Business model innovation is crucial for enhancing company performance. This study aims to investigate the relationship between the six dimensions of sustainable business model innovation and company performance among Chinese electric vehicle brands. A structural equation model is constructed based on a comprehensive literature review and hypothesis development. Using PLS-SEM, this study empirically analyzes questionnaire data collected from the top 12 electric vehicle brands in China to explore the relationship between these six core dimensions and company performance. The results indicate that innovation in “value proposition to customers”, value creation, value delivery, and “value of residual” have a significantly positive impact on the performance of Chinese electric vehicle brands. However, value capture innovation and “value of after-sales service” innovation were not found to be statistically significant. This paper provides an in-depth analysis of the mechanism through which sustainable business model innovation impacts company performance, enriching the theoretical foundation of academic research in this field and broadening its practical applications in management.

Strategic management and sustainable business model innovation (SBMI) are widely recognized important firm performance. This study develops a theoretical framework that integrates competitive strategy, SBMI, and performance, with SBMI conceptualized through the multidimensional 6V model. While the model is broadly applicable across industries, this study focuses on the electric vehicle (EV) sector in China as an empirical case to test the proposed relationships. Using survey data from 261 managerial respondents across nine major Chinese EV brands, PLS-SEM is employed to examine both direct and mediated effects of differentiation and cost leadership strategies. The results confirm that both strategies positively influence firm performance; however, the mediating roles of SBMI dimensions vary. This study contributes to the literature by demonstrating the explanatory power of the 6V-SBMI framework and offering practical insights for firms seeking to align strategic choices with sustainability-oriented innovation.

With the increasing challenges of default risk in the auto loan market, traditional risk assessment methods show significant limitations in coping with the rapidly changing market environment. This study proposes an innovative deep learning architecture—Deep Capsule Attention Network (DECAF)—which integrates capsule networks with self-attention mechanisms, effectively enhancing the intelligent detection capability for auto loan default risks. Experimental evaluations of the model’s performance in both conventional and high-risk scenarios demonstrate that the DECAF model achieved an area under the curve (AUC) of 0.924 in the conventional scenario and maintained a high performance of 0.850 in high-risk scenarios. Additionally, DECAF exhibits a significant advantage in terms of reducing false positives (FPR < 0.2), effectively minimizing misjudgments. More importantly, the study reveals that the DECAF model retains high stability even under severe market fluctuations, with only a 7.4% performance drop. These results provide new insights and solutions for financial institutions to optimize risk control strategies in dynamic market environments.