Logo image
Machine learning in explaining nonprofit organizations’ participation: a driving factors analysis approach
Journal article   Open access   Peer reviewed

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

Zhanxue Gong, Zhanxue Li, Jiawen Liu and Yeming Gong
Neural Computing and Applications, Vol.31(12), pp.8267-8277
01/12/2019

Abstract

Machine learning Artificial Intelligence AI-based Management
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.
pdf
NCA_Gong_forthDownloadView
Open Access
pdf
NCA_Gong_201912
Restricted Access

Metrics

7 File views/ downloads
23 Record Views

Details

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this contribution

Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.13 Telecommunications
4.13.807 IoT and Edge Computing
Web of Science research areas
Computer Science, Artificial Intelligence
Logo image