Search Results (266)

This study critically examines hospitality performance evaluation practices by proposing a narrative-based quantitative framework grounded in online reviews. While tourism experiences, particularly in transformative contexts, are understood as subjective and meaning-oriented, empirical evaluation remains reliant on standardized, growth-oriented indicators such as star ratings and satisfaction scores. To address this disconnect, online reviews are conceptualized not as post hoc satisfaction measures but as narrative expressions of evaluation. Drawing on sentiment analysis of narratives from eleven hotels, evaluations are operationalized through indicators and incorporated into a DEA framework. Efficiency is reframed as experiential conversion capacity—the ability of hospitality providers to transform material and organizational conditions into experiences perceived as meaningful by guests. Two aggregation configurations and a super-efficiency extension are applied to examine robustness and differentiation. The findings reveal divergence between narrative-based evaluations and star classifications, suggesting that rating systems fail to capture dimensions of meaning and affective resonance. Notably, a lower-category hotel emerges as the strongest in experiential positioning, challenging assumptions linking quality, classification, and value. From a sustainability perspective, the study contributes to critiques of tourism evaluation and supports post-growth, sufficiency-oriented approaches. Methodologically, it demonstrates how techniques can be repurposed as interpretive tools when grounded in narrative data. Full article

Virtual influencers are increasingly used in influencer marketing, yet it remains unclear whether trust generated by an artificial persona can be transferred to endorsed brands in the same way as trust generated by human influencers. This study examines artificial intelligence (AI)-mediated trust transfer among female consumers by comparing virtual and human influencers across four social media platforms. Drawing on source credibility, parasocial interaction, social presence, trust transfer, and brand attachment perspectives, we propose that influencer type is associated with brand outcomes through three observable social-cue pathways: perceived authenticity, parasocial interaction, and social presence. These cues are expected to be associated with influencer trust, which is then associated with brand trust, brand attachment, purchase intention, and recommendation intention. Using 78,432 female consumer comments from Xiaohongshu, Instagram, Weibo, and Douyin, matched across 12 virtual–human-influencer pairs, we construct text-derived linguistic indicators—proxies rather than validated psychometric constructs—through keyword dictionaries, sentiment classification, and standardized composite scoring. The results show that human influencers are associated with higher perceived authenticity, parasocial interaction, and social presence than virtual influencers. The strongest association in the model is the trust-transfer path from influencer trust to brand trust, and the indirect path is associated with a substantial share of the observed covariation between influencer type and brand attachment. Product type further qualifies these patterns: the human-influencer advantage is stronger for hedonic products than for utilitarian products. These findings suggest that virtual influencers should not be understood as universal substitutes for human influencers. Instead, their effectiveness depends on whether AI-mediated personas can generate the social and authenticity cues that the literature associates with trust transfer in a given product context. Full article

This paper proposes the Continuous Operational Feedback Loop (COFL) architecture, a fully localized, event-driven operational monitoring and response system for Direct-to-Consumer (D2C) commerce. The architecture integrates the n8n workflow engine with on-premise large language model (LLM) inference via the Ollama framework, forming a containerized stack deployable on commodity CPU-only edge hardware (~USD 1640). Using a multi-source dataset of 1800 records constructed from publicly available e-commerce corpora and evaluated with a silver-standard automated labeling protocol, empirical validation demonstrates an end-to-end latency of 3.22 s and a macro-F1 sentiment classification score of 0.836—representing 98.2% of the full-precision baseline and 94.0% of cloud GPT-4o API generation quality measured by ROUGE-L—at approximately 1/200th of the per-request inference cost. A systematic quantization ablation study across six model-quantization configurations establishes LLaMA 3 8B Q4_K_M as the Pareto-optimal selection for the target hardware. An Analytic Hierarchy Process (AHP) multi-criteria framework with criterion weights derived from published literature confirms the COFL implementation achieves a higher composite score than cloud API deployment under the stated evaluation assumptions. Failure mode and effects analysis (FMEA) is summarized to characterize system reliability under identified failure scenarios. Full article

In the context of the growing experience economy, themed transformation has emerged as a key strategy for shopping malls seeking to enhance offline consumer experiences. However, existing studies on environmental experiences have largely focused on static factors, overlooking the potential interaction effects between the environment and experience behaviors. Based on 23,541 comments posted in 2024–2025 about 16 highly recommended Chinese-themed shopping malls on social media platforms, this study used natural language processing (NLP) to construct lexicons covering 11 environmental categories and 7 behavioral categories. The Light Gradient Boosting Machine (LightGBM) model, combined with SHapley Additive exPlanations (SHAP), was then employed to assess the relative importance of the environmental and behavioral elements and to reveal their interaction effects on customer sentiment. The results show that, among environmental elements, decoration is the most important predictor of sentiment, and among behavioral elements, leisure contributes the most. Leisure performs the strongest interactions with decoration and store category, suggesting that these couplings are most closely associated with positive emotional experience. These findings point toward a design logic centered on environment–behavior interdependencies, help to propose differentiated optimization strategies for various themed shopping malls, and offer a new perspective on the complex interplay among environment, behavior, and emotional experience. Full article

Against the backdrop of intensifying global climate change, climate policy uncertainty (CPU) and investor sentiment have become critical factors influencing the stability of financial markets. In this study, a quantitative index of investor sentiment is constructed using stock trading volume, turnover rate, price-to-earnings ratio, circulating market value, and the consumer confidence index. The QVAR-DY model is employed to analyze the risk contagion mechanisms among CPU, investor sentiment, and China’s financial sub-markets across different quantiles. Furthermore, five machine learning models—LSTM, BiLSTM, CNN, XGBoost, and LightGBM—are used to forecast risk spillover indices, and their performance is compared with three benchmark models (ARIMA, Persistence, and HistMean) to systematically evaluate the advantages of machine learning models in capturing tail risk spillover effects. The findings reveal significant cross-market risk contagion in financial markets, characterized by asymmetry. The level of risk spillover under extreme conditions is substantially higher than under normal conditions, indicating high sensitivity to extreme events and major policies. CPU exhibits the most pronounced spillover effect on the money market, while investor sentiment has the greatest impact on the stock market. The stock, real estate, and commodity markets act simultaneously as sources of risk and receivers of shocks. In terms of forecasting performance, LightGBM performs best under normal conditions, whereas LSTM achieves the highest prediction accuracy under extreme conditions. Full article

The rapid growth of user-generated reviews on e-commerce platforms has created a significant decision-making challenge for both consumers and sellers, particularly in morphologically rich low-resource languages such as Turkish. This study proposes a unified artificial intelligence framework for Turkish e-commerce review intelligence by integrating transformer-based sentiment classification, instruction-tuned large language model summarization, and explainable fuzzy logic-based product evaluation within a single end-to-end architecture. A balanced dataset containing 183,333 Turkish reviews was constructed from Trendyol, Amazon Turkey, and Hepsiburada using LLM-assisted annotation and stratified downsampling. Experimental evaluations demonstrated that the fine-tuned BERTurk 128k model achieved a macro F1-score of 0.9243 on the held-out test set. To overcome the limitations of multilingual news-oriented summarization models on informal review text, the framework employed the Turkish instruction-tuned Kumru-2B model together with structured prompt engineering to generate sentiment-aware abstractive summaries. In addition, a Mamdani-type fuzzy inference system was designed to combine sentiment distribution, seller reliability, star ratings, and review volume into an interpretable product-level score. The complete pipeline was integrated into a FastAPI and React-based web platform capable of processing approximately 850 reviews in under 60 s. The findings demonstrate that domain-specific Turkish language models combined with explainable reasoning mechanisms can provide accurate, scalable, and human-interpretable decision support for large-scale e-commerce environments. Full article

Collecting and analysing data after an earthquake is essential to determine its impact. In 2014, the European Mediterranean Seismological Centre launched the LastQuake system. Its app collects reports on the intensity users feel, along with comments that provide situational awareness. However, text data collected through crowdsourcing platforms is unstructured. Therefore, natural language processing techniques such as sentiment analysis and aspect-based sentiment analysis are necessary to extract meaningful information. On the 26 November 2019, following an earthquake in Albania, the LastQuake app recorded 28,220 reports with user comments. For the current analysis, we sampled comments posted on the exact day of the earthquake, in Albanian: 1678 comments (6%). The most frequent polarity detected in comments from LastQuake app users was negative (52%), followed by positive and neutral. However, manual classification is time-consuming and not feasible during the emergency phase. Therefore, we tested the accuracy of two automatic classification models for sentiment analysis: ‘troberta’ and ‘txlm’. These models were fine-tuned using already-classified text data from the 2020 Aegean earthquake. Using the manual classification as the reference to evaluate the accuracy of the automatic classification models for sentiment analysis yields accuracies of 71% for the ‘troberta’ model and 56% for the ‘txlm’ model. Full article

The advent of social media has transformed brand communication to put storytelling at the center of building engagement and awareness. But the role of long-term brand value in virality is an essential challenge. This paper conducts a bibliometric and thematic analysis from the fields of marketing, psychology, and media studies published between 2015 and 2025, examining the correlation between narrative design and audience response, separating short-term popularity and long-term consumer appeal. The analysis was based on a structured literature review and qualitative methodological framework, using the literature sourced through Scopus, Web of Science, PsycINFO, and Google Scholar published between 2015 and 2025. Thematic coding searched for emotional tones, devices used in the narration, types of metrics, and contextual factors in inclusion and exclusion criteria. The findings indicate a divide in quantitative measures, such as likes and shares, and qualitative measures, such as sentiment and resonance stories. Story elements such as authenticity, the depth of the characters, and video-based content had a major effect on the two types of engagement. Storytelling effectiveness was also mediated by influencer participation, algorithmic interactions, and audience demographics. The results confirm that meaningful storytelling with hybrid metrics contributes to stronger brand–consumer relationships. Future studies ought to shift to predictive modeling and focus on the ability of AI to dictate personalized brand stories in diverse cultures. Full article

Children increasingly consume online video content, creating a growing need for scalable approaches to support content moderation workflows. However, directly identifying harmful or policy-violating content, such as violence, sexual content, or self-harm, remains a complex task that typically requires specialized classifiers and domain-specific annotations. In this context, sentiment analysis can provide complementary information by capturing affective signals expressed through language and visual cues. This study does not treat sentiment polarity as a direct indicator of unsafe or policy-violating content. Instead, it explores multimodal sentiment analysis as an auxiliary triage signal that may help prioritize content for human review or identify segments requiring further inspection. This paper investigates the feasibility of using large vision–language models (LVLMs) for zero-shot multimodal sentiment analysis on utterance-aligned video segments. We evaluate two LVLMs, LLaVA-OneVision-7B and Qwen2.5-VL-7B, under three input settings: text-only, vision-only, and multimodal, using a conversational TV-series dataset consisting of short utterance-level video segments and transcripts. The results show that multimodal sentiment inference can provide useful screening signals without task-specific fine-tuning, although the benefits are model-dependent. LLaVA-OneVision-7B consistently outperforms Qwen2.5-VL-7B and benefits more clearly from combining textual and visual inputs, whereas Qwen2.5-VL-7B shows limited improvement across modality settings. We also analyze the trade-off between frame sampling and image resolution. Finally, we discuss limitations related to dataset scope, annotation subjectivity, class imbalance, and the need for broader validation before real-world deployment. Full article

With the advancement of the “dual carbon” goals, the electric vehicle market has experienced explosive growth, and user review mining has become key data support for industrial quality improvement and low-carbon transportation transition. Addressing the limitations of existing sentiment classification methods in long-distance feature capture, cross-sentence semantic association, and emotional feature focus, this study proposes a BERT-Bi-xLSTM-Attention fusion model: BERT pre-trained semantic representation extracts deep contextual information, Bi-xLSTM models long-range dependency relationships, and the Attention mechanism locates sentiment-critical markers. Based on multi-platform review data from Chinese Autohome, Yiche, and China Quality Inspection Network, experiments show that the model achieves Accuracy, Recall, Precision, and F1 values of 0.9323, 0.9326, 0.9321, and 0.9328, significantly outperforming baseline models. A “sentiment-topic” fusion analysis framework is constructed, identifying five positive themes and four negative themes, revealing the dual emotional characteristics of range, driving experience, and smart features. Temporal analysis finds that negative attention to intelligent system reliability has continued to rise from 2021 to 2024, becoming an emerging user pain point. Combined with the above findings, it is recommended that consumers comprehensively evaluate multi-attribute experiences when purchasing; manufacturers prioritize optimizing user-concerned attributes; and policymakers improve industrial standards and regulatory mechanisms. This promotes high-quality development of electric vehicles, contributes to the realization of carbon neutrality goals in the transportation sector, and facilitates sustainable transportation development. Full article

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

This research investigates the interactive influence of live-streaming background design and food processing levels on consumer behavior. Our findings reveal that for minimally processed agricultural products, an origin-based background (e.g., broadcasting directly from a farm) significantly enhances purchase intention compared to a standard studio background. We demonstrate that this effect is driven by a heightened perception of freshness. Conversely, for highly processed agricultural goods, the influence of background type is attenuated. Furthermore, we identify rural sentiment as a critical boundary condition, revealing that the positive effect of origin-based backgrounds is amplified among consumers with stronger emotional ties to rural life. These results offer nuanced insights into how digital broadcasting environments can be strategically aligned with product characteristics to optimize marketing outcomes. Full article

E-commerce web content is dynamic and diverse, necessitating continuous monitoring and adaptation. This presents researchers with the challenge of discovering methods to improve delivered services. Hence, integrating natural language processing (NLP), Machine Learning (ML), Deep Learning (DL), and sentiment analysis (SA) provides businesses with robust frameworks to utilize customer feedback and enhance decision-making. Therefore, we introduce a novel dataset collection methodology that captures the dynamic relationships between e-commerce web content and consumer sentiment. Additionally, we introduce a novel, real-consumer-based quality metric on product content through FG-CSrP, extending SA into a new Fine-Grained Consumer Sentiment related to the Product. We evaluated our dataset using baseline models: Deep Neural Network (DNN), Long Short-Term Memory (LSTM), DistilBERT, and twelve automatically optimized models created by AutoGluon-Tabular across three scenarios, each with varying feature inputs (numerical, textual, and both). We then applied Explainable Artificial Intelligence (XAI) to the DNN model to explain feature importance in prediction. Our findings showed that LightGBMXT outperformed the other models in two out of three scenarios, and XAI interpretations highlighted the significant role of vendor-provided web content details in consumer sentiment. Overall, our approach provides actionable insights that can help vendors improve e-commerce strategies and strengthen customer engagement. Full article

African swine fever (ASF) remains a major challenge for global animal disease prevention and control. Public sentiment during ASF, along with farmer and consumer behavior, are underexplored factors in ASF control. This study examines how public sentiment influences farmers’ risk perceptions and consumers’ behavioral responses, including consumption substitution intention and pork price expectations, and assesses the implications of these behaviors for disease control effectiveness. Using provincial panel data from China (June 2021–November 2022), sentiment analysis of 1.19 million Weibo posts, and a micro-level survey of 920 farmers, we combine panel regression, spatial econometric analysis, and micro-level behavioral evidence. Results show that public sentiment significantly elevates farmers’ risk perception, which may influence reporting decisions, marketing timing, and biosecurity investment, thereby increasing the complexity of surveillance and disease control. Sentiment intensifies substitution intentions and shapes pork price expectations, leading to reduced demand for formally marketed pork and potential shifts to lower-traceability or less-inspected channels. Spatial analysis indicates that the half-decay distance for amplifying ASF risk via sentiment is about 1300 km, providing parameters for cross-jurisdictional coordination. These findings support integrating socio-behavioral indicators into veterinary early warning systems and designing targeted disease risk communication under a broader One Health framework. Full article

Artificial intelligence (AI) is increasingly embedded in digital marketing, enabling organisations to personalise communication, analyse consumer data, and optimise decision-making processes. Despite its widespread adoption, limited empirical research has examined whether AI-driven digital marketing contributes to responsible consumption and production, as articulated in Sustainable Development Goal 12 (SDG 12). Grounded in a capability-based and marketing intelligence framework, this study investigates the mechanisms through which AI-driven digital marketing influences responsible marketing outcomes. Using survey data from 120 professionals in multinational corporations (MNCs) operating in South Africa, the study examines how AI-driven digital marketing influences responsible marketing outcomes aligned with Sustainable Development Goal 12 (SDG 12), with particular emphasis on the mediating roles of predictive consumer analytics and sentiment-based consumer understanding as distinct dimensions of AI-enabled marketing intelligence. Instead, its influence operates indirectly through sentiment-based consumer understanding, while predictive consumer analytics show no significant effect. These results suggest that AI contributes to responsible consumption primarily when it enhances firms’ capacity to interpret consumer values, emotions, and ethical concerns. The study advances the digital marketing and sustainability literature by reframing AI as a relational and sense-making capability while offering practical guidance for aligning AI-driven marketing strategies with SDG 12 in emerging markets. Full article