Hong-Yu Shao , Shi-Miao Zhang , Wei Guo , Jiang Sun , Lei Wang , Zhi-Xing Chang
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引用次数: 0
Abstract
In the rapidly evolving technological market environment, the cognitive gap between users and product management teams is increasingly widening, and the design conflicts arising during product development significantly hinder the speed and accuracy of product iterations. Meanwhile, with the advancement of intelligent technologies, online communities have become key platforms where users express their experiences and perceptions of products. Therefore, efficiently uncovering users’ implicit cognition during the design process and analyzing the cognitive conflicts between users and product management teams are of great importance for improving product design and enhancing user satisfaction. Currently, product design and optimization rarely address conflict issues between users and product management teams from a cognitive science perspective. In this study, we propose a discriminative model based on design cognitive conflicts and utilize data from China’s largest automotive online community—Autohome—for analysis. By constructing an ICA model (Importance-Conflict Analysis Model), we identify key product attributes requiring focused improvement, and subsequently generate optimal product improvement solutions based on the CPT model (Cumulative Prospect Theory Model). Theoretically, this study defines the concept of design cognitive conflict and addresses the lack of focus on the perspective of product management teams in traditional product design. Practically, the research provides guidance for product management teams in forming psychological expectations and conducting effective product design and improvement throughout the design process.
期刊介绍:
Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.