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Sustainable Investment in a Supply Chain in the Big Data Era: An Information Updating Approach

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  • Yanping Cheng

    (Library, Central China Normal University, Wuhan 430079, China)

  • Yunjuan Kuang

    (Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China)

  • Xiutian Shi

    (School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Ciwei Dong

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

Abstract

We are now living in the big data era, where firms can improve their decision makings by adopting big data technology to utilize mass information. To explore the effects of the big data technology, we build an analytical model to study the sustainable investment in a supply chain, consisting of one manufacturer and one retailer, by using Bayesian information updating approach. We derive the optimal sustainable investment level for the manufacturer and the optimal order quantity for the retailer. Comparing the results with and without the big data technology, we find that whether the manufacturer should make more sustainable investment when the retailer adopts the big data technology depends on the service level at the retailer side. Interestingly, it is not always optimal for the retailer to adopt the big data technology. We identify the conditions under which the manufacturer and retailer are better off with the big data technology. In addition, we investigate the impact of the number of observations regarding the market information and find that the optimal decisions and profits increase in the number of the observations, if and only if the service level is low.

Suggested Citation

  • Yanping Cheng & Yunjuan Kuang & Xiutian Shi & Ciwei Dong, 2018. "Sustainable Investment in a Supply Chain in the Big Data Era: An Information Updating Approach," Sustainability, MDPI, vol. 10(2), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:403-:d:130194
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    Cited by:

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    3. Wenbing Luo & Ziyan Tian & Shihu Zhong & Qinke Lyu & Mingjun Deng, 2022. "Global Evolution of Research on Sustainable Finance from 2000 to 2021: A Bibliometric Analysis on WoS Database," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
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    6. Andreea Chițimiea & Mihaela Minciu & Andreea-Mariana Manta & Carmen Nadia Ciocoiu & Cristina Veith, 2021. "The Drivers of Green Investment: A Bibliometric and Systematic Review," Sustainability, MDPI, vol. 13(6), pages 1-25, March.
    7. Ciwei Dong & Qingying Li & Bin Shen & Xun Tong, 2019. "Sustainability in Supply Chains with Behavioral Concerns," Sustainability, MDPI, vol. 11(15), pages 1-7, July.
    8. Sebastiano Cupertino & Gianluca Vitale & Angelo Riccaboni, 2018. "L?impatto dei Big Data sulle attivit? di pianificazione & controllo aziendali: In caso di studio di una PMI agricola Italiana," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(3), pages 59-86.
    9. Shi, Xiutian & Shen, Bin, 2019. "Product upgrading or not: R&D tax credit, consumer switch and information updating," International Journal of Production Economics, Elsevier, vol. 213(C), pages 13-22.
    10. Lei Xu & Runpeng Gao & Yu Xie & Peng Du, 2019. "To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain," Sustainability, MDPI, vol. 11(8), pages 1-14, April.

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