Machine learning in explaining nonprofit organizations’ participation: a driving factors analysis approach
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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.
KeywordsMachine learning Nonprofit organization Smart city Public satisfaction
This work is supported by National Natural Science Foundation under Grant No. 71620107002 and National Social Science Foundation under Grant No. 91538204.
Complice with ethical standards
Conflict of interest
The authors have no competing interests.
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