Search Results (183)

With the rapid development of internet technology, live-streaming sales have emerged as a new form of online shopping and gradually become an important channel for consumer purchases. This paper investigates the decision-making problem in a dual-channel supply chain under the live-streaming sales model, focusing on the optimal decisions and profit distribution between the retailer and live-streamer when a Valuation Adjustment Mechanism (VAM) is in place. We construct a dual-channel supply chain model consisting of a retailer and a live-streamer, then analyze the decision-making and profits under three scenarios: centralized decision-making, decentralized decision-making without VAM, and decentralized decision-making with VAM. The results show that the proper use of a VAM can increase the profits of all supply chain participants; furthermore, different sales target-setting methods should be adopted for products with different price levels. The sales target serves as the core of the incentive mechanism and directly influences the streamer’s effort and pricing strategies. When formulating sales strategies, retailers and live-streamers should comprehensively consider the decision-making structure, the incentives and risks associated with using a VAM, and the coordinated development of multiple channels to maximize overall supply chain profits. Full article

With the gradual maturation of live-streaming e-commerce, some merchants invest substantial resources in hiring celebrity streamers, attempting to leverage their influence and credibility to enhance consumer trust and achieve long-term growth. However, against the backdrop of high return rates and low repurchase rates, whether this celebrity-driven operational model can truly achieve sustainable development remains questionable, and relevant research on this issue remains limited. Therefore, guided by Source Credibility Theory and informed by the post-consumption perspective of Expectation–Confirmation Theory, this study employs partial least squares structural equation modeling (PLS-SEM) to examine consumer trust formation and repurchase intention. The results indicate that perceived product quality, perceived interactivity, and perceived attractiveness significantly enhance consumer trust, which in turn positively influences repurchase intention. However, perceived discount has no significant effect on trust, suggesting that relying solely on low-price strategies may be insufficient to support trust-based repurchase intention. Furthermore, the MGA results show that streamer type does not significantly moderate the relationship between perceived characteristics and consumer trust. These findings suggest that celebrity identity alone may not be sufficient to reshape the trust-formation mechanism in live-streaming e-commerce. This study provides theoretical and practical insights for understanding consumer trust formation and promoting the sustainable development of live-streaming e-commerce. Full article

Sustainable higher education calls for programmes that bring together pedagogical innovation, territorial relevance and global partnerships. This article examines the curricular design of the Multiplatform Communication degree at Universidad Técnica Particular de Loja (UTPL), Ecuador, presenting it as a sustainable initiative geared towards local and intercultural communities and aligned with both national development planning and international standards. The study is based on the Constitution of the Republic of Ecuador, the National Development Plan and the Organic Code on the Social Economy of Knowledge. The methodology is both quantitative and qualitative, employing a literature review, content analysis, surveys and interviews. The findings highlight the demand from employers and students for curricular skills focused on intercultural communication, the promotion of cultural heritage, media literacy and the production of sustainable content through international partnerships with production companies, live-streaming platforms and academic mobility programmes. The projected curricular design, it is concluded, amounts to a case of academic leadership oriented towards sustainability, with relevance for university governance in contexts facing territorial challenges. Full article

AI digital-human livestreaming has become a key format of e-commerce livestreaming due to its low cost and round-the-clock operation. How the technical features of digital humans work together with consumers’ psychological perceptions to drive consumption is an important academic and practical question. Existing studies mostly adopt linear analytical paradigms. They focus on the anthropomorphic appearance features of digital humans. Few studies interpret the synergistic effects of technical–emotional factors from a configurational perspective. Empirical tests on boundary conditions under different platform ecosystems are also scarce. Drawing on the SOR–PAD theoretical framework, this study takes 18,690 livestream bullet-screen comments collected from 40 AI digital-human livestream rooms across Jingdong, Baidu, and Meituan as the research material. It conducts a mixed empirical analysis, combining text mining, machine learning, and fsQCA. The dependent variable, purchase behavior tendency, is obtained from an SVM model trained by matching bullet-screen comments with desensitized background transaction data. This indicator acts as a proxy measure for subjective purchase intention. The SVM model achieves an accuracy of 0.97, recall of 0.94, and an F1-score of 0.95. The findings are as follows. First, four technical features, including professionalism, simulation fidelity, responsiveness, and personalization, together with the three psychological perceptions of pleasure, arousal, and trust, constitute critical antecedents of high purchase behavior tendencies. Second, no single necessary condition can trigger a high purchase behavior tendency. Five equivalent technical–emotional configuration paths are identified, including response-oriented, pleasure-oriented, and multiple-synergy types. Third, the conversion effects of the configuration paths show clear platform boundaries. Different e-commerce formats match differentiated configuration paths. This study introduces configurational causal logic into the existing SOR–PAD framework. It expands the application boundary of this framework for AI digital-human livestreaming scenarios. It supplements the empirical evidence of consumer behavior under human–computer interactions. It also offers practical references for platforms and merchants to operate AI digital-human livestreams. Full article

Financial media risk is jointly driven by multisource content, including news reports, corporate announcements, social media posts, short videos, and livestreams, while content authenticity, propagation velocity, asset relevance, and trading infrastructure conditions can simultaneously influence short-term market fluctuations. To address the limitations of existing methods, including their reliance on either textual information or market sequences, insufficient source verification, and inadequate alignment of asynchronous multimodal signals, FMRP-Net is proposed for artificial intelligence-driven sensing. Event semantics, risk categories, and asset association information are first extracted through a pretrained financial language model and cross-modal consistency analysis. A dual-layer hardware reliability perception module is then employed to integrate sensing evidence from cameras, microphones, terminal inertial signals, server temperature, power consumption, network traffic, and transmission latency. Heterogeneous temporal propagation graphs, cross-frequency alignment, and bidirectional propagation–market coupling are further incorporated to jointly predict market direction, volatility, risk level, and propagation trends. Experimental results demonstrate that FMRP-Net achieved an Accuracy of $0.832$, a Macro-F1 of $0.824$, a ROC-AUC of $0.891$, an MCC of $0.665$, and a PR-AUC of $0.883$ for market direction prediction over future horizons of 5, 15, 30, and 60 min, indicating a balanced performance in terms of Precision and Recall. For volatility prediction, MAE, RMSE, and MAPE values of $0.0178$, $0.0271$, and $12.46\%$ were obtained, respectively, together with an $R^2$ of $0.812$. In the ablation study, the media risk Macro-F1 and source reliability AUC reached $0.842$ and $0.929$, respectively, while the propagation-scale prediction error was reduced to $0.109$ and the average early-warning lead time reached $10.6$ min. These results demonstrate that the integration of multimedia semantics, hardware sensing evidence, and propagation structures can effectively improve the accuracy, stability, and interpretability of financial market prediction and risk early warning. Full article

With the rapid development of artificial intelligence, AI-driven algorithmic goal setting has become an important mechanism of digital platform management. In the livestream e-commerce industry, platforms increasingly use algorithmic systems to assign tasks, monitor performance, and regulate streamers’ work. Although this intensive and dynamic form of algorithmic management can improve operational efficiency, it may also be associated with potential ethical risks. Drawing on Conservation of Resources Theory, Emotional Labor Theory, and Sociotechnical Systems Theory, this study examines whether AI-driven algorithmic goal setting is associated with streamers’ unethical selling behavior through emotional dissonance and whether AI transparency moderates this relationship. Using a three-wave time-lagged survey design, data were collected from 427 livestream e-commerce streamers in China. SPSS-based hierarchical regression analysis and bootstrapping were employed to test the proposed moderated mediation model. The results showed that AI-driven algorithmic goal setting was significantly and positively associated with streamers’ unethical selling behavior and that emotional dissonance partially mediated this relationship. Furthermore, the positive relationship between AI-driven algorithmic goal setting and emotional dissonance, as well as the corresponding indirect relationship with unethical selling behavior, was weaker at higher levels of AI transparency. These findings identify emotional dissonance as an important psychological mechanism linking algorithmic performance demands with unethical selling behavior and indicate the conditional buffering role of AI transparency. This study extends algorithmic management research to the livestream e-commerce context and provides practical implications for enhancing algorithmic transparency and reducing ethical risks in platform governance. Full article

As artificial intelligence and digital human technologies become increasingly integrated into livestream commerce, AI anchors are becoming important marketing agents. Yet prior research has focused primarily on their visual characteristics, leaving dynamic vocal cues largely unexplored. Drawing on social response theory and perceived authenticity research, this study examines the nonlinear association between AI anchor pitch variability and consumer engagement, together with a proposed psychological pathway and boundary condition. Study 1 analyzes 4322 product-presentation segments nested within 330 AI-anchored livestreams and 85 independent accounts on Douyin. Negative binomial models, formal boundary-slope tests, and additional specifications using account and livestream-session fixed effects, a correlated-random-effects decomposition, and viewer-minutes exposure provide robust evidence of an inverted U-shaped association between pitch variability and real-time danmaku engagement. Evidence concerning appearance-realism moderation is conditional and specification-sensitive across alternative pitch operationalizations, exposure definitions, and within-account specifications. Study 2 uses a preregistered multi-stimulus mixed design with four AI anchors, four products, and three between-participants pitch-variability conditions. Correctly scaled planned contrasts show that moderate pitch variability produced greater perceived authenticity and engagement intentions than the average of the two endpoint conditions. A 2-1-1 multilevel analysis yielded an indirect-effect pattern consistent with the proposed role of perceived authenticity. Models allowing treatment effects to vary across the 16 included anchor-product combinations showed a positive average moderate-pitch advantage, although its magnitude varied across stimuli. These findings extend livestream-commerce research from human streamers to AI-mediated communication while indicating that appearance-realism moderation, stimulus-level generalization, and causal mediation require further replication. Full article

As VR live streaming becomes an emerging form of immersive retailing, understanding how consumers respond to streamers’ social cues is increasingly important. Drawing on social presence theory, this research examines how streamer gaze in VR live streaming influences purchase intention and investigates the underlying mechanism and boundary condition. We propose that streamer gaze increases purchase intention by enhancing consumers’ feeling attended to, and in turn, social presence, and that this process is shaped by virtual space scale. Three studies provide convergent support for this framework. Study 1, using a retrospective survey and structural equation modeling with 386 participants, offers initial evidence for the proposed relationships. Study 2, using an immersive VR experiment with 160 participants, provides causal support for the serial mediation mechanism. Study 3, using a 2 × 2 immersive VR experiment with 240 participants, further shows that the positive effect of streamer gaze is stronger in smaller virtual spaces than in larger ones. This research contributes to the literature on VR retailing, live-streaming commerce, and social presence by identifying streamer gaze as a concrete nonverbal cue that shapes consumer responses in immersive selling environments. Full article

Firms are increasingly introducing AI assistants into human-led live-streaming rooms, yet it remains unclear how such assistance should be configured and when it creates economic value. We develop an analytical model that compares human-only live-streaming selling with human–AI collaborative live-streaming selling. The firm sets the selling price in both modes and, under collaboration, jointly chooses the AI capability level. The model captures two channels through which AI may create value: enhancing the effectiveness of the human host and generating demand spillover beyond the room’s baseline conversion. We derive the equilibrium price, AI capability, demand, and profit, and we identify the conditions under which collaboration outperforms human-only live-streaming selling. The results show that AI capability is more valuable when paired with a stronger host, whereas the effect of product quality on AI investment is not necessarily positive. Lower AI cost may support a higher selling price by enabling a more capable selling process. Moreover, demand improvement and profit improvement need not occur simultaneously. Extensions examine AI-only live-streaming selling and imperfect AI assistance. Full article

Livestream commerce has become a major retail interface, yet limited attention has been paid to how streamers’ language shapes consumer responses. This research examines the effect of streamer sensory language on consumer purchase in livestream commerce. Drawing on one field study using Douyin livestream data (Study 1: 31,254 min-level observations from 526 livestream sessions involving 136 streamers) and two experiments (Study 2: N = 200; Study 3: N = 320), we show that sensory language increases consumer purchase responses. Study 1 demonstrates a positive association between streamer sensory language and real-time purchase behavior and provides initial evidence for the moderating role of product type. Study 2 establishes the causal effect of sensory language on purchase intention and tests the serial mediation of mental imagery vividness and perceived authenticity. Study 3 further shows that the effect is stronger for experience goods than for search goods. Full article

This paper presents AERO-GUARD, a formally verified drone authentication and road surveillance system that integrates Kyber post-quantum key encapsulation, physical unclonable functions (PUFs), decentralized IPFS-based identity storage, and blockchain-anchored audit logging. AERO-GUARD operates across three phases, key provisioning, enrollment, and authentication, enforcing mutual authentication, replay resistance, and privacy-preserving comparison through an off-chain evaluator (OCE) that performs homomorphic subtraction on encrypted PUF responses without accessing plaintext secrets. The protocol is modeled and verified using ProVerif 2.05 under the Dolev–Yao adversary model. To evaluate the system beyond theoretical verification, a simulation environment was developed to replicate realistic road conditions, incorporating a simulated road network and a virtual drone traversing monitored routes. An AI model is deployed to perform real-time detection of suspicious and anomalous activities along the road. All detection events are surfaced through a centralized monitoring dashboard that provides authorized personnel with live alerts, a drone camera livestream with detection annotations, and contextual drone telemetry, enabling timely and informed incident response. Formal verification results demonstrate that AERO-GUARD satisfies the targeted security properties, including mutual authentication, secrecy preservation, and replay resistance, confirming the protocol’s resilience against common authentication attacks. Full article

In live-streaming e-commerce systems, massive volumes of user-generated danmaku constitute a critical yet underutilized source of business intelligence. However, transforming such unstructured, noisy, and highly context-dependent textual data into structured and actionable knowledge remains a fundamental challenge for enterprise information systems. To address this issue, this study proposes TaSC-LLM, an LLM-enabled topic recognition method for constructing interpretable topic measurements from unstructured user-generated content. The proposed framework integrates topic taxonomy construction and zero-shot classification into a unified semantic reasoning pipeline. Unlike conventional topic modeling or supervised classification approaches, TaSC-LLM leverages chain-of-thought reasoning, multi-stage taxonomy induction, sliding window context modeling, and self-consistency verification to eliminate reliance on predefined label spaces and annotated training data. This design allows the system to dynamically construct and update topic taxonomies while ensuring interpretability, robustness, and cross-scenario adaptability. Empirical evaluation on three large-scale live-streaming e-commerce danmaku datasets shows that TaSC-LLM achieves strong taxonomy coverage, classification accuracy, and agreement with expert annotations. The findings suggest that LLM-based reasoning can help convert unstructured user-generated text into interpretable topic measures for downstream empirical and managerial analysis. While the present evaluation is conducted offline, TaSC-LLM provides a methodological foundation for future business applications that can be further examined under multi-session, multi-platform, and deployment-oriented conditions. Full article

This article uses Clavicular, a controversial livestreamer who became a prominent public face of looksmaxxing in early 2026, as a trace-supported case study of fringe diffusion under platform conditions. Using public media traces, platform snapshots, and public visibility indicators, it examines how a niche masculine self-optimization culture becomes broadly legible. The article introduces diagnostic masculinity—a platformed style of masculine authority built on naming, ranking, and promising correction of bodily lack—as a working concept to explain why looksmaxxing resonates with boys and men beyond platform affordances alone. Drawing on scholarship on digital masculinity, platformization, and meme circulation, it argues that Clavicular’s rise depended on the alignment of coded vernacular, high-volume livestreaming, clipping infrastructure, controversy, monetization, and mainstream media translation. The article proposes a five-stage model of containment failure—vernacular incubation, infrastructural assembly, controversy acceleration, mainstream translation, and parodic consolidation—through which fringe masculine self-optimization becomes culturally portable without becoming fully normalized. The case shows how platform systems do not merely amplify fringe masculinities but package them into forms that can circulate across media environments that would otherwise remain separate. Full article

Real-time interaction and immersive experience in livestream e-commerce greatly boost consumers’ impulse buying, yet few studies explore how streamer interaction quality affects impulse buying via emotional bonds. Drawing on the SOR framework, this study constructs a moderated mediation model with parasocial interaction as the mediator and self-construal as the moderator. Combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), we empirically analyze 406 valid questionnaires. The results show that: (1) four dimensions of streamer interaction quality (expertise, empathy, entertainment, reliability) positively predict impulse buying intention, with partial mediation of parasocial interaction; (2) the moderation effect is asymmetric: interdependent self-construal strengthens the positive link between parasocial interaction and impulse buying intention, while independent self-construal exerts no significant moderation; (3) fsQCA detects six equivalent configurations triggering high impulse buying intention, uncovering complementarity and substitution among interaction dimensions. This study clarifies the emotional transmission mechanism in livestream scenarios and offers tiered implications: streamers should uphold credibility as the baseline and adopt differentiated interaction strategies based on individual strengths; platforms should quantify interaction quality and implement targeted training and precision matching based on configurational paths; and merchants should match streamer characteristics to product types, strengthen group belonging for interdependent consumers, and reduce decision-making barriers for independent consumers. Full article

Focusing on the dual pathways of emotional contagion and cognitive reinforcement, this study explores consumer interaction mechanisms in e-commerce live streaming and investigates the moderating role of live streaming assistants. Using valid real-time panel data spanning 4671 min across 40 Taobao Live sessions, this research adopts the LIWC-based text mining approach to extract indicators of user emotional expression and cognitive expression from bullet comments and matches minute-level multi-source data for empirical analysis. The empirical results indicate that early emotion expression and cognitive expression are significantly associated with intertemporal reinforcement patterns, with emotional contagion being more closely linked to instant purchase decisions and cognitive reinforcement being more closely linked to user follow behavior. While assistant intervention is associated with a more active overall interaction atmosphere across the two psychological pathways, it is negatively correlated with product sales, suggesting a dual-edged association. By introducing human assistants into the analytical framework and empirically distinguishing differentiated outcome associations related to emotional and cognitive mechanisms, this study extends the application of consumer-behavior theories to real-time livestream interaction contexts and offers cautious practical implications for livestream operation and assistant deployment. Full article