Mobile app stores from the user's perspectives

YesThe use of smartphones has become more prevalent in light of the boom in Internet services and Web 2.0 applications. Mobile stores (e.g., Apple’s App Store and Google Play) have been increasingly used by mobile users worldwide to download or purchase different kinds of applications. This has prompted mobile app practitioners to reconsider their mobile app stores in terms of design, features and functions in order to maintain their customers’ loyalty. Due to the lack of research on this context, this study aims to identify factors that may affect users’ satisfaction and continued intention toward using mobile stores. The proposed model includes various factors derived from information systems literature (i.e., usefulness, ease of use, perceived cost, privacy and security concerns) in addition to the dimensions of mobile interactivity (i.e. active control, mobility, and responsiveness). The study sets out 13 hypotheses that include mediating relationships (e.g., perceived usefulness mediates the influence of ease of use, active control, responsiveness and mobility; perceived ease of use mediates the influence of active control). As well as outlining the proposed research method, the research contributions, limitations and future research recommendations are also addressed

Ranking online consumer reviews

YesProduct reviews are posted online by the hundreds and thousands for popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers and researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness scores. The helpfulness score is predicted using features extracted from review text, product description, and customer question-answer data of a product using the random-forest classifier and gradient boosting regressor. The system classifies reviews into low or high quality with the random-forest classifier. The helpfulness scores of the high-quality reviews are only predicted using the gradient boosting regressor. The helpfulness scores of the low-quality reviews are not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and makes all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3–4 newer high-quality reviews are placed in the top ten reviews along with 5–6 older reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.Ministry of Electronics and Information Technology (MeitY), Government of India for financial support during research work through “Visvesvaraya PhD Scheme for Electronics and IT”