Search Results (97)

Generation Z (Gen Z) consumers exhibit a distinctive multi-platform purchase behavior: they habitually discover products through social commerce (S-commerce) stimuli—including influencer content and livestream shopping—yet systematically migrate to e-marketplaces to complete their transactions. Despite the strategic importance of this behavioral pattern for platform operators, brand managers, and policymakers, the underlying mechanisms remain poorly understood. This study advances a Consumer Decision Journey framework comprising five stages—Social Commerce Stimuli, Discovery, Risk Evaluation, Marketplace Evaluation, and Purchase Decision—and integrates Stimulus-Organism-Response theory and the Elaboration Likelihood Model to explain how Gen Z consumers navigate cross-platform purchase decisions. Employing a sequential explanatory mixed-methods design, we conducted a large-scale survey (n = 423 Gen Z respondents) analyzed via Partial Least Squares Structural Equation Modeling, complemented by 18 in-depth qualitative interviews. Results confirm that influencer persuasion, livestream engagement, and perceived product authenticity drive product discovery; price comparison mediates, and price sensitivity moderates the discovery-to-risk pathway. Critically, perceived risk in S-commerce paradoxically accelerates cross-platform migration and elevates trust in e-marketplaces. Trust in e-marketplaces and logistics reliability each moderate the risk-to-purchase relationship. Qualitative analysis reveals that cross-platform behavior is a deliberate, internalized strategy among Gen Z—a platform arbitrage norm. These findings have substantial implications for S-commerce platform design, influencer marketing strategy, and e-marketplace positioning in emerging economies. Full article

The rise of digital platforms has transformed marketing landscapes, with live-streaming emerging as a powerful tool for engaging audiences and shaping consumer behavior. While live-streaming e-commerce is rapidly expanding in Chinese and North American markets, empirical research comparing live-streaming impulse buying (LSIB) across cultural contexts remains limited. This study examined how atmospheric cues (ACs) are associated with LSIB in China and the United States through hedonic value (HV) and utilitarian value (UV), while also considering cultural value boundary conditions. Data were collected from 396 Chinese and 408 American consumers through online survey platforms. The measurement structure was first assessed using multi-group confirmatory factor analysis, and the main structural relationships were then tested using controlled multi-group latent structural equation modeling (SEM). Composite score path models were estimated as robustness checks, and moderation hypotheses were examined using interaction regressions on composite scores. In both countries, AC was positively associated with HV and UV, and HV was positively associated with LSIB. In the U.S. sample, UV was negatively associated with LSIB, whereas the corresponding association was not significant in China. Formal Wald tests did not indicate statistically significant cross-country differences in the focal structural paths. On the HV pathway, collectivism strengthened the relationship between AC and HV in China, and long-term orientation strengthened the relationship between AC and HV in the U.S. The findings suggested that the core stimulus–organism–response (S-O-R) mechanism replicated across two market contexts, while cultural orientations mainly condition the hedonic route. The study contributed to cross-context understanding of live-streaming consumption and provides evidence-based implications for digital marketing strategy. Full article

The rapid rise of live-streaming e-commerce has fostered a new “content clipping” model, in which secondary creators edit and republish anchors’ live-streaming content to promote product sales. While this model can expand market reach and enhance revenue, it also introduces copyright disputes, regulatory challenges, and profit-sharing conflicts among platforms, anchors, and secondary creators. This study develops a three-party evolutionary game model to examine strategic choices regarding platform regulation, anchor authorization, and secondary content creation. Results reveal that excessive regulation may undermine equilibrium and profitability, while appropriate authorization can balance risk and reward. Secondary creators’ participation is sensitive to commission rates and cost–benefit trade-offs. This research contributes to the literature by integrating copyright governance into live-streaming e-commerce game theory and offers actionable insights for designing regulatory mechanisms, optimizing authorization policies, and fostering sustainable multi-party collaboration. Full article

Virtual anchor livestreams have rapidly become an efficient e-commerce format, attracting large audiences through distinctive avatars and real-time interactivity. However, technical constraints and the physical limitations of virtual anchors limit the ability to evoke the vicarious proximal sensory perceptions of products. Therefore, it is essential to investigate strategies that compensate for this deficiency and enhance the effectiveness of live streaming to meet consumer demand. This study explores the effect of a virtual anchor’s proximal sensory fit in product descriptions on purchase intention through two experiments. The results show that a higher sensory fit positively affects purchase intention. The chain mediating effect of perceived authenticity and attractiveness is significant. Product display positively moderates the relationship between sensory fit and purchase intention. This study provides new theoretical perspectives and practical guidance for virtual anchor marketing, emphasizing that marketing effectiveness can be enhanced through reasonable product display and sensory descriptions. Full article

This study is based on the practical context of the livestream e-commerce industry’s shift from “traffic competition” to “quality competition”. Addressing the limitations of existing research that predominantly focuses on streamers’ external traits while overlooking intrinsic qualities and frequently employs linear models that oversimplify the decision-making processes of consumer purchasing behavior (CPB), a theoretical framework grounded in the Elaboration Likelihood Model (ELM) is developed to explain how streamer traits drive consumer trust and identification through dual pathways. This study adopted a mixed-method approach combining structural equation modeling (SEM) and artificial neural networks (ANNs). By analyzing 408 valid questionnaires, it systematically investigated the driving mechanisms through which streamer traits affected consumers’ trust and identification. The study found that streamers’ integrity significantly enhanced perceived trust and perceived identification via the central route. While awareness could strengthen identification, it had no significant effect on trust building, revealing the inherent tension between “traffic” and “quality”. ANN analysis further demonstrated that the nonlinear combination of traits more effectively predicts consumer responses than traits. This study provided empirical support for the “quality transformation” of livestream e-commerce from both theoretical and methodological perspectives, offering important implications for platforms to develop a quality assessment system centered on trust and identification and to optimize the streamer cultivation mechanism. Full article

The revitalization of time-honored brands has been identified as a strategic priority in China’s national development agenda, yet many long-established brands continue to face declining market share and limited engagement with younger consumers. E-commerce livestreaming, characterized by real-time interaction and broad digital reach, has emerged as a potentially powerful channel for reversing this trend. However, the mechanisms through which livestreaming strengthens brand identification with time-honored brands among young consumers remain poorly understood. Drawing on the stimulus–organism–response (SOR) framework, social presence theory (SPT) and parasocial interaction theory (PSI), this study investigates how streamer characteristics—popularity, professionalism, and interactivity—shape brand identification with time-honored brands in livestreaming environments. Survey data were collected from 434 young consumers aged 18–40 who had viewed time-honored-brand livestreams within the previous six months. Structural equation modelling was used to test a model incorporating social presence, perceived authenticity of time-honored brands, and trust in time-honored brands as sequential mediators and consumer–streamer relationship strength as a moderator. The results show that streamer popularity and professionalism significantly enhance both social presence and perceived brand authenticity, whereas streamer interactivity primarily strengthens social presence. Social presence and perceived authenticity both increase trust in time-honored brands, which in turn predicts brand identification with time-honored brands. Streamer popularity and professionalism influence brand identification through two serial mediation pathways—via social presence and trust, and via authenticity and trust—while streamer interactivity operates mainly through the social presence–trust pathway. In addition, consumer–streamer relationship strength amplifies the effect of social presence on trust but attenuates the effect of authenticity on trust. By integrating SOR with SPT and PSI in a moderated serial mediation framework, this study provides new insight into how livestreaming transforms external cues into durable brand identification with time-honored brands among young consumers. The findings extend time-honored branding theory into digitally mediated commerce and offer evidence-based guidance for the strategic renewal of time-honored brands in the platform economy. Full article

With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed, coordinated, and intertwined across heterogeneous data modalities, posing substantial challenges to data-driven platform governance and early risk identification. Existing approaches often fail to jointly model cross-modal temporal semantics, the gradual evolution of weak abnormal signals, and organized group-level manipulation. To address these challenges, a data-driven multimodal abnormal behavior detection framework, termed MM-FGDNet, is proposed for large-scale live-streaming environments. The framework models abnormal behaviors from two complementary perspectives, namely temporal evolution and cooperative group structure. A cross-modal temporal alignment module first maps video, text, audio, and user behavioral signals into a unified temporal semantic space, alleviating temporal misalignment and semantic inconsistency across modalities. Building upon this representation, a temporal fraud pattern modeling module captures the progressive transition of abnormal behaviors from early incipient stages to abrupt outbreaks, while a cooperative manipulation detection module explicitly identifies coordinated interactions formed by organized user groups and automated accounts. Extensive experiments on real-world multi-platform live-streaming e-commerce datasets demonstrate that MM-FGDNet consistently outperforms representative baseline methods, achieving an AUC of 0.927 and an F1 score of 0.847, with precision and recall reaching 0.861 and 0.834, respectively, while substantially reducing false alarm rates. Moreover, the proposed framework attains an Early Detection Score of 0.689. This metric serves as a critical benchmark for operational viability, quantifying the system’s capacity to shift platform governance from passive remediation to proactive prevention. It confirms the reliable identification of the “weak-signal” stage—rigorously defined as the incipient phase where subtle, synchronized deviations in interaction rhythms manifest prior to traffic inflation outbreaks—thereby providing the necessary time window for preemptive intervention against coordinated manipulation. Ablation studies further validate the independent contributions of each core module, and cross-domain generalization experiments confirm stable performance across new streamers, new product categories, and new platforms. Overall, MM-FGDNet provides an effective and scalable data-driven artificial intelligence solution for early detection of coordinated abnormal behaviors in live-streaming systems. Full article

The growth of e-commerce live streaming has expanded sales channel options for fresh agricultural suppliers. This study investigates a two-echelon supply chain consisting of a fresh agricultural supplier and downstream retailers. Using differential game theory, we examine the supplier’s preservation technology level and product greenness, analyzing and comparing equilibrium strategies under three different modes: e-commerce platform sales mode (SP), head streamer sales mode (SH) and ordinary streamer sales mode (SN). The results demonstrate that SP is the dominant strategy when retailers’ marginal profits are low. Conversely, under high marginal profit conditions, the optimal selection depends on streamer cooperation costs: SH is preferred with low head streamer costs; widening cost gaps introduce temporal considerations between SH and SN; further gap expansion makes SN optimal. Furthermore, product greenness is related to supplier’s marginal profit, while the preservation technology level is jointly determined by supplier’s marginal profit and retailers’ inspection costs. Finally, combinations of these modes are also investigated. 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