Search Results (920)

This theoretical essay aims to analyze some of the socio-economic innovations introduced by Platform Capitalism Specifically, it focuses on two main aspects: first, the digital platform as a radical organizational innovation. Digital platforms represent a structural novelty in the market economy, signaling a new organization of production and labor. Second, the essay examines the role of platforms in directly generating value through the concept of “network value”. To this end, it explores the function of “business intelligence” as a strategic and competitive tool. Finally, the paper discusses the key issues associated with platform capitalism, which could threaten its social sustainability and contribute to economic and financial instability. These issues include the increasing commodification of everyday activities, the devaluation of paid labor in favor of free production driven by platform users (the so-called prosumers), and the emergence of proprietary and financial monopolies. Hence, digital platforms do not inherently ensure comprehensive social and environmental sustainability unless supported by targeted economic policy interventions. Conclusively, it is emphasized that defining robust social welfare frameworks—which account for emerging value creation processes—is imperative. Simultaneously, policymakers must incentivize the proliferation of cooperative platforms capable of fostering experimental circular economy models aligned with ecological sustainability. Full article

Fuel price increases have long been a contentious issue in Indonesia, sparking intense public and political debates. This study examines how digital media, particularly Kompas.com and Tempo.co, shape public discourse on fuel price hikes through mediatization. Using discourse network analysis, this study compares the political narratives surrounding fuel price increases during the administrations of Susilo Bambang Yudhoyono (2013) and Joko Widodo (2022). The findings reveal a shift in dominant discourse—opposition to price hikes was prominent in both periods, with government authority and economic justification emphasized in 2013, whereas concerns over rising living costs and social unrest dominated in 2022. This study highlights how mediatization has transformed policymaking from deliberative discussions into fragmented media battles, where digital platforms amplify competing narratives rather than facilitating consensus. Kompas.com predominantly featured counter-discourses, while Tempo.co exhibited stronger pro-government narratives in 2013. This study suggests that while digital media plays a crucial role in shaping policy perceptions, it does not necessarily translate into policy influence. It contributes to the broader understanding of the media’s role in policy debates. It underscores the need for more strategic government communication to manage public expectations and mitigate political unrest surrounding fuel price adjustments. Full article

This study investigates how young Chinese consumers across generational lines engage with wine, addressing three key research questions: What motivates their wine purchases? What sensory preferences do they exhibit? And through which channels do they prefer to buy wine? Based on a qualitative design combining focus groups and semi-structured interviews, the study identifies significant generational differences between millennials and post-millennials. Millennials treat wine as a social tool for networking and status, while post-millennials view wine as a medium of personal identity shaped by digital culture. Similarly, millennials prefer a balance of traditional and digital retail, whereas post-millennials favour online platforms. Experiential consumption follows the same pattern, from formal tourism to virtual tastings. By linking these findings to institutional and cultural theories of consumer behaviour, the study contributes to a nuanced understanding of wine consumption in an emerging market. It provides practical implications for wine marketers aiming to localize their strategies for younger Chinese segments. Full article

The purpose of the present research is to analyse the negative outcomes associated with the excessive Instagram dependency of those users that access the application through their smartphones. An empirical study was conducted through online interviews using structured questionnaires, resulting in 342 valid responses, with the target population being young users over 18 years old who access Instagram daily. Research shows that dependency on Instagram is primarily driven by individuals’ need for orientation and understanding, with entertainment being a secondary motivation. The results indicate that dependency on the social network is positively associated with excessive use, addiction, and Instastress. Furthermore, excessive use contributes to personal and social problems and increases both stress levels and mindfulness related to the platform. In turn, this excessive use intensifies addiction, which functions as a mediating variable between overuse and Instastress, mindfulness, and emotional exhaustion. This study offers valuable insights for academics, mental health professionals, and marketers by emphasizing the importance of fostering healthier digital habits and developing targeted interventions. Full article

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article

This study explores the determinants of startup intention within the context of e-commerce platform-based startups in South Korea. We employ an extended technology acceptance model (TAM) that integrates individual, social, and entrepreneurial characteristics. A two-step analytical approach is applied, combining variable extraction through data mining and hypothesis testing using structural equation modeling. The results indicate that personal and social factors—such as entrepreneurial mindset and social influence—positively affect perceived usefulness, while job relevance and exposure to successful startup models enhance perceived ease of use. In contrast, security concerns and technological barriers negatively impact these relationships, posing critical obstacles to platform-based startups. This study extends the TAM framework to the platform-based startup context, offering theoretical contributions and proposing policy implications, including promoting digital literacy, developing entrepreneurial networks, and addressing security and regulatory issues. These insights offer a deeper understanding of how platform environments shape entrepreneurial behavior, providing practical guidance for startup founders, developers, and policymakers. Full article

During the course of genome mining initiatives, we identified a marine-derived Micromonospora, assigned here as strain WMMD956; the genome of WMMD956 appeared to contain a number of features associated with everninomicins, well-known antimicrobial orthosomycins. In addition, LCMS-based hierarchical clustering analysis and principal component analysis (hcapca) revealed that WMMD956 displayed an extreme degree of metabolomic and genomic novelty. Dereplication of high-resolution tandem mass spectrometry (HRMS/MS) and Global Natural Product Social molecular networking platform (GNPS) analysis of WMMD956 resulted in the identification of several analogs of the previously known everninomicin. Chemical structures were unambiguously confirmed by HR-ESI-MS, 1D and 2D NMR experiments, and the use of MS/MS data. The isolated metabolites, 1–3, were evaluated for their antibacterial activity against methicillin-resistant Staphalococcus aureus (MRSA). Full article

Social network platforms have a big impact on the development of companies by influencing clients’ behaviors and sentiments, which directly affect corporate reputations. Analyzing this feedback has become an essential component of business intelligence, supporting the improvement of long-term marketing strategies on a larger scale. The implementation of powerful sentiment analysis models requires a comprehensive and in-depth examination of each stage of the process. In this study, we present a new comparative approach for several feature extraction techniques, including TF-IDF, Word2Vec, FastText, and BERT embeddings. These methods are applied to three multilingual datasets collected from hotel review platforms in the tourism sector in English, French, and Arabic languages. Those datasets were preprocessed through cleaning, normalization, labeling, and balancing before being trained on various machine learning and deep learning algorithms. The effectiveness of each feature extraction method was evaluated using metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a new metric that measures the execution time for generating word representations. Our extensive experiments demonstrate significant and excellent results, achieving accuracy rates of approximately 99% for the English dataset, 94% for the Arabic dataset, and 89% for the French dataset. These findings confirm the important impact of vectorization techniques on the performance of sentiment analysis models. They also highlight the important relationship between balanced datasets, effective feature extraction methods, and the choice of classification algorithms. So, this study aims to simplify the selection of feature extraction methods and appropriate classifiers for each language, thereby contributing to advancements in sentiment analysis. Full article

Health professionals and institutions, as users and influencers, use social networks to disseminate information and knowledge about health issues, in the case of organ donation and transplantation (ODT) to spread the social benefits of the process and increase the positive attitude towards ODT. The aim of this work was to analyse the perception and use of social networks by university students of health sciences to determine whether, in their opinion, social platforms are suitable for the promotion of ODT after participating in an educational programme specialising in ODT and communication. The students indicated that social networks are a good medium for disseminating messages about ODT, with WhatsApp standing out as the most appropriate after the programme. Eighty-six per cent say that social media can positively influence the attitude towards ODT and 65% have started to follow ODT institutional accounts on social media. Addressing communication in specialisation programmes in the field of health and ODT raises awareness of the responsible use of social media among university health students and strengthens their capacity as prescribers of the social benefits of ODT. Full article

This study examines the added value of territorial cooperation within the LEADER approach, a key pillar of the EU’s rural development policy. Both interterritorial and transnational cooperation projects empower Local Action Groups (LAGs) to tackle common challenges through innovative and community-driven strategies. Drawing on over 3000 projects since 1994, LEADER cooperation has proven its ability to deliver tangible results—such as joint publications, pilot projects, and shared digital platforms—alongside intangible benefits like knowledge exchange, improved governance, and stronger social capital. By facilitating experiential learning and inter-organizational collaboration, cooperation enables stakeholders to work across territorial boundaries and build networks that respond to both national and transnational development issues. The interaction among diverse actors often fosters innovative responses to local and regional problems. Using a mixed-methods approach, including case studies of Italian LAGs, this research analyses the dynamics, challenges, and impacts of cooperation, with a focus on learning processes, capacity building, and long-term sustainability. Therefore, this study focuses not only on project outcomes but also on the processes and learning dynamics that generate added value through cooperation. The findings highlight how territorial cooperation promotes inclusivity, fosters cross-border dialogue, and supports the development of context-specific solutions, ultimately enhancing rural resilience and innovation. In conclusion, LEADER cooperation contributes to a more effective, participatory, and sustainable model of rural development, offering valuable insights for the broader EU cohesion policy. Full article

Amid structural changes driven by the greening and digital transformation of the maritime industry, the demand for career transitions of seafarers with onboard experience to shore-based positions—particularly ship superintendents—is steadily increasing. However, the current lack of a systematic education and career development framework to support such transitions poses a critical challenge for shipping companies seeking to secure sustainable human resources. The aim of this study was to develop a competency-based training program that facilitates the effective transition of seafarers to shore-based ship superintendent roles. We integrated a developing a curriculum (DACUM) analysis with competency-based job analysis to achieve this aim. The core competencies required for ship superintendent duties were identified through three expert consultations. In addition, social network analysis (SNA) was used to quantitatively assess the structure and priority of the training content. The analysis revealed that convergent competencies, such as digital technology literacy, responsiveness to environmental regulations, multicultural organizational management, and interpretation of global maritime regulations, are essential for a successful career shift. Based on these findings, a modular training curriculum comprising both common foundational courses and specialized advanced modules tailored to job categories was designed. The proposed curriculum integrated theoretical instruction, practical training, and reflective learning to enhance both applied understanding and onsite implementation capabilities. Furthermore, the concept of a Seafarer Success Support Platform was proposed to support a lifecycle-based career development pathway that enables rotational mobility between sea and shore positions. This digital learning platform was designed to offer personalized success pathways aligned with the career stages and competency needs of maritime personnel. Its cyclical structure, comprising career transition, competency development, field application, and performance evaluation, enables seamless career integration between shipboard- and shore-based roles. Therefore, the platform has the potential to evolve into a practical educational model that integrates training, career development, and policies. This study contributes to maritime human resource development by integrating the DACUM method with a competency-based framework and applying social network analysis (SNA) to quantitatively prioritize training content. It further proposes the Seafarer Success Support Platform as an innovative model to support structured career transitions from shipboard roles to shore-based supervisory positions. Full article

In social networks like community Question-Answering (cQA) services, members interact with each other by asking and answering each other’s questions. This way they find counsel and solutions to very specific real-life situations. Thus, it is safe to say that community fellows log into this kind of social network with the goal of satisfying information needs that cannot be readily resolved via traditional web searches. And in order to expedite this process, these platforms also allow registered, and many times unregistered, internauts to browse their archives. As a means of encouraging fruitful interactions, these websites need to be efficient when displaying contextualized/personalized material and when connecting unresolved questions to people willing to help. Here, demographic factors (i.e., gender) together with frontier deep neural networks have proved to be instrumental in adequately overcoming these challenges. In fact, current approaches have demonstrated that it is perfectly plausible to achieve high gender classification rates by inspecting profile images or textual interactions. This work advances this body of knowledge by leveraging lexicalized dependency paths to control the level of abstraction across texts. Our qualitative results suggest that cost-efficient approaches exploit distilled frontier deep architectures (i.e., DistillRoBERTa) and coarse-grained semantic information embodied in the first three levels of the respective dependency tree. Our outcomes also indicate that relative/prepositional clauses conveying geographical locations, relationships, and finance yield a marginal contribution when they show up deep in dependency trees. Full article

The proliferation of social media platforms has triggered an unprecedented increase in multimodal fake news, creating pressing challenges for content authenticity verification. Current fake news detection systems predominantly rely on isolated unimodal analysis (text or image), failing to exploit critical cross-modal correlations or leverage latent social context cues. To bridge this gap, we introduce the SCCN (Semantic-enhanced Cross-modal Co-attention Network), a novel framework that synergistically combines multimodal features with refined social graph signals. Our approach innovatively combines text, image, and social relation features through a hierarchical fusion framework. First, we extract modality-specific features and enhance semantics by identifying entities in both text and visual data. Second, an improved co-attention mechanism selectively integrates social relations while removing irrelevant connections to reduce noise and exploring latent informative links. Finally, the model is optimized via cross-entropy loss with entropy minimization. Experimental results for benchmark datasets (PHEME and Weibo) show that SCCN consistently outperforms existing approaches, achieving relative accuracy enhancements of 1.7% and 1.6% over the best-performing baseline methods in each dataset. Full article

This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). The proposed cloud-based business intelligence model automates Extract, Transform, Load (ETL) processes, enables real-time analytics, and secures data integrity and transparency through blockchain-enabled audit trails. By implementing the proposed solution, Smart City and public service providers can significantly improve operational efficiency, including a 15% reduction in costs and a 12% decrease in fuel consumption for waste management, as well as increased citizen engagement and transparency in Smart City governance. The digital twin component facilitated scenario simulations and proactive resource management, while the participatory governance module empowered citizens through transparent, immutable records of proposals and voting. This study also discusses technical, organizational, and regulatory challenges, such as data integration, scalability, and privacy compliance. The results indicate that the proposed approach offers a scalable and sustainable model for Smart City transformation, fostering citizen trust, regulatory compliance, and measurable environmental and social benefits. Full article

The rapid advancement of Internet of Things technologies has significantly enhanced cross-regional communication among geographically and linguistically diverse populations on social platforms yet simultaneously accelerated the propagation of rumors across multilingual networks at unprecedented velocity. Therefore, this study focuses on investigating the spatiotemporal propagation dynamics and cross-lingual diffusion characteristics of rumors. Distinguished from conventional approaches, we innovatively formulate a hybrid dual-layer rumor containment model through a reaction–diffusion framework that explicitly incorporates the coupling control effects of media layers with independent propagation dynamics. Furthermore, we rigorously prove the differentiability of control-to-state mappings, which enables the derivation of necessary optimality conditions for the optimal control problem. Finally, comprehensive simulations validate both the adaptability and effectiveness of our media-based spatiotemporal control strategies in multilingual environments. Full article