IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0305639.html
   My bibliography  Save this article

Revolutionizing cross-border e-commerce: A deep dive into AI and big data-driven innovations for the straw hat industry

Author

Listed:
  • Junjie Dai
  • Xiaoyan Mao
  • Pengyue Wu
  • Huijie Zhou
  • Lei Cao

Abstract

This paper investigates the impact of artificial intelligence (AI) and big data analytics on optimizing cross-border e-commerce efficiency for straw hat manufacturers in Zhejiang Province, China. It identifies market and consumer demand trends through machine learning analysis of comprehensive e-commerce data and leverages generative AI to revolutionize production and marketing processes. The integration of AI-generated content (AIGC) technology facilitates streamlined design-to-production cycles and rapid adaptation to market changes and consumer feedback. Findings demonstrate that the application of AI and big data significantly enhances market responsiveness and sales performance for straw hat enterprises in cross-border e-commerce. This research contributes a novel framework for employing AI and big data to navigate the complexities of international commerce, providing strategic insights for small and micro enterprises seeking to expand their global market footprint.

Suggested Citation

  • Junjie Dai & Xiaoyan Mao & Pengyue Wu & Huijie Zhou & Lei Cao, 2024. "Revolutionizing cross-border e-commerce: A deep dive into AI and big data-driven innovations for the straw hat industry," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-28, December.
    • Handle: RePEc:plo:pone00:0305639
    DOI: 10.1371/journal.pone.0305639
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305639
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0305639&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0305639?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    2. Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
    3. Gomez-Herrera, Estrella & Martens, Bertin & Turlea, Geomina, 2014. "The drivers and impediments for cross-border e-commerce in the EU," Information Economics and Policy, Elsevier, vol. 28(C), pages 83-96.
    4. Bing Han & Muhammad Rizwanullah & Yane Luo & Rahim Atif, 2024. "The role of cross-border E-commerce on the export of goods and services," Electronic Commerce Research, Springer, vol. 24(2), pages 1367-1384, June.
    5. Sandesh Achar, 2019. "Early Consequences Regarding the Impact of Artificial Intelligence on International Trade," American Journal of Trade and Policy, Asian Business Consortium, vol. 6(3), pages 119-126.
    6. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    7. Jia Zhuo & Zaoli Yang, 2022. "Consumer Demand Behavior Mining and Product Recommendation Based on Online Product Review Mining and Fuzzy Sets," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, October.
    8. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    2. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    3. Subodha Kumar & Rakesh R. Mallipeddi, 2022. "Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4488-4500, December.
    4. Rafael Andersson Lipcsey, 2023. "The Transformative Effects of AI on International Economics," Papers 2312.06679, arXiv.org.
    5. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    6. 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).
    7. Maria De‐Arteaga & Stefan Feuerriegel & Maytal Saar‐Tsechansky, 2022. "Algorithmic fairness in business analytics: Directions for research and practice," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3749-3770, October.
    8. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    9. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    10. Emilia Vann Yaroson & Soumyadeb Chowdhury & Sachin Kumar Mangla & Prasanta Kumar Dey, 2024. "Unearthing the interplay between organisational resources, knowledge and industry 4.0 analytical decision support tools to achieve sustainability and supply chain wellbeing," Annals of Operations Research, Springer, vol. 342(2), pages 1321-1368, November.
    11. Liu, Weihua & George Shanthikumar, J. & Tae-Woo Lee, Paul & Li, Xiang & Zhou, Li, 2021. "Special issue editorial: Smart supply chains and intelligent logistics services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    12. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    13. Görkem Sariyer & Mustafa Gokalp Ataman & Sachin Kumar Mangla & Yigit Kazancoglu & Manoj Dora, 2023. "Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations," Annals of Operations Research, Springer, vol. 328(1), pages 1073-1103, September.
    14. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.
    15. Julian Senoner & Bernhard Kratzwald & Milan Kuzmanovic & Torbjørn H. Netland & Stefan Feuerriegel, 2023. "Addressing distributional shifts in operations management: The case of order fulfillment in customized production," Production and Operations Management, Production and Operations Management Society, vol. 32(10), pages 3022-3042, October.
    16. Feifei Han & Jiao Guan, 2024. "Investment in Data Analytics with Manufacturer Encroachment," Mathematics, MDPI, vol. 12(9), pages 1-18, April.
    17. Yan, Ran & Liu, Yan & Wang, Shuaian, 2024. "A data-driven optimization approach to improving maritime transport efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    18. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    19. Robert P. Rooderkerk & Nicole DeHoratius & Andrés Musalem, 2022. "The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3727-3748, October.
    20. Zhe (James) Zhang & Shivendu Shivendu & Peng Wang, 2021. "Is Investment in Data Analytics Always Profitable? The Case of Third‐Party‐Online‐Promotion Marketplace," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2321-2337, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0305639. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.