Investigating the relationship between price, rating, and popularity in the Blackberry World App Store

Context: App stores provide a software development space and a market place that are both different from those to which we have become accustomed for traditional software development: The granularity is finer and there is a far greater source of information available for research and analysis. Information is available on price, customer rating and, through the data mining approach presented in this paper, the features claimed by app developers. These attributes make app stores ideal for empirical software engineering analysis. Objective: This paper1 exploits App Store Analysis to understand the rich interplay between app customers and their developers. Method: We use data mining to extract app descriptions, price, rating, and popularity information from the Blackberry World App Store, and natural language processing to elicit each apps’ claimed features from its description. Results: The findings reveal that there are strong correlations between customer rating and popularity (rank of app downloads). We found evidence for a mild correlation between app price and the number of features claimed for the app and also found that higher priced features tended to be lower rated by their users. We also found that free apps have significantly (p-value < 0.001) higher ratings than non-free apps, with a moderately high effect size (Â12=0.68). All data from our experiments and analysis are made available on-line to support further investigations

Mobile consumer behaviour on apps usage: The effects of perceived values, rating, and cost

The rapid development of technology and the use of smartphones and mobile applications (apps) have become significant and influential in today’s living. Despite the growing amount of research on mobile app usage, little is known on the role they play in shaping the behaviour of the mobile consumer. By deploying the framework, this study revises Sheth’s fundamental propositions of consumption values. While the fit of values has been recognised by many behavioural studies, insight into the theoretical interface in the mobile communication studies remains unsubstantiated. Therefore, this study explores the perceived consumption values on mobile apps behaviour and investigates the role of the rating of the apps and cost in influencing the behaviour. A total of 392 questionnaires were collected through an online survey with 9 hypotheses examined. The analysis of the structural equation modelling determined a final model with four significant factors (functional, social, emotional, and conditional values). It confirmed that the usage is influenced by the apps rating and cost towards a certain degree. This study indicates the potential significance variables in mobile consumer literature and sheds light on mobile communication marketing in the area of apps marketing, specifically in designing an effective user-experience (UX) apps for mobile consumers

A survey of app store analysis for software engineering

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges

Software Engineering in the Age of App Stores: Feature-Based Analyses to Guide Mobile Software Engineers

Mobile app stores are becoming the dominating distribution platform of mobile applications. Due to their rapid growth, their impact on software engineering practices is not yet well understood. There has been no comprehensive study that explores the mobile app store ecosystem's effect on software engineering practices. Therefore, this thesis, as its first contribution, empirically studies the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. The study highlights the importance of a mobile application's features as a deliverable unit from developers to users. The study uncovers the involvement of app stores in eliciting requirements, perfective maintenance and domain analysis in the form of discoverable features written in text form in descriptions and user reviews. Developers discover possible features to include by searching the app store. Developers, through interviews, revealed the cost of such tasks given a highly prolific user base, which major app stores exhibit. Therefore, the thesis, in its second contribution, uses techniques to extract features from unstructured natural language artefacts. This is motivated by the indication that developers monitor similar applications, in terms of provided features, to understand user expectations in a certain application domain. This thesis then devises a semantic-aware technique of mobile application representation using textual functionality descriptions. This representation is then shown to successfully cluster mobile applications to uncover a finer-grained and functionality-based grouping of mobile apps. The thesis, furthermore, provides a comparison of baseline techniques of feature extraction from textual artefacts based on three main criteria: silhouette width measure, human judgement and execution time. Finally, this thesis, in its final contribution shows that features do indeed migrate in the app store beyond category boundaries and discovers a set of migratory characteristics and their relationship to price, rating and popularity in the app stores studied

Effect of App Market Conditions on Permissions Usage by App Developers

The common mechanism for controlling security and privacy data on mobile platforms is through the app permissions model. Platform owners evolve the model through changes to the APIs provided to app developers. This however places increased responsibility on app developers to determine the privileges they need to deliver the app’s functionality. In this paper, we investigate the factors influencing an app developer to seek permissions for privileged access in the context of the Android mobile platform. We find that the apps facing higher competition in their category or targeted at more mature audiences seek more permissions from users. However, apps charging higher prices for downloads ask for a lower number of permissions. The findings suggest that market conditions incentivize app developers to seek more privileges whereas a revenue stream such as download price does not. Therefore, more control exercised by the platform owner on the conditions of their app market would be more beneficial to the platform from the security and privacy perspective

Customer Rating Reactions Can Be Predicted Purely Using App Features

In this paper we provide empirical evidence that the rating that an app attracts can be accurately predicted from the features it offers. Our results, based on an analysis of 11,537 apps from the Samsung Android and BlackBerry World app stores, indicate that the rating of 89% of these apps can be predicted with 100% accuracy. Our prediction model is built by using feature and rating information from the existing apps offered in the App Store and it yields highly accurate rating predictions, using only a few (11-12) existing apps for case-based prediction. These findings may have important implications for require- ments engineering in app stores: They indicate that app devel- opers may be able to obtain (very accurate) assessments of the customer reaction to their proposed feature sets (requirements), thereby providing new opportunities to support the requirements elicitation process for app developers

Branded apps in Spain as a means of communicating trends in fashion

Apps are of great interest and curiosity for the users of mobile phones, being already highly unloaded and used for the users of smartphones. The fashion mobile applications represent only a fraction of the mobile global applications (almost 0,2 %), nevertheless they are starting to deserve special attention from designers and researchers in this area. Brands want to know about the new trends in the market to be able to continue surprising and impressing their public. In this study, we seek to identify the most relevant aspects of the applications that help to improve the image of the Spanish prêt-à-porter fashion brands such as Zara, Pull and Bear, Stradivarius, Shana, Bimba y Lola,... The study considers the position in the ranking of downloads, the category, the opinions, valuations of the users and the criteria described by Jami Lawrence (2010): amusement, saving of time and / or comfort in the use. Findings: In this analysis, we seek to identify the most relevant aspects of the applications that help to improve the image of the brand, in this particular case of the Spanish prêt-à-porter fashion brands. - It is indispensable that the applications entertain the user, save time and / or comfort, we have observed that they are these characteristics those that are valued by the users. - Another factor that is valued in the applications, is that the application should make sense for the user and to have an added value that doesn't necessarily have to be related with the nature of the brand. - But it is important that applications to be developed have to be coherent with the identity of the brand. As in any other area, the appearance of new mass media, like the mobile, is the introduction of new challenges. – Finally, it is important for the brand to adapt constantly to the new technologies, but we must not forget that a precarious incorporation and badly developed app it can be more harmful than beneficial

Mobile consumer behaviour on apps usage: the effects of perceived values, rating, and cost

The rapid development of technology and the use of smartphones and mobile applications (apps) have become significant and influential in today’s living. Despite the growing amount of research on mobile app usage, little is known on the role they play in shaping the behaviour of the mobile consumer. By deploying the framework, this study revises Sheth’s fundamental propositions of consumption values. While the fit of values has been recognised by many behavioural studies, insight into the theoretical interface in the mobile communication studies remains unsubstantiated. Therefore, this study explores the perceived consumption values on mobile apps behaviour and investigates the role of the rating of the apps and cost in influencing the behaviour. A total of 392 questionnaires were collected through an online survey with 9 hypotheses examined. The analysis of the structural equation modelling determined a final model with four significant factors (functional, social, emotional, and conditional values). It confirmed that the usage is influenced by the apps rating and cost towards a certain degree. This study indicates the potential significance variables in mobile consumer literature and sheds light on mobile communication marketing in the area of apps marketing, specifically in designing an effective user-experience (UX) apps for mobile consumers

App Store Analysis for Software Engineering

App Store Analysis concerns the mining of data from apps, made possible through app stores. This thesis extracts publicly available data from app stores, in order to detect and analyse relationships between technical attributes, such as software features, and non-technical attributes, such as rating and popularity information. The thesis identifies the App Sampling Problem, its effects and a methodology to ameliorate the problem. The App Sampling Problem is a fundamental sampling issue concerned with mining app stores, caused by the rather limited ‘most-popular-only’ ranked app discovery present in mobile app stores. This thesis provides novel techniques for the analysis of technical and non-technical data from app stores. Topic modelling is used as a feature extraction technique, which is shown to produce the same results as n-gram feature extraction, that also enables linking technical features from app descriptions with those in user reviews. Causal impact analysis is applied to app store performance data, leading to the identification of properties of statistically significant releases, and developer-controlled properties which could increase a release’s chance for causal significance. This thesis introduces the Causal Impact Release Analysis tool, CIRA, for performing causal impact analysis on app store data, which makes the aforementioned research possible; combined with the earlier feature extraction technique, this enables the identification of the claimed software features that may have led to significant positive and negative changes after a release

Adherence of popular smoking cessation mobile applications to evidence-based guidelines

Background Smoking remains one of the major preventable causes of chronic diseases. Considering the promising evidence on the effectiveness of mobile technology for health behaviour change, along with the increasing adoption of smartphones, this review aims to systematically assess the adherence of popular mobile apps for smoking cessation to evidence-based guidelines. Methods The United Kingdom Android and iOS markets were searched in February 2018 to identify smoking cessation apps. After screening, 125 Android and 15 iOS apps were tested independently by two reviewers for adherence to the National Institute of Care and Excellence (NICE) Smoking Cessation Guidelines for Self-Help Materials and the Five A Guidelines for Smoking Cessation. Pearson chi square tests were run to examine differences between the two operating systems. Results A majority of apps across both operating systems had low adherence (fulfils 1–2 out of 5 guidelines) to the Five A Guidelines (65.7%) and low adherence (fulfils 1–3 out of 9 guidelines) to the NICE Smoking Cessation Guidelines for Self-Help Materials (63.6%). Only 15% of mobile apps provided information about the benefits of nicotine replacement therapy (NRT), and even fewer provided information regarding types of NRT products (7.1%) or how to use them (2.1%). In addition, only a minority of apps arrange follow-up appointments or provide additional support to help smokers quit. Conclusion Similar to previous mobile app reviews dating back to 2014, our findings show that most mobile apps do not follow existing smoking cessation treatment guidelines, indicating little change regarding the availability of evidence-based mobile apps for smoking cessation in the UK market. Smokers seeking to quit, tobacco control policy makers and software developers need to work together to develop apps that are in line with the latest clinical guidelines and strategies to maximise effectiveness