App Success Research Articles

Review Analyzer

This project focuses on leveraging generative AI to analyse user reviews from the Play Store for diverse applications. By utilizing advanced large language models (LLMs), the system processes extensive user feedback to identify key trends, sentiments, and actionable insights. The AI analyses reviews to categorize overall user sentiment (positive, negative, or neutral), highlight recurring issues, and identify popular feature requests. The generative AI’s capabilities enable it to provide nuanced suggestions to developers, such as improving app functionality, addressing common complaints, and implementing features aligned with user preferences. Additionally, the system can generate concise summaries of user reactions, offering developers a clear understanding of their app's strengths and areas needing improvement. This data-driven approach enhances app development by prioritizing updates based on real user needs, improving user satisfaction, and fostering higher ratings on the Play Store. The integration of generative AI streamlines the review analysis process, ensuring actionable recommendations for sustained app success. Key Words: Generative AI, Large Language Models (LLMs), sentiment analysis, App improvement, review analysis 1.INTRODUCTION This project utilizes generative AI to analyze Play Store reviews across various applications, providing developers with actionable insights. By leveraging large language models (LLMs), the system processes user feedback to identify sentiment trends, highlight common issues, and suggest improvements. This approach enables developers to understand user reactions effectively and prioritize updates, ultimately enhancing app quality, user satisfaction, and ratings. The integration of AI streamlines review analysis, ensuring data-driven decisions for better app performance.

Dating App Users: Interpersonal Styles and Self-Reported Mating Success.

In this study (N = 495), dating apps were conceptualized as digital leks. We examined how interpersonal (narcissism, Machiavellianism, psychopathy), sexual (sociosexual attitudes, desires, and behavior), and search (satisficing, alternatives) styles relate to mating success through dating apps (dates and sex). Individuals with a faster life history strategy, particularly men high in psychopathy and sexual desires, report more mating success via dating apps. Women who were easier satisficed also experienced more Tinder-sex. These findings clarify the nuanced roles of sociosexuality facets in dating app success, enriching the discourse on digital mating behaviors. Using the life history framework, this research advances our understanding of how personality shapes real-world mating outcomes that originate in digital contexts.

Patient experiences with a smartphone application dedicated to postoperative recovery after elective arthroplasty

IntroductionIn the rapidly evolving landscape of digital health technologies, the widespread use of smartphones has paved the way for innovative applications designed to enhance healthcare experiences. This introduction underscores the global prevalence of smartphone users and their potential in healthcare, particularly exemplified by therapeutic apps such as the ‘Post Op’ smartphone application. Focused on postoperative support, this app prioritises user-friendly design, data recording, and adherence to national standards. The study aims to evaluate the app's success in achieving these objectives, highlighting its continuous improvement based on real user feedback and its potential impact on improving patient experiences and recovery processes. Methods and materialsA cohort of elective joint arthroplasty procedure patients at Kettering General Hospital (KGH), spanning a timeframe of one to 12 months post-surgery were surveyed over the telephone regarding their experience of the ‘Post Op’ smartphone utilising a custom, well-structured standardised questionnaire. ResultsThe study surveyed 42 consecutive patients who had undergone elective joint arthroplasty procedures at Kettering General Hospital, including 22 total hip replacement (THR) and 20 total knee replacement (TKR) patients. The respondents, with a median age of 66 years, provided overwhelmingly positive feedback about the ‘Post Op’ smartphone application. On a scale of 1–6, the mean ease-of-use score was 5.31, with only two THR patients providing lower scores due to difficulties with photography. However, the app's overall functionality, particularly in uploading photos, received a mean score of 4.78. Despite some technical issues, 97.6 % of respondents would recommend the app, emphasising its positive impact on confidence in the recovery and reducing unnecessary healthcare visits. Narrative responses highlighted the app's simplicity, support, and prompt communication with healthcare teams. Overall, the feedback indicates a high level of satisfaction with the ‘Post Op’ application among elective arthroplasty patients, showcasing its potential benefits for postoperative care. ConclusionThe study provides valuable insights into the impact of the ‘Post Op’ smartphone application on postoperative care, acknowledging both its successes and areas for enhancement. Further research and iterative improvements are crucial for optimising patient experiences and ensuring the app aligns effectively with healthcare needs.

Family-based mobile application needs in childhood obesity management in South Korea: a qualitative study.

It is essential to investigate the users' needs in developing user-centered, family-based applications (apps) for managing childhood obesity. This study conducted focus group interviews with ten parents, ten children, and four app developers to investigate the need to develop a family-based mobile app for childhood obesity. The interview period was from July 17 to August 30, 2023. The data were analyzed using thematic analysis. The results highlighted 31 key themes critical to family-based app development, including the need for comprehensive content promoting healthy lifestyle habits, tailored expert support, and motivational factors for app engagement, which were categorized as intrinsic, extrinsic, and social motivations and technological features. Participants also highlighted potential challenges, such as app usability issues and the need for a user-friendly interface. The pivotal role of parents in guiding and supporting their children's health behaviors was emphasized, particularly in creating a supportive environment that encourages sustained engagement with the app. These findings provide a comprehensive understanding of the needs and preferences of parents, children, and app developers in managing childhood obesity. Emphasis on integrating healthy lifestyle content, providing expert support, and incorporating motivational strategies is crucial for the app's success. Additionally, addressing potential difficulties and recognizing the pivotal role of parents are essential considerations. These insights will inform the design and development of a user-centered mobile app that supports families in managing childhood obesity.

Analisis penerapan UI/UX dalam aplikasi media sosial: Dampaknya terhadap keterlibatan pengguna dan loyalitas platform

This research focuses on applying UI/UX design principles in social media applications and analyzing their impact on user engagement and platform loyalty in the long term. With increasing competition among social media platforms, user experience has become a major determining factor in app success. This research explores how UI/UX elements such as efficient navigation structure, consistent visual aesthetics, interface responsiveness, and content personalization play a role in increasing user engagement, time spent on the app, and user intent to return to the platform. Using quantitative and qualitative approaches, data was collected through user surveys and analysis of behavioral data from several leading social media platforms. The research results show that optimal UI/UX design not only significantly influences user engagement levels, but also increases user loyalty, reduces churn rates, and increases retention in the long term. This study provides strategic recommendations for social media application developers in designing interfaces that support sustained engagement and positive user experiences

Data Analysis and Prediction of Of Various app

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Exploratory Data Analysis and Prediction of Rating Of Various app on Google Play Store

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Pembuatan Aplikasi Computer Based Test Berbasis Website Menggunakan Algoritma Fisher-Yates Shuffle

This research focuses on the development of a web-based Computer Based Test (CBT) Application for Madrasah Tsanawiyah (MTs) Faqih Hasyim in Sidoarjo, East Java. This application aims to overcome conventional evaluation problems with paper and pen, such as cheating, scoring errors, and data management difficulties. Using web technologies such as HTML, CSS, PHP, and JavaScript, the application integrates the Fisher-Yates Shuffle algorithm to randomize the order of questions, reducing the possibility of cheating during the exam. Equipped with session tokens, exam timing, and result reports, the app improves the overall efficiency of the evaluation process. This study refers to previous research and highlights the importance of adopting technology to simplify assessment and facilitate online data management. The Fisher-Yates Shuffle algorithm, used to generate a unique sequence of questions for each student during the exam, minimizes the risk of cheating. The app implements a staged exam, ensuring students who meet the minimum criteria proceed to the next stage. In addition, the app achieves a degree of randomization of the exam content, eliminating patterns that may affect the exam results. The feasibility testing results show that the app is highly feasible, with a feasibility percentage of 92.4%. This conclusion signifies the app's success in achieving its development goals and making a positive contribution in easing the exam grading process. It is recommended to simplify the interface for senior users, improve the responsiveness of the website display for mobile access, and add a time limitation method in future research. Keywords: Fisher-Yates Shuffle Algorithm, Computer Base Test, Question Randomization.

Ni-kshay SETU, a Digital Health Intervention for Capacity Building in Tuberculosis under the National TB Elimination Program in India: A comprehensive mHealth app review

This mHealth application review explores the evolution, design, and impact of Ni-kshay SETU, a digital health intervention tailored for India's National Tuberculosis Elimination Program (NTEP). Faced with escalating TB incidences, the NTEP prioritized capacity-building, leading to the conception of Ni-kshay SETU. This review describes the narrative of Ni-kshay SETU's concept from the beginning to the present day. It starts with the National Strategic Plan 2017–25, which says that capacity building of health care workers is important for reduction of tuberculosis morbidity and mortality by 2025. The digital platform materialized as a web app with Android and iOS versions, constituting a user-friendly interface. The app, embodying a ready reckoner and decision-making tool, offers cadre-specific modules in local languages, promoting ease of comprehension and applicability. The app's development, rooted in a consultative process, aligns with the nuanced needs of healthcare personnel across various geographies and cadres. With more than 40,400 subscribers and 1,450,000 visits, Ni-kshay SETU serves as a notable example of digital health intervention, providing a comprehensive and context-aware approach to TB care. However, it's important to acknowledge the limitations of Ni-kshay SETU. While the app aims to address various challenges in TB care, its effectiveness is contingent on factors such as digital literacy and access to smartphones. Additionally, the app's success relies on consistent updates and continuous user engagement for its usage, posing potential challenges in sustaining the momentum. In summary, Ni-kshay SETU is not just a tool for building capacity but also acts as a driver for affordable digital solutions in areas with limited resources, aiming to strengthen healthcare systems on the path to eliminating TB.

Uncovering the quality factors driving the success of mobile payment apps

This study comprehensively analyzed the key quality factors of mobile payment applications that shape consumer perceptions and their influence on market performance. Leveraging text analytics techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentence-level sentiment analysis, we analyzed a vast dataset of 230,940 reviews of nine mobile payment apps. Our investigation revealed four significant quality factors discussed in online consumer reviews: payment convenience, security, customer service, and app design. Crucially, this study demonstrated the substantial impact of positive and negative evaluations of these factors on the market performance of mobile payment apps, with numerical ratings acting as mediators. This study breaks new ground by empirically validating the link between post-usage evaluations and the app success. Additionally, it also empirically validated the link between post-usage evaluations and app success, elucidating the role of quality factors. The insights can empower app developers and marketers to enhance user experience and attain market dominance.

A Novel Web-Based and Mobile Application to Measure Real-Time Moral Distress: An Initial Pilot and Feasibility Study.

Moral distress (MoD) is a vital clinical indicator linked to clinician burnout and provider concerns about declining patient care quality. Yet it is not routinely assessed. Earlier, real-time recognition may better target interventions aimed at alleviating MoD and thereby increase provider well-being and improve patient care quality. Combining two validated MoD instruments (the Moral Distress Thermometer [MDT] and the Measure of Moral Distress for Healthcare Professionals [MMD-HP]), the authors developed a novel mobile and Web-based application environment to measure and report levels MoD and their associated causes. This app was tested for basic feasibility and acceptability in two groups: graduate nursing students and practicing critical care nurses. The MDT app appears feasible and acceptable for future use. All participants (n = 34) indicated the MDT app was satisfying to use, and 91.2% (n = 31) indicated the app was "very appropriate" for measuring MoD. In addition, 84.2% (n =16) of practicing nurses indicated the app fit either "somewhat well" (47.4%, n = 9) or "very well" (36.8%, n = 7) into their typical workday, and 68.4% (n = 13) said they were either "extremely likely" or "somewhat likely" to use the app daily in clinical practice. Education about moral distress and its associated causes proved important to the MDT app's success. It is ready for future validity and reliability testing, as well as examining usability beyond nursing, longitudinal data monitoring, and possible leveraging to pre- and postintervention evaluation studies.

The psychological influence of dating app matches: The more matches the merrier?

Swipe-based dating apps characteristically provide quantitative social feedback in the form of matches. Surveys suggest a link between dating app success and well-being, but the nature of this correlation has yet to be examined. In an experiment with 125 undergraduate women, we manipulated dating app feedback: When accepting a profile, participants had either a high (27/31) or a low chance (3/31) of receiving matches. We found no effect of chance of matches on women’s loneliness or fear of being single. However, a higher chance of matches led to higher partner choice overload. Furthermore, in those who accepted seven or more profiles, women in the low chance of matches condition reported higher loneliness than women in the high chance of matches condition. This could mean that matches are rewarding for women with a high approach orientation. Manipulating social feedback in a dating app paradigm seems suitable to study the effects of social acceptance and ostracism.

How the app designs influence the app use and optimization measures in Chinese internet market

With the rapid expansion of Chinese Internet mobile app market, being conscious of the mobile app success determinants performs a vital aspect for enterprises. To overcome the common strait that many enterprises fail to achieve profitability by exploiting commercial mobile applications on account of few app downloads and low customer utilization rate, this essay mainly focuses on the predicament that application operation is confronted with in the current Chinese mobile app market and interprets three general interfering factors affecting app profitability. With selected data and correlation analyses, it confirms the impact icon's aesthetic design, customer satisfaction and customer loyalty on improving the profitability of mobile applications. According to the research results, some feasible optimization measures are proposed for mobile application developers. The results could help assist entrepreneurs in making profits by improving app user utilization. However, some objective factors and limitations are ignored in the discussion of influencing factors and more in-depth researches are suggested.

Factors for success of awake prone positioning in patients with COVID-19-induced acute hypoxemic respiratory failure: analysis of a randomized controlled trial

BackgroundAwake prone positioning (APP) improves oxygenation in coronavirus disease (COVID-19) patients and, when successful, may decrease the risk of intubation. However, factors associated with APP success remain unknown. In this secondary analysis, we aimed to assess whether APP can reduce intubation rate in patients with COVID-19 and to focus on the factors associated with success.MethodsIn this multicenter randomized controlled trial, conducted in three high-acuity units, we randomly assigned patients with COVID-19-induced acute hypoxemic respiratory failure (AHRF) requiring high-flow nasal cannula (HFNC) oxygen to APP or standard care. Primary outcome was intubation rate at 28 days. Multivariate analyses were performed to identify the predictors associated to treatment success (survival without intubation).ResultsAmong 430 patients randomized, 216 were assigned to APP and 214 to standard care. The APP group had a lower intubation rate (30% vs 43%, relative risk [RR] 0.70; CI95 0.54–0.90, P = 0.006) and shorter hospital length of stay (11 interquartile range [IQR, 9–14] vs 13 [IQR, 10–17] days, P = 0.001). A respiratory rate ≤ 25 bpm at enrollment, an increase in ROX index > 1.25 after first APP session, APP duration > 8 h/day, and a decrease in lung ultrasound score ≥ 2 within the first 3 days were significantly associated with treatment success for APP.ConclusionIn patients with COVID-19-induced AHRF treated by HFNC, APP reduced intubation rate and improved treatment success. A longer APP duration is associated with APP success, while the increase in ROX index and decrease in lung ultrasound score after APP can also help identify patients most likely to benefit.Trial registration: This study was retrospectively registered in ClinicalTrials.gov at July 20, 2021. Identification number NCT04477655. https://clinicaltrials.gov/ct2/show/NCT04477655?term=PRO-CARF&draw=2&rank=1

The customer retail app experience: Implications for customer loyalty

Retailer mobile applications are one of the principal retail purchase and information search channels. Customer experience is key to retail app success. However, its dimensions and impact on retailer performance have been the subject of only a limited number of studies. This research builds on existing customer retail app experience literature by considering four dimensions as precursors of satisfaction with the retailers’ app and customer loyalty (cognitive, affective, relational and sensorial). Data were collected from a sample of 545 retailer app users and analysed using PLS-SEM. The results demonstrate that the affective dimension has the most influence and they highlight the importance of the sensory experience, which even surpasses the cognitive experience. The effect of the relational dimension on customer satisfaction, meanwhile, could not be positively confirmed. Analysis of the moderating effect of gender, age and device type used identifies effects that have not, until now, been demonstrated in current literature.

Intra-platform competition: the role of innovative and refinement evolution in app success

This study distinguishes between two types of app evolution based on its content: innovative evolution and refinement evolution. Drawing on product innovation and app evolution literature, we reveal the antecedents of those two types of evolution and their impacts on app success. The model was tested using a matched survey and objective data collected from 218 apps on Google Play. The results indicate that online customer monitoring routine is positively associated with both innovative evolution and refinement evolution. Furthermore, while refinement evolution is positively associated with app market performance, innovative evolution exerts its influence on app performance through refinement evolution.

Success factors driving consumer reuse intention of mobile shopping application channel

PurposeDue to fierce competition in the mobile retailing market, it is desirable to identify the success factors driving consumers to reuse a mobile shopping application. This research intends to develop a model for mobile shopping app success by integrating an e-commerce systems success (ESS) model and sales promotion benefits and parasocial interaction literatures.Design/methodology/approach302 useable online questionnaires were obtained. The data analysis was conducted using the structural equation modelling.FindingsThe results indicate the validity of the ESS model in predicting consumers' reuse intention of mobile shopping apps where three quality dimensions of system, information and service facilitate both perceived value and user satisfaction, which in turn generates reuse intention. Furthermore, savings and entertainment denoting the utilitarian and hedonic sales promotion benefits have positive impact on perceived value but have no influence on satisfaction. Parasocial intention between consumers and sellers facilitates both value perception and satisfaction.Originality/valueThis research contributes to the mobile retailing literature by identifying the success factors driving consumers' continuance intention of mobile shopping apps. Theoretically, it validates and extends the ESS model in mobile shopping app context by identifying savings, entertainment and parasocial interaction as additional success factors. Based on the findings, two approaches are proposed to suggest mobile retailers design a successful mobile shopping app.

The effect of app store strategy on app rating: The moderating role of hedonic and utilitarian mobile apps

Concerns about the performance of mobile apps have spurred a stream of research on factors contributing to the success of mobile apps in the major app stores. However, identified success factors have not been synthesized into a unified framework. Similarly, reported findings typically address main effects. The present study introduces the concept of app store strategy to synthesize the mobile app success factors that developers implement in the major app stores. The study synthesizes four such factors consisting of app size, app versions (major and minor), revenue model and mobile app names. Further, drawing on IS literature, the study distinguishes between hedonic and utilitarian mobile apps. Using a dataset of mobile apps from Play Store (N = 8325), the study investigates to what extent app store strategy interacts with the hedonic and utilitarian value of mobile apps in driving app rating. The study employed a combination of traditional regression and machine learning approaches to test proposed hypotheses. The findings make a two-fold contribution to the literature first by introducing the concept of app store strategy to synthesize mobile app success factors and second by examining hedonic and utilitarian mobile apps as a boundary condition in the relationship between app store strategy and app rating.

Investigating the Relationship between the Effectiveness of App Evolution and App Continuance Intention: An Empirical Study of the U.S. App Market

Researchers have shown app evolution to continuously lead to app success from the developer perspective. However, few studies have explored app success from the user perspective, which limits our knowledge about the role that app evolution has in app success. Building on app evolution literature and the technology acceptance model (TAM), we investigate the influence that effectiveness of app evolution has on perceived app usefulness, perceived ease of use, and app continuance intention (a proxy for app success from the user perspective). We collected survey data from 299 app users on both the Google Play and Apple’s App Store platforms in the United States. Our findings indicate that effectiveness of strategic evolution and effectiveness of evolution speed directly affect perceived app usefulness, while effectiveness of operational evolution and effectiveness of evolution speed directly affect perceived app ease of use. In addition, perceived app usefulness and perceived app ease of use constitute two key factors that lead to app continuance intention. Perceived ease of use affects users’ app continuance intention both directly and indirectly through perceived app usefulness. This study enhances our knowledge about the relationship between effectiveness of app evolution and app continuance intention. Such knowledge has particular importance in helping small firms or startups with limited resources understand how to retain app users. We also discuss limitations and directions for future research.

Systematic evaluation of mobile fitness apps: Apps as the Tutor, Recorder, Game Companion, and Cheerleader

Mobile fitness applications are innovating the ways in which smartphone users self-manage their health. Prior research found that app functions may impact app efficacy. However, research to date has not sought to systematically investigate how different combinations of app functions impact user response to apps, especially adoption intent. This article describes two studies on mobile fitness app characteristics and user attitudes. Study One used content analysis and hierarchical cluster analysis on 98 iPhone fitness apps and identified four app clusters: “Tutor”, “Recorder”, “Game Companion”, and “Cheerleader.” Tracking was the predominant function in current market, but tracking-focused Recorder apps received lowest user ratings among all app clusters. Users favored Tutor apps that combine exercise education and tracking, and Game Companion apps that combine gamification, tracking, and social functions. Function combinations, rather than standalone functions, impact app success. Following a Reasoned Action Approach, Study Two found various effects of individual differences (age, gender, BMI, eHealth literacy, smartphone experience, function preference) on user attitude toward different fitness app types. A comparison between two studies demonstrated a mismatch between market offerings and user needs regarding app functions. Implications of results for mobile fitness app design to improve consumer health and for theories are discussed.