Applying Rough Set Theory to Evaluate Network Marketing Performance of China's Agricultural Products

被引:1
|
作者
Jiang, Hua [1 ]
Ruan, Junhu [1 ]
机构
[1] Hebei Univ Engn, Sch Econ & Management, Handan, Peoples R China
关键词
agricultural products; network marketing; performance evaluation; rough set theory; measures and suggestions;
D O I
10.4304/jcp.5.8.1264-1272
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Agricultural products network marketing means fully introducing e-commerce systems into the sale process of agricultural products, using information technologies to publish and collect the demand and price information, and relying on agricultural production bases and logistics distribution systems to enhance brand images, improve customer services, develop online marketing channels and ultimately expand marketing activities. It is very important to evaluate the performance of agricultural products network marketing. The paper proposed a performance evaluation model of agricultural products network marketing based on rough set theory. The model used genetic algorithm to reduce the initial decision table and, through calculating the importance of the decision attributes in the reduced decision table, identified the weight coefficients of various influencing indexes, effectively overcoming the subjectivity of weight coefficient determination in the current evaluation models. In the same time, the paper evaluated the network marketing performance of six agricultural enterprises and the results were in line with the actual results, which verified the validity of the proposed evaluation model. Lastly, the paper, according to the reduced results of the initial index system, proposed specific measures and suggestions to improve the network marketing performance of agricultural products.
引用
收藏
页码:1264 / 1272
页数:9
相关论文
共 50 条
  • [31] Routing Optimization of Computer Network Based on Rough Set Theory and Application of Optimization Algorithm
    Cai, E.
    PROCEEDINGS OF THE WORLD CONFERENCE ON INTELLIGENT AND 3-D TECHNOLOGIES, WCI3DT 2022, 2023, 323 : 101 - 109
  • [32] Evaluation of the Virtual Network Laboratory Exercises Using a Method Based on the Rough Set Theory
    Dobrilovic, Dalibor
    Brtka, Vladimir
    Berkovic, Ivana
    Odadzic, Borislav
    COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, 2012, 20 (01) : 29 - 37
  • [33] Research on Case Retrieval Model Based on Rough Set Theory and BP Neural Network
    Wang, Xiaohui
    2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION, 2009, : 117 - 120
  • [34] Missing attribute value prediction based on artificial neural network and rough set theory
    Setiawan, N. A.
    Venkatachalam, P. A.
    Hani, A. F. M.
    BMEI 2008: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS, VOL 1, 2008, : 306 - 310
  • [35] Improved convolutional neural network combined with rough set theory for data aggregation algorithm
    Cao, Junqin
    Zhang, Xueying
    Zhang, Chunmei
    Feng, Jiapeng
    JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 11 (02) : 647 - 654
  • [36] Improved convolutional neural network combined with rough set theory for data aggregation algorithm
    Junqin Cao
    Xueying Zhang
    Chunmei Zhang
    Jiapeng Feng
    Journal of Ambient Intelligence and Humanized Computing, 2020, 11 : 647 - 654
  • [37] A Fault Diagnosis Modeling Method Combined RBF Neural Network with Rough Set Theory
    Zhou Liuyang
    Shi Yuwen
    Tang Pengcheng
    Zhang Hui
    2009 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL II, 2009, : 501 - +
  • [38] Applying variable precision rough set model for clustering student suffering study's anxiety
    Yanto, Iwan Tri Riyadi
    Vitasari, Prima
    Herawan, Tutut
    Deris, Mustafa Mat
    EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (01) : 452 - 459
  • [39] An Integrated Methodology of Artificial Neural Network and Rough Set Theory for Analyzing IVF Data
    Durairaj, M.
    Nandhakumar, R.
    2014 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING APPLICATIONS (ICICA 2014), 2014, : 126 - 129
  • [40] A Promising Method of Knowledge Acquisition Using a Combination of Bayesian Network and Rough Set Theory
    Chen, Chih-Cheng
    Tseng, Ming-Lang
    Hsu, Wei-Ting
    ICOSCM 2009 - PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON OPERATIONS AND SUPPLY CHAIN MANAGEMENT, 2009, 3 : 994 - 1000