Factors Driving Mobile App Users to Pay for Freemium Services

With the popularity of smart mobile devices, mobile applications (most commonly referred to as an App) have gradually grown up to be a huge commercial market. Therefore, as the variety and download counts of Apps in the application stores of the two biggest operating systems increase, how to make a profit from Apps has become the most concerned issue for developers. Today the freemium strategy is widely observed in mobile App markets. Freemium is a business model by which an App is offered free of charge, but a premium is charged for advanced features. Hence, the purpose of this study is to explore the factors driving mobile App users to pay for freemium services based on value-based adoption model. An online survey was conducted to collect empirical data in order to test the research model. The results of PLS analysis indicate that an App user’s intention to pay is determined by perceived value, a thorough comparison of benefits and sacrifices, and trust of developer. Furthermore, perceived value will be affected by perceived effort and perceived usefulness while the App user has no experience on premium service. Finally, the implications for practitioners and researchers are discussed

Robust Recovery of Subspace Structures by Low-Rank Representation

In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Rank Representation (LRR), which seeks the lowest-rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that LRR well solves the subspace recovery problem: when the data is clean, we prove that LRR exactly captures the true subspace structures; for the data contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for the data corrupted by arbitrary errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace segmentation and error correction, in an efficient way.Comment: IEEE Trans. Pattern Analysis and Machine Intelligenc