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Machine learning in explaining nonprofit organizations’ participation : a driving factors analysis approach

Author

Listed:
  • Zhanxue Gong

    (EM - EMLyon Business School)

  • Xiyuan Li
  • Jiawen Liu
  • Yeming Gong

Abstract

The construction of smart cities requires the participation of nonprofit organizations, but there are still some problems in the analysis of driving factors of participation. Based on this, using the structural equation model as the research method, a public satisfaction relationship model, based on the machine learning, for nonprofit organizations participating in the construction planning of smart cities was constructed in this study. At the same time, corresponding assumptions are set, and data are collected through questionnaires. Afterward, the Likert tenth scale was used to score questionnaire questions, and deep learning was conducted in conjunction with the model. The research shows that the model established in this study has good analytical results and has certain practical effects. It can provide suggestions for optimization and can provide theoretical references for subsequent research.

Suggested Citation

  • Zhanxue Gong & Xiyuan Li & Jiawen Liu & Yeming Gong, 2019. "Machine learning in explaining nonprofit organizations’ participation : a driving factors analysis approach," Post-Print hal-02880932, HAL.
    • Handle: RePEc:hal:journl:hal-02880932
    DOI: 10.1007/s00521-018-3858-6
    Note: View the original document on HAL open archive server: https://hal.science/hal-02880932
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    References listed on IDEAS

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    1. M. Mazhar Rathore & Anand Paul & Awais Ahmad & Gwanggil Jeon, 2017. "IoT-Based Big Data: From Smart City towards Next Generation Super City Planning," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 28-47, January.
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    Cited by:

    1. Ana De Las Heras & Amalia Luque-Sendra & Francisco Zamora-Polo, 2020. "Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era," Sustainability, MDPI, vol. 12(22), pages 1-25, November.

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    More about this item

    Keywords

    Machine learning; Artificial Intelligence; AI-based Management; Machine Learning; non-profit organization; smart city; public satisfaction;
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