User-generated Research Articles

EXPRESS: Status Downgrade: the Impact of Losing Status on a User-generated Content Platform

Non-financial incentives such as badges, ranks, and status are often used to encourage user participation on online platforms. This study focuses on the effect of one such incentive, “status,” in the context of a thirdparty restaurant-review platform. In contrast to previous research that has mainly focused on the effects of such incentives on subsequent contributions from users who gained statuses, we explore how the intrinsic and perceived quality of content generated by users is impacted after users lose their statuses. Using natural language processing (NLP) techniques to extract quality metrics from online reviews in our dataset, we exploit a quasi-experimental setting and demonstrate that even though the intrinsic quality of reviews significantly decreases after a reviewer is demoted by a platform, consumers on the platform nonetheless perceive these reviews as disproportionately useful. We draw on inequity theory and the elaboration likelihood model to theoretically support our empirical results, as well as conduct mechanism analyses to rule out alternative explanations. Furthermore, we find that temporal associations with a platform or with an elevated status do not moderate the effect of status loss on the intrinsic and perceived quality of reviews written post-demotion. The implications of our findings are significant for platform managers who manage the design of status-driven recognition systems and must determine how the change in status should be displayed on the platform.

How do affective and cognitive involvement affect tourism intentions: a perspective on blogger impact

ABSTRACT Social Network Services (SNS) platforms are crucial sources of travel information. Xiaohongshu, a highly esteemed platform in China and globally, provides travel guides and lifestyle information. Empirical studies on how influencers, as well as the affective and rational factors of travel user-generated content (UGC) affect travel intentions are limited. Based on involvement theory and dual-system theory, this study uses topic modelling, sentiment analysis, and semantic network visualization to analyze travel posts and comments on Xiaohongshu. It reveals the affective and functional factors of posts by bloggers of varying impact and their effects on audience affective and cognitive involvement, processing systems, and travel intentions. The results are as follows: (1) Posts by medium-impact bloggers are more successful in generating and enhancing travel intentions. (2) Affective factors trigger affective involvement and processing, driving heuristic travel intentions; functional factors drive cognitive processing through cognitive involvement, leading to cognitive travel intentions. (3) The proper emotional intensity of affective and functional factors effectively transmits information, builds audience confidence, and maximises the enhancement of travel intentions. This study enriches the research viewpoint and theoretical foundation of enhancing travel intentions, providing practical recommendations for tourism practitioners in creating appropriate and effective emotional promotional content.

Viral Dynamics on Social Media: Enhancing Brand Engagement through Meme Marketing Strategies

This research delves into the transformative power of meme marketing in shaping brand engagement and consumer perception within digital ecosystems. By conducting a nuanced examination of meme-centric marketing strategies, this study elucidates how brands leverage viral content to foster a sense of community and enhance customer loyalty. It employs a methodological approach that combines qualitative analysis and digital analytics to assess the impact of memes on audience engagement metrics and brand sentiment. The findings underscore the importance of authenticity and the strategic alignment of meme content with brand identity and audience values. The study also highlights the role of omni-channel distribution strategies in amplifying reach and engagement, offering actionable insights for marketers aiming to capitalize on the dynamic nature of digital culture. Additionally, it points to the growing significance of user-generated content in meme marketing, advocating for brands to foster participatory cultures that encourage audience creativity. The research sets the stage for future investigations into the evolving landscape of digital marketing, suggesting further exploration into the intersection of technology, culture, and consumer behavior in the age of memes.

Using machine learning to develop customer insights from user-generated content

Uncovering customer insights (CI) is indispensable for contemporary marketing strategies. The widespread availability of user-generated content (UGC) presents a unique opportunity for firms to gain a nuanced understanding of their customers. However, the size and complexity of UGC datasets pose significant challenges for traditional market research methods, limiting their effectiveness in this context. To address this challenge, this study leverages natural language processing (NLP) and machine learning (ML) techniques to extract nuanced insights from UGC. By integrating sentiment analysis and topic modeling algorithms, we analyzed a dataset of approximately four million X posts (formerly tweets) encompassing 20 global brands across industries. The findings reveal primary brand-related emotions and identify the top 10 keywords indicative of brand-related sentiment. Using FedEx as a case study, we identify five prominent areas of customer concern: parcel tracking, small business services, the firm's comparative performance, package delivery dynamics, and customer service. Overall, this study offers a roadmap for academics to navigate the complex landscape of generating CI from UGC datasets. It thus raises pertinent practical implications, including boosting customer service, refining marketing strategies, and better understanding customer needs and preferences, thereby contributing to more effective, more responsive business strategies.

The Rise of Participatory Culture and User Content in the Digital Era: A Case Study of Wanyan Huide Fans

In the digital era, with the development of a new generation of information technology, the live broadcast industry is developing rapidly. Due to the impact of COVID-19 and China's national policy, the number of users of live broadcast platforms has grown rapidly during COVID-19, such as Tiktok, Kuaishou, Baidu and other platforms, and a large number of users have poured in, and live broadcasts have become an emerging industry in China. In the process of the rapid development of live broadcasting, some types of live broadcast have also been differentiated, the most popular of which is live broadcasting. In addition, there is also a very popular chat live broadcast. This paper chooses Henry Jenkins's participatory culture and UGC (User-Generated Content) as the theoretical framework and takes the recently popular "perceptual psychology Wanyan Huide" as the research object. Through semi-structured in-depth interviews, this paper explores the reasons for Wanyan Huide's popularity, analyzes the characteristics of his fans, and finally reveals the motivation of his fans for secondary creation.

Tiramisù: making sense of multi-faceted process information through time and space

AbstractKnowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

Mesterséges intelligencia és a rendészetelmélet

Aim: The aim of the publication is to demonstrate the usability of ChatGPT, which is widely available free of charge and quickly gaining popularity, at a general user level in the field of law enforcement theory and police science. Methodology: The paper shows the limitations and potential of the program by providing direct instructions to the system and the texts generated by the program without any changes, and by briefly explaining them. Findings: The revolutionary rise of artificial intelligence, in particular the general availability of large language models, has a major impact on police theory and could be a driver of future change, with both opportunities and threats. Artificial intelligence can play a major role in the scientific development of police theory, including all aspects of the establishment of law enforcement (professional publications, university education, professional organisations, tradition), but this requires the commitment of those working in the field. The programme under consideration currently has its limitations, but the exponential development that is expected will challenge the science of law enforcement theory, as well as law enforcement itself. Value: On the one hand, the author hopes that the publication will bring artificial intelligence, including ChatGPT, to prospective users who are eager to try and exploit it in their scientific work. On the other hand, it draws attention to the limitations of the current version of the programme.

Game-theoretic implications for uncovering the effects of virtual tipping in complex user networks

Consumer-generated media (CGM), including social live streaming service (SLSS), often incorporate gamification elements to engage users. Online virtual tips and gifts, such as “bits” in Twitch, exemplify this approach by fostering interactive dynamics between live streamers and their audience. However, issues remain in understanding the impact of (virtual) tips on user behavior and how platform commissions, which are partly collected from the tips provided by other users, influence the platform’s overall revenue and user behavioral strategies. This study considers a CGM/SLSS platform, such as Twitch, as a variation of the public goods game and proposes a game-theoretical model integrated with a platform-mediated tipping system with a certain commission rate. In experiments, the proposed model was applied to artificial and actual SLSS networks, demonstrating that the influence of a tipping system on user behavior is significant, impacting the motivation for live streaming and the effort to improve its quality. Depending on individual preferences for psychological or monetary reward, the commission rate is determined by the platform depending on the users’ network positions, such as the degree of connectivity, which corresponds to the number of followers/followees in SLSS networks. Our findings offer valuable insights into the design and management of SLSS platforms and further suggest that there may be an appropriate commission rate to maximize platform revenue.

AI-based big data analysis of user-generated content to understand adherence in oral hormone receptor-positive advanced breast cancer treatment: A literature review and patient-centered approach using natural language processing

AI-based big data analysis of user-generated content to understand adherence in oral hormone receptor-positive advanced breast cancer treatment: A literature review and patient-centered approach using natural language processing

Effect of Sulfuric Acid Immersion on Electrical Insulation and Surface Composition of Amorphous Carbon Films

Sulfuric acid is a concern for contacts within electronic devices, and the application of amorphous carbon films as thin electrical insulating coatings for small coils requires full investigation of its effects. Five types of amorphous carbon films were fabricated on Si substrates under different deposition conditions using vacuum coating systems. Based on their optical constants (ISO 23216:2021(E)), the films were classified into three types: hydrogenated amorphous carbon (a-C:H), polymer-like carbon (PLC), and graphite-like carbon (GLC). The structure, surface composition, and electrical insulation properties of the films were evaluated before and after immersion in sulfuric acid. Although the PLC and a-C:H showed progression of surface oxidation due to sulfuric acid immersion, none showed obvious changes in their structure or DC dielectric breakdown field strength due to sulfuric acid immersion, proving their stability. Furthermore, the PLC and a-C:H, which had a relatively low extinction coefficient, exhibited excellent insulation properties. Our results suggest that amorphous carbon films can be useful as thin insulating films for small coils that may come in contact with sulfuric acid. Our study offers a valuable tool for general users in the industry to facilitate selection of electrical insulating amorphous carbon films based on optical constants, such as extinction coefficients.

Do Psychological Ownership and Communicative Presence Matter? Examining How User-Generated Content in E-Commerce Live Streaming Influences Consumers' Purchase Intention.

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.

Does user-generated video content motivate individuals to visit a destination? A non-visitor typology

Νon-visitors comprise a lucrative but underexplored segment in the tourism literature. This study fills a research gap on non-visitors by proposing a new typology contributing to a better understanding of their travel motivations and intentions to visit or not a destination, after being exposed to user-generated video content online. Semi-structured in-depth interviews facilitated via two tourist videos of Raja Ampat, Indonesia, available on YouTube, were conducted with 31 Indonesians and 30 British non-visitors. Findings indicate the presence of five types of non-visitors based on travel motivations: neophiles, nationalists, narcissists, volunteers, and reluctant non-goers. The study contributes to a better understanding of non-visitors’ motivations across different cultures and sheds some light on how user-generated video content shapes visit intentions among those who have never visited a destination. In practical terms, the study offers prudent knowledge on enhancing the possibility that non-visitors will turn into goers.

Beyond static rankings: A tourist experience-driven approach to measure destination competitiveness

In the dynamic field of destination management, maintaining a destination's competitiveness requires understanding the evolving preferences of tourists. However, current research often adopts a static approach, failing to capture the dynamic nature of tourist needs and the evolving competitiveness of a destination. To address this, we introduce a novel approach using user-generated content from various social media platforms over a six-year period to assess key attributes that influence destination competitiveness. The results indicate that attributes in deciding destination competitiveness are not fixed, with some remaining stable factors in competitiveness, while others fluctuate over time. Attributes that even alter their competitive standing could significantly impact overall destination competitiveness. This research contributes a dynamic model that allows destination managers to adapt strategies in real time, aligning with current market conditions and enhancing competitiveness in the tourism industry.

User-Generated Content as a Brand Image Builder: An Explanatory Study on Instagram's JackArmy Fashion Brand Image

User-Generated Content as a Brand Image Builder: An Explanatory Study on Instagram's JackArmy Fashion Brand Image

The influence of social media content on attitude, destination image and intention of female Muslim travelers to visit halal destinations: comparison between UGC and FGC

Purpose This study aims to investigate how user-generated content (UGC) and firm-generated content (FGC) in social media affect attitudes toward halal tourism destinations, destination image and visit intentions among female Muslim (Muslimah) travelers. It also examines the role of travel habits as the moderating variable. Design/methodology/approach This study adopts the Theory of Interpersonal Behavior (TIB) and the Theory of Planned Behavior (TPB) to explore the motivation of Muslimah travelers to visit halal destinations. The survey was completed by 284 Indonesian Muslimah respondents who had visited halal tourism destinations in Indonesia. The SEM-PLS statistical technique was used to verify the associations between variables. Findings This study’s findings offer valuable insights for both theoretical understanding and practical applications. They suggest that both UGC and FGC significantly predict attitudes toward halal tourism and destination image, thereby influencing the visiting intentions of Muslimah travelers. Although UGC does not directly influence visiting intentions, it does play a significant role in shaping visiting intentions through its mediating effect on attitudes toward halal tourism destinations. In addition, the results indicate that habit positively moderates the relationship between attitudes toward halal tourism and visiting intentions. Originality/value This research contributes uniquely and significantly in two aspects. First, it merges the TIB with the TPB to elucidate the decision-making process of Muslimah travelers when selecting halal destinations. This integration illuminates new insights into the intentions of Muslimah tourists. Second, this study examines the respective influences of UGC and FGC on the visiting intentions of Muslimah travelers to halal tourism destinations. By comparing UGC and FGC, the research provides valuable insights into the decision-making processes of Muslimah tourists. Given Indonesia’s status as home to the largest Muslim population globally, this study offers a novel perspective on the traveling behavior of Muslimahs and practical recommendations for the Indonesian tourism sector in the establishment and promotion of Muslim-friendly destinations within the country.

Sustainable Brand Resilience: Mitigating Panic Buying through Brand Value and Food Waste Attitudes Amid Social Media Misinformation

This study investigates the relationship between user-generated content on social media and panic buying, with a focus on how attitudes towards food waste and brand value act as moderating variables. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from an online survey with 370 responses were analyzed. The findings show that user-generated content significantly contributes to the dissemination of inaccurate information, which in turn triggers panic buying. However, the impact of inaccurate information on panic buying is moderated by consumer attitudes towards food waste and brand value. Specifically, consumers with negative attitudes towards food waste and high brand trust are less likely to engage in panic buying when exposed to misinformation. These results suggest that promoting responsible consumption and leveraging brand value can mitigate the adverse effects of misinformation during crises. This study contributes to sustainability by providing insights into developing strategies for retailers and policymakers to manage consumer behavior, emphasizing the importance of accurate information and brand communication in reducing panic buying tendencies.

Satisfaction and sustainability concerns in whale-watching tourism: A user-generated content model

This study examines the satisfaction and sustainability concerns of whale-watching tourists by analysing user-generated content (UGC) on social media. A satisfaction model was developed and estimated utilising an ordered probit analysis with UGC data from TripAdvisor over the last 13 years that includes a specific whale-watching lexicon. The model addresses most of the physical, human, environmental, experiential, and operational aspects of the activity, including consumers’ feelings and sustainability concerns. The significance of the variables in the model was proven with the available empirical data. The findings provide a comprehensive description of the underpinnings of whale watchers’ preferences and concerns. The evidence reveals compelling social trends towards higher sustainability concerns influencing satisfaction, providing valuable information for the industry and its decision-makers for understanding preferences for sustainability in whale-watching tourism.

A blockchain-based platform for incentivizing customer reviews in the grocery industry

Nowadays, user-generated content is pivotal for many companies: people trust other customers' opinions more than any brand advertisement. Brands are aware of this and try to promote and motivate their customers to create high-quality content. However, this way of operating is still at an early stage: there is a lack of fairness, as companies typically do not provide a validation system, or if they do, it is not based on a transparent solution, and often there is no reward for creating unique and high-quality content. In this paper, we focus on the problem of incentivizing the user's creation of content in the form of customer reviews in the online grocery industry. Specifically, we illustrate the solution to the problem devised in the Re-Taled project by relying on blockchain technology. We developed a decentralized ecosystem of consumers, influencers, and manufacturers, where content creators are rewarded for their contribution according to a framework that provides incentives in the form of both reputation and monetization. Blockchain technology is used to certify the content's authenticity and compensate content creators with a cryptographic token. We illustrate the technical choices of the solution together with its software architecture and implemented platform. In particular, we introduce the framework used to validate the trustworthiness of user-generated content and favor fairness and transparency within the platform.

Creating Emordle: Animating Word Cloud for Emotion Expression.

We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the extent of the emotion delivered. We also invited general users to create their own emordles based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.

Firm’s Online Controversial Events: How User-Generated Content Affects Consumer Engagement

Firm’s Online Controversial Events: How User-Generated Content Affects Consumer Engagement