Research on the Purchase Intention of Children's Educational Products Based on Feedforward Neural Network and Factor Analysis

Ningyan Chen

doi:10.54097/ijeh.v5i2.1986

Authors

Ningyan Chen

DOI:

https://doi.org/10.54097/ijeh.v5i2.1986

Keywords:

Sales mechanism, Children's edu cation products, Purchase intention, Feedforward neural network, Factor analysis.

Abstract

To optimize the current marketing mechanism, this paper first discusses the basic concept of children's educational products and purchase intention and then discusses the influencing factors of product purchase intention. Finally, a comprehensive research method of feedforward neural network and factor analysis is designed to study the purchase intention of children's educational products. The results show that in terms of interest, consumers pay more attention to consumer products based on the cultivation of learning interest, with the highest weight proportion of approximately 72%. It can be seen that the research method of this paper can clearly understand the research results of consumers' purchasing intention. This research not only provides a reference for promoting the sales of children's educational products but also makes a contribution to market development.

Downloads

Download data is not yet available.

References

Kim D Y, Chun S H, Jung Y, et al. Phthalate plasticizers in children's products and estimation of exposure: Importance of migration rate. International journal of environmental research and public health, 2020, 17(22): 8582.

Wang D, Fang T. Forecasting Crude Oil Prices with a WT-FNN Model. Energies, 2022, 15(6): 1955.

Zhao L, Luo S, Yang G, et al. Chaos analysis and stability control of the MEMS resonator via the type-2 sequential FNN. Microsystem Technologies, 2021, 27(1): 173-182.

Sahoo A, Ghose D K. Imputation of missing precipitation data using KNN, SOM, RF, and FNN. Soft Computing, 2022, 26(12): 5919-5936.

Dockrell J E, Connelly V, Walter K, et al. Assessing children's writing products: the role of curriculum based measures. British Educational Research Journal, 2015, 41(4): 575-595.

Pan H, Zhou H. Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce. Electronic Commerce Research, 2020, 20(2): 297-320.

Chen Y, Wu Q, Shao L. Urban cold-chain logistics demand predicting model based on improved neural network model. International Journal of Metrology and Quality Engineering, 2020, 11: 5.

Chi T, Ganak J, Summers L, et al. Understanding perceived value and purchase intention toward eco- friendly athleisure apparel: Insights from US millennials. Sustainability, 2021, 13(14): 7946.

Li J, Cui T, Yang K, et al. Demand forecasting of e-commerce enterprises based on horizontal federated learning from the perspective of sustainable development. Sustainability, 2021, 13(23): 13050.

Akkaya M. Understanding the impacts of lifestyle segmentation & perceived value on brand purchase intention: An empirical study in different product categories. European research on management and business economics, 2021, 27 (3) : 100155.

Aldakheel F, Satari R, Wriggers P. Feed-forward neural networks for failure mechanics problems. Applied Sciences, 2021, 11 (14) : 6483.

Sales L F, Pereira A, Vieira T, et al. Multimodal deep neural networks for attribute prediction and applications to e-commerce catalogs enhancement. Multimedia Tools and Applications, 2021, 80(17): 25851-25873.

Zhang Z, Sun W, Yu Y. Research on the development of marine regional E-commerce based on the analysis of cloud computing and grey prediction method. Journal of Coastal Research, 2020, 115(SI): 333-337.