Fine-Grained Attribute Analysis of User Satisfaction for NEVs: An Interpretable Importance–Performance Analysis Based on Online Reviews

The rapid growth of the new energy vehicle (NEV) industry has intensified competition among manufacturers, making user satisfaction a strategic priority. Importance–performance analysis (IPA) provides a viable framework for user satisfaction analysis by examining attribute importance and performance from user reviews. However, existing attribute importance assessment methods lack transparency, and IPA outputs remain coarse-grained. This study aims to develop an improved IPA framework that equips manufacturers with a data-driven approach to product improvement, derived from analysing user satisfaction through consumer reviews. To achieve this, a constrained priority linear programming model is proposed to enhance interpretability in importance measures, and the traditional IPA framework is extended by further diagnosing underperforming attributes through the integration of negative review analysis and the chi-square test, thereby deriving attribute-specific improvement directions. Empirical analysis of 8424 reviews from the Autohome platform demonstrates the effectiveness of the proposed method, and a cross-platform analysis of 4000 reviews from PCauto further confirms its robustness. Results identify that endurance and handling are priority attributes requiring resource allocation, followed by intelligence, interior, and comfort as attributes that need to maintain their advantages.