Search Results (815)

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

News recommendation systems must simultaneously address multiple challenges, including dynamic user interest modeling, nonlinear popularity patterns, and diversity recommendation in cold-start scenarios. We present a Popularity-Aware Dynamic Multi-Perspective Fusion Network (PAD-MPFN) that innovatively integrates three key components: adaptive subspace projection for multi-source interest fusion, logarithmic time-decay factors for popularity bias mitigation, and dynamic gating mechanisms for personalized recommendation weighting. The framework uniquely combines sequential behavior analysis, social graph propagation, and temporal popularity modeling through a unified architecture. Experimental results on the MIND dataset, an open-source version of MSN News, demonstrate that PAD-MPFN outperforms existing methods in terms of recommendation performance and cold-start scenarios while effectively alleviating information overload. This study offers a new solution for dynamic interest modeling and diverse recommendation. Full article

Entrepreneurial intention research requires further examination of systemic relationships between constructs of comprehensive models that will more closely approximate the ontological realities of individuals. This research jointly examines relationships among basic individual values and constructs from the theory of planned behavior, entrepreneurial orientation, and entrepreneurial intention while accounting for multiple contextual factors. Situational factors are accounted for by random determination of mediators, examination of 23 model configurations, and use of quasi-random samples (e.g., respondents with different demographic factors from different organizations, industries, and regions) from two culturally and economically contrasting countries, the United States and India. Models were analyzed using PLS-SEM. Contrary to the “common view”—the idea that Western countries are individualistic and Asian and Latin American countries are collectivistic—individual personal focus values and passion relationships were stronger for India’s sample than for the United States. Contrary results were found for basic individual social focus values and subjective norms relationships. Results show a lack of stark disparities between the two country samples. Hence, it seems that between-country differences have been overemphasized, while more attention to context and to within-country variability is required. This study expands the entrepreneurial orientation nomological network by jointly considering basic individual values and TPB’s and EO’s constructs anteceding entrepreneurial intention and examining a large set of model configurations while accounting for multiple situational factors in two culturally and economically contrasting countries. Full article

Entrepreneurship serves as a vital engine of economic development, yet the mechanisms translating entrepreneurial intention into behavior have gradually emerged. This study employs bibliometric analysis of 61 SSCI-indexed articles (2014–2024) using CiteSpace to examine co-authorship networks, co-citation patterns, and research hotspots. Our findings demonstrate that individual-level factors (personality traits, entrepreneurial self-efficacy, and entrepreneurship education) drive both entrepreneurial intention and entrepreneurial behavior. More importantly, environmental factors (university milieu, regional social legitimacy, and national cultural dimensions) moderate the relationship between entrepreneurial intention and behavior. The study also identifies a temporal pattern in the entrepreneurial intention–behavior correlation. These results advance theoretical understanding of the intention–behavior transition and offer practical insights for entrepreneurship education and policy design. Full article

This article explores the integration of artificial intelligence (AI) in digital marketing through social and mobile networks and its role in fostering sustainable consumer behavior. AI enhances personalization, sentiment analysis, and campaign automation, reshaping marketing dynamics and enabling brands to engage interactively with users. A quantitative study conducted on 501 social media users evaluates how perceived benefits, risks, trust, transparency, satisfaction, and social norms influence the acceptance of AI-driven marketing tools. Using structural equation modeling (SEM), the findings show that social norms and perceived transparency significantly enhance trust in AI, while perceived benefits and satisfaction drive user acceptance; conversely, perceived risks and negative emotions undermine trust. From a sustainability perspective, AI supports the efficient targeting and personalization of eco-conscious content, aligning marketing with environmentally responsible practices. This study contributes to ethical AI and sustainable digital strategies by offering empirical evidence and practical insights for responsible AI integration in marketing. Full article

Current technological development has made the Internet and new technologies increasingly present in people’s lives, expanding their opportunities but also potentially posing risks for dysfunctional use. This study aims to identify psychopathological factors associated with dysfunctional ICT use, extending the evidence beyond the well-established relationships with mood disorders to include personality disorders (i.e., cluster C in particular). A total of 711 participants (75.70% female; Mage = 28.33 years, SD = 12.30) took part in the data collection. Firstly, the results showed positive correlations between higher levels of addictive patterns for the Internet, social networks, smartphones and applications, and video games and higher levels of borderline symptoms as assessed by the Borderline Symptom List 23—Short Version. Moreover, scores reflecting high addictive patterns also positively correlated with general narcissistic traits as indicated by the total score of the Narcissistic Personality Inventory 13—Short Version and those specifically described by its Entitlement/Exploitativeness dimension, as well as with higher levels of almost all the personality traits assessed by the Personality Inventory for DSM 5—Brief Form (i.e., negative affectivity, detachment, disinhibition, and psychoticism). These findings broaden the still scarce body of evidence on the relationship between personality disorders and dysfunctional ICT use, which, however, needs to be further explored. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. 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

Background: Understanding the multifactorial determinants of human longevity remains a major scientific challenge. Certain regions of the world—so-called “longevity hotspots”—exhibit a notably high prevalence of centenarians; one such region is the province of Ourense, in north-western Spain. Objectives: This study aimed to analyse, for the first time, the nutritional factors associated with healthy longevity among centenarians, as well as those linked to longevity irrespective of health status, in the province of Ourense. Methods: A cross-sectional, retrospective, observational, mixed-methods study was conducted. A population of 261 individuals aged 100 or over residing in Ourense was identified. A sample of 156 participants was included in the quantitative analysis; from this sample, 25 centenarians were selected for in-depth qualitative analysis through personal interviews. Results: Dietary patterns aligned with the Southern European Atlantic Diet (SEAD), combined with strong social bonds and a culture of self-sufficiency, appear to be key contributors to exceptional longevity in this population. Conclusions: Remarkable longevity in Ourense is associated with a combination of factors: adherence to an SEAD-style dietary pattern, an active and uncomplicated lifestyle, and strong social support networks. Full article

The drastic and historic fall in natural rubber prices has prompted many smallholders around the world to abandon rubber plantations in favor of other survival alternatives. In the Lomela and Lodja territories of Sankuru Province (Democratic Republic of Congo), where a historical absence of a rubber market prevails, local communities have conserved rubber plantations inherited from the colonial era (dating back to 1955). Data collected from 401 households enabled us to identify the perceptions and determinants of rubber plantation conservation. The results show that households are highly dependent on forest ecosystem services. Agriculture is the main activity for 81.3% of respondents, in the context of extreme poverty where daily incomes amount to 0.33 USD/person. The patriarchal system favored men, who inherited 97% of the plantations. Men perceived the conservation of the plantations as beneficial, while women perceived it as serving external project interests. Perceptions were significantly influenced by gender, age, social and legal organization, geographical origin, mode of acquisition, main activity, diversification of income sources, membership in a tribal mutuality, access to the informal mutual aid networks, membership in an association and contact with extension services. Conservation was positively and significantly correlated with geographical origin, membership in an association, contact with extension service, consideration of plantations as natural heritage and the ecosystem services provided. These results underline that rubber plantations cannot be understood only in terms of rubber production, but also in terms of their socio-ecological and heritage dimensions. Full article

Background: Endometriosis is a chronic, inflammatory, estrogen-dependent gynecological disease in which endometrial-like tissue grows in areas outside the uterus. This condition may significantly influence women’s life course and mental health. Personal, behavioral, social, and environmental factors play a crucial role in predicting these outcomes. The current study aimed to compare the Perceived Life Course Impact (PLCI) and mental health of women with Endometriosis to those without the disease, as well as to explore the factors associated with PLCI and mental health. Methods: This cross-sectional study surveyed 543 Israeli women (270 with Endometriosis, 273 without). Participants completed a validated questionnaire assessing perceptions of life course impact in several life domains (intimacy and relationships, employment, education) and mental health. Multiple regression analyses were used to identify factors associated with PLCI and mental health among participants. Results: Women with Endometriosis reported a significantly greater negative perceived impact on intimacy and relationships, employment, and education, with poorer mental health, as compared to the control group. Women with Endometriosis also experienced higher social negativity and lower social support. Multiple regression analyses indicated social negativity as a strong predictor of negative PLCI. Additionally, menstrual pain, social support, and healthcare accessibility were found to be significant predictors of both PLCI and mental health. Conclusions: Comprehensive care for women with Endometriosis requires a multidisciplinary approach, with interventions focused on improving healthcare accessibility, enhancing social support networks, and mitigating social negativity within interpersonal environments. Full article