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Sleep disorders are a global challenge, affecting a quarter of the global population. Mobile health (mHealth) sleep apps are a potential solution, but 25% of users stop using them after a single use. User satisfaction had a significant impact on continued use intention. This China-US comparison study aimed to mine the topics discussed in user-generated reviews of mHealth sleep apps, assess the effects of the topics on user satisfaction and dissatisfaction with these apps, and provide suggestions for improving users' intentions to continue using mHealth sleep apps. An unsupervised clustering technique was used to identify the topics discussed in user reviews of mHealth sleep apps. On the basis of the two-factor theory, the Tobit model was used to explore the effect of each topic on user satisfaction and dissatisfaction, and differences in the effects were analyzed using the Wald test. A total of 488,071 user reviews of 10 mainstream sleep apps were collected, including 267,589 (54.8%) American user reviews and 220,482 (45.2%) Chinese user reviews. The user satisfaction rates of sleep apps were poor (China: 56.58% vs the United States: 45.87%). We identified 14 topics in the user-generated reviews for each country. In the Chinese data, 13 topics had a significant effect on the positive deviation (PD) and negative deviation (ND) of user satisfaction. The 2 variables (PD and ND) were defined by the difference between the user rating and the overall rating of the app in the app store. Among these topics, the app's sound recording function (β=1.026; P=.004) had the largest positive effect on the PD of user satisfaction, and the topic with the largest positive effect on the ND of user satisfaction was the sleep improvement effect of the app (β=1.185; P<.001). In the American data, all 14 topics had a significant effect on the PD and ND of user satisfaction. Among these, the topic with the largest positive effect on the ND of user satisfaction was the app's sleep promotion effect (β=1.389; P<.001), whereas the app's sleep improvement effect (β=1.168; P<.001) had the largest positive effect on the PD of user satisfaction. The Wald test showed that there were significant differences in the PD and ND models of user satisfaction in both countries (all P<.05), indicating that the influencing factors of user satisfaction with mHealth sleep apps were asymmetrical. Using the China-US comparison, hygiene factors (ie, stability, compatibility, cost, and sleep monitoring function) and 2 motivation factors (ie, sleep suggestion function and sleep promotion effects) of sleep apps were identified. By distinguishing between the hygiene and motivation factors, the use of sleep apps in the real world can be effectively promoted.

In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. This article investigates the use of a text mining approach to aid sentiment analysis in the pursuit of understanding what customers are saying about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. The authors analyse the relationship between unstructured customer sentiment in the form of verbatim feedback and structured data in the form of user review ratings or satisfaction ratings to explore the question of whether customers say what they really think when given the opportunity to provide free text feedback as opposed to how they rate a product on a scale of one to five. Using various Sentiment Analysis approaches, the authors assign a sentiment score to a piece of verbatim feedback and then categorise it as positive, negative, or neutral. Using this normalised sentiment score, they compare it to the corresponding rating score and investigate the potential business insights. The results obtained indicate that a business cannot rely solely on a standalone single metric as a source of truth regarding customer experience. There is a significant difference between the customer ratings score and the sentiment of their corresponding review of the product. The authors propose that it is imperative that a business supplements their customer feedback scores with a robust sentiment analysis strategy.

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This research aimed to conduct sentiment analysis on user reviews of Indihome, one of Indonesia's leading internet service providers, using data collected from Twitter. With a growing user base of 7 million, Indihome achieved an impressive revenue growth rate of 48% by the end of 2019. To assess user satisfaction, sentiment analysis was performed on tweets containing the keyword "indihome" between July 1 and July 11, 2022. A total of 10,000 tweets and retweets were collected and subjected to data preprocessing, manual labeling, and training using the Naive Bayes Classifier method. The training phase involved 1,000 tweets, equally divided between positive and negative sentiments, manually labeled for accuracy. For evaluation, 200 labeled tweets were used as testing data. The sentiment analysis results were remarkable, with 98% accuracy achieved for the training data and 91% for the testing data. These findings demonstrate the effectiveness of the Naive Bayes Classifier in accurately determining sentiment from Indonesian-language tweets about Indihome's services. By analyzing user opinions expressed on Twitter, this study provides valuable insights into the level of user satisfaction with Indihome as an internet service provider. Leveraging sentiment analysis applications on Twitter social media enables companies like Indihome to gain a deeper understanding of customer sentiment and make informed decisions to improve their services. The research showcases the importance and effectiveness of sentiment analysis in evaluating user feedback and enhancing customer satisfaction in the internet service industry.

The 'MySuara' app is a personalised augmentative and alternative communication (AAC) mobile application designed for Malaysian special education classrooms, offering multilingual support and a unique dual-screen modelling feature to enhance teacher-student communication. Besides, it allows the customisation of symbol sets to meet individual student needs, providing a personalised solution for teachers and caregivers. This study aims to assess the usability of the 'MySuara' mobile app using a developed questionnaire, as usability is quantifiable and crucial for user satisfaction. Conducting such evaluations can unveil flaws in the system and reveal user demands, which is vital for successful app development. Usability testing is essential during development to ensure high user satisfaction and successful app usage. However, limited usability models exist for assessing mobile application features. Therefore, 'MySuara' was evaluated using a questionnaire developed based on the Mobile Learning Application Usability model and the People at the Centre of Mobile Application Development (PACMAD) model. Two questionnaires adapted from Parsazadeh et al. (2018) and Marcial et al. (2018), were combined to create the augmentative and alternative communication usability questionnaire (AACUQ) for 'MySuara' app. The PACMAD was deemed the most suitable for evaluating 'MySuara' due to its tailored focus on mobile application usability, encompassing attributes identified as crucial factors impacting usability. The AACUQ usability questionnaire comprised eight attributes: effectiveness, efficiency, satisfaction, learnability, memorability, error handling, cognitive load, and timeliness. Permissions were secured from the authors before integrating the questionnaires, with adjustments made by removing unsuitable items and modifying others. The AACUQ, comprising 40 items on a five-point Likert scale, was administered via Google Forms, including a consent form, demographic section, and questionnaire for participant response. Fourteen participants, 12 special education teachers and two speech therapists, voluntarily participated in the survey. Data collection occurred after participants tested the 'MySuara' app. Overall, participants expressed satisfaction with the app, with mean values for each attribute above average. The study discusses limitations and implications, concluding 'MySuara' is suitable for its intended purpose.

Mobile systems and mobile applications have gained a lot of attention both in research and industry over the last years. The market share of mobile devices is constantly growing. More and more, users expect their mobile devices to provide functionality similar to the one they know from their desktop computers, but also to provide additional, mobile device specific functionality, like location based services. However, software development for mobile devices is still cumbersome and a methodology geared towards supporting the development of such mobile applications is still lacking. While there is broad agreement that developing for mobile devices is more difficult than traditional software engineering, we lack a precise understanding that goes beyond anecdotic evidence of why this is the case and how the situation can be ameliorated. We do not precisely know where and how the development of mobile application differs from “normal” software development and where new methodologies are needed. Aspects that need to be addressed in this context are, among others, the questions of software architectures for mobile applications, approaches to the software design for mobile applications and how the consequences of being mobile, e. g., changing location, changing context, changing connectivity, heterogeneous software and platforms influence the software development process and what design approaches do reflect the specialties of mobile applications and systems appropriately. While this special issue cannot provide a definite or complete answer to these questions, it does provide answers to some important aspects. In the following, we will first try to get a more thorough understanding of some of the main differences between mobile and classical application development and will then discuss how the papers contained in this special issue address these differences.

Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we targeted the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts.

Arabic language suffers from the lack of available large datasets for machine learning and sentiment analysis applications. This work adds to the recently reported large dataset BRAD, which is the largest Book Reviews in Arabic Dataset. In this paper, we introduce HARD (Hotel Arabic-Reviews Dataset), the largest Book Reviews in Arabic Dataset for subjective sentiment analysis and machine language applications. HARD comprises of 490587 hotel reviews collected from the Booking.com website. Each record contains the review text in the Arabic language, the reviewer’s rating on a scale of 1 to 10 stars, and other attributes about the hotel/reviewer. We make available the full unbalanced dataset as well as a balanced subset. To examine the datasets, we implement six popular classifiers using Modern Standard Arabic (MSA) as well as Dialectal Arabic (DA). We test the sentiment analyzers for polarity and rating classifications. Furthermore, we implement a polarity lexicon-based sentiment analyzer. The findings confirm the effectiveness of the classifiers and the datasets. Our core contribution is to make this benchmark-dataset available and accessible to the research community on Arabic language.

Purpose: This study aims to understand the impact of user reviews and ratings on mobile app download intention and the moderating impact of perceived usefulness on the strength of this relationship. Design/methodology/approach: A scenario-based survey method was selected following the positivism research paradigm. For this purpose, 304 responses were collected using different versions of the same scenario. Findings: The statistical results show that 37.5% of Sri Lankan consumers' mobile app download intention is explained by mobile app ratings and reviews, which have been found to be a significant influencing factor in potential users' intentions to download the app. However, perceived usefulness does not significantly moderate this relationship. Originality: This study is the first attempt to understand the impact of user reviews and ratings on mobile app download intention in the Sri Lankan context. Implications: The findings encourage Sri Lankan mobile app developers and owners to closely monitor and respond to user reviews and ratings to better meet user expectations and improve app ratings.

The rapid growth of mobile banking applications has transformed the financial landscape, providing convenience and accessibility to users worldwide. All 11 commercial banks in Albania provide mobile banking options to their customers. Smartphones have become the primary platform for digital interactions, with mobile banking emerging as the preferred method for over 73% of online banking users in the country. Recent literature suggests that user-friendly features, ease of navigation, simple language, continuous updates, robust safety measures, and active reviewer engagement contribute significantly to app downloads, ongoing usage, conversion rates, and customer satisfaction. Ultimately, these factors enhance the efficiency of banking operations. This study analyses reviews of Albanian mobile banking applications on the Google Play Store to identify trends and patterns. The findings reveal a correlation between bank size, mobile banking app ratings, and reviewer engagement. Extending the analysis to Western Balkan banks confirms a similar bank size, app rating, and user engagement tendency. This work underscores the necessity of a quality assurance system to effectively apply customer feedback for useful mobile banking application updates.

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Mobile apps play an important role in COVID-19 tracing and tracking, with different countries taking different approaches. Our study focuses on 17 government owned COVID-19 Contact Tracing Apps (CTAs) and analyze them using a proposed COVIDTAS framework. User satisfaction is not directly related to the COVIDTAS score or the interaction between users and the app developers. To increase adoption of CTAs, government leadership must offer assurance to its citizens that their identify will be concealed and emphasize the benefits of CTAs as it relates to shared public health. While no country has topped the list on all three major factors (COVIDTAS Score, User Reviews, and User Ratings), the CTA from India seems to have above average performance on all three factors.

Of growing interest in the area of improving the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedback, which can be costly in time and resources and alter the pattern of use within the search experience, some research has explored the collection of implicit measures as an efficient and useful alternative to collecting explicit measure of interest from users.This research article describes a recent study with two main objectives. The first was to test whether there is an association between explicit ratings of user satisfaction and implicit measures of user interest. The second was to understand what implicit measures were most strongly associated with user satisfaction. The domain of interest was Web search. We developed an instrumented browser to collect a variety of measures of user activity and also to ask for explicit judgments of the relevance of individual pages visited and entire search sessions. The data was collected in a workplace setting to improve the generalizability of the results.Results were analyzed using traditional methods (e.g., Bayesian modeling and decision trees) as well as a new usage behavior pattern analysis (“gene analysis”). We found that there was an association between implicit measures of user activity and the user's explicit satisfaction ratings. The best models for individual pages combined clickthrough, time spent on the search result page, and how a user exited a result or ended a search session (exit type/end action). Behavioral patterns (through the gene analysis) can also be used to predict user satisfaction for search sessions.

“Essential Security Practices for Fortifying Mobile Apps” is a definitive guide designed to empower developers, security professionals, and organizations with the knowledge and tools needed to secure mobile applications in an increasingly complex digital landscape. This comprehensive book covers every aspect of mobile app security, providing actionable insights and practical strategies to protect apps from the ever-evolving threat landscape. The book begins with an Introduction to Mobile App Security (Chapter 1), where readers are introduced to the critical importance of securing mobile apps. It discusses common security threats, the principles of security by design, and the unique challenges faced in mobile app development. The chapter also includes case studies of major security breaches, offering real-world context to the concepts discussed. In Secure Development Lifecycle (Chapter 2), the book emphasizes the importance of integrating security into the Software Development Lifecycle (SDLC). Topics such as threat modeling, secure coding practices, and penetration testing are explored in depth. This chapter also covers the role of continuous integration in maintaining security and the necessity of incident response planning and security training. Authentication and Authorization (Chapter 3) dives into the mechanisms that ensure only authorized users have access to mobile apps. It covers secure authentication methods, including multi-factor authentication and token-based authentication, as well as best practices for session management and password storage. The chapter provides guidelines to avoid common authentication flaws that could compromise app security. Data Protection and Privacy (Chapter 4) addresses the crucial aspects of encrypting data both at rest and in transit. It discusses secure data storage, privacy by design principles, and the handling of personally identifiable information (PII). Readers will learn about secure data deletion and compliance with data protection regulations, as well as the use of secure cryptographic libraries. Network Security (Chapter 5) explores the protocols and techniques needed to secure communication between mobile apps and their backend systems. The chapter covers the correct use of TLS/SSL, protecting against man-in-the-middle attacks, and securing backend APIs. It also discusses the importance of monitoring and logging network activity to detect and respond to potential threats. The book then moves on to Secure Coding Practices (Chapter 6), providing guidance on avoiding common coding vulnerabilities through input validation, error handling, and defensive programming techniques. It also covers secure code reviews and the best practices for using mobile platform APIs securely. Mobile Device Security (Chapter 7) shifts focus to the security of the devices on which mobile apps run. The chapter discusses mobile device threats, secure device configuration, and the use of mobile device management (MDM) solutions. It also addresses the security features of mobile devices, such as secure boot and remote wiping, which are essential for protecting against device theft or loss. In Secure Deployment and Distribution (Chapter 8), readers will learn about secure strategies for deploying and distributing mobile apps. Topics include code signing, protecting against repackaging and tampering, and securely distributing sensitive data. The chapter also covers handling updates and patches, ensuring that apps remain secure after deployment. Security Testing and Validation (Chapter 9) provides an in-depth look at the various types of security testing, including automated and manual testing techniques. It discusses penetration testing best practices, vulnerability assessment, and the integration of security testing into CI/CD pipelines. Regular security audits and addressing found vulnerabilities are also emphasized. Incident Response and Management (Chapter 10) guides readers in building an effective incident response team and creating a robust incident response plan. The chapter covers the steps for detecting, analyzing, containing, eradicating, and recovering from security incidents. Post-incident analysis and communication with stakeholders are also discussed, alongside legal and regulatory considerations. Finally, Future Trends in Mobile App Security (Chapter 11) explores the emerging threats and advancements in mobile security technologies. The chapter discusses the role of artificial intelligence in security, the integration of IoT with mobile devices, and the potential of blockchain in enhancing mobile security. It also highlights the importance of user education and awareness in safeguarding mobile applications in the future. “Essential Security Practices for Fortifying Mobile Apps” is an invaluable resource for anyone involved in the development, deployment, and management of mobile applications, offering a thorough understanding of the security challenges and the strategies needed to overcome them.

BackgroundA growing number of cancer and hematopoietic stem cell transplant (HSCT) survivors require long-term follow-up with optimal communication schemes, and patients' compliance is crucial. Adolescents have various unmet needs. Regarding self-report of symptoms and health status, users of mobile apps showed enhanced compliance. Currently, HSCT aftercare at the HSCT outpatient clinic of the St. Anna Children’s Hospital in Vienna, Austria, is based on handwritten diaries, carrying various disadvantages. Recently, we developed the prototype of a web-based, self-monitoring gamified mobile app tailored for adolescents: the INTERACCT (Integrating Entertainment and Reaction Assessment into Child Cancer Therapy) app.ObjectiveThis observational, prospective study evaluated the usability of the INTERACCT app for tracking real-time self-reported symptoms and health status data in adolescent HSCT patients and a healthy matched control group. The primary outcome of the study was the quality of the self-reported medical information. We hypothesized that the mobile app would provide superior medical information for the clinicians than would the handwritten diaries.MethodsHealth data were reported via paper diary and mobile app for 5 consecutive days each. The quality of medical information was rated on a 5-point scale independently and blinded by two HSCT clinicians, and the duration of use was evaluated. A total of 52 participant questionnaires were assessed for gaming patterns and device preferences, self-efficacy, users’ satisfaction, acceptability, and suggestions for improvement of the mobile app. Interrater reliability was calculated with the intraclass correlation coefficient, based on a two-way mixed model; one-way repeated-measures analysis of variance and t tests were conducted post hoc. Descriptive methods were used for correlation with participants’ demographics. For users’ satisfaction and acceptability of the mobile app, the median and the IQR were calculated.ResultsData from 42 participants—15 patients and 27 healthy students—with comparable demographics were evaluated. The results of our study indicated a superiority of the quality of self-reported medical data in the INTERACCT app over traditional paper-and-pencil assessment (mobile app: 4.14 points, vs paper-based diary: 3.77 points, P=.02). The mobile app outperformed paper-and-pencil assessments mainly among the patients, in particular among patients with treatment-associated complications (mobile app: 4.43 points, vs paper-based diary: 3.73 points, P=.01). The mobile app was used significantly longer by adolescents (≥14 years: 4.57 days, vs ≤13 years: 3.14 days, P=.03) and females (4.76 days for females vs 2.95 days for males, P=.004). This corresponds with a longer duration of use among impaired patients with comorbidities. User satisfaction and acceptability ratings for the mobile app were high across all groups, but adherence to entering a large amount of data decreased over time. Based on our results, we developed a case vignette of the target group.ConclusionsOur study was the first to show that the quality of patient-reported medical information submitted via the INTERACCT app embedded in a serious game is superior to that submitted via a handwritten diary. In light of these results, a refinement of the mobile app supported by a machine learning approach is planned within an international research project.