Search Results (32)

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

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

With the rapid expansion of user-generated content (UGC) platforms, digital empowerment has emerged as a crucial strategy for enhancing platform competitiveness. This study investigates how different digital empowerment models—no digital empowerment, free digital empowerment, and paid digital empowerment—affect platform outcomes. Employing a Salop model with multi-homing content creators, we analyze the impact of empowerment strategies on content quality, consumer engagement, and platform profitability. The findings reveal that digital empowerment functions not only as a technical tool but also as an incentive mechanism and structural supply adjustment strategy. It creates a behavioral feedback loop whereby empowered creators improve content quality, which increases consumer retention and word-of-mouth dissemination, thereby boosting traffic, advertising revenue, and overall platform profit. Among the three models, paid digital empowerment generally yields the highest content quality and platform profitability. The effectiveness of each strategy is moderated by creators’ data utilization capability, market competition intensity, advertising monetization potential, and word-of-mouth effects. These results provide theoretical and managerial insights into the design of differentiated empowerment strategies that align with platform goals and creator characteristics in competitive UGC environments. Full article

In UGC-based knowledge trading platforms, the abundance of personalized content often leads to varying quality levels. By incorporating embedded advertising, platforms can incentivize knowledge producers to produce high-quality content; however, the uncertainty in managing embedded advertisements increases the complexity of pricing knowledge products. This paper examines the impact of embedded advertising on the pricing of knowledge products, aims to maximize the profits of both knowledge producer and the platform. Based on Stackelberg game theory, two pricing decision models are developed under different advertising management modes: the platform-managed mode (where the platform determines the advertising intensity) and the advertiser-managed mode (where the advertiser determines the advertising intensity). The study analyzes the effects of UGC product quality, consumer sensitivity to advertising, and power structure on knowledge product pricing, and derives threshold conditions for optimal pricing. The results indicate that (1) When the quality of UGC knowledge product exceeds a certain threshold, platform-managed advertising becomes profitable. (2) Under the platform-managed mode, both the platform and knowledge producer can adopt price-increasing strategies to enhance profits. (3) Under the advertiser-managed mode, the platform can leverage differences in power structure to optimize revenue, while knowledge producer can actively enhance his pricing power to achieve mutual benefits with the platform. This study provides theoretical support and practical guidance for advertising cooperation mechanisms and pricing strategies for knowledge products in UGC-based knowledge trading platforms. Full article

This study examines how social media platforms influence tourism marketing strategies, consumer perceptions, and travel behaviors, addressing their sustainability implications. It aims to evaluate the current state of research on social media in tourism marketing, identify dominant trends, assess empirical evidence of impact, and critically highlight research gaps. The analysis focuses on three core marketing outcomes: destination image, travel intention, and user engagement—and includes a section examining sustainability considerations across environmental, sociocultural, and economic dimensions. The study uses a systematic critical review of 147 peer-reviewed academic articles published between 2015 and 2025, combined with a meta-analysis of 38 quantitative studies that report statistical effect sizes. The meta-analysis uses a random-effects model to compare the influence of different platforms and study contexts. Moderator variables include geographic region, platform type, and methodological design. Findings show that social media marketing has a statistically significant positive effect on destination image (Cohen’s d = 0.61), travel intention (d = 0.54), and user engagement (d = 0.43). The analysis also reveals geographic bias, limited research on emerging platforms, and a lack of longitudinal and ethical inquiry. Findings suggest that tourism researchers and marketers may have to adopt more context-sensitive, interdisciplinary, and ethical approaches. Critical sustainability concerns emerge, including “overtourism”, cultural commodification, digital inequities, and algorithmic biases. Further studies may focus on specific platform-related behaviors, long-term impacts, and integrated online strategies appropriate for global tourism diversity. Lastly, this paper advocates for context-sensitive, interdisciplinary, and ethically grounded approaches to ensure sustainable digital tourism marketing strategies. Full article

Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. This compels enterprises to elevate their crisis response standards and construct new response frameworks. Based on an empirical analysis of 3,135,675 social media dissemination data points from 94 corporate crisis incidents, this study explores effective crisis response patterns for enterprises through three dimensions: response timing, methods, and content. The key findings indicate that traditional crisis response timelines prove inadequate for social media scenarios, whereas intervention during the ascending phase of dissemination significantly curtails crisis propagation cycles. Beyond formal statements, informal responses demonstrate equivalent mitigation effects, with combined formal–informal approaches yielding optimal outcomes. The comparative analysis of four content strategies (downplaying, supporting, denying, and reframing) reveals differentiated impacts on dissemination volume and duration, highlighting an inherent trade-off between these parameters. This research contributes to the crisis management theory in social media contexts while providing actionable guidance for enterprises to establish systematic crisis response methodologies. The results emphasize temporal sensitivity in response deployment, strategic content formulation, and multimodal communication integration. Full article

As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries. Full article

(1) Background: In the current competitive market environment, accurately forecasting user needs is crucial for business success. By analyzing user-generated content (UGC) on social network platforms, enterprises can mine potential user needs and discern shifts in these needs, thereby enabling more efficient and precise product design that aligns with user needs. For newly launched products with a limited presence in the market, the scarcity of UGC poses a challenge to businesses seeking to predict user needs from small datasets. (2) Methods: To address this challenge, this paper proposes a model using correlation analysis (CA) and linear regression (LR) combined with multidimensional gray prediction (a CA-LR-GM (1, N) model) to help enterprises use small sample data to predict user needs. Using the UGC of the Xiaomi SU7 as a case study, this paper demonstrates the prediction of user needs for the vehicle and refines the prediction outcomes through an optimization design informed by the principle of optimal key feature distribution. (3) Results: The findings validate the feasibility of the proposed theoretical framework, offering a technical solution for the identification and prediction of user need trends. (4) Conclusions: This research puts forward strategic recommendations for enterprises regarding the optimization of their products. Full article

A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism domain. This framework seeks to address the challenges LLMs face in providing precise and contextually appropriate responses to domain-specific queries in the tourism field. TravelRAG extracts information related to tourist attractions from User-Generated Content (UGC) on social media platforms and organizes it into a multi-layer knowledge graph. The travel knowledge graph serves as the core retrieval source for the LLM, enhancing the accuracy of information retrieval and significantly reducing the generation of erroneous or fabricated responses, often termed as “hallucinations”. As a result, the accuracy of the LLM’s output is enhanced. Comparative analyses with traditional RAG pipelines indicate that TravelRAG significantly boosts both the retrieval efficiency and accuracy, while also greatly reducing the computational cost of model fine-tuning. The experimental results show that TravelRAG not only outperforms traditional methods in terms of retrieval accuracy but also better meets user needs for content generation. Full article

E-commerce live streaming has become a lucrative global industry. As the main carrier to convey information in live broadcasting, user-generated content (UGC)—and especially bullet screens—are crucial in influencing users’ purchase intentions. However, the influence of bullet screens’ multidimensional information characteristics on consumers’ decision-making processes requires further exploration. Additionally, most existing studies start with the short-term effects of live product realization, and must address how to enhance customers’ psychological ownership using new means of live streaming marketing to obtain long-term sustainable brand-building effects. This study introduces psychological ownership and the communicative presence as mediating variables based on the theory of elaboration likelihood modeling to explore the mechanism of the influence of UGC’s multidimensional features on viewers’ purchase intentions in live e-commerce broadcasting rooms. We collected 404 valid online questionnaires and tested our hypotheses using structural equation modeling. These findings indicate that UGC emotions, quality, and their interaction significantly and positively affect purchase intentions. Moreover, psychological ownership and the communicative presence mediate UGC’s effect on purchase intentions. These results provide a new perspective for understanding consumer behavior in live e-commerce to improve marketing effectiveness of e-commerce live streaming platforms. Full article

A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores the potential of web and immersive technologies, specifically the Virtual Romania (VRRO) platform, in enhancing sustainable tourism by redirecting tourist traffic to lesser-known destinations and boosting user engagement through interactive experiences. Our research examines how virtual tourism platforms (VTPs), which include web-based and immersive technologies, support sustainable tourism, complement physical visits, influence user engagement, and foster community building through social features and user-generated content (UGC). An empirical analysis of the VRRO platform reveals high user engagement levels, attributed to its intuitive design and interactive features, regardless of the users’ technological familiarity. Our findings also highlight the necessity for ongoing enhancements to maintain user satisfaction. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism can modernize travel experiences and contribute to the evolution of the broader tourism ecosystem by supporting sustainable practices and fostering community engagement. Full article

Social media has become an essential tool for travel planning, with tourists increasingly using it to research destinations, book accommodation, and make travel arrangements. However, little is known about how tourists use social media for travel planning and what factors influence their intentions to use social media for this purpose. This thesis aims to understand tourists’ intentions to use social media for travel planning. Specifically, it investigates the factors influencing tourists’ intentions to use social media for planning travel to Saudi Arabia. It develops a machine learning (ML) classification model to assist Saudi tourism SMEs in creating effective digital marketing strategies for social media platforms. A survey was conducted with 573 tourists interested in visiting Saudi Arabia, using the Design Science Research (DSR) approach. The findings support the tourist-based theoretical framework, showing that perceived usefulness (PU), perceived ease of use (PEOU), satisfaction (SAT), marketing-generated content (MGC), and user-generated content (UGC) significantly impact tourists’ intentions to use social media for travel planning. Tourists’ characteristics and visit characteristics influenced their intentions to use MGC but not UGC. The tourist-based ML classification model, developed using the LinearSVC algorithm, achieved an accuracy of 99% when evaluated using the K-Fold Cross-Validation (KF-CV) technique. The findings of this study have several implications for Saudi tourism SMEs. First, the results suggest that SMEs should focus on developing social media content that is perceived as useful, easy to use, and satisfying. Second, the findings suggest that SMEs should focus on using MGC in their social media marketing campaigns. Third, the results suggest that SMEs should tailor their social media marketing campaigns to the characteristics of their target tourists. This study contributes to the literature on tourism marketing and social media by providing a better understanding of how tourists use social media for travel planning. Saudi tourism SMEs can use the findings of this study to develop more effective digital marketing strategies for social media platforms. Full article

Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era. Such videos have increasingly become a significant means for users to captivate audiences and amplify their online influence. Given this, predicting the popularity of dance videos on UGC platforms has drawn significant attention. Methods: This study postulates that body movement features play a pivotal role in determining the future popularity of dance videos. To test this hypothesis, we design a robust prediction framework DanceTrend to integrate the body movement features with color space information for dance popularity prediction. We utilize the jazz dance videos from the comprehensive AIST++ street dance dataset and segment each dance routine video into individual movements. AlphaPose was chosen as the human posture detection algorithm to help us obtain human motion features from the videos. Then, the ST-GCN (Spatial Temporal Graph Convolutional Network) is harnessed to train the movement classification models. These pre-trained ST-GCN models are applied to extract body movement features from our curated Bilibili dance video dataset. Alongside these body movement features, we integrate color space attributes and user metadata for the final dance popularity prediction task. Results: The experimental results endorse our initial hypothesis that the body movement features significantly influence the future popularity of dance videos. A comprehensive evaluation of various feature fusion strategies and diverse classifiers discern that a pre–post fusion hybrid strategy coupled with the XGBoost classifier yields the most optimal outcomes for our dataset. Full article

Designer UGC (user-generated content) communities serve as the epicenter of contemporary innovation and creativity, offering a platform for a broad design community to showcase their talents, communicate, and collaborate. Grounded in Self-Determination Theory, this study constructs a research model aiming to delve deeply into the underlying driving factors influencing users’ intention to share their works within these communities. Through online surveys targeting UGC community users and employing structural equation modeling, this research validates the determinants affecting their sharing intentions and dissects the pathways of each influencing factor. The findings reveal that in designer UGC communities, factors such as autonomy, competence, relatedness, online social support, and value fit have a significant positive impact on users’ intention to share their works. This study offers profound insights into the intrinsic logic behind user behaviors in the design creativity domain, also providing robust guidance for the operation and management of online community platforms. Full article

This paper develops a duopoly model for user-generated content (UGC) platforms, which compete for consumers and content producers in two-sided markets characterized by network externalities. Each platform has the option to invest in a content quality assurance (CQA) system and determine the level of advertising. Our model reveals that network effects are pivotal in shaping the platforms’ optimal strategies and user behavior, specifically in terms of single vs. multi-homing. We find that when network effects for producers are weak, consumers tend to engage in multi-homing while producers prefer single-homing. Conversely, strong network effects lead to the opposite behavior. Furthermore, our model demonstrates that user behavior and network effects dictate whether a platform is incentivized to incorporate advertisements and/or invest in CQA. Generally, weak network effects prompt a platform to invest in a CQA system, unless both consumers and producers engage in multi-homing. Our model’s results highlight the importance for platform companies to evaluate the extent of network effects on their platform in order to anticipate user behavior, which subsequently informs the optimal CQA and advertising strategy. Full article