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Data mining of customer choice behavior in internet of things within relationship network

Author

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  • Yan, Yuwei
  • Huang, Chuanchao
  • Wang, Qian
  • Hu, Bin

Abstract

Internet of Things has changed the relationship between traditional customer networks, and traditional information dissemination has been affected. Smart environment accelerates the changes in customer behaviors. Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298 customers' click browsing records as training data, and collected 50 customers who used the platform for the first time as research objects. and use the smart customer relationship network correspond to cyber intelligence to build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement over the traditional equal assignment method. In addition, customer's consumer experience can be enhanced with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational fusion technologies.

Suggested Citation

  • Yan, Yuwei & Huang, Chuanchao & Wang, Qian & Hu, Bin, 2020. "Data mining of customer choice behavior in internet of things within relationship network," International Journal of Information Management, Elsevier, vol. 50(C), pages 566-574.
    • Handle: RePEc:eee:ininma:v:50:y:2020:i:c:p:566-574
    DOI: 10.1016/j.ijinfomgt.2018.11.013
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    Citations

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    Cited by:

    1. Sudesh Sheoran & Sanket Vij, 2023. "A Consumer-Centric Paradigm Shift in Business Environment with the Evolution of the Internet of Things: A Literature Review," Vision, , vol. 27(4), pages 431-442, August.
    2. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    3. Pantano, Eleonora & Viassone, Milena & Boardman, Rosy & Dennis, Charles, 2022. "Inclusive or exclusive? Investigating how retail technology can reduce old consumers’ barriers to shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    4. Baltuttis, Dennik & Häckel, Björn & Jonas, Claudius Michael & Oberländer, Anna Maria & Röglinger, Maximilian & Seyfried, Johannes, 2022. "Conceptualizing and Assessing the Value of Internet of Things Solutions," Journal of Business Research, Elsevier, vol. 140(C), pages 245-263.
    5. Gheorghe Zaman & Nicoleta Valentina Florea & Constantin Aurelian Ionescu & Dan Marius Coman & Doina Constanta Mihai & Nicoleta Luminita Gudanescu Nicolau, 2022. "Mathematical Model for Determining Costs of Unsatisfied Customers of HoReCa Industry," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 24(59), pages 268-268.

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