双渠道下制造商融资策略:零售商融资VS平台融资
Financing Strategy of the Manufacturer Under Dual-Channel:Retailer Financing VS Platform Financing
李红萍 1徐扬 2袁孝勇 3龚本刚 4陈久美4
作者信息
- 1. 安徽农业大学经济管理学院,合肥 230036
- 2. 安徽财经大学金融学院,蚌埠 233030
- 3. 南京财经大学国际经贸学院,南京 210023
- 4. 安徽工程大学经济与管理学院,芜湖 241000
- 折叠
摘要
针对双渠道供应链中资金约束制造商融资策略问题,研究了零售商融资和平台融资两种融资模式下制造商、零售商和平台三者的定价决策和利润,考察了制造商初始资本、市场规模和线下渠道所占市场份额对制造商采用两种融资模式的影响.研究结论表明:当制造商的初始资本较高时,制造商偏好平台融资;当制造商的初始资本较低时,同时当市场规模较大且线下渠道所占市场份额较大时,或者当市场规模较小且线下渠道所占市场份额较小时,制造商倾向于采用零售商融资;反之,制造商倾向于采用平台融资.此外,相对于零售商融资,平台融资能使制造商获得更高的产品销量.研究结论为双渠道供应链中制造企业的融资模式选择提供了策略指导.
Abstract
To address the financing strategy of the capital-constrained manufacturer in a dual-channel supply chain,this paper studies the pricing decisions and profits of the manufacturer,retailer and platform under two financing modes:Retailer financing and platform financing,and analyzes the effects of the manufacturer's initial capital,the market size and the market share of offline channel on the manufacturer's adoption of two financing modes.This study shows that:When the manufacturer's initial capital is high,the manufacturer favors platform financing;When the manufacturer's initial capital is low,and if the market size is large and the market share of offline channel is large,or if the market size is small and the market share of offline channel is small,the manufacturer prefers retailer financing;Conversely,the manufacturer favors platform financing.In addition,compared to retailer financing,platform financing enables the manufacturer to achieve higher product sales.The findings of this study provide strategic guidance for the choice of financing modes for manufacturing firms in dual-channel supply chain.
关键词
双渠道供应链/资金约束/零售商融资/平台融资Key words
Dual-channel supply chain/capital constraints/retailer financing/plat-form financing引用本文复制引用
基金项目
国家自然科学基金青年项目(72101003)
国家自然科学基金青年项目(72201001)
国家自然科学基金青年项目(72101108)
安徽省高等学校科学研究重点项目(2022AH050861)
安徽农业大学引进和稳定人才科研资助项目(yj2020-77)
安徽省社科规划一般项目(AHSKY2021D18)
安徽工程大学引进人才科研启动基金项目(2020YQQ061)
国家自然科学基金面上项目(72071002)
出版年
2024
NETL
NSTL
万方数据