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

Zhanxue Gong, Xiyuan Li, Jiawen Liu () and Yeming Gong ()
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Zhanxue Gong: Wuhan University [China]
Xiyuan Li: Wuhan University [China]
Jiawen Liu: HUST - Huazhong University of Science and Technology [Wuhan]
Yeming Gong: EM - EMLyon Business School

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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.

Keywords: Machine learning; Artificial Intelligence; AI-based Management; Machine Learning; non-profit organization; smart city; public satisfaction (search for similar items in EconPapers)
Date: 2019-12-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
Note: View the original document on HAL open archive server: https://hal.science/hal-02880932
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Neural Computing and Applications, 2019, 31 (12), 8267-8277 p. ⟨10.1007/s00521-018-3858-6⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02880932

DOI: 10.1007/s00521-018-3858-6

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