Design Model of Cultural and Creative Products Using User Perception Demand Mining
People are becoming more and more aware of the value of design throughout a product’s entire life cycle as a result of the fierce competition for industrial products that exists today. The life of a product involves its design, manufacture, sale, and use, and how well these links are managed determi...
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Published in | Computational intelligence and neuroscience Vol. 2022; pp. 1 - 12 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
United States
Hindawi
21.08.2022
John Wiley & Sons, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | People are becoming more and more aware of the value of design throughout a product’s entire life cycle as a result of the fierce competition for industrial products that exists today. The life of a product involves its design, manufacture, sale, and use, and how well these links are managed determines the product’s positioning in terms of value in the eyes of consumers. The key to the functional integration of the design is monitoring the entire process and applying the user’s emotional needs. A useful tool for assessing users’ emotional needs is the perceptual image of a product. An artificial intelligence-driven method for product perceptual design is proposed, and its efficacy is demonstrated by the design of an optometer. This method addresses the issues of incomplete measurement and insufficient sample collection in the traditional users’ perceptual cognition measurement. The findings demonstrate that extracting users’ perceptual cognition through text mining can assist designers in better understanding users’ perceptual needs, resulting in designed products that are more likely to meet users’ expectations for satisfaction. A design approach that can increase users’ psychological acceptance of products and boost their competitiveness is the perceptual design method, which combines human and artificial intelligence. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Correction/Retraction-3 Academic Editor: Zhao Kaifa |
ISSN: | 1687-5265 1687-5273 1687-5273 |
DOI: | 10.1155/2022/6339184 |