; Finally, based on the integrated model of technology acceptance and use (UTAUT), the theory of perceived risk and the theory of trust, the conceptual model of influencing factors of initial information adoption by users of social e-commerce is constructed, and the key influencing factors are identified by using Delphi method and DEMATEL method. The experimental results show that the degree of centrality of the new technology application is the largest, 5.250, which is the key factor influencing the initial information adoption of social e-commerce users. User satisfaction has the largest influence on the continuous information adoption intention of social e-commerce users, with the influence factor reaching 1.223, followed by IT self-efficacy (0.948), user relationship network structure (0.771), social e-commerce platform quality (0.637), perceived usefulness (0.419) and emotional attachment intensity (0.409).

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference26 articles.

1. Research on Data Mining of the Internet of Things Based on Cloud Computing Platform;Zhang;IOP Conference Series Earth and Environmental Science,2018

2. The Internet of Things: A Survey;Li;Information Systems Frontiers,2015

3. Urban Planning and Building Smart Cities Based on The Internet of Things Using Big Data Analytics”;Rathore;Computer Networks,2016

4. The Virtual Object as a Major Element of the Internet of Things: A Survey;Nitti;IEEE Communications Surveys & Tutorials,2016

5. Everything You Wanted to Know about Smart Cities: The Internet of Things Is The Backbone;Mohanty;IEEE Consumer Electronics Magazine,2016

Cited by 20 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Improved DeepFM-Based Model with LSTM on Click-Through-Rate;Proceedings of the 5th International Conference on Computer Information and Big Data Applications;2024-04-26

2. An In-Depth Analysis of Data Warehouse and Data Mining-Aligned Business Intelligence Tools and Methodologies in E-Commerce;2024 6th International Conference on Management Science and Industrial Engineering;2024-04-24

3. Prediction Model for E-Commerce User Purchase Behavior Based on Data Mining;2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB);2024-04-19

4. Minimizing Overhead through Blockchain for Establishing a Secure Smart City with IoT Model;Scalable Computing: Practice and Experience;2024-04-12

5. Computer security technology in E-commerce platform business model construction;Heliyon;2024-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3