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    Abstract
2014 (Vol. 6, Issue: 4)
Article Information:

An Empirical Study on Evaluation of Marketing Channel Risk in Agricultural Products via PCA and Cluster Analysis

Yazhou Xiong, Shujie Yun, Jingyuan Xing, Xiaorong Huang and Wei Shu
Corresponding Author:  Jingyuan Xing 

Key words:  Agricultural Products, cluster analysis, marketing channel risk, Principal Components Analysis (PCA), , ,
Vol. 6 , (4): 552-557
Submitted Accepted Published
January 20, 2014 February 10, 2014 April 10, 2014
Abstract:

In order to better guard against the marketing channel risk in agriculture products, it is very important that how to conduct a fair and reasonable quantitative evaluation on marketing channel risk. As an important channel by which the enterprises connect the market and consumers, marketing channels play a very important role on the enterprise operation and development. And the development issue of the "agriculture, rural, farmers" has always been one of the most important problems of Chinese government in recent years. The demand of agricultural products in China has been transmitted from the overall shortage to the relative surplus stage, which leads to increasingly fierce competition on marketing of the agricultural products. To make marketing channel operation of the agricultural products enterprise be healthy and effective, it is necessary to carry out effective risk management of marketing channel. This study builds an evaluation index system on marketing channel risk of agricultural products, which includes 2 first-level indicators, 13 second-level indicators. Then the study uses the statistical data, Principal Component Analysis (PCA) and cluster analysis to evaluate and analyze the marketing channel risk of agricultural products. Finally some corresponding measures and suggestions are put forward to strengthen the risk prevention.
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  Cite this Reference:
Yazhou Xiong, Shujie Yun, Jingyuan Xing, Xiaorong Huang and Wei Shu, 2014. An Empirical Study on Evaluation of Marketing Channel Risk in Agricultural Products via PCA and Cluster Analysis.  Advance Journal of Food Science and Technology, 6(4): 552-557.
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ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
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