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

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  • 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
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    References listed on IDEAS

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    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.
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