Understanding User Engagement Research Articles

Understanding user engagement in mobile health applications from a privacy management perspective.

Mobile health applications (mHealth apps) have surged in popularity for their role in promoting knowledge exchange and providing emotional support among health consumers. However, this enhanced social connectivity via these apps has led to an escalation in privacy breaches, potentially hindering user engagement. Drawing upon the communication privacy management theory, this study proposes a moderated mediation model to link social privacy concerns to user engagement in mHealth apps. An online survey involving 1149 mHealth app users was conducted in China to empirically validate the proposed model. Results indicated that social privacy concerns were negatively related to user engagement in mHealth apps, and perceived privacy of the app partially mediated this relationship. Moreover, perceived control positively moderated the indirect relationship between social privacy concerns and user engagement via perceived privacy. Specifically, the negative impact of social privacy concerns on perceived privacy was mitigated for users who reported higher levels of perceived control, indicating that when users feel more in control of their personal data, they are less affected by concerns over social privacy. Theoretically, this study has the potential to help scholars understand user engagement in mHealth apps from a privacy management perspective. Practically, the results of this study could assist mobile app providers and health professionals in devising evidence-based strategies to enhance social engagement and promote effective and sustainable use of mHealth apps among health consumers.

Best practices to evaluate the impact of biomedical research software-metric collection beyond citations.

Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation. To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress. More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website.

ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph

ROBO-SPOT: Detecting Robocalls by Understanding User Engagement and Connectivity Graph

A two-actor model for understanding user engagement with content creators: Applying social capital theory

The emergence of video sharing platforms has given rise to the creation and consumption of tourism-related content. However, there is limited knowledge about the characteristics of content creators that enhance users' engagement with their content. The present study aims to fill this gap by examining creator characteristics and their impact on three tiers of user engagement. Tourism-related content, comprising 366 videos across six destinations, was extracted from YouTube using three social media analytic tools: VidIQ, TubeBuddy, and SocialBlade. The data were analyzed using PLS-SEM with SmartPLS 4.0. The findings reveal that channel subscribers positively influence user engagement at three levels – views, likes, and comments. However, a higher number of video uploads negatively impacts engagement. Furthermore, older videos tend to garner more views, but users' tendency to like the videos decreases over time. In addition, we extracted 23,993 comments and performed sentiment analysis on users’ comments using Python-based VADER social media sentiment analysis tool. The compound-based sentiment analysis reveals that 59.5 percent of users show positive sentiments toward tourism-related content on YouTube while only 9.3 comments were negative, and 31.2 percent of sentiments remain neutral. Temporal analysis shows the rising trend in qualitative user engagement from 2010 to 2023, highlighting a growing interest in consuming and interacting with tourism-related content. This study discusses its theoretical contributions and managerial implications for content creators, destination managers, and advertising agencies.

From Virality to Veracity: Understanding User Engagement and Information Accuracy in Breaking Events

This research explores the intricate dynamics of user engagement, information virality, and accuracy during breaking events within the timeframe of 2017-2022. Employing a mixed-methods approach, we analyze user behaviors on social media platforms, exploring the quantitative metrics of likes, shares, and comments, while also unraveling the qualitative dimensions through participant interviews. The study uncovers a nuanced spectrum of user engagement, ranging from information-seeking tendencies to profound emotional responses, unveiling the complex interplay that shapes digital interactions during critical moments. The investigation into information virality reveals the significant impact of emotional appeal, novelty, and alignment with societal concerns on the dissemination of information. A meticulous dissection of network dynamics elucidates the influential roles played by influencers and the intricate dance of user sharing patterns. Simultaneously, the scrutiny of information accuracy establishes a positive correlation between accurate information and trust in traditional sources, unveiling the multifaceted consequences of misinformation. In the broader context of existing literature, our findings align with the growing body of research emphasizing the pivotal role of social media in shaping narratives during breaking events. This study contributes to a deeper understanding of the challenges posed by the digital landscape and highlights the need for adaptive strategies that balance information dissemination and accuracy.

Understanding user Engagement with Digital Assistance Technology

Understanding user Engagement with Digital Assistance Technology

A content analysis: analyzing topics of conversation under the #sustainability hashtag on Twitter

Abstract This study aimed to identify and understand the major topics of discussion under the #sustainability hashtag on Twitter (now known as “X”) and understand user engagement. The sharp increase in social media usage combined with a rise in climate anomalies in recent years makes the area of sustainability with respect to social media a critical topic. Python was used to gather Twitter posts between January 1, 2023, and March 1, 2023. User engagement metrics were analyzed using a variety of statistical analysis methods, including keyword-frequency analysis and Latent Dirichlet Allocation (LDA), which were used to identify significant topics of discussion under the #sustainability hashtag. Additionally, histograms and scatter plots were used to visualize user engagement. LDA analysis was conducted with 7 topics after trials were run with various topics and results were analyzed to determine which number of topics best fit the dataset. The frequency analysis provided a basic overview of the discourse surrounding #sustainability with the topics of technology, business and industry, environmental awareness, and discussion of the future. The LDA model provided a more comprehensive view, including additional topics such as Environmental, Social, and Governance (ESG) and infrastructure, investing, collaboration, and education. These findings have implications for researchers, businesses, organizations, and politicians seeking to align their strategies and actions with the major topics surrounding sustainability on Twitter to have a greater impact on their audience. Researchers can use the results of this study to guide further research on the topic or contextualize their study with existing literature within the field of sustainability.

Understanding User Engagement Assessment and Modernization in a Chronic Disease Management App on A-B-C Platform

The study examines user engagement and satisfaction with a chronic disease management app on the A-B-C platform. It explores demographic details and factors influencing engagement, including usability, features, personalization, social support, and accessibility. Specific app elements such as goal-setting, education resources, communication, reminders, and wearables integration are scrutinized, alongside user barriers like technological challenges, awareness gaps, time constraints, and privacy issues. Using a purposive sample of 200 participants, including patients, caregivers, healthcare professionals, and app users, data analysis employs descriptive statistics to inform app modernization efforts. The study demonstrates how usability and robust social support features influence positive user engagement. It also identifies areas for improvement in notifications, educational content, and accessibility. Key app features contributing to satisfaction include personalized goal-setting, comprehensive educational resources, and communication support, while challenges encompass technological barriers, a lack of awareness, time constraints, and privacy concerns. Recommendations include tailored marketing, feature enhancements, and addressing barriers, with proposed plans focusing on user education, data privacy, and app performance. Timely execution of these plans is crucial, with a focus on integrating data privacy measures and security software to ensure a trustworthy user experience.

An Empirical Study of User Engagement in Influencer Marketing on Weibo and WeChat

Understanding social media users’ engagement is one of the most crucial steps in a successful deployment of an influencer marketing campaign. On mainstream social media platforms, opinion leaders are the major channels to spread opinions and media content to a large population of consumers. Therefore, analyzing how users respond to the blogs of opinion leaders lies at the core of understanding user engagement in social media. In this article, to study the factors that have great impacts on user engagement, we first collect a cross-platform opinion leaders’ blogs dataset, which includes 344 643 blogs published by 93 opinion leaders who have accounts on both Weibo and WeChat, the two most popular social media platforms in China. Based on this dataset, we conduct both characteristics study and semantic study to investigate the impact factors of user engagement with respect to blogs of opinion leaders. To find out the associations between user engagement and practically accessible attributes of opinion leaders and their blogs, we adopt state-of-the-art (SOTA) sentiment analysis models to process the blog data, develop a normalization technique to alleviate the issue caused by the heterogeneity of fall-out intervals of blogs, and devise a saliency method to compute the integrated gradients of sentences in blogs. Utilizing these computational tools, we reveal that user engagement on the two platforms agrees on some common factors, such as the number of tokens of blogs. Meanwhile, the two platforms differ in some aspects. For example, the semantic patterns that can improve the level of user engagement on the two platforms are very different. Our analysis can provide advertisers with valuable insights on how to plan an influencer marketing campaign on the two platforms.

Determinants of Word-of-Mouth in the Virtual Reality Market: A Focus on Aesthetic Attributes and Perceived Value

In the burgeoning domain of virtual reality (VR), there’s undeniable progress in technology. However, a distinct gap remains in understanding user engagement with VR devices. This study delves into the factors determining user satisfaction and word-of-mouth (WOM) recommendations related specifically to VR devices. Using partial least squares structural equation modeling, this research analyzes empirical data directly sourced from VR device users. The results indicate that the interface convenience of VR devices significantly impacts emotional value. Moreover, the design and shape of the VR devices resonate profoundly with both emotional and social values. In contrast, the color of the screen displayed by VR devices doesn’t significantly influence these values. The study also uncovers a unique relationship between emotional value and WOM. Additionally, social value has a direct positive influence on both user satisfaction and WOM recommendations. These insights offer a deeper understanding of the consumer mindset within the VR device segment, illuminating key factors driving WOM and enriching our comprehensive understanding of the VR device landscape.

Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers

This study aims to explore the determinants of user behaviors toward an artificial intelligence (AI) tool, ChatGPT, focusing on university students and office workers. In this study, we present a comprehensive model to understand user engagement with AI tools, specifically focusing on ChatGPT. The model is grounded on four primary stages, each containing distinct variables: 1) fundamental (comprising perceived intelligence and system quality), 2) knowledge and service (covering knowledge acquisition, application, personalization, and trust), 3) gain with user tendency (encompassing utilitarian benefits, individual impact, satisfaction, and personal innovativeness), and 4) behavior (including behavioral intention, continued usage, and Word-of-Mouth (WOM)). A total of 13 variables have been examined. A survey was conducted on a sample of 645 university students and office workers, and the collected data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal significant associations between perceived intelligence and knowledge management, and personalization. System quality also significantly impacts knowledge management and personalization. Knowledge acquisition and application were found to significantly affect utilitarian benefits and individual impact, but not satisfaction. Personalization significantly influenced utilitarian benefits, individual impact, and satisfaction. Trust significantly impacts behavioral intention. Utilitarian benefits and individual impact had a positive effect on satisfaction, behavioral intention, and WOM. Personal innovativeness was significantly associated with behavioral intention. Behavioral intention significantly affected usage and WOM, while usage did not significantly associate with WOM. Among control variables, only age affects behavioral intention. This study also confirmed the indirect effects and conducted a multi-group analysis (MGA) between students and workers. MGA results show that there are significant differences in three relationships (personalization-satisfaction, utilitarian benefits-WOM, and behavioral intention-WOM) between students and workers. This research extends the understanding of AI tool usage and provides theoretical and practical insights for researchers, practitioners, and policymakers in AI and related fields.

A Systematic Analysis of Infinite Scrolling in Digital Interfaces: A Good Accompanied by Bad and Ugly

In the digital age, user interfaces play a pivotal role in shaping user experiences and determining the success of websites, applications, and social media platforms. Infinite scrolling has emerged as a widely adopted feature, transforming the way users interact with and consume content. This paper presents a systematic analysis of the implications, advantages, and drawbacks of infinite scrolling in various digital interfaces. By amalgamating insights from existing literature and examining interface design principles, this study seeks to provide a comprehensive understanding of the impact of infinite scrolling on user behaviour and interface performance. The analysis commences with an exploration of the psychological and behavioural aspects of infinite scrolling. In understanding user engagement, attention span, and content consumption patterns, this research aims to uncover the psychological mechanisms that make infinite scrolling captivating and encourage users to explore content for extended durations. Studies have shown that infinite scrolling can create a sense of fluidity and continuity, enticing users to stay engaged for longer periods and discover more content than traditional pagination or load more button approaches. This seemingly infinite supply of information aligns with the human desire for novelty and curiosity, which contributes to increased user retention. In addition to user-centric aspects, this analysis addresses the technical implications of infinite scrolling. Infinite scrolling introduces dynamic content loading, which can impact website performance and data handling. As users scroll through large volumes of content, the continuous loading of data can lead to slower page loading times, potentially resulting in frustration and abandonment. Moreover, search engine optimization (SEO) can be affected, as infinite scrolling may hinder search engines' ability to index and rank content accurately. Developers and designers must consider these technical challenges and adopt optimization techniques to ensure a smooth and efficient user experience. International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 07 | July - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM24808 | Page 2 Despite its allure and benefits, this systematic analysis also unveils several drawbacks associated with infinite scrolling. The never-ending content flow can lead to increased cognitive load, making it harder for users to retain information or recall previously viewed content. Additionally, infinite scrolling may present accessibility challenges for users with disabilities, as they may find it difficult to navigate and locate specific information without clear navigation cues. In conclusion, this systematic analysis provides a comprehensive understanding of the impact of infinite scrolling on user behaviour, satisfaction, and technical considerations. By synthesizing existing knowledge and offering practical recommendations, this research contributes to the ongoing discussion about the role of infinite scrolling in shaping the future of digital interfaces. As technology continues to evolve, informed decision-making and user-centric design will remain pivotal in leveraging the advantages of infinite scrolling while mitigating its drawbacks to create more enjoyable and accessible digital experiences. Keywords: Infinite Scrolling, Digital Technology, Content, User Behaviour

Helping Each Other Quit Online: Understanding User Engagement and Real-life Outcomes of the r/StopSmoking Digital Smoking Cessation Community

Despite decades of prevention, tobacco addiction is still a widespread health concern responsible for around 8 million deaths per year. Existing digital smoking cessation solutions such as social media are becoming increasingly popular and represent a novel approach to find community support. However, little is known about how they affect smoking behavior. This research aims to understand what motivates people to join online communities and how their participation affects their attitudes and behaviors. To do so, this article conducts an in-depth analysis of the popular Reddit r/StopSmoking thread through three complementary studies. Using the transtheoretical model and the uses and gratification theory, Study 1 aims at understanding the link among motivation factors, engagement, and outcomes through a user survey. Study 2 aims at understanding the engagement by analyzing the content of 10 years of user interaction data. Study 3 attempts to gain further knowledge of interactions by examining the reaction of the community to a crisis situation such as that of the recent COVID-19 pandemic. Findings convey the fact that participation in such communities has a favorable impact on the change process toward quitting. Results show that providing social support to others is the biggest contributing factor for participating in the community. User interactions analysis confirmed that survey responses were accurate reflections of actual user activity. Regarding the impact of the COVID-19 crisis, results suggest that it increased levels of stress and depression in the community while decreasing active engagement, indicating that there may be opportunities for improvement in dealing with tough situations.

User Engagement Comparison between Advergames and Traditional Advertising Using EEG: Does the User’s Engagement Influence Purchase Intention?

In the context of human–computer interaction (HCI), understanding user engagement (UE) while interacting with a product or service can provide valuable information for enhancing the design process. UE has been a priority research theme within HCI, as it assesses the user experience by studying the individual’s behavioral response to some stimulus. Many studies looking to quantify the UE are available; however, most use self-report methods that rely only on participants’ answers. This study aims to explore a non-traditional method, specifically electroencephalography, to analyze users’ engagement while interacting with an advergame, an interactive form of advertising in video games. We aim to understand if a more interactive type of advertising will enhance the UE and whether, at the same time, it would influence the user’s purchase intention (UPI). To do this, we computed and compared the UE during the interaction with an advergame and a conventional TV commercial while measuring the participants’ brain activity. After the interaction with both types of advertising, the UPI was also evaluated. The findings demonstrate that a more interactive advertisement increased the participants’ UE and that, in most cases, a UE increment positively influenced the UPI. This study shows an example of the potential of physiological feedback applications to explore the users’ perceptions during and after the human–product interaction. The findings show how physiological methods can be used along with traditional ones for enhancing the UE analysis and provide helpful information about the advantages of engagement measurement in HCI applications.

Exploiting Post-Click Behaviors for Recommender System

In recent years, recommender systems become an effective technique to address the information overload problem on the Web, targeting the delivery of most relevant items and information to individual users based on their historical behaviours. To build an effective recommender system, implicit feedback like user click has been harvested. Since click data is usually very noisy, recent works also leverage dwell time as a proxy to optimize user engagement. However, dwell time is just one-dimensional post-click behaviour. The multi-dimensional post-click behaviours, e.g., reading comments, browsing images, and other sub-modules, have not been fully exploited. To address this issue, in this paper, we study leveraging post-click behaviours to improve user engagement in recommendation systems. We first take an E-commerce recommender system as an example to define post-click behaviours and demonstrate their effects with respect to user conversions. Based on the analysis, we then propose a tree-based labelling model, which provides a new perspective to understand user engagement beyond CTR or dwell time. The labelling model can be further incorporated into state-of-the-art recommendation methods. Extensive experiments on the dataset from JD.com, a real-world E-commerce website, demonstrate the competitive performance of the proposed method in both offline and online scenarios.

Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses to leave a social networking platform, it may cause a cascading dropping out among her friends. In many scenarios, it would be a good idea to persuade critical users to stay active in the network and prevent such a cascade because critical users can have significant influence on user engagement of the whole network. Many user engagement studies have been conducted to find a set of critical <i>(anchored)</i> users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In order to fully utilize the power of anchored users in evolving networks, existing studies have to mine multiple sets of anchored users at different times, which incurs an expensive computational cost. To better understand user engagement in evolving network, we target a new research problem called <i>Anchored Vertex Tracking</i> (AVT) in this paper, aiming to track the anchored users at each timestamp of evolving networks. Nonetheless, it is nontrivial to handle the AVT problem which we have proved to be NP-hard. To address the challenge, we develop a greedy algorithm inspired by the previous anchored <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure&#x0027;s evolution. The extensive experiments conducted on real and synthetic datasets demonstrate the performance of our proposed algorithms and the effectiveness in solving the AVT problem.

Designing Player Intent through “Playful” Interaction

Designing Player Intent through “Playful” Interaction

Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review

BackgroundDigital mental health interventions (DMHIs), which deliver mental health support via technologies such as mobile apps, can increase access to mental health support, and many studies have demonstrated their effectiveness in improving symptoms. However, user engagement varies, with regard to a user’s uptake and sustained interactions with these interventions.ObjectiveThis systematic review aims to identify common barriers and facilitators that influence user engagement with DMHIs.MethodsA systematic search was conducted in the SCOPUS, PubMed, PsycINFO, Web of Science, and Cochrane Library databases. Empirical studies that report qualitative and/or quantitative data were included.ResultsA total of 208 articles met the inclusion criteria. The included articles used a variety of methodologies, including interviews, surveys, focus groups, workshops, field studies, and analysis of user reviews. Factors extracted for coding were related to the end user, the program or content offered by the intervention, and the technology and implementation environment. Common barriers included severe mental health issues that hampered engagement, technical issues, and a lack of personalization. Common facilitators were social connectedness facilitated by the intervention, increased insight into health, and a feeling of being in control of one’s own health.ConclusionsAlthough previous research suggests that DMHIs can be useful in supporting mental health, contextual factors are important determinants of whether users actually engage with these interventions. The factors identified in this review can provide guidance when evaluating DMHIs to help explain and understand user engagement and can inform the design and development of new digital interventions.

GeoLifecycle

As Location-Based Social Networks (LBSNs) have become widely used by users, understanding user engagement and predicting user churn are essential to the maintainability of the services. In this work, we conduct a quantitative analysis to understand user engagement patterns exhibited both offline and online in LBSNs. We employ two large-scale datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19 million tips in Yelp and Foursquare, respectively. We discover that users keep traveling to diverse locations where they have not reviewed before, which is in contrast to "human life" analogy in real life, an initial exploration followed by exploitation of existing preferences. Interestingly, we find users who eventually leave the community show distinct engagement patterns even with their first ten reviews in various facets, e.g., geographical, venue-specific, linguistic, and social aspects. Based on these observations, we construct predictive models to detect potential churners. We then demonstrate the effectiveness of our proposed features in the churn prediction. Our findings of geographical exploration and online interactions of users enhance our understanding of human mobility based on reviews, and provide important implications for venue recommendations and churn prediction.

Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index.

BackgroundChildhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics.ObjectiveThe aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement.MethodsThe Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant’s date of birth.Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models.ResultsThe overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including “app only” users or “both” app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005).ConclusionsThe EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions.