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Firms must strategically manage their responses to user comments to keep users engaged on their social media business pages. The question of whether, how, and when a firm should respond to user comments to achieve favorable outcomes is of great interest to researchers and practitioners. We focus on these questions and study the effects of initial user comments and firm responses on subsequent user engagement on social media business pages. In particular, we theorize and examine how two features of initial user comments (i.e., sentiment and controversialness) and two features of firm responses (i.e., uniqueness and timeliness) jointly affect the volume and sentiment of subsequent user comments. By analyzing data from the Facebook business pages of multiple U.S. retailers (10,312 firm posts from 37 firms and over 1 million user comments), we found that firms are more likely to respond to negative comments (than positive or neutral comments) but less likely to respond to controversial comments (which evoke diverse opinions and emotions). Further, we found that engaging with negative and controversial comments and promptly responding to comments are linked to an increase in the volume of subsequent user comments but also to a more negative sentiment in these comments. We also found that providing unique responses improves the volume and sentiment of subsequent user comments. Our findings offer theoretical and practical insights into firms' response management on social media. [ABSTRACT FROM AUTHOR]

ObjectivesDespite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR.Materials and methodsWe developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles.ResultsWe applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors.DiscussionWe developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement.ConclusionGoing forward, the BDC framework can be used to study engagement with similar dashboards.

User engagement, a key factor in the success of social media platforms, has long been based on permanent content. A recent paradigm shift in platform design has led large social media providers to implement ephemerality features that by default make shared content disappear after a certain amount of time. However, very little is known about how ephemerality features affect user engagement and behavior in social media. Drawing upon the technology affordance perspective, we conducted a qualitative multimethod study involving individual interviews and focus groups. Our findings show that the affordances arising from features with varying degrees of ephemerality (i.e., snaps and stories) differ from those of permanent content features in terms of self-presentation, browsing others' content, and communication. Adopting a multidimensional conceptualization of user engagement, we show the positive (e.g., more content sharing) and negative (e.g., cognitive burden from context loss) effects for snaps and stories that should be cautiously considered by social media platforms aiming to introduce such features. Finally, we reveal new user behaviors that relate to sharing snapshots of fleeting value as snaps or experiences of transient value as stories. [ABSTRACT FROM AUTHOR]

Social technologies on online review platforms enable social interactions among users, such as establishing following relationships and commenting on others' posts. Although it is well recognized that more socially engaged reviewers tend to be more active and generate content of higher quality, our knowledge about the impact of social interactions on peer evaluations of reviews is limited. To address this issue, we use a unique dataset from a major online review platform and find that, ceteris paribus, reviews posted by more socially engaged users receive more helpfulness votes than those posted by less socially engaged users. Similarly, users tend to vote more for reviews written by their mutual followers than for those written by nonfollowers. In addition, we find that less socially engaged users review a broader range of products and services but are less likely to stay on a platform, which may further contribute to the inflation of peer evaluations (toward online reviews). Our study provides unique empirical evidence regarding the influence of social interactions on review evaluations. Furthermore, we caution researchers and practitioners against utilizing review helpfulness scores as a sole measure for review quality and diagnosticity. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSIn this exploratory research, a gamification approach for crowdsourcing fake account detection in social networks has been proposed and evaluated for the first time. Gamification usually tries to satisfy the users’ psychological needs. However, motivation could also be based on human physiological needs. Among these needs are health, shelter, and clothes. In the gamification world, we can translate them into monetary or cash rewards. We have investigated the effects of both these rewards on users’ motivations in participating and detecting fake accounts by conducting two in-the-wild exploratory experiments and two surveys. The experimental results showed that users prefer physiological rewards over psychological rewards, and an increase in the reward (0.75 euro in this study) resulted in a 365% increase in user participation and a 28.88% decrease in number of accurate reports in the first experiment. Similarly, in the second experiment, we observed that higher monetary rewards resulted in higher user acquisition and higher user engagement, but the quality of the reports was 10% lower than the low monetary rewards. This indicates that physiological rewards can potentially make users over-motivated or, in other words, overgamified. To tackle this problem, we proposed a gamification approach that benefits from both psychological and physiological rewards and gradually increases them. The evaluation results showed a 15% increase in the quality of the reports, a 26% increase in interaction time using the proposed method, and a 19% increase in user acquisition to lower monetary rewards. It also showed a 57% increase in commitment to participating in the survey compared with other groups. Both survey results show that around 40% of the users prefer to receive between 0.50 and 1 euro for their services. Also, women showed more quality responses and commitment in completing the participation than men.A gamification approach was proposed to motivate the users to join and participate in fake account detection activities on social media.High physiological rewards increase user acquisition and engagement but decrease the quality of the user reports.Psychological rewards alone do not successfully motivate users to participate and engage in this activity.The proposed gamification method uses both physiological and psychological rewards and can increase the quality of the report while minimizing the expenses. [ABSTRACT FROM AUTHOR]

Summary The use of recommender systems in mobile health apps for weight control has grown, but user app uptake and engagement remain limited. The objective of our scoping review was to explore the influence of recommender systems on mHealth app user engagement, identify the theoretical frameworks that have been applied on digital health interventions designed for weight management, and examine the key aspects that support tailoring user engagement through recommender systems. Based on existing literature, we identified 13 articles on recommender systems for weight management. Themes emerged, including theoretical underpinnings, authors' domain knowledge, user motivation, and design. Most studies used constructs from the social cognitive theory. We found inconsistencies between authors' domain knowledge and the intervention's content, with few professionals from the health and psychology fields. Only 46% of articles measured user engagement, whereas gamification and tailored messages were common app features. Despite some positive weight change results, more attention is needed toward implementing behavior theory and other strategies to promote app user engagement. Future studies should more accurately measure user motivation and identify the best features and behavioral constructs to increase app user interaction. Larger studies with a more diverse population are needed to generalize findings and evaluate weight loss maintenance and physical activity habits among users of recommender system. [ABSTRACT FROM AUTHOR]

Due to the rise of social media, the spread of fake news has been a serious threat to information integrity and the credibility of news sources. To address this challenge, there is an urgent need to develop robust and effective strategies to curb the spread of false information. There are two key contributions to fake news research in this work. First, we introduce the Psycho-Linguistic News Content and Comments (PL-NCC) dataset, a consolidated dataset derived from two prominent fake news datasets, NELA-GT and Fakeddit. Our dataset leverages linguistic and psychological features from both news articles and user comments to enhance the classification accuracy of benchmark models. Second, we propose the News Content and Comments (NCC) classification model, which utilizes the psychological features extracted from our PL-NCC dataset. By incorporating a feed-forward layer into a deep learning model, this approach enhances the effectiveness of the extracted features for more accurate fake news detection. Our model achieves a classification accuracy of over 90%, surpassing several existing baseline results. [ABSTRACT FROM AUTHOR]

Are social media posts with emoji more engaging? Guided by the media richness theory, this study explores the relationship between visual (i.e., emoji) and textual content, and how they collectively impact user engagement with peer-to-peer accommodation brands. A three-stage sequential design using naturalist data, including text mining, frequent/rare itemset mining, and one-way ANOVA, was used. This study revealed that the combination of { Travel Tips and Inspiration, } and { Interaction and Motivation, } tended to result in an increased amount of likes and shares in social media posts. By theoretically revealing and empirically examining the complex relationship between verbal and visual content, this study enriches the theoretical understanding of media richness in tourism brands. Practically, this study provides actionable guidelines for tourism brands to increase user engagement by effectively using visual-verbal content. [ABSTRACT FROM AUTHOR]

In recent years, e-sports has experienced a rapid surge in popularity, attracting a vast and diverse audience. As this industry continues to evolve, understanding and predicting e-sport engagement becomes increasingly vital for stakeholders, including game developers, tournament organizers, sponsors, and marketers. Machine learning classification algorithms offer a powerful approach to analyse and forecast user engagement in e-sports, thereby enabling the industry to tailor experiences to individual preferences and behaviours. Thus, this study investigates the level of engagement classification technique of data mining using predictive modelling operations with four different classes, namely strongly agree, either agree or disagree, disagree, and strongly disagree. Machine learning algorithms, particularly classification models, have proven to be effective in analysing large and complex datasets related to e-sport engagement. This study applies statistical techniques to categorize users based on 59 attributes of 106 instances to predict the engagement levels. By training on historical user data, six classification algorithms from two groups, namely bayes and rules, have been used to identify patterns and trends that are indicative of different engagement levels, with the accuracy ranges from 76% to 92%. For feature selection, the result shows that participating in activities, enjoying exchanging ideas, and playing with like-minded gamers were the top three ranking dimensions contributing to the level of engagement. Machine learning classification algorithms have the potential to revolutionize how e-sport engagement is understood and optimized. By analysing diverse data points and leveraging advanced predictive techniques, machine learning algorithms enable stakeholders to tailor e-sport experiences to individual preferences and behaviours, ultimately enhancing user engagement and satisfaction. [ABSTRACT FROM AUTHOR]

In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering usermovie interaction data, including user feedback and movie characteristics, which is standardized to ensure consistency before model training. Leveraging its adaptable learning rate and neighborhood function, the Enhanced SOM effectively identifies subtle data nuances. Personalized movie suggestions are then generated by exploiting the Enhanced SOM's capacity to identify similar users and films. Integration of hybrid filtering techniques enriches recommendation quality, blending collaborative filtering algorithms, which leverage user-item interactions, with content-based filtering, which utilizes movie attributes such as genres and descriptions. This amalgamation results in suggestions that harmoniously combine diverse filtering methodologies. The proposed solution's efficacy is rigorously evaluated by comparing suggestion accuracy and user satisfaction against predefined benchmarks. Extensive real-world dataset testing corroborates the effectiveness of the Enhanced SOM-based movie recommendation approach. Furthermore, the system offers flexibility through options for parameter adjustment, grid size variations, and neighborhood function modifications to further refine recommendation accuracy. Collectively, these elements underscore the efficacy of the proposed method in furnishing tailored movie recommendations. When coupled with hybrid filtering techniques, the implementation of Enhanced SOMs emerges as a reliable model for content platforms seeking to enhance user experiences by delivering precise movie recommendations, coupled with scalability and adaptability. [ABSTRACT FROM AUTHOR]

Within digital media, the effectiveness of content material advice systems is pivotal for boosting user engagement and satisfaction. This study's article delves into the development and implementation of a singular set of rules recommendation gadgets based totally on the principles of support Vector device (SVM), a distinguished machine learning approach. The objective is to address the demanding situations faced by using traditional recommendation structures, such as media content problems, by leveraging the type and regression talents of SVM. The methodology encompasses the usage of a large dataset of person interactions and alternatives extracted from diverse virtual media systems. This statistic is then processed via an SVM version and relationships among user behaviors and content material characteristics. The particular issue of this technique lies in its adaptability and precision in dealing with excessive-dimensional facts, which is ordinary in digital media environments. The SVM model is high-quality-tuned to optimize content recommendation via not most effective matching consumer choices but additionally introducing a degree of content material variety to combat echo chambers. This research evaluates the performance of the SVMbased recommendation system towards traditional algorithms via a sequence of metrics inclusive of accuracy, range, and consumer engagement charges. This assessment gives insights into the efficacy of SVM in delivering extra applicable and various content to users, thereby enhancing their digital media experience. [ABSTRACT FROM AUTHOR]

AbstractThis study investigates effective feedback mechanisms to maintain human engagement in interactive machine learning (IML) systems, focusing on social media platforms. We developed “Loop,” an IML system based on human-in-the-loop (HITL) principles that recommends content while encouraging users to report inaccuracies for model refinement. Loop implements three types of artificial intelligence (AI) feedback on user reports: (a) machine learning (ML)-centric, (b) personal-centric, and (c) community-centric feedback. In addition, we evaluated the relative effectiveness of these feedback types under two different task criticality scenarios: high and low. A user study with 30 participants was conducted to evaluate Loop through questionnaires and interviews. Results showed that participants preferred algorithmic improvements for personal benefit over altruistic contributions to the community, especially for low-criticality tasks. Furthermore, personal-centric feedback had a significant impact on user engagement and satisfaction. Our findings provide insights into the effectiveness of machine feedback in HITL-ML systems, contributing to the design of more engaging and effective IML interfaces. We discuss implications and strategies for encouraging proactive user engagement in HITL-ML-based systems, emphasizing the importance of tailored feedback mechanisms. [ABSTRACT FROM AUTHOR]

Library spaces play a crucial role in fostering user engagement, particularly for students in academic settings by creating welcoming environment that blend inspiring spaces with engaging resources and programming, academic libraries can make significant contributions to overall student engagement. This study employed descriptive regression research design to determine the influence of library space design on student engagement. A total number of 100 students from selected schools in Regions XI and XII were the respondents of the study using convenience sampling technique. Mean and simple linear regression analysis were employed to analyze the data. The findings showed that student's level of perception towards library space design was very high while level of user engagement was found to be high. Among the indicators of library space design, aesthetics and atmosphere got the highest mean ratings while on the aspect of user engagement, library visits yielded the highest mean rating. Moreover, the results showed that student's perception on the library space design influence user engagement. Base on the findings it is recommended that the school administrators should prioritize allocating adequate resources for continuous library space renovation, conduct comprehensive assessments to library personnel and users, and streamlining access to library resources to meet expectations of students who are comfortable with technology. [ABSTRACT FROM AUTHOR]

The article "Leveraging Technology to Transform Sexual and Reproductive Health Services" in the Bangladesh Journal of Medical Science examines how technology, including telemedicine and mobile health apps, is reshaping the delivery of sexual and reproductive health services. It underscores the advantages of these innovations in enhancing access, care quality, and patient empowerment, while also addressing concerns about data privacy, digital literacy, and equity. The document delves into ethical considerations, disparities in digital access, and the significance of health equity in telemedicine and e-health services, emphasizing the need for collaboration among stakeholders for a safe and inclusive integration of technology in SRH services. [Extracted from the article]

In 2014, the EU introduced the lead candidate procedure to raise citizens' awareness and interest in the European Parliament (EP) elections and, thereby, voter turnout. We study the use of personalization, centralized personalization (focusing on lead candidates), emotional personalization, and private personalization on Facebook by political parties across 12 countries during the 2014 and 2019 EP campaigns and the effects of personalization on user engagement. A standardized quantitative content analysis of 14,293 posts by 227 political parties shows that about half of the Facebook posts were personalized, but there is no general trend of rising personalization. While emotional personalization increased, parties hardly ever posted about their lead candidates and their private lives. Variations are not due to structural (e.g., party and media systems) or geographical/cultural factors. Positive effects are found for the use of emotional personalization attracting a higher volume of user reactions (likes, reactions, shares, and comments) in both elections. [ABSTRACT FROM AUTHOR]

Interactive educational videos have grown in popularity in the higher education setting over the last two decades. This exploratory study investigates the mediating and moderating effects of user engagement (UE) on the relationships between the antecedents of UE (i.e., intrinsic load, extraneous load, germane load, and task value) and learning satisfaction. A cross-sectional survey study was conducted in June 2023. We conveniently sampled 49 year-one undergraduate students, who completed a self-report questionnaire consisting of items pertaining to cognitive load, task value, user engagement, and learning satisfaction. Data was analysed using hierarchical linear regression models. Findings revealed that reward fully mediated the relationship between germane load and learning satisfaction. In addition, reward was also found to moderate the relation between (a) germane load and learning satisfaction, and (b) task value and learning satisfaction. These findings highlight the importance of creating rewarding experiences when designing interactive educational videos. [ABSTRACT FROM AUTHOR]

Incentivized blockchain-based online social media (BOSM), where creators and curators of popular content are paid in cryptocurrency, have recently emerged. Traditional social media ecosystems have experienced significant bot involvement in their platforms, which has often had a negative impact on both users and platforms. BOSM can provide additional direct financial incentives as motivation for both bots' and human users' engagement. Using the panel vector autoregression and regression discontinuity in time framework, we analyze two distinct data sets from Steemit, the largest and most popular BOSM, to study the impact of bot engagement on human users and the impact of changes in financial reward on user engagement. Interestingly, our findings demonstrate that while increased engagement by bots is positively associated with engagement by human users, the association between bot engagement and human user engagement decreases as the number of votes for a post increases. We also find that shifts in economic incentives significantly influence the behavior of both human users and bots. This research provides significant insights on how social media platforms can leverage economic incentives to influence user behavior and, more importantly, leverage bots' activity to increase the engagement of their human users. [ABSTRACT FROM AUTHOR]

Abstract The challenge of sustaining user engagement in eHealth interventions is a pressing issue with significant implications for the effectiveness of these digital health tools. This study investigates user engagement in a cognitive-behavioral therapy-based eHealth intervention for procrastination, using a dataset from a randomized controlled trial of 233 university students. Various machine learning models, including Decision Tree, Gradient Boosting, Logistic Regression, Random Forest, and Support Vector Machines, were employed to predict patterns of user engagement. The study adopted a two-phase analytical approach. In the first phase, all features of the dataset were included, revealing ‘total_minutes’—the total time participants spent on the intervention and the eHealth platform—as the most significant predictor of engagement. This finding emphasizes the intuitive notion that early time spent on the platform and the intervention is a strong indicator of later user engagement. However, to gain a deeper understanding of engagement beyond this predominant metric, the second phase of the analysis excluded ‘total_minutes’. This approach allowed for the exploration of the roles and interdependencies of other engagement indicators, such as ‘number_intervention_answersheets’—the number of completed lessons, ‘logins_first_4_weeks’—login frequency, and ‘number_diary_answersheets’—the number of completed diaries. The results from this phase highlighted the multifaceted nature of engagement, showing that while ‘total_minutes’ is strongly correlated with engagement, indicating that more engaged participants tend to spend more time on the intervention, the comprehensive engagement profile also depends on additional aspects like lesson completions and frequency of platform interactions.

This article introduces the FIRST Values Framework, a conceptual marketing thinking tool designed to foster empathy in library marketing by aligning strategies with core values and community needs. The framework enhances user-centered design and engagement efforts through five values: fellow-feeling, identification, responsiveness, self-awareness, and thoughtfulness. The literature discusses the significance of empathy in libraries, empathetic marketing, relationship marketing, and incorporating empathy into staff training for service design, highlighting the essential need for empathic connections in library services. The article discusses the integration of empathy across different contexts by briefly comparing the FIRST Values Framework with existing models like design thinking and human-centered design. Practical steps for implementing the framework in libraries are provided, ensuring marketing and outreach efforts are empathetic, user-centered, and impactful.

Abstract Introduction Digital mental health interventions (DMHIs) overcome traditional barriers enabling wider access to mental health support and allowing individuals to manage their treatment. How individuals engage with DMHIs impacts the intervention effect. This review determined whether the impact of user engagement was assessed in the intervention effect in Randomised Controlled Trials (RCTs) evaluating DMHIs targeting common mental disorders (CMDs). Methods This systematic review was registered on Prospero (CRD42021249503). RCTs published between 01/01/2016 and 17/09/2021 were included if evaluated DMHIs were delivered by app or website; targeted patients with a CMD without non-CMD comorbidities (e.g., diabetes); and were self-guided. Databases searched: Medline; PsycInfo; Embase; and CENTRAL. All data was double extracted. A meta-analysis compared intervention effect estimates when accounting for engagement and when engagement was ignored. Results We identified 184 articles randomising 43,529 participants. Interventions were delivered predominantly via websites (145, 78.8%) and 140 (76.1%) articles reported engagement data. All primary analyses adopted treatment policy strategies, ignoring engagement levels. Only 19 (10.3%) articles provided additional intervention effect estimates accounting for user engagement: 2 (10.5%) conducted a complier-average-causal effect (CACE) analysis (principal stratum strategy) and 17 (89.5%) used a less-preferred per-protocol (PP) population excluding individuals failing to meet engagement criteria (estimand strategies unclear). Meta-analysis for PP estimates, when accounting for user engagement, changed the standardised effect to -0.18 95% CI (-0.32, -0.04) from − 0.14 95% CI (-0.24, -0.03) and sample sizes reduced by 33% decreasing precision, whereas meta-analysis for CACE estimates were − 0.19 95% CI (-0.42, 0.03) from − 0.16 95% CI (-0.38, 0.06) with no sample size decrease and less impact on precision. Discussio n Many articles report user engagement metrics but few assessed the impact on the intervention effect missing opportunities to answer important patient centred questions for how well DMHIs work for engaged users. Defining engagement in this area is complex, more research is needed to obtain ways to categorise this into groups. However, the majority that considered engagement in analysis used approaches most likely to induce bias.

Abstract The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-preserving representation of user engagement on online social platforms. Leveraging deep LSTM neural networks to analyze more than 100 million Snapchat sessions from almost 80.000 users, we demonstrate that patterns of active and passive use are predictable from past behavior ( $${R^2}$$ R 2 =0.345) and that the integration of context features substantially improves predictive performance compared to the behavioral baseline model ( $${R^2}$$ R 2 =0.522). Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. Further, we show that a large proportion of variance can be accounted for with minimal behavioral histories if momentary context is considered ( $${R^2}$$ R 2 =0.442). These results indicate the potential of context-aware approaches for making models more efficient and privacy-preserving by reducing the need for long data histories. Finally, we employ model explainability techniques to glean preliminary insights into the underlying behavioral mechanisms. Our findings are consistent with the notion of context-contingent, habit-driven patterns of active and passive use, highlighting the value of contextualized representations of user behavior for predicting user engagement on online social platforms.

The challenge of sustaining user engagement in eHealth interventions is a pressing issue with significant implications for the effectiveness of these digital health tools. This study investigates user engagement in a cognitive-behavioral therapy-based eHealth intervention for procrastination, using a dataset from a randomized controlled trial of 233 university students. Various machine learning models, including Decision Tree, Gradient Boosting, Logistic Regression, Random Forest, and Support Vector Machines, were employed to predict patterns of user engagement. The study adopted a two-phase analytical approach. In the first phase, all features of the dataset were included, revealing ‘total_minutes’—the total time participants spent on the intervention and the eHealth platform—as the most significant predictor of engagement. This finding emphasizes the intuitive notion that early time spent on the platform and the intervention is a strong indicator of later user engagement. However, to gain a deeper understanding of engagement beyond this predominant metric, the second phase of the analysis excluded ‘total_minutes’. This approach allowed for the exploration of the roles and interdependencies of other engagement indicators, such as ‘number_intervention_answersheets’—the number of completed lessons, ‘logins_first_4_weeks’—login frequency, and ‘number_diary_answersheets’—the number of completed diaries. The results from this phase highlighted the multifaceted nature of engagement, showing that while ‘total_minutes’ is strongly correlated with engagement, indicating that more engaged participants tend to spend more time on the intervention, the comprehensive engagement profile also depends on additional aspects like lesson completions and frequency of platform interactions. [ABSTRACT FROM AUTHOR]

Social media are used by populist radical right parties for anti-gender campaigns to mobilize against the 'gender ideology'. Anti-gender hate speech targets women, sexual minorities, and feminist activists and therefore poses a threat to their participation in society. In this study, we examine the engagement with anti-gender hate speech on social media in Germany and Hungary, using anti-gender, homophobic, and sexist Facebook posts as examples. These countries were chosen because of the cultural differences in the way gender issues and discrimination are dealt with, as well as the position of populist radical right parties. Employing a choice-based conjoint design, we analyze which content and source characteristics as well as cultural and individual factors influence users' engagement with such postings. Findings show country differences in the acceptance (likes, shares) and disapproval (flagging behavior) of anti-gender hate speech but also with respect to relevance of the three main topics of hate speech (anti-gender, homophobia, sexism). [ABSTRACT FROM AUTHOR]

Digital health interventions (DHIs) can facilitate positive health outcomes. User engagement (UE) plays an important role in DHI efficacy. Yet, DHIs vary in functionality, design and intended outcomes, underscoring the importance of incremental, user-centred design to understand engagement in specific settings. This study explores the relationship between user engagement and DHI implementation in three design iterations, or rounds, of a unique, multi-week asynchronous intervention that leverages online discussion and problem-solving therapy (PST). The intervention seeks to engage older adults to improve problem solving skills relating to the intervention focus, health aging (two rounds) and Lewy Body Dementias (LBD) caregiving (one round). The PST component drew upon personas, a common user-centered design method, in a novel way. Exit interviews were conducted at the end of each round to understand participants' experiences. Using thematic analysis, we identified factors that contributed to social engagement ('engaging with others') and learning engagement ('engaging with content') with the DHI. The findings demonstrate how iterative changes in the design and delivery of a DHI can contribute to user engagement, increasing the likelihood of knowledge acquisition and developing problem-solving skills as part of health self-management. [ABSTRACT FROM AUTHOR]

TikTok, a short video-sharing platform, has captured a massive audience since its inception in 2016. This article explores the evolution of rural media in China, the burgeoning TikTok rural market, and the unique landscape of rural TikTok users. By investigating the development trajectory of TikTok and probing into the individual usage patterns, motivations, and preferences of rural TikTok users, this study aims to gain deeper insights into their distinct characteristics. Furthermore, this study employs two theoretical frameworks, the uses and gratifications theory and the media enjoyment theory, to analyze the causal relationship between Chinese TikTok rural users and their direct demands and satisfaction levels. Leveraging a combination of big data crawling and comprehensive questionnaire surveys, we provide data-driven perspectives on rural TikTok users' experiences, motivations, and challenges. Our findings reveal that TikTok has become a compelling entertainment and social interaction source for rural users, aligning with its original positioning. The adaptability of TikTok's content curation algorithms caters to diverse age groups, contributing significantly to user satisfaction. However, content quality and influencer representation disparities persist as challenges for rural TikTok users. This research offers valuable insights into the practical issues faced by rural TikTok users and presents recommendations for their enhanced engagement with the platform. By bridging the urban–rural digital divide, this study contributes to a more equitable media landscape in China and underscores the platform's role in reshaping the dynamics of knowledge dissemination and entertainment consumption in the knowledge-based economy of the twenty-first century. [ABSTRACT FROM AUTHOR]

This article introduces the FIRST Values Framework, a conceptual marketing thinking tool designed to foster empathy in library marketing by aligning strategies with core values and community needs. The framework enhances user-centered design and engagement efforts through five values: fellow-feeling, identification, responsiveness, self-awareness, and thoughtfulness. The literature discusses the significance of empathy in libraries, empathetic marketing, relationship marketing, and incorporating empathy into staff training for service design, highlighting the essential need for empathic connections in library services. The article discusses the integration of empathy across different contexts by briefly comparing the FIRST Values Framework with existing models like design thinking and human-centered design. Practical steps for implementing the framework in libraries are provided, ensuring marketing and outreach efforts are empathetic, user-centered, and impactful. [ABSTRACT FROM AUTHOR]

Purpose: This study explores central questions related to the connection between social media marketing activities (SMMAs), user engagement and the self-brand connection of restaurant Instagram pages. The study examines the mediating role of user engagement between SMMAs and self–brand connections. Also, this study explores the connection between SMMAs and user engagement through the moderating role of gender and trust. Design/methodology/approach: A convenience sample method was employed to collect data from customers (18–24 years old). A structural equation modeling approach and PROCESS macro were applied based on 298 online questionnaires completed by customers who follow restaurant Instagram pages. The mediating effect for user engagement and the moderating effect for gender and trust were performed. Findings: The findings revealed that SMMAs have a significant positive influence on self–brand connection and user engagement. Further, user engagement acts as a mediator between SMMAs and self–brand connection. The results illustrate the importance of SMMAs in enhancing user engagement in light of gender and trust. Practical implications: This paper presents significant managerial implications for restaurant businesses about how SMMAs can effectively enhance user engagement behavior and self–brand connection on Instagram pages. Originality/value: This research developed a theoretical model to understand how SMMAs might enhance user engagement in the restaurant industry by invoking gender and trust as moderating variables in the relationship between SMMAs and user engagement. This paper offers new theoretical and practical contributions that add value to social media marketing (SMM) literature by testing the moderated–mediation model of these constructs in the hospitality sector. [ABSTRACT FROM AUTHOR]

Fueled by remarkable developments in mobile and wearable technologies coupled with the power of the Internet a multibillion industry constructed on digital platforms, Connected Fitness, has emerged. Sustained engagement is a crucial prerequisite for healthy behavior, and platforms that offer connected fitness services must contend with the challenge of engaging new users. In this study, we examine whether a virtual race, an event in which runners can participate remotely in an organized activity, can serve as an effective strategy to amplify users' long-term engagement related to healthy behavior. We study the impact of offering virtual races to runners on a digital fitness platform on their engagement with the platform and level of physical activity. Leveraging the match of the theme of the virtual races and users' zodiac signs as the instrument to support causal inference, we find that a virtual race increases the engagement of new users by 42.8 days and total spending on related e-commerce on the platform by 48.4% during our study period. We also find that the positive impact of virtual races transcends the typical mechanism of social connections; results show that virtual races can increase the engagement with the platform even when their social connections are low. Finally, we find that existing users who participate in a virtual race outrun those in the control group by 4.41 km in a three-week period. Our study highlights the potential of new forms of digital interactions, powered by mobile and wearable technologies, on user behavior in fitness platforms. This paper was accepted by Anindya Ghose, information systems. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4945. [ABSTRACT FROM AUTHOR]

Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamification for Lexical Ambiguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity. [ABSTRACT FROM AUTHOR]

Introduction: Digital mental health interventions (DMHIs) overcome traditional barriers enabling wider access to mental health support and allowing individuals to manage their treatment. How individuals engage with DMHIs impacts the intervention effect. This review determined whether the impact of user engagement was assessed in the intervention effect in Randomised Controlled Trials (RCTs) evaluating DMHIs targeting common mental disorders (CMDs). Methods: This systematic review was registered on Prospero (CRD42021249503). RCTs published between 01/01/2016 and 17/09/2021 were included if evaluated DMHIs were delivered by app or website; targeted patients with a CMD without non-CMD comorbidities (e.g., diabetes); and were self-guided. Databases searched: Medline; PsycInfo; Embase; and CENTRAL. All data was double extracted. A meta-analysis compared intervention effect estimates when accounting for engagement and when engagement was ignored. Results: We identified 184 articles randomising 43,529 participants. Interventions were delivered predominantly via websites (145, 78.8%) and 140 (76.1%) articles reported engagement data. All primary analyses adopted treatment policy strategies, ignoring engagement levels. Only 19 (10.3%) articles provided additional intervention effect estimates accounting for user engagement: 2 (10.5%) conducted a complier-average-causal effect (CACE) analysis (principal stratum strategy) and 17 (89.5%) used a less-preferred per-protocol (PP) population excluding individuals failing to meet engagement criteria (estimand strategies unclear). Meta-analysis for PP estimates, when accounting for user engagement, changed the standardised effect to -0.18 95% CI (-0.32, -0.04) from − 0.14 95% CI (-0.24, -0.03) and sample sizes reduced by 33% decreasing precision, whereas meta-analysis for CACE estimates were − 0.19 95% CI (-0.42, 0.03) from − 0.16 95% CI (-0.38, 0.06) with no sample size decrease and less impact on precision. Discussion: Many articles report user engagement metrics but few assessed the impact on the intervention effect missing opportunities to answer important patient centred questions for how well DMHIs work for engaged users. Defining engagement in this area is complex, more research is needed to obtain ways to categorise this into groups. However, the majority that considered engagement in analysis used approaches most likely to induce bias. [ABSTRACT FROM AUTHOR]

The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-preserving representation of user engagement on online social platforms. Leveraging deep LSTM neural networks to analyze more than 100 million Snapchat sessions from almost 80.000 users, we demonstrate that patterns of active and passive use are predictable from past behavior ( R 2 =0.345) and that the integration of context features substantially improves predictive performance compared to the behavioral baseline model ( R 2 =0.522). Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. Further, we show that a large proportion of variance can be accounted for with minimal behavioral histories if momentary context is considered ( R 2 =0.442). These results indicate the potential of context-aware approaches for making models more efficient and privacy-preserving by reducing the need for long data histories. Finally, we employ model explainability techniques to glean preliminary insights into the underlying behavioral mechanisms. Our findings are consistent with the notion of context-contingent, habit-driven patterns of active and passive use, highlighting the value of contextualized representations of user behavior for predicting user engagement on online social platforms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Justice services have begun to integrate the use of mobile applications into treatment, support, and rehabilitative programs for forensic clients. One such application that been adopted to support forensic clients is "eRecovery": a smartphone application that provides clients recovering from a substance addiction with support for managing relapse. In this article, we report on evaluation findings from a trial of eRecovery in an Australian Community Justice Centre, and reflect on several issues relating to fostering and sustaining client engagement with similar applications within forensic and justice settings. We propose the Service Design Engagement Model to organize, visualize, and describe the stages and factors important to adoption, appropriation, and on-going routine use of the software by forensic clients. The model recognizes the role of contextual and environmental factors in supporting users through the early stages of engagement, and the importance of user agency in longer-term engagement with therapeutic apps. [ABSTRACT FROM AUTHOR]

Online news platforms have become users' primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users' participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation. [ABSTRACT FROM AUTHOR]

As an emerging category of non-human influencers, virtual influencers (VIs) have received increasing attention on social media. While VIs generate high user engagement, the underlying factors that drive social media engagement are poorly understood. Informed by communication theory, this study aims to explore Instagram users' perceptions of VIs and the key source and content factors that prompt user engagement in tourism contexts. An initial netnographic observation of Instagram posts was followed by five online focus group interviews using a photo elicitation technique. The results indicated that Instagram users had mixed feelings towards VIs; however, most were receptive to VI marketing provided that ethical concerns had been addressed. Key source and content factors that stimulate user engagement with VIs are integrated into a proposed framework. The findings will assist tourism practitioners and content creators to design VI marketing campaigns that incorporate and leverage important influencer and post-related features. [ABSTRACT FROM AUTHOR]

Many people with Type 2 diabetes (T2D) who could benefit from digital health technologies (DHTs) are either not using DHTs or do use them, but not for long enough to reach their behavioral or metabolic goals. We aimed to identify subgroups within DHT adopters and non-adopters and describe their unique profiles to better understand the type of tailored support needed to promote effective and sustained DHT use across a diverse T2D population. We conducted latent class analysis of a sample of adults with T2D who responded to an internet survey between December 2021 and March 2022. We describe the clinical and psychological characteristics of DHT adopters and non-adopters, and their attitudes toward DHTs. A total of 633 individuals were characterized as either DHT "Adopters" (n = 376 reporting any use of DHT) or "Non-Adopters" (n = 257 reporting never using any DHT). Within Adopters, three subgroups were identified: 21% (79/376) were "Self-managing Adopters," who reported high health activation and self-efficacy for diabetes management, 42% (158/376) were "Activated Adopters with dropout risk," and 37% (139/376) were "Non-Activated Adopters with dropout risk." The latter two subgroups reported barriers to using DHTs and lower rates of intended future use. Within Non-Adopters, two subgroups were identified: 31% (79/257) were "Activated Non-Adopters," and 69% (178/257) were "Non-Adopters with barriers," and were similarly distinguished by health activation and barriers to using DHTs. Beyond demographic characteristics, psychological, and clinical factors may help identify different subgroups of Adopters and Non-Adopters. [ABSTRACT FROM AUTHOR]

In the field of artificial intelligence-based fitness apps, the effective integration of behavior change techniques (BCTs) is critical for promoting physical activity and improving health outcomes. However, the specific BCTs employed by apps and their impact on user engagement and behavior change are not explored sufficiently. This study investigates the Freeletics fitness app through a mixed-methods approach to evaluate the use of BCTs. In the quantitative analysis, fifteen unique BCTs were identified based on the Behavior Change Technique Taxonomy (V1). In the qualitative analysis, user reviews (n =400) were examined to understand perspectives on the app's effectiveness in promoting behavior change. Goal setting, action planning, self-monitoring of behavior, and social support were among the most prevalent BCTs identified in the Freeletics app, and their effectiveness in enhancing user engagement and promoting behavior change was also highlighted by user reviews. Among the areas of improvement identified in the study were the need for simplifying personalization options and addressing user concerns regarding the specificity of feedback. The study underscores the importance of integrating BCTs effectively within AI-based fitness apps to drive user engagement and facilitate behavior change. It contributes valuable insights into the design and implementation of BCTs in fitness apps and offers recommendations for developers, emphasizing the significance of goal setting, feedback mechanisms, self-monitoring, and social support. By understanding the impact of specific BCTs on user behavior and addressing user concerns, developers can create more effective fitness apps, ultimately promoting healthier lifestyles and positive behavior change. [ABSTRACT FROM AUTHOR]

Mobile apps can effectively promote healthy and sustainable eating, but their success depends on initial user engagement. This study aimed to increase initial user engagement by tailoring app descriptions to users' regulatory focus (prevention versus promotion). Four conditions were created by combining two types of messages: regulatory concern (safety versus growth) and anticipated emotion (positive versus negative). The safety message emphasized the protection of health and the environment, while the growth message emphasized the improvement of well-being and environmental conservation. The message with the positive anticipated emotion described satisfaction with using the app, while the message with the negative anticipated emotion described dissatisfaction with not using the app. Participants' attitudes, desires, and intentions, as well as downloads of the app were measured to assess the effectiveness of the message. Promotion-focused individuals showed greater desire and were more likely to download the app when the message matched their focus (growth + positive anticipated emotions) than when it did not (safety + negative anticipated emotions). Conversely, prevention-focused individuals did not show increased engagement with regulatory congruent messages. These results suggest that tailoring app descriptions to users' regulatory focus may increase initial engagement among promotion-focused individuals. Further research is needed to investigate methods to increase engagement among prevention-focused individuals. [ABSTRACT FROM AUTHOR]

AbstractThe objective of the present study was to examine the effects of using gamification implemented in immersive virtual reality for the promotion of library services in the university library including knowledge acquisition, knowledge retention and user engagement. In terms of research procedure, we divided the participants into Gamified and Non-Gamified virtual reality groups, and both groups were asked to perform the pretest questionnaire of cognitive assessments. Then, participants were informed to use virtual reality to finish all the levels within one month in the library lab setting of the university. We performed post-tests to evaluate the knowledge acquisition, including a delayed post-test of three months to verify the knowledge retention. We also collected the user data to analyze the participant behavior. Results showed no significant differences among the Gamified and Non-Gamified groups in terms of knowledge acquisition, but showed significant difference in knowledge retention and user engagement. These findings supported the presumption that the gamified virtual reality approach can improve the library user’s access and knowledge retention of library services. [ABSTRACT FROM AUTHOR]

With the rise of digital media, conspiracy theories infamous for their emotional manipulation have challenged science epistemology and democratic discourse. Despite extensive literature on misinformation and the role of emotion in persuasion, less is understood about how emotion is used in conspiracy and debunking messages on video platforms and the impact of emotional framing on public engagement with science on social media. Our article fills this gap by analyzing thousands of YouTube videos that propagate or debunk COVID-19 conspiracy theories from March to May 2020. We found that conspiracy and debunking videos used the emotions of trust and fear differently depending on the issue framing of the conspiracy. Our article also reveals a dilemma facing debunking messages—when debunking videos used more trust-related emotions, these videos received more likes yet fewer views. These findings shed new light on the role of emotion on user engagement with misinformation and its correction on digital platforms. [ABSTRACT FROM AUTHOR]

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

With the rise of social media, material can be easily shared and discovered, with some postings becoming viral and others going undetected. The main factors that influence the generation of viral material on social media are investigated in this research. This study uses data from well-known social media sites to identify and analyse the elements that matter most for material to become viral. These elements include emotional appeal, timeliness, platform algorithms, user involvement, and the kind of content. In order to understand how these factors affect the probability of material swiftly spreading across digital networks, the research used a mixed-method approach, combining quantitative data analysis with qualitative interviews with content producers. The results show that the three main factors that cause content to become viral are emotional resonance, publishing timing, and optimising for particular platforms. Researchers also found that algorithmic preferences and audience interaction tactics played a part in increasing content reach. For content producers and marketers looking to maximise content exposure in a competitive online context, this article gives practical consequences and helps to the knowledge of digital marketing tactics. [ABSTRACT FROM AUTHOR]

Negativity is a common feature of current online political communication during elections. Previous studies on negativity and its impact on user engagement focused mainly on Western European countries. Considering the political particularities of the Central and Eastern European countries, the present study focused on Czechia, Hungary, Lithuania, the Republic of Moldova, and Romania, where national election campaigns took place from 2020 to 2022. We aimed to investigate comparatively different topics prevalent in the negative messages and look at users' engagement with negative communication. We applied manual content analysis of N = 4,095 Facebook posts published four weeks before the elections by political parties elected in the national parliament. Results showed significant differences across countries using negative messages and the associated topics. The posts' highest rate of negative statements was identified in Czechia (52%), while the lowest was in Lithuania (17%). There are topics consistently associated with negative statements across most countries, such as corruption, economy and finance, foreign policy, labor, and social issues. However, given that those elections took place during the Covid-19 pandemic in Czechia, Lithuania, Romania, and the Republic of Moldova and, in Hungary, after Russia invaded Ukraine, we also identified contextual topics such as health, war, and conflicts with other countries and defense that were mainly associated with negative messages. Furthermore, negative posts generate more reactions and comments than posts containing no negative statements. Addressing foreign policy in negative posts generates significantly more reactions and comments. [ABSTRACT FROM AUTHOR]

Canada's federal elections in 2019 and 2021 produced a similar outcome--a minority Liberal government. These back-to-back elections provide an ideal context to understand trends in digital campaigning strategies and assess how the pandemic influenced campaigns' use of social media. We examine how the three leaders of the major parties used Facebook in 2019 (n = 712) compared to 2021 (n = 979). The Conservative leader O'Toole posted more frequently than other candidates in 2021, fitting with the equalization theory of digital campaigning. In 2019 and 2021, the incumbent prime minister, Trudeau, received the most user engagement on his Facebook posts despite calling a snap election during a pandemic and less than two years into his mandate. These findings support normalization theories of digital campaigning with evidence of an accumulating incumbent advantage. The Covid-19 pandemic sidelined attention to climate change. We argue that the Liberal government owned both issues; we expected Trudeau to have greater attention to and user engagement for these policy posts. In general, Facebook posts about the pandemic yielded greater user engagement than posts that did not mention the pandemic. Candidates tested new campaign strategies in 2021, particularly making calls to interact with them; these posts yielded higher user engagement than posts that did not include a call to interact. While candidates used new social media campaign strategies, voter turnout declined from 2019 to 2021. These findings have implications for other democratic systems and the future of digital campaigning. [ABSTRACT FROM AUTHOR]

This study empirically examines the complex dynamics between social media entities, brands, and users, advancing user engagement and emotional attachment. It introduces a novel affordance perspective to dissect the symbiotic relationships that shape marketing and communication management. Analysing data from 424 U.S. social media users, this research explores the interplay between platform-user and brand-user affordances and their impact on reflective engagement and emotional attachment in digital environments. Findings affirm a substantial impact of affordances on emotional attachment to platforms, while also redefining the role of social influence, which contrasts with its conventional moderating role in user engagement literature. By moving beyond a functionalist affordance view to a relational one, this paper brings to the fore the triad of platform-user, brand-user, and user-reflection affordances, providing strategic insights for marketers and setting the stage for future research on user interaction dynamics. [ABSTRACT FROM AUTHOR]