User Engagement Research Articles

A Diffusion Modeling-Based System for Teaching Dance to Digital Human

The introduction of artificial intelligence (AI) has triggered changes in modern dance education. This study investigates the application of diffusion-based modeling and virtual digital humans in dance instruction. Utilizing AI and digital technologies, the proposed system innovatively merges music-driven dance generation with virtual human-based teaching. It achieves this by extracting rhythmic and emotional information from music through audio analysis to generate corresponding dance sequences. The virtual human, functioning as a digital tutor, demonstrates dance movements in real time, enabling students to accurately learn and execute dance postures and rhythms. Analysis of the teaching outcomes, including effectiveness, naturalness, and fluidity, indicates that learning through the digital human results in enhanced user engagement and improved learning outcomes. Additionally, the diversity of dance movements is increased. This system enhances students’ motivation and learning efficacy, offering a novel approach to innovating dance education.

Meta Learning to Rank for Sparsely Supervised Queries

Supervisory signals are a critical resource for training learning to rank models. In many real-world search and retrieval scenarios, these signals may not be readily available or could be costly to obtain for some queries. The examples include domains where labeling requires professional expertise, applications with strong privacy constraints, and user engagement information that are too scarce. We refer to these scenarios as sparsely supervised queries which pose significant challenges to traditional learning to rank models. In this work, we address sparsely supervised queries by proposing a novel meta learning to rank framework which leverages fast learning and adaption capability of meta-learning. The proposed approach accounts for the fact that different queries have different optimal parameters for their rankers, in contrast to traditional learning to rank models which only learn a global ranking model applied to all the queries. In consequence, the proposed method would yield significant advantages especially when new queries are of different characteristics with the training queries. Moreover, the proposed meta learning to rank framework is generic and flexible. We conduct a set of comprehensive experiments on both public datasets and a real-world e-commerce dataset. The results demonstrate that the proposed meta-learning approach can significantly enhance the performance of learning to rank models with sparsely labeled queries.

A Mediating Effect of Customer Experience on the Relationship between Metaverse Usage and Purchase Intention: A Conceptual Development

This study examines the mediating effect of customer experience on the relationship between metaverse usage and purchase intention, providing a conceptual framework to understand consumer behavior within virtual environments. As the metaverse rapidly evolves into a significant platform for interaction and commerce, understanding how user engagement translates into purchasing decisions is crucial for businesses and marketers. The framework posits that metaverse usage encompasses the frequency and depth of interactions users have with virtual spaces, which significantly influences their overall customer experience. A positive customer experience is characterized by factors such as satisfaction, emotional connection, and engagement, all of which are critical in shaping consumers' purchase intentions. When users perceive their interactions in the metaverse as enjoyable and meaningful, they are more likely to develop a favorable attitude towards the brands and products featured in these environments. The study integrates insights from established models, including the Technology Acceptance Model (TAM) and the Stimulus-Organism-Response (SOR) model, to highlight how various elements of the metaverse such as perceived usefulness, perceived ease of use, and perceived enjoyment which is affect customer experience. By establishing customer experience as a mediator, this research illustrates how metaverse usage not only influences direct purchasing behavior but also shapes the emotional and cognitive processes that underpin decision-making. Ultimately, this conceptual development aims to provide actionable insights for businesses looking to optimize their strategies in the metaverse. By understanding the pathways through which customer experience mediates the relationship between metaverse engagement and purchase intention, companies can create more compelling virtual experiences that foster customer loyalty and drive sales. This research contributes to the growing body of literature on digital consumer behavior, emphasizing the importance of customer experience in shaping purchasing decisions in virtual environments.

An Overview of Tools and Technologies for Anxiety and Depression Management Using AI

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” and “depression”. Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for overreliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.

Promoting Lusi Island Tourism Through an Augmented Reality Application

Background: The integration of augmented reality (AR) in tourism offers innovative enhancements to visitor experiences. Specific Background: This study aims to develop a research framework for AR-based tourism, focusing on effective tools for user interaction with 3D models. Knowledge Gap: There is a lack of systematic evaluation methods for assessing AR applications in tourism. Aims: The objective is to design and test an AR application that accurately displays 3D models to engage visitors. Results: Analysis shows that all tested 3D objects render correctly with specific markers, and functionality is confirmed across various menus. Tracking capabilities demonstrate effective visibility across multiple distances and lighting conditions, though limitations exist in low-light settings. Novelty: This research presents a structured approach to AR tourism development, highlighting the importance of system performance analysis. Implications: Findings enhance the understanding of AR's role in tourism and stress the need for rigorous testing in application design, informing future innovations in the field. Highlights : Effective tools enhance visitor interaction with AR applications. Rigorous testing ensures accurate rendering of 3D objects. Performance evaluation identifies limitations in varying conditions. Keywords: Augmented Reality, Tourism, 3D Models, System Analysis, User Engagement

The Impact of Meaningful Influencer Collaborations on Hospitality Businesses

Influencer marketing has become a significant concept in the hospitality sector, transforming traditional marketing and customer engagement methods. This research paper aims to investigate the impact of meaningful collaborations among the stakeholders within the hospitality industry. Additionally, it focuses on its impact on brand awareness, customer collaboration, and booking rates. By analyzing a range of social media influencers, this study aims to explore effective strategies and outcomes for marketing campaigns. The insights gained will provide a deeper understanding of how strategic partnerships can be leveraged to optimize investments and foster growth in the hospitality industry. The study is exploratory in nature with elements of primary and secondary data with a qualitative approach. A stratified sampling was used to ensure representation of different hospitality establishments (hotels, restaurants, etc.), influencer categories (mega, macro, micro, nano influencers) and the other stakeholders of the industry. The findings of this research will contribute to a deeper understanding of influencer marketing within the hotel industry and offer practical recommendations for hotel managers and marketing professionals seeking to leverage these strategies to enhance their brand and market presence.

Development and Evaluation of an AI-Based Chatbot for Preventing Social Media Addiction: A Waterfall Model Approach

The rapid growth of social media has transformed interaction and communication patterns, but it has also led to the rise of social media addiction, particularly among teenagers and young adults. This addiction, marked by compulsive usage and negative impacts on mental health and daily life, necessitates effective interventions. This research explores the development and evaluation of an AI-based chatbot designed to mitigate social media addiction by employing cognitive and behavioural strategies. The study utilizes the Waterfall model—a structured, sequential approach—in the chatbot’s development, encompassing stages from needs analysis to maintenance. The chatbot’s effectiveness was assessed through rigorous testing and user feedback. The methodology included problem analysis, system design, implementation, testing, and iterative improvements. A comprehensive needs analysis identified the psychological and behavioural factors contributing to social media addiction, leading to the design of a prototype chatbot integrated with AI for dynamic content adaptation and real-time feedback. The implementation phase focused on coding and system integration, followed by rigorous testing using Black Box Testing and the System Usability Scale (SUS) to ensure functionality and user-friendliness. Results indicate that the chatbot significantly reduced social media addiction scores, with a mean decrease from 55.21 to 50.17, supported by a highly significant p-value of <0.0001. User satisfaction was high, particularly regarding ease of use and information quality. However, user engagement declined over time, highlighting the need for ongoing content updates and feature enhancements. This study contributes to the field by providing insights into the application of the Waterfall model in AI chatbot development and offers a scalable solution for addressing social media addiction, with implications for future digital interventions in mental health.

Service‐dominant logic and customer engagement based value proposition framework in peer‐to‐peer accommodation: A two‐study approach

AbstractIn the competitive travel accommodation market, peer‐to‐peer accommodation (P2PA) providers are in desperate need of a value proposition framework that will assist them in guiding the development of an ‘engaged’ customer base. However, currently, the P2PA literature does not provide such a framework that includes a comprehensive list of tangible and intangible values driving customer engagement (CE). Underpinned by the service‐dominant (S‐D) logic theory, we adopted a big data‐based, two study approach to identify these values (and their composites) that drive CE in P2PA. Study 1 was conducted to identify important tangible and intangible values driving customers to engage with P2PA. This qualitative study adopted a text‐mining and sentiment analysis approach which was performed on 499,160 customer reviews. The subsequent quantitative study (Study 2) used a multi‐factor ANOVA analysis (i.e., Automatic Linear Modelling) to examine 19,060 listed houses using their star ratings and other available quantitative data (e.g., accommodation cost, response rate and number of rooms). Our findings present an S‐D logic‐based value proposition framework for enhancing CE in P2PA. This framework shows that customers consider seven tangible‐intangible values (i.e., accommodation, perceived enjoyment, perceived friendliness, perceived convenience, surrounding, perceived response and trustworthiness) in creating three values (i.e., re‐adopt, recommend, and refer P2PA) for P2PA‐platform providers and listed‐houses. Overall, by developing an S‐D logic‐based framework and identifying salient values in P2PA value co‐creation, this research extends both the P2PA literature and S‐D logic theory. We also provide insightful recommendations for P2PA providers.

Impact of AI Chatbots on Customer Engagement: An Investigating in The Role of Customer Trust in Online Services of Telecom Egypt Context

Impact of AI Chatbots on Customer Engagement: An Investigating in The Role of Customer Trust in Online Services of Telecom Egypt Context

Development of an integrated model for SME marketing and CRM optimization

The development of an integrated model for optimizing marketing and customer relationship management (CRM) in small and medium-sized enterprises (SMEs) presents a crucial advancement in enhancing business competitiveness and customer engagement. This paper proposes a unified model that merges scalable software solutions for social media analytics with AI-driven CRM to provide a comprehensive framework for SMEs to enhance their marketing strategies and customer interactions. Social media platforms have become vital channels for customer outreach, engagement, and feedback, yet SMEs often face challenges in leveraging these platforms due to limited resources and expertise. Integrating social media analytics into CRM systems offers businesses the ability to analyze customer behaviors, preferences, and sentiment on a large scale. The proposed model utilizes artificial intelligence (AI) to streamline customer interactions, enabling personalized communication and improving customer retention. By incorporating AI into CRM, SMEs can automate routine tasks, such as responding to customer inquiries and segmenting audiences based on their social media activity. This allows for more efficient marketing strategies and enables businesses to proactively address customer needs, ultimately driving customer loyalty and long-term growth. Furthermore, the unified model focuses on scalability, ensuring that SMEs can adopt and adapt these technologies according to their evolving needs and resources. This paper also explores the benefits of integrating these technologies, including the potential for improved data-driven decision-making, enhanced customer experience, and increased operational efficiency. In conclusion, this integrated model presents a strategic pathway for SMEs to optimize their marketing and CRM efforts through the adoption of scalable, AI-driven tools that enhance customer engagement and boost business performance. Keywords: SME Marketing, CRM Optimization, Social Media Analytics, AI-Driven CRM, Customer Engagement, Scalable Software Solutions, Business Performance, Personalized Communication, Customer Retention, Data-Driven Decision-Making.

Public Participation Monitoring: Social Media Data Mining and Analysis of User Engagement Patterns

Social Media Platforms (SMP) like Twitter and Facebook have become public influencing medium in today’s digital age which facilitate community interactions. These necessitates the monitoring of public participation in these SMP. This work performs such an study by examining the user engagement patterns in social media in Indonesia during the national elections (#Pemilu2024), Ramadan celebrations (#Ramadan2024), and climate change discussions (#ClimateChange). The data for the study was collected during a period of six months from (January to June 2024) using event-specific hashtags and keywords. To identify the user engagement patterns datamining and Natural Language Processing (NLP) tools were utilized. The findings show that the Twitter platform has higher user engagement in morning with news updates and visual updates at evening. The Facebook show user engagement in afternoon with videos and evening engagement with shared articles. The Sentiment Analysis (SA) and network analysis was performed over the dataset and the findings have shown that higher positive sentiments towards elections and Ramadan but argumentative towards climate change discussions. The Twitter show rapid communication effectiveness compared to Facebook. Further the youth prefer faster update and older expect detailed content sharing.

Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review.

Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users. This study aims to show how research has considered user experiences and behavioral approaches in designing mHealth interventions for AF detection, screening, and management; the phases of designing complex interventions from the UK Medical Research Council (MRC) were referenced: namely, identification, development, feasibility, evaluation, and implementation. Studies published until September 7, 2022, that examined user experiences and behavioral approaches associated with mHealth interventions in the context of AF were extracted from multiple databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines were used. A total of 2219 records were extracted, with only 55 records reporting on usability, user experiences, or behavioral approaches more widely for designing mHealth interventions in the context of AF. When mapping the studies onto the phases of the UK MRC's guidance for developing and evaluating complex interventions, the following was found: in the identification phase, there were significant differences between the needs of patients and health care workers. In the development phase, user perspectives guided the iterative development of apps, interfaces, and intervention protocols in 4 studies. Most studies (43/55, 78%) assessed the usability of interventions in the feasibility phase as an outcome, although the data collection tools were not designed together with users and stakeholders. Studies that examined the evaluation and implementation phase entailed reporting on challenges in user participation, acceptance, and workflows that could not be captured by studies in the previous phases. To realize the envisaged human behavior intended through treatment, review results highlight the scant inclusion of behavior change approaches for mHealth interventions across multiple levels of sociotechnical health care systems. While interventions at the level of the individual (micro) and the level of communities (meso) were found in the studies reviewed, no studies were found intervening at societal levels (macro). Studies also failed to consider the temporal variation of user goals and feedback in the design of long-term behavioral interventions. In this systematic review, we proposed 2 contributions: first, mapping studies to different phases of the MRC framework for developing and evaluating complex interventions, and second, mapping behavioral approaches to different levels of health care systems. Finally, we discuss the wider implications of our results in guiding future mHealth research.

Uncovering How Information Quality Shapes Diverse User Engagement on Content Community Platforms: Harnessing Deep Learning for Feature Extraction

Uncovering How Information Quality Shapes Diverse User Engagement on Content Community Platforms: Harnessing Deep Learning for Feature Extraction

Digital Official Letter Management Using E-Buddy Sidoarjo Application

Background: The rise of digital technology has revolutionized local governance by enhancing efficiency and transparency. Specific Background: In this context, the E-Buddy application facilitates the digital management of official correspondence, yet its effectiveness has not been thoroughly examined. Knowledge Gap: Current literature lacks a detailed analysis of the communication dynamics and bureaucratic structures affecting the success of such digital systems. Aims: This study investigates the implementation of the E-Buddy application, emphasizing communication, resource allocation, executor disposition, and bureaucratic structure. Results: Findings reveal that the application's effectiveness is undermined by limited feature utilization and insufficient adherence to standard operating procedures (SOPs), resulting in miscommunication and inefficiencies. Novelty: The research underscores the importance of effective communication and structured SOPs in optimizing digital systems in governance. Implications: The study recommends enhancing training and establishing clear SOPs to improve implementation outcomes and contribute to more effective governance. Highlights : Effective communication is crucial for successful implementation. Insufficient adherence to SOPs hampers operational efficiency. Training and support enhance user engagement and application effectiveness. Keywords: Digital Governance, E-Buddy Application, Implementation, Communication, Bureaucratic Structure

Deciphering the evolution of metaverse - A techno-functional perspective in digital marketing

The metaverse has disrupted the traditional marketing practices and it has potential to transform entire world of marketing activities with thrilling immersive experiences. This study provides an analysis of evolving field of metaverse marketing in the context of information systems using state of the art bibliometric and scientometric tools coupled with machine learning algorithms. Utilizing 257 documents from Scopus database that published between 1996 and 2024, this research maps and unveils the development of metaverse marketing from its inception and the role of information systems in its evolution. The analysis of literature resulted in five main emerging themes of the role of information systems in metaverse marketing research as User Experience, Customer engagement, Convergence of metaverse Technology, Design of virtual goods & experience and Global Social Interaction. The major sub-themes of the study are User Behaviors and Preferences, Branding on virtual environment, Virtual reality, Virtual wearables and Virtual Socialization. This study also reveals the emerging trends and gaps in literature that pave the ways for future research expansion in the information systems and metaverse marketing. Few of the important future research areas identified are understanding user experience, design of immersive customer engagement strategies, customer virtual presence and Security & privacy concerns of the users on metaverse platform.

Factors Affecting Reconsumption Intention in Herbal Medicine: Evidence from Indonesia

This study aims to investigate the mediating role of customer engagement on the effect of trust on reconsumption intention. This study was conducted on herbal medicine consumers in Indonesia. Using a quantitative approach involving 425 herbal consumers in Indonesia with purposive sampling techniques using criteria who are 18 years old and have consumed herbal medicine at least 2 times in the past month. Data analysis using covariance-based structural equation modelling (CB-SEM) using AMOS application. The study found that trust had no significant effect on reconsumption intention. Customer engagement has a significant effect on reconsumption intention. Trust has a significant effect on customer engagement. Then, customer engagement acts as a complete mediation on the influence of trust on reconsumption intention in herbal medicine consumers in Indonesia. These results suggest marketers need to pay attention to engagement to increase reconsumption intention using consumer trust.

Examining the impact of online service convenience on user engagement and intention to continuously use e-resources: mediating role of attitude

Purpose This study aims to analyze the mediating role of attitude in understanding the impact of online service convenience on user engagement and intention to continuously use e-resources. Five components of online service convenience have been adopted for the present study: These are access convenience, search convenience, evaluation convenience, transaction convenience and possession/ post-possession convenience. Design/methodology/approach A total of 500 PhD scholars and faculty members from two different states of India were approached to provide their responses through a structured questionnaire, which resulted in 224 complete responses. These responses were analyzed using structural equation modeling. The statistical analysis has been performed with the help of Smart-PLS 3. Findings The study findings have proven the proposed conceptual model by showing a significant relationship between all the variables. Furthermore, it has been identified that the attitude of the users also positively impacts intention to continuously use e-resources. Originality/value This study contributes to the literature on e-resources by providing implications for students, faculties, managers and society.

Effectiveness of VR Technology in the Design of the Bali Megarupa V Virtual Exhibition

Virtual exhibitions offer a promising avenue for art appreciation in the digital age. However, limited research explores how these online spaces can foster emotional connections with art, a crucial aspect of the art experience. This study investigates the potential of immersive virtual reality (VR) to enhance engagement and emotional impact in virtual art exhibitions. Employing a multi-method approach combining literature review, user testing, and prototype development, the research identifies and evaluates design elements contributing to increased user engagement. This research employed a multi-method approach to understand how users engage with virtual art exhibitions. The study identified and evaluated the impact of specific design elements on user engagement by combining a literature review, user testing with a VR prototype, and subsequent analysis. The study focuses on critical elements like interactivity, personalization, storytelling, and multi-sensory experiences. User testing with 55 participants revealed the effectiveness of immersive design in fostering exploration, contemplation, and a sense of presence in the virtual space. This immersive design is the strategic integration of elements like interactivity, personalization, and storytelling within the VR environment. The research underscores the significance of immersive design, defined as the strategic integration of design elements to cultivate an emotional connection within a VR environment. This encompasses aspects like interactivity, personalization, and the incorporation of storytelling within the experience. Finally, the study offers practical recommendations for curators and designers to create virtual art experiences that resonate more deeply with audiences.

The Effect of Customer Value, Trust, and Customer Satisfaction on Customer Loyalty Mediated By Customer Engagement (Empirical Study: Customer Music Course In Jakarta)

This study aims to investigate the impact of customer value, trust, and customer satisfaction on customer loyalty, with customer engagement as a mediating variable, focusing on music course customers in Jakarta. Utilizing a quantitative approach, data was collected from 95 respondents who have been customers of music courses for more than three months and are over 18 years old. The data was analyzed using Partial Least Square (PLS) with the SmartPLS 3.0 software. The results of the study indicate that trust and customer value positively and significantly influence customer engagement, which in turn significantly enhances customer loyalty. However, customer satisfaction did not show a significant direct effect on either customer engagement or customer loyalty. Additionally, while customer engagement mediates the relationship between customer value and customer loyalty, it does not mediate the relationship between trust or customer satisfaction and customer loyalty. The findings suggest that building customer engagement through perceived value can strengthen loyalty among music course customers. Limitations of this study include a focus on a specific geographic location (Jakarta) and the relatively small sample size, which may affect the generalizability of the results.

Analyzing User Psychology and Behavior in Short-Form Video Shopping Platforms: An Integrated TAM and ISS Model Approach

This research investigates the psychology and behavior of users engaging in short-form video shopping platforms (SVSP) from a user experience perspective. By extending the Technology Acceptance Model (TAM) and the Information System Success Model (ISS), an intention-to-use model for SVSP was developed. We conducted a survey on participants of SVSP shopping through the Questionnaire Star platform, obtaining 1,026 valid responses. Structural equation modeling (SEM-AMOS) was utilized to validate the hypotheses. Key findings include: perceived usefulness positively influences user engagement, purchase intention, and satisfaction; information quality positively impacts purchase intention; perceived ease of use negatively affects user engagement and satisfaction. Indirect effects on use intention were observed via mediation variables. Additionally, price negatively moderates the relationship between satisfaction, user engagement, and purchase intention. These findings contribute to theory and emphasizes user experience and provides actionable insights for sellers to boost engagement and satisfaction, along with managerial strategies to optimize short-form video live shopping experiences.