Search Results (117)

Rural e-commerce has spurred profound changes in rural production and living patterns. Taking the policy of E-commerce Entering Rural Areas as a quasi-natural experiment, based on the data from fixed observation points in rural China, this paper examines how rural e-commerce development affects rural households’ green production practices. The results show that (1) while rural e-commerce has generally led to a 5% increase in farmers’ chemical fertilizer use, its promoting effect on farmers’ chemical fertilizer input has been gradually weakening over time. (2) Crop planting types moderate the relationship between rural e-commerce and farmers’ fertilizer input behaviors. For farmers mainly planting food crops, rural e-commerce increases their chemical fertilizer use by 6.87%, while for those mainly planting cash crops, rural e-commerce reduces their chemical fertilizer use by 4.25%. (3) Mechanism analysis reveals that service outlet construction and e-commerce training for farmers are the main channels through which rural e-commerce drives farmers to increase fertilizer input, while brand cultivation is a channel through which rural e-commerce inhibits farmers’ fertilizer input, and this influence channel only exists among farmers mainly planting cash crops. Full article

E-commerce live streaming is increasingly constrained by the “anchor dilemma” of talent shortages and reputational volatility. Virtual anchors are viewed as a critical nexus for intelligent and sustainable e-commerce transformation, offering scalable and low-carbon potential. Yet, their user experience and perception remain underexplored. Methodologically, this study adopts a mixed empirical design combining literature review, expert interviews, and a structured questionnaire survey (N = 309), followed by reliability testing, paired-sample t-tests, and Importance–Performance Analysis (IPA) to assess user expectations and perceptions. The integrated analysis resulted in a framework of fourteen evaluative attributes, within which spectacle and cross-platformity emerged as distinguishable dimensions observed in participants’ assessments. The results show that expectations (M = 4.41) significantly exceed perceptions (M = 3.74), with all 14 importance–performance gaps reaching significance. Interactivity, professionalism, and technological maturity emerged as priority areas for improvement, while spectacle and novelty were confirmed as key advantages, and credibility and emotional bonding outperformed expectations. Based on these findings, a phased strategy is proposed: short-term optimization of interaction and knowledge support, mid-term development of human–AI collaboration and platform adaptability, and long-term establishment of governance and commercialization ecosystems. The study enriches virtual anchor research and highlights that enhancing core competencies is essential to transform novelty into enduring sales and brand equity, providing a practical pathway for e-commerce’s intelligent and sustainable transformation. Full article

With the rapid advancement of virtual human technology and live-streaming e-commerce, virtual anchors have increasingly become key interactive entities in the digital economy. However, emerging issues such as fake reviews, abnormal tipping, and illegal transactions pose significant threats to platform financial security and user privacy. To address these challenges, a multimodal emotion–finance fusion security recognition framework (MSF-Net) is proposed, which integrates visual, audio, textual, and financial transaction signals to achieve cross-modal feature alignment and multi-signal risk modeling. The framework consists of three core modules: the multimodal alignment transformer (MAT), the fake review detection (FRD) module, and the multi-signal fusion decision module (MSFDM), enabling deep integration of semantic consistency modeling and emotion–behavior collaborative recognition. Experimental results demonstrate that MSF-Net achieves superior performance in virtual live-streaming financial security detection, reaching a precision of 0.932, a recall of 0.924, an F1-score of 0.928, an accuracy of 0.931, and an area under curve (AUC) of 0.956, while maintaining a real-time inference speed of 60.7 FPS, indicating outstanding precision and responsiveness. The ablation experiments further verify the necessity of each module, as the removal of any component leads to an F1-score decrease exceeding 4%, confirming the structural validity of the model’s hierarchical fusion design. In addition, a lightweight version of MSF-Net was developed through parameter distillation and quantization pruning techniques, achieving real-time deployment on mobile devices with an average latency of only 19.4 milliseconds while maintaining an F1-score of 0.923 and an AUC of 0.947. The results indicate that MSF-Net exhibits both innovation and practicality in multimodal deep fusion and security risk recognition, offering a scalable solution for intelligent risk control in data-driven artificial intelligence applications across financial and virtual interaction domains. Full article

With the rapid development of live streaming e-commerce, opening live streaming sales channels for online product sales has become an important strategic priority for enterprises. This paper examines information technology-driven operational decisions in live streaming e-commerce supply chains, including sales mode selection, technology investment, and information sharing. We develop game-theoretic models for reselling and agency selling modes under three scenarios: no technology investment, investment without information sharing, and investment with information sharing. Our findings show that under the reselling mode, e-tailers tend to accept information sharing when the live streaming creates high extra value or when the market demand is underestimated. Under the agency mode, e-tailers usually reject sharing. Regarding technology investment, platforms are motivated to invest under the reselling mode when the market is underestimated regardless of sharing; in contrast, under the agency mode, platforms only invest when e-tailers accept sharing and the market is severely underestimated, the sharing fee is moderate, and the agency fee and hassle cost are low. A win-win outcome arises when the agency fee is low and live streaming generates low extra value with high hassle costs, making the agency optimal for both e-tailers and platforms; conversely, when the agency fee is high and live streaming generates high extra value with low hassle costs, the reselling becomes optimal. This study provides important strategic guidance and policy insights for supply chain members, enabling firms to make better decisions and thus gain a competitive advantage in the face of fierce market competition. Full article

Live-streaming e-commerce is a popular promotional method for attracting more customers in the era of the platform economy. This study investigates a better strategy for live-streaming sales for a manufacturer in an e-platform-based supply chain. Specifically, the manufacturer has two options—self-broadcasting or key opinion leader (KOL) live-streaming—to sell products on the e-platform, which acts as a reseller and agency seller in the dual-channel supply chain. We established three Stackelberg models for the supply chain and derived the equilibrium decisions and profits of supply-chain members under three scenarios: without live-streaming, KOL live-streaming, and manufacturer self-broadcasting. After a comparative analysis, we obtained the following key results: First, even if the influence level of the KOL is within a low range, the manufacturer should change the strategy from no live-streaming e-commerce to KOL live-streaming, with a decrease in the agency fee charged from the e-platform. However, with the continued decline in agency fee, the manufacturer should change the strategy from KOL live-streaming to self-broadcasting. Furthermore, we found a win–win zone such that both supply-chain members are better off under the KOL scenario when both the agency fee of the e-platform and the influence level of the KOL are in higher ranges. Our study not only provides a theoretical basis for the enterprise to choose the appropriate live-streaming mode but also helps it to further increase profits and reduce operational risks. In the future, we will further incorporate theoretical analyses into empirical study to discover more interesting results. Full article

Consumer digital hoarding is becoming increasingly common in agricultural live social e-commerce, where the abundance of product information, seasonal promotions, and origin-based narratives make consumers more inclined to accumulate digital content such as product links, coupons, and live-stream screenshots. This phenomenon not only affects consumers’ digital mental health, consumption behavior, and decision-making ability, but also poses challenges to agricultural merchants and platforms in terms of customer conversion, precision marketing, and supply chain management. Drawing on the SOR model and integrating construal level theory, this paper constructs a research framework to analyze the key factors influencing consumers’ willingness to digitally hoard in the context of agricultural live social e-commerce. Based on a questionnaire survey of 322 consumers, and using the Ordered Probit (O-Probit) model, the empirical results show that emotional interaction significantly influences digital hoarding intention through the chain mediating effects of emotional attachment and fear of missing out (FOMO). Furthermore, social distance and immersion serve as boundary conditions in this mechanism. Our findings not only deepen the understanding of consumer digital hoarding behavior in agricultural live e-commerce, but also provide new insights for agricultural merchants and platforms to better design interaction strategies, balance consumers’ digital accumulation with actual purchasing conversion, and enhance the efficiency of agricultural product marketing. Full article

E-commerce virtual anchors’ live-streaming sales represent a critical intersection of virtual human technology and artificial intelligence (AI), paving the way for new developments in digital marketing. Recent advancements in AI have significantly enhanced the intelligence of virtual anchors, driving increased consumer acceptance. As intelligence is a key determinant of user adoption, this study employs a mixed-methods approach combining grounded theory and quantitative analysis to conceptualize, structure, and measure virtual anchor intelligence. The grounded theory results reveal that virtual anchor intelligence encompasses multiple capabilities—manifested in guidance, recognition, analysis, and feedback during human–computer interaction, which enable effective hosting and sales performance. Four core dimensions of intelligence are identified: guidance intelligence, recognition intelligence, analysis intelligence, and feedback intelligence. Following established protocols for scale development, we constructed and validated an 18-item measurement scale, demonstrating strong reliability and validity. Empirical findings indicate that while guidance intelligence exerts an adverse effect on consumer participation, the remaining dimensions have positive effects, mediated by perceived shopping value. This study provides a comprehensive framework for understanding and measuring virtual anchor intelligence, elucidating its underlying mechanisms. The findings lay a theoretical foundation for future research on e-commerce virtual anchors but also offer practical implications for optimizing live-streaming strategies and advancing the design of virtual humans. Full article

Within the domain of live-streaming e-commerce, the application of artificial intelligence (AI) technologies presents novel growth opportunities. Live-streaming sales by AI streamers have attracted widespread attention as a new form. To promote the application of AI streamers in live-streaming e-commerce, this paper mainly addresses the following question: How do AI streamers in live-streaming e-commerce influence consumer trust and purchase intention? Based on this, guided by the stimulus–organism–response (SOR) theory, we construct a research model to study the impact of AI streamers’ characteristics and scenario fit on purchase intention through trust mediation. Subsequently, 410 valid samples of consumers who watch AI streamers promoting products are collected through an online questionnaire. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are integrated to investigate both the influence mechanisms and configurational pathways affecting purchase intention. This study reveals that AI streamers’ image characteristics (cuteness, vitality) and scenario fit are positively associated with purchase intention. However, AI streamers’ ability characteristics (professionalism, responsiveness) are not positively associated with purchase intention. In addition, consumer trust partially mediates the relationship between these key factors and purchase intention. Further, consumer innovativeness significantly negatively moderates the effect of AI streamers’ characteristics and scenario fit on consumer trust. Moreover, the influence mechanisms diverge substantially between incremental and breakthrough products. Full article

In response to the growing complexity of e-commerce warehouse management driven by the expansion of live-streaming and cross-border businesses, this study tackles the critical challenge of product demand forecasting. We propose an intelligent forecasting approach based on a multi-model fusion framework, constructing a Stacking ensemble that integrates XGBoost, LightGBM, and CatBoost as base learners. Hyperparameter optimization is systematically conducted using grid search combined with cross-validation. To account for periodic trends, seasonal fluctuations are modeled as explicit temporal features, and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is incorporated to perform joint forecasting by capturing residual time-dependent patterns. Experimental results show that the proposed fusion model consistently outperforms all individual base learners across multiple metrics, including R², RMSE, MAE, MAPE, Precision, Recall, and Accuracy. Furthermore, the mean cosine similarity of the forecast sequences reaches 0.986, underscoring both the stability of seasonal representations and the model’s robustness in capturing demand variations. This method effectively improves the accuracy of e-commerce product demand forecasts, offering reliable data support for inventory management and allocation strategies. Full article

This article focuses on the negative evaluation losses resulting from consumers’ dissatisfaction with the traceability level in agricultural product logistics activities. Based on the supply chain (SC) under the “agricultural product cooperative + live-streaming e-commerce” model, and in accordance with previous research, combined with the differences in the dominant role and the bearers of profit and loss in the SC. By applying Stackelberg game theory, different types of decisions are analyzed and solved. The research results show: (1) Centralized decision-making has advantages in terms of traceability level and SC profit performance. (2) In non-centralized decision-making models, when manufacturers bear negative evaluation losses and retailers act as the leaders of the SC, the overall decision-making effect is more ideal. When manufacturers or retailers act as the leaders of the SC and both bear the traceability costs, the decision-making effect is basically the same. (3) The traceability level sensitivity coefficient and the traceability level effect on traceability both have positive effects on the growth of SC profits and the improvement of the traceability level. (4) The maximization of profits for both parties can be achieved through the coordination of contracts. This study can enhance the traceability level of the agricultural product SC, encourage SC members to increase their investment in the traceability, reduce the profit impact from negative evaluations, and provide a reference for the sustainable development of the agricultural product SC. Full article

Existing research on live-streaming e-commerce consumption behavior is mostly limited by a single disciplinary framework, unable to systematically parse the mechanism of macro-policies and cultural values on intergenerational consumer psychology. This study takes ASEAN cross-border live-streaming e-commerce as a scenario, integrates theories of economics, political science, and sociology, and constructs an innovative three-layer analysis model of “macroeconomic system–meso-market–micro-behavior” based on multi-source data from 2020 to 2024. It empirically explores the formation mechanism of intergenerational differences in impulse buying. The results show that the behavior differences of different groups are significantly driven by income gradient, cross-border policies (tariff adjustment and consumer protection regulations), and collectivism/individualism cultural orientations. The innovative contribution of this study is reflected in three aspects: Firstly, it breaks through the limitation of a single discipline, and for the first time, it incorporates structural variables such as policy synergy effect and family structure change into the theoretical framework of impulse buying, quantifying and revealing the differentiated impact of institutional heterogeneity in ASEAN markets on intergenerational behavior. Secondly, it reconstructs the transmission path of “cultural values–family structure–intergenerational behavior” and finds that the inhibitory effect of collectivism on impulse buying tends to weaken with age. Thirdly, it proposes a “policy instrument–generational response” matching model and verifies the heterogeneous impact of the same policy (such as tariff reduction) on different generations. This study fills the gaps in related research and can provide empirical support for ASEAN enterprises to formulate stratified marketing strategies and for policymakers to optimize cross-border e-commerce regulation. which is of great significance to promote the sustainable development of the regional live-broadcast e-commerce ecology. Full article

Rapid advancements in artificial intelligence and the Internet of Things (IoT) have fueled the growth of furniture, transforming traditional home environments into intelligent living spaces. As consumer adoption accelerates, understanding user concerns and sentiment trends becomes crucial for brands to refine product offerings and enhance market competitiveness. This study systematically investigates consumer concerns and sentiment trends toward furniture products by analyzing user-generated reviews across two major e-commerce platforms: Jingdong and Taobao. Leveraging advanced text-mining methods including TF-IDF keyword extraction, hierarchical clustering, Graph of Words–Latent Dirichlet Allocation (GoW-LDA) topic modeling, and BERT-based sentiment analysis, this research identifies critical user preferences, product satisfaction factors, and platform-specific behavioral patterns. Results reveal distinct cross-platform differences; Jingdong users prioritize service quality, brand trust, and logistical efficiency, whereas Taobao users emphasize product aesthetics, material selection, and cost-effectiveness. The sentiment analysis demonstrates that Jingdong users exhibit more consistent and positive feedback, while sentiment on Taobao displays higher variability due to product-quality discrepancies and price sensitivity. Full article

Facial recognition systems are increasingly used for authentication across domains such as finance, e-commerce, and public services, but their growing adoption raises significant concerns about spoofing attacks enabled by printed photos, replayed videos, or AI-generated deepfakes. To address this gap, we introduce a multi-layered Face Recognition-as-a-Service (FRaaS) platform that integrates passive liveness detection with active challenge–response mechanisms, thereby defending against both low-effort and sophisticated presentation attacks. The platform is designed as a scalable cloud-based solution, complemented by an open-source SDK for seamless third-party integration, and guided by ethical AI principles of fairness, transparency, and privacy. A comprehensive evaluation validates the system’s logic and implementation: (i) Frontend audits using Lighthouse consistently scored above 96% in performance, accessibility, and best practices; (ii) SDK testing achieved over 91% code coverage with reliable OAuth flow and error resilience; (iii) Passive liveness layer employed the DeepPixBiS model, which achieves an Average Classification Error Rate (ACER) of 0.4 on the OULU–NPU benchmark, outperforming prior state-of-the-art methods; and (iv) Load simulations confirmed high throughput (276 req/s), low latency (95th percentile at 1.51 ms), and zero error rates. Together, these results demonstrate that the proposed platform is robust, scalable, and trustworthy for security-critical applications. Full article

The rapid rise of live streaming e-commerce has transformed retail dynamics; however, the allocation of after-sales service costs between live streaming salespeople and manufacturers remains a critical, unresolved issue, exacerbated by fairness concerns among stakeholders. Utilizing a Stackelberg game model where manufacturers act as leaders and live streaming salespeople as followers, this study examines the impact of cost allocation on profit distribution and supply chain efficiency. The framework incorporates a coefficient for fairness concerns and an after-sales effort to develop nine decision-making scenarios. Analysis demonstrates that perceptions of fairness significantly reshape cost-sharing strategies: when manufacturers assume after-sales responsibilities, their scale effects reduce marginal costs, maximizing overall supply chain profit. Conversely, when a live streaming salesperson bears costs, excessive focus on fairness reduces total supply chain efficiency, even if short-term profits are gained through premium pricing. These results validate that the Stackelberg game model combined with fairness concerns and after-sales efforts balances efficiency–profit dual objectives, providing a sustainable governance framework for live streaming e-commerce ecosystems. Full article

This study investigates how AI-driven virtual anchors affect consumers’ purchase intentions by identifying their key attributes, underlying mechanisms, and configurational interplay. We integrate latent Dirichlet allocation (LDA), structural equation modeling (SEM), and fuzzy-set qualitative comparative analysis (fsQCA) into a unified methodological framework. Empirical evidence demonstrates that the public visibility of virtual anchors exerts a significant positive impact on purchase intention, whereas professionalism, responsiveness, and personalization primarily cultivate consumer pleasure and trust, yet exert limited direct influence on purchase decisions. Emotional states—arousal, pleasure, and trust—mediate the relationship between anchor characteristics and purchase intention. fsQCA further reveals that high purchase intention emerges when responsiveness serves as a necessary condition, trust operates as a pivotal hub, and arousal/pleasure function as emotional conduits; conversely, low purchase intention is chiefly attributable to deficiencies in visibility, responsiveness, and trust. By synthesizing the SOR (stimulus-organism-response) model with the PAD (Pleasure-Arousal-Dominance) emotion theory, this research extends theoretical insights into consumer behavior within e-commerce live-streaming contexts and provides actionable guidance for optimizing virtual anchor strategies, thereby advancing both standardization and innovation in the industry. Full article