Mobile Apps Acceptability: A Meta-Analysis Model for Google Play

Mobile Apps Acceptability: A Meta-Analysis Model for Google Play

Usman Shehzaib (Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan), Javed Ferzund (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, Pakistan) and Muhammad Asif (Department of Computer Science, University of Lahore, Pakpattan, Pakistan)
DOI: 10.4018/IJITWE.2018100101

Abstract

This article describes how the mobile app market is growing day by day. Mobile app stores have created the opportunity for the users to publicly provide feedback on mobile apps that they have installed or used. In this way, users are involved in the design and development of mobile apps, which was done by designers and developers before. Online user reviews are a useful source to know the user's perception about mobile apps and thus provide a way of co-value creation. This article is conducted to investigate the factors affecting the acceptability of mobile Apps. Main purpose of this article is to use online reviews for construction of a model instead of using existing acceptance theories. The model proposed in this research is based on the analysis of reviews and app information extracted from the Google Play Store. The ratings and number of installs are two key indicators of the popularity of an app. Other characteristics like price, category and size also influence the user's selection of an app. The findings showed the appropriateness of the proposed model and hypotheses for evaluating mobile apps acceptability and popularity. This article provides mobile app developers and marketers with an insight into the mobile app popularity and acceptability dynamics.
Article Preview
Top

1. Introduction

Mobile applications are software systems operating on mobile devices, evolve day by day, making ubiquitous information access possible at anytime and anywhere (Kaasinen et al. 2000). Mobile app market is growing in terms of users and downloads (Agarwal et al. 2010). It is a different kind of software repository where developers and users can collaborate in some way (Anam and Yeasin, 2013). Mobile app stores have opened the possibility for the users to publicly provide feedback on mobile apps that they have installed or used. This development has changed the behavior of users from passive to active. Generally, a review is a brief text written by a user which contains user’s opinion or point of view about a particular app. In addition to review, a user has the option to rate a particular app on an ordinal scale. Google play store is a famous example of app review systems which displays histograms of ratings and comments by users. Before this, only designers and developers were considered enough for delivering mobile apps. The privilege of sharing mobile app experience is playing a vital role in mobile app industry as it represents real users’ experiences. However, the success of app is calculated by its number of downloads and the revenues generated from the mobile app (Lim et al., 2015). Technology acceptance research has a long practice in the field of information systems research. This is due to the fact that community always wanted to know in advance whether a certain technology will be flourishing and understand causalities leading to this acceptance (Platzer, 2010).

Online user reviews are becoming popular to know the user’s perception about mobile apps. Users are sharing their real-world experiences which cannot be achieved in lab settings. For example, Facebook app available on Google Play is receiving around 2000 reviews on daily basis (Chen et al., 2014). In addition, users are spending enough time to play with the apps and voluntarily participating in providing the feedback. User’s reviews can play a significant role from developer’s perspectives as well as can fulfill user’s needs. The soliciting contributions of a large number of users can influence the quality of service. User reviews are being widely exploited in a variety of domains and mobile apps is one of them. Mobile app reviews have gained a lot of attention despite the noise and irrelevancy in reviews. However, the quality of reviews can influence revenues generated and also affect the rate of growth (Vasa et al., 2012). Timely and constructive user feedback can be critical for developers to remove bugs, to add features, and improve user experience (Chen et al., 2014). User reviews can be a way of knowing about an app’s success and its problems but is a difficult task to perform as it requires intensive text mining techniques. There should be an easy and user-friendly way to know an app.

According to Ter Chian Tan and Vasa (2011), reviews by a user are a way of co-value creation. From this perspective, user reviews can create co-value for both developers and customers. User’s feedback can give confidence to other users to decide whether to use a particular mobile app or not. The mobile app reviews are a way of collecting user feedback on various aspects such as bugs, functionality, performance and quality. But it is a challenging task to extract knowledge from these reviews. For the development of high quality apps, it is essential to recognize the key factors that define mobile applications, which if intelligently collected and utilized, can facilitate the delivery of truly excellent, demanding, user friendly apps that fulfill user’s requirements (Flora, Wang, and Chande, 2014).

On the other side, good ratings and positive feedback can improve the returns on investments for developers. However, negative feedback or improvement suggestions can help the developers to know, where to focus on the development of mobile apps. The unstructured way of providing user’s feedback has both positive and negative aspects. However, mobile app reviews can be a useful way to exchange user experiences. There is a range of users (novice to expert) sharing experiences on app stores. Expert users share their experience for other users and share useful insights for the designers (Anam and Yeasin, 2013). But to get maximum benefit from these reviews is a challenging task, as it demands intensive text mining algorithms to be implemented.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 16: 4 Issues (2021): Forthcoming, Available for Pre-Order
Volume 15: 4 Issues (2020): 2 Released, 2 Forthcoming
Volume 14: 4 Issues (2019)
Volume 13: 4 Issues (2018)
Volume 12: 4 Issues (2017)
Volume 11: 4 Issues (2016)
Volume 10: 4 Issues (2015)
Volume 9: 4 Issues (2014)
Volume 8: 4 Issues (2013)
Volume 7: 4 Issues (2012)
Volume 6: 4 Issues (2011)
Volume 5: 4 Issues (2010)
Volume 4: 4 Issues (2009)
Volume 3: 4 Issues (2008)
Volume 2: 4 Issues (2007)
Volume 1: 4 Issues (2006)
View Complete Journal Contents Listing