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Incorporating social media observations and bounded rationality into fashion quick response supply chains in the big data era

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  • Choi, Tsan-Ming

Abstract

In this paper, we study the fashion quick response program with social media observations, demand forecast updating, and a boundedly rational retailer. We analytically find that the likelihood of having good social media comments on the product plays a critical role in affecting the value of quick response, and its impact is mediated by the fashion retailer’s prior attitude towards the market demand. We then demonstrate how a Pareto improving situation can be achieved under quick response, and uncover that manipulating social media comments can benefit the manufacturer under the surplus sharing contract, but not under the two-part tariff contract.

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  • Choi, Tsan-Ming, 2018. "Incorporating social media observations and bounded rationality into fashion quick response supply chains in the big data era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 386-397.
    • Handle: RePEc:eee:transe:v:114:y:2018:i:c:p:386-397
    DOI: 10.1016/j.tre.2016.11.006
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    7. Zhao, Tianyi & Xu, Xiaoping & Chen, Ya & Liang, Liang & Yu, Yugang & Wang, Ke, 2020. "Coordination of a fashion supply chain with demand disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
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    11. Wei, Ying & Xiong, Sijia & Li, Feng, 2019. "Ordering bias with two reference profits: Exogenous benchmark and minimum requirement," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 229-250.
    12. Choi, Tsan-Ming, 2019. "Blockchain-technology-supported platforms for diamond authentication and certification in luxury supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 17-29.
    13. Qiu, Ruozhen & Sun, Yue & Zhou, Hongcheng & Sun, Minghe, 2023. "Dynamic pricing and quick response of a retailer in the presence of strategic consumers: A distributionally robust optimization approach," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1270-1298.
    14. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
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    16. Gurinder Kaur & Ronald Kander, 2023. "Supply Chain Simulation of Manufacturing Shirts Using System Dynamics for Sustainability," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    17. Govindan, Kannan & Rajeev, A. & Padhi, Sidhartha S. & Pati, Rupesh K., 2020. "Supply chain sustainability and performance of firms: A meta-analysis of the literature," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    18. Choi, Tsan-Ming & Guo, Shu & Luo, Suyuan, 2020. "When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
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    20. Shi, Chun-lai & Geng, Wei, 2021. "To introduce a store brand or not: Roles of market information in supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    21. Choi, Tsan-Ming & Liu, Na, 2019. "Optimal advertisement budget allocation and coordination in luxury fashion supply chains with multiple brand-tier products," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 95-107.
    22. Xu, Jian & Duan, Yongrui, 2020. "Pricing, ordering, and quick response for online sellers in the presence of consumer disappointment aversion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).

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