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基于Logistic分析的安徽特色农产品网络营销发展影响因素 被引量:2

Influencing Factors of Characteristic Agricultural Products Network Marketing Development in Anhui Based on Logistic Analysis
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摘要 目的:进一步深化对特色农产品网络营销的认识和理解,不断优化网络营销策略。方法:以安徽省主要区域特色农产品的主体购买意愿为调查对象,分析消费者网购特色农产品的行为特征,设置李克特五级量表,采取Logistic回归模型对网络营销的产品、平台、环境、商家等因素进行分析。结果:明确产品质量、商家售后服务和物流体系是影响网络营销发展的主要关联因素,且物流体系达到极显著水平。结论:从构建城乡融合物流体系、加强特色农产品标准化品牌化建设和拓展宣传渠道与营销路径等角度提出对策和建议。为提升特色农业产业和农产品核心竞争力提供参考依据。 Objective:To further deepen the understanding of the network marketing of characteristic agricultural products,and constantly optimize the network marketing strategy.Methods:It analyzes the behavior characteristics of consumers'online shopping of characteristic agricultural products by taking the main willingness to buy the main regional characteristic agricultural products in Anhui Province as the investigation object,and sets up a Likert five level scale.Logistic regression model was used to analyze the factors of network marketing,such as product,platform,environment and business.Results:It was clear that product quality,business after-sales service and logistic system were the main related factors affecting the development of network marketing of characteristic agricultural products,and the logistic system reached a very significant level.Conclusion:Based on this,the paper puts forward countermeasures and suggestions from the perspectives of building urban-rural integrated logistic system,strengthening the construction of characteristic agricultural product standardization brand,and expanding publicity channels and marketing paths,so as to provide reference for improving the core competitiveness of characteristic agricultural industry and agricultural products.
作者 戴先红 DAI Xianhong(School of Financial Management,Hefei University of Economics,Hefei 230011,China)
出处 《安徽科技学院学报》 2022年第5期102-108,共7页 Journal of Anhui Science and Technology University
基金 国家级大学生创新创业训练计划项目(202113616002) 安徽省大学生创新创业训练计划项目(S202013616006)。
关键词 特色农产品 网络营销 LOGISTIC回归模型 Characteristic agricultural products Network marketing Logistic regression model
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