In-app Purchases Research Articles

Ai Art In Mobile: Topic Modeling Of User Reviews For Generative Ai Applications

The generative AI concept, which enables users to create text, image, and video content through prompts, is revolutionizing the content side and AI applications in marketing. Despite the increasing popularity of generative AI applications, the market perception regarding generative AI remains underexplored. This study aims to explore the generative AI market perception through the context of mobile applications with the help of user reviews. The study follows a structured approach including identifying the generative AI mobile applications, assessing the context through rating scores and install amounts of mobile applications, and using a topic modeling approach (BerTopic) for online reviews to identify the topics included in the conversation. 8159 user reviews from 22 mobile applications are used as sample of the study and the average rating score for the sample found as 4,06 which signals a positive perception of market. The study concludes top ten topics as; “Dissatisfaction About Amount of Advertisements in app”, “NSFW Content and Moderation”, “Praise of Application”, “Functionality Problems & Crashes”, “Payment Necessity and Trial Problems”, “In-App Purchase Restoration Problems”, “Specific Feature in App”, “Chat Function”, “Credit System” and “Excess of Ads”. The study reveals the main issues of Ai Art mobile applications for marketing decision-making processes.

Selling Bonus Actions in Video Games

In the mobile video games industry, a common in-app purchase is for additional “moves” or “time” in single-player puzzle games. We call these in-app purchases bonus actions. In some games, bonus actions can only be purchased in advance of attempting a level of the game (pure advance sales (PAS)), yet in other games, bonus actions can only be purchased in a “spot” market that appears when an initial attempt to pass the level fails (pure spot sales). Some games offer both advance and spot purchases (hybrid advance sales). This paper studies these selling strategies for bonus actions in video games. Such a question is novel to in-app tools selling in video games, and it cannot be answered by previous advance selling studies focusing on end goods. We model the selling of bonus actions as a stochastic extensive form game. We show how the distribution of skill among players (i.e., their inherent ability to pass the level) and the inherent randomness of the game influence selling strategies. For casual games, where low-skill players have a sufficiently high probability of success in each attempt, if the proportion of high-skill players is either sufficiently large or sufficiently small, firms should adopt PAS and shut down the “spot” market. Furthermore, the player welfare-maximizing selling strategy is to sell only in the spot market. Hence, no “win-win” strategy exists for casual games. However, PAS can be a win-win for hardcore games, where low-skill players have a sufficiently low success probability for each attempt. This paper was accepted by Hemant Bhargava, information systems. Funding: C. T. Ryan received funding from NSERC Discovery [Grant RGPIN-2020-06488]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.02348 .

Supporting parents with acutely ill children: Environment scan and user evaluation of mobile applications (the SuPa kids project)

BackgroundChildren are one of the biggest users of emergency departments in the UK, sometimes utilising services when experiencing acute illnesses that can be managed at home. mHealth can be an efficacious way for parents/guardians to manage acute illnesses. It has also become a ubiquitous resource to promote public health interests within a resource constrained health system. However, to be effective, co-design is necessary with concepts such as usefulness, ease of use, trustworthiness and security underpinning the utility of mHealth interventions for acute childhood illness. ObjectiveThe objective of this study was to conduct an environmental scan of mHealth apps for parental assessment and management of acute childhood illnesses in 0- to 5-year-olds, review the content and functionality of each identified app against the list of desired attributes identified in previous research and synthesise publicly available reviews from end users to demonstrate the quality of current apps in the marketplace and identify areas for improvement. MethodApp characteristics were mapped to an a priori defined list of desirable attributes. End user reviews were mapped to a modified Technology Acceptance Model framework that included overarching themes of: Perceived Usefulness (PU), Perceived Ease of Use (PEU), Perceived Trustworthiness (PT) and Perceived Security (PS) using reflexive thematic analysis. ResultsOverall, 15 apps were included in the analysis (Table 2). Fourteen of the 15 apps were available on both the iOS and Android platforms. ‘Baby Illness, Prevention, Cure’ was only available on the Android store. All apps were free to download and did not restrict access to information or features via in-app purchases. Baby and child first aid (British Red Cross) was the earliest app to be released (2014). This app was updated annually, with the most recent update being 2022. The most recent apps to be released (2018) were ‘AskFirst’ (formerly Ask NHS), ‘Essex Child Health’ and ‘The Lullaby Trust Baby Check’, updated in 2022, 2020 and 2019, respectively. No individual app met all the desirable attributes highlighted by parents in previous research. Both ‘Healthier Together’, and ‘The Lullaby Trust Baby Check’ included all but one desirable attributes (video), as did the ‘’CATCH (Common Approach to Children’s Health), which did not utilise a traffic light system. Apps that were locality specific were not rated by users (Berkshire Child Health, Cheshire Child Health, Child Health Guide Newham, Child Health HMR, Oldham Child Illness and Walsall Healthy Child). All other apps were rated from 2/5 to 5/5 stars. When considering localities, the ‘CATCH’ app was only supported in Halton, Cheshire, Knowsley, Liverpool, St Helens and Vale Royal. Further investigation evidenced no correlation between inclusion of the desirable attributes and app rating. ConclusionOverall, this research has highlighted several aspects of best practise when developing mHealth apps for the management of acute childhood illnesses, for increasing PU, PEU, PT and PS; and also, places importance on co-design.

Investigating the influence of mobile game addiction on in-app purchase intention in PUBG mobile: the mediating roles of loyalty, negative e-WOM and perceived risk

Investigating the influence of mobile game addiction on in-app purchase intention in PUBG mobile: the mediating roles of loyalty, negative e-WOM and perceived risk

Development of Smart Pill Expert System Based on IoT

An improved version of the smart pill expert system known as SPEC 2.0 is presented in this paper to give a knowledge of IoT in healthcare. At the designated moment, the system strives to accurately dispense the recommended dosage of medication. The user-friendly interface of SPEC 2.0 is one of its standout features since it makes it simple for people of all ages to utilize among all the smart medicine dispensers. The main objective of this system is to deliver control and monitoring capabilities via an android application, free of in-app purchases or subscriptions. Numerous features, including the capacity to send alerts and SMS messages regarding pill distribution, are included in the android application. With the help of this tool, users can keep on schedule with their medicine and receive frequent reminders. In order to successfully manage the issue of overdosage, the system lays a special emphasis on preventing the consumption of medication in excess amounts. Users of SPEC 2.0 can reduce their risk of adverse drug reactions by following the directions for dose intervals. Daily tests have been conducted to ensure that SPEC 2.0 works as intended, and thorough records have been kept of each test. These tests offer important information about the system's dependability and its capacity to deliver precise drug dosages on a regular basis. In conclusion, the improved smart pill expert system reveals its potential to dramatically increase medicine adherence and avoid any health concerns brought on by ingesting the wrong dosage.

Price Discount and Impulse Buying at Diamond Game Mobile Legend Bang Bang

Mobile Legends Bang Bang (MLBB) is one of the most popular mobile games in Indonesia. Since its launch in 2016. It can be played by more than one person in an area, usually 5 vs 5 and the main goal of the Mobile Legends Bang Bang game is to destroy the opponent's base. This game also utilizes in-app purchases for its monetization, and diamond is the term for money in the mobile legends Bang Bang game to make purchases in the Mobile legends Bang Bang game. Therefore, researchers are interested in conducting research on purchasing diamonds on the Mobile Legends Bang Bang site. This study aims to determine how much influence the price discount has on impulse buying in Mobile Legends Bang Bang diamonds. The research method used by the authors is descriptive and verification research, while the sample in this research is consumers of the Mobile Legends Bang Bang game. The research was conducted January – Mei 2023 with a sample of 100 respondent who were Mobile Legends Bang Bang. The data analysis technique used is a Hypothesis Test design with Validity Test, Reliability Test, Normality Test, Simple Linear Regression Analysis, Correlation Coefficient, Determination Coefficient, and t test. The results of the study show that the price discount has a significant effect on impulse buying.

Factors of consumer choice of music streaming services in Russia and China

The streaming music service is one of the most important elements of digital ecosystems. Subscriptions, pre-orders, and in-app track purchases are extremely valuable for developers of streaming services, who are interested in promoting them among users. Previous studies do not contain comparative analyses of consumer behaviour of music streaming services for users in different countries. The purpose of the study is to compare the factors of choice of Russian users and users from China. The study consists of in-depth interviews with Russian service employees, followed by a quantitative survey that helps to identify the main factors of choosing a music streaming service. We discovered differences in the significance of factors for Russian and Chinese users. Logistic regression analysis allowed us to identify a variety of choice factors with different significance for customers who use the service; in order to succeed, ecosystems including music streaming services should immerse themselves in the particularities of consumer behaviour in a certain country, taking into account the uniqueness of the music market.

What Motivates Mobile Legends: Bang Bang Players' Loyalty and In-App Purchases? Investigation of Perceived Values Effect: Playfulness, Addiction, and Good Price (Evidence from Indonesia)

What Motivates Mobile Legends: Bang Bang Players' Loyalty and In-App Purchases? Investigation of Perceived Values Effect: Playfulness, Addiction, and Good Price (Evidence from Indonesia)

Free apps and paid apps: monetization strategies for health apps in the Portuguese market

PurposeThe rapid advancement of technology has transformed the health-care industry and enabled the emergence of m-Health solutions such as health apps. The viability and success of these apps depends on the definition of a monetization model appropriate to their specificities. In this sense, the purpose of this paper is to study the mechanisms of monetization of health apps, to stablish how alternative revenues determine if a health app is to be free or paid.Design/methodology/approachProbability models are used to identify the factors that explain if a health app is free or paid.FindingsResults show that the presence of alternative monetization mechanisms negatively impacts the likelihood of a health app being paid for. The use of personal data to customize advertising (the monetization of “privacy capital”) or the inclusion of ads on the app are alternative means of monetization with potential to decrease the likelihood of a health app being paid for. The possibility of in-app purchases has a lower negative impact on the probability of a health app being paid for. The choice of platform to commercialize an app is also a strategic decision that influences the likelihood of an app being paid for.Originality/valueThis work stands out for bringing together the two largest platforms present in Portugal and for focusing on the perspective of revenue and monetization of health apps and not on the perspective of downloads.

Comparison of utility of tobacco cessation mobile applications with regard to Indian scenario

The study was aimed to determineperceptions and reviews of the currently available tobacco cessation mobile applications (apps) by online and on-field surveys. In Phase I, the most commonly used tobacco cessation applications with the highest rating from Google Play Store were selected. The most relevant, positive and critical reviews of these apps were recorded. In Phase II, tobacco users with smart phone & internet connectivity and ready to give verbal consent were included in the study. The same apps were allocated to the study participants. At the end of the 2nd week of app use, the participants were subjected to a questionnaire for feedback on liked, disliked & most used features, frequency of app use, utility, expectations, and suggestions for app. The selected apps were QuitNow, Smoke Free, Quit Tracker, Stop Smoking – EasyQuit and Quit Smoking – SSC. The positive responses in Phase I were customer support, tracking statistics, reward system, craving management, user friendliness, and informative. The negative responses were lacking customer support, less user friendly, pushing for in-app purchases. In phase II, 40 tobacco users were included. 8 study participants were allocated for each app. The liked features were time, health & money, tracking, and gaming feature. The disliked features were advertisements (adds) and language barriers. The suggestions/expectations of study participants were removal of adds, user-friendliness, craving management, and local language. Our research compared and analysed few of the most used tobacco cessation applications used by millions of users but still could not find an ideal tool to deliver the aid for tobacco cessation through the mHealth approach. Use of pros & unique features and mitigation of cons may prove helpful for increasing compliance. An approach should be taken to understand more of the user's perspective and expectations rather than seeing it as a commercial opportunity.

Disentangling consumers’ negative reactions to impulse buying in the context of in-app purchase: Insights from the affect-behavior-cognition model

Deciphering how consumers react to impulse purchases is vital for sellers to bolster consumers’ post-purchase experience and induce subsequent purchase behavior. Contextualized to in-app purchases, this study draws on the Affect-Behavior-Cognition (ABC) model to disentangle the relationships among distinct categories of negative post-purchase reactions and the moderating influence of product appeal on these relationships. Empirical data was collected from an online survey of 376 in-app buyers and Covariance-Based Structural Equation Modelling (CB-SEM) was employed to validate the research model. Analytical results indicate that awareness of breakdown in self-control exerts a positive effect on post-purchase regret while regret positively impact the negative emotions of anger and guilt, both of which in turn drive the concealment of purchased good or service. In-app product appeal is discovered to reinforce the relationships between regret and anger/guilt as well as the relationship between anger and concealment while simultaneously attenuating the relationship between guilt and concealment. This study sheds light on impulsive in-app purchases by delivering a deeper understanding of the interdependencies among constituent post-purchase reactions in the context of in-app impulse buying.

The Effect of Perceived Value on In-App Purchase Intention in Mobile Legends through Loyalty as a Mediating Variable

The research design used in this research is explorative research with a quantitative approach. The research location taken by the researcher is in Malang City. This study is intended to analyze the effect of perceived value which includes emotional value, social value, quality/performance, and price/value for money on in-app purchase intention through loyalty to Mobile Legends game players in Malang City. The population in this study is Mobile Legends game players from teenagers to adults in Malang City. The population in this study is Mobile Legends game players from teenagers to adults in Malang City. The number of samples in this research was 110 samples. Overall, it can be seen that the dominant influence on In-App Purchase Intention is Quality/Performance, Price/Value for Money, Loyalty, and Social Value. This proves that to increase the intention to make purchases in the application, it must increase social value, quality, and price according to the quality obtained in the Mobile Legends game and make players loyal to the game. Social Value, Quality/Performance, Price/Value for Money and Loyalty are variables that significantly influence In-App Purchase Intention. There needs to be development in further research such as variables that are more relevant in accordance with the times and indicators that are more diverse.

SENTIMENT ANALYSIS OF LEAGUE OF LEGENDS: WILD RIFT REVIEWS ON GOOGLE PLAY USING NAÏVE BAYES CLASSIFIER

League of Legends: Wild Rift is a mobile game with more than 48 million downloads worldwide. The game publishers could earn profit from selling item in the game (in-app purchases). Performance of player and players' impressions during the first week usually determine whether players would made in-app purchases or not. Therefore, it is important to understand the player opinions so that the game publisher could encourage the players to increase the in-app purchases. Therefore, this research utilized sentiment analysis to study the player opinions about the League of Legends: Wild Rift game based on the reviews given by the players on the Google Play Store. The sentiment analysis was applied by using Naive Bayes Classifier (NBC) algorithm which was well known for achieving good accuracy in the sentiment analysis task. In addition, data preprocessing and feature extraction should be carried out properly to increase the accuracy of the classifier. Therefore, this research investigated the impact of using stemming and transformation of informal words into formal words in the preprocessing stages, then compared two feature extraction algorithms, namely Term Frequency – Inverse Document Frequency (TF-IDF) and Bag of Words (BOW). From the experiment, it was found that the use of stemming could decrease the accuracy of the classifier, but the use of transformation of non-standard words into standard words could improve the performance of the classifier, for both feature extractions, BOW and TF-IDF. In this case, BOW feature extraction was able to achieve better performance, compared to TF-IDF. The best model was achieved when not using stemming, applying the transformation of informal words into formal words, and using BOW bigram feature extraction, with the accuracy of 79,3%, precision of 82.10%, recall of 83.50%, and f1-score of 82,8.10%.

Smartphone Apps for Smoking Cessation: Systematic Framework for App Review and Analysis.

Cigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown. The goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users. We conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022. We initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation. Numerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.

A recommending system for mobile games using the dynamic nonparametric model

We contribute to the literature on recommendation systems by proposing the Dynamic Nonparametric (DNP) model that incorporates a fully dynamic Bayesian approach into the traditional collaborative filtering and hence captures the change of customer preferences in the mobile game industry with higher flexibility. Using the extensive log data of mobile gamers, we demonstrate that our DNP model closely predicts game usage patterns with higher accuracy than other widely used benchmarks. We also present various applications that reveal the managerial relevance of our model. Personalized recommendation lists of games generated by the DNP model outperform those by benchmarks in terms of adoption rates and repeated usage. Our model can also be used to improve the prediction performance of future in-app purchases and to decide the optimal size of recommendation sets based on types of gamers.

Fraud App Detection of Google Play Store Apps Using Decision Trees

Abstract: Mobile applications have become an integral part of our lives, but it's challenging to determine their safety and reliability. To address this, a system has been developed that uses ratings, reviews, in-app purchases, and ads as parameters to predict app safety. Three machine learning models – Decision Tree, Logistic Regression, and Naïve Bayes - were compared to evaluate system effectiveness. The Decision Tree model performed the best, with 85% accuracy, an F1 score of 0.815, recall of 0.85, and precision of 0.87. These results highlight the reliability of the Decision Tree model for assessing app safety. Using machine learning models in this way automates evaluation and provides more accurate and consistent results. This system represents a promising step towards ensuring the safety and reliability of mobile applications, which are increasingly important in our daily lives

The dark side of mobile apps: when and how technical security matters for in-app purchases?

PurposeThis research aims to explore the dark side of mobile applications by investigating the role of apps' technicality and app security in the mechanism of user satisfaction, app intention and customers' continuance tendency to make in-app purchases.Design/methodology/approachDrawing on attitude-behavior-context (ABC) theory, the study proposed a conceptual framework and examined the framework using a structural equation modeling (SEM) approach based on data collected from app users from New Zealand.FindingsThe results reveal the correlation between user satisfaction and in-app purchase with a mediator of app continuance intention (ACI). In particular, the results show that app technicality (AT) has a positive correlation with user satisfaction as an antecedent. App security and hedonic value are positively correlated with user satisfaction.Originality/valueThe research has three critical research implications. First, this research advances the understanding of the dark side of mobile apps by showing how app security influences customers' in-app purchases. Secondly, this study reveals and offers empirical evidence for the mechanism between app security and user satisfaction. Finally, the study provides empirical evidence of AT as a distal antecedent for in-app purchases.

Implementation of Blockchain technology

Transaction volumes throughout the world are growing very fastly and that result in the complexities, vulnerabilities, inefficiencies, and higher costs of current transaction systems. The growth of ecommerce, online banking, and in-app purchases, is increasing and is more popular among the people around the world. And transaction volumes are increasing with the advent of the Internet of Things (IoT). Objects, such as laptop, washing machines and groceries are running low and cars that deliver themselves to your door. To meet these challenges and others we need faster payment methods that are trustworthy and require no specialized equipment with no chargebacks or monthly fees and offer a good bookkeeping solution for ensuring transparency.

Demand for in-app purchases in mobile apps—A difference-in-difference approach

Using five “freemium” mobile app games on six European markets we examine the effect of price changes on conversion rate, number of users and viewing of rewarded videos. Our difference-in-difference estimation relies on games being available on both the Apple and Google platforms with price changes on only one platform. Our main identification comes from exogenous adjustments of Apples prices in 2021. Own-price elasticities of conversion are in the -1 to -4 range. Watching of rewarded videos decreases as in-app prices decrease with an average elasticity of around 0.5, but overall play is not affected by changes in-app prices.

The Impact Of Perceived Values and Peer Influences Toward In-App Purchase Intention In Mobile Game

Latar Belakang : Dalam beberapa dekade terakhir, Internet dan smartphone sering dianggap sebagai kebutuhan dasar bagi kehidupan manusia. Menurut survei yang dilakukan oleh Datareportal.com, pengguna internet meningkat sebesar 3,5 persen pada tahun ini hingga Oktober 2022, mencapai 5,07 miliar saat kita memasuki kuartal terakhir tahun ini. 171 juta pengguna baru selama 12 bulan terakhir telah meningkatkan penetrasi internet global menjadi 63,5%.
 Tujuan : Bertujuan untuk menyatakan kembali proposisi bahwa game seluler, terutama game sosial, mengharuskan pemain untuk berkomunikasi dengan pemain lain dan berdiskusi tentang game itu sendiri, dan diskusi sering kali condong ke arah cara membelanjakan uang mereka secara efisien untuk pembelian dalam aplikasi.
 Metode : Dalam penelitian ini akan digunakan metode "Normal P-P Plot" untuk menentukan apakah data tersebut didistribusikan secara normal atau tidak.
 Hasil : Hasil ini menunjukkan bahwa pengguna game seluler lebih cenderung membeli Pembelian Dalam Aplikasi ketika mereka melihat sesuatu yang mereka butuhkan atau inginkan dalam game, baik untuk tujuan estetika atau praktis. Kemungkinan untuk membeli juga akan ditentukan oleh genre atau jenis permainan.
 Kesimpulan: Dapat diartikan bahwa tidak ada pengaruh yang signifikan antara Nilai Hedonik terhadap Niat Pembelian Dalam Aplikasi. Ini menunjukkan bahwa meskipun motivasi hedonik memainkan peran besar dalam hal keterlibatan dalam game itu sendiri, itu tidak berarti itu akan memiliki dampak yang cukup besar dalam hal niat Pembelian Dalam Aplikasi.