Search Results (1,040)

In the digitally saturated hospitality environment, research on digital transformation remains dominated by macro-level adoption trends and user-generated content, while the potential of micro-level web-behavioural data remains largely untapped. Recent systematic reviews highlight a fragmented body of literature and note that hospitality studies seldom address first-party behavioural data or big-data analytics capabilities. To address this gap, we collected clickstream, navigation and booking-funnel data from five luxury hotels in the Mediterranean and employed big-data analytics integrated with simulation modelling—specifically fuzzy cognitive mapping (FCM)—to model causal relationships among digital touchpoints, managerial actions and customer outcomes. FCM is a robust simulation tool that captures stakeholder knowledge and causal influences across complex systems. Using a case-study methodology, we show that first-party behavioural data enable real-time insights, support knowledge-based decision-making and drive digital service innovation. Across a 12-month panel, visitor volume was strongly associated with search traffic and social traffic, with the total-visitors model explaining 99.8% of variance. Our findings extend digital-transformation models by embedding micro-level behavioural data flows and simulation modelling. Practically, this study offers a replicable framework that helps managers integrate web-analytics into decision-making and customer-centric innovation. Overall, embedding micro-level web-behavioural analytics within an FCM framework yields a decision-ready, replicable pipeline that translates behavioural evidence into high-leverage managerial interventions. Full article

With the global trend of population aging, human-centered development that integrates medical convenience with daily life quality has become a critical necessity. However, conceptual frameworks, evaluation methods, and spatial prototypes for such ‘healthcare–daily-life’ development remain limited. This study proposes Hospital-Oriented Development (HOD) as a framework to promote collaborative development by considering both hospital accessibility and urban development intensity, derived from multi-sourced urban data. First, a conceptual framework was established, consisting of three dimensions, i.e., network accessibility, facility completeness, and environmental comfort, which was then characterized by twelve indicators based on urban morphological features. Second, these indicators were quantitatively evaluated through detailed values measured among 20 exemplary hospitals in Shanghai selected via user-generated content. Finally, HOD performance and morphology informed the spatial prototype. The results reveal confidence intervals for each indicator and recommended spatial features. Numerically, there was a positive correlation between facility completeness and network accessibility, but a negative correlation with environmental comfort. Spatially, a context-specific HOD prototype for China was developed. This study proposes the concept of HOD, delivers quantitative measurements, and develops a spatial prototype via empirical research, providing theoretical insights and evidence to support the improvement in healthcare environments from a human-centered perspective. Full article

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

With the proliferation of multimedia data, controllable summarization generation has become a key focus in Artificial Intelligence Content Generation. However, many traditional methods lack precise control over output length, often resulting in summaries that are either too verbose or too brief, thus failing to meet diverse user needs. In this paper, we propose a length-customizable approach for multimodal image-text summarization. Our method integrates combinatorial optimization with deep learning to address the length-control challenge. Specifically, we formulate the summarization task as a knapsack optimization problem, enhanced by a greedy algorithm to strictly adhere to user-defined length constraints. Additionally, we introduce a multimodal attention mechanism to ensure balanced and coherent integration of textual and visual information. To further enhance semantic alignment, we employ a cross-modal matching strategy for image selection based on pre-trained vision-language models. Experimental evaluations on the MSMO dataset and validate against baselines like LEAD-3, Seq2Seq, Attention, and Transformer that our method achieves a ROUGE-1 score of 40.52, ROUGE-2 of 16.07, and ROUGE-L of 35.15, outperforming existing length-controllable baselines. Moreover, our approach attains the lowest length variance, confirming its precise adherence to target summary lengths. These results validate the effectiveness of our method in generating high-quality, length-constrained multimodal summaries. Full article

We consider combinatorial multi-access coded caching with private caches, where users are connected to two types of caches: private caches and multi-access caches. Each user has its own private cache, while multi-access caches are connected in the same way as caches are connected in a combinatorial topology. A scheme is proposed that satisfies the following three requirements simultaneously: (a) Linear Function Retrieval (LFR), (b) content security against an eavesdropper, and (c) demand privacy against a colluding set of users. It is shown that the private caches included in this work enable the proposed scheme to provide privacy against colluding users. For the same rate, our scheme requires less total memory accessed by each user and less total system memory than the existing scheme for multi-access combinatorial topology (no private caches) in the literature. We derive a cut-set lower bound and prove optimality when $r\ge C-1$. For $r<C-1$, we show a constant gap of 5 under certain conditions. Finally, the proposed scheme is extended to a more general setup where different users are connected to different numbers of multi-access caches, and multiple users are connected to the same subset of multi-access caches. Full article

With the widespread integration of Artificial Intelligence-Generated Content (AIGC) into e-commerce platforms, understanding how users perceive, evaluate, and respond to such content has become a critical issue for both academia and industry. This study examines the influence mechanism of AIGC Content Quality (AIGCQ) on users’ Purchase Intention (PI) by constructing a cognitive model centered on Trust (TR) and Perceived Risk (PR). Additionally, it introduces two moderating variables—Ethical Concern (EC) and Perceived Platform Responsibility (PLR)—to explore higher-order psychological influences. The research variables were identified through a systematic literature review and expert interviews, followed by structural equation modeling based on data collected from 507 e-commerce users. The results indicate that AIGCQ significantly reduces users’ PR and enhances TR, while PR negatively and TR positively influence PI, validating the fundamental dual-pathway structure. However, the moderating effects reveal unexpected complexities: PLR simultaneously amplifies both the negative effect of PR and the positive effect of TR on PI, presenting a “dual amplification” pattern; meanwhile, EC weakens the strength of both pathways, manifesting a “dual attenuation” effect. These findings highlight the nonlinear cognitive mechanisms underlying users’ acceptance of AIGC, suggesting that PLR and EC influence decision-making in more intricate ways than previously anticipated. By uncovering the unanticipated patterns in moderation, this study extends the boundary conditions of the trust–risk theoretical framework within AIGC contexts. In practical terms, it reveals that PLR acts as a “double-edged sword,” providing more nuanced guidance for platform governance of AI-generated content, including responsibility frameworks and ethical labeling strategies. Full article

Older adults often face significant challenges in understanding medical information due to cognitive aging and limited health literacy. Existing simplification models, while effective in general domains, cannot adapt content for elderly users, frequently overlooking narrative tone, readability constraints, and semantic fidelity. In this work, we propose GENSIM—a Generative Expert-Narrated Simplification Model tailored for age-adapted medical text simplification. GENSIM introduces a modular architecture that integrates a Dual-Stream Encoder, which fuses biomedical semantics with elder-friendly linguistic patterns; a Persona-Tuned Narrative Decoder, which controls tone, clarity, and empathy; and a Reinforcement Learning with Human Feedback (RLHF) framework guided by dual discriminators for factual alignment and age-specific readability. Trained on a triad of corpora—SimpleDC, PLABA, and a custom NIH-SeniorHealth corpus—GENSIM achieves state-of-the-art performance on SARI, FKGL, BERTScore, and BLEU across multiple test sets. Ablation studies confirm the individual and synergistic value of each component, while structured human evaluations demonstrate that GENSIM produces outputs rated significantly higher in faithfulness, simplicity, and demographic suitability. This work represents the first unified framework for elderly-centered medical text simplification and marks a paradigm shift toward inclusive, user-aligned generation for health communication. Full article

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

The axle box of a railway vehicle is a critical component, and its maintenance involves complex procedures that are difficult to convey with traditional, document-based manuals. To address these challenges, augmented reality (AR)-based educational content was developed to digitize maintenance training and enhance its effectiveness. The content’s implementation was guided by a systematic storyboard, which was based on interviews with skilled staff. It also utilized specialized algorithms to improve the accuracy of mechanical measurement work and the efficiency of User Interface (UI) generation. The user experience of the developed content was comprehensively evaluated using a combination of two methods: a formative evaluation through direct observation of work performance and a post-survey administered to 40 participants. As a result of the evaluation, the mean work success rate was 62.5%, demonstrating the content’s high efficiency as a training tool. The overall mean score from the post-survey was 4.11, indicating high user satisfaction and perceived usefulness. A one-way ANOVA was performed and revealed a statistically significant difference in post-survey scores among the four age groups. The developed content was found to be more effective for younger participants. The results confirm the high potential of AR as a digital educational method for complex maintenance work. Full article

Social media has dramatically impacted all sectors of society, including public communication and governmental relations. Most countries have increasingly incorporated it into their communication strategies. However, there is little research on this subject. This study tackled this gap by analyzing the existing literature and comprehending the objectives, determinant factors, and consequences of social media use by governments. It investigated the practice of such measures on Portuguese governmental communication to understand the low levels of engagement identified through the research. The governmental accounts were subjected to two types of analysis to achieve a practical means of classification. The exploratory analysis of the @govpt account data (18,071) tweets used various methods specific to user-generated content. Fourteen public agencies’ tweets (39,965) underwent the transparency, participation, collaboration, and comfort (TPCC) index computations, developing four factors to calculate the public sector’s digital communication success. The TPCC Index results revealed the lowest development rates, with participation and collaboration being the least developed factors. Only 107 mentions were found across 59,036 tweets, and explicit co-creation terms appeared 303 times. Furthermore, the analyzed accounts did not progress to the deeper stages of connection with governments’ possible exploration n. This research’s main achievements and contributions consist of contemplating the Portuguese case study while proposing and validating the TPCC Index metrics’ modifications for X data analysis. Full article

The purpose of this study is to explore and analyse user perceptions of peri-rural landscapes in the Seoul metropolitan region, using Icheon City as a case study. While the multifunctionality of peri-rural areas—providing ecological, cultural, and socioeconomic benefits—is increasingly recognised, the perceptual and experiential dimensions remain underexplored in South Korea. To address this gap, 10,578 Naver Blog posts were collected and refined, resulting in 8078 valid entries. Methodologically, this study introduces an innovative approach by integrating centrality analysis with latent Dirichlet allocation (LDA) topic modeling of user-generated content, supported by a bespoke dictionary of 170 local landscape resources. This combined framework allows simultaneous examination of structural associations and thematic narratives within user perceptions. The results indicate that resources such as Seolbong Urban Park, Seolbong Mountain, and the Cornus Fruit (sansuyu) Villages function as symbolic hubs in the perceptual network, while thematic clusters capture multi-dimensional concerns spanning leisure, ecology, culture, suburbanization, and real estate. Synthesised together, these findings demonstrate that user perceptions construct peri-rural landscapes not as isolated sites, but as spatially cohesive and thematically interconnected systems that mediate between urban and rural domains. Overall, this study contributes to metropolitan planning discourse by highlighting perceptual dimensions alongside functional and ecological dimensions. It shows that users cognitively construct peri-rural landscapes as systems that are both spatially cohesive and thematically interconnected, and that function as spaces that link urban and rural areas. Crucially, this study provides a replicable framework for using user-generated content to inform the planning and management of peri-rural landscapes in metropolitan areas. Full article

In recent years, large multimodal models (LMMs), such as ChatGPT 4o and DeepSeek R1—artificial intelligence systems capable of multimodal (e.g., image and text) human–computer interaction—have gained traction in industrial and civil engineering applications. Concurrently, insufficient real-world drone-view data (specifically close-distance, high-resolution imagery) for civil engineering scenarios has heightened the importance of artificially generated content (AIGC) or synthetic data as supplementary inputs. AIGC is typically produced via text-to-image generative models (e.g., Stable Diffusion, DALL-E) guided by user-defined prompts. This study leverages LMMs to interpret key parameters for drone-based image generation (e.g., color, texture, scene composition, photographic style) and applies prompt engineering to systematize these parameters. The resulting LMM-generated prompts were used to synthesize training data for a You Only Look Once version 8 segmentation model (YOLOv8-seg). To address the need for detailed crack-distribution mapping in low-altitude drone-based monitoring, the trained YOLOv8-seg model was evaluated on close-distance crack benchmark datasets. The experimental results confirm that LMM-prompted AIGC is a viable supplement for low-altitude drone crack monitoring, achieving >80% classification accuracy (images with/without cracks) at a confidence threshold of 0.5. Full article

Digital transformation has re-engineered tourism marketing and how destination branding competes for tourist attention, yet scholarship offers little systematic quantification of these changes. Drawing on 160 peer-reviewed studies published between 1990 and 2025, we combine grounded-theory thematic synthesis with a random-effect meta-analysis of 60 datasets to trace branding performance across five technological eras (pre-Internet and brochure era: to mid-1990s; Web 1.0: 1995–2004; Web 2.0: 2004–2013; mobile first: 2013–2020; AI-XR: 2020–2025). Results reveal three structural shifts: (i) dialogic engagement replaces one-way promotion, (ii) credibility migrates to user-generated content, and (iii) artificial intelligence–driven personalisation reconfigures relevance, while mobile and virtual reality marketing extend immersion. Meta-analytic estimates show the strongest gains for engagement intentions (g = 0.57), followed by brand awareness (g = 0.46) and image (g = 0.41). Other equity dimensions (attitudes, loyalty, perceived quality) also improved on average, but to a lesser degree. Visual, UGC-rich, and influencer posts on highly interactive platforms consistently outperform brochure-style content, while robustness checks (fail-safe N, funnel symmetry, leave-one-out) confirm stability. We conclude that digital tools amplify, rather than replace, co-creation, credibility, and context. By fusing historical narrative with statistical certainty, the study delivers a data-anchored roadmap for destination marketers, researchers, and policymakers preparing for the AI-mediated decade ahead. Full article

The growing prevalence of digital labor platforms has fundamentally transformed business models by creating interconnected value systems that redefine how work is organized, delivered, and monetized in today’s digital economy. This study examines platform-based business model innovation through the lens of value co-creation processes, analyzing user-generated content from digital work platforms including Reddit, FlexJobs, Toptal, and Deel. Using Latent Dirichlet Allocation (LDA) topic modeling on 342 semantically filtered reviews from platform workers, we identified six key themes characterizing stakeholder experiences: User Experience and Platform Evaluation (23.77%), Financial Concerns and Time Management (18.49%), Platform Satisfaction and Recommendation System (16.60%), Paid Services and Investment Strategies (15.09%), Job Search Processes and Remote Work Alternatives (13.96%), and Overall Platform Performance and Account Management (12.08%). These findings reveal how digital platforms create value through complex interactions between technology infrastructure, governance mechanisms, and stakeholder experiences within interconnected ecosystems. The dominance of user experience concerns over purely economic considerations challenges traditional labor economics frameworks and highlights the critical role of platform design in worker satisfaction. Our analysis demonstrates that successful plsatform business models depend on balancing technological capabilities with human-centered value propositions, requiring innovative approaches to ecosystem orchestration, stakeholder engagement, and value distribution. The study contributes to understanding how digital business models can leverage interconnected value systems to drive sustainable innovation, offering strategic insights for platform design, ecosystem governance, and business model optimization in the digital era. Full article