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Research on Customer Perceived Value Evaluation of New Chinese-Style Clothing Based on PSO-BP Neural Network.
- Source :
- Scientific Programming; 10/11/2022, p1-13, 13p
- Publication Year :
- 2022
-
Abstract
- In the current era that consumers pursue personalized experience, in order to optimize the customer experience of new Chinese-style clothing products and improve the evaluation procedures of new Chinese-style clothing products, based on the theory of customer perceived value, this paper constructs the evaluation index and evaluation model of new Chinese-style clothing customer perceived value. This study is divided into three stages: firstly, through literature research and interview, thirty-seven elements of the evaluation index of customer perceived value of new Chinese-style clothing are defined; secondly, through questionnaire survey and exploratory factor analysis, seven dimensions of the evaluation index of the customer perceived value of new Chinese-style clothing were extracted, which were cultural and educational value, aesthetic value, creative value, green value, engineering value, social value, and quality value, respectively; thirdly, we propose a PSO-BP neural network to evaluate the customer perceived value of new Chinese-style clothing, and we choose twenty-two and eight new Chinese-style clothing as training samples and test samples, respectively. The experimental results show that the PSO-BP neural network can accurately evaluate the customer perceived value of new Chinese-style clothing, and its error is controlled by 2.5% compared with the traditional BP neural network. The research results show that enterprises can improve the quality of product design through the new Chinese-style clothing customer perceived value evaluation indicators and models, and then improve their sustainable competitive advantage, so as to achieve the sustainable development of the new Chinese-style clothing industry ultimately. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10589244
- Database :
- Complementary Index
- Journal :
- Scientific Programming
- Publication Type :
- Academic Journal
- Accession number :
- 159720872
- Full Text :
- https://doi.org/10.1155/2022/9273429