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  • Thumbnail for 5.0.0 Shades of Android: Determining App Preferences from User Reviews
    5.0.0 Shades of Android: Determining App Preferences from User Reviews by Richmond, Will

    In the burgeoning personal and business market of mobile apps, user reviews are a critical quality information source, significantly affecting app download rates and sales. For five Android music apps, this paper investigates determining user preferences from user reviews using a manual content analysis.User reviews were scored for usability, functional, nonfunctional and control factors using an ordered logit model, applying constructs of the Technology Acceptance Model and TaskFunctional Fit theory. This methodology is loosely based on previous iOS manual content analysis. Usability and functionality were hypothesized to increase user reviews, while functionality critiques, especially nonfunctional factors, were hypothesized to negatively affect user reviews. Negative, nonfunctional use comments had the largest effect on reviews, while usability only slightly improved user ratings. Most positively predicted factors, including functionality and ease of use, had no effect on user ratings. Overall results differ from Apple iOS user reviews, but emotion and user’s experience with the mobile app environment have very pronounced ratings effects for both platforms. Finally, analyzing all five apps separately for user preferences highlights the disparity in user review content across apps, even within a similar category.