业务
供应链
信息共享
投资(军事)
产业组织
提交
产品(数学)
模式(计算机接口)
服务(商务)
运营效率
代理(哲学)
信息技术
逆向物流
佣金
偏爱
综合后勤保障
电子商务
商业
信息不对称
信息系统
营销
供应链管理
服务提供商
服务水平
欧盟委员会
作者
Xiaogang lin,Zichao Liao,Shuai Yan,Yiwen Bian
摘要
ABSTRACT E‐commerce sales have become increasingly common and important. Manufacturers generally wholesale products to platforms that subsequently resell to customers (reselling), or directly sell to customers through platforms by paying a commission rate (agency selling). In both selling formats, manufacturers can choose between the platforms' logistics and their own logistics for product delivery, generating four typical modes: (1) reselling+platform logistics (RP); (2) reselling+manufacturer logistics (RM); (3) agency selling+platform logistics (AP); and (4) agency selling+manufacturer logistics (AM). Motivated by the fact that platforms possess superior demand information and apply it to improve operational decisions, this paper investigates how a platform strategically shares information to induce a manufacturer to adopt a more efficient mode. We find that the manufacturer's mode preference is significantly influenced by information asymmetry. If the platform possesses highly accurate information (i.e., the information accuracy level is high), the manufacturer prefers the AP mode, and this preference strengthens as the platform's logistics investment efficiency improves. Otherwise, the manufacturer prefers the AM (RP) mode if the logistics improvement efficiency is low (high). However, when the platform's information accuracy level is high but investment efficiency is low, the platform will commit to sharing information to induce the manufacturer to adopt the AM (RP) mode rather than the AP mode if logistics improvement efficiency is low (high). Notably, as the platform's logistics investment efficiency gradually improves, these induced changes in mode selection can be reversed under certain conditions, while the negative impact of information sharing on consumer surplus and social welfare becomes increasingly significant.
科研通智能强力驱动
Strongly Powered by AbleSci AI