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Analysis of green e-commerce supply chain considering delivery time under reward–penalty mechanism based on confidence level

Author

Listed:
  • Gao, Rong
  • Hua, Kexin
  • Wang, Xiaosheng
  • Wei, Jie

Abstract

The rapid growth of e-commerce and the increasing demand for green consumption are spurring the development of the green e-commerce supply chain, which poses new challenges, including the discrepancy between actual and promised delivery time of green products sold on e-commerce platforms.Due to the indeterminates in the market environment, we do not have sufficient relevant data to deduce the demand for new green products.Therefore, considering the consumer’s perception of the delivery time difference, we investigate the pricing and sales mode selection problem of the green e-commerce supply chain using uncertainty theory under four combination scenarios of two approaches (no incentives, consumer incentives) and two e-commerce sales modes (wholesale sales mode, platform sales mode). Instead of expected utility maximization, we assume that an e-commerce platform and a green manufacturer seek to maximize profits under uncertain demand based on greenness and delivery time difference with a certain confidence level, from which two centralized models and four Stackelberg decentralized models are constructed.The results show that supply chain members’ choice of sales mode is associated with risk attitude.In addition, the impact of promised delivery time on the optimal profitability of the supply chain member who is responsible for product delivery is related to the strength of consumer incentives under the reward and penalty mechanism. More importantly, the introduction of the consumer reward and penalty mechanism can effectively improve the greenness of products, the efficiency of delivery, the optimal profits of supply chain members and social welfare.

Suggested Citation

  • Gao, Rong & Hua, Kexin & Wang, Xiaosheng & Wei, Jie, 2025. "Analysis of green e-commerce supply chain considering delivery time under reward–penalty mechanism based on confidence level," Socio-Economic Planning Sciences, Elsevier, vol. 99(C).
  • Handle: RePEc:eee:soceps:v:99:y:2025:i:c:s0038012125000527
    DOI: 10.1016/j.seps.2025.102203
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