IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0238152.html
   My bibliography  Save this article

Assessing the application of big data technology in platform business model: A hierarchical framework

Author

Listed:
  • Xiaomin Du
  • Yang Gao
  • Linlin Chang
  • Xiangxiang Lang
  • Xingqun Xue
  • Datian Bi

Abstract

This research aims to create a hierarchical framework for the development of a platform business model based on big data. However, this hierarchical framework must consider unnecessary attributes and the interrelationships between the aspects and the criteria. Hence, fuzzy set theory is used for screening out the unnecessary attributes, a decision-making and trial evaluation laboratory (DEMATEL) is proposed to manage the complex interrelationships among the aspects and attributes, and interpretive structural modeling (ISM) is used to divide the hierarchy and finally construct a hierarchical framework. The results reveal that (1) value proposition and community building in value production are fundamental links; (2) information technology and information management in value production are technical supports; (3) customer development in value marketing is the power source; and (4) value acquisition is the last link, which is established on the basis of and influenced by value marketing and value network. This hierarchical framework aims to guide the platform toward the application of big data. This study also proposes engagement of stakeholders for promoting value creation and establishing a sound business model from multiple levels and links.

Suggested Citation

  • Xiaomin Du & Yang Gao & Linlin Chang & Xiangxiang Lang & Xingqun Xue & Datian Bi, 2020. "Assessing the application of big data technology in platform business model: A hierarchical framework," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
    • Handle: RePEc:plo:pone00:0238152
    DOI: 10.1371/journal.pone.0238152
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238152
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0238152&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0238152?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. Jun, Seung-Pyo & Park, Do-Hyung & Yeom, Jaeho, 2014. "The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 237-253.
    2. 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.
    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. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    3. 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).
    4. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    5. Zhang, Abraham & Wang, Jason X. & Farooque, Muhammad & Wang, Yulan & Choi, Tsan-Ming, 2021. "Multi-dimensional circular supply chain management: A comparative review of the state-of-the-art practices and research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    6. Lei Wang & Ram Gopal & Ramesh Shankar & Joseph Pancras, 2022. "Forecasting venue popularity on location‐based services using interpretable machine learning," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2773-2788, July.
    7. Suyuan Luo & Tsan‐Ming Choi, 2022. "E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2107-2126, May.
    8. Fredström, Ashkan & Parida, Vinit & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak, 2022. "What is the Market Value of Artificial Intelligence and Machine Learning? The Role of Innovativeness and Collaboration for Performance," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    9. Srinivas, Sharan & Ramachandiran, Surya & Rajendran, Suchithra, 2022. "Autonomous robot-driven deliveries: A review of recent developments and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    10. Lee, Junmin & Kim, Keungoui & Kim, Jiyong & Hwang, Junseok, 2022. "The relationship between shared mobility and regulation in South Korea: A system dynamics approach from the socio-technical transitions perspective," Technovation, Elsevier, vol. 109(C).
    11. Fink, Alexander A. & Klöckner, Maximilian & Räder, Tobias & Wagner, Stephan M., 2022. "Supply chain management accelerators: Types, objectives, and key design features," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    12. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    13. Liu, Weihua & Wang, Siyu & Lin, Yong & Xie, Dong & Zhang, Jiahui, 2020. "Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    14. Guangmei Cao & Yuesen Wang & Honghu Gao & Hao Liu & Haibin Liu & Zhigang Song & Yuqing Fan, 2023. "Coordination Decision-Making for Intelligent Transformation of Logistics Services under Capital Constraint," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    15. Andriosopoulos, Kostas & Bigerna, Simona & Bollino, Carlo Andrea & Micheli, Silvia, 2018. "The impact of age on Italian consumers' attitude toward alternative fuel vehicles," Renewable Energy, Elsevier, vol. 119(C), pages 299-308.
    16. 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.
    17. Havinga, Maik J.A. & de Jonge, Bram, 2020. "Condition-based maintenance in the cyclic patrolling repairman problem," International Journal of Production Economics, Elsevier, vol. 222(C).
    18. Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    19. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    20. Ma, Deqing & Hu, Jinsong, 2022. "The optimal combination between blockchain and sales format in an internet platform-based closed-loop supply chain," International Journal of Production Economics, Elsevier, vol. 254(C).

    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:pone00:0238152. 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.