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Impacts of CSR and demand disruption on the optimal decisions in logistics service supply chain: a comparison of four analytical models

Author

Listed:
  • Xuehua Ji

    (Beijing Jiaotong University
    Curtin University)

  • Shaochuan Fu

    (Beijing Jiaotong University)

  • Hadrian Geri Djajadikerta

    (Curtin University)

  • Tricia Ong

    (Edith Cowan University)

  • Yong Ye

    (Taizhou University)

Abstract

Enterprises’ awareness and environmental crises induce the significance of corporate social responsibility (CSR), but demand disruption makes the members of logistics service supply chain (LSSC) cautious about profits. How do the CSR and demand disruption affect the optimal decisions of the LSSC? This study develops the two-echelon chain consisting of a logistics service integrator (LSI) and a functional logistics service provider (FLSP). Based on decentralized and centralized decision-making modes, the scenarios without and with demand disruption are studied. By comparing four analytical models, the results show that: (i) CSR could induce a lower price, and higher green innovation level and social welfare of the chain. A higher FLSP’s CSR ratio promotes her utility, but hurts the benefit and utility of LSI; (ii) as the disrupted demand increases, the level of service innovation, consumer surplus and corporate utilities ascend. It differs from logistics service price, which is determined by changing demand and CSR effort level; (iii) the channel utility and green innovation under demand disruption reach equilibrium in the centralized optimization, which greatly benefits the environment and logistics firms. The findings represent practical insights for mitigating the potential risk of demand volatility, and encourage logistics enterprises to proactively incorporate CSR.

Suggested Citation

  • Xuehua Ji & Shaochuan Fu & Hadrian Geri Djajadikerta & Tricia Ong & Yong Ye, 2025. "Impacts of CSR and demand disruption on the optimal decisions in logistics service supply chain: a comparison of four analytical models," Annals of Operations Research, Springer, vol. 353(3), pages 917-948, October.
  • Handle: RePEc:spr:annopr:v:353:y:2025:i:3:d:10.1007_s10479-025-06652-2
    DOI: 10.1007/s10479-025-06652-2
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