User-generated Research Articles

Lights, camera, action! Adding showmanship to the hospitality servicescape and experiencescape to attract customers: a management perspective

PurposeRestaurants are challenged to secure attraction attributes that provide motives, satisfaction and experiences for guests. The social interaction between server and guest can provide. Our case study aims to examine the perspectives of hospitality executives towards showmanship and server performances in restaurants to enhance servicescape and elevate guest experiences.Design/methodology/approachA literature review examined employee showmanship as part of the cocreation of gastronomic experiences and servicescape. Additional data were obtained from semi-structured interviews with 15 hospitality executives affiliated with Macao’s integrated resorts (IRs), and the information was examined using thematic analysis.FindingsThe study outcomes showed management support for showmanship performed by servers or chefs in cocreating immersive memorable dining experiences and social media and user-generated content (UGC). This was perceived to distinguish a restaurant from the competition. However, server talent and showmanship interaction with guests must be well managed, and bundled with additional restaurant servicescape attributes.Practical implicationsThe comments from hospitality management provide key insights towards implementing and maintaining showmanship which will involve resource challenges including server artisan talent and restaurant design.Originality/valueServer showmanship social interaction and value cocreation with guests are rarely discussed, although a key part of guest restaurant motivation and in creating an immersive experience. We address the limited understanding of hospitality management assessment towards developing showmanship, providing practical perspectives to assist restaurant and bar management to integrate and develop showmanship to enhance customer dining experiences.

The influence of social media marketing on consumer behavior in the retail industry: A comprehensive review

The pervasive impact of social media marketing on consumer behavior within the retail industry serves as the focal point of this comprehensive review. This paper aims to dissect the intricate relationship between social media marketing initiatives and their effects on consumer purchasing patterns, brand loyalty, and engagement. By collating and examining a broad spectrum of literature, including empirical studies, theoretical frameworks, and case analyses, this review endeavors to provide a holistic understanding of how social media platforms are reshaping consumer interactions with retail brands. Without adopting a primary research methodology, this review critically analyzes existing research findings to identify prevailing trends, effective strategies, and the resultant consumer responses to social media marketing in the retail context. The analysis delves into the mechanisms through which social media influences consumer perceptions, engagement practices, and ultimately, purchasing decisions. It highlights the transformative power of social media in marketing, emphasizing the shift towards more interactive, personalized, and consumer-driven communication strategies. Key insights reveal that social media marketing exerts a profound influence on consumer behavior, mediated by factors such as content authenticity, the extent of consumer engagement, and the strategic use of influencers and user-generated content. These elements are shown to enhance brand visibility, strengthen consumer-brand relationships, and influence purchasing behavior. The paper posits that social media marketing represents a critical element in the contemporary retail marketing mix, with significant implications for consumer behaviour. Retailers must leverage innovative social media strategies to engage consumers effectively. The review also suggests avenues for future research, particularly in the context of evolving social media trends and their impact on consumer behavior. This synthesis aims to equip practitioners and scholars with a nuanced understanding of the role of social media marketing in shaping consumer dynamics within the retail sector. Keywords: Social Media Marketing, Consumer Engagement, Brand Awareness, Content Marketing, Influencer Marketing, Personalization, Customer Relationship Management (CRM).

Exploring the Relationship between Social Media Engagement, Customer Reviews, and Brand Perceptions: A Comprehensive Study in Retail Industry

This research investigates the intricate relationships between social media engagement, customer reviews, and brand perceptions in the retail industry. Through a qualitative analysis of customer-generated content and social media interactions, key themes and patterns emerged, shedding light on the factors influencing brand sentiment and consumer behavior. The findings highlight the impact of positive brand experiences, strategic social media engagement, user-generated content, transparency, and authenticity in shaping brand perceptions and fostering customer loyalty. The study underscores the importance of delivering exceptional customer experiences, engaging meaningfully on social media platforms, and prioritizing transparency and authenticity to enhance brand perception and build trust in the digital era.

User-generated content effectiveness in co-creation of online higher educational services

ABSTRACT Improvement of student services is increasingly important for institutions providing higher education in today's competitive market. Consequently, leveraging the online content generated by students is an effective means of actively involving customers in the educational process. As a result, the co-creation of innovative educational services through user-generated content (UGC) has become imperative. Engaging students in the service development process requires the adoption of an appropriate New Service Development (NSD) model, enabling the implementation of a co-creation strategy. The present study investigates this phenomenon by examining user-generated content on a website dedicated to higher education services. Employing the thematic analysis approach, 46 themes were identified that can be utilized in the development of new higher educational services. The findings demonstrate the potential of user-generated content in implementing a co-creation strategy at every stage of the new service development process.

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.

Modification Mechanisms of Descriptive Norm Perceptions Toward Vaping: The Role of Behavior Prevalence and Group Size in an Online Setting

ABSTRACT We often rely on descriptive norm perceptions as a mental shortcut for decision making. However, less is known about how such perceptions are shaped and modified by our experiences in day-to-day life. The interactive nature of the current media environment offers opportunities for individuals to access others’ health behavior choices through online user-generated content. Within a setting of online comment boards, the current study examined the descriptive norm perception modification process toward vaping with a large-scale experiment that systematically varied levels of exposure to online commenters’ vaping behavior choice indications. Findings revealed a significant positive effect of behavior prevalence on descriptive norm perceptions, which in turn were positively associated with vaping intention. This set of results was observed only when a sufficient total amount of comment exposures was ensured. The study provided empirical evidence for the underlying mechanism of the “quasi-statistical sense,” which helps people draw conclusions about behavior prevalence and may influence their behavioral decision making. Theoretical and practical implications are discussed.

AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system’s ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks.

Exploring the evolving landscape of human-centred crisis informatics: current challenges and future trends

Abstract Modern Information and Communication Technology (ICT) has been used in safety-critical situations for over twenty years. Rooted in Human-Computer Interaction (HCI) and related disciplines, the field of crisis informatics made considerable efforts to investigate social media use and role patterns in crises, facilitate the collection, processing and refinement of social media data, design and evaluate supportive ICT, and provide cumulative and longitudinal research. This narrative review examines contemporary challenges of human-centred crisis informatics and envision trends for the following decade, including (I) a broadening scope of crisis informatics, (II) the professionalisation of cross-platform collaboration of citizen communities and emergency services, (III) expert interfaces for explainable and multimodal artificial intelligence for user-generated content assessment, (IV) internet of things and mobile apps for bidirectional communication and warnings in disruption-tolerant networks, as well as (V) digital twins and virtual reality for the effective training of multi-agency collaboration in hybrid hazards.

Exploring Guests’ Satisfaction and Dissatisfaction with Homestay Experiences: A Netnographic Study of a Rural Tourism Destination in Vietnam

ABSTRACT Few studies have used online reviews to gain useful insights into homestay guests’ satisfaction. This study responds to demands from the existing literature on homestay tourism in a rural destination context to identify the factors that contribute to tourists’ satisfaction and dissatisfaction by examining homestay experiences in Vietnam’s Ben Tre province. User-generated content on Booking.com was analyzed using a netnographic approach. The data comprised 656 online posts. The findings suggest that guests achieve satisfaction from host families’ attitudes and language abilities, high-quality facilities in the bedrooms and grounds, authentic cuisine, a peaceful location, the availability of complementary services, and affordable prices. The findings contradict studies suggesting that homestay guests may seek familiarity, and this was largely not evident in terms of tourist food consumption while at the destination. In addition, the findings challenge studies indicating that prices have a strong influence on tourists’ satisfaction with homestay tourism.

DEPRESSION DETECTION BY ANALYZING SOCIAL MEDIA POST OF USER: A REVIEW

In contemporary times, identifying depression at an early stage has become a critical issue within the field of psychology. Mental health problems, notably depression, afflict a significant portion of the global population, with an estimated 300 million individuals currently affected. Leveraging the vast amount of user-generated content on social media platforms, researchers are exploring ways to utilize this data to identify potential mental health issues in users. Depression remains a pressing concern in society and a topic of ongoing research globally. Despite numerous studies attempting to understand various mood disorders such as depression, anxiety, and stress using activity logs from devices like smartphones, accurately predicting depressive episodes remains challenging. Social media analysis has emerged as a popular approach to tackle this challenge. In this paper, we propose a system for assessing depression and detecting suicidal ideation by predicting suicidal tendencies based on the severity of depression. Our study aims to develop effective machine learning algorithms for identifying potential signs of depression in individuals through their social media posts. To achieve this, we trained and tested classifiers to discern whether a person exhibits signs of depression using features extracted from their social media activities. Various machine learning algorithms were employed to train and classify users into different levels of depression on a scale ranging from 0 to 100%. Data in the form of posts were collected and categorized into depressed or non-depressed using machine learning algorithms. Our primary contribution lies in exploring a range of features and their effectiveness in detecting the degree of depression. We aim to build a deep learning model capable of categorizing users with depression by learning from individual-level labels to infer post-level labels. By amalgamating the probabilities of post label categories, we can create temporal posting profiles to classify users with depression. Our findings reveal distinct variations in posting behaviors between depressed and non-depressed users, as indicated by the combined probabilities of post label categories. In our study, we utilized natural language processing (NLP) techniques, employing the BERT algorithm, to efficiently identify potential depression indicators in social media content. This approach offers a more accessible and effective means of detecting depression. Keywords— Machine Learning, NLP, BERT Algorithm, Depression, Classification, Social Media Post.

The effects of user- and marketer-generated content on customer satisfaction: A textual analysis approach

Online reviews and management responses are important user-generated content (UGC) and marketer-generated content (MGC) in social media platforms, which directly reflect the customer’s and merchant’s feedback on a specific consumption and may further affect customer satisfaction. However, as the targeted feedback to UGC, the impact of MGC and the total impact between UGC and MGC are still understudied. In this work, we proposed a systematic analysis procedure for unravelling the impacts of online catering UGC and MGC on customer satisfaction in-depth. By studying the collected 130,412 review data from a leading online catering review website in China, we empirically extracted several new UGC topics using a topic model, thereby expanding the customer value factor set from previous literature; we also quantitatively analyzed their effectiveness in explaining customer satisfaction. We further integrated the UGC and MGC, and causally elaborated upon the usefulness of targeted responses in customer relationship management through difference-in-difference (DID) analysis. The findings validated that the effectiveness of management response on customer future satisfaction is relatively limited compared to the impact brought by the rating variables in UGC, but targeted management responses can perform more effectively in improving customer satisfaction. In addition, moderation roles based on the consumption price and the subjective emotional tendency of customers revealed in the textual comments were also investigated. Thus, this study helped to form a comprehensive understanding of customer satisfaction by contributing to a better quantification of customer value factors and understanding of the influence mechanism for customer satisfaction through the integration of UGC and MGC. Several managerial implications for optimizing customer relationship management in online catering markets were also provided.

Video platforms’ advertising and pricing decisions in the presence of derivative videos

Video platforms’ advertising and pricing decisions in the presence of derivative videos

The Impact of User-Generated Content on Brand Perception: A Case Study of Social Media Platforms

In the digital era dominated by social media, understanding the impact of User-Generated Content (UGC) on brand perception is indispensable for businesses. This study explores the nuanced dynamics of UGC, focusing on its positive impacts such as authenticity, increased brand awareness, and improved brand image. It also addresses challenges, including negative UGC and the need for quality control. The research methodology employs a case study approach, examining a specific brand's engagement with UGC on social media platforms through content analysis and social listening. The case study reveals that UGC enhances memory, trust, and social influence, leading to increased engagement and conversion. The authenticity of UGC fosters stronger brand connections, aligning with shifting consumer preferences towards more genuine content. The findings underscore the significance of UGC in shaping consumer behavior and influencing purchasing decisions, urging businesses to embrace UGC strategies for building trust, connecting authentically, and driving sales. The findings underscore the significance of UGC in shaping consumer behavior and influencing purchasing decisions. The study highlights the importance for businesses to embrace UGC strategies for building trust, connecting authentically with their audience, and ultimately driving sales in today's digital landscape. By leveraging the power of UGC, businesses can enhance their brand perception, engage with their audience more effectively, and achieve their marketing objectives in an increasingly competitive marketplace. Keywords: User-generated content (UGC), Brand Perception, Social Media Engagement, Consumer Behavior, Trust, Authenticity, Brand Loyalty, Marketing Strategy, Social Influence, Customer Engagement, Content Analysis, Social Listening, Brand Awareness, Conversion Rate, Community Building, Reputation Management, Influencer Marketing, Content Moderation, Brand Identity, Consumer Sentiment.

Impact of Influencer Marketing and User-Generated Content on Millennials\' Purchase Decisions

Abstract: In the digital era, influencer marketing and user-generated content (UGC) have significantly transformed marketing strategies, particularly affecting millennials' purchasing decisions. This research delves into the impact of these digital marketing strategies on millennials, focusing on the importance of trust, authenticity, and the integration of influencer marketing with UGC. Influencers, due to their perceived credibility and relatability, play a pivotal role in shaping consumer behavior, but their influence depends heavily on the authenticity of their endorsements. Similarly, UGC contributes to shaping consumer perceptions through genuine expressions of consumer experiences, highlighting the value of transparency and community in digital marketing. The study employs a mixed-methods approach, analyzing the responses of millennials to influencer marketing and UGC, and presents an integrated view of how these strategies influence purchasing decisions. The findings underscore the necessity for brands to forge authentic partnerships with influencers, emphasize storytelling, and actively engage with and promote UGC to resonate with millennials. Integrated marketing campaigns that effectively blend the persuasive power of influencers with the authenticity of UGC have been shown to generate curiosity, trust, and excitement among millennials, although skepticism remains a challenge due to concerns over authenticity. This research contributes to the evolving landscape of digital marketing strategies, offering insights into the complex web of influences that shape millennials' consumer behavior. It highlights the need for marketers and brands to navigate the intricacies of trust, authenticity, and community engagement in a digital context, providing a roadmap for effectively leveraging influencer marketing and UGC to engage the millennial audience.

Bibliometrics of Brand-related Social Media Content

This study presents bibliometric analysis of brand-related content on social media. By filtering by topic in the WOS database, publications between 2000 and 2021 on two types of social media content, firm-generated content (FGC) and user-generated content (UGC), are examined. For FGC, 47 articles in the database are reviewed, while 3502 articles are included in the analysis for UGC. The research results found that while the FGC studies of the researchers mainly were “Business,” the UGC articles were “Computer Science Information Systems” predominantly. In addition, the journal that gives the most place to studies with the FGC topic is the Journal of Marketing. On the other hand, the journals New Media & Society and Sustainability published 46 articles each for UGC studies. As a result of the co-word network analysis, although there were five themes in the map of the FGC articles, more than ten themes were found in the map of the UGC articles. The research results are expected to shed light on researchers who will work on brand-related social media content in the following years.

착용형 증강현실 기반 체험형 콘텐츠 연구

This paper proposes AR stone tower content, an experiential content based on wearable augmented reality (AR). Although wearable augmented reality is gaining attention, the acceptance of the technology is still focused on specialized applications such as industrial sites. On the other hand, the proposed AR stone tower content is based on the material of 'stone tower' so that general users can relate to it and easily participate in it, and it is organized to utilize space in a moving environment and find and stack stones based on natural hand gestures. The proposed AR stone tower content was implemented in the HoloLens 2 environment and evaluated by general users through a pilot exhibition in a small art museum. The evaluation results showed that the overall satisfaction with the content averaged 3.85, and the content appropriateness for the stone tower material was very high at 4.15. In particular, users were highly satisfied with content comprehension and sound, but somewhat less satisfied with object recognition, body adaptation, and object control. The above user evaluations confirm the resonance and positive response to the material, but also highlight the difficulties of the average user in experiencing and interacting with the wearable AR environment.

Pengembangan Sistem Evaluasi OSCE Berbasis Aplikasi Ponsel untuk Mahasiswa Kedokteran di Indonesia

Evaluation of Objective Structured Clinical Examination (OSCE) is required to assess medical students’ skills in order to meet the Indonesian medical doctor competency standards. This study presents an innovative method for developing a mobile app-based OSCE evaluation system in Med Stud Games (MSG) and explores its condition. To evaluate MSG, we use the focus group discussion (FGD) method. We use a purposeful sampling approach, consisting of a content expert, medical education expert and medical students. The result of an experience in using MSG for two OSCE training was discussed in FGD. Data was analyzed using in vivo coding and divided into categories to advantages, problems, content, interface, and future improvements. The FGD results in 19 codes. Advantages are categorized as easy to use, fast, practical, and automation timer and scoring. Problems categorized in distraction potential of incoming calls and notifications and online only. Content results in the validity of score and checklist quality. Interface results in font size and layout. Future improvement results in saving and sharing data, open feedback and text, and user-generated content. MSG improve assessment and feedback qualities for medical students. Further observational studies are needed to assess OSCE validation to be used in exam settings

Exploring the Impact of Social Media on Public Opinion Formation: A Comparative Analysis

Abstract: This study investigates the influence of social media on the formation of public opinion, employing a comparative analysis with traditional media platforms. In the digital age, social media has become a ubiquitous source of information and communication significantly impacting the way individuals perceive and engage with societal issues. This research seeks to examine the mechanisms through which social media shapes public opinion, including factors such as algorithmic curation, user-generated content, and network effects. By juxtaposing its influence with that of traditional media, this study aims to provide insights into the evolving dynamics of public opinion formation in contemporary society.

General Radiography Imaging Usage and Effective Dose of Inpatients: Based on Data from Inpatients in 2018

General Radiography Imaging Usage and Effective Dose of Inpatients: Based on Data from Inpatients in 2018

The Role of User-Generated Content in the Sustainable Development of Online Healthcare Communities: Exploring the Moderating Influence of Signals

Grounded in signaling theory, this study explores the influence of user-generated content (UGC) within online healthcare communities on patient purchasing behavior, with the overarching goal of advancing the development of online medical consultation services and contributing to the sustainable evolution of the online healthcare community. Leveraging publicly available data from the “Haodf.com”, we construct an empirical model of online medical consultation purchases, integrating principles from signaling theory and trust theory. Our analysis scrutinizes the effects of various forms of UGC on patient purchasing behavior, alongside the moderating influence of associated signals. The results demonstrate that knowledge-sharing articles authored by doctors and patient ratings positively impact consultation service purchases, whereas public displays of doctors’ past consultation records impede such transactions. Furthermore, external signals were found to moderate the relationship between UGC and consultation service purchases. The implications of these findings offer actionable insights for stakeholders invested in online healthcare communities.