Journal of Theoretical and Applied Electronic Commerce Research

With worsening energy and environmental issues, new energy vehicles (NEVs) have emerged as the future of the automotive industry, as they aim to address the high energy consumption and carbon emissions of traditional fuel vehicles. However, due to the industry’s short development history, limited available data, and incomplete supporting systems, most existing NEV research focuses on theoretical analysis, which hinders the achievement of accurate sales predictions. Today, online reviews influence consumer decisions and thus provide a new perspective for sales forecasting. Based on consumer behavior theory and neural network principles, our research selects factors influencing NEV sales (covering economics, technological, policy, and consumer dimensions, including preprocessed crawled online reviews), constructs an index system screened via grey relational analysis, and establishes five models (SARIMA, GRU, Seq2Seq, Attention-GRU, Attention-Seq2Seq) for training and testing. The study supports the use of online reviews in NEV sales prediction and proves that the model based on cutting-edge technology of Attention-Seq2Seq can outperform the other four methods presented above. Through this, the current contributions advance marketing innovation by helping NEV stakeholders understand relevant information using a predictive model from online reviews, which leads to precise product improvement and optimal distribution of resources as well as precise adoption of marketing strategies.

This study examines if peers’ digital transformation affects focal firms’ greenwashing, addressing the literature gap of insufficient focus on industry interactions via institutional theory. Using a sample of Chinese listed companies, the paper conducts an empirical analysis and finds that the digital transformation of peer enterprises significantly inhibits the greenwashing behavior of focal enterprises. This inhibitory effect is realized through three key mechanisms: the competitive peer spillover effect of digital transformation, the suppression of peer spillover in greenwashing behavior, and the convergence effect of industry-wide information disclosure quality. Moreover, this inhibitory effect is particularly pronounced in industries characterized by low short-termism tendencies, high technology intensity, high pollution levels, and fierce competition. Further research confirms that the initial emergence of highly digitalized enterprises in an industry triggers a “catfish effect,” and once the proportion of digitalized enterprises exceeds 50%, the inhibitory effect on greenwashing behavior becomes significantly stronger.

Influencer marketing practice predominantly favors same-niche partnerships; however, industry evidence suggests that unconventional pairings can sometimes outperform traditional matches, highlighting a knowledge gap regarding how collaboration structure influences consumer response. Drawing on Optimal Distinctiveness Theory, this research examines whether cross-niche collaborations enhance brand evaluations by fulfilling concurrent desires for inclusion and differentiation. Four controlled experiments (total N = 1482) employing Instagram-style stimuli and behavioral intention measures compare same- versus cross-niche alliances while manipulating or measuring consumers’ need for uniqueness and category expertise. Across studies, cross-niche (versus same-niche) collaborations consistently improve brand attitude, an effect fully mediated by heightened perceptions of brand innovation. Moderation analyses reveal that this indirect effect is amplified among consumers high in need for uniqueness but attenuated—and in some cases reversed—among category experts, who tend to prefer the depth signaled by same-niche partnerships. Alternative explanations, including curiosity, source credibility, and message liking, are empirically ruled out. These findings extend Optimal Distinctiveness Theory to influencer branding by identifying collaboration type as a market-level cue signaling innovation while refining influencer effectiveness models through a dual-contingency framework that incorporates motivational and knowledge-based audience factors. From a managerial perspective, the results suggest deploying cross-niche alliances when targeting novelty-seeking or novice segments while preserving niche purity or offering technical justification when addressing expert consumers, thereby aligning collaboration strategies with audience composition.