She Wants Safety, He Wants Speed: A Mixed-Methods Study on Gender Differences in EV Consumer Behavior

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis.