Crowdsourcing is a famous technique to get innovative ideas and soliciting contribution from a large online community particularly in e-business. This technique is contributing towards changing the current business techniques and practices. It is also equally famous in analysis and design of m-business services. Mobile app stores are providing an opportunity for its users' to participate and contribute in the growth of mobile app market. App reviews given by users usually contain active, heterogeneous and real life user experience of mobile app which can be useful to improve the quality of app. Best to our knowledge, the strength of mobile app reviews as a crowdsource is not fully recognized and understood by the community yet. In this paper, we have analysed a crowdsourcing reference model to find out which features of crowdsource are present and are related to our case of mobile app reviews as a crowdsource. We have analyzed and discussed each construct of the reference model from the perspective of mobile app reviews. Moreover, app reviews as a crowdsourcing technique is discussed by utilizing the four pillars of the reference model: the crowd, the crowdsourcer, the crowdsourcing, and the crowdsourcing platform. We have also identified and partially validated certain constructs of the model and highlighted the significance of app reviews as a crowdsource based on existing literature. In this study, only one crowdsourcing reference model is used which can be a limitation of our study. The study can be further investigated and compared with other crowdsourcing reference models to get better insights of app reviews as a crowdsource. We believe that the understanding of app reviews as a crowdsourcing technique can lead to the further development of the mobile app market and can open further research opportunities.

Mobile app reviews are gaining importance as a crowd source to improve the quality of mobile apps. Mobile app review systems are providing a platform for users to share their experiences and to support in decision making for a certain app. Developers, on the other side, are utilizing the review system to get real-life user experience as a source of improving their apps. This paper has analyzed existing review system and proposed few recommendations for the current review system to improve the quality of app reviews. The proposed review system can help for collection and analysis of user reviews to make it more meaningful with less intensive data mining techniques. The proposed system can help the end users to get an overview of mobile apps. The recommendations in this paper are derived from the existing literature related to app reviews and will help to improve the current review systems for better app reviews from users as well as developers perspective.